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.

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.

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 each PSU, giving a total of 3600 households. Four reserve units were selected in each sample PSU to act as replacement units for non-response cases. In a few cases in which the rate of migration was particularly high and more than four of the originally selected households could not be found for the interview, additional households for the same PSU were randomly selected. The sampling frame was divided into four regions (strata), Coastal Area, Central Area, Mountain Area, and Tirana (urban and other urban). These four strata represent the domains of estimation. They were further divided into major cities, other urban, and rural areas (Table 1). The EAs were allocated proportionately to the number of housing units in these areas.

3 The World Bank (2002), Basic Information Document, Living Standard Measurement Study, Albania, Development Research Group.

Four survey instruments were used to collect information for the 2002 Albania LSMS: Household questionnaire; Diary for recording household food consumption; a) Community questionnaire; b) Price questionnaire.

Table 1. Domains of Estimation (Regions), Districts and Major Cities in the Domains of Estimation

Region 1 Region 2 Region 3 Tirana Coastal area Central area Mountain area Districts* Lezhë Kuçove Devoll Kukes Tirana urban Kurbin Skrapar Kolonjë Has Tirana other urban Kavajë Krujë Pogradec Tropoje Mallakaster Peqin Mirdite Bulqize Lushnje Gjirokastër Puke Diber Delvine Permet Malesi e Madhe Gramsh Sarande Tepelenë Mat Librazhd Tirana (rural) Major Durres Shkoder Berat Cities Fier Elbasan Korçë Vlore *Divided into other urban and rural areas.

2.3. Stage Zero: are the Census and the LSMS comparable? The two sources of data have been fully analysed in order to identify the common concepts and to construct the common variables to be compared. The original Census and LSMS variables have been transformed in order to get comparable variables. The list of those 38 common variables has been divided into three categories: i) household dwelling conditions and presence of durable goods (23 variables); ii) household head characteristics (8 variables); iii) household socio-demographic characteristics (7 variables). Since some variables collected in the LSMS survey presented some missing values it was decided to impute them in order to avoid the loss of statistical units (and therefore degrees of freedom) in the estimation of the linear regression model with variance components (see Section 3). The imputation procedure was based on the “sequential regression multivariate imputation” (SRMI, Raghunathan et al., 2001) approach as implemented in the imputation software IVE-ware. The variables which underwent the imputation procedure were: i) type of building; ii) inhabited dwelling surface. Each of the 38 variable distributions from the Census were compared with the corresponding weighted distribution from the LSMS. A chi-square test was used for the comparisons. The main decision to be taken concerns the choice of the potential variables to be included in the regression model as explanatory

variables. According to the chi-square test, only 9 out of 38 LSMS distributions fit the census counterpart very well. This leads to a trade-off between the use of many explanatory variables (not highly comparable with those in the census) and the use of few explanatory variables (loosing part of the explanation of the variability in the dependent variable in the model). To overcome this problem it was decided to reduce the number of categories of most of the variables in order to obtain new distributions (mainly dummies) which were similar, as far as possible, to those in the census. In this way all the 38 variables could be used.

3. Stage One: the estimation of stratum-specific linear regression models with variance components for imputing expenditures

The basic idea can be explained in a simple way as follows. WE begin with a data source which is richer in context but small in size, such as an intensive sample surveys, which contains the target variables (income or consumption levels). A regression model of the target variable, with a set of covariates as regressors, is estimated. The idea is to use this model to impute the target variable on to households in a large data set such as the Census. For this purpose it is necessary that the covariates used in the model are common to the data sources and are comparable. In practice the methodology follows two steps: a) the use of survey data to estimate a prediction model for household consumption; b) the use of this model to simulate the expenditure of each household in the census and the derivation of poverty/inequality measures with their relative prediction error. In the context of this work the smaller sample survey is the LSMS (2002) survey and the larger one is the Population Census (2001). The key assumption is that the model estimated from the survey data apply to the census observations. This requires comparability between the two sources in units, concepts, definitions and classifications, as well as in time. Our examination of the data indicates broad comparability. It should be noted, however, that the reference periods for the two sources differ by somewhat over one year, which may be important in the circumstance of a Transition Country.

3.1. A prediction model for consumption

This step (Stage One) consists in developing an accurate empirical model of a logarithmic transformation of the household per-capita total consumption expenditure (rent and health expenditure excluded). Geographical differences in the level of prices are taken into account (LSMS variable rpcons3).

In the model the covariates are variables defined in exactly the same way as in the smaller sample data (LSMS) and in the census. Denoting by ln ych, the logarithm consumption expenditure of household h in cluster c, a linear approximation to the conditional distribution of ln ych, is considered: TT (1) ln yEch,,=+ln ych| xch,uch,=xch,β +uch,

T where xch, is the vector of covariates and uch, is the error component. Previous experience with survey analysis4 suggests that the proper model to be specified has a complex error structure, in order to allow for a within-cluster correlation in the disturbances as well as heteroschedasticity. To allow for a within cluster correlation in disturbances, the error component is specified as follows:

uch,=ηc+ εc,h (2) where η and ε are independent of each other and not correlated to the matrix of explanatory variables. Since residual location effects can highly reduce the precision of poverty/inequality measure estimates, it is important to introduce some explanatory variables in the set of covariates which explain the variation in consumption expenditure due to location. For this reason introducing the means of each covariate into the model covariates is proposed. This is calculated over all the census households in the 450 census enumeration areas which correspond to the 450 PSU selected in the LSMS sampling scheme. The enumeration areas correspond to clusters in the LSMS data. Preliminary analyses on the Albanian LSMS suggest that the expenditure behaviour is locally different. In order to avoid forcing the parameter estimates to be the same for the whole country it was, therefore, decided to estimate separate regression models for the following areas: a) Coastal area – rural - (Stratum 1 - rural); b) Coastal area – urban - (Stratum 1 - urban); c) Central area – rural - (Stratum 2 - rural); d) Central area – urban - (Stratum 2 - urban); e) Mountain area (Stratum 3); f) Tirana (Stratum 4). The final results of this first stage are the GLS estimates of the selected model estimated on the LSMS data. In order to reach these final results a number of preliminary steps have to be performed. The initial step of this Stage One aims at verifying whether or not weighting helps in the prediction model. The Hausman test implemented here (Deaton, 1997) considers, as null hypothesis, the fact that the regressions are homogenous across strata, so that the estimator obtained by a weighted procedure and that

4 Elbers, Lanjouw and Lanjouw (2003).

obtained by an unweighted procedure are both unbiased (the difference between them has zero expected value); by contrast when heterogeneity and design effect are important the two expectations will differ. In practice, in order to implement the test an auxiliary regression model is estimated which has all the usual regressors as well as all regressors interacted with household weights (household expansion factors) as regressors; then we test whether the estimated parameters on these interacted regressors are jointly zero, using a standard F-test. If the test fails to reject the null hypothesis, then it is necessary to use household weights in the analysis. The test was applied to each selected model for each stratum (except the Coastal area - Stratum 1 rural - and the Central area - Stratum 2 rural - where the test is non applicable as the weights are the same for all units). The results of the test are specified in Table 2: we reject the null hypothesis in Stratum 1 urban, Stratum 2 urban and Stratum 4 and we fail to reject the null hypothesis for Stratum 3. That is, weights need to be used only for Stratum 3.

Table 2. Hausman test of population weights

Stratum (Region) 1 2 3 4 Coastal Coastal Central Central Mountain Tirana area area area area area (rural) (urban) (rural) (urban) Number of weights 1 4 1 6 2 3 Hausman test of population weights Not F-test=1.19 Not F-test=1.35 F-test=2.32 F-test=1.13 (Deaton, 1997) applicable F(23,987)

Weights No No No No Yes No

The initial estimate of β in equation (1) is obtained from OLS (weighted with survey sampling weight for Stratum 3), the proportion of deviance explained by the model ranges between 0.56 and 0.64. With consistent estimate of β, the residuals from the regression are used as estimates of the overall disturbances uˆch, . The residual is decomposed into uncorrelated household and location components as follows: ueˆˆch,,=+ηc ch

The estimated location components (ηˆc ) are the within-cluster means of the overall residual. The household component estimates ( ech, ) are the overall residual net of location components; these values are used to estimate the variance of εch, . To allow for heteroschedasticity in the household component, a model is chosen which best explains its variation. The covariates of this model can be the

usual regressors as well as their squares or interactions between variables (see Table 3 for details of the estimated model).

Table 3. Model for Heteroschedasticity in εc,h Coastal Coastal Central Central Mountain Tirana area area area area area (rural) (urban) (rural) (urban) House surface less 0.052*** -0.035** than 40m2 (0.020) (0.017) Car 0.054** -0.060* (0.0246) (0.035) Child 0-5 -0.007** (0.004) Household size -0.646*** -1.417** 0.086* (0.228) (0.564) (0.045) Highest education low -0.028*** (0.090) # non working people 0.012** (0.005) EA means variables Water inside 0.029* (0.016) Car 0.054** (0.025) Room business -0.926*** (0.347) Interaction variables 0.007* 0.022*** 0.017* (Household size)2 (0.004) (0.008) (0.009) (Household size)3 -0.001* (0.00006) House surface less -0.029** than 40m2*Child 0-5 (0.013) Yhat -2.198** -4.344** (0.977) (2.067) (Yhat)2 0.106*** 0.211** (0.052) (0.104) Yhat*hhsize 0.064*** 0.136** (0.022) (0.056) Yhat*heater -0.003* (0.002) Constant 11.4903** 0.057*** 22.413** -0.090*** 0.073*** 0.213*** (4.653) (0.014) (10.319) (0.008) (0.006) (0.064) R2 0.060 0.039 0.33 0.010 0.019 0.018 *denotes significance at the 10% level, **at the 5% level, and ***at the 1% level. 2 The dependent variable of the model is (εˆch,−εˆc.) . Standard errors are White robust estimates.

A logistic model of the variance εch, conditional on the chosen set of regressors, z is estimated (bounding the prediction between zero and a maximum

A = (1.05)*max( ech, )):

2 ech, ln 2 = z'ch,α + rch,. (3) Ae− ch,

Let exp(z'ch, α) = B and using the delta method the household specific variance is estimated as: AB 1 AB(1− B) σˆ 2 =+var(r) . (4) ch,   3  12++BB (1)

2 The variance of ση is estimated non-parametrically, allowing for heteroschedasticity in εch, (see Appendix 2 of Elbers, Lanjouw and Lanjouw, 2002). The two variance components are combined in order to calculate the estimated variance covariance matrix ( Σˆ ) of the overall residual of the original model. Once Σˆ is calculated the original model can be estimated by GLS. The results are shown in Table 4. For each model (one for each stratum or sub-stratum) the significance of the cluster effect has been tested by the Lagrange multiplier test for random effects, a test that Var (ηc ) = 0; such a hypothesis has been rejected at 5% level. The estimated share of the location component with respect to the total 22 residual variance is represented by ρ = ση /σu . The ρ values range between about 4% to 21%, urban area (Coastal area, Central area and Tirana) shows the lowest effect of the local component, the other rural (Coastal area and Central area) and Mountain areas show a much more significant local component effect. The idea of estimating different models for each stratum or sub-stratum seems to be appropriate both in terms of local effect and in terms of covariates; in fact different subsets of covariates are significant for each model. The results of the GLS regression are reported in Table 10 in the Appendix. The significant parameter for each stratum/sub-stratum is the possession of a car. The other significant parameters in almost all the strata/sub-strata are the household size (as logarithmic transformation), the level of education and the number of nonworking persons in the household. With regards to the possession of durable goods the most important factor is a refrigerator, followed by a TV and heater. Considering the EA mean variables it can be observed that the variables relating to the migration before 1990 and having a separate kitchen are significant in three of the six strata. The results from this first step consist of a set of estimated GLS parameters for the regression coefficient βˆ , the associated variance covariance matrix and the disturbances at the cluster and the household level. As for the disturbances, attention is focused on their distribution. The results of the tests on normality (Shapiro-Wilk, Kolmogorov-Smirnov and Cramer-von Mises) are in Table 4. We

conclude that the hypothesis of normality is rejected in almost all cases, only for the household residuals of the Central urban area we fail to reject the null hypothesis.

Table 4. Diagnostics for selected Stage One models specification (p-values are in parenthesis)

Coastal Coastal Central Central Mountain Tirana area area area area area (rural) (urban) (rural) (urban) Observations 520 480 520 479 1000 600 Number of groups 65 60 65 60 125 75 R-squared within 0.5557 0.5466 0.5376 0.5379 0.5588 0.5944 R-squared between 0.6116 0.8000 0.7274 0.6589 0.6777 0.8071 R-squared overall 0.5721 0.6280 0.6046 0.5614 0.6027 0.6501 Sigma uc,h 0.1370 0.0879 0.1306 0.0757 0.1492 0.0653 Sigma εc,h 0.2953 0.3060 0.2855 0.3093 0.2886 0.3167 Rho 0.1773 0.0762 0.1731 0.0565 0.2109 0.0408 Test for Normality

Distribution of uc,h 0.9734 0.9820 0.9758 0.9728 0.9886 0.9762 Shapiro-Wilk (<0.01) (<0.01) (<0.01) (<0.01) (<0.01) (<0.01) 0.0848 0.0652 0.0611 0.0805 0.0634 0.0656 Kolmogorov-Smirnov (<0.01) (<0.01) (<0.01) (<0.01) (<0.01) (0.01) 0.4342 0.3114 0.3617 0.4224 0.5421 0.4333 Cramer-von Mises (<0.01) (<0.01) (<0.01) (<0.01) (<0.01) (<0.01)

Distribution of εc,h 0.9905 0.9912 0.9831 0.9963 0.9935 0.9944 Shapiro-Wilk (<0.01) (<0.01) (<0.01) (0.34) (<0.01) (0.07) 0.0477 0.0320 0.0296 0.0245 0.0285 0.0343 Kolmogorov-Smirnov (<0.01) (0.15) (0.15) (0.15) (0.05) (0.08) 0.2149 0.1176 0.0723 0.0473 0.1509 0.1274 Cramer-von Mises (<0.01) (0.07) (0.25) (0.25) (0.02) (0.04)

3.2. Simulation on expenditure, poverty/inequality indicators and relative standard error The parameter estimates obtained from the previous step are applied to the census data so as to simulate the expenditure for each household in the census. A set of 100 simulation was conducted. For each simulation a set of the first stage parameters was drawn from their corresponding distribution simulated at the first stage: the beta coefficients, β , are drawn from a multivariate normal distribution with mean βˆ (the coefficients of the GLS estimation) and variance covariance matrix equal to the one associated with βˆ . Relating to the simulation of the residual terms ηˆc and ech, , assumption of any specific distributional form has been avoided by drawing directly from the estimated residuals: for each cluster the residual drawn is ηc and for each household εch, .

The simulated values are based on both the predicted logarithm of expenditure x 'ch, β , and on the disturbance terms ηc and εch, using a bootstrap procedure: T yxˆch,,=exp( chβ +ηεc+c,h) . (5)

The full set of simulated yˆch, values is used to calculate the expected value of each of the poverty measures considered. For each of the simulated consumption expenditure distributions a set of poverty and inequality measures is calculated, as is their mean and standard deviation over all the 100 simulations.

