Poverty and Inequality Mapping in Albania: Final Report
Gianni Betti, 28 February 2003
Contents: 1. Introduction 2. The data sources 3. Linear regression models with variance components 4. Poverty and Inequality measures Annex 1: Poverty and Inequality Maps Annex 2: Comparison between census and LSMS sources and description of the variables Annex 3: The imputation procedure Bibliography Annex 4: Distributions of the variables (attached Excel file Distributions.xls)
1. Introduction
The World Bank, in collaboration with the Department for International Development (DfID), is assisting the Government of Albania in the establishment of a permanent poverty monitoring and policy evaluation system in Albania. The current project aims at creating a reliable and sustainable system of household surveys for the timely production of reliable and relevant statistical information so to assist policy-makers in the design, implementation and evaluation of economic, social and environmental programs. As a part of the activities envisaged under the project, a poverty and inequality mapping analysis, foreseen as part of the project, is being carried out based on the methodology fully described in Elbers, Lanjouw and Lanjouw (2002). This methodology combines census and survey information to produce finely disaggregated maps which describe the spatial distribution of poverty and inequality in the country.
1 In fact, 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 or of sufficient size at low levels of aggregation to yield statistically reliable estimates. Household budget surveys or Living Standard Measurement 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 sufficiently large enough to allow disaggregation have little or no information regarding monetary variables. The basic idea is to estimate a linear regression model with local variance components using the information from the smaller and richer data sample, in the Albanian Living Standard Measurement Study (LSMS) conducted in 2002, including some aggregate information from the Population and Housing Census or other sources available for all the statistical units in the sample (i.e. from the General Census of Agricultural Holdings). The vector of covariates utilised in the regression model should be restricted to those variables that can also be linked to households in the census. 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 is to be made of a set of poverty measures based on the Foster-Green-Thorbecke indexes (for α=0,1,2), the Sen index and an absolute poverty line calculated using the information contained in the rich sample survey, as well as 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. Moreover, bootstrapping standard errors of the welfare estimates will be computed so as to assess the precision of the estimates. This report is made up of four sections and four annexes. After this introduction, section two is devoted to the comparison and the harmonisation of the data sources, giving special attention to the Census and LSMS data sets. In section three the estimated linear regression models with variance components are reported and there is a full description of how the Montecarlo simulation has been considered in order to prepare the statistical information for calculating bootstrapping standard errors of poverty and inequality measures. Section four 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;
2 d) The 36 Districts; e) The 384 Communes; f) The 11 Mini-municipalities which the city of Tirana is divided into. Annex one reports poverty and inequality maps for Prefectures, Districts, Communes and Municipalities. Annex two fully describes the comparison made between the various data sources and the list of common variables; annex three discusses how we have treated missing information in the LSMS data set, while annex four, consisting of the attached Excel file, reports the distributions of the whole set of variables used in this work.
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 three main sources of statistical information available in Albania are: The Population and Housing Census (PHC) – 2001. The Living Standard Measurement Study (LSMS) – 2002. The General Census of Agricultural Holdings – 1998 and other sources.
2.1 The Population and Housing Census1
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, 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. 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.
1 INSTAT (2002), The Population of Albania in 2001.
3 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 the dwelling. d) Individual questionnaire: to be completed for all the members of the household who are present, or absent for less than 1 year (to be defined in the roster). 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) – 20022.
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 basis for the LSMS sampling frame. The final sample design for the 2002 LSMS included 450 PSUs and 8 households in each PSU, for a total of 3600 households. Four reserve units were selected in each sample PSU to act as replacement unit in 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 other rural (Table 1). The EAs were allocated proportionately to the number of housing units in these areas.
2 The World Bank (2002), Basic Information Document, Living Standard Measurement Study, Albania, Development Research Group.
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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 ( Other Kavajë Krujë Pogradec Tropoje Urban ) 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
Four survey instruments were used to collect information for the 2002 Albania LSMS: a) Household questionnaire b) Diary for recording household food consumption c) Community questionnaire d) Price questionnaire.
