(2003) and is often referred to as the “ELL” poverty mapping method. The assumptions and data employed for ELL maps are further elaborated upon in Bedi, Coudouel, and Simler (2007). However, a pre-requisite for using the ELL approach is access to micro-level census data. In Central Asia, census data either do not exist (for instance, in Uzbekistan), are relatively dated (for instance, in the Kyrgyz Republic and Tajikistan), or are not made available for these purposes (for instance, in Kazakhstan). In such cases, the most common alternative approach is the Fay-Herriot (FH) method, which is adopted to generate the results described in this report.

The FH method allows estimation of indicators and rates using a combination of survey data and district-level indicators from available sources that are less subject to imprecision, such as administrative data or remote sensing. In this report, most of the publicly available sources used are derived from satellite imagery. The FH approach proceeds by matching accurate area-based information with indicators that are aggregated to the level of interest in the survey (the district, in this case). Starting from the relatively imprecise estimates from the survey, a statistical model is developed, which attempts to explain the variation of the welfare indicator at the district level (in this case, either the poverty rate, average consumption per capita, or the share of the middle class at the district level). Once the model is estimated, the direct survey estimates also enter into the final area-level results: the final estimated area-level poverty rate is a weighted average of the observed and model-based estimates for cases in which both estimates are present. For areas that do not appear in the survey data, the results rely entirely on estimates derived from the statistical model.

The report is structured as follows. The remainder of this section reviews the definitions of the key indicators to be estimated. Section II reviews the data used in the study (relying on both survey microdata, and complementary satellite/administrative data). The approach used for the maps described in this report is discussed in more detail in section III. Section IV includes the district-level maps for Central Asia. Section V briefly discusses the results and gives two examples of uses for the maps (comparing migration rates to poverty rates and locating program locations on maps). The annexes include detailed tables of the results at the level of districts (rayons) throughout Central Asia, validation and robustness exercises, and the regression models used.

# **I.I – Definitions of Poverty in Central Asia**

The World Bank regularly produces internationally comparable estimates of poverty as part of its mandate. In 2017, the World Bank updated all such estimates to a new set of poverty lines, and at the same time, began applying up-to-date purchasing power parity (PPP) conversion factors for each country (based on estimates of price differences in 2011). The objective of calculating the World Bank’s internationally comparable poverty estimates is to estimate the prevalence of poverty in terms of a single global standard. Measures created on this basis are in turn used to monitor progress towards development goals set by the World Bank, the member states of the United Nations, and other partners.

The World Bank poverty estimates contrast with national official poverty estimates in Central Asia in several ways. The most common official poverty approaches in the region consider local patterns of 8