Water Storage and Poverty faculteit...Water Storage and Poverty Impact analysis of small reservoirs...
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Water Storage and Poverty Impact analysis of small reservoirs on the well-being of rural households in Burkina Faso
A.C. van der Kraan Master Thesis January 2008
Water Storage and Poverty Impact analysis of small reservoirs on the well-being of rural households in Burkina Faso
Master thesis submitted in partial fulfilment
of the requirements of the degree of Master of Science
Faculty of Civil Engineering and Geosciences
Department of Water Resources Management
Delft University of Technology
in cooperation with
Small Reservoirs Project
Anneke van der Kraan
Final Report
January, 2008
– Thinking differently about water is essential for achieving our triple
goal of ensuring food security, reducing poverty, and conserving ecosystems –
Water for food, water for life
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Preface
This is the final report for my master thesis project. The research is carried out at the section of
Integrated Water Resources Management of the Faculty of Civil Engineering and Geosciences of Delft
University of Technology (DUT). The project is conducted under supervision of Prof. Dr. Ir. Nick van de
Giesen.
The research is part of the Small Reservoirs Project (SRP)1, which is embedded in the CGIAR
Challenge Program Water for Food. The objectives of the SRP are (1) at basin/watershed level: to
promote and support the planning, development and management of small reservoir ensembles, (2) at
local/community level: to support use of small multi-purpose reservoirs that are properly located, well
designed, operated and maintained in sustainable fashion, and economically viable while assuring
they improve the livelihoods of the local residents (SRP, undated).
This research focuses on the second objective – more specific – the improvement of livelihoods due to
use of small multi-purpose reservoirs. The aim is to understand the interdependences between the
presence – or absence – of these reservoirs and the well-being of rural households living in their
vicinity.
I would like to thank my committee members for their advice and critics.
Prof. Dr. Ir. Nick van de Giesen – Chair of the department of Water Resources Management at
the faculty of Civil Engineering and Geosciences at Delft University of Technology, The
Netherlands.
Dr. Ir. Olivier Hoes – Lecturer at the department of Water Resources Management at the
faculty of Civil Engineering and Geosciences at Delft University of Technology, The
Netherlands and consultant for Neelen & Schuurmans Consultants in Utrecht, The
Netherlands.
1 The full title is: Planning and evaluating ensembles of small, multi-purpose reservoirs for the improvement of
smallholder livelihoods and food security: tools and procedures. The brief title is: Small Multi-purpose Reservoir
Ensemble Planning.
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Dr. Eric Molin – Associate professor at the department of Transport Policy and Logistics
Organisation at the faculty of Technology, Policy and Management at Delft University of
Technology, The Netherlands.
Ir. IJsbrand de Jong – Senior water resources specialist at the department of Rural
Development Operations Eastern and Southern Africa at the World Bank in Washington DC,
United States of America.
Dr. Ir. Rhodante Ahlers – Senior lecturer in Water Management at the department of
Management and Institutions at the UNESCO-IHE Institute for water education in Delft, The
Netherlands.
Delft, January 20th, 2008
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Summary
People living in Burkina Faso face highly variable rainfall, experience droughts and floods and
resulting have insecure livelihoods. Not only the year-to-year rainfall has a high variance, more
important is the variable distribution of rain over the growing season. To overcome dry periods, people
have built small reservoirs. According to several sources there are between 1400 and 2000 reservoirs
of all sizes in Burkina Faso. They are an important socio-economic infrastructure, as their use
supports many different purposes. This research focuses on the question how the use of small multi-
purpose reservoirs affects the well-being of poor rural livelihoods. The aim is to gain insight into the
relation between the presence – or absence – of small multi-purpose reservoirs and the state of well-
being of rural households living in the vicinity of those dams. So far, the extent and direction of the
relations between various dimensions of poverty and socio-economic values of small reservoirs are
based on assumptions. Knowledge of – positive and negative – impacts will be of significant value for
planning and management of these reservoirs. Therefore, the main research question is stated as:
“What is the relationship between the presence of small multi-purpose surface water reservoirs and
the state of well-being of rural households?”
First concern is identification of the socio-economic values of small reservoirs in rural areas in Burkina
Faso. Therefore, a literature review is conducted on the values of water ecosystems in general and
small surface water reservoirs in particular. Not so much the (monetary) economic value of water is
regarded, more the economic characteristics; water as a natural asset that is used by agriculture and
households, and so provides a means for human well-being. Identified socio-economic values – goods
and services – are water supply of domestic, agricultural and animal use, raw material, food and
nutrient supply, and other uses like recreation and education. Water from small reservoirs is used in
and around the house, e.g. for cleaning, bathing, washing, cooking. Generally, it is not a source for
drinking water; that is extracted from the groundwater; however, in areas where rainfall is very low
people may have no other choice than to use reservoir water. Additionally, general agriculture and
other agricultural purposes – such as fruit trees and vegetable gardens – are served. The diversion of
water to home gardens may contribute substantially to a varied diet or increase the household income.
Livestock may depend directly on water from small reservoirs, in addition to profiting from the higher
availability of fodder from crop stubble. Easier access to water can also contribute the development of
local economic activities, be it small scale and informal such as brick making, beer brewing, and mat
weaving.
Second concern is the need to define poverty as to be relevant to the aim and context of this research,
and to be compatible with data availability. Recurrently, a literature review is conducted on the various
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concepts of poverty, and related dimensions. In order to define poverty the following steps are taken:
(1) Definition of the dimensions of poverty: related to the concepts of poverty adapted, a range of
dimensions is identified; (2) Selection of indicators of poverty: for every (sub)dimension one or more
indicators or proxies can be identified. These indicators are mainly determined by the data source: a
multi-topic household survey over 8,500 households covering the whole country of Burkina Faso. The
reported socio-economic values correspond with poverty dimensions that fall under the poverty lines
and basic needs concept, and therefore these are selected to represent poverty. As income, health
and nutrition being the main dimensions that explain poverty, their sub-dimensions are supporting
them. These sub-dimensions are measures of access (proximity + availability) to resources and
services, measures on health, nutrition and income levels and expenditures on resources and assets.
Consequently, the definition of poverty is formulated as: “Poverty is the lack of sufficient access to
financial and material assets, and public and natural resources, as to ensure being nutritioned and
healthy.”
Based on the qualitative analysis a (theoretical) cause-effect diagram showing the relations within and
between ‘storage’ and ‘poverty’ is drawn up. Together these relations form the conceptual model,
which is verified and quantified in the second part of the research. A valid cause-effect diagram is
essential for the outcomes of this research, as it functions as the conceptual basis for testing. Applied
methods for quantitative analysis are bivariate correlation analysis and multiple regression analysis,
both performed by using SPSS software package. This statistical approach allows quantify the system
of relations step-by-step as to finally estimate series of multiple regression equations between and
within (sub)systems of dependence and independence relations. The value of this approach is its
ability to estimate the impact of interventions in parallel with the interaction between multiple factors
within the poverty reduction process.
The analysis reveals that the main (positive) direct effect of small reservoirs is income generation and
education. Mainly employment rates and education levels benefit from storage. This leads to the new
hypothesis that when small reservoirs are more proximate this leads to considerable time savings.
Access to small reservoirs does not seem to contribute directly to improved nutritional status. The
state of food security is mostly determined by purchase of nutritional products and sufficient access to
stocks, while autoconsumption (self-sufficiency) of nutritional products plays a minor role. In turn,
expenditures on food are mainly determined by income levels and market access. There is no
evidence found that small reservoirs have a positive direct impact on food supply from (irrigated)
agriculture, dairy farming or aquaculture – that are considered as the main socio-economic values of
small reservoirs.
The concern is that the presence of small reservoirs would cause higher prevalence of water-related
diseases; water resources development in general has often been blamed for negative impacts on
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human health such as increased spread of malaria and schistosomiasis. Fortunately, the analysis
shows that this concern is dispensable; there is no evidence found that reservoir density relates to
gauges of water-related diseases (fever and diarrhoea). However, neither better sanitation nor
availability of improved potable water sources contribute to the reduction of water-related diseases. In
fact, the prevalence of water-related diseases is not explained by the variables within the model. We
can conclude population density is an important motivation for improved accessibility of water storage
(small reservoirs), and other resources (food market, potable water) and services (schools, health
services) become better accessible. Moreover, access to (public) transport contributes significantly to
the accessibility of resources and services.
The applied statistical techniques puzzle out the existence and strength of relations, however, the
direction remains only given in by theory. In general, the strength of the relations and the explained
variance by the regression models is low to medium; hence, we should be careful drawing strong
conclusions. Relations are weak due to (1) measurement error and the use of proxies to represent
concepts, and (2) disaggregation of variables. First proposed solution is re-defining of the
questionnaire as to upgrade the data by re-formulating survey questionnaires as to obtain data of (at
least) interval level. Additionally, re-formulating should lead to more reliable answers (reduce
measurement error) and more accurate indicators (to omit imperfect representation of concepts).
Secondly, we propose geo-referencing of the survey to be able to estimate the real proximity of small
reservoirs.
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Glossary
Access For this research access is defined in a fairly simple way by only including
proximity and availability in the definition.
Covariance The averaged sum of combined difference.
Direct use values Direct use values arise from direct interaction with water resources. They may
be consumptive, such as use of water for irrigation or the harvesting of fish, or
they may be non-consumptive such as recreational swimming, or the aesthetic
value of enjoying a view.
Dummy variable A dummy variable is a numerical variable used in regression analysis to
represent subgroups of the sample.
Excreta Faeces and urine.
Food security Food security exists when all people, at all times, have access to sufficient,
safe and nutritious food to meet their dietary needs and food preferences for
an active and healthy life. The causal factors of food insecurity are equally
physical, economic and socio-political.
Homoscedasticity Homoscedasticity is the statistical assumption that the variance of the
dependent variable is the same for all the data.
Human capital Human capital is the attributes of a person that are productive in some
economic context.
Indirect use values Indirect use values are associated with services provided by water resources
but that do not entail direct interaction. E.g. they are derived from flood
protection provided by wetlands or the removal of pollutants by aquifer
recharge.
Interval level The interval measurement level represents quantitative data with a constant
unit of measurement, that have an arbitrary zero point. Therefore, it is not
possible to state that any value on an interval scale is a multiplication of any
other value on the scale.
Linearity Linearity is the statistical assumption that the relationship between variables is
a straight line.
Livelihood Refers to the means of gaining a living, including livelihood capabilities,
tangible assets and intangible assets.
Malnutrition The condition caused by deficiencies or imbalances in energy, protein and/or
other nutrients. Signs include wasting (thinness), stunting (shortness), or
being underweight.
Measurement error The degree to which observed values are not representative for ‘true’ values.
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Multi-collinearity Multicollinearity refers to a situation of collinearity of independent variables.
Collinearity of two variables means that strong correlation exists between
them, making it difficult or impossible to estimate their individual regression
coefficients reliably.
Non-use values Non-use values are derived from the knowledge that a resource is maintained.
By definition, they are not associated with use of the resource or tangible
benefits that can be derived from it.
Nominal level The nominal measurement level is considered the lowest. It assigns numerical
values as labels to identify categorical data.
Normality Refers to the statistical assumption that at all variables and all combinations of
the variables are normally distributed (bell-shaped curve).
Option values Option value is the satisfaction that an individual derives from the ensuring
that a resource is available for the future given that the future availability of the
resource is uncertain. It can be regarded as insurance for possible future
demand for the resource.
Ordinal level In the ordinal measurement level categorical data are ordered or ranked in
relation to the amount of the attribute possessed. However, the scale is really
non-quantitative, because it indicates only relative positions in an ordered
series.
Outlier Observations with a unique combination of characteristics identifiable as
distinctly different from the other observations.
Poverty For this research poverty is formulated as follows: poverty is the lack of
sufficient access to financial and material assets, and public and natural
resources, as to ensure being nutritioned and health.
Poverty line The threshold below which a given household or individual will be classified as
poor.
Poverty mapping Refers to the use of maps in policy making and targeting assistance,
particularly in the areas of food security and environmental management.
Poverty maps are spatial representations of poverty assessments.
Quasi-option values Quasi-option value is derived from the potential benefits of waiting for
improved information prior to giving up the option to preserve a resource for
the future. This is based on a desire to take advantage of the prospect of
improved information in the future and act on subsequent revision of
preferences.
Ratio level The ratio measurement level represents the highest form of measurement
precision because they possess the advantages of all lower scales plus an
absolute zero point.
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Small reservoirs In this research small reservoirs are defined as surface water reservoirs with a
surface until 100 hectare.
Stunted Low height for age (shortness).
Underweight Low weight for age.
Wasted Low weight for height (thinness).
Well-being Generally, well-being is the experience of good quality of life. Within this
research well-being is used as substitute (inversely proportional) for poverty.
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Table of Contents
Preface ....................................................................................................................................... i Summary ...................................................................................................................................iii Glossary ...................................................................................................................................vii Table of Contents....................................................................................................................... xi Figures and Tables ...................................................................................................................xiv
1. Introduction ........................................................................................................................ 3 1.1 Context of the research .............................................................................................................. 3 1.2 Problem definition....................................................................................................................... 4
2. Approach............................................................................................................................ 7 2.1 Theory on socio-economic values.............................................................................................. 7 2.1.1 Framework for valuation......................................................................................................... 8 2.2 Conceptualisation of poverty .................................................................................................... 10 2.2.1 Dimensions of poverty.......................................................................................................... 10 2.2.2 Indicators of poverty............................................................................................................. 12 2.3 Methods for quantification of linkages...................................................................................... 13 2.3.1 Cause-effect relations .......................................................................................................... 13 2.3.2 Selection of statistical techniques ........................................................................................ 14 2.3.3 Correlation analysis.............................................................................................................. 16 2.3.4 Regression analysis ............................................................................................................. 17 2.4 Outline of the report.................................................................................................................. 20
3. Socio-Economic Values of Small Reservoirs........................................................................ 23 3.1 Water as a natural asset .......................................................................................................... 23 3.2 Values and classifications found in literature ........................................................................... 23 3.3 Socio-economic values of small reservoirs .............................................................................. 25 3.4 Characteristics of small reservoirs ........................................................................................... 27 3.5 Indicators of storage................................................................................................................. 29
4. Definition of Poverty .......................................................................................................... 31 4.1 Poverty in literature................................................................................................................... 31 4.2 Classification of dimensions ..................................................................................................... 31 4.3 Relevant dimensions of poverty ............................................................................................... 33 4.4 Indicators of poverty ................................................................................................................. 33
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5. Linking Storage to Poverty ................................................................................................. 39 5.1 Storage as explanatory factor for poverty ................................................................................ 39 5.2 Linking storage and poverty ..................................................................................................... 40 5.2.1 Links between storage and poverty ..................................................................................... 41 5.2.2 External explanatory factors................................................................................................. 44
6. Verification of Relationships ............................................................................................... 49 6.1 Model specification................................................................................................................... 49 6.2 Bivariate correlation analysis.................................................................................................... 50 6.2.1 Direct links between storage and poverty ............................................................................ 50 6.2.2 Indirect links (and interdependencies) between storage and poverty ................................. 53 6.2.3 Links with external factors.................................................................................................... 58 6.3 Discussion ................................................................................................................................ 63
7. Quantification of Relationships ........................................................................................... 65 7.1 Model specification................................................................................................................... 65 7.2 Multiple regression analysis ..................................................................................................... 66 7.2.1 External factors .................................................................................................................... 66 7.2.2 Income.................................................................................................................................. 67 7.2.3 Nutrition................................................................................................................................ 72 7.2.4 Health ................................................................................................................................... 77 7.3 Discussion ................................................................................................................................ 83
8. Interpretation .................................................................................................................... 85 8.1 Interpretation of the country-scale analysis.............................................................................. 85 8.2 Urban versus rural environment ............................................................................................... 88
9. Reflection ......................................................................................................................... 91 9.1 Technical validation.................................................................................................................. 91 9.2 Evaluation of the scope ............................................................................................................ 92
10. Conclusions and Recommendations ................................................................................... 95 10.1 Conclusions.......................................................................................................................... 95 10.2 Recommendations for future work ....................................................................................... 98
Bibliography ........................................................................................................................... 101
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Appendix A. Values of Water............................................................................................... 109 Appendix B. Selection of Indicators...................................................................................... 113 Appendix C. Data Screening ............................................................................................... 121 Appendix D. Explorative Correlation Analysis........................................................................ 133 Appendix E. Explorative Regression Analysis ....................................................................... 135 Appendix F. Validation Tables............................................................................................. 137 Appendix G. Interpretation Tables ........................................................................................ 139
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Figures and Tables
Figures
Figure 1-1: Location of Burkina Faso ...................................................................................................... 3 Figure 2-1: Framework for valuation ....................................................................................................... 9 Figure 3-2: Reservoir density distribution.............................................................................................. 29 Figure 4-1: Classification of dimensions of poverty............................................................................... 32 Figure 5-2: Conceptual model – the cause-effect diagram ................................................................... 47 Figure 6-1: Correlations between storage and income ......................................................................... 50 Figure 6-2: Correlations between storage and nutrition ........................................................................ 51 Figure 6-3: Correlations between storage and health ........................................................................... 52 Figure 6-4: Main correlations within the poverty-storage system.......................................................... 62 Figure C-1: Boxplot for indicators of income ....................................................................................... 122 Figure C-2: Boxplot for indicators of expenditures on health services................................................ 123 Figure C-3: Scatterplot ‘household size’ * ‘total value of expenditures on health services’ ................ 124 Figure C-4 left: Scatterplot ‘household size’ * ‘value of expenditures on nutritional products’............ 125 Figure C-4 right: Scatterplot ‘household size’ * ‘value of autoconsumption of nutritional products’.... 125 Figure C-5: Scatterplot ‘household size’ * ‘total income’ ..................................................................... 125 Figure C-6: Scatterplot ‘length of the child’ * ‘weight of the child’ ....................................................... 126 Figure C-7: Scatterplot ‘reservoir density’ * ‘population density’ ......................................................... 126
Tables
Table 3-1: Values of small multi-purpose reservoirs ............................................................................. 26 Table 4-2: Selected indicators for income............................................................................................. 34 Table 4-3: Selected indicators for nutrition............................................................................................ 35 Table 4-4: Selected indicators for health............................................................................................... 37 Table 5-1: Storage and poverty links..................................................................................................... 40 Table 6-1: Correlations for interdependencies: income ........................................................................ 54 Table 6-2: Correlations for interdependencies: nutrition ....................................................................... 55 Table 6-3: Correlations for interdependencies: health .......................................................................... 57 Table 6-4: Correlations external factors: external factors ..................................................................... 58 Table 6-5: Correlations external factors: income .................................................................................. 59 Table 6-6: Correlations external factors: nutrition ................................................................................. 60 Table 6-7: Correlations external factors: health .................................................................................... 61 Table 7-1: Regression model for reservoir density ............................................................................... 67
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Table 7-2: Regression model for population density............................................................................. 67 Table 7-3: Regression model for total revenue (direct model) .............................................................. 68 Table 7-4: Regression model for total revenue (complete model) ........................................................ 68 Table 7-5: Regression model for income from (owned) resources ....................................................... 69 Table 7-6: Regression model for income from employment ................................................................. 70 Table 7-7: Regression model for income from entrepreneurship.......................................................... 70 Table 7-8: Regression model for level of education.............................................................................. 71 Table 7-9: Regression model for proximity of primary school ............................................................... 71 Table 7-10: Regression model for proximity of secondary school ........................................................ 71 Table 7-11: Regression model for occurrence of food insecurity (direct model)................................... 72 Table 7-12: Regression model for occurrence of food insecurity (complete model)............................. 72 Table 7-13: Regression model for value of expenditures on nutritional products ................................. 73 Table 7-14: Regression model for value of autoconsumption of nutritional products ........................... 73 Table 7-15: Regression model for access to stocks (of cereals) until next harvest .............................. 74 Table 7-16: Regression model for proximity of food market ................................................................. 74 Table 7-17: Regression model for livestock holding ............................................................................. 75 Table 7-18: Regression model for landholding...................................................................................... 75 Table 7-19: Regression model for prevalence of malnutrition (direct model) ....................................... 76 Table 7-20: Regression model for prevalence of malnutrition (complete model).................................. 76 Table 7-21: Regression model for recent prevalence of disease (complete model)............................. 77 Table 7-22: Regression model for chronic prevalence of disability....................................................... 78 Table 7-23: Regression model for recent prevalence of fever .............................................................. 79 Table 7-24: Regression model for recent prevalence of diarrhoea....................................................... 79 Table 7-25: Regression model for value of expenditures on health services ....................................... 80 Table 7-26: Regression model for access to (improved) toilets ............................................................ 81 Table 7-27: Regression model for access to (improved) garbage disposal.......................................... 81 Table 7-28: Regression model for proximity of potable water source................................................... 81 Table 7-29: Regression model for availability of (improved) potable water source .............................. 82 Table 7-30: Regression model for proximity of health services ............................................................ 82
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1. Introduction
Although poverty is often considered a matter of institutions, governance and infrastructure, water
resources play a vital role in economic growth, human health and reduction of poverty in the semi-arid
and sub-humid savannah areas of West Africa. Therefore, throughout Burkina Faso – and other parts
of West Africa – many (small) dams and reservoirs have been built. They are catalyst for change,
initiate income generating activities, allow people to cope better with hungry periods of the year and to
diversify their diets.
1.1 Context of the research
Most of Burkina Faso belongs to the Sahel zone, the agricultural region between the Sahara and the
coastal rain forests. The land is green in the south, with forests and fruit trees, and dessert in the
north. Large parts of central Burkina Faso are located on a savannah plateau, with fields, brush, and
scattered trees. The mean annual rainfall varies from 1200 mm in the south-western part of the
country to less than 600 mm in the north (Coche, 1998). The climate is tropical with hot, wet summers
and warm, dry winters during which the hot, dry and dusty Harmattan wind blows. The rainy season
lasts approximately four months – May/June to September – and is shorter in the north of the country.
Droughts are often a chronic problem, especially in northern regions.
Like in most developing countries, a large part of the
population depends on agriculture for their income.
This is reflected in the fact that the agricultural sector
is the most important in Burkina Faso, followed by live-
stock husbandry. Over 80% of the labour force is
working in agriculture, only a small fraction is involved
in industry and services. Irrigation development is
currently growing exponentially, especially small-scale
irrigation at community level. This growth is not only
reflected in an increasing total number of dams, but
also in the conversion of drinking and cattle water
storage reservoirs into irrigation supply basins (Van de
Giesen et al., 2000). Figure 1-1: Location of Burkina Faso
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People living in Burkina Faso face highly variable rainfall, experience droughts and floods and
resulting have insecure livelihoods. Not only the year-to-year rainfall has a high variance, more
important is the distribution of rain over the growing season (Van de Giesen et al., 2000). To
overcome dry periods, people have built small reservoirs. These reservoirs are an important socio-
economic asset to the poor rural communities. According to several sources there are between 1400
and 2000 reservoirs of all sizes in Burkina Faso (SRP, undated; Coche, 1998). They typically provide
water for irrigation, livestock watering, fishing and domestic use. But they also serve a purpose for
wildlife watering and recreational purposes. Even if the water levels in the reservoirs run low, the
reservoir’s bottom can be exploited in order to make bricks out of the clay-rich soil (Poolman, 2005).
Most reservoirs have a rather shallow depth and consequently a small storage capacity. For this
reason many of them are seasonal; they store water during the wet season, as to be used during dry
periods and (part of) the dry season. Most permanent reservoirs are concentrated in central Burkina
Faso (Coche, 1998). Compared to large reservoirs (> 100 ha), small surface water reservoirs are less
reliable and effective for water conservation. Their small volume does not allow for seasonal or annual
carry-over, and the high surface area to volume ratio leads to high evaporation losses (Keller et al.,
2000). Small reservoirs have the advantage to large reservoirs that they are – in many cases – a less
strong threat to society and environment. Additionally, they are operationally efficient; they response
rapidly to precipitation runoff, are flexible, close to the point of use, and require relatively few parties
for management.
1.2 Problem definition
In Burkina Faso, where rainfall is highly variable and droughts are frequent, storage of freshwater is
essential to secure livelihoods during the dry season. Therefore, many (small) dams and reservoirs
have been built. Unfortunately, the qualitative and quantitative impacts of (clusters of) small reservoirs
on these livelihoods are quiet unknown. This research focuses on the question how the use of small
multi-purpose reservoirs affects the well-being of poor rural livelihoods. The aim is to gain insight into
the relation between the presence – or absence – of small multi-purpose reservoirs and the state of
well-being of rural households living in the vicinity of those dams. So far, the extent and direction of
the relations between various dimensions of poverty and socio-economic values of small reservoirs
are based on assumptions. Knowledge of – positive and negative – impacts will be of significant value
for planning and management of these reservoirs.
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Accordingly, the main research question is stated as:
What is the relationship between the presence of small multi-purpose surface water
reservoirs and the state of well-being of rural households?
Research questions and approach
In order to answer this question, three sub-questions are formulated:
1. What are the socio-economic values of small multi-purpose surface water reservoirs to poor
rural households in Burkina Faso?
2. How can we define ‘poverty’ within the scope of this research?
3. Which dimensions of poverty are (in)directly related to the presence (or absence) of small
multi-purpose reservoirs? What is the statistical strength of these relations? Which external
factors play a significant role?
In this research poverty and well-being are considered as (inversely proportional) substitutes.
Scope of the research
This research provides an impact analysis of the presence of small multi-purpose reservoirs on the
well-being of people living in Burkina Faso on macro scale. It focuses on the complex system of
factors that describe the origin of the link between storage and poverty. Principally, the scale of
aggregation for this research is – determined by the data source – the household level (or attributed to
the household level). Therefore, micro scale effects as inter-household poverty distribution is not
considered, but the analysis neither goes very broad; it does not comprehend effects of e.g.
globalization and aid. Later in the report poverty is to be defined to fit the purpose of this research.
As the aim is to regard this system in a generalized way, the influence of temporal and spatial
dynamics can not be included. Although it is recognized that poverty should be regarded as an
(infinite) process wherein time is an important parameter, available data resources are static (i.e.
household survey).
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2. Approach
In this chapter the methodological considerations are drawn up and theoretical frameworks are
shaped. Section 2.1, 2.2 and 2.3 each describe how to come to an answer of respectively research
question one, two and three. Finally, this chapter gives the outline of the report.
2.1 Theory on socio-economic values
As stated in the Dublin Principles by the International Conference on Water and the Environment
1992, water should be regarded as an economic good. Later, at the 2nd World Water Forum 2000,
agreement was reached that the full resource value – economic, social, cultural and environmental –
should be recognized in water management decisions (Agudelo, 2001).
The most common reason for undertaking a valuation of ecosystems is to assess the contribution of
ecosystems to social and economic well-being. But purely economic valuation of water often overlooks
two important dimensions: (1) Environmental values, such as the role of water flows in maintaining
biodiversity and ecosystem integrity. (2) Social values, which – at its most basic – can mean simply
using water to grow food to eat (FAO, 2006). The environmental dimension of water is essential to
sustain the basis for economic development, growth and poverty reduction (Kemper et al., undated).
The aim of this research is to identify the socio-economic values of small multi-purpose reservoirs in
rural areas in Burkina Faso. Therefore, a literature review is conducted on the values of water
ecosystems in general, and small surface water reservoirs in particular. Hereby, the term ‘value’ is
used to describe the importance placed on these ecosystems by individuals, which includes not only
income generation due to the use of its goods and services, but also other benefits it provides for
human welfare. Not so much the (monetary) economic value of water is regarded, more the economic
characteristics; water as a natural asset that is used by agriculture and households, and so provides a
means for human well-being.