2.3 The General Census of Agricultural Holdings3 and other sources
Another important source of information in Albania is the General Census of Agricultural Holdings; according to the approved calendar, the household interviewing process started on the first of June 1998. The census was accomplished by means of a direct interview with the manager (owner) of agricultural households. The census was extended to 466.809 agricultural holdings (private and public), 2968 villages and cities, 368 communes and municipalities, 36 districts and 12 prefectures. Some aggregate statistics (mainly at commune/municipality level) are to be used in the estimation of the consumption model in section 4, in order to explain the variability due to local differences. A list of potential interesting variables is reported as follows: a) Number of holdings (Physical person and Public) b) Total area (ha) (Physical person e Public) c) UAA (ha) (Physical person and Public) d) Total cultivated land by the way of tilling (by hand, with equidaes, mechanically) e) Agriculture holdings selling animal products by classes of UAA
5 Moreover, further information such as number of postal offices, banks or supermarkets (per communes) should be taken into account. The main aim of this section consists in comparing the common information collected by the census and the sample survey. The two sources of data have been fully analysed in order to identify the common concept and to construct the common variable to be compared. The original Census and LSMS variables have been transformed in order to get comparable variables. Table A2 in Annex 2 reports the list of those common variables divided into three categories: a) Household dwelling conditions and presence of durable goods (23 variables). b) Household head characteristics (8 variables) c) 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 4). The imputation procedure was based on the “sequential regression multivariate imputation” (SRMI) approach adopted by the imputation software (IVE-ware), and is fully described in Annex 3. The variables which underwent the imputation procedure were: a) Type of building b) Inhabited dwelling surface c) Highest level of education achieved by any member of the household. Each of the 38 variable distributions from the Census were compared with the corresponding distribution from the LSMS, with the weighted distribution from the LSMS and, for the above three variables, with the imputed LSMS distribution. A chi-square test was used for the comparisons. The distributions are reported in the attached excel file (Distributions.xls). The first decision to be taken lies in whether or not to consider the weights in the estimation of the linear regression model in the LSMS data set. Comparing the census distributions with the corresponding two in the LSMS (not weighted and weighted), in 21 cases the original distribution fits better than the weighted distribution. Moreover in the 17 cases where the weighted distribution fits better, for five variables the difference between the two distributions is so small that they can be considered the same, whereas for the other two cases, the distributions are very different from the census one and therefore they cannot be included in the
3 INSTAT (2000), General Census of Agricultural Holdings 1998.
6 regression model. Taking into account all the above considerations it seems necessary NOT to consider the weights in the estimation of the linear regression models (see section 4). The second decision to be taken lies in 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. Anyway, some variables have been excluded because they are not comparable at all. The list of the new variables to be used as potential regressors in the estimation of the model is reported in Table A2 in Annex 2.
3 The estimation of stratum-specific linear regression models with variance components for imputing expenditures
The basic idea can be explained in a simple way. Having data from a smaller and a richer data-sample such as a sample survey and a census, a regression model of the target household-level variable, given a set of covariates based on the smaller sample can be estimated. Restricting the set of covariates to those that can also be linked to households in the larger sample, the estimated distribution can be used to generate the distribution of the consumption expenditure (yh) for the population or sub-population in the larger sample given the observed characteristics. Therefore the conditional distribution of a set of welfare measures can now be generated and the relative point estimates and standard errors can be calculated. Practically the methodology follows two steps: a) the survey data are used to estimate a prediction model for the consumption b) simulation of the expenditure for each household of the census and poverty/inequality measures are derived 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 census (2001). The key assumption is that the model estimated from the survey data apply to census observation, of course the assumption is most reasonable if the survey and census year is the
7 same, unfortunately it is not our case, so when interpreting results we need to consider that the poverty estimates obtained refer to the census year.
3.1 A prediction model for consumption
This step 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 the exact 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:
T T ln ych = E[ln ych | xch ]+ uch = xch β + uch [1]
Previous experience with survey analysis4 suggests that a model to be specified with a complex error structure, in order to allow for a within-cluster correlation in the disturbances and allow for heteroschedasticity. The disturbance term is specified as follows:
uch = ηc + ε ch [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 welfare 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 we propose introducing into the model covariates the means of each covariate calculated over all the census households in the 450 census enumeration areas (EA) which correspond to the 450 PSU selected in the LSMS sampling scheme. The enumeration areas correspond to clusters in the LSMS data. Some preliminary analyses on the Albanian LSMS suggest that the expenditure behaviour is locally different so, in order to avoid forcing the parameter estimates to be the same for the whole country it has been decided to estimate separate regression models for the following areas: o coastal area –rural- (stratum 1-rural),
4 Elbers, Lanjouw and Lanjouw (2002).
8 o coastal area –urban- (stratum 1-urban), o central area –rural- (stratum 2-rural), o central area –urban- (stratum 2-urban), o mountain area (stratum 3), o Tirana (stratum 4).
