The Geography of Welfare - World Bankdocuments.worldbank.org/curated/en/306301502329834751/...The Geography of Welfare in Benin, Burkina Faso, Côte d’Ivoire, and Togo Nga Thi Viet

The Geography of Welfare in Benin, Burkina Faso, Côte d’Ivoire, and TogoNga Thi Viet Nguyen and Felipe F. Dizon

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November 2017

The Geography of Welfare in Benin, Burkina Faso, Côte d’Ivoire, and Togo

Nga Thi Viet Nguyen and Felipe F. Dizon

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Photo CreditsCover page (top): © Georges TadonkiCover page (center): © Curt Carnemark/World BankCover page (bottom): © Curt Carnemark/World BankPage 1: © Adrian Turner/FlickrPage 7: © Arne Hoel/World BankPage 15: © Adrian Turner/FlickrPage 32: © Dominic Chavez/World BankPage 48: © Arne Hoel/World BankPage 56: © Ami Vitale/World Bank


iii

Acknowledgments

This study was prepared by Nga Thi Viet Nguyen and Felipe F. Dizon. Additional contributions were made by Brian Blankespoor, Michael Norton, and Irvin Rojas. Marina Tolchinsky provided valuable research assistance. Administrative support by Siele Shifferaw Ketema is gratefully acknowledged.

Overall guidance for this report was received from Andrew L. Dabalen.

Joanne Gaskell, Ayah Mahgoub, and Aly Sanoh pro-vided detailed and careful peer review comments.

The team greatly benefited from the valuable support and feedback of Félicien Accrombessy, Prosper R. Backiny-Yetna, Roy Katayama, Rose Mungai, and Kané Youssouf. The team also thanks Erick Herman Abiassi, Kathleen Beegle, Benjamin Billard, Luc Christiaensen, Quy-Toan Do, Kristen Himelein, Johannes Hoogeveen, Aparajita Goyal, Jacques Morisset, Elisée Ouedraogo, and Ashesh Prasann for their discussion and comments.

Acknowledgments


v

Abbreviations and Acronyms

AIDS Acquired Immune Deficiency Syndrome

CGIAR Consultative Group for International Agricultural Research

CMU Country Management Unit

ECOWAS Economic Community of West African States

EMC Continuous Multi-Sectoral Survey (Enquête Multisectorielle Continue)

EMICOV Integrated Modular Household Well-Being Survey (Enquête Modulaire Intégrée sur les Conditions de Vie des Ménages)

ENV Household Living Standards Survey (Enquête sur le Niveau de Vie des Ménages)

FAO Food and Agriculture Organization

PFR Rural Land Plans (Plans Fonciers Ruraux)

FEWSNET Famine Early Warning Systems Network

GADM Global Administrative Area Database

GDP Gross Domestic Product

HI Herfindahl Index

HIV Human Immunodeficiency Virus

ICT Information and Communication Technology

IFPRI International Food Policy Research Institute

LMI Low and Middle Income

NEG New Economic Geography

OECD Organisation for Economic Co-operation and Development

PAD Project Appraisal Document

PFR Rural Land Use Plan (Plan Foncier Rural)

PID Project Information Document

PPP Purchasing Power Parity

QUIBB Basic Well-Being Indicator Questionnaire (Questionnaire des Indicateurs de Base du Bien-être)

R&D Research and Development

SCD Systematic Country Diagnostic

SHIP Survey-Based Harmonized Indicators Program

SMS Short Message Service

SSA Sub-Saharan Africa

TFP Total Factor Productivity

UN United Nations

WAEMU West African Economic and Monetary Union

WDI World Development Indicator

Abbreviations and Acronyms


viiTable of Contents

Table of Contents

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Abbreviations and Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .v

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

Chapter 1: Location and Prosperity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1Motivation and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Regional Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Chapter 2: Geography of Welfare—Three Building Blocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Natural Endowment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Agglomeration Economies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Market Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

Chapter 3: Spatial Disparities in Welfare and Poverty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Leading and Lagging Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

Poverty Rates, Poverty Mass, and Poverty Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Access to Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Quality of Life and Characteristics of Poor People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Chapter 4: Geographical Differences in Agricultural Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32Employment in Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Agricultural Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Assets, Inputs, and Output Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Chapter 5: Putting It All Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Relationship between Welfare and Agricultural Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Three Sets of Explanatory Variables for Three Building Blocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Correlates of Welfare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51

Correlates of Agricultural Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Chapter 6: Policy Recommendations and Further Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56Urbanization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Agricultural Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Fiscal Transfers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Safety Net Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Limitations and Further Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63


The Geography of Welfare in Benin, Burkina Faso, Côte d’Ivoire, and Togoviii

Appendix A: Agroecological Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Appendix B: Market Accessibility Index—Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Appendix C: Extra Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Appendix D: Summary of Findings on Agricultural Activities across Countries, by Zone . . . . . . . 83

Appendix E: Agricultural Data—Notes on Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Maps, Tables, Figures, and BoxesMap 1.1: Climate Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Map 2.1: Abundant in Terms of Precipitation Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Map 2.2: Agroecological Zones, by Country . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Map 2.3: Concentration of Agglomeration Economies in the South . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

Map 2.4: Road Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Map 2.5: Concentration of High Market Access in the South, around Capitals and Economic Capitals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

Map 3.1: Cluster of Leading Areas in the South, around the Capitals and the Economic Capitals, or along Country Borders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Map 3.2: In Three out of Four Countries, the North is Remarkably Poorer than the South . . . . . . . . .21

Map 3.3: Lower Poverty Rates in Areas around the Capitals and the Economic Capitals, and along the Country Border. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

Map 3.4: High Poverty Density in and around the Capitals and the Economic Capitals, and in the South. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Map 3.5: High Variation in Access to Public Services across Space (except cellphone coverage) . . . 25

Map 3.6: Low Service Coverage in Areas with High Poverty Incidence . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Map 3.7: Higher Diversity in Food Consumption Basket and Lower Food Share from Own Production in the South . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Map 3.8: Variation in Key Food Consumed across Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Map 4.1: Employment in Agriculture Relative to Other Sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Map 4.2: Cash Crops across Agroecological Zones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Map 4.3: Cash Crops across Côte d’Ivoire’s Agroecological Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Map 4.4: Maize Yields across Agroecological Zones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

Map 4.5: Cash Crop Yields across Agroecological Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Map 4.6: Use of Inputs and Farm Land across Agroecological Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Map 4.7: Land Tenure Security across Agroecological Zones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Map 4.8: Sale of Agricultural Produce. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47

Table 1.1: Economic per Capita Growth by International Standards. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Table 1.2: Geographical Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Table 3.1: Better Housing Conditions for Poor Households in Urban Areas or in Favorable Agroecological Zones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Table 3.2: Fewer Family Members, Lower Dependency Rates, More Likely to be Female-Headed Households, and Less Likely to Have No Education among Poor Households in Urban Areas and Favorable Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Table 5.1: Statistical Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51


ixTable of Contents

Table 5.2: Factors Associated with Spatial Differences in Poverty: Coastal Location, Population Density, and Market Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Table 5.3: Role of Geographical Differences in Agricultural Productivity, Natural Endowments (temperature, latitude, elevation, coastal location), and Spending on Fertilizer . . . . . . . . . . . . . . . . . . 54

Figure 2.1: Higher Share of Population Living in Urban Areas but Slower Urban Population Growth by African Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Figure 3.1: Large Wealth Gap between Leading and Lagging Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

Figure 3.2: Many Leading Areas in Low-density Locations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Figure 3.3: Low Market Access in Many Leading Areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Figure 3.4: Majority of the Poor Living in Low-density Areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Figure 3.5: Large Gaps in Public Service Coverage between the Most Sparsely and Most Densely Populated Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Figure 4.1: Percentage of Population Engaged in Agriculture, by Poor and Nonpoor . . . . . . . . . . . . . . 33

Figure 4.2: Crops Grown by Poor and Nonpoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Figure 5.1: Correlation between Poverty and Agricultural Productivity. . . . . . . . . . . . . . . . . . . . . . . . . . 50

Box 4.1: Summary of the agriculture sector based on various World Bank documents (Project Information Document [PID], Project Appraisal Document [PAD], and Systematic Country Diagnostic [SCD]) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Box 4.2: Background on land reform in Benin, Côte d’Ivoire, and Burkina Faso . . . . . . . . . . . . . . . . . . 45


xiExecutive Summary

Executive Summary

West Africa is at the heart of Africa’s transforma-tion. With a gross domestic product (GDP) growth rate of more than 5 percent annually, it is the fast-est growing region in the continent. Yet poverty rates remain high, even by African standards. In the four West African countries covered in this report, namely Benin, Burkina Faso, Côte d’Ivoire, and Togo, nearly half of the population lives on less than US$1.90 a day at 2011 purchasing power parity (PPP). This means over 25 million people live in extreme poverty.