Parallel with wetlands
Since extensive research is being done on the values of wetlands, an argument to draw a parallel with
wetlands is noted;
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During the Convention of Wetlands, signed in Ramsar (Iran, 1971) wetlands are defined as: "Wetlands are areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six meters" (Article 1.1). In addition Ramsar sites "may incorporate riparian and coastal zones adjacent to the wetlands, and islands or bodies of marine water deeper than six meters at low tide lying within the wetlands" (Article 2.1). Furthermore, there are human-made wetlands such as fish and shrimp ponds, farm ponds, irrigated agricultural land, salt pans, reservoirs, gravel pits, sewage farms, and canals (Barbier et al., 1997).
As the convention adopts an extremely broad approach in determining wetlands, and considering the
similarities on valuation concepts for wetlands and general ecosystems found in literature, the parallel
is considered admissible.
2.1.1 Framework for valuation
Foregoing research done on the valuation on ecosystems provides a wide range of approaches,
conceptual frameworks and terms. Terms as resources, attributes, functions and services are often
used in literature; however, authors give different definitions or use them in a different context. They
are useful in classifying the socio-economic and ecological values of water, if and only if, the
conceptual framework in which they are applied is clarified.
The concept of Total Economic Value
The concept of Total Economic Value (TEV) is a widely used framework for analysis of the utilitarian
value of ecosystems (Alcamo et al., 2003). This concept divides the values derived from ecosystems
into two main categories: use values and non-use values. Typically, use values involve some human
interaction with the resource whereas non-use values do not (Barbier et al., 1997). They can be
divided into direct use values – which arise from direct interaction with water resources – and indirect
use values that are linked with services provided by water resources, e.g. flood protection. There is
another form of indirect use values called (quasi)option value2. It deals with the current satisfaction
and future improved information. Non-use values are usually associated with existence values.
Turner et al. (2000) proposes a framework based on Total Economic Value that describes the
interface between ecology and economy, and integrates the ecological interdependencies. It shows
2 See Glossary
9
that in order to value water resources one has to establish their functions – the link between the
ecosystem characteristics, structures and processes – and the goods and services they provide and
that are valued by society. It gives a summary of the complex relationship between economics and
ecology.
Conceptual framework developed for this research
Since none of the frameworks provided in literature fits the scope of this research – more specific the
purpose of this first question – we have combined the framework of Total Economic Value (derived
from Barbier et al., 1997) with the framework proposed by Turner et al. (2000), and designed a
valuation framework for the specific scope of this research: (non-monetary) valuation of small multi-
purpose surface water reservoirs.
The socio-economic value of small surface water reservoirs is divided into two categories: goods and
services. Goods refer to the natural products harvested or used by communities such as water supply
for domestic, agricultural and livestock purposes, and natural products as wildlife and fish. Services
support life by indirect use or existence. Which and how many goods and services a reservoir can
provide depend on its characteristics: the physical features, natural environment and internal
processes. It should be noted that the development and use of small surface water reservoirs may
change the natural environment, and thus the reservoir characteristics.
PhysicalFeatures
Reservoir character istics
ExternalEnvironment
InternalProcesses
Goods
Values of small reservoirs
Services
Figure 2-1: Framework for valuation
10
2.2 Conceptualisation of poverty
What is poverty? Poverty is hunger. Poverty is lack of shelter. Poverty is being sick and not being able to see a doctor. Poverty is not having access to school and not knowing how to read. Poverty is not having a job, is fear for the future, living one day at a time. Poverty is losing a child to illness brought about by unclean water. Poverty is powerlessness, lack of representation and freedom (Website PovertyNet).
As a multi-dimensional phenomenon, poverty is defined and measured in various ways. The
formulation of the definition determines how we analyze poverty and understand its dimensions. While
the main understandings of the term include material and economic needs, increasingly, the notion of
what constitutes basic needs has expanded to encompass not only food, water, shelter and clothing,
but also access to other amenities such as education, credit, participation, security and dignity (Hulme
et al., 2001).
This second research question concerns the need to define poverty in a way to be relevant to the aim
and context of this research, and to be compatible with data availability. Therefore, a literature review
is conducted on the various definitions and concepts of poverty, and the related dimensions –
including the implications for the poverty indicators – in order to consider a broad range of dimensions
before formulating the definition of poverty. Further, it is recognized that poverty may be closely
related or correlated with inequity, vulnerability, underdevelopment and social exclusion. In order to
define poverty for this research the following steps are taken:
• Dimensions of poverty. Related to the concepts or approaches of poverty adapted, a range of
main- and sub-dimensions can be identified;
• Indicators of poverty. For every (sub)dimension one or more indicators or proxies can be
identified. These indicators are mainly determined by the data sources available.
2.2.1 Dimensions of poverty
The first step concerns the selection of relevant dimensions of well-being. Traditionally, poverty
assessment is focussed on monetary dimensions – income, expenditures and/or consumption – based
on the assumption that human well-being is determined by the material standard of living3. Nowadays,
3 The material standard of living includes the own production, since this is an important asset for most poor.
11
it is increasingly recognised that poverty measures based on these money-metric dimensions reflect a
static concept, offering only a limited picture of household well-being (Falkingham et al., 2002). It
ignores non-monetary dimensions, like health, life expectancy, education, literacy and access to public
goods or common-property resources, as well as dimensions of human capabilities like deficient social
relations, insecurity and lack of empowerment. Although the introduction of non-income measures
complicates the analysis, it nevertheless provides a more complete assessment of poverty in its
different dimensions. Moreover, it permits deeper analysis of the causes of poverty (Lok-Dessallien,
undated b).
Three concepts of poverty: income (poverty lines), basic needs and human capabilities
Most human welfare and poverty dimensions can be grouped into three major concepts of poverty:
income/consumption lines, basic needs and human capabilities. As others, these concepts are derived
from perceived causes of poverty, divided into physiological and sociological deprivation.
Both the income (poverty lines) and the basic needs concept are based on physiological deprivations
(Lok-Dessallien, undated a). The basic needs approach considers dimensions of poverty that are most
related to physical survival. The main understandings of the term include material and economic
needs, however, in a broader interpretation it goes beyond food needs. The UNDP (1997) describes
poverty in the basic need perspective as: “Poverty is deprivation of material requirements for minimally
acceptable fulfilment of human needs, including food. This understanding of deprivation goes well
beyond the lack of private income; it includes the need for basic health and education and essential
community services. It also recognizes the need for employment and participation.”
While the basic needs approach is especially useful with respect to access to public non-marketable
goods and services, the income/consumption or poverty line approach has become the most used tool
to define poverty in terms of having resources to satisfy needs, normally placed in the sphere of
private consumption. It consists in attributing a monetary value to a set of basic goods and services,
and identifying as poor those whose income is lower than a defined minimum: the poverty line (Rocha,
1998). Note that using income as an indicator to measure the basic minimum requires a strong
assumption: different people have the equal needs and derive equal welfare from a given income.
Relative poverty lines go beyond basic necessities and concern also the distribution of assets, hence
inequity.
The human capability concept of poverty focuses on expanding people’s opportunities and spans both
physiological and sociological sides of deprivation (Lok-Dessallien, undated a). This concept of
poverty represents the absence of some basic capabilities to function. Concepts of poverty based on
sociological deprivations are based on underlying structural inequities. Meaning that access to
12
external assets – such as credit, land, infrastructure and common property – and internal assets – as
health, nutrition and education – are not always equally distributed. Despite the clear distinction
between poverty and inequity, analysis of poverty often employs indicators of equity because of
inherent linkages between them (Lok-Dessallien, undated a).
2.2.2 Indicators of poverty
A considerable amount of literature exists on poverty indicators. By selecting indicator(s) for each
dimension of poverty one should take known characteristics of poverty in a given society and the
availability of data on the living conditions of the population into account (Rocha, 1998). Indicators
need to be direct, unambiguous and relevant. Attention needs to be paid to indicators that are
substitutes for each other, e.g. time and distance, expenditures and consumption.
Data gathering
The data available are in the form of a multi-topic household survey: the ‘Questionnaire des
Indicateurs de Base de Bien-être’ (QUIBB)4 performed between April and July 2003. This
questionnaire is written on behalf of the National Institute of Statistics and Demography (INSD) of
Burkina Faso, and is meant for gathering the data needed for the economic and social management of
the country. It is, in design, a known way for collecting information about households’ characteristics,
measures of access, usage and degree of satisfaction in matters of social service (INSD, 2003). The
questionnaire contains more than 54,000 individuals divided over 8,500 households. The complete
country of Burkina Faso is covered, divided into 425 zones. The main advantage of this type of survey
is that the variables can be correlated with reasonable accuracy, since the same sample is used for
different modules of the survey (Lok-Dessallien, undated c). Data on poverty are available on
household or individual scale.
4 Translated: questionnaire of indicators of basic well-being
13
2.3 Methods for quantification of linkages
Basis for answering the third question is given by the qualitative analysis on dimensions of poverty
and socio-economic values of water5. All links between poverty and storage are to be visualized in a
cause-effect diagram, including their interdependencies and some external factors. The diagram
functions as the conceptual model that provides the basis for further research. Subsequently, the
hypothetical relations are tested by means of correlation and regression analysis. This section
describes the theory behind causality, the selection of the statistical techniques and the basic
assumptions they behold.
2.3.1 Cause-effect relations
In order to link indicators of storage and poverty a cause-effect diagram is drawn, which shows the
theoretical causal relations between and within both components. Causality always implies at least
some relationship of dependency between the cause and the effect, i.e. change in one variable is
assumed to result in change in another variable. A valid cause-effect diagram is essential for the
outcomes of this research, as it functions as the conceptual basis for testing. As Hair et al. (1998)
states: “The strength and conviction with which we can assume causation between two factors lies not
in the analytical methods chosen, but in the theoretical justification provided to support the analyses.”
Note that a causal diagram shows only the – positive or negative – type of direction of an impact; it
does not show the direction of the causality.
Dependence relationships are sometimes, but not always, hypothesized to be causal in nature. Causal
relationships are the strongest type of inference made in applying (multi)variate statistics. Therefore,
they can be supported only when the following conditions for causality exist (Hair et al., 1998):
• Covariance between cause and effect, as to indicate sufficient association between the two
variables;
• Temporal antecedence of the cause versus the effect, meaning the cause must occur before
the effect;
• Non-spurious association must exist between the cause and effect, hence lacking alternative
causal variables that explain away the relationship;
• Theoretical support must exist for the relationship between the cause and effect.
5 Hereinafter ‘storage’ refers to the socio-economic values of small reservoirs, and ‘poverty’ refers to the
(sub)dimensions of poverty.
14
2.3.2 Selection of statistical techniques
To select the appropriate statistical techniques for testing we must return to the research question,
which requests the direction and extent of the relations. In addition, the quality of the available data
and the assumptions underlying the statistical techniques must be taken into account.
Data quality assessment
In general, as to select the appropriate statistical technique(s), the following pre-tests should be
performed: (1) outlier detection, and (2) missing data analysis.
Outliers are observations with a unique combination of characteristics identifiable as distinctly
different from the other observations. Typically, it is judged to be an unusually high or low value on a
variable, or a unique combination of values across several variables that make the observation stand
out from the others (Hair et al., 1998). Outlier detection deals with identification and, possibly, deletion
of these extreme values. Note that outliers should be considered within the context of the research;
hence, they can be problematic as well as beneficial. When beneficial, outliers may be indicative of
characteristics of the population. In contrast, problematic outliers are not representative for the
population and can seriously distort statistical tests.
The main concern of missing data analysis is to identify patterns and relationships underlying the
missing data, in order to maintain as close as possible the original distribution of values when any
remedy is applied. The extent to which missing data occurs is of second concern. Missing data are of
importance since they influence sample size and possibly bias results (when not missing at random).
Therefore, the first step in the analysis is to determine the type of the missing data, i.e. whether data
are (completely) missing at random or not missing at random. Dependent on the randomness a
remedy or imputation method can be chosen. Second step is to determine whether the extent of
missing data is low enough to not affect the results, even if it operates in a non-random matter (Hair et
al., 1998). Rule of thumb is that missing data under 10% for an individual case can generally be
ignored, except when the missing data occur in a specific non-random fashion. Additionally, the
number of cases with no missing data must be sufficient for the selected analysis technique if
replacement values will not be substituted (imputed) for the missing data (Hair et al., 1998).
Assessing underlying assumptions
The available data – more in particular selected indicators – should meet the requirements of the
selected statistical techniques. These are threefold:
15
1. Selected data should satisfy the statistical assumptions underlying the (multi)variate
technique, e.g. normality, homoscedasticity and linearity6. Testing the data for compliance with
the underlying assumptions deals with the foundation upon which the technique provides
statistical inferences and results. Some techniques are less affected by violating certain
assumptions – which is termed robustness – but in all cases meeting some of the
assumptions will be critical to a successful analysis (Hair et al., 1998).
2. Selected data should satisfy the required level of measurement. A critical factor in selection
and application of statistical techniques is the measurement level of the dependent and
independent variables. Data can be classified into two categories – categorical or continuous
– based on their characteristics. The measurement level is critical in determining which
techniques are applicable, with considerations made for both independent and dependent
variables.
3. For each subject in the study there must be related pairs of scores, i.e. sets of measurements
are obtained on the same individuals or on pairs of individuals.
Selection
The hypothetical causal relations are tested by means of correlation and regression analysis. These
techniques are closely related, as correlation provides the basis for regression analysis. Two variables
are said to be correlated if changes in one variable are associated with changes in the other variable.
The concept of association, represented by the correlation coefficient is fundamental to regression
analysis by describing the relationship between two variables (Hair et al., 1998). Thus, the function of
determining correlations within this research is to pre-define interesting combinations of variables for
regression analysis. The higher the correlation between variables, the better the prediction by
regression will be.
A quick scan of the data tells us that the above mentioned requirements are not satisfied by nearly all
indicators of poverty, and therefore, it is concluded that – since the data do not meet the requirement
of normal distribution – so called non-parametric tests should be applied. Problem is that not for all
parametric tests a non-parametric alternative exists. Therefore, also parametric alternatives are
discussed in the sections here below.
6 See Glossary
16
2.3.3 Correlation analysis
The first statistical test selected is (bivariate) correlation analysis. Correlation analysis is primarily
concerned with finding out whether or not a relationship exists. The correlation coefficient determines
the (quantitative) extent to which two variables are related, and whether there exists a positive or
negative relation – referred to as the type of the relationship. Note that correlation coefficients do not
indicate the direction of the causality.
Technically, the correlation coefficient represents the standardized measure of covariance (Field,
2005). Therefore, the value of the correlation coefficient varies between +1 and –1. Both of the
extremes represent perfect linear relationships between the variables, and zero represents the
absence of a linear relationship. The sign of the correlation coefficient indicates whether there exists a
positive or negative relation – referred to as the type of the relationship. This is not an indication of the
direction of the causality. For correlations involving dichotomous variables, the sign of the correlation
(positive or negative relation) depends entirely on the coding; hence this requires extra attention when
interpreting.
Correlation coefficients
There are alternative types of correlation coefficients. Relevant correlation coefficients are:
• Pearson product moment correlation coefficient;
• Spearman rank order correlation coefficient;
• Point-biserial correlation coefficient.
Pearson’s correlation (rp). This parametric statistic requires at least data of the interval level7 for it to
be an accurate measure of the linear relationship between two variables. However, in establishing
whether the correlation coefficient is significant, more assumptions are required: for the test statistic to
be valid data have to be normally distributed. In any case, if the data are non-normal or are not
measured at the interval level then non-parametric tests should be performed (Field, 2005).
Spearman’s correlation (rs). When data have been measured at only the ordinal8 level they are said
to be non-parametric and Pearson’s correlation is not appropriate. Therefore, in these cases
Spearman’s correlation coefficient is used. Spearman’s coefficient is a non-parametric statistic, and
so, can be used when the data violate parametric assumptions such as normal distribution. It is also
applied when data are classified to be of ordinal measurement level, i.e. the categories are ordered in
a meaningful way.
7 See Glossary
17
Point-biserial correlation coefficient is used when one of the two variables is (discrete) dichotomous.
The point-biserial correlation is mathematically equivalent to the Pearson correlation, that is, cases
where we have one continuously measured variable and a dichotomous variable. Therefore, in these
cases it is common practice to apply the Pearson correlation.
2.3.4 Regression analysis
The objective of regression analysis is to predict a single dependent (criterion) variable form one or
more independent (predictor) variables. When the problem involves a single dependent variable, the
statistical technique is called simple regression. When the problem involves more dependent
variables, it is termed multiple regression (Hair et al., 1998). Multiple regression is more complicated
than simple regression, but the basic principle is the same; estimating the linear combination of
predictors that correlate maximally with the outcome variable. In itself, linear regression is a parametric
technique. Non-parametric alternatives are (multi-nominal) logistic regression or ordinal regression.
However, Hair et al. (1998) states that regression analysis has been shown to be quite robust even
when the normality assumption is violated.
Regression equation
In regression, each independent variable is weighted by the regression analysis procedure as to
ensure maximal prediction from the set of independent variables. In the case of multiple regression for
explanatory purposes – as is the scope of this research – all of the independent variables should be
on comparable scale, i.e. standardized. The standardized coefficients denote the relative contribution
of the independent variables to the overall prediction, although correlation among the independent
variables complicates the interactive process. The set of weighted independent variables form the
regression model: a linear combination of the independent variable that best predicts the dependent
variable (Hair et al., 1998). The model is fitted is linear, meaning it is based on a set of straight lines.
The mathematical technique used to establish the best fitting line is called method of least squares.
The general form of the standardized regression equation reads as follows:
β β β ε= + + + +1 1 2 2( ... )i n n iY X X X (eq. 2-1)
where Yi is the outcome, and Xi the predictor variable of the i-th case’s score. β1, …,βn are the
standardized regression coefficients that represent the number of standard deviations that the
outcome will change as a result of one standard deviation change in the predictor when all other
predictors are kept constant (Field, 2005). As they are directly comparable, they provide a better
insight into the relative importance of individual predictors. The intercept is omitted since the
18
standardized regression model always goes through the origin (0;0), as the mean of all coefficients is
zero and their standard deviation one. There is a residual term εi, which represents the error of fit.
The line of best fit is found by ascertaining which line, of all possible, results in the least amount of
difference between observed data points and the estimated line (Field, 2005). We are interested the
residuals – that are the differences in the vertical – since we use the line to predict values of Y from
values of the X variable. As the sum of positive and negative residual values tend to cancel each other
out, the square of the differences is used, hence the sum of squares represents the accuracy of the
estimated line.
Assumptions and requirements
The basic assumptions of regression are equal to those of correlation analysis: linearity, normality and
homoscedasticity. As regression analysis is based on the concept of correlation, the linearity of the
relationship between dependent and independent variables is crucial. Regression analysis also poses
requirements upon the level of measurement and sample size. In case the dependent variable is
categorical, (multi-nominal) logistic regression8 or ordinal regression is appropriate. When the
independent variables are categorical, with more than two categories, they must be converted into a
set of dichotomous variables by dummy variable coding.
Additional assumptions underlying multiple regression are (Ho, 2006):
• Independence of error terms. In regression, it is assumed that the predicted value is not
related to any other prediction; hence each predicted value is independent. The Durbin-
Watson statistic informs about whether the assumption of independent errors is tenable;
• Normality of the error distribution. It is assumed that errors of prediction – differences between
the obtained and predicted dependent variable scores – are normally distributed. Violation of
this assumption can be detected by a visual check of the frequency distribution of residuals;
• No (almost) perfect multicollinearity. For unbiased multiple regression analysis there should be
no perfect linear relationship between predictors – i.e. no high correlations. Multicollinearity
can be diagnosed by assessing the correlation matrix and the collinearity diagnostics. The
latter provide some measures of whether there is collinearity in the data. Specifically, it
provides the variance inflation factor (VIF) and tolerance statistics.
8 In case of multiple categories of the dependent variable, multi-nominal logistic regression is applied.
19
Overall model fit
Measures of the overall model fit are given by R and R-square. In case of several predictors the
correlation coefficient represents the correlation between the observed values of Y, and the values of
Y predicted by the multiple regression model. Consequently, the resulting R-square – also called
coefficient of determination – can be interpreted as follows: it is the amount of variation in the outcome
variable accounted for by the model.
However, the fact that the estimated model is significant does not imply the model to be a good
representation of reality. For this assumption, also the test statistic requires a significant confidence
level. The test statistic – that is again a measure of the variance, i.e. variance explained by the model
divided by the variance not explained by the model (Field, 2005) – is a measure of the goodness-of-fit
of the model. For regression analysis we review the t and F test statistics. Fischer (1991) described
this in his criterion that when this probability falls below .05, it gives sufficient confidence to assume
the value of test statistic is indicating that the model is generalizable; i.e. representing the population.
Model parameters
In order to assess the individual contribution of variables the model parameters – beta values – and
the significance of these values is assessed. Earlier is explained that the regression coefficients
β1,…,βn represent the change in the outcome resulting from a unit change in the predictor when all
other variables in the equation are kept constant. Also, if a predictor has a significant impact on the
prediction of the outcome then this coefficient (βi) should be different from zero. In multiple regression
the significance of the t-test indicates whether the regression coefficient is different from zero. As a
general rule, if this observed significance is less than 0.05, then the result reflects a genuine effect
(Field, 2005).
Logistic regression
In cases we aim to estimate regression relations where the outcome variable (dependent variable) is
dichotomous logistic regression is used. Logistic regression is multiple regression but with an outcome
variable that is a categorical dichotomy and predictor variables that are continuous or categorical
(Field, 2005). Logistic regression is limited, however, to prediction of only a two-group (binary)
dependent measure. Thus, in cases for which three or more groups form the dependent measure –
but in a non-ordered manner – multi-nominal logistic regression should be applied.
In logistic regression the overall model fit is assessed by the Hosmer & Lemeshow test. The chi-
square test statistic provides an indication of the goodness-of-fit of the model. It tests the hypothesis
that the observed data are significantly different from the predicted values from the model. So we want
20
a non-significant value for this test – indicating that the model is a fair representation of the data. The
squared regression coefficient in linear regression is replaced by Nagelkerke R2. In terms of
interpretation this can be similarly interpreted to the R2 in linear regression; it provides a gauge of the
substantive significance of the model.
The assessment of the individual predictors is based on the Wald statistic. If the logistic coefficient B is
significantly different from zero then it can be assumed that the predictor is making a significant
contribution to the prediction of the outcome. Hence, the Wald statistic is basically identical to the t-
statistic in linear regression. The direction of the relation can be directly assessed from the sign of the
B-value, or indirectly from the exponentiated coefficients Exp(B); less than one are negative, greater
than one are positive. The magnitude is best assessed by the Exp(B), with the percentage change in
the dependent variable is shown by (Hair et al., 1998):
= −% ( ( ) 1) *100change Exp B (eq. 2-2)
For the aforementioned interpretation to be reliable the confidence interval of Exp(B) should not
include one. If the confidence interval ranges from less than one to more than one, then this would
limit the generalizability of the model parameter, i.e. the direction of this relationship may be unstable
in the population as a whole (Field, 2005).
2.4 Outline of the report
Chapter 1 gives the background of the research. It first illustrates the context of the research by
describing relevant features of livelihood in Burkina Faso and the role small reservoirs play in that
(Section 1.1). Secondly, it presents the problem definition, research questions and research
perspective (Section 1.2).
Chapter 2 describes the methodological considerations and theoretical frameworks applied in the
research. Section 2.1 describes the framework for identifying socio-economic values of small
reservoirs. Section 2.2 defines the context of poverty concepts – income (poverty lines), basic needs
and human capabilities – throughout which relevant dimensions and indicators are identified. Within
those concepts lies the definition of poverty for this research. Section 2.3 explores the theory behind
causality, as to enable design of a valid conceptual model for this research. This model functions as
the base for quantitative analysis. Sections 2.3.2 to 2.3.4 go deeper into the selection of appropriate
statistical techniques and the basic assumptions they behold.
21
Chapter 3 elaborates on the framework for valuation given in Section 2.2. It gives a short overview of
the literature survey done on socio-economic values of water. Based on this, Section 3.3 provides a
description of goods and services provided by small reservoirs. However, which and how many are
provided depends on the reservoir characteristics, which are described in Section 3.4. Finally, Section
3.5 gives the indicators of storage used for this research.
Chapter 4 classifies the dimensions of poverty into the context of poverty concepts. Based on this it
gives the definition of poverty for this research. Subsequently, Section 4.4 gives proxies and indicators
for each sub-dimension of poverty.
Chapter 5 functions as the transition between the theoretical and statistical phase of this research. By
linking the socio-economic values to dimensions of poverty it provides the theory – visualized by
means of the cause-effect diagram – that will be the base for the application of statistical methods.
Moreover it describes detailed, in terms of (sub)dimensions and indicators, all relations that are
visualised in this conceptual model.
Chapter 6 deals with verification of proposed relations by means of bivariate correlation analysis. It
first gives the basic statistical knowledge needed prior to understand, implement and interpret the
analysis. The results of the correlation analysis are described and visualized in Section 6.2. The
chapter is closed with a short discussion (Section 6.3).
Chapter 7 analyzes the relative contribution of variables and their interaction effects by means of
multiple regression analysis. Again, the basic statistical knowledge needed prior to understand,
implement and interpret statistical test is discussed (Section 7.1). Section 7.2 describes the results of
the regression analysis split up into storage, income, nutrition and health. The chapter is closed with a
short discussion (Section 7.3).
Chapter 8 deals with the overall interpretation of the statistical tests. The aim is to get insight into the
complete system. Section 8.1 deals with the interpretation of the complete system on country scale.
And Section 8.2 additionally analyzes the system for separate environments: rural versus urban.
Chapter 9 provides a reflection on the performed research; it encompasses the validation of the
statistical tests by means of split-sample validation (Section 9.1) and looks deeper into the
authentication of the scope and assumptions of the research (Section 9.2).
Finally, Chapter 10 draws the overall conclusions, gives answers to the research questions and
provides recommendations for future work.
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3. Socio-Economic Values of Small Reservoirs
This chapter provides a short overview of the conducted literature review on the values of water and
discuss the different classifications used. Results of the complete literature review are found in
Appendix A. Next, values – the goods and services – are identified that are provided by small surface
water reservoirs in particular. Therefore, we use the framework for valuation of small reservoirs as
discussed in Chapter 2, Section 2.1.1. Finally, a list of selected values and their indicators is
presented.
3.1 Water as a natural asset
Surface water can be considered as a natural resource, the value of which resides in its ability to
create flows of goods and services over time. It provides a basis to many livelihood strategies, like
agriculture, fisheries and cattle farming, and thus has an essential socio-economic value. Not so much
the (monetary) economic value of water is regarded in this research, more the economic
characteristics; water as a natural asset that is used by poor rural households, and as such provides a
means for poverty reduction, social and economic development and food security.
3.2 Values and classifications found in literature
Since the early seventies, attempts have been made to provide a more systematic listing of the many
benefits of natural ecosystems to human society, and to design methods for assigning values to those
benefits (De Groot, 1992). The result is a large variety of frameworks and concepts for valuation.
Below, a brief summary of these frameworks and concepts is given. The complete overview of the
conducted literature review on the values of water can be found in Appendix A.
The classification of Barbier et al. (1997) is based on the framework of Total Economic Value (TEV),
and applied by the Ramsar Convention on Wetlands. It distinguishes use values from non-use values
based on the criteria of human interaction with an environmental resource. Non-use values refer to
current or future values associated with a resource which rely merely on its continued existence and
are unrelated to use. Use values are grouped according to whether they are direct or indirect. Direct
use values involve both commercial and non-commercial activities, with some of the activities often
being important for the subsistence needs of local populations in developing countries. The indirect
use values emanate from supporting or protecting activities. A special category of use values is option
24
value, which arises because an individual may be uncertain about his/her future demand for a
resource and/or its availability in the future. Quasi-option value is simply the expected value of the
information derived from delaying exploitation and conversion of the resource today.
