Estimation of urban infrastructure deficit in developing ... · Estimation of urban infrastructure...

POLITECNICO DI MILANO

PhD in Management, Economics and Industrial Engineering

Estimation of urban infrastructure deficit in developing

countries as a function of historical cement consumption

Thesis Tutor: Federico Caniato

Thesis Supervisor: Matteo Kalchschmidt

Thesis by:

Guillermo Grassi id# 10269280

XXIX Cycle

September 2017

Acknowledgements

I want to thank Prof. Angel Caniato and Prof. Matteo Kalchschmidt for kindly welcoming

me into academia, their support, contributions and dedication during the last four years.

My special thank goes to my father Enrique, my sister Eliana and my brother Ezequiel,

who are simply the best family I could have ever wished for. If Karma is a real thing, I must

have save humanity in my previous life to deserve you all. You are to me that house light a

sailor desperately looks for in the middle of a maelstrom.

But most of all, I want to thank you mamá. From your universally beautiful soul I learned

the importance of effort, gratitude, solidarity, loyalty, the beauty of arts, how to do the

things right and the right thing. I thank you now and I will thank you to the end of time for

blessing me with your eternal light. I hope these words reach you out somewhere near a

lake surrounded by white jasmines and full of ducks.

Estimation of urban infrastructure deficit in developing countries as a function of

historical cement consumption

V

ABSTRACT

According to the United Nations’ 2014 estimations, 881 million people live in slums under

the life-threatening conditions associated to urban infrastructure deficits (Todaro and

Smith, 2012). This research proposes the application of Duncan et al. (2001) and

Kahneman and Tversky (1977) methodologies to diagnose urban infrastructure deficits in

developing countries while offering a valid long-term forecasting technique for cement

consumption. Whereas governments need to understand current urban infrastructure

shortages and have a valid forecasting methodology for planning resource allocations;

industrial companies can benefit from efficiently assessing attractive related investment

opportunities in developing countries. The objective is to use references of historical

cement consumption in advanced economies to quantify the current deficit on urban

infrastructure in developing countries and to simulate potential demand evolution through

the application of analogy forecasting methodologies. The initial assumption for this thesis

is that while the level of urban infrastructure and cumulative cement consumption per

capita are closely related, the cement demand cycle in advanced economies has similar

pattern regimes. This similarity can be used to foresee future demand development in

developing countries. The empirical work is based on data collected for 129 countries

containing hundred-year period observations of cement consumption and other measures

to characterize the local urban infrastructure development. The results shown by our

forecasting methodology (pooled models) are promising as the simulation of growth

predictions obtained are substantially more accurate than the outcomes provided by

standard models in all analyzed metrics. For instance, the pooled models predicted with

higher accuracy the Cement demand per capita measured in Mean Absolute Percentage

Error (28% as compared with 105% achieved by the standard models in average). The

resulting economic cost estimations to narrow the urban infrastructure deficits in

developing countries are alarming. Out of the 129 countries in scope, 76 suffer Cement

stock per capita deficits with the most severe cases concentrated in Africa and Asia. As per

our methodology based on reference values, narrowing the urban infrastructure deficit

globally could demand USD ~12.9 trillion for housing construction and USD 24.7 trillion

investment in public-structures. We found support for the validity of our estimations on the

review of current literature such as Woetzel et al. (2014) for housing financial needs and

Bughin, Manyika and Woetzel (2016) for public-structure investment.



VI

INDEX OF CONTENT

ABSTRACT……………………………………………………………………………..V

INDEX OF FIGURES…..…………………………………………………………...…XII

INDEX OF TABLES .…………………………………………………………….…..XIV

EXCECUTIVE SUMMARY………………………………………………………..…XV

LITERATURE REVIEW…………………………………………..……………………………………..XV

THEORETICAL FRAMEWORK RESEARCH QUESTIONS …………………………………...........XIX

RESEARCH METHODOLOGY…………………..……………………………...................................XXII

RESULTS AND DISCUSSIONS………………………………..………………..............................XXVIII

CONCLUSIONS, LIMITATIONS AND FURTHER DEVELOPMENTS……………………..…XXXVIII

1 INTRODUCTION ....................................................................................................... 1

Research background ............................................................................................ 3

1.1.1 A fundamental construction material............................................................. 3

1.1.2 Consequences of urban infrastructure deficit ................................................ 4

1.1.3 Cement demand forecasting .......................................................................... 4

Thesis structure and general considerations.......................................................... 5

1.2.1 Thesis structure .............................................................................................. 5

1.2.2 General considerations................................................................................... 7

2 LITERATURE REVIEW .......................................................................................... 10

Cement overview ................................................................................................ 11

2.1.1 The product .................................................................................................. 11

2.1.2 Cement history at a glance ........................................................................... 12

2.1.3 Modern production process and cement types............................................. 15

2.1.4 Cement substitutes and supplementary materials ........................................ 18

2.1.5 Economics of the cement business .............................................................. 18

2.1.6 Cement industry ........................................................................................... 22

Economic relevance of urban infrastructure: cement as an enabler.................... 26

2.2.1 Urban infrastructure development ............................................................... 26



VII

2.2.2 Economic relevance of urbanization ........................................................... 31

2.2.3 Economic relevance of public-structure development ................................ 34

Demand forecasting ............................................................................................ 37

2.3.1 Demand forecasting methodologies ............................................................ 37

2.3.2 Specificities of the cement demand in the context of forecasting ............... 40

2.3.3 Forecasting of cement demand .................................................................... 47

Concluding remarks ............................................................................................ 50

3 THEORETICAL FRAMEWORK ............................................................................. 52

Reference class forecasting ................................................................................. 52

Forecasting by analogy ....................................................................................... 58

Other supporting researches ................................................................................ 63

Research questions .............................................................................................. 65

3.4.1 Research question 1 ..................................................................................... 65

3.4.2 Research question 2.a .................................................................................. 66

3.4.3 Research question 2.b .................................................................................. 67

3.4.4 Research question 3 ..................................................................................... 69

3.4.5 Research questions in the context of the literature gaps .............................. 70

4 RESEARCH METHODOLOGY .............................................................................. 71

Process description .............................................................................................. 71

Exploratory analysis: .......................................................................................... 72

4.2.1 Classifications of countries in groups: ......................................................... 72

4.2.2 Datasets and variables.................................................................................. 85

4.2.3 Data analysis overview and description .................................................... 106

Explanatory analysis ......................................................................................... 108

4.3.1 Equivalence groups .................................................................................... 108

4.3.2 Long-term forecasting model for cement .................................................. 112



VIII

4.3.3 Estimation of urban infrastructure deficit .................................................. 117

4.3.4 Developing countries – case study ............................................................ 137

5 RESULTS AND DISCUSSIONS ........................................................................... 139

Data analysis overview and description ............................................................ 139

5.1.1 Cross-sectional analysis ............................................................................. 139

5.1.2 Descriptive statistics .................................................................................. 147

Equivalence groups ........................................................................................... 153

Long term-forecasting model for cement ......................................................... 156

5.3.1 Subgrouping and construction of local models ......................................... 156

5.3.2 Construction of coefficients for subgroups’ pooled models ...................... 156

5.3.3 Empirical validation................................................................................... 159

Urban infrastructure deficit ............................................................................... 164

5.4.1 Definition of reference values ................................................................... 164

5.4.2 Estimation of economic cost ...................................................................... 168

5.4.3 Additional cement capacity – Industry economics .................................... 175

Developing countries – Nigeria’s case study .................................................... 179

5.5.1 Social, political and economic background: .............................................. 179

5.5.2 Nigerian’s extreme urban infrastructure deficit ......................................... 181

5.5.3 Potential growth of Nigerian cement demand ........................................... 183

5.5.4 Implications for the Nigerian cement industry .......................................... 187

6 CONCLUSIONS, LIMITATIONS & FURTHER DEVELOPMENTS ................. 190

General scope .................................................................................................... 190

6.1.1 Conclusions ............................................................................................... 190

6.1.2 Limitations and further development ........................................................ 192

Long-term forecasting model for cement ......................................................... 193

6.2.1 Conclusions ............................................................................................... 193



IX

6.2.2 Limitations and future developments ........................................................ 194

Urban infrastructure deficit ............................................................................... 196

6.3.1 Conclusions ............................................................................................... 196

6.3.2 Limitations and future developments ........................................................ 197

7 ANNEXES ............................................................................................................... 199

Classification of countries by economic development ..................................... 199

Variables – 2013 figures unless stated differently ............................................ 206

Material content for a 50-square meter house ................................................... 217

Highways as % of total paved roads in advanced countries ............................. 218

Construction cost comparison in developing countries, 2016 values ............... 219

Descriptive statistics of Cement stock per capita (tons) 2013 by economic cluster

and geographic region.................................................................................................. 220

Descriptive statistics Advanced countries vs remaining countries, selected

variables ....................................................................................................................... 221

Countries with multiple peaks of Cement consumption per capita – Cement stock

per capita comparison .................................................................................................. 222

Countries with multiple peaks of Cement consumption per capita – GDP per

capita Constant comparison ......................................................................................... 223

Countries with multiple peaks of Cement consumption per capita – Italy ... 224

Pooled models used for the empirical validation of individual advanced

economies .................................................................................................................... 225

Forecasting models 1913-2013 – empirical validation subgroup 1 .............. 226




Residuals – empirical validation equivalence subgroup 2 ............................ 237

Variation between known and predicted values* (1/2) ................................. 238



X

Variation between known and predicted values (2/2) ................................... 239

Deficit of urban infrastructure by countries .................................................. 240

Identification of Nigeria’s corresponding equivalence group ....................... 246

Nigerian local cement producers – 2013* ..................................................... 247

8 REFERENCES ........................................................................................................ 248

Books, papers and reports ................................................................................. 248

Databases and websites ..................................................................................... 257

8.2.1 Databases ................................................................................................... 257

8.2.2 Websites ..................................................................................................... 257



XI

INDEX OF FIGURES

Figure 1. Thesis structure - illustration ................................................................................ 6

Figure 2. Customary proportions of materials used in concrete (source: Portland Cement

Association) ....................................................................................................................... 11

Figure 3. Cement regional consumption (source: Global Cement Report, 2015 and U.S.

Geological Survey) ............................................................................................................ 15

Figure 4. Cement production process ................................................................................ 16

Figure 5. Typical cost structure of producing grey cement (source: Jeffries, 2012) ......... 19

Figure 6.Cement prices 2013, selected global cement producers (source: company annual

reports, 2013) ..................................................................................................................... 22

Figure 7.Cement consumption per capita – 2013 Top ten countries (source: Global Cement

Report, 2015) ..................................................................................................................... 25

Figure 8. Public-structure deficit evolution – access to clean water and sanitation (source:

World Bank 1994 and World Health Organization 2013 .................................................. 31

Figure 9.Urban population as % of total (source: United Nations 2014) .......................... 32

Figure 10. GDP purchase power parity - current per capita and Capital stock per capita by

country (source: World Bank, 2017 and International Monetary Fund, 2017) ................. 34

Figure 11. Forecasting methods (source: Chambers et al., 1971, Makridakis and

Wheelwright, 1977, Armstrong, 2001 and Chase et al., 2006) ......................................... 38

Figure 12. 2013 Cement consumption per capita and GDP per capita by country (source:

Global Cement Report, 2015 and World Bank, 2017) ...................................................... 43

Figure 13. Illustration of Bayesian inference (source: Analytics Vidhya, 2016) .............. 58

Figure 14. Time-series with four Pattern Regimes, Duncan et al. (2001) ........................ 60

Figure 15. Illustration of similarity of cement consumption patterns between countries . 66

Figure 16. Illustration of the saturation point in the Cement consumption per capita ...... 67

Figure 17. Relation between Cement stock per capita and development level of urban

infrastructure ...................................................................................................................... 68

Figure 18. Illustration of potential long-term Cement consumption per capita development

........................................................................................................................................... 69

Figure 19. Process description of the research methodology ............................................ 71

Figure 20. Datasets comparison of Cement stock per capita figures ................................. 92

Figure 21. Data analysis overview and description - process overview .......................... 106



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Figure 22. Illustration of the correlational co-movement approach for the selection of

equivalence groups .......................................................................................................... 109

Figure 23. Illustration of expert judgement approach for selection of the equivalence groups

......................................................................................................................................... 111

Figure 24. Illustration of model-based clustering approach for the selection of equivalence

groups within advanced countries ................................................................................... 112

Figure 25. Illustration of subgrouping and construction of individual model ................. 113

Figure 26. Illustration of the construction of coefficients for each subgroups model ..... 114

Figure 27. Illustration of subgroup model forecasting method for validation through

removal of local model .................................................................................................... 115

Figure 28. Illustration of accuracy comparison between actual values against pooled and

standard model results ..................................................................................................... 117

Figure 29. Illustration of the process for estimating urban infrastructure deficit ............ 118

Figure 30. Illustration of a 50-square meter house cement requirements (slab floor-roof,

wall brickwork and plastering) ........................................................................................ 122

Figure 31. Illustration of the potential public-structure (roads) construction enabled by one

cubic meter of concrete .................................................................................................... 125

Figure 32. Zuirch’s Andreastrasse project site illustrating different thickness in concrete

building slabs, bridges deck / columns and road-surfaces (Source: Grassi, 2017) .......... 126

Figure 33. Cement consumption in public-structure during the initial stages of capital

formation.......................................................................................................................... 130

Figure 34. Example of city concrete roads in Zurich, Switzerland (source: Grassi, 2017)

......................................................................................................................................... 134

Figure 35. Illustration of road characteristics used for the computation of cost of public-

structure construction....................................................................................................... 135

Figure 36. Germany development for Cement stock per capita, Cement consumption per

capita and GDP per capita Constant for the 1913-2013 period ....................................... 142

Figure 37. Cement stock per capita Variation coefficient (standard deviation ÷ mean) . 148

Figure 38. Advanced vs remaining countries - selected variables.................................. 150

Figure 39. Equivalence subgroups within advanced economies ..................................... 154

Figure 40. Illustration of quadrant range criteria ............................................................. 155

Figure 41. Simulation of Japan’s Cement consumption per capita development for the

1913-2013 period with pooled and standard models ....................................................... 160



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Figure 42. MAPE, MAD and Variation of saturation point prediction ........................... 162

Figure 43. Cement stock per capita - urban infrastructure relation - 2013 logarithm form

......................................................................................................................................... 164

Figure 44. Urban infrastructure basic level - Cement stock per capita reference value .. 165

Figure 45. Urban infrastructure deficit – Comparison between results and literature review

......................................................................................................................................... 171

Figure 46. Urban infrastructure deficit in developing countries [2013 - USD per capita]

......................................................................................................................................... 172

Figure 47. Economic value of urban infrastructure - extreme deficit (average) ............. 173

Figure 48. Economic value of urban infrastructure - high deficit (average) ................... 174

Figure 49. Economic value of urban infrastructure moderated deficit (average) ............ 175

Figure 50. Potential resulting cement supply and demand balance ................................. 177

Figure 51. Nigerian suburbs in Lagos (source: AMP-Pinterest, retrieved in 2017) ........ 179

Figure 52. Comparison of Nigeria and advanced economies average - selected urban

infrastructure metrics (2013) ........................................................................................... 181

Figure 53. Nigerian urban infrastructure deficit – Comparison between results and literature

review (2013) ................................................................................................................... 182

Figure 54. Nigeria’s Cement consumption per capita and stock forecast at GDP per capita

Constant growth of 8.0% per annum ............................................................................... 185





Figure 57. Nigerian cement supply and demand balance in 2013 and 2041 ................... 189



XIV

INDEX OF TABLES

Table 1. Real construction value by segment – 2013 selected countries (source: Business

Monitor International, 2015) ............................................................................................. 27

Table 2. Population living in slums in developing countries (source: United Nations

Millennium Development Goals Report 2015) ................................................................. 28

Table 3. Substandard housing, selected countries (source: Woetzel et al., 2014) ............. 29

Table 4. Estimation of national cement losses due to earthquakes during the 1913-2013

period (source: https://en.wikipedia.org/wiki/List_of_earthquakes_in_Japan and Cabinet

Office of Japan 2011) ........................................................................................................ 47

Table 5. Research questions in the context of the identified literature gap ....................... 70

Table 6. Economic classification by organism (source: World Bank, 2013 and International

Monetary Fund 2013) ........................................................................................................ 73

Table 7. Geographic regions (source: World Bank 2013) ................................................. 77

Table 8. Cement consumption (average): Gulf Cooperation Country members and regions

(source: Cembureau 1994, The Global Cement Report 2015, International Monetary Fund

2016) .................................................................................................................................. 84

Table 9. Description of variables ....................................................................................... 86

Table 10. Typical concrete pavement thickness (source: Byers, 2014) .......................... 124

Table 11. Tunnel length as percentage of paved roads length by country (source:

https://en.wikipedia.org) .................................................................................................. 127

Table 12. Largest arch-gravity dams in advanced countries (source:

https://en.wikipedia.org and World Statistical Review, 1998, and International Cement

Review, 2015) .................................................................................................................. 128

Table 13. Cost of construction in Sao Paulo (Brazil 2016), land cost excluded (source:

Turner & Townsend 2016) .............................................................................................. 131

Table 14. Correlations of variables – 2013 ...................................................................... 140

Table 15. Average Cement stock per capita 2013 by economic cluster and geography . 149

Table 16. Equivalence subgroups and models (Standard and Pooled) ............................ 157

Table 17. Subgroup 2: validation of pooled model – Japan’s example .......................... 159

Table 18. Quantity of countries by level of Cement stock per capita deficit and region 166

Table 19. Reference values by equivalence subgroup ..................................................... 168

Table 20. Comparison of Nigerian forecasted scenarios and subgroup 1 ....................... 187

Table 21. Summary of research questions, related elements and results......................... 191



XV



XVI

EXCECUTIVE SUMMARY

This Executive-PhD thesis is expected to contribute to the research field by providing

insights to diagnose urban infrastructure deficits’ in developing countries while offering a

valid long-term forecasting methodology for cement consumption. According to the United

Nations’ estimation for 2014, 881 million adults and children live in life-threatening

conditions due to urban infrastructure deficits. While governments need to understand

current urban infrastructure shortages and have a valid forecasting methodology for

planning resource allocations; industrial companies can benefit from assessing efficiently

large and risky investment opportunities in developing countries. The objective is to use

references of historical cement consumption in advanced economies to quantify the current

deficit on urban infrastructure in developing countries and to simulate potential demand

evolution through the application of analogy forecasting methodologies. The initial and

main assumption for this research project is that while the level of urban infrastructure and

cumulative cement consumption per capita are closely related, the cement demand cycle in

advanced economies has similar pattern regimes. This similarity can be used to foresee

future demand development in developing countries. Although we centered the forecasting

part of this research on cement consumption, we expect some of our findings to be of

interest to other forecasting problems holding similarities. We retain our results to be

beneficial for the study of new product launch and demand in the upper stages of the supply

chain where accounting for the stock in lower stages of the supply chain is required.

LITERATURE REVIEW

The literature review covers three central topics throughout the understanding of the

objective within this research, main supporting concepts and current state of the art. Firstly,

the cement industry was reviewed from the most relevant angles. The second section

addressed the relation between cement consumption with urban infrastructure quality and

availability together with its social-economic impact. Thirdly, a review was provided

around the substantive findings on demand forecasting methodologies and the specificities

of the cement demand in the context of forecasting.

Cement overview

Cement is a key ingredient in the concrete production, the most widely manmade used

material due to its versatility, availability and relatively low cost (Arezoumandi et al.,



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2013). When used to build urban infrastructure such as houses, roads, bridges and tunnels

(Kosmatka et al., 2002), cement concrete lasts for many years before replacement is needed

(Courland, 2011).

As other capital-intensive industries, cement production requires large operations and a

continuous production process to avoid high production cost per unit through efficiency

(Grant, 1991). A small cement plant able to produce one million tons a year would costs

around USD 200 million (The Economist, 2013), leading to a payback ranging from 10 to

30 years. The production of cement requires enormous amounts of both thermal and

electrical energy, consuming between 12% and 15% of total industrial use of energy

(Madlool et al., 2011). Thus, acute swings in energy cost can erode profitability up to loss-

levels. Because of its product bulky characteristics, the cement business holds logistics

limitation inherent to the industry (Caniato et al., 2011). Transportation of cement tends to

be costly limiting the size of efficient plants despite the presence of economies of scale.

Consequently, distribution costs are therefore balanced out against economies of scale to

determine the radius a plant (typically 300 km) can economically serve (Porter, 1980). Due

to the above-mentioned elements, a proper understanding of the demand function is

fundamental for the cement industry assessment of long-term investment opportunities.

Because of its local-market characteristics and required economies of scale, cement prices

are specifically affected by excess of capacity. When fixed costs are high relative to

variable costs as it happens in the cement production, firms will accept marginal business

opportunities at any price that covers variable costs (Grant, 1991), with disastrous

consequences on profitability. Cement prices tend to be higher, in markets far from large

exporters as China, Japan and Turkey, and in landlocked countries as Switzerland.

Although the lower demand that followed the 2008 world financial crisis drove small and

old cement plants out of business, the persistent low utilization rate (International Cement

Review, 2014) resulted in competitive prices ranging from 104 to 110 USD per ton of

cement, depending on market exposure.

In 2013, the global cement market accounted for USD 250 billion in revenues (The

Economist, 2013) with 4’033 million tons sold with about 82% of the demand concentrated

in Asia, followed by the Americas 7%, Europe 6% and remaining 5% consumed in Africa



XVIII

and Oceania (Global Cement Report, 2015). While top three largest consumers are China

(2’400 million tons), India (253.9 million tons) and United States (81.7 million tons), at

per capita level most intense markets are Qatar, Saudi Arabia and Bahrain with demand

exceeding 1’500 kg per capita. Following a series of recent mergers and acquisitions

(Lafarge-Holcim and Heidelbergcement-Italcementi), the current market outside China is

mainly served by only four large competitors, LafargeHolcim, HeiderlbergCement, Cemex

and Buzzi Unicem, with the largest companies in the Chinese market being Anhui Conch

and CNBM.

Economic relevance of urban infrastructure: cement as an enabler

From a contemporary angle, and following the notion of cement as the material of choice

for construction (Arezoumandi et al., 2013), cement is an enabler of urban infrastructure

(Aitcin, 2000, Deverell, 2012; Kang and Li, 2013). Cement allows the construction of the

physical components and systems serving a country (Fulmer, 2009) and facilitates

economic integration (Todaro and Smith, 2012). Urban infrastructure buildings can be

divided in three typologies depending on its intended purpose. The first typology is housing

buildings (also called residential), which consider any type of building providing

accommodation to dwellers. Second typology is public-structure buildings, which

compromises roads, electricity, water and sanitation, communications and the like. The

third type are the commercial or non-residential buildings, commonly defined as those

intended to generate a profit such as offices, private hospitals, retail and industrial sites.

Deficits of urban infrastructure holds social and economic consequences. Among many

others, inadequate housing in large urban centers often relates to serious diseases ranging

from bronchitis to cholera outbreaks. Exposure to toxic gases in unventilated shanty town

houses and the constant exposure to untreated sewage running open increases the risks of

virulent infections (Todaro and Smith, 2012). Although the population living in developing

countries’ slums decreased from 39% to ~30% in fifteen years as percentage of total,

absolute numbers continue to increase driven by high urbanization rates and population

growth in developing countries (United Nations, 2015). The lack of public-structures is

also alarming. In 1994, the World Bank warned about the lack of access to clean water and

sanitation in the developing world together with many other basic public-structure

shortages (one billion and two billion people affected respectively). Many years after, the



XIX

number of people who still do not have access to improved sanitation facilities grew to two

and a half billion people despite public efforts (World Health Organization, 2013) driven

by urbanization processes and population. The United Nations (2014) last revision on world

urbanizations prospects claims that more people live in urban areas than in rural areas, with

54% per cent of the world’s population residing in urban areas in 2014 up from 30% in

1950. By 2050, 66% of the world’s population is expected to be urbanized. While the notion

of a strong link between urbanization and economic growth is widely diffused, the review

on the work of Chen et al. (2014), Todaro and Smith (2012) and Zhang and Song (2003)

addresses causality sustaining that it is the economic growth driving urbanization and not

vice versa.

Demand forecasting

The main distinction within the different forecasting methods rests between Quantitative

and Qualitative methods. Whereas Quantitative methods are based on: firstly, the idea that

data related to past demand can be used to predict future demand (time-series) and

secondly, the notion of cause-and-effect of relationships (causal or regressive), the

Qualitative techniques comprehend those methodologies subjective or judgmental based

on estimates from relevant sources such as consumers and experts (Chase et al., 2006).

Osenton (2000) through its theory of natural limits and Palacios Fenech and Tellis (2016)

on their review on consumption claimed that every product or service has a natural

consumption level, after which further investment to grow are ineffective. In the same line,

Aitcin (2000) claims is that it is possible to establish a direct relationship between the

consumption of cement and the economic development of a country. As a country develops,

there is an increasing need for public-structure development and consequently an increase

in the consumption of cement, however the growth of cement consumption slows down

when the standard of living reaches a certain level (Aitcin, 2000). The implementation of

available methodologies for the cement long-term demand forecasting presents limitations

usually related to the use of individual time-series projections. Empirical evidence

demonstrates that forecasting accuracy in cement demand can be improved using integrated

forecasting systems (Caniato et al., 2011) due to the current limitations of both qualitative

and quantitative techniques when used in isolation. Cement demand forecasts generally

rely on individual country data and thus lose the value provided by distributional

information (Kahneman and Tversky, 1977). As exemplified by the work of Hung and Wu



XX

(1997), Deverell (2012), Kang and Li (2013) and Birshan et al. (2015), ignoring the

historical cement demand evolution in other countries, particularly mature economies,

limits the understanding of demand long-term patterns, particularly of the cement stock /

saturation point.

Concluding remarks - gaps

The review of the current literature revealed substantial gaps offering the opportunity to

bridge them through the development of this research work:

The literature review on markets sizing / trends (e.g. Global Cement Report, 2015;

and International Cement Review, 2014) appeared will covered. However, within

the reviewed material, the true demand potential (based on necessity) of most

developing world remains unquantified in its full dimension.

The accumulated knowledge in terms on urban infrastructure, its driving forces (i.e.

urbanization process and public investment in public-structure) and its economic

relevance is extensively covered by several authors, specially through the work of

Todaro and Smith (2012). However, the perspectives on urban infrastructure deficit

are limited to quantifications of housing units (Woetzel, 2009 and Dasgupta, 2014)

and public-structure investment estimations (The World Bank, 1994; World Health

Organization, 2013; Green et al., 2015 and Maier, 2015). An efficient quantification

methodology of the resources needed to narrow urban infrastructure gaps to

minimum functional levels is still missing, particularly in the developing world.

Lastly, whereas the work of Hung and Wu (1997) only illustrates the limitation of

missing the external view proposed by Kahneman and Tversky (1977), the claims

of Deverell (2012) and Kang and Li (2013) are restricted to providing a cement

stock reference to estimate the Chinese cement demand potential. Current reviewed

literature lacks insights about cement consumption patterns and thus of thorough

understanding of the demand function and its prescriptive application to estimate

the potential in developing countries.

THEORETICAL FRAMEWORK

The theoretical foundations of this research are mainly based on work of Kahneman and

Tversky (1977) on reference class forecasting and Duncan et al. (2001) on analogy

forecasting. We structured our research methodology on these premises providing a solid



XXI

and compatible theoretical-analytical background to our research. The absence of a known

and diffused cement forecasting methodology which benefits from comparable cases

(external view – analogies) opens an opportunity to build a prescriptive long-term

technique theoretically sustained by the work of Kahneman and Tversky (1977) and

Duncan et al. (2001).

Reference class forecasting - internal vs external view

The tendency to neglect distributional data is connected to the adoption of what Kahneman

and Tversky (1977) termed “internal approach” to prediction, where one focuses on the

constituents of the specific problem rather than on the distribution of the outcomes of

similar cases likely producing underestimation. Evidence suggests that people are

insufficiently sensitive to distributional data even when data are available and rely

primarily on singular information even when is scanty and unreliable, or give insufficient

weight to distributional information (Kahneman and Tversky, 1973; and Tversky and

Kahneman, 1977). The adoption of “external approach” which treats the specific problem

as one of many could help to overcome this bias by relating the problem at hand to the

distribution of the problem for similar projects. Kahneman and Tversky (1977) reference

class presented an approach aimed to be applied to both the prediction of uncertain

quantities and the prediction of probability distribution.

Analogy forecasting

Forecasting by analogy assumes that since two diverse types of phenomena share the same

behavioral patterns, it is possible to predict the future outcome of one by observing the

historical development of the other. In this line, Duncan et al. (2001) research provided a

detailed approach to the use of analogies for forecasting. Aligned with what Kahneman and

Tversky (1977) called reference class, Duncan et al. (2001) fundamentally defined

equivalence group as the groups of products or services which are often analogous in ways

that make them follow similar time-series patterns causing their time-series to covary over

time. Duncan et al. (2001) coined the name Bayesian Pooling by blending the “Bayesian”

concept of improving beliefs based on new data and “pooling” as per its reference to

drawing and combining information from analogous time-series.



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Research questions

During the literature review of this research work, unexplored areas were identified

opening the opportunity to improve and enlarge the span of the existing knowledge

particularly related to urban infrastructure deficits and the long-term cement demand

function. The fulfilment of our primary objectives required additional inputs, currently

unavailable, in the form of a data or methodology. Table I summarizes the research

questions in the context of the literature gaps identified during the corresponding review.

Table I: Research questions in the context of the identified literature gap

Research questions Identified literature gaps (limited or unavailable knowledge)

Understanding of

urban

infrastructure

deficit in

developing

countries

Cement long-term

forecasting

methodology

Market potential

as a function of

deficits on the

Cement stock

levels

RQ1: Are the

pattern regimes of

long-term cement

demand per capita

similar in advanced

countries?

The central answer of

this question serves to

initially validate the

use of the theoretical

framework (Duncan et

al., 2001).

RQ2.a: What is the

natural consumption

level for the cement

demand in different

coutries?

Answering RQ2.a and RQ2.b provides the necessary insights to

estimate efficiently deficits of urban infrastructure in developing

countries, bridging the claims of Osenton (2004) for saturation

point and Aitcin (2000), Deverell (2012), Kang and Li (2013)

and Birshan et al. (2015) in terms of urban infrastructure link to

cement consumption.

RQ2.b: How does

the cement natural

consumption level

relates to urban

contextual factors?

RQ3: How does

economic growth

influence the cement

demand in the long

term?

Answering RQ3 allows to obtain the

required specific parameters to estimate the

potential growth of Cement consumption

per capita in developing economies as a

function of the economic development.

Thus, completing the initial claims of

Aitcin (2000).



XXIII

RESEARCH METHODOLOGY

The research methodology was organized in two main phases. The exploratory analysis,

aimed to cover the rationale behind the regional and economic categorization of countries,

the description of the variables and the main objectives of the data analysis. The

explanatory part covers the long-term forecasting model structure, the steps involved on its

construction, and the processes for both the diagnose of urban infrastructure deficit and the

forecasting of the selected case-study country (developing market).

Exploratory analysis

The Exploratory analysis was aimed to address firstly, the main rational behind the

countries’ classification with a focus on the widespread practices and most proper use of

them considering the requirements of this research. Secondly, the Datasets and variables

section structured the descriptions of the variables used from a conceptual perspective.

Thirdly, in the section Data analysis overview and description, we provided a description

of the most relevant variables through cross-section and descriptive statistics analysis

revealing stablished relationships together with their particularities and potential elements

of causality.

Classification of countries

Considering Aitcin (2000) arguments over the relation between economic development and

cement consumption, one of the very first tasks of the research methodology required the

classification of countries as per their stage of economic development. Furthermore,

characterizing the economic development was also required considering the additional

need of finding the cement natural consumption level (saturation point), likely having taken

place in countries where the consumption cycle is completed (Osenton, 2004). The

geographic classification was also retained a relevant part of the exploratory analysis.

Geographic classification was expected to enable understanding of geographical patterns

with substantial relevance in the cement consumption requiring to be controlled in our

forecasting model. Out of the 160 countries for which some sort of relevant information

related to cement consumption was found, only 129 countries remained forming part of the

main body of analysis. A screening process was designed to eliminate misleading or

incomplete observations potentially affecting the results robustness.



XXIV

Datasets and variables

The deficit estimation and long-term forecasting models were validated through a

collection of time-series covering a hundred-year period from 1913 to 2013 and single data-

point mainly corresponding to the year 2013 for the 129 countries in scope. Four different

type of variables at country level were constructed: Cement, Urban infrastructure,

Economic development and Country inherent (Table II).

Table II: Description of variables

Variable Group Unit Period

Cement consumption per

capita

Cement kg 1913-2013

Cement stock per capita Cement tons 1913-2013

Public-structure quality Urban infrastructure 1-7 scale Single value

Capital stock per capita Urban infrastructure 2011 constant Intl.$* Single value

Population living in slums Urban infrastructure % of total population Single value

GDP per capita Constant Economic development 1990 Intl.$* Time-series

GDP per capita Current Economic development 2011 Intl.$* Single value

Human Development Index Economic development 1-100 scale Single value

Political stability Economic development 1-100 scale Single value

Urbanization level Country inherent % of total population Single value

Temperature average Country inherent Celsius degrees Single value

Country size Country inherent Km2 Single

Elevation over sea level Country inherent Meters (average) Single

Intl.$: International dollars

Data analysis overview and description

This phase, we aimed to identify and describe the most relevant variables through cross

sectional - correlations and descriptive statistics analysis revealing stablished relationships

together with their particularities affecting cement consumption development. We

structured this phase in two steps, firstly a cross-sectional analysis following Canning



XXV

(1998) involving all variables and countries within scope. The second step covered the

descriptive statistics of most relevant variables to obtain a set of perspectives supporting

the description of patterns on the cement consumption related to economic development

level or geography. Most important, this analysis was designed to sustain the case of

advanced economies as an equivalence group mainly through the statistical analysis

covering the Cement stock per capita variable in the context of all economic groups and

regions.

Explanatory analysis

The explanatory analysis was organized around four steps. The first step covered the

definition of the equivalence groups. The second step dealt with the elements related to the

estimation of urban infrastructure deficit in developing countries. The third step was aimed

to articulate and describe the methodology used to develop the long-term forecasting

model. Lastly, the insights and results provided by the three previous steps were

implemented and analyzed in-depth in a selected developing economy (case study).

Equivalence groups

With the objective of identifying analogous time-series for pooling data that correlates

highly over time, we followed Duncan et al. (2001) suggestions of combining three

approaches functional to our objectives (i.e. correlational co-movement, expert judgment

and model-based clustering.

Long-term forecasting model for cement

We organized the construction of our model in a sequential process. Firstly, we covered

the subgrouping (leveraging on the inputs from previous steps) and construction of local

models (based on polynomial regression grade two). The first task was followed by the

construction of coefficients for subgroups’ models combining the local models and finally

completed with the empirical validation of our long-term forecasting model adequacy.

Subgrouping: This approach was implemented through regressing the Cement

consumption per capita and GDP Constant per capita variables for advanced countries from

1913 until saturation point. Afterwards, the β coefficients of the linear regression and the

levels of Cement consumption per capita reached at moment of peak were compared to



XXVI

identify similarities. The timeframe was allegedly limited to the moment of Cement

consumption per capita peak to enable a compatible-prescriptive comparison of the β

coefficients with those of developing countries which have not reached a saturation point

yet.

Local models: Once the subgroups were defined, the next step involved the construction of

individual (local) models of each country times-series (Cement consumption per capita and

GDP per capita Constant) to enable the construction of subgroups model’s coefficients. We

found that a quadratic regression efficiently fits the bell-shaped cement demand function

through squaring in one term the predictor variable (GDP per Capita Constant). The linear-

in parameters and the a priori strong fitting indicated the suitable use of a quadratic

regression (Armstrong, 2001) for the construction of the individual models.

Construction of coefficients for subgroups’ pooled models: This task required drawing

information from analogous country time-series to allow the construction of the forecasting

model (Duncan et al., 2001). Once the local models were defined, we combined them to

form the pooled subgroups models. Both α and β coefficients for each subgroup model

were obtained by averaging the coefficients of the quadratic regression covering the 1913-

2013 period for each advanced economy within the corresponding equivalence subgroup

(Equation I). Although both the dependent variable (Cement consumption per capita) and

the independent variable (GDP per capita Constant) are per capita values and thus already

scaled by the population as a divisor, following Duncan et al. (2001) procedure, the

processes of re-scaling and homogenizing were avoided by having regressed each time-

series.

Equation I: Pooled model

𝐶𝑒𝑚𝑒𝑛𝑡 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖𝑡

= 𝛼 ∗ 𝐺𝐷𝑃 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡𝑖𝑡2 + 𝛽

∗ 𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡𝑖𝑡 + 𝐶

𝑖: 𝑡𝑎𝑟𝑔𝑒𝑡 𝑐𝑜𝑢𝑛𝑡𝑟𝑦



XXVII

Empirical validation: This step was aimed to verify the adequacy of our pooled model to

forecast the long-term demand of cement. The validation test was constrained by design to

the advanced economies as those are the one expected to account for completion of all

demand stages (growth-saturation-decline-maintenance) allowing for the evaluation of the

model’s prediction accuracy for the full cycle. The predicted values were compared against

both the actual (observed) values and the predicted values from a standard model which

did not benefit from the value of the distributional information (Kahneman and Tversky,

1977). The standard model corresponds to an equivalent quadratic function constructed

with the local time-series information from 1913 to the demand saturation point.

Estimation of urban infrastructure deficit

The modelling to assess the urban infrastructure deficits was aimed to estimate the gap

between advanced and developing countries in terms of the provision of urban

infrastructure, and together with constructing a valid long-term cement demand forecasting

at the core of this research’s ultimate objectives. The methodology used to assess the urban

infrastructure deficit in developing countries was organized around two broad steps:

definition of reference values and cost estimation of required constructions.

The definition of reference values required firstly the verification that the variable Cement

stock per capita can be used as a valid proxy to assess the level of urban infrastructure in a

country. The second step covered the definition of the reference value to be used to estimate

the gap(s) of urban infrastructure between advanced and developing countries as function

of the Cement stock per capita levels. In the scope of our research, the reference value

relates to the minimum level of urban infrastructure needed in a country to deal with basic

society needs such as housing, sanitation, transport, health, education. The definition of the

minimum level of urban infrastructure corresponds to the saturation point concept

(Osenton, 2004) and the claims of Aitcin (2000), Deverell (2012), and Kang and Li (2013)

in relation to basic infrastructure achievement linked to the cement consumption. The

saturation points of Cement consumption per capita in advanced countries were identified

and averaged to a single figure as to define representative level(s) of Cement stock per

capita at saturation point (Equation II) and to calculate / diagnose the stock deficit of

developing countries (Equation III).



XXVIII

Equation II: Reference value of Cement stock per capita for basic urban infrastructure

𝐵𝑎𝑠𝑖𝑐 𝑢𝑟𝑏𝑎𝑛 𝑖𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎

=∑ (𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑠𝑎𝑡𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑝𝑜𝑖𝑛𝑡

𝑛𝑖 )

𝑛

𝑖, 𝑛: 𝑎𝑑𝑣𝑎𝑛𝑐𝑒𝑑 𝑐𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠

Equation III: Estimation of deficit of Cement stock per capita in developing countries

𝐷𝑒𝑓𝑖𝑐𝑖𝑡 𝑜𝑓 𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013

= 𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑣𝑎𝑙𝑢𝑒𝑎𝑑𝑣𝑎𝑛𝑐𝑒𝑑 𝑐𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠

− 𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013

𝑖: 𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑖𝑛𝑔 𝑐𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠

The cost estimation of required constructions was designed to provide an initial

representation, in many cases alarming, of the monetary resources required for public and

private administrations in developing countries to achieve a basic level of urban

infrastructure. These cost figures were conceived as estimations driven by a trade-off

between the efforts required in terms of accuracy improvement attempts and resulting

benefits. This step required a set of computation to estimate the relevant costs such as

materials, labor and engineering involved in closing the urban infrastructure deficits were

based on assumption around two questions. Firstly, how much can be built with the cement

stock per capita gap (as addressed previously) and secondly how much would those

constructions cost. In relation to the first question we estimated that 6.22 square meters of

housing or 21.14 square meters of public-structure (specifically in the form of roads) can

be completed with one ton of cement. These figures are mainly based on the insights of

Kosmatka et al. (2002) and van Oss (2005) for concrete technology, Vanderwerf (2007)

and the Center for Sustainable Systems (2014) for matters related to housing construction;

and Byers (2014) together with the American Society of Civil Engineers (2013) covering

the public-structure related inputs. In relation to the second question, we chose to focus on

square meter references to homogenize findings with the previously define metrics

facilitating the final computations. We estimated a construction cost of USD 352.35 per

square meter for residential construction and USD 53.80 per square meter of public-



XXIX

structure construction as 2013 building cost values based on the corresponding review of

relevant literature and market surveys.

Developing countries – case study

Through the case study we aimed to condensate the knowledge expansion and

methodological results generated during the length of this research through a detailed

review of a selected developing country. This detailed review was expected to

contextualize and validate our findings, primarily through a bottom up approach assessing

the current (2013) urban infrastructure status and projections by reviewing and applying

the Data analysis overview and description outcomes, Urban infrastructure deficit insights

and our long-term cement demand forecast methodology.

RESULTS AND DISCUSSIONS


We clustered the observations by variable type (Cement, Urban infrastructure, Economic

development and Country inherent) with a particular focus on the relation between Cement

stock per capita and the availability of urban infrastructure (as per its variables: Public-

structure quality, Capital stock per capita, and Population living in slums).

Cross-sectional analysis

Cement variables: The first insight to address is on how Cement Stock per capita and

Cement consumption per capita relate to the rest of the variables. The cross-sectional

analysis indicated that the correlations of Cement stock per capita with all other variables

are stronger than those of Cement consumption per capita, particularly in the relation with

economic development variables. This weaker relation between the current level of cement

consumption and economic development of a country suggest the existence of a non-linear

relationship and confirms the expected long-term patterns of cement consumption

supported by the claims Aitcin (2000); Deverell (2012); and Kang and Li (2013) in terms

of cement consumption decline when a certain level of urban infrastructure is reached.

Urban infrastructure variables: The high correlation found between Cement stock per capita

and the Urban infrastructure variables, Public-structure quality (0.84*), Capital stock per

capita (0.88*) and Population living in slums (-0.76*) have a double importance. Firstly,



XXX

related to the social / economic relevance of urban infrastructure deficits claimed by the

World Bank (1994), Canning (1998), Todaro and smith (2012), Srinivasu and Rao (2013),

United Nations (2015) and the International Monetary Fund (2017). Secondly, supports the

initial interpretation of urban infrastructure deficits connection to the shortages of

cumulative cement consumption (cement stock). The tight relation of Urban infrastructure

variables with economic development is illustrated by the high correlation results between

GDP per capita Constant with Public-structure (0.91*), Capital stock per capita (0.90*) and

Population living in slums (-0.74*). The interpretations of these relationships are sustained

by Schawab (2014) and the International Monetary Fund (2017) claims. The strong

correlation between Cement stock per capita and Urban infrastructure variables supports

the general aim of inferring urban infrastructure deficit as a function of historical levels of

cumulative cement consumption in line with Aitcin (2000), Deverell (2012) and Kang and

Li (2013).

Economic development variables: The observed high correlation results between Cement

stock per capita and the four economic related variables (GDP per capita Constant 0.88*,

GDP per capita Current 0.91*, Human Development Index 0.90* and Political stability

0.63*) are a central support to our objective of building a long-term forecasting model. This

positive relation clearly indicates that more economically developed a country is, the higher

the amount of cement placed in the form of concrete buildings forming urban infrastructure.

Among all the thirteen variables, the Economic variables type hold the highest relation with

the cement demand development, particularly when represented by the Cement stock per

capita variable (following the non-linearity aspects mentioned in the initial consideration

of the cross-sectional analysis). The confirmation of the strong relation between cement

consumption and economic development is key a contribution to the objective of building

a cement demand long-term forecasting model based on the inputs of economic growth.

Country inherent variables: Except for Urbanization level, correlation coefficients between

Cement stock and the Country inherent variables resulted low; and not significant in the

case of Elevation. While the positive correlation between Urbanization level and Cement

stock per capita (0.77*) suggest that countries with a high level of urbanization tend to

account for higher levels of cumulated cement consumption, the causation of one over the

other (if any), remains unexplained only in the context of a correlational analysis.



XXXI

According to Chen et al. (2014), Todaro and Smith (2012) and Zhang and Song (2003) it

is the economic development what to drive the Urbanization level, an engine of urban

infrastructure. Consequently, and following this notion, it surfaces as axiomatic that it is

economic development what drives the cement consumption and not the urbanization level.

Descriptive statistics

Within the scope / objectives of this research and resulting from the descriptive statistics

analysis, economic clusters appear to be more homogenous groups in terms of Cement

stock per capita as compared with geographic regions (Figure I).

Figure I. Cement stock per capita -Variation coefficient (standard deviation ÷ mean)

between groups

As previously shown by the outcomes of the correlational analysis, Cement stock per

capita and GDP per capita Constant and Current, both expressions of economic

development, appear to have a very strong positive correlation (0.88* and 0.92*

respectively) being the likely causal variables following the claims of Aitcin (2000) and

International Cement Review (2014). The coefficient of variation (standard deviation ÷

mean) of Cement stock per capita was analyzed within economic groups and geographic

regions to verify the homogeneity and similarity within time-series of advanced economies.



XXXII

Equivalence groups

We obtained a set of four equivalence groups of advanced countries sharing analogous

Cement consumption per capita and GDP per capita Constant time-series to predict the

potential cement consumption in developing countries following (Kahneman and Tversky

(1977) and Duncan et al. (2001) guidance. Firstly, we validated the correlational co-

movement of advanced economies by analyzing the general homogeneity of advanced

economies in relation of Cement consumption per capita, Cement stock per capita and GDP

per capita Constant. Advanced economies showed a higher degree of consumption pattern

similarity for the observed variables as compared with the remaining countries and

appeared to have completed a full consumption cycle (growth-saturation-decline-

maintenance). The application of model-based clustering, the relation between the Cement

consumption per capita at peak and the linear regression coefficient β (additional kg of

cement consumption per capita per ‘000 USD of increment in GDP per capita Constant)

allowed for the formation of prescriptive equivalence subgroups.


The results shown by the pooled models in the empirical validation are promising as the

growth simulation predictions obtained through our approach were substantially more

accurate than the outcomes provided by the standard model in all analyzed metrics: Mean

Absolute Deviation (MAD), Mean Square Error (MSE), Mean Absolute Percentage Error

(MAPE), Variation on the average Cement consumption per capita and Variation of

saturation point measured in numbers of years. The higher prediction accuracy of the

pooled models observed in the Figure II summarizes MAPE and the prediction of saturation

point for both the pooled and the standard models. The findings are in line with the claims

of Hung and Wu (1997), Deverell (2012) and Kang and Li (2013) on the value provided

by observing the historical data of comparable economies to understand long-term demand

patterns. The pooled models predicted with higher accuracy the Cement demand per capita

in every equivalence group with a total average MAPE of 28% as compared with 105%

achieved by the standard models. In terms of predicting the saturation point, whereas the

pooled models’ variation with the actual values averaged 15 years difference, the standard

model’s inaccuracy doubled that of the pooled models (29 years). The average Cement

consumption per capita, also corresponding to the period comprehended between the

saturation point and 2013, shows a higher accuracy for the pooled models. While the



XXXIII

standard model registered a variation of 86% in average for all the advanced economies in

scope, pooled models were more accurate registering a variation of only 19%.

Figure II: Simulation Cement consumption per capita – MAPE and Variation of

saturation point prediction

Urban infrastructure deficit

Reference values

Once the relation between cement stocks and the development of urban infrastructure was

confirmed, we computed the Cement stock per capita levels required for a basic level of

urban infrastructure following the claims of Osenton (2004) in relation to saturation point

and Aitcin (2000), Deverell (2012), and Kang and Li (2013) in relation to basic

infrastructure achievement. As previously described in the research methodology, the

reference value relates to the minimum level of urban infrastructure needed in a country to

deal with basic society needs such as housing, sanitation, transport, health and education.

Subgroup 4

Total average

Saturation point

Subgroup 1

Subgroup 2

Subgroup 3

Subgroup 4

Total average

99% 86%

160%

61%105%

34% 20% 31% 29% 28%

Su

bg

rou

p 1

Su

bg

rou

p 2

Su

bg

rou

p 3

Subgro

up 4

Tota

l av

erag

e

Standard model Pooled model

MAPE [%]

29 27 31 30 29

15 13 17 16 15

Su

bg

rou

p 1

Su

bg

rou

p 2

Su

bg

rou

p 3

Subgro

up 4

Tota

l av

erag

eStandard model Pooled model

Saturation point [years of variation vs actual]



XXXIV

The results indicate that advanced economies built up in average 13.7 tons of cement to

reach a basic level of public-structure. In average, lower-medium-income and low-income

countries account for only 47.4% and 13.1% respectively of the Cement stock per capita

required to constitute a basic level of urban infrastructure.

Estimations of economic cost

The resulting economic cost estimations to narrow the housing and public-structure deficits

in developing countries are alarming. For instance, many Sub-Saharan countries would

need to invest over 7 times their GDP to achieve a minimum functional level of urban

infrastructure. The Figure III illustrates in a heatmap the economic cost expected to narrow

urban infrastructure deficits concentrated in the world’s poorest regions, particularly in the

Sub-Saharan Africa, South Asia and Caribbean (i.e. Haiti).

Figure III: Urban infrastructure deficit in developing countries [2013 - USD per capita]

Residential and non-residential segment: In line with Millennium Development Goals

Report published by United Nations in 2015, we found the current (2013) deficit in

developing countries to be extremely high. As per the estimations resulting from our

methodology, narrowing the residential and non-residential deficit globally would cost

USD ~25.9 trillion (~12.9 trillion into housing and ~12.9 trillion allocated to the non-

residential segment). We found support for our housing estimation on the work of Woetzel

et al. (2014) estimating the total economic resources to narrow the housing gap in



XXXV

developing markets to be circa USD 16 trillion. We consider our results and Woetzel et al.

(2014) findings to be similar, with the difference (USD ~ 3 trillion) potentially driven by

elements of scope.

Public-structure segment: As in the residential and non-residential case, the estimated

economic resources required to mitigate the deficit are staggering. Our results indicate that

a USD 24.7 trillion investment would be required in the long-term. We found our results

in line to those of Bughin, Manyika and Woetzel (2016) when observed in an annual base.

For instance, while Bughin, Manyika and Woetzel (2016) annual investment cost accounts

for USD 874 billion, our total results account for USD 823 billion annually (spread in a

thirty- year period). We decided to annualize the total investment required using a thirty-

year period considering the historical average capital formation cycle undergone in

advanced countries (Samans, Blanke and Corrigan, 2015). We consider the annual

differences of USD 51 billion to be most likely driven by particular pieces of public-

structure excluded from the scope of this research as addressed previously.