How is it possible that the subregion’s high eco-nomic growth cannot translate into higher levels of prosperity? The answer lies in where one looks, as national averages often mask large disparities at subnational levels.

Recent literature on the new economic geography suggests that within-country disparities may be a natural outcome of the development process. As a country develops, economic activity clusters in regions endowed with more favorable agroecologi-cal conditions, more abundant natural resources deposits, or simply a better location. More eco-nomic opportunities in turn attract more people in search of jobs, which consequently increases pop-ulation density in one area over another. Arguably, the higher concentration of people and economic activity leads to economies of scale. Such ben-efits can be further enhanced with the existence of market access for products, labor, and ideas, which continues to boost these regions’ income and attractiveness to people and firms. This vir-tuous cycle of development makes it difficult for poor regions to catch up.

This report aims to assess the spatial disparities in economic development along four important dimensions:

1. It provides stylized facts of the underly-ing forces behind within-country inequality, namely natural endowment, agglomeration economies, and market access. These are the three building blocks of the economic geogra-phy literature;

2. It examines spatial disparities in welfare and poverty. As the agricultural sector is a

cornerstone of the economy in this subregion, the report explores geographical differences in agricultural activity;

3. It quantifies the roles of natural endowment, agglomeration economies, and market access in determining the spatial distribution of wel-fare and agricultural productivity; and

4. It suggests a number of policy guidelines that may help improve shared prosperity across space.

However, we acknowledge that since poverty is a multidimensional concept, many other fac-tors could potentially contribute to the observed within-country inequality, yet they are not cov-ered by the scope of this study. These may include social elements such as ethnicity, nutrition, and health; economic conditions such as prices and markets; and political dimensions such as institu-tions and conflict.

A Tale of Two RegionsIn terms of agroecological endowment, two dis-tinct groups emerge within the subregion. Being the most northerly and the only landlocked coun-try in the set, Burkina Faso has a noticeably differ-ent agroecosystem, being generally drier and less fertile. It also has the most dispersed population in the subregion. The rest—Benin, Côte d’Ivoire, and Togo—are coastal and located at the same latitude and therefore share similar agroecological endowments. Of the four countries, Togo has the highest population density.

Within each of the three coastal countries, there also exists a tale of two regions, with the South having more favorable conditions for agricultural activities, access to the sea, and higher popula-tion density than the North. Thus, the pattern of market access is similar to that for natural endow-ment and agglomeration, with higher levels of market access concentrating in the South.

The picture is slightly different in landlocked Burkina Faso. While it shares a similar geographi-cal pattern of agroecological characteristics as


The Geography of Welfare in Benin, Burkina Faso, Côte d’Ivoire, and Togoxii

the one found in the neighboring countries (i.e., North vs. South), its population and market access concentrate only in the Central region, home of the two largest cities: Ouagadougou and Bobo-Dioulasso.

A Tale of Two EconomiesIn the three coastal countries, the North is mark-edly poorer and has a larger share of population employed in the agriculture sector than the South. In Togo particularly, poverty in the far North may be more than three times as high as in the far South. However, the pattern of poverty is reversed in Burkina Faso, in part because of the possession of livestock among northern residents.

The spatial distribution of crops grown also var-ies between the North and South. Cash crops, or crops produced for commercial value, are more prevalent in the South (except cotton) in terms of the proportion of farmers growing them. However, the geographical coverage of cotton production is quite different and resembles a belt cover-ing the southern parts of Burkina Faso and the northern parts of Benin, Côte d’Ivoire, and Togo. Interestingly, this cotton belt overlaps with areas of higher agglomeration distant from the capital city of each country.

Looking beyond monetary poverty and agricultural activity, the quality of life of the poor as measured by the extent of food intake diversification, access to basic services, and housing conditions increases significantly from North to South. In other words, given two poor individuals with similar incomes, the one living in the South enjoys a more diverse food basket, has a higher chance of having access to electricity and sanitation, and has a higher probability of living in a house with either a con-crete roof or brick walls than his fellow citizen in the North. These patterns are consistent across all four countries.

At subnational levels, differences in welfare are even more pronounced between leading and lag-ging locations.1 A location is defined as “leading” if per capita consumption for an average household living there is higher than the national average. A

1 Subnational levels consist of communes in Benin, provinces in Burkina Faso, departments in Côte d’Ivoire, and prefectures in Togo.

typical household in a leading area may consume as much as seven times more overall than a similar household in a lagging area. This gap is highest in Benin and lowest in Burkina Faso.

Notably, many of the leading areas have not yet maximized the benefits of agglomeration econ-omies. This observation is especially clear in Burkina Faso and Côte d’Ivoire, where approxi-mately half of the leading regions are located in low-density areas. This suggests scope for greater concentration of economic activities and labor in these locations in order to further take advan-tage of economies of scale and boost economic development.

Moreover, in each country, there exists geographi-cal pockets of poverty that may be resistant to policy-induced changes. These lagging areas are characterized by a combination of high poverty rates and a low number of poor people per square kilometer. As a result, the unit cost of a poverty targeted program may be extremely high in these areas. Given budget constraints, the government may not be able to reach this population group.

Spatial Disparities ExplainedThere has been a long ongoing debate in the eco-nomic geography literature on whether a location’s levels of per capita income and other economic dimensions are determined by geographical and ecological variables. Many researchers have pro-vided evidence supporting the view that such links are strong, while others have argued that the role of geography in explaining spatial patterns of per capita income operates through various direct channels (e.g., productivity and trade) or indirect channels (e.g., choice of political and economic institutions), with little direct effect of geography on incomes.

How therefore does this play out in the case of Benin, Burkina Faso, Côte d’Ivoire, and Togo?

As it turns out, except for being coastal or land-locked, agroecological characteristics do not appear to be directly associated with a location’s per capita income. The relationship between geog-raphy and welfare is indeed being mediated by agglomeration economies and market access. In other words, locations favorable to growth


xiiiExecutive Summary

will either attract people or experience stronger population growth and at the same time receive investments in infrastructure. Thus, when con-trolling for population density and market access, the correlation between welfare and geographical variables (except for being located along the coast) is no longer significant.

If natural endowment plays any role in explain-ing spatial disparities in welfare, the key factor is location near the coast. Given two locations with the exact same population density and market access, the one on the coast is 21 percent richer than the one located inland. The fact that the eco-nomic benefits of coastlines remain strong (i.e., they have not been arbitraged away by migration or increased market access) reveals the untapped potential for economic development provided by access to international trade for the three coastal countries (Benin, Côte d’Ivoire, and Togo).

However, the story is quite different when look-ing at agricultural productivity measured by maize yields. This report focuses on maize yields because this food crop is fairly prevalent across all four countries and is found in most areas in each country. This allows for some degree of comparability for yields across zones and across countries.

What is notable is the persistence of the correla-tion between geography and agricultural produc-tivity regardless of whether population density, market access, or farm inputs are taken into account. In contrast to the new economic geog-raphy literature suggesting that agglomeration economies and market access can help farmers take advantage of better prices, a wider selection of agricultural inputs, and better markets for har-vested crops, this link is weak in the subregion. This finding implies that there may in fact be two types of agriculture: a subsistence agriculture, whereby most crops are cultivated for home consumption and where investments are less sensitive to mar-ket access, and a commercial agriculture, which might concentrate along coastlines and benefit from higher investment in inputs.