4. Poverty and Inequality measures

From the estimated distribution of the monetary variable in the census data set or in any of its sub-populations, an estimate was made of a set of poverty measures based on the Foster-Greer-Thorbecke indices (for α =0,1,2), the Sen index and an absolute poverty line calculated using the information contained in the rich sample survey. In addition, a set of inequality measures based on the Gini coefficient, the Gini coefficient of the poor, and two general entropy (GE) measures (with parameter c=0,1) are estimated. These measures are described in the Inset 1 below. Bootstrap standard errors of the welfare estimates are computed so as to assess the precision of the estimates. The procedure for estimating the poverty and inequality measures was applied for the whole of Albania and disaggregated at seven levels: a) Rural – urban level; b) The four strata used in sampling the LSMS; c) The six strata for which the linear regression models have been estimated; d) The 12 Prefectures; e) The 36 Districts; f) The 374 Communes/Municipalities; g) The 11 Mini-municipalities into which the city of Tirana is divided. For any given location, the means constitute the point estimates, while the standard deviations are the bootstrap standard errors of these estimates. Inset 1. Poverty and Inequality measures

The family of measures proposed by Foster-Greer-Thorbecke is formally defined ε 1 p yy− as FGT = piwhere ε is a measure of the sensitivity of the index ∑ nyi=1 p

to poverty, y p is the poverty line and p is the subset of poor individuals. When the parameter is 0, the measure is simply the headcount ratio, which simply measures the proportion of the population that is counted as poor. The headcount ratio merely measures the incidence of poverty, but not its intensity, i.e. measures how many poor individuals there are and not how poor they are. When the parameter is 1, the measure is the relative poverty gap, an index measuring poverty intensity; it can be interpreted as the cost of eliminating poverty (relative to the poverty line), because it shows how much would have to be transferred to the poor to bring their incomes up to the poverty line. When the parameter is 2, the measure is the poverty severity index; for all positive values of the parameter, the measure is strictly decreasing in the living standard of the poor. The most widely used single measure of inequality is the Gini coefficient. The Gini coefficient is based on the Lorenz curve, a cumulative frequency curve that compares the income distribution with the uniform distribution that represents 2 n yy− equality. The Gini coefficient is defined as Gini i. i . The Sen = 2 ∑  nyi=1  index tries to combine the effects of the dispersion of the poor, the incidence of their poverty, and the distribution of poverty within the group. It can be defined 2 p yy− as Sen =pip +1−i . The measures of general entropy are ∑() (1pn+ )i=1 yp n n 1 yi 1 yyii defined as GE(0) =−∑ log and as GE(1) = ∑log , nyi=1  nyi=1  y which is the well known Theil index.

The disaggregation into four strata is very useful for comparing the results obtained by the Census to those obtained by LSMS and reported in Table 5 (Source: The World Bank, 2003). The census-based predictions are very consistent with those from LSMS: with the sole exception of the Head Count ratio in Stratum 1, in none of the four strata we are able to reject the null hypothesis that the estimate based on census is equal to the LSMS survey mean at 95% confidence level; in the case of the Head count ratio in the Stratum 1, we can reject the null hypothesis at 95% level, but not at 99% level.

Table 5. Head Count Ratio and Per-capita Consumption: comparison between LSMS and Census* Head count Head count Consumption Consumption LSMS Census LSMS Census ALBANIA 25.39 28.60 7,800.82 7,569.67 (1.32) (1.28) (117.68) (120.21)

STRATUM 1 20.60 26.64 8,419.25 8,148.48 (2.22) (1.94) (218.07) (249.18) STRATUM 2 25.57 29.49 7,496.12 7,177.76 (2.32) (2.32) (193.63) (222.95) STRATUM 3 44.54 40.85 6,168.34 6,181.78 (2.51) (1.60) (149.86) (120.69) STRATUM 4 17.82 18.01 9,042.59 8,981.39 (2.06) (1.09) (304.96) (140.85) * Standard errors are in parentheses. Those for LSMS are estimated according to Levinson (2001).

Table 6. Poverty and inequality indices (%)

Head count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con* ALBANIA 28.60 6.96 2.48 29.54 12.38 5.29 14.28 15.05 7,569.67 (1.28) (0.44) (0.19) (0.52) (0.27) (0.40) (0.53) (0.77) (120.21) RURAL 36.26 9.06 3.27 27.72 12.57 7.45 12.65 13.74 6,586.25 (2.18) (0.73) (0.32) (0.72) (0.33) (0.76) (0.74) (1.35) (190.99) URBAN 18.09 4.08 1.40 28.94 11.74 2.78 13.78 14.02 8,919.82 (0.86) (0.27) (0.12) (0.54) (0.33) (0.22) (0.53) (0.57) (170.92) STRATUM 1 26.64 6.48 2.32 31.57 12.40 4.83 16.36 17.68 8,148.48 (1.94) (0.65) (0.28) (1.15) (0.40) (0.58) (1.24) (1.88) (249.18) STRATUM 2 29.49 7.00 2.43 27.35 11.94 5.36 12.16 12.43 7,177.76 (2.32) (0.76) (0.33) (0.54) (0.46) (0.71) (0.51) (0.52) (222.95) STRATUM 3 40.85 10.98 4.20 27.40 13.56 9.43 12.25 12.41 6,181.78 (1.60) (0.63) (0.31) (0.55) (0.34) (0.67) (0.51) (0.52) (120.69) STRATUM 4 18.01 4.11 1.42 29.35 11.88 2.80 14.18 14.54 8,981.39 (1.09) (0.38) (0.17) (0.63) (0.52) (0.30) (0.65) (0.70) (140.85) Stratum 1 urban 15.63 3.80 1.40 30.25 12.81 2.54 15.34 15.19 9,935.96 (1.84) (0.61) (0.28) (0.94) (0.78) (0.47) (1.04) (1.00) (467.70) Stratum 1 rural 34.84 8.47 3.00 28.87 12.23 6.89 13.97 16.54 6,816.09 (3.22) (1.09) (0.46) (1.74) (0.48) (1.09) (1.94) (3.87) (283.67) Stratum 2 urban 19.48 4.08 1.29 26.34 10.59 2.79 11.28 11.42 8,168.94 (1.56) (0.48) (0.20) (0.64) (0.50) (0.39) (0.56) (0.58) (163.15) Stratum 2 rural 34.41 8.43 2.99 27.02 12.25 6.80 11.87 12.23 6,689.88 (3.45) (1.13) (0.48) (0.86) (0.53) (1.14) (0.80) (0.85) (327.31) *Con stands for percapita consumption expenditure; standard errors are in parenthesis.

According to both sources, Stratum 4 (Region of Tirana) is better off in terms of per capita consumption and percentage of individuals below the poverty line (head count), while in the Stratum 3 (Mountain area) there seems to be the highest proportion of poor individuals. Table 6 reports poverty and inequality measures and their bootstrap errors calculated on the basis of the Census, for the whole of Albania, and disaggregated

at rural – urban level, by four strata (Regions) and by rural/urban type for the Coastal and Central regions (Stratum 1 and 2). The last panel in the Table shows the disaggregation of Central and Coastal strata by rural/urban areas. According to the four poverty indices considered (Head count, FGT(1), FGT(2), Sen) the Mountain region is still the worst off, while in rural areas both in the Coastal and Central strata more than one third of the population is poor. However the region of Tirana shows, according to the Gini coefficient index and the two General Entropy indices used, higher inequality in the distribution of per capita consumption.

Table 7. Poverty and inequality indices by PREFECTURE (%)*

Head Gini- Prefecture FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con count poor 1: BERAT 26.42 5.81 1.89 25.64 11.00 4.29 10.65 10.85 7,233.45 (2.16) (0.67) (0.27) (0.52) (0.44) (0.59) (0.46) (0.47) (193.63) 2: DIBËR 42.77 11.88 4.65 28.31 13.95 10.36 13.09 13.39 6,125.03 (2.04) (0.84) (0.41) (0.57) (0.38) (0.91) (0.55) (0.60) (153.13) 3: DURRËS 24.77 6.07 2.18 31.23 12.49 4.42 15.99 16.46 8,412.58 (1.38) (0.48) (0.22) (0.96) (0.40) (0.42) (1.00) (1.14) (259.03) 4: ELBASAN 31.84 7.54 2.61 26.60 11.89 5.91 11.48 11.74 6,852.57 (1.74) (0.57) (0.24) (0.42) (0.32) (0.55) (0.37) (0.38) (147.41) 5: FIER 29.71 7.05 2.46 28.83 11.99 5.43 13.49 14.03 7,365.17 (2.51) (0.82) (0.34) (0.98) (0.42) (0.77) (0.91) (1.05) (201.87) 6: GJIROKASTËR 19.38 4.11 1.31 27.43 10.68 2.79 12.26 12.62 8,393.35 (1.90) (0.54) (0.21) (0.64) (0.48) (0.43) (0.61) (0.70) (284.64) 7: KORÇË 26.95 6.09 2.03 27.03 11.27 4.52 11.82 12.09 7,405.28 (2.67) (0.84) (0.34) (0.57) (0.49) (0.76) (0.52) (0.53) (239.50) 8: KUKËS 39.98 10.59 3.99 27.53 13.27 8.99 12.32 12.53 6,282.05 (1.90) (0.76) (0.37) (0.61) (0.43) (0.81) (0.56) (0.57) (150.55) 9: LEZHË 36.68 9.83 3.76 30.74 13.54 8.11 15.59 17.24 6,898.25 (2.17) (0.85) (0.40) (1.07) (0.44) (0.86) (1.26) (2.67) (203.69) 10: SHKODËR 32.77 8.43 3.14 28.60 13.06 6.72 13.38 13.59 7,025.02 (3.26) (1.14) (0.50) (0.60) (0.57) (1.10) (0.61) (0.61) (308.93) 11: TIRANË 23.44 5.51 1.92 29.48 12.00 3.96 14.22 14.66 8,201.84 (1.19) (0.39) (0.17) (0.46) (0.31) (0.33) (0.46) (0.52) (134.69) 12: VLORË 18.26 4.14 1.42 33.52 11.72 2.82 18.63 20.93 9,817.49 (1.58) (0.47) (0.19) (2.12) (0.45) (0.37) (2.55) (4.42) (502.02) * Standard errors are in parentheses.

Table 7 reports the measures calculated at Prefecture level. We note that both poverty and inequality are very spatially heterogeneous among Prefectures. In the Prefecture of Vlore there is the highest per capita consumption and the lowest percentage of poor people (18,3%), whereas according to the Gini coefficient consumption is very concentrated (33,5%). On the other hand, the Prefecture of Diber seems to be the worst off with only 6125 lek per month of per capita consumption, and the highest percentage of poor individuals (42,8%).

Poverty and inequality measures disaggregated at District and Commune/Municipality levels are reported in the Appendix. Table 8 reports the measure disaggregated at Mini-municipality (for the City of Tirana) level. The poverty and inequality maps corresponding to the above Tables are presented in the Appendix (Figures 1-6). We note that poverty is spatially heterogeneous among Municipalities within the same District, but not very much among Districts within the Prefecture to which they belong.

Table 8. Poverty and inequality indices by Mini-Municipality of Tirana City (%)

Mini Head municipality count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 1 16.92 3.81 1.30 28.33 11.68 2.55 13.24 13.46 8,963.78 (1.26) (0.43) (0.19) (0.73) (0.66) (0.33) (0.72) (0.78) (176.52) 2 15.76 3.48 1.17 29.66 11.40 2.29 14.44 14.76 9,510.04 (1.21) (0.38) (0.17) (0.70) (0.59) (0.29) (0.71) (0.73) (178.42) 3 14.89 3.18 1.06 27.71 11.19 2.09 12.63 12.94 9,133.99 (1.17) (0.35) (0.15) (0.83) (0.59) (0.27) (0.79) (0.93) (201.79) 4 20.50 4.70 1.64 27.37 11.95 3.31 12.35 12.43 8,206.32 (1.32) (0.45) (0.21) (0.58) (0.61) (0.37) (0.57) (0.55) (158.31) 5 11.48 2.42 0.80 29.68 11.12 1.53 14.46 14.83 10,424.63 (0.90) (0.25) (0.11) (0.73) (0.57) (0.18) (0.72) (0.79) (198.43) 6 24.76 5.72 1.98 28.52 11.84 4.20 13.44 14.41 7,806.92 (2.14) (0.67) (0.28) (1.70) (0.55) (0.59) (1.77) (2.72) (328.50) 7 15.71 3.59 1.26 28.97 12.07 2.40 13.87 14.10 9,308.23 (1.10) (0.35) (0.16) (0.61) (0.63) (0.27) (0.62) (0.63) (192.80) 8 15.74 3.45 1.17 28.25 11.48 2.29 13.13 13.35 9,137.55 (1.21) (0.37) (0.17) (0.61) (0.66) (0.29) (0.60) (0.62) (202.50) 9 16.01 3.71 1.32 29.59 12.30 2.50 14.48 14.68 9,423.56 (1.18) (0.40) (0.19) (0.68) (0.73) (0.31) (0.71) (0.72) (186.16) 10 9.06 1.67 0.49 27.96 9.51 1.00 12.72 13.12 10,541.28 (1.02) (0.26) (0.09) (0.73) (0.59) (0.17) (0.66) (0.75) (297.74) 11 30.60 7.57 2.74 27.86 12.61 5.91 12.71 12.96 7,115.61 (2.22) (0.85) (0.40) (0.79) (0.63) (0.79) (0.75) (0.75) (175.10) * Standard errors are in parentheses.

For instance the Prefecture of Berat shows an headcount ratio of 26,4%, whereas the three Districts within the Prefecture range from 22,9% (Skaprar) to 27,8% (Berat); the Prefecture of Gijrokaster shows an headcount ratio of 19,4%, whereas the three Districts within the Prefecture range from 18,3% (Permet) to 22,0% (Telepene). On the other hand, the District of Kukes shows an headcount of 40,6%, whereas the 15 Municipalities within the District range from 21,4% to 79,5%. Figures 7-12 in the Appendix show the level of spatial heterogeneity at any level, considering two measures: the headcount ratio and the per-capita consumption. Two thirds of Prefectures have both headcount and per-capita consumption estimates that are significantly different from the corresponding values at national

level (Figures 7 and 8). In contrast with this, less than 20% of Districts have the same estimates that are significantly different from the Prefecture to which they belong (Figures 9 and 10). Finally, at Municipality level, more than 40% of Municipalities have headcount and per-capita consumption measures that are significantly different from the District to which they belong (Figures 11 and 12). Table 9 reports decomposition of one of the general entropy class inequality measures (GE(1), Theil Index) into its within-area and between-area components at various levels of aggregation. By definition, all of the inequality is within groups when the group in question is the whole country, or all rural areas or all urban areas of the country; and all of the inequality is between-groups when each household is considered a separate group. GE(1) index is decomposable so that we are able to distinguish between the inequality due to differences between a certain level of disaggregated areas (Prefectures, Districts, Communes/Municipalities, etc…) and the inequality due to the differences between households present within areas. From Table 9 we can see that in the whole country and in both rural and urban areas, a large portion of the inequality is due to within-group inequality, even when the groups are relatively small, such as Communes or Municipalities. Approximately, 9% of the inequality in Albania is between Prefectures, 12,5% between Districts, and 17% between Communes/Municipalities.

