HOW INDUSTRIAL DEVELOPMENT MATTERSTO THE WELL-BEING OF THE POPULATION

Some Statistical Evidence

Vienna, 2020

Acknowledgement

This publication was prepared by Kamila Facevicova and Petra Kynclova under the general supervi-sion of Shyam Upadhyaya, Chief Statistician of UNIDO. The final editing of the report was doneby Niki Rodousakis.

Copyright c© 2020 United Nations Industrial Development Organization

The designations employed and the presentation of the material in this document do not imply theexpression of any opinion whatsoever on the part of the Secretariat of the United Nations IndustrialDevelopment Organization (UNIDO) concerning the legal status of any country, territory, cityor area or of its authorities, or concerning the delimitation of its frontiers or boundaries, or itseconomic system or degree of development.

Designations such as "developed", "industrialized" and "developing" are intended for statisticalconvenience and do not necessarily express a judgment about the stage reached by a particularcountry or area in the development process. Mention of firm names or commercial products doesnot constitute an endorsement by UNIDO.

Material in this publication may be freely quoted or reprinted, but acknowledgement is requested,together with a copy of the publication containing the quotation or reprint.

For reference and citation, please use: United Nations Industrial Development Organization, 2020.How industrial development matters to the well-being of the population: Some statistical evidence.Vienna.

All photos c© UNIDO, Freepik, unless otherwise stated.

Contents

I INTRODUCTION

II INDUSTRIAL DEVELOPMENT

III HUMAN DEVELOPMENT

IV POVERTY AND INEQUALITY

V EMPLOYMENT

VI EDUCATION

VII HEALTH

VIII REFERENCES AND APPENDICES

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Appendix I - List of countries and areas included in selected groupings 63

I INTRODUCTION

Introduction

The 2030 Agenda for SustainableDevelopment, agreed by all UN Member Statesin 2015, is a global action plan for people,planet and prosperity, which is relevant nowand in the future. Building on the MillenniumDevelopment Goals (MDGs), the 17 SustainableDevelopment Goals (SDGs) and 169 targets havebeen established to drive economic prosperityand social well-being while protecting the envi-ronment. The 2030 Agenda aims to leave no onebehind and thus represents a shared blueprint forboth developed and developing countries (UN,2017).

The United Nations Industrial DevelopmentOrganization (UNIDO) is fully committedto contributing to the achievement of theSDGs, while delivering on its mandate tosupport Member States in achieving inclusiveand sustainable industrial development (ISID).SDG 9 calls for building resilient infrastructure,promoting sustainable industrialization and fos-tering innovation. Due to the SDG’s interlinkednature, many of UNIDO’s activities contributeto more than just SDG 9.

The main objective of this report is toprovide statistical evidence on how closelyindustrial development is linked to people’s liv-ing conditions and the quality of their lives.Countries’ development is measured by their eco-nomic growth, defined in terms of rising levelsof gross domestic product (GDP). The main dis-advantage of using GDP as the main measure ofdevelopment is that it does not capture importantquality of life elements as well as inequalities,which are essential for the assessment of anycommunity’s well-being. It also disregards theeffects that economic production and the associ-ated increased demand for energy, food, servicesand consumer goods have on the environment

(Stiglitz et al., 2018).Although ’well-being’ has become a widely

used term, promoted by both researchers andpolicymakers, there is no established definition.Well-being is a very broad concept that can be de-scribed by many dimensions and defining it hasbeen the focus of many research papers (Dodgeet al., 2012). Considering the significance ofwell-being for sustainable development, estab-lishing well-designed measurement frameworksis crucial for policy-making (Llena-Nozal et al.,2019).

The OECD Framework for Measuring Well-Being and Progress was introduced to monitornot only the economic performance of countries,but people’s living conditions as well. It buildsaround three distinct components: 1) materialconditions, 2) quality of life, and 3) sustainabil-ity, each with their relevant dimensions. Basedon this framework, the biennial OECD report“How’s Life?” (OECD, 2017) presents a compre-hensive set of internationally comparable well-being indicators for OECD and partner countries.At the same time, the OECD created the “BetterLife Index” as a communication tool to engagedirectly with data users. The set of indicators is,however, dominated by subjective perspectivesof the surveyed population (OECD, 2013).

The development of this framework to mea-sure the level of well-being and its progress isa step towards making such information publiclyavailable and to highlight priority areas for ac-tion followed by a reshaping of national policies.The available information allows researchers toextend the analysis by further indicators and toinvestigate potential linkages.

It is indisputable that the achievement ofSDG 9 is linked to meeting the other Goalsand targets of the 2030 Agenda. Inclusive and

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sustainable industrialization drives sustained eco-nomic growth, the creation of decent jobs and in-come (SDG 8); it helps reduce poverty (SDG 1),hunger (SDG 2) and inequalities (SDG 5 and10), while improving health and well-being(SDG 3), increasing resource and energy effi-ciency (SDG 6, 7, 11, 12) and reducing green-house gas and other polluting emissions, includ-ing from chemicals (SDG 13, 14, 15).

There is strong evidence that citizens livingin developed industrialized countries enjoy farmore prosperous and healthy lives than thosewho reside in least developed countries (LDCs)(Upadhyaya and Kepplinger, 2014). The former

benefit from high levels of education, better so-cial security and health services, sophisticated transport and communication networks, and ac-cess to information, knowledge, technology and financial facilities required by businesses. This report presents empirical evidence on the corre-lation between industrial development and other dimensions of sustainable development, with a view to improving the understanding of these correlations among policymakers at both na-tional and international level. The report does not, however, draw on the causal analysis of the variables under study.

IIINDUSTRIAL DEVELOPMENT

Industrial Development

Industrial development unleashes dynamicand competitive economic performance whichgenerates income and employment, facilitatesinternational trade and increases resource effi-ciency, and is thus a major driver of povertyalleviation and shared prosperity.

Although industrialization contributes tothe universal objective of economic growth,its impact differs depending on the country’sstage of development. In developed economies,industrial growth is reflected in achieving higherproductivity, embracing new technologies, in-telligent production processes and reducing theeffects of industrial production on the environ-ment and climate. For developing economies, in-dustrialization implies structural transformationof the economy from traditional sectors such asagriculture and fishery to modern manufacturingindustries fuelled by innovation and technology.Such an expansion of the manufacturing sectorcreates jobs, helps improve incomes and thusreduces poverty, introduces and promotes newtechnologies and produces essential goods andservices for the market.

World manufacturing production reachedUSD 13,543 billion (at constant 2010 prices) in2018, reducing the global MVA growth rate from3.8 per cent in 2017 to 3.5 per cent in 2018. Thisslowdown has primarily been attributed to theincrease in trade and tariff barriers between theUnited States, China and the European Union,exposing the markets to a high level of uncer-tainty, limiting investments and future growth.The deceleration in production was observed inall major country groups.

Industrialized economies continued to dom-inate global manufacturing production, how-ever, their share dropped from 67.7 per centin 2007 to 55.7 per cent in 2017 (Figure 1).

This long-term trend illustrates the relocationof manufacturing production from industrializedeconomies to the developing world. Developingand emerging industrial economies have main-tained a strong pace of manufacturing growth,much higher than that of the world and of indus-trialized economies.

MVA in developing and emerging industrialeconomies is dominated by China which in-creased its MVA share from 13.5 per cent in 2007to 24.3 per cent in 2017. Emerging industrialeconomies excluding China accounted for 16.4per cent of global manufacturing productionin 2017, while the share of other developingeconomies and LDCs was negligible at 2.8 percent and 0.8 per cent, respectively.

