ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 1 Analysis of Spatial
Public Disclosure Authorized Patterns of Settlement, Internal Migration, and Welfare Inequality in Zimbabwe Public Disclosure Authorized Public Disclosure Authorized
Rob Swinkels Therese Norman Brian Blankespoor
WITH Nyasha Munditi
Public Disclosure Authorized Herbert Zvirereh World Bank Group April 18, 2019
Based on ZIMSTAT data Zimbabwe District Map, 2012
Zimbabwe Altitude Map ii ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
TABLE OF CONTENTS
ACKNOWLEDGMENTS iii ABSTRACT v EXECUTIVE SUMMARY ix ABBREVIATIONS xv 1. INTRODUCTION AND OBJECTIVES 1 2. SPATIAL ELEMENTS OF SETTLEMENT: WHERE DID PEOPLE LIVE IN 2012? 9 3. RECENT POPULATION MOVEMENTS 27 4. REASONS BEHIND THE SPATIAL SETTLEMENT PATTERN AND POPULATION MOVEMENTS 39 5. CONSEQUENCES OF THE POPULATION’S SPATIAL DISTRIBUTION 53 6. POLICY DISCUSSION 71 AREAS FOR FURTHER RESEARCH 81 REFERENCES 83 APPENDIX A. SUPPLEMENTAL MAPS AND CHARTS 87 APPENDIX B. RESULTS OF REGRESSION ANALYSIS 99 APPENDIX C. EXAMPLE OF LOCAL DEVELOPMENT INDEX 111 ACKNOWLEDGMENTS
This report was prepared by a team led by Rob Swinkels, comprising Therese Norman and Brian Blankespoor. Important background work was conducted by Nyasha Munditi and Herbert Zvirereh. Wishy Chipiro provided valuable technical support. Overall guidance was provided by Andrew Dabalen, Ruth Hill, and Mukami Kariuki. Peer reviewers were Luc Christiaensen, Nagaraja Rao Harshadeep, Hans Hoogeveen, Kirsten Hommann, and Marko Kwaramba. Tawanda Chingozha commented on an earlier draft and shared the shapefiles of the Zimbabwe farmland use types. Yondela Silimela, Carli Bunding-Venter, Leslie Nii Odartey Mills, and Aiga Stokenberga provided inputs to the policy section. Han Herderschee contributed to the conceptualization of the work. Assistance was provided by Aimee Niane and Farai Sekeramayi Noble. Staff of the World Bank Harare office provided useful inputs during presentations of early findings. The team gratefully acknowledges the collaboration of the Zimbabwe National Statistics Agency (ZIMSTAT).
iii iv ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE ABSTRACT
Zimbabwe has an unusual settlement pattern due to its colonial legacy. A sizeable proportion of the rural population lives in densely populated areas that are far away from the main road network and poorly connected to markets. This study using data from ZIMSTAT’s 2012 poverty map and the 2002 and 2012 population census suggests the highest ward poverty rates and lowest social service delivery outcomes are found here. These communal lands are characterized by a deep spatial poverty trap. In 2012 two thirds of the extreme poor lived in these communal lands, and preliminary estimates suggest that this had risen to three quarters by 2017. Social outcomes such as education levels are typically also lowest here . Many of these spatial poverty traps are prominent in the northwest but are also found in other parts of the country. These communal lands typically have below average agricultural production potential and were designated during colonial times as lands where native African farmers could live and farm, creating space for the development of large-scale commercial farms mostly for people of European descent. Fast track land reform during the 2000s attempted to reverse the unequal land distribution. It led to substantial population movements, but insufficient communal farmers benefited to adequately resolve the spatial poverty trap and many remain with a weak asset base. During 2002-2012 around 410,000 people moved to the white commercial farm areas (urban and rural) of which around 290,000 came from communal lands and 110,000 from Harare. However, around 140,000 people moved in the other direction: from the commercial farming areas to communal lands. In 2012, 65 percent of the rural population still lived in communal lands (down from 70 percent in 2002).
v vi ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
Nationwide, rural multi-dimensional poverty did not drop between 2001/2 and 2011/12. During the economic crisis of the 2000s, GDP fell, urbanization slowed, and a large part of the population went back to subsistence farming. Between 1999-2014, the proportion of the population engaged in unpaid or self- employed farming rose from 46 to 60 percent and average labor productivity in agriculture fell by 55 percent, according to labor force survey data. Fast track land reform did not structurally tackle the spatial poverty traps prevalent in the communal lands. While the communal areas are often densely populated they are located far from good roads. The existing trunk road network connects the towns located in the thinly populated commercial farm areas which were built to connect goods markets rather than people. High domestic transport costs between the northern maize-surplus and the southern maize-deficit production regions have contributed to the segmentation of the maize grain market1, according to a World Bank study of 2015, which has direct implications in terms of food security and demand stimulus for agricultural production in the high population density, high-poverty areas. Extreme poverty in communal lands is structurally higher than in areas dominated by other land-use types keeping everything else the same. Even when controlling for other factors such as gender ratios, distance factors, natural zones and education outcomes, people in communal lands have extreme poverty rates that are 5–7 percent higher than those living in other farm type areas. This reflects the structural lack of economic opportunities in these areas and the deep disadvantages and inequality of opportunity that people in these areas face. Several policy measures exist for tackling the spatial poverty traps. First, the government should ensure social service delivery policies are spatially blind in their design and universal in their coverage. This concerns the practical regulations that govern the social services such as education, health, and water and sanitation across the country as well as their affordability and how these are financed through tax and transfer mechanisms. To compensate for the lack of budget resources for non-wage expenses, the Government of Zimbabwe has expanded the use of user fees and charges for various social services. This has resulted in lower financing for basic services in poorer areas. Without appropriate mechanisms to equalize financing of basic services, Zimbabwe could find it difficult to reduce the current inequality of opportunity.
1 Mahdi, Bonato and Herderschee. 2015. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE vii
In the education sector, for example, the collection of revenue through private fees is skewing resources in a highly regressive manner (i.e. weighted towards the better-off). The decision to impose fees in all schools, while funding teacher salaries in private schools, left many vulnerable children at risk and transferred benefits to less poor households, according to the recently completed public expenditure review (PER) of the primary and secondary education sector. The currently constrained fiscal situation calls for focus on protecting the vulnerable, better coordination of the multiple social protection programs and better targeting of these programs to the most needy to provide them with opportunities to improve their lives. Second, the government should ensure adequate connectivity to these lagging areas through policies and investments to facilitate spatial integration. Examples include roads, railways, and communication systems that facilitate the movement of goods, services, people, and ideas locally, nationally, and internationally. Better connectivity will also help some people move out of these areas if they are too densely populated and they do not have the land assets they need to make a decent living. Third, there is a need to enact new laws to further decentralize decision making to Provincial and Metropolitan Councils and to improve distributional equity by aligning transfers with local needs and revenue capabilities. viii ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE EXECUTIVE SUMMARY
This report aims to assess the spatial dimensions of settlement, internal migration, and welfare inequality in Zimbabwe, explore their linkages and implications, and identify policy options for addressing spatial disparities in social outcomes. The study looks at where people are today, unpacks urbanization trends, and reviews population density and connectivity. It assesses the reasons behind the spatial settlement patterns and discusses the consequences of the population’s spatial distribution in terms of poverty, nonfarm employment, and service delivery. Finally, it presents policy implications. The data used for this study mostly consist of statistics that originate from three ZIMSTAT reports (i) Zimbabwe Profile: Population Census 2002, (ii) National Report: Population Census 2012, and (iii) The food poverty atlas: small area food poverty estimation. The latter is based on the PICES 2011/12 survey and the 2012 population census. District summary data from the Central Business Register 2013 were also used, in addition to labor force survey data. The settlement pattern in Zimbabwe is unusual. First, population densities outside the major secondary cities (City Councils) are low—lower than in areas located farther away. This counts for the cities of Kadoma, Kwekwe, Gweru, Masvingo and to a lesser extent Mutare. Second, we see that there are many areas with a higher-than-average population density that are far away from road connections. This includes the relatively densely populated areas in the west and north of the country. Third, in the areas close to Harare and Bulawayo and the Harare–Bulawayo road, as well around Marondera, there are considerably more men than women, while the opposite is the case in rural areas farther away. Between 2002 and 2012, the population in Zimbabwe grew by only 14 percent, or 1.3 percent per year. Annual population growth dropped from 3.9 percent in 1983 to 1.1 percent in 2003 and has currently stabilized at 2.3 percent per year. The fast drop in population growth was related to several factors, including a rise in
ix x ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
the mortality rate caused by the human immunodeficiency virus (HIV) epidemic; a drop in the fertility rate; and mass emigration, mostly to South Africa, following the economic crisis of the 2000s. The 32 percent urbanization rate in Zimbabwe is relatively low and and its growth has slowed down considerably since 2002. Internal migration in Zimbabwe is among the highest in the world. According to data from the population census 2012, 23 percent of the population aged 10 years and older that year indicated they lived in a different district in 2002. Most of the internal migrants in Zimbabwe (59 percent) live in rural areas, while the proportion of migrants is highest in the smallest towns, where they formed 43 percent of the population. In Harare, this figure is 16 percent. Some of the rural districts, typically those around the capital, Harare, have a high influx of migrants. Circular migration is common. The number of people moving from rural to urban areas during 2002–12 (508,000) was only marginally higher than those who moved in the other direction: from urban to rural areas (492,000). Municipalities and small towns are growing faster in a relative sense than Harare and City Councils, suggesting the distribution of the urban population has somewhat shifted toward these smaller urban areas. Most of the cross-district migration during 1992–2002 and 2002–12 was from rural to rural. Net outflows of rural areas were highest in Masvingo, Manicaland, and Midlands. Mashonaland West was the only province with high net rural inflow. Many of the larger secondary cities (City Councils) were at the heart of the areas that traditionally contained large European commercial farms and that had a low population density. This counts in particular for Kadoma, Kwekwe, Gweru, and Masvingo. These cities were well connected with a good road network, but they linked together markets of agricultural and other goods rather than people. Rural population increase during 2002–12 was much higher in the former white commercial farm areas that were subjected to fast track land reform in the 2000s than the other land types. The census data suggest that the rural population in these areas increased by around 35 percent, or 670,000 people, compared to 3 percent, or around 160,000, in the communal lands. (The total population grew by 13 percent during this period, according to ZIMSTAT data.) The proportion of the rural population who lives in the former white commercial farm areas rose from 25 to 30 percent. During 2002-2012 around 410,000 people moved to the white commercial farm areas (urban and rural) of which around 290,000 came from communal lands and 110,000 from Harare. However, around 140,000 people moved in the other direction: from the commercial farming areas to communal lands. In 2012, 65 percent of the rural population still lived in communal lands, down from 70 percent in 2002. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE xi
Urban economic opportunities declined since the onset of the economic crisis, following fast track land reform of the early 2000s. This also played a role in the reverse trend of urban to rural migration. People moved to urban areas in search of a better life, but when economic conditions deteriorated, costs of living went up, and housing quality appeared poor, many returned to rural areas. The number of people conducting unpaid or self-employed work in the agricultural sector rose from 45 to 60 percent during 1999–2014, according to Labour Force Survey (LFS) data, while those in self-employment outside agriculture also went up from 12 to 16 percent. Throughout the 2000s, the government also adopted several policies to remove slumdwellers from Harare and other urban areas, which impacted an estimated 150,000 people, according to some authors.2
Statistical analysis suggests people’s movements during 2002–12 were influenced by the availability of nonfarm jobs. Population increase between 2002 and 2012 was significantly higher in areas where the proportion of the population working outside agriculture in 2002 was higher, controlling for other factors. It was also significantly higher in the post–fast track land reform areas. The high concentration of rural people in various remote and poorly connected areas where agricultural production conditions are not optimal (mostly the communal lands) has led to entrenched poverty there. The population in such areas is caught in a spatial poverty trap. The highest ward poverty rates are found in these relatively densely populated and remote areas, located away from main roads. Many are found in the north and west of the country and far from road networks. High domestic transport costs between the northern maize-surplus and the southern maize-deficit production regions have contributed to the segmentation of the maize grain market,3 which has direct implications in terms of food security and demand stimulus for agricultural production in the high population density, high-poverty areas. Social service delivery outcomes in the more remote and often densely populated areas are low, contributing to the spatial poverty trap. For example, there is a large concentration of people who have not completed secondary education and are not in school in the outer west, north, and southwestern parts of the country. The quality or affordability of social service delivery in these areas may be problematic.
2 Potts 2011. 3 Mahdi, Bonato and Herderschee. 2015. xii ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
Small towns have relatively high average extreme poverty rates: 12 to 13 percent, about double the figure of larger towns. Their relatively high poverty rates are concerning because these urban centers are growing rapidly in size, attract a growing proportion of migrants, and should be the small engines of progress and welfare growth through structural transformation. A large proportion (40 percent in 2012) of the population in these small towns obtains their main income from agriculture, suggesting that only limited shifts to industry and services take place where labor productivity tends to be higher. Population densities in small towns and municipalities (excluding Chitungwiza) are low, making service delivery to its population expensive.
The high average poverty rate of small towns is likely related to the quality of social services, which tends to be poorer there compared to other urban types, albeit still better than in rural areas. For example, on average only 46 percent of households in small towns had access to electricity in 2012 compared to 84 to 92 percent in the three other types of urban centers. However, variation among small towns is very high. Education outcomes in smaller towns are also much lower than in other urban centers but also highly variable. Ongoing monitoring of urban development through the urban service level benchmarking effort confirms that the quality of infrastructure services differs highly among smaller towns, suggesting the differences may be caused by variation in management of these small towns rather than more structural reasons.
Wards in communal land areas have extreme poverty rates that are 5 to 7 percent higher than other farm type areas when controlling for other factors. The latter include gender ratios, education service outcomes, connectivity, and natural zones. This would suggest these areas have a structural lack of economic opportunities and the people in these areas face deep disadvantages and inequality of opportunity.
The spatial distribution of extreme poverty has a clear gender dimension. Ward level extreme poverty rates are significantly and negatively related to the women-to-men ratio, keeping everything else the same, with poverty rates being higher when there are relatively more women than men. Ward level extreme poverty rates are also negatively related to mean years of schooling and connectivity to other people and – as said – are positively related to being located in communal land areas, and also to being situated in the extensive farming areas and in the high rainfall areas.
Several policy measures exist for tackling the spatial poverty traps. First, the government should ensure social service delivery policies are spatially ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE xiii
blind in their design and universal in their coverage. This concerns the practical regulations that govern the social services such as education, health, and water and sanitation across the country as well as their affordability and how these are financed through tax and transfer mechanisms. To compensate for the lack of budget resources for non-wage expenses, the Government of Zimbabwe has expanded the use of user fees and charges for various social services. This has resulted in lower financing for basic services in poorer areas. Without appropriate mechanisms to equalize financing of basic services, Zimbabwe could find it difficult to reduce the current inequality of opportunity.
In the education sector, for example, the collection of revenue from private fees is skewing resources in a highly regressive manner (i.e. weighted towards the better-off). The decision to impose fees in all schools, while funding teacher salaries in private schools, left many vulnerable children at risk and transferred benefits to less poor households, according to the recently completed public expenditure review (PER) of the primary and secondary education sector. The currently constrained fiscal situation calls for focus on protecting the vulnerable, better coordination of the multiple social protection programs and better targeting of these programs to the most needy to provide them with opportunities to improve their lives.
Second, the government should ensure adequate connectivity of these lagging areas through policies and investments to facilitate spatial integration. Examples include roads, railways, and communication systems that facilitate the movement of goods, services, people, and ideas locally, nationally, and internationally. Better connectivity will also help some people move out of these areas if they are too densely populated and they do not have the land assets they need to make a decent living.
Third, spatially targeted programs are needed, and transfers need to be aligned with local needs and revenue capabilities. There is a need to enact new laws to further decentralize decision-making to Provincial and Metropolitan Councils. This would facilitate distributional equity by providing an opportunity to align transfers with local needs and revenue capabilities.
There is also a need to more systematically track the achievements and development gaps of each province and district in terms of different service delivery, infrastructure, and local governance indicators. This would enable tracking progress in local development and align resources to needs. The data on various local development indicators could be combined into a single comparable measure (a local development index, or LDI). xiv ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
With most of the extreme poor located in communal land areas, agricultural policies as well as those governing health, education and infrastructure services need to ensure that they deliver better results in these areas. Government expenditure in agriculture currently is highly skewed toward maize input and output subsidies, which may come at the expense of other important agricultural services such as research and development (R&D).
For Zimbabwe’s cities and towns to become centers of growth and poverty reduction, they must create working land markets. Currently, the demand for land cannot always be met due to weak property rights and poor land governance. Consequently, towns and cities develop in a fragmented and disconnected way. Urban planning is problematic. Cities in Zimbabwe typically have master plans that are outdated. Many of the more remote urban areas do not have services. A high minimum plot size has led to low density areas and some densification away from the city center. Obtaining financing for investment in urban areas is currently difficult, especially for bulk infrastructure, because local authorities do not have the means. Developers determine what is possible in terms of service delivery, and cities and towns depend on these developers, in particular when they own the land. This needs to be addressed, together with improved collaboration across administrative boundaries. ABBREVIATIONS
AfDB African Development Bank BEAM Basic Education Assistance Module CSO Central Statistical Office FTLRP Fast Track Land Reform Program GDP gross domestic product GNI gross national income GNU Government of National Unity HIV human immunodeficiency virus HSCT Harmonized Social Cash Transfer ICT information communication technology LDI local development index LFS Labour Force Survey MPI Multidimensional Poverty Index PER public expenditure review PICES Poverty Income Consumption and Expenditure Survey R&D research and development SADC Southern African Development Community UCAZ Urban Councils Association of Zimbabwe UNDP United Nations Development Programme UNFPA United Nations Population Fund UNICEF United Nations International Children’s Emergency Fund ZANU–PF Zimbabwe African National Union—Patriotic Front ZIMSTAT Zimbabwe National Statistics Agency ZINARA Zimbabwe National Road Administration
xv xvi ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 1 INTRODUCTION AND OBJECTIVES
Zimbabwe is a lower-middle-income country4 with a per capita gross national income (GNI) that is barely higher than it was 35 years ago. It presents characteristics of deep fragility, having suffered the largest peacetime contraction of any economy during 1998–2008 (figure 1.1, panel a) and record hyperinflation. But it has also displayed remarkable resiliency that has preserved many of its middle-income achievements of the 1990s, such as a high literacy rate (89 percent in 2014).5 The latter is close to the value of upper-middle-income
FIGURE 1.1 Progress Along Economic Growth and Literacy Rate, 1980–2017 a. Gross national income per capita, b. Literacy rate of 15+ years old, Atlas method, 1980–2017 (current US$) 1980–2016 (%)
2,000 100
1,600 90 80 1,200 70 800 60
400 50
0 40 1980 1986 1992 1998 2004 2010 2016 1980 1986 1992 1998 2004 2010 2016
Zimbabwe Sub-Saharan Africa Zimbabwe Sub-Saharan Africa Upper middle income Lower middle income Source: World Development Indicators Database (data.worldbank.org).