Table 9. Decomposition of the GE(1) inequality index (Theil). Level of Decomposition Number of Units Within-Group Between-Group % Between-Group Inequality Inequality Inequality Albania 1 15.05 0 0 Urban - rural 2 13.85 1.20 8.0 Strata 4 14.51 0.54 3.6 Strata – urban / rural 6 13.65 1.40 9.3 Prefectures 12 13.71 1.34 8.9 Districts 36 13.17 1.88 12.5 Communes/ Municipalities 374 12.50 2.55 16.9 Rural 1 13.74 0 0 Communes 309 11.40 2.34 17.0 Urban 1 14.02 0 0 Municipalities 65 13.27 0.75 5.3

5. Concluding remarks

In this paper we have estimated various measures of poverty and inequality for small administrative units in Albania, combining the 2001 Population and Housing Census with the 2002 Living Standards Measurement Study survey data. We believe the poverty rates produced are quite precise at Stratum, Prefecture and District levels, and are precise enough to be of value to policy-makers and researchers even at Communes level. The main findings of research like the present one are potentially very useful for policy-makers. We find, for instance, 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. The quality of the modelling has benefited from the fact that the LSMS survey in Albania followed the Population Census closely in time as well as in the basic concepts and definitions used. We would strongly recommend such coordination between data sources for the production of useful small area estimates, especially for Transition Countries, which tend to be subject to rapid changes in living conditions.

Acknowledgements

Poverty and inequality maps described in this paper were prepared under the project “Poverty & Inequality Mapping in Albania” funded by the World Bank by means of the Project Coordination Unit of “Social Services Delivery Project”, Tirana, Albania. We would like to thank Gero Carletto, Giulio Ghellini, Jane Lanjouw, Peter Lanjouw and Vijay Verma for very helpful comments on earlier versions of this paper.

REFERENCES

BETTI, G., BALLINI, F., and NERI, L. (2003). Poverty and Inequality Mapping in Albania, Final Report to the World Bank, July 2003. DEATON, A. (1997). The Analysis of Household Surveys: A Microeconometric Approach to Development Policy. John Hopkins Press and The World Bank, Washington, D.C. ELBERS, C., LANJOUW, J.O., and LANJOUW, P. (2002). Micro-level Estimation of Welfare, Working Paper n. 2911, The World Bank, Washington, D.C. ELBERS, C., LANJOUW, J.O., and LANJOUW, P. (2003). Micro-level Estimation of Poverty and Inequality. Econometrica, 71, 355-364. INSTAT (2000). General Census of Agricultural Holdings 1998. INSTAT (2002). The Population of Albania in 2001. LEVINSON, R. (2001). Sample design for the 2002 Living Standards Measurement Survey (LSMS), Final Report to the World Bank, November 2001. RAGHUNATHAN, T.E., LEPKOWSKI, J., VAN VOEWYK, J., and SOLENBERGER, P. (2001). A Multivariate Technique for Imputing Missing Values Using a Sequence of Regression Models, Survey Methodology, 27, 85-95. THE WORLD BANK (2002). Basic Information Document, Living Standard Measurement Study, Albania, Development Research Group. THE WORLD BANK (2003). Construction of the consumption aggregate and estimation of the poverty line, LSMS 2002 – Albania.

APPENDIX

Figures 1&2. Head Count Ratio and Per Capita Consumption by Prefectures.

Figures 3&4. Head Count Ratio and Per Capita Consumption by District.

Figures 5&6. Head Count Ratio and Per Capita Consumption by Municipality.

Figures 7&8. Prefectures Level versus Albania Head Count Ratio and Per Capita Consumption

Figures 9&10. District Level versus Prefecture Level Head Count Ratio and Per Capita Consumption

Figures 11&12. Commune Level versus District Level Head Count Ratio and Per Capita Consumption

Table 10. Regression results by Strata: GLS estimates and standard errors (in parentheses) Coastal Coastal Central Central Mountain Tirana area area area area area (rural) (urban) (rural) (urban) House surface less than -0.1550*** 0.1534*** 40m2 (0.0326) *0.0586) House surface 40m2- -0.0779** -0.984*** 69m2 (0.0310) (0.221) Material: Brick or stone -0.0703* (0.0373) Separate kitchen 0.1525*** (0.0450) Wc inside 0.1117*** 0.1074** (0.0336) (0.0481) Water inside 0.0862** (0.343) TV 0.2189*** 0.1866* 0.1148*** (0.0655) (0.1012) (0.0401) Parabolic 0.1270*** 0.1230*** (0.0258) (0.0470) Refrigerator 0.2215*** 0.1871*** 0.1154*** 0.1205*** (0.0415) (0.0604) (0.0346) (0.0268) Heater 0.0843** 0.0837** 0.1350*** (0.0351) (0.0407) (0.0321) Air conditioning 0.3450*** 0.2338*** (0.1206) (0.0514) Computer 0.2412*** (0.0473) Car 0.1854*** 0.3809*** 0.3829*** 0.3146*** 0.3635*** 0.3469*** (0.0468) (0.0420) (0.0563) (0.0466) (0.0490) (0.0406) Washing machine 0.1090*** 0.129*** 0.1371*** 0.2233*** 0.2065*** (0.0350) (0.0371) (0.0358) (0.0397) (0.0423) Rooms per person 0.1242*** 0.1180*** 0.1513*** 0.0871** (0.0430) (0.0399 (0.0498) (0.0376) Possession of 0.1734*** 0.2221*** agricultural land (0.0410) (0.0528) Child 0-5 -0.0364*** (0.0124) Household size -0.2810*** -0.1608*** (0.0416) (0.0422) Household size squared 0.0150*** 0.0136*** (0.0038) (0.0037) Log household size -0.1991*** -0.4960*** -0.4873*** 0.2848*** (0.0667) (0.0448) (0.0337) (0.0595) Highest education low -0.2623*** -0.2344*** -0.2457*** -0.1021** (0.0562) (0.0714) (0.0497) (0.0407) Highest education -0.1953*** -0.1750** -0.1356*** 0.1023*** medium (0.0557) (0.0711) (0.0433) (0.0228) Highest education high 0.1154*** 0.2532*** 0.1230*** (0.0428) (0.0368) (0.0317) Migration since 1990 0.0724 (0.0566) # non working people -0.1038*** -0.0508*** -0.1245*** -0.0395*** -0.1061*** (0.0162) (0.0127) (0.0200) (0.0085) (0.0159) Spouse age 0.0045** (0.0022) Spouse age squared -0.00006* (0.00003) EA means variables Plastered 0.1504*** (0.0510) House before 1945 0.7621***

(0.2933) House 1961 - 1980 0.2371* 0.1139** (0.1387) (0.0636) House 1981 - 1990 0.2607* 0.1327** (0.1446) (0.0543) House surface less than -0.2540** 40m2 (0.1043) House surface 40m2- -0.2233*** 69m2 (0.0818) Material: Brick or stone 0.3526*** (0.0710) Separate kitchen 0.1963*** -0.1957*** -0.1393*** (0.0685) (0.0711) (0.0489) Wc inside -0.1837* -0.2065*** (0.1056) (0.0695) Water inside 0.2863*** (0.1000) Washing machine 0.3913*** (0.1109) Rooms per person -0.3248*** (0.1651) Rooms business -2.4634* 1.7243*** (1.4723) (0.6603) Possession of -0.2074* agricultural land (0.1216) Parabolic 0.3327*** (0.0952) Car 1.3868*** (0.4435) Computer 5.2594* (2.8557) Child 0-5 -0.3899** (0.2067) Household size -1.8065*** (0.4824) Household size squared 0.2099*** (0.0537) Log household size -0.4963* (0.2946) Female householder 1.0824*** (0.3051) Highest education low -0.3623** (0.1850) Highest education -0.7322*** -0.5318*** medium (0.2497) (0.1807) Highest education high 0.9408*** (0.2192) Migration before 1990 -0.4699** -0.6859*** -0.7746*** (0.2117) (0.1442) (0.2407) Migration since 1990 0.3834*** -0.5549*** (0.1406) (0.1564) # non working people 0.0477** (0.0308) Spouse age squared -0.00027*** (0.0001) Spouse work -0.2669*** (0.0921) Constant 13.1177*** 808641*** 11.18*** 10.0322*** 9.3215*** 9.1857*** (1.0738) (0.1605) (0.6387) (0.2536) (0.1194) (0.1081)

Table 11. Poverty and inequality indices by DISTRICT(%)

Head Gini- District count FGT(1) FGT(2) Gini poor Sen GE(0) GE(1) Con 1: BERAT 27.80 6.19 2.03 25.84 11.09 4.64 10.81 11.05 7,132.25 2.46 0.77 0.31 0.57 0.47 0.70 0.50 0.51 207.01 2: BULCUIZË 59.46 18.80 8.02 26.48 15.63 18.76 11.47 11.52 4,872.82 2.68 1.41 0.78 0.65 0.54 1.74 0.58 0.58 157.85 3: DELVINË 14.50 3.17 1.05 42.84 11.19 2.05 30.83 35.36 13,400.59 2.34 0.67 0.27 6.16 0.79 0.49 9.40 12.44 2,113.08 4: DEVOLL 27.89 6.09 1.96 26.21 10.70 4.57 11.05 11.39 7,203.81 4.16 1.26 0.49 0.71 0.65 1.16 0.61 0.64 369.79 5: DIBËR 38.14 10.07 3.79 27.98 13.25 8.40 12.74 12.95 6,472.22 2.01 0.79 0.38 0.70 0.48 0.82 0.66 0.66 161.23 6: DURRËS 21.78 5.38 1.96 31.60 12.71 3.80 16.50 16.76 8,996.73 1.65 0.55 0.25 1.02 0.52 0.47 1.10 1.19 354.51 7: ELBASAN 28.61 6.55 2.20 26.68 11.45 4.95 11.53 11.81 7,170.90 2.32 0.73 0.30 0.47 0.44 0.68 0.42 0.43 209.63 8: FIER 26.82 6.26 2.17 29.02 11.87 4.68 13.69 14.14 7,710.72 2.33 0.74 0.30 1.0 3 0.43 0.66 0.97 1.08 227.85 9: GRAMSH 35.24 8.47 2.97 25.13 12.07 6.90 10.27 10.38 6,392.57 2.46 0.84 0.37 0.67 0.50 0.84 0.56 0.56 168.85 10: GJIROKASTËR 18.33 3.93 1.27 28.55 10.85 2.65 13.32 13.82 8,783.22 1.94 0.55 0.22 0.91 0.59 0.44 0.90 1.10 323.58 11: HAS 47.52 13.56 5.37 27.97 14.16 12.30 12.73 13.07 5,770.89 3.13 1.38 0.71 1.02 0.68 1.58 0.95 1.03 228.69 12: KAVAJË 27.98 6.76 2.40 29.52 12.22 5.10 14.18 14.55 7,689.25 2.56 0.84 0.35 0.94 0.43 0.76 0.90 0.98 262.13 13: KOLONJË 20.53 4.38 1.45 25.58 11.10 3.10 10.76 10.79 7,863.55 2.70 0.73 0.29 0.71 0.71 0.60 0.64 0.63 327.84 14: KORÇË 25.68 5.79 1.92 27.35 11.25 4.23 12.12 12.36 7,601.15 2.24 0.71 0.29 0.59 0.47 0.63 0.53 0.56 219.50 15: KRUJË 33.32 8.05 2.81 26.64 12.03 6.39 11.50 11.70 6,742.49 2.36 0.83 0.37 0.50 0.50 0.81 0.45 0.45 198.43 16: KUÇOVË 24.40 5.25 1.70 25.07 10.91 3.82 10.22 10.29 7,350.99 1.79 0.54 0.22 0.54 0.51 0.47 0.47 0.45 168.99 17: KUKËS 40.61 10.71 4.01 27.39 13.16 9.14 12.18 12.43 6,230.81 2.17 0.88 0.42 0.67 0.48 0.92 0.61 0.63 164.17 18: KURBIN 38.50 10.51 4.10 28.15 13.90 8.87 13.01 13.11 6,423.69 2.94 1.15 0.55 0.64 0.57 1.20 0.60 0.61 229.02 19: LEZHË 35.04 9.28 3.51 33.49 13.31 7.52 18.52 21.45 7,463.12 2.84 1.12 0.53 2.03 0.59 1.12 2.55 5.24 343.33 20: LIBRAZHD 37.13 9.27 3.35 26.15 12.56 7.70 11.14 11.32 6,356.48 2.53 0.87 0.38 0.65 0.45 0.90 0.57 0.59 185.12 21: LUSHNJË 32.74 7.88 2.76 28.65 12.06 6.25 13.30 13.97 7,044.25 2.91 0.98 0.42 0.99 0.45 0.95 0.91 1.09 198.99 22: MALËSI E MADHE 30.51 7.10 2.41 27.40 11.54 5.51 12.14 12.62 7,115.24 4.57 1.38 0.55 0.92 0.60 1.34 0.85 0.94 488.03 23: MALLKASTËR 33.29 8.03 2.83 27.09 12.15 6.43 11.91 12.23 6,793.93 3.35 1.11 0.47 0.87 0.53 1.08 0.76 0.85 255.55 24: MAT 37.53 9.56 3.47 27.64 12.55 7.94 12.41 12.93 6,518.10 4.74 1.74 0.77 0.94 0.74 1.78 0.89 1.01 395.92 25: MIRDITË 37.02 9.83 3.71 27.91 13.26 8.13 12.70 12.84 6,562.18 4.11 1.51 0.71 0.83 0.84 1.56 0.82 0.78 349.77

26: PEQUIN 38.25 9.42 3.33 26.16 12.14 7.85 11.07 11.39 6,303.18 2.96 1.16 0.54 0.87 0.69 1.19 0.76 0.80 195.51 27: PËRMET 18.26 3.69 1.13 25.77 10.13 2.48 10.78 10.92 8,245.90 2.46 0.67 0.25 0.59 0.60 0.53 0.51 0.52 334.91 28: POGRADEC 30.63 7.12 2.42 26.70 11.52 5.51 11.52 11.82 6,995.67 3.59 1.19 0.50 0.70 0.62 1.13 0.61 0.64 274.13 29: PUKË 42.34 12.13 4.88 29.55 14.44 10.65 14.30 14.64 6,302.69 6.30 2.38 1.09 0.99 0.78 2.60 1.07 1.10 547.14 30: SARANDË 14.03 3.07 1.03 32.68 11.34 1.98 17.69 18.81 10,763.33 1.47 0.40 0.16 2.25 0.50 0.29 2.55 3.62 648.94 31: SKAPRAR 22.95 4.81 1.52 25.19 10.54 3.43 10.29 10.46 7,527.73 2.96 0.78 0.29 0.71 0.51 0.68 0.60 0.65 332.95 32: SHKODËR 31.44 8.00 2.96 28.50 12.94 6.29 13.29 13.44 7,141.41 2.70 0.95 0.43 0.60 0.57 0.91 0.59 0.59 249.20 33: TEPELENË 22.03 4.72 1.52 26.28 10.75 3.32 11.21 11.32 7,853.82 2.32 0.66 0.26 0.59 0.57 0.56 0.53 0.52 273.04 34: TIRANË 22.76 5.32 1.85 29.43 11.95 3.79 14.17 14.63 8,278.93 1.30 0.42 0.18 0.50 0.35 0.35 0.50 0.56 151.37 35: TROPOJË 33.25 8.24 2.96 26.88 12.48 6.57 11.76 11.86 6,758.61 2.51 0.89 0.41 0.72 0.57 0.88 0.64 0.66 216.82 36: VLORË 19.55 4.47 1.54 32.32 11.80 3.09 17.28 19.11 9,329.39 1.76 0.53 0.22 1.54 0.48 0.43 1.79 3.16 413.76

