Poverty Mapping Research Articles

Crop research to benefit poor farmers in marginal areas of the developing world: a review of technical challenges and tools.

Abstract Despite great successes in crop research for the developing world, many poor farmers in marginal areas have not benefited. There is increasing recognition that crop research can and should benefit these farmers. This paper reviews some of the key technical issues related to the challenge of formulating and carrying out crop research that is targeted, relevant and appropriate to these farmers, with a particular emphasis on crop improvement. It examines the reasons that underpin the lack of research impacts, and some of the key challenges and tools available to make this happen. The challenges include breeding for marginal environments, incorporating risk and vulnerability as important dimensions of crop research, recognizing the demand for multiple traits and diverse varieties, as well as addressing neglected and underutilized species important to the poor. The tools are comprised of a combination of crop environmental classification systems with poverty mapping to target and prioritize research at the global and national levels, the use of participatory research approaches, particularly in plant breeding, as well as non-market valuation methodologies to make research products relevant and appropriate to the poor at the local level. The paper briefly examines the promise of modern biotechnology.

Targeting agricultural research to benefit poor farmers: Relating poverty mapping to maize environments in Mexico

We explore approaches for targeting agricultural research to benefit poor farmers. Using small area estimation methods and spatial analysis, we generated high-resolution poverty maps and combined them with geo-referenced biophysical data relevant to maize-based agriculture in Mexico. We used multivariate classification and cluster analysis to synthesize biophysical data relevant for crop performance with rural poverty data. Results show that the rural poor are concentrated in particular regions and under particular circumstances. Formal maize germplasm improvement trials were largely outside the core areas of rural poverty and there was little evidence for direct spillover of improved germplasm. Agro-climatic classification used for targeting breeding is useful but often ignores some important factors identified as relevant for the poor. Combining this method with poverty mapping improves stratifying and targeting crop breeding efforts to meet the demands of resource-poor farmers. We believe this integrated approach will help increase benefits from agricultural research to poor rural communities.

Methods, results and policy implications of poverty and food security mapping assessments

The importance of poverty reduction to the world development agenda has motivated greater interest in the geographic dimensions of poverty and food security. This special issue of Food Policy includes examples of poverty and food security mapping used to support policy development in agricultural and rural areas. The volume includes eight country case studies and one cross-country comparison that illustrate advances in our capacity to assess welfare over large areas and at detailed spatial resolutions. Poverty mapping facilitates assessments of the role of environmental factors on the broad spatial pattern of poverty and food security. Evaluating proximity and accessibility in welfare outcomes can improve our knowledge of poverty patterns and processes. Spatial statistics can enhance our understanding of geographic and neighbourhood effects on poverty and food security outcomes. The development of effective policies requires increased collaboration among stakeholders, researchers and policy makers in constructing and using poverty and food security maps.

Spatial clustering of rural poverty and food insecurity in Sri Lanka

We mapped poverty, with reference to a nutrition-based poverty line, to analyse its spatial clustering in Sri Lanka. We used the Divisional Secretariat poverty map, derived by combining the principal component analysis and the synthetic small area estimation technique, as the data source. Two statistically significant clusters appear. One cluster indicates that low poverty rural areas cluster around a few low poverty urban areas, where low agricultural employment and better access to roads are key characteristics. The other indicates a cluster of high poverty rural areas, where agriculture is the dominant economic activity, and where spatial clustering is associated with factors influencing agricultural production. Agricultural smallholdings are positively associated with spatial clustering of poor rural areas. In areas where water availability is low, better access to irrigation significantly reduces poverty. Finally, we discuss the use of poverty mapping for effective policy formulation and interventions for alleviating poverty and food insecurity.

Poverty Mapping with Aggregate Census Data: What is the Loss in Precision?

AbstractSpatially disaggregated maps of the incidence of poverty can be constructed by combining household survey data and census data. In some countries (notably China and India), national statistics agencies are reluctant, for reasons of confidentiality, to release household‐level census data, but they are generally more willing to release aggregated census data, such as village‐ or district‐level means. This paper examines the loss in precision associated with using aggregated census data instead of household‐level data to generate poverty estimates. The authors show analytically that using aggregated census data will result in poverty rates that are biased downward (upward) if the rate is below (above) 50%, and that the bias approaches zero as the poverty rate approaches zero, 50%, and 100%. Using data from Vietnam, it is found that the mean absolute error in estimating district‐level poverty rates is 2.5 percentage points if the census data are aggregated to the enumeration‐area level means, and 3–4 percentage points if the data are aggregated to commune or district level. Finally, the authors propose a method for reducing the error using variances calculated from the census. When this approach is applied to the Vietnam data, this method can cut the size of the aggregation errors by around 75%.