4 Defined as having a GNI per capita of $995 or more in 2017. 5 World Bank 2016.
1 2 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
countries (figure 1.1, panel b). After a period of recovery during 2009–13, the fragilities have reemerged in the face of economic mismanagement and political factionalism, and GNI/capita growth has flattened, even if it picked up somewhat in 2017. From the mid-1990s to the late 2000s, Zimbabwe shifted toward a period of international isolation and turbulent economic decline. During this period, the Mugabe administration embarked on a series of radical efforts to redistribute economic assets, including the Fast Track Land Reform Program (FTLRP) and the indigenization policy. These reforms weakened the economy and allowed further capture of the state by political and economic elites. Between 1998 and 2008, GNI fell by 50 percent, and record hyperinflation caused the failure of critical services, the collapse of productive capacity, and the widespread migration of skilled workers.6 The strong industrial and agricultural bases of the economy have eroded. The value added of its industrial sector more than halved from US$4.3 billion in 1999 to US$2.0 billion in 2008, while the agricultural value added reduced to one-third of its value during this period, from US$2.9 billion to US$0.9 billion (figure 1.2, panel a).
FIGURE 1.2 Sectoral Growth and Poverty Change a. Value added by sector, 1987–2017 b. Multidimensional poverty trends, 2001, (constant 2010 US$)(billions) 2007, and 2011–12 (%)
16.0 25
14.0 20 12.0
10.0 15 8.0
6.0 10
4.0 5 2.0
0.0 0 1987 1992 1997 2002 2007 2012 2017 2001 2007 2011–12 Services Multidimensional poverty (k=4) Agriculture, forestry, and fishing Multidimensional poverty (k=3) Industry (including construction) Source: Stoeffler, Alwang, Mills, and Taruvinga 2015, Source: World Development Indicators Database. using ZIMSTAT data (PICES 2001, 2007, and 2011).
6 World Bank 2016. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 3
Since the late 2000s, Zimbabwe has pursued a broad shift in economic policy, which helped usher in a period of economic and social recovery. A newly formed Government of National Unity (GNU) was able to stabilize the economy. The GNU undertook deep economic reforms, including establishing a multi currency system, which ushered in gross domestic product (GDP) growth averaging 11.3 percent per year from 2010–12. The agricultural and industrial sectors recovered somewhat, and social indicators began to improve. Multidimensional poverty dropped between 2007 and 2011–12 (figure 1.2, panel b). In 2013, the Mugabe administration returned to power, and reformers in the Zimbabwe African National Union—Patriotic Front (ZANU–PF) undertook pragmatic economic reforms, including maintaining the multicurrency regime. The economic growth trend is now some 2 percent below the average of sub-Saharan Africa, partly resulting from exchange rate misalignment, even if growth in 2017 picked up. Confidence started to evaporate in 2012 (coinciding with the commodity super-cycle ending, which particularly hit the mining sector), and the investment-to-GDP ratio declined sharply. The decline in Zimbabwe’s growth coincided with a gradual depreciation of the South African rand versus the U.S. dollar, which had an immediate impact on Zimbabwe’s competitiveness because Zimbabwe uses the U.S. dollar, and South Africa is its largest trading partner.7
Despite deteriorating living conditions in FIGURE 1.3 urban areas throughout the 2000s, in 2012, Multidimensional Poverty monetary and multidimensional poverty in Urban and Rural Areas were much higher in rural areas than in (k = 3) (in %) urban areas. The impact of the economic 30 crisis in the mid-2000s on multidimensional poverty was felt particularly in rural areas. 25
However, although the rural Multidimensional 20 Poverty Index (MPI) has dropped somewhat since 2007, the urban MPI has not, suggesting 15 urban living conditions have not improved 10 since then (figure 1.3). 5 The international literature suggests 0 that in developing countries, disparities 2001 2007 2011 2001 2007 2011 between urban and rural areas, or between Urban Rural lagging and leading areas, are typically large. Source: Stoeffler, Alwang, Mills, and Taruvinga 2015.
7 World Bank, AfDB, and UNDP 2018. 4 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
These disparities in welfare correspond to disparities in economic density. Welfare tends to be higher in places with high economic density, usually urban areas, and lower in places away from it.8 Reducing “economic distance” (the distance between areas where economic activity is concentrated and areas that lag) is key for economic growth and poverty reduction in lagging areas. To reduce their own economic distance, people often move closer to economic density.9
Urban areas are important centers of growth and drivers of poverty reduction. This happens through “agglomeration effects,” that include, for example, better networks and flow of new ideas as well as better access to services and infra structure, allowing firms to grow and flourish. “Structural transformation” then follows: as economic growth occurs, labor typically moves out of agriculture into the manufacturing and service sectors,10 where labor productivity and thus wages tend to be higher. Urban demand for agricultural products then increases, raising incomes of the remaining farmers and pushing up rural wages. Regrettably, many governments see labor mobility and rural-urban migration as undesirable and try to restrict the internal movement of people. This is typically out of fear of urban unemployment or overburdening of city infrastructure or for electoral/political reasons. The policy challenge is not how to keep people from moving, but rather, how to prevent them from moving for the wrong reasons, as argued by the 2009 World Development Report, “Reshaping Economic Geography.”11 When people are pushed out of rural areas by the lack of security or basic services, migration is beneficial for the migrant but not always for the nation. “Pull” migration is better than “push,” and basic services should be delivered to people wherever they live. Migration due to push factors is unlikely to add to agglomeration benefits but is likely to exacerbate the urban congestion policy makers prefer to avoid.12 The 2009 World Development Report concludes that spatial convergence of welfare requires “institutions that unite,” “infrastructure that connects,” and, where integration is hardest, “interventions that target.” While evidence suggests that urban incomes are higher than rural ones, the analysis often takes place at an aggregate level, and the urbanization policy debate is often simply narrowed to the issues relating to large cities.13
8 World Bank 2017. 9 World Bank 2017. 10 de Brauw, Mueller, and Lee 2014. 11 World Bank 2009. 12 World Bank 2017. 13 Christiaensen and Kanbur 2018. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 5
Thereis a growing recognition that the distinction between secondary towns (or small towns) and large cities may be particularly central for analysis and for policy. They are differentiated by migration patterns, by spillovers to their rural hinterland, and ultimately by the poverty reduction return to public investment in one location or the other.14 Emerging evidence15 suggests that migration out of agriculture into the rural nonfarm economy and secondary towns yields more inclusive growth patterns and faster poverty reduction than agglomeration in mega cities. This suggests that patterns of urbanization and spatial distribution of the population more broadly deserve increased attention when striving for faster poverty reduction.
This report aims to assess the spatial dimensions of settlement, internal migration, and welfare inequality in Zimbabwe, explore their relationship and implications, and identify policy options for addressing spatial disparities in social outcomes. It is exploratory in nature and identifies areas for further research to continue to unravel the drivers of the pattern that is observed. The study looks at where people are today (chapter 2), unpacks urbanization trends, and reviews population density and connectivity (chapter 3). Chapter 4 assesses the reasons behind the spatial settlement patterns and looks at Zimbabwe’s historical land allocation, land reform, and economic crisis in the 2000s. Chapter 5 discusses the consequences of this spatial distribution of the population in terms of poverty, nonfarm employment, and service delivery outcomes. Chapter 6 discusses policy implications.
The conceptual framework for the study16 can be described as follows. Urbanization has the potential to reduce poverty in both rural and urban areas. Rural poverty reduces if the rural poor migrate to urban areas (figure 1.4, section a). It decreases further if migrants to urban areas send remittances to family members who remained behind. Urbanization also leads to an increased urban demand for agricultural products, which in turn can lead to wage convergence between urban and rural areas (figure 1.4, section b). Urban poverty reduces when nonpoor rural migrants move to urban areas (figure 1.4, section c), and agglomeration effects and better access to social services in urban areas play an important role in enhancing economic opportunities in urban areas and reduce urban poverty. At the same time, congestion can lower productivity and reduce urban dynamics, leading to potential
14 Christiaensen and Kanbur 2018. 15 Christiaensen and Todo 2013; Christiaensen, De Weerdt, and Todo 2013. 16 Based on World Bank 2017. 6 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 1.4 Conceptual Framework on the Link Between Urbanization and Poverty Reduction
A Rural B – Escape of the rural poverty – Sending remittances poor reduction – Increased demand for + outmigration of the agricultural products non-poor – Wage convergence
Rural to urban Urbanization migration
Urban D C – Agglomeration effects – Arrival of the nonpoor poverty – Better access to social + arrival of poor migrants reduction services + congestion + high living costs
Source: World Bank 2017. Note: “− ” implies a poverty-reducing effect and +“ ” a poverty-increasing effect.
increases in poverty. High living costs in urban areas can also increase urban poverty as market failures may prevent wages from moving upward to compensate for this (figure 1.4, section d). The data used for this study consist of summary statistics from three ZIMSTAT reports (i) Zimbabwe Profile: Population Census 2002, (ii) National Report: Population Census 2012, and (iii) The Food Poverty Atlas: small area food poverty estimation. The latter is based on the PICES 2011/12 survey and the 2012 population census. Summary statistics for each of the 1969 wards and 92 rural districts and urban centers were combined with other spatial data to construct a spatial development database that was used for the analysis conducted for this report. District summary data from the Central Business Register 2013 were also used. There were some uncertainties in the merging of the 2012 ward level boundaries with those from 2002, especially in urban areas and some caution is warranted when comparing ward level data of 2012 with those of 2002. While two observations exist (2002 and 2012) for population census questions such as ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 7
household characteristics, main sector of employment (formal and informal), and population movements, only one observation of poverty levels is available (for 2012). This prevents an analysis of changes in poverty levels and limits the research to identifying factors that can explain the spatial difference in poverty levels as they were in 2012. Excellent data are available on migration patterns during 1992–2002 and 2002–12 because the census questionnaires contain detailed questions on this. The assessment excludes international migration, as the census only covered people who live inside Zimbabwe, and no questions were asked on people who have left the country. We also made use of findings from the recent Jobs Diagnostic for Zimbabwe conducted by the World Bank that analyzed data of the past four Labor Force Surveys (LFSs).
2 SPATIAL ELEMENTS OF SETTLEMENT: WHERE DID PEOPLE LIVE IN 2012?
This chapter briefly discusses spatial elements of population settlement in Zimbabwe. It looks at urbanization trends, and population densities within as well as outside each of the urban size categories. It also reviews spatial differences in women-to-men ratios of the population.
In Zimbabwe, five different official categories of urban areas are distinguished (table 2.1a). These are City Councils, Municipalities, Town Councils, and Local Boards. For the purposes of our analysis, we have added Metropoles for the capital, Harare, and we have combined Town Councils and Local Boards into one category (“small towns”) because these include only a small part of the population. One-third of the Zimbabwean population lives in urban areas: 11 percent in the capital, Harare; 10 percent in the six City Councils; 5 percent in the nine Municipalities, and 5 percent in the 16 small towns. An urban classification based on population size gives a somewhat different grouping. Chitungwiza, for example, is officially labelled as a municipality, but with a population of 356,000, it is the third-largest urban settlement in Zimbabwe and much larger than all City Councils except Bulawayo. Similarly, Epworth is classified as a Local Board but contains 176,000 people and is the fourth-largest urban settlement in the country. Both Chitungwiza and Epworth are located near Harare. Gwanda is a municipality, but with a population of 20,000 people, it falls in the smallest size category (table 2.1b).
9 10 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
TABLE 2.1a Official Classification of Zimbabwe Urban Settlements
Metropoles City Councils Municipalities Town Councils Local Boards (Small towns) Harare* Bulawayo Bindura Beitbridge Hwange Mutare Marondera Chipinge Ruwa Kadoma Chinhoyi Chiredzi Epworth Gweru Chegutu Gokwe Town Lupane Kwekwe Kariba Karoi Chirundu Masvingo Victoria Falls Mvurwi Gwanda Norton Redcliff Plumtree Chitungwiza Rusape Shurugwi Zvishavane Population in 2012 1,485,231 1,299,771 690,107 622,734 Range of population size 1,000,000+ 90,000–650,000 20,000–80,000 plus 8,000–70,000 40,000–170,000 one of 356,000 (Chitungwiza) Proportion of total population in 2012* 11% 10% 5% 5% Proportion of total urban population in 2012* 35% 32% 17% 15%
Sources: Government of Zimbabwe, World Bank, and UCAZ 2016; ZIMSTAT 2012. * Harare is also classified as a City Council but grouped separately here.
The current 32 percent urbanization rate in Zimbabwe is below the average of 39 percent for sub-Saharan Africa and certainly much below the average of the Southern African region, where national urbanization rates vary between 43 and 68 percent. The metropole Harare, with a population of almost 1.5 million in 2012, counts for 35 percent of Zimbabwe’s urban population, followed by 32 percent who lived in City Councils (table 2.1a). Three-quarters of the urban population lives in cities larger than 100,000 (table 2.1b). Although the urbanization rate has grown steadily over the past 30 years in the sub-Saharan Africa continent, available data suggest it has stalled in Zimbabwe since 2002. Between 2002 and 2012, the urbanization rate dropped from 34.5 to 32.8 percent, according to the United Nations Population Fund (UNFPA) ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 11
TABLE 2.1b Classification of Zimbabwe Urban Centers by Population Size, 2012
Population size of urban center 1,000,000+ 100,000–1,000,000 50,000–100,000 20,000–50,000 8,000–20,000 Metropole Large cities Medium-size towns Small towns Smallest towns Harare Bulawayo Masvingo Chiredzi Kariba Chitungwiza Chinhoyi Ruwa Plumtree Mutare Norton Chegutu Mvurwi Epworth Lupane Zvishavane Shurugwi Gweru Marondera Bindura Kadoma Chiredzi Beitbridge Kwekwe Ruwa Hwange Chegutu Victoria Falls Redcliff Karoi Rusape Chipinge Gokwe Gwanda Total population in 2012 1,485,231 1,736,249 530,318 356,862 46,035 Proportion of total population 11% 13% 4% 2.5% 0.5% Proportion of urban population 35% 42% 13% 8% 2%
Sources: Government of Zimbabwe, World Bank, and UCAZ 2016; ZIMSTAT 2012. figures used in the World Development Indicators (figure 2.1, panel a) or remained at 32 percent, according to ZIMSTAT estimates (figure 2.1, panel b).17 During this period, the size of Zimbabwe’s capital, Harare, increased only by around 5 percent to around 1.5 million people,18 while the national population grew by 13 percent19. Bulawayo, the country’s second-largest city, witnessed a drop from 675,000 to 650,000 people.
17 The 2017 urbanization rate is also estimated at 32 percent (ZIMSTAT 2017). 18 This would be around 2 million if one adds the nearby municipality of Chitungwiza (356,000 people), the nearby Local Board of Epworth (100,000 people), and Harare Rural. 19 According to the ZIMSTAT census reports. It was 17 percent according to UNFPA data. 12 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 2.1 Urbanization Trend a. Urban population, 1990–2017 b. Population size, urban and rural, (as % of total population) 2002 and 2012
41 14,000,000
39 12,000,000 37 10,000,000 32% 35 8,000,000 33 6,000,000 31
29 4,000,000
27 2,000,000
25 – 1989 1993 1997 2001 2005 2009 2013 2017 2002 2012 Zimbabwe Sub Saharan frica Rural rban Source: World Development Indicators Database. Sources: Zimbabwe CSO 2002; ZIMSTAT 2012.
Some researchers doubt whether indeed a deurbanization is taking place and claim that unadjusted urban/rural boundaries mean the demographic growth associated with urban sprawl has not been captured. Beacon Mbiba,20 for example, argues that population growth that has been rapid in rural areas around large cities like Harare should, in fact, be designated as urban, and the same is true in small and intermediate urban settlements. He illustrates this using satellite pictures of various small towns. The official definition of an urban area in Zimbabwe is that a settlement should be “compact,” have at least 2,500 inhabitants, and most of the employed should be engaged in nonagricultural employment.21 However, Mbiba argues that the Zimbabwe National Statistics Agency (ZIMSTAT) categorizes these areas as rural even if their population is above 2,500. This is confirmed by the census data, and indeed, in actual practice, areas are only counted as urban if they have been officially reclassified as urban/towns by the government. Although official figures are likely to underestimate urbanization in Zimbabwe, the conclusion that urbanization in Zimbabwe has at least slowed down is likely to still hold. The census data show that indeed between 2002 and 2012 the population in the rural districts and urban settlements around Harare grew by
20 Mbiba 2017. 21 ZIMSTAT 2012, p. 25. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 13
37 percent while the country’s total population grew by only 13 percent. Adding the population of all these districts to Harare Urban would double the city’s size to 2.9 million and raise the urbanization rate to 44 percent. However, not all these districts surrounding Harare can be regarded as urban. And census data suggest about one-quarter came from Harare while another one-quarter came from other urban areas. If we assume that half of Harare’s fast-growing neighboring rural districts should in fact be classified as urban, the national urbanization rate in 2012 would be 38 percent—6 percent higher than ZIMSTAT’s estimate for 2002 and close to the average for sub-Saharan Africa. It would increase the rise in urbanization during 2002–12 to 1.4 percent year—higher than the 1.2 percent per year rise for the whole country. However, if the growth in the urbanization rate during 1990–2002 was maintained during 2003–12, the projected proportion of people living in urban areas in 2012 would still be somewhat higher, at 39 percent. Moreover, as argued by Potts (2018), for various African countries, not all small towns may qualify as truly urban. We conclude, therefore, that although the urbanization rate may be underestimated, urbanization during 2002–12 in Zimbabwe has slowed down, unlike the rest of sub-Saharan Africa. Further research into the settlement patterns of all peri-urban areas (for example, using satellite pictures) is needed to assess to what extent an upward revision of the Zimbabwe’s urbanization rate is indeed justified. See also chapter 3.
The levels of urbanization can also be measured through population density. Here we find large differences among urban types and size groups. Population densities in City Councils and Town Councils/Local Boards (small towns) are low. Taking all small towns together (adding the total area of all small towns and dividing this by its total population) gives a population density in 2012 of only 325 per square kilometer (km2)—much below the official criteria for urban areas of 2,500 people/km2. This also gives a low population density in City Councils (517 per km2) (figure 2.2, panel a). Towns of between 50,000 and 100,000 people also have a very low density, but density here grew relatively fast and almost doubled between 2002 and 2012 (figure 2.2, panel b). Low population densities of urban centers make service delivery to its population expensive.