Additional cement capacity: As per 2013 figures, we found that in total 33’435 million tons

of cement would be required globally to achieve a minimum level of urban infrastructure

in developing markets. When annualized in a thirty-year period, additional 1’115 million

should consumed by these countries. Although according to the Global Cement Report

(2015), current cement industry installed capacity (5’695 million tons a year, 2014 figures)

should match new demand level, many elements such as reserves depletion and pollution

would limit the industry ability to deal with the incremental volumes constituting a

substantial supply and demand unbalance.

Developing countries – Nigeria’s case study

Nigeria was chosen among other developing countries to condensate the knowledge

expansion and methodological results generated during the length of this research due to

three specific factors around its potential growth profile: Firstly, Nigeria has one of the

largest populations of youth in the world. Secondly, the urban infrastructure deficit in

Nigeria is alarming with most of its population living in inadequate houses and an almost

non-existing public-structure. Thirdly, Nigeria’s abundance of natural resources (e.g.



XXXVI

Africa’s biggest oil exporter) could support its long-term economic growth during its

transition towards a Middle-High income economy (source: World Bank, 2017).

Nigeria’s extreme urban infrastructure deficit

Nigeria’s urban infrastructure metrics indicates very low levels of development. As per

2013 figures, Nigeria’s Cement stock per capita lagged 10.7 tons behind the reference

values to achieve a basic level of urban infrastructure. Whereas its Public-structure quality

level accounts for almost one third of that of advanced countries, its Capital stock per capita

accounts for insignificant USD 5’700 compared with USD 95’100-average in advanced

economies. These shortages on the cumulative cement consumption translate in the need

for substantial investments to narrow the current urban infrastructure alarming deficits.

Figure IV: Nigerian urban infrastructure deficit (2013) – results vs. literature review

According to our estimations, Nigeria would require a total expenditure of USD ~707

billion for housing development and USD ~1.3 trillion for public-structure development

(USD 45.4 billion when annualized in a 30-year period) to improve current sub-standards.

We found support to our results in the work of the Olotuah and Taiwo (2015) for the

estimations of housing cost and the African Development Bank (2012) in relation to the

public-structure requirements (Figure IV).

Literature review

Infrastructure

Results

Literature review

707.0 757.5

Res

ult

s

Lit

erat

ure

revie

w

Housing - USD billion

45.454.5

Res

ult

s

Lit

erat

ure

rev

iew

Infrastructure - USD billion

annualized



XXXVII

Potential growth of Nigerian cement demand

The implementation of our cement long-term forecasting model was executed following

the guidelines described the Research methodology section: Prescription of equivalence

subgroup, definition of economic growth scenarios and generation of long-term cement

consumption forecast.

Prescription of equivalence group: The first step towards the implementation of our cement

long-term forecasting model to assess the potential future developments of Nigerian cement

market was to identify its equivalence sub-group (Duncan et al., 2001). Nigeria’s β

coefficient of 81.78 (additional kg of Cement consumption per capita per USD 1’000

increases in GDP per capita Constant) appeared to correspond to the growth pattern of

prescriptive subgroup 1.

Definition of economic growth: We developed three different economic growth scenarios

(High, Base and Low growth) to provide the independent inputs (GDP per capita Constant)

to the subgroup pooled model referencing our assumptions to the Nigerian’s 6.6 % annual

growth registered during the 10-year period corresponding to 2003-2013. Considering the

estimative nature of our model, we assumed a band contained by high and low scenarios

from a potential 6.0% base of annual growth as follows. High economic growth scenario:

8.0% per annum; Base economic growth scenario: 6.0% per annum and Low economic

growth scenario: 4.0% per annum. We retained relevant to clarify that our aim during this

exercise was not to predict the long-term economic growth in Nigeria, but rather to

understand the potential cement demand behavior under different economic growth

assumptions.

Long-term cement consumption forecasts: Three long-term Cement consumption per

capita forecasts were generated using the pooled model prescription for the sub-group 1

and the economic growth scenarios (independent inputs - GDP per capita Constant)

developed in the previous step. We focused on the observation of four main metrics at

saturation point and compared them with the historical reference of the advanced countries

conforming the subgroup 1 (Table III). In the High economic growth scenario, the Cement

consumption per capita expects 27 years of sustained growth (2014-2041) until its

saturation point reaching a Cement stock per capita of 18 tons. At saturation point,



XXXVIII

Nigeria’s GDP per capita Constant accounted for USD 17’200 and a Cement consumption

per capita of 1’001 kg. In the Base economic growth scenario, the Cement consumption

per capita expects 37 years of sustained growth (2014-2051) until its saturation point

reaching a Cement stock per capita of 24 tons. At saturation point, Nigeria’s GDP per capita

constant accounted for USD 18’300 and a Cement consumption per capita of 1’002 kg. In

the Low economic growth scenario, the cement saturation point is reached in the year 2063

after 49 years of sustained growth reaching a GDP per capita Constant of USD 18’700, a

Cement consumption per capita of 1’000 kg and a Cement stock per capita of 35 tons.

Table III: Comparison of Nigerian and subgroup 1 forecasts

Forecast Time length to

peak [years] –

for subgroup 1

commencing in

1945

At saturation point

Stock per

capita [tons]

Consumption

per capita [kg]

GDP per capita

Constant

[USD]

Subgroup 1 43.25 16 897 15’000

Nigeria-High 27 18 1’001 17’200

Nigeria-Base 37 24 1’002 18’300

Nigeria-Low 49 35 1’000 18’700

Implications for the Nigerian cement industry

We analysed the current (2013) and potential supply and demand balance for the high

growth scenario at saturation point (2041) factoring Nigerian’s population growth.

According to the United Nations (2017), Nigerian’s population by 2041 is expected to

reach ~333 million people. Under the high economic growth scenario, Nigerian

Consumption per capita was estimated at 1’001 kg, which would correspond to a total

demand of 333.3 million tons per annum (Figure V). To cope with this growing market, we

estimate that the Nigerian cement industry would be required to install additionally ~12.4

million tons of capacity every year, corresponding to an investment of 1.9 to 2.5 USD

billion in production capacity only (Alsop, 2005).



XXXIX

Figure V. Nigerian cement supply and demand balance in 2013 and 2041

CONCLUSIONS, LIMITATIONS AND FURTHER DEVELOPMENTS


Through the implementation of our research methodology we were able to validate the

adequacy of forecasting by analogy to predict the potential cement consumption

development in developing countries (Kahneman and Tversky, 1977 and Duncan et al.,

2001). Our methods appear to tackle the problematic of cement long-term forecasting

raised by Hung and Wu (1997) and Birshan et al. (2015) leveraging on the initial findings

of Aitcin (2000) regarding economic development and cement consumption relation. While

our analogy forecasting methodology showed valid results for the cement long-range

forecasting models, we retain that its accuracy could be improved through: The use of

shrinkage factors (Duncan et al., 2011) to facilitate adjustment to countries’ specificities;

Emphasizing on the identification and screen out of disruptive economic growth events to

improve the predicting strength of our model’s coefficients (Armstrong, 2001); Capturing

the maintenance level following the demand decline, we suggest the potential

implementation of a higher degree for the pooled models (i.e. polynomial regression grade

three) or alternatively limit the cement demand decline to a maintenance reference value;

Lastly, while the nature of our model is prescriptive, practitioners and other potential

interested users of our methodology are suggested to adjust the subgroup models to a

specific market by selecting countries’ time-series that appear to relate the most to the target

country (when possible).

Capacity 2013

Under capacity 2041

Potential consumption 2041

21.2 21.1

333.3312

Co

nsu

mp

tio

n

20

13

Cap

acit

y

20

13

Un

der

cap

acit

y

20

41

Po

ten

tial

con

sum

pti

on

20

41

Cement supply and demand balance: 2013 and 2041

[million tons]



XL


Our findings validated the general aim of inferring current and future urban infrastructure

deficit in developing countries as a function of cumulative cement consumption supported

by Osenton (2004) claims on the notion of natural consumption (saturation point). The

construction of reference values for a basic level of urban infrastructure allowed us to

diagnose alarming shortage of global cement consumption. Out of the 129 countries in this

research-work’s scope, 76 countries suffer Cement stock per capita deficits (mainly

concentrated in Africa and Asia) with different levels of severity. Following a set of

assumption described during the research methodology, we estimated that while USD

~25.9 trillion would be required to narrow deficits in the residential (USD 12.9 trillion) and

non-residential (USD 12.9 trillion) segments, USD 24.7 trillion should be invested to

improve public-structure in the long-term. Although the estimations’ outcomes appeared

to be aligned with other reviews elaborated through bottom up approaches, we retain some

adjustments could improve the robustness of the findings while triggering a continuation

with new research lines. The first improvement opportunity comes from a better

understanding of the saturation point in the cement consumption. While we used a

systematic procedure during the identification of saturation points of cement demand, we

still consider that other alternative procedures could be developed and implemented to

increase the validity of our reference values. In terms of applying the use of our reference

values to estimate urban infrastructure, we suggest practitioners willing to adopt our

findings to benefit from the granularity we provided through the equivalence subgroups to

increase diagnosing accuracy (use of individual values of each prescriptive subgroup). For

the estimation of the economic costs related to narrow the urban infrastructure deficits, we

also encourage practitioners to consider the use of revised figures adjusted to specific

market conditions for the assessment of an individual country deficit (city or region) to

increase the accuracy of predictions. Finally, in our assumptions for the segment relevance

we relied mainly on the claims of Samans, Blanke and Corrigan (2015) on public

expenditure in public-structure cycle; however, when analyzing an individual country, we

suggest considering potential factors that could influence the linearity of our 65%:35%

ratio assumption until the saturation point of cement consumption is achieved.



1

1 INTRODUCTION

This Executive-PhD thesis summarizes the main efforts of a four-year research project

commenced in the year 2013 with the instrumental support of Prof. Caniato and Prof.

Kalchschmidt. Primarily, this initiative fulfils the knowledge transfer agreement signed

between Holcim Ltd. and Politecnico di Milano with the aim of providing an adequate

environment connecting professional industrial experience with the highly scientific

structural support provided by Politecnico di Milano’s department of Management,

Economics and Industrial Engineering. This research project was a part-time undertaking

aimed to provide a contribution to the research field and to the practice, both from a

managerial and a policy/institutional standpoint.

As a corporate strategist executive with Holcim Ltd., the leading cement manufacturer, I

was exposed several times to the economic complexities of urban infrastructure

development in developing countries, the devastating social implications of its deficit, and

its tight relation with the cement consumption. For instance, although the population living

in slums has decrease in the last years as percentage of total, the number of people living

in unacceptable conditions has increased due to population growth and large urbanization

processes taking place in the developing word (United Nations, 2015). As per 2014

estimates, 881 million adults and children (United Nations, 2015) live at significant risk of

being exposed to the life-threatening conditions of urban infrastructure deficits (Todaro

and Smith, 2012; and United Nations, 2015). According to Aitcin (2000); Deverell (2012);

and Kang and Li (2013), the cement demand reaches a saturation point following the

completion of major parts of the residential, commercial and public structure

developments. Based on this argument, we used references of historical consumption in

advanced economies to quantify the current deficit on urban infrastructure in developing

countries and to simulate potential demand evolution through the application of analogy

forecasting methods (Duncan et al, 2001; and Kahneman and Tversky, 1977). A main

assumption of this research project is that while the level of urban infrastructure and

Cement stock per capita (cumulative cement consumption) are closely related, the cement

demand cycle in advanced economies has similar pattern regimes. This thesis is expected

to contribute to the research field by providing the necessary insights to diagnose urban

infrastructure deficit in developing countries while offering a valid long-term forecasting



2

methodology for cement consumption. A proper understanding of deficiencies on the

cement consumption and its potential development is crucial for both the public and private

sector. While governments need a diagnosis of the current urban infrastructure shortages

and a valid forecasting methodology for planning resource allocations, industrial

companies can benefit largely from properly assessing attractive investment opportunities.

Although we centered the forecasting part of this research on cement consumption, we

expect some of our findings to be of particular interest to other forecasting problems

holding similarities to the main dynamics affecting the cement demand. We retain our

results to be beneficial for the study of new product launches, intermediate products

(producers-durable goods) and the demand in the upper stages of the supply chain where

accounting for the stock in lower stages of the supply chain is required. A summary of this

research’s structure is provided in the section 1.2.1 (Thesis structure) for the readers’

convenience.

Preliminary versions related to this research project were presented in academic

conferences. The first paper, focused on the assessment of alternative demand forecasting

methodologies, was presented in June-2015 during the 2nd Annual European Doctorate in

Industrial Management Conference (EDIM) held at Politecnico di Milano. The second

paper, focused on estimation of urban infrastructure deficit in developing countries, was

presented in July-2017 during the Restructuring of the Global Economy Conference

(ROGE), held in University of Oxford. This second paper was awarded as best presentation

for the track Growth and Economic development (plenary session). In the next paragraphs,

the main aspects related to the research background are briefly covered together with the

overall thesis organization synapsis.



3

Research background

The strong relation between cement consumption and urban infrastructure level (Aitcin,

2000, Deverell, 2012; and Kang and Li, 2013) suggests that a correct understanding of the

cement demand long-term cycle could allow for the diagnose of urban infrastructure

deficits.

1.1.1 A fundamental construction material

The Cement overview section (2.1) offers a complete review of cement from its origins in

Neolithic-hunter campfires (Courland, 2011), to the modern economics of the cement

industry, passing through its production process and common substitutes. However, the

most relevant introductory facts of cement are centered on two elements, its diffusion and

durability. In terms of diffusion, cement is the key ingredient in concrete production, the

most widely manmade used material in the world due to its versatility, availability and

relatively low cost (Arezoumandi et al., 2013). In terms of its durability, in the form of

urban infrastructure, cement concrete lasts for many years before replacement is needed

(Courland, 2011). As described in the section 2.3.2 (Specificities of the cement demand in

the context of forecasting), modern concrete lifespan depends on its quality but also on the

environment. For example, while Monteiro (2013) claimed that current buildings are

designed to last 100 to 120 years, Courland (2011) argued a duration of 75 to 100 years

before demolishing is needed.


Economist, 2013), and although its supply was concentrated in a few global companies, the

industry has not necessarily created values for its companies or investors in their pursuit of

a growth marked with chronic overcapacity (Birsham et al., 2015). As other capital-

intensive industries, the expensive assets used in the cement production require large

operations to avoid high production cost per unit (Grant, 1991). Therefore, considering the

financial risks and the logistics limitation inherent to the cement industry (Caniato et al.,

2011), producers’ accurate demand forecasting is crucial to achieve positive financial

return.



4

1.1.2 Consequences of urban infrastructure deficit

Urban infrastructure refers to all buildings intended to provide housing or public-structure

in the form of physical components and systems serving a country (Fulmer, 2009), and thus

its deficit holds serious social and economic consequences affecting the life of millions of

people (Todaro and Smith, 2012).

As argued by Todaro and Smith (2012), the poorest urban dwellers are often at greater risk

of being exposed to dangerous conditions in slums. Woetzel et al. (2014) estimates that

worldwide 330 million urban households lack of decent housing or are financially stretched

by housing costs at a level of giving up other basic needs. If current trends of urbanization

and income growth persist, the affordability housing gap will grow up to 440 million urban

households by 2025 affecting 1.6 billion people (Woetzel et al., 2014). Although the

portion of urban population living in slums in the developing regions fell to ~30 percent in

2014 from ~39 percent fifteen years before, the absolute numbers of urban residents living

in slums continue to grow (United Nations, 2015).

Besides the problematic of housing, urban infrastructure deficits are also largely driven by

a global decreasing in public-structure investment. The alarming claims from the

International Monetary Fund in its World Economic Outlook (2014) indicated that stock of

public capital, which mainly reflects the public-structure availability, has declined

significantly as a share of output over the past three decades in advanced, emerging and

developing economies. While progress has been made in terms of basic needs, 768 million

people still lack access to clean water and 2.5 billion to adequate sanitation. Green et al.

(2015) argued that the world needs more public-structure than governments can deliver

estimating USD 57 trillion required to build new and refurbish existing public-structure

between 2013 and 2030 (including advanced and developing economies).

1.1.3 Cement demand forecasting

Current forecasting methodologies offer several qualitative and quantitative options;

however, these approaches appear inadequate to forecast cement long-term consumption.

Current practises ignore the historical demand patterns in other countries missing the value

of external view (Kahneman and Tversky, 1977). The work of Hung and Wu (1997),



5

Deverell (2012), Kang and Li (2013) and Birshan et al. (2015), revealed that ignoring the

historical demand evolution in other countries, particularly mature economies, limits the

understanding of cement demand long-term patterns.

Following previous arguments and Aitcin (2000) claims that establishes a direct

relationship between the cement consumption and the economic development of a country,

we based the theoretical foundations of this research on the work of Kahneman and Tversky

(1977) on reference class forecasting and Duncan et al. (2001) on analogy forecasting. The

absence of a known and diffused cement forecasting methodology based on the “external

approach”, which treats the specific problem as one of many (Kahneman and Tversky,

1977), opened a contribution opportunity to build prescriptive long-term technique

theoretically sustained by analogy forecasting methods.

The fulfilment of our primary objectives required additional inputs to the current literature

in the form of a data or methodology. Therefore, in the theoretical framework section (3.4)

of this research, we proposed a set of four research questions whose answers facilitated

both the construction of a long-term forecasting model and quantitative assessment of

urban infrastructure deficit in developing countries.

Thesis structure and general considerations

1.2.1 Thesis structure

The thesis has been organized around building continuous knowledge from most general

concepts related to the cement industry and urban infrastructure components to the

necessary specific insights that sustained the methodological approach. The Figure 1

illustrates the main building blocks that form the core structure of this research project

together with its corresponding flow.



6

Figure 1. Thesis structure - illustration

The literature review section (2) was aimed to initiate the reader to the main objectives of

this research from a content perspective, to provide the fundamental supporting concepts

and current state of the art covering the cement product and industry, the economic and

social relevance of urban infrastructure and the general and cement specific forecasting

methodologies.

The theoretical framework section (3) was organized to describe the work of Kahneman

and Tversky (1977) on reference class forecasting and Duncan et al. (2001) on analogy



7

forecasting while revealing the objective of its selection among other methods. The same

section contains the development of the research questions based on the gaps identified

during the corresponding literature review.

The section 4 elaborates on the research methodology, designed to answer the research

questions through the combined findings of the exploratory analysis supporting all phases

of the explanatory analysis. While the exploratory analysis aims to familiarize the reader

with the main characteristics of the datasets, variables and objectives, the explanatory part

covers the analytical backbone of this research (both the processes to quantify urban

infrastructure deficit in developing countries and the long-term forecasting model for

cement consumption).

The results and conclusions are presented in the section 5, where we focused on

highlighting the main stablished relations between Cement, Urban infrastructure,

Economic development and Country inherent variables, the adequacy of the cement

demand forecasting methodology based on analogies and the findings in terms of urban

infrastructure deficits in developing countries.

Finally, the Conclusions, limitations and further developments section (6) bundles up the

most relevant concluding aspects of this research work, particularly in terms of contribution

novelty to the field in combination with a set of recommendations aimed to trigger the

continuation of this research line. We expect new research projects to eventually confirm

and improve the robustness of our findings with a city granularity level approach supported

with bottom-up methodologies when necessary and possible.

1.2.2 General considerations

Software and on-line platforms

Literature review was mainly supported by abstract and citation databases of peer-reviewed

literature such as Scopus and other platforms as ResearchGate. Computations have been

mainly carried out throughout the use of STATA/SE statistical software package and

Microsoft 360 Excel software particularly for correlation and regression analysis.



8

The use of interchangeable words

Through the body of this thesis, the words country, economy, nation and state are used

interchangeably in alignment with the World Bank definitions, and thus referring to a

territory with separate statistics but not necessarily politically independent. The words

demand and consumption are as well used interchangeably. In this research, both the

expressions “total” and “per capita” demand or consumption, refer to the “market demand”,

and not to the “individual demand” in the sense of consumer overall willingness to buy a

product as its prices changes (Pindyck and Rubinfeld, 2005).

Countries classification

Although the economic and geographic classifications of countries are thoroughly

addressed in the section 4.2.1, we retained useful for the reader to provide an advance on

the main criteria. Developing country refers to any country which is not considered and

advanced economy as per the International Monetary Fund economic classifications. In

some cases, and when duly clarified, developing countries will refer particularly and only

to low and lower-middle income countries following the World Bank economic

classifications.

Units of measures

Along this document, the word ton refers to metric tons. Cubic meter / m3, kilograms / kg,

kilometer / km and percentage / % are used interchangeably. USD refers to the United

States dollar and all values and metrics correspond to the year 2013 unless is specified

accordingly.

Variables

The thirteen variables used throughout the analysis of this research and fully described in

the section 4.2.2 (Datasets and variables) are always shown capitalized to distinguish them

from other concepts sharing similar wording. Therefore, while Cement consumption per

capita refers to the variable under its provided definition, the term cement consumption

refers only to its plain meaning.



9

Abbreviations

Abbreviations are generally avoided for the readers convenience, except for Gross

Domestic Product (GDP) and Gross National Income (GNI) as per its extensive use during

the thesis.



10

2 LITERATURE REVIEW

The literature review is aimed to cover three central topics providing understanding of their

objective within this research, main supporting concepts and current state of the art. The

most relevant literature was reviewed through the use of abstract and citation databases of

peer-reviewed literature such as Scopus, ResearchGate, specialized publications and other

relevant material.

The section 2.1 provides firstly, a cement overview from a product perspective

addressing its historical origins, modern production process, substitutes,

supplementary materials, the economics of the business from investment to pricing,

and industry dynamics including markets and main cement producer companies

with a global presence.

Considering the close relation between the level of cement consumption and

resulting urban infrastructure quality and availability, in the section 2.2, the

economic relevance of urban infrastructure is reviewed from the perspective of

cement as an enabler. We explore the social implications around urban

infrastructure deficits, and the economic relevance of both urbanization and public-

structure development as drivers for urban infrastructure development.

The section 2.3 is structured around the substantive findings on demand forecasting

methodologies, the specificities of the cement demand in the context of forecasting

and lastly the limitations of current techniques for cement demand forecasting.

While cement is considered a durable good, its particularities such as durability and

application as intermediate material are addressed from a consumption point of

view.



11

Cement overview

2.1.1 The product

Cement is a key ingredient in the concrete production, the most widely manmade used

material due to its versatility, availability and relatively low cost (Arezoumandi et al.,

2013). When used to build urban infrastructure, cement concrete lasts for many years

before replacement is needed (Courland, 2011). As described by van Oss (2005), hydraulic

cements are binding agents used in concrete and most mortars production. Under specific

proportions (Figure 2), the mix of cement, water, aggregates and sometimes additives

results in concrete properly used to build highways, streets, parking areas, bridges, high-

rise buildings, dams, homes, floors, sidewalks, driveways, and numerous other applications

(Kosmatka et al., 2002).

Typically, 1 ton of cement yields 3.4 to 3.8 cubic meters of concrete, weighing 7 to 9 tons

(van Oss 2005). The Center for Sustainable Systems (2014) describes the magnitude of the

cement concrete use with the following two illustrations of the early 2000`s. While the

United States average single-family house built in year 2000, required 19 tons of concrete,

all commercial buildings constructed in 2001 used in total 19 million tons of cement.

Although cement is an intermediate product used for the production of concrete or mortars,

and aggregates happen to be the largest component for concrete production, through the

following paragraphs we described why this research is centered on cement.

Figure 2. Customary proportions of materials used in concrete (source: Portland Cement

Association)

15% 18% 8% 28% 31%

Cement Water Air FineAggregates

CoarseAggregates

Air entrained concreteby absolute volume



12

Firstly, as argued by van Oss (2005) it is the hardened cement paste that binds the

aggregates together which serve mainly as low-cost fillers resulting in all of the concrete’s

strength of the concretes. The relevance of cement within the mix is similar to that of the

concrete case when it comes to hydraulic mortars, which is used in masonry construction

to bind together bricks, blocks and stones. The difference is that only fine aggregates are

incorporated and the cement used is more plastic in character.

Secondly, whereas cement is mainly single-purpose material for its use in construction,

aggregates are also used in the chemical-metallurgical industry, for agricultural purposes

to neutralize soil acidity while stimulating fertilizers efficiently and for several other

applications (Lafarge, 2013). In the case of concrete, although it is almost exclusively used

in construction, available information related to its demand naturally excludes mortar

volumes, and thus ignoring an important portion of cement consumption.

Thirdly, in addition to the cement higher relevance as a material in terms of technical

properties and specific use in construction, other drawbacks for the concrete and aggregates

data gathering are originated in their industrial characteristics. The aggregates and concrete

commercial activities tend to operate at elevated levels of informality derived from their

fragmented supply base and often low law enforcement, particularly in emerging countries

and likely in the past decades of current advanced countries (Hartley, 2009).

2.1.2 Cement history at a glance

As argued by van Oss (2005), although the use of different cement types in constructions

is a very old practice, there are still uncertainties on who first used it and in what form.

Courland (2011) situates the most popular theory on lime’s discovery at a Neolithic

limestone campfire scenario. He argues that after weeks of a sustained fire, the hunters-

gatherers might have noted how the stone next to the flames becomes dissected and breaks

off into clumps that collapse into a white powder. The serendipity description continues

with these modern humans eventually understanding that in combination with water, this

white powder hardens back. The following paragraphs summarize van Oss (2005) synopsis

of the cement history which condensed the work of several other authors such as Lea

(1970), Bogue (1955), Klemm (2004), Lesley (1924) and Wilcox (1995).



13

Ancient applications

Although the Romans are recognized for a leap improvement in both the quality and

applications of hydraulic cement, there are previous records of its use. The first binder

likely used for blocks laying was the plain mud still in use mainly for adobe construction

in many parts of the world and bitumen in some parts of ancient Mesopotamia. With some

differences, earliest use of mortars took place in Egypt (crude gypsum), Greece and Crete

(lime mortars). Archaeological sites in Egypt revealed that some building foundations were

even made of gypsum concrete using limestone quarry and/or construction debris as coarse

aggregates.

Following their learning on various types of mortars from the Greeks, the most significant

improvement towards the diffused used of cement was the Roman use of pozzolan-lime

cements incorporating volcanic ash. The use of pozzolana, volcanic ash quarried from the

Vesuvius slopes near the village of Pozzuoli, provided pozzolan-lime cement with high

strength allowing for significant increase in its coarse content. The use of pozzolan-lime

mortars and concrete throughout the Roman Empire varied in applications from sea walls

for artificial harbours to nowadays standing buildings such as the Pantheon in Rome.

Post roman cement

During centuries after the collapse of the Roman Empire, sporadic interest remained in

reproducing the characteristics of the old Roman cements. Active research into improving

the quality of cements was active in Western Europe by the eighteenth century, when John

Smeaton discovered that strong hydraulic lime mortar could be made from calcining

limestones rich in clay, giving birth to natural cement.

Until the mid-19th century, natural cement remained the dominant cement type in most of

Europe and the U.S. Smeaton’s discovery inspired research into ways to improve the

quality, and/or reduce the variability, of natural cements leading to the making of so-called

artificial cement, most probably through the contribution of French engineer Louis J. Vicat.

His research showed how high quality hydraulic limes could be made from ordinary

limestone, in the absence of argillaceous limestones or cement rock.



14

Portland cement

Along with other several patents issued in England and France for various types of

hydraulic limes and cements, one was granted to Joseph Aspdin, an English brick mason

and experimenter for a product that he called Portland cement, a reference alluded to the

Portland stone. By the mid-1840s Several efforts to duplicate the quality of Aspdin’s

Portland cement yielded in important ideas, particularly the need to heat the raw materials

to much higher temperatures than needed to calcine the limestone. Nowadays only

superficial similarities remain between modern portland cements and Aspdin’s, however

the name has been retained by the industry due to its reputation and the modern material

being still an artificial cement made from limestone and argillaceous raw materials.

Market growth

By the end of the nineteenth century, two inventions prepared the ground for the adoption

of cement as a main construction material. While reinforced cement opened up new uses

for concrete such as high-rise buildings and suspended slab structures, the invention of the

rotary kiln allowed increasing the capacity substantially. However, it was not before the

second half of the twentieth century that the cement production commenced a strong

growth. As can be observed in the in Figure 3, while Western Europe initially led the

regional growth, since 1980 Asia has experienced a tremendous cement consumption

growth mainly driven by China, which accounts for half of total world consumption. The

lower absolute consumption observed for the Americas appears to be a combination of two

elements. While the population in the Americas accounts for almost double the European,

its proportionally lower cement consumption appears to be related to the deficit in

consumption across the whole continent, where only Canada and United States are

advanced economies out of the twenty-nine countries in scope. Nowadays, almost all

cement produced is portland cement or portland-based with the five top producers being

China, India, the United States, Iran and Turkey (U.S. Geological Survey, 2015).



15

Figure 3. Cement regional consumption (source: Global Cement Report, 2015 and U.S.

Geological Survey)

2.1.3 Modern production process and cement types

Nowadays portland cement production is the result of pulverizing clinker, which consists

primarily of hydraulic calcium silicates but also contains some calcium aluminates, calcium

alumino ferrites and one or more forms of calcium sulfate (gypsum) added in the final

stages. Since Aspdin’s invention, the process to mass produce portland cement has

incorporated multiple and complex steps.

Cement modern production process

The exhaustive work of Kosmatka et al. (2002) describing the characteristics of the cement

production is briefly summarized below (Figure 4).

Calcareous (e.g. lime stone) and argillaceous materials (e.g. clay) are transported

from the quarry, crushed, milled, and proportioned into a mixture with the desired

chemical composition.

The ground raw material is fed into the upper end of a kiln which could be of wet

of dry process depending on the water content of the blend. The mix passes through

the rotating kiln where the burning fuel forced into the lower end of the kiln raises

Africa

Oceania

-

500

1'000

1'500

2'000

2'500

3'000

3'500

4'000

19

35

19

39

19

43

19

47

19

51

19

55

19

59

19

63

19

67

19

71

19

75

19

79

19

83

19

87

19

91

19

95

19

99

20

03

20

07

20

11

Europemillion tons

Asia

AmericasAfrica-Oceania



16

temperatures between 1400°C and 1550°C changing the raw material chemically

into cement clinker.

The clinker is cooled and then pulverized simultaneously with the addition of small

amount of gypsum to regulate the setting time of the cement and to improve

shrinkage and strength development properties. In the grinding mill, clinker is

ground to a fine grey powder called portland cement.

Figure 4. Cement production process

Cement types

There are a number of different types of hydraulic cement, most of which are still in use

today, at least occasionally. Based on van Oss (2005) descriptions, the following list

summarizes the main cements types.

Pozzolan-lime cements: These include the original Roman cements and are an

artificial mix of pozzolans with lime. Little, if any, of this material is currently

manufactured.



17

Hydraulic lime: This lime is made from the calcination of clay-rich limestones.

Hydraulic lime was the active ingredient in natural cements and today is used in

some specialized mortars applications, but is only produced in a few locations.

Natural cements: Made at different temperatures, these cements are made out of

argillaceous limestone or cement rock or intimately interbedded limestone and clay

or shale, with few, if any, additional raw materials. Due to the absence of significant

blending of raw materials, the quality of natural cements varies regionally is mainly

dependent on the composition of the local cement rock. Most natural cement plants

worldwide switched to the production of portland cement because of its recognized

superiority.

Portland cement: As previously described, Portland is currently the most

commonly used cement and its manufacture involves the uniform mixing of raw

materials. This allows for standardized properties of the finished cement, regardless

of where it was made. The American Society for Testing and Materials denotes five

different types of portland cements depending on their properties, being the type I

known as ordinary portland cement.

Portland-limestone cements: These cements are formed by combining large

amounts (6% to 35%) of ground limestone to portland cement. Portland-limestone

cements are in common use in Europe for relatively low strength construction

applications.

Blended cements: Blended cements, also called composite cements, are mixes of

a portland cement (generally Type I) with one or more supplementary cementing

materials such as slag, silica fume or fly ash commonly making up about 5%–30%

by weight of the final blend.

Masonry cements: These are portland based cements to which other materials have

been added to for plasticity. The most common additives are unburned ground



18

limestone and/or lime. Masonry cements are used for blocks binding and space

control in masonry construction.

Aluminous cements: Made from a mix of limestone and bauxite as the main raw

materials, aluminous cements are used for refractory applications and fast-

hardening concrete. These expensive cements are produced in small quantities for

a few companies worldwide.

2.1.4 Cement substitutes and supplementary materials

Portland cement competes as a construction material with concrete substitutes, such as

aluminum, asphalt, clay brick, fiberglass, glass, gypsum, steel, stone, and wood. However,

the largest substitution factor to portland cement comes from the increasingly use of some

materials, especially fly ash and ground granulated blast furnace slag, which due to its

hydraulic cementitious properties is being used as components of finished blended cements

(U.S. Geological Survey, 2017). Besides the economic reasons behind the adoption by

cement producers of supplementary cementitious materials, there is also a relevant

environmental factor. As argued by Lothenbach et al. (2011), the use of these leads to a

significant reduction in CO2 emissions per ton of cementitious material where no

additional clinkering process is required. In the case of global producer Holcim, now

forming part of LafargeHolcim, the adoption of cementitious supplement led to a clinker

factor reduction from ~80% in 1990 to ~70% in 2010, and consequently to a CO2 reduction

of 21% per ton of cement produced (Holcim, 2011). In terms of the future development of

cement concrete consumption, while the clinker factor might experience further but mild

decreases through new technologies, the sustained adoption of cement concrete as a main

construction material is not expected to change considerably in the next decades.

2.1.5 Economics of the cement business

The cement industry is highly sensitive to business risks associated to its intrinsic

characteristics, particularly the required expensive assets, high energy consumption,

environmental impact and logistic (market) limitations. Therefore, an accurate

understanding of future demand development is crucial to achieve positive financial return.



19

In the following paragraphs, the main profitability drivers in the cement production

industry are addressed.

Investment required and production cost

As other capital-intensive industries, the depreciation of expensive assets (Figure 5) used

in the cement production require large operations and a continuous production process to

avoid high production cost per unit through efficiency (Grant, 1991). A small cement plant

able to produce 1 million tons a year would cost around USD 200 million (The Economist,

2013). In other words, the cost of constructing of a new production line is equivalent to

more than two years of sales at its full capacity (Lafarge, 2014), leading to a payback over

investment ranging from 10 to 30 years.

Alsop (2005) provides additional investment cost insights from a per-ton perspective.

Where green-field cement plants cost USD 150-200 per ton of annual production, kiln

expansions account for USD 80-150 depending on the excess capacity of ancillary

equipment and storage. As per Alsop (2005) estimates, half of the cost is driven by the

purchase of production equipment (quarrying, milling, kiln, storage and electrical controls),

while the remaining accounts for erection, freights, buildings, capitalized costs, spares and

contingencies. Consequently, large continuous manufacturing process required to achieve

low unitary cost are often at risk of underutilized capacity unable to contain fixed costs

(Betts, 2009).

Figure 5. Typical cost structure of producing grey cement (source: Jeffries, 2012)

28% 12% 33% 27%

DepreciationRaw materials and consumables

Energy Labour, maintenance and other production

costs

Based on 2011 figures



20

Although the raw materials costs represent a large portion of the total production cost, its

relevance holds a higher predictability as compared with other cost items (i.e. energy).

Quarrying expenditure are usually materialized in long-term royalty agreement or other

type of mining rights ownership contracts issued by public administrations. On the other

hand, energy cost appears as a more critical cost item. Cement production requires

enormous amounts of both thermal and electrical energy. According to Madlool et al.

(2011), cement industry consumes approximately 12% to 15% of total industrial use of

energy, and thus, acute swings in energy cost can erode profitability up to loss-levels. In a

typical modern cement plant, thermal energy (i.e. used during the burning process)

accounts for 20% to 25% of the cement production cost, with the electrical energy

consumption (i.e. used grinding process) being about 110 to 120 kWh per tonne of cement

produced (Madlool et al., 2011).

Besides its environmental / polluting impact, CO2 emissions pose a high economic risk for

cement manufacturers. The cement industry is considered one of the largest CO2 producers

emitting volumes as high as 5% of total global anthropogenic CO2 emissions (Gartner,

2004). The third phase of the European Union Emission Trading Scheme corresponding to

the 2013 to 2020 is expected to increase the CO2 cost pressure as the certificates allowances

for manufacturers converge. During July 2017, the price of the CO2 certificates

corresponding to the European Union Emission Trading Scheme traded over five Euro per

certificate.

Distribution

Because of its product bulky characteristics, the cement business holds logistics limitation

inherent to the industry (Caniato et al., 2011). Transportation of cement tends to be costly

limiting the size of efficient plants despite the presence of economies of scale.

Consequently, distribution costs are therefore balanced out against economies of scale to

determine the radius a plant can economically serve (Porter, 1980). As described previously

in section 2.1.3, an additional logistic complexity in the cement production is driven by the

technical need to locate a plant over a limestone deposit. While according to Kolb (2001),

cited in Alsop (2005), limestone occurrence is frequent (e.g. covering 1.5% of earth crust),

dismantling a transferring a cement plant to a more attractive market is a rare event.



21

Although small cement volumes are shipped longer distances through waterways, a typical

cement plant is competitive in a radius of no more than 300km for the most common types

of cement (LafargeHolcim, 2015). The seaborne volumes traded across borders are

estimated to be only around 3% of global production (The Economist, 2013).

Pricing – main drivers

Besides cost structure elements such as raw material, labour and energy, due to its local-

market characteristics and required economies of scale, cement prices are specifically

affected by excess of capacity. As argued by Grant (1991), when fixed costs are high

relative to variable costs as it happens in the cement production, firms will accept marginal

business opportunities at any price that covers variable cost, with disastrous consequences

on profitability.

The addition of new production plants, particularly in emerging markets, and the market

collapse following the events of the 2008 world financial crisis, generated a systematic

excess capacity and this pressure on prices (International Cement Review, 2014). Excess

capacity is not only experienced in a given local market by a falling demand or additional

capacity. As described by The Economist (2013), countries with excess capacity and

coastal cement loading facilities in deep waters ports often dump spare output in nearby

coastal states, lowering the prices in the receiving markets. Cement prices tend to be higher,

in markets far from large exporters as China, Japan and Turkey, and in landlocked countries

as Switzerland. While the lower demand that followed the 2008 world financial crisis drove

small and old cement plants out of business, the persistent low utilization rate (International

Cement Review, 2014) resulted in competitive prices ranging from 104 to 110 USD per

ton of cement, depending mainly on market exposure (Figure 6).

However, a new cement industry trend in pricing strategy appears to advocates for the need

to depart from the commodity product perspective towards value differentiation. As argued

by Birsham et al. (2015), for cement companies unable to pursue a low-cost model

(customary competitive strategy in cement’s commoditized industry), differentiation might

be a possibility by customer focusing through premium pricing and operational excellence.

This strategy can create value, especially when the pursuing company is in possession of a



22

known and trusted brand. Birsham et al. (2015) claim that premium-selling strategies,

which require possession of known brand, tend to be more successful within business-to-

business-to-consumer models.

Figure 6.Cement prices 2013, selected global cement producers (source: company

annual reports, 2013)

2.1.6 Cement industry

Competitive landscape

Following a series of recent mergers and acquisitions (Lafarge-Holcim and

Heidelbergcement-Italcementi), the current market outside China is served mainly by the

four largest competitors, LafargeHolcim, HeiderlbergCement, Cemex and Buzzi Unicem.

Largest companies in the Chinese market are Anhui Conch and CNBM.

LafargeHolcim: With a local presence in 80 countries, LafargeHolcim was founded

in 2015 following the merger of Lafarge and Holcim, currently is the largest

building material provider. The company operates in the cement, concrete, and

aggregates business, has 90’000 employees, 353.3 million tons of installed cement

capacity, 2’300 plants (including over 1’400 concrete, over 600 aggregates and over

200 cement plants). LafargeHolcim 2016 all business segments revenues accounted

for CHF 27.54 billion (LafargeHolcim, 2016).

110 105 104 104 -

20

40

60

80

100

120C

emex

Hei

del

ber

gC

em

Holc

im

Laf

arg

e

USD per tone of cement - 2013

Figures converted to US Dollars by 31st December 2013 exchange rate.



23

HeidelbergCement: After the takeover of the Italian cement producer Italcementi,

HeidelbergCement became the second largest cement producer in the world after

LafargeHolcim. The company operates as well in the concrete and aggregates

business, it has around 60’000 employees working in more than 3’000 production

sites in around 60 countries. HeidelbegCement 2016 all business segments revenues

accounted for USD 16.96 billion (HeildelbergCement, 2016).

Cemex: Operating in 50 countries, Cemex competes in the cement, concrete and

aggregates businesses. The company has about 41’000 employees, 93 million tons

of installed cement capacity and 1’914 production sites. Cemex 2016 all business

segments revenues accounted for USD 13.4 billion (Cemex, 2016).

Buzzi Unicem: Headquartered in Italy, Buzzi Unicem operates in 12 countries with

approximately 10’100 employees. With presence in the cement, concrete and

aggregates business, the company has 38 million tons of installed cement capacity

and 484 production sites. Buzzi Unicem 2016 all business segment revenues

accounted for USD 2.98 billion (Buzzi Unicem, 2016).

Anhui Conch: Headquartered in the Anhui province, Anhui Conch is the largest

cement manufacturer in mainland China. The company has a cement production

capacity of 313 million tons and an aggregates production capacity of 24.9 million

tons. Anhui Conch 2016 all businesses segment revenues accounted for USD ~8.5

billion (Anhui Conch, 2016).

CNBM: Through the ownership of three cement companies, CNBM 2015 sales

accounted for ~280 million tons of cement and ~72 million cubic meters of

concrete. CNMB 2015 all businesses segment revenues accounted for USD ~15

billion (CNBM, 2015).

Cement market


Economist, 2013). The following summary, mainly based on the last figures provided by



24

the Global Cement Report (Global Cement Report, 2015), describes the largest cement

markets and consumption trends.

Although since World War II cement consumption has maintained a robust growth, the

greatest development in cement production kicked off in the 1980s driven by Asia, which

currently accounts for more than half of total world consumption (van Oss, 2005). Whereas

China’s market grew at 9.1% annually from 1993 to 2003, global consumption excluding

China experienced a growth rate of 2.6%. The following ten-year period (2003-2013)

showed a similar pattern with an annual Chinese growth of 10.8% trailed by a global growth

of 3.5%.

In 2013, worldwide cement consumption reached 4’033 million tons with about 82% of the

demand concentered in Asia, followed by the Americas 7%, Europe 6% and remaining 5%

consumed in Africa and Oceania. At country level, top ten markets were China (2’400

million tons), India (253.9 million tons), United States (81.7 million tons), Brazil (70

million tons), Russia (63.4 million tons), Turkey (67.2 million tons), Indonesia (59.9

million tons), Saudi Arabia (56.6 million tons), Iran (53.7 million tons) and Egypt (50

million tons).

However, the cement use intensity measured at per capita consumption, shows different

leading markets. The main factor driving the cement consumption intensity is the stage of

economic development which relates to the lack of urban infrastructure (Todaro and Smith,

2012). Out of the top ten countries in terms of consumption per capita during 2013, six

belong to oil based economies which enjoyed great growth in the last decades fueled by

the enormous wealth generated by the oil business (Figure 7). As will be later discuss in

this work, considering the objectives of this research, countries with extremely abnormal

levels of consumption in what relates to normal urban infrastructure needs are considered

out of scope (i.e. Persian Gulf wealthy oil based economies).



25

Figure 7.Cement consumption per capita – 2013 Top ten countries (source: Global

Cement Report, 2015)

Brazil

Russia

Turkey

Indonesia

Saudi Arabia

Iran

Egypt

-

500

1'000

1'500

2'000

2'500

3'000

Qat

ar

Sau

di

Ara

bia

Bah

rain

Ch

ina

Lib

ya

Om

an

Ku

wai

t

Mac

au

Leb

ano

n

Sin

gap

ore

kg



26

Economic relevance of urban infrastructure: cement as an enabler

The relevance of cement in the development of human societies can be illustrated by the

discovery of the archaeological sites Göbekli Tepe and Nevali Cori in east Turkey.

Courland (2011) summarized the arguments, against long-standing theories, that it was

religion and not agriculture what brought together the first permanent human settlements.

As proven by these twelve thousand old sites, agriculture appeared centuries after the first

kiln-based crafts and the religious motivated buildings that brought together the

cooperative efforts of many people. Courland (2011) also credits the chemical properties

of lime, it heats up when combined with water forming a “rock”, as the potential source of

magical set of beliefs and rituals that might have led to the first inter-tribal communities.

From a contemporary angle, and considering the notion of cement as an enabler of urban

infrastructure development (Aitcin, 2000, Deverell, 2012; Kang and Li, 2013), in the

following paragraphs we address the relevance of the later from an economic development

perspective. We review firstly, the main notions around urban infrastructure, secondly the

relation between economic development and urbanization process and thirdly, the

economic impact of public and private capital investment in public-structure.

2.2.1 Urban infrastructure development

Main definitions

In alignment with the Fulmer (2009), in this research, urban infrastructure compromises all

buildings intended to provide housing or public-structure in the form of physical

components and systems serving a country. While housing or residential, considers any

type of building providing accommodation to dwellers, public-structure compromises

roads, electricity, water and sanitation, communications, and the like which facilitates and

integrates economic activities (Todaro and Smith, 2012). Commercial or non-residential

buildings, commonly defined as those intended to generate a profit such as offices, retail

and industrial, are also encompassed as a part of urban infrastructure. Considering cement

being the material of choice for construction (Arezoumandi et al., 2013), the following

sections focus on the physical components of urban infrastructure leaving aside any forms

of non-physical public-structure such as institutions. As can be observed in Table 1, the



27

segment allocation of urban infrastructure investment varies considerable depending on the

stage of economic development, whereas advanced economies such as Germany tend to

focus investment on public-structure, developing countries construction value is usually

concentrated in the residential and non-residential segment, particularly private investment

in housing (source: Business Monitor International 2015).

Table 1. Real construction value by segment – 2013 selected countries (source: Business

Monitor International, 2015)

Country Real construction

value [USDb]

Residential &

non-residential

[%]

Public-structure

[%]

Germany 153.8 27% 73%

Chile 9.3 64% 36%

Argentina 29.2 70% 30%

China 665.2 71% 29%

South Africa 11.8 47% 53%

Urban infrastructure deficit diagnosis

Deficits of urban infrastructure holds social and economic consequences. Our review of

housing deficits covers the findings of Todaro and Smith (2012), Woetzel et al. (2014) in

terms of affordability gap and Dasgupta et at. (2014) on housing supply response to the

urbanization process. The main insights on public-structure deficits arose from the review

of The World Bank (1994), World Health Organization (2013) reports, the work of Green

et al. (2015) and Maier (2015).

Housing: As argued by Todaro and Smith (2012), though generally urban dwellers

are likely to have higher incomes, the poorest are often at greater risk of being

exposed to dangerous conditions as illustrated by their description of a typical urban

slum in Asia. In an Asian shanty town, chronic and acute bronchitis are common

due to the exposure to health-threatening pollutants generated by cooking fuels and

poor ventilations equivalent to smoking daily several packs of cigarettes. Untreated



28

sewage running open and contaminated waters increase risks of infectious diseases

while fuel cost to boil water absorbs a large portion of daily earnings. In addition

to the medical fees, the opportunity cost of time spent travelling to distant hospital

and waiting in overcrowded rooms make medical treatment very expensive

resulting in lack of attention. In slums nearby industrial areas, the streets are usually

contaminated with lead originated in combined emissions from sub-standard

automobiles and factories. As a result of the exposure to these factors and schooling

absence, children acquired both physical and mental impairments preventing them

to meet basic academic standards.

United Nations (2015) claims that the lives of those living in slums have improved

significantly in the last 15 years with more than 320 million people gaining access

to either improved water, sanitation, durable housing or less crowded housing

conditions. However, although the portion of urban population living in slums in

the developing regions fell to ~30% in 2014 from ~39% in fifteen years (Table 2),

the absolute numbers of urban residents living in slums continue to grow. This

phenomenon is fueled by current high urbanization rates and population growth

without the provision of appropriate land and housing policies. United Nations

(2015) estimates that while the proportion of urban population living in slums has

fallen significantly in almost all regions, as per 2015 over 880 million urban

residents live in slum conditions, compared to 792 million reported in 2000. The

largest declines have been recorded in Eastern Asia, South-Eastern Asia and

Southern Asia.

Table 2. Population living in slums in developing countries (source: United Nations

Millennium Development Goals Report 2015)

Population

living in

slums

1990 1995 2000 2005 2010 2014

Millions 689 749 792 830 872 881

% of total

population

46.2 42.9 39.4 35.6 32.6 29.7



29

Woetzel et al. (2014) argues that the provision of decent housing appears to be a

recurrent challenge for nations not limited to solving the slums problem in the

developing world. The struggle of housing affordability also emerges in expensive

capitals where hundreds of millions of people find it hard to afford a decent housing.

As per Woetzel et al. (2014) findings, the current 330 million urban households at

risk are expected to raise to about 440 million urban households by 2025 affecting

1.6 billion people. As can be observed in Table 2, only ten countries concentrate

584 million people living in substandard houses.

Table 3. Substandard housing, selected countries (source: Woetzel et al., 2014)

Country Substandard housing

units in urban areas

[million]

People living in

substandard houses

[million]

Share of total urban

households in each

nation [%]

China 62 210 29

India 28 135 33

Nigeria 11 50 63

Brazil 11 47 27

Indonesia 7 30 23

Bangladesh 6 32 62

Russia 5 13 12

Pakistan 4 32 47

Philippines 4 18 41

Iran 4 17 30

In line with the claims of Aticin (2000), Deverell (2012) and Kang and Li (2013) on cement

market saturation driven by achievements of urban infrastructure levels, Dasgupta et al.

(2014) argues that housing investment follows an S-shaped trajectory ramping up at a GDP

per capita of USD ~3’000 and reaching a turning point at USD ~36’000. However, the fast

urbanization process taking place in low-income countries, particularly in Africa and parts

of Asia, is likely preparing the ground for lack of necessary capital investment support.

This claim is supported by Todaro and Smith (2012) work arguing that high levels of

urbanization are being reached in developing countries, particularly in Africa, at lower



30

levels of per capita income than those of developed countries when were at a comparable

stage of development.