China has been heading the list of the tenlargest manufacturers worldwide since 2010,with a share of 24.3 per cent in world MVAin 2017, followed by the United States witha share of 15.0 per cent (Figure 1). China’smanufacturing production is inching closer toEurope’s, which accounted for 25.5 per cent in2017. The remaining countries in the list of topten manufacturers are Japan, Germany, India,the Republic of Korea, Italy, France, Brazil andIndonesia. Together, these countries accountedfor over 70 per cent of global MVA in 2017.

Despite being home to over 12 per cent ofworld population, LDCs only accounted for 0.8per cent of total worldwide manufacturing pro-duction in 2017. By comparison, industrializedeconomies with a share of around 17 per cent ofglobal population, accounted for over 55 per centof global manufacturing output. It is thus crucialfor LDCs to expand their capacities to reachan overall higher growth trajectory (UNIDO,2019e).

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Figure 1: MVA and its distribution by country groups, billions of constant 2010 US dollars (left).Top 10 largest manufacturing producers in the world in 2017, share of countries’ MVA in globalMVA (right).Source: UNIDO MVA 2019 Database (UNIDO, 2019d)

Manufacturing value added per capita

A country’s level of industrial developmentis often reflected by its MVA per capita whichmeasures a countries’ level of manufacturing pro-duction relative to their population size. Valueadded is a sector’s net output after adding up alloutputs and subtracting all intermediate inputs. Itis calculated without deducting the depreciationof fabricated assets or the depletion and degra-dation of natural resources. The origin of valueadded is determined by the International Stan-dard Industrial Classification (ISIC), revision 3.Data are expressed in constant 2010 US dollarsand sourced from the UNIDO MVA Database(UNIDO, 2019d). Manufacturing refers to indus-tries that belong to ISIC divisions 15-37.

The process of industrialization is facinga number of fundamental challenges, particu-larly because industrial production capacity hasbeen concentrated in a few countries, includingChina, the United States, Japan and Germany.The regional patterns are depicted in Figure 2

indicating that the highest levels of MVA percapita are attained in North America and Europe.Despite the rapid growth of MVA per capita indeveloping and emerging industrial economies,they still lag significantly behind industrializedcountries.

Global MVA per capita has continued togrow, accounting for USD 1,736 in 2017 com-pared to USD 1,251 in 2000. The median valueof MVA per capita in 2017 was around USD 500,i.e. the majority of countries in the world achieveonly USD 500 compared with USD 5,770 for in-dustrialized economies.

Figure 2 shows that the lowest levels ofMVA per capita are mostly located in LDCs inAfrica and Asia. Somalia (USD 2), Timor-Leste(USD 7) and Sierra Leone (USD 9) registered thelowest MVA per capita in 2017, while Liechten-stein (USD 44,349), Ireland (USD 24,077) andSan Marino (USD 15,462) had the highest MVAper capita in 2017.

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Figure 2: World map of MVA per capita in 2010 constant US dollars, all measured in 2017.Source: UNIDO MVA 2019 Database (UNIDO, 2019d)

Competitive Industrial Performance index

The Competitive Industrial Performance(CIP) index represents a composite measureto benchmark industrial competitiveness acrosseconomies, providing valuable information oncountries’ strengths and weaknesses in nationalmanufacturing industries. By promoting com-petitiveness, economic efficiency in the alloca-tion of scarce resources can be maximized whilegreater prosperity for the population is gener-ated.

An increase in industrial competitivenesscan contribute to a country’s overall prosper-ity in many different ways. For example, itcan encourage more investment from nationaland international firms, increase industries’ re-silience to external shocks, including surgesin commodity prices or economy-wide reces-sions. Competitiveness is decisive if a coun-try’s industrial sector is to flourish, it determines

the pace and quality of the country’s structuralchange as its economy develops, as well as theextent to which these changes will contribute tosociety’s well-being. The industrial sector’s con-tribution to prosperity depends on its capacity toproduce manufactured goods, to exchange thosegoods in global markets and to specialize in com-plex production processes (UNIDO, 2019b).

The CIP Index is widely used byinternational development agencies to rank coun-tries within the context of their developmentpriorities. The global manufacturing ranking isbased on the analysis of eight indicators reflect-ing three dimensions: 1) the capacity to produceand export manufactured goods, 2) the extent oftechnological deepening and upgrading, and 3)the impact on the world market.

The 2019 edition of the CIP Index assesses150 economies covering around 99 per cent of

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global manufactured exports and MVA in 2017.Figure 3 presents the distribution of CIP scoresin the world. The countries at the top of the2017 CIP ranking are Germany (0.515), Japan(0.404), China (0.369), the Republic of Korea

(0.365) and the United States (0.355). By con-trast, the economies with the lowest levels ofmanufacturing capacity include Burundi, Eritreaand Tonga.

Figure 3: World map of CIP index scores, all measured in 2017.Source: UNIDO CIP 2019 Database (UNIDO, 2019a)

III HUMAN DEVELOPMENT

Human Development

Human development is a concept that goesbeyond economic growth and is defined as theprocess of expanding people’s freedoms and op-portunities and improving their well-being. Thehuman development approach aims to increase

the richness of human life rather than the rich-ness of the economy human beings live in. Peo-ple and their opportunities and choices are themain focus of human development.

Figure 4: World map of HDI scores, all measured in 2017.Source: UNDP HDI 2018 (UNDP, 2018)

Improving people’s lives by expanding theirfreedoms and opportunities is a principle that isapplicable to everyone in the world. The humandevelopment approach thus reinforces the pledgeto leave no one behind as promoted by the 2030Agenda for Sustainable Development, which in-

corporates human development in a number ofdimensions.

Significant progress has been made overthe past 25 years on many fronts of humandevelopment, with people living longer, morepeople rising out of extreme poverty and fewer

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people suffering from malnutrition. Humandevelopment has enriched human lives —- unfor-tunately, however, not all have benefited equally,and even worse, not everyone has been included(UNDP, 2016).

There are many reasons why industrializa-tion is fundamental for human development.First, its is an essential means for incorporat-ing technological progress and innovation andpromoting its use. That is, it offers a unique op-portunity for learning, improvement and transfor-mation. Moreover, industrialization empowers

people to access productive resources by expand-ing human capabilities through education, skillsdevelopment, and sociocultural changes as wellas to produce goods that are essential for nu-trition, health care, and other human needs toimprove the quality of life.

The Human Development Index (HDI) de-veloped by UNDP, together with its adjustedversion that takes inequality factors into ac-count, belong to the important measure of humandevelopment.

Human Development Index

The Human Development Index (HDI) is acomposite index that focuses on three basic di-mensions of human development: (i) the abilityto lead a long and healthy life, measured by lifeexpectancy at birth; (ii) the ability to acquire

knowledge, measured by mean years of school-ing and expected years of schooling; and (iii)and the ability to achieve a decent standard ofliving, measured by gross national income percapita.

Figure 5: Comparison of the HDI with MVA per capita and the CIP index, all measured in 2017.Source: UNDP HDI 2018 (UNDP, 2018), UNIDO CIP 2019 Database (UNIDO, 2019a) and UNIDO MVA 2019

Database (UNIDO, 2019d)

The HDI’s 2018 Update presents values for189 economies around the world from 1990 upto the most recent data available for 2017. TheHDI’s regional patterns are illustrated on theworld map in Figure 4. We find clear similaritieswith the global distribution of MVA per capita

(Figure 2). Countries with the highest HDIscores are located in Europe and North America,i.e. industrialized economies. By contrast, LDCsin Africa and Asia are clustered at the bottom ofthe HDI ranking. The top five countries in theglobal HDI ranking are Norway (0.953), Switzer-

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land (0.944), Australia (0.939), Ireland (0.938)and Germany (0.936). The bottom five are alllocated in Africa, namely – Burundi (0.417),Chad (0.404), South Sudan (0.388), the CentralAfrican Republic (0.367) and Niger (0.354).