When looking at ward level population densities of urban settlements and their distribution, we note that variation across urban wards can be very high: from about 250 to 25,000 people per km2 (figure 2.3, panel a). This is especially the case in the capital, Harare. Smaller urban settlements have only a few areas that are densely populated (say, more than 5,000 people/km2). The median density (represented by the blue line in figure 2.3) is lowest in the smallest urban size category (8,000 to 20,000 people) while the mean (shown by the red line in 14 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 2.2 Population Density* of Urban Centers (people/km2) a. By official urban classification b. By urban size category
2,500 2,500
2,000 2,000
1,500 1,500
1,000 1,000
500 500
0 0
Rural Rural 1 million Metropoles City CouncilsMunicipalities 8,000–50,000 50,000–100,000 100,000–1 million
T. Councils Loc. Boards 2002 2012 2002 2012 Sources: Government of Zimbabwe, World Bank, and UCAZ 2016; ZIMSTAT 2012. Note: The high population density of municipalities is caused by high the density of Chitungwiza. * Total population in urban type/total area of urban type.
FIGURE 2.3 Distribution of Urban Ward Population Density by Size of Urban Center 2012 a. Urban areas with more than 1 million people b. Urban areas with 100,000 to (Harare) 1 million people
100 100
80 80
60 60 Percent 40 Percent 40
20 20
0 0 0 10,000 20,000 30,000 0 10,000 20,000 30,000 Population density (pop km2) in 2012 Population density (pop km2) in 2012 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 15
FIGURE 2.3 (Continued) Distribution of Urban Ward Population Density by Size of Urban Center 2012 c. Urban areas with 50,000 to 100,000 people d. Urban areas with 20,000 to 50,000 people
100 100
80 80
60 60
Percent 40 Percent 40
20 20
0 0 0 10,000 20,000 30,000 0 10,000 20,000 30,000 Population density (pop km2) in 2012 Population density (pop km2) in 2012
e. Urban areas with 8,000 to 20,000 people f. Rural areas
100 100
80 80
60 60 Percent Percent 40 40
20 20
0 0 0 10,000 20,000 30,000 0 10,000 20,000 30,000 Population density (pop km2) in 2012 Population density (pop km2) in 2012 Mean Median Source: Based on data from ZIMSTAT 2012.
figure 2.3) is lowest in the 50,000 to 100,000 people urban category. Even in the capital, Harare, the median is only about 4,200 people/km2 (figure 2.3, panel a). A recent study by the World Bank and UK Aid22 concluded that African cities are typically spatially dispersed and scattered in small neighborhoods. The study claims that without adequate roads or transport systems, commuting can be slow and costly, denying workers access to jobs throughout the larger urban area. Population densities outside urban centers differ largely across the urban types and are especially low outside City Councils/large cities. The 2012 population density map (figure 2.4a) suggests that rural population densities just outside the six City Councils, for example, are remarkably low (except Mutare),
22 Lall, Henderson, and Venables 2017. 16 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 2.4a Population Density and Zimbabwe’s Primary Cities and City Councils (a Ring Indicates a Radius of 100km)
Harare Mutare
Kadoma
Kwekwe
Gweru
vingo Bulawayo Mas
Source: Based on summary data from ZIMSTAT 2012. Note: circles show most large cities are located in thinly populated areas.
and lower than in areas located farther away from them. Unusually, there are many areas with a high population density that are far away from the large urban centers, such as districts in the northeast (east of Kadoma and Kwekwe), southwest (south and east of Masvingo town), and northeast (north east of Harare) (see the dark red areas in figure 2.4a). Population density is not strongly correlated with average rainfall as a large proportion of these densely populated areas receive relatively low rainfall. Unlike in most of sub-Saharan Africa, rural population density in Zimbabwe can be high in relatively dry areas (figure 2.4b). ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 17
FIGURE 2.4b Population Density and Agro-Ecological Zone
Proportion dot density 2012 and atural egions L B P eospatial perations Support Team, e elopment ata roup and obs CCS (2018)
Source: Based on summary data from ZIMSTAT 2012. Note: The blue dots represent population density; the background color represents an agro-ecological zone, or natural region. 1 = specialized and diversified >( 1000mm), 2 = intensive (750–1000mm), 3 = semi-intensive (650–800 mm), 4 = semi-extensive (450–650 mm), 5 = extensive (<650mm).
Harare and the medium-size towns (50,000 to 100,000 people) appear to be much less “isolated,” in terms of the number of people living nearby, than the City Councils or the large cities (100,000–1 million people). A closer look at the data on population densities by distance from urban centers confirms that the capital, Harare, and the medium-size towns are surrounded by, on average, much denser populated areas than the large cities. In Harare, 20km from the city center the density is, on average, still around 403 people/km2 (or around 6 when expressed as a natural logarithm, see figure 2.5). But for the City Councils (most of which are large cities), population densities drop rapidly to around 55 people/km2 (or around 4 when presented as a logarithm) at 20km outside the center. Kadoma, Kwekwe, Gweru, Masvingo, and to a lesser extent Mutare, are all surrounded by 18 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 2.5 Logged Population Density and Distance to Urban Areas Along the Road (0–100km) (Ward Level Averages and Fitted Polynomials) a. Harare b. Bulawayo (City Council)
10 10 (logged) (logged) 2 2 8 8
6 6
4 4
2 2
0 0 Population density, pop km
Population density, pop km 0 20 40 60 80 100 0 20 40 60 80 100 istance (along roads) to Harare, km istance (along roads) to Bulawayo City Council, km c. Kadoma (City Council) d. Kwekwe (City Council)
10 10 (logged) (logged) 2 8 2 8
6 6
4 4
2 2
0 0
Population density, pop km 0 20 40 60 80 100 Population density, pop km 0 20 40 60 80 100 istance (along roads) to Kadoma City Council, km istance (along roads) to KweKwe City Council, km e. Gweru (City Council) f. Masvingo (City Council)
10 10 (logged) (logged) 2 8 2 8
6 6
4 4
2 2
0 0 Population density, pop km 0 20 40 60 80 100 Population density, pop km 0 20 40 60 80 100 istance (along roads) to Gweru City Council, km istance (along roads) to Masvingo City Council, km ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 19
FIGURE 2.5 (Continued) Logged Population Density and Distance to Urban Areas Along the Road (0–100km) (Ward Level Averages and Fitted Polynomials) g. Mutare h. Medium-size towns (50,000–100,000 people)
10 10 (logged) (logged) 2 8 2 8
6 6
4 4
2 2
0 0 Population density, pop km
0 20 40 60 80 100 Population density, pop km 0 20 40 60 80 100 istance (along roads) to Mutare City Council, km Distance (along roads) to closest medium−sized town, km i. Small towns (20,000–50,000 people) j. Smallest towns (8–20,000 people)
10 10 (logged) (logged) 2 2 8 8
6 6
4 4
2 2
0 0 0 20 40 60 80 100 Population density, pop km
Population density, pop km 0 20 40 60 80 100 Distance (along roads) to closest small town, km Distance (along roads) to closest small town, km Source: Based on ZIMSTAT 2012. Note: The blue dot (•) represents the average population density at 20km distance from the urban center. 20 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
areas with relatively low population densities. Outside medium-size towns, these drops are much less sudden: 20km away from them, the average population density is 640/km2 (the log of which is 6.5), which is higher than 20km outside Harare. Small towns are also surrounded by, on average, much more scarcely populated areas (figure 2.5 panel i). Medium-size towns appear to be much less “isolated,” in terms of the number of people living nearby, than City Councils. Variation in ward population density across wards when moving away from urban centers is high for each of the City Councils and the size categories. Many of the rural areas with a higher-than-average population density are located far away from road connections. This includes the relatively densely populated areas in the west, east, and far north of the country. (See the black circles over the light-colored areas in figure 2.6.) These are far away from not only trunk
FIGURE 2.6 Population Density and Road Network
Population density 2012 and t e oad etwor L B P eospatial perations Support Team, e elopment ata roup and obs CCS (2018)
Major road Secondary road Feeder road Population density (pop m2) 0 - 10 11 - 25 26 - 50 51 - 100 101 - 500 501 - 1000 1001 - 5000 5001 - 30000 Protected areas
Source: Based on summary data from ZIMSTAT 2012. Note: The red lines represent the major roads while the thin black lines represent the secondary road network; the background color represents population density; the black circles demonstrate some of the densely populated and poorly connected areas. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 21
roads but also secondary and tertiary roads. The main trunk roads that connect Harare to Bulawayo and to some extent also the Harare–Masvingo road mostly run through scarcely population areas, even if they pass through various City Councils. Transport connectivity among people, measured through a spatial indicator referred to as “market potential,” confirms that various high population density areas are poorly connected to other people. Market potential is a spatial analysis term used to refer to an algorithm that reflects distance of people to other people weighted by the travel time along the road. Three of the four areas with a relatively high population density but poor connection to the road network (see circles in figure 2.7 copied from figure 2.6) have very low “market potential”
FIGURE 2.7 Population Weighted Travel Times of Each Ward to All Other Wards (“Market Potential”)
Sources: Based on summary data from ZIMSTAT 2012 and international geospatial databases. 22 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
and, thus, are isolated, while the fourth one (in the extreme north) has medium “market potential.” Satellite pictures of nighttime lights taken in 2018 confirm the country’s urbanization pattern. Current electrification in urban centers is high, with an access rate of 85 percent, but low in rural areas, with only 15 percent having access to electricity. The satellite map clearly shows the dominance of Harare, Chitungweza and Bulawayo in the lighted urban areas. Nighttime lights in the rest of the country are sparse and seem to be concentrated around the Harare–Bulawayo and Harare–Chinhyoi roads (figure 2.8, panel a). Various densely populated areas appear to have no electricity. The regional nighttime light picture shows the dominance of South African urban areas among all the urban areas in the region, in particular the areas between Beitbridge in Zimbabwe and greater Johannesburg/ Pretoria. This emphasizes the importance of these areas as market centers for the Zimbabwean economy and the importance of good connectivity with these centers. There are striking differences in the ratio of women to men across rural areas. Close to Harare and Bulawayo and the Harare–Bulawayo road, especially southeast of Gweru town and northeast of Kadoma town, and in Chikomba District there are more men than women, with women-to-men ratios varying between 0.8 to 1.0. However, the opposite is the case in rural areas farther away, where women-to-men ratios vary from 1.0 to 1.3 (figure 2.9a). Nationwide data suggest that in urban areas, the average proportion of women is higher than in rural areas (52.5 versus 51.7 percent), but urban-rural differences are small and, thus, the spatial disparities in sex ratio appear to be largely across rural areas. One factor possibly at play is artisanal mining, which tends to be a male occupation and is concentrated on the “Great Dyke,” a geological formation located along the Harare–Bulaway road (see map on page i and figure 2.9b). Indeed, the women-to- men ratio appears to be significantly related to the proportion of people working in mines, although the correlation coefficient is low. Large areas in particular in the southeast—Manicaland and Masvingo—and the southwest have many more women than men. International migration is thought to have played less of a role, as almost as many women as men are thought to have migrated to South Africa.23 Further research is needed to understand the causes of the high women-to-men ratios in many rural areas.
23 Crush and Tavera 2010. FIGURE 2.8 Nighttime Lights: Zimbabwe and Southern Africa a. Zimbabwe nighttime lights, August 2018
This is a time-enabled image service providing access to monthly cloud-free composites of Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) low light imaging data: detections of lighting from cities, towns, villages, combustion sources and lit fishing boats. https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html
b. Southern Africa nighttime lights, 1996–2010
Harare Lusaka
Bulawayo
Greater Johannesburg
Source: NASA 24 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 2.9a Women-to-Men Ratios, 2012
omen to men ratio in wards, 2012 L B P eospatial perations Support Team, e elopment ata roup and obs CCS (2018)
Source: Based on summary data from ZIMSTAT 2012.
Chapter summary
In this chapter we reviewed population settlement patterns. We noted that although there are many small towns, three-quarters of the urban population lives in cities larger than 100,000 people, and more than one-third of all urban people— or 11 percent of all Zimbabweans—lives in Harare (1.5 million). Only 7 percent of Zimbabweans live in medium-size or small towns. The current 32 percent urbanization rate in Zimbabwe is below the average of 39 percent for sub-Saharan Africa, and available data suggest urbanization in Zimbabwe has stalled or at least slowed down since 2002, even if the census data show that between 2002 and 2012 the population in the rural districts (and urban settlements) around Harare grew three times faster than the total population, implying that the true ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 25
FIGURE 2.9b Zimbabwe Gold Deposits
Sources: Pan African Resources PLC and customdigitalmaps.com. urbanization rate may be higher if the peri-urban population growth around Harare is taken into account.
Population densities of urban wards vary highly, suggesting the urban population is spatially dispersed and scattered across various high- and low-density neighborhoods. The urbanization literature suggests that this potentially undermines the beneficial agglomeration effect of urban centers. Population densities outside urban centers differ largely among the types of urban centers and are especially low outside City Councils/Harare. The medium-size towns appear to be much less “isolated” in terms of the number of people living nearby. Population density is not strongly correlated with average rainfall, unlike in many other parts of the African continent. Many of the rural areas with a higher-than-average population density are located far away from road connections and located in some of the dryer areas. This includes the relatively densely populated areas in the west, east, and far north of the country. 26 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
There are striking differences in the ratio of women to men across rural areas which may be related to mining activities.
This report is exploratory in nature, and various areas for further research are identified. This chapter suggests that further research is needed on, first, urbanization trends in Zimbabwe that take into account the population growth of peri-urban areas, as some of these could be classified as urban. Such research could include an assessment of trends in built-up areas using satellite pictures. Second, the extent of urban dispersion across scattered settlements within large cities and its impact on access to the jobs market and economic activity deserves further investigation. Third, the reason for the high women-to-men rations in various rural areas should be further studied. 3 POPULATION MOVEMENT DURING 2002-2012
This chapter will look at changes in population settlement patterns during 2002–12. During this period Zimbabwe had one of the largest internal migration flows in the world. We assess the characteristics of these population movements, reviewing origin and destination patterns.
Between 2002 and 2012 population growth in Zimbabwe was low. The number of people living in Zimbabwe rose from 12.7 to 14.5 million according to United Nations Population Fund (UNFPA) estimates—or from 11 million to 12.5 million according to Zimbabwe National Statistics Agency (ZIMSTAT) figures. The UNFPA figures suggest a growth of only 17.5 percent or 1.6 percent per year while the ZIMSTAT figures suggest these figures are 13 percent and 1.2 percent per year. Annual population growth dropped from 3.9 percent in 1983 to 1.1 percent in 2003 and has currently stabilized at 2.3 percent per year. The fast drop in population growth was related to a rise in the mortality rate caused by the human immunodeficiency virus (HIV) epidemic, as well as a drop in the fertility rate and mass emigration following the economic crisis of the 2000s. Conservative estimates put the number of Zimbabwean emigrants in 2010 between 1 million and 2.5 million, while advocacy groups estimate the figure to be closer to 5 million.24 Most of the emigrants, among which were many skilled (but also unskilled) professionals, went to South Africa. The 2011 South Africa Census placed the number of international migrants in South Africa at 2,173,409, or about 4.2 percent of the country’s total population at that time.
24 Muzondidya 2011, 116–117, quoted in Mareyanadzo, Ogude, and Bangwayo 2018.
27 28 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
According to that census, 672,308 migrants were from Zimbabwe and accounted for 30.9 percent of the total migrant population and 45.5 percent of all migrants from Southern African Development Community (SADC). A large proportion of all migrants (11 percent) chose not to disclose their country of origin.25
The change in population density between 2002 and 2012 varied highly across the country, suggesting that emigration was higher in some areas than others and that substantial population movements have taken place within the country during this period, even if the proportion living in urban areas barely changed. Many areas saw decreases of 50 percent and more while in others, population density rose by 100 percent or more (figure 3.1).
FIGURE 3.1 Percentage Change in Population Density, 2002–12
Change (%) in Population Density 2002 - 2012 WORLD BANK GROUP Geospatial Operations Support Team, Development Data Group and Jobs CCSA (2018)
Sources: Based on summary data from ZIMSTAT 2012 and Zimbabwe CSO 2002 and international geospatial databases.
25 World Bank 2018a. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 29
Many people appear to have moved to low population density rural areas, while others moved into the already densely populated areas around Harare. At the national level no statistical relationship was found between the population increase during 2002–12 and population density (figure A.1 in appendix A). Figure 3.2 shows the increase in population numbers (represented by red dots) were high in some areas with low population density (dark blue background color), but it was also high in other areas with a high population density (light green or yellow background color), such as around Harare. Population increase was high around Harare, in rural parts of Kadoma District and Makonde District (west of Chinhoyi) in Mashonaland West, and also in rural parts of Chiredzi and Bikita Districts in Masvingo.
FIGURE 3.2 Population Increase in Absolute Numbers on Population Density
Population density 2012 Population change 2002 - 2012 WORLD BANK GROUP Geospatial Operations Support Team, Development Data Group and Jobs CCSA (2018)
Source: Based on summary data from ZIMSTAT 2012 and Zimbabwe CSO 2002. Note: The red dots represent the population increase in absolute numbers; the background color represents population density. 30 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
As mentioned in chapter 2, the population in areas officially defined as “Harare Urban” barely grew in size during 2002–12. But if we look at the areas surrounding Harare urban, we find that the population there increased substantially, by 400,000 people (figure 3.3, panel a) or 18 percent. This includes some of the urban settlements around Harare such as Epworth, which grew by 50 percent, and Ruwa, which saw its population increase by 139 percent. The rural districts surrounding Harare, such as Goromonzi, Seke, Zvimba, and Mazowe, also expanded and grew by 33 to 49 percent (figure 3.3, panel b and figure 3.3, panel c). The country’s total population grew by only 13 percent. Thus, substantial population movements have taken place toward the outer areas of Harare. About half of those who moved to these areas came from rural areas, one-quarter came from Harare, and another one-quarter came from other urban areas.
The population increase in areas surrounding Harare is also evident from land cover maps of 1995, 2005, and 2015. These suggest built-up areas (referred to as “urban” in figure 3.3d, see red areas) have increased substantially during the past 20 years26.
Internal migration in Zimbabwe is among the highest in the world. According to the Population Census report 2012, 23 percent of the population of 10 years and older that year indicated they lived in a different district in 2002. In 2002 this was even higher, when 25 percent said they lived in a different district than in 1992. The developing country average proportion of internal migrants has been estimated at around 12 percent,27 and Zimbabwe has the world’s second-highest intensity of internal migration between zones, after Chile (corrected for the average number of people in the zones that are distinguished for migration and given GDP per capita).28
Most of the internal migrants in Zimbabwe (59 percent) live in rural areas, while the proportion of migrants is highest in the smallest towns. Of all internal migrants in 2012, 58 percent originate in rural areas while the remaining 42 percent come from urban areas. 16 percent come from Harare (figure 3.4, panel a). In 2012 the proportion of migrants was highest in the towns with a population between 50,000 and 100,000 people and between 20,000 and 50,000 people, where these formed 43 to 44 percent of the population, much above the national average of 23 percent. The proportion of migrants is growing fastest in the smallest towns (8,000–20,000 people) (figure 3.4, panel b).