Public-structure: More than twenty years ago, the World Bank (1994) warned that

in the developing world one billion people lack access to clean water and two billion

to sanitation. Transportation networks, energy generation and telecommunications

presented serious shortages as well. A review on recent public-structure literature

does not appear to show the necessary improvement. Although as illustrated by the

Figure 8, the World Health Organization (2013) reports an improvement of 23.2%,

768 million people still lack access to clean water. On the other hand, the number

of people who still do not use an improved sanitation facility grew 25% reaching

2.5 billion. In terms narrowing the gap, Green et al. (2015) argues that the world

needs more public-structure than governments can deliver estimating USD 57

trillion required to build new and refurbish existing public-structure between 2013

and 2030 (including the maintenance and replacement of public-structure in

advanced economies). In the same line, Maier (2015) claims that by consensus

estimates from the Organisation for Economic Co-operation and Development, the

Boston Consulting Group and the World Bank, the estimated annual global public-

structure investment need is about US $D 3.7 trillion, of which only about 70% is

being met.



31

Figure 8. Public-structure deficit evolution – access to clean water and sanitation

(source: World Bank 1994 and World Health Organization 2013

Summarizing the main elements described above, as argued by Kessides (2005) and Foster

et al. (2011), urban infrastructure development is crucial to alleviate poverty and to promote

economic growth. A progressive deficit reduction in both housing adequacy and public-

structure availability is expected to improve the life of billions of people. Thus, the main

drivers supporting the urban infrastructure development requires to be address. The

following sections are aimed to describe from a social and economic perspective the roles

of the urbanization and investment in public-structure in supporting urban infrastructure

development.

2.2.2 Economic relevance of urbanization

Under its basic definition, the Oxford dictionary defines urbanization as the process of

making an area more urban. However, common urbanizations processes are driven by

population shift from rural to urban areas (Todaro and Smith, 2012) changing the

proportion of people living in urban places (Antrop, 2004).

The United Nations (2014) last revision on world urbanizations prospects claims that more

people live in urban areas than in rural areas, with 54% per cent of the world’s population

1'000 768 2'000 2'500

19

94

20

13

19

94

20

13

Improvement23.2%

Deterioration25%

People with lack of access to clean water [millions]

People with lack of access to sanitation [millions]



32

residing in urban areas in 2014 up from 30% in 1950. By 2050, 66% of the world’s

population is expected to be urbanized (Figure 9). Urbanization levels are substantially

different by geographic region. Whereas North America, Latin America - Caribbean and

Europe account for high levels (82%, 80% and 73% respectively), Africa and Asia remain

mostly rural, 40% and 48% of their respective populations living in urban areas, and thus

with the highest urbanization potential.

Figure 9.Urban population as % of total (source: United Nations 2014)

The positive association between urbanization and per capita income is one of the most

obvious and striking facts of the development process, and thus, the more economically

developed the country, the greater the chances of a large share of population living in urban

areas (Todaro and Smith, 2012). The reasons behind this link are highlighted by Black and

Henderson (2011) who argue metropolitan areas benefit from localized information and

knowledge sharing concentrating most of the non-agricultural economic activity and thus

making the cities the engines of economic growth.

While the notion of a strong link between urbanization and economic growth is widely

diffused, the review on the work of Chen et al. (2014), Todaro and Smith (2012) and Zhang

30%

54%66%

80%73%

40%48%

19

50

20

14

20

50

Lat

in A

mer

ica

Euro

pe

Afr

ica

Asi

a

Regional - 2014Worldwide evolution

Urban population as % of total



33

and Song (2003) address causality sustaining that it is the economic growth driving

urbanization and not vice versa.

Chen et al. (2014) argue that although empirical findings support the notion of

urbanization and GDP levels link, considerable evidence sustains that a given

country fails to obtain desired economic benefits from accelerated urbanization,

especially if government-led.

In the same line, Todaro and Smith (2012) claim that shares of urban population as

% of total are being reached in the emerging world at lower levels of per capita

income than those reached by the developed countries at a comparable stage of

development. The main example provided by Todaro and Smith (2012) is Africa,

where urbanization is not associated with industrialization, as it was in the now-

developed economies. Populations sizes also appear to set a difference as in most

part of the developing world, cities attract much larger amount of people in

comparison to what happen in the cities of the now-developed world.

Zhang and Song (2003) thorough review of the migrations process in China

between 1978 and 1999 demonstrate that the causal link runs from economic

growth to migration, and not vice versa.

As Figure 10 shows, urbanization trends indicate that almost all world’s populations growth

will be driven by the development of urban areas as rural migrations to urban areas continue

in the developing world. Todaro and Smith (2012) argue that the unprecedented size of

these urban agglomerations questions how these cities will cope with the economic,

environment and politics complexities of such acute concentrations of people. For example,

China’s megalopolises, urban centers with a population over ten million people, are

expected to reach thirteen in 2020 from only three in 2000 fueled by large-scale internal

migration and government initiatives (Economist Intelligence Unit, 2012).



34

2.2.3 Economic relevance of public-structure development

The relation between countries investment in public-structure and the resulting

improvement in their productivity growth as well as quality of life has been at the center of

the debate for many years. As observed in Figure 10, there is an evident positive relation

between the GDP per capita and the capital stock per capita, which represents the

aggregation of public, private and public-private partnership investments into the creation

of public-structure systems (International Monetary Fund, 2017).

Figure 10. GDP purchase power parity - current per capita and Capital stock per capita

by country (source: World Bank, 2017 and International Monetary Fund, 2017)

In the following paragraphs, we address the main arguments from leading authors and

macroeconomic institutions in favor of a positive relation. The review covers, firstly those

assigning a key role to public-structure, secondly those describing specific roles and lastly

those claims over the macro-economic impact of public investment in public-structure.

Srinivasu and Rao (2013) argue that infrastructural investments play a strategic but

indirect role in the development process by increasing productivity of land, labor

and capital. Todaro and Smith (2012) assigned a key role to the social and economic

public-structure as a facilitator and integrator of economic activities. In the 2012

revision to their work on economic development, Todaro and Smith (2012)

R² = 0.925

-

50'000

100'000

150'000

200'000

250'000

300'000

- 20'000 40'000 60'000 80'000 100'000 120'000 140'000 160'000

GDP ppp current per capita - 2013 [USD]

Capital stock - 2013 [USD]



35

illustrate the relevance of social-economic public-structure acting as a supplement

to direct productive investment with a practical example. A farmer invest in a new

tractor may lead to increasing productivity, but without adequate transport facilities

to get this additional product to local markets, her investment may not add to

national food production.

Canning (1998) describes an annual database of physical public-structure stocks for

a cross section analysis of 152 countries for the period 1950–95 containing six

measures of public-structure. Among many finds, the most relevant is Canning

(1998) claims that growth regressions suggest that the number of telephone main

lines per capita does have a significant impact on subsequent growth rates of GDP

per capita but that the other public-structure variables such as kilometers of road,

of paved road, and of railway line do not. Paved roads and electricity generating

capacity may have an influence in economic growth in subtle ways, for example in

combinations of geographical factors at initial stages of development.

The World Bank (1994) claims that the precise linkage between public-structure

and development are still open to debate, however argues that public-structure

capacity grows step by step with economic output. Therefore, a 1% growth in

public-structure stock translates in 1% growth in GDP across all countries. The

World Economic Outlook (2014) proposes empirical estimations on the

macroeconomic impact of public investment in the medium term (four years after

the shock) to identify the investment spending multipliers (percentage point

increase in output per an unanticipated 1.0 percentage point of GDP increase in

investment). Whereas outcome for advanced economies reached 1.5 percentage

points, developing economies only accounted for 0.5 to 0.9. In the same direction,

Wu et al. (2010) support the hypothesis that government spending contributes to

economic growth except for the low-income countries suggesting a link to

government inefficiency and inferior institutions.

The International Monetary Fund (2017) claims that in theoretical models of

economic growth, the public capital stock, representing the network of physical

assets over time such as roads, airports, electric utilities, public schools, hospitals,



36

and prisons, acts as direct input factor of the production function resulting into

higher productivity growth and living standards.



37

Demand forecasting

As defined by the Oxford dictionary, forecasting relates to the prediction or estimation of

a future event or trend. In the words of Armstrong (2001), forecasting is important for

individuals predicting events such as a success in a marriage and for organizations required

investments or decisions based on forecasts. Executives today consider some kind of

forecasting in virtually every decision they make relating to demands and its influencing

factors such as seasonality, sudden changes in demand levels, price-cutting, competition,

strikes and large swings of the economy (Chambers et al., 1971).

The following three subsections commence with review the main forecasting

methodologies and alternatives selection process of most adequate techniques, continue

exploring the specific characteristics of the cement demand function focusing on the

concept of cement stock and finalize exploring the most relevant works on cement demand

forecasting.

2.3.1 Demand forecasting methodologies

As the complexity of industrial and commercial dynamics increase, it does the implications

of forecasting methodologies and the importance given to the field. In order to manage the

increasing diversity and complexity of forecasting problematics, many forecasting

techniques have been developed on the last years (Chambers et al., 1971). Nevertheless,

researchers still suggest the existence of a substantial gap in terms of available

methodologies and what is desirable and attainable (Makridakis and Wheelwright, 1977).

As observed in Figure 11, although researchers use different dimensions to group existing

methodologies, the proposed diverse classifications tend to hold similarities. The main

distinction within the different forecasting methods rests between Quantitative and

Qualitative methods. Whereas Quantitative methods are based on: firstly, the idea that data

related to past demand can be used to predict future demand (time-series) and secondly, the

cause-and-effect of relationships (causal or regressive), the Qualitative techniques

comprehend those methodologies subjective or judgmental based on estimates and

opinions from relevant sources such as consumers and experts (Chase et al., 2006).

Estimation of urban infrastructure deficit in developing countries as a function of historical cement consumption

38

Figure 11. Forecasting methods (source: Chambers et al., 1971, Makridakis and Wheelwright, 1977, Armstrong, 2001 and Chase et al.,

2006)



39

In terms of forecasting methodology selection, most researchers share common

considerations when choosing the right technique such as the context of the forecast, the

availability of historical data, required accuracy and forecasting period. However, the work

of different researchers offers specific views and tastes over key aspects to consider. The

insights on the reviewed authors cover focus on lifecycle, timeline factor, data pattern

availability.

Life-cycle: Chambers et al. (1971) proposes that when a company wishes to forecast

a product, it must consider the stage of the product’s life cycle as the availability of

data and the possibility of establishing relationships between the factors are

expected to depend directly on the product maturity. Therefore, the life-cycle stage

is a prime factor to determine the most suited forecasting. At early stages of a

product life-cycle, qualitative methods appear to be more suitable forecasting

options, while later on (and once enough historical data becomes available)

quantitative techniques tend to offer higher performance.

Time-line: Chase et al. (2006) emphasize the role played by the time-line / horizon

in the methodology selection with long-term forecasts requiring the use of several

approaches to increase accuracy likelihood. In the same direction, Kotler and Keller

(2006) assign a substantial weight to the time-line with a focus on the importance

of understanding the degree of demand stability when choosing a forecasting

methodology.

Data-pattern: Makridakis and Wheelwright (1977) also focus on the time horizon

of the forecasting exercise but giving particular emphasis to the implications of the

data pattern type. Armstrong (2001) proposes to follow a methodology tree to select

the most appropriate forecasting techniques commencing firstly by dividing the

options for demand forecasting methods into those based primarily on judgment

and those based on statistical sources depending on the data availability.



40

2.3.2 Specificities of the cement demand in the context of forecasting

The cement consumption, and hence its future demand, appears to hold particular

characteristics when compared with other common goods of its types, specifically durable

goods. Therefore, the current literature review of cement’s market saturation point, the

relevance of its cumulative consumption and the factors affecting require to be explored

and clarified on advance to the content provided in section covering cement forecasting.

Demand of durable goods

The hardened cement past (in the form of concrete) can be considered the paramount of

durable goods as per its common definitions, tangible goods that normally survive many

uses (Kotler and Keller, 2006). According to Cooper (1994), a durable good has ability to

perform its required function over an extended period of time under normal conditions of

use without excessive maintenance. Although durable goods are often finished goods

(consumer-durable goods) such as industrial machinery, durable goods can also be

intermediate products (producer-durable goods) such as cement or raw steel (Amadeo,

2017). However, to drawn further parallels besides its durability appear to be difficult due

to the cement specificities. Some of the microeconomic questions posed by durable goods

(Waldman, 2003) does not appear as elements influencing the cement consumption.

Among durable goods, three characteristics specific to cement consumption: its strong

government regulations, indirect application, and commodity perception, appear to depart

the cement consumption dynamics from those of typical durable goods. Below we

transcribe Waldman (2003) proposed set of questions associated to durable goods

consumption followed by our perspectives for the particular case of cement.

Durability choice and the related issue of “planned obsolescence.”: “do firms have

an incentive to reduce durability below the efficient level so that units break down

quickly? Also, to what extent do firms have an incentive to introduce new products

that make old units obsolete?” Waldman (2003).

Cement case: considering the basic characteristic of cement such as strength and

durability, and the strict official regulations that dictate its specifications (van Oss,

2005), choices related to planned durability and obsolesces with consequences on

its demand do not appear to be valid alternatives for cement manufacturers.



41

Timing issues: “How are current prices and marketing strategies affected by a

producer’s actions tomorrow that affect the future value of units the producer sells

today?” Waldman (2003).

Cement case: Being an intermediate good used in most cases as an input for

construction (O`Sullivan and Sheffrin, 2003), the cement used in the form of

concrete or mortar and forming part of a building structure lacks a future price on

its own. In addition, cement standard specifications and stable manufacturing

procedures limit manufacturers capacity for new product launching with the

potential to affect the future demand and prices of current products.

Information asymmetry: “In many durable goods markets, buyers are unable to

judge the quality of durable units offered for sale. As a result, the problem of

adverse selection can arise, where sellers withdraw high-quality units from the

market because consumers are unable to perceive high quality and are thus

unwilling to pay a high price for it.” Waldman (2003).

Cement case: Although cement producers do market special and increasingly

innovative cements, as mentioned previously, most cement sold volumes account

for portland cement priced in a commodity fashion with very limited capacity for

product differentiation.

Saturation point

Through its theory of natural limits, Osenton (2000), claims that every product or service

has a natural consumption level, equivalent to a saturation point, after which further

investment to grow are ineffective. In his work, Osenton (2000) states that the natural

consumption level of a product is determined after a number of years of sales and marketing

investment (usually around 20–25 years) before saturation is reached. Palacios Fenech and

Tellis (2016) review five product categories across 86 countries between 1977 and 2011 to

find that product consumption reached a peak at about 56% of market penetration followed

by a dramatic drop. In cement consumption, this point of market saturation would indicate

a turning point towards a “petrified” consumption (Kotler and Keller, 2006) mainly driven

by urban infrastructure maintenance. In other words, the turning point signals a



42

fundamental shift in cement consumption from construction of new buildings to their

maintenance mainly (with a much lower use of cement), and thus when the first declines,

the second is expected to grow due to the increasing obsolescence of the existing buildings

stock.

Aitcin (2000) claims is that it is possible to establish a direct relationship between the

consumption of cement and the economic development of a country. As a country develop,

there is an increasing need for public-structure development and consequently in an

increased consumption of cement, however the growth of cement consumption slows down

when the standard of living reaches a certain level (Aitcin, 2000). Three reasons are

understood to drive the process. In a potential decreasing order of relevance: firstly, the

urbanization process has peaked; secondly, the major parts of the public-structure needs

have been built and thirdly, technological progress results in better technical uses of

concrete, so that it is possible the use of less material (Aitcin, 2000). In line with Osenton

(2004), Aitcin (2000) also argues that most materials are facing a saturated market in

industrialized markets where only maintenance, replacement and the natural progression

of the market are the driving demand forces.

In the same line, Deverell et al. (2012) explore the cement stock per capita reached in Asian

countries and well-developed coastal provinces in China when annual construction demand

peaked. Deverell et al. (2012) argues that as construction demand tends to be a one-off

event non-recurring for a long time, once a basic urban infrastructure has been built around

each person, there is no need to continue building. Deverell et all. (2012) claims that the

cement consumption per capita peaked in advanced Asian economies once cement stock

reached the eighteen tons per capita.

Resulting from a research study focused on the Chinese market, Kang and Li (2013) find

that as with steel, cement is consumed over many years, as buildings constructed today will

be standing for many years into the future. Among other insights, Kang and Li (2013) argue

that cement cumulative consumption provides good understanding of a country’s cement

requirement for future urbanization needs.



43

The propositions of Aitcin (2000) are also sustained by the International Cement Review

(2014) claims as follows: most emerging markets are currently on the rising part of the

cement consumption bell-curve, the cement consumption per capita rises in the initial

stages of economic development before peaking once gross domestic product per capita

reaches the levels of advanced economies (Figure 12).

Figure 12. 2013 Cement consumption per capita and GDP per capita by country (source:

Global Cement Report, 2015 and World Bank, 2017)

Cement stock

Considering Aitcin (2000) claims that relates the cement consumption saturation to the

completion of an urbanization process and major parts of the public-structure, it results

necessary to review the cumulative aspect of the cement consumption (i.e. cement stock)

and main elements affecting it.

As mentioned previously, when applied in the form of concrete for buildings’ construction,

cement is expected to last for many years before replacement is needed. Therefore, every

new building, regardless whether is dedicated to housing, commercial or public-structure

use, will add to the existing stock composed by constructions of different ages. For the

objectives of this research, the cement stock represents its cumulative consumption that

forms part of all standing buildings to the year of measure. Consequently, the cement sock

MADAGASCAR

RWANDA

BURKINA FASO

HAITI

UGANDA

ZIMBABWE

MALI

SIERRA LEONE

BENIN

AFGHANISTAN

NEPAL

SENEGAL

TANZANIA

CAMEROONKENYA

-

100

200

300

400

500

600

700

800

900

1'000

- 20'000 40'000 60'000 80'000

Cement consumption per capita - 2013 [kg]

GDP ppp current per capita - 2013 [USD]



44

level will increase by the addition of new buildings as described above, but also be reduced

through demolition activities both man-planned or unplanned such as earthquakes. We

understand the cement “net-stock” as the result of subtracting from the cement stock the

reductions due to demolition both man-planned or unplanned.

As it is later elaborated in following chapters, the cement stock at saturation point is a key

element in the scope of this research, and thus the main elements of concern on demolition

events, both man-planned or unplanned and its impact on the “net-stock” are reviewed and

described.

Hong et al. (2016) supports our conceptualization of the cement “net-stock”. In their work

on housing stock and impact on energy and materials, Hong et al. (2016) develop a building

stock turn over model, were newly constructed building floor areas are added to the existing

stock, and the demolished residential and commercial buildings are subtracted to project

future needs.

Man-planned demolition

Modern concrete lifespan depends on its quality but also on the environment. While

Monteiro (2013) claims that current buildings are designed to last 100 to 120 years,

Courland (2011) argues a duration of 75 to 100 years before demolishing is needed.

However, in the scope of this research, we retain that the use of one or many demolition

factors to estimate the net cement stock could be inaccurate, inefficient and most important

unnecessary as described below:

Different building codes: The differences from country to country in terms of

government building codes result in different regulations when it comes demolition

requirements. Visscher and Meijer (2006) carried out a comparative study of

building regulations in several European countries to concluded that the broad

spectrum of different systems forms a major barrier for further harmonization of

building regulations in Europe.



45

High granularity: A bottom up approach to identify required demolition on a single

year is expected to be a challenging exercise due the enormous data granularity.

This exercise implies to survey the year of construction and material used (quantity

and condition) of all housing, commercial and public-structure buildings. An

example of this complexities can be grasped through observing the substantial

number involved in the likely resource-demanding surveys handled by the United

Sates census. The U.S. Department of Housing and Urban Development (2011)

conducted and exhaustive analysis in 2009 surveying 76’420 houses to conclude

that 13.3% of standing houses were built before 1939. In terms of public-structure

records, the American Society of Civil Engineers (2013) surveyed 84’000 dams,

300 commercial harbors and 607’380 bridges among many other public-structure

buildings such as waterways, rails and schools. Close to the million inspections, a

bottom-up approach appears to be a titanic endeavor for any public office.

The needs of this research: As prompted previously, a substantial emphasis of this

research is given to the market saturation understanding, specifically of advanced

economies which are the ones deemed to have already reached a market peak. By

the time that most advanced economies reached a market saturation, further

illustrated in the results section, neglectable amounts of applied cement were older

than 70 years and consequently sensitive to demolition requirements (based on data

from the Cembureau 1994). Therefore, introducing a man-planned demolition

factor to estimate a “net-stock” appears to be unnecessary for the scope of this

research.

Unplanned demolition

Although the destruction caused by some specific unplanned events such as wars and

natural disasters are expected to have an impact on the cement stock levels, we found minor

benefits of controlling for it and thus unnecessary. The summary below provides a

description of these two event types particularities and their potential impact.

Wars: Since the use of cement as a main construction material ramped-up after the

second world war, the impact of the conflict in the perspective of nowadays cement



46

stock is minor. We find on the analysis of the destructions in Germany, one of the

most bombed countries during the second world war (Hewitt, 2010), a sound

support for this argument. Germany’s 2.3 tons of Cement stock per capita were one

of the highest in the world by 1939 (based on Cembureau 1994 data), and as it is of

common knowledge, by the end of the second world war was heavily affected by

the allied bombing. Brakman et al. (2004) argues that although in average 40% of

dwellings in German cities were destroyed, a total of seven million people lost their

homes (10% of total population) causing a potential loss of cement stock equivalent

to ~1% of 2013 Cement stock per capita in Germany (31.1 tons) assuming that all

the physical public-structure of every single person who lost her home was

destroyed. Although controlling for the second world war impact on the cement

stock does not appear to be necessary for advanced economies involved, more

recent cases of military conflicts with devastating effects in countries such as Iraq,

Afghanistan and Syria are expected to require individual analysis on the cement

stock and level of destruction.

Natural disasters: Among all the natural disasters, both inland and offshore

earthquakes appear to be an impressive source of building destruction (Arslan and

Korkmaz, 2007). We review the examples of heavily affected countries in the last

hundred years, and took Japan to illustrate that controlling for the effects of

earthquakes on cement stock appears unnecessary for this research. Some of the

massive Japanese earthquakes such as the Great East Japan earthquake of 2011

brought unprecedent devastation (Yeh et al., 2013) to the cities of Miyagui, Iwate

and Fukushima leaving homeless about 6% of their population (source: Cabinet

Office of Japan 2011). However, when assessed from a national and long-period

perspective, the impact on the cement stock appear minor. As no specific

information in terms of cumulative cement stock losses was found, the destruction

was estimated using death toll over total population as an approximation of the

potential urban infrastructure destruction. In Table 4, the destruction caused by the

earthquakes was estimated converting the reported number of fatalities to a number

of displaced people using a factor of 22 people displaced per every person killed

(source: Cabinet Office of Japan 2011). Assuming the cement stock per capita of



47

every displaced people was lost, the total destruction caused by earthquakes

accounts for less than 1% of Japanese cumulative consumption during the 1913 to

2013 period.

Table 4. Estimation of national cement losses due to earthquakes during the 1913-2013

period (source: https://en.wikipedia.org/wiki/List_of_earthquakes_in_Japan and Cabinet

Office of Japan 2011)

Fatalities [# of

people]

Displaced

estimated [# of

people]

Cement stock

loss per capita

estimated [tons]

Cement stock

2013 [tons]

Variation [%]

182’158 ~4’000’000 ~0.11 30.6 <1%

To summarize above introduced elements, we retain that controlling for the impact of

demolition (man-planned or unplanned) in the cement stock at national level would add an

unnecessary level of complexity with a model minor improvement in terms of forecasting

accuracy.

2.3.3 Forecasting of cement demand

Usually built around individual time-series projections, the implementation of available

methodologies for the cement long-term demand forecasting presents limitations. Caniato

et al. (2011) examined the integration and implementation of quantitative and judgmental

forecasting (focused on the short/medium term) through an action research case in the

Italian cement industry. Empirical evidence demonstrates that forecasting accuracy in

cement demand can be improved using integrated forecasting systems due to the current

limitations of both qualitative and quantitative techniques when used in isolation. Cement

demand forecasts generally rely on individual country data and thus lose the value provided

by distributional information (Kahneman and Tversky, 1977). As exemplified below with

the work of Hung and Wu (1997), Deverell (2012), Kang and Li (2013) and Birshan et al.

(2015), ignoring the historical demand evolution in other countries, particularly mature

economies, limits the understanding of demand long-term patterns, particularly of the

cement stock.

https://en.wikipedia.org/wiki/List_of_earthquakes_in_Japan



48

The criticality of understanding the main elements of the cement demand turning

point previously described can be illustrated by Hung and Wu (1997), who

forecasted the quantities of cement demand in Taiwan in 1993, 1994, and 1995

using monthly data from January 1982 to December 1995. Their findings suggest

that the forecasting performances of all six models employed (decomposition, Holt-

Winters seasonal smoothing, univariate ARIMA, transfer function, intervention

model, and vector ARIMA) are less satisfactory in the year of demand turning point

(1994) than in the years of 1993 and 1995.

Deverell (2012) and Kang and Li (2013) studies on the Chinese market used a

shared understanding on the cumulative demand concept to forecast the cement

demand evolution. When analyzing the potential cement demand growth in

Western China, Deverell (2012) used reference values from other developed Asian

markets and coastal China. As result, Deverell (2012) estimated a continuation of

growth during the next two or three years from the current cement stock per capita

of 14 tons in 2012 until markets reaches 18 tons. In the same line of thought of

Deverell (2012), Kang and Li (2013) compared the Chinese cement stock in 2012

with that of Japan and U.S. claiming that China consumption peak would be reached

four to five years until reaching the cumulative levels per capita achieved by the

United States (18 tons) and Japan (26 tons). Both in the case of Deverell (2012) and

Kang and Li (2013), their research omitted the relation between economic growth

and cement consumption and limited the development of the second one to a

cumulative regional target (cement stock). Our research differs from the work of

Deverell (2012) and Kang and Li (2013) in two fundamental and innovative aspects.

Firstly, while incorporating the saturation point conceptual-element as well, our

model is mainly driven by the country’s economic development. Secondly, we

based our model in analogical time-series globally and not regional according to

Duncan et al. (2001) guidance, therefore fully benefiting from the external-view of

a larger universe (Kahneman and Tversky, 1977). The characteristics of our long-

term forecasting model are fully described in the Research methodology section

(4.3.2).



49

Birshan et al. (2015) claimed that cement companies often fail to recognize demand

development switching from growth to maturity stage resulting in production

capacity overbuilt as happened in South Korea and may be currently happening in

China. Birshan et al. (2015) also unveiled the lack of knowledge in terms of cement

demand behavior collapse following the impact of disruptive situations such as

financial crisis and construction bubbles in Italy, Spain, and the United States.



50

Concluding remarks

The review of the current literature has revealed substantial gaps. We retain relevant the

need to bridge them through the development of this research work. Our findings are

expected provided further knowledge to efficiently estimate urban infrastructure deficits

and long-term cement demand in developing countries.

The literature review on the cement product appears well covered, especially what

regards to its technical characteristics and properties (e.g. van Oss, 2005),

production process (e.g. Kosmatka, 2002), cost (e.g. Alsop, 2005) and industry in

general. The markets coverage in terms of size and trends seems as well properly

addressed by industry reports such as the Global Cement Report or the International

Cement Review. However, within the reviewed material, the true demand potential

(to reach a basic level of urban infrastructure) of most developing world remains

unquantified in its full dimension.

The accumulated knowledge in terms on urban infrastructure, its driving forces (i.e.

urbanization process and public investment in public-structure) and its economic

relevance is extensively covered by several authors, specially through the work of

Todaro and Smith (2012). The perspectives on urban infrastructure deficit are

limited to pure quantifications of housing units (Woetzel, 2009 and Dasgupta, 2014)

and public-structure investment (The World Bank, 1994; World Health

Organization, 2013; Green et al., 2015 and Maier, 2015). Thus, an efficient

quantification of economic resources needed to close the urban infrastructure gap

to a minimum functional level remains missing, particularly in the developing

world.

Lastly, we claim that there is an opportunity to improve the cement long-term

forecasting methodologies. Whereas the work of Hung and Wu (1997) illustrates

the limitation of missing the external view proposed by Kahneman and Tversky

(1977), the claims of Deverell (2012) and Kang and Li (2013) are restricted to



51

providing a cement stock reference to estimate the Chinese cement demand

potential. Most important, current reviewed literature lacks insights about cement

consumption patterns and thus of thorough understanding of the demand function

and its prescriptive application to estimate the potential in developing countries.



52

3 THEORETICAL FRAMEWORK

The theoretical foundations of this research are mainly based on work of Kahneman and

Tversky (1977) on reference class forecasting and Duncan et al. (2001) on analogy

forecasting. As a result of the literature review findings, we structured our research

methodology on these premises, which provided a solid and compatible theoretical

background to our research satisfying specific analytical needs. The absence of a known

and diffused cement forecasting methodology which benefits from comparable cases opens

an opportunity to build prescriptive long-term technique theoretically sustained by the work

of Kahneman and Teverski (1977) and Duncan (2001). Reference class forecasting is

covered firstly as we find its propositions of an introductory and general tenure, secondly,

we embark on the description of the detailed framework proposed by Duncan et al. (2001)

in their work on analogy forecasting. Sustained by the explored theoretical framework, in

the last segment of this section, research questions are described previously to addressing

our research methodology.

Reference class forecasting

Armstrong (2001) argues that a formal use of analogies can help in expert forecasting as

might reduce biases due to optimism or an unrealistic view of one’s capabilities. This claim

can be tracked down almost 30 years to the original work of Kahneman and Tversky (1977).

The following paragraphs provide a summary of the conceptual structure behind the

reference class forecasting as proposed by Kahneman and Tversky (1977).

Benefits of reference class forecasting

Kahneman and Tversky work (1977) has been highly influential in describing the

relevance, implementation and usefulness of the reference class forecasting eventually

contributing to Kahneman Nobel Prize award in 2002. In their 1977 paper, “Intuitive

prediction: Biases and Corrective Procedures”, Kahneman and Tversky (1977) claimed that

the accuracy of the intuitive judgment used to make vital decisions are often limited to:

Systematic rather than random judgment errors, manifesting bias instead of

confusion.



53

Presence of common biases confirmed by the lack of significant differences

between judgment processes within different professional.

Erroneous intuitions resembling visual illusions as remains compellingly attractive

even when the person is fully aware of their nature.

Singular and distributional data

Kahneman and Tverski (1977) distinguish two types of information available to the

forecaster, singular and distributional. Whereas singular information, or case data, refers to

evidence about the particular case being considered, Distributional information, or base-

rate data, refers to knowledge about the distribution of outcomes in similar situations. The

common tendency to ignore distributional information is understood as a a major cause of

error in intuitive prediction.

Internal and external view

Evidence suggests that people are insufficiently sensitive to distributional data even when

data are available and rely primarily on singular information even when is scanty and

unreliable, or give insufficient weight to distributional information (Kahneman and

Tversky, 1973; and Kahneman and Tversky, 1977). The tendency to neglect distributional

data is connected to the adoption of what Kahneman and Tversky (1977) termed “internal

approach” to prediction, where one focuses on the constituents of the specific problem

rather than on the distribution of the outcomes of similar cases likely producing

underestimation. The adoption of “external approach” which treats the specific problem as

one of many could help to overcome this bias by relating the problem at hand to the

distribution of the problem for similar projects.

Through the use of reference class, Kahneman and Tversky (1977) present an approach

aimed to be applied to both the prediction of uncertain quantities and the prediction of

probability distribution. This approach encompasses elicitation and correction of intuitive

forecast to deal with two common prediction biases, the non-regressiveness of predictions

and the overconfidence in the precision of estimates.



54

Non-regressive predictions

Kahneman and Tversky (1977) claim that there is considerably evidence that people often

predict by matching prediction to impression, failing to take uncertainty into account.

Predictions are allowed to match impressions only in the case of perfect predictability. In

the intermediate cases, most common, the prediction should be regressive, i.e. falling

between the class average and the value that best represents one’s impression of the case

at hand. The main error of non-regressive prediction is that ignores the regression towards

the mean, the mathematical consequence of the presence of uncertainty.

The proposed procedure to eliminate the non-regressive of prediction consists on a five-

step approach:

Selection of a reference class: This step requires to identify a class to which the case

at hand can be referred meaningfully, and for which the distribution of outcomes is

known, or can be assessed with reasonable confidence. When forecasting the sale

of a new book or a film, the reference class will require the selection of an

appropriate class of books or films for which the distribution of sales or revenue is

known.

Assessment of the distribution for the reference class: In the absence of direct

distribution information for the reference class, Kahnemand and Tversky (1977)

suggest exploring related data to estimate distribution values for the target

reference.

Intuitive estimation: In this step, the forecaster is required to make intuitive

estimates using the singular information accumulated for the particular case. These

biases of these predictions, which are likely non-regressive, are aimed to be

corrected in the following steps obtaining more accurate estimates.

Assessment of predictability: The forecaster is expected to assess the degree

predictability level of the available information. For linear predictions, the adequate

measure of predictability will be ρ, the correlation between predictions and



55

outcomes. In the absence of historical information, the forecaster must rely on

subjective assessment of predictability with different degrees of statistical

sophistications.

Correction of the intuitive estimate: The non-regressive initial estimation should be

now adjusted toward the average of the reference class. Under general conditions,

the difference between the intuitive estimate and the reference class average should

be adjusted by the correlation coefficient ρ, providing an estimate likely free of non-

regression error.

Kahneman and Tverski (1977) addressed several potential objections likely to arise in the

interaction between analyst and expert. One general objection could question the basic need

for regressiveness as this procedure would yields to conservative predictions not far from

the class average. To this objection, Kahneman and Tversky (1977) point out that whereas

a fallible predictor could correctly predict a few exceptional cases, chances of erroneously

identifying many other as exceptional is high.

The over confidence-effect

As demonstrated by a considerable amount of research, the existence of a consistent bias

in the setting of confidence intervals and probability distributions has been stablished.

Kahneman and Tversky (1977) defined as surprises, when the actual value of an uncertain

event falls outside the confidence interval. Evidence also shows that the degree of

overconfidence tends to increase with ignorance. On the other hand, overconfidence

appears to have a lower level of occurrence when the experts manage a considerable

amount of information about the conditional distribution of the outcomes. Potential

explanations to this improvement might be related to the recurrence of an identifiable

pattern of indicators followed by different outcomes on separate occasions, allowing the

forecaster to gain understanding of the outcomes distribution linked to that pattern. As

descrbied below, Kahneman and Tversky (1977) define four factors associated to the

overconfidence effect:



56

Lack of sensitiveness to determine the quality of the information, for example

trusting small samples of unreliable data.

Oversensitivity to the consistency of available data: A situation where experts tend

to draw more confidence from a small body of consistent data than from a much

larger body of less consistent data. For example, the public is likely to feel more

confident on a conclusion that was unanimously supported by a three-people panel

than from a conclusion supported by ten experts out of twelve.

Conditionality: It occurs when adopting unstated assumptions regarding the

assessed quantity, for example, estimating future revenue of a company without

considering the possibility of war, depression or sabotage.

Anchoring: Refers to the biasing effect of an initial value on subsequent judgments

as one usually considers a best guess before assessing extreme fractals when

constructing a probability distribution over a quantity.

Kahneman and Tversky (1977) suggestion to eliminate the overconfidence bias is to use

the correlation between predictions and outcomes in the reference class (i.e. ρ), to adjust

the confidence interval to be applied to the individual case.

Barriers to reference class forecasting

Along with other findings during their review on reference class forecasting, Flyvbjerg and

Techn (2006) claim that there are 2 types of sources for forecasting inaccuracy with

different implication in terms of application success, optimism bias and strategic

misinterpretation.

The optimism biases assume that managers and forecasters are making honest mistakes and

have an interest in improving accuracy. Thus, the potential use of the outside view and

reference class forecasting is expected to be efficient and effective as forecasters will

welcome the method. However, where strategic misrepresentation is the main cause of

inaccuracy, differences between estimated and actual costs and benefits are explained by



57

political and organizational pressures. Although manager and forecasters would still enjoy

the reference class forecasting benefits in terms of accuracy, different motivations might

twist their interest on it.



58

Forecasting by analogy

Forecasting by analogy assumes that since two diverse types of phenomena share the same

behavioral patterns, it is possible to predict the future outcome of one by observing the

historical development of the other. Duncan et al. (2001) research provide a detailed

approach to the use of analogies for forecasting. In line of what Kahneman and Tversky

(1977) call class reference, Duncan et al. (2001) defined equivalence group as the groups

of products or services which are often analogous in ways that make them follow similar

time-series patterns causing their time-series to covary over time.

The Bayesian pooling approach is a Duncan et al. (2001) coined-name by blending the

“Bayesian” concept of improving beliefs based on new data (Figure 13) and “pooling” as

per drawing and combining information from analogous time-series. Duncan et al. (2001)

approach provides four major benefits: requires few parameters for estimation, builds

directly in conventional time-series models, adapts to pattern changes in time-series and

finally allows for rapid adjustments on pooled data (screens out adverse effects of outliers).

The following paragraphs cover Duncan et al. (2001) claims over the Bayesian polling

approach convenience over other methods and an outline of its applications described in

detailed steps.

Figure 13. Illustration of Bayesian inference (source: Analytics Vidhya, 2016)



59

Convenience of Bayesian pooling over other methods

Duncan et al. (2001) suggest that the convenience of Bayesian pooling is as well enhanced

by the limitations of other methods such as Panel-data (fixed and random effects models).

While Bayesian pooling is focused on pattern forecasting, panel-data methods are:

Specifically used for other purposes such as controlling for nuisance cross sectional

variation while estimating multivariate causal model.

Weakened by assuming that coefficients of causal variables are constant across

observational units when often is not the case.

Constrained by assuming that cross-sectional variation remaining after all causal

terms have been included in the model can be eliminated by adjusting the intercept

terms (Sayrs 1989). In contrast, bayesian pooling coefficients are expected to vary

from group to group within the population of time-series to be forecasted.

The authors describe Bayesian pooling further advantages over conventional time-series

methods for forecasting time-series that are volatile or are characterized by multiple time-

based patterns. The identification of equivalence group’s different pattern regimes,

described as the intervals in which parameters of a time-series model are stable (Duncan et

al., 2001) is a key element considering the cyclical nature of the cement demand (Figure

14). This feature provides a radical advantage for the estimation of long-term consumption.



60

Figure 14. Time-series with four Pattern Regimes, Duncan et al. (2001)

Bayesian pooling process

The process for implementation of the Bayesian pooling has the following steps (Duncan

et al., 2001):

1. Selection of the target series, described as the equivalence group of analogous time-

series for the time-series of interest: This step considers the identifications of

analogous time-series for pooling with the objective of find time-series that

correlate highly over time after synchronizing in the case of non-contemporaneous

series. Duncan et al. (2001) describes four different approaches for the

identification of equivalence groups. Firstly, the correlational co-movement

grouping which considers the selection of time-series that correlate highly with the

target series. Secondly the model-based clustering, which considers clustering

time-series using multivariate causal factors. Thirdly expert judgment, which

requires an expert use judgment for grouping. Lastly the strawman comparison,

which suggest pooling all the time-series to compare with the forecasting results

yielded by the previous three approaches.



61

2. Scaling each time-series to make pooled data homogeneous: Duncan et al. (2001)

provide three different ways to homogenize time-series removing differences in

magnitudes and variances. One alternative proposes to simply standardize each

time-series (subtract its sample mean and divide by its sample standard deviation).

A second alternative pointed out as not yet applied to Duncan et al. (2001)

knowledge would require regressing the target time-series on each equivalence

group time-series, and use estimated target series values as standardized data. This

alternative has been used throughout out our research and will be described in a

detailed manner in the methodological section. The third approach for scaling,

appropriate for both univariate or multivariate applications, suggest the use of

dimensionless dependent variables, for example, percentages of totals (Greis and

Gilstein, 1991) or percentage growth rates (Zellner and Hong, 1989).

3. Construction of local and group models: This step encompasses the construction of

a conventional time-series model for the target series and a separate model for the

group data. In the case of multivariate time-series models, the same model

specification is required for both the local model of the target series and the group

model. Whereas the local model estimation uses only the target observation unit’s

time-series, the group model uses the pooled data of the equivalence group. Duncan

et al (2001) suggest the possibility to recursively adjusts the time-series level

(current mean) so that, in effect, the intercept becomes the last historical time period

using Adaptive Bayesian pooling (Duncan et al., 1993, 1994, 1995a, 1995b;

Szczypula, 1997).

4. Combination of local model and group model parameters using Bayesian

“shrinkage” weights to form the pooled model. The shrinkage formulas are weights,

inversely proportional to variances of estimated parameters, for combining local

and group parameter estimates.

5. Forecasting with the pooled model.



62

6. Readjustment of target series forecasts to the raw data level: finally, if the target

series was transformed in step 2, the process needs to be reversed to produce

forecasts at the raw data level.



63

Other supporting researches

Green and Armstrong (2007) claim that people often use analogies when forecasting in an

unstructured manner, and thus subject to biases, especially for emotionally charged topics.

In their work on structured analogies for forecasting, Green and Armstrong (2007)

demonstrate that following a judgmental procedure, unaided experts’ forecast predicting

decisions made in eight conflicts situations were little better than chance at 32% accurate.

In contrast, forecasts following structured-analogies were 46% accurate, and reached 60%

accuracy when experts were able to rely on more than one analogy when predicting the

conflict outcome.

Green and Armstrong (2007) work suggests that although is clear that the information

provided by informal analogies should be useful for forecasting, in some situations people

will choose inferior analogies if they do not use a structured approach. Green and

Armstrong (2007) claim that people tend to choose analogies that are easy for them to recall

and to confirm their beliefs falling into judgmental biases. The procedure for forecasting

with structured analogies consists of a five steps process:

Describe the target situation: It requires an administration preparing an accurate,

comprehensive, and concise description of the target situation through the advice

of unbiased experts or from experts with opposing biases.

Select experts: The second step comprehends the administrator to decide on the

selection and quantity of experts on the target situation to be recruited. A mian

driver on the number of experts needed is the available knowledge they might have

about analogous situations, the variability in responses among experts, and the

criticality of obtaining accurate forecasts.

Identify and describe analogies: Experts are required to describe as many analogies

as possible, ignoring the degree of the similarity to the target situation and secondly

to match their analogies' outcomes with target outcomes.



64

Rate similarity: Experts are required to list similarities and differences between

their analogies and the target situation rating them, potentially using a pre-defined

scale.

Derive forecasts: Lastly, the administrator should construct a procedure for deriving

a forecast from experts' analogies providing logical consistency and replicability.

Among the many rules suggested by Green and Armstrong (2007), one suggest the

selection of the analogy that the expert rated as most similar to the target adopting

the outcome implied by that analogy as the forecast.



65

Research questions

The core objective of this research is to provide a valid methodology to forecast the long-

term cement demand in developing countries while simultaneously diagnosing urban

infrastructure deficits as a function of cement stock levels. During the literature review of

this research work, unexplored areas were identified opening the opportunity to improve

and enlarge the span of the existing knowledge. The fulfillment of our primary objectives

requires additional inputs, currently unavailable, in the form of a data or methodology.

While the process outlined by the work of Duncan et al. (2001) is used in this research as

the theoretical framework behind the long-term cement demand methodology and deficit

assessment, current literature fails to provide required knowledge for its application and

interpretation. The following four proposed research questions in this section are mainly

aimed to deliver key understanding on the potential formation-validity of equivalence

groups and the interaction of key variables, particularly urban infrastructure to cement

consumption and cement demand evolution to economic development.

Some of the variables mentioned in this section such as GDP per capita Constant or Cement

stock per capita are fully described later on during the research methodology process.

While the reason behind this choice is to limit the content of this section focusing only on

the research questions, we retain necessary to briefly introduce some variables facilitating

the development of the research questions’ purpose. From our perspective, leaving their

complete description as a goal to be achieved in the section 4 (Datasets and variables)

appears to be beneficial for the readers convenience in terms of self-mapping the several

elements and concepts composing the body of this research. While firstly we fully describe

the four research questions, the end of this sections offers a summary in the context of the

literature gaps identified previously (Table 8).

3.4.1 Research question 1

Current literature, particularly the work of Hung and Wu (1997) assessing the Taiwanese

cement consumption trends during the 1993-1995 period, right before its demand peak,

illustrates the limitation of missing the external view proposed by Kahneman and Tversky

(1977). The search for alternative methodologies during the literature review uncovered



66

the lack of insights about common cement consumption patterns and thus of thorough

understanding of the demand function behaviour. To assess the suitability of the analogy

forecasting methodology, suggested by our literature review as a suitable theoretical

framework to build an alternative methodology, we firstly are required to analyze the

potential to determine equivalence groups (Duncan et al. 2001). The identification of

exiting equivalence groups stand at the center of the analogy forecasting to validate the

potential benefit of using reference class (Kahneman and Tversky, 1977). As will be later

described in more detail through the research methodology section, due to the prescriptive

nature of our model, the focus of the equivalence groups are intentionally limited to those

economies which appear to have already reached a mature level of cement consumption

and thus completed a consumption cycle (Aitcin, 2000). By comparing the evolution

pattern of cement consumption per capita and GDP per capita constant during the 1913-

2013 period in advanced economies, the first research question to be answered arises

(Figure 15).

Research Question 1: Are the pattern regimes of long-term cement demand per capita

similar in advanced countries?

Figure 15. Illustration of similarity of cement consumption patterns between countries

3.4.2 Research question 2.a

Before embarking into understanding the relation between urban infrastructure levels,

particularly the deficit aspect, as a function of Cement stock per capita, it is necessary

define when does cement consumption saturates. As previously discussed in the section



67

addressing the specificities of the cement demand, we share the notion that the turning point

in the function of cement consumption per capita has the capacity to signal that a natural

consumption level has been reached (Osenton, 2004). Research question 2.a is limited to

identify and relate the levels of Cement stock per capita and GDP per capita Constant

reached in advanced economies at the point of maximum consumption per capita aiming

to identify similarities among countries (Figure 16).

Research Question 2.a: What is the natural consumption level for the cement demand in

different countries?

Figure 16. Illustration of the saturation point in the Cement consumption per capita

3.4.3 Research question 2.b

Once that the general wonder over the existence and magnitude of a natural level of cement

consumption per capita has been answered, the following step leads to the exploration of

the research question 2.b. The assessment of the relation between historical cement

consumption and the contextual characteristics of the urban infrastructure is expected to

simultaneously provide: firstly, a validation on the correlation’s significance between

cement stock and urban infrastructure availability; and secondly a review of the elements

influencing the development of these two variables. Current literature encourages this line

of work, particularly following Aitcin (2000), Deverell (2012) and Kang and Li (2013)

claims.



68

One of the three reasons listed by Aitcin (2000) potentially slowing down cement

consumption once a certain standard of living has been reached stablishes that the major

parts of the public-structure needs have been built as illustrated in the Figure 17. Although

no particular parameter is defined, the outcomes of his global work clearly proposes a

causal link between the maturity of the cement demand to the erection of most of the major

public-structure needed

In the same line, the studies on Asian countries conducted by Deverell et al. (2012) and

Kang and Li (2013) sustain a relation between market saturation and the development of

basic infrastructure. While Deverell et al. (2012) argues that once a basic urban

infrastructure has been built, there is no need to continue pouring cement in the form of

building, Kang and Li (2013) claim that cement cumulative consumption works as an

efficient estimator of a country’s cement requirement for future urbanization needs.

Although both Deverell et at. (2012) and Kang and Li (2013) provide defined references

of market saturation (~18 tons per capita), these are mostly constrained to Asian advanced

economies and for the particular applications on the Chinese cement market. Thus, this

research work proposes and requires a much broader and rigorous understanding of this

relation (Cement stock per capita and urban infrastructure level).

Research Question 2b: How does the cement saturation point level relates to urban

contextual factors?

Figure 17. Relation between Cement stock per capita and development level of urban

infrastructure



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3.4.4 Research question 3

The fundamental difference between Deverell et al. (2012) and Kang and Li (2013)

contributions as compared with those of Aitcin (2000) are based on the later claims &

insights over the relation between economic development and cement consumption.

Whereas Deverell et al. (2012) and Kang and Li (2013) relate cement consumption to levels

of urban infrastructure only, Aitcin (2000) suggests that it is possible to establish a direct

relationship between the consumption of cement and the economic development of a

country. Although Aitcin (2000) also finds a link between Cement stock per capita and

levels of urban infrastructure development, his starting point is grounded on the impact of

a countries’ economic development on the cement demand. In the same line of thought, the

International Cement Review (2014) also argues that Cement consumption per capita rises

in the initial stages of economic development before peaking once gross domestic product

per capita reaches the levels of advanced economies. Inputs from the literature appear

concisely clear on proposing a relation between the degree of economic development and

the level of cement consumption. However, though this might perhaps sound obvious for

many observers, the characteristics of this relation are still unexplored, what leads us to the

third research question of this research. Answering to this question would allow to obtain

the required specific parameters to estimate the potential growth of Cement consumption

per capita in developing economies.

Research Question 3: How does economic growth influence the cement demand in the long

term?

Figure 18. Illustration of potential long-term Cement consumption per capita development



70

3.4.5 Research questions in the context of the literature gaps

The following table provides a summary of the research questions in the context of the

literature gaps identified during the corresponding review. Detailed descriptions of the

literature gap are as well developed in concluding remarks of the literature review section.

Table 5. Research questions in the context of the identified literature gap

Research

questions

Identified literature gaps (limited or unavailable knowledge)

Estimation &

understanding of

urban infrastructure

deficit in developing

countries

Cement (specific)

long-term

forecasting

methodology

Cement market

potential as a

function of deficits

on the Cement

stock levels

RQ1: Are the

pattern regimes of

long-term cement

demand per

capita similar in

advanced

countries?

The central answer

of this question

serves to initially

validate the use of

the theoretical

framework (Duncan,

2001)

RQ2.a: What is

the natural

consumption level

for the cement

demand in

different

countries?

Answering RQ2.a and RQ2.b provides the necessary insights to

estimate deficits of urban infrastructure in developing countries,

bridging the claims of Osenton (2004), Aitcin (2000), Deverell

(2012), Kang and Li (2013) and Birshan et al. (2015)

RQ2.b: How does

the cement

natural

consumption level

relates to urban

contextual

factors?

RQ3: How does

economic growth

influence the

cement demand in

the long term?

RQ3 allows to obtain the required specific

parameters to estimate the potential growth

of Cement consumption per capita in

developing economies as a function of the

economic development. Thus, completing

the initial claims of Aitcin (2000)



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4 RESEARCH METHODOLOGY

Process description

The research methodology was organized in two main phases. The exploratory analysis,

aimed to cover the rationale behind the regional and economic categorization of countries,

the description of the variables and the main objectives of the descriptive statistics. The

explanatory part covers the long-term forecasting model structure, the steps involved on its

construction, and the processes for both the diagnose of urban infrastructure deficit and the

case-study (featuring a deep analysis) of a selected developing country.

As described in the Figure 19, the process was designed to answer the four research

questions through the combined findings of the exploratory analysis supporting all phases

of the explanatory analysis.