A comparison of HDI scores with MVA percapita in 2017 (Figure 5) reveals a positive as-sociation between both indicators. High HDIscores are observed in industrialized countrieswith a high MVA per capita. The distributionof HDI values creates clusters of countries cor-responding to country groupings by stage ofindustrial development.

The countries with the highest HDI valuesbelong to the group of industrialized economies,while low HDI values are typically found inLDCs. Samoa is an interesting example, whichreported a very high HDI value (0.71) in 2017,normally achieved by the group of other devel-oping economies, and Samoa thus seems to haveuntapped industrial potential.

Similar patterns are also evident when wecompare the HDI with the CIP index (Figure 5).HDI scores are positively correlated with theindustrial competitiveness of nations.

Figure 6: Progression of the relationship between the HDI and MVA per capita and the CIP indexsince 1995.Source: UNDP HDI 2018 (UNDP, 2018), UNIDO CIP 2019 Database (UNIDO, 2019a) and UNIDO MVA 2019

Database (UNIDO, 2019d)

Figure 6 depicts the relationship betweenHDI scores and MVA per capita (left) andCIP scores (right) in five subsequent time pe-riods since 1995. Both indicators reflect coun-tries’ industrial performance and show improv-ing trends over time. An overall growth in HDIscores is clearly linked to increases in MVA percapita and CIP scores. A change in the distri-bution of HDI scores over the last 20 years isvisible. The median values are slowly rising and

the values of countries with low HDI scores areincreasing more rapidly than those of economiesat the top of the HDI ranking.

A slowdown in the growth of HDI scores canbe observed in recent years. This overall trendcan be explained not only by the global financialcrisis of 2008-2009, but also by the natural limitof various HDI components, i.e. life expectancy,years of schooling and rates of enrolment cannotgrow indefinitely.

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Inequality-adjusted Human Development Index

The Inequality-adjusted Human DevelopmentIndex (IHDI) adjusts the HDI for inequality inthe distribution of each dimension across the pop-ulation. The IHDI combines a country’s averageachievements in health, education and incomewith how those achievements are distributedamong the country’s population by “discount-ing” each dimension’s average value accordingto its level of inequality. The IHDI equals theHDI when there is no inequality across a popula-tion but falls below the HDI scores as inequalityrises. In this sense, the IHDI measures thelevel of human development when inequality isaccounted for.

The difference between the IHDI and HDIis the human development cost of inequality,also termed loss to human development due toinequality. The IHDI identifies inequalities inthe different dimensions, and can inform poli-

cies to reduce inequality, and leads to a bet-ter understanding of inequalities across popula-tions and their contribution to the overall humandevelopment cost. A recent measure of inequal-ity in the HDI, the coefficient of human inequal-ity, is calculated as an average inequality acrossall three dimensions of the HDI, namely – theability to lead a long and healthy life, the abilityto acquire knowledge and the ability to achievea decent standard of living (UNDP, 2018).

The IHDI measure corresponds to the ref-erence year 2017. It uses the HDI indicatorsfor 2017, measures of inequality that are basedon the most recent household surveys availablefrom 2006 to 2017 and life tables that refer tothe period 2015-2020. The development of theIHDI over time is only available from 2010 to2017.

Figure 7: Comparison of the IHDI with MVA per capita and the CIP index, all measured in 2017.Source: UNDP IHDI 2018 (UNDP, 2018), UNIDO CIP 2019 Database (UNIDO, 2019a) and UNIDO MVA 2019

Database (UNIDO, 2019d)

The adjustment for inequality seems to re-duce the HDI scores, regardless of the stage ofindustrial development in accordance with coun-try groups (Figure 7). As is the case for the HDI,

the IHDI scores are also closely related to thevalues of both MVA per capita and CIP, withhigh values typical for industrialized economies.

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Figure 8: Progression of the relationship between the IHDI and MVA per capita and the CIP indexsince 2010.Source: UNDP IHDI 2018 (UNDP, 2018), UNIDO CIP 2019 Database (UNIDO, 2019a) and UNIDO MVA 2019

Database (UNIDO, 2019d)

Despite data sparsity for the calculationof IHDI scores and thus limited availability,Figure 8 suggests an overall upward trend overthe period 2010-2017. Although the HDI scoresare adjusted to corresponding levels of inequality,the link with industrial development indicatorsremains very strong suggesting positive correla-tion patterns and overall improvement over time.

The HDI and IHDI as composite measures ofhuman development are crucial indicators usedby policymakers and researchers alike. How-ever, new challenges to human development,

particularly inequality and environmental sus-tainability, require concerted measurement andmore-in-depth analysis. Data availability isexpanding with new opportunities to measureinnovation and disaggregation, and possibili-ties to establish new partnerships to track theprogress towards achieving the 2030 Agendafor Sustainable Development. For this reason,other indicators should be considered as sup-plementary to human development measuresto track countries’ achievement of sustainabledevelopment.

IVPOVERTY AND INEQUALITY

Poverty and Inequality

Economic growth driven by industrialdevelopment is often essential for reducing ab-solute poverty. Nevertheless, economic growth

may also be associated with increased incomeinequality, which does not automatically addressthe entire poverty problem.

Figure 9: World map of the poverty headcount ratio at USD 1.90 per day, most recent availablevaluesSource: The World Bank’s Poverty and Equity Database (World Bank, 2019a)

Inclusive and sustainable industrialdevelopment, when adequately linked to formaljob markets and health, safety and environmen-tal standards, is widely recognized as havinga crucial impact on job creation, sustainablelivelihoods, technology and skills development,food security and equitable growth – some of

the key requirements for eradicating poverty by2030.

There is evidence that rapid industrializa-tion has lifted several millions of people out ofpoverty by providing them with jobs and an in-come. Yet progress has been uneven and manyremain stuck in a poverty trap, particularly in

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areas where industrialization levels remain lowor have stagnated. Countries, especially in sub-Saharan Africa, are lagging far behind and theshare of poor people has actually even increased

in some countries. Inclusive and sustainableindustrial development can contribute to povertyreduction efforts and ensures that “no one is leftbehind” by 2030.

Poverty headcount ratio at USD 1.90 per day (2011 PPP)

When assessing poverty in a given country,and trying to define how best to reduce it, one nat-urally chooses a poverty line considered appro-priate for that country. Poverty lines across coun-tries vary in terms of their purchasing power, andhave a strong economic gradient, implying thatricher countries tend to adopt higher standards of

living in their definition of poverty. But to consis-tently measure global absolute poverty in termsof consumption we need to treat two people withthe same purchasing power over commoditiesthe same way—both are either poor or not poor-— even if they live in different countries.

Figure 10: Comparison of the poverty headcount ratio at USD 1.90 a day with MVA per capita andthe CIP index, all measured in the period 2015-2017Source: The World Bank’s Poverty and Equity Database (World Bank, 2019a), UNIDO CIP 2019 Database (UNIDO,

2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

Poverty measures based on internationalpoverty lines attempt to hold the poverty line’sreal value constant across countries, as is thecase when making comparisons over time. Thepoverty headcount ratio at USD 1.90 a day isan indicator that expresses the percentage of thepopulation living on less than USD 1.90 a dayat 2011 international prices. As a result of revi-sions in PPP exchange rates, poverty rates forindividual countries cannot be compared withpoverty rates reported in earlier editions (World

Bank, 2019a).

The distribution of the poverty headcount ra-tio at USD 1.90 a day (2011 PPP) is shown inFigure 9. Since this indicator was only reportedfor a fraction of countries in 2017, the worldmap combines the most recent values availablefor each country. The majority of figures were re-ported for the period of 2013-2017, however, forsome of the African countries, only data from be-fore 2010 were available. The map shows cleardifferences between African and South Amer-

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ican countries, where the ratio of the popula-tion below the international poverty line is muchhigher than in the rest of the world. The highestvalues were reported for Madagascar with 77.6per cent in 2012, the Democratic Republic of theCongo with 76.6 per cent in 2012 and Burundiwith 71.8 per cent in 2013.

