1. The INTRODUCTION

1.1 BACKGROUND

Preparation of this review to map rural poverty in developing countries was motivated by a study, carried out by Nelson et al. (1997) on behalf of the Technical Advisory Committee (TAC) of the Consultative Group on International Agricultural Research (CGIAR), on CGIAR research priorities for marginal lands. The study differentiated between marginal agricultural land (MAL) and favored agricultural land (FAL) and estimated their extent and the number of people living in these areas. According to this analysis, about 634 million rural poor are living in marginal lands, of which 375 million (59%) are in Asia. The report also shows that only 25% of the 374 CGIAR projects endorsed in 1997 are fully targeted at poverty alleviation in MAL, suggesting a need "to sharpen its [CGIAR's] strategic focus on poverty alleviation particularly in setting priorities for research related to marginal rural areas."

The relevance of such research targeting the poor was highlighted by a study by the International Food Policy Research Institute (IFPRI) which found that public investments in low-potential rainfed areas, for example with high-yielding varieties, irrigation, and education, would increase agricultural productivity and reduce rural poverty in India, providing a greater gain per unit of additional investment than similar investments in irrigated or high-potential rainfed areas (Fan and Hazell, 1997). Similarly, a study based on the 1992-93 Living Standard Measurement (LSMS) Study survey of Vietnam found that the highest increase in net crop income would occur in Vietnam's two poorest regions: the Northern Uplands and the North Coast (van de Walle, 1996).

These two studies took advantage of disaggregated data on population, incidence of poverty, land use, and infrastructure. But in many developing countries the empirical basis for characterizing and mapping marginal lands is so weak and at times unavailable as to make policy recommendations meaningful. Similar limitations are apparent in the Nelson et al. study: the soil and length of growing period maps used to define MAL and FAL included no information on land cover or use, population data were only available at the first subnational level, and a constant poverty rate was applied for all areas within a country.

In its review of Nelson et al., the TAC (1996) identified the following key limitation in our understanding of the nature and distribution of marginal lands: "the lack of readily available data in a geo-referenced framework, in particular with respect to the incidence and nature of poverty and probability of land degradation by land type." The TAC recommended a "review of available data on poverty and land degradation in relation to these marginal lands."

In response to this observation, United Nations Environment Programme (UNEP)/GRID-Arendal contracted with World Resources Institute (WRI) to carry out a review of poverty mapping (see Appendix 1 Terms of Reference - Poverty Mapping Assessment, page 75). This review is part of an ongoing collaborative project between UNEP and CGIAR to strengthen the use of Geographic Information Systems (GIS) in agricultural research, assist in the production of reliable statistical and cartographic products, for example on poverty and land use quality, and contribute to further development of global databases relevant to agricultural research. WRI had previously mapped human development indicators in West Africa to support regional priority setting by the Abidjan-based Regional Office of the U.S. Agency for International Development (USAID).

1.2 POVERTY MAPS - APPLICATIONS AND USERS

Most national poverty assessments using household and community surveys have compiled data that allow disaggregation by broad categories such as urban and rural areas, socio-economic characteristics such as household types and educational backgrounds, and major geographical regions such as a coastal, forest, and savanna zone. These poverty assessments have helped in
  • defining poverty,
  • describing the situation and problem,
  • identifying and understanding causes of poverty,
  • developing programs and formulating policies, and
  • selecting interventions and guiding allocation of resources.
Geographically disaggregated data from these assessments can be displayed in a map. Figure 1, page 3, shows such a thematic poverty map for Uganda. Poverty maps can provide quickly information on the spatial distribution of poverty. However, the coarse resolution of Figure 1 is too limiting for many applications. Analysts are beginning to produce poverty maps at finer resolution, by geo-referencing surveys and integrating these data with other information. Linking poverty assessments to maps provides new benefits in addition to the applications of poverty assessments outlined above:
  • Poverty maps make it easier to integrate data from various sources such as surveys, censuses, and satellites and from different disciplines such as social, economic, environmental data. This can help in defining and describing poverty. For example, by comparing spatial patterns of income with educational level, access to services, and market integration, different dimensions of human well-being can be examined or even be integrated.
  • A spatial framework allows to switch to new units of analysis, for example from administrative boundaries to ecological boundaries, and access new variables like community characteristics, not collected in the original survey.
  • Identifying spatial patterns with poverty maps can provide new insights into the causes of poverty, for example how much are physical isolation and poor agro-ecological endowments impediments to escape poverty. This in turn affects what type of interventions to consider.
  • The allocation of resources can be improved. Poverty maps can assist in where and how to target antipoverty programs. Geographic targeting, as opposed to across the board subsidies, has been shown to be effective at maximizing the coverage of the poor while minimizing leakage to the non-poor (Baker and Grosh, 1995). Research examining narrow geographic targeting at the community level is currently being conducted in Burkina Faso (see 5.3.2, page 60). With appropriate scale and robust poverty indicators, poverty maps can assist in the implementation of antipoverty programs, for example by promoting subsidies in poor communities and cost recovery in less poor areas.
    Figure 1 Poverty Map of Uganda
       
  • Poverty maps with a high resolution can support efforts to decentralize and localize decision making.
  • Maps are a powerful tool to visualize spatial relationships and can be very effective in reaching policy makers, providing an additional return on investments in survey data, which often remain unused and unanalyzed after the initial report or study is completed.
  • Internationally comparable poverty maps applying a consistent set of indicators at subnational level can improve decision making and strategic planning of international development organizations that had to rely mostly on national level indicators.
The CGIAR-System as a whole and specific Centers are potentially important users of poverty maps, especially since they are working within an agro-ecological framework and require assessments at both administrative and biophysical levels. Specific applications include broad strategic planning and priority setting exercises and analyses, for example examining the impacts of specific agro-technological interventions on poverty. Other users of poverty maps include organizations that have poverty alleviation or reduction as their mandate, for example, at the international level, UNDP, the World Bank, and bilateral development agencies. Here, poverty maps of various scales could be used for planning, analyses, and monitoring.

1.3 SPATIALLY DISAGGREGATED DATA - AT WHAT SCALE OR RESOLUTION?

The final applications of these poverty maps ultimately determine the appropriate scale or resolution. For example, data can be analyzed at the individual, household, village, community, administrative, national, or regional level. Whether comparing countries, setting research priorities, studying causes and effects, developing a baseline for monitoring, or targeting specific project interventions, each application requires a specific resolution for reference units. Data can generally be aggregated from the individual to the macro level, and analysts need to balance detail and coverage required for analysis with the cost of data collection. A coarse resolution or a scale too small neglects the heterogeneity within each unit and provides insufficient detail for decision making, a fine resolution or a scale too large increases the cost of compiling, managing, and analyzing the data. In addition, data at coarse resolution, for example national poverty indicators, usually are more readily available and cover a wider geographic area.

It is difficult to pre-determine an ideal resolution or scale that would be a perfect framework to guide all research priorities for marginal lands. The TAC and CGIAR Centers need to define the purpose and specific applications of their poverty mapping more precisely and determine how accurately they want to reflect the spatial distribution of poverty. Ultimately, multiple assessments and scales will be necessary, and the optimal scale will be determined by the loss attached to errors of identification in locating the poor.

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