Table 12.1. Poverty and inequality indices by Commune/Municipality (PREFECTURE of BERAT, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor BERAT SINJE 48,68 13,38 5,09 25,43 13,23 12,26 10,47 10,68 5531,27 6,59 2,63 1,24 1,41 1,08 3,01 1,19 1,22 394,79 LUMAS 42,68 10,86 3,95 24,71 12,52 9,51 9,94 10,06 5856,36 5,60 2,04 0,94 1,32 1,15 2,23 1,10 1,09 363,22 CUKALAT 40,33 9,88 3,43 25,60 11,69 8,36 10,55 10,90 6142,72 5,52 2,12 0,97 1,77 1,14 2,19 1,47 1,56 358,57 TERPAN 37,54 8,60 2,83 23,42 10,95 7,11 8,82 8,96 6127,19 5,94 1,99 0,83 1,36 0,95 2,04 1,04 1,05 392,15 ROSHNIK 35,58 8,42 2,88 24,43 11,65 6,86 9,69 9,76 6334,20 4,92 1,54 0,64 1,34 1,00 1,53 1,12 1,06 377,21 OTLLAK 33,18 7,52 2,47 25,30 10,94 5,97 10,30 10,62 6637,98 5,42 1,78 0,74 1,20 0,95 1,75 0,99 1,05 425,62 VELABISHT 30,78 6,80 2,20 24,15 10,84 5,27 9,45 9,67 6658,16 4,75 1,38 0,53 0,92 0,73 1,34 0,74 0,78 390,86 POSHNJE 28,08 5,65 1,69 23,04 9,76 4,24 8,54 8,72 6776,91 4,59 1,27 0,47 0,89 0,75 1,18 0,66 0,69 382,95 KUTALLI 27,97 5,97 1,89 23,66 10,50 4,50 9,06 9,17 6841,53 4,50 1,30 0,51 0,81 0,82 1,23 0,65 0,63 375,44 URA VAJGURORE 27,44 5,82 1,83 24,83 10,42 4,33 9,93 10,09 7064,79 3,48 1,03 0,41 0,72 0,69 0,95 0,59 0,60 292,51

VERTOP 20,53 4,15 1,26 26,94 9,85 2,87 11,71 12,17 8146,77 4,67 1,28 0,48 1,80 0,94 1,06 1,59 1,85 694,62 BERAT 19,49 4,00 1,25 25,53 10,36 2,73 10,58 10,72 7999,53 1,63 0,45 0,18 0,54 0,47 0,37 0,46 0,47 167,69 KUÇOVE KOZARE 35,84 7,90 2,54 23,24 10,68 6,42 8,68 8,87 6206,30 4,27 1,33 0,53 0,99 0,74 1,35 0,74 0,77 269,31 PERONDI 23,17 5,30 1,86 24,69 11,99 3,90 10,12 10,01 7404,87 3,63 1,05 0,45 1,07 1,14 0,93 0,97 0,88 419,49 KUCOVE 20,93 4,25 1,31 24,96 10,19 2,95 10,08 10,18 7738,47 2,36 0,64 0,24 0,66 0,59 0,53 0,55 0,55 235,34 SKRAPAR LESHNJE 44,07 10,90 3,82 22,53 11,71 9,74 8,23 8,30 5639,73 10,01 3,49 1,53 1,88 1,75 3,90 1,39 1,46 576,70 POTOM 35,16 7,57 2,38 23,46 10,13 6,21 8,89 9,36 6330,48 8,94 2,67 1,03 1,43 1,33 2,74 1,10 1,28 598,53 ZHEPE 32,13 7,47 2,54 24,99 11,32 6,04 10,16 10,38 6738,68 9,47 2,85 1,14 1,61 1,38 2,95 1,35 1,44 785,61 GJERBES 31,50 7,21 2,50 23,50 11,69 5,96 9,16 9,27 6595,46 10,59 2,97 1,17 1,31 1,48 3,16 1,09 1,09 860,06 VENDRESHE 27,78 6,04 1,95 23,44 10,74 4,57 8,95 8,98 6851,63 6,66 1,66 0,62 1,29 1,45 1,58 1,04 1,03 585,36 QENDER 19,84 3,87 1,14 23,93 9,46 2,67 9,27 9,36 7804,15 5,89 1,44 0,49 1,26 0,96 1,22 1,03 1,05 781,81 COROVODE 19,09 3,72 1,10 24,26 9,65 2,51 9,52 9,62 7826,10 2,77 0,69 0,25 0,70 0,69 0,56 0,56 0,58 289,87 POLIÇAN 19,06 3,60 1,05 23,42 9,45 2,45 8,87 9,04 7644,95 3,22 0,79 0,27 0,87 0,73 0,63 0,67 0,70 344,96 BOGOVE 18,89 4,45 1,53 27,74 11,53 3,01 12,85 12,53 8884,50 5,23 1,53 0,65 2,18 1,78 1,26 2,14 1,99 1007,61 CEPAN 18,74 4,16 1,41 26,64 11,49 2,91 11,73 11,84 8441,17 6,56 1,61 0,62 2,03 2,38 1,40 1,90 1,91 943,26

Table 12.2. Poverty and inequality indices by Commune/Municipality (PREFECTURE of DIBER, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor BULQIZE MARTANESH 76,02 28,51 13,65 25,99 17,77 31,53 11,09 11,17 3908,06 5,70 4,71 3,17 1,77 1,90 5,97 1,63 1,56 351,60 OSTREN 67,69 22,83 10,13 25,37 16,27 24,11 10,54 10,53 4375,88 6,00 3,28 1,87 1,26 1,18 4,28 1,13 1,08 328,23 GJORICE 64,30 20,08 8,33 24,95 14,77 20,64 10,08 10,31 4626,95 5,30 2,90 1,63 1,46 1,19 3,63 1,21 1,25 295,60 SHUPENZE 63,12 20,47 8,87 26,23 15,86 21,00 11,27 11,36 4675,55 5,23 2,65 1,45 1,34 1,03 3,37 1,17 1,19 304,53 ZERQAN 62,15 20,49 8,93 27,06 15,90 20,68 11,89 12,05 4743,28

4,29 2,56 1,54 1,32 1,17 3,16 1,19 1,19 275,96 KLENJE 61,63 19,34 8,25 24,80 15,33 19,88 10,19 10,10 4717,65 8,89 4,50 2,45 1,78 1,65 5,65 1,54 1,46 489,41 FUSHE BULQIZE 52,03 14,35 5,48 24,66 13,26 13,56 9,85 10,06 5292,30 6,27 2,82 1,40 1,00 1,16 3,32 0,83 0,87 373,52 BULQIZE 46,17 12,58 4,86 25,16 13,68 11,43 10,39 10,37 5619,16 5,04 1,96 0,93 1,01 0,84 2,23 0,86 0,84 290,81 DIBER SELISHTE 61,06 18,94 7,87 25,21 14,83 19,07 10,31 10,47 4772,90 6,10 3,16 1,71 1,20 1,17 3,91 1,03 1,01 336,93 LUZNI 55,13 15,24 5,81 23,65 13,20 14,75 9,07 9,20 5075,76 6,37 2,89 1,44 1,07 1,12 3,47 0,84 0,85 339,02 MUHURR 55,08 16,28 6,60 25,48 14,43 15,79 10,58 10,65 5119,49 6,38 2,93 1,52 1,01 1,23 3,53 0,91 0,81 365,73 KASTRIOT 54,77 16,56 6,84 26,42 14,91 15,96 11,42 11,57 5158,29 4,53 2,09 1,11 1,10 0,90 2,50 0,99 1,01 266,39 FUSHE CIDHEN 54,66 15,48 6,03 24,86 13,64 14,91 10,02 10,21 5154,55 5,79 2,72 1,42 1,38 1,22 3,27 1,14 1,17 314,08 ZALL REC 51,70 14,76 5,75 26,68 13,63 13,76 11,47 11,75 5455,15 5,68 2,44 1,23 1,32 1,17 2,81 1,18 1,16 365,56 QENDER TOMIN 44,35 11,50 4,21 27,49 12,66 10,14 12,23 12,98 6015,81 4,22 1,62 0,75 1,37 0,78 1,78 1,23 1,54 259,32 ZALL DARDHE 44,13 11,43 4,19 25,73 12,59 10,13 10,70 10,98 5873,41 6,38 2,55 1,23 1,43 1,32 2,83 1,27 1,31 389,89 SLLOVE 42,08 11,30 4,31 25,66 13,33 9,86 10,79 10,77 5931,44 5,63 2,29 1,14 1,43 1,47 2,47 1,29 1,21 355,38 ARRAS 40,44 10,25 3,71 25,29 12,47 8,75 10,38 10,48 6050,75 4,64 1,72 0,80 1,23 1,05 1,82 1,06 1,06 325,97 LURE 32,99 7,75 2,66 26,97 11,49 6,18 11,74 12,14 6842,90 5,85 1,95 0,85 1,26 1,30 1,95 1,12 1,17 500,19 MAQELLARE 32,63 7,58 2,58 25,45 11,49 6,02 10,48 10,66 6665,91 4,54 1,54 0,66 0,97 0,88 1,51 0,83 0,85 337,55 MELAN 30,67 6,88 2,30 25,29 11,23 5,39 10,40 10,64 6802,98 4,81 1,47 0,60 1,46 0,96 1,40 1,20 1,30 366,36 KALA E DODES 29,45 6,31 2,03 23,33 10,60 4,91 8,85 8,97 6681,45 6,14 1,83 0,74 1,36 1,20 1,74 1,08 1,06 494,12 PESHKOPI 10,11 1,86 0,54 24,21 9,44 1,14 9,57 9,63 9290,61 2,01 0,45 0,15 0,81 0,73 0,31 0,63 0,65 392,28 MAT BAZ 57,01 16,40 6,39 25,03 13,59 16,04 10,10 10,48 5062,19 7,08 3,17 1,56 1,23 1,04 3,89 1,00 1,06 411,42 RUKAJ 52,36 14,52 5,59 25,16 13,34 13,84 10,35 10,63 5334,27 8,14 3,46 1,66 1,73 1,18 4,17 1,50 1,59 494,37 LIS 43,31 11,03 3,95 26,34 12,17 9,66 11,18 11,61 6027,97 7,03 2,59 1,15 1,67 1,12 2,84 1,44 1,56 530,17

ULEZ 42,98 10,84 3,83 25,59 11,87 9,42 10,55 11,04 5971,54 7,36 2,70 1,20 1,44 1,20 2,87 1,21 1,33 505,02 KLOS 40,70 10,40 3,80 26,03 12,65 8,95 11,02 11,25 6119,02 5,35 1,93 0,86 1,32 0,78 2,12 1,14 1,23 397,00 XIBER 40,19 11,13 4,34 27,51 13,66 9,67 12,44 12,52 6336,58 9,76 3,40 1,56 2,08 1,65 3,78 2,02 1,93 883,32 MACUKULL 39,36 10,28 3,80 26,83 12,52 8,82 11,65 11,91 6350,61 9,47 3,58 1,64 1,63 1,54 3,85 1,46 1,49 784,73 GURRE 38,82 10,03 3,66 27,17 12,26 8,65 12,00 12,56 6491,61 11,33 3,95 1,74 1,78 1,51 4,34 1,65 1,78 1010,10 SUÇ 38,70 9,26 3,16 25,31 11,40 7,76 10,31 10,67 6246,30 6,83 2,38 1,03 1,55 1,22 2,45 1,30 1,40 462,99 KOMSI 32,73 8,22 2,95 28,78 12,22 6,50 13,42 13,74 7118,25 5,56 2,04 0,93 1,70 1,28 1,97 1,62 1,73 519,30 DERJAN 30,93 7,57 2,67 33,79 11,69 5,97 18,74 21,47 8099,90 8,71 2,94 1,25 3,23 1,60 2,85 3,57 4,63 1194,12 BURREL 22,53 4,72 1,48 24,84 10,42 3,34 10,01 10,12 7533,07 3,11 0,89 0,35 0,71 0,70 0,77 0,59 0,59 282,73

Table 12.3. Poverty and inequality indices by Commune/Municipality (PREFECTURE of DURRES, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor DURRES ISHEM 43,37 11,18 4,09 26,02 12,61 9,83 10,97 11,30 5943,63 5,68 2,14 0,97 1,23 0,94 2,33 1,04 1,10 366,85 GJEPALAJ 37,68 9,13 3,22 24,79 12,06 7,67 9,99 10,11 6209,95 5,46 1,79 0,77 1,05 0,86 1,89 0,87 0,89 393,17 KATUND I RI 37,14 9,37 3,40 28,17 12,54 7,75 12,90 13,46 6610,68 3,48 1,32 0,61 1,85 0,80 1,34 1,72 2,11 293,23 SUKTH 35,91 9,48 3,58 28,77 13,30 7,77 13,53 13,79 6756,89 3,50 1,26 0,58 1,24 0,66 1,29 1,16 1,28 331,60 MANEZ 32,95 8,15 2,93 27,49 12,40 6,49 12,31 12,54 6881,23 3,80 1,29 0,57 1,12 0,73 1,27 1,00 1,09 314,25 RASHBULL 31,45 7,63 2,70 26,72 12,16 5,99 11,64 11,76 6928,77 3,91 1,32 0,57 0,97 0,70 1,28 0,84 0,88 341,34 MAMINAS 25,04 5,63 1,89 26,86 11,27 4,14 11,74 11,99 7586,49 4,66 1,34 0,54 0,97 0,95 1,21 0,86 0,92 471,58 DURRES 15,11 3,76 1,41 31,11 13,09 2,50 16,30 16,22 10338,09 2,28 0,77 0,36 1,11 1,05 0,60 1,26 1,23 577,59 XHAFZOTAJ 11,88 2,23 0,66 26,10 9,60 1,40 11,05 11,25 9352,55 2,76 0,62 0,21 1,05 0,84 0,45 0,91 0,95 582,47 SHIJAK 9,21 1,74 0,51 27,51 9,52 1,04 12,37 12,45 10688,28 2,33 0,55 0,19 0,98 0,93 0,37 0,90 0,94 702,98 KRUJE NIKEL 48,10 13,35 5,13 26,50 13,46 12,15 11,36 11,59 5644,83 5,11 2,24 1,12 1,18 0,95 2,55 1,00 1,06 323,05