What Can Be Done?Within-country disparities can be a potential source of tensions between lagging and leading locations and may affect the country’s overall

growth and political stability. How can our find-ings help policy makers reduce geographical dif-ferences in welfare while boosting growth? Based on our analysis, we propose four broad policy recommendations:

1. Urbanization: We find that many of the lead-ing areas have not yet maximized the ben-efits of agglomeration economies, especially in Burkina Faso and Côte d’Ivoire. Based on the new economic geography literature, there is scope for increasing concentration in eco-nomic activities and labor in these areas to further take advantage of economies of scale and boost economic development. However, it is important to consider complementary policies to urbanization, including removing barriers to labor mobility so that people can migrate to leading areas where demand for labor and productivity are higher, and invest-ing in urban infrastructure and the provision of public services to accommodate a poten-tially larger number of users.

2. Increasing agricultural productivity: Not all rural families can move to urban locations. For those staying in the agriculture sector in rural areas, policy makers should consider improving welfare by increasing agricultural productivity. Potential areas of improvement include land tenure, irrigation, use of farm inputs such as fertilizer, and research and development.

3. Fiscal budget transfers: Geographical pock-ets of poverty exist where the costs of reach-ing the poor are very high. These areas are characterized by a combination of high pov-erty rates and low poverty density. Another set of lagging areas with little prospect of growth consists of those with unfavorable agroecological characteristics and limited opportunities to diversify into nonagricul-ture sectors. Our quantitative analyses show a persistent link between agroecologi-cal endowment and agricultural productivity regardless of whether agglomeration, market access, or farm inputs are taken into account. Our findings imply that some lagging areas may not be able to improve their welfare after all. This may call for pro-poor fiscal transfers through a system of inter-region transfers to ensure equity across leading and lagging areas.


The Geography of Welfare in Benin, Burkina Faso, Côte d’Ivoire, and Togoxiv

4. Safety net programs: Not all poor people, especially the vulnerable, can benefit from the policies proposed above. Thus, the need to maintain strong safety net programs target-ing the poor and vulnerable remains strong. New technologies such as e-vouchers and mobile transfers make it possible for such programs to reach targeted beneficiaries in

low-density areas in a cost-effective way. Moreover, safety net programs should be part of an overarching poverty reduction strategy consisting of interacting with and working alongside urban policy, agricultural produc-tivity boosting programs, and other policies aimed at eradicating poverty and reducing vulnerability.


Chapter 1

Location and prosperity

00000_Geography_Welfare-English.indd 1 12/7/17 8:59 AM

The Geography of Welfare in Benin, Burkina Faso, Côte d’Ivoire, and Togo2

Motivation and ObjectivesLocation is the most critical predictor of a per-son’s welfare (World Bank, 2009). As of today, a child born in Togo is expected to live nearly 20 years less than a child born in the United States. More-over, the child will earn a tiny fraction—less than 3 percent—of what her American counterpart will earn (World Bank, 2017).

Such disparities in income and living standards within a country are just as unsettling . An urban inhabitant in Togo’s capital, Lomé, has a 16 percent chance of being poor, and a 90 percent chance of having access to electricity. However, these probabilities are reversed for a person from a rural district in far northern Oti prefecture, where resi-dents have an 80 percent chance of falling into poverty and a mere 13 percent chance of having access to electricity.

As pointed out in the World Development Report—Reshaping Economic Geography (World Bank, 2009), such within-country disparities can pose a major challenge for policy makers as they present a potential source of increasing tensions between poorer and richer areas. Moreover, if these spatial inequalities persist or widen, they can potentially affect a country’s future growth and political stability.

Spatial differences in economic development have long been the subject of study, with a history dating back to the 4th century b.c. and expanding after World War II due to uneven post-war economic recovery and development. Until the 1980s, the study of economic geography was under scrutiny because it undermines the notion of equal opportunity among individuals. How-ever, over the past decades, the field has regained attention in mainstream development debates thanks to new theories of economic growth and empirical research in this field (Hausmann, 2001).

The recent literature on the new economic geog-raphy (NEG) implies that within-country dispari-ties may be a natural outcome of the development process, and once established, can be persistent and insensitive to policy-induced changes (see Fujita, Krugman, and Venables, 1999; Puga, 1999; Fujita and Thisse, 2002; World Bank, 2009). Thus, from a policy maker’s perspective, the success of any government policy aimed at improving shared prosperity across locations crucially depends on what drives the observed spatial inequality.

This study covers four countries in West Africa under the Country Management Unit (CMU) AFCF2, namely Benin, Burkina Faso, Côte d’Ivoire, and Togo, and makes use of four recently collected household consumption surveys . Given the data limitations, we focus on a static analysis of economic geography in this subregion. While our discussion is centered around within-country inequalities as these are more relevant to each respective government, we touch upon some aspects of cross-country differences in order to provide a regional con-text. We also emphasize that our analysis focuses mainly on the spatial distribution of welfare and poverty.

To understand the driving forces behind differ-ences of welfare and poverty across space, we base our analysis on the NEG literature and high-light its three building blocks: natural endowment, agglomeration economies, and market access. In addition, we explore the spatial distribution of sev-eral key elements that intertwine with poverty, including economic activity, agricultural produc-tivity, household demographics, and access to ser-vices (Banerjee and Duflo, 2007). We acknowledge that since poverty is a multidimensional concept, many other factors could potentially contribute to observed within-country inequality, yet are not covered by the scope of this study. These may include social elements such as ethnicity, nutri-tion, and health; economic conditions such as prices and markets; and political dimension such as institutions and conflict.

Our objectives are:

1. To provide stylized facts relevant to the three building blocks of the economic geography literature: natural endowment, agglomeration economies, and market access;

2. To examine static spatial disparities in welfare and poverty together with relevant develop-ment indicators such as poor household demographics, access to services, economic activity, and agricultural productivity;

3. To quantify the roles of natural endowment, market access, and agglomeration economies in determining the spatial distribution of wel-fare; and

4. To suggest a number of policy guidelines that may help improve shared prosperity across space.


Location and Prosperity 3

The report is organized as follows. The rest of Chapter 1 provides a glance at the regional con-text (i.e., where these countries stand in the global economy) as well as an overview of the data used. Chapter 2 introduces the three building blocks of the economic geography literature: natu-ral endowment, agglomeration economies, and market access. Chapter 3 presents stylized facts about the subregion’s spatial disparities, focus-ing on welfare, poverty, access to services, and profiles of the poor. Chapter 4 provides details of the geographical distribution of agricultural activity given the important role it plays in the subregion. Chapter 5 uses the NEG framework to explore correlates of the observed inequalities across space. Finally, Chapter 6 concludes with a policy discussion.

Regional Context The majority of the world’s extreme poor, those living on less than US$1 .90 a day at 2011 pur-chasing power parity (PPP), are concentrated in Sub-Saharan Africa (SSA) . Within the conti-nent, West Africa is home to some of the poorest nations, where approximately half of the popula-tion lives in poverty, and over three quarters of

the population have no access to improved sanita-tion (World Bank, 2017). Along key factors such as institutional quality, labor productivity, and human capital, the region’s geographical characteristics are often considered to be a key constraint on its economic development.

The four West African countries covered in this report (Benin, Burkina Faso, Côte d’Ivoire, and Togo) lie mostly in the tropical savannah climate area (Map 1.1), a common geographic disadvan-tage identified among countries lagging behind in economic development. Hausmann (2001) shows that, on average, annual economic growth rates in tropical nations are between one-half and a full percentage point lower than in temperate coun-tries. In addition, countries located in tropical areas often show more skewed income distribu-tion and poorer health conditions than their non-tropical counterparts.