The classification of Roggeri (1995) is designed for valuation of tropical freshwater wetlands. It defines
functions, resources and attributes. Functions of wetlands are due to their role in many natural
phenomena and processes. Resources can be used in order to obtain products or services. Finally,
they provide attributes – or qualities – such as biological diversity. All functions, attributes and
resources are goods and services which have a value for human beings. Note that all values are
closely linked to the resource biological, chemical and physical characteristics, and to the interaction
between these. Therefore, not all possible goods and services are automatically provided by the
resource. Furthermore, the role a resource plays in a given process may vary considerably, both in
significance and quality.
The classification of De Groot (1992) is based on environmental function evaluation, thus includes not
only the harvestable goods (nature in the narrow sense) and land-use values, but also refers to other
benefits of the natural environment which are less tangible. Environmental functions are defined as the
capacity of natural processes and components to provide goods and services that – directly of
indirectly – satisfy physiological and psychological human needs. De Groot divides the environmental
functions into four classes:
• Regulation function. The capacity of natural and semi-natural ecosystems to regulate essential
ecological processes and life support systems, which contribute to the maintenance of a
healthy environment by providing clean air, water and soil;
• Information function. Natural ecosystems contribute to the maintenance of mental health by
providing opportunities for reflection, spiritual enrichment, cognitive development and
aesthetical experience;
• Carrier function. Implies that natural and semi-natural ecosystems provide space and a
suitable substrate or medium for many human activities such as habitation, cultivation and
recreation;
• Production function. Nature provides many resources, ranging from food and raw materials to
energy resources and genetic material.
None of the above classifications perfectly suits the purpose of this research, since the focus of this
research lies on small multi-purpose reservoirs in (semi)arid areas. Therefore, a new framework for
non-monetary valuation of small reservoirs in arid and semi-arid areas is designed, the content of
which is described in the next sections. The proposed framework combines the principles of Total
Economic Value (derived from Barbier et al., 1997) with the framework proposed by Turner et al.
(2000).
25
3.3 Socio-economic values of small reservoirs
Many different classifications of ‘small reservoirs’ can be found in literature. According to Van de
Giesen et al. (2000) small reservoirs are defined as to have a surface smaller than 5 hectares,
reservoirs with a surface between 5 and 100 hectares are classified as medium and with surfaces
exceeding 100 hectares as large. For this research, reservoirs with a surface area till 100 hectares are
defined as ‘small’.
Small reservoirs are multi-purpose structures, whose uses include irrigation, livestock watering, brick
making, domestic use and recreation. Chapter 2, Section 2.1.1 presented the framework that is used
to identify the values – goods and services – of small surface water reservoirs. The socio-economic
value is divided into two main categories: goods and services. Goods refer to the natural products
harvested or used by communities such as water supply for domestic, agricultural and livestock
purposes, materials as clay and reed, and natural products as wildlife and fish. Services support life by
indirect use or existence, i.e. by functioning. Numerous processes and activities would be
considerably altered or disappear if the reservoir no longer exists or not performs properly. For
example, reservoirs protect downstream agricultural land by mitigating floods and droughts. Existence
might also have an intrinsic value based on ethical or aesthetical criteria. Goods and services are not
necessarily of socio-economic value, such value derives from the demand from human society. The
extent and scale at which each particular value is provided depends on several factors which include
quantity and quality of the water, productivity, accessibility, availability of alternative sources of water,
and communities’ network (Dinar et al., 1995). These factors are classified in the framework as the
reservoir characteristics. The framework leaves out the spatial and temporal dynamics, hence the
option and quasi-option values proposed by Barbier et al. (1997).
Below, a general list of reservoir goods and services at the household level is presented. It provides a
convenient starting point for identifying which values a reservoir is likely to contain. Note that it is often
inappropriate to assess a reservoir for all the values in a standard mode, since not all reservoirs
perform all functions to the same degree or magnitude, if at all (Smith et al., 1995).
26
Table 3-1: Values of small multi-purpose reservoirs
Goods provided by small reservoirs Services provided by small reservoirs
Water supply for domestic use
basic consumption needs
brewing traditional beer
Water supply for agricultural use
communal agriculture
fruit trees and vegetable gardens
Water supply for animal use
livestock grazing and watering
wildlife cropping and harvesting
Raw material supply
building material
energy material
Food and nutrient supply
fisheries and aquaculture
gathering of natural products and medicines
Other uses
recreation and tourism
education and cultural heritage
Flood and drought mitigation
Groundwater recharge
Water quality improvement
nutrient retention
sediment retention
salinity control
water treatment
Micro-climate stabilization
Biodiversity maintenance
role in the life cycle of some species
Small reservoirs can be considered a socio-economic infrastructure, as it is used for many different
purposes. The multiple use of reservoir water has different positive and negative effects on human
health and rural development; hence they affect rural livelihoods by contributing to health, reducing
costs of living and even by providing additional income. A great many of the poor in urban and rural
settings base their livelihoods on informal activities; e.g. small-scale cropping, livestock rearing, agro-
processing and other micro-enterprises. In many of these activities adequate water supply is a crucial
enabling resource: used in, or necessary for, the activity itself; freeing time (by reducing time spent
collecting water); or as a key element in improved health that in turn enables people to do work
(Moriarty et al., 2004).
27
Small reservoirs typically support – besides general irrigation – other agricultural purposes, such as
fruit trees and vegetable gardens and livestock watering. Livestock may depend directly on water from
small reservoirs, in addition to profiting from the higher availability of fodder from crop stubble. The
diversion of water to home gardens may contribute substantially to a varied diet or increase the
household income. These gardens – often the responsibility of women – may have vegetables, herbs,
and trees bearing nutritious fruit. Small reservoirs can also be a direct source of protein and micro-
nutrients in the form of aquatic plants, fish, and other organisms. Additionally, domestic purposes,
such as laundry, bathing, washing household utensils, soaking grains, cooking, drinking, house
cleaning, sanitation benefit from the presence of small reservoirs. Note that a small reservoir is not a
source for drinking water; that is extracted from the groundwater. However, in areas where rainfall is
very low, people have no other choice than to use surface water (Boelee et al., 2000).
Easier access to water can also contribute the development of local economic activities, be it small
scale and informal such as brick making, butcher shops, washing vehicles, pottery, mat weaving are
served. These rural industries may provide employment and contribute to local income generation
(Boelee et al., 2000). Besides, small reservoirs may supply a range of products that can be used as
fertilizer, energy and construction material.
Water resources development in general has often been blamed for negative impacts on human
health such as increased spread of malaria and schistosomiasis, however, in many cases the multiple
use of surface water reveals important contributions to improved health and livelihoods. While the
consumption of untreated surface water holds certain risks to human health, the higher availability of
water through the presence of irrigation systems may actually improve health. Water-washed and
even water-borne diseases are reduced with increased use of water for bathing and for consumption
(Boelee et al., 2000; Lipton et al., 2003).
3.4 Characteristics of small reservoirs
Reservoir characteristics describe the features of the ecosystem that the reservoir is part of, and that
determine the extent and scale at which values are provided to human society. They are classified into
three (interrelated) categories: physical features, natural environment and internal structure. Reservoir
characteristics are in turn influenced by the values they provide; development and use of small surface
water reservoirs may change the reservoir characteristics. Turner et al. (2000) defines characteristics
as “those properties that describe a wetland area in the simplest and most objective possible terms.
They are a combination of generic and site-specific features”. The interactions among hydrology and
geomorphology, saturated soil and vegetation determine the general characteristics and the
significance of the processes that occur in any given ecosystem. These processes also enable the
28
development and maintenance of the ecosystem structure which in turn is the key to the continuing
provision of goods and services.
Small reservoirs – physical features
There exists an enormous variety in the physical features of small reservoirs. Many are earthen dams
constructed across a river or stream bed, so as to obstruct the natural stream flow and contain this
water in type of retention basin (Balasz, 2006). Therefore, the shape – slope and size – and volume of
reservoirs depend on the features of the stream channel beds and bottom valleys they are constructed
in. Especially in semi-arid areas, the presence and capacity of most small dams is variable – both
within seasons and between seasons – due to rainfall variability, evaporation, seepage, siltation and
water withdrawals. Small reservoirs often have one or two outlet structures. Their dams are usually
made out of earth with a clay core, in cases protected by rocks to prevent dam crest erosion. Often,
tropical rains cause overflowing and thereby damage the dam wall. The presence, dimensions and
quality of a spillway are of great importance for the dam’s durability (Liebe, 2002); i.e. the sustainability
and integrity of the dam. However, for this research, we assume reservoirs function as designed.
Small reservoirs – external environment
The external environment that is most close to the reservoir is the natural environment. This compiles
all natural conditions that influence the reservoir performance. Think of weather and climate
conditions, hydrology and geology. Besides the natural environment the social environment, economic
environment, political environment, etc can be recognized such as land-use, demography and
institutional setting.
Small reservoirs – internal processes
The internal processes are closely related to the services which a reservoir provides. They are
categorized into hydrological processes, chemical processes and biological processes. Under
hydrological processes are meant the – long and short term – storage of (sub)surface water and the
interaction with groundwater. Biological and chemical processes are the cycling of nutrients, removal
and retention of elements, and the export of organic carbon.
29
3.5 Indicators of storage
Supply of goods and services can be quantified by measures of proximity and availability. Another
important indicator is water quality, since it determines the variety of activities for which the water can
be used for. Unfortunately, no data are available on reservoir water quality, thus, it is assumed the
water is appropriate for all types of uses with the exception of the use as potable water source.
Proximity to the reservoir is measured through assessing the average distance to a reservoir from
any point within the province that the reservoir belongs to. The provincial scale is appropriate since no
useful (georeferenced) data is available on the location of the households (or zones) that are
interviewed in the QUIBB. Another useful measure of reservoir accessibility is reservoir density at
provincial scale. Both indicators introduce an error in the real proximity of reservoirs, since borders of
the provinces are no physical boundaries in reality.
0 – 0.10
0.10 – 0.50
0.50 – 1.00
1.00 – 1.50
1.50 – 2.00
2.00 – 3.00
3.00 – 4.00
Reservoir density [#/100 km2]
Figure 3-2: Reservoir density distribution
30
Water availability is not easy to measure. The most appropriate indicator is the reservoir volume or
stored volume of water at the end of the rainy season. Another indicator would be the reservoir
surface area – obtained from (digitalized) satellite images – that is input to empirical formulae to
estimate volumes. In that case images towards the end of the rainy season are required, in order to be
as accurate as possible. So far these images are not available. In any case, indicators face the
problem of seasonality. We have learned that most reservoirs are seasonal, since they have a rather
shallow depth and consequently a small storage capacity (Coche, 1998). The hypothesis would now
be that the larger the reservoir, the more impact it would have on (the reduction of) poverty. Here an
error is introduced, since not all reservoirs are seasonal or can be considered equally influenced by
seasonality. To overcome this problem we assume that seasonality does not exist; hence, all
reservoirs are always filled.
31
4. Definition of Poverty
This chapter gives the definition of poverty for this research, meaning we explicitly define the
dimensions of poverty considered and the indicators that measure them. Before listing the dimensions
of poverty relevant in this study we give a short overview of the dimensions of poverty – or well-being
– found in literature.
4.1 Poverty in literature
The theme of the website of the World Bank says: “Working for a world free of poverty.” With poverty
described as: “Living below a minimum level of income, such as a US$1 per day per person, often
defines poverty. However, poverty is also a lack of adequate food, shelter, health, education, and
influence over decisions that affect one’s life. Of the six billion people on our planet, three billion live in
developing countries in conditions that fit those definitions of poverty” (Website World Bank).
Also the Human Development Report 1997 (UNDP, 1997) defines poverty beyond income: “Human
poverty is more than income poverty; it is the denial of choices and opportunities for living a tolerable
life.”
The International Fund for Agricultural Development states that: “Poverty is not only a condition of low
income and lack of assets. It is a condition of vulnerability, exclusion and powerlessness. It is the
erosion of their capability to be free from fear and hunger and have their voices heard” (Website
IFAD).
These are some illustrative examples of definitions of poverty by some NGO’s. Since we want to think
in terms of dimensions of poverty, we find here income, food, shelter and education. Looking further,
also more abstract dimensions as opportunities, vulnerability and powerlessness are found. The
coming sections will elaborate on the many different dimensions of poverty and the indicators that
support them.
4.2 Classification of dimensions
When regarding dimensions of poverty from the perspective of the three concepts of poverty
addressed in Chapter 2, Section 2.2.1, we can give the following broad list of (complementary)
32
dimensions. Note that the following scheme is not the only interpretation possible, since borders of
concepts are flexible and poorly defined.
Poverty Lines Basic Human Needs Human Capabilities
Income
Consumption
Expenditures
Nutrition
Health
Education
Sanitation
Energy
Shelter
Potable water
Resources and assets
Public services
Food security
Employment
Literacy
Malnutrition
Sickness
Disability
Powerlessness
Vulnerability
Participation
Inequity
Choices and opportunities
Underdevelopment
Security
Rights and dignity
Social exclusion
Fear for the future
Livelihood sustainability
Figure 4-1: Classification of dimensions of poverty
33
4.3 Relevant dimensions of poverty
From this list we can exclude dimensions that tend to go into the direction of the human capability
concept, since these fall outside of the scope of this research; that should consider the dimensions of
poverty that are (almost) directly influenced by the socio-economic values of small reservoirs, and
indicators are best to be measured at household level. These dimensions are fairly un-directly related
to the presence – or absence – of small reservoirs, and it can be stated that they are more directly
influenced by many other factors. Moreover, these dimensions are not easy to operationalizable and
measurable, neither indicators are available.
The dimensions that fall under the poverty lines and basic human needs concept can be seen as the
most basic and more directly influenced by water access, and therefore, are selected to represent
poverty. As income, health and nutrition being the main dimensions that explain poverty, their sub-
dimensions are supporting them. These sub-dimensions are measures of access (proximity and
availability), measures on health, nutrition and income levels and expenditures on resources and
assets.
Definition of poverty for this research
Based on the above analysis we are able to give a founded working definition of poverty for this
research:
Poverty is the lack of sufficient access to financial and material assets, and public and natural
resources, as to ensure being nutritioned and healthy.
So as well-being to be defined as not being poor.
4.4 Indicators of poverty
Further, this chapter will briefly elaborate on the (sub)dimensions of poverty and investigate indicators
that measure them, in parallel with the exploration of the available data. Data source is the
‘Questionnaire des Indicateurs de Base de Bien-être’ (QUIBB)9 performed between April and July
2003 on more than 10,000 households in Burkina Faso (INSD, 2003). The selected indicators are
given in a table for each main dimension. Note between brackets refers to the measurement scale of
the indicator. More detailed information on selected indicators is given in Appendix B.
9 Translated: Questionnaire of indicators of basic well-being
34
Income
A common method used to measure poverty is based on income or consumption levels. In literature,
an extensive discussion about the choice between these monetary indicators of poverty can be found.
Most analysts argue that, provided the information on consumption obtained from a household survey
is detailed enough, consumption will be a better indicator of poverty measurement than income
(Coudouel et al., 2002). For this research we advocate for income as a main dimension of poverty, but
include expenditures on food and health in the analysis as proxies for consumption. Hereby, also the
potential weakness of monetary indicators in poor rural agrarian economies is covered.
Table 4-2: Selected indicators for income
Sub-dimension Indicator
Income diversication –
Total income Total revenue (continuous, CFA/yr)
Income diversication –
Income from employment
Salary from public sector (continuous, CFA/yr)
Salary from private sector (continuous, CFA/yr)
Income diversication –
Income from entrepreneurship
Revenue from rent (continuous, CFA/yr)
Revenue from interest (continuous, CFA/yr)
Income diversication –
Income form (owned) resources
Revenue from agriculture (continuous, CFA/yr)
Revenue from dairy farming (continuous, CFA/yr)
Education –
Education level Highest level of education reached (categorical, 4 items)
Education –
Access to schools
Proximity of primary school (categorical, 5 items)
Proximity of secondary school (categorical, 5 items)
Nutrition
Nutrition is often called an investment in development, since better nutrition has proven to increase
intellectual capacity and thus increases the (future) ability to obtain other types of assets that are
essential for development. Literature shows that foetal and infant undernutrition affects children’s later
school enrolment, educational attainment, cognitive ability, and lifetime earnings and labour
35
productivity (Haddad, 2002). In this perspective, nutrition is closely related to the other dimensions of
poverty, i.e. health and income.
Multiple methods are available for measuring malnutrition. One alternative approach is measuring
nutritional outcomes by the investigation of child nutritional status based on anthropometric surveys.
Malnutrition is diagnosed when individuals' anthropometric measurements in terms of weight, height
and age fall below international reference standards. Poor growth in infants and children, as well as
underweight in adults may be the consequence of both inadequate food intake and poor absorption of
food caused by environmental factors, such as infections or inadequate parental care (FAO, 2003).
Three of the most reliable indices for malnutrition are stunting, wasting and underweight. Stunting is
an indicator of chronic malnutrition, the result of prolonged food deprivation and/or illness. Wasting is
an indicator of acute malnutrition, the result of more recent food deprivation or illness. Underweight is
used as a composite indicator, to reflect both acute and chronic malnutrition, although it cannot
distinguish between them (Nandy et al., 2003). Notice that these three indicators show considerable
overlap, for this research we argue to use one single variable that integrates them.
The dimension nutrition is divided into three sub-dimensions: (1) food security, (2) physical state
related to famine and (3) expenditures on food. Food security exists when all people, at all times, have
access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an
active and healthy life (Website FAO SPFS).
Table 4-3: Selected indicators for nutrition
Sub-dimension Indicator
Food security –
Occurrence of food insecurity
Occurrence of problems satisfying nutrition needs (categorical,
5 items)
Food security –
Access to food market Proximity of (local) food market (categorical, 5 items)
Food security –
Access to owned resources
Surface of landholding (continuous, # hectares)
Number of livestock (continuous, # cattle)
Food security –
Access to (direct) nutritional
resources
Access to stocks (of cereals) until the next harvest
(dichotomous, 2 items)
Value of autoconsumption of nutritional production (continuous,
CFA/month)
36
Famine –
Malnutrition
Child is wasted (low weight for height) (dichotomous, 2 items)
Child is stunted (low height for age) (dichotomous, 2 items)
Child is underweight (low weight for age) (dichotomous, 2
items)
Child is stunted and/or wasted and/or underweight
(dichotomous, 2 items)
Food consumption –
Expenditures on food
Value of expenditures on nutritional products (continuous,
CFA/month)
Health
Beyond its intrinsic value to individuals, health clearly is a central issue in poverty reduction and
overall human development. Indeed, three of the Millennium Development Goals (MDGs) call for
global health improvements by 2015, among which the reduction of child mortality (OECD/WHO,
2003). The international community agreed upon that enjoying the highest attainable standard of
health is one of the fundamental rights of every human being. For poor people especially, health is
also a crucially important economic asset – i.e. form of human capital – that increases an individual
capability.
Waterborne diseases are due to the consumption of (with pathogenic micro-organisms) contaminated
water, or from species which existence – or lifecycle – is related to water (Website WHO).
Contaminated water that is used in food preparation can be the source of foodborne diseases.
According to the World Health Organization, diarrhoeal disease is responsible for the deaths of 1.8
million people every year. It is estimated that 88% of that burden is attributable to unsafe water supply,
sanitation and hygiene, and is mostly concentrated on children in developing countries (WHO, 2007).
Sanitary measures are the interventions – usually construction of facilities such as latrines – that
improve the management of excreta10 (WSSCC, 2005). It encompasses all services that maintain
hygienic conditions to prevent diseases, as the collection and treatment of wastewater, either
centralized or on-site by e.g. septic tanks and latrines. In a broader sense, sanitation also includes the
collection and disposal of solid wastes.
Of all water-related diseases malaria is the most severe one. Where Sub-Saharan Africa already
carries the highest per capita burden of disease in the world, malaria is the single most important
disease in this – being responsible for nearly one million deaths and 300 to 500 million clinical cases
every year (MARA/ARMA, 1998). In this research we use the occurrence of fever and diarrhoea as
10 See Glossary
37
proxy for prevalence of waterborne diseases. Additionally, sanitation access levels are measured by
manner of garbage and toilet wastewater disposal.
The dimension health is explained by three sub-dimensions: (1) health security, (2) physical health
and (3) expenditures on health. Health security encompasses having sufficient access to (public)
health services, (reliable) potable water and sanitation facilities.
Table 4-4: Selected indicators for health
Sub-dimension Indicator
Health security –
Access to health services Proximity of (public) health service (categorical, 5 items)
Health security –
Access to potable water
Proximity of potable water source (categorical, 5 items)
Availability of (improved) potable water source (categorical, 3
items)
Health security –
Access to sanitation facilities
Access to (improved) toilets (categorical, 4 items)
Access to (improved) garbage disposal (categorical, 6 items)
Physical –
Disease and disability
Recent prevalence of disease (dichotomous, 2 items)
Chronic prevalence of handicap or injury (dichotomous, 2
items)
Physical –
Water-related disease
Recent prevalence of diarrhoea (dichotomous, 2 items)
Recent prevalence of fever (dichotomous, 2 items)
Health consumption –
Expenditures on health services
Value of expenditures on consultation (CFA/past month)
Value of expenditures on medical analysis (CFA/past month)
Value of expenditures on medicines (CFA/past month)
Value of expenditures on hospitals (CFA/past month)
Value of expenditures on other medical services (CFA/past
month)
Total value of expenditures on health services (CFA/past
month)
39
5. Linking Storage to Poverty
The previous chapter identified income, nutrition and health as main dimensions that explain poverty.
This chapter determines the theoretical (inter)relations between the selected dimensions of poverty
and the socio-economic values of small reservoirs. By doing so, it provides the conceptual model that
will be the base for the application of statistical methods in the next phase of this research.
5.1 Storage as explanatory factor for poverty
Earlier (Chapter 3, Table 3-1) we identified the socio-economic values – goods and services – that
small multi-purpose reservoirs can provide to households living nearby them. We recall that goods
provided are domestic, agricultural and animal water supply, and food, nutrient and raw material
supply. It is hypothesized that these goods have a – more or less – direct and often positive relation to
health, nutrition or income; hence poverty. Services that can be provided by small reservoirs are flood
and drought mitigation, groundwater recharge, water quality improvement, micro-climate stabilization
and biodiversity maintenance. They support enhance alleviation by indirect use or functioning. The
availability of numerous goods would be considerably altered or disappear if the reservoir no longer
exists or performs its functions properly. For this reason services are addressed to have a secondary
impact on indicators of poverty.
In the table below a brief overview of possible impacts of small reservoirs on (sub)dimensions of
poverty. The table shows that socio-economic values of small reservoirs result in an impact on all
three main-dimensions of poverty. These relations are also present in the cause-effect diagram that is
given at the end of this chapter.
40
Table 5-1: Storage and poverty links
Goods supplied by small reservoirs Impact on household poverty
Domestic water supply
Improved access to sanitation facilities
Improved access to nutritional resources by watering small
gardens
Improved access to nutritional resources by watering small
animals
Access to (better quality) potable water
Agricultural water supply Access to means of production of direct nutritional resources
Access to means of production to increase income
Animal water supply Access to means of production of direct nutritional resources
Access to means of production to increase income
Food and nutrient supply
Access to direct nutritional resources
Access to means of production of direct nutritional resources
Access to means of production to increase income
Raw material supply
Access to means of production of direct nutritional resources
Access to means of production to increase income
Access to materials to improve housing
5.2 Linking storage and poverty
In this section we describe the hypotheses on the linkages between storage and poverty, which are
visualized in the cause-effect diagram. We already, briefly, described the origin of the links between
storage and poverty (Table 5-1), but it is also recognize that the actual impact that can be achieved by
productive and household uses of small reservoirs will depend on interdependencies within the
storage-poverty interactive system and external factors. By interdependencies are meant the relations
between (sub)dimensions within either the storage or poverty component. There is a need to analyze
the interrelations; there can be a dependence of assets or uses onto each other, and competition can
occur between different assets (goods and services) or uses. As is seen from the cause-effect
diagram, within the poverty component, different (sub)dimensions or indicators are not independent
from each other; they are correlated. Furthermore, as there is a limited amount of water available, the
priority uses of water influence the overall effectiveness. Below, the assigned (inter)relations are
justified, but we start with evaluating the theory behind causality.
41
Below, the theory and hypothesis on cause and effect between and within storage and poverty is
described exhaustively. Notes are made about the non-spuriousity and direction of hypothesized
relations. In most cases it is easy to determine which variable affects the next, however, in some
cases the temporal antecedence of cause and effect is difficult to determine. Only after application of
correlation analysis the ‘true’ existence of the causality is evaluated. This chapter deals with the
practical significance of correlations.
5.2.1 Links between storage and poverty
Storage - Income
Starting from the hypothesized links between storage11 and the income dimension of poverty: in the
cause-effect diagram income diversification is differentiated into three proxies that each have different
indicators – these are not mentioned in the diagram for simplicity reasons – see also Chapter 4. For
the link between storage and the indicator ‘total revenue’ it is hypothesized that in general a household
total income will increase with improved accessibility to water. Not only directly – through increased
access to means of production – but also indirectly; as time savings and improved health and
nutritional status will provide human capital12, hence enable employment.
Many scientists – like Bloom et al. (2000) – argue that the relation between health and income is one
of mutual reinforcement, i.e. the direction of the causality is not only from income to health, but runs
both ways. Better health leads to higher income, but there is also a positive feedback effect, giving rise
to a beneficial situation where health and income improvements are mutually reinforcing.
For the proxy income from (owned) resources (that are agriculture and dairy farming), the hypothesis
is that with increased access to water – assumed that there is sufficient availability – the income from
(owned) resources will increase. This is, in turn, based on the theory that production of agricultural and
dairy farming products depends on water access, and will increase with improved resources. However,
the increased production potential might not result in increased income since it might not be sold for
money, or can be used for own consumption (self sufficiency). Hence, the strength of the relation may
be diminished by interference of autoconsumption. Also external factors like the access to (local)
markets and milieu of residence (urban versus rural) are relevant in this concern.
11 Hereinafter the term ‘storage’ refers to the socio-economic values of small reservoirs measured by reservoir
density (province scale).
12 See Glossary
42
The proxy income from entrepreneurship (composed of rent and interest revenues) is not directly
influenced by improved access to small reservoirs. However, storage may lead to increased total
income, better education and time savings, hence revenue from rent and interest as well.
The proxy income from employment (built up from salary from private and public sector) has a similar
indirect relation to storage; hence by means of considerable time savings and improved human
capital. With decreased distance to a source of water people spent less time fetching, and so the
hypothesis is that they would use their time for instance in employment. As already suggested before,
time savings give the opportunity to perform other activities; not necessarily employment. Obtaining
water often involves significant inconvenience of time spent in collection. Time spent collecting water
should be closely related to the distance to the source; the time required to collect water reduces the
time remaining for other activities such as cooking or farming, being employed or going to school
(DOW, 2001). Therefore, all activities that concur for time may diminish the strength of this relation.
More specifically, all indicators within the (main)dimension income are in a way related.
We classify education as sub-dimension of income. Many will argue that education to be a main
dimension of poverty; we subscribe the important role of education in the poverty reduction process,
however, we have chosen to give it the current status within this research. The hypothesis is that there
is a positive relation between storage and education level. Like income diversification, education will
benefit from improved health and nutritional status; hence, illness will negatively influence education
level. A more direct relation to storage would be due to time savings, as one spent less time on
fetching water it is possible to attain education. Many literature sources mention the drawback water
fetching has on education – particularly that of girls (Boelee et al, 2000; Website Unicef). Influential to
this relation would be the access to education facilities and other time consuming activities.