A comparison of the poverty headcount ratioat USD 1.90 a day (2011 PPP) with the valueof MVA per capita and the CIP index is de-picted in Figure 10. It reveals substantially dif-ferent patterns for various levels of industrial

development. Industrialized economies andemerging industrial economies have very lowpoverty headcount ratio values concentratedaround a value of zero. Other developingeconomies and least developed countries, onthe other hand, report values of 10 per cent andhigher. For instance, in Zambia, 57.5 per centof the population lived below the internationalpoverty line, and in Malawi, 70.3 per cent did.Figure 10 indicates that as countries industrial-ize the percentage of people living in poverty isdecreasing significantly.

Figure 11: Progression of the relationship between the poverty headcount ratio at USD 1.90 perday and MVA per capita and the CIP index since 1995.Source: The World Bank’s Poverty and Equity Database (World Bank, 2019a), UNIDO CIP 2019 Database (UNIDO,

2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

The systematic decline of the overall valueof the poverty headcount ratio over the last 20years is depicted in Figure 11. Major differencesare visible over the different time periods, par-ticularly as regards lower MVA per capita val-ues and CIP scores in least developed countries.The percentage of the population living on lessthan USD 1.90 per day has decreased by half

since 1995–1999. In the case of developed coun-tries with an MVA per capita that is higher thanUSD 1,000 or with a CIP score of more than 0.5,the decline has not been as dramatic. Figure 11demonstrates that the poverty headcount ratioat USD 1.90 per day decreased over the last 20years from around 10 per cent to almost zero incountries that have successfully industrialized.

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Inequality-adjusted Income Index

The inequality-adjusted income index is partof the calculation process of the HDI index andrepresents the HDI income index adjusted for

inequality in income distribution based on datafrom household surveys (UNDP, 2018).

Figure 12: Comparison of the inequality-adjusted income index with MVA per capita and the CIPindex, all measured in 2017.Source: UNDP IHDI 2018 (UNDP, 2018), UNIDO CIP 2019 Database (UNIDO, 2019a) and UNIDO MVA 2019

Database (UNIDO, 2019d)

Figure 13: Progression of the relationship between the inequality-adjusted income index and MVAper capita and the CIP index since 2010.Source: UNDP IHDI 2018 (UNDP, 2018), UNIDO CIP 2019 Database (UNIDO, 2019a) and UNIDO MVA 2019

Database (UNIDO, 2019d)

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A comparison of the value of the inequality-adjusted income index with MVA per capita andthe CIP scores indicates a clear relationship be-tween these indicators (Figure 12). The high val-ues of the inequality-adjusted income index aretypical for countries with a high MVA per capitaand for economies at the top of the CIP rank-ing. Moreover, the distribution of the inequality-adjusted income index also follows the countryclassification in accordance with its level of in-dustrialization. One interesting exception of thistrend is Timor-Leste, which has one of the low-

est MVA per capita values but a high value ofits inequality-adjusted income index which ac-counts for 0.55. Timor-Leste was not includedin the CIP index and ranking in 2017.

Although the values of the inequality-adjusted income index have only been availablesince 2010, a comparison of their median valuesin the time periods 2010–2014 and 2015–2017indicates a relatively stable pattern of the rela-tionship with MVA per capita and the CIP scores(Figure 13). A moderate growth of the index’soverall value over time is noticeable.

Multidimensional Poverty Index

Another international measure of poverty ispublished by UNDP in its Human DevelopmentReports Series as the Global MultidimensionalPoverty Index (MPI). The MPI includes health,education and standard of living indicators todetermine a population’s degree of poverty. Itcontributes to the measurement of acute povertyacross more than 100 developing countries since2010, when it replaced the Human Poverty Index

(UNDP and Oxford Poverty, 2019).The global MPI is disaggregated by age

group and geographic area to display povertypatterns within countries. It is further brokendown to highlight which particular deprivationscharacterize poverty and drive its reduction or in-tensification. These analyses often play a crucialrole for policymakers.

Figure 14: Comparison of the MPI, reported for the period 2007-2018, with MVA per capita andthe CIP index, measured in 2017.Source: UNDP MPI 2019 (UNDP and Oxford Poverty, 2019), UNIDO CIP 2019 Database (UNIDO, 2019a) and UNIDO

MVA 2019 Database (UNIDO, 2019d)

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The relationship of the global MPI withMVA per capita and the CIP scores is depictedin Figure 14. Although the global MPI coversdeveloping economies only, both plots supportthe findings based on the poverty headcount ratioat USD 1.90 a day. LDCs, in particular, regis-tered a decline in the MPI as they experienced

industrial growth. Countries with an MVA percapita of more than USD 1,000 or a CIP scoreabove 0.1 have a global MPI that is close to zero.Figure 14 also highlights that two countries havevery low global MPI values together with a lowMVA per capita and CIP index: Iraq and Mal-dives.

V EMPLOYMENT

Employment

Sustained economic growth requires a struc-tural transformation of the economy towardsactivities with higher levels of productivity.Structural transformation towards inclusive andsustainable industrial development serves as anengine to create the competitive job opportuni-ties necessary in both developed and developing

countries today. Besides quantity, it is the qualityof jobs that matters. When labour productivityincreases, industry upgrades employment oppor-tunities to higher skilled and higher paid jobs,accompanied by increases in social protectionand worker security (UNIDO, 2015).

Figure 15: World map of total employment rates reported in 2017.Source: ILO modelled estimates, November 2018 (ILO, 2019)

The importance of employment as a path-way to economic development, social inclusionand well-being has long been recognized. Mostdeveloping economies struggle with high unem-

ployment or underemployment. Many peoplecan barely sustain themselves from what theyearn. This is why creating new jobs, but alsoimproving the incomes and working conditions

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of existing jobs, is extremely important. Aseconomies develop, jobs are reallocated fromagriculture and other labour-intensive primaryactivities to industry and finally to the servicessector. Since the industrial revolution, man-

ufacturing has been at the core of structuralchange, consistently creating higher levels ofoutput and employment, and leading to unprece-dented growth in incomes (UNIDO, 2013).

Total employment rate

Total employment rate is defined as a mea-sure of the extent to which available labour re-sources (people available to work) are beingused. It is calculated as the ratio of the employedto the working age population. Employmentrates are sensitive to the economic cycle, but areconsiderably affected by governments’ highereducation and income support policies in thelonger term and by policies that facilitate the em-ployment of women and disadvantaged groups.

Data were sourced from the ILO modelledestimates database and are available from 1991.The ILO modelled estimates series serve asa complete set of internationally comparable em-ployment statistics, including both nationally re-ported observations and imputed data for coun-tries with missing data. The imputations are

produced through a series of econometric mod-els maintained by the ILO. Estimates for coun-tries with very limited labour market informationhave a high degree of uncertainty, however (ILO,2019).

Figure 15 presents the variance of the totalemployment rate across most continents. Thelowest employment rates for 2017 were reportedfor the State of Palestine (72.6 per cent), SouthAfrica (72.7 per cent), North Macedonia (77.6per cent) or Greece (78.5 per cent). Some coun-tries had very high employment rates, like Qatar(99.9 per cent) and Niger (99.7 per cent). Val-ues above 99 per cent were not reported for anyof the European countries, where the best per-formers were Iceland (97.3 per cent) and CzechRepublic (97.1 per cent).