As shown in Table 1 .1, these four West African countries are poor by international standards . Not only is their per capita GDP lower than the average of Low and Middle Income (LMI) countries, it is also lower than African averages. Even though these economies, especially for Burkina Faso and Côte d’Ivoire, have grown at the impressive rate of approximately 5 percent per year, annual growth in per capita GDP still falls behind those of their

MAP 1 .1 Climate Classification

Source: Kottek et al., 2006.



peers, in part because of relatively fast population growth.

A related fact is the high levels of population density across all four countries . Within this subregion, Togo is the densest, with the number of people per square kilometer being about three times higher than SSA averages, and twice the average of LMI countries. Population density levels in the other three countries are also well above international averages.

Interestingly, the subregion’s relatively high population density and high GDP density (defined as GDP per square kilometer), which are often considered favorable elements for eco-nomic development (World Bank, 2009), do not translate into higher levels of prosperity . In three out of four countries—Benin, Burkina Faso, and Togo—about half of the population lives in extreme poverty, a rate that is even higher than African averages. Even in Côte d’Ivoire, the only middle-income country in the group, with a per capita GDP

above US$3,500 PPP per year, one in every four persons still lives on less than US$700 annually. A possible explanation for this mismatch between population density and economic prosperity may be the vast unevenness in economic development within a country’s border. Chapter 3 will explore this aspect further.

DataAs discussed above, national level comparisons mask large disparities at subnational levels. This section describes the data used to explore within-country disparities in welfare, poverty, and other development indicators.

Statistical Data

This study makes use of four recently collected household consumption surveys: the Benin Inte-grated Modular Household Well-Being Survey

TABLE 1 .1 Economic per Capita Growth by International Standards

Levels

GDP, 2015 (PPP,

$billion)

GDP per Capita,

2015 (PPP $)

GDP Density 2015 ($/km2,

thousands)Population

(million)

Population Density

(ppl/km2)

$1 .90 Poverty Rate (%)

Benin 22 2,057 198 11 96 67.8Burkina Faso 31 1,696 112 18 66 43.7Côte d’Ivoire 80 3,514 251 23 71 27.9Togo 11 1,460 196 7 134 49.2Sub-Saharan Africa

3,718 3,714 157 1,001 42 41.0

Low & Middle Income

61,047 9,911 645 6,159 65 12.6

Annual Growth (2010–2015)(percent) (percent) (percent) (percentage point)

Benin 4.3 1.5 2.7 3.67Burkina Faso 5.9 2.8 3.0 –2.32Côte d’Ivoire 5.8 3.3 2.4 –0.16Togo 4.8 2.0 2.7 –1.26Sub-Saharan Africa

4.3 1.5 2.8 –1.56

Low & Middle Income

5.2 3.9 1.3 –1.93

Source: World Bank, 2017.


Location and Prosperity 5

(Enquête Modulaire Intégrée sur les Conditions de Vie des Ménages, EMICOV 2015), the Burkina Faso Continuous Multi-Sectoral Survey (Enquête Multi-sectorielle Continue, EMC 2014), the Côte d’Ivoire Household Living Standards Survey (Enquête sur le Niveau de Vie des Ménages, ENV 2015), and the Togo Basic Well-Being Indicator Questionnaire (Questionnaire des Indicateurs de Base du Bien-être, QUIBB 2015).

While earlier household consumption surveys for each country are also available (i.e., Benin EMICOV 2010, Burkina Faso GHS 2009, Côte d’Ivoire ENV 2011, and Togo QUIBB 2011), their lack of compa-rability with more recent surveys limits our capa-bility to observe changes in geographical patterns of welfare and poverty over time.2 Therefore, we focus on a static analysis of economic geography in this subregion.

We also take advantage of a number of harmo-nized data sets from the Survey-Based Harmo-nized Indicators Program (SHIP) produced by the World Bank, which aim to compile in a consistent format consumption aggregates and other house-hold indicators such as demographics and assets from household budget surveys in the SSA sub-region. However, our four surveys were collected only recently and have not been fully processed in SHIP at the time of the writing.

In general, household consumption surveys, including those used in this report, are designed to produce welfare measures and development outcomes at the national level and, in some cases, at the first subnational level (e.g., regions). Dis-aggregating the data into lower administrative unit levels may pose two risks: lack of represen-tativeness, and imprecise estimates.3 On the one hand, households who live in a small geographical area and were interviewed for the surveys may not represent the wider population. On the other, the limited number of households reporting the information of interest leads to higher odds of ending up with missing information (e.g., access to improved toilets), or, when information is avail-

2 Household consumption surveys are considered comparable if all three of the following criteria are consistent across surveys: (i) the sample size is nationally representative; (ii) the data were collected during the same period; and (iii) the surveys rely on the same report-ing instrument and reporting period (Beegle et al., 2016).

3 As a rule of thumb, estimates are considered sufficiently precise if the relative standard error (measured as standard error divided by the mean) is less than 10 percent.

able, wide variance (e.g., outliers). To arrive at a balance between data and administrative coher-ence such that conclusions can be useful from a policy and development perspective, we focus on second subnational level data (i.e., communes for Benin, provinces for Burkina Faso, departments for Côte d’Ivoire, and prefectures for Togo). It is important to note that even at these subnational levels, we can limit but not entirely eliminate the two shortcomings discussed above.

We rely on the agriculture and land modules con-tained in these surveys to capture the geography of agriculture from the perspective of households. Unlike administrative data on agricultural pro-duction, our approach is more likely to be biased toward smallholder agriculture instead of large commercial farms. While livestock is a major source of income for households in Sahel regions, the data are not available across all four countries in the subregion, and this dimension is therefore excluded from our analysis.

Geographic Data

To construct a market access index, we used the road network provided by DeLorme (2015). While an ideal index should capture access to all modes of transportation (e.g., air, coast, rail, etc.), we did not have access to such data at the time of writ-ing. Thus, our index is limited to reflecting domes-tic market access to roads.

For our multivariate regressions in Chapter 5, we constructed six continuous agro ecological variables at the administrative unit level for each country:4 temperature, precipitation, soil quality, latitude, elevation, and ruggedness. The temperature variable is a long-run (1960–1990) annual average taken from Hijmans et al. (2005), and precipitation is taken from HarvestChoice/ International Food Policy Research Institute (IFPRI) and University of Minnesota (2016), which mea-sures annual average over the period 1960–2014. Soil quality is measured as organic carbon soil content (fine earth fraction) at 60–100cm depth taken from HarvestChoice/IFPRI and University of Minnesota (2016). Ruggedness is based on Nunn and Puga (2012). Elevation, given in meters, is taken from Isciences (2008).

4 Communes for Benin, provinces for Burkina Faso, departments for Côte d’Ivoire, and prefectures for Togo.



Except for HarvestChoice/IFPRI and University of Minnesota (2016), these data consist of a 30-arc-second grid solution, equivalent to 1 3 1 km, thus making it possible to aggregate the data to the administrative unit level. For its part, grid resolu-tion for HarvestChoice/IFPRI and University of

Minnesota (2016) data is 5-arc-minute, or roughly 10 3 10 km. Thus, some smaller administra-tive units will not have data. For such areas, we impute a given variable as the average of that vari-able across its neighboring administrative units (Table 1.2).

TABLE 1 .2 Geographical Data Sources

Data SourcesAdministrative boundaries National statistical services, Global Administrative Area

Database (GADM)Agroecological zones (Benin, Togo) Food and Agriculture Organization (FAO) of the United

Nations, Togo’s Ministry of the Environment and Forestry Resources

Climate zones Kottek et al. (2006)Elevation Isciences (2008)Livelihood zones (Burkina Faso, Côte d’Ivoire)

Famine Early Warning Systems Network (FEWSNET), AGRHYMET

Population density at subnational levels

National statistical services

Precipitation HarvestChoice/IFPRI and University of Minnesota (2016)Road network DeLorme (2015)Ruggedness Nunn and Puga (2012)Soil quality HarvestChoice/IFPRI and University of Minnesota (2016)Temperature Hijmans et al. (2005)


ChAPTER 2

Geography of Welfare— Three Building Blocks



This chapter defines three spatial scales as step-ping stones for the spatial analysis of welfare and agricultural activity to follow in Chapters 3 and 4. These scales are based on the three building blocks found in the economic geography literature.