The models are estimated using the household survey data (LSMS), the results are in Table 2. For each model the significance of the cluster effect has been tested by the Breusch and Pagan (1980)
Lagrange multiplier test for random effects, a test that Var(ηc) = 0; such a hypothesis has been rejected at 5% level. Then we have tested the hypothesis that the random effects ηc and the regressors xch are correlated by means of the Hausman's (1978) specification test. The test returns a significant result, therefore the GLS estimator (random-effects estimator) is efficient. The estimated share of the location component with respect to the total residual variance is represented
2 σ η by Rho= 2 . The rho values falls from about 2.9% to 30.8%, the central urban area and Tirana show σ u the lower effect of the local component, 6.2% and 2.9% respectively while the other areas show a very relevant local component effect from 19% of the coastal urban area to 30% of the central one. The idea of estimating different models for each stratum or sub-stratum seems to be proper either in terms of local effect of in terms of covariates. In fact different subset of covariates are significant for each model. The parameters which are significant for each stratum/sub-stratum are related to the level of education and the possession of a car. The other significant parameters in almost all the strata/sub- strata are the household size, the possession of durable goods (TV, refrigerator, heater, washing machine), the variable indicating the number of rooms per person and the number of non working people in the household. Considering the EA mean variables it can be observed that the variables relating to the level of education are significant in three of the six strata (coastal and central urban area and mountains) while the variable indicating migration after 1990 (instead of before 1990) is significant for the coastal urban area, the central rural area and the mountain stratum. It may be considered that, the results from this first step consist of a set of estimated parameters for the regression coefficient βˆ , the associated variance covariance matrix and parameters of the disturbance distributions.
9 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. The simulated values are based on both the ~ ~ ~ predicted logarithm of expenditure x'ch β , and on the disturbance terms ηc and ε ch using bootstrapped methods:
T ~ ~ ~ ln yˆ ch = exp(xch β +ηc + ε ch ) [3]
For each household, the two disturbance terms are drawn from distributions described by the parameter estimates in the first step. The beta coefficients, are drawn from a multivariate normal distribution with mean βˆ and variance covariance matrix equal to the one associated to βˆ . For each household the disturbance terms are drawn from distributions described by parameters estimated in the previous step. Some diagnostic procedures have been used in order to verify the normality of the disturbance terms.
The full set of simulated yˆ ch is used to calculate the expected value of each of the poverty measure considered . The simulation step has been repeated 100 times, drawing a new set of beta coefficients, the related disturbance terms and finally the simulated consumption expenditure. For each of the simulated consumption expenditure distributions a set of poverty and inequality measures has been calculated as has their mean and standard deviation over all the 100 simulations.