Total child labour rate

Child labour is often defined as work thatdeprives children of their childhood, their po-tential and their dignity, and that is harmful totheir physical and mental development. Notall work carried out by children should be clas-sified as child labour that must be eliminated.This includes activities such as helping theirparents around the home, assisting in a fam-ily business or earning pocket money outsideschool hours and during school holidays. Dataon child labour are provided by the Statistical In-formation and Monitoring Programme on ChildLabour (SIMPOC), which is the statistical arm ofthe International Programme on the Eliminationof Child Labour (IPEC).

The challenge of ending child labour remainsformidable. A total of 152 million children — 64million girls and 88 million boys -– are engagedin child labour globally, accounting for nearly

one in ten children worldwide. Nearly half of allchildren engaged in child labour — 73 millionchildren in absolute terms -– perform hazardouswork that directly endangers their health, safety,and emotional development. Children in em-ployment, a broader measure comprising bothchild labour and the permitted forms of employ-ment involving children of a legal working age,account for 218 million (ILO, 2017).

Figure 16 presents the relationship be-tween the child labour and countries’ industrialdevelopment. Data are available from the house-hold surveys conducted mostly in developingeconomies. The largest share of child labour isreported in the agricultural sector. Child labourfills the income gap in many countries. Ascountries industrialize, income increases and theshare of child labour declines.

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Figure 16: Comparison of the total child labour rate, reported for 2010-2016, with MVA per capitaand the CIP index, both measured in 2017.Source: ILOSTAT 2019 (ILO, 2017), UNIDO CIP 2019 Database (UNIDO, 2019a) and UNIDO MVA 2019 Database

(UNIDO, 2019d)

VI EDUCATION

Education

Education is in every sense one of the fun-damental factors of development. No countrycan achieve sustainable economic developmentwithout substantial investment in human capi-tal. Education enriches people’s understandingof themselves and the world. It improves thequality of their lives and leads to broad social

benefits for individuals and society. Educationraises people’s productivity and creativity andpromotes entrepreneurship and technological ad-vances. In addition, it plays a crucial role insecuring economic and social progress and im-proving income distribution.

Figure 17: World map of adjusted net enrolment rate in primary school, most recent availablevalues.Source: UNESCO Institute for Statistics 2019 (UNESCO, 2019)

There is a two-way relationship betweenindustrial development and education. Growthin industrialization creates high demand for

a skilled and trained workforce thereby encour-aging education among youth, while at the sametime providing revenues that can then be directed

40

towards further developing education. A lack ofjob opportunities for youth in developing coun-tries, however, has forced many to emigrate toindustrialized countries, which has been the rootcause for international human trafficking, oneof the worst humanitarian problems the globalcommunity faces today. Only industrializationand the creation of employment opportunities

can provide the livelihoods that allow people inmany developing countries to flourish in theirplace of origin.

This section presents statistical evidencebased on the main education indicators – theadjusted net enrolment rate in primary school,the net enrolment rate in secondary school andthe inequality-adjusted education index.

Adjusted net enrolment rate in primary school

The adjusted net enrolment rate in primaryschool is represented as the total number ofpupils of official primary school age who areenrolled in primary or secondary education. Theindicator is expressed as a percentage of the cor-responding population. Data are collected annu-ally by the UNESCO Institute of Statistics fromofficial responses to its education surveys. Whilethe net enrolment rate only reflects the number

of pupils in the official primary school age groupat the primary education level, the adjusted netenrolment rate extends this measure to those ofthe official primary school age range who havereached the secondary education level becausethey might have accessed primary education ear-lier than the official entrance age or might haveskipped a grade due to exceptional performance.

Figure 18: Comparison of the adjusted net enrolment rate in primary school with MVA per capitaand the CIP index, all measured in the period 2015-2017.Source: UNESCO Institute for Statistics 2019 (UNESCO, 2019), UNIDO CIP 2019 Database (UNIDO, 2019a) and

UNIDO MVA 2019 Database (UNIDO, 2019d)

The global distribution of the adjusted netenrolment rate in primary school is presentedin Figure 17. Due to the high sparsity of thedata in recent years, the latest available valuefor each country was used. The lowest net ad-

justment enrolment rates in primary school aregenerally observed in the sub-Saharan Africanregion. The countries with the lowest reportedvalue of the adjusted net enrolment rate in pri-mary school were South Sudan (32.25 per cent

41

in 2015), Afghanistan (26.77 per cent in 1993)and Somalia (14.45 per cent in 1980). The bestperforming countries were Canada, Singaporeand Viet Nam, all reporting more than 99.9 percent enrolment in primary school.

Figure 18 illustrates the relationship betweenthe adjusted net enrolment rate in primary schooland indicators of industrial development. Bothplots support the positive synergies between edu-

cation and industrialization. The higher variabil-ity of the adjusted net enrolment rates in primaryschool is demonstrated by countries with lowindustrial performance, i.e. other developingeconomies and least developed countries. Theadjusted net enrolment rates in primary school incountries with higher MVA per capita values andCIP scores are exclusively above 90 per cent.

Figure 19: Progression of the relationship between the adjusted net enrolment rate in primaryschool and MVA per capita and the CIP index since 1995.Source: UNESCO Institute for Statistics 2019 (UNESCO, 2019), UNIDO CIP 2019 Database (UNIDO, 2019a) and

UNIDO MVA 2019 Database (UNIDO, 2019d)

The positive correlation between the ad-justed net enrolment rate in primary school andthe high level of industrial development hasalso been observed in the long-term perspec-tive. Figure 19 demonstrates how progress inenrolment in primary education clearly fosters

industrial development and as countries industri-alize, they require high skilled workers. More-over, the rapid improvement of the adjustednet enrolment rate in primary school has beenrecorded in LDCs over the last 20 years.

Net enrolment rate in secondary school

Many countries have pledged their commit-ment to achieve higher levels of education thanuniversal primary education only and have ex-tended their target to include the secondary ed-ucation level. The net enrolment rate is the ra-tio of pupils of official school age who are en-rolled in school to the population of the corre-sponding official school age. Secondary educa-

tion completes the provision of basic educationthat began at the primary level, and aims to laythe foundations for lifelong learning and humandevelopment, by offering more subject- or skill-oriented instruction provided by more special-ized teachers. Data are collected annually bythe UNESCO Institute of Statistics from officialresponses to its education surveys.

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Figure 20: Comparison of net enrolment rate in secondary school with MVA per capita and the CIPindex, all measured in the period 2015-2017.Source: UNESCO Institute for Statistics 2019 (UNESCO, 2019), UNIDO CIP 2019 Database (UNIDO, 2019a) and

UNIDO MVA 2019 Database (UNIDO, 2019d)

Figure 21: Progression of the relationship between net enrolment rate in secondary school andMVA per capita and the CIP index since 1995.Source: UNESCO Institute for Statistics 2019 (UNESCO, 2019), UNIDO CIP 2019 Database (UNIDO, 2019a) and

UNIDO MVA 2019 Database (UNIDO, 2019d)

In comparison to the adjusted net enrol-ment rate in primary school, the relationship be-tween industrial development and the net enrol-ment rate in secondary school is much stronger.A clear positive correlation pattern is visible in

Figure 20. The countries’ classifications accord-ing to level of industrial development are per-ceptibly separated and thus suggest an importantrole of industrialization for education. In LDCswith a low MVA per capita, the enrolment rate is

43

usually reported to be below 50 per cent. By con-trast, the net enrolment rate in secondary schoolin industrialized economies with a high MVAper capita was above 80 per cent in 2017.

The change in the progression of the netenrolment rate in secondary school over timeis depicted in Figure 21. An overall improve-

ment in net enrolment rates in secondary schoolseems to be positively correlated with industrialdevelopment. The global secondary school en-rolment rate has increased over the past 20 years.While LDCs reported enrolment rates close tozero in the period 1995-2004, a rapid upwardtrend has been noted since 2010.