We start with the building block found in the tra-ditional economic geography literature: natural endowment. A region is expected to be better off than others if it is endowed with a more favorable agroecosystem, more natural resources, or simply a better location. We then add two main elements from any NEG model as well as from any modern theory of location:5 agglomeration economies (Mar-shall, 1920; Krugman, 1991; Porter, 1998; Hender-son, 2014), and access to markets, that is, markets for goods, labor, and ideas (Smith, 1776; Fujita and Thisse, 2002). Finally, we assess what these three elements look like in each country: Benin, Burkina Faso, Côte d’Ivoire, and Togo.

The core idea of NEG is that a location is not an isolated geographical area but is affected by its rela-tionships or connections with neighboring locations. Agglomeration economies ensure that economic activity is concentrated in areas that are better located to benefit from increasing returns to scale. Access to markets then captures the levels of trans-portation costs and the degree of labor mobility between locations. High levels of market access (i.e., free movement of goods and people across space) combined with increasing returns to scale will cre-ate spatial disparities in economic activities, and therefore poverty. In this study, we do not cover tangible costs such as road tolls or legal require-ments for residency or non-tangible costs such as discrimination or ethnic or religious differences that may be associated with market access.

Our main findings are:

1. In all four countries, there seems to exist a tale of two regions—North vs. South for the coastal countries (Benin, Côte d’Ivoire, and Togo), and Center vs. the Rest for landlocked Burkina Faso.

2. Within a country, the North generally has the least favorable agroecological

5 For a detailed discussion of NEG, see, for example, Fujita, Krug-man, and Venables (1999); Fujita and Thisse (2002); Baldwin et al., (2003); Brakman, Garretsen, and Van Marrewijk (2009); Combes, Mayer, and Thisse (2008). For major NEG models, see Krugman (1991); Krugman and Venables (1995); Venables (1996); and Puga (1999).

characteristics for agricultural activities, while the South is the most endowed.

3. Similarly, agglomeration economies clus-ter mainly in the South . The exception is landlocked Burkina Faso, where the densest population is in the central region, home of its capital, Ouagadougou.

4. Market access follows the same pattern . Most land areas in the North have very limited access to markets, while high levels of market access concentrate around the capitals, the economic capitals, and along the coast in the South.

Natural Endowment In this section, we examine each country’s natu-ral endowments (i.e., agroecological endowment), standardize existing classifications of agroeco-logical zones, and regroup these into four broad zones ranging from least favorable (Zone 1) to most favorable (Zone 4). This recategorization allows us to overlay patterns of welfare, poverty, agricultural productivity, and economic activity over agroecological zones in a consistent and sys-tematic manner across countries.

Agriculture plays a key role in the four coun-tries of interest . This sector generates about a third of GDP value each year. In Togo, nearly half of GDP in 2015 came from agriculture alone. In addition, the agriculture sector provides jobs to about half of the workforce (World Bank, 2017). In an agricultural economy, agroecological endow-ments determine not only what crops are planted or livestock is raised in each location but also what returns can be obtained for any crop harvested or livestock herded. These consequently affect the region’s agricultural productivity and welfare (see, for example, Diamond, 1997).

Compared to the rest of SSA, West Africa is relatively abundant in precipitation, with most of its land in the savannah and grassland areas, and it benefits from a tropical climate (Map 2.1, Map 2.2, and Appendix A). Within the subregion, two distinct groups emerge. Being the northern-most and the only landlocked country, Burkina Faso has noticeably different agroecological char-acteristics, being generally drier and less fertile. The rest—Côte d’Ivoire, Benin, and Togo—are


Geography of Welfare—Three Building Blocks 9

coastal and located at the same latitude, thus sharing similar climates and precipitation levels.

A clear picture immediately comes to the fore-front: In all four countries, Zone 1 is in the north, while Zone 4 is concentrated in the south . Inter-estingly, Zones 4 in Benin, Côte d’Ivoire, and Togo are also coastal, which is generally considered an advantage for economic development (Map 2.2). Overviews of each zone are detailed below.

Zone 1 represents the Sahelian and Sudanian savannah areas, the driest of all four zones within a country. There is only one rainy season, which is also relatively short. However, precipitation levels vary considerably across countries, starting from the lowest in Burkina Faso to the highest in Côte d’Ivoire.

• Burkina Faso: Zone 1 is typical Sahelian, with four months of rainfall per year, accumulating approximately 400–500 mm. The soil is sandy and of poor quality.

• Côte d’Ivoire: While the annual precipitation level of 1,000–1,100 mm for five months of the year is lower than those in other zones within Côte d’Ivoire, it is the highest when compared

with Zone 1 in Burkina Faso, Benin, and Togo. The area is characterized by savannah, with a mixture of woodland and grassland.

• Togo: This zone has very similar characteristics in terms of savannah landscapes and average amounts of rainfall, albeit slightly lower, compared to Zone 1 in Côte d’Ivoire.

• Benin: The climate is Sudano-Sahelian with a unimodal rainfall pattern of 700–1,000 mm per year. The area is marked with a vast expanse of arable land in ferrosol soil.

Zone 2 is also characterized by unimodal rainfall patterns, albeit with slightly higher precipitation levels and a longer rainy season than in Zone 1. The capital of Burkina Faso, Ouagadougou, is located in this zone.

• Burkina Faso: This Sudano-Sahelian zone receives about 600–800 mm of rainfall per year. However, it has poor quality soil and faces serious land erosion problems.

• Côte d’Ivoire: Vegetation types are the same as in Zone 1, i.e., woodlands, grassland, and savannah. However, precipitation levels are higher, at 1,100–1,300 mm per year. The zone

MAP 2 .1 Abundant in Terms of Precipitation Levels

Source: Funk et al., 2015.



is typically characterized by flat terrain and ferrosol soil.

• Togo: The climate is Sudano-Guinean, with savannah landscapes.

• Benin: This zone shares similar characteristics with Togo’s Zone 2.

Zone 3 receives more plentiful rainfall than the other two zones.

• Burkina Faso: The zone transits into a savannah ecosystem and a Sudanian climate. Precipitation levels are 800–900 mm per year (unimodal rainfall curves) along with good soil quality. The zone is also endowed with large forests and vast areas of animal reserves.

• Côte d’Ivoire: The zone covers two distinct ecosystems: a Guinean type in the mountains, and a Sudanian type in the flatlands. Average annual rainfall is 1,250–1,500 mm.

• Togo: This zone has a Guinean climate and is largely made up of the Togo Mountains, which can reach nearly 1,000 meters in height at Mount Agou.

• Benin: Being a transitional zone, it has no clear distinction between the two rainy seasons. The landscape, however, is similar to Zone 2, which is woody savannah with tropical ferruginous soil.

Zone 4 has the most abundant rainfall and the most fertile soil. It houses the capital Lomé in Togo and the economic capitals in Côte d’Ivoire and Benin, Abidjan and Cotonou, respectively.

• Burkina Faso: This zone shares a similar ecosystem and climate with Zone 3. However, it receives more rainfall, at about 900–1,100 mm per year.

• Côte d’Ivoire: The coastal zone receives up to 1,750 mm of rainfall per year.

• Togo: This is a coastal zone with a subequatorial climate. However, precipitation levels are lower than in other zones, at about 750–1,000 mm annually.

• Benin: This zone has a subequatorial climate, with two rainy and two dry seasons. The soil type is mostly ferralitic, including relics of forest.

MAP 2 .2 Agroecological Zones, by Country

Sources: AGRHYMET, 2016; Dixon and Holt, 2010; FAO, 2001, 2009a, 2009b; Ministère de l’Environnement et des Ressources Forestières, 2003, 2014; Vissoh et al., 2004.