KODER THUMANE 35,56 8,56 2,98 25,88 11,86 6,99 10,84 11,09 6491,44 5,01 1,64 0,69 0,82 0,75 1,68 0,71 0,71 397,22 CUDHI 33,92 7,98 2,70 25,82 11,12 6,51 10,77 11,09 6736,83 9,90 3,07 1,25 1,75 1,47 3,14 1,54 1,56 885,85 FUSHE KRUJE 31,56 7,39 2,52 26,74 11,63 5,76 11,55 11,80 6923,60 2,64 0,89 0,38 0,61 0,52 0,84 0,54 0,57 220,02 BUBQ 30,51 6,75 2,18 25,68 10,74 5,18 10,61 10,94 6898,58 4,34 1,33 0,53 0,98 0,76 1,25 0,83 0,89 371,28 KRUJE 24,63 5,46 1,81 25,60 11,22 3,98 10,68 10,75 7415,22 2,48 0,73 0,30 0,71 0,61 0,64 0,61 0,63 238,31

Table 12.4. Poverty and inequality indices by Commune/Municipality (PREFECTURE of ELBASAN, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor ELBASAN LABINOT MAL 57,23 17,06 6,90 25,98 14,33 16,78 10,93 11,23 5070,48 7,08 3,22 1,64 1,19 1,15 3,91 1,04 1,05 425,81 GJINAR 49,06 13,46 5,11 25,50 13,20 12,44 10,48 10,71 5541,21 7,66 3,04 1,45 1,26 1,13 3,60 1,05 1,09 508,13 LABINOT FUSHE 47,70 12,97 4,91 25,80 13,15 11,79 10,79 11,08 5626,02 5,91 2,48 1,22 1,47 1,12 2,84 1,25 1,30 378,21 GRACEN 46,88 12,84 4,83 27,52 12,86 11,50 12,21 12,77 5871,87 7,25 3,18 1,56 2,26 1,43 3,52 2,02 2,13 581,48 MOLLAS 40,55 9,53 3,21 23,26 11,30 8,13 8,71 8,88 5905,82 5,78 1,92 0,81 0,98 0,91 2,08 0,74 0,77 358,30 TREGAN 39,67 9,04 2,98 22,56 11,01 7,69 8,21 8,36 5906,70 6,31 2,00 0,80 0,90 0,89 2,14 0,67 0,68 378,92 GJEGJAN 39,27 9,51 3,31 24,92 11,80 8,04 10,02 10,23 6131,54 5,28 1,94 0,86 1,30 0,98 2,03 1,07 1,10 348,32 SHALES 38,11 8,78 2,92 23,21 11,17 7,32 8,69 8,83 6038,55 4,91 1,70 0,72 1,07 0,90 1,75 0,82 0,83 289,86 GREKAN 37,36 8,98 3,11 24,89 11,76 7,45 10,04 10,27 6238,94 5,14 1,86 0,82 1,33 0,99 1,89 1,07 1,12 328,61 KAJAN 36,34 8,25 2,71 24,19 10,91 6,75 9,41 9,64 6286,16 5,56 1,79 0,73 1,43 0,92 1,82 1,11 1,18 360,35 PAPER 36,10 8,61 2,93 26,07 11,52 6,99 10,93 11,24 6492,19 4,44 1,56 0,68 1,35 0,91 1,59 1,15 1,20 359,05 ZAVALINE 32,43 7,39 2,45 25,91 10,85 5,94 10,83 11,31 6840,18 9,15 2,90 1,21 1,58 1,36 2,97 1,36 1,45 875,61 KLOS 32,18 6,93 2,18 24,32 10,33 5,48 9,51 9,85 6637,68 7,10 2,02 0,76 1,13 0,98 1,98 0,91 0,96 556,44 SHUSHICE 31,72 7,43 2,54 25,65 11,58 5,83 10,70 10,90 6762,83 4,55 1,44 0,60 1,44 0,89 1,41 1,25 1,32 451,30

SHIRGJAN 28,55 6,00 1,86 23,75 10,25 4,57 9,09 9,33 6812,65 6,05 1,60 0,57 0,81 0,65 1,52 0,63 0,67 508,72 BELSH 28,51 5,95 1,83 24,60 10,18 4,48 9,72 10,04 6923,51 4,05 1,11 0,41 1,07 0,61 1,02 0,85 0,97 362,09 BRADASHESH 28,46 6,25 2,02 25,64 10,82 4,73 10,64 10,99 7055,29 4,75 1,35 0,51 1,22 0,73 1,27 1,02 1,13 471,11 GOSTIME 27,52 5,87 1,86 25,02 10,44 4,42 10,11 10,40 7086,55 5,67 1,58 0,60 1,06 0,86 1,52 0,88 0,92 544,66 CERRIK 26,84 5,82 1,88 25,34 10,82 4,34 10,39 10,58 7153,68 3,33 1,00 0,40 0,87 0,73 0,91 0,73 0,75 277,38 FUNAR 22,09 4,37 1,31 24,92 9,26 3,21 9,98 10,40 7711,49 9,35 2,42 0,88 1,28 1,51 2,16 1,06 1,09 1073,58 FIERZ 20,00 3,80 1,09 23,64 9,22 2,59 9,02 9,12 7670,29 4,61 1,10 0,38 1,41 1,01 0,89 1,08 1,10 549,34 ELBASAN 19,33 4,01 1,26 26,35 10,46 2,73 11,29 11,49 8187,55 1,78 0,53 0,21 0,65 0,56 0,42 0,57 0,59 194,15 RRASE 19,02 3,16 0,80 20,54 7,78 2,15 6,75 6,87 7275,46 6,77 1,42 0,42 0,91 1,05 1,13 0,59 0,61 626,93 GRAMSH LENIE 57,19 15,92 6,10 23,38 13,27 15,73 8,89 9,09 4971,80 7,97 3,35 1,65 1,35 1,24 4,18 1,05 1,09 390,13 KUSHOVE 56,33 15,62 6,04 22,82 13,44 15,49 8,52 8,54 4968,83 8,79 3,82 1,89 1,54 1,45 4,65 1,19 1,15 434,38 TUNJE 46,44 11,21 3,87 22,38 11,65 10,16 8,09 8,19 5507,30 6,39 2,30 1,05 1,12 1,19 2,63 0,85 0,87 344,69 KUKUR 45,30 11,57 4,21 23,72 12,54 10,39 9,15 9,18 5614,47 5,80 2,10 0,96 1,15 1,01 2,37 0,91 0,90 339,00 SULT 43,72 10,57 3,74 22,03 11,99 9,51 7,98 7,97 5592,55 7,88 2,65 1,14 1,37 1,20 2,99 1,00 1,01 411,60 KODOVJAT 43,19 11,09 4,07 24,25 12,74 9,78 9,59 9,60 5766,07 5,20 1,94 0,90 1,07 1,02 2,16 0,86 0,87 333,41 SKENDERBEG AS 43,15 10,33 3,58 22,04 11,62 9,16 7,92 7,96 5632,25 7,44 2,61 1,17 1,35 1,29 2,91 0,99 0,99 384,07 POROCAN 35,45 8,27 2,81 23,97 11,40 6,81 9,34 9,51 6304,98 7,38 2,30 0,95 1,12 1,28 2,35 0,92 0,90 555,00 PISHAJ 32,26 7,43 2,52 24,57 11,46 5,88 9,81 9,93 6571,17 4,08 1,35 0,58 1,02 0,96 1,30 0,85 0,85 302,51 GRAMSH 18,82 3,65 1,10 23,55 9,83 2,50 9,01 9,12 7698,94 2,97 0,70 0,25 0,72 0,63 0,58 0,54 0,56 326,76 LIBRAZHD LUNIK 69,36 22,63 9,71 24,33 15,46 24,11 9,61 9,77 4323,47 4,83 2,90 1,69 1,12 1,04 3,72 0,89 0,90 255,01 STEBLEVE 60,26 18,67 7,87 24,94 14,97 18,88 10,26 10,19 4806,38 8,35 4,53 2,67 2,08 2,41 5,67 1,98 1,73 466,59 ORENJE 49,44 12,97 4,77 23,98 12,72 12,00 9,30 9,45 5409,64 5,36 2,10 0,98 0,87 0,86 2,48 0,69 0,71 293,06 HOTOLISHT 46,81 12,12 4,43 24,31 12,67 10,94 9,57 9,72 5570,90

4,02 1,49 0,69 0,99 0,79 1,70 0,82 0,83 243,16 STRAVAJ 38,93 9,20 3,15 24,00 11,59 7,79 9,31 9,44 6077,97 5,89 1,89 0,80 0,89 1,03 2,03 0,73 0,71 377,02 QUKES 34,72 8,07 2,75 24,11 11,52 6,56 9,44 9,52 6345,41 4,72 1,56 0,66 0,89 0,86 1,55 0,72 0,70 348,22 PRENJAS 34,62 8,10 2,78 24,01 11,63 6,61 9,38 9,42 6340,57 5,24 1,72 0,73 1,09 0,92 1,77 0,87 0,86 352,20 RRAJCE 33,95 7,63 2,54 23,83 11,22 6,17 9,21 9,38 6374,29 4,54 1,37 0,55 0,74 0,70 1,36 0,60 0,59 318,34 QENDER 32,85 7,64 2,60 25,16 11,57 6,09 10,27 10,43 6608,63 4,39 1,36 0,56 0,91 0,69 1,36 0,75 0,78 332,84 POLIS 32,06 7,33 2,49 24,48 11,42 5,82 9,77 9,86 6603,56 4,69 1,50 0,65 1,03 1,05 1,46 0,86 0,82 364,49 LIBRAZHD 13,25 2,55 0,76 25,46 9,74 1,62 10,56 10,64 8991,57 2,89 0,69 0,24 1,23 0,91 0,50 1,06 1,06 506,40 PEQIN KARINE 46,24 11,99 4,41 24,44 12,66 10,82 9,75 9,96 5621,54 5,89 2,29 1,15 1,80 1,47 2,61 1,49 1,53 364,48 GJOÇAJ 44,91 11,12 3,89 23,78 11,86 9,88 9,11 9,33 5685,90 6,35 2,29 1,00 1,20 0,98 2,58 0,93 0,98 342,36 PAJOVE 43,50 11,38 4,21 25,71 12,79 10,02 10,73 10,94 5885,38 5,58 2,33 1,14 1,61 1,22 2,55 1,38 1,44 359,56 SHEZE 41,53 10,64 3,82 28,20 12,31 9,09 12,86 13,66 6311,40 5,19 1,97 0,92 2,32 1,14 2,09 2,12 2,50 401,27 PERPARIM 32,29 7,09 2,28 24,44 10,66 5,57 9,62 9,86 6612,75 4,86 1,50 0,61 1,15 0,99 1,47 0,93 0,93 355,71 PEQIN 26,73 6,01 2,01 25,88 11,27 4,49 10,89 11,02 7260,58 3,75 1,15 0,48 0,89 0,90 1,06 0,77 0,76 337,01

Table 12.5. Poverty and inequality indices by Commune/Municipality (PREFECTURE of FIER, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor FIER RUZHDIE 41,16 9,54 3,22 21,98 11,29 8,37 7,85 7,89 5777,60 8,64 2,73 1,09 1,10 0,88 3,02 0,78 0,77 487,66 KURJAN 40,87 9,90 3,47 23,15 11,92 8,57 8,74 8,76 5839,33 6,17 2,14 0,92 1,15 0,93 2,32 0,85 0,87 367,96 STRUMM 39,82 9,73 3,46 23,62 12,18 8,36 9,10 9,08 5942,45 5,43 1,86 0,80 1,06 0,81 1,99 0,82 0,86 348,71 FRAKULL 39,55 9,63 3,41 23,95 12,12 8,24 9,34 9,38 5989,60 5,14 1,76 0,77 0,83 0,86 1,84 0,67 0,66 337,60 KUMAN 38,11 8,94 3,09 22,86 11,71 7,60 8,53 8,52 5986,46 6,06 1,99 0,83 0,92 0,86 2,10 0,69 0,69 362,46 PORTEZ 37,52 9,06 3,20 23,97 12,09 7,61 9,39 9,41 6115,62 4,94 1,64 0,71 0,84 0,81 1,73 0,67 0,67 325,75

DERMENAS 36,51 8,78 3,09 24,46 12,06 7,30 9,76 9,79 6238,63 4,55 1,55 0,69 0,86 0,97 1,60 0,75 0,73 309,77 ZHARREZ 36,47 8,39 2,84 22,97 11,41 6,99 8,57 8,58 6120,79 6,03 1,82 0,72 0,69 0,71 1,93 0,52 0,52 389,04 LEVAN 35,87 8,77 3,12 25,87 12,25 7,22 10,88 11,07 6437,00 4,50 1,47 0,62 0,92 0,70 1,49 0,78 0,81 324,11 MBROSTAR 35,04 7,96 2,67 23,75 11,32 6,53 9,15 9,27 6294,29 5,14 1,55 0,62 0,97 0,66 1,59 0,74 0,81 327,04 CAKRAN 34,04 7,99 2,76 23,91 11,81 6,49 9,34 9,32 6358,37 4,48 1,38 0,57 0,70 0,65 1,40 0,56 0,57 321,98 LIBOFSHE 32,31 7,36 2,47 24,96 11,30 5,84 10,11 10,31 6631,11 4,94 1,49 0,59 1,08 0,68 1,44 0,87 0,92 351,02 QENDER 28,14 6,19 2,04 24,89 11,06 4,72 10,05 10,16 6983,31 4,21 1,21 0,47 0,93 0,65 1,11 0,77 0,81 349,08 ROSKOVEC 27,25 6,14 2,07 26,79 11,32 4,64 11,66 11,97 7326,73 4,40 1,34 0,54 1,10 0,89 1,22 0,96 1,03 401,33 TOPOJE 27,01 5,95 1,97 24,65 11,11 4,50 9,90 9,99 7035,14 4,72 1,34 0,52 0,93 0,82 1,23 0,75 0,77 417,30 PATOS 22,24 4,98 1,68 25,99 11,42 3,55 11,07 11,08 7764,12 3,06 0,89 0,36 0,69 0,71 0,77 0,62 0,61 347,70 FIER 10,55 2,40 0,86 28,06 12,19 1,52 13,23 12,99 10630,03 1,56 0,48 0,23 0,93 1,20 0,36 0,98 0,89 548,15 LUSHNJE HYZGJOKAJ 45,13 11,31 4,06 22,74 12,22 10,24 8,42 8,39 5575,58 8,39 2,91 1,22 1,17 0,98 3,26 0,85 0,87 453,96 FIERSHEGAN 43,14 10,79 3,86 24,92 12,25 9,48 10,08 10,38 5861,46 5,58 1,99 0,86 1,37 0,75 2,18 1,10 1,32 333,26 ALLKAJ 41,97 10,26 3,61 23,14 11,91 8,96 8,70 8,73 5777,20 6,58 2,35 1,03 1,10 0,99 2,55 0,83 0,86 372,36 GRABIAN 41,93 10,84 3,99 26,24 12,76 9,45 11,22 11,49 6053,41 5,95 2,19 0,98 2,01 0,89 2,40 1,70 1,99 399,98 BUBULLIME 41,43 9,99 3,47 23,07 11,81 8,66 8,64 8,68 5807,09 5,95 1,97 0,82 0,98 0,80 2,14 0,75 0,75 340,88 KARBUNARE 40,65 9,60 3,30 22,66 11,62 8,33 8,34 8,36 5840,21 6,96 2,25 0,92 1,01 0,83 2,50 0,73 0,73 391,53 RREMAS 39,59 9,82 3,51 24,41 12,24 8,39 9,70 9,74 6038,36 5,92 2,13 0,96 1,13 1,06 2,28 0,93 0,91 416,08 DUSHK 39,38 9,54 3,36 23,79 12,05 8,16 9,21 9,22 5993,07 5,47 1,92 0,84 0,97 0,86 2,09 0,77 0,77 335,31 KRUTJE 39,15 9,32 3,22 23,91 11,74 7,91 9,26 9,38 6030,30 5,29 1,75 0,72 0,86 0,68 1,83 0,67 0,69 329,70 GRADISHTE 38,70 9,34 3,27 24,70 11,96 7,90 9,90 10,05 6131,80 4,92 1,65 0,70 0,97 0,75 1,75 0,78 0,84 326,80 BALLAGAT 37,69 8,71 2,95 22,55 11,30 7,36 8,27 8,27 6015,12 7,49 2,40 0,99 1,23 1,13 2,59 0,93 0,91 467,16 TERBUF 36,46 9,02 3,23 26,94 12,33 7,49 11,92 12,64 6516,72

