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Locating the Poor: Spatially Disaggregated Poverty Maps for Sri Lanka

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Locating the Poor: Spatially Disaggregated Poverty Maps for Sri Lanka Research Report 96 Locating the Poor: Spatially Disaggregated Poverty Maps for Sri Lanka Upali A. Amarasinghe, Madar Samad and Markandu Anputhas International Water Management Institute P O Box 2075, Colombo, Sri Lanka i IWMI receives its principal funding from 58 governments, private foundations, and international and regional organizations known as the Consultative Group on International Agricultural Research (CGIAR). Support is also given by the Governments of Ghana, Pakistan, South Africa, Sri Lanka and Thailand. The authors: Upali A. Amarasinghe, Madar Samad, and Markandu Anputhas are Senior Researcher, Principal Researcher, and Research Officer, respectively, of the International Water Management Institute, Colombo, Sri Lanka. This study, as part of the joint initiative of poverty mapping by FAO, UNEP and CGIAR, was supported by the Government of Norway. The authors appreciate the valuable comments of both Dr. Norbert Henninger of WRI and Dr. Hugh Turral of IWMI; the support extended by various staff members of the Census and Statistics Department and the Samurdhi Authority of Sri Lanka for data collection; and the staff of the IWMI Remote Sensing/GIS unit for various software inputs of the Geographic Information System. Amarasinghe, U. A.; Samad, M.; Anputhas, M. 2005. Locating the poor: Spatially disaggregated poverty maps for Sri Lanka. Research Report 96. Colombo, Sri Lanka: International Water Management Institute. /poverty / mapping / analysis / estimation / households / employment / irrigation programs / rain / water availability / Sri Lanka/ ISSN 1026-0862 ISBN 92-9090-617-0 Copyright © 2005, by IWMI. All rights reserved. Cover map shows the spatial variation of the percentage of poor households below the poverty line across Divisional Secretariat divisions in Sri Lanka except those in the Northern and Eastern provinces. Please send inquiries and comments to: [email protected] Contents Summary v Introduction 1 Poverty Mapping 2 Spatial Clustering of Poor Areas 7 Determinants of Poverty and Spatial Clustering 9 Poverty Maps in Geographical Targeting 18 Policy Discussion and Conclusions 20 Annex A. Estimation Methodology of Subnational Poverty 23 Annex B. Principal Component Analysis in Estimating Auxiliary Index 27 Annex C. Identifying Spatial Clustering of Poor Areas 28 iii iii Summary Historically, Sri Lanka has placed a high value on basic human needs: channeling assistance to the rural areas to promote food security and employment, and assuring that the poor have access to primary health care and basic education. This policy has resulted in a high level of achievements in some areas of human well-being such as education and health. Yet, the achievements to date in terms of improving household incomes and food security, especially in rural areas, are rather disap- pointing. A quarter of the population still lives below the official poverty line. A key question is, despite the achievement in vital areas of human welfare like health and education, why does income poverty continue to persist among a sizable segment of the population? This study attempts to answer this question on the premise that inadequate spatially disaggregated information on poverty and inefficient targeting of resources for poverty alleviation are the major reasons for the slow progress in reducing the income poverty. The study generates poverty maps for Sri Lanka at subdistrict level (Divisional Secretariat Divi- sion [DS division]) by combining the small-area estimation and the principal-component methods. The report identifies who the poor are and where they live. The report also demonstrates how poverty maps can assist in identifying the spatial patterns of clustering of poor areas, the reasons for such clustering and how to use maps for geographical targeting of poverty-alleviation interventions. The spatial autocorrelation analysis shows two statistically significant clusters: one indicating low- poverty rural DS units that cluster around a few low-poverty urban DS divisions, and the other indicat- ing high-poverty rural DS divisions that cluster around high-poverty rural DS divisions. A high nonagri- cultural employment and improved physical infrastructure such as roads are key characteristics of the first cluster. Spatial clustering of poor areas in the second cluster is significantly associated with factors influencing agricultural production, such as access to, and availability of, land and water resources. A large number of small landholding sizes are significantly associated with spatial cluster- ing of poor areas in the second cluster. In the drier areas, inadequate access to irrigation supplies is a factor that explains