Storage - Nutrition
The sub-dimension food security is measured by four proxies of which occurrence of food insecurity is
seen as the most meaningful. The hypothesis is that the better access to water resources, the fewer
problems satisfying nutritional needs occur. This is based on the assumption that small reservoirs
provide a means to practice (small holder) agriculture and dairy farming, and to gather nutritional
products from in and around the reservoir.
Two other indicators within this dimension are ‘value of autoconsumption of nutritional products’ and
‘access to stocks of cereals until next harvest’, where the latter is seen as a proxy for stocks of
nutritional products in general. Both indicators have a causal relation to the satisfaction of nutritional
needs. With increased value of autoconsumption and sufficient stocks it is more likely that less
problems satisfying nutritional problems occur. All is – of course – relative to the number of household
43
members. For the relation to storage it is hypothesized that as (agricultural) production increases both
autoconsumption levels as well as the availability of stocks will benefit from this. Unfortunately, no
direct or suitable indicators for production levels are available for this research.
The second sub-dimension – famine – is a result of food insecurity. It encompasses the
anthropometric measures related to weight, length and age of children under five. It is widely accepted
that poor nutrition in a child is reflected in failure to grow sufficiently (Nandy et al., 2003). The indicator
‘malnutrition’ is used as an aggregate measure of the indices wasted, stunted and underweight.
Food consumption levels are included by the indicator ‘expenditures on nutritional products’. These
are indirectly influenced by storage. Storage may lead to increased income levels, and so, people may
tend to spend more on nutritional products. It is a known phenomenon that as incomes rise, food
habits change in favour of more nutritional and more diversified diets (Molden, 2007). Note that, we
should verify this relation for autoconsumption levels; in case these are sufficient to ensure food
security, expenditures on food are less likely to increase. Moreover, access to (local) food markets is a
limiting factor for expenditures on food. Note that measures on expenditures are relative to the number
(and age) of household members.
Finally, indicators for the proxy access to owned resources are ‘surface of landholding’, ‘number of
livestock’. As the density of reservoirs increases local communities have access to sources for
livestock watering and irrigation water supply. Therefore, they are able to sustain a larger number of
livestock and find more beneficial agriculture practices. Naturally, the more of these resources
available the more input for income generation and food security the household beholds. Indirectly,
even measures of health security should improve. It is hypothesized that income from (owned)
resources and access to (direct) nutritional products show the most direct relations with access to
owned resources. While income from other sources (than agriculture and dairy farming) as well as
education levels may diminish.
Storage - Health
Likewise to nutrition, we have defined health security as a sub-dimension of health. It encompasses
the access to (public) health services, potable water and sanitation facilities. Only access to (public)
health services is considered as external influencing factor, since it is not related to the presence – or
absence – of small reservoirs. One may argue that access to potable water should be regarded as
external. We recall the assumption that the reservoirs’ water is appropriate for all types of uses with
the exception of the use as potable water source. However, literature reveals that in cases reservoir
water will be used for drinking water purposes (Boelee et al., 2000), or that the presence of a reservoir
will locally raise the groundwater table (Savadogo, 2006), accommodating improved digging and
44
pumping of (potable) water. Therefore, we include a direct link between storage and the proxy access
to potable water.
The proxy access to sanitation facilities is not considered to be influenced by the presence of a water
source. Two indicators represent this proxy: ‘access to (improved) toilets’ and ‘access to (improved)
garbage disposal; thus, measures of hygiene that require water are not included.
A clear sub-dimension of health is physical health. We already mentioned several times the impact
physical health has on other dimensions of poverty. But how does storage influence physical health?
Actually, here we find the only (possibly) negative relation in the system. In literature, many examples
can be found where it can go wrong with the human interaction with surface water, called water-
related diseases. One important issue is malaria. While malaria prevalence is highly dependent on the
presence of (stagnant) surface water, many other factors will play a role, e.g. rainfall and temperature
(Website MARA/ARMA). In this research, fever is used as an indicator for malaria prevalence. The
hypothesis is that with higher reservoir densities the number of people suffering from malaria
increases. Secondly, sources of water-related diseases are unsafe drinking water and lack of
sanitation and hygiene. Main symptom is considered to be diarrhoea (WHO, 2007), this is used as a
second indicator for the treat storage has it.
Note that the above hypotheses are quite interesting from the research point of view, however, they
are influenced by a variety of factors that are kept outside the scope of this research (therefore, not
included in the conceptual model). This implies that we should be most careful in drawing any
conclusions on this issue. Moreover, malaria prevalence is highest just after the rainy season (Website
MARA/ARMA; Beiersmann et al., 2007), while the QUIBB interviews are performed just before and at
the start of the rainy season.
Health consumption is measured by the indicator ‘value of expenditures on health services’. This is
indirectly influenced by storage. Either when physical health status increases or decreases this has an
influence on expenditures on e.g. consultation, medicines and medical analysis. Also with improved
welfare and income levels, people might make use of health services in a more moderate way.
Therefore, access to (public) health services is a determining factor in this relation. Be aware that
measures of expenditures are relative to the household size.
5.2.2 External explanatory factors
The actual benefits of small reservoirs will depend on other constraints faced by poor people like
availability of labour, skills, infrastructure, equipment, presence and knowledge of markets for products
45
and services, transport, quality control standards (Moriarty, 2003). All these factors originate from
outside the conceptual model (represented by the cause-effect diagram). They mainly influence the
direction and extent of impacts, rather than the existence of the impact itself. Possible factors should
be identified and assessed on their influence onto the proposed causal relations. The reservoir
characteristics – described in Chapter 3, Section 3.4 – serve as the framework for identifying external
factors. Recall that reservoir characteristics are the physical features, external environment and
internal processes. Some relevant external factors are weather and climate conditions, land-use,
hydrology and geology, demography and community composition, and the quality and presence of
different (social-economic) infrastructures. Many of these indicators are not included in the analysis, as
they fall outside the scope of this research.
Some external factors are present in the cause-effect diagram, since they are seen as proxy within
(sub)dimensions of poverty; they are supporting the definition of the (sub)dimension. This set of
relations can be considered as a first verification on the extent of the relations between storage and
poverty. Availability and proximity of (communal) services and resources is important to many poor
households. For instance, access to a (local) food market is relevant to food security; the amount of
expenditures on nutritional products and income from (owned) resources depend on it. Since trade
may not be done in monetary terms, also the relation to nutritional satisfaction should be tested. Also
access to schools determines people’s possibilities to overcome literacy, and so provide capabilities.
Health security includes the external factor access to (public) health service; with improved proximity
to hospitals and clinics, physical health is likely to increase and expenditures on health may increase
as well.
Other external factors are not included in the diagram because they represent a more generic
influence on the system. Examples of the latter are ‘age of the individual’ and ‘number of household
members’, which are used to test for the relativeness of expenditures, income and consumption levels.
Main controlling external variables are ‘population density’, ‘milieu of residence’, and ‘proximity of
(public) transport’.
As regards the relation between poverty and population density it is not surprising to find that the
direction of the relationship is neither obvious nor simple. World Bank economist Pritchett (1997) is
bolder: “Solid evidence that population growth is a cause or even an exacerbating condition of poverty
cannot be given because there is none". For this research, it is hypothesized that population density
generally positively influences the access to (communal) services and resources, as exploitation
becomes more beneficial due to higher demand. However, the hypothesis is that the relation between
storage and population density is one of mutual enforcement. One may reason as above: where
population density is higher the demand for water resources increases, and thus, it is beneficial to
build a reservoir. The opposite reasoning is also cogent: people tend to settle there were water
46
resources are available. Hence, the temporal antecedence between cause and effect is not at all
clear. Aim is to find evidence for the leading direction of this relation, whereby other factors influencing
the relation are taken into account.
For the external factor ‘milieu of environment’, so whether a household is situated in a rural or urban
environment, it is hypothesized that in more urbanized areas – mostly situated in central Burkina Faso
– access to (communal) services and resources is higher. Furthermore, indicators that are related to
livelihood strategies may show differences, depending on environmental factors.
The influence of access to (public) transport is hypothesized to be mainly on access to services and
resources. This is supported by literature; the availability of physical infrastructure (roads) improves
the access and availability of food, access to health services and education facilities (Thimm, 1993).
Consequently, roads play a major role in achieving food security.
Explanation of Figure 5-2:
The figure shows the conceptual framework for the storage-poverty interactive system. The arrows
indicate the hypothesized relations, type of relation and direction of causality. A plus in the diagram
means that when the value of the independent variable increases, the value of the dependent variable
increases as well; hence, this does not always mean there is a positive effect. Once again, the main
dimensions of poverty are divided into sub-dimensions. Proxies and indicators are given in the light-
coloured boxes. Their item scales can be found in Appendix B.
access toprimary/
secondaryschool
access to(local)food
market
access to(public)healthservice
level ofeducation
totalrevenue
recentprevalence of disease
value ofexpenditures
on healthservices
occurrenceof problemssatisfyingnutritional
needs
value ofexpenditureson nutritional
product
Health security
Income diversication
Physical health
Food security
Education
Food consumption
Health consum
ption
HEALTH NUTRITION INCOME
recentprevalenceof diarrhoea
recentprevalence
of fever
chronicprevalenceof handicap
or injury
access to(improved)garbagedisposal
access to(improved)
toilets
reservoirdensity
(provincescale)
STORAGE
incomefrom
employment
incomefrom
(owned)resources
incomefrom
entrepre-neurship
-
+
+
-
+
+ +
+
+ +
-
+
+
-+
-
access toowned
resources
+
Famine
prevalence ofmalnutrition
-
+
access to(improved)source ofpotablewater
-
+
access tostocks (ofcereals)until nextharvest
value ofautocons-umption
of nutritionalproducts
+
-
+
-
+
+
-
+
Figure 5-2: Conceptual model – the cause-effect diagram
49
6. Verification of Relationships
This chapter aims to verify the hypotheses as stated in the previous chapter. By applying correlation
analysis on each of the linkages visualized in the conceptual model the existence of a (statistical)
significant relation is verified; hence, hypotheses are accepted or rejected. The correlation coefficient
also determines the (quantitative) extent to which two variables are related, and whether the type of
relationship is positive or negative. Note that correlation coefficients do not indicate the temporal
antecedence of the cause versus the effect; this should be pre-defined by theory (see Section 5.2).
This chapter first gives some basic knowledge that is required to interpret the outcomes of the
correlation analysis. Main outcome of this chapter is the test results of bivariate correlation, visualized
by means of the cause-effect diagram. Further, it will briefly discuss each test individually. Later –
Chapter 8 – will look at the model as a whole, integrate all statistical tests performed and give answers
to the research questions. Additionally, a more exploratory approach is applied by testing relations that
are not hypothesized by theory as direct (see Appendix D).
6.1 Model specification
We are interested in testing the experimental hypothesis (or prediction) that there exists a relation
between specific indicators of poverty and storage. The reverse possibility – stating that no relation
exists – is called the null-hypothesis. As to confirm or reject the null-hypothesis, inferential statistics
are reviewed. Basically, we calculate the probability that the estimated coefficient is not occurring by
chance. As this probability decreases, greater confidence is gained that the experimental hypothesis is
actually correct, and that the null-hypothesis can be rejected. Literature sources (Hair et al., 1998;
Field, 2005; Fischer, 1991) suggest that only when the probability of a genuine result – i.e. the result is
not found by change – is 95% or more it can be accepted as being true. This implies that the
probability of the estimation being found by change is 5% or less. In statistical terms this is called a
statistically significant finding.
From the data screening process (see Appendix C) we have learned that none of the indicators are
normally distributed. Theory prescribes that in case of violation of the normality assumption, the
Spearman’s correlation coefficient should be selected. In cases that one of the variables is
dichotomous Pearson correlation analysis is applied. The Pearson correlation coefficient is denoted as
rp, the Spearman correlation coefficient is denoted as rs.
50
incomefrom
employment
incomefrom
(owned)resources
level ofeducation
totalrevenue
proximityof primary/secondary
school
Income diversication
Education
INCOME
reservoirdensity
(provincescale)
STORAGE
incomefrom
entrepre-neurship
+.173+.147
+.269
-.144
+.194
Note all correlations are tested one-tailed, unless mentioned otherwise. Also, all ordinal variables are
considered as of interval measurement level, this requires extra attention when interpreting (the sign
of) the correlation coefficient.
6.2 Bivariate correlation analysis
The strategy for testing is to build the model bottom-up. Hence, it starts with assessing direct links
between storage and poverty, followed by the indirect linkages and interrelations (that lead to the
indirect links). Finally, correlations of external factors with the storage-poverty interactive system are
estimated.
6.2.1 Direct links between storage and poverty
Storage - Income
The hypothesis is that between storage and
income diversication there should be a positive
relation of some importance. So when the
reservoir density increases, the total income will
increase; hence a positive correlation. The test
confirms this hypothesis: rs = +.173 (p<.01). The
analysis provides the indication that storage has
a negative relation to income from (owned)
resources: rs = -.144 (p<.01). Of which the
correlation with revenue from agriculture and
from dairy farming are respectively rs = -.114
(p<.01) and rs = -.088 (p<.01). Indicating that
with higher reservoir density, the income from
(owned) resources will decrease. This is the
opposite from the hypothesis that – from all
other revenues – these revenues are the most
influenced by the presence of small reservoirs.
All other sources of income show a positive
relation to storage.
Figure 6-1: Correlations between storage and income
51
proximityof (local)
foodmarket
occurrenceof problemssatisfyingnutritional
needs
prevalence ofmalnutrition
value ofexpenditureson nutritional
product
Food security
Famine
Food consumption
NUTRITION
reservoirdensity
(provincescale)
STORAGE
access tostocks (ofcereals)until nextharvest
value ofautocons-umption
of nutritionalproducts
+.229
(+).041
+.058
(-).082
-.254
surface of landholding
number oflivestock
-.318
-.214
Remarkable is the relatively high positive correlation between storage and income from employment:
rs = +.269 (p<.01). Therefore, the interpretation may be that the benefits of reservoirs are highly
influenced by time savings and improved human capital. A similar conclusion can be drawn for the
relation between storage and education level. Where we hypothesize a positive relation, the test
confirms this: rs = +.194 (p<.01).
Storage - Nutrition
An important hypothesis within this research is
that food security should improve due to the
proximity of small reservoirs. Hence, the
hypothesis is that the better access to water
resources – i.e. higher density – the fewer
problems satisfying nutritional needs the
household faces. However, the indicator
‘occurrence of problems satisfying nutritional
needs’ does not show to have a high direct
correlation to storage, moreover, the correlation
is positive: rs = +.058 (p<.01), suggesting that
with higher densities the household faces more
often problems satisfying her nutritional needs.
In a way this outcome is supported by the
following results: the indicator ‘value of
autoconsumption’ shows a relatively high, but
negative correlation: rs = -.254 (p<.01), indicating
that with increasing reservoir density, the value
of autoconsumption is decreasing. This is
neither consistent with the hypothesis. Also the
indicator ‘access to stocks until the next harvest’
shows a high, and negative correlation to
storage: rp = (-).082 (p<.01). The interpretation is
that stocks are not sufficient due to higher
reservoir densities.
Figure 6-2: Correlations between storage and nutrition
52
chronicprevalenceof handicap
or injury
recentprevalenceof diarrhoea
recentprevalenceof disease
recentprevalence
of fever
value ofexpenditures
on healthservices
Health security
Physical health
Health consum
ptionHEALTH
reservoirdensity
(provincescale)
STORAGE
access to(improved)garbagedisposal
access to(improved)
toilets
+.118
.000
(+).011
availability of(improved)
potablewatersource
proximity ofpotablewatersource
proximityof (public)
healthservice
+.201
+.056
For both indicators of access to owned resources the correlation coefficient is relevant, but negative;
suggesting that with higher reservoir densities the surface of owned land and number of owned cattle
decreases. And, although the correlation between storage and ‘prevalence of malnutrition’ does not
show to be important, also this correlation is not consistent with the hypothesis that nutritional status
should improve due to storage. Only ‘expenditures on nutritional products’ are positively influenced by
storage: rs = +.229 (p<.01), suggesting that expenditures increase with higher reservoir density.
Summarizing, no evidence is found that storage positively influences measures of food security, nor
direct sources of nutritional products. Again we should apply tests for controlling factors as household
size and access to (local) food markets. For the value of autoconsumption a controlling variable would
be production levels, however, there are no indicators available to perform a partial correlation test.
Storage - Health
An interesting hypothesis concerns the human
interaction with small reservoirs. The hypothesis
is that with higher reservoir density the
prevalence of waterborne diseases will increase.
Main symptoms for these diseases are
considered diarrhoea for unreliable drinking
water, sanitation and hygiene, and fever for the
main water related disease malaria; hence we
use these as indicators for the threat storage
has on physical health. Tests show that the
correlation between storage and ‘prevalence of
diarrhoea’ is insignificant, and the correlation to
‘prevalence of fever’ is unimportant: rp = (+).011
(p<.01). The conclusion may be that the direct
negative impact of storage on (physical) human
health is negligible. The hypothesis is that
storage has a positive effect on access to
potable water. It may be the case that the
proximity and availability of a potable water
source improves with more proximate
reservoirs. The analysis shows that the
correlations are respectively rs = +.056 (p<.01),
Figure 6-3: Correlations between storage and health
53
and rs = +.201 (p<.01), suggesting that reservoir density has no important impact on the proximity of
potable water, but that the availability of an (improved) potable water source generally increases.
As storage has no real negative effect on the prevalence of waterborne diseases and a positive effect
on access to potable water we assume that the relation between storage and ‘expenditures on health’
is positive. This hypothesis is rejected by the statistical test: rs = +.118 (p<.01), suggesting that with
increased reservoir density also expenditures on health increase. Note that measures on health
security and physical health are not the only determining factors influencing the value of expenditures
on health; additional tests need to be performed.
6.2.2 Indirect links (and interdependencies) between storage and poverty
Income
Education is hypothesized to have a positive impact on all sources of income. The test shows this is
true for the relation to total revenue, income from employment and income from entrepreneurship.
Again income from (owned) resources is violating this hypothesis: rs = -.251, suggesting that
individuals with a lower education level have more income from (owned) resources, and less income
from employment. Overall, the higher level of education, the more income is obtained.
It is hypothesized that, in general, total revenue will not only increase directly from improved
accessibility of water, but also indirectly as storage potentially leads to improved health and nutritional
status; hence will have a positive effect on human capital. The relation between income and nutrition
is one of mutual dependence. As income levels increase the expenditures on nutritional products may
increase as well. This is supported by the statistical test: rs = +.442. Consequently, increased food
consumption levels are likely to lead to improved food security and nutritional status. In turn, these
improvements may have a positive impact on income and education levels, due to time savings and
improved human capital. This is confirmed by the correlation analysis between ‘occurrence of
problems satisfying nutritional needs’ and ‘total revenue’: rs = -.180, suggesting that with less
nutritional problems income levels increase. The same positive effect is seen for the relation between
‘occurrence of problems satisfying nutritional needs’ and ‘level of education’: rs = -.106.
Likewise, the relation between income and health is one of mutual dependence. As increased income
level may enable higher expenditures on health. Unfortunately, the latter are mainly determined by
other factors, and only limited by income. Nevertheless, the analysis shows there is a positive relation:
rs = +.144 (p<.01). The hypothesis is now that, due to improved nutrition and higher expenditures on
health, the physical health of individuals will improve. However, the correlation analysis does not show
54
interesting correlations from indicators of prevalence of diseases and disability to ‘total revenue’.
Neither education levels are influenced by disease and disability.
Table 6-1: Correlations for interdependencies: income
Independent variable Dependent variable Coefficient
Total revenue rs = +.256 (p<.01)
Income from (owned) resources rs = -.251 (p<.01)
Income from employment rs = +.402 (p<.01)
Level of education
Income from entrepreneurship rs = +.168 (p<.01)
Expenditures on nutritional products rs = +.442 (p<.01) Total revenue
Expenditures on health services rs = +.144 (p<.01)
Occurrence of problems satisfying
nutritional needs Total revenue rs = -.180 (p<.01)
Recent prevalence of disease Total revenue Insignificant
Chronic prevalence of handicap or injury Total revenue rp = (-).009 (p<.01)
Nutrition
Although storage does not seem to have a positive direct impact on measures of nutrition, we are
interested in the interrelations within this dimension. According to the conceptual model, the proxy
access to owned resources has a positive relation to ‘value of autoconsumption of nutritional products’
and ‘access to stocks until next harvest’. All test results show that with increasing land and livestock
holding both autoconsumption levels as access to stocks are positively influenced. Additionally,
access to owned resources is hypothesized to have a positive effect on income from (owned)
resources. The analysis confirms this hypothesis as correlations are relatively high and positive.
It is hypothesized that the occurrence of food insecurity reduces due to higher autoconsumption
levels. Unfortunately, the analysis does not show an interesting correlation. Remarkable is the
relatively high positive contribution of ‘access to stocks (of cereals) until next harvest’: rp = (-).375. For
completeness we note that the correlation between ‘value of autoconsumption of nutritional products’
and ‘access to stocks (of cereals) until next harvest’ is relatively high, and positive: rp = (+).170 (2-
tailed). Conclusion may be that autoconsumption does not have a direct relation to ‘occurrence of
problems satisfying nutritional needs’, but that ‘access to stocks until next harvest’ has. We apply a
controlling test for the impact of ‘expenditures on nutritional products’. The analysis suggests that
more a higher level expenditures on food lead to less food insecurity.
55
Note that there is a trade-off between expenditures on food and autoconsumption levels. The
correlation coefficient rs = -.350 (2-tailed), suggests that when expenditures increase, autoconsumption
levels decreases. The overall conclusion may be that stocks (of cereals) are mostly formed by own
production than from marketed products. Consequently, we may conclude that expenditures on
nutritional products have a major impact on food security, relative to value of consumed own food
production. Note that we should apply tests to control for access to local markets, measures of income
(later in this section), household size and milieu of residence.
Table 6-2: Correlations for interdependencies: nutrition
Independent variable Dependent variable Coefficient
Value of autoconsumption of nutritional
products rs = +.525 (p<.01)
Access to stocks (of cereals) until next
harvest rp = (+).161 (p<.01)
Surface of landholding
Income from (owned) resources rs = +.392 (p<.01)
Value of autoconsumption of nutritional
products rs = +.487 (p<.01)
Access to stocks (of cereals) until next
harvest rp = (+).137 (p<.01)
Number of livestock
Income from (owned) resources rs = +.532 (p<.01)
Value of autoconsumption of nutritional
products
Occurrence of problems satisfying
nutritional needs rs = -.023 (p<.01)
Occurrence of problems satisfying
nutritional needs rp = (-).375 (p<.01)
Access to stocks (of cereals) until next
harvest
Value of autoconsumption of nutritional
products rp = (-).170 (p<.01)
Occurrence of problems satisfying
nutritional needs rs = -.129 (p<.01)
Value of autoconsumption of nutritional
products rs = -.350 (p<.01)
Value of expenditures on nutritional
products
Access to stocks (of cereals) until next
harvest rp = (+).039 (p<.01)
Occurrence of problems satisfying
nutritional needs Prevalence of malnutrition rp = (-).054 (p<.01)
Recent prevalence of disease rp = (-).063 (p<.01) Prevalence of malnutrition
Chronic prevalence of handicap or injury rp = (-).031 (p<.01)
Value of expenditures on nutritional
products Value of expenditures on health services rs = +.218 (p<.01)
56
As ‘occurrence of problems satisfying nutritional needs’ is considered to be the main indicator of food
security it should have a (positive) relation to famine. Recall that as indicator for famine we use the
aggregate measure malnutrition that represents whether or not a child is wasted and/or stunted and/or
underweight13. The relation between ‘occurrence of problems satisfying nutritional needs’ and
‘prevalence of malnutrition’ is hypothesized to be direct: with little or no nutritional problems the
prevalence of malnutrition should diminish. Unfortunately, the correlation is rather weak and the sign
of the coefficient rejects the hypothesis. Neither other indicators of food security show interesting
correlations with ‘prevalence of malnutrition’. Overall conclusion is that all correlations from food
security and food consumption to famine are weak.
Naturally, measures of nutrition are related to measures of health. When assessing the relation
between nutritional status and heath status we find that both indicators are weak related to
malnutrition. Therefore, we can draw the conclusion that no real relation between measures of famine
and physical health exists. Note that all indicators of both are dichotomous, so this interpretation is
biased by the measurement scale. Neither other measures of nutrition show important correlations to
measures of physical health and health consumption. For the relation from ‘expenditures on nutritional
products’ to ‘expenditures on health services’: rs = +.218, suggesting that the increase of food
consumption leads to increasing healthcare consumption, which is a rather unexpected result. Overall
we can conclude that the relation between food security and physical health are not very strong in this
research. We also recognize that improved nutritional status (‘prevalence of malnutrition’) and – more
indirectly – food insecurity (‘occurrence of problems satisfying nutritional needs’) are factors
influencing an individuals’ health status.
Health
Many external factors play a role in health security, however, the proxy access to sanitation facilities is
considered to be internal. It is hypothesized that with improved access to sanitation facilities –
measured by manner of garbage and toilet wastewater disposal – the prevalence of diarrhoea will
decrease. However, the analysis shows no relations. Besides improved sanitation also indicators of
access to potable water are hypothesized to influence the prevalence waterborne diseases. Again, the
results do not allow conclusions since correlations are dispensable. We have already seen that the
direct relation between storage and waterborne diseases is also of low extent; this might explain the
weak relations that we find here.
13 See Glossary
57
While it is hypothesized that storage has a direct impact to the prevalence of waterborne diseases,
many other diseases are mainly due poor nutritional status – primarily by famine, secondarily by food
security and food consumption – or external factors. Prevalence of disease is largely related to the
prevalence of water related diseases. No important correlations are found between waterborne
diseases and ‘chronic prevalence of handicap or injury’.
Table 6-3: Correlations for interdependencies: health
Independent variable Dependent variable Coefficient
Access to (improved) toilets Recent prevalence of diarrhoea Insignificant
Access to (improved) garbage disposal Recent prevalence of diarrhoea rp = (-).010 (p<.05)
Recent prevalence of diarrhoea Insignificant Proximity of potable water source
Recent prevalence of fever rp = (-).016 (p<.01)
Recent prevalence of diarrhoea rp = (-).011 (p<.01) Availability of (improved) potable water
source Recent prevalence of fever rp = (-).012 (p<.01)
Recent prevalence of diarrhoea Recent prevalence of disease rp = (+).377 (p<.01)
Recent prevalence of fever Recent prevalence of disease rp = (+).663 (p<.01)
Recent prevalence of disease Value of expenditures on health services rp = (+).086 (p<.01)
Chronic prevalence of handicap or injury Value of expenditures on health services rp = (+).023 (p<.01)
Access to (improved) toilets Value of expenditures on health services rs = +.143 (p<.01)
Access to (improved) garbage disposal Value of expenditures on health services rs = +.079 (p<.01)
The third sub-dimension is health care consumption. Expenditures on health care are hypothesized to
be indirectly influenced by storage: due to chancing physical health status expenditures on e.g. health
case, medicines and medical analysis change in the opposite direction. Also with improved welfare
and income levels, people might use health services in a more moderate way. The overall influence of
storage on health may be negative, however, we should be careful to conclude upon this since the
above analysis did not indicate strong negative relations between storage and (waterborne) diseases,
the relation is hypothesized to be indirect and many other (external) factors should be considered. The
correlation analysis suggests that health care consumption levels are not strongly influences by
disease and disability.