26 This was also confirmed by Wania et al. 2014. 27 UN Population Division 2013, quoted in Lucas 2015. 28 Lucas 2015. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 31
FIGURE 3.3 Population Growth in Harare and Surrounding Areas, 2002–12 a. Number of people (in thousands) b. Relative population increase Harare and surrounding districts (percentage) Harare and adjacent rural distrcts Harare and adjacent 18% Harare Urban rural distrcts 3% Chitungwiza Harare Urban
Zvimba Chitungwiza Zvimba Mazowe Mazowe Goromonzi Goromonzi Epworth Epworth Seke Seke Harare Rural Harare Rural Ruwa Ruwa – 500 1,000 1,500 2,000 2,500 3,000 0% 50% 2002 2012 100% 150% 200% 250% 300% c. Population density in 2002 and 2012
Source: Based on summary data from ZIMSTAT 2012 and Zimbabwe CSO 2002. 32 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 3.3d Land Cover Maps of Harare and Surroundings in 1995, 2005, and 2015
Source: European Space Agency (ESA) CCI land cover satellite data. Note: 300 m annual global land cover time series from 1992 to 2015. https://www.esa-landcover-cci.org/?q=node/175
FIGURE 3.4 Internal Migration b. Proportion of population who are migrants* by urban size and rural areas, 2002 and 2012
0.5 0.46 a. Internal migrants* in 2012 and their origin 0.44 (as proportion of total population) 0.42 0.4 0.36 Harare, 0.35 0.34 16% 0.32 0.3 0.28
Rural Bulawayo, areas, 5% 0.19 58% 0.2 0.18
Other cities, 9% Proportion migrated, means Proportion migrated, 0.1
Municipalities 12% 0.0
Rural >1 million 8,000−50,000 50,000−100,000 100,000−1 million 2002 2012 Sources: Based on Zimbabwe CSO 2002 and ZIMSTAT 2012. * Those who indicated they lived in a different district 10 years ago. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 33
Although in rural districts the average proportion of migrants is only 18 percent, the variability is high, and some of the rural districts have a high influx of migrants. The data suggest that these are concentrated around the densely populated areas around Harare. This includes Harare Rural (where in 2012 77 percent of the population of 10 years and older lived in another district 10 years ago) and other districts surrounding the capital such as Goromonzi, Seke, Marondera, Zvimba, Bindura, Chegutu, and Mazowe rural districts (where 26 to 40 percent was a migrant in 2012). High migrant proportions are also found in rural areas near Bulawayo (Umguza), near Kadoma (Kadoma rural), Gweru (Gweru Rural), and in Masvingo (Chiredzi). The spatial distribution of districts with a high proportion of migrants has remained similar between 1992–2002 and 2002–12 (figure 3.5). The relatively densely populated rural areas in Manicaland and the northern parts of Masvingo (see figure 2.4a in chapter 2) appear to have lower numbers of migrants.
Most of the cross-district migration during 1992–2002 and 2002–12 was from rural to rural (632,000 people). The number of people moving from rural to urban areas (508,000) during 2002–12 was only marginally higher than those who moved in the other direction, from urban to rural areas (492,000), confirming findings from other researchers of migration in Zimbabwe that circular migration is common.29 Urban-to-rural migration has increased while rural-to-urban migration has dropped (figure 3.6, panel a). Those who left rural
FIGURE 3.5 Proportion of the Population at Ward Level Who Is Migrant (Lived Somewhere Else 10 Years Ago) a. 2002 b. 2012
Proportion migrants 2002 WORLD BANK GROUP Geospatial Operations Support Team (2018) Proportion migrants 2002 WORLD BANK GROUP Geospatial Operations Support Team (2018)
Source: Based on summary data from ZIMSTAT 2012. Note: Darker areas suggest a higher proportion of migrants. 34 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
areas mostly went to other rural areas (632,000 people), followed by Harare (205,000 people) and then municipalities, towns, and cities (103,000 people). More people migrated to municipalities and towns (352,000) than to Harare (302,000), with most of these (around 200,000) coming from rural areas. In 2012 Harare had received most of its migrants from rural areas, but about one- third of the migrants (about 113,000 people) had come from other urban areas (figure A.4 in appendix A).
Net migration, the percentage of population who moved in minus the percentage of people who left, when expressed as a percentage of the population who lives there, was also highest in municipalities and small towns (figure 3.3, panel b). These urban centers are growing faster in a relative sense than Harare and City Councils. Even if this change is relative and from a low base, it suggests the distribution of the urban population has slowly shifted toward these smaller urban areas. Indeed, the proportion of the
FIGURE 3.6 Internal Migration Between Urban and Rural Areas a. Rural-to-urban and urban-to-rural migration b. Net migration (% of population who moved patterns, 1992–2002 and 2002–12 in minus % of people who left) by urban type (number of migrants) during 1992–2002 and 2002–12
700,000 14% 12% 600,000 10% 500,000 8% 400,000 6% 4% 300,000 2% 200,000 0% –2% 100,000 –4% 0 –6% Rural to Rural to Urban to Urban to rural urban rural urban Harare and LBsRural areas 1992–2012 2002–2012 City Councils MunicipalitiesTown Councils 1992–2012 (%) 2002–2012 (%) Source: Based on summary data from ZIMSTAT 2012.
29 For example, Potts 2011. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 35
population who lives in small towns rose from 4 to 6 percent between 2002 and 2012, while the proportion who lived in city councils dropped from 11 to 10 percent.
International evidence suggests that secondary town development (and rural nonfarm growth) could lead to a more inclusive growth patterns than metropolization. This could be because more of the poor find their way to small secondary towns (and the nonfarm economy) than to distant cities.30 Reasons could be lower migration costs and ability to maintain and exploit closer social ties with the area of origin. Circular migration and commuting to the towns to find off-farm employment likely also play a role.
Considering rural areas, Mashonaland East and Mashonaland West had the highest inflow of migrants during 2002–12 (almost 200,000 people). However, rural outflow was also highest in Mashonaland East and was high in Masvingo as well (almost 200,000 people). Net outflows of rural areas were highest in Masvingo, Manicaland, and Midlands. Mashonaland West was the only province with high net rural inflow. Looking at urban areas, we see a substantial movement into and out of Harare (which here includes Harare Rural), with 346,000 people leaving and 460,000 people moving in.
The area lighted at night (9 p.m.) decreased in many urban areas between 1996 and 2010, according to satellite pictures (figure 3.7). This suggests urban stagnation and lack of urban dynamism. Other studies31 that also used nighttime light imagery came to the same conclusion and found that the mining and agricultural towns in particular were affected, blaming Zimbabwe’s economic decline.32
Chapter summary
This chapter looked at recent population movements. It was found that between 2002 and 2012, population growth in Zimbabwe was low. However, the change in population density varied highly across the country, suggesting that emigration rates varied across areas and that substantial population movements have taken place within the country during this period, even if the proportion of
30 Christiaensen, De Weerdt, and Todo 2013. 31 Li, Ge, and Chen 2013. 32 Chingozha and von Flintel 2017b. 36 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 3.7 Compound Annual Growth Rate of Nighttime Light, 1996–2010
Source: NASA, based on time series of Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) low light imaging data: detections of lighting from cities, towns, villages, combustion sources and lit fishing boats. https://ngdc. noaa.gov/eog/viirs/download_dnb_composites.html. Blue and yellow color indicates negative growth rate of night time lights while orange and red colors indicate in increase.
the population living in areas officially designated as urban areas barely changed. Many people appear to have moved to low population density rural areas, while others moved into the already densely populated areas around Harare. The areas surrounding Harare saw a substantial population increase of 18 percent or around 400,000 people. Internal migration in Zimbabwe is among the highest in the world: 23 percent of the population that is 10 years and older in 2012 lived in a different district in 2002. Most of the migration was rural to rural, although the proportion of the population who is a migrant is highest in the smallest towns. Mashonaland West ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 37
was the only province with high net rural inflow. Net outflows of rural areas were highest in Masvingo, Manicaland, and Midlands. Further research is needed to explain past population movements. This would include assessing to what extent high rural-to-rural migration is related to high population inflows to rural areas around Harare. Another area of further study would be to explain why population increase in rural areas such as Mashonaland West was so high while other rural areas saw a relatively high net out-migration. Land reform is likely to have played a role (see chapter 4), but further research is needed to explain the large differences across districts and provinces.
4 REASONS BEHIND THE SPATIAL SETTLEMENT PATTERN AND POPULATION MOVEMENTS
This chapter discusses the factors that can explain the unusual spatial settlement pattern, population movements, and slowdown in urbanization in Zimbabwe. It looks at Zimbabwe’s colonial legacy of land allocation and postcolonial land reform as well as the subsequent economic crisis of the 2000s and the role this played in defining population settlement patterns and migration.
The colonial Land Apportionment Act of 1930 allocated land to different population groups and is responsible for Zimbabwe’s unusual settlement pattern where most large cities (City Councils) are situated in areas with a low population density. It has also resulted in areas with a higher-than-average population density located far away from main roads and sometimes even from secondary and tertiary roads. The increase in urban-to-rural migration, and high levels of migration within rural areas that were noted in chapter 3, were likely related to land reform – as mentioned- and the subsequent economic crisis. The halt in the urbanization rate can possibly also be linked to government efforts to prevent migration to Harare by demolishing informal settlements that had sprung up. Many of the larger City Councils were at the heart of the “European areas” designated in the 1930 land act (see also next paragraphs). These cities have traditionally been surrounded by large “white” commercial farms with a
39 40 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
low population density. This is true in particular for Kadoma, Kwekwe, Gweru, and Masvingo, but less for Bulawayo and Mutare (and Harare) (red circles in figure 4.1). These cities were well connected with a good road network but linked together markets of agricultural and other goods rather than people. Figure 4.1 shows that areas with low population numbers (few blue dots) in 2002 largely correspond with the former white commercial farm areas (purple background color), except in areas north and northeast of Harare. In contrast, population numbers were high in the communal lands (formerly called “tribal trust lands,” yellow background color) located far away from the large secondary cities. In 2002, the median ward level population density in rural areas in communal
FIGURE 4.1 Population Density in 2002 and Land Type
Population dot density 2002 and Land apportionment 1930 WORLD BANK GROUP Geospatial Operations Support Team, Development Data Group and Jobs CCSA (2018) Harare Kadoma
Kwekwe
Bulawayo
Mutare
Gweru Land apportionment Large scale commercial farms post 2000 land reform Small-scale commercial farms / old native purchase areas National Park
Communal lands Undesignated land Old resettlement (1980-2000 land reform) Masvingo Population density: 1 dot = 1,000 people
Land apportionment map by T, Chingozha
Sources: Based on summary data from ZIMSTAT 2012 and international geospatial databases. Spatial data on the Land Apportionment Act are from Tawanda Chingozha (Stellenbosch University). Note: The blue dots represent population density in 2002. The red circles are the large cities (Harare and City Councils) and demonstrate many are surrounded by thinly populated rural areas. The background color represents the land type. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 41
lands was almost three times higher than in each of the other rural farmland- type areas (table 4.1).
The colonial land act led to a situation whereby in 1963, the native African population of more than 2.5 million was squeezed inside 50,000 square miles of tribal trust lands, which often consisted mostly of infertile and dry areas typically, although not all, located on the fringes of the country, far away from the road network (figure 4.1) (Ranga 2004). The settlers, who numbered just over 200,000, enjoyed the other 75,000 square miles, including the more fertile high-rainfall areas around Harare, but also the rangelands in the south of the country (figure 4.1). Areas referred to as “old resettlement” consist of farmland sold by white commercial farmers and resettled after independence, between 1980 and 2000. Small-scale commercial farms are areas that were formerly designated as “native purchase areas,” which was land native Zimbabweans could purchase before independence.
Zimbabwe underwent large-scale land reform during the 2000s. In 2000, before the fast track land reform started, around 3.5 million hectares had been resettled, and in 2010, another 7.6 million had been added (table 4.2).
Census data confirm that rural population increase during 2002–12 was much higher in the former white commercial farm areas that were subjected to fast track land reform in the 2000s than in any of the other land types. The census data suggest that the rural population in these areas increased by around 35 percent, or 670,000 people, compared to 3 percent, or around 160,000, in the communal lands. The proportion of the rural population who lives in the former white commercial farm areas rose from 25 to 30 percent (figure A.3 in appendix A). A large majority of the rural population, however, remains located in the communal lands (figure 4.2, panels a and b).
TABLE 4.1 Population Density 2012 by Land Type (People/km2)
Median Land apportionment Urban Rural Harare Urban and other 6,056 27 Former white commercial farmland resettled after 2000 880 18 Old resettlement schemes resettled during 1982–2000 17 Small-scale commercial farms 12 Communal lands 4,762 42
Source: Based on summary data from ZIMSTAT 2012. 42 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
TABLE 4.2 Changes in the Distribution Structure of the Land (Millions of Hectares)
Land category 1980 2000 2010 Communal areas 16.4 16.4 16.4 Old settlement 0.0 3.5 3.5 New resettlement: A1 (small) 0.0 0.0 4.1 New resettlement: A2 (large) 0.0 0.0 3.5 Small-scale commercial farms 1.4 1.4 1.4 Large-scale commercial farms 15.5 11.7 3.4 State farms 0.5 0.7 0.7 Urban land 0.2 0.3 0.3 National parks and forest land 5.1 5.1 5.1 Unallocated land 0.0 0.0 0.7
Source: Scoones et al. 2011.
Zimbabwe’s land reform offered incentives for the urban class to migrate back to rural areas to take up farming,33 and some studies indicate that the fast track land reform program may indeed have allocated land plots to poor urban households34 contributing to the rise in urban-to-rural migration noted above. These findings suggest that land reform is likely to have contributed considerably to the high levels of internal migration in Zimbabwe. Other researchers have suggested that between 2000 and 2010 some 170,000 households35 were resettled. At a household size of 4.2, this would be equivalent to around 700,000 people which is close to the number we arrive at (an increase of 670,000 people, which includes natural population growth), based on the population census data, when we look at both rural and urban population in these areas. The increase in population numbers in some former white commercial farm areas is much higher in certain areas than in others (figure 4.3). For example, as noted above, the south of Makonde District in Mashonaland West and parts of Kadoma District saw a remarkable rural influx, which all took place in the former
33 Moyo et al. 2013; Scoones et al. 2011. 34 Moyo 2010 and Murisa 2010, quoted in Chingozha and von Fintel 2017b. 35 Sam Moyo et al. 2013. Chigumira (2010) estimates the number of resettled households at 156,000 on 7 million hectares of land. FIGURE 4.2 Population Change 2002–2012 by Land Class (Farm Land Type)
a. Rural population change by 1930 land class, 2002–12
6,000,000
5,649,005 5,490,824
4,000,000
2,571,352
Rural population 2,000,000 1,898,593
220,167 196,933 213,191 131,550 0 Large Scale Small-Scale Communal Undesignated Commercial Commercial Lands Land Farm Areas Farm Areas 2002 2012
b. Rural and total population change, 2002–12
Large Scale Commercial Farm Areas
Small-Scale Commercial Farm Areas
Communal Lands
Undesignated Land
National Park
0 200,000 400,000 600,000 800,000 1,000,000 Population increase Rural pop Total pop Source: Based on summary data from Zimbabwe CSO 2002 and ZIMSTAT 2012; shapefiles of land types from Tawanda Chingozha, Stellenbosch University. Note: ‘Large Scale Commercial Farms’ are areas formerly designated as ‘White Commercial Farm Areas’ or ‘European Areas’, while ‘Small-Sale Commercial Farm Areas’ areas are the former ‘Native Purchase’ areas’. Communal farms were formerly referred to as ‘Tribal Trust land’. 44 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 4.3 Population Increase 2002–2012 in Absolute Numbers on Farmland Type
Land Apportionment and opulation an e RL BA R U eo patial peration Support eam e elopment ata roup and ob SA
Land apportionment Large scale commercial farms post 2000 land reform Small-scale commercial farms / old native purchase areas National Park
Communal lands Undesignated land Old resettlement (1980-2000 land reform
opulation in rea e dot people
Land apportionment map by T, Chingozha
Sources: Based on summary data from Zimbabwe CSO 2002 and ZIMSTAT 2012; shapefiles of land types from Tawanda Chingozha, Stellenbosch University. Note: The red dots represent a population increase in absolute numbers; the background color represents the farmland type.
white commercial farm areas. This could be explained by the type of resettlement— that is, whether the resettled farmers were smallholders with, say, 6 hectares of land or whether they had much larger farms. The first are referred to as “A1” resettlement farmers and the second as “A2.” Further research is needed to confirm whether the high population influx was indeed higher in where “A1” farmers were common. A migration flow chart confirms the circular migration that took place as well as the population movements to the land reform areas. The largest migration flows in terms of number of people were (1) from the communal lands to other ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 45
FIGURE 4.4 Migration Between Farmland Types, 2002–12 Migration between land classes in Zimbabwe
ttlement 1982- rese 2000 (in 100,000s) Old Urban 0 0 7 2 6 e r 4 ra n 5 a a H rb U 4
6 3
2 C o m R 8 m 1 u r u a n l a
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Sources: Based on data from Zimbabwe CSO 2002 and ZIMSTAT 2012. ‘White resettlement 2000’ refers to land resettled post 2000 during the fast track land reform. ‘S. rural’ refers to ‘small-scale commercial farms’ which are the old ‘native purchase’ areas. 46 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
communal lands, followed by (2) communal lands to Harare, and (3) communal areas to the urban areas of the former white commercial farm areas that were resettled post 2000. During 2002–2012 around 410,000 people moved to the white commercial farm areas (urban and rural) of which around 290,000 came from communal lands and 110,000 from Harare. However, around 140,000 people moved in the other direction: from the commercial farming areas to communal lands (figure 4.4). Additional research is needed to further unravel and explain these population movements.
