Global Poverty Mapping in Support of CGIAR Research Priorities

Transcription

1 CONSULTATIVE GROUP ON INTERNATIONAL AGRICULTURAL RESEARCH SCIENCE COUNCIL Global Poverty Mapping in Support of CGIAR Research Priorities This study report, commissioned by the Standing Panel on Priorities and Strategies (SPPS) has been provided earlier to the Science Council. The scoping study was designed to evaluate the possibilities of using existing initiatives and data, or new research, to develop poverty mapping for the purpose of research priority setting. This report will be discussed by the SPPS panel meeting during SC3 and is provided here for the information of participants and observers to that meeting. SCIENCE COUNCIL SECRETARIAT FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS MARCH

2 Global Poverty Mapping in Support of CGIAR Research Priorities Consultant s Report P K Thornton 30 November Mentone Terrace, Edinburgh EH9 2DF, Scotland, UK Tel , fax , p.thornton@cgiar.org 2

3 Executive summary The research carried out by the Centres of the Consultative Group on International Agricultural Research (CGIAR) system needs to be reviewed frequently to help ensure its effectiveness in meeting CGIAR goals. The world within which agricultural research for development is practised is extremely dynamic. The Science Council has taken the leadership in initiating a process of System-level priority setting, consisting of a multi-faceted approach that is both analytical and broadly consultative with stakeholders, donors, and scientists within and without the CGIAR system. Given recent advances in the use of GIS techniques, the possibility exists that poverty mapping could be one useful element of a broader prioritysetting process. This document is the report of a 10-day scoping study, and proposes some activities in the short- to medium-term that build on a large amount of previous work, to try to bring together, at a global level, databases on agro-ecological conditions, degree of market integration, and indicators of poverty. The last few years have seen considerable interest in poverty mapping as a tool for characterisation and targeting, and work continues at global, national and community levels. Poverty mapping at the global level is generally constrained by data availability and the problems of identifying poverty or proxy indicators that can be harmonised across countries and regions. Country-level poverty mapping studies, using techniques based on small-area estimation, seem to be burgeoning, and nearly 30 countries now have (or will shortly have) national poverty maps generated in this way. Poverty mapping is also being done at higher resolutions still, particularly at the community level, to gain a better understanding of the causal links between poverty and community or household characteristics, and thereby to be able to target more effectively interventions that are truly pro-poor. With funding from the Government of Norway and the Poverty and Food Insecurity Mapping Project of FAO, seven country case studies were carried out between 2002 and 2004 in Latin America, Africa and Asia, with the object of ascertaining whether there are some general lessons that can be drawn concerning the geo-spatial determinants of poverty in different places. If such lessons could be drawn, they could add considerably to the credibility of global poverty maps, hampered as they are by data constraints and low analytical rigour. A brief summary of these studies is provided here. The reports on these case studies are still being finalised, and a thorough synthesis study is planned but has not yet been carried out. 3

4 Two specific activities are proposed here, that can be done concurrently. One is to carry out a relatively rapid developing-country mapping exercise, that makes use of the global data sets assembled by FAO and others, and uses sub-national indicators of nutrition and vulnerability as proxies for poverty levels. This should provide a first-cut map that associates agroecological conditions, degree of market integration, and poverty, at a coarse resolution, that should still prove useful for Science Council priority-setting goals but can be refined considerably over time. The second activity proposed is a regional study that can build on some of the seven case studies referred to above and is centered on higher-resolution databases that exist for regions such as East Africa and parts of south-east Asia. One candidate region for such a study would include Kenya and Uganda, where high-resolution poverty maps based on small-area estimation have been completed within the last 12 months and high-resolution natural resource databases also exist. The outputs of any regional studies would help to define ways of linking country studies to global poverty maps, and would also help to identify the geo-spatial determinants of poverty in particular places and the degree to which such relationships could be used at higher levels of aggregation. Evidence from the seven case studies and elsewhere suggests that there can be considerable heterogeneity in the determinants of rural poverty. The implication of this is that poverty alleviation efforts will have to be targeted at much smaller intervention (or recommendation) domains than have perhaps been considered in the past. This suggests that serious investment of resources and intellectual effort into targeting for poverty reduction will be needed, if the CGIAR and its programmes are to be effective in alleviating poverty. In the longer term, the CGIAR Centres need to be involved in setting up and pursuing harmonised approaches to data collection and analysis, and collaboration needs to be catalysed between the CGIAR and other international organisations concerned with agricultural research for development. 4

5 Contents Executive summary 3 List of Tables 6 1 Background 7 2 A few notes on the status of poverty mapping 9 3 CSI Country Case Studies 15 4 Next steps 26 References 44 Acronyms 47 Appendix 1: Terms of Reference 48 Appendix 2: Contacts 50 5

