Poverty and Inequality Mapping in Transition Countries

POVERTY AND INEQUALITY MAPPING IN TRANSITION COUNTRIES Laura Neri, Francesca Ballini, Gianni Betti Working paper n. 52, January 2005 POVERTY AND INEQUALITY MAPPING IN TRANSITION COUNTRIES Laura Neri, Francesca Ballini and Gianni Betti1 ABSTRACT In this paper we estimate various measures of poverty and inequality for small administrative units in a Transition Country –Albania – and we prepare the corresponding maps. Poverty and inequality maps - spatial descriptions of the distribution of poverty and inequality - are most useful to policy-makers and researchers when they are finely disaggregated, i.e. when they represent small geographic units, such as cities, municipalities, districts or other administrative partitions of a country. We aim at performing poverty and inequality mapping primarily using data from a Population Census, in conjunction with an intensive small scale national sample survey. The methodology adopted, described in Elbers, Lanjouw and Lanjouw (2003), combines census and survey information to produce finely disaggregated maps. The basic idea is to estimate a linear regression model with local variance components using information from the smaller and richer sample data - in the case of Albania the Living Standard Measurement Study (LSMS) conducted in 2002 – in conjunction with aggregate information from the 2001 Population and Housing Census. The main findings of research are potentially very useful for policy-makers. As expected, we find that in Albania there is considerable heterogeneity of poverty rates across administrative units. The particular spatial pattern of this heterogeneity has important policy implications for poverty alleviation programmes: at the highest level we observe a large spatial heterogeneity among Prefectures; this spatial heterogeneity is much less pronounced among Districts within the same Prefecture; however, it is pronounced again at the lowest level among Municipalities within the same District. What this means for the practitioner and the policymaker is that it is important to disaggregate down to the Commune level when analysing issues and planning interventions, as this will add substantially in terms of precision of the targeting of resources when compared to stopping at the District level. Key words: Poverty and inequality, regression models with variance components, Population and Housing Census, Transition Countries. 1. Introduction Poverty and inequality maps - spatial descriptions of the distribution of poverty and inequality - are most useful to policy-makers and researchers when they are finely disaggregated, i.e. when they represent small geographic units, such as cities, municipalities, districts or other administrative partitions of a country. In order to produce poverty and inequality maps, large data sets are required which include reasonable measures of income or consumption expenditure and which are representative and of sufficient size at low levels of aggregation to yield statistically reliable estimates. Household budget surveys or living standard surveys covering income and consumption usually used to calculate distributional measures are rarely of such a sufficient size; whereas census or other large sample surveys large enough to allow disaggregation have little or no information regarding monetary variables. Often the required small area estimates are based on a combination of sample surveys and administrative data. In this paper we aim at performing poverty and inequality mapping primarily using an alternative source of data: data from a Population Census, in conjunction with an intensive small scale national sample survey. The methodology adopted, described in Elbers, Lanjouw and Lanjouw (2003), combines census and survey information to produce finely disaggregated maps which describe the spatial distribution of poverty and inequality in the country under investigation. We intend to adopt this methodology to the case of Albania. The basic idea is to estimate a linear regression model with local variance components using information from the smaller and richer sample data - in the case of Albania the Living Standard Measurement Study (LSMS) conducted in 2002 – in conjunction with aggregate information from the 2001 Population and Housing Census, supplemented by some other sources (e.g. the General Census of Agricultural Holdings). The estimated distribution of the dependent variable in the regression model (monetary variable) can therefore be used to generate the distribution for any sub- population in the census conditional to the sub-population’s observed characteristics. From the estimated distribution of the monetary variable in the census data set or in any of its sub-populations, an estimate has to be made of a set of poverty measures, such as the Sen and the Foster-Green-Thorbecke indices and a set of inequality measures such as the Gini coefficient and general entropy measures. To assess the precision of the estimates, standard errors of the poverty and inequality measures need to be computed using an appropriate procedure such as bootstrapping. 1 Department of Quantitative Methods, University of Siena, Italy; {neri,ballini,betti2}@unisi.it. 2 Four important aspects of this methodology should be noted at the outset. Firstly, information from the Census is required at micro (household and individual) level; however micro-level linkage between Census and survey data is not required. Secondly, the vector of covariates utilised in the regression model implies that those variables have to be present in both sources. Thirdly and most importantly, the common variables in the sources must be sufficiently comparable; comparability requires the use of common concepts, definitions and measurement procedures. Moreover, especially in Transition Countries with rapid changes in living conditions, it is important that reference periods for the data sets are as close as possible to each other. This paper is made up of five sections and an Appendix. Section 2 is devoted to the comparison and the harmonisation of the data sources, giving special attention to the Census and LSMS data sets. In Section 3 the estimated linear regression models with variance components are reported and there is a full description of how the Montecarlo simulation has been used to prepare the statistical information for calculating bootstrapping standard errors of poverty and inequality measures. Section 4 reports the above described indices calculated for the whole of Albania and disaggregated at six levels: a) The four strata used in sampling the LSMS; b) The six strata for which we have estimated the linear regression models; c) The 12 Prefectures; d) The 36 Districts; e) The 374 Communes/Municipalities; f) The 11 Mini-municipalities into which the city of Tirana is divided. Section 5 notes some important policy implications. The Appendix reports poverty and inequality maps for Prefectures, Districts and Communes. 2. The sources The Republic of Albania is geographically divided into 12 Prefectures. These are divided into Districts which, in turn, are divided into Municipalities and Communes. The Communes contain all the rural villages and the very small cities. The Capital of Albania, Tirana, is also divided into 11 Mini-municipalities. The two main sources of statistical information available in Albania are the Population and Housing Census (PHC) – 2001 and the Living Standard Measurement Study (LSMS) – 2002. 2.1. The Population and Housing Census2 The census was conducted in April 2001, and the moment as reference was considered midnight of 31 March 2001. The 2001 census introduced some essentially new concepts in data collection methods as well as in definitions, 2 INSTAT (2002), The Population of Albania in 2001. 3 mainly the concept of an open population was introduced in order to asses the consequences of emigration and internal migration. For the April 2001 General Census of Population and for Housing Census purposes, the cities and the villages have been divided into 9,834 Enumeration Areas (EAs) which were established throughout the country and generally involved about 80-120 dwellings per area. The fieldwork of the census was based on a four-part questionnaire with questions at four different levels: a) Building questionnaire: to be completed only for the first or only dwelling in the building. b) Dwelling questionnaire: to be completed for all the inhabited dwellings in the building. c) Household questionnaire: to be completed for all the households (if more than one) in each dwelling. d) Individual questionnaire: to be completed for all the members of the household who are present, or absent for less than 1 year. At the end of march 2001 in Albania there were 726,895 households with 3,069,275 persons (1,347,281 in the labour force) living in 512,387 buildings. 2.2. The Living Standard Measurement Study (LSMS) – 20023 The 2002 LSMS was carried out between April and June, with some field activities extending into August and September. The survey work was undertaken by the Living Standards unit of INSTAT (Albanian National Statistics Office), with the technical assistance of the World Bank The Population and Housing Census (PHC) performed in mid-2001, provided the country with a much needed updated sampling frame which is one of the building blocks for the household survey structure. In fact the 9,834 Enumeration Areas formed the primary sampling units (PSUs) for the LSMS sampling frame. The final sample design for the 2002 LSMS included 450 PSUs and 8 households in

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