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Table 2: Regression results by Strata: GLS estimates and standard errors (in parentheses) Coastal Coastal Central Central Mountain Tirana area (rural) area area area area (urban) (rural) (urban) Observations 520 480 520 479 1000 600 R-squared within 0.5578 0.5550 0.5746 0.5351 0.5696 0.5791 R-squared between 0.6241 0.6834 0.5685 0.6431 0.6520 0.8015 R-squared overall 0.5779 0.5965 0.5716 0.5556 0.5991 0.6365 Sigma uc 0.1451 0.1502 0.1881 0.0818 0.1565 0.0573 Sigma εci 0.2991 0.3090 0.2816 0.3183 0.2965 0.3330 Rho 0.1905 0.1912 0.3085 0.0619 0.2179 0.0287 One dwelling house -0.0864*** (0.0316) House construction 1945 - -0.1406*** 1960 (0.0465) House construction 1961 - 0.2318** 1980 (1.1323) House construction since 0.1412*** -0.0747** 1991 (0.0416) (0.0304) House inhabited surface -0.0861* -0.1027*** -0.1614*** less than 40m2 (0.0480) (0.0369) (0.0608) House inhabited surface -0.0857*** -0.0789*** 40m2-69m2 (0.0329) (0.0240) Wc inside 0.1141*** 0.1052** (0.0340) (0.0483) Water inside 0.0798** (0.0349) TV 0.2225*** 0.1455** 0.1966* 0.1155*** (0.0664) (0.0604) (0.1044) (0.0414) Parabolic 0.0716** 0.1107*** 0.1264*** (0.0362) (0.0268) (0.0490) Refrigerator 0.2297*** 0.1898*** 0.1184*** 0.1138*** (0.0419) (0.0601) (0.0352) (0.0279) Heater 0.0873** 0.1396*** 0.0849*** 0.1154** (0.0356) (0.0321) (0.0289) (0.0496) Air conditioning 0.2555** 0.2182*** (0.1231) (0.4334) Computer 0.1777* 0.2446*** (0.0974) (0.0494) Car 0.1848*** 0.3569*** 0.3710*** 0.3222*** 0.3371*** 0.3495*** (0.0473) (0.0428) (0.0556) (0.0481) (0.0510) (0.0425) Washing machine 0.1129*** 0.1214*** 0.1340*** 0.2262*** (0.0356) (0.1214) (0.0353) (0.0410) Rooms per person 0.1345*** 0.1149*** 0.1688*** 0.1311*** 0.1479*** (0.0436) (0.0365) (0.0514) (0.0412) (0.0390) Possession of agricultural 0.1726*** 0.1546*** land (0.0417) (0.0540) Child 0-5 -0.0449*** (0.0127) Household size -0.2848*** -0.0710*** -0.3979*** -0.1649*** -0.2165*** -0.1379*** (0.0420) (0.0197) (0.0312) (0.0345) (0.0234) (0.0408) Household size squared 0.0154*** 0.0278*** 0.0139*** 0.0081*** 0.0083** (0.039) (0.0029) (0.0039) (0.0018) (0.0039) Highest education low -0.2655*** -0.1728*** -0.2268*** -0.2446*** -0.2510*** -0.2252*** (0.0568) (0.0527) (0.0683) (0.0509) (0.0380) (0.0461) Highest education medium -0.2126*** -0.0992** -0.1705** -0.1351*** -0.1536*** -0.1116*** (0.0562) (0.0437) (0.0682) (0.0444) (0.0347) (0.0332) Migration since 1990 0.0994* -0.0655** (0.0586) (0.0289) # non working people -0.0930*** -0.0572*** -0.1225*** -0.1047***
11 (0.0186) (0.0142) (0.0206) (0.0200) Spouse age 0.0049*** 0.0058** (0.0022) (0.0023) Spouse age squared -0.00007*** -0.0001*** (0.00003) (0.00003) To be continued… Enumeration Area means variable
Rooms business 1.3422** (0.6538) Brick or stone 0.3302*** (0.0719) Plastered 0.1165** (0.0513) House construction 1961 - 0.1535** 1980 (0.0671) Separate kitchen -0.1384*** (0.0510) Wc inside -0.1909* -0.2114*** (0.1105) (0.0669) Car 0.7365* (0.4173) Computer 5.5708* (2.9687) Household size -1.8993*** (0.5008) Household size squared 0.2212*** (0.0558) Spouse work -0.2463*** (0.0958) Highest education low -0.9050*** -0.4045** -0.8820*** (0.2651) (0.1933) (0.2355) Highest education medium -0.9838*** -0.8266*** -1.2437*** (0.3355) (0.2612) (0.3305) Migration before 1990 -0.5321** -0.5363*** (0.2200) (0.1757) Migration since 1990 -0.3258* 0.2659** -0.5897** (0.1688) (0.1312) (0.2468)
Constant 13.2319*** 10.0938*** 9.9999*** 10.0881*** 10.3502*** 9.5694*** (1.1177) (0.2637) (0.1176) (0.2642) (0.2867) (0.1173)
* denotes significance at the 10%, **at the 5% level, and ***at the 1% level.
4 Poverty and Inequality measures
The procedure for estimating the poverty and inequality measures has been applied 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 the linear regression models have been estimated; c) The 12 Prefectures; d) The 36 Districts;
12 e) The 384 Communes; f) The 11 Mini-municipalities in which the city of Tirana is divided. For any given location, the means constitute the point estimates, while the standard deviations are the bootstrapping standard errors of these estimates.