6 List of Tables Table 1. Countries with poverty maps based on small-area 13 estimation. Source: Henninger (2003), updated Table 2. Comparison of the seven GRID-Arendal/FIVIMS poverty mapping studies Table 3. Explanatory variables in the seven CSI-FIVIMS poverty assessments. Table 4. Global thematic maps in van Velthuisen et al. (2004) 29 Table 5. Demographic and Health Surveys (DHS) with anthropometry. Source: website as at 9 November 2004 Table 6. End-decade Databases, Multiple Indicator Cluster Surveys (MICS), 2000: MICS2 national reports at as at 9 November Table 7. Activity 1: proposed short-term interim global analysis Table 8. Activity 2: proposed medium-term regional/country 38 level analysis Table 9. Possible spatial data layers for mapping pathways out 40 of poverty or intervention domains related to two livestock research initiatives 6

7 1 Background Rates of change in the interlocking fields of science, agriculture, global trade and development are very rapid, and the research carried out by the Centres of the Consultative Group on International Agricultural Research (CGIAR) system needs to be frequently reviewed, to help ensure that CGIAR goals can continue to be met in an effective manner (Science Council, 2004). The recent past has seen a tendency for dispersion of CGIAR research initiatives, due in part to current donor funding mechanisms that can lead to overlap in Centres research portfolios. Given that many of the research-for-development needs have to address challenging issues related to poverty reduction and sustainable livelihoods for many millions of the rural poor, the CGIAR Centres responses need to be multi-faceted and systems orientated. The CGIAR as a whole has considerable comparative advantage in mobilizing research capacity across Centres, and in developing effective partnerships with NARS and advanced research institutes, in designing and implementing complex research-for-development projects to address such needs. The Science Council is in the process of developing a set of System Priorities that are designed to increase participation by stakeholders in priority setting and assist donors in allocating their resources to the CGIAR to projects with potentially large impacts. Once these System Priorities are broadly accepted by donors and stakeholders, and internalized into Centre Medium-Term Plans, they will contribute to a performance measurement system to translate logframes and milestones into objective indicators of performance (Science Council, 2004). Given the dynamics of the milieu within which international agricultural research for development is practised, priority setting has to be a continuous process, and as new information and new data become available, priority setting should be revisited for refinement, so as to make the science portfolio of the CGIAR as effective and as poverty-focussed as possible. In the past, priorities of the CGIAR were set by considering relative priorities and resource allocation among commodities, facilitated by congruence analysis based on the value of 7

8 production. It seems that this method of priority setting is now unlikely to be effective, given that the goals of the CGIAR are more complex and the value of production criterion does not work for non-market values such as prioritising germplasm conservation or assisting NARS. In addition, such a method makes it very difficult to assess new issues and opportunities, and there has been a sea-change in funding modalities (Science Council, 2004). The Science Council has taken the leadership in initiating a process of System-level priority setting. This has consisted of a multi-faceted approach that is both analytical and broadly consultative with stakeholders, donors, and scientists within and outside the CGIAR system. This process makes use of the following: Deductive approaches that include a broad analysis of new challenges and opportunities, development of criteria to achieve poverty reduction through agricultural research that can be used to screen future proposals, and updated congruence analysis to establish the future relative importance to be given to different commodities and different sectors by region in the light of anticipated supply and demand changes. Historical approaches, including review of evolving research portfolios for the CGIAR and other research institutions and international organizations, and analysis of long-run trends in budget allocation across outputs, crops, sectors, undertakings, regions, and Centres. Inductive approaches that centre on a broadly consultative approach inviting the formulation of demand for incremental research by stakeholders and of potential supply of research by scientists. Given recent advances in the use of GIS techniques for the characterization of agroecological potential and for poverty mapping, the Science Council expressed the desire to actively pursue this approach as an element of its priority setting function, as an added element to the variety of deductive approaches that is available for priority setting. Accordingly, a 10-day scoping study was carried out to see what could be achieved in the short- to medium-term concerning the matching in a geo-referenced fashion of the following: 8

9 A typology of agro-ecological-based farming systems and commodities. A typology of degrees of market integration (travel time to a major urban or international market). A typology of poverty that characterizes: (i) the number, incidence, depth, and inequality of income of the poor, (ii) the same for malnutrition and hunger, (iii) levels of insecurity, such as weather variability and lack of water control. Time was spent at FAO from 3-5 November and 8-12 November 2004, interacting with the Science Council and Dr Ergin Ataman and colleagues, gathering relevant information for the scoping study. The terms of reference for the work are given in Appendix 1. The structure of this report is as follows. Section 2 contains a few notes on the current status of global-scale mapping efforts, and on the current status of high-resolution poverty maps at the third or higher sub-national administrative level. Section 3 contains a short summary of some major features of seven country case-studies carried out under the umbrella of the CGIAR s Consortium for Spatial Information, with money from the Government of Norway and FAO s Poverty and Food Insecurity Mapping Project. Finally, section 4 contains some specific suggestions for further work towards a set of global databases and analyses for use in priority setting. 2 A few notes on the current status of poverty mapping The field of poverty mapping has been quite lucky in the clarity of the overviews that have been written on the subject over the last few years. Reviews by Henninger (1998), Deichmann (1999), Henninger and Snel (2002), and Davis (2003) give excellent background information and a wide variety of examples. In the earliest of these reviews, Henninger (1998) noted that the empirical basis in many developing countries for characterizing and mapping land resources was very weak and was often unable to contribute to the formulation of policy recommendations. Fortunately, the situation seems to have improved markedly since then, and this must be due, at least in part, to widespread recognition of the fact that national poverty assessments can help to define 9