5,64 1,88 0,81 1,90 0,80 1,98 1,77 2,56 441,69 KOLONJE 36,15 8,57 2,96 24,49 11,68 7,08 9,72 9,83 6290,28 5,75 1,89 0,79 1,03 0,88 1,90 0,82 0,84 379,38 GOLEM 35,96 8,34 2,84 23,57 11,54 6,90 9,03 9,10 6212,36 5,66 1,70 0,68 0,96 0,75 1,75 0,75 0,81 368,17 DIVJAKE 33,59 7,90 2,72 25,89 11,74 6,35 10,88 11,17 6639,12 4,41 1,38 0,57 1,02 0,65 1,37 0,86 0,90 377,12 LUSHNJE 15,68 3,60 1,25 29,26 11,97 2,38 14,23 14,14 9636,08 1,30 0,40 0,18 1,00 0,73 0,31 1,03 1,01 396,12 MALLAKAST ER NGRAÇAN 46,61 11,51 4,11 21,82 11,96 10,73 7,80 7,81 5463,57 11,38 3,90 1,64 1,27 1,46 4,44 0,95 0,91 604,00 ARANITAS 44,68 11,29 4,08 24,57 12,46 10,08 9,83 10,01 5744,41 5,43 1,93 0,85 1,81 0,77 2,11 1,44 1,63 369,12 HEKAL 42,85 10,79 3,91 23,90 12,48 9,55 9,31 9,34 5779,86 6,73 2,32 1,00 1,09 0,97 2,57 0,87 0,90 399,40 GRESHICE 40,77 9,91 3,50 23,47 11,96 8,66 8,99 9,07 5896,13 8,18 2,69 1,13 1,36 1,22 2,87 1,04 1,10 467,95 KUTE 36,83 9,18 3,35 24,52 12,56 7,74 9,88 9,78 6232,56 6,80 2,24 0,99 1,59 1,16 2,38 1,28 1,28 507,36 QENDER 35,81 8,34 2,85 24,03 11,55 6,88 9,40 9,55 6262,79 5,53 1,74 0,70 1,01 0,74 1,77 0,80 0,89 367,19 SELITE 34,51 8,13 2,83 23,44 11,73 6,76 9,03 9,01 6337,33 9,19 2,71 1,09 1,59 1,27 2,84 1,22 1,22 644,43 FRATAR 33,46 7,84 2,70 26,00 11,61 6,31 10,99 11,37 6665,92 5,46 1,81 0,76 1,48 0,93 1,77 1,25 1,41 434,14 BALLSH 15,96 3,68 1,29 27,03 11,83 2,47 12,17 11,96 9005,99 3,47 1,09 0,49 1,24 1,44 0,86 1,22 1,10 551,00

Table 12.6. Poverty and inequality indices by Commune/Municipality (PREFECTURE of GJIROKASTER, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor GJIROKAST ER LAZARAT 32,62 7,66 2,62 25,48 11,43 6,10 10,51 10,63 6704,32 6,52 2,13 0,90 1,45 1,28 2,10 1,25 1,27 528,53 LUNXHERI 28,93 6,93 2,41 26,39 11,70 5,30 11,40 11,51 7164,30 6,21 2,01 0,86 1,94 1,27 1,89 1,76 1,78 612,56 DROPULL POSHTEM 28,00 6,65 2,31 28,27 11,85 5,01 13,09 13,60 7461,77 3,72 1,26 0,58 2,15 1,16 1,17 2,05 2,34 494,52 DROPULL SIPERM 24,17 5,11 1,60 25,90 10,35 3,67 10,84 11,09 7547,85 4,37 1,20 0,45 1,11 0,87 1,08 0,95 0,97 463,79 QEN.LIBOHIVE 19,79 4,11 1,29 28,57 10,14 2,88 13,47 14,98 8534,57 6,55 1,74 0,67 2,43 1,50 1,47 2,41 3,32 971,26

ODRIE 16,86 3,22 0,94 22,89 10,96 2,36 8,49 8,58 8044,03 8,32 1,95 0,70 1,37 3,93 1,56 1,05 1,08 1082,11 LIBOHOVE 16,27 3,08 0,90 26,96 9,34 2,03 11,73 12,28 8684,91 4,20 1,02 0,36 1,18 1,19 0,79 1,03 1,16 560,62 CEPO 14,59 2,76 0,79 34,45 9,15 1,76 19,44 22,32 10582,39 4,24 1,00 0,35 3,87 1,22 0,76 4,54 6,33 1190,79 ANTIGONE 14,49 2,85 0,86 28,83 10,18 1,89 13,52 13,79 9722,91 5,42 1,22 0,41 2,77 2,05 0,88 2,60 2,81 1058,31 ZAGORIE 14,22 3,22 1,17 27,96 13,88 2,39 13,04 12,74 9813,94 7,01 1,93 0,78 2,01 4,14 1,57 2,06 1,88 1350,08 PICAR 13,42 2,41 0,67 27,81 8,65 1,54 12,50 12,90 9622,80 5,13 1,19 0,40 2,36 1,70 0,87 2,13 2,34 941,63 GJIROKASTER 10,56 2,06 0,62 26,30 9,97 1,26 11,34 11,32 9897,08 1,37 0,34 0,12 0,74 0,61 0,23 0,65 0,65 370,87 POGON 9,78 1,56 0,40 26,97 9,70 1,07 11,78 12,37 9964,35 4,94 1,05 0,36 3,42 3,93 0,81 3,05 3,52 1240,96 PERMET DISHNICE 32,57 7,16 2,28 25,51 10,35 5,64 10,46 10,96 6740,00 7,29 2,19 0,86 1,81 1,22 2,12 1,48 1,65 540,02 BALLABAN 28,77 6,48 2,12 26,93 10,81 4,92 11,70 12,08 7296,44 6,71 1,97 0,78 1,75 1,25 1,87 1,58 1,71 731,38 SUKE 28,16 5,96 1,85 24,06 10,06 4,47 9,31 9,44 6935,86 5,97 1,78 0,69 1,21 1,14 1,65 0,96 0,98 474,37 CARCOVE 18,02 3,67 1,14 25,46 10,07 2,51 10,54 10,65 8301,54 5,81 1,58 0,61 1,25 1,58 1,32 1,09 1,15 779,72 FRASHER 17,51 3,20 0,90 22,42 9,39 2,22 8,16 8,25 7755,54 6,01 1,44 0,52 2,00 2,09 1,12 1,45 1,52 736,10 KELCYRE 16,27 3,27 1,04 24,76 10,53 2,21 10,05 10,10 8283,15 3,14 0,72 0,26 0,89 0,86 0,58 0,73 0,73 475,18 PETRAN 15,03 3,01 0,93 25,85 10,02 1,99 10,89 10,97 8818,56 4,67 1,26 0,51 1,52 1,62 1,01 1,30 1,34 742,42 QENDER PISKOVE 12,14 2,16 0,61 24,72 8,95 1,37 9,92 10,12 9072,97 4,10 0,90 0,30 1,09 1,36 0,66 0,92 0,98 810,65 PERMET 11,58 2,05 0,57 24,33 8,90 1,26 9,60 9,72 8999,11 1,90 0,46 0,16 0,89 0,78 0,31 0,73 0,75 294,63 TEPELENE BUZ 54,60 14,95 5,64 24,29 12,98 14,36 9,52 9,82 5168,59 6,26 2,88 1,46 1,32 1,39 3,41 1,07 1,16 359,80 LUFTINJE 34,58 8,19 2,80 25,48 11,48 6,61 10,48 10,69 6534,74 5,83 1,99 0,86 1,15 1,11 2,00 0,93 0,99 479,74 KRAHES 23,59 5,10 1,65 26,15 10,83 3,68 11,09 11,18 7687,77 4,56 1,19 0,45 1,16 0,98 1,04 1,02 1,05 511,30 LOPES 20,33 3,79 1,06 23,66 8,80 2,68 8,96 9,19 7720,26 8,40 2,08 0,70 1,44 1,37 1,71 1,14 1,16 934,97 MEMALIAJ 20,20 4,05 1,23 24,54 9,99 2,77 9,75 9,81 7744,96 2,44 0,66 0,25 0,73 0,76 0,53 0,59 0,61 245,74 QENDER 19,87 3,89 1,14 25,27 9,43 2,64 10,30 10,56 7947,34

4,51 1,14 0,40 1,25 0,96 0,92 1,04 1,07 529,54 QESARAT 17,94 3,52 1,06 25,18 9,73 2,40 10,27 10,43 8209,33 5,49 1,32 0,47 1,40 1,30 1,07 1,15 1,18 653,58 FSHAT MEMALIAJ 16,29 3,03 0,86 25,25 9,01 1,98 10,30 10,51 8485,05 4,45 1,08 0,37 1,41 1,33 0,82 1,18 1,23 661,01 TEPELENE 14,27 2,67 0,77 25,21 9,31 1,69 10,29 10,35 8779,09 2,08 0,50 0,18 0,73 0,72 0,37 0,61 0,62 316,04 KURVELESH 11,75 2,13 0,60 25,03 9,11 1,33 10,22 10,32 9319,12 4,00 0,92 0,33 1,58 1,95 0,63 1,38 1,36 945,27

Table 12.7. Poverty and inequality indices by Commune/Municipality (PREFECTURE of KORÇE, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor DEVOLL HOCISHT 33,80 7,71 2,55 25,09 11,05 6,18 10,13 10,38 6576,07 6,10 1,86 0,74 1,06 0,89 1,90 0,88 0,93 475,42 MIRAS 32,85 7,17 2,28 24,30 10,53 5,70 9,49 9,84 6555,76 6,33 1,90 0,74 0,90 0,80 1,91 0,72 0,83 499,66 BILISHT 30,19 6,62 2,12 25,00 10,62 5,10 10,06 10,33 6834,15 5,55 1,67 0,65 1,02 0,90 1,58 0,83 0,88 417,76 PROGER 28,51 6,38 2,09 27,06 10,88 4,85 11,81 12,19 7326,35 5,98 1,87 0,75 1,62 1,03 1,75 1,41 1,52 668,20 BASHKIA BILISHT 12,80 2,37 0,69 25,14 9,33 1,49 10,25 10,37 8967,35 2,48 0,59 0,20 0,85 0,82 0,42 0,72 0,76 411,59 KOLONJE CLIRIM 56,94 17,79 7,47 26,30 14,92 17,38 11,30 11,43 5035,52 8,13 4,08 2,27 2,62 1,95 4,88 2,30 2,38 469,79 BARMASH 29,91 6,31 1,93 22,93 9,89 4,88 8,45 8,54 6704,13 8,85 2,38 0,89 1,50 1,26 2,28 1,07 1,17 669,84 NOVOSELE 24,92 6,12 2,44 25,93 14,44 4,96 11,57 11,12 7523,85 8,82 2,38 0,92 2,23 3,05 2,24 2,00 1,83 895,31 QENDER ERSEKE 24,87 5,14 1,61 24,86 10,26 3,78 10,00 10,25 7304,31 5,04 1,43 0,54 1,07 0,98 1,25 0,89 0,93 514,57 LESKOVIK 19,43 4,68 2,01 26,18 15,22 3,69 12,66 11,90 8087,02 7,37 1,77 0,60 2,18 3,20 1,46 2,51 2,31 1057,04 16,71 3,14 0,91 24,01 9,36 2,09 9,33 9,52 8052,16 3,65 0,94 0,34 1,10 1,22 0,72 0,88 0,94 460,31 ERSEKE 14,95 2,75 0,78 24,27 9,16 1,77 9,52 9,62 8410,95 2,67 0,63 0,22 0,80 0,82 0,47 0,65 0,65 383,98 MOLLAS 14,21 2,53 0,69 24,58 8,58 1,63 9,72 9,94 8621,61 5,16 1,15 0,38 1,45 1,30 0,88 1,16 1,21 810,00 KORÇE VRESHTAS 48,67 12,76 4,65 24,75 12,51 11,66 9,84 10,13 5526,28 5,60 2,33 1,10 1,15 0,94 2,64 0,92 0,95 340,41 MOGLICE 42,01 9,94 3,35 23,11 11,25 8,68 8,62 8,88 5832,56