Figure 19. Process description of the research methodology



72

Exploratory analysis:

This section was aimed to describe the general organization, objectives and methodology

of the exploratory process. Firstly, the main rational behind the economic and geographic

categorization of countries was addressed, with a focus on the widespread practices and

most proper use of them considering the requirements of this research. A detail description

on the countries exclusion process was provided together with the final list of outliers and

the main drivers. In the Datasets and variables section (4.2.2), we summarized the

descriptions of the variables used from a conceptual perspective. We also offered an

exhaustive explanation on how the datasets were treated and completed to reconstruct

missing information when needed. In the Data analysis overview and description (4.2.3),

we provided a description of the most relevant variables through cross-section and

descriptive statistics analysis, revealing stablished relationships together with their

particularities and potential elements of causality.

4.2.1 Classifications of countries in groups:

Economic clusters:

Considering Aitcin (2000) arguments over the relation between economic development and

cement consumption, one of the very first tasks of the research methodology required the

classification of countries as per their stage of economic development. Furthermore,

considering the need of finding the cement natural consumption point, likely having taken

place in countries where the consumption cycle is completed (Osenton, 2004),

characterizing the different levels of economic development was needed.

To determine a level of economic development, two main well diffused and accepted

sources were combined and used simultaneously. As illustrated in the Table 6, The World

Bank classification was used to distinguish between four different type of economies by

income level: high, upper-middle, lower-middle and low. The International Monetary Fund

interpretation of advanced economies was used to select the most developed economies

within the World Bank’s high-income group.



73

Table 6. Economic classification by organism (source: World Bank, 2013 and

International Monetary Fund 2013)

World Bank – 2013 International Monetary Fund - 2013

Classification GNI (USD)

Classification

High-income >12’745 Advanced (only 34 out of the 53

countries considered High-income by

the World Bank)

Emerging market and developing

economies

Upper-middle-

income

4’126-12’745

Lower-middle-

income

1’046-4’125

Low- income <1’046

We justified this choice on the fact that the World Bank high income group also contains

countries which have likely not completed a consumption cycle. In 2013, 19 out of the 53

countries classified as high income by the World Bank criteria were considered as emerging

markets and developing countries by the International Monetary Fund. The strictness of the

International Monetary Fund classification appeared to suit better the methodology and

objectives of this research, which focuses on the identification of reference values of

cement demand by observation of full consumption cycles over time. For instance, the 2013

Cement stock per capita in some high-income countries as per World Bank’s economic

classification were Chile (12.86 tons), Uruguay (13.56 tons), and Trinidad and Tobago

(21.3 tons), differing from the 31 tons average for the advanced economies as per the

International Monetary Fund classification. This concept is described exhaustively during

the variables description (section 4.2.2). The following paragraphs summarize the main

assumption followed by both organism in order to guide the reader towards the

understanding of our choice.



74

The World Bank economic classification: In its World Development Indicators database,

the World Bank classifies 217 economies composed by 189 World Bank member countries

and 28 other economies with populations larger than 30’000 inhabitants. Besides the

economic classification, the World Bank also provides two additional classifications; by

geographic region and by the operational lending. Every year, the first day of July,

countries are re-classified based on the estimate of their Gross National Income (GNI) per

capita corresponding to the previous calendar year. The resulting income groupings remain

unchanged for the following twelve months although some GNI per capita might be

occasionally revised.

The World Bank separates countries in four income groupings, depending on their level of

GNI in U.S. dollars, converted from local currency using the World Bank Atlas method.

Per capita figures result from the combined work of the multiple economist working locally

in the World Bank country units responsible for the GNI estimations and the World Bank’s

demographers from a variety of sources for the population numbers. The income thresholds

for the low, upper-middle, lower-middle and high income groups were established in 1989

following previously defined operational thresholds and are inflation adjusted annually at

the beginning of the World Bank's fiscal year. For the inflation adjustments, the World

Bank currently uses the change in a deflator collected from inflation measures of Japan, the

United Kingdom, the United States, and the Euro area.

The World Bank’s choice of GNI over other metrics as a measure of income level is related

to the use of GNI in the methodology to determine its operational lending policy. The

World Bank claims that while a country GNI per capita does not completely represent its

level of economic development, it acts as an efficient indicator in turns closely correlated

with other measures of life quality. Among other limitations, the World Bank highlights

that GNI might be underestimated in lower-income economies with high levels of informal

economic activity, might lack of reflection over distribution income inequalities, and

finally can be occasionally biased due to arbitrary official exchange rates.

Although as it will later on be described, this research focuses on the use of GDP

specifically as a core variable during the development of the forecasting model, we retain



75

that the World Bank’s use of GNI as measure of income does not poses any conflict in

terms of methodological consistency. Within the scope of this research, the use of the

World Bank’s economic classification is limited to the application of the proposed

categories without utilizing any of its figures for computations, therefore avoiding

inconsistencies potentially caused by the combined arithmetic use of GNI and GDP figures.

International Monetary Fund: In its 2013 World Economic Outlook report, the

International Monetary Fund describes the main aspects of the two economic groups which

conform its economic classification, the advanced economies; and the emerging market

and developing economies (as clarified in the Introduction section, this second category is

referred across this thesis document as “developing”). In contrast to the groupings provided

by the World Bank, the International Monetary Fund does not set its classification criteria

strictly on economic terms. The International Monetary Fund argues that the objective of

their on-going classification is to facilitate analysis by providing a reasonably meaningful

method of organizing data for the country members.

For example, while the World Bank positions rich oil economies such as Brunei, Oman,

Saudi Arabia, Bahrain, Kuwait, United Arab Emirates and Qatar at the top of the

classification raking (solely driven by GNI levels), the International Monetary Fund criteria

excludes them from the advanced economies group. As summarized by the International

Monetary Fund (2016), some of the elements driving these economies out of the advanced

countries group are particularly related to the lack of economic diversification and the

limited role of the public sector acting as an enabler for the private sector.

Although the International Monetary Fund two-grouping categorization into advanced, and

emerging market and developing economies appears more general than the World Bank’s

four groups driven by its GNI screening, the former also provides specific and functional

sub-grouping. The International Monetary Fund separates the advanced economies in

subgroups such as the Group of Seven (G7) composed by the largest seven countries in

terms of GDP out of the 35 advanced economies that composed the 2013 list (United States,

Japan, Germany, France, Italy, the United Kingdom and Canada). The members of the Euro

area are also distinguished as a subgroup. The group of emerging markets and developing



76

economies is composed by the 154 remaining members which are not include in the

advanced economies classification. The subgrouping of emerging market and developing

economies mainly answers to a regional rationale consisting of central and eastern Europe

(sometimes also referred to as emerging Europe); Commonwealth of Independent States;

developing Asia; Latin America and the Caribbean; Middle East; North Africa,

Afghanistan, and Pakistan; and sub-Saharan Africa. The International Monetary Fund also

sub-classifies the emerging market and developing economies according to analytical

criteria such as composition of export earnings and other income from abroad; a distinction

between net creditor and net debtor economies; and other forms of groupings. For example,

the analytical criterion by source of export earnings distinguishes between two categories,

fuel and nonfuel.

Nielsen (2011) review over the economic classification methodologies of the United

Nations Development Program, the World Bank and the International Monetary Fund

offers a set of concluding remarks regarding the validity of the country classification used

in this research methodology. Although the used methods are different, the structure of the

three different taxonomies are similar in the sense that 20–25 percent of countries are

designated as developed by each method. Nielsen (2011) review criticize to a certain extent

the lack of clarity of the existing taxonomies regarding how they distinguish among country

groupings. While the World Bank fails to describe the threshold between developed and

developing countries, the United Nations Development Program does not provide a

rationale for the ratio of developed and developing countries and the International

Monetary Fund classification system lack of threshold use.

Geographic regions:

Countries geographic classification is an important part of the exploratory analysis as to

detect and understand geographical patterns with substantial relevance in the cement

consumption which might need to be controlled in our forecasting model. The descriptive

value of a proper geographic distinctions allowed us to assess cement consumption levels

by region and how it relates to main regional economic conditions, climate, terrain

complexity, cultural treats or choice of construction techniques.



77

To observe for these regional dynamics, countries were grouped following the World

Bank’s geographic classification in regions and sub-regions as illustrated by Table 7. The

World Bank groups all its 189 members plus other 28 other economies with population of

more than 30’000 people. The term country, economy and nation are used interchangeably

by the World Bank and in the scope of this research to refer to a territory with separate

statistics but not necessarily politically independent.

Oceania was merged to Asia due its geographic proximity and high inter-regional migration

flows. Furthermore, as the Oceania region is only composed by Australia, Fiji and New

Zealand, the significance of its statistical findings was expected to offer a low level of

relevance. All countries in the scope of this research criteria are covered in Annex 1.

Table 7. Geographic regions (source: World Bank 2013)

Region Africa America Asia Europe

Sub-region Norther

Southern

Western

Eastern

Central

North

Central

South

Caribbean

Southern

Western

Eastern

South-western

Central

Oceania

Northern

Southern

Western

Eastern

Excluded countries:

Out of the 160 countries listed in the annex 1, for which some sort of relevant information

related to cement consumption was initially found, only 129 countries remained forming

part of the main body of analysis. The screening process taking place in the exploratory

phase was designed to eliminate observations potentially affecting the results robustness.

As described below, the main two reasons for the exclusion of a country were: firstly, the

lack of relevant data and secondly, abnormal cement consumption. Although all the data-

variables are fully described in the data section, we introduce briefly some concepts (e.g.

Cement stock per capita) during the description of the exclusion process to facilitate readers

understating of the rationale behind.



78

Lack of relevant data: A set of countries was excluded due to the absence of critical data.

This exclusion comprehends particularly but not only the ex-Union of Soviet Socialist

Republics or largely influenced counties (Armenia, Azerbaijan, Belarus, Estonia, Georgia,

Latvia, Lithuania, Moldova, Kazakhstan, Kyrgyzstan, Russia, Tajikistan, Turkmenistan,

Ukraine, Uzbekistan, Czech Republic and Slovakia). Although recent (i.e. 2013)

information in terms of cement consumption and other economic development metrics was

gathered, the availability of historical information during the period between the post

second world war and the dissolution of the Union of Soviet Socialist Republics was

limited or non-existing.

To avoid losing the statistical and analytical value of a larger country dataset, several

attempts to obtain the missing data were made such as the retrospective use of allocation

key with base in 1991. However, our linear reconstruction of cement consumption data for

the now independent states that constituted the Union of Soviet Socialist Republics probed

to be inaccurate. The complexity and limited available information on the internal cement

production-distribution flows requires an exhaustive research project on its own. As argued

by Abouchar (1969), during 1927-1940 period, the Soviet transportation utilization

measured by rail-ton millage rose by 408% while tonnage originated increased by 306%.

Also, the increase on the average length of the cement haul appears to be attributed to an

irrational demand of transportation caused by poor marketing, irrational allocation of

product mixed and regional misallocation of investments (Abouchar, 1969). Finally, we

retained that the benefits of enlarging the country dataset by reconstructing the unknown

cement consumption of the ex-Union of Soviet Socialist Republics states were outweighed

by the risks of presenting wrong and misleading information.

Other countries with missing information that needed to be excluded were the Democratic

People’s Republic of Korea, Eritrea, New Caledonia, Cyprus and Luxembourg. While the

case for Eritrea, New Caledonia, Cyprus and Luxembourg relates to the lack of historical

economic development information, specifically the GDP Constant per capita, the

problematic for People’s Republic of Korean is routed in the broad absence of official

information. Although some data related to cement consumption is accessible through



79

estimation and speculation, other critical ones related to economic activity and vital for this

research are not available (Smith, 2006). In the same line of action that lead to disregard

ex-Union of Soviet Socialist Republics states, we retained that the large efforts to build a

likely inaccurate set of data for Democratic People’s Republic of Korea did not justify the

time and effort required.

Abnormal cement consumption: Before describing the exclusion process of countries

holding abnormal levels of cement consumption, stablishing the meaning and extent of

normality within the scope of this research is required. The fulfillment of this objective

leads to the initial description of the Cement stock per capita, which will be addressed in

more details during both the exploratory and explanatory analysis. The Cement stock per

capita, normally expressed in tons, represents the accumulation of all previous cement

consumption per capita to the year of measure. For example, the Cement stock per capita

in the year 2000 corresponds to the aggregation of all the Cement consumed per capita

from 1913 to 2000 (1913 corresponding to the first year-data recorded). The exclusion of

countries in this group is mainly driven by their lack of representation of other countries

and by their wealth level not appearing to be a necessary measure of development level.

As previously addressed by Osenton (2000), Aitcin (2000), Deverell et al. (2012), and Kang

and Li (2013) in the section Specificities of the cement demand in the context of forecasting

(2.3.2), there are a set of three fundamental propositions which opens the gate to the

definition of normal range of Cement consumption per capita.

Firstly, Aitcin (2000) and in more general terms the findings of the International

Cement Review (2014) claim is that it is possible to establish a direct relationship

between the consumption of cement and the economic development of a country.

This proposition indicates that for certain level of economic development, for

instance measured in terms of GDP per capita, there is a corresponding level of

cement consumption.

Secondly, both Osenton (2000), and Palacios Fenech and Tellis (2016) described a

sort of standard level of consumption peak. While Osenton (2000) claimed that



80

every product or service has a natural consumption level after which further

investment to grow are ineffective, Palacios Fenech and Tellis (2016) found their

set of reviewed products reached a peak at about 56% of market penetration

followed by a dramatic drop.

Thirdly Aitcin (2000), Deverell et al. (2012) and Kang and Li (2013) sustain the

idea of a cement demand drop once a certain level of urban infrastructure has been

reached. Whereas Aiticin (2000) proposed a general approach by claiming that

growth of cement consumption slows down when the standard of living reaches a

certain level, the work of Deverell (2012) and Kang and Li (2013) in Asian markers

directly proposes reference values (18 tons per capita) in the same line of thought.

Although the complete findings on the natural level of cement consumption are described

in the result section of this research, the solid propositions of Osenton (2000), Palacios

Fenech and Tellis (2016), Aitcin (2000), International Cement Review (2014), Deverell et

al. (2012), and Kang and Li (2013) motivated us to run an initial analysis to screen out

country outliers. For that, we compared the consumption level of all countries measured in

terms of Cement stock per capita which enabled the exclusion of some countries under two

typological groups (Table 8):

Superfluous level of cement consumption: This group contains countries with

abnormal cement consumption levels in the sense of exaggerated for what

represents normal infrastructure needs. Although some of these countries (i.e.

Brunei and the United Arab Emirates) were already screened out due to the absence

of data related to GDP Constant per capita, their abnormal level of cement

consumption is discussed within these paragraphs to support the general “outlier”

conceptualization. The group is composed mainly by wealthy oil based economies

Brunei, Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates.

The reasons behind this abnormal consumption level appear to be rooted in the

specific dynamics of their hyper economic growth potentially fueling related

expressions such as luxury building / overdesign, tourism activities, migration

process and housing oversupply driven by real state bubble. Schmid (2006)



81

consolidates a large portion of these expression under the term “economies of

fascination”, which focus on theming everyday life, for instance with the artificial

islands in the form of a Palm tree in Dubai, one of the United Arab Emirates. The

pharaonic dimension of this construction is quantified by de Jong et al. (2003)

arguing that only one contractor supplied 70 million cubic meters of sand, out of a

total of 94 million cubic meter required. Supporting Schmid (2006) interpretation

of Dubai’s growth drivers, Davidson (2009) criticized the planners and visionaries

of Dubai’s model claiming that real estate, luxury tourism, and a construction

industry should never have been allowed to become the main foundation of the

economy.

A different example of superfluous cement consumption level within this group of

counties is illustrated by the Qatar preparations towards the 2022 football World

cup. Zimbalist (2015) claims that while the preparation of Qatar 2022 World Cup

is estimating a total expenditure of USD 200 billion, similar events required only a

fraction (USD 40 billion for China’s 2008 Olympic Games, USD 50 billion

Russia’s 2014 Olympic Games and USD 20 billion for Brazil’s 2014 World Cup).

The staggering difference exposes the alternative intentions behind these buildings

which appeared to depart from true sporting functionality towards an exhibit of

excess. In another attempt to exemplify the potential impact caused by large and to

certain extent ephemeral projects, Zimbalist (2015) describes a specific dynamic of

irrational excesses taking place during the city of New York candidacy for the 2012

Summer Olympic Games. Zimbalist (2015) argues that only the six-square-block

concrete slab required underneath a stadium structure would have cost USD 400

million, with an expected post-Olympic use of only 15 days a year.

The construction of the Bahrain Financial Harbor located in Manama provides

another solid example related to the excess building of these outlier countries. The

large-scale commercial development project required a USD 1.5 billion investment

and its construction was mainly completed in 2009 after five years, adding an

additional floor area of 380’000 square meters. As reported by the BBC (2013), due

to its low occupancy, the whole project was perceived locally as a national disgrace.



82

Four years after the project was finished, only a mini-mart and a coffee shop was

open in a shopping center between the two towers conforming the main body of the

development, which were mostly empty too. The common pattern of overbuilt and

oversold office towers around the region was worsened in Bahrain due to tourism

collapse following the social unrest and protests demanding democratic reforms and

an end to religious discrimination of Shia Muslims. AlShehabi and Suroor (2016)

claimed that both land reclamation and dispossession played a key role in the real

estate development in Bahrain. By 2008, more than 20 additional mega projects

were planned to create more than 60’000 luxurious residential units with

corresponding commercial and office space.

The claims of Mariscotti and Pickles (2016) offer a set of concluding remark for

the superfluous cement consumption dynamics in Gulf Cooperation Council area.

Their work on the energy rich Gulf monarchies (Bahrain, Kuwait, Oman, Qatar,

Saudi Arabia and United Arab Emirates) describes how the real estate overcapacity

has reached unprecedent scales with the value of the projects under construction in

2016 being four times the value of all projects constructed during the previous ten.

In line with Schmid (2006) understanding of the economies of fascination,

Mariscotti and Pickles (2016) claimed that the Gulf Cooperation Council area has

become a test bed for leading architects to launch their most audacious and

ambitious projects. While as per 2016 figures the regions accounted for 2% of

global GDP, in a potentially alarming and disproportionate fashion, it holded 27 of

the 100 tallest buildings in the world. Mariscotti and Pickles (2016) concluded their

summary claiming that since 2006, the value of cancelled projects has been worth

almost twice as much as the value of the ones completed, given currently announced

projects only a 35% change of completion.

While Brunei faces a different geographic exposure, several signs appear to support

a situation of growing superfluous cement consumption. Begawan (2016) claims

that the current massive residential oversupply has taken a token in the rental market

and eventually in the housing price. In a similar character to the Gulf Cooperation

Council, Brunei’s large wealth predominantly related to oil activities, has drove the



83

construction of large scale residential and non-residential projects generating a real

estate overcapacity recently becoming evident. As reported by the Oxford Business

Group (2016), while housing purchases rose by 9.7% in 2015 versus 2014 due to

cheap available lending, the lending for land purchases and construction fell by

9.5% during the same period signaling a clear deacceleration in the construction

activity.

Cement consumption and economic development misalignment: This group

contains the cases of Libya and Lebanon, where the registered levels of Cement

stock per capita of these countries appeared to be extremely high in relation of their

level of economic development.

Although Lebanese Cement stock per capita (~40 tons as per 2013 figures) is not

extremely high in comparison with the Gulf oil wealthy economies, it appears

certainly disconnected to its GDP per capita levels. At USD 14’500 in 2013,

Lebanon GDP per capita is closer to the range of upper middle countries (up to

USD 14’601) which in turns corresponds to an average Cement stock per capita of

14.2 tons (approximately only one third of Lebanese levels). In an extensive review

on the Lebanon economy, Sadikov et at. (2012) claims help to clarify particular

dynamics affecting the construction market in Lebanon, and consequently the

cement demand. Sadikov et al. (2012) argued that substantial part of the

construction activity in Lebanon is driven by non-residents in two forms. Firstly,

under the Lebanese strategy to expand in the region, Lebanese banks allowed real-

estate loans to non-residents. Secondly, accounting for USD 6.7 billion in 2010,

Lebanon is one of the world’s largest recipients of remittances as percentage of

GDP (17%). In combination, these two elements contribute to the construction

market development, resulting in levels of consumption per capita which seem to

be unrelated with the real internal economic development of the country.

Although Libya, might be perceived at first sight as a case of superfluous level of

cement consumption similar to wealthy oil based economies, its abnormality

appears to relate more to economic misalignment, perhaps caused by the historical



84

political context following the subsequent years to the Al-fatah revolution of 1969.

Ngab (2007) claimed that this stage was characterized by large public spending in

all sectors, particularly in construction, financed mainly by revenues generated

through the high oil prices that followed the 1973 oil crisis (source: U.S.

Department of State). In 2013 figures, Libya’s abnormal level of Cement stock per

capita (44 tons per capita, three times larger than that of other upper middle-income

countries) appeared to relate to two dynamics identified by Ngab (2007). Firstly, a

strong focus on the use of cement as main and often sole construction material in

residential construction. Secondly, the development of large projects such as the

great manmade river waterpipe network. During the transformation in the

construction industry that shifted from indigenous construction practices to a

cement based industry, 97% of the construction in Libya used cement-based

material, regardless of location, cost, or environmental conditions. This

indiscriminate used of the cement during the 1970 decade, placed Libya as one of

the world leaders in terms of cement consumption per capita.

Table 8. Cement consumption (average): Gulf Cooperation Country members and

regions (source: Cembureau 1994, The Global Cement Report 2015, International

Monetary Fund 2016)

2013 values Gulf

Cooperation

Council

Advanced

economies

High

income

Upper

Middle

income

Lower

Middle

income

Low

income

GDP Current

per capita

[USD]

68’673 43’482 24’538 14’601 5’927 1’790

Cement

stock per

capita [tons]

59.0 31.0 20.1 14.2 6.5 1.8

Cement

consumption

per capita

[kg]

1’670 397 380 431 260 83.4



85

The main elements previously described related to the process for exclusion of 31 countries

from this research can be summarized as follows:

22 countries lacking relevant data (mainly historical Cement consumption per

capita and GDP constant per capita): Armenia, Azerbaijan, Belarus, Estonia,

Georgia, Latvia, Lithuania, Moldova, Kazakhstan, Kyrgyzstan, Russia, Tajikistan,

Turkmenistan, Ukraine, Uzbekistan, Czech Republic, Slovakia Democratic

People’s Republic of Korea, Eritrea, New Caledonia, Cyprus and Luxembourg.

7 countries with superfluous level of cement consumption: Brunei, Bahrain,

Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates (Brunei and United

Arab Emirates key-data such as GDP per capita Constant was also missing).

2 countries with cement consumption and economic development misalignment:

Lebanon and Libya.

4.2.2 Datasets and variables

To confirm the validity of our deficit estimation and long-term forecasting models we

collected time-series covering a hundred-year period from 1913 to 2013 and single data-

point mainly corresponding to the year 2013 for 129 countries. In most cases, only low

levels of conditioning were required to construct four different type of variables at country

level, Cement, Urban infrastructure, Economic development and Country inherent

variables (Table 9). Due to the extensiveness of the data, the Annex 7.2 lists all the variables

with their corresponding value for 2013. The hundred-year historical data for the variables

Cement consumption per capita and Cement stock per capita and GDP per capita Constant

are available upon request. For the readers guidance, in text body of this research, variables

are indicated with the first letter capitalized to distinguish them from other related

expressions.



86

Table 9. Description of variables

Variable Group Unit Period Type Source

Cement

consumption

per capita

Cement kg Time-

series:

1913-2013

Raw data World

Statistical

Review (1998)

and

International

Cement Review

(2015)

Cement stock

per capita

Cement tons Time-

series:

1913-2013

Constructed

using

Cement

consumption

per capita

World

Statistical

Review (1998)

and

International

Cement Review

(2015)

Public-

structure

quality

Urban

infrastructure

1-7 scale Single

value: 2013

Raw data Global

Competitiveness

Report (2013-

2014)

Capital stock

per capita

Urban

infrastructure

2011

constant

International

Dollars

Single

value: 2013

Constructed

aggregating

raw data

IMF Fiscal

Affairs

Department

(retrieved 2017)

Population

living in

slums

Urban

infrastructure

% of total

population

Single

value: 2014

Raw data United Nation's

Millennium

Development

Goals database

GDP per

capita

Constant

Economic

development

1990

International

Dollars

Time-

series:

1913-2013

Raw data Maddison

project

(retrieved 2013)



87

GDP per

capita

Current

Economic

development

2011

International

Dollars

Single

value: 2013

Raw data World Bank

(retrieved 2017)

Human

Development

Index

Economic

development

1-100 scale Single

value: 2013

Raw data Human

Development

Report (2016)

Political

stability

Economic

development

1-100 scale Single

value:

1996-2013

average

Constructed

averaging

raw data

Worldwide

Governance

Indicators

(retrieved 2015)

Urbanization

level

Country

inherent

% of total

population

Single

value: 2013

Raw data World Bank

(retrieved 2017)

Temperature

average

Country

inherent

Celsius

degrees

Single

value:

1961-1999

average

Raw data World Bank’s

Climate change

knowledge

portal (retrieved

2017)

Country size Country

inherent

Km2 Single Raw data United Nations

Statistics

Division

(retrieved 2017)

Elevation

over sea level

- average

Country

inherent

meters Single Raw data Country

Geography

Data, Portland

State University

(retrieved 2015)

Cement variables:

As one of the main objectives within this research is to forecast the long-term growth of

the cement demand in developing markets, Cement consumption per capita is the variable

to predict at country level, and thus core variable in our analysis together with the resulting

Cement stock per capita. The following paragraphs offer a description for the two cement



88

variables, Cement consumption per capita - 1913-2013 [kg] and Cement stock per capita -

1913-2013 [tons], as well as a review of the datasets from which data was gathered.

Cement consumption per capita - 1913-2013 [kg]: This variable measures the

average annual cement consumption per capita in kg at national level. The

extended records allowed us to observe for long-term consumption pattern and

to estimate Cement stock per capita levels.

Cement stock per capita - 1913-2013 [tons]: This variable is constructed by the

accumulation of all previous cement consumption per capita to the year of

measure in tons. For example, the Cement stock per capita in the year 2000

corresponds to the aggregation of all the Cement consumed per capita from 1913

to 2000.

Two main sets of data were required to obtain cement related variable’s values for the 1913-

2013 period. The World Statistical Review, special edition issued in 1998 by the European

Cement Association based in Belgium covering the 1913-1995 period and The Global

Cement Report, 11th Edition issued in 2015 by International Cement Review Organization

for the 1996-2013 period. The reason behind the choice of two separate dataset was driven

by their coverage, while the World Statistical Review (1998-edition) covers the 1913-1995

period, the earliest data provided by the Global Cement Report (11th-edition) corresponds

to 1990.

The World Statistical Review (corresponding to the 1913-1994 period): In 1998, the

European Cement Association, based in Brussels published a special edition of its

worldwide renowned report. The European Cement Association represent the cement

industry in Europe agglomerating within its full members national associations and

European companies, except for Malta and Slovakia. Among the different activities, the

European Cement Association acts as spokesperson for the cement industry before the

European Union institutions and other governmental authorities communicating the

industry’s position and policy developments on relevant matters such as technical,



89

environmental, energy and employee health. The European Cement Association is staffed

by multinational professionals supported by inputs from its members through four working

groups and ad hoc teams playing a significant role in the world-wide sustainable

development of cement and the ready-mixed and precast concrete industries.

The World Statistical Review, compiled and published for the first time in 1957, tabulated

country by country data on world cement production, trade and consumption since 1913.

In response to many requests (source: European Cement Association) an eighth edition was

published covering until 1995. The statistics reported in the review were obtained from

members in cooperation with worldwide cement association, national statistical

institutions, the United Nations and other private sources. In terms of methodology, the

World Statistical Review claims that the cement consumption was calculated as the

addition of cement production and import less exports, unless specific data for domestic

consumption was available (per capita values obtained applying population size data from

the United Nations official bulletins). The worldwide aggregated difference between

cement exports and cement import figures appears to be a result of:

Large importing countries are not always required to do make custom declarations.

Re-exports are rarely recorded.

Imports for military activities and defense purposes are usually not recorded.

Cement imports to be sold in markets forming part of unofficial territories,

particularly in certain African regions, are normally not recorded in official

statistics.

We evaluated the impact on these matters to be generally neglectable and thus no attempt

to control for them was considered. Towards the end of this section, a validation of the

data- robustness is provided. In terms of missing data, we found data breaks mainly related

to the First and the Second World Wars periods and other scattered but minor omissions.

In the following paragraphs, we described the technique used to fill missing information

when we retained necessary. First and second World War periods: Due to the breaks in the

entries provided by the European Cement Association’s World Statistical Review during

the Great Wars periods, missing years were interpolated using geometric progression



90

(compounded annual growth). This linking technique was also used by Canning (1998) in

his work on global physical public-structure for 152 countries covering the 1950-1995

period.

The 1914-1919 break due to First World War: Whereas Canning (1998) used only

two figures (immediate previous and immediate subsequent to the break), before

estimating the interpolated figures, we retained more appropriate to use an average

of three years to create the previous and subsequent values to the break. The

objective behind this choice was to avoid or mitigate biased interpolations driven

by impact of abnormal values recorded at one (or both) ends of the break. As the

1913 year is the first available entry, for the 1914-1919 break, the interpolated

numbers were estimated by inflating the 1913 volume with compounded annual

growth resulting from the 1913 volume and the 1920-21-22 average volume

(Equation 1).

The 1939-1946 break due to the Second World War: In the same fashion as the

1914-1919 break, the 1939-1946 break was estimated by inflating the 1938 volume

with the compounded annual growth resulting from the 1936-37-38 average volume

and the 1947-48-49 average volume (Equation 1).

Scattered breaks: In the few cases where isolated breaks present, the same

procedure used to reconstruct the missing data due to the First and Second Wars

was implemented to link the break’s previous and subsequent values.



91

Equation 1. Interpolation equations for breaks in the Cement consumption per capita

dataset

𝐶𝐴𝐺𝑅 (𝑡0, 𝑡𝑛) = (𝑉(𝑡𝑛) ÷ 𝑉(𝑡0))1÷(𝑡𝑛−𝑡0) − 1

1914-1919 break:

V(t0): Ccpc1913

V(tn): µCcpc(1920,1921, 1922)

1939-1946 break:

V(t0): µCcpc(1936,1937,1938)

V(tn): µCcpc(1947,1948, 1949)

Ccpc: Cement consumption per capita

The Global Cement Report (corresponding to the 1995-2013 period): The eleventh edition

of this report published in 2015 covers 1995-2013 period. Issued by the International

Cement Review organization every two years, The Global Cement Report is considered an

industry reference featuring market information for over 170 countries. In terms of

methodology, this report includes all types of grey and blended cement (equivalent to the

figures named construction cement by the European Cement Association’s World

Statistical Review report). Although white cements records are excluded from The Global

Cement Report, we estimated its impact to be neglectable. As per the Global Cement

Directory (2015), the global production capacity of white cement reached 13.3 million tons

in 2013, which accounts for 0.32% of the 4’139.52 million tons corresponding to the total

cement consumption (Global Cement Report, 2015). To obtain per capita figures, the

International Cement Review Organization used Population Reference Bureau figures.

U.S. Geological Services – validity of the cement datasets: To verify the validity of both

the World Statistical Review 1998 and the Global Cement Report 2015 datasets, the

Cement consumption per capita values were compared with the information provided by

the Mineral Yearbook prepared by the United States Geological Survey since 1935. The



92

variation of the resulting Cement stock per capita from the two data sates, the preferred one

used in this research conformed by the World Statistical Review 1998 in combination with

the Global Cement Report 2015, and the Mineral Year Book were compared as illustrated

in the Figure 2. The histogram shows that countries with a Cement stock per capita

maximum variation of +/-20% accounted for 77.4% of total countries for the 1935-2013

period.

Figure 20. Datasets comparison of Cement stock per capita figures

The information gathered from the multiple Mineral Yearbooks was disregarded as the core

source of information for this research (and only used as a validation / comparison source)

due to the elements described in the following paragraphs. Although most of the countries

showed a high level of similarities for the recorded Cement consumption per capita figures

(main input to conform the Cement stock per capita), the noticed discrepancies are believed

to be based in the characteristics of the United States Geological Service’s Mineral Year

Book data as follows:

The Mineral Year Book only reports national total figures, therefore per capita

levels required to be estimated using historical information on population

increasing the chance of incurring into errors.

13%

3%

45%

0%

52%

22%

18%

18%

7%

0%

29%

48%

29%

3%

16%10%

0

5

10

15

20

25

30

35

40

45

50

-50

%

-45

%

-40

%

-35

%

-30

%

-25

%

-20

%

-15

%

-10

%

-5%

0%

5%

10

%

15

%

20

%

25

%

30

%

35

%

40

%

45

%

50

%

Number of countries

Cement stock per capita variation per dataset



93

The earliest information available corresponds to 1935, thus failing to cover the

desired hundred-year period we were able to gather through the combination of the

World Statistical Review 1998 and the Global Cement Report 2015.

Instead of consumption volumes, The Mineral Year Book reports cement

production volume at country level. While at global level this element might not

result in a substantial mislead, at country level could become an element of

distortion. However, to still be able to utilize this rich source of information for

validation purposes of the other more complete data bases, a direct outreach (though

electronic mail) was done in 2014 to United States Geological Services’ specialist,

Mr. Hendrik van Oss. Through this contact, Hendrik Van Oss, who at the moment

of the contact enjoyed a long career as commodity specialist for cement, coal

combustion by products and ferrous slag, confirmed that the figures published in

the Mineral Year Book referred to the cement produced locally by the countries

using both clinker produced in the same country or imported form another county.

As clinker is more likely to be exported than cement products for economic reasons

mainly related to transport cost, the cement produced in a country is expected in

most of the cases to be consumed internally. The above-mentioned clarification

indicates that the Mineral Year Book cement production data is still valid as a

source of comparison with no necessary requirements to control for potentially

biasing international cement flows.

As the Mineral Year Book is published every year, change occurring in the

geopolitical map were difficult to track and implement. The dissolution and

formation of new states required to recalculate retrospectively the historical annual

cement production values of these newly form states. Some examples of this

situations are: Zambia and Zimbabwe formed out of the dissolution of the

Rhodesias, the 1945 split of Korea into the Democratic People's Republic of Korea

(North Korea) and the Republic of Korea (South Korea), Vietnam separation

between North and South during the 1954-1975 period. The Union of Soviet

Socialist Republics dissolution, and to a lesser extent of Yugoslavia and



94

Czechoslovakia, are probably the most relevant case of formation of new states.

Whereas re-creating the historical values for Cement consumption per capita would

have been extremely time consuming and potentially inaccurate, the data contained

in the World Statistical Review 1998 and the Global Cement Report 2015 also omit

historical values for these states. Thus, as was described in the previous paragraphs,

these countries were excluded from this research.

Urban infrastructure variables:

The selection of the urban infrastructure variables was done according to the suggestions

derived from two generic claims. Firstly, the World Bank (1994), Canning (1998), Todaro

and smith (2012), Srinivasu and Rao (2013), United Nations (2015) and the International

Monetary Fund (2017) findings in terms of the social consequences of urban infrastructure

deficit. Secondly, but not less important, the claims of Aticin (2000), Deverell (2012) and

Kang and Li (2013) on cement market saturation driven by achievements of urban

infrastructure levels. Dasgupta et al. (2014) findings on housing investment following a S-

shaped trajectory ramping up at a GDP per capita of USD ~3’000 also provided support to

Aticin (2000), Deverell (2012) and Kang and Li (2013) findings. The three selected

variables (Public-structure quality, Capital stock per capita and Populating living in slums)

were introduced to support the estimation of urban infrastructure deficit as a function of

the cement stock shortages after the correlation between these variables were verified. In

the absence, at least not found by the authors during the literature review, of a published

single indicator of urban infrastructure level by a recognized global organization, we

considered that the combination of these three variables would provide a consistent

illustration of the Urban infrastructure level in a country.

Public-structure quality - 2013 [Scale]: The first of the these three-urban

infrastructure variables was selected to reflect on a country level of urban

infrastructure from the extensiveness and efficiency of its public-structure network.

The dataset, measured in a 1 to 7 scale, with 7 being the most desirable outcome,

was gathered from the Global Competitiveness Report, edited by Klaus Schwab and

published by the World Economic Forum in 2014. The report ratifies the



95

importance of public-structure for the development of countries’ economy through

its interpretation of the public-structure’s role to generate competitiveness. Schwab

(2014), as the report editor, claims that well-developed public-structure networks

significantly impact in economic growth by reducing the negative effect of distance

in transportation while connecting the nation at low cost. Furthermore, the quality

and extensiveness of public-structure networks supports the reduction of poverty

by providing access of less-developed communities, workers and entrepreneurs to

core economic markets. The 2013-2014 version of this report featured 148

economies, containing a detailed profile for each economy in scope covering over

hundred indicators. The Global Competitiveness Report aim to study and

benchmark the many factors contributing to national competitiveness to stimulate

discussion about best ways to improve competitiveness. The World Economic

Forum, through the Global Competitiveness Report, defines competitiveness as the

set of institutions, policies, and factors that determine the level of productivity, and

thus prosperity prospect of a country. The level of productivity, in turn, sets the

level of prosperity that can be reached by an economy. Schwab (2014) argues that

the many determinants driving productivity and competitiveness such as

specialization and the division of labor, investment in physical capital and public-

structure and education and training are not mutually exclusive, an element

captured in the report by including a perspective on many components. These

elements are in turns weighted and grouped in twelve pillars of competitiveness,

with Public-structure quality (named “infrastructure quality” in the Global

Competitiveness Report) being the second pillar only after Institutions.

Capital Stock per capita - 2013 [constant 2011 International Dollars]: The

introduction of this variables was aimed to identify and quantify public-structure

deficits in economic value, once its correlation with Cement stock per capita was

stablished. The variable Capital Stock per capita represents the aggregation of

public, private and public-private partnership investments and is measured in

constant 2011 International dollars. The dataset was gathered from the Investment

and Capital Stock Dataset, 1960-2015 (2017 version) released by the International

Monetary Fund’s Fiscal Affairs Department. The file contains an estimation of



96

stock of public capital for 170 countries from 1969 to 2015. To produce a Capital

Stock per capita per country, the aggregation of these values was divided by the

total population data provided by the United Nations retrieved in 2015 (Equation

2). The focus of the International Monetary Fund on dedicating resources to the

record and publications of global stock of public capital is centered on its relevance

claims. As highlighted during the literature review section, the Capital stock is a

key input in the creation of a network of physical assets, including economic public-

structure and social public-structure such as roads, airports, electric utilities, public

schools, hospitals and prisons (International Monetary Fund, 2017). The

International Monetary Fund claims that it is necessary to look at the stock instead

of focusing on annual inflows for two reasons. Firstly, it is existing network volume

that provide productive services and secondly, public-structure assets are subject to

depreciation, hence the need for examination the stock net of depreciation. To

estimate the Capital stocks levels, the International Monetary Fund relies on the

perpetual inventory method compiling various databases of public, private, and

public-private partnerships. The raw investment data is transformed into real-cost

in constant 2011 USD under the assumptions of depreciation rates and on the initial

capital stock series to derive the net real-cost stocks. While the depreciation rates

are time and country-group varying, the initial capital stock is derived using the

synthetic time-series approach. The International Monetary Fund claims that the

benefit of this approach is based on a convenient unified and standardized

framework comparable across countries, however the estimates do not rely on

detailed asset-level investment information.

Equation 2. Formation of Capital stock per capita variable

𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013

= (𝑃𝑢𝑏𝑙𝑖𝑐 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑖2013 + 𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑖2013

+ 𝑃𝑢𝑏𝑙𝑖𝑐 & 𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑖𝑛𝑣𝑒𝑠𝑚𝑒𝑛𝑡𝑖2013) ÷ 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖2013

𝑖 = 𝑐𝑜𝑢𝑛𝑡𝑟𝑦



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Population living in slums - 2014 [% of total]: We incorporated this variable under

the notion that high proportions of people living in inadequate households tend to

reflect, to a great extent, the urban infrastructure deficits of a country. The figures

represent the percentage of the total population of a country which lives in slums

and was gathered from the United Nation's Millennium Development Goals

database, available for the year 2014 for developing countries only. Although the

time horizon of this research ends in 2013, we retain that for this particular variable,

information related to 2014 is relevant and consistent considering the expected

relative slow change of rates. As per described in the literature review section for

housing deficit, during the period 2000 to 2014, the population living in slums was

reduced by 10 percentage points, which corresponds to a change of ~0.7% per

annum. The reduction of the population living in slums has an impact across the

eight objectives set by the Millennium Development Goals, with targets for 2015

ranging from reducing extreme poverty rates to stop HIV/AIDS spread. The goals

were agreed on a blueprint by all the world’s countries and all the world’s leading

development institutions materializing large and combined efforts to fight poverty.

Following their description of the health risks and development drawbacks faced in

a daily base by the slum dwellers, Todaro and Smith (2012) claimed that is no

wonder why improvement on lives of slum dwellers is a core part of the Millennium

Development Goals.

Economic development variables:

The main objective of introducing these four variables was to understand the relation

between cement consumption and economic growth, and the potential of the later as

predictor of long-term demand. Adding different levels of analytical angles through the

incorporation of multiple variables, also enabled us to enrich the findings accomplished

during the exploratory section of this research. While the selection of the GDP per capita

Constant and Current was aimed to relate the cement demand development with extended

periods of economic growth, the selection of allegedly non-pure economic indicators

responded to the intention of providing different angles of potential causality in the

development of urban infrastructure and thus on the cement consumption level.



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GDP per capita Constant - 1913-2013 [1990 International Dollars]: The data

provided by The Maddison Project database offered a singular opportunity to assess

the long-term relation between economic growth and cement consumption globally,

and therefore to address the last of the four research questions, “How does

economic growth influence the cement demand in the long term?

As argued by Bolt and Zanden (2013), the work of British economist Angus

Maddison estimating GDP and population in the world since Roman times are of

great value to the academic community. Following Angus Maddison death in 2010,

the continuation of the Maddison project was carried on in cooperation of

specialized scholars. In term of accuracy of the Maddison project dataset, Bolt and

Zanden (2013) argued that estimates of the national accounts of countries in the

past are subject to certain margins of error, usually based on partial data and

assumptions about the relation between these data (i.e. tax proceedings and the

related economic activities). While the values contained in last hundred years, the

time horizon of these research, are expected to be fairly precise, estimations on the

19th century Sub-hara Africa or pre-Colombian Latin America might be more

sensitive to errors. As argued by Bolt and Zanden (2013) review on Maddison’s

work, most of the criticism (i.e. Pomeranz, 2000) to the accuracy of this database

relates to distant past such as underestimation of real incomes in large parts of Asia

during the 18th and early 19th century or the growth overestimation in Europe

between 1300 and 1800. In terms of methodology for the GDP estimations, one

main element currently utilized in the Maddison project database refers to

information on real wages to infer changes in GDP per capita growth. Bolt and

Zanden (2013) argue that although Maddison had reserves for the use of this

information considering potential inference errors as labor is only a part of GDP,

since Allen (2001) work on real wages in Europe between 1300 and 1914, a large

number of studies has been published validating many assumptions of the Maddison

project database.

The currency used in this database corresponds to 1990 Constant International

Dollar, which appears appropriate for the needs of this research requiring a standard



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measure for comparison acting as an anchor for long-run real exchange rates

(Taylor, Peel and Sarno, 2001). International dollar, also known as Geary–Khamis

dollar, is a hypothetical unit of currency that holds the same purchasing power

parity that the U.S. dollar had in the United States at a given point in time and thus

showing how much a local currency unit is worth within the country's borders

(Khamis, 1984). The International dollar is based on the concepts of purchasing

power parities of currencies and the international average prices of commodities

(Khamis, 1984). As argued by Hill (1998), the Geary-Khamis method can often

produce overestimation of some poorer countries' per capita incomes, relative to

richer countries, by as much as 70% with important consequences. To verify the

validity of the constant dollar, we introduced the GDP per capita Current variable.

While the validity of the Maddison project data is addressed fully in the results

section, we retained appropriate for the reader’s understating of the methodological

approach to advance that the validity and adequacy of the dataset was confirmed.

GDP per capita Current - 2013 [2011 International Dollars]: Continuing with the

closing remarks of the previous paragraph, we chose the World Bank 2013 GDP

per capita current value (retrieved in 2017) to verify the adequacy of the GDP per

capita Constant and to provide additional inputs through a cross sectional analysis.

This GDP per capita Current values are expected to offer a more accurate reflection

of a country current economic development for 2013, and thus a solid source of

validation for “constant” values provided by the Maddison project prompted by Hill

(1998) claims. While the Maddison project GDP per capita figures are shown in

1990 prices, the World Bank data current prices database are expressed in the value

of the currency for that particular year avoiding the adjustments required for the

effects of price inflation but potentially causing unwanted deviations (Taylor, Peel

and Sarno, 2001). Except for uncommon instances of deflation, a country's current

price series on a local currency basis is expected to be higher than its constant price

series in the years succeeding the constant price base year. However, when the

values are converted to a common currency (e.g. U.S. dollars), currency devaluation

since the base year may cause the current dollar series to be lower than the constant

dollar series (World Bank data helpdesk).



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Human Development Index – 2013 [Scale]: The incorporation of this variable was

aimed to provide an additional layer in understanding the relation between cement

consumption and economic development from a people’s capabilities reference

point. In addition, the multiple correlation analysis taking place in the exploratory

analysis section is expected provide valuable insights in terms of cement

consumption relation with people wellbeing. The Human Development Index,

published by the United Nations Development Program is measured in a 1-100

scale (100 representing the highest value for human development) and was created

to emphasize that people and their capabilities should be the ultimate criteria for

assessing the development of a country, not economic growth alone. According to

Jahan and Jespersen (2016), the Development Index is a summary measure of

average achievement in three key dimensions of human development: a long and

healthy life, being knowledgeable and have a decent standard of living. These three

dimensions are assessed from different angles, while the health dimension is

assessed by the life expectancy at the moment of birth, the education dimension is

measured by mean of years of schooling for adults over 25 years and expected years

of schooling for children when entering to school. Thirdly, the standard of living

dimension is measured by gross national income per capita using logarithm of

income to reflect the diminishing importance of income with increasing gross

national income. Finally, the three dimensions scores are aggregated into a

composite index using geometric mean.

Political stability [Scale]: The Political stability variable was introduced to add an

additional level of understanding of the cement consumption in the perspective of

economic development and the influence of political stability on it (Alesina et al.,

1992). Political stability represents the likelihood of political instability and/or

politically-motivated violence, including terrorism measured in a 1-100 scale, with

100 representing the highest level of stability. The dataset was gathered from the

Worldwide Governance Indicators (retrieved in 2015) based on the original work

of Kaufmann, Kraay and Mastruzzi (2010). Their work covers over 200 countries

and territories, measuring political stability together with other five dimensions of

governance starting in 1996. In turns, these dimensions aggregate several individual



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variables obtained from multiple sources such as survey, and public, private, and

non-governmental organizations sector experts worldwide. A decision in terms of

use of the time scope of the political stability data had to be made between the three

different options that were foreseen. The first option was to use every single year

of the information provided as a single observation, however this option was

disregarded as could only cover the 1996-2013 period and thus lacking much of the

full period of this research (1913-2013). A second option was to only use the 2013

value only as is the case to most of the single-value variables used in this research.

This option was eliminated as would misleadingly provide a one-year view of a

dynamic variable as it is political stability. Finally, the third option was to calculate

a simple average of the timeframe provided (1996-2013) for each country and thus

obtaining a single value (Equation 3). We retained that this approach (the third

option) offered the best solution in terms of providing a single figure representing

the political stability level during the longest and most complete period available.

Although the 1996-2013 average does not represent the hundred-year period

covered by other variables (i.e. Cement consumption per capita, Cement stock per

capita and GDP per capita Constant) it does offer a fairly complete historical

representation. Therefore, the Political stability average relates better to variables

illustrating the result of several years of accumulation (Cement stock, Public-

structure quality, Capital stock and other variables which are the result of long term

policies such as Population living in slums).

Equation 3. Formation of Political stability average

𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙 𝑠𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑎𝑣𝑒𝑟𝑎𝑔𝑒𝑖 = ( ∑ 𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙 𝑠𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖) ÷ (2013 − 1996)

2013

𝑡=1996

𝑖 = 𝑐𝑜𝑢𝑛𝑡𝑟𝑦

Country inherent variables:

The three country inherent variables were selected to support the understanding of the

historical cement consumption in relation to countries’ specific characteristics. We



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considered that a long-term forecasting tool was expected to benefit from a proper

understanding on the relation between cement consumption development and country

inherent characteristics as reflected in the selection of these three variables: Urbanization

level, Temperature, Country size and Elevation over sea level.

Urbanization level - 2013 [% of total population]: As covered in the literature

review section, while the notion of a strong link between urbanization and economic

growth is widely diffused, the review on the work of Chen et al. (2014), Todaro and

Smith (2012) and Zhang and Song (2003) finds that it is economic growth driving

urbanization and not vice versa. Consequently, common urbanizations processes

are driven by population shift from rural to urban areas looking for better economic

prospects, and thus changing the proportion of people living in urban places

(Antrop, 2004). Through the incorporation of the Urbanization level variable, we

aimed to verify the extent and characteristics of the economic development and

urbanization level relation in the context of cement consumption. The dataset was

retrieved from the World Bank indicators in 2017 which in turns is based on World

Urbanization Prospects’ provided by the United Nations since 1988 (lastly updated

in 2014). Although the World Bank working definition of urban population refers

to people living in urban areas, the original source (United Nations World

Urbanization Prospects report, 2014) follows the definitions used in each country.

In its report, United Nations (2014) claims that the definitions are generally those

used by national statistical offices in carrying out population census, however

adjusted were carried out whenever possible and required to maintain consistency

in the cases of definition changes between census.

Temperature - average [Celsius degrees]: The incorporation of this variable was

aimed to increase understanding about the influence of temperature ranges in the

quantity of cement used to build urban infrastructure in different countries. The

Temperature – average variable averages the weather temperature recorded from

1961 to 1999 period by the World Bank’s Climate change knowledge portal.