58
6.2.3 Links with external factors
The selected paths include factors that origin from outside the storage – poverty interactive system
(represented by the causal relation diagram). They mainly influence the direction and extent of
impacts, rather than the existence of the impact itself. Some factors are present in the causal relation
diagram, since they are seen as indicator within (sub)dimensions of poverty; they are supporting the
definition of the (sub)dimension. For the underlying theory we direct to Chapter 5, Section 5.2.
External factors - Storage
Three external factors influence storage – reservoir density – that are population density, type of the
environment (urban versus rural) and the proximity of (public) transport. So far, the direction of the
causal relation between storage and those factors is not always clear; e.g. high reservoir density may
cause more people to find a living around them, but on the other hand, high population densities may
urge communities to built small reservoirs. The correlation analysis shows the following results:
Table 6-4: Correlations external factors: external factors
Independent variable Dependent variable Coefficient
Population density Reservoir density rs = +.751 (p<.01)
Milieu of residence Reservoir density rp = (-).485 (p<.01)
Proximity of (public) transport Reservoir density rs = +.103 (p<.01)
Suggesting that with higher reservoir densities also population densities increase, or, that with higher
population densities also reservoir densities increase. This relation is expected, however, the question
remains which of the two factors acts as driving force. Further, the analysis gives that reservoirs are
more densely in rural environments, and that where reservoirs are densely distributed, (public)
transport is more easily reached.
External factors – Income
The external factor that is included in the dimension income is access to primary/secondary schools.
Naturally, the main impact should be on ‘level of education’ (is a more direct impact). The analysis
shows that proximity of primary schools has the least impact; however, both results suggest that with
improved access to schools education levels increase. Secondly, the hypothesis is that with increased
access to schools (both primary as secondary) measures of income should increase. The correlation
analysis to ‘total revenue’ confirms the hypothesis with the following correlations. Differentiated tests
59
show that the main impact is on income from employment, while there is a negative impact on income
from (owned) resources.
Other external factors influencing the dimension income are population density and access to (public)
transport. The hypothesis is that population density leads to increasing proximity of schools, and
indirectly, to higher income and education levels. This is confirmed by the correlation analysis. Also
access to transport positively influences the above measures of income.
Table 6-5: Correlations external factors: income
Independent variable Dependent variable Coefficient
Level of education rs = +.260 (p<.01)
Total revenue rs = +111 (p<.01)
Income from employment rs = +.286 (p<.01)
Proximity of primary school
Income from (owned) resources rs = -.319 (p<.01)
Level of education rs = +.385 (p<.01)
Total revenue rs = +.271 (p<.01)
Income from employment rs = +.431 (p<.01)
Proximity of secondary school
Income from (owned) resources rs = -.311 (p<.01)
Proximity of primary school rs = +.297 (p<.01)
Proximity of secondary school rs = +.409 (p<.01)
Total revenue rs = +.224 (p<.01)
Population density
Level of education rs = +.272 (p<.01)
Proximity of primary school rs = +.487 (p<.01)
Proximity of secondary school rs = +.621 (p<.01)
Total revenue rs = +.164 (p<.01)
Proximity of (public) transport
Level of education rs = +.269 (p<.01)
External factors – Nutrition
One external factor for nutrition is included in the causal relation diagram, namely access to (local)
food market. It is hypothesized that this indicator will influence measures of food security,
expenditures on nutritional products and income from (owned) resources. The test for bivariate
correlations suggest that with increased proximity of food markets, the value of autoconsumption
decreases, but the expenditures on food increases. Additionally, it is hypothesized that with more
60
proximate food markets, income from (owned) resources increases, however the test shows the
opposite. Remarkably is that, when we test the relation between the proximity of a food market and
‘total revenue’ we do find a positive correlation: rs = +.120.
External factors influencing the access to a (local) food markets are population density, milieu of
residence and access to (public) transport. The hypothesis is that population density, in general,
positively influences the access to (communal) services and resources. The correlation analysis
confirms this hypothesis with respect to the proximity of food markets: rs = +.274. Additionally, in an
urban environment food markets are more proximate.
Table 6-6: Correlations external factors: nutrition
Independent variable Dependent variable Coefficient
Occurrence of problems satisfying
nutritional needs rs = -.095 (p<.01)
Value of autoconsumption of
nutritional products rs = -.247 (p<.01)
Value of expenditures on nutritional
products rs = +.284 (p<.01)
Income from (owned) resources rs = -.291 (p<.01)
Proximity of (local) food market
Total revenue rs = +.120 (p<.01)
Population density Proximity of (local) food market rs = +.274 (p<.01)
Milieu of residence Proximity of (local) food market rp = (-).320 (p<.01)
Proximity of (public) transport Proximity of (local) food market rs = +.601 (p<.01)
Occurrence of problems satisfying
nutritional needs rs = +.088 (p<.01)
Household size
Total food consumption rs = +.421 (p<.01)
Naturally, we should control indicators of nutrition for household size. With increasing number of
household members it is more likely that the household faces problems, hence score high on
‘occurrence of problems satisfying nutritional needs’, and have higher food consumption levels. The
analysis shows that the occurrence of nutritional problems is not highly correlated to the number of
household members, even though, the result suggests that with increasing household size problems
satisfying needs occur more frequently. Food consumption levels (summated) are more sensitive to
‘household size’: rs = +.421.
61
External factors – Health
As configured in the causal relation diagram, we assume better physical health with increased access
to (public) health services. The correlation analysis confirms the positive impact of access to (public)
health services to indicators of health security, physical health and health consumption. Correlations to
measures of physical health are tested negligible.
Table 6-7: Correlations external factors: health
Independent variable Dependent variable
Expenditures on health services rs = +.123 (p<.01)
Recent prevalence of disease rp = (+).019 (p<.01)
Proximity of (public) health services
Chronic prevalence of handicap or injury Insignificant
Proximity of (public) transport Proximity of (public) health services rs = +.631 (p<.01)
Proximity of (public) health services rs = +.361 (p<.01)
Proximity of potable water source rs = +.145 (p<.01)
Availability of (improved) potable water
source rs = +.198 (p<.01)
Access to (improved) toilets rs = +.397 (p<.01)
Population density
Access to (improved) garbage disposal rs = +.203 (p<.01)
The hypothesis is that population density leads to increasing access to (communal) services and
resources. For each of the above indicators higher population densities have a positive effect.
Explanation of Figure 6-4:
The figure shows the correlations within the storage-poverty system. The arrows show the hypothetical
relations and the results of the correlation analysis. The colours indicate whether the hypothesis is
confirmed (blue) or rejected (red) by the statistical test.
+.095
proximityof (local)
foodmarket
proximityof (public)
healthservice
totalrevenue
recentprevalenceof disease
value ofexpenditures
on healthservices
occurrenceof problemssatisfyingnutritional
needs
value ofexpenditureson nutritional
products
Health security
Income diversication
Physical health
Food security
Education
Food consumption
Health consum
ption
HEALTH NUTRITION INCOME
recentprevalenceof diarrhoea
recentprevalence
of fever
chronicprevalenceof handicap
or injury
access to(improved)garbagedisposal
access to(improved)
toilets
STORAGE: reservoir density (province scale)
incomefrom
employment
incomefrom
(owned)resources
incomefrom
entrepre-neurship
Famine
prevalence ofmalnutrition
access tostocks (ofcereals)until nextharvest
value ofautocons-umption
of nutritionalproducts
-
number oflivestock
proximityof primary
school
proximityof secondary
school
level ofeducation
surface of landholding
availability of(improved)
potablewatersource
proximityof potable
watersource
.000+.218 +.144 +.442
-.129
(-).054
(-).375
-.023
(+).170 -.350
-.095
+.525+.487
+.284
-.291
+.392
+.532
+.201
+.056
-.214
-.318
-.106 -.180-.251
+.402
+.168
+.123
+.143
.000 .000
.000
.000
(+).023
(+).086
.000
+.385
+.260
-.144 +.269 +.147
+.173
+.194
.000
Figure 6-4: Main correlations within the poverty-storage system
63
6.3 Discussion
In this chapter we have seen that:
• Most correlations between storage and poverty and within poverty are representing small to
medium effects. Throughout the analysis we regard the relative strength of the correlation
coefficient; hence, correlations above 0.10 are referred to as interesting or important. There
are two possible causes for finding relatively low correlations:
Measurement error: the degree to which the observed values are not representative of
‘true’ values. Sources can be data entry errors, imprecision of the measurement (e.g.
representation of concepts) and the inability of respondents to accurately provide
information. Thus all variables used must be assumed to have some degree of
measurement error (Hair et al., 1998). The measurement error results more deviation
around the linear relationship, and thus, in less strong correlation coefficients;
Different scales of aggregation: variables are disaggregated to lower scale for testing.
Hereby they loose part of their variance which results in less strong correlation
coefficients;
• Accuracy of the chosen indicators for physical health and famine may be low since we do not
find many – hypothesized or not hypothesized – interesting correlations to other indicators.
This phenomenon might be due to their measurement level, measurement scale or extent of
missing data. However, due to the measurement scale (individual) – and related sample size
(N = 54035) – of measures of physical health, one may expect to find significant correlations
easier. As Hair et al. (1998) states: “With regard to sample size we need to be aware that at
any given alpha level, increased sample sizes always produce greater power of the statistical
test”;
• So far, the analysis did not go deeper into the effect of milieu of residence (urban versus rural)
onto the storage-poverty interactive system. However, the current analysis suggests there
may be important effects of both (external) factors. Therefore, a split-sample test is applied in
a later stage of this research – see Chapter 8.
65
7. Quantification of Relationships
So far, the existence of relationships between two variables is verified and quantified. However, the
conceptual model (Figure 5-2) shows that in most cases more than one variable is affecting one other.
As the preceding analysis encompasses only one-to-one relations it does not tell anything about the
contribution of several causes on one effected variable, nor does it take relations between causes into
account. In this research, multiple regression analysis is applied to assess the relative contribution of
different independent variables on one dependent variable and involve interaction effects between
independent variables. Note that linear regression is a parametric technique. Non-parametric
alternatives are (multi-nominal) logistic regression or ordinal regression. However, Hair et al. (1998)
states that regression analysis has been shown to be quite robust even when the normality
assumption is violated.
7.1 Model specification
A good strategy to adopt for multiple regression modelling is to include predictor variables for which
there are sound theoretical reasons for expecting them to predict the outcome. Field (2005) suggests
running a regression analysis in which all predictors are entered into the model and examine the
output to see which predictors contribute substantially to the models ability to predict the outcome.
Once established which variables are important, re-run the analysis including only the important
predictors and use the resulting parameter estimates to define the regression model. However, in this
research we face three problems to this approach:
1. The complete model containing all explanatory variables – about 40 for this research – may
become too complex. Chen et al. (2004) even suggest this can result in inaccurate estimation
of the parameters and instability of the model structure.
2. The proposed conceptual model shows several relations of mutual dependence that require
iterative testing. This can not be embraced in only one multiple regression model, but requires
sequences of models. Statistical techniques are available for estimating systems of multiple
regressions; however, the available data violate all assumptions underlying those techniques.
3. Theory shows there is no single indicator representing ‘poverty’; hence, we know multiple
outcome variables. Fortunately, the anterior correlation analysis does provide sufficient insight
into the interactive system to provide a convenient starting point for regression analysis.
66
The modelling strategy is now to estimate an overall regression model for each main-dimension of
poverty and to dissect for its included variables. Additionally, a regression model for storage is
estimated. Note that the type of regression analysis is determined by the measurement level of the
dependent variable.
Method of entering
A great deal of care should be taken in selecting predictors for the model because the values of the
regression coefficients depend upon the variables in the model (Field, 2005). Therefore, the predictors
included, and the way in which they are entered into the model can be of great impact. Only when
predictors are all completely uncorrelated the order of variable entry has a minor effect on the
parameters calculated. Often, actually usually, this is not the case.
Field (1995) suggests that in case there is a sound theoretical literature available, the model should be
based on this past research. As a general rule, the fewer predictors the better and certainly include
only predictors for which a good theoretical grounding is available. Note to respect also the minimum
required sample size of 20 cases per included predictor variable. Since the aim of this research is to
explain (inter)dependencies between storage and poverty, forced entry (or enter method) is applied. In
this method predictors are all forced into the model simultaneously. This method relies on theoretical
reasons for including predictors. However, no decision about the order of entry needs to be made. In
this research, both the causal relation diagram as the correlation analysis provides the theoretical
base for including predictor variables.
7.2 Multiple regression analysis
In this section the results of multiple regression analysis are given. For each main dimension of
poverty – income, nutrition and health – a representative indicator is chosen, for which a more
extensive model is estimated. For all variables included in this ‘complete’ regression model only the
relative contribution of only the direct effects are assessed. For a broad overview of the results of the
regression analysis see Appendix E.
7.2.1 External factors
Reservoir density
Throughout this research the indicator used to represent proximity of small reservoirs is ‘reservoir
density’. In most relations storage is a determining parameter, however, there are also variables that
determine storage. The hypothesis is that both population density and income determine storage.
67
Note that the hypothesis does not explicitly state that the direction of the relation is from population
density towards storage; however, this direction is used during for this analysis.
The preceding analysis has already show reservoir density and population density are highly
correlated. This result is supported by the regression analysis as the explained variance by the
variables model is 79.6%. The relative contribution of population density is 89.8%, while income has a
small negative contribution (β= -3.6%). Clearly, population density is an important determining factor
behind storage.
Table 7-1: Regression model for reservoir density
Dependent variable Independent variable βi Sig.
Reservoir density Population density +.989 .000
Total revenue -.036 .000
Population density
Factors within this research that possibly influence population density are reservoir density – as
people tend to settle there were water resources are available – and the difference between rural and
urban environments.
Table 7-2: Regression model for population density
Dependent variable Independent variable βi Sig.
Population density Reservoir density +.787 .000
Milieu of environment (-).216 .000
From the above analysis the temporal antecedence between cause and effect remains unclear (as
expected). However, the hypothesis that population density generally positively influences the access
to (communal) services and resources, as exploitation becomes more beneficial due to higher demand
remains to be tested below.
7.2.2 Income
Total Revenue
Naturally, total revenue is directly influenced by its diversified sources – entrepreneurship,
employment and (owned) resources – but also by level of education, physical health, food insecurity
and storage play a role. The total variance (R2) explained by these factors is 76.4%. Clearly, the
68
differentiated measures of income sources (1st three variables) determine the most variance on the
dependent variable. Prevalence of disease and disability does not have an effect on the total revenue.
Table 7-3: Regression model for total revenue (direct model)
R2= 76.4%
Dependent variable Independent variables βi Sig.
Total revenue Income from (owned) resources +.575 .000
Income from employment +.443 .000
Income from entrepreneurship +.448 .000
Level of education +.037 .000
Recent prevalence of disease .000 .955
Chronic prevalence of handicap and injury (+).002 .414
Occurrence of problems satisfying nutritional needs -.029 .000
Reservoirs density +.047 .000
Factors that are indirectly related to the total revenue are access to schools, food markets and
transport, and population density. After re-estimating the regression model including all variables the
explained variance on the dependent variable increases to 77.2%. The indicators of physical health
remain insignificant.
Table 7-4: Regression model for total revenue (complete model)
R2= 77.2%
Dependent variable Independent variables βi Sig.
Income from (owned) resources +.584 .000
Income from employment +.430 .000
Income from entrepreneurship +.448 .000
Level of education +.009 .000
Recent prevalence of disease (-).002 .436
Chronic prevalence of handicap and injury (-).002 .346
Occurrence of problems satisfying nutritional needs -.013 .000
Proximity of (local) food market +.017 .000
Total revenue
Proximity of primary school -.014 .000
69
Proximity of secondary school +.058 .000
Reservoir density -.066 .000
Proximity of (public) transport +.026 .000
Population density +.113 .000
Cleary, income from agriculture and dairy farming has the largest and positive contribution to total
revenue (β= 58.4%), followed by income from employment (β= 43%) and income from
entrepreneurship (β= 44.8%). Population density has a small relative contribution (β= 11.3%), while
the relative contribution of reservoir density is negative (β= -6.6%). The low regression coefficient for
level of education (β= 0.9%) was not expected based on the correlation analysis. Therefore, it is
interesting to know which variables determine most of the variance on the three diversified sources of
income.
Income from owned resources
Livestock holding has the highest relative contribution to this source of income (β= 26.8%),
especially when compared to the contribution of land holding (β= 2.5%). Reservoir density,
education level and proximity of (local) food market are negatively related to income from owned
resources. This is consistent with the results of the correlation analysis, however, inconsistent with
the hypotheses.
Table 7-5: Regression model for income from (owned) resources
R2= 10.5%
Dependent variable Independent variables βi Sig.
Proximity of (local) food market -.063 .000
Surface of landholding +.025 .000
Number of livestock +.268 .000
Reservoir density -.053 .000
Level of education -.033 .000
Occurrence of problems satisfying nutritional needs -.046 .000
Recent prevalence of disease (+).005 .272
Income from
(owned) resources
Chronic prevalence of handicap and injury (-).002 .544
70
Income from employment
The analysis shows that chronic prevalence of disability does not play a role in the explanation of
income from employment. Most determining is level of education, followed by the positive
contribution of storage. Furthermore, the role of food insecurity is positive, and larger than on any
other source of income.
Table 7-6: Regression model for income from employment
R2= 16.7%
Dependent variable Independent variable βi Sig.
Level of education +.302 .000
Occurrence of problems satisfying nutritional needs -.108 .000
Recent prevalence of disease (+).011 .007
Chronic prevalence of handicap and injury (-).003 .492
Income from
employment
Reservoir density +.165 .000
Income from entrepreneurship
The total explained variance by included variables is 1.9%, thus far too low to draw any valid
conclusions upon.
Table 7-7: Regression model for income from entrepreneurship
R2= 1.9%
Dependent variable Independent variables βi Sig.
Level of education +.094 .000
Occurrence of problems satisfying nutritional needs -.022 .000
Recent prevalence of disease (+).003 .476
Chronic prevalence of handicap and injury (+).004 .387
Income from
entrepreneurship
Reservoir density +.069 .000
Level of education
All included variables contribute significantly to the explained variance on ‘level of education’.
Likewise to the correlation analysis the largest contribution is from ‘proximity of secondary school’
(β= 30.8%), while the relative contribution of ‘proximity of primary school’ is much smaller (β=
71
4.3%). Reservoir density is the second determining variable, moreover, the coefficient is positive
(β= 16.1%) – suggesting the hypothesis that storage contributes to considerable time savings.
Table 7-8: Regression model for level of education
R2= 19.4%
Dependent variable Independent variable βi Sig.
Proximity of primary school +.043 .000
Proximity of secondary school +.308 .000
Occurrence of problems satisfying nutritional needs -.089 .000
Recent prevalence of disease (-).004 .336
Chronic prevalence of handicap and injury (-).016 .000
Level of education
Reservoir density +.161 .000
Proximity of primary and secondary schools
The proximity of schools is hypothesized to be influenced by two external variables: population
density and proximity of (public) transport. The analysis shows that the influence of both is
positive, but that the influence of proximity of (public) transport has a 50% larger effect.
Table 7-9: Regression model for proximity of primary school
R2= 27.8%
Dependent variable Independent variables βi Sig.
Proximity of (public) transport +.443 .000 Proximity of
primary school Population density +.187 .000
Table 7-10: Regression model for proximity of secondary school
R2= 47.6%
Dependent variable Independent variables βi Sig.
Proximity of (public) transport +.554 .000 Proximity of
secondary school Population density +.298 .000
72
7.2.3 Nutrition
In the conceptual model, the indicator ‘prevalence of malnutrition’ is the end-indicator for the
dimension nutrition. However, food insecurity (indicator ‘occurrence of problems satisfying nutritional
needs’) is seen as a more overall measure for nutritional status. Therefore, this indicator is used as
starting point for the regression analysis on nutrition.
Occurrence of problems satisfying nutritional needs
Directly, food insecurity is influenced by the value of autoconsumption and expenditures on nutritional
products, access to stocks (of cereals) until next harvest and (local) food markets. Consistent with the
correlation analysis the contributions of expenditures on food and access to stocks are largest and
positively influencing food security.
Table 7-11: Regression model for occurrence of food insecurity (direct model)
R2= 16.9%
Dependent variable Independent variables βi Sig.
Value of expenditures on nutritional products -.126 .000
Value of autoconsumption of nutritional products +.042 .000
Access to stocks (of cereals) until next harvest (-).389 .000
Occurrence of
problems satisfying
nutritional needs
Proximity of (local) food market -.066 .000
Indirectly, also income, land and livestock holding, reservoir density, and external factors population
density and access to (public) transport play a role. The relative contribution of food markets becomes
insignificant. Important determining variables are population density (β= -34.7%) and reservoir density
(β= 32.9%). Although their extent is equal, the sign of population density indicates a positive impact on
food insecurity – i.e. improvement towards food secure livelihood – while reservoir density has a
negative impact. As expected based on the correlation analysis, access to stocks is an important
determining factor (β= 38%).
Table 7-12: Regression model for occurrence of food insecurity (complete model)
R2= 20.9%
Dependent variable Independent variables βi Sig.
Value of expenditures on nutritional products -.064 .000 Occurrence of
problems satisfying Value of autoconsumption of nutritional products +.047 .000
73
Access to stocks (of cereals) until next harvest (-).380 .000
Proximity of (local) food market -.024 .054
Total revenue -.083 .000
Number of livestock -.053 .000
Surface of landholding -.005 .640
Reservoir density +.329 .000
Population density -.347 .000
nutritional needs
Proximity of (public) transport -.069 .000
Again, differentiated regression models are estimated to investigate the determining factors for each of
the indicators, wherein only direct relations to the dependent variable are included.
Value of expenditures on nutritional products
Clearly, income has the highest relative importance to the dependent variable (β= 38.8%),
followed by the proximity of (local) food markets (β= 14.6%). The mutual dependency with direct
nutritional products is – as hypothesized – negative, however, of low relevance.
Table 7-13: Regression model for value of expenditures on nutritional products
R2= 19.1%
Dependent variable Independent variables βi Sig.
Total revenue +.388 .000
Proximity of (local) food market +.146 .000
Value of autoconsumption of nutritional products -.060 .000
Value of expenditures
on nutritional
products
Access to stocks (of cereals) until next harvest (-).035 .000
Value of autoconsumption of nutritional products
Important determining parameters are – compatible with the correlation analysis – surface of
landholding (β= 25.1%) and number of livestock (β= 14.3%). Unfortunately, also in this analysis
the coefficient for storage is negative (β= -9.8%); thus rejects the hypothesis that small reservoirs
support self sufficiency.
Table 7-14: Regression model for value of autoconsumption of nutritional products
R2= 15.8%
74
Dependent variable Independent variables βi Sig.
Access to stocks (of cereals) until next harvest (+).101 .000
Value of expenditures on nutritional products -.041 .000
Surface of landholding +.251 .000
Number of livestock +.143 .000
Value of
autoconsumption of
nutritional products
Reservoir density -.098 .000
Household has access to stocks (of cereals) until next harvest
As the dependent variable is dichotomous the regression model is estimated by logistic
regression. Assessment of the χ2-statistic reveals that the model is not a good fit of the data (p=
.000); i.e. the hypothesis that the observed data are significantly different from the by the model
predicted values is confirmed. Also the variance explained by the model is low with Nagelkerke R2
.072 (7.2%). Hence, no genuine conclusions upon this indicator are possible.
Table 7-15: Regression model for access to stocks (of cereals) until next harvest
R2= 7.2%
Dependent variable Independent variables B Sig. Exp(B)
Value of expenditures on nutritional products .000 .348 1.000
Value of autoconsumption of nutritional products .000 .000 1.000
Surface of landholding +.007 .000 1.013
Number of livestock +.013 .000 1.007
Access to stocks (of
cereals) until next
harvest
Reservoir density +.280 .902 1323
Proximity of (local) food market
Access to (local) food markets is influenced by two external factors: population density and access
to (public) transport. The relative contribution of population density is 10.2%, and access to
(public) transport is the largest determining factor: β= 56.9%.
Table 7-16: Regression model for proximity of food market
R2= 36.7%
Dependent variable Independent variables βi Sig.
Proximity of (local) Proximity of (public) transport +.569 .000
75
food market Population density +.102 .000
Number livestock
The only direct determining variable for number of livestock in the conceptual model is reservoir
density. This variable explains 2.8% of the variance of the dependent variable; the standardized
regression coefficient (β) is -.167. As the explaining power of this only predictor variable is low, a
more exploratory approach is applied though including ‘total revenue’ and ‘population density’ in
the equation. Herein the regression coefficient for ‘reservoir density’ becomes insignificant, but the
additional variables contribute to the explained variance; that increases to 4.6% (still too low to
draw any valid conclusions upon). The analysis shows that, as expected, population density has a
negative contribution (β= -19.5%) and income has a positive contribution (β= 11.5%).
Table 7-17: Regression model for livestock holding
R2= 4.6%
Dependent variable Independent variable βi Sig.
Reservoir density -.008 .745
Total revenue +.115 .000
Number of livestock
Population density -.195 .000
Surface of landholding
Again, the only determining variable for surface of landholding in the conceptual model is reservoir
density. This variable explains 9.5% of the variance of the dependent variable; the standardized
regression coefficient (β) is -.309. Again, a more exploratory approach is applied, including ‘total
revenue’ and ‘population density’ in the equation. The inclusion of these variables leads to an
explained variance of 10%, wherein the role of income is insignificant. Both remaining variables
relate negatively to the surface of landholding.
Table 7-18: Regression model for landholding
R2= 10.0%
Dependent variable Independent variable βi Sig.
Reservoir density -.187 .000
Total revenue -.016 .137
Surface of
landholding
Population density -.134 .000
76
Prevalence of malnutrition
Only occurrence of food insecurity is hypothesized to have a direct relation to the prevalence of
malnutrition. Recall, the indicator malnutrition represents whether or not the child (younger than 5
years) is stunted, wasted or undernutritioned14. Since the dependent variable is a dichotomous dummy
variable, the model is estimated using binary logistic regression. The assessment of the χ2-statistic
reveals that the model is a good fit of the data (p= .086). However, the variance explained by the
model is negligible as Nagelkerke R2 is .007 (0.7%). Therefore, no conclusions are drawn upon this
model.
Table 7-19: Regression model for prevalence of malnutrition (direct model)
R2= 0.7%
Dependent variable Independent variable B Sig. Exp(B)
Prevalence of
malnutrition Occurrence of problems satisfying nutritional needs -.182 .000 .833
Although the correlation analysis does not show any relevant correlations with the prevalence of
malnutrition, a more exploratory regression analysis is applied including all indictors of the dimension
nutrition, storage, income, indicators of physical health, access to (improved) toilets and access to
potable water, plus external factors population density and access to (public) transport. This broader
analysis reveals that disease and disability are most (positive) influential to malnutrition (χ2-statistic
insignificant, R-square 4.2%).
Table 7-20: Regression model for prevalence of malnutrition (complete model)
R2= 4.2%
Dependent variable Independent variable B Sig. Exp(B)
Occurrence of problems satisfying nutritional needs -.177 .000 .837
Access to stocks (of cereals) until next harvest +.124 .255 1.132
Value of expenditures on nutritional products .000 .261 1.000
Value of autoconsumption of nutritional products .000 .166 1.000
Surface of landholding -.001 .527 .999
Prevalence of
malnutrition
Number of livestock -.003 .101 .997
14 See Glossary
77
Proximity of (local) food market -.051 .215 1.052
Reservoir density +8.871 .396 7119
Total revenue .000 .030 1.000
Recent prevalence of disease +.766 .000 2.152
Chronic prevalence of handicap or injury +1.020 .029 2.772
Availability of (improved) potable water source -.027 .888 .974
Proximity of (potable) water source +.058 .311 .944
Access to (improved) toilets +.390 .003 .677
Population density .000 .983 1.000
Proximity of (public) transport +.074 .054 .928
7.2.4 Health
Theoretically, ‘recent prevalence of disease’ is a good overall indicator for the dimension health.