significant spatial clustering. Further, the DS division poverty maps show that the incomes of 45 percent of households that are benefiting from the samurdhi—a program for reducing the severity of poverty—are not below the official poverty line. The study shows that geographical targeting of the poorest DS divisions in the Samurdhi Financial Program could decrease the severity of food insecurity and may even lead to lowering the incidence of poverty in the DS divisions and help reduce substantial disparities of welfare fund allocation among the DS divisions. The present study is a subnational poverty mapping analysis based on secondary data of the Population and Agriculture Census and Consumption and Expenditure Survey of Sri Lanka. The results show a good overview of the spatial variation of poverty at finer resolution than what is currently available at the district level. The study shows that finer-resolution poverty maps can be used to identify where the poor live and analyze and underlie location-specific causes of poverty more effectively than from aggregate statistics. v Locating the Poor: Spatially Disaggregated Poverty Maps for Sri Lanka Upali A. Amarasinghe, Madar Samad and Markandu Anputhas Introduction Sri Lanka’s achievements in some areas of alleviation programs. This is especially true human welfare, such as health and education, when the subnational units are large or have have been described as remarkable for a low- diverse occupational patterns. income country. Its life expectancy at birth (74 It is well recognized that geographic years) and adult literacy rate (92%) are higher targeting, as opposed to across the board-based than the world averages of 63 years and 77 interventions, is more effective at maximizing the percent, respectively (UNDP 2003). Infant coverage of the poor while minimizing leakage to mortality rate (19 per 1,000 live births), and the the nonpoor. Geo-referenced poverty maps when combined primary, secondary and tertiary school integrated with more conventional sources of enrolment ratio (66%) are comparable to the information serve two main purposes. First, they levels of upper middle-income economies (UNDP help identify poor areas. Second, they assist in 2003). Despite these achievements in social analyzing location-specific causes of poverty. welfare, poverty continues to be a major problem These in turn help design better geographically in the country. It is estimated that, at present, targeted interventions. about one-quarter of the population lives below Many countries use poverty maps in various the national poverty line (DCS 2003a). ways in their poverty-alleviation programs Over the years, many poverty alleviation (Henninger and Snel 2002). Nicaragua uses programs have been launched in Sri Lanka. poverty maps to determine resource allocation But, many doubt whether the benefits of these for poverty alleviation and provides expanded interventions actually reached those intended health-care coverage to the poorest areas. In because of shortcomings in identifying and Cambodia, poverty maps assist the World Food locating the poor. The poverty information is Program to distribute food to the neediest areas presently compiled from household or and the Asian Development Bank to identify the community surveys, and presented sector-wise poorest areas for rural development project (rural, urban and estate sectors) and spatially work. The Republic of South Africa uses (in terms of administrative districts in the poverty maps to distribute grants equitably country). The aggregate poverty information on among municipalities. It also used these maps these scales is useful for broad national-level to identify high-risk areas for preventing the interventions such as food subsidies, income- cholera outbreak in 2001 and to develop crime support schemes and other national social- prevention strategies. Many other countries use welfare programs aimed at assisting the poor. or expect to use poverty maps for geographical But, often they are too coarse for designing targeted resource allocation and policy and targeting location-specific poverty- formulation.1 1Reports of poverty mapping and their uses in other countries are available at http://population.wri.org 1 In Sri Lanka, there has not been any effort d. to demonstrate the use of poverty maps in to analyze in detail the spatial patterns of geographical targeting of poverty-alleviation poverty with the aid of finer-resolution poverty programs. maps. This report is an attempt to fill this void. The rest of the report is organized as follows. The overall aim of this report is to Section 2 describes the spatial variation of poverty demonstrate the potential use of poverty maps maps at the DS level, a lower administrative unit in policy interventions in poverty-alleviation than a district. Section 3 identifies the clusters of programs.
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