Inequality-adjusted education index

The inequality-adjusted education index ispart of the calculation process of the HDI indexand represents the HDI education index adjustedfor inequality in the distribution of years ofschooling based on data from household surveys(UNDP, 2018).

Figure 22 depicts a strong relationship be-tween the inequality-adjusted education indexand industrial development variables. Coun-

tries with high values of MVA per capita andCIP scores generally also achieve high scoresin the inequality-adjusted education index andvice versa. Moreover, economies are orga-nized in accordance with their classification bylevel of industrial development, which indicatesa substantial correlation between education andindustrial development.

Figure 22: Comparison of the inequality-adjusted education index with MVA per capita and theCIP index, all measured in 2017.Source: UNDP IHDI 2018 (UNDP, 2018), UNIDO CIP 2019 Database (UNIDO, 2019a) and UNIDO MVA 2019

Database (UNIDO, 2019d)

Both plots in Figure 23 reveal a strong rela-tionship between both variables throughout thelast 20 years, which remains unchanged. Fur-

thermore, the relationship seems to be constantlyincreasing over time (1995-2017), regardless ofindustrial development group.

44

Figure 23: Progression of the relationship between the inequality-adjusted education index andMVA per capita and the CIP index since 1995.Source: UNDP IHDI 2018 (UNDP, 2018), UNIDO CIP 2019 Database (UNIDO, 2019a) and UNIDO MVA 2019

Database (UNIDO, 2019d)

VII HEALTH

Health

Universal health coverage remains a ma-jor global challenge. Health is a crucial socialand economic asset – a cornerstone of humandevelopment. Many deaths and injuries could beprevented with timely and affordable access toappropriate pharmaceutical products and relatedhealth care services.

Inclusive and sustainable industrialdevelopment prioritizes high-level innova-tion and scientific research, including thedevelopment of new medical treatments, vac-

cines and medical technologies. Through theirexpertise and resources, locally operating phar-maceutical and medical equipment industries canplay a decisive role in meeting the global healthchallenge by developing innovative, safe andeffective pharmaceutical products and workingwith other stakeholders to make them available,affordable and accessible to people who lackthem, especially the most marginalized groups(UNIDO, 2015).

Figure 24: World map of life expectancy at birth reported in 2017.Source: The World Bank, World Development Indicators 2019 (World Bank, 2019b)

48

Industrial development has also been asso-ciated with negative environmental effects suchas pollution, climate change, habitat destructionand over-exploitation of natural resources, andthus has a fundamental impact on human health.Nonetheless, the process of industrialization hasradically changed in recent years. One of the pre-requisites for industry to flourish in a sustainablemanner is the availability of a guaranteed supplyof affordable and clean energy, together with im-proved resource efficiency. Many countries have

introduced norms and regulations to ensure thatadverse impacts are minimized, starting with theplanning stage, choice of location and adoptionof best available technologies.

Five indicators linked to human health wereselected for this report to demonstrate their levelof association with industrial development – lifeexpectancy at birth, infant mortality rate, lifetimerisk of maternal death, prevalence of undernour-ishment and people using at least basic drinkingwater services.

Life expectancy at birth

Life expectancy at birth refers to how long,on average, a new-born can expect to live if cur-rent death rates do not change. However, theactual age-specific death rate of any particularbirth cohort cannot be known in advance. If ratesfall, actual life spans will be higher than the lifeexpectancy calculated using current death rates.

Life expectancy at birth is one of the most fre-quently used health status indicators. Increasesin life expectancy at birth can be attributed toa number of factors, including rising living stan-dards, improved lifestyle and better education, aswell as greater access to quality health services.This indicator is measured in number of years.

Figure 25: Comparison of life expectancy at birth with MVA per capita and the CIP index, allmeasured in 2017.Source: The World Bank, World Development Indicators 2019 (World Bank, 2019b), UNIDO CIP 2019 Database

(UNIDO, 2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

Life expectancy at birth refers to the averagenumber of years a new-born is expected to live ifmortality patterns at the time of the child’s birthremain constant in the future. In other words, it

considers the number of deaths among peopleof different ages in a given year, and providesa snapshot of these overall mortality characteris-tics of the population for that year.

49

The data used in this report come from theWorld Development Indicators (WDI) database.The figures are taken from various data sources:(1) the United Nations Population Division.World Population Prospects: 2019 Revision; orderived from male and female life expectancy atbirth from sources such as: (2) census reportsand other statistical publications from nationalstatistical offices, (3) Eurostat: DemographicStatistics, (4) the United Nations StatisticalDivision. Population and Vital Statistics Re-port (various years), (5) U.S. Census Bureau:International Database, and (6) Secretariat of thePacific Community: Statistics and DemographyProgramme.

The regional differences in life expectancy atbirth (in years) between countries are depicted inFigure 24. In most of African countries, life ex-pectancy is below 65 years, which is much lowerthan that in the rest of the world. More specifi-cally, the life expectancy in Sierra Leone is 52years and only 23 years in the Central AfricanRepublic or Chad. By contrast, Algeria reporteda life expectancy of above 73 years. The high-

est life expectancy rate for 2017 was reportedby Hong Kong ROC (85), Japan, China MacaoSAR and Switzerland (all around 84 years).

The close positive relationship betweenindustrial development and life expectancy atbirth is shown in Figure 25. Countries witha long life expectancy rate are typically thosewith high MVA per capita or CIP scores and viceversa. On the other hand, a relatively high vari-ability can be observed within the group of otherdeveloping economies, including countries witha high life expectancy like Lebanon or Cuba (80years), as well as countries with a very low lifeexpectancy at birth, namely Nigeria and Côted’Ivoire (54 years).

Looking at the overall trend of life ex-pectancy at birth and industrial development overthe last 20 years, extremely positive progress hasbeen made (Figure 26). The improvement is sub-stantial in all countries, regardless of their stageof industrial development measured by MVAper capita or CIP scores. The progress made byLDCs is obviously notable.

Figure 26: Progression of the relationship between life expectancy at birth and MVA per capita andthe CIP index since 1995.Source: The World Bank, World Development Indicators 2019 (World Bank, 2019b), UNIDO CIP 2019 Database

(UNIDO, 2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

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Infant mortality rate

The infant mortality rate represents the ratioof the number of deaths of children under oneyear of age in a given year to the number of livebirths in that year. The value is expressed per1,000 live births. The main sources of mortalitydata are important registration systems and director indirect estimates based on sample surveys orcensuses.

Estimates of neonatal, infant, and childmortality tend to vary by source and method fora given period and place. Years of available esti-mates also vary by country, making comparisonsacross countries and over time difficult. To makeneonatal, infant, and child mortality estimates

comparable and to ensure consistency acrossestimates by different agencies, the UnitedNations Inter-agency Group for Child MortalityEstimation (UN IGME), which comprises theUnited Nations Children’s Fund (UNICEF), theWorld Health Organization (WHO), the WorldBank, the United Nations Population Division,and other universities and research institutes, de-veloped and adopted a statistical method thatuses all available information to reconcile dif-ferences. The method uses statistical modelsto obtain a best estimate trend line by fittinga country-specific regression model of mortalityrates against their reference dates.

Figure 27: Comparison of infant mortality rate per 1,000 live births with MVA per capita and theCIP index, all measured in 2017.Source: The World Bank, World Development Indicators 2019 (World Bank, 2019b), UNIDO CIP 2019 Database

(UNIDO, 2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

Despite declining infant mortality rates,marked disparities exist across regions and coun-tries. The infant mortality rate is typically higherin economies with lower levels of industrialdevelopment (Figure 27). Although industrial-ized countries report values very close to zero,the opposite is visible for LDCs, i.e. the CentralAfrican Republic with 87.6 and Sierra Leonewith 81.7 deaths per 1,000 live births. Thesetwo countries also reported the lowest life ex-

pectancy rates, indicating a close relationshipbetween these two indicators.