10 poverty, describe the extent of the problem, identify and understand the causes of poverty, develop programmes and formulate policies, and select interventions and guide allocation of resources (Henninger, 1998). As various authors have noted, high-resolution poverty maps can support efforts to decentralize and localize decision making, and are a powerful tool for visualizing spatial relationships. Internationally comparable poverty maps that apply a consistent set of indicators at sub-national level can assist in decision making and strategic planning of a wide variety of international development organizations. Given the very clear (and relatively recent) focus of the CGIAR system Centres on poverty alleviation, these Centres are and will remain key users of the information contained in poverty maps for broad strategic planning and priority setting purposes, and for examining the impacts of specific agro-technological or policy-related interventions on poverty. A recent activity has been the project "Improving Methods for Poverty and Food Insecurity Mapping and Its Use at Country Level". This is a joint initiative by FAO, UNEP and the CGIAR, running from 2001 to 2004, funded by the Government of Norway. The project outputs originally anticipated were as follows: 1 The establishment of a global collection of poverty maps and GIS databases that would be available to interested users and network members, eventually being used for decision-making. 2 A synthesis of the state-of-the-art tools and methods for mapping food insecurity, poverty and vulnerability in various contexts. 3 Poverty and food insecurity mapping case studies in a number of countries. 4 An active and functional web-based network of individuals and institutions exchanging information and jointly improving the techniques and practical utility of food insecurity and poverty mapping systems for improved programming at national, regional and global levels. The project was implemented by FIVIMS (Food Insecurity and Vulnerability Information and Mapping System), Poverty and Food Insecurity Mapping Project, the Environment and 10

11 Natural Resources Service (SDRN) of FAO, UNEP/GRID-Arendal, and CIAT, representing the CSI (Consortium on Spatial Information) of the CGIAR Centres. This work has resulted in seven case studies at the country and sub-national levels, two of which were funded by FIVIMS, with the object of attempting to draw general lessons of the spatial determinants of poverty in different places. The CGIAR Centres involved were CIAT, CIMMYT, IITA, IFPRI, ILRI, IWMI and IRRI. As might be expected, there is currently a considerable tradeoff involved between geographic coverage and detail: high-resolution poverty maps at the third or fourth sub-national administrative level contain a wealth of detail, but the coverage of countries that have developed these is still very patchy, and the methods used (or definitions of poverty used) are not always compatible from one country to another. On the other hand, while global poverty maps have complete (or near-complete) coverage, their value is constrained by both the global data sets available with which to construct them and an incomplete understanding of the determinants of poverty in different places. One objective of the CSI case studies was to ascertain whether some general principles can be drawn. These case studies are summarised in section 3 below. 2.1 Country poverty maps Various methods for spatial location of the poor have been put forward. Davis (2003) discusses seven: small-area estimation; multivariate weighted basic-needs index; combination of qualitative information and secondary data; primarily qualitative; extrapolation of participatory approaches; direct measurement of household-survey data; and direct measurement of census data. In their section on methods of poverty mapping, Henninger and Snel (2002) discuss just two: small-area estimation, and other methods. Small-area estimation is a statistical technique that combines survey and census data to estimate welfare or other indicators for disaggregated geographical units such as municipalities or rural communities. Small-area estimation applies parameters from a predictive model to identical variables in a census or auxiliary database; the assumption is that the relationship defined by the model holds for the larger population as well as the original sample. Two principal methods of small-area estimation for poverty mapping have emerged. The first uses census data on household units (Hentschel et al., 2000), and is the only method where statistical properties have been and continue to be thoroughly investigated. The second uses average values from disaggregated geographical units such as communities or small 11