Urban economic opportunities declined since the onset of the economic crisis, following fast track land reform of the early 2000s.36 This also played a role in the reverse trend of urban-to-rural migration. People had moved to urban areas in search of a better life, but when economic conditions deteriorated, costs of living went up, and housing quality appeared poor, many returned to rural areas. Coping with hyperinflation, rising unemployment, and water and power shortages, urban dwellers began to reconsider the option of pursuing a rural livelihood.37 As noted above, some were able to obtain land through the land reform program. Potts (2011) suggests that the relative valuation of rural lifestyles and livelihoods increased significantly as the Harare economy declined (see box 4.1). The census data show that those who left Harare during 2002–12 (almost 300,000 people) mostly went to rural areas. Rural areas received almost half of their migrants from urban areas, and mostly from Harare (about 200,000 people) (figure A.4 in appendix A). Zimbabwe is not the only country that experienced an increase in urban-to-rural migration. Côte d’Ivoire, for example, experienced a similar phenomenon during its crisis period in the 2000s.38
Labour Force Survey data show that Zimbabwe experienced a substantial move of labor into agriculture during 1999–2014, suggesting “reverse structural transformation” took place. Labor productivity in the agricultural sector more than halved. The proportion of people conducting nonfarm work in 2014 was low: only 17 percent of those who work, while 83 percent was active in the agricultural sector. The number of people who mainly depended on the agricultural sector for
36 Potts 2011. 37 Groves 2012. 38 Beauchemin 2011. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 47
BOX 4.1 Making a Living in Harare (Case Study)
“In 1999 Mrs Gambanga moved to Harare from Gutu communal area, where she had been born. At first she worked as a domestic in Msasa Park, a high-income residential area. By 2001 she was living in Kuwadzana Extension, a formally planned, low-income, high-density area on the western edge of the city. She was married to a security guard who earned Z$6,800 per month but food, transport and rent alone cost them Z$4,820. She was no longer working and was a home- carer. When asked about her standard of living in Harare and her views on the pros and cons of urban life, she expressed strong dissatisfaction. According to her, there was not much difference between employment opportunities in Harare and those in the communal areas, for ‘unemployment is a problem everywhere’. She was worried about the ‘price hikes on everything’. She felt that her standard of living would be better in the rural areas of Gutu. They had some land there and had managed to produce maize and groundnuts the previous year. This rural production was important to them and had contributed to their cash income and to their food consumption in Harare. Mrs Gambanga said she and her husband planned to leave Harare soon—in less than a year. In Gutu she felt she had ‘opportunities to survive out of my own effort’. Another reason for leaving Harare was that her husband’s income was ‘so little’.”
Source: Potts 2011.
their income rose by 960,000 between 1999 and 2014.39 In contrast, employment (formal and informal) in the industrial sector dropped by around 130,000 and rose by 340,000 in the services sector (figure 4.5). The value added per worker in the agricultural sector more than halved from an already low level. Workers went back to self-employment in agriculture. The number of people conducting unpaid or self-employed work in the agricultural sector rose sharply
39 World Bank 2018b. 48 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 4.5 Farm and Nonfarm Employment, 1990–2014
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
0 1999 2004 2011 2014 Agriculture paid Non-agriculture paid Non-agriculture self-employed Agriculture self-employed and unpaid Sources: World Bank 2018b, based on ZIMSTAT data (Labor Force Surveys 1999, 2004, 2011 and 2014) and World Development Indicators Database.
during 1999–2014 (from 46 to 60 percent), while those in self-employment outside agriculture also went up (from 12 to 16 percent). However, the welfare impact of these population movements was limited as rural non-monetary poverty did not fall between 2001 and 2011 (Figure 1.3). The number of people receiving agricultural and non-agricultural wages dropped from 42 to 26 percent during 1999–2011, reflecting diminishing wage-earning opportunities during the economic crisis of the 2000s. It then rose again somewhat in absolute numbers during 2011–14 (figure 4.5), when Zimbabwe seemed to be recovering from the crisis.
The census data from 2002 and 2012 suggest that the drop in the percentage of the population with nonfarm-income-generating activities Zimbabwe experienced during 2002–12 was most dramatic (between 10 and 40 percent) in northern Zimbabwe in areas in a wide circle around Harare (covering parts of Mashonaland West, Central, and East), as well as near Bulawayo, Kadoma, and Kwekwe (figure 4.6). These clearly were the areas that suffered from the economic decline during this period. While the proportion of the population who gained their income from nonfarm work declined on average, this differed highly among wards. Some of the rural areas that witnessed a relative high population increase ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 49
FIGURE 4.6 Change in the Proportion of People with Nonfarm Jobs, 2002–12
an e in proportion o people it non arm ob RL BA R U eo patial peration Support eam e elopment ata roup and ob SA
an e proportion all non arm ob
–0.42––0.10 –0.09––0.05 –0.04––0.01 0.00–0.01 0.02–0.05 0.06–0.10 0.11–1.00 missing Protected Areas water
Sources: Based on data from Zimbabwe CSO 2002 and ZIMSTAT 2012. saw the proportion of people dependent on nonfarm work decrease substantially (for example, areas north of Harare), possibly due to smallholders obtaining land through land reform. But this pattern is not consistent (for example, we see the opposite in areas south of Kadoma) because it most likely also depends on whether nonfarm jobs in the areas disappeared due to the collapse of the industrial sector, whether artisanal mining work grew in importance, and whether the base figure was high or low. Further research is needed to confirm the drivers of the change in nonfarm job opportunities. Throughout the 2000s, the government also adopted several policies to remove slumdwellers from Harare and other urban areas. This often was accompanied with violence, providing an even greater impetus for many to leave Zimbabwe’s cities. In 2005, inhabitants of shantytowns in Harare became the victims of a government campaign named “Operation Murambatsvina,” which 50 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
aimed at pushing people out of the city. Potts (2011) estimates that about 150,000 people were made homeless as a result of this campaign. The operation was not limited to Harare, and clearances were also ordered in Bulawayo and smaller towns and trading centers around the country. As mentioned, emigration, mostly to South Africa, driven by the economic crisis of the 2000s, likely also contributed to the lack of urban population growth. The drop in the population of Bulawayo is most striking as emigrants probably disproportionally originated from urban areas and from the southwest of the country, closest to South Africa. The population movements during 2002–12 were positively correlated with the availability of nonfarm jobs and higher population densities. Population increase 2002–12 was significantly higher in areas where the proportion of the population working outside agriculture in 2002 was higher, controlling for other factors. It was also significantly higher in the post–fast track land reform areas and positively correlated to population density in 2002, as shown by the regression analysis (table 4.3). The findings suggest people’s movements in 2002–12 were influenced by the availability of nonfarm jobs, and they were attracted to areas with higher population densities, suggesting agglomeration takes place. It also confirms that population increase was positively related to whether areas were in the fast track land reform zones and to the early (pre–fast track land reform) resettlement areas. Even though these had been resettled years before, regression analysis results show they were still attracting people during 2002–12. In addition, population increase was somewhat significantly positively related to whether areas where better connected to where other people live (“market potential”), controlling for other variables. Regression analysis suggests that the positive relationship of population movement with population density and with the proportion of people in nonfarm work may be driven by urban centers. When we control for these urban centers, we find that population change is toward lower population density areas and to areas where the employment rate was lower in 2002 (table 4.3, Model B). Population change is positively correlated to urban centers, with high coefficients for small towns and urban centers. Controlling for this, we find that population change is negatively related to population density and the employment rate, meaning that population increase is higher where population density and the employment rate is lower. Controlling for urban centers, we also find that an increase in population density was higher when there were more men than women, which may be related to the situation in the former white commercial farming areas. The full set of regressions results is presented in appendix B. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 51
TABLE 4.3 Results of Regression Analysis of Ward Level Summary Data Showing Variables That Were Significantly Correlated with the Relative Change in Population Density, 2002–12
Variables significantly correlated with a change in population density Model A Model B Standard Standard VARIABLES Coefficient error Coefficient error Proportion in nonfarming occupations, 2002 0.635** (0.266) Population density, 2002, logged 0.132*** (0.044) –0.208*** –0.065 Market access (“market potential”) 0.092* (0.053) Employment rate, 2002 0.558 (0.413) –1.082** (0.440) Women-to-men ratio, 2002 0.622 (0.454) –0.438* (0.263) Mean years of schooling, 2002 0.004 (0.044) Change in mean years of schooling, 2002–12 –0.085 (0.068) Proportion working in mining (dummy) –0.058 (0.110) 0.057 (0.174) Land class type dummies (Base: 5. Tribal Trust) 1. Large-scale commercial farm areas (resettled 0.463*** (0.137) after 2000) 2. Old resettlement areas (resettled during 0.152** (0.064) 1980–2000) 3. Previous white: other 0.012 (0.100) 4. Small-scale commercial farms (former “native –0.074 (0.059) purchase” areas) Natural region dummies (Base: 5. Region IV: Semi-Extensive Farming) 1. Region I: Specialized and Diversified Farming –0.059 (0.071) 2. Region IIA: Intensive Farming 0.068 (0.074) 3. Region IIB: Intensive Farming –0.002 (0.069) 4. Region III: Semi-Intensive Farming 0.008 (0.049) 5. Region V: Extensive Farming 0.044 (0.072) Location type dummies (Base: Rural Area) Harare 0.838*** (0.321) City Councils 0.861*** (0.314) Municipality 1.162*** (0.339) Town Councils and Local Boards 2.401* (1.248) Constant 1.811** (0.735) 1.719*** (0.239) Observations (wards) 1,404 1,485 R-squared 0.117 0.147
Sources: Based on data from Zimbabwe CSO 2002 and ZIMSTAT 2012. Note: Robust standard errors in parentheses. See appendix B for more results. *** p<0.01, ** p<0.05, * p<0.1. 52 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
Chapter summary
This chapter discussed factors that explain Zimbabwe’s spatial settlement pattern and population movements. The colonial Land Apportionment Act of 1930 is responsible for Zimbabwe’s unusual settlement pattern. Many of the larger secondary cities (City Councils) have traditionally been surrounded by large white commercial farms with a low population density. The native African population was largely confined to the often-dry and less-fertile tribal trust lands/communal lands, which were often poorly connected to the road network. Large-scale land reform during the 2000s contributed to internal migration toward rural areas. The population in the former white commercial farm areas rose by about 700,000 people. Inflows were highest from communal lands, followed by inflows from Harare. Declining economic opportunities since the onset of the economic crisis following fast track land reform of the early 2000s played a role in the reverse trend of urban-to-rural migration. Labor Force Survey data confirm there was a substantial move of labor into agriculture during 1999–2014. Most went back to self-employment. Emigration and policies to remove slumdwellers from Harare and other urban areas also contributed to urban-to-rural migration. The population movements during 2002–12 were positively correlated with the availability of nonfarm jobs and higher population densities. But when controlling for urban settlement areas, we find that this no longer holds and that instead, population change is negatively related to population density and the employment rate. Further research is needed on the drivers of population movements. The increase in population numbers in some parts of the former white commercial farm areas is much higher in other parts. Further research is needed to assess whether this is related to the type of resettlement and consisted of smallholder (type A1) or larger (type A2) resettlement farms. Further study is also needed to confirm the drivers of the change in nonfarm job opportunities, and to explain why population change is toward areas where the employment rate was lower in 2002 (when controlling for urban centers) and the role existing education levels and sex ratios in areas played in migration toward these areas. 5 CONSEQUENCES OF THE POPULATION’S SPATIAL DISTRIBUTION
This chapter assesses the consequences of the unusual spatial distribution of the Zimbabwe population in terms of poverty and other indicators of well-being, such as access to social services. It first looks at the impact in rural areas and then assesses urban areas.
Rural areas
The high concentration of rural people in some remote and poorly connected areas where agricultural production conditions are not optimal (mostly the communal lands40) has led to entrenched poverty there. These residents are caught in a spatial poverty trap. The 2012 extreme poverty map41 shows that the highest ward poverty rates are found in these relatively densely populated and remote areas, located away from main roads. Many are found in the north and west of the country and located away from road networks (figure 5.1a). This includes Nkayi, Lupane, and Tsholotsho Districts in Matabeleland North; Gokwe South District in Midlands; and the southern parts of Hurungwe and Makonde Districts in Mashonaland West. Other pockets of high extreme poverty are found on the outer edges of the country, such as in the north (Centenary and Mbire Districts in Mashonaland Central), in the northeast (around Mudzi District in Mashonaland
40 The former tribal trust lands. 41 Completed by ZIMSTAT in 2016 and based on the national food poverty line of US$2 purchasing power parity per day, or about US$1 in actual money in Zimbabwe.
53 54 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 5.1a Ward Level Extreme Monetary Poverty Rate in 2012 (Number of Poor
as a Proportion of Total Population)WORLD BANK and GROUP Road Geospatial OperationsTransport Support Team, Network Development Data Group and Jobs CCSA (2018)
Food Poverty prevalence 0.01–0.2 0.21–0.3 0.31–0.4 0.41–0.5 0.51–0.6 0.61–0.7 0.71–0.8 0.81–1 District boundaries
Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012.
East), and in the southeast (around Chipinge district in Manicaland). “Hotspot” analysis, a geospatial analytical technique that looks at the spatial clustering of a phenomenon, confirms that extreme poverty is concentrated in the west and north of the country (figure 5.1b) and the lowest is in the central parts and south of Harare. Maize grain markets in Zimbabwe are characterized by segmentation between the northern-surplus and southern-deficit production regions, due to high domestic transport costs between these zones, according to a recent World Bank study.42 Other reasons include high transaction and market entry costs for small traders who are the main agents for arbitrage. Market cointegration analysis showed that maize grain prices in low-production areas such as Matabeleland North
42 Mahdi, Bonato and Herderschee. 2015. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 55
FIGURE 5.1b Hotspot Analysis of Poverty, 2012
Hot Spot Anakysis of Poverty rate, 2012 WORLD BANK GROUP Geospatial Operations Support Team, Development Data Group and Jobs CCSA (2018)
Source: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012. Note: Areas in red are spatially clustered high-poverty areas, and blue areas are spatially clustered areas of lower poverty and South, where there are various pockets of high poverty, do not have a long-term relationship with prices in the main producing areas of Zimbabwe. These areas are concentrated in the northern and eastern parts of the country. The segmentation of the maizegrain market has direct implications in terms of food security and demand stimulus for agricultural production in the high population density, high-poverty areas. Although surplus and deficit regions are connected through the Harare–Bulawayo road, the maize traded through this channel is mostly destined for the milling industries that service these large urban hubs as opposed to the small markets that service vulnerable and food-insecure households. Although maize meal markets are well integrated, rural households in deficit areas rely largely on unprocessed maize grain for their needs during parts of the year and, therefore, this does not necessarily counteract the inefficiencies from 56 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
segmented maize markets and their impact on poverty and food security.43 It is possible that labor markets are also segmented, but no Labor Force Survey data are yet available for analysis, which prevents us currently from testing this hypothesis. Maps showing the location of the extreme monetary poor as well as the spatial distribution of the population density suggest that a large proportion of the rural poor live in areas that are densely populated (figure 5.2), although, as mentioned, poverty rates can also be high in some scarcely populated areas. In contrast to many other countries, in Zimbabwe the distribution of the number of extreme monetary poor follows a mostly similar
FIGURE 5.2 Number of Poor People and Population Density, 2012
Population density 2012 and Poverty dot density 2012 WORLD BANK GROUP Geospatial Operations Support Team, Development Data G
Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012. Note: The red dots represent the number of poor people; the background color represents population density in 2012; the yellow circles show areas where a high number of extreme poor overlaps with high population density areas.
43 Ibid ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 57
distribution of the proportion of the extreme monetary poor. Perhaps notable exemptions are rural areas northeast of Harare (Central Mashonaland) where extreme poverty rates in 2012 were relatively low but because of relatively high population density, the number of extreme poor is substantial. Although poverty rates are negatively correlated with population density, this is entirely driven by rural and urban differences. The correlation between rural population density and rural living standards is relatively weak (figure 5.3). In other countries, poverty rates typically tend to be high in scarcely populated areas, also in rural areas, and higher population density is often associated with higher welfare and literacy rates and better access to tap water, toilets, and electricity.44 However, in Zimbabwe, this relationship is not straightforward. Areas with a higher population density tend to have lower extreme monetary poverty rates, but this relationship weakens when only considering rural wards, as these poverty rates differ enormously for a given rural population density. Wards that have a high population density but low poverty rate are nearly all urban. However, some urban wards have a population density that is low and more typical for rural areas. Many of these are small towns and tend to have high poverty rates.
FIGURE 5.3 Population Density and Poverty Rates 0.8
0.6
0.4 Poverty prevalence
0.2
0.0 0 2 4 6 8 10 Logged population density, pop/km2 Rural wards Urban wards Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012.
44 Gollin, Kirchberger, and Lagakos 2016. 58 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
Rural poverty is disproportionally concentrated in the communal lands, suggesting many of the extreme poor in these areas have not yet benefited from land reform in the neighboring former white commercial farm areas. In 2012, two-thirds of the extreme poor lived in the communal lands, whereas only 47 percent of the population lived in these areas. The former white commercial farm areas had relatively low numbers of extreme poor in 2012 compared to the communal lands (figure 5.4). In 2012, the extreme poverty rate was 20 percent in the former white commercial farm areas and 26 percent in the communal lands. It was the lowest (17 percent) in the small-scale commercial farms (former “native purchase areas”), suggesting
FIGURE 5.4 Poverty Density and Farmland Type
Poverty dot density 2012 and Land apportionment 1930 WORLD BANK GROUP Geospatial Operations Support Team, Development Data Group and Jobs CCSA (2
Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012. Spatial data on the Land Apportionment Act are from Tawanda Chingozha (Stellenbosch University). Note: The red dots represent poverty density; the background color represents the farmland type. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 59
that in these areas, farming may lead to higher welfare of the population than elsewhere (table 5.1). Social service delivery outcomes in the more remote, poor, and often densely populated areas are low. This would suggest the quality or the affordability of social service delivery in these areas is problematic. For example, there is a large concentration of people who have not completed secondary education and are not in school in the outer west, north, and southwestern parts of the country (figure 5.5, panel a). The concentration of those without education and not in school shows a similar pattern. The wards with the highest levels of people who have completed secondary education are found around Harare and Bulawayo as well as along the Harare–Masvingo road and the Harare–Mutare road (figure 5.5, panels a and b).
Similarly, the social service delivery outcomes regarding drinking water, sanitation, and electricity are very unequally divided across the country, with the areas away from Harare, Bulawayo, and Mutare, and especially the outer fringes of the country, witnessing low access rates. The relatively densely populated and remote parts in the north west of the country, where extreme poverty is high, also appear to consistently receive poorer services (figure 5.6, panels a through f).
The location of dams and reservoirs for water supply services also seems to be concentrated in the often thinly populated, relatively well-off areas, suggesting that these will be unable to benefit those living in the high-poverty rural areas (figure 5.7).
