# **I – Introduction**

Measurement of poverty rates, average per capita consumption, the size of the middle class, and other welfare indicators is traditionally conducted using survey data. To allow for frequent monitoring and to contain the costs of gathering detailed information, such surveys usually visit only a small sample of the population. When this sample of the population is representative, welfare surveys provide reliable estimates of poverty incidence for the entire population at a small fraction of the cost that would be required to survey each person in the country.

However, this approach necessarily leads to sampling errors, and consequently, a typical household income or expenditure survey cannot produce statistically reliable welfare estimates for small geographic units. This study starts with nationally (and regionally) representative data from each of the four Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan and Uzbekistan) for which they are available. The analysis then proceeds to sharpen the reliability of the survey estimates to allow reporting at a level below what is traditionally reported (moving from “oblast” level estimates, to “rayon” level estimates). Because no such survey data are available for Turkmenistan, the country is excluded from the analysis.

There are two survey types used in the approach described in this report. In Kazakhstan and the Kyrgyz Republic, welfare estimates are derived from the Household Budget Surveys (HBS) conducted by the national statistical agencies. In Tajikistan and Uzbekistan, HBS data were either inappropriate for this analysis, or unavailable. Instead, the welfare estimates for these countries are derived from the baseline surveys for two World Bank studies: Listening to Tajikistan (L2T) and Listening to the Citizens of Uzbekistan (L2CU).

Each of these four surveys are designed to be representative at the national level in the respective country, for rural and urban areas, and at the region (oblast) level. For this reason, the national official rates of poverty monitored in each country are not usually published for administrative units below the regional level (with some exceptions). However, many government administrative activities are undertaken at the district level, and regions are a relatively high level of spatial aggregation for targeting policies that are sensitive to the needs of poor and vulnerable people. Likewise, policies that are intended to grow the middle class and average income could benefit from greater disaggregation than is directly available.

Small area estimation (SAE) techniques sharpen the precision of welfare measures to enable reporting at highly disaggregated geographic units. Using statistical models for imputation, SAE approaches provide estimates of indicators for small areas that would be impossible to reliably construct with traditional survey data alone. The results are often used to target policies and assign resources to have greater poverty-reducing impact or are intended to address the concerns of specific welfare groups at the local level. In many countries and regions, poverty maps have been used to highlight geographic variations, simultaneously display different dimensions of well-being, understand income determinants, and to both design and select interventions.

A variety of such methods have been devised to overcome the increasing imprecision of welfare estimates as they are disaggregated. The standard approach to SAE, used by the World Bank and applied in most cases when enough data are available, is described in Elbers, Lanjouw, and Lanjouw 7