Agglomeration EconomiesThe second building block—agglomeration economies—is a vital engine of innovation and growth and plays a powerful role in explaining within-country inequality . As countries develop over time, economic activity clusters in certain locations to take advantage of both economies of scale and knowledge exchanges (Marshall, 1920; Krugman, 1991; Porter, 1998; Henderson, 2014). Greater economic opportunities in turn attract people who migrate in search of jobs, which con-sequently boosts population density in one area over another (World Bank, 2009).

As discussed in Chapter 1, this subregion is rela-tively densely populated by African standards . In terms of urbanization, as reported by the various national statistical services, the share of popula-tion residing in urban areas in three countries—Togo, Benin, and Côte d’Ivoire—is relatively high, at over 40 percent of the total population, a rate that is higher than the SSA average. However, growth rates for urbanization are slower, with Burkina Faso having the lowest share of population living in urban areas, at less than 30 percent. Nevertheless, the country is catching up, with impressive urban population growth of nearly 6 percent annually (Figure 2.1).

To go beyond the urban-rural dichotomy seen in the literature and, more importantly, to arrive at a consistent and systematic classification across countries,6 we further distinguish localities into

6 Each national statistical agency has a different definition of “urban locality.”

four groups: ultra-remote rural, rural, urban, and ultra-dense urban . Density thresholds used to cat-egorize these groups are based on the Organisation for Economic Co-operation and Development—OECD (1994), Uchida and Nelson (2010), and Buys, Chomitz, and Thomas (2005). Specifically:

a. Ultra-remote rural localities are defined as those having fewer than 50 people per square kilometer.

b. Rural areas are locations with population den-sity of between 50 and 150 people per square kilometer.

c. Urban areas are characterized by having popu-lation density of between 150 and 300 people per square kilometer.

d. Ultra-dense urban localities are areas with more than 300 people per square kilometer.

As shown in Map 2.3, in three out of four coun-tries, namely Benin, Côte d’Ivoire, and Togo, urban and ultra-dense urban localities are concentrated mainly in the South, coinciding with the most favorable agroecological zone (Zone 4) as well as coastal areas. Landlocked Burkina Faso is an exception in that the densest part of the country is located in the central region, around the capital Ougadougou.

Compared to other countries in the subregion, Burkina Faso has the most dispersed population. Apart from ultra-dense Kadiogo Province, home to the capital Ougadougou, the rest of the country is made of ultra-remote rural or rural areas. Based on our classification, the country does not even have urban localities. In contrast, Togo’s population is

FIGURE 2 .1 Higher Share of Population Living in Urban Areas but Slower Urban Population Growth by African Standards

Urban population (% total) Urban population growth (annual %)

0

CIV

BEN

TGO

SSA

BFA

BFA

SSA

TGO

CIV

BEN

20 40 60 0 2 4 6 Source: World Bank, 2017.



the densest, with the only ultra-remote rural loca-tion being found in the Central region, where the Togo Mountains lie.

Market AccessBased on NEG theory, the benefits of agglomera-tion economies can be further enhanced with the existence of good access to markets for products, labor, and ideas (Mayer, 2008). A region with better market access will attract more eco-nomic activity and labor, leading to an agglomera-tion advantage over time. With increasing returns to scale, the region can then afford to reinvest in market access and further reinforce its advantage. This can start a virtuous cycle of development, which is good for economic growth and poverty reduction but also makes it difficult for disadvan-taged regions to catch up (World Bank, 2009).

In this section, we start with a brief glance at the current road network, a core factor in mar-ket access. We then proceed to calculate each country’s market access index based on the most

commonly used modified model of the “classical” approach.7

As highlighted in NEG literature, access to markets must be considered beyond a coun-try’s border . This element is especially crucial for landlocked countries such as Burkina Faso. For Burkina goods to reach new markets and for Burkina people to receive more products from the outside world, there must be good transport con-nections to neighboring countries. As shown in Map 2.4 several primary roads connect large cities in Burkina Faso to their neighbors’ coastal ports in Benin, Côte d’Ivoire, and Togo.

Among the four countries, landlocked Burkina Faso has a relatively extensive primary and secondary road network that extends to all four agroecological zones. In contrast, coastal countries such as Côte d’Ivoire and Benin concentrate domestic transpor-tation systems in coastal and dense areas (Zone 4) and neglect remote regions (Zones 1 and 2).

7 For more details of the classical and modified models, see, for example, Deichmann (1997) and Lall, Shalizi, and Deichmann (2004).

MAP 2 .3 Concentration of Agglomeration Economies in the South

Source: National statistical agencies.



MAP 2 .4 Road Networks

Source: DeLorme, 2015.

To measure market access, we first follow the classical model in the literature, as follows:

Domestic market access for a given location along a road network is a function of the weighted sum of populated locations of all other locations dis-counted by travel time on the road.8

�

MAi =Pjt ij-q

j∑

where MAi is market access in location i, Pj is the population in location j, tij is travel time between locations i and j, and q is a trade elasticity param-eter. We then apply the most commonly used modified model as it is more relevant to coun-tries with geographically limited data on populated locations.9

8 Examples from the literature with similar market access include Harris (1954); Hanson (2005); Emran and Shilpi (2012); Jedwab and Storeygard (2015); Berg, Blankespoor, and Selod (2016); and Don-aldson and Hornbeck (2016).

9 See Lall, Shalizi, and Deichmann (2004); Yoshida and Deichmann (2009); and Ballon et al. (n.d.).

�MAi = Pj ⋅e

(−t ij

− b

2a2)

j∑

where Pj is the population in location j, t ij is travel time between locations i and j, and a and b are trade elasticity parameters based on Deichmann (1997). We then summarize market access at an administrative level for each country by converting the market access results to an inverse distance weighted grid and taking the mean of the grid in the administrative level. The spatial distribution of the market access indicator is presented in Map 2.5. (Appendix B provides further details of the construction of our market access indicator.)

There are two key limitations to our model . First, we only consider land transportation (e.g., road networks), thus potentially underestimating market access index in coastal areas, where sea access is available. Second, our model computes domestic market access with locations across borders not being taken into account. As a result, market access index of areas along a country’s border may also be underestimated.



Two striking facts emerge . First, across all four countries, the North generally has lim-ited access to market . Second, areas with high market access cluster in the South, around the capitals and the economic capitals—Cotonou (Benin), Ouagadougou (Burkina Faso), Abidjan

(Côte d’Ivoire), and Lomé (Togo). However, not all coastal areas are born equal, as shown by Côte d’Ivoire, where the western coastal side of the country does not enjoy the same levels of market access as the eastern side, at least not until it reaches closer to the border with Liberia.

MAP 2 .5 Concentration of High Market Access in the South, around Capitals and Economic Capitals

a. Benin b. Burkina Faso

c. Côte d’Ivoire d. Togo

Source: Authors’ calculations based on data from DeLorme (2015).


Chapter 3

Spatial Disparities in Welfare and poverty



This chapter visually presents the geographical distribution of welfare and poverty and relates it to the three key elements of economic geog-raphy: natural endowment, agglomeration, and market access. As described in Chapter 2, spatial disparities in welfare could be a natural outcome of the cycle of development: as a country devel-ops, economic activity concentrates in certain regions to take advantage of economies of scale, with these regions in turn attracting more people looking for job opportunities, which consequently increases population density. With increasing returns to scale, these regions can continue to invest in access to markets and thus further rein-force their advantage. However, this development process makes it more difficult for poorer areas to catch up.

If economic development is inevitably uneven within a country, where are the leading and lag-ging areas located, and where do the majority of the poor live? These are among the questions we aim to answer in this chapter. Within the domain of welfare and poverty, we focus on four important dimensions: leading and lagging regions, poverty measures (including poverty rates, poverty mass, and poverty density), access to basic services, and profile of the poor.

Arguably, the stylized facts presented in this chap-ter are useful for policy makers for several reasons. First, they can guide budget allocation across administrative units by identifying which regions have fallen behind in terms of economic devel-opment, which regions have forged ahead, and more importantly, the magnitude of the income gap between them.