Weather temperature appears to affect the use of cement in two ways, weather

related deterioration of concrete, and choice of materials. Kosmatka et al. (2002)



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claimed that concrete duration is affected by the cycles of freezing and thawing,

and thus adequate strength and entrained air is requited. Concretes containing

supplementary cementing materials require careful attention during application

such as adequate entrained air content to provide the same resistance to freezing

and thawing cycles as a concrete mainly produced with portland cement. De-icing

chemicals used for snow and ice removal are known for causing surface scaling in

inadequately air-entrained or non-air-entrained concrete during freezing. Above

described Kosmatka (2002) claims suggest that weather conditions could drive the

amount of cement used in the concrete due to different production cost of concretes

using mainly portland cement vs others concretes which incorporate supplementary

binding materials but require specific attentions to avoid deterioration. Secondly,

the work of Lstiburek (2010) summarizes how extreme weather temperature

influence the choice of construction material. The moisture flow by air leakage and

vapor diffusion from the inside to the outside (at -40 degrees Celsius) appears to be

the main source of concern driving the materials’ choice. Because minuscule gaps

leaking air can lead to icicles and frost boles, Lstiburek (2010) suggestion for air

and vapor barrier includes several materials such as wood, plywood, impermeable

membrane, oriented strand board sheathing or draining building wrap, none of

which contain or refer to cement. To summarize the above described ways on which

weather temperature affect the amount of cement use, while cold temperatures can

have an impact in the amount of portland cement used in the concrete production

to avoid early deterioration, it can also drive the type of materials used, potentially

substituting cement.

Country size [Km2]: The addition of Country size as a country inherent variable

relates to its potential influence on the cement consumption, and thus to the need of

understanding the main characteristics of this relation. The main suggestion on

these matters come from Canning (1998) who argued that an increase in area

significantly increases road length, and thus sustaining the assumption that larger

countries require extensive public-structure to connect urban centers. It is

understood that as this variable does not cover insights on distances between cities

or population density, some of the findings could led to potential misleading



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interpretations and thus required to be treated carefully. Following the line of

Canning (1998) who used country land area in his work on world public-structure

stocks, and in the absence of alternative methods, we retained that the incorporation

of the Country size variable would serve to enrich our analysis. Two analytical

alternatives were analyzed to avoid the potential misleading results above

mentioned. The first one was to use a factor of both land area and population

density, however the available population density indicators to which we came

across such as the one available through World Bank database provided only

national level indicators and thus overlook cases where population is highly

concentrated in certain areas. A clear example of this potentially misleading

situation is Australia, which accounts for one of the lowest national population

densities in the world (3 inhabitants per square km as compared with the 6’997 in

Hong Kong). However, Australian population is mostly concentrated in both the

east and west coast resulting in highly densely populated cities such as Sydney with

400 inhabitants/km2 and Melbourne with 453 inhabitants/km2 (source: Australian

Bureau of Statistics, 2016); twice as much the density of small countries like

Switzerland (source: United Nations Statistics Division). The second alternative

was to attempt a bottom up approach to review by country the quantity and size of

cities, and corresponding connecting distances. This option was disregarded as

would have required large efforts, an endeavor which should perhaps being framed

as a project itself. The Country size data was gathered form the United Nations

Statistics Division.

Elevation over sea level - average [meters]: We incorporate this variable as a proxy

of terrain complexity under the suggestions of Zhou et al. (2009) who claimed that

difficult terrains need complex and thus concrete intense public-structure.

Therefore, we understand that a long-term forecasting tool is expected to benefit

from a proper understanding on the relation between terrain complexity and cement

consumption. The main assumption that suggests the use of this variable as proxy

of terrain complexity is based on the proposition that countries with high elevation

averages have a system(s) of large natural elevations which might complicate the

activity of construction. As in the case of the variable Country size, a single figure



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representing the average elevation of a country could lead to misleading

interpretation and thus its related findings would have to be treated carefully. As

this variable is expressed in average, avoiding misleading results would require that

both the forms of elevations such as hills or mountains and inhabitants were equally

distributed within the country land extension. In order to develop more robust

alternatives to this situation, a second proxy for terrain complexity was considered.

Under the assumption that arable land tends to be flat and free of substantial terrain

complexities, the indicator arable land as % of land area sourced by World Bank’s

World Development Indicators was assessed. The reasons for its exclusion as a

potential proxy for terrain complexity was that this indicator defines arable land as

land under temporary crops, temporary meadows for mowing or for pasture, land

under market or kitchen gardens, and land temporarily fallow. However, land

abandoned as a result of shifting cultivation or unused potentially arable land are

excluded. This omission results in an incomplete representation of the reality as

illustrated by the example of Argentina, which in 2002 accounted for 10.18% of

arable land as % of land area under the definition of the World Bank’s World

Development Indicator, however its real (potential) arable land accounted for ~50%

or the total land (Cordoba Chamber of Commerce, 2002). The Elevation over sea

level average dataset was sourced by the Country Geography Data, Portland State

University (Retrieved 26 April 2015).

Other country inherent variables: Other potentially relevant information was

examined as to be integrated to the country inherent variables, particularly the

composition of the population pyramids. As per Boucher (2016), most developing

countries present an expansive type of population pyramid (“larger percentage of

the population in the younger age cohorts”) as compared with the constrictive or

stationary shape typically present in developed countries. Due of the expansive

pyramid shape dominance within the developing countries, we did not retain

necessary to incorporate population demographic elements as an additional variable

to avoid incremental complexity with limited gains.



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4.2.3 Data analysis overview and description

In the data analysis overview and description phase, we aimed to identify and describe the

most relevant variables through correlations analysis, revealing stablished relationships

together with their particularities affecting cement consumption development. This phase

is structured in two steps, firstly a cross-sectional analysis involving all variables and

countries within scope, and a second step covering the descriptive statistics of most relevant

variables (Figure 21).

Figure 21. Data analysis overview and description - process overview

Cross sectional-analysis:

Following the work of Canning (1998) when describing a database of world public-

structure stocks for the 1950-1995 period, we chose the most relevant and complete year



107

(2013) to run a correlational analysis. Previously to embarking into the correlational

analysis, to reduce the impact of wide-ranging quantities and different metrics, all

variables’ figures were converted to logarithm base 10. The interpretation of variables

correlations was done clustering the variable type (Cement, Urban infrastructure,

Economic development and Country inherent) aiming to identify common patterns

between variables particularly related to the cement stock per capita and the availability of

urban infrastructure (as per the variables Public-structure quality, Capital stock per capita,

and Population living in slums).

Descriptive statistics:

As in the case of the cross-sectional analysis, the objective carrying out descriptive

statistics analysis was to obtain a set of perspectives which could help us to describe

patterns on the cement consumption related to economic development level or geography.

Most important, as is covered later in the Equivalence groups section (4.3.1), this analysis

was designed to sustain the case of advanced economies as an equivalence group. We

structured the descriptive statistics task around three steps. Firstly, a descriptive statistical

analysis covering the Cement stock per capita variable in the context of all economic groups

and regions (Advanced economies, High income, Upper medium income, Lower middle

income, Low income, Africa, Asia-Oceania, Europe and Americas). Secondly, a combined

economic and regional perspective depicting the arithmetic average of Cement stock per

capita 2013 by geographic region and a broke down by economic cluster. Thirdly, the

advanced economies comparison with all other countries in three dimensions (Cement

consumption per capita, Cement stock per capita and GDP per capita Constant). Within all

the sixteen-metrics provided by the descriptive statistics analysis, we focused on three core

outcomes: mean, standard deviation and most particularly in the variation coefficient

(Equation 4). The value of the variation coefficient is based on its capacity to illustrate how

homogenous a group is, specifically in terms of the most relevant variables on scope

(Cement consumption / Cement stock per capita and GDP per capita Constant). Although

the causality of GDP per capita Constant over the consumption of cement is developed in

the results section, its initial reference based on the findings of Aitcin (2000) and

International Cement Review (2014) are advanced in this section for the sake of the readers

guidance.



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Equation 4. Example of Coefficient of variation equation of Cement stock per capita

𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑜𝑓 𝑣𝑎𝑟𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013

=𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013

𝑀𝑒𝑎𝑛 𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013

𝑖 = 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑔𝑟𝑜𝑢𝑝 𝑜𝑟 𝑔𝑒𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐 𝑟𝑒𝑔𝑖𝑜𝑛

Explanatory analysis

The explanatory analysis was arranged around the methodological approach on four steps.

The first step covered the process to define the equivalence groups. The second step

described the methodology used to develop the long-term forecasting model. The third step

addressed the elements related to the estimation of urban infrastructure deficit in

developing countries. Lastly, the results provided by the urban infrastructure deficit and

long-term demand assessment were analyzed in depth in selected developing country

throughout a case study.

4.3.1 Equivalence groups

As described during the description of the Forecasting by analogy section (3.2), at the core

of the Bayesian pooling methodology is the identification of equivalence groups. Formed

by the Cement consumption per capita and GDP per capita Constant analogous time-series

of advanced countries, the equivalence groups hold the capacity to predict the potential

cement consumption development in developing countries (Kahneman and Tversky, 1977

and Duncan et al., 2001). With the objective of identifying analogous time-series for

pooling data that correlates highly over time after synchronizing (in the case of non-

contemporaneous series), we opted for following Duncan et al. (2001) suggestion of

combining correlational co-movement, expert judgment and model-based clustering

approaches. Furthermore, in the scope of our methodology, the three approaches are

functional to different objectives. As is addressed in the following section (4.3.3), the

correlational co-movement and expert judgment approaches serve primarily to provide

evidence of general similarities within advanced economies to define reference values of

Cement stock per capita for the estimation of urban infrastructure deficit in developing

countries. On the other hand, the model-based clustering was implemented to allow us the

definition of subgroups within the advanced economies that share narrower similarities in



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terms of patterns of cement consumption cycle, thus improving the accuracy of the long-

term forecasting model (section 4.3.2)

Correlational co-movement (Figure 22): This first approach, aimed to select time-

series (Cement consumption per capita and GDP per capita Constant covering the

1913-2013) of advanced countries that correlate highly, has a two-fold objective.

First objective, to validate the homogeneity of the advanced economies as

compared with developing countries in relation to the Cement consumption per

capita, Cement stock per capita and GDP per capita Constant. To accomplish this

task, we relied on the descriptive statistics outcomes from the data analysis

overview and description section (4.2.3). We reviewed two points in time as to

observe variables values (Cement consumption per capita, Cement stock per capita

and GDP per capita Constant). The reviewed points were year 2013, since as

mentioned previously is the year that accounts for the most complete datasets, and

the moment of saturation point in the Cement consumption per capita.

Second objective, to validate the completion of the Cement consumption per capita

cycle (growth, saturation, decline and maintenance) in advanced economies

through individual plotting of each time-series and visual confirmation of pattern

similarity.

Figure 22. Illustration of the correlational co-movement approach for the

selection of equivalence groups



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Expert judgment (Figure 23): We found on the expert judgment approach the best

alternative to solve a particular situation which occurred during the Cement

consumption per capita development in some advanced economies. While it is a

central objective of this research to understand the cement consumption saturation

point in advanced economies, we focused on those demand peaks which relate to

the achievement of a basic level urban infrastructure (Aitcin, 2000, Deverell, 2012

and Kang and Li, 2013). Thus, to benefit from its predictive value when assessing

deficit level of developing countries, we retained necessary to distinguish between

a consumption peak related to achievement of a basic urban infrastructure from

those fueled by other drivers such as speculation (e.g. cheap mortgages), tourism

(e.g. second-homes, hotels) or public-structure overdesign (e.g. corruption)

(Birshan et al., 2015).

To achieve this task, we followed a four-step structured approach. Firstly, we

reviewed the time-gap between peaks to understand whether a delayed second peak

corresponded to an economic hiccup (short cycle) or rather to a dynamic unrelated

to the development of basic urban infrastructure. This necessity to differentiate

between periods-length relates to dichotomic characteristics, while a typical

economic cycle lasts circa six years (source: United States National Bureau of

Economic Research), the build-up of a housing market bubble is expected to

develop in fifteen or more years (Holt, 2009). Secondly, we assessed the Cement

stock per capita and GDP per capita Constant value at first peak to understand if

these values relate to those of similar economies at point of peak or whether are

substantially different. Thirdly, we assessed the Cement stock per capita at the

moment of second peak to evaluate if the Cement stock per capita value achieved

at this second peak appears to be exaggerated in comparison with those of similar

economies at saturation point. Lastly, we reviewed available literature to unearth

historical evidence that could explain the drivers that led to a double peak such as

speculation markets drivers or corruption scandals. In the Conclusions, limitations

and further developments chapter (6) we open the discussion on how this



111

problematic could be potentially structured as to strengthen the robustness of this

choice and provide initial insights for specific cases.

Figure 23. Illustration of expert judgement approach for selection of the

equivalence groups

Model-based clustering (Figure 24): the model-based clustering provided the final

elements for the conformation of equivalence subgroups within the advanced

economies. This approach was implemented through regressing Cement

consumption per capita and GDP Constant per capita variables for advanced

countries and comparing similarities in the β coefficients but also in the levels of

Cement consumption per capita reached at moment of peak. The regression

timeframe was allegedly limited to the moment of Cement consumption per capita

peak (saturation point) as to generate a set of β coefficients which could be

comparable to those of developing countries. As the cement demand function in

developing countries were not expected to have reached a saturation point, the

opportunity for comparison with advanced economies required the regression to be

limited to the inflexion point. The β coefficients of the advanced countries

equivalence subgroups were averaged and later used to prescribe an equivalence

subgroup to matching developing countries. By regressing each time-series, both

the process of re-scaling and homogenizing were avoided (Duncan et al., 2001).



112

Figure 24. Illustration of model-based clustering approach for the selection of

equivalence groups within advanced countries

4.3.2 Long-term forecasting model for cement

The methodology to develop the long-term forecasting model resumes from the process

stablished previously in the Equivalence groups section (4.3.1) and was organized in three

steps following Duncan et al. (2001) suggestions on the implementation of Bayesian

pooling methodology. The first step, covering the subgrouping and construction of local

models; the second step, corresponding to the construction of coefficients for subgroups’

models and the third step, dedicated to the empirical validation of our long-term forecasting

model adequacy.

Subgrouping and construction of local models (Figure 25)

As described previously during the Equivalence groups section (4.3.1), three approaches

were combined following (Duncan et al., 2001) procedure for the definition of equivalence

groups formed by time-series that correlate highly over time. While the first two

approaches, correlational co-movement and expert judgment, were designed to provide

evidence of general similarities within advanced economies to assess urban infrastructure

deficits in developing countries, the model-based clustering allowed us to find subgroups

of advanced economies sharing narrower similarities in terms of cement consumption

patterns.



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Subgrouping: To consolidate the readers’ understanding of the chosen methodology

process, we retained necessary to briefly recap the main aspects of the model-based

clustering. This approach was implemented through regressing Cement consumption per

capita variable and GDP Constant per capita variable for advanced countries from 1913

until saturation point. Afterwards, the β coefficients of the linear regression and the levels

of Cement consumption per capita reached at moment of peak were compared to identify

similarities. The timeframe was allegedly limited to the moment of Cement consumption

per capita peak to enable a compatible-prescriptive comparison of the β coefficients with

those of developing countries which have not reached a saturation point yet. Note that as

the peak of Cement consumption per capita for developing countries is supposedly unknow

yet, its matching equivalence group would be limited to the value of β only (for the linear

regression 1913-2013) and thus in some cases two matching Groups shall be considered.

Local models: Once the subgroups were defined, the next step involved the construction of

individual (local) models of each country times series (Cement consumption per capita and

GDP per capita Constant) to feed the construction of subgroups model’s coefficients. As

illustrated in the Figure 25, the local model is based on a polynomial function of degree 2.

We found that a quadratic regression efficiently fits the bell-shaped cement demand

function through squaring in one term the predictor variable (GDP per Capita Constant).

The linear-in parameters and the a priori strong fitting indicated the suitable use of a

quadratic regression (Armstrong, 2001) for the construction of the individual models.

Figure 25. Illustration of subgrouping and construction of individual model



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(*) The illustrated trend-line in the Figure 25 relates to the expected strong relationship

between the value of the β and the cement consumption at peak. This assumption is

confirmed and discussed in the Results and discussions chapter (5).

(**) β: Corresponds to the additional cement consumption pc [kg] per 1'000 USD

increment on GDP pc Constant from 1913 to peak, as defined in section 4.3.1 (Figure 24).

Construction of coefficients for subgroups’ pooled models (Figure 26)

The second step required the drawing of information from analogous time-series to allow

the construction of the forecasting models (Duncan et al., 2001). Once the local models

were defined in the previous step, we combined them to form the pooled subgroups models.

As illustrated in the Figure 26, both α and β coefficients for each subgroup model were

obtained by averaging the coefficients of the quadratic regression covering the 1913-2013

period for each advanced economy within the corresponding equivalence subgroup.

Although both the dependent variable (Cement consumption per capita) and the

independent variable (GDP per capita Constant) are per capita values and thus already

scaled by the population value as a divisor, following Duncan et al. (2001) procedure, both

the process of re-scaling and homogenizing were avoided by having regressed each time-

series. As is described in the empirical validation, the process contemplated the constant

term to be optimized in each individual use of the forecast model.

Figure 26. Illustration of the construction of coefficients for each subgroups model



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Empirical validation

The empirical validation was implemented to verify the adequacy of the pooled model to

forecast the long-term demand of cement. The validation was constrained to advanced

economies as those have completed all stages (growth-saturation-decline-maintenance) and

therefore allowed for the evaluation of the model’s prediction accuracy for the full cycle.

The simulation of the demand growth in the advanced countries was carried out through

the following procedure:

As illustrated in the Figure 27, the subgroup pooled model was adjusted by

recalculating the α and β coefficients without the contribution of the local model

(country being forecasted) to avoid misleading results product of a self-

representative estimator. Following the exclusion of the local model, the subgroup’s

pooled model new α and β coefficients were used to forecast the Cement

consumption per capita development with the inputs of GDP per capita Constant as

independent variable. It is important to highlight that although the contribution of

the local model was subtracted, the time-series period used to recalculate α and β

coefficients for the empirical validation comprehended the full period (1913-2013).

Constraining these time-series to an earlier point in time would not have allowed

the quadratic regression coefficients to provide the bell-shape required to capture

the consumption decline after the saturation point is reached.

Figure 27. Illustration of subgroup model forecasting method for validation through

removal of local model



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In each forecasting exercise, the constant term was optimized using linear

programming to mimic the function of a shrinkage factor improving the prediction

accuracy. The optimization was implemented by minimizing the average of the

squared difference between the observed and the predicted values of Cement

consumption per capita for the period comprehended between 1913 and the

saturation point. The choice of limiting the period to the saturation point allowed

for a fair comparison against the predictions of the standard model, described in the

following step.

Equation 5. Forecasting methodology - optimization of the constant term

𝑀𝑖𝑛𝛴𝑡=1913

𝑠𝑝 (𝜀𝑖2)

𝑠𝑝 − 1913

𝑠𝑝: 𝑦𝑒𝑎𝑟 𝑜𝑓 𝑠𝑎𝑡𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑝𝑜𝑖𝑛𝑡

𝜀: 𝑒𝑟𝑟𝑜𝑟

Finally (Figure 28), the predicted values were compared against both the actual

(observed) values and the predicted values from a standard model which did not

benefit from the value of the distributional information (Kahneman and Tversky,

1977). The standard model corresponds to an equivalent quadratic function

constructed with the local time-series information from 1913 to the saturation point.

We compared the observed values against the predicted values of the subgroup

pooled model and the predicted values from the standard model in different metrics:

o For the period between the actual saturation point and 2013: Mean Absolute

Deviation (MAD), Mean Square Error (MSE), Mean Absolute Percentage

Error (MAPE) and variation on the average Cement consumption per capita.

o For the 1913-2013 period: variation of the saturation point prediction

measured in numbers of years and variation on the average Cement

consumption per capita.



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Figure 28. Illustration of accuracy comparison between actual values against pooled and

standard model results

4.3.3 Estimation of urban infrastructure deficit

Together with the construction of a valid methodology for cement demand long-term

forecasting, providing a valid process to assess the urban infrastructure deficit in

developing countries is at the core of this research’s ultimate objectives. The assessment of

urban infrastructure deficit is aimed to estimate the gap between advanced and developing

countries in terms of the provision of urban infrastructure. For the objectives of this

research, urban infrastructure compromises all buildings intended to provide housing or

public-structure in the form of physical components and systems serving a country (Fulmer,

2009). These buildings can be divided in three typologies depending on their intended

purpose. The first typology is housing buildings (also called residential), which consider

any type of building providing accommodation to dwellers. The second typology is public-

structure buildings, which compromises roads, electricity, water and sanitation,

communications, and the like which facilitates and integrates economic activities (Todaro

and Smith, 2012). The third type are the commercial or non-residential buildings,

commonly defined as those intended to generate a profit such as offices, private hospitals,

retail and industrial sites. The methodology used to assess the urban infrastructure deficit

in developing countries is organized around two broad steps: definition of reference values

and cost estimation of required constructions (Figure 29).



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Figure 29. Illustration of the process for estimating urban infrastructure deficit

4.3.3.1 Definition of reference values

The definition of reference values required firstly the verification that the variable Cement

stock per capita can be used as a valid proxy to assess the level of urban infrastructure in a

country. To accomplish this task, we built on the Data analysis overview and description

section (4.2.3) findings, adding supplementary levels of analysis to support understanding

the relation between Cement stock per capita and the urban infrastructure variables: Public-

structure quality, Capital stock per capita and Population living in slums.

The second step encompasses the definition of the reference value to be used to estimate

the gap(s) of urban infrastructure between advanced and developing countries as function



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of the Cement stock per capita levels. The reference value relates to the minimum level of

urban infrastructure needed in a country to deal with basic society needs such as housing,

sanitation, transport, health and education. The definition of the minimum level of urban

infrastructure corresponds to the saturation point concept (Osenton, 2004) and the claims

of Aitcin (2000), Deverell (2012), and Kang and Li (2013) in relation to basic infrastructure

achievement linked to the cement consumption. The outcomes of the previous section

(Equivalence groups 4.3.1) in terms of saturation point of Cement consumption per capita

in advanced countries were averaged to a single figure as to define representative level(s)

of Cement stock per capita at saturation point (Equation 6).

Equation 6. Reference value of Cement stock per capita for basic urban infrastructure

level

𝐵𝑎𝑠𝑖𝑐 𝑢𝑟𝑏𝑎𝑛 𝑖𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎

=∑ (𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑠𝑎𝑡𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑝𝑜𝑖𝑛𝑡

𝑛𝑖 )

𝑛

𝑖, 𝑛: 𝑎𝑑𝑣𝑎𝑛𝑐𝑒𝑑 𝑐𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠

The gaps (deficit of Cement stock per capita) were estimated as the difference between the

current (2013) Cement stock per capita of each developing country and the reference value

corresponding to the basic urban infrastructure (Equation 7).

Equation 7. Estimation of deficit of Cement stock per capita 2013 in developing countries

𝐷𝑒𝑓𝑖𝑐𝑖𝑡 𝑜𝑓 𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013

= 𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑣𝑎𝑙𝑢𝑒𝑎𝑑𝑣𝑎𝑛𝑐𝑒𝑑 𝑐𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠

− 𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013


Reference value: Basic level of urban infrastructure per capita



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4.3.3.2 Estimation of economic cost

This section was designed to provide an estimation of the monetary resources required for

the public and private administrations in developing countries to achieve a basic level of

urban infrastructure. As is described in the following paragraphs, the cost figures to narrow

the deficit are estimations driven by a compromise between the efforts required and

resulting benefits of accuracy improvement attempts. This section’s process was aimed to

provide an initial representation of the urban infrastructure deficit, in some cases alarming

due to the magnitude, while triggering future areas of research to improve accuracy. In the

chapter Conclusions, limitations and further developments (6), a series of potential research

lines on this matter are suggested to ameliorate the robustness of our findings.

We found that the definitions and estimation of costs such as materials, labor and

engineering involved in closing the urban infrastructure deficits, required a set of

computation based on assumption around two questions. Firstly, how much can be built

with the cement stock per capita gap? and secondly how much would those constructions

cost?

How much can be built with the cement stock per capita gap?

We structured the answer to the first question following a set inputs in the form of

guidelines provided by current literature. We estimated that 6.22 square meters of housing

or 21.14 square meters of public-structure (specifically in the form of roads) can be

completed with one ton of cement. These figures are mainly based on the insights of

Kosmatka et al. (2002) and van Oss (2005) for concrete technology, Vanderwerf (2007)

and the Center for Sustainable Systems (2014) for matters related to housing construction;

and Byers (2014) together with the American Society of Civil Engineers (2013) covering

the public-structure related inputs.

First input to these calculations follows the guidance of and Kosmatka et al. (2002) and

van Oss (2005) claiming that the content of cement in one cubic meter of concrete is

generally ~270 kg. The second input relates to what can be constructed with one cubic

meter of concrete (construction yield), where a distinction between residential & non-

residential, and public-structure buildings was required.



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Yield – residential & non-residential constructions: As claimed by the Vanderwerf (2007),

the concrete slab floor thickness for residential construction able to receive occasionally

heavy loads is 15.25 cm, resulting in 6.6 square meters of slab floor per cubic meter of

concrete. For the calculation of the cement content in the mortar and plaster used for the

brickwork and wall plastering respectively, we were required to define the dimensions of

a typical house to stablish a ratio between floor square meters and walls size.

We defined the house dimension in 50-square meters (Figure 30) following the guidance

of typical house sizes in Japan (40 square meters), India (Maharashtra 28-45 square meters)

as per Woetzel et al. (2014), China (Hong Kong 47 square meters), Russia (57 square

meters) as per World Bank (2014) and Argentina (Buenos Aires 52 square meters, source:

www.clarin.com). Although as claimed by the World Bank (2014), advanced economies

tend to have larger houses in terms of square meters (Australia 214, Canada 181, Denmark

137, France 112, Germany 109, Italy 81, Spain 97 and the United States 201), the 50-

square meter assumption for our computation relates to house units aimed to narrow a

housing shortage and thus avoiding superfluous / luxury spaces. For the computation of the

mortar required for the brickwork, we used a customary ratio 0.0197 cubic meters of mortar

per square meter of wall (source: www.concremax.com.pe). Following van Oss (2005), we

estimated the cement content in one cubic meter of mortar to be similar of that of concrete

in the customary proportion of 1:1:6 (cement:clay:sand), equivalent to 12.5% of cement

per cubic meter (compared with the 13% in the case of concrete). For the computation of

the plaster material required for wall-plastering, we also used a customary application of

7.5 kg of cement per square meter of wall surface considering a thickness of ~2.5 cm

(source: http://www.brickwarehouse.co.za). The Annex 7.3 contains and describes all the

technical characteristics related to the construction of the 50-square meter house such as

columns size – spread, slab thickness and footings depth.

http://www.concremax.com.pe/

http://www.brickwarehouse.co.za/



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Figure 30. Illustration of a 50-square meter house cement requirements (slab floor-roof,

wall brickwork and plastering)

The concrete requirements for a house construction are expected to vary depending on the

floor-walls ratio usually driven by the house size and the type of materials used. While the

Center for Sustainable Systems (2014) claimed that an average American house built in

2013 was ~250 square meters large (wooden roof) and required one cubic meter of concrete

every 5.9 square meters of construction, under our estimations, the typical 50-square meter

house required one cubic meter every ~2.5 square meters of floor surface).

As a final word regarding housing, we acknowledge that the used approach holds

limitations for the estimation of potential square meters of housing construction and thus

we provide in the chapter Conclusions, limitations and further developments (6) a set of

suggestions to continue improving the accuracy on this research line. While we retained

that the estimations based on a typical 50-square meters house capture the most relevant

insights, the final outcomes are expected to be mildly affected by the following elements.

Firstly, although the mortar needs for the brickworks was considered, the type or

size of brick was not due to the immense granularity of the choices in different

markets. Clay bricks are often used in construction however, sand-lime and

concrete bricks are common as well. In terms of size, for our mortar calculation we

used a common brick size (12cm x 23 cm x 7 cm), and thus different block size

would affect the amount of mortar required to bind the bricks. Bigger bricks mean

less bricks required to complete the same wall surface, and thus less mortar required



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to bind them. Also, in many markets, other materials such as wood, glass, gypsum

or even fabrics (mainly for internal walls) are used for wall construction which

could affect the total amount of cement used in mortar for brickworks.

We allegedly omitted to consider doors and windows as our typical house has only

one internal wall, and thus the potential cement savings from the empty surfaces

left by openings (doors and windows) were expected to compensate the mortar

requirements for some extra wall(s) for a toilette or a separated kitchen if desired.

Floor covering was also excluded in our typical 50-square meter house considering

the large material options available. While the installation of ceramic tiles would

require cement, many other choices such as carpets, wooden floors, synthetic

materials, paint and even polished slab concrete-floor would not require additional

use of cement.

Although we omitted estimations of non-residential buildings, we assumed the

commercial constructions (most likely the largest part of the non-residential

segment) such as offices, stores and industrial sites to be similar in terms of

constructions costs. We retained that although other non-residentials buildings are

highly diverse in terms of construction requirements (e.g. sports stadium), its low

level of occurrence did not justify investing high efforts to estimates its impact. For

example, in the United States, there is only one stadium every 1.5 million

inhabitants (with a capacity of 20’000 people or more, source: wikipedia.org). The

construction of an average Unites States’ stadium with a capacity of 50’000 people

requires ~13’500 tons of cement (source: Wembley National Stadium archive),

which represents only 0.03% of the Cement stock corresponding to a population of

1.5 inhabitants in the United states (~40.6 million tons of cement, as per 2013

figures).

Yield - public-structure constructions: The yield estimation of one cubic meter of concrete

(equivalent to 0.27 tons of cement) in terms of potential square meters of public-structure

construction (roads) was based on the values provided by Byers (2014) in terms of concrete

pavement thickness (Table 10). While three different thickness of concrete pavement are



124

required depending on traffic load, subgrade, and climate, we focused on the maximum

thickness (17.5 cm) corresponding to city streets, secondary roads, and small airports

category which represents most of the road system in advanced counties.

Table 10. Typical concrete pavement thickness (source: Byers, 2014)

Thickness (1) City streets,

secondary roads,

and small

airports

(2) Primary roads

and interstate

highways

(3) Large airports

Minimum 10 cm 17.5 cm 20.0 cm

Maximum 17.5 cm 28.0 cm 46.0 cm

Although the pavement thickness required for the construction of large airports is

considerable higher (more than double), we worked under assumption that the incremental

quantities of cement required for these projects’ type were expected to be of minor impact

at national level as illustrated by the following example. While the United States have 499

airports, only about 6% (29) are considered to be large airports corresponding to the

category 3 of concrete pavement thickness. Furthermore, large airports material

requirements are dwarfed by the quantity of existing road length adding up to 6.8 million

kilometers (American Society of Civil Engineers, 2013).

The incremental cement used in primary roads and interstate highways (second category)

as compared with the first category (city streets, secondary roads and small airports) was

disregarded as well due to its minor impact while the attempt to include it proportionally

was expected to add up unnecessary complexity to the computations. As described in the

Annex 7.3, the highways (expressways) account in average for only 2% of total paved roads

in most advanced countries.

Based on the above-mentioned inputs and our set of assumption, 1 cubic meter of concrete

(corresponding to 0.27 tons of cement) approximately yields to 5.71 square meters of

public-structure construction in the form of roads (Figure 31).



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Figure 31. Illustration of the potential public-structure (roads) construction enabled by

one cubic meter of concrete

While the reasons behind our choice towards the first category on concrete pavement

thickness (city streets, secondary roads, and small airports) were already described above

in terms of its higher relevance as compared with the other two categories, the selection of

17.5 cm thickness relates to our intention to cover as well other public-structure needs.

Thus, the maximum thickness per square meter within the first category range (10 cm to

17.5 cm) was chosen to consider different forms of structural public-structure potentially

requiring higher strength.

As in the case of residential construction, we acknowledge that while our approach was

designed to capture the largest part of the public-structure needs (roads, parking’s,

industrial sites surfaces and bridge decks), the construction of other public-structure pieces

such bridges and ports foundations, concrete piping systems, railway sleepers or tunnel

liner segments weren’t directly addressed (Figure 32). The main reason for this

methodological choice is that due to the lower cement use into these pieces of public-

structure, we considered that the incremental accuracy from a higher analytical granularity

did not compensate the required efforts.



126

Figure 32. Zuirch’s Andreastrasse project site illustrating different thickness in concrete

building slabs, bridges deck / columns and road-surfaces (Source: Grassi, 2017)

The following paragraphs provide a perspective on the estimated magnitude of other typical

structures to confirm the validity of our approach.

Tunnels: The review of tunnels length in a random selection of advanced economies

(except for China) revealed that the tunnel’s length in km account in average for

less than 0.4% of total paved roads’ length (Table 11). Although this list is by no

means exhaustive and reflects only the gathered information, we trusted its

representativeness for most advanced countries following the illustration provided

by the Switzerland case. While the Helvetic country accounts for the highest

average elevations over sea level (1’350 meters) within advanced countries, a proxy

for terrain complexity, and for a one of the best public-structure quality levels

(ranked in fifth place in the Global Competitiveness Report 2013-2014), its tunnels’

length as percentage of paved roads length still remained at a low 0.6%. Therefore,



127

we considered unlikely that other advanced economies not covered in the Table 11

accounted for tunnels’ length share of paved roads length substantially higher than

the case of Switzerland, consequently requiring specific attention.

Table 11. Tunnel length as percentage of paved roads length by country (source:

https://en.wikipedia.org)

Country Paved roads

length [km]

Tunnels length

[km]

Tunnels length

share of paved

roads length [%]

China 4'046'300 8'053 0.20%

Germany 645'000 183 0.03%

Italy 487'700 900 0.2%

Japan 992'835 4'026 0.4%

Republic of

Korea

91'195 649 0.7%

Netherlands 139'124 34 0.0%

Norway 75'754 865 1.1%

Sweden 140'100 20 0.0%

Switzerland 71'464 403 0.6%

Average 0.36%

Dams: Despite its colossal sizes, particularly the arch-gravity type, the low

magnitude of concrete built dams in the context of a country total physical public-

structure can be illustrated by a list of the largest examples. As described by the

Table 12, which lists some the largest arch-gravity dams ever built in advanced

countries, the cement requirement for their construction represents only a relatively

small amount when considered in perspective of the total cement stock in a country.

While cement content estimation of each dam was based on the concrete used by

applying a factor of 0.27 tons of cement per cubic meter of concrete (van Oss,

2005), the total cement stock was calculated by multiplying the Cement stock per

capita 2013 times the corresponding country population. As can be observed, the

largest project (Swiss Grande Dixence dam) accounts for only 0.45% of current

stock of cement (2013). As out of the eleven largest arch-gravity dams, the three

biggest accounted for only 0.65% of Switzerland’s cement stock (2013), we



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concluded the total magnitude of the all Swiss concrete dams was not expected to

account for more than 2.4% of total cement stock.

Table 12. Largest arch-gravity dams in advanced countries (source:

https://en.wikipedia.org and World Statistical Review, 1998, and International

Cement Review, 2015)

Dam name Height

[meters]

Country Concrete

used

[m3]

Cement used

[tons]*

% of

Cement

stock**

Kölnbrein 200 Austria

1'580'000

426'600

0.13%

Zillergründl 186 Austria

1'373'000

370'710

0.12%

Daniel-

Johnson

214 Canada

2'200'000

594'000

0.07%

Kurobe 186 Japan

1'582'845

427'368

0.01%

Almendra 202 Spain

2'186'000

590'220

0.04%

Grande

Dixence

285 Switzerland

6'000'000

1'620'000

0.45%

Mauvoisin 250 Switzerland

2'030'000

548'100

0.15%

Contra 220 Switzerland

660'000

178'200

0.05%

Glen Canyon 216 United

States

4'110'000

1'109'700

0.01%

Hoover 221 United

States

3'333'460

900'034

0.01%

Dworshak 218 United

States

760'000

205'200

0.00%

(*) Based on a cement factor of 0.27 tons of cement per cubic meter of concrete

(van Oss, 2005). (**) Cement stock is calculated by multiplying Cement stock per

capita 2013 times total country population.

Based on the above described examples for tunnels and arch-gravity dams, we concluded

that although the cement use intensity and construction costs of other pieces of public-

structure are likely to be different to that of the roads system, its impact in our estimations

to be limited. This is based on the expectations of the low magnitude of these structures in

the context of the total physical public-structure stocks (particularly those cement based).



129

Segment relevance – the role of capital formation cycle: The final input related to original

question, how much can be built with the Cement stock per capita gap, relates to the use of

concrete by segment (residential & non-residential, and public-structure). As anticipated in

the section Urban infrastructure development (2.2.1), the incidence of the segments tends

to vary depending on the stage of economic development. Whereas in developing countries

housing usually accounts for > 60% of the total cement consumed (e.g. Colombia 65%,

Ecuador 67%, Mexico 60%, Nigeria 74%), same segment in advanced economies is less

relevant (e.g. Belgium 44%, Australia 50%, United States 32%); (source: Business Monitor

International 2016 and multiple national urban planning agencies).

Most important, during the stages of capital formation, cement consumption in the public-

structure segment tends to grow substantially. Samans, Blanke and Corrigan (2015)

claimed that public expenditure in public-structure (as % of GDP) peaked by the mid-1970

in advanced countries followed by a period of contraction plunging to one third and in some

cases even one fourth of the values reached at peak. By observing the Investment and

Capital Stock Dataset covering the 1960-2015 period, we found that from 1960 to the mid-

1970, the investment in capital stock grew over 60% in most of the currently considered

advanced economies (source; International Monetary Fund’s Fiscal Affairs Department,

2017 version). As previously addressed in the section 1.1.2, Consequences of urban

infrastructure deficit, the International Monetary Fund in its World Economic Outlook

(2014) claimed that the investment in public capital has declined substantially as a share of

output over the past three decades across advanced countries.

As illustrated in the Figure 33, we estimated the average share of cement consumption by

segment to be 65% dedicated to public-structure and 35% for residential and non-

residential segments during the period of capital formation. We obtained the ratio at peak

of cement consumption by extrapolating the increase of public expenditure in public-

structure as percentage of GDP following the claims of Samans, Blanke and Corrigan

(2015) and our findings from the Capital Stock Dataset published by the International

Monetary Fund’s Fiscal Affairs Department (2017 version).



130

Figure 33. Cement consumption in public-structure during the initial stages of capital

formation

To obtain a division between residential and non-residential segments we reviewed

literature covering statistical construction permits in different advanced countries. We

found that both in Europe and in the United states, the residential and non-residential

segments share a similar number of permits released annually. For instance, excluding the

periods of high market speculation (i.e. 2008 housing bubble), the quantity of permits

granted in Europe for residential and non-residential had minor annual variations between

each other (Source: http://ec.europa.eu/eurostat). The work of Mullins (2006) covered the

development of residential and non-residential development in the United States from 1990

to 2006 describing a similar relevance for each segment except during the steepest years of

the housing bubble ending in the sub-prime crisis (Atif and Amir, 2009). Mullins (2006)

claims were based on the analysis of construction people employed in each of the two

segments. Finally, we concluded that a breakdown in equal parts is a representative split

between residential and non-residential construction requirements for the objectives of this

thesis.

How much would those constructions cost?

Once that we have defined how much can be constructed with the urban infrastructure gap

measured in terms of Cement stock per capita, the natural next step corresponded to

defining the economic resources required to undertake and accomplish those constructions.

As the building cost per square meter in developing countries differs from segment to

segment (described in the Table 13), we found indicative constructions cost for both

http://ec.europa.eu/eurostat



131

residential and public-structure separately. For the housing segment, we reviewed the work

of Turner and Townsend (2016), Woetzel et al. (2014) and the African property and

construction cost guide (2016). For the public-structure segment, we found support on the

claims of Collier, Kirchberger and Söderbom (2014), Carruthers (2013), and Mubila,

Moolman and Van Zyl (2014).

We chose to focus on square meter references to homogenize the findings with the

previously define metrics facilitating the final computations. We estimated USD 352.35

per square meter for residential construction and USD 53.80 per square meter of public-

structure construction as 2013 building cost values.

Table 13. Cost of construction in Sao Paulo (Brazil 2016), land cost excluded (source:

Turner & Townsend 2016)

Buildings Construction segment Cost per square

meter [USD]

Airport Public-structure-Commercial 1’660

Car park (multi-story) Public-structure-Commercial 450

School (primary) Public-structure-Commercial 690

Hospital (day center) Public-structure-Commercial 870

Aged care Public-structure-Commercial 580

Hotel (three stars) Commercial 840

Offices (business park) Commercial 690

Shopping mall (large) Commercial 620

Warehouse (basic factory units) Commercial 510

Townhouse (medium standard) Residential 480

Apartment low-rise Residential 520

Apartment high-rise Residential 640

Constructions cost - residential: To define the cost of residential construction, we reviewed

leading industry guidance for developing countries. We focused on construction typologies

compatible with the typical 50-square meter house used to defined the yield of cement and



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concrete in term of potential square meters of housing construction. The Annex 7.5

describes international construction cost in fourteen cities located in developing countries

in Africa, Asia, Eastern Europe and Latin America as per Turner and Townsend (2016),

Woetzel et al. (2014) and the African property and construction cost guide (2016). While

both Turner and Townsend (2016) and the African property and construction cost guide

(2016) appear to reference current market values only, Woetzel et al. (2014) also provides

target costs for low construction solutions. Thus, Turner and Townsend (2016) reported the

average 2016 at USD 532.72 per square meter (USD 462.7 per square meter when adjusted

to 2013 values), and Woetzel et al. (2014) USD 242 (in reported 2013 values). Turner and

Townsend (2016) average corresponding to 2013 was obtained by deflating 2016 average

(532.72 USD per square meter) using the International Monetary Fund inflation / average

consumer prices for emerging market and developing economies (retrieved in August

2017) as follows: 2014 - 4.69%; 2015 - 4.71%; 2016 - 4.37%. Woetzel et al. (2014) insights

on low construction cost targets are fundamentally aligned with the objectives of our

research addressing the global housing challenge. While market constructions costs are a

given, we expect public administrations in developing countries to aim for lower

constructions cost in their attempts to narrow the housing deficit in line with Woetzel et al.

(2014) findings. Therefore, we retained that the resulting arithmetic average (USD 352.35

per square meter) between the townhouse construction values provided by Woetzel et al.

(2014) (USD 242 - 2013) and Turner and Townsend (2016) (USD 462.7 - 2013) were a

valid compromise for residential construction reference values. Its representativeness

within the objectives of this research is supported through the following arguments.

Firstly, the values are in line within the different sources as shown in the Annex

7.5, where Turner and Townsend (2016) reported figures are compared with

Woetzel et al. (2014) and the African property and construction cost guide (2016).

Furthermore, and likely most important, reported constructions costs in each

different town tend to be similar (e.g. Turner and Townsend (2016) mean of 532.72

and a standard deviation of 95.82 results in a variation coefficient of only 18%,

which we considered low in a context of global estimations). Although reported

data is limited to fourteen markets, it covers a wide geographic range including



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Africa, Asia, Eastern Europe and Latin America and thus offering a representative

figure to estimate the construction economic resources.

Secondly, as all gathered data-sources (Turner and Townsend, 2016; the African

property and construction cost guide, 2016; and Woetzel, 2014) report values

correspond to cities and not to countries, we concluded that the representativeness

of using an average benefit from the distributional data of the external view

(Kahneman and Tversky, 1977). We expected this approach to help to mitigate the

distortive impact of temporary construction overpricing or underpricing in some

markets. Furthermore, as all gathered dataset cover a limited amount of developing

markets, attempting to use individual country data would have left unaddressed the

quantification of deficit for many other developing countries.

Thirdly, considering that narrowing the urban infrastructure deficit in developing

countries is expected to occur over decades, and that construction costs are subject

to changes as in any other economic activity, the expected subtle benefits of

defining a cost for residential construction by country would be offset by the

substantial time efforts required. Some of the most sensitive and common elements

driving residential construction cost changes often relate to inflation processes,

industry consolidation-fragmentation and an imbalance in housing supply-demand.

Construction cost - public-structure: To define the cost of public-structure construction,

we reviewed different valid sources reporting actual costs of public-structure projects,

particularly related to the constructions of new transportation systems compatible to the

first road category previously described (City streets, secondary roads, and small airports,

Figure 34). At the core of our review stood the work of Collier, Kirchberger and Söderbom

(2014) which analyzed the World Bank’s Roads Cost Knowledge System database. The

World Bank’s efforts to build this knowledge platform commenced in 2001 and were

motivated by the aim of developing a knowledge system on road work costs for developing

countries, in which an institutional memory of cost averages and ranges could be stablished

to ultimately improve reliability on cost estimates while reducing overruns risks (World

Bank, 2006). Collier, Kirchberger and Söderbom (2014) found that the 2004 average cost



134

for a one-lane new road was 91’788 USD per km. We converted this value to 2013 prices

resulting in 158’571 USD per km using the International Monetary Fund inflation /average

consumer prices for emerging markets and developing economies (retrieved in August

2017) as follows: 2005 - 5.90%; 2006 - 5.81%; 2007 - 6.47%; 2008 -9.20%; 2009 - 5.03%;

2010 - 5.60%; 2011 - 7.09%; 2012 - 5.2% and 2013 - 5.50%).

Carruthers (2013) review of several construction road works in developing countries

located south and east Mediterranean region (e.g. Algeria, Egypt, Israel, Jordan, Lebanon,

Libya, Morocco, Syria, Tunisia, Turkey and Palestine) revealed an average cost of

construction of 150’000 USD per km to build a new one lane road in 2010 prices. Using

the same inflating methodology as used for Collier, Kirchberger and Söderbom (2014), the

2010 Carruthers (2013) work corresponded to 179’333 USD per km in 2013 prices.

Figure 34. Example of city concrete roads in Zurich, Switzerland (source: Grassi, 2017)

Mubila, Moolman and Van Zyl (2014) analyzed 172 road construction projects in Africa

with the aim of obtaining a unit costs for comparison purposes and understanding the extent

of project cost overruns. Their review covered maintenance of paved and unpaved roads,



135

rehabilitation of paved roads, and construction of new roads using information from the

African Development Bank (2007) and the Africa Infrastructure Country Diagnostic

(2008). Among many findings, Mubila, Moolman and Van Zyl (2014) found that the

construction of one lane costed in average 147’100 USD per km in 2006 prices (for projects

over one hundred km in length). We used the same inflating methodology as per the two

previous works to convert Mubila, Moolman and Van Zyl (2014) 2006 prices to 2013

resulting in 226’780 USD per km. As in the case of the residential construction, the last

step was to estimate the cost per square meter as to be able to compute the total cost

estimation to narrow the urban infrastructure deficit in developing countries. Therefore, we

firstly averaged the values in 2013 prices of the different sources (Collier, Kirchberger and

Söderbom, 2014; Carruthers, 2013; and Mubila, Moolman and Van Zyl, 2014) obtaining

an average single cost for road construction of USD 188'228 per km of one lane road. To

obtain the square meter cost we first defined the average lane width in 3.5 meters as per

guidance of different standards. While the Interstate Highway standards for the U.S.

Interstate Highway System uses a 3.7-meter standard for lane width, European roads width

vary by country ranging from 2.5 to 3.50 meters. We chose 3.5 meter as an intermediate

value within the standards of advanced nations under the assumption that developing

countries will tend to adopt safer regulations as public-structure quality improves. Thus,

we estimated the public-structure (roads) cost of construction per square meter to be USD

53.80 (Figure 35).

Figure 35. Illustration of road characteristics used for the computation of cost of public-

structure construction



136

Final cost computation: The final step in the cost estimation to close the urban

infrastructure deficit in developing countries comprehends the combinations of all elements

described in the previous paragraphs (i.e. Deficit of cement stock per capita, Segment

relevance, Construction yield and Construction cost) in one single computation. The

incorporation of population size was aimed to provide a measure of the economic resources

needed at national level by simply multiplying the per capita values times the number of

inhabitants in each developing country in scope (Equation 8).

Equation 8. Final cost computation to reduce urban infrastructure deficit in developing

countries

𝑇𝑜𝑡𝑎𝑙 𝑐𝑜𝑠𝑡𝑖2013

= [(𝐷𝑒𝑓𝑖𝑐𝑖𝑡 𝑜𝑓 𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013

∗ 𝑆𝑒𝑔𝑚𝑒𝑛𝑡 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑐𝑒𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙 ∗ 𝑌𝑖𝑒𝑙𝑑𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙

∗ 𝐶𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙)

+ (𝐷𝑒𝑓𝑖𝑐𝑖𝑡 𝑜𝑓 𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013

∗ 𝑆𝑒𝑔𝑚𝑒𝑛𝑡 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑐𝑒𝑖𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒 ∗ 𝑌𝑖𝑒𝑙𝑑𝑖𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒

∗ 𝐶𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑜𝑛 𝑐𝑜𝑠𝑡𝑖𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒) ] ∗ 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑠𝑖𝑧𝑒𝑖2013

𝑇𝑜𝑡𝑎𝑙 𝑐𝑜𝑠𝑡𝑖2013

= [(𝐷𝑒𝑓𝑖𝑐𝑖𝑡 𝑜𝑓 𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013 ∗ 0.35 ∗ 6.22

∗ 352.35)

+ (𝐷𝑒𝑓𝑖𝑐𝑖𝑡 𝑜𝑓 𝑐𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013 ∗ 0.65 ∗ 21.14

∗ 53.8) ] ∗ 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑠𝑖𝑧𝑒𝑖2013

𝐷𝑒𝑓𝑖𝑐𝑖𝑡 𝑜𝑓 𝑐𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎: 𝑎𝑠 𝑝𝑒𝑟 𝑏𝑎𝑠𝑖𝑐 𝑢𝑟𝑏𝑎𝑛 𝑖𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒

𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑣𝑎𝑙𝑢𝑒𝑠

𝑖: 𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑖𝑛𝑔 𝑐𝑜𝑢𝑛𝑡𝑟𝑦



137

4.3.3.3 Estimation of specific cement cost

Whereas the previous sections were developed with the objective of estimating the urban

infrastructure deficit, the estimation of cement cost was aimed to isolate the cement portion

from the complete cost equation. To estimate the cost of cement, the Cement stock per

capita gap was multiplied but an average cost of cement per ton. Although cement prices

might vary by markets, as seen in the section Economics of the cement business (2.1.5),

largest cement manufacturers with different geographical exposure accounted for similar

average selling prices in 2013 (Cemex USD/ton 110; Heidelberg USD/ton 105; Holcim

USD/ton 104 and Lafarge). We thus decided to use an average of these four different prices

resulting in USD/tons 105.75.

Equation 9. Estimation of cement cost to close urban infrastructure deficit

𝐶𝑒𝑚𝑒𝑛𝑡 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013

= (𝑅𝑒𝑓𝑒𝑟𝑒𝑐𝑒 𝑣𝑎𝑙𝑢𝑒 − 𝐶𝑒𝑚𝑒𝑛𝑡 𝑠𝑡𝑜𝑐𝑘 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖2013)

∗ 𝐶𝑒𝑚𝑒𝑛𝑡 𝑝𝑟𝑖𝑐𝑒2013


𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑣𝑎𝑙𝑢𝑒: 𝑏𝑎𝑠𝑖𝑐 𝑙𝑒𝑣𝑒𝑙 𝑜𝑓 𝑢𝑟𝑏𝑎𝑛 𝑖𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒

𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎

𝐶𝑒𝑚𝑒𝑛𝑡 𝑝𝑟𝑖𝑐𝑒: 𝑈𝑆𝐷 105 𝑝𝑒𝑟 𝑡𝑜𝑛

4.3.4 Developing countries – case study

The case study was aimed to condensate the knowledge expansion and methodological

results generated during the length of this research through a detailed review of a

developing country. This detailed review was expected to contextualize and validate our

findings, primarily through a bottom up approach assessing the current (2013) urban

infrastructure status and projections following a set of three steps:

Data analysis overview and description: Firstly, a review was provided for the

findings summarized during the cross-sectional analysis in terms of stablished



138

relationships and how those relate to the subject country covering Cement, Urban

infrastructure, Economic development and Country inherent variables’ type.