Table 3: Poverty and inequality indices (%) Head count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con ALBANIA 28,41 6,94 2,47 30,01 12,33 5,23 14,69 15,44 7686,77 1,07 0,36 0,16 0,42 0,21 0,33 0,43 0,66 119,42 STRATUM 1 26,18 6,48 2,35 31,82 12,59 4,80 16,64 17,79 8276,49 2,07 0,72 0,32 1,11 0,47 0,64 1,23 1,84 271,25 STRATUM 2 30,17 7,18 2,48 27,80 11,86 5,50 12,51 12,82 7190,29 1,89 0,67 0,29 0,49 0,41 0,62 0,45 0,47 157,56 STRATUM 3 37,65 9,98 3,76 28,31 13,31 8,28 13,05 13,30 6545,07 1,78 0,71 0,35 0,53 0,41 0,73 0,51 0,52 141,08 STRATUM 4 17,95 4,12 1,42 30,23 11,82 2,78 15,02 15,39 9250,01 1,33 0,45 0,21 0,63 0,66 0,36 0,67 0,70 200,93 Stratum 1 urban 14,45 3,45 1,26 29,82 12,66 2,28 14,88 14,68 10070,61 1,84 0,56 0,24 0,73 0,64 0,43 0,78 0,74 493,56 Stratum 1 rural 34,93 8,74 3,16 29,87 12,52 7,09 14,87 17,42 6939,00 3,38 1,19 0,53 1,68 0,57 1,19 1,90 3,67 295,67 Stratum 2 urban 19,38 4,16 1,35 27,10 10,91 2,84 11,98 12,11 8350,21 1,44 0,44 0,18 0,59 0,45 0,35 0,55 0,56 179,22 Stratum 2 rural 35,49 8,66 3,04 27,07 12,06 7,01 11,84 12,22 6619,19 2,67 0,97 0,43 0,56 0,48 0,96 0,51 0,54 210,00
Table 3 reports poverty and inequality measures and their bootstrapping errors for the whole of Albania, and disaggregated by four strata and by rural/urban type for the Coastal and Central regions (stratum 1 and 2). The disaggregation into four strata is very useful for comparing these results to those obtained by LSMS and reported in Table 4. 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 Mountain area there seems to be the highest proportion of poor individuals. However, the two sources show slightly different figures for the Coastal and Central areas: per capita consumption estimated in the LSMS is higher than that simulated in the census data set; this phenomenon is not evident in the Mountain and in the City of Tirana. The main explanation for this is the fact that information in the two data sets has been collected in two successive years, 2001 and 2002. In that period the two Coastal and Central areas experienced a quick change in life-style and high increase in the purchase of durable
13 goods. This phenomenon has already occurred in Tirana City, but it has not yet started in the Mountain Region. Since the linear regression model for estimating the per capita consumption in the census data set has involved many durable goods as regressors, the per capita consumption is lower in the year 2001 (census) and the head count ratio is higher (since the poverty line is an absolute one). The last panel in Table 3 shows the disaggregation of Central and Coastal strata also by rural/urban areas; according to the four poverty indices considered (Head count, FGT(1), FGT(2), Sen) the Mountain region is still the worse off, while in both rural areas in the Coastal and Central strata more than one third of the population is poor. However the region of Tirana shows higher inequality in the distribution of per capita consumption (according to the Gini coefficient index and the two General Entropy indices used). Table 4: Poverty and inequality indices according to LSMS (%) Head count FGT(1) FGT(2) Gini Theil Con ALBANIA 24,58 5,47 1,82 29,8 15,0 7948 STRATUM 1 19,88 4,22 1,43 28,6 13,5 8556 STRATUM 2 24,88 5,43 1,70 28,2 13,5 7642 STRATUM 3 43,24 10,68 3,87 29,3 14,1 6280 STRATUM 4 17,35 3,69 1,27 31,4 16,6 9216
Table 5 reports the measures calculated at Prefecture level; in the Prefecture of Vlore there is the highest per capita consumption and the lowest percentage of poor people (16,57%), whereas according to the Gini coefficient consumption is very concentrated (33,61%). On the other hand, the Prefecture of Diber seems to be the worse off with only 6211 lek per month of per capita consumption, and the highest percentage of poor individuals (42,10%).
