Country Policy Brief Poverty in Europe March 2016 Poverty and Equity Global Practice Pinpointing Poverty in Latvia Rates of poverty and social exclusion vary individual EU member states, have developed widely across European Union (EU) member a set of high-resolution poverty maps.2 The states, and there is also a high degree of greater geographical disaggregation of the variability in living standards within member new poverty maps reveals which parts of states. In its 2014–20 multiannual financial these larger regions have particularly high framework, the EU budgeted €1 trillion to rates of poverty and require greater attention support growth and jobs, which will contrib- in poverty reduction programs. ute to the goal of reducing the number of The poverty maps for Latvia confirm exist- people living at risk of poverty or social exclu- ing knowledge about poverty in Latvia, but also sion by 20 million by the year 2020. To con- reveal new insights. The EC has relied on the tribute to this goal, the Government of Latvia NUTS 2 territorial classification3 to determine has set a national goal of reducing by 121,000 eligibility for aid from European Structural and the number of people at risk of poverty or Investment Funds and for program planning. living in households with low work intensity.1 In smaller countries such as Latvia, the NUTS Success depends on d ­ eveloping the appro- 2 classification corresponds to the entire na- priate policies and programs and targeting tional territory, that is, with no sub-national di- them effectively. However, the EC has previ- visions (map 1, panel a). The EU-SILC in Latvia ously had to rely on sub-national data at a rel- is representative at the statistical region level atively high level of aggregation for program (NUTS 3), and the Central Bureau of Statistics planning and the allocation of EU funds. The reports risk of poverty estimates at that level. EC and the World Bank, in cooperation with Using small area estimation techniques, it Map 1  At-Risk-of-Poverty Rates, Latvia a. NUTS 2 classification b. Statistical subregions (NUTS 3) 12.93 12.93–15.93 15.93–19.1 19.1–23 19.2 23–24.9 24.9–28.1 Source: Estimates using data from the 2012 EU-SILC and 2011 Population and Housing Census collected by the Latvia Central Statistical Bureau. Note: The risk of poverty rates are defined using the EU standard of 60 percent of median national equivalized income after social transfers. 1 was possible to improve the precision of these Map 2  Population Living below the Poverty poverty estimates for the six regions of Latvia. Threshold, Latvia According to these estimates, there is quite a bit of heterogeneity across regions (map 1, panel b). Riga and Pieriga in northern central Latvia are the only regions where the inci- dence of poverty is below the national average. Estimates of poverty in other regions range from 21 percent to as high as to 28 percent in Latgale in southeastern Latvia. Knowing which regions have higher poverty rates can help more efficiently target resources for de- velopment and poverty reduction. 52,613 52,613–55,365 Targeting poor areas alone can have limita- 55,365–59,169 tions. Policy makers have an interest both in 59,169–85,255 areas where poverty is high and in areas that Source: Estimates using data from the 2012 EU-SILC and 2011 have the most poor people. These two are not Population and Housing Census collected by the Latvia Central the same: areas that are poor may also be Statistical Bureau. sparsely populated, whereas large cities tend to have low poverty rates, but large numbers of poor people because of the large popula- one must understand why these places are tions. For example, Riga Region has the lowest poor. The reasons are likely to vary from estimated risk of poverty, 12.9 percent, which place to place and may include inadequate is less than one-half the risk in the poorest infrastructure, lack of economic activity, region, Latgale (28.0 percent). However, be- an insufficiently skilled workforce, or other cause Riga Region is much more populous, reasons. Poverty maps provide more finely each of the two regions has approximately grained information on sub-national varia- 85,000 people living at risk of poverty. While tions in poverty than was previously avail- the remaining four regions have somewhat able and can potentially improve resource heterogeneous p ­ overty incidence, they all have allocation. The maps also force more think- a similar number of poor people, in the range ing on how best to allocate resources aimed of 50,000–60,000 (map 2). at improving standards of living, balancing While poverty estimation at the regional the targeting of poor areas and poor people. level adds a significant nuance to national es- While the appropriate combination of ap- timates, more revealing spatial heterogeneity proaches will vary by country, the maps pro- is possible if census microdata are employed. vide important information to help improve The initial poverty maps in Latvia were lim- policies and programs to combat poverty ited to the six statistical regions because cen- and social exclusion. sus microdata were not available at that time. The processing of the microdata has now Notes been completed, and it is planned to revisit 1. Latvia, Ministry of Economics. 2015. “National Reform Programme of Latvia for the Implementation the poverty mapping exercise using census of the ‘Europe 2020’ Strategy: Progress Report.” April, and EU-SILC microdata. By using micro- Ministry of Economics, Riga, Latvia. data, one will be able to estimate the risk of 2. These maps combine aggregate data from the 2011 poverty for much smaller geographical units population census and the 2012 EU-SILC survey. and provide much higher-­ resolution poverty 3. The NUTS (Nomenclature des Unités Territoriales Statistiques) classification is a hierarchical system estimates than are possible directly from of dividing up the economic territory of the the EU-SILC. It is expected that reasonably European Union for the development of regional © 2016 International Bank for Reconstruction precise poverty estimates can be obtained for statistics, regional socioeconomic analysis, and the and Development / The World Bank. Some rights reserved. The findings, interpretations, Latvia’s 119 municipalities and cities. framing of EU regional policies. To date the NUTS 2 and conclusions expressed in this work do not Poverty maps do not provide all the an- classification has been used for determining eligibil- necessarily reflect the views of The World Bank, its ity for aid from European Structural Funds. Below Board of Executive Directors, or the governments swers. They must be combined with other the NUTS 3 classification areas are defined accord- they represent. The World Bank does not guarantee information, including local expertise, to ing to Local Administrative Units (LAU). Most EU the accuracy of the data included in this work. This work is subject to a CC BY 3.0 IGO license inform decision making. After identifying member states have LAU 1 and LAU 2 divisions, but (https://creativecommons.org/licenses/by/3.0/ the areas or populations in greatest need, some only have LAU 2. igo). 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