12 towns instead of household-unit data (Bigman et al., 2000). Easier access to data makes this method attractive, but the error associated with estimation for units of different sizes in terms of population has not been thoroughly investigated. In general, all methods have their own advantages and disadvantages, and choice of method is not trivial, but depends on factors such as the purpose, data availability, technical capacity, and the trade-off between effort and knowledge of errors (Davis and Siano, 2001). Small-area estimation techniques are becoming quite widely applied, despite the fact that they have considerable data and technical requirements, and this is largely because the statistical properties of the models used and the estimates derived can be subjected to scrutiny a key factor in contributing to strengthening the credibility of the resulting maps. Table 1 shows a list of countries that have developed, are developing, and may soon develop high-resolution poverty maps based on small-area estimation, updated from Henninger (2003). 2.2 Global poverty mapping In an ideal world, highly disaggregated poverty maps would exist for all developing countries, and these could be harmonised appropriately and used for priority setting purposes. Despite the fairly rapid development of high-resolution poverty maps, it is clear that there is as yet very incomplete coverage of countries. In addition, the problems related to the harmonisation issue may be substantial. For global priority setting in the near future, what are the options, if poverty mapping is to be included in the process? There have been some recent efforts to work from the top down : to take available global data sets, and attempt to develop global maps (with complete coverage), at some level of resolution, that are comparable across countries and regions. The basic problems associated with these efforts relate to resolution of the underlying databases, standardisation of data across different regions and countries, and identification of appropriate proxies that serve the purposes of the analysis. 12

13 Table 1. Countries with poverty maps based on small-area estimation. Source: Henninger (2003), updated. Maps Developed Maps in the process of being developed Countries that may develop poverty maps soon Panama Mexico Zambia Ecuador Bolivia India Nicaragua Morocco Philippines Guatemala Tanzania Turkey Mozambique Indonesia Ethiopia Malawi Thailand Madagascar China Republic of South Africa Georgia Kenya Uganda Cambodia Vietnam China Albania Bulgaria Bangladesh One example of this is the recent DFID-funded work at ILRI on attempting to locate poor livestock keepers globally. The central element of this was a global livestock system classification (Seré and Steinfeld, 1996) that was mapped using global datasets of human population density, irrigated lands, land use/land cover, urban areas, and length of growing period. For these livestock systems, poverty data from various sources were attached to produce a set of poverty maps by production system by country (Thornton et al., 2002). Examination of case study results and more detailed country data showed that there were often higher incidences of poverty in sparsely populated and remote areas (measured by the 13

14 headcount, the percentage of poor living below a poverty line) and sometimes in low-potential, marginal agricultural areas. These spatial patterns, however, did not appear in other locations, and given insufficient quantitative data to generalise across regions or to identify other general patterns, the global maps produced were based on national-level poverty rates. In the ILRI study, national poverty lines from the World Bank were used, and to take account of the fact that the proportional importance of livestock to household income streams differs from one culture to another and within production systems, estimates were used of the number of poor livestock keepers globally, broken down to three categories: extensive graziers, rainfed mixed farmers, and landless livestock keepers (LID, 1999). These fixed proportions were then applied to the numbers of poor in each system using nationally-defined rural poverty rates. The resulting numbers of poor livestock keepers (some 600 million in developing countries) were certainly very coarse at the global level, but the results of this mapping exercise were used for helping to set animal health (Perry et al., 2002) and other priorities within ILRI, and they were also used within DFID for various purposes (Sarah Holden, personal communication). A second example is a recent analysis of mountain environments and populations globally (Huddleston et al., 2003). In this study, the distribution of mountainous regions was mapped globally, based on elevation and local elevation ranges, and overlaid with population densities (urban and rural), to identify population densities at different elevations. The study then considered the livelihoods available to mountain people, on the basis of type of land cover, various land-use categories, and broad system types. The work achieves a break-down of area and rural population of the six classes of mountain region, broken down by 18 system types. Finally, vulnerability is considered in terms of a set of constraints imposed by climate (moisture and temperature), terrain slope, and soil quality, degree of isolation measured as lack of access to infrastructure, and malnutrition and poor health. The number of vulnerable people (defined in this way) is then calculated by region and by mountain class some 245 million people, in all developing and transition countries. In addition to this, a global analysis is being completed that seeks to map the geophysical factors that influence agricultural production and rural vulnerability on a global basis (van Velthuisen et al., 2004). This is a contribution of FAO to the outputs of the project "Improving Methods for Poverty and Food Insecurity Mapping and Its Use at Country Level", referred to above, and constitutes part of the FIVIMS Global GIS Database (FGGD) that is 14