However, in the preceding correlation analysis we have seen that this indicator does not correlate high
to other indicators within the conceptual model, and that there are doubts about the reliability of the
indicator. Therefore, we also apply a complete analysis – including indirect relations and external
factors – for the indicator ‘value of expenditures on health services’.
Recent prevalence of disease
Besides the water-related diseases (with indicators fever and diarrhoea) this indicator encompasses
also eye, nose, ear, throat and skin problems. Chronic prevalence of disability, recent prevalence of
water-related disease, expenditures on health services and their proximity, and the prevalence of
malnutrition are – consistent with the conceptual model – directly influencing the prevalence of
diseases. Indirectly related factors are access sanitation facilities and access to potable water
sources, total income, storage, population density and access to (public) transport. Added to the
model the χ2-statistic reveals that the model is a good fit of the data (p= .727). Also the variance
explained by the model is satisfying as Nagelkerke R2 is .787 (78.7%). The analysis shows that
prevalence of malnutrition and chronic prevalence of disability are determining factors for disease.
Table 7-21: Regression model for recent prevalence of disease (complete model)
R2= 78.7%
Dependent variable Independent variable B Sig. Exp(B)
Recent prevalence of Chronic prevalence of handicap or injury -1.492 .047 .225
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Total value of expenditures on health
services .000 .001 1.000
Prevalence of malnutrition +.646 .033 1.907
Recent prevalence of fever -24.876 .990 .000
Recent prevalence of diarrhoea -24.742 .992 .000
Proximity of (public) health service +.071 .390 .931
Access to (improved) toilets -.090 .663 1.095
Access to (improved) garbage disposal +.068 .413 .934
Availability of (improved) potable water
source +.465 .152 1.592
Proximity of potable water source +.041 .746 .959
Total revenue .000 .725 1.000
Reservoir density +8.153 .666 3474
Population density +.002 .277 1.002
disease
Proximity of (public) transport -.023 .764 1.023
Chronic prevalence of handicap or injury
Chronic prevalence is – according to the conceptual model – determined by prevalence of water-
related diseases, expenditures and proximity of health services and prevalence of malnutrition. As
we fit a logistic regression model to these data the χ2-statistic reveals that the model is a good fit
of the data (p= .401). The variance explained by the model is 1.7% according to Nagelkerke R2.
Although this value is too low to really interpret, it can be noticed that the prevalence of
malnutrition is the only significant indicator in the model, with B is -5.582 (p= .016) and Exp(B)
3.025.
Table 7-22: Regression model for chronic prevalence of disability
R2= 1.7%
Dependent variable Independent variable B Sig. Exp(B)
Recent prevalence of disease -1.371 .065 .254
Recent prevalence of fever -24.836 .990 .000
Recent prevalence of diarrhoea -24.660 .992 .000
Total value of expenditures on health services .000 .000 1.000
Chronic prevalence of
handicap or injury
Proximity of (public) health service +.176 .003 .839
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Prevalence of malnutrition +.633 .029 1.883
Recent prevalence of fever
As the outcome variable is dichotomous, a logistic regression model is estimated. The χ2-statistic
reveals that the model is not a good fit of the data (p= .000). The variance explained by the model
is (Nagelkerke R2) 0.8%. Hence, no valid conclusions can be drawn upon the outcomes to the
regression analysis.
Table 7-23: Regression model for recent prevalence of fever
R2= 0.8%
Dependent variable Independent variable B Sig. Exp(B)
Total value of expenditures on health services .000 .000 1.000
Proximity of (public) health service +.025 .238 .975
Access to (improved) toilets +.114 .032 .892
Access to (improved) garbage disposal -.047 .029 1.049
Availability of (improved) potable water source +.066 .451 13068
Proximity of potable water source +.066 .083 .936
Recent prevalence of
fever
Reservoir density +.316 .904 1.372
Recent prevalence of diarrhoea
Likewise to fever, the prevalence of diarrhoea is determined by access to reservoirs and potable
water, access to sanitation facilities and health services, expenditures on health services. The χ2-
statistic reveals that the model is not a good fit of the data (p= .029). The variance explained by
the model is (Nagelkerke R2) 0.5%. Again, no valid conclusions can be drawn upon the outcomes
to the regression analysis.
Table 7-24: Regression model for recent prevalence of diarrhoea
R2= 0.5%
Dependent variable Independent variable B Sig. Exp(B)
Total value of expenditures on health services .000 .000 1.000
Proximity of (public) health service -.020 .592 1.020
Access to (improved) toilets +.077 .430 .926
Recent prevalence of
diarrhoea
Access to (improved) garbage disposal -.075 .050 1.077
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Availability of (improved) potable water source -.437 .004 .646
Proximity of potable water source -.012 .838 1.012
Reservoir density -2.924 .546 .054
Value of expenditures on health services
The value of expenditures on health services is hypothesized to be directly influenced by physical
health, the proximity of (public) health services and income. These parameters together explain 4.0%
of the variance. Indirectly, water-related diseases, improved sanitation and potable water, malnutrition
and food insecurity, storage and external factors population density and proximity of transport are of
influence. The explained variance increases to 5.5%, thus still is considered too low. Both variables
from the dimension nutrition (‘malnutrition’ and ‘food insecurity’) do not have significant regression
coefficients. The analysis shows that the three most important determining factors for expenditures on
health are total income (β=11.4%), chronic prevalence of disability (β=10%) and availability of
(improved) potable water source (β=7.2%). Hence, the expenditures on health care increase due to
these factors. Note that due to the low explained variance we should be careful drawing conclusions
upon the results.
Table 7-25: Regression model for value of expenditures on health services
R2= 5.5%
Dependent variable Independent variable βi Sig.
Recent prevalence of disease (+).100 .000
Chronic prevalence of handicap or injury (+).018 .000
Proximity of (public) health service +.032 .000
Total income +.115 .000
Recent prevalence of fever -.018 .003
Recent prevalence of diarrhoea -.020 .000
Access to (improved) toilets +.020 .000
Access to (improved) garbage disposal +.028 .000
Availability of (improved) potable water source +.072 .000
Proximity of potable water source -.040 .000
Occurrence of problems satisfying nutritional needs +.003 .445
Reservoir density +.024 .012
Total value of
expenditures on health
services
Population density +.052 .000
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Proximity of (public) transport +.007 .226
Access to (improved) toilets
In turn, access to improved sanitary facilities – i.e. improved toilets – is hypothesized to be
dependent on income and population density.
Table 7-26: Regression model for access to (improved) toilets
R2= 27.3%
Dependent variable Independent variable βi Sig.
Population density +.427 .000 Access to (improved)
toilets Total revenue +.232 .000
Access to (improved) garbage disposal
The only factors influencing garbage disposal are income and population density (within this
research). The explained variance is 8.0% with regression coefficients respectively β=4.8% and
β=27.1%. Clearly, income plays a minor role.
Table 7-27: Regression model for access to (improved) garbage disposal
R2= 8.0%
Dependent variable Independent variable βi Sig.
Population density +.271 .000 Access to (improved)
garbage disposal Total revenue +.048 .000
Proximity of potable water source
We are interested in the impact of storage and income, and external factors population density
and proximity of (public) transport on the proximity of potable water sources. The proximity of
(public) transport is the most influential variable (β= 30.8%), followed by population density (β=
13.2%). Both income as storage have a negative influence (respectively β= -2.7% and β= -7.4%).
Table 7-28: Regression model for proximity of potable water source
R2= 11.1%
Dependent variable Independent variable βi Sig.
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Reservoir density -.074 .001
Population density +.132 .000
Proximity of (public) transport +.308 .000
Proximity of potable
water source
Total revenue -.027 .011
Availability of (improved) potable water source
As we test the impact of storage, income and population density on the availability of improved
potable water sources we get the following results: the relative contribution of population density is
largest with 35.5%, followed by the positive contribution of income 20.5% and the negative
coefficient for storage (β= -5.3%).
Table 7-29: Regression model for availability of (improved) potable water source
R2= 16.1%
Dependent variable Independent variable βi Sig.
Reservoir density -.013 .546
Proximity of potable water source +.131 .000
Population density +.255 .000
Proximity of (public) transport +.164 .000
Availability of
(improved) potable
water source
Total revenue +.194 .000
Proximity of (public) health service
Within the scope of this research the proximity of (public) health services is determined by
population density and proximity of (public) health services. The total variance explained by these
variables is 46.4% and the relative contribution is respectively 24.7% and 56.9%.
Table 7-30: Regression model for proximity of health services
R2= 27.3%
Dependent variable Independent variable βi Sig.
Population density +.247 .000 Proximity of (public)
health service Proximity of (public) transport +.569 .000
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7.3 Discussion
• In general, the explained variance by the indicators included in the regression model is low to
medium. Exceptions are the complete models for storage, income and health, where R-square
is almost 80%. Hence, we should be aware that most of the dimension nutrition is not largely
explained by variables included in the model.
• Main assumptions underlying linear regression analysis are linearity, normality and
homoscedasticity. From the data screening process we have learned that none of the
indicators are normally distributed. However, Hair et al. (1998) states that regression analysis
has been shown to be quite robust even when the normality assumption is violated.
Assessment of the standardized residual plot shows that in many cases the assumption of
linearity is violated. Hence, the regression equation is not determined by a set of straight lines.
Due to the aforementioned violations heteroscedasticity is occurring as well.
• Additional assumptions are independence of error terms and normality of the error distribution.
The Durbin-Watson statistic informs about whether the assumption of independent errors is
tenable. Residual terms should be uncorrelated; i.e. independent. In most cases the
requirement of independent errors is satisfied. On the contrary, the normality of error
distribution is often violated
• One important assumption for unbiased multiple regression analysis is the requirement of no
perfect multicollinearity15. There should be no perfect linear relationship between predictors –
i.e. no high correlations. Multicollinearity can be diagnosed by assessing the correlation matrix
and the collinearity diagnostics. Fortunately, in most cases the assumption of no perfect
multicollinearity is satisfied. However, as reservoir density and population density are highly
correlated they are a source of collinearity. Possible solution is to exclude one out of two
variables from the analysis – here population density explains reservoir density at high level
(90%) thus can be excluded as explanatory variable – or summarize both variables in one
other variable
• Assessment of the goodness-of-fit indicates that all linear regression models are a good
representation of the population; hence, the models are generalizable. Note that this
conclusion is lessened by low values of R-square.
15 See Glossary
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8. Interpretation
In this chapter we provide the overall interpretation of the results from the correlation and regression
analyses. The aim is to get insight into the overall storage – poverty interactive system. As we have
mentioned before – discussion on correlation analysis – from the estimated correlation model on
country scale we learned that the variable ‘milieu of residence’ showed high correlations with many
other variables. This may implicate that the overall system – and driving forces – in a rural
environment shows to be different than when we regard the urban environment. This hypothesis can
only be tested by comparing the model for rural and urban sub-sets. Moreover, the research question
refers to rural households.
8.1 Interpretation of the country-scale analysis
Income
As hypothesized, the impact of small reservoirs on the total income – thus from all possible sources –
is positive. This total income is highly determined by its differentiated sources, wherein income from
(owned) resources has the largest stake. Access to small reservoirs seems to have a large positive
impact on education levels and employment rates (income from employment). This leads to the new
hypothesis that when small reservoirs are more proximate this leads to considerable time savings.
Obtaining water often involves significant inconvenience of time spent in collection. This reduces the
time remaining for other activities such as cooking or farming, being employed or going to school
(DOW, 2001).
Adversely, a negative relation of storage to income from (owned) resources – agriculture and dairy
farming – is found, while it was expected that this relation would be positive as well. It is found that
livestock holding has the highest contribution to income from (owned) resources. In turn, education
has a positive effect on all sources of income, with the exception of income from (owned) resources.
The proximity of schools leads to higher education levels, and consequently to higher income rates
(with the exception of income from agriculture and dairy farming). Remarkable is that the contribution
of secondary schools to education levels is much larger than that of primary schools.
Nutrition
Access to small reservoirs does not seem to have a positive – nor highly negative – impact on any
measure of the dimension nutrition. Food insecurity nor autoconsumption levels nor land and livestock
holding are positively related to reservoir density. Hence, there is no evidence found that small
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reservoirs have a positive direct impact on food supply from (irrigated) agriculture, dairy farming or
aquaculture – that are considered (throughout this research) as the main socio-economic values of
small reservoirs. Explanation may be that most small reservoirs are non-carryover; hence lacking to
secure water supply until the end of the dry season to sufficiently water livestock and serve basic
domestic needs or to provide in cash-crop opportunities.
Autoconsumption levels are highly determined by land and livestock holding (owned resources). This
supports the hypothesis that in poor rural societies food security is dependents on own production
(self suffiency). However, the indicator for food insecurity – occurrence of problems satisfying
nutritional needs – does not seem to benefit from autoconsumption nor from land and cattle holding,
since coefficients are low. Moreover, the importance of stocks (of cereals) and consumption of
marketed nutritional products occur to be most important. In turn, the total income is the determining
factor for expenditures on food; hence can be seen as driving force behind food security. We clearly
see there is a positive feedback loop from income to expenditures on nutritional products to food
insecurity (occurrence of problems satisfying nutritional needs) back to income. Unfortunately, the
analysis does not make clear what determines the availability of stocks.
Although access to owned land and livestock does not show to be an important factor in the alleviation
of food insecurity, it relates positively to both autoconsumption levels, as well as to income from
agriculture and dairy farming; consequently the total income. The role of local food markets in this
process is not clear; higher access to food markets leads to higher levels of expenditures on food, and
thus, contributes to food security, while when markets are at further distance values of
autoconsumption increase. However, income from (owned) resources does not seem to benefit from
more proximate markets.
The analysis reveals that disease and disability are most influential to malnutrition, while it does not
show malnutrition is related to measures of food security and food consumption levels. Explanation
may be that food access alone does not yield food security; food adequacy – quality besides quantity
– and physical ability to absorb nutrients (usually affected by disease) are determining factors (POST,
2006; Mwaniki, undated)
Health
The concern – and hypothesis – is that the presence of small reservoirs would cause higher
prevalence of water-related diseases. Fortunately, the analysis shows that this concern is
dispensable; there is no evidence found that reservoir density relates to gauges of water-related
diseases (fever and diarrhoea). However, better sanitation – improved latrines and garbage
evacuation – nor availability of improved potable water sources contribute to the reduction of water-
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related diseases. In fact, the prevalence of water-related diseases is not explained by factors within
the conceptual model.
Small reservoirs positively influence the availability of improved potable water sources – as they
possibly support groundwater raising (Savadogo, 2006) – and the option to have improved latrines.
However, compared to the impact of high population density the influence of storage on potable water
is low. Hence, driving force behind improved sources of potable water and, besides, behind improved
sanitation is population density.
Remarkable is that food consumption levels, access to (potable) water resources and improved
sanitation do not lead to a reduction of healthcare consumption. Moreover, no effects are found from
these factors onto prevalence of disease, disability or famine. Factors that importantly increase the
expenditures on health are income level, proximity of health services, and prevalence of disease and
disability; hence as people can access health services better, the use tends to increase.
Overall
Most likely population density is an important driving force behind the presence of small reservoirs,
while income is in the current analysis insignificant. Generally, the analysis shows that in more densely
populated areas resources (food market, potable water) and services (schools, health services)
become better accessible. Also access to (public) transport contributes significantly to the accessibility
of resources and services.
It can be concluded that the impact of small reservoirs on poverty is originating from income
generation and education – thus partly by time saving and improved human capital. Both food and
health security improve as total incomes increases, since more is spent on nutritional products and
health services. The extent of this relation is in turn influenced by measures of access to (local) food
markets and (public) health services, and consequently by access to (public) transport and population
density.
Question that comes out of this analysis is the relation that small reservoirs have with food security
and income from agriculture and dairy farming. As storage has a negative impact on autoconsumption
levels – opposite from the hypothesis – we may conclude that reservoir resources are not so much
used for own consumption, rather than to market products. However, as additionally land and livestock
holding, and income from these resources are found to have a negative relation to small reservoirs
this analysis does not provide evidence that established socio-economic values of small reservoirs are
properly provided, obtained or used.
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8.2 Urban versus rural environment
Possibly, driving forces differ between the rural and urban milieu of residence. It is hypothesized that
in a rural environment people mainly practice agriculture and dairy farming, therefore have higher
levels of autoconsumption and have more income from these resources. And so, storage possibly
plays a major role in sustaining their livelihoods, hence alleviate poverty levels. Where in the urban
environment expenditure levels are higher and income is not so much from agriculture and access to
services and assets is better.
Income
In the rural environment most income is obtained from agriculture and dairy farming. Besides other
sources of income also income from agriculture and dairy farming are positively related to the
presence of small reservoirs; the latter the most. However, land and livestock holding is not positively
influenced by the presence of small reservoirs.
In the urban environment most income is obtained from employment and entrepreneurship. The
overall contribution of small reservoirs on all sources of income is positive, however, with the
exception of income from owned resources. Income from employment relatively has the highest
contribution to the total income. Consequently, the role of education in employment is large – also
compared to the rural environment. Employment rates and education levels are more (positively)
influenced by food security, while physical health does not seem to have any relevance.
Nutrition
Neither in the rural nor in the urban environment access to small reservoirs contributes to the
alleviation of food insecurity, level of autoconsumption of nutritional products nor access to stocks (of
cereals). The conclusion that small reservoir do not have a positive direct impact on food attainment is
strengthened by this analysis.
In both environments access to stocks and expenditures on food are determining the state of food
(in)security, whereby the latter is mainly determined by income levels and market access. Basically,
the impact of income levels on nutrition (food insecurity and consumption) is higher in the urban
environment. Note the disputable role of autoconsumption; the regression analysis shows a negative
effect in both cases, while the correlation analysis shows a minor positive effect in the rural
environment. Likewise to the overall analysis, autoconsumption levels are highly determined by
access to owned resources (land holding more than livestock husbandry). The difference in
importance between the rural and urban environment herein is negligible. Only in the urban
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environment livestock and land holding are positively influenced by the presence of small reservoirs,
but negatively by higher population densities. In the rural environment income is the (positive)
determining factor for the ownership of these resources.
Health
A clear distinction between the rural and urban environment are the determining factors for the
prevalence of disease. While in the rural environment disease is mainly related to disability and
famine, in the urban environment access to sanitation facilities are more important. Unfortunately, the
prevalence of water-related diseases is not explained by the variables within the model.
In the urban environment, access to improved sanitation facilities and improved potable water are
most determined by income and population density. In the rural environment income plays a minor
role in access to improved sanitation facilities and improved potable water. There proximity of
(potable) water and transport are the most important factors.
Neither in the rural environment nor the urban environment we can draw unquestionable conclusion
upon the interaction between small reservoirs, potable water, sanitation facilities and the prevalence of
(water-related) diseases; no substantial correlations nor high explained variances are reached.
External factors
In both environments the presence of small reservoirs is highly explained by population density; in
neither environment income levels play a role in this. Remarkably is that – compared to the urban and
overall system – in the rural environment access to small reservoirs is less explained by both
parameters.
In the urban environment population density has higher impact on the access resources (food market,
potable water) and services (health centres, schools), including sanitation facilities. However, the role
of transport remains the main determining factor – and is even more important in the rural
environment.
Finally, two findings should be noted:
• In the urban environment total income increases with increased population density, however,
in the rural environment there is no relation between both.
• In the rural environment there is a less strong negative influence of population density to the
amount of livestock and land holding by a household.
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9. Reflection
This chapter does not only give the (technical) validation of the statistical test, additionally the
research as a whole is evaluated by discussing the validity of the scope. This evaluation concerns –
amongst others – the width of the perspective, the assumptions made and whether the approach used
is influencing the validity of outcomes.
9.1 Technical validation
In order to assess the accuracy of the statistical models we apply validation by data splitting. This
approach involves randomly splitting of the dataset (in half), re-computing the model and then
comparing the resulting models. If a model can be generalized, then it must be capable of accurately
predicting equal outcomes to different samples (Field, 2005). Note that the requirement of minimum
sample size should still be satisfied (see Chapter 7).
The split-sample validation of the correlation analysis shows that the absolute difference between
datasets is on average only 0.01 (1%), with a maximum of 0.1 (10%). These results show that the
accuracy of the tests is confirmed. As regression analysis is founded on correlation analysis there is
no need to apply additional split-sample validation of the correlation analysis.
Benchmarking the storage-indicator
In order to assess the accuracy and validity of the indicator ‘reservoir density (province scale)’ we
compare the outcomes of the correlation analysis for relations with storage for the identical relation
using the indicator ‘average distance to a reservoir’. The analysis shows that the absolute difference
between outcomes using different indicators is on average 0.02 (2%), with a maximum of 0.31 (31%).
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9.2 Evaluation of the scope
• Limitations of the indicators for storage
The indicator used to represent storage – socio-economic values of small reservoirs – reservoir
density knows two drawbacks:
As the indicator is measured at the province level this implicates that province borders act like
physical borders in the analysis, while in reality province borders do not function as physical
borders – e.g. people can easily cross the province border to obtain assets and resources.
Hence, the measurement scale of the indicator introduces an error;
The principal scale of aggregation for this research is – determined by the data source – the
household level. Hence, the reservoir density at province scale is attributed to all households
in a certain province. Again a spatial error is introduced, as the real proximity of reservoirs is
not equal for each household within the province.
• Limitations of the definition of poverty
As explained in Chapter 4, concepts of poverty that tend to go into the direction of the human
capability concept, since these fall outside of the scope of this research. Many experts on poverty
will debate to take into account – at least – socio-demographic variables as sex, age and
household headship. Cavendish (1999) argues that collection and use of environmental resources
is also strongly linked to the sex of the individual. Additionally, different (quantity and quality)
resources are used by different individuals and households at different ages. Further, resource
use can also be affected by household structure; an imperfect proxy here is to stratify the data by
household headship. Ommittance of inter-household poverty distribution assumes less complexity
of the poverty process, while in reality this is a significant factor in the succeeding of poverty
alleviating measures (e.g. small reservoirs) as not all household members benefit equally.
• Imperfect representation of concepts
It should be recognized that in some cases the indicator(s) used to represent a concept do not suit
perfectly. A good example is income from entrepreneurship is proxy is composed of revenue from
rent and interest, while it is preferred to include income that is obtained from small enterprises or
informal businesses. However, these measures are not available. Other examples are imperfect
measures of water-related diseases – as more direct measures are unavailable – and no
measures of production levels – as these are best to measure the benefits from small reservoirs.
93
• Limited inclusion of spatial dynamics
The current research discards most cases of spatial dynamics as it analyses the relation between
water storage and poverty at country scale, and additionally between the rural and urban
environment. Reasons for this limited scope are:
Assessment of the outcomes of the analysis for four different climate zones did not give any
clear results; possibly due to inaccuracy of the definition of the zones;
Poverty mapping is not applied.
The aim of this research goes beyond community differences. As to assess local differences,
fieldwork trips are needed.
• Exclusion of temporal dynamics
The current research discards all temporal dynamics both between years as within the year.
Consequence is that poverty is less depicted as a process and that the influence of seasonality in
reservoir water supply is assumed not to exist. Moreover, there is a discrepancy in time between
the household survey of 2003 and the reservoir database of 2004.
• Limited inclusion of external factors
So far, the external factors included are population density, milieu of residence (rural versus
urban) and access to (public) transport. This limited number is mainly due to data availability
constraints. However, ideally factors derived from hydrology and climate, livelihood diversification,
and local demographics, economics and socio-politics should be included. It should be recognized
that both macro (including globalisation effects) and micro processes play a role. For example,
good infrastructure does not only make food markets better accessible – and so enable a
household to increase their income and food security – it also influences the prices of inputs and
outputs, improves levels and efficiency of use of inputs, and can even change the composition of
the labour market by creating opportunities for non-farm employment (Thimm, 1993).
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10. Conclusions and Recommendations
10.1 Conclusions
Although poverty is often considered a matter of institutions, governance and infrastructure, water
resources play a vital role in economic growth, human health and the reduction of poverty in the
savannah areas of West Africa. Therefore, throughout Burkina Faso – and other parts of West Africa –
many (small) dams and reservoirs have been built. They are an important source of water for many,
mostly poor, rural communities.
This research focuses on the question how the use of small multi-purpose reservoirs affects the well-
being of poor rural livelihoods. The aim is to give insight into the interdependences between the
presence – or absence – of small multi-purpose surface water reservoirs and the state of well-being of
rural households living nearby them. As we have knowledge of the extent and direction of the relations
between various dimensions of poverty and socio-economic values of small surface water reservoirs
their planning and management will be more sufficient. By answering the research question the driving
forces behind change and the role of small reservoirs in that is explained. But first we aim to answer
the sub-questions:
1. What are the socio-economic values of small multi-purpose surface water reservoirs to poor rural
households in Burkina Faso?
The reason for undertaking a valuation of small reservoirs is to assess their overall contribution to
social and economic well-being. Hereby, the term value is used to describe the importance placed
on the ecosystem by individuals, which includes not only income generation due to the use of its
goods and services, but also other benefits it provides for human welfare. Hence, not so much the
(monetary) economic value of water is regarded, moreover the economic characteristics; water as
a natural asset that is used by agriculture and households, and so provides a means for
livelihoods.
The concept of ‘total economic value’ (TEV) is a widely used framework for analysis of the
utilitarian value of ecosystems. We have combined this framework (derived from Barbier et al.,
1997) with the framework proposed by Turner et al. (2000), and designed a valuation framework
for the specific scope of this research: non-monetary valuation of small reservoirs. In this, the
socio-economic value of small reservoirs is divided into goods and services. Goods refer to the
96
natural products harvested or used by communities, while services support life by indirect use or
existence – hence by functioning. Which and how many goods and services a reservoir can
provide depend on its characteristics: the physical features, natural environment and internal
processes.
Identified socio-economic values – goods and services – that small multi-purpose reservoirs can
provide to household living nearby them are water supply of domestic, agricultural and animal use,
raw material, food and nutrient supply, and other uses like recreation and education; Water from
small reservoirs is used in and around the house, e.g. for cleaning, bathing, washing, cooking.
Additionally, general agriculture and other agricultural purposes – such as fruit trees and
vegetable gardens – are served. Livestock may depend directly on water from small reservoirs, in
addition to profiting from the higher availability of fodder from crop stubble. The diversion of water
to home gardens may contribute substantially to a varied diet or increase the household income.
Easier access to water can also contribute the development of local economic activities, be it
small scale and informal such as brick making, beer brewing, and mat weaving. Generally, it is not
a source for drinking water; that is extracted from the groundwater; however, in areas where
rainfall is very low people may have no other choice than to use reservoir water (Boelee et al.,
2000).
Clearly, indicators of storage are water quality, water availability and proximity of small reservoirs.
Due to data restriction only the latter indicator is used in this research. Hence, the actual proxy for
the values of small reservoirs is represented by reservoir density at province scale.
2. How can we define ‘poverty’ within the scope of this research?
As a multidimensional phenomenon, poverty is defined and measured in various ways. The
formulation of the definition determines how we analyze poverty and understand its dimensions.
While the main understandings of the term include material and economic needs, increasingly, the
notion of what constitutes basic needs has expanded to encompass not only food, water shelter
and clothing, but also access to other assets such as education, credit, participation, security and
dignity (Hulme et al., 2001).