Secondly, the country subject was assessed in the context of the general descriptive

statistics analysis. The set of perspectives obtained were expected to support the

pattern description on the cement consumption and its relation to economic

development level or geography.

Urban infrastructure deficit: The outcomes of the Estimation of urban infrastructure

described in section (4.3.3) were verified with current (2013) reports specialized on

public-structure development, particularly the Business Monitor International

Public-structure report. The main aim of this review was to add granularity and

support to the results obtained through the reference values resulting from the

advanced economies benchmark. The identified deficit (in terms of cement gap and

its corresponding monetary cost) in the subject country was contextualized to real

planned projects in transport public-structure, residential and non-residential, and

utilities public-structure (particularly related to water and sanitation provision).

Long-term cement demand forecast: We applied the methodology described in the

section 4.3.2 to estimate the potential long-term cement demand in the subject

country under different economic growth scenarios. Firstly, the subject country was

allocated to a subgroup following the model-based clustering (Duncan et al., 2001).

Secondly, three different economic growth scenarios (long-term) are defined to

provide the independent inputs (GDP per capita Constant) to the pooled model.

Thirdly, the three long-term Cement consumption per capita forecasts were

generated using the pooled model. Its results were compared with available

information in specialized reports, particularly the Business Monitor International

report and the Global Cement Report publication.



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5 RESULTS AND DISCUSSIONS

This section is aimed to describe the main insights observed during both the exploratory

and the explanatory analysis sections in the context of the research questions. Through the

implementation of this thesis’ research methodology, the following paragraphs present the

results on:

Firstly, the potential adequacy of our pooled model(s) for the cement long-term

demand forecasting based on Duncan et al. (2001) guidance on analogy forecasting.

Secondly, the quantification and validation through relevant sources of the urban

infrastructure deficit in developing countries as a function of the cumulative cement

consumption.

Thirdly, the implementation of the previously accumulated knowledge to diagnose

the urban infrastructure deficits and potential development of the cement demand

in Nigeria.


As organized in the Research methodology section, we present the results of: the cross-

sectional analysis involving all variables and countries within scope, and the descriptive

statistics covering the most relevant variables.

5.1.1 Cross-sectional analysis

We followed the work of Canning (1998) when describing a database of world public-

structure stocks for the 1950-1995 period as described in the Exploratory analysis section.

We chose the 2013 as the most relevant year and ran a correlational analysis of logged

variables to reduce the impact of wide-ranging quantities and different metrics (Table 14).

We clustered the observations by variable type (Cement, Urban infrastructure, Economic

development and Country inherent) with a particular focus on relations between Cement

stock per capita and the availability of urban infrastructure (as per its variables: Public-

structure quality, Capital stock per capita, and Population living in slums).


140

Table 14. Correlations of variables – 2013

(*) indicates significance level of 5%

All variables in log except

those in %

Cement stock

pc

Cement

consumption

pc

Public-

structure

quality

Capital stock

pc

Population in

slums

GDP pc

Current

GDP pc

Constant

Human

Development

Index

Political

stability

average

Urbanization

level

Temperature

average Country size Elevation

Cement stock pc 1

Cement consumption pc 0.7966* 1

0.0000

Public-structure quality 0.8398* 0.6296* 1

0.0000 0.0000

Capital stock pc 0.8786* 0.7064* 0.8474* 1

0.0000 0.0000 0.0000

Population in slums -0.7557* -0.7224* -0.7297* -0.7118* 1

0.0000 0.0000 0.0000 0.0000

GDP pc Current 0.9182* 0.7281* 0.8775* 0.9494* -0.7619* 1

0.0000 0.0000 0.0000 0.0000 0.0000

GDP pc Constant 0.8811* 0.6881* 0.9064* 0.9011* -0.7366* 0.9511* 1

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Human Development Index 0.8981* 0.6846* 0.8903* 0.9112* -0.8186* 0.9688* 0.9384* 1

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Political stability average 0.6309* 0.3884* 0.6807* 0.6857* -0.3007* 0.6634* 0.6544* 0.6733* 1

0.0000 0.0000 0.0000 0.0000 0.0108 0.0000 0.0000 0.0000

Urbanization level 0.7672* 0.5345* 0.6593* 0.7211* -0.5614* 0.7696* 0.7232* 0.7550* 0.5389* 1

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Temperature average -0.4753* -0.2825* -0.5134* -0.5539* 0.2886* -0.5419* -0.5620* -0.5836* -0.5599* -0.3748* 1

0.0000 0.0015 0.0000 0.0000 0.0154 0.0000 0.0000 0.0000 0.0000 0.0000

Country size -0.2101* -0.1496 -0.1511 -0.0973 -0.0264 -0.1279 -0.121 -0.1375 -0.3137* -0.0508 -0.0272 1

0.0168 0.0918 0.1071 0.2826 0.8268 0.1536 0.1917 0.1247 0.0003 0.5687 0.7635

Elevation -0.1774 0.0007 -0.2020* -0.1453 -0.015 -0.1964* -0.1926* -0.1889* -0.2345* -0.1583 -0.0103 0.1885* 1

0.0557 0.9943 0.0379 0.1230 0.9042 0.0362 0.0428 0.0441 0.0109 0.0884 0.9133 0.0418



141

Cement stock per capita vs Cement consumption per capita

The first insight to address is on how Cement Stock per capita and Cement consumption

per capita relate to the rest of the variables in scope. As shown in the Table 14, the

correlations of Cement stock per capita with all other variables are stronger than those of

Cement consumption per capita, particularly in the relation with economic development

variables. The weaker relation between the current level of cement consumption and

economic development of a country appear to suggest the existence of a non-linear

relationship. This outcome confirms the expected long-term patterns of cement

consumption and supports the claims of Aitcin (2000), Deverell (2012), and Kang and Li

(2013) in terms of cement consumption decline when a certain level of urban infrastructure

is reached. This claim appears evident in advanced economies which have completed a full

cycle of consumption (growth-saturation-decline-maintenance). As can be observed

through the example of Germany (Figure 36), Cement consumption per capita built up until

it peaked in 1972 followed by a demand decline towards a maintenance stage, despite of a

continuous growth on GDP per capita Constant. From its saturation point in 1972 until

2013, the German Cement consumption per capita contracted ~52% from 680 kg to 329

kg. On the other hand, both its GDP per capita Constant and Cement stock per capita

increased c.a. two-fold (190% and 230% respectively).

The patterns of the historical cement consumption development in Germany relate

substantially to the findings of Osenton (2000) and Fenech and Tellis (2016) on saturation

point. In line with Osenton (2000) claims in his theory of natural limits, in which the

saturation point of a product is usually determined after 20-25 years of sales and marketing

investment, German saturation point achieved in 1972 took place 25 years after the

consumption ramp-up that followed the second world war. While Palacios Fenech and

Tellis (2016) claimed that product consumption reaches a peak at about 56% of market

penetration followed by a dramatic drop, German Cement stock per capita in 1972

corresponded to 50% of the Cement stock per capita tration into maintenance stage,

assuming it happened in 2002 approximately (Cement consumption per capita in Germany

between 2002 and 2013 has ranged between 314 kg and 367 kg signaling a maintenance

consumption level). Although the German example appeared to be consistently aligned



142

with the literature according to Palacios Fenech and Tellis (2016), we suggest cautiousness

about initial interpretations considering the cemend particularities described in section

2.3.2.

Therefore, the turning point of Cement consumption per capita in advanced countries likely

caused by the achievement of a saturation point (Aitcin, 2000; Osenton, 2004; Deverell,

2012; and Kang and Li, 2013) and its consequently change in regime appears to weaken its

linear relation with other variables.

Figure 36. Germany development for Cement stock per capita, Cement consumption per

capita and GDP per capita Constant for the 1913-2013 period

Urban infrastructure variables

The high correlations found between Cement stock per capita and the Urban infrastructure

variables, Public-structure quality (0.84*), Capital stock per capita (0.88*) and Population

living in slums (-0.76*) have a double importance. Firstly, related to the social / economic



143

relevance, and secondly supports the initial interpretation of urban infrastructure deficits

connection to the shortages of cumulative cement consumption (cement stock).

The high correlations allows to connect the magnitude of the Cement stock per capita to

the serious social and economic consequences of urban infrastructure deficits claimed by

the World Bank (1994), Canning (1998), Todaro and smith (2012), Srinivasu and Rao

(2013), United Nations (2015) and the International Monetary Fund (2017). The tight

relation of Urban infrastructure variables with economic development is illustrated by the

high correlation results between GDP per capita Constant with Public-structure (0.91*),

Capital stock per capita (0.90*) and Population living in slums (-0.74*). The

interpretations of these high relations are sustained by Schawab (2014) and the

International Monetary Fund (2017) claims. Schwab (2014) argued that public-structure

networks significantly impact in economic growth by reducing the negative effect of

distance in transportation while connecting the national at low cost. The International

Monetary Fund (2017) claimed that Capital stock is key element in the creation of the

physical assets that form the economic public-structure of a country.

The second relevance of the close relation between cumulative consumption of cement and

the physical public-structure systems relates to the confirmation of the fundamental claims

of Aitcin (2000), Deverell (2012) and Kang and Li (2013) on cement market saturation

driven by the achievements of a certain level of urban infrastructure. Therefore, the strong

correlation between Cement stock per capita and Urban infrastructure variables support the

general aim of inferring urban infrastructure deficit as a function of historical levels of

cumulative cement consumption.

Economic development variables

The observed high correlation results between Cement stock per capita and the four

economic related variables (GDP per capita Constant, GDP per capita Current, Human

Development Index and Political stability) are a central support to our objective of building

a long-term forecasting model. This positive relation clearly indicates that more

economically developed a country is, the higher the amount of cement placed in the form

of concrete buildings forming urban infrastructure. Among all the thirteen variables, the



144

Economic variables type hold the highest relation with the cement demand development,

particularly when represented by the Cement stock per capita variable (following the non-

linearity aspects mentioned in the initial consideration of the cross-sectional analysis

“Cement stock per capita vs Cement consumption per capita”). The confirmation of the

strong relation between cement consumption and economic development is key

contribution to the objective of building a cement demand long-term forecasting based on

the inputs of economic growth.

As expected, GDP per capita Constant holds a slightly lower coefficient (0.88*) as

compared with GDP per capita Current (0.91*), potentially explained due to the Current

expression offering a more accurate representation a country’s level of wealth in 2013 (year

chosen for the cross-sectional analysis). We retain important to remind the reader that the

selection of GDP per capita Constant as the independent variable in our long-term

forecasting model was driven our need to properly represent the development of a hundred-

year period and its use in our computations.

The relevance on the high statistical relation (0.90*) between Cement stock per capita and

Human Development Index lays on the characteristics of the latter, which decouples from

a traditional income perspective to focus on people and people’s capabilities development.

As the Human Development Index, published by the United Nations Development

Program, measures the people’s average achievement in terms of a long and healthy life,

knowledge and standard of living, we found its close relation with the Cement stock per

capita variable an important element adding to the overall interpretation of urban

infrastructure’s economic and social relevance.

Although still strong, Political stability present the lowest coefficient (0.63*) of correlation

with Cement stock per capita within the Economic development variables. Plausible

explanations for this lower coefficient could be connected to two elements. Firstly, to levels

of construction still taking place under low political stability. Secondly, to the role of

different period lengths, while Cement stock per capita accounts for a hundred-year period,

Political stability averaged the 1996 to 2013 period only. Nevertheless, the significance

and relevance of Political stability and its potential implications with the development of



145

urban infrastructure and consequently with cement stock lay on the characteristics of the

variable. While Political stability is composite of different dimensions of governability

(Kaufmann, Kraay and Mastruzzi, 2010), its notion of contribution to economic growth

(Alesina et al., 1992) fueling the development of urban infrastructure is manifested by its

correlation with both GDP Current (0.66*) and Constant (0.65*).

Country inherent

Except for Urbanization level, correlation coefficients between Cement stock and the

Country inherent variables resulted low and not significant in the case of Elevation.

Nevertheless, we considered valuable to introduce potential lines of explanation for these

low relations opening the discussion and eventually triggering other lines of research in the

field.

While the positive correlation between Urbanization level and Cement stock per capita

(0.77*) suggest that countries with a high level of urbanization tend to account for higher

levels of cumulated cement consumption, the causation of one over the other (if any),

remains unexplained only in the context of a correlational analysis. As it was addressed in

the section Economic relevance of urbanization (2.2.2), while the notion of a strong link

between urbanization and economic growth is widely diffused, the review on the work of

Chen et al. (2014), Todaro and Smith (2012) and Zhang and Song (2003) sustained that it

is the economic growth driving urbanization and not vice versa. In this line of thought,

there is evidence that suggest endogeneity in the Cement stock per capita and Urbanization

level relation. According to Chen et al. (2014), Todaro and Smith (2012) and Zhang and

Song (2003) it is the economic development what to drive the Urbanization level, one of

the engines of urban infrastructure (as per the previous studies and theoretical

considerations described in section 2.2.2). Consequently, it surfaces as axiomatic that it is

economic development what drives the cement consumption and not the urbanization level.

The relation between Cement Stock and remaining Country inherent variables appeared to

be weak and thus, as it was said previously mentioned, our initial descriptions of any

potential relations need to be taken carefully and are only aimed to open discussions and

hopefully more granular researches.



146

Although weak to a certain extent, the negative correlation between Temperature and

Cement stock per capita (-0.48*) appear to indicate that the higher the annual average

temperature of a country, the lower the cement stock. As mentioned for the Urbanization

level case, while the correlations fail to describe causality, the review of Kosmatka et al.

(2002) and Lstiburek (2010) works appear to provide an initial line of thought for this

relation, by no means conclusive.

Kosmatka et al. (2002) claims that concrete duration is affected by the cycles of freezing-

thawing and by the effects of the de-icing chemicals opened a door for potential explanation

on cement use intensity in cold countries. On the other hand, Lstiburek (2010) argued how

extreme weather conditions (-40 degrees Celsius) influence the choice of other construction

materials such as wood, plywood and impermeable membrane rather than cement. Based

on the two previous arguments (Kosmatka et al., 2002 and Lstiburek, 2010), while the

intensity of cement use in cold weathers appear to increase in public-structure, other

construction materials gain traction in the construction of housing. These are the advances

of potential line of thinking, and considering that the correlation between Temperature and

Cement stock per capita (-0.48*) is similar to the relation between Temperature and GDP

per capita Current (-0.54*), we suggest controlling for endogeneity in future researches.

Country size (-0.21*) relationship with Cement stock resulted in a low coefficient.

Although cement consumption is expected to be higher due to the notion of more resources

needed in large countries to connect distant urban centers ipso facto, the negative

coefficient could initially reveal an opposed perspective. In his work on world public-

structure stocks, Canning (1998) claimed that an increase in land area increases road length

while it has statistically insignificant impact on paved road provision. As land area

increases it does the distance between urban centers, and thus the resources needed to

connect them including the use of cement. However, under previous circumstances, it could

also be possible to assume that smaller countries with shorter distance between urban

centers might tend to develop better and more road connections than the larger countries,

which could focus on trains, airplanes and vessels to connect urban centers, thus consuming

less cement for road building.



147

Elevation over sea level, used in the scope of this research as a proxy for terrain complexity,

held the lowest relation coefficient with Cement stock per capita among all variables and

presented no statistical significance. With the limited information related this variable, we

considered different alternatives that justified the weak relation. Firstly, it is possible that

the correlation’s poor-quality is the result of multiple dynamics, which although related to

the Elevation over sea level, affect the construction in opposed ways. For example,

complex geographies are expected to require a complex (Zhou et al., 2009) and thus cement

intense urban infrastructure. For example, Switzerland accounts for the highest average

elevations over sea level (1350 meters), and also for a high tunnel-length as percentage of

total paved roads (0.6%) within advanced economies. On the other hand, terrain complexity

could act as a deterrent for construction, resulting in less building activity in high elevation

countries and thus less use of cement. A second explanation to the weak correlation could

be associated with lack of capacity of the Elevation over sea level variable per-se to mimic

terrain complexity. As anticipated in the variables’ description in the Datasets and variables

section (4.2.2), as this variable is expressed in average, an unequal distribution of the

elevation forms such as hills and the inhabitants throughout the country’s extension could

cause biased and thus misleading results.

5.1.2 Descriptive statistics

Between groups: Economic clusters vs Geographic regions:

Within the scope and objectives of this research, economic clusters appear to be more

homogenous groups in terms of Cement stock per capita as compared with geographic

regions. As previously shown by the outcomes of the correlational analysis, Cement stock

per capita and GDP per capita Constant and Current, both expressions of economic

development appear to have a very strong positive correlation (0.88* and 0.92*

respectively) with economic development being the likely causal variable following the

claims of Aitcin (2000) and International Cement Review (2014). We verified the

homogeneity and similarity within time-series of advanced economies through conducting

three different analysis ranging from the most general to the most particular interpretations.

Firstly, the descriptive statistics of Cement stock per capita 2013 resulting from Annex 7.6

and illustrated in the Figure 37, indicate that the variation coefficients of the economic



148

clusters average are lower (42%) as compared with the geographic regions average (67%).

In addition, the variation coefficient of the advanced economies is the lowest (22%) among

all classification (geographic and economic). We retain useful for the reader to remind that

the variation coefficient (standard deviation ÷ mean) illustrates how homogenous a group

is through quantifying the magnitude of the standard deviation as percentage of the mean.

Figure 37. Cement stock per capita Variation coefficient (standard deviation ÷ mean)

The second analysis covers a description of the combined economic and regional

perspective from a different angle, depicting the arithmetic average of Cement stock per

capita 2013 by geographic region and a broke down by economic cluster. As it can be

observed in the Table 15, the difference in countries’ cement stock, relates higher to the

economic development stage than it does to a geographical factor. Cement stock per capita

variations appear to be substantially narrower within economic clusters than within

geographic regions. While the maximum variations in the Cement stock per capita (against

the total average) in advanced, high, medium and low-income countries across different

geographies is 17.7%; 21.7%, 54% and 2.1% respectively, the maximum variation within

Africa, Americas, Asia-Oceania and Europe regions is 64.4%; 98.7%: 116.5% and 36.3%

respectively. This observation is aligned with the low correlations resulting between the

Country inherent variables and Cement stock per capita: Temperature (-0.47*), Country

Advanced

High

Medium

Low

Africa

Americas

Asia - Oceania

Europe

82%

22%30%

54%63%

101%

51%

83%

32%

All c

ou

ntr

ies

Ad

van

ced

Hig

h

Med

ium

Lo

w

Afr

ica

Am

eric

as

Asi

a -

Oce

an

ia

Eu

rop

e

Geographic 67% (average)

Economic 42% (average)

Variation coefficient by group [%]



149

size (-0.21*) and Elevation over sea level (-0.18). We assume this alignment to be based

on the expectation that geographic regions tend to share the same landscapes features such

as temperature, however playing a negligible role in terms of cement consumption as

compared with economic development following our findings.

Table 15. Average Cement stock per capita 2013 by economic cluster and geography

Average of

Cement

stock per

capita (tons)

2013

Africa Americas Asia-

Oceania

Europe Total

average

Maximum

difference

vs Total

average

Advanced 26 32 31 31 17.7%

High 19 24 20 21.7%

Medium 9 10 11 17 11 54%

Low 2 2 2 2 2.1%

Total

average

5 13 15 27 14

Maximum

difference

vs Total

average

64.4% 98.7% 116.5% 36.3%

Thirdly, we observed plotted times series for Cement consumption per capita and GDP per

capita Constant to: firstly, confirm pattern similarity in the shape and regimes of the cement

consumption function within advanced economies, and secondly to verify their completion

of full-cycle (growth-saturation-decline-maintenance). In addition, we compared the

variation coefficient of three core variables (Cement stock per capita, Cement consumption

per capita and GDP per capita Constant) within advanced against all remaining countries.

As observed in the Figure 38 (which in turns results from the Annex 7.7), the variation

coefficients for advanced economies are considerably lower as compared with the

variations of the remaining countries demonstrating its higher homogeneity. While the

variation coefficients for these three variables in all remaining countries are circa 80% or



150

higher, values for the advanced countries group account for 22% for Cement stock per

capita; 54% for Cement consumption per capita and 22% for GDP per capita Constant. The

correlational co-movement of these variables within advanced countries is further

demonstrated through the low variation coefficients at the year of demand saturation:

Cement stock per capita 28%; Cement consumption per capita 42% and GDP per capita

Constant 22%.

Figure 38. Advanced vs remaining countries - selected variables

Duncan et al. (2001) suggestion on the use of expert judgment was implemented to define

the year of saturation point in the presence of potential ambiguity. As shown in the Annexes

7.8, 7.9 and 7.10 (with the illustration of Italy), we identified a few advanced countries that

presented two differentiated peaks in the development of their historical cement

consumption (New Zealand, Norway, Australia, United States, Ireland, Greece, Spain,

Italy, Israel, Hong Kong and Singapore). We followed the fours-steps approach structured

in the Research methodology section (Equivalence groups 4.3.1) to determine the demand

peak that corresponds to the achievement of a basic level urban infrastructure (Aitcin, 2000,

Deverell, 2012 and Kang and Li, 2013) instead of other driven by elements such as

speculation (Birshan et al., 2015). The revision of the time gap between both peaks of

Cement consumption per capita indicates long-periods in most of these countries (over

Cement consumption per capita

GDP per capita Constant

Cement stock per capita






78%85% 85%

22%

54%

22%28%

42%

22%

Advanced countries at

year of saturation point

All countries excluding

advanced 2013

Variation coefficient [%]

Advanced countries

2013






151

twenty years) except for Norway and Singapore (thirteen years in both cases). We

understand that due to the considerable length, these extended time-lag between peaks

appear to be disconnected from an economic hiccup which could have deaccelerated

growth and consequently postponing the real saturation point to a second demand peak.

Both the Cement stock per capita and GDP per capita Constant values at first peak relate

closer to those of the reference values obtained from other advanced economies at

saturation point (13.7 tons average and USD 12’760 average respectively) as compared

with the values corresponding to the second peak. While the variation between the

reference values for Cement stock per capita and GDP compared with the first peak are -

6.6% and -7.9% respectively, second peak account for much higher variations (94.9% and

61.7% respectively). Lastly, we found support for these findings through revising the

historical context and most relevant events that drove a second peak in the cement

consumption. While Birshan et al. (2015) on United States, Italy and Spain blamed it solely

on the construction bubble busted through the 2008 world financial crisis, Buck (2017)

added corruption as a main component for particular case of Spain.

Buck (2017) claimed that the in 2007, Spain accounted for more new housing

permits than Germany, France, Great Britain Britain and Italy combined and

embarked in sinking billions of public economic resources into pharaonic one-off

events as Valencia’s city of Arts and Science complex.

Romei (2016) argued that Italian property market kept falling since its collapse in

2008 due to a persisting large supply of unsold housing units with the number of

property transactions falling from 860’000 in 2006 to 403’000 in 2013.

Waldron (2014) review on the Irish construction bubble places its origins in the

mid-1990s when a deregulation in the banking sector and government commitment

to expand home ownership developed in the massive collapse of an oversupplied

house market in 2007.

Smith (2014) claimed that in 2013, only 3’600 property transactions were registered

in Athens, down from the 250’000 in 2005 fueled by the speculation driven by



152

Olympic games euphoria. Consequently, the Bank of Greece estimated that since

the commence the Greek debt crisis in 2010, property values nationwide have

dropped by around 32%.

The steep cement demand growth in Hong Kong between mid-1980s and 1996,

which fueled a second demand peak and derived in a massive property crash

plunging the houses price by 70% during the Asian financial crisis, also appeared

to be driven by a situation of supply / demand imbalance and consequently

overbuilding. Worried by the increasing Chinese immigration, Hong Kong public

administration launched the Home Ownership Scheme program which targeted a

disproportioned house supply (source: Also Sprach, 2011).

As for the housing bubble in Hong Kong, the Asian financial crisis mainly rooted

in real estate speculation (Palma, 2000), derived in the mid-1990 construction

collapse of Taiwan, Singapore, and the Republic of Korea. In the case of the latter,

the real estate speculation did not cause a second demand peak but rather appeared

to have contributed to a steep growth of cement consumption until its collapse,

unrelated to the development of urban infrastructure needs.

The claims of Collyns and Senhadji (2002), support the understanding of the above

examples of speculative real-estate bubbles along with a description of the sequences.

Collyns and Senhadji (2002) claimed that the provision of financial services and the

allocation of resources improved by the liberalization of financial systems and the

increased globalization of capital markets have contributed to the increasing pronunciation

of financial cycles with dramatic fluctuations in asset prices. Typically, these financial

cycles are generated by a wave of optimism underpinned by favorable developments in the

real side of the economy. This optimism generated during financials cycles fuels among

other elements the underestimation of risk and consequently the over-investment in

physical capital (e.g. residential developments). It is when the eventual realignment of

expectation with fundamentals occurs that the imbalances generated during the boom are

suddenly corrected (Collyns and Senhadji, 2002).



153

Equivalence groups

Following the procedure described in the Research methodology section (Equivalence

groups 4.3.1), we obtained a set of four equivalence groups of advanced countries sharing

analogous Cement consumption per capita and GDP per capita Constant time-series to

predict the potential cement consumption in developing countries (Kahneman and Tversky,

1977 and Duncan et al., 2001). As per the results described in the results of the Data

analysis overview and description (5.1.1 and 5.1.2), we were able to validate the first two

steps for the formation of the equivalence groups. Finally, through the execution of the

third step, the model-based clustering, we achieved the final formation of the subgroups.

Firstly, we validated the correlational co-movement of advanced economies by analyzing

the general homogeneity of advanced economies in relation of Cement consumption per

capita, Cement stock per capita and GDP per capita Constant. Advanced economies

showed a higher degree of consumption pattern similarity for the observed variables as

compared with the remaining countries and appeared to have completed a full consumption

cycle (growth-saturation-decline-maintenance). Before carrying out the model-based

clustering to define the set of equivalence groups, it was required to define the demand

peak for each country (in the case of multiple peaks) that related to the achievement of a

basic level urban infrastructure (Aitcin, 2000; Deverell, 2012; and Kang and Li, 2013). As

covered in the Descriptive statistics results (5.1.2), we followed Duncan et al. (2001)

guidelines for the implementation of the expert judgement approach combining a

comparison of the characteristics of the different peaks in a country together with a

historical review of the potential events driving cement demand development. Once that

peaks related to saturation points (Osenton 2004; Aitcin, 2000; Deverell, 2012; and Kang

and Li, 2013) were defined, the model-based clustering was implemented as per the

methodology defined previously (section 4.3.1) to obtain the desired set of subgroups. The

relation between the Cement consumption per capita at peak and the linear regression

coefficient β (additional kg of cement consumption per capita per ‘000 USD of increment

in GDP per capita Constant) allowed for the formation of four subgroups (Figure 39).

Figure 40 illustrates the criteria used to define the clusters (quadrant ranges) following

model-based clustering (Duncan et al., 2001).



154

Subgroup 1: β = ~75-~100

Formed by Switzerland, Austria, Greece, and Portugal.

Subgroup 2: β = ~55-~75

Formed by Slovenia, Japan, Germany, France, Italy, Belgium and Israel.

Subgroup 3: β = ~35-~55

Formed by United Kingdom, Netherlands, Sweden, Denmark, Finland and Norway.

Subgroup 4: β = ~15-~35

Formed by New Zealand, Canada, Australia and United States.

Figure 39. Equivalence subgroups within advanced economies

(*) Corresponding for the period 1913 to saturation point of each individual country.

TaiwanRepublic of Korea

Singapore

Switzerland

Austria

Greece

Slovenia

Portugal

Japan

GermanyFrance

ItalyBelgium

Israel

United Kingdom

Netherlands

Sweden

Denmark

Finland

Norway

New Zealand

Canada

Australia

United States

Spain

Ireland

Hong Kong

R² = 0.5762

-

200

400

600

800

1'000

1'200

1'400

1'600

0 10 20 30 40 50 60 70 80 90 100 110 120 130

Cement consumption pc at peak [kg]

β: Additional cement consumption per capita [kg] per USD 1'000 increment on GDP pc Constant*



155

Figure 40. Illustration of quadrant range criteria

As can be noticed from the Figure 39, a few countries (Hong Kong, Taiwan, Republic of

Korea, Singapore, Spain and Ireland) did not appeared to conform any of the four-

subgroups as per the defined clustering criteria due to the specific characteristics of their

cement demand development. In all cases, these countries share a very steep growth during

the 15-20 years preceding the demand collapse, and as mentioned during the analysis of

double peaks of consumption (section 5.1.2), likely fueled by overbuilding of residential,

public-structure or both sectors. The cement demand development of these countries

appeared to be influenced by misleading elements potentially unrelated to normal urban

infrastructure development. While Hong Kong, Spain and Ireland failed to fall into any of

the subgroups 1 to 4, for the Taiwan, Republic of Korea and Singapore case, only Taiwan

and Republic of Korea appeared to conform a quadrant, however unable to provide the

possibility for empirical validation (lack of distributional information according to

Kahneman and Tversky, 1977). In the chapter Conclusions, limitations and further

developments (6), we propose a set of potential methodological approaches to address this

specific. In addition, the maximum Cement consumption per capita reached by these

countries helps to illustrate the potential abnormality of these construction market

development. While the average Cement consumption per capita in the advanced countries

at saturation point was ~600 kg, these countries achieved in average twice as much

accounting for 1’270 kg (Hong Kong 854 kg, Taiwan 1’332 kg, Republic of Korea 1’358

kg, Singapore 1’752 kg, Spain 1’255 kg and Ireland 1’073 kg, generally during second

consumption peak).



156

Long term-forecasting model for cement

Our long-term forecasting model was developed around the three steps defined in the

Research methodology (4.3.2). Firstly, the subgrouping and construction of local models;

secondly, the construction of coefficients for subgroups’ models; and finally, the empirical

validation of the model. The construction of the model, as well as its empiric validation is

based on the observations of hundred-year period datasets (for the Cement consumption

per capita and GDP per capita Constant variables) and thus sensitive to elements related to

long-term macroeconomic cycles. We addressed these particularities in the empirical

validation and provided initial paths to improve the model’s accuracy related to hidden

elements. The results shown by the pooled models are promising as the growth simulation

prediction obtained through our approach were substantially more accurate than the

outcomes provided by the standard model in all analyzed metrics: Mean Absolute

Deviation (MAD), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE),

variation on the average Cement consumption per capita and variation of saturation point

measured in numbers of years.

5.3.1 Subgrouping and construction of local models

Following the four equivalence subgroups identified in the previous section (Equivalence

groups 5.2), we proceeded with the construction of the local models for each individual

advanced country time times series (variables Cement consumption per capita and GDP

per capita Constant). As described in the next step, the construction of the equivalence

subgroup coefficients was fed by the local models described in detail in the Table 16. The

expected suitability of the quadratic regression for the construction of the local model’s

due to the linear-in parameters and the a-priori strong fitting (Armstrong, 2001) was

confirmed by the high coefficients of determinations with only two countries out of twenty-

one accounting for a coefficient lower than 0.70 (United States 0.64 and Sweden 0.51).

5.3.2 Construction of coefficients for subgroups’ pooled models

The pooled model α and β coefficients were obtained by drawing of information from

analogous time-series (equivalence subgroups) to allow the construction of the forecasting

model (Duncan et al., 2001).


157

Table 16. Equivalence subgroups and models (Standard and Pooled)

Country β Constant R² Peak Cons.pc α β Constant R² α P-value β P-value Constant R²

Switzerland 75.0 -419.4 0.97 1972 938 -0.2 79.4 -441.9 0.97 -4.0 0.00 154.8 0.00 -788.2 0.90

Austria 93.8 242.6 0.93 1972 793 2.0 69.9 -186.8 0.93 -3.5 0.00 117.0 0.00 -265.8 0.92

Greece 98.6 -146.1 0.96 1979 760 0.5 93.7 -138.2 0.96 -3.9 0.00 124.5 0.00 -177.9 0.88

Portugal 80.2 -113.2 0.98 2001 1'098 1.3 63.2 -81.1 0.99 -2.8 0.00 106.1 0.00 -149.4 0.89

Subgroup 1 86.9 -3.5 125.6

Slovenia 70.3 93.2 0.98 1981 819 -0.7 79.0 -110.4 0.98 -3.9 0.00 113.1 0.00 -171.7 0.91

Japan 66.5 86.1 0.98 1973 715 -0.4 70.5 -93.3 0.98 -3.7 0.00 109.8 0.00 -164.8 0.96

Germany 71.7 -152.8 0.92 1972 680 1.5 52.9 -102.6 0.93 -4.2 0.00 113.5 0.00 -239.0 0.87

France 57.2 -124.2 0.96 1974 593 0.6 47.8 -96.8 0.96 -3.3 0.00 96.8 0.00 -222.2 0.89

Italy 68.3 -89.9 0.98 1980 738 -2.9 107.0 -177.2 0.99 -3.3 0.00 106.1 0.00 -168.0 0.94

Belgium 56.1 -62.6 0.93 1978 748 -2.7 99.4 -209.2 0.94 -2.3 0.00 80.9 0.00 -122.7 0.87

Israel 60.4 -4.1 0.83 1975 717 -5.5 117.1 -102.0 0.86 -3.1 0.00 97.3 0.00 -68.0 0.85

Subgroup 2 63.4 -3.4 102.5

United Kingdom 45.6 -161.6 0.95 1973 357 -1.0 61.4 -218.1 0.95 -1.8 0.00 57.0 0.00 -154.9 0.75

Netherlands 47.9 -127.2 0.93 1971 455 2.3 12.5 -15.7 0.94 -2.0 0.00 67.9 0.00 -168.6 0.89

Sweden 54.8 -126.2 0.99 1969 502 -0.2 57.2 -133.2 0.99 -1.8 0.00 52.4 0.00 -61.0 0.51

Denmark 45.4 -121.7 0.97 1973 519 1.8 16.1 -19.3 0.98 -2.0 0.00 63.6 0.00 -151.4 0.71

Finland 47.7 -47.4 0.97 1974 479 -2.3 74.3 -105.9 0.98 -1.5 0.00 49.3 0.00 -26.2 0.78

Norway 45.6 -61.4 0.95 1974 429 -2.0 70.6 -125.7 0.96 -1.2 0.00 46.9 0.00 -34.6 0.80

Subgroup 3 47.8 -1.7 56.2

New Zealand 36.6 -104.0 0.93 1974 369 0.5 28.9 -75.7 0.93 -1.7 0.00 54.7 0.00 -144.1 0.76

Canada 33.3 -30.6 0.83 1974 433 -0.6 43.7 -66.0 0.83 -1.5 0.00 48.7 0.00 -62.0 0.72

Australia 31.6 -61.5 0.90 1988 435 -2.4 79.5 -262.4 0.95 -1.2 0.00 52.3 0.00 -143.6 0.93

United States 18.9 57.6 0.63 1973 381 -0.3 25.6 28.7 0.63 -0.5 0.00 25.6 0.00 41.1 0.64

Subgroup 4 30.1 -1.2 c-f: 95% 45.3 c-f: 95%

Linear regression - 1913 to peak

(criteria for subgrouping)

Standard model

Quadratic regression - 1913 to peak

Pooled model

Quadratic regression - 1913 to 2013



158

As shown in Table 16, both α and β coefficients for each subgroup model were obtained

by averaging the coefficients of the individual (local) quadratic regressions covering the

1913-2013 period for each advanced economy within the corresponding equivalence

subgroup. The resulting pooled models are described in the Equation 10, Equation 11,

Equation 12 and Equation 13.

Equation 10. Subgroup 1 - pooled model


= −3.5 ∗ 𝐺𝐷𝑃 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡𝑖𝑡2 + 125.6

∗ 𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡𝑖𝑡 + 𝑂𝑝𝑡𝑖𝑚𝑖𝑧𝑒𝑑 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡









Equation 13. Subgroup 4 – pooled model






159

5.3.3 Empirical validation

We verified the adequacy of the pooled models for long-term cement demand forecasting

through the empirical validation of its prediction accuracy. As described during the

development of the research methodology, the validation was constrained to advanced

economies as those have completed all stages (growth-saturation-decline-maintenance) and

therefore allow for the efficient evaluation of the model’s prediction accuracy during the

full consumption cycle. We use the example of Japan (subgroup 2) to illustrate the

procedure for the empiric validation. As shown in the Table 17, a pooled model was built

with the remaining countries of Japan’s equivalence subgroup, and later on used to predict

the Cement consumption per capita for the period saturation point to 2013 based on Japan’s

GDP per capita Constant development. The Annex 7.11 provides a description of each

pooled model used during the empirical validation, which implies the elimination of the

local model for the country being-forecasted from the equivalence subgroup pooled model

to avoid self-prediction bias. This procedure is fully described in the section 4.3.2

Empirical validation.

Table 17. Subgroup 2: validation of pooled model – Japan’s example

Country α P-value

(c-l 95%)

β P-value

(c-l 95%)

Constant R2

Slovenia -3.9 0.00 113.1 0.00 -171.7 0.91

Germany -4.2 0.00 113.5 0.00 -239.0 0.87

France -3.3 0.00 96.8 0.00 -222.2 0.89

Italy -3.3 0.00 106.1 0.00 -168.0 0.94

Belgium -2.3 0.00 80.9 0.00 -122.7 0.87

Israel -3.1 0.00 97.3 0.00 -68.0 0.85

Pooled

model

-3.36 101.3 -143.46*

(*) The constant term for the pooled model was optimized following the procedure

described in the section 4.3.2.



160

The Figure 41 illustrates the simulation of Japan’s Cement consumption per capita

development using both the pooled and standard model. While the actual saturation point

of the Cement consumption per capita in Japan was reached in 1973, the pooled model

predicted it for a level of GDP per capita Constant of USD 14’800, which takes place 71

periods (years) after the first independent value used, corresponding to 1984 in a time-line.

Using the same procedure as in the Japan’s case, we compared actual against predicted year

of saturation point for the remaining advanced economies. A variation of only 11 years in

a hundred-year period together with a low MAPE (12% compared with 103%

corresponding to the standard forecast) supports the adequacy of the pooled model in terms

of accuracy.

Figure 41. Simulation of Japan’s Cement consumption per capita development for the

1913-2013 period with pooled and standard models

The pooled model based on a quadratic regression also showed its adequacy for the forecast

of cement long-term demand through linear-in parameters (random distribution of residuals

Annex 7.16) and the a-priori strong fitting (Armstrong, 2001). The standard model, also

based on a quadratic regression, relied on Japan’s individual country data only losing the

y = -0.3528x2 + 70.489x - 93.282

y = -3.3664x2 + 101.3x - 143.46

0

200

400

600

800

1000

1200

1400

0 5 10 15 20 25

Observed Pooled model Standard

Poly. (Standard) Poly. (Pooled model)

GDP per capita Constant ['000 USD]

[kg]

Japan

Year of saturation point: actual 1973

Year of saturation point: pooled 1984



161

value provided by distributional information (Kahneman and Tversky, 1977). As shown in

the Figure 41, its substantial deviation from the observed values (MAPE of 103% vs 12%

achieved with the pooled model, Annex 7.17) are in line with the claims of Hung and Wu

(1997), Deverell (2012) and Kang and Li (2013). Japan’s example supports the crucial

value provided by observing the historical cement demand of comparable economies to

understand cement’s demand long-term patterns. The Annexes 7.12, 7.13, 7.14 and 7.15

contain the simulation of the Cement consumption per capita growth for the each advanced

economy in scope in the same fashion of Figure 41. Overall, the results shown by the pooled

models are highly positive, the growth simulation prediction obtained through our approach

were substantially more accurate than the outcomes provided by the standard model. As

shown in Annexes 7.17 and 7.18, we used the different customary metrics defined in the

research methodology to measure the predictions’ accuracy as compared with the actuals

values: Mean Absolute Deviation (MAD), Mean Square Error (MSE), Mean Absolute

Percentage Error (MAPE), variation on the average Cement consumption per capita and

variation of saturation point measured in numbers of years. The accuracy metrics for the

pooled models were superior for every country except for Australia. Although no

substantial difference was observed between the pooled and the standard prediction

accuracy in Australia (MAPE: 31% and 29% respectively), both models appeared to have

a moderate accuracy as compared with the average of all advanced countries. In any

respect, this does not indicate a lack of adequacy for the pooled model, but rather a high

accuracy reached by the standard model. The observation of other cases accounting for

small accuracy differences between the pooled and standard a model (e.g. Norway MAPE

26% and 34% respectively) indicate a potential relation between the shape of the demand

function in the previous years before its peak. It appears that when the Cement consumption

per capita growth deaccelerates before reaching its saturation point, the quadratic

regressions in which the standard model is built also captures the typical bell-shape of the

cement demand function. In the Conclusions, limitations and further developments chapter,

we summarize a set of potential elements contributing to these dynamics as well as further

lines of research to improve the analogy forecasting model. The higher prediction accuracy

of the pooled models is observed in the Figure 42 summarizing MAPE / MAD averages

and the prediction of saturation point by equivalence group for both the pooled and the

standard models. The pooled models predicted with higher accuracy the Cement



162

consumption per capita in every equivalence group with an average total MAPE of 28% as

compared with 105% achieved by the standard models. In terms of predicting the saturation

point, whereas the pooled models’ variation with the actual values averaged 15 years

difference, the standard model’s inaccuracy doubled that of the pooled models (29 years

average variation). Because of MAPE known asymmetry (Armstrong, 1985), reports

higher errors when the predicted value is larger than the observed value and lower vice

versa, we verified MAPE results examining the MAD outcomes. The MAD also indicated

that the pooled models predicted with higher accuracy the Cement demand per capita in

every equivalence group with an average total MAD of 109 kg as compared with 387 kg

indicated by the standard models.

Figure 42. MAPE, MAD and Variation of saturation point prediction

4

Total average

Saturation point

1

2

3

4

Total average

MAD

1

2

3

99% 86%160%

61%105%

34% 20% 31% 29% 28%

1 2 3 4

Tota

l

aver

age

Standard model Pooled modelMAPE [%]

29 27 31 30 2915 13 17 16 15

1 2 3 4

Tota

l

aver

age

Standard model Pooled modelSaturation point [years of variation vs actual]

567 379 418

177 387

174 103 82 96 109

1 2 3 4

Tota

l

aver

age

Standard model Pooled modelMAD [kg]

Subgroups

Subgroups

Subgroups



163

The average Cement consumption per capita, also corresponding to the period

comprehended between the saturation point and 2013, shows a higher accuracy for the

pooled models. While the standard model registered a variation of 96% in average for all

the advanced economies in scope, pooled models registered a variation of only 19% (7.18).



164


As described in the Research methodology chapter (section 4.3.3), the process to assess

urban infrastructure deficits in developing countries required a series of computations

around two core successive steps. Firstly, the definition of reference values, and secondly

the economic resources required to narrow the gap on urban infrastructure levels.

5.4.1 Definition of reference values

Cement stocks and urban infrastructure levels

The construction of reference values required initially to confirm the relation between

cement stocks and the development of urban infrastructure following the claims of Aitcin

(2000), Deverell (2012), and Kang and Li (2013). To achieve this objective, we relied on

the results of the cross-sectional analysis addressed in the section 5.1.1. The high and

significant resulting correlations found between Cement stock per capita and the Urban

infrastructure variables confirmed the existence of a tight relation between urban

infrastructure levels and the historical cement consumption.

Figure 43. Cement stock per capita - urban infrastructure relation - 2013 logarithm form

The Figure 43 illustrates the relation between Cement stock per capita and the urban

infrastructures variables (Public-structure quality, Capital stock per capita and Population

living in slums) in its log form. The coefficient of determination (R2) of these relationships

-0.55954675

-0.375985405

-0.215646692

-0.069402236

-0.06405225

0.046185541

0.063310864

0.082535873

0.115539904

0.119756724

0.147014446

0.154178074

0.201862455

R² = 0.7052

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

-2.0 0.0 2.0

Pub

lic-s

truc

ture

q

ualit

y


R² = 0.7719

0.0

1.0

2.0

3.0

4.0

5.0

6.0

-2.0 0.0 2.0

R² = 0.5711

- 10 20 30 40 50 60 70 80 90

100

-2.0 0.0 2.0

Cement stock per capita Cement stock per capita

Cap

ital

sto

ckp

er c

apita

Po

pul

atio

n liv

ing

in s

lum

s



165

(0.7052, 0.7719 and 0.5711 respectively) are validated by the results of the cross-sectional

analysis, considering that correlation coefficients previously obtained (Table 14: 0.84, 8.88

and -0.76 respectively) must equal the R2 square root as follows (Public-structure quality

(0.84 = √0.7052); Capital stock per capita (0.88 = √0.7719) and Population living in

slums (-0.76 = √0.5711). We suggest that the existence of this relation validates the

possibility to infer the deficits levels on urban infrastructure through the shortages of

cumulative cement consumption (cement stock) in a particular country.

Reference values

The Figure 44 shows the Cement stock per capita levels required for a basic level of urban

infrastructure following the claims of Osenton (2004) in relation to saturation point and

Aitcin (2000), Deverell (2012), and Kang and Li (2013) in relation to basic infrastructure

achievement. As previously described in the research methodology, the reference value

relates to the minimum level of urban infrastructure needed in a country to deal with basic

society needs such as housing, sanitation, transport, health and education. The results

indicate that advanced economies built in average 13.7 tons of cement to reach a basic level

of urban infrastructure, with a Cement stock per capita currently (as per 2013 figures)

accounting for 31 tons.

Figure 44. Urban infrastructure basic level - Cement stock per capita reference value

Advanced at peak

31.023.1

14.2 13.76.5 1.8

7.211.9

Ad

van

ced

Hig

h

Up

per

med

ium

Bas

ic

Lo

wer

med

ium

Lo

w

Cement stock per capita 2013 [tons] - averageDeficit to basic urban infrastructure level



166

In average, lower-medium-income and low-income countries account for only 47.4% and

13.1% respectively of what is required to constitute a basic level of urban infrastructure.

The validity of the reference value (13.7 tons) in terms of representativeness was previously

addressed during the descriptive statistics results (section 5.1.2) by comparing its

coefficient of variation with that of the Cement stock per capita for all remaining countries.

While the variation coefficient for all countries (excluding advanced) accounted for 78%,

the advanced economies registered substantially lower variation values (28% at saturation

point and 22% as per 2013 figures) illustrating its homogeneity. As shown in the Table 18,

out of the 129 countries in scope, 76 countries suffer Cement stock per capita deficits,

however with different levels of severity. While Africa concentrates most countries with

highest deficit followed by Asia-Oceania (Fiji is the only country corresponding to

Oceania), Latin America appears to have a milder gap towards reaching a minimum level

of urban infrastructure with Haiti presenting the highest deficit in the region. We would

expect the poorest countries in the ex-Union of Soviet Socialist Republics previously

excluded (as per section 4.2.1) to be also part of the deficit pools in Europe (Eastern and

Northern) and Asian (Central and Western). Annex 7.19 offers a detailed list of each

country with Cement stock per capita deficit for a basic level of urban infrastructure.

Table 18. Quantity of countries by level of Cement stock per capita deficit and region

2013 Cement stock per capita deficit [tons] Total

Quantity

of

countries

per

region

[#]

Regions <3.5 3.5-7 7-10.5 >10.5

Africa 3 3 10 20 36

Asia-

Oceania

1 3 8 5 17

Europe 1 1

Latin

America

13 4 4 1 22

Total 17 11 22 26 76



167

As illustrated in the Table 19, reference values corresponding to the individual equivalence

subgroups were estimated as well. The relation between differences among the subgroups

and the characteristics of the subgroups’ members appear to be self-evident. For instance,

countries with a high β and high level of Cement consumption per capita at saturation point

(subgroup 1) account for a higher reference value (16 tons). Nevertheless, while we retain

these differences between subgroups to be relevant and therefore to be considered for the

conduction of granular analysis in a particular country, we rely on the advanced countries

total average (13.7 tons) for the initial estimation of urban infrastructure deficit. The

following paragraphs are aimed to describe the reasons behind this choice:

Firstly, the variation between the subgroups’ reference values is relatively low.

While the average is 13.7 tons, the extreme values are +2.3 tons and -2.7 tons.

Furthermore, from a long-term perspective angle, the values of the extreme

subgroups represent only 7.4% and 8.7% from the 2013 Cement stock per capita

average in advanced countries (31 tons).

Secondly, as the cluster-base model (Duncan et al., 2001) require calculating the

parameters β and Cement consumption per capita at saturation point, the use of

individual reference values by equivalence subgroup would require estimating the

β parameter and two potential scenarios for market saturation for each of the

hundred developing countries in scope.

As described above, the first and second points would increase substantially the

research efforts with a potentially subtle accuracy improvement on the deficit

assessment considering the estimative nature of this thesis’s objectives.

Nevertheless, in the section 5.5.2179, Case studies of developing countries, we

applied the model-based clustering methodology as implementation guidance for

the reader while verifying and discussing its outcomes.



168

Table 19. Reference values by equivalence subgroup

Equivalence subgroup Beta (as

per

model-

based

clustering)

Reference

values (basic

urban

infrastructure

level)

# Countries

1 Switzerland, Austria, Greece, and

Portugal.

~75-~100 16

2 Slovenia, Japan, Germany, France, Italy,

Belgium and Israel.

~55-~75 14

3 United Kingdom, Netherlands, Sweden,

Denmark, Finland and Norway.

~35-~55 11

4 New Zealand, Canada, Australia and

United States.

~15-~35 13

Total - 13.7

5.4.2 Estimation of economic cost

The resulting economic cost estimations to narrow the housing and public-structure deficits

in developing countries are alarming. For instance, many Sub-Saharan countries would

need to invest over 7 times their GDP to achieve a minimum functional level of urban

infrastructure. In the Annex 7.19, a detailed list of per capita and total values is provided

with estimations of construction cost for each country accounting for Cement stock per

capita levels below reference value.

The results were obtained by answering the two questions designed and presented in the

research methodology section 4.3.3.2, Estimation of economic cost. Firstly, we answered

how much can be built with the cement stock per capita gap, and secondly, we moved

towards how much would those constructions cost. For residential and non-residential

segments, we focused on the construction of low-budget building typology covering



169

primarily housing needs but in turns also representing commercial constructions such retail

stores and small care centers. For the public-structure segment, we focused on road building

considering it takes most of the resources required in terms of cement consumption. The

construction cost of other public-structure pieces such as power generation plants and

telecommunications network are excluded as previously described during the development

of the research methodology sections. In the following paragraphs, we provide an

aggregated perspective by construction segment and a detailed description of the results at

region and country level.