15 still under development. The major thematic layers used in this study are listed in section 4.1 below. Despite the sometimes heroic assumptions that are needed in such global analyses, the information that can be provided would seem to be very useful for global priority setting. The quality and resolution of global databases is improving constantly, and so these topdown analyses should be able to be refined through time. There are still considerable problems to be overcome, however. Progress towards higher-resolution studies is hampered by the coarsest-resolution data layer used in the analysis, and this may be very difficult to overcome. There can also be serious issues caused by inconsistency in data definitions and collection methods. Nevertheless, the global analyses referred to above contain a wealth of data that may be used in various ways for a coarse global analysis of poverty, and an activity to do this is suggested in section 4 below. 3 CSI Country Case Studies For the third output of the project "Improving Methods for Poverty and Food Insecurity Mapping and Its Use at Country Level", seven case studies were funded (two by FIVIMS), and the reports of this work are still in the draft stage. A thorough synthesis of these case studies is pending, but what follows here is a brief description of each and a table that attempts to pull together some comparative information on them. Details (and much of the text below) were taken from the project draft reports as at early November 2004 and from information on the project website, Geospatial dimensions of poverty and food security - a case study of Mexico The objective of this case study was to develop new methodologies for characterizing spatial and temporal variation in food insecurity and poverty in Mexico. Emphasis was given to statistically rigorous analyses that account for complexities in analyzing spatial data from diverse sources. The diversity of environments and socio-economic conditions of Mexico make the country an ideal platform for studying variation in food security and human welfare. It is considered to have one of the largest inequalities in the distribution of wealth and human welfare in the world. The project used a range of spatial and econometric methodologies, emphasizing their testing and evaluation. Approaches tested included non-parametric methods 15

16 of interpolation, and spatial and temporal auto-regression techniques. An initial multi-variate regression model for monthly household expenditure in rural communities (as an indicator for likely food security), that includes components of human welfare, education, indigenous grouping, environment, market access and policy, was developed using household level data from a national survey using variables compatible with those in national census and also GISderived information. The initial model constructed, after various iterations of development, was found to explain approximately 45% of the variance when used as a predictor of rural household average per capita monthly expenditure. The predictive model was run for rural communities throughout Mexico and model outputs classified according to nationally-defined poverty lines defined in terms of monthly expenditure required to meet the minimum basic human food requirements. These results were then interpolated using ordinary kriging. Kernel density interpolation of communities predicted to be below the poverty line confirmed the existence of potential hot-spots of clustered vulnerable communities in certain regions of Mexico, particularly southern-central and the south-west. 3.2 Improved mapping and spatial analysis of food security and poverty in Ecuador This case study was designed to identify the lack of access to food within Ecuador and test hypotheses regarding the causes of food insecurity in Ecuador. It involved identifying how access to food varies spatially, how access to food varies temporally, and crucially, how the factors that contribute to food insecurity change over space and time. Participatory methods were used to generate hypotheses of drivers of food insecurity. These drivers were mapped, along with a food security variable of food consumption per capita. Small area estimation methods were used to generate food consumption estimates at the administrative unit with a median population of 3000 persons. Food consumption was analysed spatially to determine whether the distribution of the food insecure is in any way non-random and shows spatial structure. The case study then investigated the relationship between food consumption and drivers using various methods including multiple regression and geographically weighted regression models. Study results showed that, for the spatial analysis of food consumption at the district scale, conditions within the district account for much of the variation in food consumption values. All the maps produced by this case study were published on an interactive map server that is accessible to a wide range of users and easy to update and add information. 16

17 3.3 Use of geo-spatial predictive drivers for reducing malnutrition levels of poor rural households in Nigeria The objectives of this case study were to identify and parameterise key factors and socioeconomic and/or policy drivers that affect poverty, malnutrition and under-nourishment levels in Nigeria. The core assumption of the research was that rural malnutrition is closely linked to rural livelihoods and poverty, which in turn are closely related to the prevalent economic conditions (input/output markets, access to roads, transport, marketing information) and the entrepreneurial potential of the household (education, age of head of household). The study built on two surveys, the first a collaboration between IITA and the Project Co-ordination Unit of the Federal Ministry of Agriculture and Rural Development to map food demand in conjunction with rural and urban poverty in ten states in Nigeria, and the second a complementary study at IITA with the National Planning Commission investigating food consumption and nutrition in 12 States. Models were developed using regression and results interpolated using kriging to describe driving factors of poverty and malnutrition. As at November 2004, access to a full write-up of the analyses that had been completed was not possible. 3.4 Mapping and beyond - improving welfare policy design and targeting through spatial analysis of poverty and vulnerability: a study of Malawi Building on IFPRI's research into poverty in Malawi, this case study examined the spatial content of the various levels of economic vulnerability of Malawi households. For any given shock, the vulnerable are those whose welfare will be adversely affected to a larger degree than is the norm for the population. Examining the issue from a spatial perspective, the study sought a closer understanding of just which spatial factors determine which households in Malawi are particularly likely to see a disproportionate reduction in their welfare due to a range of negative shocks. What are the key correlates of the incidence and spatial variability of household poverty and economic vulnerability in Malawi? Spatial regression methods were used, in conjunction with small-area poverty maps of the country with a broad range of socio-economic and agro-ecological datasets, including temporal data, to identify key correlates of aggregate household poverty and economic vulnerability levels. Household panel survey analysis was carried out to inform the investigation of spatial correlates of household vulnerability. A spatial correlation analysis was carried out of various poverty 17