Second and along the same lines, the geographi-cal targeting of programs designed to alleviate poverty can benefit from the identification of geo-graphical areas with high prevalence of poverty, or poverty rates, defined as the share of population living below US$1.90 a day at 2011 PPP. In addition to poverty rates, information on the number of the poor, or poverty mass, is handy when it comes to cost estimates of a social policy targeted to the poor, such as social safety net programs.

Third, public investments in service delivery pro-grams designed for the poor can be prioritized accordingly. In this context, services may come in many forms and include social services such as primary education for all, economic services such

as irrigation systems for poor farmers, or infor-mation services such as mobile phone coverage. The spatial distribution of poverty density, defined as the number of poor people per square kilome-ter, and maps of current public services coverage are critical for policy makers to decide whether a new service delivery program can be offered or an existing program can be expanded in a cost-effective way. If so, how many locations can the programs reach, and where are these locations to be found? The coverage of such programs depends heavily on the projected costs (e.g., upfront invest-ment such as schools, piping for water connec-tions, electricity lines and poles, etc.), which in turn are largely determined by the density of users and the current status of public service coverage.

Finally, by assessing how the characteristics of the poor differ across space from food consumption patterns to household demographics, we aim to help governments quickly identify affected groups in cases of shock (e.g., rise in commodity prices, etc.) or policy reforms related to food products, such as maize subsidies. Within a country, the poor display distinct characteristics and face dif-ferent challenges in each location. For example, factors with significant impact on the poor’s wel-fare in the North, such as maize price, may play a lesser role than those in the South.

To preview our main findings:

1. There is a large income gap between lead-ing and lagging areas . This gap is highest in Benin and lowest in Burkina Faso.

2. Many of the leading areas have not reached their full potential . In other words, many have not yet maximized the benefits of agglomera-tion economies (especially in Burkina Faso and Côte d’Ivoire) or of market access.

3. Within a country, there is wide variation in poverty rates, such that the North is mark-edly poorer than the South (except in Burkina Faso).

4. The poverty mass—the number of poor people—is highest in low-density areas (in Burkina Faso, Côte d’Ivoire, and Togo). This pattern suggests that the cost for service delivery programs to physically reach the poor could be relatively high, especially as access to public services such as improved toilets, piped water, and electricity differs greatly across


Spatial Disparities in Welfare and Poverty 17

space, with the North having lower coverage than the South.

5. In each country, there exist geographical pockets of poverty, which may be resis-tant to policy-induced change . Related to the main findings, #3 and #4 above, these areas are characterized by a combination of high poverty rates and low poverty densities. Therefore, the unit cost of a poverty-targeted program could be extremely high in these areas. Given budget constraints, the govern-ment may not be able to reach this population group.

6. Looking beyond monetary poverty (i.e., US$1.90 a day at 2011 PPP), the quality of life of the poor measured by the extent of food intake diversification and housing con-ditions varies across space, with geographi-cal patterns mimicking those observed with monetary poverty.

Leading and Lagging AreasAs discussed in Chapter 2, the accumulation of wealth in one area but not in another may be a natural outcome of the development path (made plausible through the evolution of agglomeration and expansion in market access). The question is therefore: What regions are benefiting from the fruits of development and which are falling behind? More importantly, how wide is the gap between them?

In fact, there are only a few leading areas and many lagging ones (Map 3.1. We define a loca-tion as “leading” if per capita consumption for an average household living there is higher than the national average. Out of 107 departments in Côte d’Ivoire,10 only 19 are leading. This figure is 14 out

10 Although Côte d’Ivoire has 108 departments, the 2001 ENV survey covers only 107 of them.

MAP 3 .1 Cluster of Leading Areas in the South, around the Capitals and the Economic Capitals, or along Country Borders

Sources: Authors’ calculations based on Benin EMICOV 2015, Burkina Faso EMC 2014, Côte d’Ivoire ENV 2015, and Togo QUIBB 2015.

Note: A location is defined as “leading” if consumption per capita for an average household living there is higher than the national average.



of 77 communes for Benin, 11 out of 45 provinces for Burkina Faso, and 9 out of 36 prefectures for Togo.11

The limited number of areas with per capita con-sumption above the national average suggests

11 For Benin, leading communes are Abomey, Abomey-Calavi, Adjarra, Bohicon, Cotonou, Dassa-Zoumé, Houeyogbe, Natitingou, Ouèssé, Parakou, Porto-Novo, Sakété, Savé, and Sèmè-Kpodji. Lead-ing provinces in Burkina Faso are Boulgou, Comoé, Houet, Kadiogo, Nahouri, Noumbiel, Oudalan, Poni, Sanmatenga, Séno, and Yagha. For Côte d’Ivoire, Abidjan is one of the leading departments, the rest being Abengourou, Aboisso, Adzopé, Bangolo, Bettié, Blolequin, Bouaflé, Dabou, Duékoué, Gagnoa, Grand-Bassam, Guéyo, Guiglo, San-Pedro, Sikensi, Tabou, Yamoussoukro, and Zuénoula. For Togo, leading prefectures are Bassar, Cinkassé, Danyi, Golfe, Lacs, Lomé, Ogou, Tchaoudjo, and Vo.

a large wealth gap between leading and lagging locations . As shown in Figure 3.1 the difference in income between the top three leading areas and the bottom three lagging ones could be as high as a factor of 7 (in the case of Benin). In this regard, Burkina Faso is the least unequal country, with a ratio of approximately 3.5 between the province with the highest income and the poorest one.

Surprisingly, many of these leading areas have not yet maximized the benefits of agglomeration economies . This observation is especially clear in Burkina Faso and Côte d’Ivoire (Figure 3.2). In a country with sparse population such as Burkina Faso, a majority of the better-off provinces are still located in ultra-remote areas. The fact that

FIGURE 3 .1 Large Wealth Gap between Leading and Lagging Areas

0

500

Con

sum

ptio

n pe

r ca

pita

l, $

PP

P 2

011

Copa

rgo

Bouk

oum

beCo

bli

Sem

e-kp

odji

Porto

nov

o

Cont

onou

1,000

1,500

Benin

0

500

Con

sum

ptio

n pe

r ca

pita

l, $

PP

P 2

011

Sour

ou

Loro

um

Zond

oma

Houe

t

Noum

biel

Kadio

go

1,000

2,000

1,500

Burkina Faso

0

500

Con

sum

ptio

n pe

r ca

pita

l, $

PP

P 2

011

Oum

e

Teng

rela

Sipil

ou

Guey

o

Guigl

o

Abidj

an

1,000

2,500

2,000

1,500

Côte d’Ivoire

0

500

Con

sum

ptio

n pe

r ca

pita

l, $

PP

P 2

011

Douf

elgou

Blitta

Akeb

ouLa

cs

Lom

e Co

mm

une

Golfe

1,000

2,000

1,500

Togo

Sources: Authors’ calculation based on Benin EMICOV 2015, Burkina Faso EMC 2014, Côte d’Ivoire ENV 2015, and Togo QUIBB 2015.



these provinces have not taken advantage of increasing returns to scale by urbanizing could partially explain why the wealth gap between rich and poor provinces is relatively low in Burkina Faso. We notice a similar pattern in Côte d’Ivoire, where nearly half of the leading departments are located in rural areas. However, some of the lead-ing departments in the country host large-scale farmers, who need land of considerable size for their operations, thus explaining their low-density locations.

It is important to point out a mixed picture in Benin . While some leading communes have low population density, many lagging ones are situ-ated in either urban or ultra-dense urban areas. Why, therefore, do these locations, which could enjoy the benefits of agglomeration externalities, remain poor? In fact, these dense but lagging com-munes cluster in the South near the coast of Benin (Map 2.3 and Map 3.1). While the poor might enjoy

agglomeration economies, the nature of the eco-nomic activities in this area, which consists mostly of informal trading with Nigeria, might attract large populations of poor migrants (Golub, 2012).