Imputed welfare estimates in regression analysis

We discuss the use of imputed data in regression analysis, in particular the use of highly disaggregated welfare indicators (from so-called 'poverty maps'). We show that such indicators can be used both as explanatory variables on the right-hand side and as the phenomenon to explain on the left-hand side. We try out practical ways of adjusting standard errors of the regression coefficients to reflect the error introduced by using imputed, rather than actual, welfare indicators. These are illustrated by regression experiments based on data from Ecuador. For regressions with imputed variables on the left-hand side, we argue that essentially the same aggregate relationships would be found with either actual or imputed variables. We address the methodological question of how to interpret aggregate relationships found in such regressions. © Oxford University Press 2005; all rights reserved.

Targeting the water-poor through water poverty mapping

This paper shows how water poverty mapping using census data and the Water Poverty Index can be used to identify effectively the most water-poor households in a region for the targeting of water supply development policies and projects. The main findings come from a case study conducted in the Estcourt municipal district in South Africa where simple water poverty maps were developed using readily available data sources at three different scales: enumerator area, place names and sub-catchment. The efficiency of targeting the most water-poor households using the different scales of water poverty maps were measured by comparing both the inclusion and exclusion rates of targeting and comparing them with other similar targeting studies. The distribution of water poverty within a community was also compared with the results of a detailed household questionnaire conducted as part of the broader development of the water poverty index (WPI). The main conclusion from the study is that water poverty mapping is a strong visual extension of the WPI that has great potential for providing a practical way for water management authorities and decision makers to identify and target the most water poor households and monitoring the impacts and tangible benefits of water supply development policies.

Autologistic regression model for poverty mapping and analysis

Poverty mapping in developing countries has become an increasingly important tool in the search for ways to improve living standards in an economically and environmentally sustainable manner. Although the classical econometric methods provide information on the geographic distribution of poverty, they do not take into account the spatial dependence of the data and generally they do not consider any environmental information. Methods which use spatial analysis tools are required to explore such spatial dimensions of poverty and its linkages with the environmental conditions. This study applies a spatial analysis to determine those variables that affect household poverty and to estimate the number of poor people in the target areas.

Mapping livestock and poverty: a tool for targeting research and development

Detailed geographical profiles of poverty and inequality can be extremely valuable to governments, non-governmental organizations, and multilateral institutions that aim to strengthen the impact of their spending on poverty. Some developing countries use information on the geographical distribution of welfare indicators to guide the allocation of resources among local agencies or government as a first step in reaching the poor. Poverty maps can also be important tools for researchers.

Estimating Poverty Over Time and Space: Construction of a Time-Variant Poverty Index for Costa Rica

This paper presents the construction of a spatially explicit, nationally disaggregated measure of poverty over time in Costa Rica. The paper first describes the two possible methods considered for the construction of a poverty map: principal component analysis (PCA) versus small area estimation. Next, reasons for choosing PCA and a description of its application both at one point in time (1973) and over time are presented together with the resulting poverty maps. The methodology applied represents a methodological innovation in that the resulting poverty map is time variant rather than concentrated in a single moment in time. A comparison of the results obtainable using various techniques and a discussion on the relative merits of the various options available concludes the paper.

Locating poor livestock keepers at the global level for research and development targeting

Many research and development agencies are committed to halving the number of people living in extreme poverty by 2015. Knowledge of where the poor are, and what characterises them, is patchy at best. Here we describe a global livestock and poverty mapping study designed to assist in targeting research and development activities concerning livestock. Estimates of the numbers of poor livestock keepers by production system and region are presented. While these estimates suffer from various problems, improvements in global databases are critical to improve the targeting of interventions that can meet the challenges posed by poverty and to chart progress against international development indicators.

A robust poverty profile for Brazil using multiple data sources

This paper presents a poverty profile for Brazil, based on three different sources of household data for 1996. We use PPV consumption data to estimate poverty and indigence lines. ''Contagem'' data is used to allow for an unprecedented refinement of the country's poverty map. Poverty measures and shares are also presented for a wide range of population subgroups, based on the PNAD 1996, with new adjustments for imputed rents and spatial differences in cost of living. Robustness of the profile is verified with respect to different poverty lines, spatial price deflators, and equivalence scales. Overall poverty incidence ranges from 23% with respect to an indigence line to 45% with respect to a more generous poverty line. More importantly, however, poverty is found to vary significantly across regions and city sizes, with rural areas, small and medium towns and the metropolitan peripheries of the North and Northeast regions being poorest.

Picking the Poor: Indicators for Geographic Targeting in Peru

Geographic targeting is perhaps the most popular mechanism used to direct social programs to the poor in Latin America. This paper empirically compares geographic targeting indicators available in Peru. To this effect, I combine household–level information from the 1997 Peru Living Standards Measurement Survey (LSMS) and district–level information from the 1993 Peru Population and Housing Census. I then conduct a series of simulations which estimate leakage rates, concentration curves, the impact of transfers on poverty as measured by the headcount index, poverty gap and P2 measures of the FGT family, and non–parametric (kernel) densities when transfers are based on alternative indicators. I conclude that there is substantial potential for geographic targeting in Peru. However, the differences in outcomes across geographic targeting indicators are small, and are not statistically significant. These results are in keeping with earlier work which suggests that (among reasonable alternatives) the choice of geographic targeting indicator does not have an important bearing on poverty outcomes, and are at odds with more recent research which stresses the advantage of poverty maps which “impute” consumption or income.