18 measures with selected risks faced by households and proxies for the coping strategies they might employ. Overall, the correlations were very weak. Households in most areas of Malawi appear to have adapted to their ecological conditions so that their welfare does not seem to be closely correlated with these conditions, particularly the variability in those conditions and the consequent risk to household welfare. 3.5 Livelihoods and information mapping in Kenya Recognizing that expenditure-based poverty estimates do not necessarily capture the importance of the five types of assets (physical, natural, social, financial and human) that may largely determine the livelihood options facing poor households, this case study concerned itself with livelihoods mapping in Kajiado District in Kenya. An objective of the work was to explore what is 'map-able' with respect to these five types of assets, and work closely with community members to determine which of the spatial indicators of livelihood assets identified (with their inputs) were the most useful. Another objective was to test whether the various assets really contribute to an understanding of variations in livelihoods and poverty. This was done by utilising data from a geo-referenced household welfare survey and the Kenya small-area estimation poverty map, using a spatial econometric analysis. Results indicated that variables such as access to roads and security are reasonably highly correlated with poverty headcounts. 3.6 Mapping and analysis of poverty and food insecurity in Bangladesh The objective of this case study was to develop methodologies and digital, geo-referenced sub-national data sets that allow more comprehensive analyses of the relationships between human well-being and geographical factors in areas where the poor concentrated, for better targeting of agricultural research and development programs, thereby improving food security in Bangladesh. Information was assembled on natural resource endowment and vulnerability, physical accessibility, and economic poverty, building upon approaches developed for small area estimation of poverty using statistical regression analysis linking household sample survey data with geographically comprehensive data. Spatial statistical techniques were applied to analyze the relationship of productivity factors with the resource endowment, socio-economic and risk factors for profiling the agricultural production characteristics of sub-districts, and to identify clusters of contiguous "poor" communities, using global and 18

19 local measures of spatial auto-correlation. These clusters would constitute pockets of high poverty incidence. Results indicated that poverty incidence has become more localised during the past decade. 3.7 Spatial clustering of rural poor in Sri Lanka This case study, which received substantially less funding than the other six above, used estimates of poverty at the third sub-national administrative level to test whether the poor in Sri Lanka are spatially clustered, and to determine the extent which the availability and access to water and land resources are contributory factors for the spatial patterns of poverty across geographic areas. Spatial clustering techniques were used to assess whether neighboring units were similar or dissimilar in poverty levels. Income poverty was measured as the percentage of households below the national poverty line. It also investigated the linkages of spatial clustering of rural poverty with factors influencing agriculture production. The results showed that spatial clustering of the rural poor in Sri Lanka is statistically significant. Areas with a low percentage of poor households are clustered around major urban centers. In predominantly agricultural areas, there are only limited economic opportunities for poor people to escape poverty. There are considerable differences in the type of analyses carried out and the results obtained from these seven case studies. The synthesis study should be able to draw out the common results from them, but in the meantime, Table 2 attempts to tabulate some essential features of each case study. From the available literature on each study, an attempt was made to gauge the level of effort expended in each study on data collation, analysis, and participation; these will be very approximate only. Table 3 summarises the explanatory variables that were found in each case study to be associated with poverty. It can be seen that the impact of environmental variables is mixed: they matter in some places, but not so much in others. Variables related to education (access or attainment) are important in four of the six case studies that have results -- a common finding in other studies that seek to identify correlates of poverty, such as that of Minot and Baulch (2002) in Vietnam. As is clear, the seven studies were not all of the same type. The Bangladesh study, for example, can be seen as the first stage in a longer process, that found correlations or associations between poverty and a range of factors. The Kenya example might be seen as a second stage: this work at the sub-location level (the fifth sub-national administrative level) was only possible because a 19

20 Table 2. Comparison of the seven GRID-Arendal/FIVIMS poverty mapping studies Country Mexico Ecuador Area, scale Country Country Question(s) asked Characterisation of spatial and temporal variation in food security and poverty Poverty defined how? Spatial/GIS data used Monthly per capita expenditure, at the 3 rd subnational level (municipio) Accessibility to markets Maize storage loss surfaces Climate, slope, relief What is the spatial variability of food poverty? What are its major determinants? Food poverty at the 3rd subnational administrative level Soils Access to water Access to markets Land tenure Climate Human population Poverty data used Determined in the study Derived in the study Survey data used Population Census 2000 Household income and expenditure surveys 2000, 2002 National nutrition survey 1999 Tools used Bigman-type SAE Multiple regression analysis Interpolation tools: kriging, kernal density Spatial statistics (Moran, Geary) Goodness of fit of the models Total expenditure regressions, r 2 of 0.4 to 0.5 Models over-estimated household food poverty rates Effort involved Data collation: moderate Analytical: considerable Participatory approaches: low Major outcomes Extreme rural poverty is nonuniform and clustered LSMS 1995, 1998 Census of 1990, 2001 Structured survey in one case study location for verification Multivariate regressions (SAE) of food consumption per person Spatial stats (Moran, semivariograms, GAM clustering tools) Geographically weighted regression Food poverty headcount: r 2 from Data collation: moderate Analytical: considerable Participatory approaches: moderate Data quality, accessibility issues are important 20

