This report presents the results of subnational poverty estimation using aggregate poverty statistics and how they can help policy interventions. In particular, they estimate the poverty map across the DS division level in Sri Lanka. The poverty map depicts the proportion of households below the poverty line, which is based on household expenditure for food for obtaining the minimum calorie requirement.

The allocation of resources and the design of policies tailored to local-level conditions require highly disaggregated information. Data on poverty at the local level is typically not available because most household surveys are not representative past the regional level. This volume aims to promote the effective use of Small Area Estimation poverty maps in policy making. It presents the range of policies and interventions which have been informed by poverty maps, focusing on the political economy of poverty maps and the key elements to their effective use by policy makers. The volume also looks at the future of poverty maps in terms of new techniques and new areas of application.

To rejuvenate the slow poverty alleviation process in Sri Lanka, the first step would be to better understand the geographical distribution of poverty, which in turn would require estimating poverty at a level of disaggregation lower than the district level. This policy note summarizes results and experience of a poverty mapping exercise in Sri Lanka that has been conducted in close collaboration with the Department of Census and Statistics (DCS) since 2003. The results of poverty mapping successfully indicate where pockets of severe poverty remain in Sri Lanka, and provide interesting insights-poverty measured as a percentage of population is higher in remote areas, while the absolute number of the poor is larger in urban areas. Also, preliminary results drawn from a map with very high resolution indicate that there are some pockets of poverty even in Colombo District-the growth center of the country. The aim of this report is threefold. First, the report demonstrates that the poverty mapping method developed by Elbers at al (2003) is a useful tool to illustrate the spatial heterogeneity in poverty incidence in Sri Lanka at different levels of resolution (section 2). Second, it highlights the importance of capacity building in ensuring the sustainability of the poverty mapping work (section 3). Third, it discusses new observations regarding the statistical properties of the methodology (section 4).

Publ. in association with UNEP/GRID-Arendal, Norway.

The “leave no one behind” principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country’s population. This entails detailed, granular data on population groups that extend beyond national trends and averages. The Asian Development Bank (ADB), in collaboration with the National Statistical Office of Thailand and the Word Data Lab, conducted a feasibility study to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in Thailand. This report documents the results of the study, providing insights on data collection requirements, advanced algorithmic techniques, and validation of poverty estimates using artificial intelligence to complement traditional data sources and conventional survey methods.

Contributed papers presented at the conference organized by International Water Management Institute, Irrigation Dept., Dept. of Agriculture, and Hector Kobbekaduwa Agrarian Research and Training Institute.

This research report presents the results of a global analysis of multi-temporal time series at nominal 10 kilometer pixel resolution. Statistics of irrigation at country level are derived from these maps for different seasons and for the entire year (annualized) for the nominal year of 1999. Three methods of area abstraction are used and compared, and three methods of accuracy assessment are applied. The annualized irrigated areas of the world at the end of the last millennium were about 480 Mha of which there were 263 Mha for season 1, 176 Mha for season 2, and 41 Mha for continuous cropping. Of this, Asia alone accounts for 78 percent (375 Mha) with 59 percent from China and India. The country statistics are compared with FAO country-level statistics (see Annex I). The IWMI GIAM 10 km V2.0 map were tested based on 3 sources of independent data resulting in accuracies between 84 and 91 percent with errors of omission not exceeding 16 percent and errors of commission less than 21 percent. The total area available for irrigation (TAAI; the nearest equivalent to FAO’s equipped area) was 412 Mha.