9,04 2,87 1,15 1,11 0,97 3,22 0,85 0,89 546,07 GORE 40,43 10,51 3,91 26,81 12,98 9,06 11,74 12,11 6192,75 6,04 2,06 0,91 1,57 1,05 2,20 1,43 1,55 459,85 VOSKOPOJE 38,69 10,17 3,78 28,82 12,67 8,62 13,61 14,05 6679,01 8,78 3,23 1,48 3,32 1,51 3,40 3,20 3,22 855,64 LEKAS 38,48 9,78 3,54 24,44 12,40 8,22 9,80 9,71 6110,68 7,01 2,56 1,25 2,12 2,06 2,68 1,75 1,65 439,42 PIRG 36,92 8,49 2,83 24,03 11,19 7,01 9,31 9,53 6211,00 5,08 1,67 0,69 0,87 0,79 1,73 0,69 0,73 338,34 VOSKOP 36,78 8,15 2,61 23,46 10,57 6,69 8,83 9,09 6188,02 5,86 1,87 0,76 1,19 0,96 1,91 0,92 0,98 354,73 QENDER 32,22 7,29 2,43 24,71 11,18 5,76 9,87 10,02 6617,66 4,41 1,38 0,56 0,80 0,76 1,35 0,66 0,68 360,80 POJAN 31,97 7,10 2,30 25,02 10,86 5,54 10,08 10,34 6691,61 3,78 1,21 0,49 0,80 0,66 1,17 0,66 0,69 309,68 DRENOVE 31,59 7,64 2,68 27,00 11,97 6,01 11,86 12,08 6976,61 5,47 1,71 0,72 1,24 0,99 1,70 1,17 1,24 542,80 MOLLAJ 30,34 6,86 2,27 25,66 11,04 5,31 10,66 10,92 6917,56 5,43 1,75 0,73 1,37 1,21 1,66 1,14 1,16 474,02 MALIQ 26,49 5,65 1,79 24,78 10,58 4,18 9,92 10,07 7128,97 3,40 0,97 0,37 0,84 0,71 0,87 0,68 0,72 284,77 LIBONIK 25,84 5,47 1,72 24,91 10,40 4,03 10,02 10,22 7234,47 5,09 1,38 0,52 0,91 0,79 1,26 0,76 0,76 544,31 VITHKUQ 22,63 4,60 1,40 24,05 9,76 3,27 9,36 9,45 7465,56 5,92 1,59 0,59 1,50 1,28 1,37 1,20 1,17 573,53 LIQENAS 19,64 3,73 1,07 24,29 9,09 2,56 9,51 9,80 7804,69 5,71 1,43 0,50 1,21 0,98 1,19 0,95 1,04 678,19 KORÇE 13,86 2,80 0,87 26,35 10,32 1,79 11,36 11,43 9120,51 1,47 0,41 0,16 0,66 0,57 0,30 0,59 0,62 266,18 POGRADEC VELCAN 50,56 13,98 5,37 24,91 13,45 13,04 10,08 10,23 5371,10 5,71 2,32 1,12 1,37 1,04 2,72 1,12 1,18 335,83 PROPTISHT 47,38 12,84 4,85 25,53 13,06 11,70 10,55 10,81 5630,96 7,24 3,05 1,47 1,40 1,25 3,48 1,19 1,20 465,54 DARDHAS 39,30 9,55 3,30 24,90 11,54 8,14 10,00 10,32 6164,65 8,95 3,06 1,30 1,39 1,32 3,26 1,17 1,20 626,22 HUDENISHT 37,96 8,84 2,97 24,22 11,26 7,37 9,46 9,71 6164,00 5,55 1,88 0,80 1,12 0,96 1,96 0,87 0,91 376,17 BUCIMAS 34,03 7,52 2,41 24,25 10,66 6,03 9,45 9,77 6457,85 5,85 1,79 0,69 0,87 0,76 1,80 0,69 0,75 407,29 TRABINJE 29,55 6,63 2,17 26,72 10,72 5,17 11,51 12,04 7178,46 8,49 2,58 1,02 1,90 1,19 2,49 1,63 1,87 795,10 CERRAVE 29,26 6,13 1,89 24,07 10,10 4,71 9,30 9,56 6831,04 6,62 1,84 0,69 0,93 0,85 1,77 0,74 0,79 556,06 POGRADEC 18,24 3,80 1,20 26,13 10,55 2,56 11,13 11,22 8312,02 1,83 0,52 0,21 0,74 0,53 0,41 0,65 0,67 222,00

Table 12.8. Poverty and inequality indices by Commune/Municipality (PREFECTURE of KUKES, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor HAS GJINAJ 70,70 23,44 10,14 24,58 15,37 25,18 9,79 10,04 4294,36 7,49 4,58 2,66 1,28 1,42 5,92 1,05 1,10 411,34 FAJZA 56,94 17,31 7,14 26,28 14,80 16,94 11,26 11,44 5057,35 5,48 2,57 1,38 1,44 1,16 3,16 1,25 1,25 323,77 GOLAJ 45,97 12,27 4,62 25,90 13,18 11,03 10,92 11,23 5728,11 4,35 1,77 0,87 0,99 0,92 2,04 0,87 0,90 279,83 KRUME 37,25 10,09 3,88 28,82 13,58 8,36 13,61 13,73 6665,21 3,64 1,50 0,76 1,72 1,15 1,50 1,69 1,69 353,54 KUKES ARREN 79,48 28,04 12,43 22,40 15,39 31,34 8,11 8,37 3811,25 6,58 5,07 3,17 1,50 1,63 6,43 1,09 1,17 360,98 UJMISHT 61,12 18,20 7,32 24,70 14,21 18,35 9,89 10,18 4800,37 5,29 2,77 1,52 1,18 1,19 3,40 0,97 0,97 284,50 MALZIU 58,75 17,97 7,43 26,36 14,84 17,80 11,27 11,51 4970,12 4,58 2,30 1,27 0,94 1,03 2,86 0,84 0,82 280,18 KALIS 58,55 16,62 6,48 23,81 13,60 16,59 9,26 9,52 4919,07 7,64 3,36 1,66 1,38 1,23 4,16 1,09 1,15 378,11 ZAPOD ORGJOST 56,24 15,72 6,00 25,59 13,21 15,24 10,52 11,00 5179,41 5,41 2,67 1,39 1,07 1,15 3,18 0,91 0,97 309,21 BUSHTRICE 55,98 15,91 6,17 24,77 13,52 15,43 9,96 10,28 5090,70 6,32 2,79 1,42 1,55 1,29 3,40 1,30 1,33 351,45 TOPOJAN 52,00 14,16 5,39 23,64 13,14 13,52 9,10 9,15 5243,35 8,52 3,60 1,72 1,44 1,31 4,28 1,14 1,14 465,02 KOLSH 48,17 13,11 4,95 26,22 12,87 11,99 11,15 11,58 5683,48 8,41 3,56 1,75 1,90 1,63 3,99 1,68 1,71 537,78 GRYKE CAJ 47,86 12,48 4,56 24,54 12,51 11,37 9,71 9,88 5556,53 6,14 2,59 1,25 1,26 1,26 2,93 1,05 1,08 364,16 SHISHTAVEC 40,20 9,77 3,43 25,24 12,00 8,36 10,31 10,57 6102,51 4,71 1,67 0,76 1,23 0,91 1,78 1,03 1,07 332,83 SURROJ 40,00 9,65 3,33 25,61 11,54 8,21 10,53 10,86 6201,16 6,99 2,48 1,09 1,23 1,28 2,62 1,07 1,04 469,43 SHTIQEN 39,04 9,51 3,36 25,06 12,01 8,10 10,22 10,50 6143,59 6,01 2,22 0,99 1,30 1,16 2,32 1,10 1,15 382,88 BICAJ 38,28 9,31 3,26 25,56 11,94 7,79 10,55 10,79 6257,80 4,47 1,64 0,74 0,91 0,85 1,72 0,78 0,80 311,96 TERTHORE 34,77 8,69 3,17 26,78 12,57 7,16 11,75 11,94 6658,02 6,44 2,26 1,02 1,50 1,29 2,32 1,36 1,39 541,14 KUKES 21,42 4,61 1,50 25,67 10,92 3,25 10,76 10,87 7783,86 2,93 0,84 0,34 0,93 0,74 0,71 0,80 0,80 327,43 TROPOJE LEKBIBAJ 56,89 16,55 6,55 25,54 13,87 16,18 10,54 10,84 5082,20 6,45 2,96 1,51 1,17 1,15 3,65 0,99 1,02 349,98 FIERZE 42,54 10,68 3,84 24,78 12,25 9,33 9,97 10,16 5886,57

6,03 2,28 1,02 1,37 1,18 2,39 1,12 1,20 367,45 BYTYC 40,21 10,52 3,97 26,28 13,14 9,07 11,33 11,46 6131,95 5,59 2,08 1,00 1,21 1,17 2,26 1,06 1,08 364,58 TROPOJE FSHAT 34,24 8,28 2,92 26,61 12,02 6,69 11,48 11,71 6666,44 4,41 1,59 0,71 1,19 1,06 1,57 1,07 1,11 344,91 MARGEGAJ 34,19 7,98 2,76 24,73 11,66 6,52 9,98 10,13 6449,49 5,79 1,91 0,83 1,32 1,12 1,96 1,09 1,19 384,51 LLUGAJ 29,26 6,76 2,29 26,17 11,36 5,17 11,11 11,27 7063,57 4,99 1,66 0,73 1,33 1,31 1,54 1,17 1,16 419,60 BUJAN 28,97 7,00 2,49 26,60 12,17 5,38 11,58 11,61 7115,77 4,61 1,48 0,65 1,31 1,11 1,39 1,18 1,16 448,89 B.CURRI 20,29 4,44 1,47 25,36 11,09 3,10 10,55 10,49 7896,76 3,24 0,99 0,41 1,10 1,02 0,82 0,95 0,93 396,29

Table 12.9. Poverty and inequality indices by Commune/Municipality (PREFECTURE of LEZHE, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor KURBIN MILOT 41,37 10,88 4,07 26,38 13,09 9,41 11,32 11,47 6071,83 4,37 1,66 0,77 0,82 0,80 1,80 0,72 0,74 290,63 FUSHE KUQE 40,01 10,36 3,82 26,92 12,81 8,84 11,76 12,04 6239,72 5,27 1,93 0,87 1,01 0,83 2,06 0,90 0,94 369,30 MAMURRAS 37,98 10,13 3,88 27,35 13,55 8,51 12,28 12,35 6382,83 3,05 1,18 0,56 0,88 0,67 1,19 0,81 0,81 230,18 LAÇ 36,84 10,69 4,40 29,71 14,91 8,94 14,66 14,57 6721,14 4,49 1,84 0,92 1,07 0,95 1,90 1,10 1,08 388,78 LEZHE UNGREJ 48,05 14,20 5,69 52,06 14,19 12,78 48,97 58,65 10563,56 5,04 2,27 1,16 9,68 1,03 2,56 21,27 25,58 3737,54 SHENKOLL 46,70 13,04 5,06 27,72 13,62 11,75 12,45 12,80 5848,49 5,40 2,31 1,13 1,31 0,97 2,61 1,19 1,31 369,16 ZEJMEN 46,16 12,12 4,53 24,99 12,99 11,00 10,23 10,60 5654,24 5,74 2,27 1,06 1,00 0,94 2,57 0,92 1,45 339,60 BALLDRENI I RI 42,92 11,86 4,65 26,90 13,82 10,48 11,86 11,94 5977,21 5,96 2,54 1,29 1,20 1,32 2,77 1,12 1,08 396,45 DAJÇ 42,48 11,03 4,06 26,13 12,74 9,61 11,09 11,37 6000,06 5,47 2,09 0,95 1,22 0,89 2,24 1,02 1,09 355,33 KOLÇ 42,01 11,05 4,13 26,62 12,98 9,63 11,53 11,78 6072,53 5,70 2,20 1,06 1,27 1,18 2,40 1,14 1,14 380,44 BLINISHT 40,29 10,18 3,69 26,04 12,44 8,72 11,01 11,28 6151,76 5,66 2,04 0,91 1,22 1,05 2,15 1,07 1,10 373,47 KALLMET I MADH 32,00 7,75 2,73 27,23 12,00 6,15 12,14 12,56 6976,18 6,02 1,94 0,81 1,91 1,00 1,86 1,72 1,98 614,25 SHENGJIN 20,63 4,64 1,57 27,74 11,49 3,25 12,66 13,03 8285,32

3,20 0,88 0,35 1,02 0,77 0,75 0,95 1,06 326,13 LEZHE 16,61 4,32 1,67 31,89 13,69 2,93 17,28 16,71 10403,01 2,69 0,91 0,42 1,27 1,05 0,71 1,52 1,37 598,42 MIRDITE SELITE 61,68 19,88 8,50 26,74 15,34 20,05 11,58 11,86 4808,68 6,60 3,64 2,08 1,80 1,45 4,38 1,62 1,63 424,40 FANE 54,96 16,56 6,69 28,32 14,27 15,79 12,93 13,57 5370,36 5,80 2,74 1,46 1,98 1,29 3,28 1,91 2,05 423,37 OROSH 44,47 13,00 5,25 29,98 14,48 11,47 14,71 15,19 6181,03 4,88 1,99 1,03 2,18 1,18 2,25 2,21 2,35 468,54 KTHELLE 35,86 8,97 3,21 26,52 12,12 7,40 11,41 11,64 6581,72 7,86 2,63 1,13 1,40 1,26 2,71 1,25 1,22 695,32 KAÇINAR 35,15 8,53 2,95 25,60 11,54 6,96 10,57 10,82 6526,81 8,25 2,73 1,15 1,19 1,30 2,73 1,00 1,05 664,86 RUBIK 28,98 6,40 2,07 25,14 10,80 4,85 10,21 10,41 6954,32 4,27 1,30 0,52 0,93 0,78 1,21 0,77 0,78 363,64 RRESHEN 25,69 5,82 1,96 26,38 11,38 4,29 11,32 11,41 7444,32 3,51 1,10 0,46 0,82 0,85 0,98 0,74 0,72 332,72

Table 12.10. Poverty and inequality indices by Commune/Municipality (PREFECTURE of SHKODER, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor MALESI E MADHE SHKREL 39,01 9,60 3,36 26,03 11,86 8,10 10,90 11,25 6280,88 6,78 2,36 1,02 1,11 1,03 2,47 0,97 1,01 520,70 KASTRAT 35,29 8,62 3,06 26,43 12,20 7,05 11,33 11,57 6562,58 4,78 1,57 0,67 0,92 0,76 1,62 0,82 0,81 391,62 GRUEMIRE 29,79 6,78 2,26 27,71 11,19 5,22 12,45 13,21 7231,08 5,50 1,59 0,62 1,39 0,80 1,54 1,31 1,54 613,07 KOPLIK 29,40 6,83 2,32 26,72 11,50 5,23 11,60 11,91 7118,06 4,17 1,43 0,62 1,21 1,10 1,32 1,06 1,22 349,74 QENDER 24,85 5,28 1,67 26,03 10,38 3,85 10,92 11,22 7493,96 5,08 1,43 0,55 1,06 0,87 1,27 0,90 0,94 555,08 KELMEND 21,84 4,72 1,51 27,69 10,24 3,42 12,37 12,76 8300,78 8,37 2,39 0,93 1,22 1,32 2,12 1,12 1,17 1139,54 PUKE FIERZE 60,03 21,30 10,15 29,91 18,45 21,79 15,21 15,23 4958,09 10,48 4,51 2,44 2,73 2,48 5,94 3,21 2,76 690,92 QELEZ 58,45 18,21 7,60 26,32 14,68 18,16 11,21 11,53 5006,58 10,50 5,06 2,67 1,00 1,47 6,31 0,89 0,89 642,88 GJEGJAN 54,07 17,10 7,38 28,08 15,83 16,59 13,02 13,16 5305,14 8,28 3,59 1,85 1,55 1,30 4,43 1,57 1,49 566,64 BLERIM 51,34 14,94 5,95 26,86 13,88 14,09 11,68 12,07 5478,29 9,28 4,05 1,99 1,52 1,50 4,69 1,39 1,37 597,52 FUSHE ARREZ 43,48 11,32 4,17 25,11 12,78 9,95 10,23 10,40 5820,36 5,39 1,92 0,89 1,01 1,00 2,16 0,85 0,85 329,58