How Low Can You Go? Combining Census and Survey Data for Mapping Poverty in South Africa

Poverty maps, spatial descriptions of the distribution of poverty in any given country, are most useful to policymakers and researchers when they are finely disaggregated, i.e., when they represent small geographic units, such as cities, towns or villages. Unfortunately, almost all household surveys are too small to be representative at such levels of disaggregation, and most census data do not contain the required information to calculate poverty. The 1996 South African census is an exception, in that it does contain income information for each individual in the household. In this paper, we show that the income from the census data provides only a weak proxy for the average income or poverty rates at either the provincial level or at lower levels of aggregation. We also demonstrate a simple method of imputing expenditures for every household in the census, using information in the October Household Survey (OHS) and the Income Expenditure Survey (IES) in 1995. The resulting predicted household consumption values are plausible and provide a good fit with the IES data. We also provide an example which demonstrates that poverty headcount can be imputed with fair precision for magisterial districts and for transitional local councils. Finally, our paper serves as a reminder of the value of comparing various data sources for external validation, and underlines the need to make more use of census data, which seems to be underutilized in most developing countries.

The geography of poverty: A political map of poverty under New Labour

The geography of poverty: A political map of poverty under New Labour

Exploring the environmental and political dimensions of poverty: the cases of the cities of Mar del Plata and Necochea-Quequén

This paper presents a framework to show how information drawn from different sources for any city allows the construction of poverty profiles and maps. These not only help local governments to act but they also provide a catalyst for more participatory and integrated approaches to poverty reduction. The paper also gives examples of how this framework was used in two cities in Argentina. This framework brings out the multi-dimensional nature of urban poverty, including environmental and political dimensions which are not made evident by conventional definitions of poverty. The framework also highlights the complex linkages between the different dimensions and shows how the environmental dimensions (including housing conditions) are not just visible features of poverty but also key “entry points” through which social, economic and political dimensions can be understood and addressed. Mapping environmental conditions also brings out key social and spatial inequalities.

Monitoring Targeting Performance When Decentralized Allocations to the Poor Are Unobserved

How can a central government monitor the performance of a decentralized poverty program when the incidence of the program's benefits is unobserved at the local level? This article shows that, using a poverty map and the corresponding spending allocation across geographic areas, one can identify the latent differences in mean program allocations to the poor and the non-poor. The national measure of targeting performance can also be decomposed into subgroups. An application to an antipoverty program in Argentina is used to assess the program's performance before and after reforms. Increases in funding and changes in program design brought large gains to the poor, although performance differed across provinces.

Generating Disaggregated Poverty Maps: An Application to Vietnam

Previous research shows that geographic targeting in poverty programs is not accurate unless the geographic units are small. Household surveys, however, can rarely estimate poverty rates for more than 5–10 regions. This study uses data from Vietnam to illustrate a method for generating disaggregated poverty maps. First, the relationship between poverty and household indicators is estimated using survey data. Then, census data on those same indicators are used to estimate poverty rates for each of the 543 rural districts. The results indicate that Vietnamese poverty is concentrated in the north and in districts furthest from the coast and cities.

Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador

Poverty maps provide information on the spatial distribution of living standards. They are an important tool for policymakers, who rely on them to allocate transfers and inform policy design. Poverty maps art also an important tool for researchers, who use them to investigate the relationship between distribution within a country and growth or other economic, environmental, or social outcomes. A major impediment to the development of poverty maps has been that needed data on income or consumption typically are available only from relatively small surveys. Census data have the required sample size but generally do not have the required information. This article uses the case of Ecuador to demonstrate how sample survey data can be combined with census data to yield predicted poverty rates for the population covered by the census. This poverty rates are found to be precisely measured, even at fairly disaggregated levels. However, beyond a certain level of spatial disaggregation, standard errors rise rapidly.

Geographical Targeting for Poverty Alleviation: An Introduction to the Special Issue

In the face of rising public deficits and shrinking public resources, geographical targeting may be a viable way to allocate resources for poverty alleviation in developing countries. Efficiency can be increased and leakage to the non-poor reduced substantially by targeting increasingly smaller areas. This article, and more generally the symposium on geographical targeting for poverty alleviation, proposes several techniques for augmenting data to produce more detailed poverty maps. It focuses on practical considerations in the design of geographically targeted poverty alleviation programs. In particular, it assesses the advantages and disadvantages of geographical targeting and describes how geographic information systems can be applied to improve poverty mapping.