As to determine relevant dimensions of poverty for this research we analyzed literature on three
concepts of poverty: income (poverty lines) approach, basic needs approach and human capability
concept. The dimensions that fall under the poverty lines and basic human needs concept can be
seen as the most basic and more directly influenced by the presence – or absence – of small
reservoirs, and therefore, are selected to represent poverty. The analysis leads to a founded
97
working definition of poverty: Poverty is lacking sufficient access to financial and material assets,
and public and natural resources, as to ensure being nutritioned and healthy.
As income, health and nutrition being the main dimensions that explain poverty, their sub-
dimensions are supporting them. These sub-dimensions are measures of access and availability,
measures on health, nutrition and income levels and expenditures on resources and assets.
3. Which dimensions of poverty are (in)directly related to the presence (or absence) of small multi-
purpose reservoirs? What is the statistical strength of these relations? Which external factors play
a significant role?
Statistical analysis - correlation analysis and multiple regression analysis - shows that the main
(positive) direct effect of small reservoirs is income generation and education. Mainly employment
rates and education levels benefit from storage. This leads to the new hypothesis that when small
reservoirs are more proximate this leads to considerable time savings.
Overall, food security benefits from the presence of small reservoirs. However, the direct relation
between the presence of small reservoirs and (sources of) nutrition is relevant, though not
unambiguous; the role of reservoirs differs between rural and urban environments. For example,
only in the urban environment livestock and land holding are positively influenced by the presence
of small reservoirs. In the rural environment income is the (positive) determining factor for the
ownership of these resources. Food insecurity is mainly alleviated by the presence of stocks (of
cereals) and consumption of marketed nutritional products. In turn, income is the determining
factor for expenditures on food; hence can be seen as driving force behind food security.
The analysis reveals that disease and disability are most influential to malnutrition, while it does
not show malnutrition is related to measures of food security and food consumption levels.
Explanation may be that food access alone does not yield food security; food adequacy – quality
besides quantity – and physical ability to absorb nutrients (usually affected by disease) are
additional determining factors.
Small reservoirs positively influence access to improved drinking water sources. No evidence is
found that the presence of small reservoirs relates to gauges of water-related diseases (fever and
diarrhoea). However, better sanitation – improved latrines and garbage evacuation – nor
availability of improved (potable) water sources contribute to the reduction of (water-related)
diseases.
98
We can conclude population density is an important motivation for improved accessibility of water
storage (small reservoirs), and other resources (food market, potable water) and services
(schools, health services) become better accessible. Moreover, access to (public) transport
contributes significantly to the accessibility of resources and services.
What is the relationship between the presence of small multi-purpose surface water reservoirs and the
state of well-being of rural households?
We can conclude that the impact of small reservoirs on the poverty system is originating from income
generation – thus partly by time saving and improved human capital. Nearby reservoirs enable to
engage other activities as farming, going to school or developing small scale industries. Income is the
driving force behind food and health security, which – in turn – lead to improved human capital.
Population density is an important determining factor in access to small reservoirs. In more densely
populated areas resources (markets, water) and services (schools, health services, public transport)
become better accessible.
The applied statistical techniques puzzle out the existence and strength of relations, however, the
direction remains only given in by theory. In general, the strength of the relations and the explained
variance by the regression models is low to medium; hence, we should be careful drawing strong
conclusions. Relations are weak due to (1) measurement error and the use of proxies to represent
concepts, and (2) disaggregation of variables. Possible solutions are given below.
10.2 Recommendations for future work
• Improvement of the storage data
So far – in this research – the presence of small reservoirs is represented by the proximity of
those reservoirs. No (sufficient) data on water availability and water quality are available.
However, more powerful assessment of the relation between water storage and poverty can be
reached as estimates of reservoir volumes (in time) are included. The hypothesis would than be
that larger reservoirs have more impact on poverty alleviation. Furthermore, as to be able to
assess the impact of seasonality, water volumes over time – and at least at the beginning and end
of the rainy season – should be involved, using either satellite images or ground surveys.
Apart from extending the number of features covered, additionally, the current database should be
up-dated and attributed with the correct number and names of provinces and departments.
99
• Improvement of the poverty data
It is recognized that poverty should be regarded as a process in which different dimensions – e.g.
income, nutrition and health – have mutual relations wherein the temporal antecedence of the
cause versus the effect is not always clear. The current research provides a rather static
representation of this poverty process; as multiple regression analysis does not enable iterative
testing. However, statistical techniques as structural equation modelling do allow to regard the
system as a whole and to include iterations. Unfortunately, the current data do not allow applying
this technique due to their measurement level and statistical quality; i.e. the data should be of
interval measurement level and satisfy the assumptions underlying many multi-variate techniques
(e.g. linearity, normality and homoscedasticity). Therefore it is recommended to upgrade the data
by re-formulating survey questionnaires as to obtain data of (at least) interval level16. Additionally,
re-formulating should lead to more reliable responds (reduce measurement error) and more
accurate indicators (to omit imperfect representation of concepts).
• Geo-referencing of the household survey
As mentioned in the reflection (Chapter 9), in the current research the indicator for the proximity of
small reservoirs – reservoir density – is at province scale. Therefore, an error is introduced in the
real proximity of small reservoirs; as the real proximity of reservoirs is not equal for each
household within the province. This error can be abolished in case the household survey would be
geo-referenced. In that case – by means of GIS tools – the real distance between the household
residence or community and small reservoirs can be assessed.
• Poverty mapping
The proposed geo-referencing of (future) household surveys additionally allows poverty mapping.
Poverty mapping is comparing the spatial distribution of poverty indicators with data from other
assessments, such as access to resources and services, visualized on maps. For the follow-up of
this research the contribution of poverty mapping is that regional differences can be shown – thus
spatial dynamics is introduced in the research – multiple dimensions can be displayed in one map
and the relation between cause and effect can become clearer from the visualisation by mapping.
It should be noted that, although poverty mapping can serve as a useful (exploratory) tool in
establishing and presenting the spatial relationship between indicators, it does not proof causal
relations between indicators; this should be assessed by the appropriate (statistical) analysis
techniques.
16 See Glossary
101
Bibliography
Agudelo, J.I. (2001) “The economic valuation of water: Principles and methods”, Value of
water research report series No.5, IHE Delft, The Netherlands
Alcamo, J. et al. (2003) “Ecosystems and human well-being: A framework for assessment”,
Millennium Ecosystem Assessment, World Resources Institute,
Washington, USA
http://www.millenniumassessment.org/
Balazs, C. (2006) “Rural livelihoods and access to resources in relation to small
reservoirs: A study in Brazil’s Preto River Basin”, Masters Project,
University of California, Berkeley, USA
http://www.smallreservoirs.org/
Barbier, E.B. et al. (1997) “Economic valuation of wetlands, A guide for policy makers and
planners”, Ramsar convention bureau, Gland, Switzerland
http://www.ramsar.org/lib/lib_valuation_e.pdf
Beiersmann, C. et al. (2007) “Malaria in rural Burkina Faso: Local illness concepts, patterns of
traditional treatment and influence on health-seeking behaviour”,
Malaria Journal 2007 Volume 6 Article 106
http://www.malariajournal.com/content/6/1/106
Bloom, D.E. et al. (2000) “The health and wealth of nations”, Science 18 February 2000 Volume
287 No. 5456 pp 1207-1209
http://www.sciencemag.org/cgi/content/summary/287/5456/1207
Boelee, E. et al. (2000) “Multiple use of irrigation water in dry regions of Africa and South-
Asia”, Communication Texts, Volume 1, Session 1B-51-58,
International Conference Water and Health, Ouaga 2000, Health and
nutritional impacts of water development projects in Africa, November
21-24, 2000, Ouagadougou, Burkina Faso
Cavendish, W. (1999) “Empirical regularities in the poverty-environment relationship of
African rural households”, TH Huxley School, Imperial College,
London, UK
http://www.csae.ox.ac.uk/workingpapers/pdfs/9921text.pdf
Chen, C.K. et al. (2004) “Using ordinal regression model to analyse student satisfaction
questionnaires”, Association for Institutional Research, IR applications
Volume 1
102
Coche, A.G. (1998) “Supporting aquaculture development in Africa”, Research network on
Integration of Agriculture and Irrigation, CIFA Occasional Paper No.
23, Rome, Italy
http://www.fao.org/docrep/X5598E/X5598E00.htm
Coudouel, A. et al. (2002) “Chapter 1: Poverty Measurement and Analysis”, in A sourcebook for
Poverty Reduction Strategies: Part 1 Core techniques and cross-
cutting issues, World Bank, Washington DC, USA
http://povlibrary.worldbank.org/files/5467_chap1.pdf
De Groot, R.S. (1992) “Functions of nature: Evaluation of nature in environmental planning,
management and decision making”, Wolters-Noordhoff, Groningen,
The Netherlands
Dinar, A. et al. (1995) “Restoring and protecting the world’s lakes and reservoirs”, World
Bank technical paper No. 289, World Bank, Washington, USA
DGIRH (2004) Official list of reservoirs in Burkina Faso February 2004, Ministère de
l’Envinronnement et de l‘Eau, Direction Générale de l’Inventaire des
Ressources Hydrauliques, Ougadougou, Burkina Faso.
DOW (2001) “Rural water demand: The case of Eastern Africa”, Lessons from the
Drawers of Water II Study
http://webworld.unesco.org/water/wwap/pccp/cd/pdf/educational_tools
/course_modules/reference_documents/water/ruralwaterdemand.pdf
Falkingham, J. et al. (2002) “Measuring health and poverty: A review of approaches to identifying
the poor”, DFID Health Systems Resource Centre, London, UK
FAO (2003) “Measurement and Assessment of Food Deprivation and
Undernutrition”, Proceedings of the International Scientific
Symposium, 26-28 June 2002, Rome, Italy
http://www.fao.org/DOCREP/005/Y4249E/y4249e00.htm
FAO (2006) “What’s water worth?” Agriculture 21 Magazine March 2006, FAO,
Rome, Italy
http://www.fao.org/ag/magazine/0603sp1.htm
Field, A. (2005) “Discovering statistics using SPSS”, Second edition, Sage
Publications, London, UK
Haddad, L. (2002) “Nutrition and Poverty”, in ACC/CSN (2002), Nutrition: A foundation for
development, Geneva, Switzerland
http://www.ifpri.org/pubs/books/intnut/intnut.pdf
Hair, J.F. et al. (1998) “Multivariate data analysis”, Fifth edition, Prentice Hall, New Jersey,
USA
103
Ho, R. (1996) “Handbook of univariate and multivariate data analysis and
interpretation with SPSS”, Central Queensland University, Rock
Hampton, Australia
Hulme, D. et al. (2001) “Chronic poverty: Meanings and analytical framework”, CPRC
Working Paper 2, Chronic Poverty Research Centre, Manchester, UK
http://www.chronicpoverty.org/resources/working_papers.html
INSD (2003) "Enquête Burkinabè sur les conditions de vie des ménages: Première
phase", Manuel de l’agent enquêteur, Institut national de la statistique
et de la démographie, Ouagadougou, Burkina Faso
INSD (2004) “Projections de population du Burkina Faso (2004)”, Ministère du
l’Economie et du Développement, SecrétariatGénéral et Institut
National de la Statistique et de la Démographie (INSD),
Ouagadougou, Burkina Faso
http://www.insd.bf/actualites/Publications/f_Projections_de_population
Keller, A. et al. (2000) “Water scarcity and the role of storage in development”, Research
Report 39, IWMI, Colombo, Sri Lanka
Kemper, K. et al. (undated) “The global water challenge”, World Bank global issues seminar series
http://siteresources.worldbank.org/EXTABOUTUS/Resources/WaterP
aper.pdf#search=%22The%20global%20water%20challenge%20kem
per%22
Liebe, J. (2002) “Estimation of water storage capacity and evaporation losses of small
reservoirs in the upper east region of Ghana”, Diploma thesis,
Geographische Institute der Rheinischen Friedrich-Wilhelms-
Universität, Bonn, Germany
http://www.smallreservoirs.org/
Lipton, M. et al. (2003) “Preliminary review of the impact of irrigation on poverty: with special
emphasis on Asia”, Land and Water Development Division, FAO,
Rome, Italy
Lok-Dessallien, R. (undated a) “Review of poverty concepts and indicators”, SEPED series on
poverty reduction
http://www.undp.org/poverty/publications/pov_red/
Lok-Dessallien, R. (undated b) “Poverty profile: Interpreting the data”, SEPED series on poverty
reduction
http://www.undp.org/poverty/publications/pov_red/
Lok-Dessallien, R. (undated c) “The data: Where to find them”, SEPED series on poverty reduction
http://www.undp.org/poverty/publications/pov_red/
MARA/ARMA (1998) “Towards an atlas of malaria risk in Africa”, Durban, South Africa
104
Molden, D. (2007) “Water for food Water for life: A comprehensive assessment of water
management in agriculture”, Earthscan, London, UK and International
Water Management Institute, Colombo, Sri Lanka
Moriarty, P. et al. (2003) “The productive use of domestic water supplies: How water supplies
can play a wider role in livelihood improvement and poverty
reduction”, Thematic overview paper, IRC International Water and
Sanitation Centre, Delft, The Netherlands
Moriarty, P. et al. (2004) “Beyond domestic: Case studies on poverty and productive uses of
water at the household level”, Technical paper series No 41, IRC
International Water and Sanitation Centre, Delft, The Netherlands
Mwaniki, A. (undated) “Achieving food security in Africa: Challenges and issues”, Cornell
University, Ithaca, USA
http://www.un.org/africa/osaa/reports/Achieving%20Food%20Security
%20in%20Africa-Challenges%20and%20Issues.pdf
Nandy, S. et al. (2003) “Poverty, food and health in welfare: Current issues, future
perspectives”, International Conference on Poverty, Food and Health
in Welfare, Lisbon, Portugal
Newcome, J. et al. (2005) “The economic, social and environmental value of ecosystem
services: A literature review”, Final report for the Department for
Environment, Food and Rural Affairs, Eftec, London, UK
OECD/WHO (1993) “Poverty and Health”, DAC Guidelines and Reference Series,
Organisation for economic co-operation and development in
cooperation with World Health Organisation, Paris, France
http://whqlibdoc.who.int/publications/2003/9241562366.pdf
Pallant, J. (2001) “SPSS survival manual: A step by step guide to data analysis using
SPSS”, Open University Press. Chicago, USA
Pearce, D.W. et al. (1993) “World Without End”, Oxford University Press, Oxford, USA
Poolman, M. (2005) “Developing small reservoirs: A participatory approach can help“,
Masters Thesis, Delft University of Technology, Delft, The Netherlands
http://www.smallreservoirs.org/
POST (2006) “Food security in developing countries”, Postnote December 2006, No.
274, Parliamentary Office of Science and Technology, London, UK
http://www.imf.org/external/pubs/ft/scr/2005/cr05338.pdf
Pritchett, L. (1997) “Review of Robert D. Kaplan’s The Ends of the Earth”, Finance and
Development March 1997, International Monetary Fund and the
International Bank for Reconstruction and Development, Washington
DC, USA
http://worldbank.org/fandd/english/0397/mar97.htm
105
Rocha, S. (1998) “On statistical mapping of poverty social reality: Concepts and
measurement”, Texto para discussao No. 553, Rio de Janeiro,
Argentina
Roggeri, H. (1995) “Tropical freshwater wetlands: A guide to current knowledge and
sustainable management”, Kluwer Academic, Dordrecht, The
Netherlands
Savadogo, A.S. (2006) “Water resources management in Burkina Faso: A case study on the
potential of small dams”, WaterAid, Ouagadougou, Burkina Faso
http://www.wateraid.org/documents/plugin_documents/burkina_faso_fi
eldwork_report__cwrm.pdf
SRP (undated) “Project proposal”, Small Reservoirs Project
http://www.smallreservoirs.org/
Smith, R.D. et al. (1995) “An approach for assessing wetland functions using hydrogeomorphic
classification, reference wetlands, and functional indices”, Wetlands
research program technical report WRP-DE-9, US Army Corps of
Engineers, Washington, USA
Thimm, H.U. (2003) “Interdisciplinary evaluation of the role of infrastructure”, Proceedings
for the International Symposium on Regional Food Security and Rural
Infrastructure, 3-6 May 2003, Giessen-Rauischholzhausen, Germany
Turner, R.K. et al. (2000) “Ecological-economic analysis of wetlands: Scientific integration for
management and policy”, Ecological Economics Volume 35, special
issue, Elsevier
Turner, R.K. et al. (2004) “Economic valuation of water resources in agriculture: From the
sectoral to a functional perspective of natural resources
management”, FAO water reports No. 27, FAO, Rome, Italy
http://www.fao.org/docrep/007/y5582e/y5582e00.HTM
UNDP (2003a) “Human Development Report 2003: Human development indicators”
http://hdr.undp.org/reports/global/2003/pdf/hdr03_HDI.pdf
UNDP (2003b) “Human Development Report 2003: Technical note 1”,
http://hdr.undp.org/reports/global/2003/pdf/hdr03_backmatter_2.pdf
UNDP (2005) “Human Development Reports: Burkina Faso Country Sheet”
http://hdr.undp.org/statistics/data/countries.cfm?c=BFA
UNDP (1997) “Human Development Report 1997”, Oxford University Press, New
York, USA
http://hdr.undp.org/reports/global/1997/en/
Van de Giesen, N.C. et al. (2000)
“The Glowa Volta project: Integrated assessment of feedback
mechanisms between climate, land-use and hydrology”, Wengen-
106
2000 Workshop, Climatic Change: Implications for the Hydrological
Cycle and for Water Management, Wengen, Switzerland
http://www.glowa-volta.de/publications/printed/wengen2000.pdf
Website FAO SPFS (February 2007)
http://www.fao.org/spfs/
Website IFAD (November 2006)
http://www.ifad.org/sf/
Website MARA/ARMA (September 2007)
http://www.mara.org.za/
Website PovertyNet (June, 2007)
http://www.worldbank.org/poverty/
Website Unicef (September 2007)
http://www.unicef.org/wes/
Website WHO (August 2007)
http://www.who.int/water_sanitation_health/diseases/en/index.html
Website World Bank (November 2006)
http://web.worldbank.org/WBSITE/EXTERNAL/EXTABOUTUS/0,,cont
entMDK:20040565~menuPK:1696892~pagePK:51123644~piPK:3298
29~theSitePK:29708,00.html
Website World Concern (June 2007)
http://www.worldconcern.org/NETCOMMUNITY/Page.aspx?&pid=567
&srcid=414
WHO (2007) “Combating waterborne diseases at the household level”, The
international network to promote household water treatment and safe
storage, Geneva, Switzerland
http://www.who.int/water_sanitation_health/diseases/burden/en/index.
html
World Bank (2004) “Poverty monitoring guidance note 1: Selecting indicators”,
Washington, USA
http://poverty2.forumone.com/library/view/15138
World Bank (2005) “Data and statistics on Burkina Faso”
http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/AFRICA
EXT/BURKINAFASOEXTN/0,,menuPK:343902~pagePK:141132~piP
K:141109~theSitePK:343876,00.html
WSSCC (2005) “Sanitation and hygiene promotion: Programming guidance”, Water
supply and sanitation collaborative council and World health
organization, Geneva, Switzerland
109
Appendix A. Values of Water
Values and classifications found in literature
The following list gives a good impression of the values of water ecosystems found in literature. One
can see that different authors use different conceptual frameworks – e.g. Turner et al. (2000 & 2004),
Barbier et al. (1997) and Newcome (2005) versus De Groot (1992) versus Alcamo (2003) versus
Roggeri (1995) – and/or give different meanings to terms as goods, services, use values and non-use
values, functions, etc. Note that this list is not an exhaustive review on literature, many more have
published on this subject. In addition, it must be realised that there are probably many unknown values
that are not recognised yet, but which may have considerable (potential) benefits (De Groot, 1992).
The classification used by Turner et al. (2000 & 2004), Barbier et al. (1997) and Newcome (2005) is
based on the framework for Total Economic Value (TEV). It provides a framework for grouping values,
and there is an increasing consensus that it is the most appropriate one to use. Simply put, total
economic valuation distinguishes between use values and non-use values, the latter referring to those
current or future (potential) values associated with an environmental resource which rely merely on its
continued existence and are unrelated to use (Pearce et al., 1993). Typically, use values involve some
human interaction with the resource that is grouped according to whether they are direct or indirect.
Direct use values involve both commercial and non-commercial activities. The indirect use values
derive from supporting or protecting activities. A special category of use values is (quasi)option value,
which is uncertainty on its future use and information function.
Table A-1: Values of water combined from Barbier et al., 1997 and Newcome et al., 2005
Direct use values Indirect use values Option and quasi-option Non-use values
livestock and
cultivation
fisheries
agriculture
fibre and fuelwood
recreation
transport
wildlife harvesting
and hunting
peat/energy
aesthetic value
sediment and
nutrient cycling
flood water storage
and stream flow
regulation
groundwater
recharge
external ecosystem
support
• micro-climate
stabilisation
potential future direct
and indirect uses of
goods and services
future value of
information
biodiversity
bequest values
(value on the
conservation of
wetlands for future
generations)
existence
cultural knowledge
and traditions
110
Turner et al. (2000) goes one step further, by explicitly linking economic valuation to ecological
characterisation. This, he labels as going from wetland functioning to wetland uses. Economic values
will always relay upon the wetland performing functions that are somehow perceived as valuable by
society. Functions in themselves are therefore not necessarily of economic value – such value derives
from the existence of a demand for wetland goods and wetland services due to these functions.
Table A-2: Values of water derived from Turner et al., 2000 & 2004
Goods Services Use values Non-use values Other values
agriculture
fisheries
forestry
non-timber
forest
products
water supply
recreation
flood control
groundwater
recharge
nutrient
removal
toxics
retention
biodiversity
maintenance
consumptive
recreational
aesthetical
educational
indirect use
values
existence
bequest
philanthropic
option
quasi option
The classification used by De Groot (1992) is based on environmental function evaluation, and thus
includes not only the land-use values and harvestable goods (nature in the narrow sense), but also
refers to other benefits of the natural environment which are less tangible. Environmental functions are
defined as “the capacity of natural processes and components to provide goods and services that –
directly of indirectly – satisfy physiological and psychological human needs”. De Groot (1992)divides
the environmental functions in to four classes:
• Regulation function relates to the capacity of natural and semi-natural ecosystems to regulate
essential ecological processes and life support systems, which contributes to the maintenance of
a healthy environment by providing clean air, water and soil.
• Information function, natural ecosystems contribute to the maintenance of mental health by
providing opportunities for reflection, spiritual enrichment, cognitive development and aesthetical
experience.
• Carrier function implies that natural and semi-natural ecosystems provide space and a suitable
substrate or medium for many human activities such as habitation, cultivation and recreation.
• Production function, nature provides many resources, ranging from food and raw materials to
energy resources and genetic material.
111
Table A-3: Values of water derived from De Groot, 1992
Due to regulation
function
Due to information
function
Due to carrier
function
Due to production
function
storage and
recycling of
nutrients, human
and organic waste
groundwater re- &
discharge
flood control & flow
regulation
erosion control
salinity control
water treatment
climatic
stabilization
maintenance of
biodiversity
education and
monitoring
cultural heritage
agriculture
stock farming
(grazing)
wildlife cropping
energy
production
transport
tourism and
recreation
human
habitation and
settlement
water
food
fuel wood
medicinal resources
raw materials for
building and
industrial use
genetic resources
The framework proposed by Alcamo et al. (2003) as a first product of the Millennium Ecosystem
Assessment (MA) – a four-year international work program designed to meet the needs of decision-
makers for scientific information on the links between ecosystem change and human well-being –
places human well-being as the central focus for assessment, while recognizing that biodiversity and
ecosystems also have intrinsic value and that people take decisions concerning ecosystems based on
considerations of well-being as well as intrinsic value. Ecosystem services are the benefits people
obtain from ecosystems. These include provisioning, supporting, regulating, and cultural services,
which directly affect people, and supporting services needed to maintain the other services. Changes
in these services affect human well-being through impacts on security, the basic material for a good
life, health, and social and cultural relations. These constituents of well-being are, in turn, influenced
by and have an influence on the freedoms and choices available to people.
112
Table A-4: Values of water derived from Alcamo et al., 2003
Supporting services Provisioning services Regulation services Cultural services
nutrient cycling
soil formation
primary production
food
fresh water
wood and fibre
fuel
climate regulation
flood regulation
water purification
aesthetic
spiritual
educational
recreational
The classification of Roggeri (1995), designed for tropical freshwater wetlands. Functions are due to
their role in many natural phenomena and processes. Their resources can be used in order to obtain
products or services. Finally, they have attributes such as biological diversity. These functions,
attributes (or qualities) and resources are goods and services which have a value for human beings.
Note that they are closely linked on the one hand to the wetlands’ biological, chemical and physical
characteristics, and on the other hand to the interaction of these characteristics. Therefore, wetlands
do not automatically provide all the goods and services as mentioned below. Furthermore, the role
that wetlands can play in a given process may vary considerably, both in significance and quality.
Table A-5: Values of water derived from Roggeri, 1995
Resources Functions Attributes
agriculture, forestry, forage
production
wildlife or fish production
aquaculture
natural products
water supply
energy production
transport
tourism, recreation
research and education
nutrient retention, export
groundwater dis/recharge
flood mitigation
sediment retention
erosion control
salinity control
water treatment
climate stabilization
ecosystem stability
biological diversity
cultural or historic value
aesthetic value
113
Appendix B. Selection of Indicators
B.1 Selection of indicators for poverty
Data source available on poverty indicators is the ‘Questionnaire des Indicateurs de Base de Bien-
être’ (QUIBB)17 performed between April and July of 2003. This questionnaire is written on behalf of
the National Institute of Statistics and Demography and Demography (INSD) of Burkina Faso, and is
meant for gathering the data needed for the economical and social management of the country. It is –
in design – a known way for collecting information about household characteristics, measures of
access, usage and degree of satisfaction en matters of social service. The questionnaire uses a
methodology developed by a group of donators and institutions, e.g. World Bank, BIT, UNICEF, and
PNUD (INSD, 2003).
The questionnaire divides the country of Burkina Faso in 425 zones (representing communities), in
each of which 24 households are interviewed. The questionnaire is composed of the following
sections:
Section A Administration of the interview place, time and date
Section B Information about the informant
Section C Education
Section D Health
Section E Employment
Section F Assets of the household
Section G Resources to the household
Section I Information on children younger than 5 years
Section J Expenses on education, health and agriculture
Section K Information on agricultural activities
Section L Information on entrepreneurial activities
Section M Information on nutritional products consumed
Section N Information on non-food products consumed
Section O Information on source of income
Section P Information on different types of services
17 Translated: Questionnaire of indicators of basic well-being
114
Sections B till E, I and L concern information on individual level, and sections F, G, J, K and M till P
information on household level. Section I is information on individual level concerning the children
younger than five years. Note that some available variables do not belong to any section mentioned
above. Also, the included manuals for interviewers and controllers do not mention these ‘other’
indicators neither do they mention section J till P.
The tables below show the selected indicators on the main dimensions of poverty – health, nutrition
and income – that are selected from the broad list of variables available from the QUIBB
questionnaires. They are selected as to be the most feasible proxies for the (sub)dimensions of
poverty. The tables show – ordered per dimension – the name of the indicator, description of the
indicator, the measurement level and scale, and the item scale. This last characteristic of the data is
relevant since specific statistical procedures appoint requirements upon the minimum level of
measurement. Basically, four levels of measurement are commonly used in statistics as to describe
the nature of the information contained within numbers assigned to objects and, therefore, within the
variable:
• Nominal – the nominal measurement level is considered the lowest. It assigns numerical
values as labels to identify categorical data.