TABLE 5.1 Poverty Rate and Poverty Density by Farmland Type
Rural poverty 2012 (median) Land apportionment Rate Density* Large-scale commercial farmland resettled after 2000 20% 5 Old resettlement schemes resettled during 1982–2000 23% 5 Small-scale commercial farms (former ‘native purchase’ land) 17% 2 Communal lands 26% 11
Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012. Spatial data on the Land Apportionment Act are from Tawanda Chingozha (Stellenbosch University). * Number of poor people/km2. Median of all wards is taken. 60 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 5.5 Education Levels (Hotspot Analysis) a. Proportion who completed secondary education
Proportion w o Completed Secondary ducation 2012 RLD A R P eospatial perations Support Team Development Data roup and obs CCSA (2
Source: based on ZIMSTAT 2012 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 61
FIGURE 5.5 (Continued) Education Levels (Hotspot Analysis) b. Proportion who completed secondary education: hotspot analysis
ot Spots: Proportion w o completed Secondary ducation 2012 RLD A R P eospatial perations Support Team Development Data roup and obs CCSA (201 )
Source: Based on data from ZIMSTAT 2012. Note: The blue areas represent low secondary completion rates; the red areas represent high secondary completion rates. FIGURE 5.6 Social Service Delivery (Water, Sanitation, Electricity), 2012 a. Improved drinking water access b. Improved drinking water access: hotspot (darker areas have higher access) analysis (blue: low access; red: high access)
Proportion wit Access to Improved Drin ing ater 2012 RLD A R P eospatial perations Support Team Development Data roup and obs CCSA (201 ) ot Spots: Proportion wit Improved ater Access 2012 RLD A R P eospatial perations Support Team Development Data roup and obs CCSA (201 )
ater accesss ot spots ( etis rd)
c. Electricity access d. Electricity access: hotspot analysis (darker areas have higher access) (blue: low access; red: high access)
Proportion wit lectricity Access 2012 RLD A R P eospatial perations Support Team Development Data roup and obs CCSA (201 ) ot spots: Proportion wit lectricity Access 2012 RLD A R P eospatial perations Support Team Development Data roup and obs CCSA (201 )
lectricity access ot spots ( etis rd)
e. Improved sanitation access f. Improved sanitation: hotspot analysis (darker areas have higher access) (blue: low access; red: high access)
Proportion wit Access to Sanitation Services 2012 RLD A R P eospatial perations Support Team Development Data roup and obs CCSA (201 ) ot spots: Proportion wit Improved Sanitation Services 2012 RLD A R P eospatial perations Support Team Development Data roup and obs CCSA (201 )
Sanitation access ot spots ( etis rd)
0.00–0.05 0.06–0.10 0.11–0.15 0.16–0.20 0.21–0.51
Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 63
FIGURE 5.7 Location of Dams and Reservoir Capacity and Poverty Prevalence
Poverty prevalence 2012 and Dams RLD A R P eospatial perations Support Team Development Data roup and obs CCSA (201 )
Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012.
Urban areas
Small towns have relatively high average45 extreme poverty rates: 12 to 13 percent in 2012, about double the figure of larger towns, although still lower than rural areas where it is 27 percent (figure 5.8). Even if only 3 to 5 percent46 of the population lives in these small towns (table 2.1a and b), their relatively high poverty rates are concerning because these urban centers are growing rapidly in size, attract a growing proportion of migrants, and should be the small engines of progress and welfare growth through structural transformation.
45 Variation is high because extreme poverty rates range from 4 to 60 percent. 46 3 percent if we only look at towns with fewer than 50,000 people; 5 percent if we look at all town councils and local boards. 64 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE 5.8 Extreme Poverty by Type of Urban Center and Rural . Extrm povrt prv lnc b loc tion tp, 2012 (m n pr w rd in Zimb bw)
0.3 0.27
0.2
0.12
0.1 0.07 0.06 0.0 Food−poverty prevalence, means Food−poverty prevalence,
0.0 Metropolis City Council Municipality Town Council Rural and Local Boards
b. Food-povrt prvlnc b urbn si , 2012 (mn pr wrd in Zimbbw)
0.3 0.27
0.2
0.10 0.1 0.07 0.0 0.0 Food−poverty prevalence, means
0.0 >1 million 100,000− 50,000− 8,000− Rural 1 million 100,000 50,000
Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012.
However, a large proportion (40 percent in 2012)47 of the population in these small towns earns their main income from agriculture, suggesting that only limited shifts to industry and services where labor productivity tends to be higher takes place. Noteworthy, too, is that the extreme poverty rate of the City Councils and cities with 100,000 to 1 million people is somewhat higher than municipalities, even if
47 This was 53 percent in 2002, suggesting the proportion working in agriculture is reducing slowly. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 65
the former are, on average, bigger in size. This suggests these large cities are weaker drivers of progress than the municipalities or towns with 50,000 to 100,000 people. Although many are growing, secondary cities and small towns in Zimbabwe appear to lack the dynamism that such cities typically play in other parts of the world. The number of people employed in industrial jobs decreased during 2002–12 with the decline varying between 23 percent (Kadoma) to 38 percent (Gweru) and 45 percent (Bulawayo). This has led to their overall stagnation and a reduction of their density (see figure 2.2 in chapter 2), together with Harare. Secondary cities are typically important facilitators of job creation and labor mobility as well as structural transformation but in Zimbabwe they appear to have lost that role. In many countries, urban centers typically have a beneficial welfare impact on their surrounding rural areas,48 but in Zimbabwe, this impact differs substantially between the types of urban area. Beneficial impact is highest for Harare and the medium-size towns (figure 5.9). The poverty rate increases much faster when moving away from Bulawayo than from Harare. At a 40km distance, for example, the extreme poverty rate averages around 15 percent for Harare but 33 percent for Bulawayo while for other urban centers, it typically is 20 percent. For Mutare City Council and for small towns, the figure is also relatively high. For medium-size towns (50,000 to 100,000 people) the average poverty rate at 40km distance is lower than all other urban center types except Harare (figure 5.9). The number of extreme poor who live within 50km (along roads) of a small town (20,000–50,000 people) is 650,00049—three times higher than for Harare, and more than three times higher than for the six larger cities together (figure 5.10, panel b). However, these differences across types of urban centers are much smaller when considering a distance of 10km (figure 5.10, panel a). Within 10km of the urban center, we find that Harare has a slightly larger number of extreme poor, probably because many of the low-income neighborhoods are located within that distance from Harare center. The medium-size towns are appear to have the lowest number of poor people at 10m distance, suggesting they may be better at impacting well-being in neighboring rural areas. The location of so many of the extreme poor within the vicinity of urban centers, in particular large, medium-size and small towns (Figure 5.10b), is a sign that these have not yet been able to harness urbanization for poverty reduction,
48 Such as through the development of agro-processing (and possibly small-scale mining industries) that create job opportunities in towns and facilitate a productivity rise in agriculture. 49 About one-quarter of all the rural poor. FIGURE 5.9 Poverty Rate and Distance to Cities (0–100km) (Ward Level Averages) (Fractional Polynomials) a. Harare b. Bulawayo
0.8 0.8 0.7 0.7 0.6 0.6 0. 0. 0.4 0.4 0.3 0.3 Poverty rate Poverty rate 0.2 0.2 0.1 0.1 0.0 0.0 0 20 40 60 80 100 0 20 40 60 80 100 istance (along roads) to arare, km istance (along roads) to ulawayo City Council, km c. Large cities (100,000–1 million people) d. Medium-size towns (50,000–100,000 people)
0.8 0.8 0.7 0.7 0.6 0.6 0. 0. 0.4 0.4 0.3 0.3 Poverty rate Poverty rate 0.2 0.2 0.1 0.1 0.0 0.0 0 20 40 60 80 100 0 20 40 60 80 100 istance (along roads) to large town, km istance (along roads) to medium-si ed town, km e. Small town (20,000–50,000 people) f. Smallest town (8,000–20,000 people)
0.8 0.8 0.7 0.7 0.6 0.6 0. 0. 0.4 0.4 0.3 0.3 Poverty rate Poverty rate 0.2 0.2 0.1 0.1 0.0 0.0 0 20 40 60 80 100 0 20 40 60 80 100 istance (along roads) to small town, km istance (along roads) to smallest town, km Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012. Note: The blue dot (•) represents the average poverty rate at 40km away from the urban center. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 67
FIGURE 5.10 Number of Extreme Poor Near Different Urban Types . Within 10km b. Within 50km
400,000 400,000 379,662 338,6 6 324,3 7
224, 4 200,000 200,000 139,712 12 ,189 91,618 Number of poor 44,409 Number of poor 27,461 8,092 0 0 arare Large Medium Small Smallest arare Large Medium Small Smallest cities towns towns towns cities towns towns towns Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012. Note: Large town = 100,000 to 1 million inhabitants; medium town = 50,000 to 100,000 inhabitants; small town = 20,000 to 50,000 inhabitants; smallest towns: 8,000 to 20,000 inhabitants. There are 7 large cities, 8 medium size towns, 14 small towns and 4 smallest towns (see figure 2.1b)
despite some of them being located in relatively high-density rural areas. As mentioned, global evidence using cross-country panel data50 suggests that urbanization into secondary/small towns generally is more beneficial for poverty reduction than when it takes place in large cities or metropoles because a greater number of poor people can rise out of poverty by connecting with growth in these towns rather than large cities. In Zimbabwe, the smaller urban centers appear to be failing in this respect. The high average poverty rate of small towns is likely related to the quality of social services, which tends to be poorer there compared to other urban types, albeit still better than in rural areas (table 5.2). For example, on average, only 46 percent of households in the small and smallest towns (8,000 to 50,000 people) had access to electricity in 2012 compared to 84 to 92 percent in the three other types of urban centers. But access in rural areas was only 19 percent. However, variation among small towns is very high, as can be noted from the standard deviations (table 5.2). Education outcomes in smaller towns are also much lower than in other urban centers but also highly variable. Ongoing monitoring of urban development through the urban service level benchmarking effort51 confirms that infrastructure services differ highly among smaller towns. For example, according to this report, property coverage of direct drinking water supply differs between 14 percent in Epworth and
50 Christiaensen, De Weerdt, and Todo 2013. 51 Government of Zimbabwe, World Bank, and UCAZ 2016. 68 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
TABLE 5.2 Mean Access to Social Services per Urban Size Class (Percentage)
Completed Population size Improved Access to Improved secondary Teenage class water electricity sanitation education pregnancy* >1 million 96 92 93 60 37 100,000–1 million 95 82 95 55 41 50,000–100,000 96 82 92 56 41 8,000–50,000 94 77 83 55 43 Rural 65 19 54 31 55 Standard Deviations >1 million 1.3 9.6 13.8 3.4 6.1 100,000–1 million 4.9 26.4 5.5 4.6 7.2 50,000–100,000 1.2 6.5 4.2 3.6 5.9 8,000–50,000 5.9 11.5 10.2 3.9 4.4 Rural 19.3 16.9 21.1 8.8 9.7
Source: Based on data from ZIMSTAT 2012.
31 percent in Lupane town to 100 percent in Victoria Falls and Kwekwe. Per capita water supply and coverage of functional toilets is even more variable,52 with the latter varying between 2 percent in Epworth and 100 percent in Kwekwe. Clearly, the quality of service delivery leaves much to be desired in many urban areas. It tends to be much better in large cities than in small towns, which is not unusual, as a review found that this is the case in many sub-Saharan Africa countries.53 Ward level poverty rates are significantly negatively related to a range of variables. The relationship is strongest to the men-to-women ratio, keeping everything else the same, with poverty rates being higher when there are relatively more women than men. This is followed by mean years of schooling, with ward poverty rates being lower with more years of education, connectivity to other people (“market potential”), not being located in communal land areas, and being located in the extensive farming areas or high-rainfall areas, all other things staying equal (table 5.3). See table B2 in appendix B for full regressions results. Wards in communal land areas have a 5–7 percent higher extreme poverty rate, keeping everything else the same. This would suggest that the people in these areas face deep disadvantages and inequality of opportunity and are stuck in a spatial poverty trap.
52 Government of Zimbabwe, World Bank, and UCAZ 2016. 53 Ferré, Ferreira, and Lanjouw 2012; Coulombe and Lanjouw 2013. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 69
TABLE 5.3 Regression Analysis of Factors Associated with 2012 Poverty Levels
Dependent variable: ward level poverty rate Coefficient Standard error Population density, 2012, logged –0.004 (0.019) Market access (“market potential”) –0.133*** (0.026) Nonfarm employment rate, 2012 –0.254 (0.171) Proportion in nonfarming occupations, 2012 0.029 (0.087) Sex ratio (women-to-men), 2012 –1.456*** (0.161) Mean years of schooling, 2012 –0.519*** (0.028) Change in mean years of schooling, 2002–12 0.216*** (0.036) Land class type dummies (Base: 5. Tribal Trust) 1. Former white commercial farm areas –0.398*** (0.049) 2. Old resettlement areas –0.378*** (0.057) 3. Previous white: other –0.287*** (0.068) 4. Native purchase –0.358*** (0.061) Natural region dummies (Base: 5. Region IV: Semi-Extensive Farming) 1. Region I: Specialized and Diversified Farming –0.106* (0.062) 2. Region IIA: Intensive Farming –0.038 (0.049) 3. Region IIB: Intensive Farming –0.162*** (0.046) 4. Region III: Semi-Intensive Farming –0.007 (0.032) 5. Region V: Extensive Farming –0.447*** (0.039) Drive-distance to Town Council and Local Board dummies (Base: above 100km) Within 15km 0.116* (0.061) Between 15 and 50km 0.136*** (0.040) Between 50 and 100km 0.078*** (0.030) Constant 6.359*** (0.351) Observations 1,402
Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012. Note: Type of regression: betafit, which fits by maximum likelihood a two-parameter beta distribution to a distribution of a variable that ranges between 0 and 1 (a proportion). Robust standard errors applied (to control for heterogeneity). Robust standard errors in parentheses. See appendix B for more results. *** p<0.01, ** p<0.05, * p<0.1.
Chapter summary
The high concentration of rural people in various remote and poorly connected areas where agricultural production conditions are not optimal (mostly the communal lands) has led to spatial poverty traps. This is exacerbated by weak social service delivery outcomes in these areas. The affordability of social service delivery in these areas is likely problematic. A large 70 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
proportion of the rural poor live in areas that are densely populated, which is in contrast to other countries where most of the rural poor live in scarcely populated areas. However, statistical analysis using ward level observations suggests that the negative correlation between poverty and population density is relatively weak when looking at rural wards only. Recent population movements, partly driven by land reform, may have brought some people to areas that are less remote, but it is not clear whether their poverty situation has improved during 2002–12 because we do not have the data to assess welfare changes at ward level during this period. In Zimbabwe, the beneficial welfare impact on surrounding rural areas is higher for Harare and the medium-size towns than the larger cities and the smallest towns. The medium-size towns (50,000 - 100,000 people) appear to have the lowest number of poor people at 10 km distance, suggesting they may be better at impacting well-being in neighboring rural areas. The high average poverty rate of small towns is likely related to the quality of social services, which tends to be poorer there compared to other urban types, albeit still better than in rural areas. Ward level poverty rates are significantly negatively related to the men-to- women ratio, keeping everything else the same, with poverty rates being higher when there are relatively more women than men. This is followed by mean years of schooling. Wards in communal land areas have extreme poverty rates that are 5 to 7 percent higher than other farm type areas, when controlling for other factors. Further research is needed to assess the relationship between poverty and population density because this has large implications for service delivery. The reasons for low quality of service delivery in the outer areas of Zimbabwe also deserve further investigation. Recent population movements, partly driven by land reform, may have brought some people to areas that are less remote, but it is not clear whether their poverty situation has improved during 2002–2012 and more research is needed to assess their welfare changes. Also, there is a need to assess what is behind the positive relationship between poverty and the women- to-men ratio. 6 POLICY DISCUSSION
This study finds that there are large numbers of people living in remote but densely populated areas of Zimbabwe who face a spatial poverty trap. The settlement pattern can largely be explained by Zimbabwe’s historical land policies that led to many people living in these relatively isolated rural areas, sometimes with low agricultural production potential. Recent population movements, partly driven by land reform, may have brought some people to areas that are less remote, but it is not clear whether their poverty situation improved during 2002–12. The spatial welfare disparities not only make it hard to ensure benefits of growth are equally shared, but they also could potentially undermine the country’s development progress and weaken social cohesion. Two-thirds of the extreme poor live in communal lands. These high-poverty areas are affected by poor connectivity and are characterized by the lack of electricity infrastructure. The lowest access rate to drinking water and sanitation services are found here. Similarly, education levels are below those elsewhere. As a consequence, children born here do not have the same opportunities as those born in other parts of Zimbabwe.
Policy instruments for promoting economic integration and reducing social disparities
Several policy instruments exist for promoting more spatially balanced development progress. First, the government should ensure social service delivery policies are spatially blind in their design and universal in their coverage. Second, the government should ensure adequate connectivity of these lagging areas through policies and investments to facilitate spatial integration.
71 72 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
Ensuring social service delivery policies are spatially blind in their design and universal in their coverage concerns the practical regulations that govern the social services across the country as well as their affordability and how they are financed through tax and transfer mechanisms. To compensate for the lack of budget resources for nonwage expenses, the Government of Zimbabwe has expanded the use of user fees and charges, creating regressive financing of some basic services. These include education, health, and water and sanitation. Growing mandates of local governments to provide services have not been accompanied by increases in subnational transfers to needy areas, which complicates an already difficult situation. Without appropriate mechanisms to equalize financing of basic services, Zimbabwe could find it difficult to reduce the current welfare inequality. In the education sector, for example, the collection of revenue from private fees is skewing resources in a highly regressive manner (that is, weighted toward those better off). The decision to impose fees in all schools, although not financing the Basic Education Assistance Module (BEAM) program but rather funding teacher salaries in private schools, left many vulnerable children at risk and transferred benefits to less-poor households, according to the recently completed public expenditure review (PER) of the primary and secondary education sector.54 The sector is currently characterized by wide inequalities in the pupil-to-teacher ratio. These can vary between 5 and 84 pupils per teacher for primary education, pointing to the existence of very crowded classes as well as extremely small classes. There is also a wide variation of actual per-student spending at the school level. School level funds depend almost entirely on parents’ contributions. In poorer areas, parents can only afford to pay much lower amounts than in better- off areas, which has led to wide spatial disparities in the quality of education offered.55 It increases the risk of students dropping out, contributing to the poverty trap households in high-poverty areas face, and it leads to large inequalities of opportunities for Zimbabwean children. The currently constrained fiscal situation calls for improving the effectiveness and equality of resource utilization. In the education sector, this may require budgeting and monitoring unit costs of students. Using unit costs will help identify the need for increase or reduction in the number of class rooms, enable a discussion on who needs more resources and why, and allow the savings to be used for equalization transfers or need-based targeting investment to take place. The specification of the outcomes, and in particular outputs, to be delivered at the end of the budget cycle should also be developed in light of the unit costs.