21 Major determinants or indicators of poverty Extremely poor communities concentrated in sloping areas (with high rainfall, erosionprone soils) Data stored, managed and disseminated via a web-based system Education Indigenous language Population density Accessibility Spatial analysis techniques not well known Food poverty and poverty are highly correlated, but correlation is region-dependent Food poverty is not randomly distributed but clustered and dynamic Water availability for agriculture Accessibility In the Sierra, demography, land tenure El Niño shocks What next? Not indicated Continue management of website Promote use of results Table 2, continued Country Nigeria Malawi Area, scale Country Country Questions asked Characterisation of the livelihoods and food security status of rural poor Identify external constraints to adequate household nutrition What are the spatial determinants of poverty prevalence in small spatiallydefined populations in rural Malawi? Poverty defined how? Spatial/GIS data used Poverty index no details on how defined Tree and grass cover, soil cover, rainfall, soil fertility, population density, travel time to cities and towns Risk chain concept of household and individual economic vulnerability, applied to poverty headcounts Extensive spatial databases, new data sets generated, interpolation Poverty data used Derived in the study 1998 small-area estimation headcounts Survey data used Food Consumption and IHS Nutrition Survey 2001 Tools used Linear regressions of poverty GeoDa software for global indicators against biophysical spatial regression analyses and socio-economic variables Geographically weighted Kriging regression Goodness of fit of Not given Low to moderate explanatory 21

22 the models Effort involved Data collation: considerable Analytical: moderate so far Participatory approaches: low power Data collation: moderate Analytical: considerable Participatory approaches: low Major outcomes None drawn yet In the global model, most determinants of the prevalence of poverty were not significant Many were significant in the local models of poverty prevalence for each of the rural areas examined Very weak links between poverty and environmental variables Heterogeneous local models for poverty determinants means that poverty reduction efforts in rural Malawi need to be targeted at the district and subdistrict levels Major determinants or indicators of poverty Rainfall Vegetation To be identified General educational attainment levels, Degree to which nonagriculture livelihood strategies can be employed Proportion of dependents in the population. What next? Analysis is still in progress Further dissemination of the results Table 2, continued Country Kenya Bangladesh Area, scale District (Kajiado) Country Questions asked Which livelihood assets can be mapped? Of these, which are useful for explaining poverty? Poverty defined how? In terms of livelihoods and access to the 5 asset types In which areas are the rural poor concentrated? Which factors contribute most to poverty? Income poverty at the 3 rd subnational level Spatial/GIS data Roads, markets, facilities Human capital 22

23 used Water, landuse, soils Schools, hospitals, training Banks, credit Livestock Churches, NGOs Poverty data used Kenya SAE poverty map at 4 th & 5 th sub-national (location, sublocation) admin level (2003) Physical capital Land quality Access to education, health, electricity, off-farm employment Vulnerability to environmental stresses Determined in the study Survey data used Community surveys Census 2001 IRRI survey HIES 2000 (for prices and setting the poverty line) Ag census 1996, National Irrigation Census 2001 Tools used Participatory resource mapping Thematic mapping Loglinear Poisson regression SAE to estimate income Geographically weighted regression to determine spatial differences between poverty and explanatory variables Goodness of fit of the models Not given Predicted incomes: r Spatial variation in poverty: r Effort involved Data collation: considerable (6 mo) Analytical: moderate Participatory approaches: considerable Major outcomes Considerable stakeholder benefits in terms of awareness of links between land use, poverty, livelihoods Some key variables correlated with poverty identified, including roads and security Data collation: considerable Analytical: considerable Participatory approaches: moderate Poverty incidence has become more localised Poverty maps to be used in the PRSP process Major determinants or indicators of poverty Soil resources PPE and NDVI Livestock density Distance to town/market Road density Educational attainment Availability of infrastructure (electricity, irrigation, roads & public services) Land tenure Soil suitability 23

24 Access to education and security What next? Develop assessment and M&E tool Expanding approach to other regions Explore use of other proxies for human & social capital Improve estimates when full population census data are available Validation of the mapped results More detailed interpretation/ interaction with local stakeholders Table 2, continued Country Sri Lanka Area, scale Country Questions asked Are the poor in Sri Lanka spatially clustered? To what extent does the availability and access to water and land resources contribute to the spatial patterns of poverty? Poverty defined how? Spatial/GIS data used Basic needs approach, households below the poverty line (consumption headcounts at the 3 rd administrative level) Rainfall, irrigable area, smallholder land holding size, number of agricultural operators per household, average distance to roads and towns Poverty data used Department of Census and Statistics 2003 Survey data used Department of Census and Statistics 2003 Tools used Spatial clustering Global and local Moran s I OLS regression 24