Residential and non-residential segments:

In line with Millennium Development Goals Report published by United Nations in 2015,

we found the current (2013) deficit in developing countries to be very high and thus

alarming. As per the estimations resulting from our methodology, narrowing the residential

and non-residential deficit would cost USD ~25.9 trillion, ~12.9 into housing USD ~12.9

trillion allocated to the non-residential segment. Although these figures appear to be

massive at first sight, we found support for our housing estimation on the work of Woetzel

et al. (2014) estimating the total economic resources to narrow the housing gap in

developing markets to be circa USD 16 trillion (Figure 45). We consider our results and

Woetzel et al. (2014) findings to be similar, with the USD ~ 3 trillion difference potentially

explained through diverse computation assumption and by the following scope-elements:

China: Whereas under our estimations, China appears to have reached a minimum

level of urban infrastructure with a Cement stock per capita of ~20 tons in 2013 (~6

tons over the reference value), Woetzel et al. (2014) still considers many additional

housing units required.

Union of Soviet Socialist Republics: Our estimations exclude most of the countries

corresponding to the ex-Union of Soviet Socialist Republics as described

previously, with Russia potentially accounting with the highest impact.

Although Woetzel et al. (2014) claims that most of the global housing deficits are

driven by shortages developing countries, their estimations also include a small



170

portion corresponding to the lack of affordability in some advanced economies in

terms of financial cost.

Public-structure segment:

Following the claims Samans, Blanke and Corrigan (2017) and International Monetary

Fund in its World Economic Outlook (2014) on patterns of capital formation cycle, we

allocated a substantial portion of the cement gap to the development of public-structure.

As in the residential and non-residential case, the estimated economic resources required

to mitigate the deficits are staggering. Our results indicate that a USD 24.7 trillion

investment would be required in the long-term.

We found support for our findings through the work of Bughin, Manyika and Woetzel

(2016) on global public-structure gaps estimating that USD 14.4 trillion (USD 13.1 trillion

in 2013 prices) would be required during the fifteen-year period between 2016 and 2030,

excluding China. We found our results in line to those of Bughin, Manyika and Woetzel

(2016) when observed in an annual base (Figure 45). For instance, while Bughin, Manyika

and Woetzel (2016) annual investment cost accounts for USD 874 billion, our total results

account for USD 823 billion annually spread in a thirty- year period. We decided to

annualize the total investment required using a thirty-year period considering the historical

capital formation cycle undergone in advanced countries. In other words, as most advanced

countries reached the peak of their capital formation cycle in the thirty-year corresponding

to the period between the second world war and the mid-1970, we considered a similar

cycle for developing countries. We consider that the annual differences of USD 51 billion

are most likely driven by particular pieces of public-structure excluded from the scope of

this research as addressed previously.



171

Figure 45. Urban infrastructure deficit – Comparison between results and literature

review

Country level

The Figure 46 illustrates in a heatmap the expected deficit concentration in the poorest

regions, particularly in the Sub-Saharan Africa, South Asia and Caribbean (i.e. Haiti). The

deficit indicates these countries have failed so far to reach the minimum levels of urban

infrastructure needed to deal with basic society necessities such as housing, sanitation,

transport, health and education. In the scope of this research, the definition of the minimum

level of urban infrastructure is linked in a prescriptive fashion to the cumulative cement

consumption as per (Osenton, 2004) findings in terms of saturation point and Aitcin (2000),

Deverell (2012), and Kang and Li (2013) claims in relation to basic urban infrastructure

achievement.

However, the tangible social and economic consequences of these cement stock shortages,

and consequently urban infrastructure deficits are devastating World Bank (1994), Canning

(1998), Todaro and smith (2012), Srinivasu and Rao (2013), United Nations (2015) and the

International Monetary Fund (2017). The following paragraphs address specific country

examples per regions to illustrate the significance of urban infrastructure deficits in the

context of the economic possibilities. More granular details are provided in the next section,

Developing countries – Nigeria’s case study (5.5).

Literature review

Results

Literature review

12.916.0

Res

ult

s

Lit

erat

ure

rev

iew

Housing - USD trillion

823.0 874.0

Res

ult

s

Lit

erat

ure

revie

w


annualized



172

Figure 46. Urban infrastructure deficit in developing countries [2013 - USD per capita]

Extreme deficit: The thirty-three countries colored in black appear to require an

investment of USD 13’000 to 20’000 per capita in housing, non-residential and

public-structure as to reach a minimum level of urban infrastructure. As expected,

these countries are concentrated in the poorest regions, particularly in the Sub-

Saharan Africa, South Asia and Caribbean (i.e. Haiti). As illustrated in the Figure

47, in average of per capita values, the economic resources to narrow the urban

infrastructure deficits in these extreme deficit countries account for USD 17’442.

While the annualized value considering a thirty-year period (USD 581) is certainly

lower, it still represents ~23% of the average GDP per capita in these countries. To

put this finding in context, as Samans, Blanke and Corrigan (2017) claimed, while

advanced countries investments in public-structure peaked at 10-15% of GDP

during the periods of capital formation, public investment usually accounts for only

5%-10%. Considering that the public-structure portion of the current annualized

deficit accounts for approximately half of total, these countries would be required

to maintain very high levels of public investment as percentage of GDP (11.5%)

during a period of 30 years to reach a minimum level of public-structure.

Nevertheless, in ideal conditions, as these countries develop economically, a transit

to lower-medium income and medium income would take place reducing the

relevance of public works over public budgets. In a decreasing order of severity,



173

countries with extreme urban infrastructure deficit are: Burundi, Rwanda, Niger,

Malawi, Ethiopia, Democratic Republic of Congo, Uganda, Burkina Faso,

Bangladesh, Madagascar, Somalia, Nepal, Myanmar, Mali, Tanzania, Guinea,

Haiti, Sierra Leone, Sudan, Mozambique, Afghanistan, Kenya, Cambodia,

Cameroon, Liberia, Nigeria, Pakistan, India, Zambia, Mauritania, Angola, Benin

and Cote D’Ivoire.

Figure 47. Economic value of urban infrastructure - extreme deficit (average)

High deficit: We observed twenty-four countries accounting for urban

infrastructure deficits of USD 6’666 to 13’333 across Latin America, Southern-

Europe, Africa, South/Easter-Asia and Oceania (i.e. Fiji) regions. As illustrated in

Figure 48, in average of per capita values, the economic resources to narrow the

urban infrastructure deficits account for USD 11’264. The annualized value at USD

375 (considering a thirty-year period) is certainly lower than that of the extreme

deficit cases, however it still represents of 5% of GDP per capita. While this ratio

tends to be achievable following the Samans, Blanke and Corrigan (2017) claims

described previously, it might be certainly limited by potential inefficient public

policies driving taxation and expenditures (Hassler, Storesletten and Zilibotti

(2007). As with the extreme deficit countries, we would expect the burden of urban

infrastructure investment (both public and private) to get lighter as these countries

develop economically transiting to upper-medium-income and high-income levels.

Deficit annualized

GDP

17'442

5812'554

Def

icit

tota

l

Def

icit

ann

ual

ized

GD

P

Extreme deficit [USD per capita]



174

In a decreasing order of severity, countries with high urban infrastructure deficit

are: Indonesia, Togo, Congo, Senegal, Paraguay, Laos, Zimbabwe, Sri Lanka,

Ghana, Bolivia, Nicaragua, Fiji, Mongolia, Philippines, Guatemala, Honduras,

Yemen, Vietnam, El Salvador, Bhutan, Namibia, Bosnia and Herzegovina, Peru

and Botswana.

Figure 48. Economic value of urban infrastructure - high deficit (average)

Moderated deficit: The nineteen countries with a moderated deficit (USD less than

6’666 per capita) to reach a basic urban infrastructure level are mainly present in

Latin America, with a few exceptions in Africa and South-East Asia. Although we

called this group moderated, it does contain a broad spectrum of deficit levels.

Whereas Colombia and Djibouti still appear to hold a substantial deficit, Uruguay

and Argentina are a few cement kilograms away from reaching a basic level of

urban infrastructure. As can be observed from the Figure 49, the annualized figure

in a thirty-year period appear to be only a neglectable fraction of the GDP per capita.

However, considering that many of these countries might be already entering a final

stage of capital formation, the application of a shorter period for annualization

should be considered. For instance, if a hypothetical further 10-year growth period

is applied (instead of the 30-year mentioned earlier), the annualized deficit would

increase as a percentage of GDP to ~2%. In a decreasing order of severity, countries

with moderated urban infrastructure deficit are: Colombia, Djibouti, Brazil,

Deficit annualized

GDP

11'264

3757'090

Def

icit

tota

l

Def

icit

ann

ual

iz

ed GD

P

High deficit [USD per capita]



175

Ecuador, Gabon, Jamaica, Cuba, Suriname, Dominican Republic, Belize, Costa

Rica, Cabo Verde, Chile, Thailand, Bahamas, Mexico, Morocco, Uruguay and

Argentina.

Figure 49. Economic value of urban infrastructure moderated deficit (average)

A final consideration in terms of the magnitude of urban infrastructure deficit is required.

In the previous paragraphs we annualized the urban infrastructure deficit values through

hypothetical length of capital formation periods following the claims of Samans, Blanke

and Corrigan (2017). However, it is important to notice that its stable comparison with

GDP figures should be taken with caution. The role of many dynamic elements such as

stage of economic development, wealth distribution, political stability and corruption are

expected to drive the economic development and thus the formation of capital. In the words

of Piketty (2013), “economic growth is a multidimensional process whose very nature

makes it impossible to sum up properly with a single monetary index”.

5.4.3 Additional cement capacity – Industry economics

Through the following paragraphs, we provide an estimation of the additional cement

capacity requirements and the economic impact derived from the incremental supply

needed to narrow the urban infrastructure deficit to basic levels.

Deficit annualized

GDP

2'255 75

14'406

Def

icit

tota

l

Def

icit

annu

aliz

ed GD

P

Moderated deficit [USD per capita]



176

Production capacity requirements

As per 2013 figures, we found that in total 33’435 million tons of cement would be required

globally to achieve a minimum level of urban infrastructure in developing markets. When

annualized in a thirty-year period, additional 1’115 million should consumed by these

countries per annum. The Global Cement Report (2015) informed that the cement industry

installed capacity is 5’695 million tons a year (2014 figures), which as indicated in the

Figure 50, would appear to cover the incremental volumes to the naked eye. However, the

following elements could contribute to a substantial supply and demand unbalance:

Nominal vs real capacity: Alsop (2005) assumes a 90% annual run factor to

calculate a cement plant real production capacity affected mainly by programmed

maintenance stops. Other sources stablish the standard expected run time at 85%

(source: Holcim’s Accounting and Reporting Principles’ manual). Consequently,

the current 5’695 tons global nominal capacity indicates that the industry would be

able to produce 4’357 to 4’632 and thus unable to meet the increasing demand.

Reserves depletion: In addition to the technical aspects related to the production

capacity frontiers, the availability of raw materials (i.e. limestone) appears to be a

limiting factor to supply incremental volume. While limestone occurrence is

frequent covering 1.5% of earth crust (Kolb, 2001 cited in Alsop, 2005), a cement

plant life-span is limited to the quantity of limestone available in its deposit (Alsop,

2005). For instance, cement producer Lafarge informed to have thirty-two years of

proven and probable reserves in Asia at 2013-sales volume (Source: Lafarge, 2014).

An increment in sales of 25% as to proportionally cope with the demand growth

would reduce the reserves to 23 years (~1/3 less of the 30-year assumed period).

Distribution: As described in the section Economics of the cement business (2.1.5),

due to its bulky nature, cement distribution costs limit the size of efficient plants

despite the presence of economies of scale (Porter, 1980). Considering that a typical

a cement plant competitive radius is no more than 300km (LafargeHolcim, 2015),

the supplying of additional 1’115 million tons of cement would likely require the



177

construction of new plants, particularly in the African land-locked countries such

Botswana, Zimbabwe, Zambia, Democratic Republic of Congo, Uganda, Central

African Republic, Chad, Niger and Burkina Faso.

Growth: Although the incremental volume of 1’115 million tons of cement per

annum is the result of annualizing the total cement gap to a thirty-year period, the

evolution of the cement demand function in developing countries is expected so

behave similarly to that of advanced countries during their development stages.

Therefore, the annual cement consumption at saturation point is expected to be

approximately twice the average consumption reaching ~2’230 million tons.

Pollution: The cement industry is considered one of the largest contributor to CO2.

Gartner (2004) estimates that CO2 volumes emitted by cement manufacturers are

as high as 5% of total global anthropogenic CO2 emissions (740 kg of CO2 per ton

of cement produced). While the cement industry is currently addressing this matter

with the incorporation of substitutes such as blast-furnace slag, fly-ashes and

pozzolana, product quality specifications limit their potential (Gartner, 2004).

Therefore, the incremental cement volumes required to narrow the urban

infrastructure deficit in developing countries is expected encourage (and demand)

new production technologies to limit the environmental impact.

Figure 50. Potential resulting cement supply and demand balance

Annualized deficit

Potential consumption

Spare production capacity

4'0334'033 5'148 5'148 5'695

1'115548

Cu

rren

t

con

sum

pti

on

An

nu

aliz

ed

def

icit

Po

ten

tial

con

sum

pti

on

Sp

are

pro

du

ctio

n

cap

acit

y

Cu

rren

t

pro

du

ctio

n

cap

acit

y

(20

14

)

Cement million tons (annual - based on 2013 figures)



178

Economic impact

The incremental volumes are expected to expand the cement market size by USD ~117

billion annually (considering an average cement price of 105 USD/t described the

Economics of the cement business section, 2.1.5) generating simultaneously substantial

opportunities for cement producer’s companies, related industries such as transportation,

and public administrations through corporate taxation. The current limited production

capacity would also require the construction of hundreds of new plants to satisfy the

increasing demand. For instance, in addition to the limitations described in the previous

paragraphs, if the recent (2014 value) level of industry utilization of 73% (source: Global

Cement Report, 2015) is applied to the required new supply in developing countries (1’115

million), the industry capacity should increase production capacity to ~1’527 million tons

(1’115 million tons/0.73). The considerations of the industry overcapacity relate to the

intention of mimicking the current supply and demand balance to cover a likely scenario

in which overcapacity (potentially systematic) persists. Considering a plant construction

cost of 150-200 USD/ton of production (Alsop, 2005), the additional capacity would

require investments for USD 229 to 305 billion, generating thousands of new jobs and

value generation for equipment producers (e.g. FLSmith), plant erection companies (e.g.

Alberici) and logistic operators.



179

Developing countries – Nigeria’s case study

Through a detailed cement industry review, Nigeria’s case study was aimed to condensate

the knowledge expansion and methodological results generated during the length of this

research as described in the section 4.3.4. In the following paragraphs, we provide an

overall context and a diagnose of Nigeria’s urban infrastructure extreme deficit together

with a description of the potential development paths. Nigeria was chosen among other

developing countries for a deep case study due to three specific factors around its potential

growth profile. Firstly, with approximately 44% of its population aged under fourteen and

with a total average age of 19 years old, Nigeria has one of the largest populations of youth

in the world. Secondly, the urban infrastructure deficit in Nigeria is alarming with the

majority of the population living in inadequate houses and almost non-existing public-

structures. Thirdly, in contrast with many other developing countries, Nigeria’s abundance

of natural resources (e.g. Africa’s biggest oil exporter) could support its long-term

economic growth during its transition to a Middle-High income economy (source: World

Bank, 2017).

5.5.1 Social, political and economic background:

As per the World Bank’s 2013 classification, Nigeria is a lower-middle-income country

and the most populated country in Africa with approximately 172 million people (2013).

Figure 51. Nigerian suburbs in Lagos (source: AMP-Pinterest, retrieved in 2017)



180

Despite its current high infant mortality rate, the population is expected to grow over 2%

per annum reaching circa 180 million by 2020 and 411 million by 2050. While Nigerian’s

are divided in several ethnic groups (~250), disruptive disputes are seldom as the three

main groups account for most of the population (Hausa-Fulani 29%, Yoruba 21% and Igbo

group 18%). As per 2013, over 50% or Nigerian urban population lives in slums and only

10-20% of total countries population live in an adequate house (sources: CIA World Facts

Book and United Nation's Millennium Development Goals database, 2014). About 50.4%

of Nigerians follow the Islam and concentrate in the north. Christians are the second largest

religious group (48.2%) of the population and locate in Nigeria’s middle belt and southern

part of the country. The increasing violence of terrorist group Boko Haram’s attacks is a

main source of concern for the government which is currently perceived to be incompetent

to engage in a gradual elimination (Walker, 2014).

The fifth consecutive national elections, held in 2015 were the first peaceful transfer of

power between two opposed political parties in Nigeria’s history. The new administration

led by President Muhammadu Buhari, promised to focus on improving the Nigerian’s

living standards through fighting corruption, security matters, tackling unemployment and

economy diversification (source: World Bank, 2017). Although Nigerian regulatory

environment is considered highly autocratic mainly due to the influence of corruption,

latest surveys inform an increasing improvement in the legal system as businesses report

faster contract enforcement at lower costs. Nigerian’s high courts appear to be more

independent and consequently less prompt to corrupted rulings than the lower courts.

Nigerian commercial laws prohibit the expropriation or nationalization of foreign

companies and focuses on the protection of private property. On the other hand, Nigeria

accounts for one of the world’s least efficient and bureaucratic systems for property

registration in the world (source: Business Monitor International, 2011-2017 and

Economist Intelligence Unit, 2011).

As per 2016 figures, Nigeria is the 13th oil producer in the world and has the 11th largest

reserve (source: United States Energy Information Administration, Retrieved 27 May

2017). The country is currently in an attractive position to take advantage of foreign direct

investment due multiple reforms including the government’s strong commitment to foster



181

the private sector. Mainly driven by the fall in oil prices in mid-2014, Nigerian economy

grew by 2.7% in 2015, substantially below the 6.3% growth in 2014 and the sustained

growth of 6.6% from 2003 to 2013. During 2017, Nigeria’s economy is expected to grow

by about 1% and 2.5% in 2018 as oil output increases back to normal levels and the

accelerated implementation of public and social investment projects materialize (source:

World Bank, 2017).

5.5.2 Nigerian’s extreme urban infrastructure deficit

Nigeria's urban infrastructure development potential has long been known, however limited

due to a chronic lack of investment and high-risk business environment (source: Business

Monitor International, 2017). Nigeria’s urban infrastructure metrics indicates very low

levels of development (Figure 52). As per 2013 figures, Nigeria’s Cement stock per capita

lagged 10.7 tons behind the reference value to achieve a basic level of urban infrastructure.

Although to estimate Nigeria’s urban infrastructure deficit we used the reference value of

13.7 tons for the multiple reasons summarized in the section 5.4.1, a more granular

approach would indicate an even larger gap in terms of Cement stock per capita. The

average Cement stock per capita reached at saturation point in subgroup 1, Nigeria’s

matching subgroup (indicated and developed in the next section), accounted for 16 tons

(Table 19) and thus enlarging the gap to from 10.7 to 13 tons.

Figure 52. Comparison of Nigeria and advanced economies average - selected urban

infrastructure metrics (2013)

Qualita

Capital stock ['000 USD]

Cement stock

per capita [tons]

31

3.0

Ad

van

ced

Nig

eria

8.1

3.3

Ad

van

ced

Nig

eria

95

.1

5.7

Ad

van

ced

Nig

eria

Infrastructure

quality [1-10 scale]

reference value 13.7

Capital stock

['000 USD]



182

Whereas its Public-structure quality level accounts for almost one third of that of advanced

countries, its Capital stock per capita accounts for an insignificant USD 5’700 as compared

with the USD 95’100 in the advanced economies. These shortages on the cumulative

cement consumption translate in the need for substantial investments in the future to narrow

the current urban infrastructure alarming deficits. According to our estimations, Nigeria

would require a total expenditure of USD ~707 billion for housing development and USD

~1.3 trillion for public-structure development to improve current sub-standards. We found

support to our results in the work of the Olotuah and Taiwo (2015) for the estimations of

housing cost and the African Development Bank (2012) in relation to the public-structure

requirements (Figure 53). In line with CIA World Facts Book, Olotuah and Taiwo (2015)

claim that 75% of Nigeria’s population (corresponding to 129 million people) live in

substandard houses subject to deplorable conditions and an insanitary environment. We

estimated that the required resources to build 43 million houses (at three people per 50-

square meter house) would account for USD 757.5 billion (as per a construction cost of

USD 352 per square meter following the assumption previously described in section

4.3.3.2). We found this value similar to our estimations of USD 707 billon resulted from

the implementation of our cumulative consumption based methodology.

Figure 53. Nigerian urban infrastructure deficit – Comparison between results and

literature review (2013)

Literature review

Infrastructure

Results

Literature review

707.0 757.5

Res

ult

s

Lit

erat

ure

revie

w

Housing - USD billion

45.454.5

Res

ult

s

Lit

erat

ure

rev

iew


annualized



183

Following a mandate of Nigerian’s government, the African Development Bank

commenced in 2010 a detailed status assessment in key Nigerian infrastructural sectors and

their role within the West Africa region. As illustrated in the Figure 52, two years later the

African Development Bank (2012) suggested an investment of USD 448 billion (USD

545.34 billion in 2013 prices) for the 10-year period commencing in 2011, corresponding

to an annual investment of USD 54.5 billion in 2013 prices. The annualized value of our

estimations accounts for a required annual investment of USD 45.4 billion, considering a

thirty-year period as per the claims of Samans, Blanke and Corrigan (2015) on capital

formation previously described in section 4.3.3.2. While these figures appear to be similar,

the differences could be based on the broader coverage of the African Development Bank

(2012) including investments related to electric power generation and telecommunications

as well.

5.5.3 Potential growth of Nigerian cement demand

The implementation of our cement long-term forecasting model was executed following

the guidelines described the Research methodology section 4.3.4: Prescription of

equivalence subgroup, definition of economic growth scenarios and generation of long-

term cement consumption forecast.

5.5.3.1 Prescription of equivalence groups

The first step towards the implementation of our cement long-term forecasting model to

assess the potential future developments of the Nigerian cement market was to identify its

equivalence sub-group (Duncan et al., 2001). As indicated in the Annex 7.20, Nigeria’s β

coefficient of 81.78 (additional kg of Cement consumption per capita per USD 1’000

increases in GDP per capita Constant) appears to correspond to the growth pattern of

prescriptive subgroup 1.

5.5.3.2 Definition of long-term economic growth scenarios

We developed three different economic growth scenarios (High, Base and Low growth) to

provide the independent inputs (GDP per capita Constant) to the pooled model subgroup 1

referencing our assumptions to the 6.6 % annual growth registered during the 10-year

period corresponding to 2003-2013. Considering the estimative nature of our model, we



184

assumed a band contained by high and low scenarios from a potential 6.0% base of annual

growth as follows:

High economic growth scenario: 8.0% per annum

Base economic growth scenario: 6.0% per annum

Low economic growth scenario: 4.0% per annum

We retained relevant to clarify that our aim during this exercise was not to predict the long-

term economic growth in Nigeria, but rather to understand the cement demand behavior

under different economic growth assumptions. For indicative purposes, while Nigerian

economy grew by 2.7% in 2015 and 6.3% in 2014, the World Bank indicated an expected

1.0% growth in 2017 and 2.5% in 2018 driven by the increase of oil output and the

implementation of public investment.

5.5.3.3 Long-term cement consumption forecast

Three long-term Cement consumption per capita forecasts were generated using the pooled

model prescription for the sub-group 1 and the economic growth scenarios (independent

inputs - GDP per capita Constant) developed in the previous step. We focused on the

observation of four main metrics at saturation point and compared them with the historical

reference of the advanced countries conforming the subgroup 1. The focus metrics are:

Years of cement consumption per capita growth until saturation point; and the Cement

consumption per capita, the Cement stock per capita and GDP per capita Constant at

saturation point.

High economic growth scenario

In the High economic growth scenario, the Cement consumption per capita expects 27

years of sustained growth (2014-2041) until its saturation point reaching a Cement stock

per capita of 18 tons. At saturation point, Nigeria’s GDP per capita constant accounts for

USD 17’200 and a Cement consumption per capita of 1’001 kg.



185

Figure 54. Nigeria’s Cement consumption per capita and stock forecast at GDP per

capita Constant growth of 8.0% per annum

It is importance to notice that since the pooled model is based on a polynomial regression

grade 2, after a certain level of GDP per capita Constant, a manual adjustment is required

to reflect maintenance level-mode of consumption.

Base economic growth scenario

In the Base economic growth scenario, the Cement consumption per capita expects 37 years

of sustained growth (2014-2051) until its saturation point reaching a Cement stock per

capita of 24 tons. At saturation point, Nigeria’s GDP per capita constant accounts for USD

18’300 and a Cement consumption per capita of 1’002 kg.

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0

200

400

600

800

1000

1200

0 10 20 30 40 50

Historical consumption per capita Forecast consumption per capitaHistorical stock per capita Forecast stock per capita


Consumption per capita [kg] Stock per capita [tons]



186



Low economic growth scenario

Under this scenario, the cement saturation point is reached in the year 2063 after 49 years

of sustained growth reaching a GDP of USD 18’700, a Cement stock per capita of 35 tons

and Cement consumption per capita of 1’000 kg.



0.0

5.0

10.0

15.0

20.0

25.0

30.0

0

200

400

600

800

1000

1200

0 5 10 15 20 25




0.0

5.0

10.0

15.0

20.0

25.0

0

100

200

300

400

500

600

700

800

900

0 2 4 6 8 10 12






187

5.5.4 Implications for the Nigerian cement industry

As observed in the Table 20 comparing the three different forecasting scenarios (High-

Base-Low) in perspective of the subgroup 1 observed average values, Nigeria’s High

economic growth scenario appears to relate the most to subgroup 1 historical values

Table 20. Comparison of Nigerian forecasted scenarios and subgroup 1

Forecast Time length to

peak [years] –

for subgroup 1

commencing in

1945

At saturation point

Stock per

capita [tons]

Consumption

per capita [kg]

GDP per capita

Constant

[USD]

Subgroup 1 43.25 16 897 15’000

Nigeria-High 27 18 1’001 17’200

Nigeria-Base 37 24 1’002 18’300

Nigeria-Low 49 35 1’000 18’700

While we reckon the Nigerian long-term future development to be unknown and difficult

to estimate, we opted to utilize the outcomes of the High economic growth scenario to

simulate and discussed the required increase in production capacity. In the following

paragraphs, we provide a brief description of 2013 Nigerian cement competitive landscape

(industry - market structure) and its potential development to cope with a growing cement

demand scenario.

Nigerian market structure

Nigeria’s cement market is divided in five regions: South-West, South-East, North-Central,

North West and North East. While most of Nigeria’s wealth (~65% of GDP) is concentrated

in the South-West and South-East regions, the North Central and North East are considered

the less developed regions accounting for only 20% of GDP. By 2011, industry analysts’

consensus indicated a likely demand growth rate of ~8% per annum until 2020, with slight

higher growth expected in the South-West, South-East and North Central regions. Nigeria



188

cement is mainly bought from distributors, approximately 90% of the market, with the

remaining volume accounting for retailers and direct sales. The low cement demand share

of non-residential segment (17%) and public-structure projects (8% of total) appears to be

related to the Nigerian stage of economic development (Samans, Blanke and Corrigan,

2015), (source: Unicem, 2011, Business Monitor International 2011).

Nigerian cement industry

The Nigerian cement industry is consolidated with three companies serving approximately

three quarters of the total market (as per 2013 data). While Nigerian installed capacity

historically relayed on imports to cope with the growing local consumption, 2013 appeared

to be the first year of almost exclusive local production supply. With a 47% market share,

the Nigerian cement industry is led by Dangote Group, the largest cement producer in

Africa. Dangote is present in the South-West and North-Central regions however has

publicly announced its intentions for expansions in recent years. LafargeHolcim, Nigeria’s

second largest company, operates in the South-West, South-East and North-East. The

remaining supply base is completed by small producers such as the Empee Group

(Purechem plant) and the BUA Group (source: Unicem, 2011).

Nigerian supply and demand balance in a high economic growth scenario

We analyzed the current (2013) and potential supply and demand balance for the high

growth scenario at saturation point (2041) factoring Nigerian’s population growth.

According to the United Nations (2017), Nigerian’s population by 2041 is expected to

reach ~333 million people. Under the high economic growth scenario, Nigerian

Consumption per capita was estimated at 1’001 kg, which corresponds to a total demand

of 333.3 million tons per annum (Figure 57). To cope with this growing market, the

Nigerian cement industry would be required to install additionally ~12.4 million tons of

capacity every year, corresponding to investments of 1.9 to 2.5 USD billion in production

capacity only (Alsop, 2005) and many others activity-related opportunities.



189

Figure 57. Nigerian cement supply and demand balance in 2013 and 2041

Capacity 2013

Under capacity 2041

Potential consumption 2041

21.2 21.1

333.3312

Co

nsu

mp

tio

n

20

13

Cap

acit

y

20

13

Un

der

cap

acit

y

20

41

Po

ten

tial

con

sum

pti

on

20

41

Cement supply and demand balance: 2013 and 2041

[million tons]



190

6 CONCLUSIONS, LIMITATIONS & FURTHER DEVELOPMENTS

General scope

The many consequences of precarious urban infrastructure development in developing

countries are tremendous. Among many others, inadequate housing in large urban centers

often relate to serious diseases ranging from bronchitis to cholera outbreaks. While the use

of rudimentary cooking fuels in combination with lack of proper ventilated houses can

derive in chronic and acute bronchitis, the constant exposure to untreated sewage running

open increases the risks of virulent infections (Todaro and Smith, 2012). Although the

population living in developing countries’ slums fell in fifteen years to ~30% in 2014 from

~39% (as percentage of total), its absolute numbers continue an alarming growth driven by

high urbanization rates and population growth in developing countries (United Nations,

2015). More than two decades ago, the World Bank (1994) warned about the lack of access

to clean water and sanitation in the developing world together with many other basic

public-structure shortages (one billion and two billion people affected respectively). As in

the case of slum population absolute increase mentioned above, the number of people who

still do not have access to improved sanitation facilities grew to 25% reaching 2.5 billion

despite public efforts (World Health Organization, 2013) driven by urbanization processes

and population growth. Narrowing the urban infrastructure deficit gap appears to be a

challenge of monumental proportions, particularly due to financing constraints. The world

needs more public-structure than governments can deliver estimating USD 57 trillion

required to build new public-structure in the developing world and refurbish some of the

existing pieces in the advanced economies between 2013 and 2030 (Green et al., 2015; and

Maier, 2015).

6.1.1 Conclusions

Through posing a set of focused research questions and the design of an appropriate

methodology to address them systematically, we explored the elements that support the

diagnose of urban infrastructure deficit in developing countries, particularly as a function

of the cumulative consumption of cement. Throughout this thesis, we also managed to

verify the suitability of a valid long-term forecasting methodology for cement consumption

(Table 21). We expect our findings to be useful for public administrations and the private



191

section to diagnose and plan the allocation of resources to narrow the increasing urban

infrastructure deficit in developing countries.

Table 21. Summary of research questions, related elements and results

Research

questions

Theoretical

framework / Main

contributors

Research

methodology

Results

RQ1: Are the

pattern regimes

of long-term

cement demand

per capita

similar in

advanced

countries?

Analogy and reference

class forecasting:

Kahneman and

Tversky (1977) and


Exploratory

analysis: cross-

sectional,

descriptive

statistics analysis

and observation

of plotted

patterns.

Verification of pattern

similarity and

consumption cycle

completion (growth-

saturation-decline-

maintenance) in

advanced economies.

RQ2.a: What is

the natural

consumption

level for the

cement demand

in different

countries?

Natural consumption

levels: Osenton

(2004), and Palacios

Fenech and Tellis

(2016).

Exploratory and

explanatory

analysis: cross-

sectional,

descriptive

statistics analysis.

Implementation

of reference

values

methodology and

cement building

yield /

construction cost

by segment.

We were able to

identify what appears

to be a saturation

point in the cement

demand (13.7 tons per

capita), also what are

the economic

implications for

countries with

substantial cement

consumption deficits.

RQ2.b: How

does the cement

natural

consumption

level relates to

urban contextual

factors?

Urban infrastructure:

Todaro and Smith

(2012), Canning

(1998), Aitcin (2000),

Deverell (2012), Kang

and Li (2013), Hung

and Wu (1997) and

Birshan et al. (2015).

RQ3: How does

economic

growth

influence the

cement demand

in the long

term?

Analogy forecasting:


Cement and economic

growth: Aitcin (2000),

and Samans, Blanke

and Corrigan (2015)

especially for capital

formation.

Formation of

equivalence

groups by

implementing

Bayesian pooling.

Verification of the

pooled model(s)

adequacy to cement

long-term forecasting

through independent

inputs (GDP per

capita Constant).



192

6.1.2 Limitations and further development

While in the following sections (6.2 and 6.3) we provide a set of concluding remarks in

combination with the limitations of our research methodology and potential future lines of

research development, the following paragraphs described the general limitations posed by

the exclusion of countries (section 4.2.1) and potential lines of further developments.

Lack of relevant data: As previously address during the length of this thesis, a set

of countries was excluded due to the absence of critical data comprehending

particularly but not only the ex-Union of Soviet Socialist Republics or largely

influenced counties (Armenia, Azerbaijan, Belarus, Estonia, Georgia, Latvia,

Lithuania, Moldova, Kazakhstan, Kyrgyzstan, Russia, Tajikistan, Turkmenistan,

Ukraine, Uzbekistan, Czech Republic and Slovakia). We thus see potential for

substantial expansion of the currently gathered knowledge through the

incorporation of historical data (specifically Cement consumption per capita and

GDP per capita Constant) of these countries. While this endeavor would likely

require a thorough and dedicated revision effort on the ex-Union of Soviet Socialist

Republics public archives, the collection of this information is expected to allow

for: The creation of new sub-groups increasing the prescriptive capacity of our

models; and/or the possibility to forecast long-term cement demand development

in the developing countries contained in this group through our models.

Abnormal cement consumption: Within the countries excluded for abnormal

cement consumption, we consider the possibility of those driven by superfluous

level of cement consumption to be part of separate group (Brunei, Bahrain, Kuwait,

Oman, Qatar, Saudi Arabia and United Arab Emirates). The data collection and

further close examination of the cement functions and economic development of

these country could provide deeper understanding on urban infrastructure

development related mainly to economies of fascination. While our research

focused primarily on the potential urban infrastructure growth of developing

countries, we retain that a better understanding of the characteristics and dynamics

of superfluous level of cement consumption would provide new insights, most

likely unexplored yet following our literature review.



193


6.2.1 Conclusions

Through the implementation of our research methodology we were able to validate the

adequacy of forecasting by analogy to predict the potential cement consumption

development in developing countries (Kahneman and Tversky, 1977 and Duncan et al.,

2001). Our methods appear to tackle the problematic of cement long-term forecasting

raised by Hung and Wu (1997) and Birshan et al. (2015) leveraging on the initial findings

of Aitcin (2000) regarding economic development and cement consumption relation. Once

the presence of analogous times-series within the advanced countries (Cement

consumption per capita and GDP per capita) was verified, we formed four equivalence

subgroups using the correlational co-movement, expert judgment and model-based

clustering approach suggested by Duncan et al. (2001). We used these equivalence

subgroups to build four prescriptive forecasting models and tested their accuracy against

the actual values and those predicted by a standard forecasting models in advanced

economies (section 5.3). The results were highly promising as the growth prediction

forecasted by the pooled models were substantially more accurate than those of the standard

models in all the controlled metrics: MAD, MSE, MAPE, variation on the average Cement

consumption per capita and variation of saturation point (in year). For instance, while the

MAPE for the standard model was a high 105%, the pooled model reached an accuracy of

28% for the period corresponding to the saturation point and 2013. The forecasting

accuracy of the prescriptive equivalence subgroups demonstrated not only the value of

relying on distributional information (Kahneman and Tversky, 1977), but also the high

flexibility in terms of scenario building and simplicity of application (Duncan et al., 2001).

The convenience and flexibility of our methodology was illustrated through the

development of the Nigerian case study (section 5.5.3), in which the cement demand

growth was assessed under three different economic growth scenarios. Based on the use of

reference class and analogy forecasting suggested by Kahneman and Tversky (1977) and

Duncan et al. (2001), we managed to provide a valid structured approach to predict the

long-term potential cement demand development in developing markets, and the

consequently evolution of the urban infrastructure development.



194

6.2.2 Limitations and future developments

While our analogy forecasting methodology showed valid results for the cement long-range

forecasting models, we retain that its accuracy could be improved through the

implementation of adjustments as follows:

Using additional shrinkage factors (Duncan et al., 2011) could facility the

adjustment to countries’ specificities. Although the correlation analysis conducted

in our research methodology did not show any significant and strong relation

besides those related to economic growth and urban infrastructure, we believe that

the prediction accuracy could benefit from a deeper analysis of main demand

drivers for the target country. Potential ways to adjust the coefficient of our pooled

models to improve forecasting accuracy would require the exploration and

incorporation of other relevant hidden variables such as local construction material

products substituting the use cement.

The construction of the model, as well as its empiric validation is based on the

observations of hundred-year period datasets (for the Cement consumption per

capita and GDP per capita Constant variables) and thus sensitive to the influence of

elements related to long-term macroeconomic cycles such as major economic crisis.

We retained that the identification and screen out of disruptive economic growth

events can avoid or at least mitigate their potentially misleading influence over the

pooled model’s coefficients and thus improving the forecasting accuracy for the

target country (Armstrong, 2001).

As the pooled models are based in a quadratic function, predicted values for Cement

consumption per capita are normally expected to decrease below maintenance level

after a certain level of economic growth in terms of GDP per capita Constant. To

tackle this issue, we suggest two potential ways. First one would the use of a higher

degree for the pooled models (i.e. polynomial regression grade three). The second

alternative would imply to limit the cement demand decline to a maintenance

reference value. For instance, the 2013 average Cement consumption per capita for

advanced countries forming part of equivalence subgroups account for ~350 kg,



195

which could be related to an indicative maintenance value considering these

countries have undergone a full consumption cycle (growth-saturation-decline-

maintenance).

Finally, practitioners and other potential interested users of our methodology are

encouraged to adjust the subgroup models to a specific market by selecting

countries’ time-series that appear to relate more closely to the target country. While

the nature of the subgroups is prescriptive, we also recommend simulating forecasts

using the two closest subgroups models to the target country and averaging results

for comparison as to widen the understanding of potential future development of

the cement long-term demand in a developing country.



196


6.3.1 Conclusions

The high and significant resulting correlations found between Cement stock per capita and

the Urban infrastructure variables confirmed the existence of a tight relation between urban

infrastructure levels and the historical consumption of cement in line with the claims of

Aitcin (2000), Deverell (2012), and Kang and Li (2013). This conclusion validated our

general aim of inferring current and future urban infrastructure deficit in developing

countries as a function of cumulative cement consumption supported by Osenton (2004)

claims on the notion of natural consumption (saturation point). The identification of a

reference value corresponding to the minimum level of urban infrastructure needed in a

country to deal with basic society needs revealed an alarming shortage of global cement

consumption. The results indicated that in average, lower-medium-income and low-income

countries account for only 47.4% and 13.1% respectively of what is required to constitute

a basic level of urban infrastructure measured at 13.7 tons of Cement stock per capita. Out

of the 129 countries in this research-work’s scope, 76 countries suffer Cement stock per

capita deficits with different levels of severity. As expected, the African continent

concentrates most countries with highest deficit followed by Asia, and to a lesser extent the

poorest countries in Latin America (i.e. Haiti). The economic quantification of the urban

infrastructures deficits supported by the reference values are massive and thus alarming.

Many Sub-Saharan countries would need to invest over seven times their GDP to achieve

a minimum functional level of urban infrastructure. Following a set of assumption

described during the research methodology, we estimated that while USD ~25.9 trillion

would be required to narrow deficits in the residential (USD 12.9 trillion) and non-

residential (USD 12.9 trillion) segments, USD 24.7 trillion should be invested to improve

public-structures in the long-term. We validated our estimations for housing through the

work of Woetzel et al. (2014) estimating the economic resources to be USD ~16 trillion

and Bughin, Manyika and Woetzel (2016) annual investment estimations in public-

structure for USD 874 billion, in line with the USD 823 billion resulting from our

annualized estimations (in a 30-year period).



197

6.3.2 Limitations and future developments

As for the long-term forecasting methodology, although the estimations’ outcomes

appeared to be aligned line with other leading reviews elaborated through a bottom up

approach, we retain some adjustments could improve the robustness of our findings while

triggering a continuation with new research lines.

The first improvement opportunity comes from a better understanding of the

saturation point in the cement consumption. Through the expert judgement

approach (Duncan et al., 2001) we aimed to determine the demand peak in advanced

countries related the achievement of a basic level urban infrastructure (Aitcin, 2000;

Deverell, 2012; and Kang and Li, 2013). We still consider that other alternative

procedures could be developed and implemented to increase the validity of our

reference values. Some initial alternatives are: Firstly, the conduction of a more

thorough and granular research. While we have addressed this element

systematically within the boundaries and limitations of our research, additional

understanding of historical dynamics deriving in maximum consumption points

would help to perfection our findings. For instance, while the second peak in the

Italian cement consumption was largely driven by a real estate bubble, some

fundamental public-structure such as high-speed railways were developed after the

1st peak. Secondly, a pragmatic approach would encompass anchoring a

hypothetical demand saturation to an equidistant point between the historical peaks

and compare the new findings with our initial results. A third potential approach,

applicable particularly for those peaks related to tourism development (e.g.

hospitality constructions such as hotels and airport) would require to artificially

adjust the per capita consumption by incorporating to the countries local population

the impact of tourism. For example, in 2016, Spain received 75.3 million tourists

(source: Reuters, 2016). That implies that in average, in a single day there were

~206’000 more people adding up to the official Spanish local population. This

approach would require the review of historical data and the consideration of

seasonality as most of tourism is likely to be concentrated in the few months around

the holidays season.



198

As previously described, we used a single reference value for the Cement stock per

capita (13.7 tons) to estimate the urban infrastructure deficits in developing

countries. While we sustain the validity of using a single figure for the general

estimations (section 5.4.1), we encourage practitioners willing to adopt our findings

to benefit from the granularity we provided through the equivalence subgroup to

increase the soundness of our results (subgroup 1: 16 tons, subgroup 2: 14 tons,

subgroup 3: 11 tons and subgroup 4: 13 tons).

The estimation of the economic cost to narrow the urban infrastructures deficit was

achieved according to the assumptions built and described in the section 4.3.3 and

5.4.2 around housing and public-structure construction. Due to the broad scope of

our research, we used average construction values for both housing and public-

structure, however, we suggest utilizing revised figures adjusted to specific market

conditions for the assessment of an individual country deficit (or city / region).

These types of refinements are also suggested for the definition of a typical house

size (i.e. we used a fifty-square meter house) and for specific public-structure pieces

requirement (if known) in the target country.

Finally, in our assumptions for the segment relevance we used a 65%:35% ratio for

the split of the Cement stock per capita gap to be allocated to residential and non-

residential, and public-structure segments respectively (4.3.3.2). Far from being

arbitrary, we relied mainly on the claims of Samans, Blanke and Corrigan (2015)

on public expenditure in public-structure cycle (as % of GDP) in advanced

countries for our estimations. However, literature also describes specific dynamics

that occur during the transition of a country from developing to advanced economy.

For instance, according to Kuznet (1955) and the more recent review on the matter

notably addressed by Piketty (2013), changes in wealth concentration play a

significant role during a countries economic development, as wealth inequality

increases during the initial stages to improve (decrease) later on. Therefore, we

suggest to focus on potential ways to implement factors that could influence the

linearity of our 65%:35% ratio assumption until the saturation point is achieved.