The overall trend since 1995 suggests a sig-nificant decline in the infant mortality rate asillustrated in Figure 28. This general trendhas been particularly obvious in countries witha lower MVA per capita or CIP scores, in whichthe overall rate of infant mortality has decreasedby nearly half over the past 20 years.

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Figure 28: Progression of the relationship between infant mortality rate and MVA per capita andthe CIP index since 1995.Source: The World Bank, World Development Indicators 2019 (World Bank, 2019b), UNIDO CIP 2019 Database

(UNIDO, 2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

Lifetime risk of maternal death

Life time risk of maternal death is the proba-bility of a 15-year-old female eventually dyingfrom a maternal cause assuming that current lev-

els of fertility and mortality (including maternalmortality) do not change in the future, takinginto account competing causes of death.

Figure 29: Comparison of lifetime risk of maternal death with MVA per capita and the CIP index,all measured in 2015.Source: The World Bank, World Development Indicators 2019 (World Bank, 2019b), UNIDO CIP 2019 Database

(UNIDO, 2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

52

Similarly to the previous variables, lifetimerisk of maternal death is also closely related toindustrial development. The higher risk is con-nected to the lower value of MVA per capita andCIP score. The highest lifetime risk of mater-nal death in 2015 were reported by Sierra Leone(5.94 per cent), Chad (5.52 per cent), Somalia(4.64 per cent) and Nigeria (4.51 per cent), i.e.by countries with the highest infant mortalityrate and lowest life expectancy.

The link between lifetime risk of maternal

death and industrial development has improvedsignificantly over all time periods since 1995(Figure 30). For countries with an MVA percapita of above USD 100 and a CIP score above0.01, the values of lifetime risk of maternal deathare close to zero in all time periods. A positivetrend is observable in countries with a lowerMVA per capita and CIP scores. In LDCs, life-time risk of maternal death reduces by approx-imately half, a trend that is similar to that ob-served for infant mortality rate.

Figure 30: Progression of the relationship between lifetime risk of maternal death and MVA percapita and the CIP index since 1995.Source: The World Bank, World Development Indicators 2019 (World Bank, 2019b), UNIDO CIP 2019 Database

(UNIDO, 2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

Prevalence of undernourishment

The prevalence of undernourishment repre-sents an estimate of the share of the populationwhose habitual food consumption is insufficientto provide the dietary energy levels required tomaintain a normal active and healthy life. It isexpressed in percentages. Data on undernour-ishment come from the Food and AgricultureOrganization (FAO) and measure food depriva-tion based on the average food available for hu-man consumption per person, the level of in-

equality in access to food, and the minimumcalories required by an average person.

Good nutrition is the cornerstone of survival,health and development. Well-nourished chil-dren perform better in school, grow into healthyadults and in turn give their children a better startin life. Well-nourished women face fewer risksduring pregnancy and childbirth, and their chil-dren set off on more solid developmental paths,both physically and mentally.

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Figure 31: Comparison of the prevalence of undernourishment with MVA per capita and the CIPindex, all measured in 2015.Source: The World Bank, World Development Indicators 2019 (World Bank, 2019b), UNIDO CIP 2019 Database

(UNIDO, 2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

Figure 32: Progression of the prevalence of undernourishment and MVA per capita and the CIPindex since 2000.Source: The World Bank, World Development Indicators 2019 (World Bank, 2019b), UNIDO CIP 2019 Database

(UNIDO, 2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

The positive influence of industrialdevelopment on the prevalence of undernour-ishment is depicted in Figure 31. In general,a higher prevalence of undernourishment is typ-ical for countries at lower stages of industrial

development, i.e. in LDCs and other developingeconomies. The highest levels of undernourish-ment in 2015 were reported for countries withan MVA per capita of less than USD 100, suchas the Central African Republic (60.3 per cent),

54

Zimbabwe (48.2 per cent) and Haiti (47.5 percent). By contrast, industrialized and emergingindustrial economies are clustered together andthe prevalence of undernourishment prevails ataround 2.5 per cent for the majority of thesecountries.

Improvements in the prevalence of under-nourishment since 2000 are not as strikingas for the previously mentioned health-related

indicators (Figure 32). This notwithstanding, theprevalence of undernourishment has experiencedan overall decrease in the last 15 years. The pos-itive trend is most visible in the case of LDCs.Figure 32 reveals a similar pattern as that de-picted in Figure 31, namely that countries withthe highest prevalence of undernourishment arethose with an MVA per capita of around USD100 or CIP scores with a value of 0.003.

People using at least basic drinking water services

The indicator people using at least basic wa-ter services encompasses both people who usebasic water services as well as those who usesafely managed water services. Basic drinkingwater services is defined as drinking water froman improved source, provided collection time isnot more than 30 minutes round trip. Improvedwater sources include piped water, boreholesor tube wells, protected dug wells, protectedsprings, and packaged or delivered water.

Data on drinking water, sanitation and hy-giene are produced by the Joint MonitoringProgramme of the World Health Organization(WHO) and the United Nations Children’s Fund

(UNICEF) based on administrative sources, na-tional censuses and nationally representativehousehold surveys.

Global access to safe water and proper hy-giene education can reduce illness and deathfrom disease, leading to improved health,poverty reduction, and higher socio-economicdevelopment. Access to safe drinking water isalso considered to be a human right, not a privi-lege, for every man, woman, and child. The eco-nomic benefits of safe drinking water servicesinclude higher economic productivity, more edu-cation, and health care savings.

Figure 33: Comparison of the percentage of people using at least basic drinking water services withMVA per capita and the CIP index, all measured in 2015.Source: The World Bank, World Development Indicators 2019 (World Bank, 2019b), UNIDO CIP 2019 Database

(UNIDO, 2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

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Figure 33 shows that in countries withan MVA per capita of above USD 1,000, ba-sic drinking water services are available fornearly the entire population, with two excep-tions, namely Eswatini with 67.6 per cent andEquatorial Guinea with 49.6 per cent. The avail-ability of drinking water tends to be problematicin LDCs where low values of MVA per capita or

CIP scores imply a low percentage of the pop-ulation using drinking water services and viceversa. The lowest share of the population usingat least basic drinking water services in 2015were reported by Eritrea (19.3 per cent), PapuaNew Guinea (36.6 per cent) and Uganda (38.9per cent).

Figure 34: Progression of the percentage of people using at least basic drinking water services andMVA per capita and the CIP index since 2000.Source: The World Bank, World Development Indicators 2019 (World Bank, 2019b), UNIDO CIP 2019 Database

(UNIDO, 2019a) and UNIDO MVA 2019 Database (UNIDO, 2019d)

A higher level of industrial development hasalways been related to a higher percentage ofthe population using at least basic drinking wa-ter services. Regardless of the country’s stageof industrial development, the overall access

to basic drinking water services has increasedsince 2000. More substantial growth in this re-gard has been registered by countries at lowerstages of industrial development, as is evident inFigure 34.

VIII

References . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Appendix I - List of countries and areas included inselected groupings

REFERENCES ANDAPPENDICES

References

Dodge, Rachel et al. (2012). “The Challenge of Defining Wellbeing”. In: International Journal ofWellbeing 2.3, pages 222–235 (cited on page 7).

International Labour Organization (2017). Global estimates of child labour: Results and trends,2012-2016. Geneva: ILO (cited on pages 34, 35).

— (2019). World Employment and Social Outlook: Trends 2019. Geneva: ILO (cited on pages 33,34).