54 Government of Zimbabwe and World Bank 2017c. 55 Government of Zimbabwe and World Bank 2017c. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 73
Current social protection systems insufficiently focus on both the chronic and transitory needs of the poor, and mechanisms for targeting them are weak. The recent PER for the sector suggests that Zimbabwe’s social protection system is affected by high administration costs, owing to a lack of harmonized processes and inadequate coordination across implementing agencies. Most expenditures do not benefit the poor. To do so, it will be important to establish a clearer evidence base on program impacts and effectiveness. Innovations such as the Harmonized Social Cash Transfer (HSCT) program, which imposes strict eligibility requirements and clear mechanisms for identifying beneficiaries, including household and proxy means testing, provide clear opportunities for scaling up. Adequate connectivity of these lagging areas should be ensured through policies and investments to facilitate spatial integration. Examples include roads and railways and communication systems that facilitate the movement of goods, services, people, and ideas locally, nationally, and internationally. Data from a 2009 survey suggest that the paved regional trunk and primary roads were generally in good condition, but almost half of the secondary network and the tertiary roads were in poor shape. The worst affected were the unpaved tertiary roads56 (table 6.1). From a strategic point of view, it is recommended that unpaved regional and primary road links be improved to paved standards. It is further recommended that high-traffic-volume secondary roads that are at dirt standards be upgraded to engineered gravel standards. The investment needed in secondary and tertiary roads to reduce spatial inequality of opportunity needs to be weighed against alternative investment options, such as further investments in trunk roads. This report notes that Zimbabwe’s poorer areas tend to depend on these secondary and tertiary roads and, thus, are weakly connected to the road network. Better connectivity will help provide better access to markets and also help some people move out of these areas if they are too densely populated and do not have the land assets people need to make a decent living. More research is needed to further quantify the trade-off of investments in trunk roads versus secondary and tertiary roads. Many road management functions are currently designated to nonsector agencies, which leads to lack of uniformity in road management approaches and erodes how efficiency resources are utilized. These road management functions
56 ZINARA 2017. 74 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
TABLE 6.1 Road Conditions in Zimbabwe, 2009
Category Surface Road condition Length Percentage Good Fair Poor Total Good Fair Poor Total Regional trunk road Paved 1,961 231 115 2307 85 10 5 100 network (RTRN) Primary road network Paved 1,718 202 101 2021 85 10 5 100 Unpaved 54 107 54 215 25 50 25 100 Total 1,772 309 155 2236 79 14 7 100 Secondary road network Paved 2,217 739 1,971 4,927 45 15 40 100 Unpaved 377 3396 3,773 7546 5 45 50 100 Total 2,594 4,135 5,744 12,473 21 33 46 100 Tertiary roads Unpaved 9,438 25,169 28,315 62,922 15 40 45 100 Urban road network Paved 2,857 3,266 2,041 8,164 35 40 25 100 Unpaved 8 12 11 31 26 39 35 100 Total 2,865 3,278 2,052 8,195 35 40 25 100 Total 18,630 33,122 36,381 88,133 21 38 41 100
Source: ZINARA 2017.
should be consolidated back to the sector ministry.57 About 80 percent of the national road network (the tertiary feeder and access roads and the urban roads) is managed by several other institutions: the 32 urban councils, the 60 district councils, and the District Development Fund, which are structurally outside the authority of the ministry responsible for roads. The roads under these bodies are in a particularly poor state.58 The subsector would benefit from robust strategic planning and more rational investment decisions. From a strategic point of view, it is recommended that unpaved regional and primary road links be improved to paved standards. There is a need for policy measures to prevent erosion of the Road Fund’s efficacy due to public-private partnership arrangements, focusing on concessions, equity, and redirection of more resources toward road maintenance.59
Backbone telecommunications networks are concentrated in urban areas for commercial reasons. With most citizens concentrated in the rural areas, this implies that a majority still do not have access to effective information
57 World Bank, AfDB, and UNDP 2018. 58 World Bank, AfDB, and UNDP 2018. 59 World Bank, AfDB, and UNDP 2018. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 75
communication technology (ICT) facilities. Some form of direct incentives for operators is needed to deliver services to rural areas. The World Bank has set out three possible partnership arrangements that can be used to achieve universal access to the ICT network. These are: (1) the competitive subsidies model; (2) the shared infrastructure/consortium model, where private operators form a consortium to build and operate backbone networks in underserved areas; and (3) the incentive-based private sector model.60
Third, spatially targeted programs are needed, and transfers need to be aligned with local needs and revenue capabilities. Local authorities play a crucial role in enabling economic growth and promoting social well-being in municipalities, cities, towns, and communities61 by providing localized services such as housing and land management, local road networks, public lighting, solid waste disposal, water supply and sanitation systems, and health and education services.62 There is a need to align policies and legislation to the Constitution,63 including by enacting new laws to bring into effect further decentralization through the Provincial and Metropolitan Councils, and improve distributional equity by aligning transfers with local needs and revenue capabilities. There is a need for an overview of the achievements and development gaps of each province and district in terms of different service delivery indicators to track progress in local development and align resources to needs. These indicators could be plotted and overlaid with maps that show poverty incidence and other welfare indicators. Data on local development indicators could be obtained from the Population Census 2012 and updated regularly using administrative data systems such as those compiled for the service delivery benchmarking work and ZIMSTAT’s facilities’ survey. The data on the various local development indicators could be combined into a single comparable measure. This is the concept of estimating a local
60 AfDB 2018. 61 Government of Zimbabwe and World Bank 2017b. 62 Government of Zimbabwe and World Bank 2017b. 63 Chapter 14, Section 270, of the new 2013 Constitution of Zimbabwe stipulates that each provincial or metropolitan council is responsible for the social and economic development of its province. This includes (1) planning and implementing social and economic development activities for its province; (2) coordi- nating and implementing governmental programs in its province; and (3) monitoring and evaluating the use of resources in its province. The social and economic development activities are further highlighted in Chapter 17, Section 301, as the need to provide basic services, including educational and health facilities, water, roads, social amenities, and electricity to marginalized areas. To ensure availability of resources for the above, Section 301 also requires that “not less than five percent of the national revenues raised in any financial year must be allocated to the provinces & local authorities as their share in that year.” 76 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
development index (LDI) for each province and each of country’s 93 local authorities: the 61 rural district councils and 32 urban councils district. The LDI combines a range of local development policy relevant indicators into an overall composite index that could measure the effectiveness of the local authority, the state of infrastructure, and the access to basic services in the local area.64 Once in place, the LDI can be juxtaposed with poverty data to guide the allocation of resources and be used to assess the progress and achievements of local governments (across time or places). The LDI can be calculated from various sources of district-level disaggregated secondary data. These include such reports as the Inventory of Facility and Social Amenities (ZIMSTAT), Education Management Information System (Ministry of Primary and Secondary Education), and perhaps the Service Level Benchmarking for Urban Water Supply, Sanitation and Solid Waste Management in Zimbabwe (particularly for the 32 urban local authorities). The Zimbabwe LDI can be a composite of three equally weighted pillars, such as local administration, local infrastructure, and access to basic services. A very preliminary possible list of components and indicators is presented in appendix C. These need to be adapted to best suit the Zimbabwean context.
Raising income-earning opportunities for those living in deprived areas
Raising incomes of households living in deprived areas would require building poor peoples’ assets and making these more productive through better service delivery and building public assets. Land reform has provided many rural households with assets, but for these households to make their newly gained assets productive, land tenure arrangements and service delivery need to improve. The current lack of land tenure significantly hinders foreign and domestic investment. A systematic land registration program is needed to complete permit and lease issuance to all new farms for reducing disputes and improving access to financing. With most of the extreme poor located in communal land areas where agricultural potential is lower than other farmland, agricultural policies and service delivery also need to better service these areas. This would include ensuring agricultural research and development (R&D) is geared toward finding
64 World Bank 2016. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 77
technologies and input packages that are suitable for these environments. The areas need to be better connected to road networks so markets can be accessed, and the investment climate in nearby towns should be improved to attract firms for the generation of nonfarm jobs. Government expenditure in agriculture is highly skewed toward maize input and output subsidies, causing major budget shortfalls for other expenditure such as R&D. Policies need to be better coordinated to improve the enabling environment for investing in agriculture. Different pieces of legislation are currently administered by different ministries, which drives up the cost of doing business and limits the government’s ability to play its facilitator role as a regulator.
Policy measures for improving the functioning of urban centers
When cities function well, they are the engines of economic growth and prosperity. However, Harare and Zimbabwe’s secondary cities have stagnated, especially the large secondary cities (the City Councils), which have been particularly affected by the collapse of the industrial sector. The number of industrial jobs in Harare declined by 29 percent during 2002–12, and figures are similar in secondary cities. Metropoles and cities have also declined in density. A recent study finds that in Harare, more than 30 percent of land within 5 kilometers of the central business district remains unbuilt.65 Costs of living are high, and investment in affordable housing lags. The country’s fast-growing small towns are faced with relatively high poverty rates and relatively poor living conditions. A large part of their population (40 percent) still works in agriculture. These towns, and also the larger urban centers, face several challenges to become engines of growth for their surrounding areas: urban service provision, adequate urban planning, and institutions for land use governance. Urban service delivery is efficient when it is provided at a large scale and less so at a smaller scale. The cost per person for connective infrastructure involving pipes, roads, and cables is reduced significantly when density and population numbers are lower. Efficient urban land use requires institutions that enable the formal consolidation of land into larger parcels for development.66
65 Lall, Henderson, and Venables 2017. 66 Hommann and Lall 2018. 78 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
For Zimbabwe’s cities and towns to become centers of growth and poverty reduction, they must (1) create working land markets, (2) employ effective urban planning, and (3) ensure financing for public investments. Functioning land markets. The demand for land cannot always be met due to low density, weak property rights, and poor land governance. Consequently, towns and cities develop in a fragmented and disconnected way. This leads to informal urban expansions that pose challenges to urban planning and cost-effective infrastructure provision, thus deterring productive investments. Effective urban planning. Cities in Zimbabwe typically have master plans that are outdated. They were developed to create sprawl and to decongest. Many of the more remote areas do not have services.67 At 1000m2, the minimum plot size is too large. This has led to low density areas and some densification away from the city center. All urban master plans currently need to be approved by the Ministry, which can make ad-hoc land-use decisions that are contrary to the master plan, especially when it owns the land, when the consent of the urban administration is not required. This can lead to problems and result in incoherent planning. Enforcement capacity of urban plans is weak and slow due to limited capacity of the urban administration. Financing for investment. Obtaining financing for public investments is difficult in urban areas, especially for bulk infrastructure, because local authorities do not have the means. Developers determine what is possible in terms of service delivery, and cities and towns depend on these developers, in particular when they own the land. Collaboration across administrative boundaries is problematic. A functional land market with formal ownership records, transfer procedures, and mechanisms for consolidating parcels in response to demand is essential for Zimbabwe’s cities to become centers of growth and poverty reduction. This is the case for many other African cities.68 Enabling such markets will depend on strengthening institutions in the form of rules and regulations for land management to enable supply of services land to facilitate better use of land assets. Robust systems for assessing land values will be key. Improving urban land markets will require improving land tenure, land management, and land allocation (figure 6.1). A sound and effective land use planning and management system needs to be in place to enable effective land market transactions. To make land use regulations more effective, development controls could be complemented with
67 The small town of Epworth near Harare is a case in point with very low access to drinking water and sanitation. 68 Hommann and Lall 2018. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 79
FIGURE 6.1 The Urban Land Supply Framework
Land Land tenure Land allocation management
To regulate land uses To identi y, prepare, To define and record t roug planning, and release land t e use rig ts o land regulations, pricing, supplies or new and t eir trans er ta ation, and so on development
Typically establis ed Typically establis ed Typically undertaken at t e national level at t e local level at t e local level
Source: Hommann and Lall 2018. financial mechanisms such as land taxes, property taxes, or subsidies to provide the incentives for implementation and enforcement. City administrative bodies need to work on valuing land, recording land and protecting ownership rights, and managing land in a fair and transparent way. Urban master plans need to be translated into capital investment plans with detailed area development schemes, which also includes financing options. These plans need to be strategic, practical, and implementable and to integrate all sectors. Effective coordination among different plans is fundamental to ensure the effectiveness of planning and the efficiency of investments. Good urban planning practices must be visionary, inclusive, and transparent and also have built-in flexibilities. But the quality and appropriateness of planning instruments depends heavily on access to good-quality information. Without accurate information, city leaders will be unable to plan for the future or take coordinated actions across institutions. Providing affordable housing and adequate infrastructure services, in particular water and sanitation, should be absolute priorities as well.
AREAS FOR FURTHER RESEARCH
This report is exploratory in nature, and various areas for further research are identified. These include the following. 1. Urbanization trends in Zimbabwe that consider the population growth of peri-urban areas, as some of these could be classified as urban instead of rural. Such research could include an assessment of trends in built-up areas using satellite pictures. 2. The extent of urban dispersion across scattered settlements with large cities and its impact on access to the jobs market and economic activity. 3. Drivers of population movements. This would include assessing (i) to what extent high rural-to-rural migration is related to high population inflows to rural areas around Harare and (ii) why population increase in some rural areas was high and low in others. The relationship to land reform needs to be confirmed as well as the type of resettlement into smallholder (type A1) or larger (type A2) farms. 4. The drivers of the change in nonfarm job opportunities, and why population change is toward areas where the employment rate was lower in 2002 (when controlling for urban centers) and the role existing education levels and sex ratios in areas played in migration toward these areas. 5. The relationship between poverty and population density and the poverty impact of population movements and land reform 2002–2012. 6. Reasons for low quality social service outcomes in the outer areas of Zimbabwe. 7. The reason for the high women-to-men rations in various rural areas and what is behind the positive relationship between poverty and the women-to-men ratio.
81
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FIGURE A.1 Relationship Between Population Change, 2002–12, and Population Density, 2012 90,000
60,000
30,000
0 Population change 2002–2012
−30,000 0 2 4 6 8 10 Logged population density, pop/km2
Rural wards Rural fitted Urban wards Urban fitted Source: Based on summary data from ZIMSTAT 2012.
87 88 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE A.2 Interprovince Migration: Number of People in 2012 Who Had Moved Out to Another Province Since 2002 and Number of People Who Had Moved In from Another Province Since 2002, by Urban and Rural Areas a. Rural
Mashonaland East Rural
Mashonaland West Rural
Masvingo Rural
Mashonaland Central Rural
Manicaland Rural
Midlands Rural
Matabeleland South Rural
Matabeleland North Rural 0 50,000 100,000 150,000 200,000 250,000 Number who moved to another province Number who moved in from another province b. Urb n
Harare Bulawayo Midlands Urban Mashonaland West Urban Manicaland Urban Mashonaland East Urban Masvingo Urban Matabeleland South Urban Matabeleland North Urban Mashonaland Central Urban 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 500,000
Number that moved to another province Number that moved in from another province Sources: Based on Zimbabwe CSO 2002 and ZIMSTAT 2012. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 89
FIGURE A.3 Rural Population Shares by 1930 Land Class Rural Population Shares (2002) per 1930 Land Class Rural Population Shares (2012) per 1930 Land Class Undesignated land, Former large-scale Undesignated land, Former large-scale 2% commercial farms 2% commercial farms (post 1980 land (post 1980 land reform), reform), 25% 30%
Small-scale commercial farms, former Small-scale native commercial purchase farms, former areas, native 3% purchase areas, 3% Communal land, former Communal tribal trust land, land, former 65% tribal trust land, 71% Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016, CSO 2002 and ZIMSTAT 2012. Spatial data on the Land Apportionment Act are from Tawanda Chingozha (Stellenbosch University).
FIGURE A.4 Share of Internal Migration, by Type and Time Period (Number of People)
Rural to municipalities and towns Rural to other cities Rural to Harare Rural to rural
Harare to municipalities and towns Harare to other cities Harare to rural
Other cities to municipalties and towns Other cities to other cities Other cities to Harare Other cities to rural
Municipalities and towns to other municipalities and towns Municipalities and towns to other cities Municipalities and towns to Harare Municipalities and towns to rural 0 200,000 400,000 600,000 800,000 1992–2012 2002–12 Source: Based on data from CSO 2002 and ZIMSTAT 2012. 90 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE A.5 Poverty Density and Proportion of Migrants
Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012. Note: The red dots represent poverty density; the background color represents the proportion of migrants. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 91
FIGURES A.6a and A.6b Extreme Poverty Rate by Natural Region, 2012 0.31 0.30 0.25 0.24 0.23
0.19 0.20 0.19
Poverty rate 0.10
0.00 I IIA IIB III IV V Specialized and Intensive Intensive Semi-Intensive Semi-Extensive Extensive Diversified farming Farming Farming Farming Farming Farming (>1000mm) (750–1000mm) (750–1000mm) (650–800mm) (450–650mm) (<650mm).
Region V: Region I: Specialized and Extensive Farming, Diversified Farming, 3% 12% Region IIA: Intensive Farming, 22%
Region IIB: Intensive Farming, 6%
Region IV: Region III: Semi−Extensive Semi−Intensive Farming, 38% Farming, 18%
Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016; and ZIMSTAT 2012. 92 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE A.7 Population Share and Nonfarm Employment Shares by Location Type, 2012 a. Shares of nonfarm employment b. Population share Harare, Harare, 11% 25% Rural, City Councils, 39% 10%
Municipalities, 5%
Town Councils and LocalBoards, 6% City Councils, 17% Rural, Town Councils 68% and Local Boards, Municipalities, 8% 11% c. Number of farm jobs (% change) d. Number of nonfarm jobs (% change)
Metropolis Metropolis
City Council City Council
Municipality Municipality
Town Council Town Council and Local Boards and Local Boards Rural Rural
−80,000 −40,000 0 20,000 −50,000 0 50,000 100,000 Farm jobs % change Nonfarm jobs % change Sources: Based on Zimbabwe CSO 2002 and ZIMSTAT 2012. Note: A ‘job’ is defined as doing any work that generates income in monetary or in kind, including self employment. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 93
FIGURE A.8 Population Density and Poverty at 50km from Each of the Urban Types . H r r b. Cit Councils 2 2 10 10
8 8
6 6
4 4
2 2
Logged population density, pop/km 0 Logged population density, pop/km 0 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8 Poverty rate Poverty rate c. Municip liti s d. Sm ll towns 2 2 10 10
8 8
6 6
4 4
2 2
Logged population density, pop/km 0
Logged population density, pop/km 0 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8 Poverty rate Poverty rate Sources: Based on data from ZIMSTAT, World Bank, and UNICEF 2016 and ZIMSTAT 2012. 94 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE A.9 Poverty Density 2012 and Population Change 2002–2012
Source: Based on data from ZIMSTAT, World Bank, and UNICEF 2016. Note: The red dots represent population change; the background color represents poverty density. The blue ring represents an area with high poverty in 2012 and high population increase 2002–2012.
The relationship between the proportion of migrants in a ward and the poverty rate varies highly by urban type. It rises with increasing poverty rates for municipalities and for the Harare metropolis, yet it drops for rapidly for town councils (figure 30). Lower poverty rates in high-migrant small towns would either suggest that migrants who move to small towns are less poor or quickly become less poor, or that migrants only move to low-poor small towns. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 95
FIGURE A.10 Nighttime Lights Density 1996–2010
Source: NASA. 96 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
FIGURE A.11 Farm and Nonfarm Workers by Location Type, 2012
1.0 0.98 0.96 0.96 0.88
0.8 0.76
0.6
0.4 Share of workers 0.24 0.2 0.12
0.02 0.04 0.04 0.0 Metropolis City Council Municipality Town Council and Rural Local Boards Farm Nonfarm
2,161,362
2,000,000
1,000,000 689,439
Number of workers Number of 447,621 309,450 202,535 140,053 10,540 11,720 8,795 19,518 0 Metropolis City Council Municipality Town Council Rural and Local Boards Farm Nonfarm Source: Based on ZIMSTAT 2012. Note: Metropolis is Harare. ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE 97
FIGURE A.11 FIGURE A.12 Farm and Nonfarm Workers by Location Type, 2012 Farm and Nonfarm Employment and Distance to Urban Areas a. Farm and nonfarm workers by drive distance to urban types
1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
< 15km < 15km < 15km < 15km 15–50km 15–50km 15–50km 15–50km 50–100km 50–100km 50–100km 50–100km Harare City Council Municipality Small Towns Farm Nonfarm
b. Share of all nonfarm workers by drive distance to urban types
50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0%
<50km <50km <50km <50km 50–10km 50–10km 50–10km 50–10km Harare City Council Municipality Small Towns Source: Based on ZIMSTAT 2012.