RRAPE 42,60 11,14 4,10 26,79 12,58 9,74 11,62 12,00 6099,96 7,96 3,00 1,35 1,57 1,20 3,28 1,41 1,43 558,93 IBALLE 41,87 11,78 4,57 31,16 13,39 10,25 15,73 16,55 6708,79 10,12 3,81 1,76 2,34 1,57 4,09 2,49 2,53 1073,37 QERRET 34,69 8,45 2,96 26,20 11,69 6,95 11,09 11,34 6668,58 8,99 2,98 1,29 1,23 1,41 3,09 1,08 1,11 767,68 QAFE MAL 33,36 8,41 3,01 30,92 12,04 6,81 15,39 16,04 7559,74 9,70 3,14 1,31 1,82 1,25 3,18 1,93 1,99 1264,68 PUKE 16,99 3,42 1,05 25,57 10,01 2,26 10,63 10,72 8404,92 3,29 0,85 0,32 0,92 1,04 0,67 0,78 0,81 441,35 SHKODER SHALE 80,40 35,30 18,65 34,83 19,74 38,76 20,25 24,10 3906,47 4,42 4,85 3,86 5,21 1,99 5,82 6,48 9,10 511,32 SHOSH 63,09 20,79 8,99 27,41 15,26 21,24 12,12 12,59 4815,00 10,18 5,59 3,17 1,92 1,85 7,00 1,82 1,84 677,74 TEMAL 61,03 19,12 7,92 27,04 14,40 19,17 11,77 12,57 4939,67 8,86 4,83 2,66 1,58 1,75 5,88 1,39 1,57 568,58 HAJMEL 55,32 16,30 6,53 26,37 14,19 15,73 11,27 11,57 5197,37 5,96 2,68 1,36 1,21 1,05 3,24 1,10 1,08 359,20 PULT 54,84 18,41 8,00 35,93 15,57 17,25 21,02 22,81 6084,37 6,81 3,25 1,78 3,98 1,29 3,90 4,74 5,19 897,95 GURI I ZI 46,00 11,71 4,19 24,41 12,15 10,56 9,59 9,84 5688,81 7,99 2,94 1,30 0,90 0,98 3,36 0,73 0,73 514,19 SHLLAK 45,81 12,16 4,49 27,22 12,25 11,04 11,84 12,44 6005,26 11,24 4,69 2,23 1,45 1,86 5,28 1,29 1,37 822,63 VIG MNELLE 45,07 11,97 4,45 26,02 12,68 10,75 10,95 11,28 5859,18 9,02 3,53 1,59 1,57 1,26 3,88 1,35 1,44 605,86 POSTRIBE 42,17 10,57 3,75 25,42 12,08 9,19 10,42 10,78 6004,37 6,98 2,42 1,05 1,01 0,91 2,67 0,85 0,87 512,78 VAU DEJES 38,79 10,07 3,71 27,74 12,81 8,43 12,48 12,81 6431,77 3,82 1,45 0,69 1,33 0,89 1,50 1,25 1,30 373,16 ANA E MALIT 38,60 9,91 3,62 26,92 12,63 8,36 11,77 12,08 6375,53 6,48 2,16 0,95 1,32 0,98 2,38 1,18 1,22 504,78 DAJC 37,03 9,29 3,32 26,86 12,28 7,64 11,63 11,82 6491,62 4,36 1,59 0,71 0,99 0,83 1,59 0,87 0,92 368,48 BERDICE 35,57 8,52 2,93 26,83 11,66 6,92 11,62 12,06 6633,40 5,35 1,71 0,72 1,38 0,87 1,76 1,25 1,28 491,16 BUSHAT 31,64 7,73 2,76 28,44 12,28 6,11 13,16 13,51 7162,68 5,02 1,53 0,62 1,27 0,78 1,50 1,24 1,28 525,94 VELIPOJE 31,40 7,67 2,70 28,41 11,94 6,03 13,07 13,42 7228,45 6,49 2,18 0,95 1,36 1,12 2,16 1,29 1,33 694,19 RRETHINAT 31,13 7,57 2,67 27,30 11,98 5,97 12,09 12,32 7065,95 6,36 2,12 0,91 1,00 1,04 2,06 0,90 0,93 595,39 BARBULLUSH 30,01 6,82 2,25 29,28 10,97 5,24 13,90 14,98 7455,07 5,97 1,88 0,76 2,63 1,09 1,75 2,58 3,30 575,63 SHKODER 20,79 4,50 1,46 26,67 10,96 3,12 11,58 11,70 8062,39

1,58 0,52 0,23 0,65 0,59 0,43 0,59 0,59 165,27

Table 12.11. Poverty and inequality indices by Commune/Municipality (PREFECTURE of TIRANE, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor KAVAJE SINABALLAJ 43,20 10,99 4,01 22,85 12,55 9,75 8,57 8,43 5658,50 7,76 2,57 1,11 1,44 1,04 2,94 1,08 1,08 449,38 GOSE 41,33 10,76 3,98 25,76 12,84 9,27 10,79 10,85 6028,76 4,93 1,79 0,80 1,01 0,77 1,94 0,85 0,85 342,87 HELMES 40,28 10,52 3,91 26,76 12,92 9,00 11,67 11,92 6196,93 5,19 1,95 0,88 1,52 0,92 2,02 1,33 1,53 386,46 KRYEVIDH 38,62 9,35 3,28 24,88 11,98 7,91 10,04 10,22 6158,33 5,57 1,82 0,75 0,89 0,72 1,89 0,72 0,77 359,12 LEKAJ 36,83 8,95 3,16 25,66 12,05 7,45 10,69 10,95 6357,54 5,52 1,81 0,77 1,06 0,84 1,88 0,88 0,95 369,10 LUZ I VOGEL 34,96 8,39 2,93 26,63 11,87 6,83 11,50 11,91 6616,27 5,08 1,73 0,74 0,99 0,87 1,73 0,87 0,93 380,77 GOLEM 32,16 7,67 2,67 26,44 11,91 6,07 11,37 11,62 6820,02 4,58 1,47 0,61 1,11 0,79 1,43 0,96 1,03 366,14 SYNEJ 29,26 6,62 2,23 25,16 11,38 5,12 10,30 10,44 6912,36 4,80 1,41 0,56 0,83 0,74 1,34 0,70 0,72 401,27 RROGOZHINE 27,95 6,84 2,45 29,47 12,36 5,17 14,19 14,56 7676,52 3,25 1,07 0,47 1,26 0,73 0,98 1,21 1,32 311,38 KAVAJE 12,90 2,91 1,01 28,26 11,85 1,88 13,34 13,16 10026,09 2,28 0,63 0,26 0,82 0,86 0,47 0,84 0,78 559,92 TIRANE VAQARR 47,41 13,14 5,06 26,75 13,56 11,90 11,60 11,93 5699,53 4,45 1,95 0,99 1,26 0,97 2,17 1,13 1,18 304,99 SHENGJERGJ 45,65 11,76 4,29 23,98 12,36 10,70 9,31 9,49 5648,27 9,41 3,63 1,67 1,34 1,28 4,21 1,06 1,12 548,60 BALDUSHK 44,02 11,25 4,07 25,06 12,45 9,93 10,18 10,42 5818,35 5,61 2,06 0,94 1,46 0,97 2,32 1,20 1,28 365,66 PETRELE 42,72 11,41 4,29 27,03 13,17 9,95 11,86 12,24 6036,59 4,86 1,87 0,88 1,16 0,88 2,05 1,03 1,14 325,09 ZALL HERR 42,23 10,33 3,60 24,39 11,78 8,95 9,59 9,87 5880,84 5,62 2,06 0,91 1,04 0,96 2,19 0,83 0,87 336,57 PEZE 41,22 10,32 3,67 25,54 12,19 8,87 10,55 10,87 6045,98 5,36 2,00 0,89 1,11 0,89 2,14 0,92 0,98 357,83 KRRABE 41,15 10,36 3,78 25,46 12,62 9,05 10,61 10,99 6020,20 6,34 2,19 0,97 1,10 1,03 2,37 0,95 1,04 404,30 PREZE 40,39 10,44 3,85 27,10 12,76 8,95 11,93 12,27 6243,29 5,50 2,07 0,94 1,35 0,91 2,17 1,22 1,32 416,69 DAJT 35,88 8,99 3,22 27,98 12,36 7,31 12,68 13,07 6711,93 3,56 1,26 0,57 1,71 0,79 1,24 1,56 1,66 356,49 ZALLBASTAR 34,68 9,06 3,38 27,95 12,97 7,38 12,79 12,91 6848,38 6,88 2,24 1,00 1,75 1,33 2,30 1,71 1,67 727,69 BERZHITE 33,53 8,52 3,13 27,16 12,73 6,84 12,07 12,20 6769,70 4,68 1,66 0,77 1,37 1,21 1,64 1,30 1,27 402,85 BERXULLE 33,04 8,17 2,91 27,82 12,17 6,49 12,56 12,85 6939,43

5,12 1,82 0,83 1,68 1,15 1,78 1,55 1,57 437,53 FARK 32,86 7,59 2,56 25,80 11,38 6,03 10,75 11,08 6693,55 4,68 1,49 0,62 0,91 0,84 1,46 0,77 0,82 388,42 NDROQ 32,74 8,13 2,87 29,24 12,08 6,36 13,87 14,43 7179,32 4,44 1,51 0,69 1,88 1,15 1,48 1,85 1,88 467,57 PASKUQAN 27,76 6,37 2,14 26,59 11,24 4,84 11,48 11,79 7288,45 6,24 1,92 0,77 1,12 0,99 1,77 1,01 1,06 602,75 KAMEZ 27,09 5,95 1,94 25,76 10,83 4,48 10,74 11,03 7236,27 5,70 1,61 0,61 0,70 0,70 1,49 0,61 0,63 605,52 KASHAR 25,65 5,78 1,92 26,99 11,19 4,23 11,82 12,06 7542,32 3,57 1,06 0,43 1,14 0,80 0,93 1,05 1,07 427,62 VORE 23,50 4,93 1,54 25,73 10,38 3,51 10,69 10,92 7574,76 3,04 0,86 0,32 0,81 0,58 0,72 0,67 0,74 310,76 TIRANE 17,84 4,06 1,41 29,32 11,86 2,76 14,15 14,51 9003,02 1,08 0,37 0,17 0,63 0,52 0,29 0,65 0,70 141,19

Table 12.12. Poverty and inequality indices by Commune/Municipality (PREFECTURE of VLORE, %)

Head Gini- FGT(1) FGT(2) Gini Sen GE(0) GE(1) Con DISTRICT COMUNE count poor DELVINE DELVINE 18,06 4,04 1,36 35,40 11,39 2,74 21,18 26,23 10133,28 3,15 0,93 0,38 3,45 0,94 0,73 4,62 9,07 738,24 VERGO 15,07 3,08 0,98 26,41 10,44 2,04 11,42 11,87 8740,17 4,39 1,09 0,41 1,65 1,31 0,85 1,49 1,88 696,51 FINIQ 7,14 1,46 0,46 40,37 19,61 1,17 30,26 26,85 22627,53 4,87 1,24 0,47 5,17 18,23 0,67 9,01 7,02 6438,77 MESOPOTAN 0,26 0,03 0,01 34,01 29,96 0,23 19,52 19,00 32237,91 0,39 0,06 0,01 4,51 39,61 0,29 5,60 5,13 10284,00 SARANDE ALIKO 27,35 6,12 2,05 25,87 11,20 4,64 10,93 11,18 7195,91 5,27 1,53 0,62 1,69 1,13 1,42 1,45 1,57 465,48 LIVADHJA 26,83 6,04 2,05 25,55 11,47 4,59 10,70 10,97 7169,45 5,38 1,52 0,59 1,29 0,83 1,38 1,07 1,19 425,49 KSAMIL 23,83 5,01 1,62 23,82 10,51 3,73 9,27 9,31 7286,82 7,13 1,87 0,71 1,31 1,30 1,73 1,03 1,04 655,00 KONISPOL 21,10 4,51 1,44 26,35 10,45 3,19 11,27 11,51 8046,69 6,56 1,76 0,66 1,33 1,27 1,50 1,11 1,21 795,62 MARKAT 16,07 3,30 1,05 29,39 10,43 2,22 14,09 14,43 9399,04 4,55 1,17 0,43 2,94 1,25 0,90 2,87 3,01 818,57 XARE 14,97 3,47 1,24 27,24 12,14 2,32 12,43 12,23 9257,23 3,85 1,09 0,49 2,16 2,06 0,85 2,01 1,97 824,58 LUKOVE 10,12 2,44 0,91 30,59 12,73 1,54 15,95 15,51 12154,17 2,99 0,87 0,39 2,13 1,73 0,64 2,34 2,36 1492,34 SARANDE 8,23 1,75 0,57 28,43 11,03 1,04 13,53 13,33 11743,84 1,38 0,36 0,14 1,03 0,92 0,24 1,04 0,99 640,85 DHIVER 1,46 0,23 0,06 32,74 24,34 0,31 18,65 18,36 24543,53 1,57 0,29 0,09 3,54 27,96 0,21 4,61 4,54 7406,07

VLORE SHUSHICE 37,59 9,47 3,45 25,61 12,63 7,92 10,70 10,70 6279,57 4,16 1,49 0,68 0,96 0,84 1,56 0,82 0,80 309,56 BRATAJ 35,87 8,20 2,76 23,76 11,31 6,79 9,16 9,30 6250,36 6,17 1,86 0,74 0,91 0,85 1,90 0,71 0,73 416,10 SEVASTER 31,58 7,23 2,46 23,75 11,48 5,78 9,23 9,26 6544,17 6,53 1,94 0,79 1,10 1,06 1,96 0,88 0,87 462,55 ARMEN 31,34 7,08 2,38 23,82 11,34 5,61 9,26 9,30 6566,13 5,30 1,53 0,60 1,15 0,76 1,50 0,90 0,91 417,18 KOTE 29,24 6,67 2,28 25,53 11,56 5,19 10,62 10,74 6965,72 4,95 1,48 0,60 1,08 0,87 1,41 0,91 0,94 387,16 HORE VRANISHT 28,97 6,50 2,18 25,04 11,30 5,01 10,21 10,38 6906,68 4,91 1,41 0,55 0,98 0,86 1,33 0,80 0,79 391,76 SELENICE 27,75 7,08 2,65 28,34 12,98 5,39 13,30 13,26 7534,95 4,46 1,57 0,73 1,22 1,26 1,43 1,20 1,19 539,16 VLLAHINE 26,79 5,80 1,90 24,04 10,88 4,38 9,42 9,47 6986,39 5,08 1,46 0,57 1,11 0,90 1,32 0,89 0,86 433,78 NOVOSELE 25,75 5,56 1,82 25,43 10,94 4,14 10,51 10,77 7247,36 3,80 1,06 0,41 1,00 0,66 0,96 0,84 0,91 347,40 QENDER 23,52 5,06 1,66 32,45 10,96 3,67 17,50 20,59 8447,32 3,29 0,93 0,36 3,75 0,69 0,80 4,21 6,29 595,95 ORIKUM 20,26 4,34 1,42 26,42 10,90 3,03 11,39 11,53 8067,84 3,29 0,87 0,34 1,04 0,79 0,72 0,91 0,95 359,58 VLORE 12,73 2,95 1,05 29,83 12,11 1,90 14,87 14,78 10544,23 2,09 0,63 0,28 0,96 1,04 0,48 1,06 1,00 601,62 HIMARE 6,80 1,65 0,59 43,95 12,45 0,95 34,50 33,89 23398,67 1,46 0,44 0,20 6,77 1,74 0,28 11,65 11,72 7185,40