25 Goodness of fit of the models Statistically significant spatial clustering of poor and nonpoor units Effort involved Data collation: moderate Analytical: moderate Participatory approaches: low Major outcomes Areas with high levels of poverty are in rural districts where agriculture is the main source of livelihoods of most households Major determinants or indicators of poverty Spatial clustering of poverty is significantly associated with access to, and availability of, land and water What next? Not specified in detail Table 3. Explanatory variables in the seven CSI-FIVIMS poverty assessments Source: Hyman and Immink (2004). Country Case Study Important explanatory variables Mexico Indigenous groups Education Accessibility Population density Ecuador Accessibility Water availability El Niño Land tenure Nigeria Rainfall 25

26 Vegetation (more analysis needed) Malawi Educational attainment Non-agricultural activities Dependency ratio Kenya Soil resources Rainfall and climate NDVI (vegetation vigour) Access to education Accessibility to towns Bangladesh Educational attainment Availability of infrastructure Land tenure Flood-prone lands Soil suitability for rice cultivation Sri Lanka Access to land and water Availability of land and water national high-resolution poverty map already existed. That study involved much more participatory work with communities, in an attempt to work towards more causal explanations of why some communities are poor and others are less so. A next logical step would be to do some community testing of interventions and community assessment of research needs, and this is planned for the coming months in Kajiado (Patti Kristjanson, personal communication). 4 Next steps The current situation with regard to the requirements for a set of global poverty maps for priority setting can be summarised as follows: For a global analysis, there are now considerable holdings of spatial data at FAO and elsewhere, such that broad-scale analyses are possible for various purposes. The only global data set for consumption poverty is still at the country scale. It is not yet possible to map income or expenditure poverty headcount data at a global scale at a resolution 26

27 finer than the country, and this situation will not change in the short term. This resolution is not good enough for what is required. A rough-and-ready (and analytically not very rigorous) global analysis could, however, be made using health and nutrition data at sub-country scale as proxies for consumption poverty data, and in some cases these cluster data could be re-aggregated, possibly to derive estimates of these poverty proxies by Favourable Rural Areas and Marginal Rural Areas. Not all developing countries have such data at the sub-national level, however, so some assumptions would need to be made for such countries. Current resolution of the global spatial data sets needed for this analysis is not high enough to allow much to be said about areas that are small but important for CGIAR goals, such as the coastal zones, small island states, and forest margins; any global analysis that is possible within the next few months will not be able to deal satisfactorily with these. Once a thorough synthesis has been carried out of the seven CSI case studies, it may be possible to combine the results with other published studies of the same sort, in an attempt to draw some firmer conclusions concerning the association of key geo-spatial variables with poverty in different countries. Depending on the outcome of the synthesis study, additional analysis for some case studies may be indicated. There would seem to be value in carrying out a regional analysis for a group of countries where higher-resolution base data exist, and where the associations between geo-spatial factors and poverty can be investigated more thoroughly than is currently possible at the global level. The results of such regional level may well be able to suggest new ideas for analysis that can be applied at the global level. Accordingly, the subsections below contain some suggestions as to what might be done next. An outline of a broad-brush global analysis is presented in subsection 4.1, which can be seen as an interim product that could make some short-term input to the priority setting process, and as something that can be greatly refined and updated through time. Some suggestions are made in subsection 4.2. for a regional exploratory analysis, making use of existing poverty 27

28 maps and existing higher-resolution databases that are available for some areas of the developing world. Subsection 4.3 briefly discusses the notion of poverty intervention mapping rather than poverty mapping per se, that may be able to contribute to targeting and priority setting. There would seem to be some potential for attempting to match interventions that address particular pathways out of poverty to specific household, community and locational characteristics. Finally, subsection 4.4 lists some more general issues related to moving forward the agenda on poverty mapping and targeting within the CGIAR as a whole: this is an agenda that could benefit enormously from more cohesive and coherent collective action by the system and its international partners. 4.1 Global analysis Given the global data layers that exist, and building on several previous global studies, one activity that could be of use is to revisit a global analysis to see what more can be gleaned from relatively coarse data sets, in terms of attempting to locate Favourable Rural Areas (FRAs) and Marginal Rural Areas (MRAs). As noted above, this could be seen as an interim analysis that should be able to be completed within The analysis would still be relatively coarse in resolution, and because of coverage problems, country poverty data could not be used directly, but a number of refinements to current efforts could be envisaged, as outlined below. Agro-ecological potential: A listing of the major global thematic layers in Velthuizen et al. (2004) is shown in Table 4. Some activity may be possible to try to move from the notion of agro-ecological potential towards something more related to agricultural systems (i.e., moving from crop suitability towards crop distribution ). For example, the farming systems layer of Dixon et al. (2002) could be used, and this might be combined with the livestock systems of Seré and Steinfeld (1996) as mapped by Kruska et al. (2003). There are 28