APPENDIX B RESULTS OF REGRESSION ANALYSIS
99 TABLE B.1 Dependent Variable: Change in Population Density and Absolute Change in Women-to-Men Ratio
Dependent variable Covariates Relative population change (% change) Absolute change in women-to-men ratio Mining/construction dummy: 1 if share of employees –0.058 0.057 –0.030*** –0.024** in mining/construction > 95 percentile (0.110) (0.174) (0.011) (0.011) Population density, 2002, logged –0.132*** –0.133*** –0.208*** –0.208*** 0.018*** 0.017*** 0.007*** 0.007** (0.044) (0.045) –0.065 (0.066) (0.003) (0.003) (0.003) (0.003) Market access (“market potential”) 0.092* 0.092* –0.002 –0.002 (0.053) (0.053) (0.003) (0.003) Employment rate, 2002 0.558 0.535 –1.082** –1.048** –0.068* –0.080** 0.019 0.005 (0.413) (0.405) (0.440) (0.476) (0.037) (0.037) (0.028) (0.029) Proportion in nonfarming occupations, 2002 0.635** 0.645** –0.038** –0.033* (0.266) (0.276) (0.019) (0.020) Women-to-men ratio, 2002 0.622 0.600 –0.438* –0.415* –0.314*** –0.325*** –0.229*** –0.239*** (0.454) (0.429) (0.263) (0.233) (0.029) (0.028) (0.018) (0.018) Mean years of schooling, 2002 0.004 0.004 0.002 0.002 (0.044) (0.044) (0.004) (0.004) Change in mean years of schooling, 2002–12 –0.072 –0.070 –0.016** –0.015** (0.061) (0.061) (0.007) (0.007) Land class type dummies (Base 5: Communal Lands ) 1. Large scale commercial farms post 2000 0.463*** 0.464*** –0.000 0.001 land reform (0.137) (0.138) (0.009) (0.009) 2. Old resettlement (1980–2000 land reform) 0.152** 0.151** –0.003 –0.003 (0.064) (0.064) (0.007) (0.007) 3. Former larger scale commercial land: other 0.012 0.006 –0.009 –0.013 (0.100) (0.100) (0.008) (0.008) 4. Small-scale commercial farms/old native –0.074 –0.078 –0.019** –0.021** purchase areas (0.059) (0.056) (0.009) (0.009) Natural region dummies (Base: 5. Region IV: Semi-Extensive Farming) 1. Region I: Specialized and Diversified Farming –0.050 –0.057 0.005 0.001 (0.073) (0.073) (0.012) (0.012) 2. Region IIA: Intensive Farming 0.064 0.058 –0.024*** –0.027*** (0.069) (0.066) (0.005) (0.005) 3. Region IIB: Intensive Farming 0.003 –0.002 –0.015** –0.018*** (0.067) (0.068) (0.007) (0.006) 4. Region III: Semi-Intensive Farming 0.008 0.006 –0.010** –0.011** (0.049) (0.048) (0.005) (0.005) 5. Region V: Extensive Farming 0.066 0.065 0.021*** 0.021*** (0.052) (0.053) (0.006) (0.006) Location type dummies (Base: Rural Area) Harare 0.838*** 0.843*** 0.023 0.021 (0.321) (0.315) (0.014) (0.014) City Council 0.861*** 0.864*** 0.043*** 0.042*** (0.314) (0.311) (0.013) (0.013) Municipality 1.162*** 1.166*** 0.022 0.021 (0.339) (0.335) (0.015) (0.015) Town Council and Local Board 2.401* 2.384* 0.026* 0.033** (1.248) (1.263) (0.015) (0.016) Constant –1.811** –1.776** 1.719*** 1.677*** 0.296*** 0.315*** 0.216*** 0.233*** (0.735) (0.703) (0.239) (0.261) (0.052) (0.051) (0.021) (0.022) Observations 1,404 1,404 1,485 1,485 1,403 1,403 1,484 1,484 R-squared 0.117 0.117 0.147 0.148 0.276 0.284 0.219 0.224
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 TABLE B.2 Dependent Variable: Extreme Poverty Rate 2012 (Proportion)
Dependent variable: ward extreme poverty rate (1) (3) (5) (7) (9) (11) (13) (15) VARIABLES Model ë1 Model ë2 Model ë3 Model ë4 Model ë5 Model ë6 Model ë7 Model ë8 Population Density 2012, logged –0.004 –0.225*** –0.225*** –0.109*** –0.159*** –0.043*** –0.014 –0.249*** (0.019) (0.012) (0.012) (0.015) (0.016) (0.012) (0.014) (0.011) Market Potential, logged (excluding intra-MP) –0.140*** –0.254*** (0.028) (0.022) Employment rate 2012 –0.300* –0.751*** –0.751*** –0.361* –1.443*** –0.557*** –0.468*** –0.678*** (0.173) (0.202) (0.202) (0.188) (0.281) (0.162) (0.159) (0.199) Proportion in non-ag 2012 0.047 –0.687*** (0.086) (0.104) Women to men ratio 2012 –1.446*** –0.279 –0.279 –0.681*** –0.497*** –1.528*** –1.748*** 1.104*** (0.163) (0.200) (0.200) (0.228) (0.191) (0.156) (0.166) (0.131) Mean years of schooling 2012 –0.520*** –0.457*** –0.564*** (0.028) (0.019) (0.025) Change in mean years of schooling 2002–2012 0.214*** 0.251*** (0.036) (0.039) % migrants 2012 0.109 (0.207) Land class type dummies (Base 5: Communal Lands ) 1. Large scale commercial farms post 2000 –0.395*** –0.628*** –0.628*** –0.451*** –0.550*** –0.531*** –0.531*** land reform (0.048) (0.052) (0.052) (0.053) (0.053) (0.045) (0.050) 2. Old resettlement (1980–2000 land reform) –0.367*** –0.419*** –0.419*** –0.329*** –0.403*** –0.438*** –0.401*** (0.056) (0.060) (0.060) (0.057) (0.058) (0.052) (0.052) 3. Former larger scale commercial land: other –0.276*** –0.520*** –0.520*** –0.572*** –0.372*** –0.321*** –0.242*** (0.066) (0.082) (0.082) (0.076) (0.084) (0.058) (0.062) 4. Small-scale commercial farms/old native –0.362*** –0.749*** –0.749*** –0.646*** –0.713*** –0.419*** –0.364*** purchase areas (0.060) (0.080) (0.080) (0.075) (0.077) (0.060) (0.061) Natural Region dummies (Base: 5. Region IV - Semi-Extensive Farming) 1. Region I - Specialized and Diversified Farming –0.075 –0.136* (0.058) (0.080) 2. Region IIA - Intensive Farming 0.018 –0.137*** (0.048) (0.046) 3. Region IIB - Intensive Farming –0.136*** –0.353*** (0.045) (0.050) 4. Region III - Semi-Intensive Farming –0.005 –0.303*** (0.033) (0.043) 6. Region V - Extensive Farming –0.451*** –0.379*** (0.039) (0.042) 7. Lake Kariba –0.022 (0.052) Drive-distance to Harare dummies (Base: Above 100km) Within 15km
Between 15 & 50km
Between 50 & 100km
Drive-distance to City Council dummies (Base: Above 100km) Within 15km
Between 15 & 50km
Between 50 & 100km
(table continues on next page) TABLE B.2 (Continued) Dependent Variable: Extreme Poverty Rate 2012 (Proportion)
Dependent variable: ward extreme poverty rate (1) (3) (5) (7) (9) (11) (13) (15) VARIABLES Model ë1 Model ë2 Model ë3 Model ë4 Model ë5 Model ë6 Model ë7 Model ë8 Drive-distance to Municipality dummies (Base: Above 100km) Within 15km
Between 15 & 50km
Between 50 & 100km
Drive-distance to Town Council dummies (Base: Above 100km) Within 15km
Between 15 & 50km
Between 50 & 100km
Location type dummies (Base: Rural Area) Harare
City Council
Municipality
Town Council & Local Board
Constant 6.467*** 0.499** 0.499** 3.565*** 0.890*** 4.271*** 5.459*** –0.958*** (0.367) (0.241) (0.241) (0.410) (0.247) (0.235) (0.291) (0.151) Observations 1,402 1,439 1,439 1,438 1,435 1,439 1,402 1,485 Dependent variable: ward extreme poverty rate (17) (19) (21) (23) (25) (27) (29) VARIABLES Model ë9 Model ë10 Model ë11 Model ë12 Model ë13 Model ë14 Model ë15 Population Density 2012, logged –0.249*** –0.140*** –0.133*** –0.044*** 0.003 –0.006 –0.001 (0.011) (0.013) (0.016) (0.012) (0.013) (0.019) (0.018) Market Potential, logged (excluding intra-MP) –0.414*** –0.100*** –0.157*** (0.027) (0.028) (0.027) Employment rate 2012 –0.678*** –0.220 –1.685*** –0.505*** –0.385** –0.397** –0.228 (0.199) (0.179) (0.262) (0.157) (0.150) (0.181) (0.166) Proportion in non-ag 2012 –0.959*** 0.084 0.049 (0.098) (0.090) (0.086) Women to men ratio 2012 1.104*** 0.523*** 0.507*** –0.386*** –0.685*** –1.513*** –1.484*** (0.131) (0.136) (0.136) (0.130) (0.133) (0.160) (0.161) Mean years of schooling 2012 –0.504*** –0.622*** –0.523*** –0.537*** (0.019) (0.024) (0.028) (0.027) Change in mean years of schooling 2002–2012 0.310*** 0.211*** 0.212*** (0.036) (0.036) (0.036) % migrants 2012
Land class type dummies (Base 5: Communal Lands ) 1. Large scale commercial farms post 2000 –0.406*** –0.432*** land reform (0.047) (0.048) 2. Old resettlement (1980–2000 land reform) –0.396*** –0.384*** (0.056) (0.055) 3. Former larger scale commercial land: other –0.325*** –0.303*** (0.072) (0.073) 4. Small-scale commercial farms/old native –0.364*** –0.363*** purchase areas (0.059) (0.058) (table continues on next page) TABLE B.2 (Continued) Dependent Variable: Extreme Poverty Rate 2012 (Proportion)
Dependent variable: ward extreme poverty rate (17) (19) (21) (23) (25) (27) (29) VARIABLES Model ë9 Model ë10 Model ë11 Model ë12 Model ë13 Model ë14 Model ë15 Natural Region dummies (Base: 5. Region IV - Semi-Extensive Farming) 1. Region I - Specialized and Diversified Farming –0.136* –0.179** –0.089 –0.226*** –0.209*** –0.073 –0.046 (0.080) (0.071) (0.059) (0.057) (0.050) (0.059) (0.056) 2. Region IIA - Intensive Farming –0.137*** 0.282*** –0.191*** –0.151*** –0.176*** 0.134*** 0.106** (0.046) (0.046) (0.042) (0.040) (0.042) (0.047) (0.050) 3. Region IIB - Intensive Farming –0.353*** –0.015 –0.408*** –0.273*** –0.313*** –0.087** –0.084* (0.050) (0.049) (0.050) (0.041) (0.042) (0.042) (0.045) 4. Region III - Semi-Intensive Farming –0.303*** –0.120*** –0.325*** –0.085** –0.061* –0.007 –0.001 (0.043) (0.042) (0.041) (0.035) (0.034) (0.032) (0.033) 6. Region V - Extensive Farming –0.379*** –0.558*** –0.319*** –0.459*** –0.420*** –0.435*** –0.436*** (0.042) (0.047) (0.043) (0.038) (0.036) (0.038) (0.039) 7. Lake Kariba –0.022 –0.866*** 0.254*** 0.365*** 0.325*** (0.052) (0.074) (0.053) (0.041) (0.042) Drive-distance to Harare dummies (Base: Above 100km) Within 15km –0.149* (0.084) Between 15 & 50km –0.339*** (0.059) Between 50 & 100km –0.232*** (0.046) Drive-distance to City Council dummies (Base: Above 100km) Within 15km 0.180*** (0.065) Between 15 & 50km 0.251*** (0.051) Between 50 & 100km 0.086*** (0.032) Drive-distance to Municipality dummies (Base: Above 100km) Within 15km
Between 15 & 50km
Between 50 & 100km
Drive-distance to Town Council dummies (Base: Above 100km) Within 15km
Between 15 & 50km
Between 50 & 100km
Location type dummies (Base: Rural Area) Harare
City Council
Municipality
Town Council & Local Board
Constant –0.958*** 4.257*** –0.133 3.331*** 4.704*** 6.115*** 6.773*** (0.151) (0.375) (0.172) (0.211) (0.268) (0.372) (0.370) Observations 1,485 1,484 1,481 1,485 1,445 1,402 1,402 (table continues on next page) TABLE B.2 (Continued) Dependent Variable: Extreme Poverty Rate 2012 (Proportion)
Dependent variable: ward extreme poverty rate (31) (33) (35) (37) VARIABLES Model ë16 Model ë17 Model ë18 Model ë19 Population Density 2012, logged 0.005 –0.004 0.082*** –0.171*** (0.019) (0.019) (0.018) (0.015) Market Potential, logged (excluding intra-MP) –0.130*** –0.133*** –0.151*** (0.026) (0.026) (0.025) Employment rate 2012 –0.369** –0.254 –0.331 –0.737*** (0.180) (0.171) (0.233) (0.211) Proportion in non-ag 2012 0.093 0.029 –0.186* (0.089) (0.087) (0.100) Women to men ratio 2012 –1.532*** –1.456*** –1.122*** 0.902*** (0.165) (0.161) (0.141) (0.122) Mean years of schooling 2012 –0.511*** –0.519*** –0.494*** (0.028) (0.028) (0.029) Change in mean years of schooling 2002–2012 0.203*** 0.216*** 0.279*** (0.036) (0.036) (0.039) % migrants 2012 –0.314 (0.226) Land class type dummies (Base 5: Communal Lands ) 1. Large scale commercial farms post 2000 –0.389*** –0.398*** land reform (0.048) (0.049) 2. Old resettlement (1980–2000 land reform) –0.364*** –0.378*** (0.056) (0.057) 3. Former larger scale commercial land: other –0.304*** –0.287*** (0.071) (0.068) 4. Small-scale commercial farms/old native –0.359*** –0.358*** purchase areas (0.061) (0.061) Natural Region dummies (Base: 5. Region IV - Semi-Extensive Farming) 1. Region I - Specialized and Diversified Farming –0.099* –0.106* (0.060) (0.062) 2. Region IIA - Intensive Farming 0.098** –0.038 (0.047) (0.049) 3. Region IIB - Intensive Farming –0.098** –0.162*** (0.044) (0.046) 4. Region III - Semi-Intensive Farming –0.007 –0.007 (0.033) (0.032) 6. Region V - Extensive Farming –0.440*** –0.447*** (0.038) (0.039) 7. Lake Kariba
Drive-distance to Harare dummies (Base: Above 100km) Within 15km
Between 15 & 50km
Between 50 & 100km
Drive-distance to City Council dummies (Base: Above 100km) Within 15km
Between 15 & 50km
Between 50 & 100km
(table continues on next page) TABLE B.2 (Continued) Dependent Variable: Extreme Poverty Rate 2012 (Proportion)
Dependent variable: ward extreme poverty rate (31) (33) (35) (37) VARIABLES Model ë16 Model ë17 Model ë18 Model ë19 Drive-distance to Municipality dummies (Base: Above 100km) Within 15km –0.370*** (0.072) Between 15 & 50km –0.187*** (0.043) Between 50 & 100km –0.016 (0.030) Drive-distance to Town Council dummies (Base: Above 100km) Within 15km 0.116* (0.061) Between 15 & 50km 0.136*** (0.040) Between 50 & 100km 0.078*** (0.030) Location type dummies (Base: Rural Area) Harare –0.291*** –0.678*** (0.097) (0.080) City Council –0.304*** –0.700*** (0.088) (0.075) Municipality –0.338*** –0.562*** (0.105) (0.095) Town Council & Local Board 0.300*** 0.234** (0.093) (0.114) Constant 6.362*** 6.359*** 5.843*** –1.146*** (0.361) (0.351) (0.379) (0.149) Observations 1,402 1,402 1,446 1,486
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 APPENDIX C EXAMPLE OF LOCAL DEVELOPMENT INDEX
111 112 ANALYSIS OF SPATIAL PATTERNS OF SETTLEMENT, INTERNAL MIGRATION, AND WELFARE INEQUALITY IN ZIMBABWE
TABLE C.1 Zimbabwe Local Development Index—Possible Components and Indicators
Subindex Weight Indicators Weight Local administration 1/3 Number of staff in the local authority office Annual budget per capita of the local authority Number of fund-raising projects implemented by the local authority Capital expenditure as a percentage of the total expenditure for the local authority Local infrastructure 1/3 Percent of households with access to water (piped or clean) Percent of households with access to sanitation Percent of households with access to electricity Total length of tarred roads (in km) in the district Total length of gravel roads (in km) in the district Number of bus routes in the district ICT coverage Access to basic services 1/3 Number of primary schools (particularly functional primary schools) in the district Number of secondary schools (particularly functional secondary schools) in the district Primary students per teacher in the district Secondary students per teacher in the district Expenditure per student ($) in the district Number of primary schools with electricity in the district Number of secondary schools with electricity in the district Number of health centers in the district Number of health centers with onsite water connections/supplies Number of health centers with electricity connections Number of health facilities with functional ambulances and communication devices Population per nurse Population per doctor
Source: Inspired by various World Bank initiatives on developing a local development index including in Croatia and the Central African Republic (Hoogeveen, Hans, Roy Katayama and Gervais Chamberlin Yama) Photo credits: page iv: “Bauer” by toubibe via Pixabay.com, Creative Commons (CC0) page viii: “Girl” by toubibe via Pixabay.com, Creative Commons (CC0) page xvi: “Rainbow” by leonbasson via Pixabay.com, Creative Commons (CC0) page 8: “Fishing Boat” by toubibe via Pixabay.com, Creative Commons (CC0) page 38: “Flag” by Public_Domain_Photography via Pixabay.com, Creative Commons (CC0) page 80: World Bank Photo Collection, “Nursing staff from surrounding clinics came to support their colleagues at Holme Eden Clinic” by November 18, 2013 via Flickr, Creative Commons (CC BY-NC-ND 2.0) page 82: “Rock Formations” by toubibe via Pixabay.com, Creative Commons (CC0) page 98: “Sand” by leonbasson via Pixabay.com, Creative Commons (CC0)