• Ordinal – in case of ordinal scales categorical data can be ordered or ranked in relation to the
amount of the attribute possessed. However, the scale is really non-quantitative, because it
indicates only relative positions in an ordered series.
• Interval – represents quantitative data with a constant unit of measurement, that have an
arbitrary zero point. Therefore, it is not possible to state that any value on an interval scale is a
multiplication of any other value on the scale.
• Ratio – represents the highest form of measurement precision because they possess the
advantages of all lower scales plus an absolute zero point (Hair et al., 1998).
115
Table B-1: Selected indicators for income
Name Indicator Measurement
scale
Measurement
level Item scale
Revagric Revenue from agriculture Household Ratio CFA/yr
Reveleva Revenue from dairy farming Household Ratio CFA/yr
Salpubli Salary form public sector Household Ratio CFA/yr
Salprive Salary form private sector Household Ratio CFA/yr
Revloyer Revenue form rent Household Ratio CFA/yr
Transf Revenue from interest Household Ratio CFA/yr
Revtotal Total revenue Household Ratio CFA/yr
Revownresources1 Income from (owned) resources
(∑ revagric + reveleva) Household Ratio CFA/yr
Revemployment1 Income from employment
(∑ salpubli + salprive) Household Ratio CFA/yr
Reventrepreneur1 Income from entrepreneurship
(∑ revloyer + tranf) Household Ratio CFA/yr
Niveduc Highest level of education reached Individual Ordinal2
1: Not at all
2: Primary
3: Secondary
4: Higher
G7d Proximity of primary school Household Ordinal2
1: > 1 hour
2: 45-59 min.
3: 30-44 min.
4: 15-29 min.
5: 0-14 min.
G7e Proximity of secondary school Household Ordinal2
1: > 1 hour
2: 45-59 min.
3: 30-44 min.
4: 15-29 min.
5: 0-14 min. 1 Composed aggregate measure of variables available from INSD (2003). 2 Variable beholds an underlying continuum and therefore is treated as of interval level in the analysis.
116
Table B-2: Selected indicators for nutrition
Name Indicator Measurement
scale
Measurement
level Item scale
F3 Surface of landholding Household Ratio # hectares
F8 Number of large cattle owned Household Ratio # cattle
F10 Number of small cattle owned Household Ratio # cattle
Cattle1 Number of livestock
(Σ F8 + F10) Household Ratio # cattle
F14 Occurrence of problems satisfying
nutritional needs Household Ordinal2
1: Never
2: Rarely
3: Sometimes
4: Often
5: Always
F20 Access to stocks (of cereals) until the
next harvest Household Ordinal
0: No
1: Yes
G7b Proximity of (local) food market Household Ordinal2
1: > 1 hour
2: 45-59 min.
3: 30-44 min.
4: 15-29 min.
5: 0-14 min.
Autoali Value of autoconsumption of nutritional
products Household Ratio CFA/month
Achali Value of expenditures on nutritional
products Household Ratio CFA/month
Depali Total value of consumption of nutritional
products Household Ratio CFA/month
Wasted Child has low weight for height Infant Dichotomous 0: No
1: Yes
Stunted Child has low height for age Infant Dichotomous 0: No
1: Yes
Underweight Child has low weight for age Infant Dichotomous 0: No
1: Yes
Malnutrition1 Child is wasted and/or stunted and/or
underweight Infant Dichotomous
0: No
1: Yes
Length Length of the child Infant Ratio # cm
Weight Weight of the child Infant Ratio # kg
1 Composed aggregate measure of variables available from INSD (2003). 2 Variable beholds an underlying continuum and therefore is treated as of interval level in the analysis.
117
Table B-3: Selected indicators for health
Name Indicator Measurement
scale
Measurement
level Item scale
D3 Chronic prevalence of handicap or injury Individual Dichotomous 0: No
1: Yes
D4 Recent prevalence of disease Individual Dichotomous 0: No
1: Yes
D5a Recent prevalence of fever Individual Dichotomous 0: No
1: Yes
D5b Recent prevalence of diarrhoea Individual Dichotomous 0: No
1: Yes
G3 Access to (improved) potable water
source Household Ordinal2
1: River or lake
2: (Drilled) well
3: Inside tap
G4 Access to (improved) toilets Household Ordinal2
1: In nature
2: Ordinary
latrine
3: Latrine with
ventilated put
4: Flush with
septic put
G7a Proximity of potable water source Household Ordinal2
1: > 1 hour
2: 45-59 min.
3: 30-44 min.
4: 15-29 min.
5: 0-14 min.
G7f Proximity of (public) health service
(hospital or clinic) Household Ordinal2
1: > 1 hour
2: 45-59 min.
3: 30-44 min.
4: 15-29 min.
5: 0-14 min.
G11 Access to (improved) garbage disposal Household Ordinal2
1: Street
2: Bag
3: Put
4: Individual
garbage pile
5: Public
garbage pile
6: Garbage bin
(emptied by a
service)
118
J31 Value of expenditures on consultation Household Ratio CFA/past
month
J32 Value of expenditures on medical
analysis Household Ratio
CFA/past
month
J33 Value of expenditures on medicines Household Ratio CFA/past
month
J34 Value of expenditures on hospitals Household Ratio CFA/past
month
J35 Value of expenditures on other medical
services Household Ratio
CFA/past
month
Tothealth1 Value of expenditures on health services
(∑ J31 + J32 + J33 + J34 + J35) Household Ratio
CFA/past
month 1 Composed aggregate measure of variables available from INSD (2003). 2 Variable beholds an underlying continuum and therefore is treated as of interval level in the analysis.
B.2 Selection of external indicators
Source: INSD (2003), Questionnaire des Indicateurs de Base de Bien-être, National Institute of
Statistics and Demography and Demography (INSD), Ougadougou, Burkina Faso
Table B-4: Selected indicators for external factors
Name Indicator Measurement
scale
Measurement
level Item scale
Urbrur Milieu of residence Household Dichotomous 0: Urban
1: Rural
Hhsize Number of household members Household Ratio # people
B5 Age (of last birthday) Individual Ratio # of years
G7c Proximity of (public) transport Household Ordinal2
1: > 1 hour
2: 45-59 min.
3: 30-44 min.
4: 15-29 min.
5: 0-14 min. 1 Composed aggregate measure of variables available from INSD (2003). 2 Variable beholds an underlying continuum and therefore is treated as of interval level in the analysis.
119
Source:
A. INSD (2004), Projections de population du Burkina Faso, Ministère du l’Economie et du
Développement, Secrétariat général, et Institut National de la Statistique et de la
Démographie (INSD), Ouagadougou, Burkina Faso.
B. DGIRH (2004), Official list of reservoirs in Burkina Faso February 2004, Ministère de
l’Envinronnement et de l‘Eau, Direction Générale de l’Inventaire des Ressources
Hydrauliques, Ougadougou, Burkina Faso.
Table B-5: Population and reservoir density
No. Province PopulationA
[#]
AreaB
[km2]
Population
density A [#/km2]
ReservoirsB
[#]
Reservoir
density B [#/100*km2]
31 Balé 198765 4543 43.75 13 0.286
1 Bam 250144 4073 61.42 66 1.621
32 Banwa 261140 5825 44.83 2 0.034
2 Bazéga 236316 3874 61.01 73 1.885
3 Bougouriba 84775 3420 24.79 8 0.234
4 Boulgou 478576 6371 75.12 39 0.612
5 Boulkiemde 461393 4254 108.46 102 2.398
6 Comoé 292479 15305 19.11 28 0.183
7 Ganzourgou 305556 4131 73.96 43 1.041
8 Gnagna 370533 8585 43.16 31 0.361
9 Gourma 255906 11212 22.82 35 0.312
10 Houet 818471 11630 70.38 21 0.181
33 Ioba 176641 2634 67.05 18 0.683
11 Kadiogo 1204346 2930 411.00 94 3.208
12 Kénédougou 245618 8336 29.47 12 0.144
34 Komandjari 57965 5175 11.20 2 0.039
35 Kompienga 57388 7048 8.14 1 0.014
13 Kossi 268002 7490 35.78 0 0.000
36 Koulpélogo 221476 5395 41.05 19 0.352
14 Kouritenga 289763 2802 103.41 58 2.070
120
37 Kourwégo 135227 1657 81.63 21 1.268
38 Léraba 106632 3076 34.67 10 0.325
39 Loroum 134457 3656 36.78 32 0.875
15 Mouhoun 284220 6898 41.20 13 0.188
16 Nahouri 129778 3814 34.03 46 1.206
17 Namentenga 293466 6320 46.44 15 0.237
40 Nayala 150417 3698 40.68 5 0.135
41 Noumbiel 56452 2709 20.84 2 0.074
18 Oubritenga 237096 2790 84.97 57 2.043
19 Oudalan 161415 9957 16.21 83 0.834
20 Passoré 307668 3940 78.08 55 1.396
21 Poni 198585 7379 26.91 14 0.190
22 Sanguié 272599 5144 52.99 80 1.555
23 Sanmatenga 538068 9406 57.20 50 0.532
24 Séno 242846 6984 34.77 21 0.301
25 Sissili 183107 7105 25.77 30 0.422
26 Soum 306846 12565 24.42 41 0.326
27 Sourou 216955 6080 35.68 16 0.263
28 Tapoa 301431 14803 20.36 15 0.101
42 Tuy 206309 5717 36.08 13 0.227
43 Yagha 150058 6461 23.23 9 0.139
29 Yatenga 514218 6587 78.07 74 1.123
44 Ziro 144095 5251 27.44 35 0.667
45 Zondoma 148983 2218 67.18 15 0.676
30 Zoundwéogo 240820 3858 62.43 34 0.881 A Derived from source A. B Derived from source B.
121
Appendix C. Data Screening
In this appendix we perform the following:
• Test of extreme values by visual inspection. A ‘quick-and-dirty’ scan as to select outlier
variables that need further investigation. The goal of this inspection is actually to retain as
many entries as possible, but be aware of their existence and possible influence;
• Test of missing values. Provides information on the extent of missing data for a single
variable. In case of insignificant missing data (less than 10%) any of the imputation methods
can be applied, or the missing data can even be ignored (Hair et al., 1998);
• Test of underlying assumptions. Prevents potential distortions and biases that occur when the
assumptions are violated. The complexity of the analysis and results may mask the indicators
of assumption violations.
By examining the data we obtain a better understanding of the basic characteristics of the data and
relationships between variables. This will provide us with a better perspective on the complexity of the
statistical techniques, as to be able to better interpret the results. Also, we ensure that the data
underlying the analysis meet all of the requirements for its application.
C.1 Data quality assessment
Outlier detection
Outliers are observations with a unique combination of characteristics identifiable as distinctly different
from other observations (Hair et al., 1998). They need to be considered since they can have an effect
on any type of empirical analysis, and they must be viewed in the perspective of their
representativeness for the population. Outlier detection concerns identifying and possibly deleting
these extreme values. Outliers can be identified from a univariate, bivariate, or multivariate perspective
which of as many as possible should be utilized (Hair et al., 1998).
Univariate detection examines the distribution of observations for each (continuous) variable through
boxplots, and selects as outliers those cases falling at the outer range of the distribution. Univariate
outlier detection can be supported by examining the standard-scores (z-scores). For larger samples (n
> 80) the threshold value is four. Higher standard scores indicate extreme values (Hair et al., 1998).
The Missing Value Analysis (MVA) option in SPSS also provides an indication of the number of
extreme values, based on range of standard deviation. Bivariate detection assesses specific pairs of
variables that have a dependence relationship through scatterplots with confidence intervals at a
122
specified alpha level. Since the effort needed becomes more substantial with an increasing amount of
variables, only specific relationships are considered.
When outliers are identified and examined, we must decide upon deleting or retaining them. In this we
should seek the balance in representativeness for the population. Below, the detection procedure and
possible deletion of values for this research is described.
Step 1 – Univariate detection (only continuous variables)
• Reservoir density – Density of small reservoir (province) [household dataset]. One clear high
extreme can be detected from the visual examination. However, no values are deleted.
• Reservoir distance – Average distance to small reservoir (province) [household dataset]. Two
clear high extremes can be detected from the visual examination. However, no values are
deleted.
• Population density – Density of the population (province) [household dataset]. One clear high
extreme can be detected from the visual examination. However, no values are deleted.
• Measures of income [household dataset]. For the measures on revenues from different
sources the standardized scores indicate between 20 and 110 outliers per variable. The
boxplot of total revenue (revtotal) shows one extreme value due to an extreme value on
revenue from interest (revtransf). This value is deleted from the analysis.
Figure C-1: Boxplot for indicators of income
123
• Measures of food consumption [household dataset]. For the variables ‘achali’ and ‘autoali’ –
expenditures and autoconsumption of nutritional products – the univariate analysis (MVA)
shows around 270 extreme high values on each of the variables. It is decided not to delete
any values in this stage, since there might be connections with other variables.
• Measures of healthcare consumption [household dataset]. For the measures on values of
expenditures on health we aggregate into variable ‘tothealth’. By evaluating of z-scores we
see that – for each variable – approximately the top 30 values are considered extreme. The
boxplot shows two high values on the variable ‘tothealth’. These are removed from the dataset
together with the values on ‘value of expenditures on medical analysis’ (J32) and ‘value of
expenditures on medicines’ (J33). Now the boxplot for ‘tothealth’ does not show discontinuity.
Figure C-2: Boxplot for indicators of expenditures on health services
• Access to own resources [household dataset]. All indicators; ‘surface of owned land’, ‘number
of large cattle owned’ and ‘number of small cattle owned’ show significant numbers of extreme
(high) values. However, no values are deleted since no evidence is found that extremes
appear in a non-random manner or are related.
• Hhsize – number of household members [household dataset]. Ranges from 1 to 55. According
to the univariate statistics (MVA) households larger than 13 members are extremes. When
double-checking with the individual dataset, there is indeed a household having 55 members.
Therefore, no values are deleted.
124
• B5 – age of the individu [individu dataset]. The age ranges from 0 to 99 years. According to
the World Bank (2005) the average life expectancy is 48.5 years and the average age 17
years (World Concern Website). The univariate statistics (MVA) individuals older than 58
years are outliers (2876 (5.3%) of the values). The threshold for the z-score is exceeded for
individuals above 94 years (62 values). The boxplot does not show discontinuity, therefore, it
is concluded to delete values above 94 years.
• Agemonth – age of the child [infant dataset]. For this controlling variable the non-declared
cases are marked with the number 99. Clearly, these values are considered as outliers –
supported with z-score above 4 – and are deleted from the dataset, so instead it has become
a missing value. No other extreme values were detected, since the maximum age was 59
months.
• I5a – weight of the child [infant dataset]. The analysis shows that there are 22 extreme high
values (above 25.5 kg). No values are deleted.
• I5b – length of the child [infant dataset]. The analysis shows that there are 27 extreme low
values (below 38 cm). No values are deleted.
Step 2 – Bivariate detection (only related continuous variables)
• Scatterplot of household size over total expenditures on health [household dataset]. Most
outliers are detected as household size is relatively small, with high expenditures on health.
However, no values are deleted.
0 10 20 30 40 50
Household size
0
100000
200000
300000
Tota
l exp
endi
ture
s on
hea
lth
Figure C-3: Scatterplot ‘household size’ * ‘total value of expenditures on health services’
• Matrix scatterplot of household size over total value of autoconsumption and value of
expenditures on food [household dataset]. Remarkable outliers are when household size is
125
relatively small, with high values of food consumption, and when household size is relatively
large with low values of food consumption. However, no values are deleted.
0 10 20 30 40 50
Household size
0,00
2500000,00
5000000,00
7500000,00
Valu
e of
exp
endi
ture
s on
nut
ritio
nal p
rodu
cts
0 10 20 30 40 50
Household size
0,00
2000000,00
4000000,00
6000000,00
Valu
e of
aut
ocon
sum
ptio
n of
nut
ritio
nal p
rodu
cts
Figure C-4 left: Scatterplot ‘household size’ * ‘value of expenditures on nutritional products’
Figure C-4 right: Scatterplot ‘household size’ * ‘value of autoconsumption of nutritional products’
• Scatterplot of household size over total revenue [household dataset]. Remarkable outliers are
when household size is relatively small, with high values total income, and when household
size is relatively large with low values of total income. However, no values are deleted.
0 10 20 30 40 50
Household size
0,00
10000000,00
20000000,00
Tota
l rev
enue
Figure C-5: Scatterplot ‘household size’ * ‘total income’
126
• Scatterplot of length of the child over weight of the child [infant dataset]. The graph does not
show clear extremes, therefore, no values are deleted.
25,0 50,0 75,0 100,0
Length in cm of the child
0,0
10,0
20,0
Wei
ght i
n kg
of t
he c
hild
Figure C-6: Scatterplot ‘length of the child’ * ‘weight of the child’
• Scatterplot of population density over reservoir density [household dataset]. The graph shows
one clear extreme, however, no values are deleted since both data are supported by literature.
0,00000000 0,01000000 0,02000000 0,03000000
Density [res/km2] province level
0,00
100,00
200,00
300,00
400,00
Popu
latio
n De
nsity
[#/k
m2]
Figure C-7: Scatterplot ‘reservoir density’ * ‘population density’
127
Missing data analysis
Any statistical data analysis should start with an examination of the missing data processes. Missing
data may cause interpretation issues since the available sample size is reduced or the statistical
results are biased (when not missing at random). The main concern of missing data analysis is to
identify the patterns and relationships underlying the missing data, in order to maintain as close as
possible the original distribution of values when any remedy is applied. The extent to which missing
data occur is the second concern.
First step in the analysis is to determine the type of the missing data, i.e. (completely) missing at
random or not missing at random. Dependent on the randomness, a remedy or imputation method can
be chosen. Second step is to determine whether the extent of missing data is low enough to not affect
the results, even if it operates in a non-random matter (Hair et al., 1998). Generally, missing data
under 10% for an individual case can generally be ignored, except when the missing data occur in a
specific non-random fashion. Also, the number of cases with no missing data must be sufficient for the
selected analysis technique if replacement values will not be substituted (imputed) for the missing data
(Hair et al., 1998).
Third step is choice of imputation method, if necessary. There are two options, either the missing data
process is classified as Missing Completely At Random (MCAR), or the process is non-random or
Missing At Random (MAR). Each of both options requires a different approach towards the imputation
of missing data. In case the missing data process is classified as MCAR the following remedies can be
applied (Hair et al., 1998):
• Complete case approach (listwise). This remedy includes only those observations with
complete data. Disadvantages are that this method reduces generalizability in case of any
non-random missing data, and it reduces the sample size.
• All-available approach (pairwise). This remedy includes only valid data and does not actually
replace the missing data, but instead imputes the distribution characteristics (means and
standard deviation) or relationships (correlations) from every valid value.
• Using replacement values. These are estimated values based on relationships among
variables in the sample.
In case the missing data process is classified as MAR or non-random only one remedy is available.
This set of procedures explicitly incorporates the missing data into the analysis, either through a
process specifically designed for missing data estimation, or as an integral portion of the standard
multivariate analysis. The first involves maximum likelihood estimation techniques that attempt to
model the processes possible, e.g. the iterative EM approach. The second involves the inclusion of
missing data directly into the analysis, defining observations with missing data as a select subset of
the sample. This approach is most applicable for dealing with missing values on the independent
128
variables of a dependent relationship. All observations having missing data are coded with a dummy
variable (cases with missing values have value one, other cases have value zero), then the missing
values are imputed by the mean substitution method. Finally, the relationship is estimated by normal
means. The dummy variable represents the difference for the dependent variable between those
observations with missing data and those observations with valid data. The dummy variable coefficient
assesses the statistical significance of this difference (Hair et al., 1998).
• For none of the selected indicators for poverty in the household dataset the percentage of
missing values exceeds 1% (let alone 10%). For simplicity we exclude cases listwise.
• For none of the selected indicators for poverty at individual dataset the percentage of missing
values exceeds 1% (let alone 10%). For simplicity we exclude cases listwise.
• Famine [infant dataset] – Selected indicators are ‘malnutrition’, ‘wasted’, ‘stunted’ and
‘underwei’. The missing values on ‘stunted’ and ‘underwei’ seem to be non-random. But the
cross-tab does not actually proof non-randomness. Since we use famine as an aggregate
measure we should investigate appropriate remedies. For simplicity we exclude cases
listwise.
Table C-8: Univariate statistics famine
Table C-9: Crosstabulation famine
Low height for age (stunted) * Low weight for age (underwei)
Low weight for age
1 2 Total
1 2615 1153 3768 Low height
for age 2 943 2847 3790
Total 3558 4000 7558
• Famine [infant dataset] – Selected indicators are ‘length’ and ‘weight’. None of the indicators
has over 10% missing data. For simplicity we exclude cases listwise.
N Missing
Count Percent
wasted 7645 565 6,9
stunted 7558 652 7,9
underwei 7558 652 7,9
malnutrition 7130 1080 13,2
129
Table C-10: Univariate statistics BMI
C.2 Assessing underlying assumptions
Normality
Normality refers to the shape of the data distribution for an individual continuous variable and its
correspondence to the normal distribution. If the variation from the normal distribution is sufficiently
large, all resulting statistical tests are invalid, because normality is required to use the F and t statistics
(Hair et al., 1998).
The severity of non-normality is based on two dimensions; the shape of the offending distribution and
the sample size. The shape of any distribution can be described by measures of kurtosis and
skewness. Kurtosis refers to the peakedness or flatness of the distribution compared with the normal
distribution. Skewness is used to describe the balance of the distribution (lack of symmetry). The
effect of sample size with regard to the normality of the distribution is that in larger samples the
detrimental effects of non-normality are reduced. Non-normality can have serious effects in small
samples (less than 50 cases), but the impact effectively diminishes when sample sizes reach over 200
cases (Hair et al., 1998).
The simplest diagnostic test for normality is a visual check of the histogram that compares the
observed data values with a distribution approximating the normal distribution. A more reliable
approach is the normal probability plot, which compares the cumulative distribution of actual data
values with the cumulative distribution of a normal distribution. The normal distribution forms a straight
diagonal line. Statistical tests for normality are e.g. Shapiro-Wilks test and a modification of the
Kolmogorov-Smirnov test, that calculate the level of significance for the differences from a normal
distribution (useless in samples with less than 30 cases, or over 1000 cases) (Hair et al., 1998).
The test of normality provides the Kolmogorov-Smirnov statistic. This assesses the normality of the
distribution of scores. A non-significant result (significance value of more than .05) indicates normality.
In case the significance value is .000 it is suggesting violation of the assumption of normality. This is
quite common in larger samples (Pallant, 2001).
Missing
N Count Percent
weight 7813 397 4,8
length 7744 466 5,7
underwei 7757 453 5,5
130
Homoscedasticity
Homoscedasticity refers to the assumption that dependent variables exhibit equal levels of variance
across the range of predictor variables. Homoscedasticity is desirable because the variance of the
dependent variable being explained in the dependence relationship should not be concentrated in only
a limited range of the independent values (Hair et al., 1998). Most cases of heteroscedasticity are a
result of non-normality in one or more variables. Thus, remedying normality may not be needed due to
sample size, but may be needed to equalize the variance.
Violation of the assumption of equal variances between pairs of variables (homoscedasticity) can be
detected by either residual plots or simple statistical tests. The most common application of graphical
tests is based on the dispersion of the dependent variable across the values of either the continuous
independent variables. Visual examination departures from an equal dispersion shown by shapes as
cones (small dispersion at one side of the graph, large dispersion at the opposite side), or diamonds
(a large number of points at the centre of the distribution). The statistical test for equal variance
dispersion assesses the equality of variances within groups formed by categorical variables. SPSS
provides the Levene test for homogeneity of variance, which measures the equality of variances for a
single pair of variables (Ho, 1996). If the Levene statistic is significant at the .05 significance level or
better, we reject the null-hypothesis that there are equal variances. If more than one continuous
variable is being tested the Box’s M test is applicable (Hair et al., 1998).
Linearity
Because correlations represent only the linear association between variables, non-linear effects will
not be represented in the correlation value. This omission results in an underestimation of the actual
strength of the relationship. The most common way to assess linearity is to examine scatterplots
(straight line depicts linearity) of the variables, and to identify any non-linear patterns in the data. An
alternative approach is to run a simple regression analysis and examine the residuals. These reflect
the unexplained portion of the dependent variable, thus, any non-linear portion of the relationship (Hair
et al., 1998).
Linearity can easily be examined by residual plots. For non-linear relationships, corrective action to
accommodate the curvilinear effects of one or more independent variables can be taken to increase
both the predictive accuracy of the model and the validity of the estimated coefficients (Ho, 1996).
131
Test for normality
• All continuous data of the infant dataset (age, length, weight) are not normally distributed
according to the Kolmogorov-Smirnov test, i.e. the significance of the test-statistic is zero.
However, the visual examination for the histogram and normal Q-Q plot reveals a tendency
towards normality for the indicators ‘length of the child’ and ‘weight of the child’.
• All data of the household dataset are not normally distributed according to the Kolmogorov-
Smirnov test, i.e. the significance of the test-statistic is zero. However, the visual examination
for the histogram and normal Q-Q plot reveals that some indicators (e.g. ‘household has
problems satisfying nutritional needs’, all indicators on ‘time to reach resource/service’) look
like they are normally distributed, but none of the histograms indicates normal distribution.
Therefore, it is assumed none of the indicators are normally distributed; hence non-parametric
tests should be applied.
• All data of the individual dataset are not normally distributed; all significance values of the
Kolmogorov-Smirnov test are zero and neither the visual examination reveals tendency
towards normality.
• Indicators ‘reservoir density’ and ‘population density’ are not distributed; all significance values
of the Kolmogorov-Smirnov test are zero and neither the visual examination reveals tendency
towards normality.
Test for homoscedasticity
We apply one-way ANOVA with ‘number of the province’ as factor.
• For all variables of the infant dataset the test of homogeneity of variances has a significance
value of zero, except for the variable ‘age of the child’. For all other variables we reject the
hypothesis that there exists homogeneity; hence there exists heterogeneity.
• For all variables of the household dataset the test of homogeneity of variances has a
significance value of zero. For all variables we reject the hypothesis that there exists
homogeneity; hence there exists heterogeneity.
• For all variables of the individual dataset the test of homogeneity of variances has a
significance value of zero. For all variables we reject the hypothesis that there exists
homogeneity; hence there exists heterogeneity.
• Indicators ‘reservoir density’ and ‘population density’ are assessed to be heterogeneous.
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Appendix D. Explorative Correlation Analysis
In this appendix gives the results of the explorative correlation analysis, the outcomes of which are
drawn up in Chapter 6. This appendix is not meant for exhaustive reading, moreover as detailed
background to the performed analysis. Therefore, it is given digitally on the enclosed CD-ROM.
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Appendix E. Explorative Regression Analysis
In this appendix gives the results of the explorative regression analysis, the outcomes of which are
drawn up in Chapter 7. This appendix is not meant for exhaustive reading, moreover as detailed
background to the performed analysis. Therefore, it is given digitally on the enclosed CD-ROM.
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Appendix F. Validation Tables
In this appendix gives the results of the technical validation of the correlation analysis, the outcomes of
which are drawn up in Chapter 9. This appendix is not meant for exhaustive reading, moreover as
detailed background to the performed analysis. Therefore, it is given digitally on the enclosed CD-
ROM.
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Appendix G. Interpretation Tables
In this appendix gives the results of the correlation and regression analysis for the rural and urban
case, the outcomes of which are drawn up in Section 8.2: Interpretation of the rural versus urban
environment. This appendix is not meant for exhaustive reading, moreover as detailed background to
the performed analysis. Therefore, it is given digitally on the enclosed CD-ROM.