199

7 ANNEXES

Classification of countries by economic development

Country Region Sub-region World Bank IMF

Latvia Europe Northern

Europe

High Emerging and

developing

Chile Americas South

America

High Emerging and

developing

Bahamas Americas Caribbean High Emerging and

developing

Uruguay Americas South

America

High Emerging and

developing

United Kingdom Europe Northern

Europe

High Advanced

New Zealand Oceania Australia and

New Zealand

High Advanced

Estonia Europe Northern

Europe

High Advanced

Trinidad and

Tobago

Americas Caribbean High Emerging and

developing

Sweden Europe Northern

Europe

High Advanced

Poland Europe Eastern

Europe

High Emerging and

developing

Finland Europe Northern

Europe

High Advanced

Denmark Europe Northern

Europe

High Advanced

Russia Europe Eastern

Europe

High Emerging and

developing

Barbados Americas Caribbean High Emerging and

developing

Canada Americas Northern

America

High Advanced

Netherlands Europe Western

Europe

High Advanced

Norway Europe Northern

Europe

High Advanced

Lithuania Europe Northern

Europe

High Emerging and

developing

Croatia Europe Southern

Europe

High Emerging and

developing

Australia Oceania Australia and

New Zealand

High Advanced



200

United States Americas Northern

America

High Advanced

Puerto Rico Americas Caribbean High Emerging and

developing

France Europe Western

Europe

High Advanced

Hong Kong Asia Eastern Asia High Advanced

Japan Asia Eastern Asia High Advanced

Czech Republic Europe Eastern

Europe

High Advanced

Germany Europe Western

Europe

High Advanced

Slovakia Europe Eastern

Europe

High Advanced

Portugal Europe Southern

Europe

High Advanced

Ireland Europe Northern

Europe

High Advanced

Malta Europe Southern

Europe

High Advanced

Slovenia Europe Southern

Europe

High Advanced

Taiwan Asia Eastern Asia High Advanced

Republic of Korea Asia Eastern Asia High Advanced

Greece Europe Southern

Europe

High Advanced

Spain Europe Southern

Europe

High Advanced

Brunei Asia South-Eastern

Asia

High Emerging and

developing

Oman Asia Western Asia High Emerging and

developing

Austria Europe Western

Europe

High Advanced

Italy Europe Southern

Europe

High Advanced

Belgium Europe Western

Europe

High Advanced

Iceland Europe Northern

Europe

High Advanced

Israel Asia Western Asia High Advanced

Singapore Asia South-Eastern

Asia

High Advanced

Saudi Arabia Asia Western Asia High Emerging and

developing

Switzerland Europe Western

Europe

High Advanced



201

Bahrain Asia Western Asia High Emerging and

developing

Kuwait Asia Western Asia High Emerging and

developing

Cyprus Asia Western Asia High Advanced

Luxembourg Europe Western

Europe

High Advanced

United Arab

Emirates

Asia Western Asia High Emerging and

developing

Qatar Asia Western Asia High Emerging and

developing

New Caledonia Oceania Melanesia High Emerging and

developing

Angola Africa Middle Africa Upper-middle Emerging and

developing

Fiji Oceania Melanesia Upper-middle Emerging and

developing

Namibia Africa Southern

Africa

Upper-middle Emerging and

developing

Bosnia and

Herzegovina

Europe Southern

Europe


developing

Azerbaijan Asia Western Asia Upper-middle Emerging and

developing

Peru Americas South

America


developing

Botswana Africa Southern

Africa


developing

Colombia Americas South

America


developing

Brazil Americas South

America


developing

Ecuador Americas South

America


developing

Gabon Africa Middle Africa Upper-middle Emerging and

developing

Jamaica Americas Caribbean Upper-middle Emerging and

developing

Cuba Americas Caribbean Upper-middle Emerging and

developing

Suriname Americas South

America


developing

Dominican

Republic

Americas Caribbean Upper-middle Emerging and

developing

Belize Americas Central

America


developing

Belarus Europe Eastern

Europe


developing



202

Costa Rica Americas Central

America


developing

Thailand Asia South-Eastern

Asia


developing

Mexico Americas Central

America


developing

Argentina Americas South

America


developing

Serbia Europe Southern

Europe


developing

Panama Americas Central

America


developing

Iran Asia Southern Asia Upper-middle Emerging and

developing

Turkmenistan Asia Central Asia Upper-middle Emerging and

developing

South Africa Africa Southern

Africa


developing

Venezuela Americas South

America


developing

Algeria Africa Northern

Africa


developing

Albania Europe Southern

Europe


developing

Macedonia Europe Southern

Europe


developing

Iraq Asia Western Asia Upper-middle Emerging and

developing

Romania Europe Eastern

Europe


developing

China Asia Eastern Asia Upper-middle Emerging and

developing

Malaysia Asia South-Eastern

Asia


developing

Tunisia Africa Northern

Africa


developing

Turkey Asia Western Asia Upper-middle Emerging and

developing

Hungary Europe Eastern

Europe


developing

Kazakhstan Asia Central Asia Upper-middle Emerging and

developing

Jordan Asia Western Asia Upper-middle Emerging and

developing

Bulgaria Europe Eastern

Europe


developing

Lebanon Asia Western Asia Upper-middle Emerging and

developing



203

Libya Africa Northern

Africa


developing

Sudan Africa Northern

Africa

Lower-middle Emerging and

developing

Cameroon Africa Middle Africa Lower-middle Emerging and

developing

Nigeria Africa Western

Africa


developing

Pakistan Asia Southern Asia Lower-middle Emerging and

developing

India Asia Southern Asia Lower-middle Emerging and

developing

Zambia Africa Eastern Africa Lower-middle Emerging and

developing

Mauritania Africa Western

Africa


developing

Cote d'Ivoire Africa Western

Africa


developing

Indonesia Asia South-Eastern

Asia


developing

Congo Africa Middle Africa Lower-middle Emerging and

developing

Senegal Africa Western

Africa


developing

Paraguay Americas South

America


developing

Laos Asia South-Eastern

Asia


developing

Sri Lanka Asia Southern Asia Lower-middle Emerging and

developing

Ghana Africa Western

Africa


developing

Bolivia Americas South

America


developing

Nicaragua Americas Central

America


developing

Mongolia Asia Eastern Asia Lower-middle Emerging and

developing

Philippines Asia South-Eastern

Asia


developing

Guatemala Americas Central

America


developing

Honduras Americas Central

America


developing

Yemen Asia Western Asia Lower-middle Emerging and

developing

Vietnam Asia South-Eastern

Asia


developing



204

El Salvador Americas Central

America


developing

Bhutan Asia Southern Asia Lower-middle Emerging and

developing

Armenia Asia Western Asia Lower-middle Emerging and

developing

Djibouti Africa Eastern Africa Lower-middle Emerging and

developing

Georgia Asia Western Asia Lower-middle Emerging and

developing

Cabo Verde Africa Western

Africa


developing

Morocco Africa Northern

Africa


developing

Kyrgyzstan Asia Central Asia Lower-middle Emerging and

developing

Egypt Africa Northern

Africa


developing

Syria Asia Western Asia Lower-middle Emerging and

developing

Ukraine Europe Eastern

Europe


developing

Uzbekistan Asia Central Asia Lower-middle Emerging and

developing

Moldova Europe Eastern

Europe


developing

Burundi Africa Eastern Africa Low Emerging and

developing

Rwanda Africa Eastern Africa Low Emerging and

developing

Niger Africa Western

Africa

Low Emerging and

developing

Malawi Africa Eastern Africa Low Emerging and

developing

Ethiopia Africa Eastern Africa Low Emerging and

developing

Democratic

Republic of

Congo

Africa Middle Africa Low Emerging and

developing

Uganda Africa Eastern Africa Low Emerging and

developing

Burkina Faso Africa Western

Africa

Low Emerging and

developing

Bangladesh Asia Southern Asia Low Emerging and

developing

Madagascar Africa Eastern Africa Low Emerging and

developing



205

Somalia Africa Eastern Africa Low Emerging and

developing

Nepal Asia Southern Asia Low Emerging and

developing

Myanmar Asia South-Eastern

Asia

Low Emerging and

developing

Mali Africa Western

Africa

Low Emerging and

developing

Tanzania Africa Eastern Africa Low Emerging and

developing

Guinea Africa Western

Africa

Low Emerging and

developing

Haiti Americas Caribbean Low Emerging and

developing

Sierra Leone Africa Western

Africa

Low Emerging and

developing

Mozambique Africa Eastern Africa Low Emerging and

developing

Afghanistan Asia Southern Asia Low Emerging and

developing

Kenya Africa Eastern Africa Low Emerging and

developing

Cambodia Asia South-Eastern

Asia

Low Emerging and

developing

Liberia Africa Western

Africa

Low Emerging and

developing

Benin Africa Western

Africa

Low Emerging and

developing

Togo Africa Western

Africa

Low Emerging and

developing

Tajikistan Asia Central Asia Low Emerging and

developing

Zimbabwe Africa Eastern Africa Low Emerging and

developing

Democratic

People's Republic

of Korea

Asia Eastern Asia Low Emerging and

developing

Eritrea Africa Eastern Africa Low Emerging and

developing


206

Variables – 2013 figures unless stated differently

Countries Cement

consumption

per capita

[kg]

Cement stock

per capita

[tons]

Public-

structure

quality [scale]

Capital stock

per capita

[2011

Constant

international

dollars]

Population

living in slums

[% of total

population]

GDP per

capita

Constant

[1990

international

dollar]

GDP per

capita

Current [2011

international

dollar]

Burundi 45.9 0.3 1.9 615.9 57.9 491.6 751

Rwanda 40.6 0.4 3.2 2'031.0 53.2 1'257.5 1'569

Niger 22.3 0.6

2'323.2 70.1 551.5 908

Malawi 24.7 0.9 2.2 2'558.8 66.7 743.0 1'125

Ethiopia 57.8 0.9 2.6 1'785.7 73.9

1'377

Democratic

Republic of

Congo

29.4 1.1

582.2 74.8 293.6 697

Uganda 50.3 1.2 2.3 3'825.9 53.6 1'241.6 1'736

Burkina Faso 61.5 1.2 2.1 2'431.4 65.8 1'327.2 1'614

Bangladesh 106.8 1.3 2.4 4'739.2 55.1 1'480.3 2'942

Madagascar 21.4 1.3 2.3 1'939.5 77.2 664.1 1'414

Somalia 46.7 1.4

73.6 978.1

Nepal 134.4 1.4 1.9 4'226.4 54.3 1'325.9 2'251

Myanmar 139.7 1.6 2.0 3'878.5 41.0

4'480

Mali 97.6 1.6 3.1 2'916.4 56.3 1'011.2 1'843

Tanzania 63.7 1.7 2.3 5'409.1 50.7 889.4 2'417

Guinea 85.4 1.7 1.7 1'053.7 43.3 615.2 1'234

Haiti 28.8 1.8 2.0 5'489.8 74.4

1'686

Sierra Leone 58.3 1.9 2.1 2'145.9 75.6 971.9 1'919

Sudan 96.3 1.9

5'956.8 91.6 3'718.3 4'136


207

Mozambique 65.0 2.0 2.4 1'477.7 80.3 2'954.1 1'069

Afghanistan 195.6 2.1

62.7 1'220.5 1'942

Kenya 103.0 2.5 3.2 3'277.6 56.0 1'234.6 2'843

Cambodia 229.5 2.5 3.3 3'199.5 55.1 2'880.6 3'058

Cameroon 80.6 2.8 2.5 3'473.9 37.8 1'257.0 2'840

Liberia 81.5 2.9 2.4 1'856.6 65.7 973.1 846

Nigeria 122.7 3.0 2.3 5'652.4 50.2 1'995.1 5'638

Pakistan 139.7 3.7 2.7 5'267.9 45.5 2'597.4 4'633

India 198.4 3.8 3.7 9'667.5 24.0 3'885.6 5'267

Zambia 78.7 3.8 2.8 10'459.0 54.0 879.4 3'679

Mauritania 227.2 3.8 2.7 5'180.2 79.9 1'388.3 3'760

Angola 255.9 3.9 1.9 22'017.6 55.5 1'600.0 7'097

Benin 174.4 3.9 2.4 4'569.0 61.5 1'457.9 1'940

Cote d'Ivoire 92.5 4.2 3.1 4'519.6 56.0 1'286.2 3'028

Indonesia 230.9 4.2 4.2 23'077.9 21.8 5'396.6 10'010

Togo 129.9 4.5

2'676.0 51.2 665.0 1'341

Congo 282.2 4.6

11'150.1 46.9 2'443.9 5'950

Senegal 192.0 4.7 2.8 4'884.0 39.4 1'510.8 2'254

Paraguay 215.0 4.8 2.7 9'922.5

3'932.8 8'514

Laos 410.3 4.8 3.7 11'744.4 31.4 2'265.0 4'954

Zimbabwe 73.2 4.9 2.6 3'824.3 25.1 909.2 1'743

Sri Lanka 224.2 5.2 4.0 13'966.6

6'581.0 10'596

Ghana 210.2 5.2 3.0 5'667.4 37.9 2'389.9 3'967

Bolivia 314.4 5.4 3.0 8'572.7 43.5 3'451.6 6'303

Nicaragua 127.8 5.4 3.1 7'503.0

1'844.7 4'712

Fiji 159.0 5.8

17'885.2

8'249

Mongolia 699.5 6.3 2.9 29'516.9 42.7 1'422.5 11'132


208

Philippines 200.9 6.3 3.4 10'491.9 38.3 3'413.4 6'587

Guatemala 189.3 6.4 3.8 10'169.9 34.5 4'650.7 7'206

Honduras 191.1 7.0 2.8 11'078.6 27.5 2'416.9 4'769

Yemen 192.3 7.1 1.9 5'100.3 60.8 2'650.0 4'015

Vietnam 507.9 7.4 3.7 7'911.3 27.2 3'672.5 5'300

El Salvador 169.1 7.6 4.0 10'966.8

3'019.3 8'111

Bhutan 543.3 7.8 3.6 22'551.7

7'417

Namibia 191.8 7.8 4.2 20'736.8 33.2 5'114.2 9'459

Bosnia And

Herzegovina

347.8 7.9 3.7 16'960.6

5'373.0 10'203

Peru 353.0 8.5 3.5 14'898.3 34.2 6'619.0 11'829

Botswana 317.0 8.8 3.4 39'261.9

5'253.7 15'546

Colombia 229.6 9.5 3.5 20'607.5 13.1 7'971.7 12'725

Djibouti 173.5 9.8

10'720.8 65.6 1'468.4 3'087

Brazil 347.5 10.7 4.0 32'869.7 22.3 7'297.2 15'814

Ecuador 421.4 11.0 3.8 22'435.2 36.0 5'737.1 11'038

Gabon 339.3 11.3 2.8 48'596.7 37.0 4'369.9 18'805

Jamaica 297.5 11.7 3.5 590.7

3'644.9 8'542

Cuba 127.6 12.1

4'224.3 20'646

Suriname 524.9 12.2 3.7 47'205.6 7.3

16'274

Dominican

Republic

418.2 12.2 3.0 20'274.4 12.1 5'725.7 12'302

Belize 435.8 12.4

12'288.2 10.8

8'184

Costa Rica 297.5 12.5 3.9 21'137.2 5.5 8'780.8 14'493

Cabo Verde 453.4 12.8 2.8 18'156.1

2'775.5 6'327

Chile 308.9 12.9 4.5 29'574.3

15'635.3 22'544

Thailand 446.0 13.0 4.5 36'339.7 25.0 10'309.9 15'435

Bahamas

13.3

23'470


209

Mexico 281.6 13.3 4.1 35'232.3 11.1 8'108.1 16'528

Morocco 444.2 13.4 4.3 18'963.7 13.1 4'386.1 7'310

Uruguay 267.0 13.6 4.3 31'942.2

13'033.3 19'943

Argentina 274.8 13.6 3.5 27'300.3 16.7 11'172.3 20'252

Serbia 178.1 13.9 3.5 16'207.2

7'688.1 13'773

Panama 559.7 14.3 4.9 28'901.1 25.8 8'936.6 19'892

Egypt 547.9 15.3 3.3 9'446.4 10.6 4'245.0 10'400

Iran 709.9 15.4 4.1 37'028.4

5'905.2 16'582

South Africa 227.8 15.8 4.1 23'715.8 23.0 5'234.5 12'860

Syria 248.9 16.6

4'862.7 19.3 7'952.0

Venezuela 284.4 16.9 2.6 37'793.8

10'545.8 18'306

Algeria 597.1 17.1 3.1 29'823.0

3'618.6 13'778

Albania 544.5 17.2 3.3 21'634.1

5'682.9 10'579

Macedonia 361.9 17.7 3.6 21'294.9

6'391.5 12'687

Iraq 630.4 18.1

13'182.1 47.2 1'920.9 15'501

United

Kingdom

150.8 19.2 6.1 75'414.9

24'529.7 39'052

Romania 360.7 19.5 3.3 50'242.2

4'963.6 19'878

China 1'761.4 19.6 4.5 29'893.9 25.2 10'055.4 12'368

Malaysia 695.4 19.9 5.2 47'084.8

11'202.8 24'231

Tunisia 673.3 20.2 3.9 26'922.3 8.0 6'487.7 10'970

New Zealand 246.3 20.6 5.2 58'472.3

19'797.8 36'210

Turkey 831.8 20.6 4.5 30'223.8 11.9 9'177.2 19'229

Hungary 231.7 20.9 4.4 42'431.5

8'605.9 24'388

Trinidad And

Tobago

594.8 21.1 4.4 96'980.8

20'486.4 33'039

Sweden 232.3 22.1 5.6 100'435.1

25'620.6 45'714

Jordan 554.4 22.3 4.3 18'713.5 12.9 5'721.2 10'568


210

Poland 378.6 22.5 4.0 29'638.9

11'624.0 24'741

Finland 306.4 22.8 5.6 106'101.9

22'960.8 41'331

Denmark 239.5 23.1 5.5 119'760.2

23'231.2 46'135

Barbados 566.4 23.7 5.5 48'612.7

15'870

Canada 258.9 24.0 5.8 90'029.4

25'824.2 44'026

Bulgaria 271.6 24.4 3.9 28'926.5

9'390.9 16'647

Netherlands 249.9 25.0 6.1 107'087.4

24'051.0 48'710

Norway 424.9 25.0 5.0 134'894.9

28'154.3 67'020

Croatia 362.9 26.4 4.7 45'833.0

9'866.8 21'681

Australia 382.5 26.6 5.6 78'320.5

26'501.3 45'575

United States 257.7 27.1 5.8 102'428.7

31'670.2 52'750

Puerto Rico 184.3 27.5 4.2

14'217.8 35'024

France 301.0 27.9 6.2 98'008.7

21'807.1 39'539

Hong Kong 459.2 28.6 6.7 98'319.4

32'990.7 53'465

Japan 370.1 30.6 6.0 102'778.0

22'706.7 39'023

Germany 329.5 31.3 6.2 90'466.9

21'475.0 45'273

Portugal 271.4 31.9 5.6 75'045.1

13'448.8 27'925

Ireland 241.9 32.3 5.3 118'839.2

22'751.1 48'354

Malta 553.4 32.7 5.0 50'291.1

31'405

Slovenia 348.7 32.8 4.9 68'302.4

16'888.4 29'559

Taiwan 524.3 33.3 5.8 91'907.2

23'291.9

Republic of

Korea

904.8 33.9 5.9 80'358.0

23'300.5 32'816

Greece 216.8 35.5 4.8 60'854.9

12'149.3 26'121

Spain 231.3 35.7 6.0 91'939.6

15'908.2 32'811

Austria 598.1 37.3 5.7 114'215.7

24'697.6 47'765

Italy 363.1 37.5 5.4 96'974.6

17'505.3 36'164

Belgium 541.5 38.2 5.6 110'146.1

23'403.4 43'489


211

Iceland 215.1 38.4 5.6 81'503.8

42'387

Israel 564.1 38.4 4.9 50'887.3

20'231.7 34'256

Singapore 1'086.0 43.7 6.4 158'394.5

31'320.9 80'768

Switzerland 649.6 44.8 6.2 144'538.9

25'370.1 59'842


212

Countries Human

Development

Index [scale]

Political

stability [scale]

Urbanization

level [% of total

population]

Temperature

[Celsius

degrees]

Country size

[km2]

Elevation over

sea level

[meters]

Burundi 39.7 5.7 11.5 20.3 27'834.0 1'504.0

Rwanda 47.9 23.6 26.9 19.0 26'338.0 1'598.0

Niger 34.5 28.1 18.2 27.2 1'267'000.0 474.0

Malawi 43.9 42.4 15.9 22.0 118'480.0 779.0

Ethiopia 43.6 10.5 18.6 22.4 1'127'127.0 1'330.0

Democratic

Republic of

Congo

43.0 1.9 41.5 24.0 2'345'000.0 726.0

Uganda 47.8 14.0 15.4 22.6 241'550.0

Burkina Faso 39.6 39.2 28.2 28.2 274'200.0

Bangladesh 56.7 13.3 32.8 25.5 147'570.0 85.0

Madagascar 50.8 38.3 33.8 22.0 587'040.0 615.0

Somalia

0.4 38.6 26.8 637'657.0 410.0

Nepal 54.3 10.0 17.9 12.7 147'181.0 3'265.0

Myanmar 53.1 14.0 33.0 23.0 678'500.0

Mali 41.6 44.1 38.4 28.3 1'240'000.0 343.0

Tanzania 51.6 34.3 30.2 22.3 945'087.0 1'018.0

Guinea 41.1 10.2 36.2 25.5 245'857.0 472.0

Haiti 48.1 14.7 56.2 24.5 27'750.0 470.0

Sierra Leone 40.8 28.3 39.2 26.0 72'740.0 279.0

Sudan 47.7 2.4 33.5 26.8 1'861'484.0 568.0

Mozambique 41.3 51.5 31.7 23.7 801'590.0 345.0

Afghanistan 46.4 1.3 25.9 12.9 647'500.0

Kenya 54.4 13.8 24.8 24.5 580'367.0 762.0

Cambodia 55.0 28.3 20.3 26.9 181'035.0 126.0


213

Cameroon 50.7 28.4 53.3 24.5 475'440.0 667.0

Liberia 42.4 14.1 48.9 25.3 111'370.0 243.0

Nigeria 51.1 6.7 46.1 26.8 923'768.0 380.0

Pakistan 53.6 5.0 37.9 20.0 803'940.0 900.0

India 60.4 14.2 32.0 23.9 3'287'263.0 160.0

Zambia 58.0 53.4 40.0 21.6 752'614.0 1'138.0

Mauritania 50.4 35.8 58.6 27.7 1'030'700.0 276.0

Angola 53.0 22.2 42.5 21.5 1'246'700.0 1'112.0

Benin 47.7 62.0 43.1 27.5 112'620.0 273.0

Cote d'Ivoire 45.8 11.7 52.8 26.3 322'460.0 250.0

Indonesia 68.1 13.7 52.3 25.7 1'904'556.0 367.0

Togo 47.3 32.9 39.0 26.8 56'785.0 236.0

Congo 58.2 21.1 64.5 24.5 342'000.0 430.0

Senegal 46.3 34.9 43.1 27.9 196'190.0 69.0

Paraguay 67.7 22.7 59.2 23.5 406'750.0 178.0

Laos 57.0 37.5 36.5 23.2 236'800.0 710.0

Zimbabwe 50.1 16.8 32.7 21.0 390'580.0 961.0

Sri Lanka 75.2 14.8 18.3 26.8 65'611.0

Ghana 57.7 43.5 52.7 27.3 238'540.0 190.0

Bolivia 65.8 28.2 67.7 21.0 1'098'580.0 1'192.0

Nicaragua 62.8 35.0 58.1 24.6 129'494.0 298.0

Fiji 72.4 51.7 53.0 23.4 18'270.0

Mongolia 72.2 69.7 70.4 -0.5 1'565'000.0 1'528.0

Philippines 66.4 13.0 44.6 25.3 300'000.0 442.0

Guatemala 62.6 21.4 50.7 23.1 108'890.0 759.0

Honduras 60.4 31.4 53.5 23.4 112'090.0 684.0

Yemen 49.8 6.9 33.5 23.2 527'970.0 999.0


214

Vietnam 66.3 54.0 32.3 24.1 329'560.0 398.0

El Salvador 66.4 45.4 65.8 24.8 21'040.0 442.0

Bhutan 59.5 74.1 37.1 8.6 47'000.0 3'280.0

Namibia 62.5 68.8 44.7 20.0 825'418.0 1'141.0

Bosnia And

Herzegovina

72.9 29.5 39.5 9.0 51'129.0 500.0

Peru 73.2 19.1 78.0 19.5 1'285'220.0 1'555.0

Botswana 69.6 81.2 56.9 21.5 600'370.0 1'013.0

Colombia 71.8 6.3 75.9 24.4 1'197'411.0 593.0

Djibouti 46.8 39.3 77.2 27.6 22'000.0 430.0

Brazil 75.2 41.3 85.2 24.9 8'515'767.0 320.0

Ecuador 73.0 24.6 63.3 21.3 283'560.0 1'117.0

Gabon 67.9 55.0 86.7 25.0 267'667.0 377.0

Jamaica 71.7 40.0 54.3 24.5 10'990.0 340.0

Cuba 76.8 55.3 76.9 25.1 109'886.0 108.0

Suriname 71.3 52.4 66.1 25.8 163'270.0 246.0

Dominican

Republic

71.1 42.2 77.1 23.9 48'730.0 424.0

Belize 71.5 53.6 44.3 25.1 22'966.0 173.0

Costa Rica 76.4 68.3 75.0 23.9 51'100.0 746.0

Cabo Verde 64.3 76.7 64.1 26.0 4'033.0

Chile 83.0 64.5 89.2 8.4 756'950.0 1'871.0

Thailand 72.4 28.0 47.9 26.2 514'000.0 287.0

Bahamas 78.6 80.6

25.1 13'940.0

Mexico 75.5 28.1 78.7 20.5 1'964'375.0 1'111.0

Morocco 62.6 33.6 59.2 17.2 446'550.0 909.0

Uruguay 79.0 72.1 95.0 17.6 176'220.0 109.0

Argentina 83.3 41.5 91.5 14.2 2'780'400.0 595.0


215

Serbia 77.1 25.9 55.4 10.2 88'361.0 442.0

Panama 77.7 45.5 66.0 24.7 78'201.0 360.0

Egypt 68.9 23.9 43.0 22.1 1'001'450.0 321.0

Iran 76.4 17.7 72.3 16.9 1'648'000.0 1'305.0

South Africa 66.3 40.9 63.8 17.6 1'219'912.0 1'034.0

Syria 60.8 28.6 56.9 17.6 185'180.0 514.0

Venezuela 76.4 15.5 88.9 25.3 912'050.0 450.0

Algeria 73.4 11.0 69.5 22.6 2'381'740.0 800.0

Albania 73.2 35.2 55.4 11.3 28'748.0 708.0

Macedonia 74.4 26.0 57.0 9.9 25'333.0 741.0

Iraq 65.7 3.3 69.3 21.6 437'072.0 312.0

United Kingdom 90.2 62.1 82.1 8.3 243'610.0 162.0

Romania 79.1 51.7 54.2 8.8 238'391.0 414.0

China 72.3 30.5 53.2 6.3 9'572'900.0 1'840.0

Malaysia 77.7 51.8 73.3 25.1 329'750.0 538.0

Tunisia 72.0 45.2 66.5 19.4 163'610.0 246.0

New Zealand 91.1 93.4 86.2 10.0 269'190.0 388.0

Turkey 75.9 18.5 72.4 10.9 780'580.0 1'132.0

Hungary 82.5 75.3 70.3 10.1 93'030.0 143.0

Trinidad And

Tobago

77.1 44.8 8.7 25.9 5'128.0 83.0

Sweden 90.5 93.2 85.5 1.5 449'964.0 320.0

Jordan 74.8 34.0 83.2 18.4 92'300.0 812.0

Poland 84.0 69.4 60.6 7.9 312'685.0 173.0

Finland 88.2 98.6 84.0 1.3 337'030.0 164.0

Denmark 92.3 87.5 87.3 7.8 2'210'583.0 34.0

Barbados 78.5 86.1 31.7 0.0 430.0

Canada 91.2 82.4 81.5 -7.1 9'984'670.0 487.0


216

Bulgaria 77.9 54.4 73.3 10.4 110'910.0 472.0

Netherlands 92.0 86.3 89.3 9.2 41'526.0 30.0

Norway 94.2 94.3 79.9 0.8 324'220.0 460.0

Croatia 81.7 61.0 58.4 10.5 56'542.0 331.0

Australia 93.3 81.2 89.2 21.5 7'692'024.0 330.0

United States 91.3 60.7 81.3 6.8 9'525'067.0 760.0

Puerto Rico

58.0 93.7 24.3 9'104.0 261.0

France 88.7 64.7 79.1 10.5 675'417.0 375.0

Hong Kong 90.8 78.8 100.0 23.2 2'755.0

Japan 89.0 82.4 92.5 10.4 377'835.0 438.0

Germany 91.5 78.5 74.9 8.5 357'021.0 263.0

Portugal 82.8 81.4 62.3 15.0 88'267.0 372.0

Ireland 91.2 89.1 62.7 9.1 71'273.0 118.0

Malta 83.7 93.0 95.1 0.0 316.0

Slovenia 87.8 82.9 49.8 8.0 20'253.0 492.0

Taiwan

70.1 78.0 22.7 36'193.0 1'150.0

Republic of

Korea

89.5 57.6 82.2 10.7 98'480.0 282.0

Greece 86.3 55.9 77.3 13.7 131'940.0 498.0

Spain 87.4 44.5 79.1 13.0 504'781.0 660.0

Austria 88.4 89.6 65.9 6.2 83'858.0 910.0

Italy 87.3 66.1 68.7 11.5 301'230.0 538.0

Belgium 88.8 76.2 97.8 9.5 32'545.0 181.0

Iceland 89.9 95.3 93.9 1.4 103'000.0 557.0

Israel 89.3 12.1 92.0 19.7 26'990.0 508.0

Singapore 90.9 88.2 100.0 27.0 692.7

Switzerland 92.8 95.2 73.8 4.7 41'210.0 1'350.0


217

Material content for a 50-square meter house

Component Work Quantity Measure

[meters]

Surface /

volume

Material Quantity Cement

factor

Cement

content

[tons]

Slab floor 1 5x10x0.1525 50 m2 Concrete 7.63 m3 0.27 2.06

Roof 1 5x10x0.1525 50 m2 Concrete 7.63 m3 0.27 2.06

Columns 6 0.225x0.3x2.3 0.93m3 Concrete 0.93 m3 0.27 0.93

Footing 0.4x0.3x30 3.6 m3 Concrete 3.6 m3 0.27 0.98

Large walls Brickwork 2 2.3x10 46 m2 Mortar 0.91 m3 0.26 0.24

Small walls Brickwork 3 2.3x5 35.5 m2 Mortar 0.68 m3 0.26 0.18

Large walls Plastering 2 (4 faces) 2.3x10 92 m2 Plaster n.a. 0.0075 0.69

Small walls Plastering 3 (6 faces) 2.3x5 69 m2 Plaster n.a. 0.0075 0.52

Roof Plastering 1 (1 face) 5x10 50 m2 Plaster n.a. 0.0075 0.375

Total 8.035

Source: van Oss (2005), (Kosmatka et al. (2002). Vanderwerf (2007), www.civilprojectsonline.com, www.concremax.com.pe and

www.brickwarehouse.co.za

http://www.civilprojectsonline.com/

http://www.concremax.com.pe/



218

Highways as % of total paved roads in advanced countries

Country Paved roads [km] Of which highways

[km]

Highways as % of

total paved roads

Austria 138'696 2'208 2%

Belgium 120'514 1'756 1%

Canada 415'600 17'000 4%

Denmark 74'558 1'205 2%

Finland 50'000 700 1%

France 1'028'446 11'416 1%

Germany 645'000 12'800 2%

Greece 41'357 1'091 3%

Ireland 96'036 1'224 1%

Israel 18'566 449 2%

Italy 487'700 6'700 1%

Japan 992'835 8'428 1%

Republic of Korea 91'195 4'193 5%

Netherlands 139'124 3'654 3%

New Zealand 62'759 199 0%

Norway 75'754 393 1%

Portugal 71'294 2'613 4%

Singapore 3'425 161 5%

Spain 683'175 16'205 2%

Sweden 140'100 2'050 1%

Switzerland 71'464 1'415 2%

United Kingdom 394'428 3'519 1%

United States 4'304'715 76'334 2%

Average 2%

Source: The world factbook, CIA



219

Construction cost comparison in developing countries, 2016 values

Townhouse Apartment

low-rise

Apartment high-rise

City Country Turner

and

Townsend

(2016)

Woetzel

et al.

(2014)*

Turner

and

Townsend

(2016)

Turner

and

Townsend

(2016)

Africa

property

and

construction

cost guide

(2016)

Woetzel

et al.

(2014)

Sao Paulo Brazil 480 520 640

Rio de

Janerio

Brazil 794 (378) 1071

Beijing China 580 570

(295)

430 580 791

Bangalore India 440 400 670 741

Mumbai India 352 (176) 410

Nairobi Kenya 510 670 710 660

Kuala

Lumpur

Malaysia 380 450 640 514

Warsaw Poland 600 640 720

Moscow Russia 450 520 580

Kigali Rwanda 680 750 800 1’037

Johannesburg South

Africa

500 530 610 635

Kampala Uganda 570 670 770 759

Istanbul Turkey 670 660 860

Lome Togo (247)

Average - 532.72 572 (274)

(*) Woetzel et al. (2014) figures in brackets correspond to low cost construction target.

Woetzel et al. (2014) original values correspond to 2013 and were inflated using World

Bank inflation index retrieved in February 2017 as follows:

China: 2014 – 2.0%; 2015 – 1.44%; 2016 – 2.0%

Brazil: 2014 - 6.33%; 2015 - 9.03%; 2016 - 8.74%

India: 2014 - 6.65%; 2015 - 4.91%; 2016 - 4.94%

Turkey: 2014 - 8.85%; 2015 - 7.67%; 2016 - 7.78%

Togo: 2014 – 0.2%; 2015 – 1.8%; 2016 – 0.9%


220

Descriptive statistics of Cement stock per capita (tons) 2013 by economic cluster and geographic region

All Advanced High Medium Low Africa Americas

Asia -

Oceania Europe

Average

Economic

Average

Regions

Mean 14.1 31.0 20.1 10.7 1.8 5.2 12.9 14.8 27.3

Standard Error 1.0 1.3 2.1 0.7 0.2 0.8 1.2 2.2 1.6

Median 12.2 31.9 21.8 10.2 1.6 3.4 12.2 10.4 25.7

Standard Deviation 11.5 6.8 6.0 5.8 1.2 5.2 6.5 12.3 8.7

Sample Variance 133.2 46.0 36.6 33.8 1.3 27.4 42.4 150.1 75.3

Kurtosis -0.4 -0.7 -1.9 -0.9 1.7 1.1 0.4 -0.5 -0.4

Skewness 0.8 0.1 -0.2 0.4 1.3 1.4 0.8 0.8 -0.1

Range 44.5 25.6 14.6 22.5 4.7 19.9 25.7 42.4 36.9

Minimum 0.3 19.2 12.9 1.9 0.3 0.3 1.8 1.3 7.9

Maximum 44.8 44.8 27.5 24.4 4.9 20.2 27.5 43.7 44.8

Sum 1'815.6 899.9 161.1 707.1 47.6 206.9 372.7 472.4 763.7

Count 129.0 29.0 8.0 66.0 26.0 40.0 29.0 32.0 28.0

Largest(1) 44.8 44.8 27.5 24.4 4.9 20.2 27.5 43.7 44.8

Smallest(1) 0.3 19.2 12.9 1.9 0.3 0.3 1.8 1.3 7.9

Confidence Level(95.0%) 2.0 2.6 5.1 1.4 0.5 1.7 2.5 4.4 3.4

Variation coefficient 82% 22% 30% 54% 63% 101% 51% 83% 32% 42% 67%


221

Descriptive statistics Advanced countries vs remaining countries, selected variables

Cement

stock per

capita

Cement

consumption

per capita

GDP per

capita

Constant

Cement

stock per

capita

Cement

consumption

per capita

GDP per

capita

Constant

Cement

stock per

capita

Cement

consumption

per capita

GDP per

capita

Constant

Mean 9 285 4'645 31 397 23'022 13.7 710 12'760

Standard Error 1 24 414 1 40 969 1 58 545

Median 7 228 3'645 32 329 23'292 13 680 12'319

Standard Deviation 7 242 3'949 7 214 5'034 4 302 2'832

Sample Variance 51 58'587 15'594'235 46 45'913 25'339'644 15 90'962 8'020'120

Kurtosis -1 13 2 -1 3 0 0 1 2

Skewness 1 3 1 0 2 -0 1 1 1

Range 27 1'740 20'193 26 935 20'841 16 1'062 12'818

Minimum 0 21 294 19 151 12'149 8 357 8'149

Maximum 27 1'761 20'486 45 1'086 32'991 24 1'419 20'967

Sum 916 28'175 422'727 900 11'519 621'588 369 19'165 344'507

Count 100 99 91 29 29 27 27 27 27

Largest(1) 27 1'761 20'486 45 1'086 32'991 24 1'419 20'967

Smallest(1) 0 21 294 19 151 12'149 8 357 8'149

Confidence Level(95.0%) 1 48 822 3 82 1'991 2 119 1'120

Variation Coefficient 78% 85% 85% 22% 54% 22% 28% 42% 22%

All countries (excluding advanced) 2013 Advanced countries 2013 Advanced countries at saturation point


222

Countries with multiple peaks of Cement consumption per capita – Cement stock per capita comparison

First peak Second peak

Country Saturation point

[year]

Cement stock

per capita [tons]

Comparison vs

advanced

Saturation point

[year]

Cement stock

per capita [tons]

Comparison vs

advanced

New Zealand 1974 10.4 -24.0% 2007 18.8 38%

Norway 1974 11.2 -18.1% 1987 16.4 20%

Australia 1988 16.0 17.0% 2011 25.8 88%

United States 1973 14.0 2.1% 2005 24.8 81%

Ireland 1979 12.6 -7.8% 2008 30.3 122%

Greece 1979 11.3 -17.2% 2006 31.6 131%

Spain 1974 8.9 -35.2% 2006 31.4 129%

Italy 1980 16.0 16.7% 2006 33.4 144%

Israel 1975 13.8 1.2% 1995 26.3 92%

Singapore 1984 13.6 -0.6% 1997 27.9 104%

Average 1978 12.8 -6.6% 2003 26.7 94.9%


223

Countries with multiple peaks of Cement consumption per capita – GDP per capita Constant comparison

First peak Second peak

Country Saturation point

[year]

GDP per capita

Constant [USD]

Comparison vs

advanced

Saturation point

[year]

GDP per capita

Constant [USD]

Comparison vs

advanced

New Zealand 1974 12.9 0.9% 2007 19.3 51%

Norway 1974 11.7 -8.1% 1987 18.2 42%

Australia 1988 16.8 31.3% 2011 25.8 102%

United States 1973 16.7 30.8% 2005 30.8 142%

Ireland 1979 8.4 -34.4% 2008 24.3 91%

Greece 1979 8.9 -30.2% 2006 15.4 21%

Spain 1974 8.1 -36.1% 2006 17.6 38%

Italy 1980 12.9 1.3% 2006 19.6 54%

Israel 1975 10.1 -20.5% 1995 15.1 19%

Singapore 1984 10.9 -14.3% 1997 20.2 59%

Average 1978 11.7 -7.9% 2003 20.6 61.7%



224

Countries with multiple peaks of Cement consumption per capita –

Italy

0

5

10

15

20

25

30

35

40

0

100

200

300

400

500

600

700

800

900

19

13

19

16

19

19

19

22

19

25

19

28

19

31

19

34

19

37

19

40

19

43

19

46

19

49

19

52

19

55

19

58

19

61

19

64

19

67

19

70

19

73

19

76

19

79

19

82

19

85

19

88

19

91

19

94

19

97

20

00

20

03

20

06

20

09

20

12

Cement consumption per capita [kg]Cement stock per capita [tons]GDP per capita Constant [USD]

Italy

kg tons / GDP pc ['000]



225

Pooled models used for the empirical validation of individual

advanced economies

Subgroup Country α β Constant*

Subgroup 1 Switzerland -3.368847 115.8682 -465.3656

Austria -3.556924 128.4766 -308.7357

Greece -3.419329 125.9537 -182.8172

Portugal -3.787786 132.1118 -217.7231

Subgroup 2 Slovenia -3.335885 100.7474 -127.1125

Japan -3.366439 101.2972 -143.4619

Germany -3.283717 100.694 -193.4104

France -3.429218 103.47 -249.031

Italy -3.435552 101.9168 -136.0866

Belgium -3.602828 106.1212 -206.756

Israel -3.461545 103.3831 -79.86169

Subgroup 3 United Kingdom -1.700841 56.03148 -143.6315

Netherlands -1.661659 53.86403 -92.49157

Sweden -1.711323 56.95595 -68.58599

Denmark -1.668462 54.7182 -94.64614

Finland -1.755609 57.57321 -44.57339

Norway -1.815555 58.05068 -63.65791

Subgroup 4 New Zealand -1.047721 42.18603 -81.45794

Canada -1.135921 44.21024 -41.48516

Australia -1.239652 43.00666 -51.61831

United States -1.446332 51.88976 -105.4872

(*) The constant term for the pooled model was optimized following the procedure

described in the section 4.3.2.



226

Forecasting models 1913-2013 – empirical validation subgroup 1

0

200

400

600

800

1000

1200

1400

1600

0 5 10 15 20 25 30



[kg]

Switzerland

0

500

1000

1500

2000

2500

3000

0 5 10 15 20 25 30



[kg]

Austria



227

0

200

400

600

800

1000

1200

1400

1600

0 5 10 15 20



[kg]

Greece

0

200

400

600

800

1000

1200

0 2 4 6 8 10 12 14 16



[kg]

Portugal



228


0

200

400

600

800

1000

1200

0 5 10 15 20



[kg]

Slovenia

0

200

400

600

800

1000

1200

1400

0 5 10 15 20 25



[kg]

Japan



229

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 5 10 15 20 25



[kg]

Germany

0

200

400

600

800

1000

1200

1400

0 5 10 15 20 25



[kg]

France



230

0

100

200

300

400

500

600

700

800

900

0 5 10 15 20 25



[kg]

Italy

0

100

200

300

400

500

600

700

800

0 5 10 15 20 25



[kg]

Belgium



231

0

200

400

600

800

1000

1200

0 5 10 15 20 25



[kg]

Israel



232


0

100

200

300

400

500

600

700

800

0 5 10 15 20 25 30



[kg]

United Kingdom

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 5 10 15 20 25 30



[kg]

Netherlands



233

0

200

400

600

800

1000

1200

1400

0 5 10 15 20 25 30



[kg]

Sweden

0

200

400

600

800

1000

1200

1400

1600

0 5 10 15 20 25 30



[kg]

Denmark



234

0

100

200

300

400

500

600

0 5 10 15 20 25 30



[kg]

Finland

0

100

200

300

400

500

600

0 5 10 15 20 25 30 35



[kg]

Norway



235


0

100

200

300

400

500

600

700

800

0 5 10 15 20 25



[kg]

New Zealand

0

100

200

300

400

500

600

700

0 5 10 15 20 25 30



[kg]

Canada



236

0

100

200

300

400

500

600

0 5 10 15 20 25 30



[kg]

Australia

0

100

200

300

400

500

600

0 5 10 15 20 25 30 35



[kg]

United States



237

Residuals – empirical validation equivalence subgroup 2

-200

-150

-100

-50

0

50

100

150

200

10 15 20 25

Res

idua

ls[k

g]

Independent



238

Variation between known and predicted values* (1/2)

MAD [kg] MSE [kg] MAPE [%]

Country Standard Pooled Standard Pooled Standard Pooled

Switzerland 494 188 295'947 46'597 81% 28%

Austria 1'104 152 1'679'012 29'547 180% 25%

Greece 298 113 147'213 39'126 59% 29%

Portugal 373 245 188'484 93'557 78% 54%

Subgroup 1 567 174 577'664 52'207 99% 34%

Slovenia 243 101 92'250 15'580 47% 19%

Japan 493 63 330'114 6'009 103% 12%

Germany 773 123 735'000 17'832 200% 31%

France 580 81 410'372 9'288 160% 22%

Italy 135 100 27'574 17'508 24% 15%

Belgium 154 129 26'979 23'239 30% 24%

Israel 272 124 118'746 22'776 39% 19%

Subgroup 2 379 103 248'719 16'033 86% 20%

United

Kingdom 297 52 109'279 3'615 141% 24%

Netherlands 663 57 632'257 5'221 198% 15%

Sweden 593 133 425'857 23'207 277% 64%

Denmark 701 69 610'386 6'429 261% 25%

Finland 139 86 25'245 10'831 49% 31%

Norway 113 93 16'592 17'401 34% 26%

Subgroup 3 418 82 303'269 11'117 160% 31%

New

Zealand

219 63 59'800 4'985 87% 26%

Canada 243 85 69'838 9'063 89% 32%

Australia 132 137 30'948 25'224 29% 31%

United

States

114 98 16'932 18'533 38% 29%

Subgroup 4 177 96 44'380 14'451 61% 29%

Total 387 109 288'039 21'218 105% 28%

(*) Correspond only for the period between actual saturation point and 2013.



239

Variation between known and predicted values (2/2)

Average Cement

consumption per

capita (1913-2013)

[%]

Average Cement

consumption per

capita (saturation

point-2013) [%]

Saturation point

[years]

Country Standard Pooled Standard Pooled Standard Pooled

Switzerland 46% 17% 79% 29% 41 4

Austria 124% 16% 175% 22% 40 23

Greece 29% 8% 43% 12% 28 28

Portugal 15% 8% 60% 33% 6 6

Subgroup 1 54% 12% 89% 24% 29 15

Slovenia 24% 0% 41% 1% 27 22

Japan 66% 2% 90% 3% 40 11

Germany 104% 15% 186% 27% 40 14

France 83% 11% 150% 19% 33 8

Italy 12% 6% 20% 10% 29 7

Belgium 14% 6% 28% 12% 18 6

Israel 28% 1% 41% 2% 4 20

Subgroup 2 47% 6% 80% 11% 27 13

United

Kingdom

64% 10% 130% 20% 34 20

Netherlands 112% 9% 188% 15% 37 17

Sweden 121% 24% 241% 47% 42 24

Denmark 124% 4% 246% 8% 34 11

Finland 24% 13% 44% 23% 22 22

Norway 13% 9% 23% 16% 14 9

Subgroup 3 76% 11% 145% 21% 31 17

New Zealand 43% 10% 86% 20% 39 39

Canada 41% 13% 85% 27% 39 22

Australia 12% 13% 30% 33% 1 5

United States 17% 10% 35% 21% 40 4

Subgroup 4 28% 12% 59% 25% 30 18

Total 53% 10% 96% 19% 29 15


240

Deficit of urban infrastructure by countries

Cement gap Per capita cost [USD] Country cost [USD million]

Country

Per

capita

[tons]

Country

[million

tons] Residential

Non-

residential

Public-

structure Total Residential

Non-

residential

Public-

structure Total

Burundi

13.4

140

5'137

5'137

9'902

15'039

53'765

53'765

103'633 211'164

Rwanda

13.2

147

5'082

5'082

9'795

14'876

56'294

56'294

108'507 221'094

Niger

13.1

240

5'009

5'009

9'656

14'665

91'968

91'968

177'270 361'206

Malawi

12.8

208

4'916

4'916

9'476

14'392

79'591

79'591

153'412 312'594

Ethiopia

12.8

1'211

4'912

4'912

9'468

14'380

464'466

464'466

895'266 1'824'199

Democratic

Republic of

Congo

12.6

911

4'816

4'816

9'284

14'100

349'438

349'438

673'548 1'372'425

Uganda

12.5

458

4'799

4'799

9'250

14'050

175'522

175'522

338'321 689'365

Burkina Faso

12.5

213

4'779

4'779

9'212

13'991

81'649

81'649

157'379 320'676

Bangladesh

12.4

1'943

4'742

4'742

9'141

13'884

745'314

745'314

1'436'604 2'927'231

Madagascar

12.4

283

4'738

4'738

9'132

13'869

108'607

108'607

209'342 426'556

Somalia

12.3

126

4'705

4'705

9'069

13'774

48'310

48'310

93'119 189'739

Nepal

12.2

341

4'696

4'696

9'051

13'747

130'710

130'710

251'946 513'367

Myanmar

12.1

640

4'632

4'632

8'929

13'561

245'444

245'444

473'096 963'984


241

Mali

12.0

200

4'614

4'614

8'893

13'507

76'554

76'554

147'560 300'668

Tanzania

12.0

602

4'595

4'595

8'857

13'452

230'739

230'739

444'753 906'231

Guinea

11.9

143

4'576

4'576

8'820

13'395

54'674

54'674

105'384 214'732

Haiti

11.9

124

4'550

4'550

8'769

13'319

47'457

47'457

91'475 186'390

Sierra Leone

11.8

73

4'528

4'528

8'727

13'255

27'976

27'976

53'924 109'876

Sudan

11.8

454

4'519

4'519

8'711

13'230

174'056

174'056

335'495 683'607

Mozambique

11.7

309

4'479

4'479

8'633

13'111

118'538

118'538

228'484 465'560

Afghanistan

11.5

354

4'425

4'425

8'530

12'955

135'778

135'778

261'714 533'269

Kenya

11.2

490

4'300

4'300

8'288

12'588

187'871

187'871

362'124 737'865

Cambodia

11.2

168

4'283

4'283

8'256

12'539

64'584

64'584

124'486 253'654

Cameroon

10.8

240

4'152

4'152

8'003

12'155

92'221

92'221

177'758 362'201

Liberia

10.8

46

4'143

4'143

7'986

12'129

17'789

17'789

34'289 69'868

Nigeria

10.7

1'843

4'091

4'091

7'885

11'976

706'983

706'983

1'362'721 2'776'687

Pakistan

10.0

1'814

3'840

3'840

7'401

11'241

695'753

695'753

1'341'074 2'732'579

India

9.9

12'678

3'800

3'800

7'325

11'125

4'862'484

4'862'484

9'372'515 19'097'484

Zambia

9.9

151

3'790

3'790

7'305

11'094

57'778

57'778

111'368 226'924


242

Mauritania

9.9

38

3'781

3'781

7'289

11'070

14'644

14'644

28'227 57'516

Angola

9.8

229

3'745

3'745

7'219

10'965

87'825

87'825

169'284 344'934

Benin

9.7

100

3'729

3'729

7'188

10'917

38'492

38'492

74'195 151'179

Cote D'ivoire

9.5

206

3'651

3'651

7'037

10'687

78'935

78'935

152'149 310'019

Indonesia

9.5

2'386

3'642

3'642

7'021

10'663

915'200

915'200

1'764'062 3'594'462

Togo

9.2

64

3'533

3'533

6'809

10'342

24'477

24'477

47'181 96'135

Congo

9.0

40

3'467

3'467

6'683

10'151

15'236

15'236

29'368 59'841

Tajikistan

9.0

73

3'466

3'466

6'680

10'146

28'113

28'113

54'187 192'528

Senegal

9.0

128

3'447

3'447

6'644

10'091

49'020

49'020

94'487 86'151

Paraguay

8.8

57

3'393

3'393

6'539

9'932

21'935

21'935

42'281 87'534

Laos

8.8

58

3'387

3'387

6'529

9'916

22'287

22'287

42'959 196'164

Zimbabwe

8.7

130

3'353

3'353

6'462

9'815

49'946

49'946

96'272 262'853

Sri Lanka

8.5

174

3'261

3'261

6'286

9'547

66'926

66'926

129'001 332'057

Ghana

8.4

220

3'231

3'231

6'228

9'460

84'546

84'546

162'964 128'966

Bolivia

8.2

86

3'157

3'157

6'086

9'243

32'836

32'836

63'293 73'701

Nicaragua

8.2

49

3'156

3'156

6'084

9'240

18'765

18'765

36'171 10'451


243

Fiji

7.9

7

3'022

3'022

5'825

8'848

2'661

2'661

5'129 31'700

Mongolia

7.4

21

2'823

2'823

5'441

8'264

8'071

8'071

15'558 1'081'589

Philippines

7.4

718

2'822

2'822

5'440

8'263

275'388

275'388

530'814 171'571

Guatemala

7.3

114

2'784

2'784

5'366

8'150

43'684

43'684

84'202 79'153

Honduras

6.7

53

2'568

2'568

4'949

7'517

20'153

20'153

38'846 252'181

Yemen

6.6

167

2'515

2'515

4'847

7'362

64'209

64'209

123'764 868'171

Vietnam

6.3

576

2'419

2'419

4'663

7'082

221'048

221'048

426'074 55'346

El Salvador

6.0

37

2'314

2'314

4'460

6'775

14'092

14'092

27'162 6'650

Bhutan

5.9

4

2'244

2'244

4'325

6'569

1'693

1'693

3'264 20'644

Namibia

5.8

14

2'240

2'240

4'317

6'557

5'256

5'256

10'131 33'304

Bosnia and

Herzegovina

5.8

22

2'218

2'218

4'275

6'492

8'480

8'480

16'344 235'986

Azerbaijan

5.2

49

1'991

1'991

3'838

5'829

18'910

18'910

36'449 15'988

Peru

5.1

157

1'966

1'966

3'789

5'755

60'085

60'085

115'815 297'571

Botswana

4.9

11

1'870

1'870

3'605

5'476

4'071

4'071

7'847 5'083

Armenia

4.8

14

1'832

1'832

3'531

5'363

5'481

5'481

10'565 925'594

Latvia

4.3

9

1'662

1'662

3'204

4'866

3'344

3'344

6'446 62'587


244

Colombia

4.2

198

1'600

1'600

3'085

4'685

75'766

75'766

146'039 5'841

Djibouti

3.9

3

1'497

1'497

2'886

4'383

1'294

1'294

2'495 8'432

Georgia

3.3

14

1'274

1'274

2'455

3'729

5'201

5'201

10'024 26'423

Brazil

3.0

614

1'154

1'154

2'224

3'378

235'669

235'669

454'256 1'156

Ecuador

2.7

42

1'018

1'018

1'961

2'979

15'935

15'935

30'716 22'062

Gabon

2.3

4

901

901

1'737

2'638

1'487

1'487

2'866 636

Jamaica

2.0

6

774

774

1'492

2'266

2'147

2'147

4'138 8'399

Cuba

1.5

18

592

592

1'141

1'733

6'728

6'728

12'968 680

Suriname

1.4

1

552

552

1'064

1'615

294

294

567 21'344

Dominican

Republic

1.4

15

546

546

1'053

1'599

5'617

5'617

10'828 66'350

Belize

1.2

0

470

470

906

1'376

162

162

312 198

Belarus

1.2

11

455

455

876

1'331

4'317

4'317

8'321 62'787

Costa Rica

1.2

6

454

454

876

1'330

2'139

2'139

4'122 14'684

Cabo Verde

0.9

0

342

342

658

1'000

173

173

334 566

Chile

0.8

14

309

309

596

905

5'434

5'434

10'475 5'418

Thailand

0.7

44

250

250

483

733

16'894

16'894

32'563 211'164


245

Bahamas

0.3

0

133

133

257

391

50

50

97 221'094

Mexico

0.3

42

129

129

249

378

15'986

15'986

30'814 361'206

Morocco

0.3

10

112

112

215

327

3'739

3'739

7'207 312'594

Uruguay

0.1

0

42

42

82

124

144

144

278 1'824'199

Argentina

0.1

4

32

32

63

95

1'380

1'380

2'659 1'372'425

Total 33’435 12'823'317 12'823'317 24'717'144 50'363'779



246

Identification of Nigeria’s corresponding equivalence group

y = 81.774x - 47.471

0

20

40

60

80

100

120

140

0 1 1 2 2 3GDP per capita Constant ['000 USD]

[kg]

Nigeria



247

Nigerian local cement producers – 2013*

Company (plants) Region City Capacity 2013

UniCem South-South Calabar 2.3

Wapco South-West Sagamu 1

Wapco South-West Ewekoro 3.3

AshakaCem North-East Ashaka 1.1

Dangote Cement North-Central Obajana 6.7

Benue Cem. Company North-Central Gboko 2.3

Dangote Cement South-West Ibeshe 3

CCNN North-West Sokoto 0.5

PureChem South-West Onigbedu 0.1

Edo Cement Company South-South Okpella 0.3

AVA North-Central Edo 0.5

Total

21.1

(*) Based on projections from 2010 values as reported by company websites.



248

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Databases and websites

8.2.1 Databases

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8.2.2 Websites

https://www.clarin.com/buenos_aires/Buenos-Aires-metros-cuadrados-poblacion_0_SyJ-

E1UaD7l.html

http://www.concremax.com.pe/noticia/como-calcular-cantidad-mortero

https://datahelpdesk.worldbank.org/knowledgebase/articles/114942-what-is-the-

difference-between-current-and-constan

http://worldbank.tumblr.com/post/70192273280/average-house-size-by-country

https://www.clarin.com/buenos_aires/Buenos-Aires-metros-cuadrados-poblacion_0_SyJ-E1UaD7l.html

https://www.clarin.com/buenos_aires/Buenos-Aires-metros-cuadrados-poblacion_0_SyJ-E1UaD7l.html

https://datahelpdesk.worldbank.org/knowledgebase/articles/114942-what-is-the-difference-between-current-and-constan

https://datahelpdesk.worldbank.org/knowledgebase/articles/114942-what-is-the-difference-between-current-and-constan

http://worldbank.tumblr.com/post/70192273280/average-house-size-by-country

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