Llena-Nozal, Ana, Neil Martin, and Fabrice Murtin (2019). The economy of well-being: Creatingopportunities for people’s well-being and economic growth. OECD Statistics Working Paper.Paris: Organisation for Economic Co-operation and Development (cited on page 7).

Organisation for Economic Co-operation and Development (2013). OECD Guidelines on MeasuringSubjective Well-being. Paris (cited on page 7).

— (2017). How’s Life? 2017: Measuring Well-being. Paris, page 340 (cited on page 7).Stiglitz, Joseph E., Jean-Paul Fitoussi, and Martine Durand (2018). Beyond GDP: Measuring What

Counts for Economic and Social Performance. OECD Publishing, address = Paris, page 144(cited on page 7).

The World Bank (2019a). Poverty and Equity database. Washington (cited on pages 25–27).— (2019b). World Development Indicators database. Washington (cited on pages 47–55).United Nations Development Programme (2016). Human Development Report 2016: Human

Development for Everyone (cited on page 18).— (2018). Human Development Indices and Indicators 2018, page 119 (cited on pages 17–21, 28,

43, 44).United Nations Development Programme and Oxford Poverty and Human Development Initiative

(2019). Global Multidimensional Poverty Index 2019: Illuminating Inequalities (cited onpage 29).

United Nations Educational, Scientific and Cultural Organization (2019). UNESCO Institute forStatistics (UIS) database (cited on pages 39–42).

United Nations General Assembly (2017). Work of the Statistical Commission pertaining to the2030 Agenda for Sustainable Development. A/RES/71/313, Available at https://undocs.org/A/RES/71/313 (cited on page 7).

United Nations Industrial Development Organization (2013). Industrial Development Report 2013.Vienna: UNIDO (cited on page 34).

— (2015). The 2030 Agenda for Sustainable Development: Achieving the industry-related goalsand targets (cited on pages 33, 47).

— (2019a). CIP 2019 Database. Vienna (cited on pages 14, 18–21, 26–29, 35, 40–44, 48–55).— (2019b). Competitive Industrial Performance Report 2018. Vienna: UNIDO (cited on page 13).— (2019c). International Yearbook of Industrial Statistics 2019. Cheltenham: Edward Elgar

Publishing (cited on pages 61, 63).— (2019d). Manufacturing Value Added 2019 Database. Vienna (cited on pages 12, 13, 18–21,

26–29, 35, 40–44, 48–55).— (2019e). Statistical Indicators of Inclusive and Sustainable Industrialization: Biennial Progress

Report 2019. Vienna: United Nations Industrial Development Organization (cited on page 11).

60

Upadhyaya, Shyam and David Kepplinger (2014). How industrial development matters to the well-being of the population - Some statistical evidence. Working Paper. Vienna: United NationsIndustrial Development Organization (cited on page 8).

Methodological Note

The visualization of the indicators of inter-est and their relationship with level of industrialdevelopment is based on three types of plots.Maps allow us to study local dependencies aswell as to perform a worldwide comparison. Themost recent values for the year 2017 were se-lected where possible. In the case of sparse data,the last reported value for each country was used.The distribution of values to the classes was doneusing the Fisher-Jenks algorithm.

Relationships between the given indicatorand MVA per capita and the CIP index are visu-alized on scatter plots. Where possible, valuesfrom the same year (2015 or 2017) are com-pared. In the case of sparse data, the last avail-able values from the period 2015–2017 are com-pared with the MVA per capita and CIP scoresreported in the same year. The only exceptionis the total child labour rate, which was reportedfor the period 2010–2016 and compared with

the industrial development indicators for theyear 2017. The overall trend of the relation-ship is indicated by the Loess smoothing curve.The Loess regression is a non-parametric tech-nique that uses a local weighted regression to fita smooth curve through points in a scatter plot.Loess curves can reveal trends and cycles in datathat might be difficult to model with a paramet-ric curve. Moreover, countries are differenti-ated according to colour reflecting their stage ofindustrial development (UNIDO, 2019c). Thetop three performing countries from each group(according to the indicator of the interest) arehighlighted in the graph by using labels.

The last type of plots shows the progressionof the relationship throughout a longer time span.Each country is represented in the given timeperiod by the median value reported for the rele-vant years. The overall trend within the period isagain indicated by the Loess smoothing curve.

Appendix

Appendix I - List of countries and areas included in selected groupings 1

INDUSTRIALIZED ECONOMIES

EU2

AustriaBelgiumCzechiaDenmarkEstoniaFinlandFranceGermanyHungaryIrelandItalyLithuaniaLuxembourgMaltaNetherlandsPortugalSlovakiaSloveniaSpainSwedenUnited Kingdom

Other EuropeAndorra

BelarusIcelandLiechtensteinMonacoNorwayRussian FederationSan MarinoSwitzerland

East AsiaChina, Hong Kong SARChina, Macao SARChina, Taiwan ProvinceJapanMalaysiaRepublic of KoreaSingapore

West AsiaBahrainKuwaitQatar

United Arab Emirates

North AmericaBermudaCanadaGreenlandUnited States of America

OthersArubaAustraliaBritish Virgin IslandsCayman IslandsCuraçaoFrench GuianaFrench PolynesiaGuamIsraelNew CaledoniaNew ZealandPuerto RicoTrinidad and TobagoUnited States Virgin Islands

1International Yearbook of Industrial Statistics (UNIDO, 2019c)2Excluding non-industrialized EU economies.

64

DEVELOPING AND EMERGING INDUSTRIAL ECONOMIESBy Development

EMERGINGINDUSTRIAL ECONOMIES

ArgentinaBrazilBrunei DarussalamBulgariaChileColombiaCosta RicaCroatiaCyprusEgyptGreeceIndiaIndonesiaIran (Islamic Republic of)KazakhstanLatviaMauritiusMexicoNorth MacedoniaOmanPeruPolandRomaniaSaudi ArabiaSerbiaSouth AfricaSurinameThailandTunisiaTurkeyUkraineUruguayVenezuela (Bolivarian Rep. of)

CHINA

OTHER DEVELOPINGECONOMIES

AlbaniaAlgeriaAngola

AnguillaAntigua and BarbudaArmeniaAzerbaijanBahamasBarbadosBelizeBolivia (Plurinational State of)Bosnia and HerzegovinaBotswanaCabo VerdeCameroonCongoCook IslandsCôte d’IvoireCubaDem. People’s Rep of KoreaDominicaDominican RepublicEcuadorEl SalvadorEquatorial GuineaEswatiniFijiGabonGeorgiaGhanaGrenadaGuadeloupeGuatemalaGuyanaHondurasIraqJamaicaJordanKenyaKyrgyzstanLebanonLibyaMaldivesMarshall IslandsMartiniqueMicronesia, Fed. States ofMongoliaMontenegro

MontserratMoroccoNamibiaNicaraguaNigeriaPakistanPalauPanamaPapua New GuineaParaguayPhilippinesRepublic of MoldovaRéunionSt. Kitts and NevisSt. LuciaSt. Vincent and the GrenadinesSeychellesSri LankaState of PalestineSyrian Arab RepublicTajikistanTongaTurkmenistanUzbekistanViet NamZimbabwe

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LEAST DEVELOPEDCOUNTRIES

AfghanistanBangladeshBeninBhutanBurkina FasoBurundiCambodiaCentral African RepublicChadComorosDem. Rep. of the CongoDjiboutiEritreaEthiopia

GambiaGuineaGuinea-BissauHaitiKiribatiLao People’s Dem RepLesothoLiberiaMadagascarMalawiMaliMauritaniaMozambiqueMyanmarNepalNigerRwanda

SamoaSao Tome and PrincipeSenegalSierra LeoneSolomon IslandsSomaliaSouth SudanSudanTimor-LesteTogoTuvaluUgandaUnited Republic of TanzaniaVanuatuYemenZambia

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