Similarly, we find a mixed pattern between leading and lagging areas and market access . Although a region with higher per capita consump-tion is often shown to have better market access, this pattern does not always hold. Figure 3.3 illus-trates how many leading administrative units in fact have low market access (defined as having market access below the average value across all four countries) and vice versa. As mentioned in Chapter 2, a limitation of our market access index results from underestimates of market access values in administrative units along a country’s border and along the coast. This could explain why some leading administrative units do not have high market access.

FIGURE 3 .2 Many Leading Areas in Low-density Locations

ultra-remote rural rural urban ultra-dense urban

log

(con

sum

ptio

n pe

r ca

pita

)

2

5.5

6

6.5

7

7.5

6

6.5

7

7.5

6

6.5

7

7.5

8

6

6.5

7

7.5

8

4 6 8 10

log (population density)leading region

Benin

ultra-remote rural rural urban ultra-remoteurban

log

(con

sum

ptio

n pe

r ca

pita

)

2 3 4 5 6

log (population density)leading region

Burkina Faso

ultra-remote rural rural urban ultra-dense urban

log

(con

sum

ptio

n pe

r ca

pita

)

2 4 6 8log (population density)

leading region

Côte d’Ivoire

ultra-remote rural urban ultra-dense urbanrural

log

(con

sum

ptio

n pe

r ca

pita

)

3 4 5 6 7 8 9log (population density)

leading region

Togo




FIGURE 3 .3 Low Market Access in Many Leading Areas

Low MA High MAlo

g (c

onsu

mpt

ion

per

capi

ta)

6

5.5

6

6.5

7.0

7.5

log

(con

sum

ptio

n pe

r ca

pita

)

6

6.5

7.0

7.5

log

(con

sum

ptio

n pe

r ca

pita

)

6

6.5

7.0

7.5

log

(con

sum

ptio

n pe

r ca

pita

)

6

7

6.5

7.5

8

8 10 12 14log (market access)

leading region

log (market access)

leading region

leading region

Benin

Low MA High MA

0 5 10 15

Burkina Faso

Low MA High MA

0 5 10 15log (market access)

leading region

log (market access)

Côte d’IvoireLow MA High MA

6 8 10 12 14

Togo


Poverty Rates, Poverty Mass, and Poverty DensityThe previous section discussed leading and lag-ging areas in terms of income.12 We now turn to an equally pressing concern: Which areas experience widespread poverty incidence and which ones do not? Here we use the US$1.90-a-day poverty line at 2011 PPP to define poverty.

here again, we observe a tale of two regions: North vs . South (Map 3.2). Within each of the three coastal countries—Benin, Côte d’Ivoire, and Togo—the North, corresponding to the two least favorable agro-ecological zones (Zones 1 and 2) is markedly poorer than the South, which comprises the two most favorable zones (Zones 3 and 4). In Togo particularly, poverty in the far North may be more than three times as high as in the far South. However, the pattern of poverty is reversed in Burkina Faso. One possible explanation is the pos-session of livestock among Burkina inhabitants in

12 Measured as per capita consumption.

the North, which is the case in neighboring coun-tries such as Mali and Niger.

Not surprisingly, leading areas have the lowest poverty rates and are clustered in the South, around the capitals and the economic capi-tals, and along the country’s border (Map 3.3). This pattern of poverty is consistent with the geographical distribution of leading and lagging regions described previously.

however, what is alarming is the large varia-tion in poverty incidence between leading and lagging areas . In Benin, poverty rates can vary between 20 percent in Cotonou and nearly 100 percent in the three most lagging communes (Cobli, Copargo, and Boukoumbé). A similar range is observed in Togo, or between 15 percent in the top three prefectures (Golfe, Lacs, and Lomé) and above 90 percent in the bottom three (Tandjoaré, Akebou, and Doufelgou). For Burkina Faso, the figure ranges from about 10 percent in the rich-est provinces (Noumbiel, Nahouri, and Kadiogo) to above 80 percent in the most disadvantaged ones (Komandjoan, Zondama, and Sourou). Côte d’Ivoire tells the same story, with poverty rates



MAP 3 .2 In Three out of Four Countries, the North Is Remarkably Poorer than the South


MAP 3 .3 Lower Poverty Rates in Areas around the Capitals and the Economic Capitals, and along the Country Border




between 8 percent in the top three departments (Guéyo, Abidjan, and Tabou) and over 80 percent in the bottom three (Tengrela, Sipilou, and Oumé). Figure C.1 in Appendix C provides details of poverty rates at subnational levels for each country.

A relevant consideration for policy makers is not only the issue of where poverty rates are high but also where the poverty mass is located. Here, we define poverty mass as the number of poor people. A location with a lower poverty rate does not nec-essarily imply that it has fewer poor people when population is taken into account.

In fact, relatively better-off locations, such as the capitals or the economic capitals of coun-tries and areas in the South, have high poverty mass . In Benin and Côte d’Ivoire, nearly half of the poor congregate in Zone 4 (the South). Similarly, in Burkina Faso, about half of the poor population lives in Zone 2 in and around the capital. Mean-while, Zone 3 in Togo is home to about 40 percent of the country’s poor.

At subnational levels, some leading areas in Benin and Côte d’Ivoire host the highest number of poor people .13 These are Abomey-Calavi com-mune, suburban Cotonou in Benin, and Abidjan department in Côte d’Ivoire. In contrast, in Burkina Faso and Togo, the largest poor population is con-centrated in two of the most disadvantaged areas in terms of income: Yatenga province in Burkina Faso, and Oti prefecture in Togo.

Nevertheless, most of the poor still reside in either ultra-remote rural or rural areas, where density is lower than 150 people per kilometer (Figure 3.4).14 This strong pattern seen in Burkina Faso, Côte d’Ivoire, and Togo, where at least three out of four poor people live in low-density loca-tions, suggests that the cost of physically reach-ing the poor could be relatively high in these three countries.

13 Compared to other administrative units in the same country.

14 Following our agglomeration classification in Chapter 2.

FIGURE 3 .4 Majority of the Poor Living in Low-density Areas

0 .2 .4 .6 .8 0 .2 .4 .6 .8

ultra-dense urban

urban

rural

ultra-remote rural

ultra-dense urban

rural

ultra-remote rural

ultra-dense urban

urban

rural

ultra-remote rural

ultra-dense urban

urban

rural

ultra-remote rural

BEN BFA

CIV TGO

Percent of population

Poverty rates ($1.90/day, PPP 2011)

BEN BFA

CIV TGO

ultra-remote rural ruralurban ultra-dense urban

Number of poor people by agglomeration




The final—though no less important—dimension of poverty discussed in this section is poverty den-sity. Similar to population density, poverty density is defined as the number of poor people per square kilometer (Map 3.4). Not surprisingly, the highest density of the poor is found in the capitals or the economic capitals . Across countries, Coto-nou commune in Benin is the densest, at nearly 1,700 poor people per square kilometer, Lomé commune in Togo follows, with poverty density of 1,500, and Abidjan department in Côte d’Ivoire houses about 200 poor people per square kilo-meter. In sparsely populated Burkina Faso, where poverty density is relatively low, even the densest province—Zondoma—has only 78 poor people per square kilometer.

It is important to note stark variation in pov-erty density across administrative units, which affects the cost of various government pro-grams delivered to each location . In Benin, the difference between Cotonou commune, with the highest number of poor people per square kilo-meter, and Karimama commune, with the lowest

poverty density, is strikingly high, or nearly 200 times the number of poor people. This ratio is 120 for Togo, 73 for Côte d’Ivoire, and 31 for Burkina Faso. Figure C.2 in Appendix C lists poverty density for each administrative unit in the fo

Documents

The Geography of Welfare - World Bankdocuments.worldbank.org/curated/en/306301502329834751/...The Geography of Welfare in Benin, Burkina Faso, Côte d’Ivoire, and Togo Nga Thi Viet