14
Table 5: Poverty and inequality indices by PREFECTURE (%) Head Prefecture count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 1: BERAT 28,09 6,29 2,08 26,05 11,19 4,73 10,98 11,18 7138,83 1,84 0,58 0,24 0,38 0,34 0,52 0,33 0,34 158,44 2: DIBËR 42,10 11,60 4,49 28,53 13,75 10,02 13,23 13,53 6210,75 1,63 0,75 0,39 0,55 0,44 0,79 0,53 0,54 114,06 3: DURRËS 25,83 6,48 2,38 30,89 12,84 4,79 15,68 15,95 8209,08 1,88 0,67 0,31 0,74 0,47 0,61 0,76 0,82 283,21 4: ELBASAN 31,75 7,59 2,64 27,20 11,95 5,93 11,99 12,28 6949,84 1,46 0,50 0,22 0,39 0,27 0,48 0,35 0,38 115,48 5: FIER 29,14 7,04 2,48 29,47 12,12 5,36 14,09 14,57 7539,79 2,58 0,88 0,38 0,94 0,51 0,82 0,90 0,97 233,51 6: GJIROKASTËR 22,16 4,90 1,61 28,02 11,10 3,44 12,74 12,96 8155,75 1,59 0,51 0,21 0,53 0,43 0,42 0,50 0,52 188,80 7: KORÇË 27,53 6,41 2,18 27,95 11,62 4,77 12,66 12,93 7493,74 1,81 0,62 0,26 0,57 0,40 0,55 0,53 0,55 156,88 8: KUKËS 35,35 9,09 3,34 28,49 12,82 7,36 13,17 13,48 6788,45 2,34 0,88 0,40 0,56 0,47 0,88 0,53 0,56 195,94 9: LEZHË 36,39 9,76 3,72 30,97 13,49 8,00 15,74 17,01 6977,24 2,06 0,82 0,40 0,93 0,47 0,83 1,05 1,88 202,48 10: SHKODËR 33,54 8,49 3,07 28,83 12,59 6,73 13,47 13,77 7013,32 1,92 0,77 0,37 0,55 0,50 0,74 0,53 0,54 151,10 11: TIRANË 22,49 5,22 1,79 30,08 11,78 3,69 14,77 15,25 8474,74 1,49 0,47 0,20 0,48 0,37 0,40 0,49 0,54 176,01 12: VLORË 16,57 3,71 1,25 33,61 11,47 2,45 18,73 20,88 10249,16 1,56 0,45 0,18 2,04 0,39 0,34 2,51 4,39 503,47
Tables 6-8 report poverty and inequality measures disaggregated at District, Communes and Mini- municipality (for the City of Tirana) level. The poverty and inequality Maps corresponding to Tables 5- 7 are in the following Annex 1.
15
Table 6: Poverty and inequality indices by DISTRICT (%) Head District count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 1: BERAT 29,04 6,59 2,20 26,24 11,33 5,00 11,14 11,37 7076,05 1,87 0,61 0,26 0,43 0,37 0,56 0,37 0,39 153,52 2: BULCUIZË 56,15 17,56 7,44 27,21 15,44 17,07 12,07 12,19 5101,98 3,49 1,88 1,06 0,68 0,74 2,28 0,64 0,64 212,34 3: DELVINË 14,07 3,07 1,01 43,86 11,10 1,97 32,41 37,90 13867,19 2,41 0,65 0,26 6,15 0,77 0,48 9,68 12,92 2217,93 4: DEVOLL 29,45 6,84 2,31 27,23 11,48 5,19 11,97 12,23 7195,18 2,60 0,88 0,37 0,62 0,54 0,81 0,55 0,56 211,32 5: DIBËR 34,75 8,94 3,29 28,49 12,88 7,20 13,19 13,46 6835,17 2,17 0,83 0,39 0,61 0,46 0,83 0,58 0,61 185,10 6: DURRËS 23,66 6,07 2,29 31,35 13,26 4,41 16,28 16,34 8640,40 2,36 0,85 0,39 0,87 0,62 0,75 0,94 0,95 367,02 7: ELBASAN 29,32 6,83 2,32 27,26 11,63 5,19 12,04 12,34 7193,56 1,77 0,60 0,26 0,48 0,38 0,55 0,43 0,46 141,95 8: FIER 26,08 6,16 2,14 29,76 11,91 4,54 14,38 14,80 7950,24 2,31 0,76 0,32 0,99 0,48 0,67 0,96 1,02 250,51 9: GRAMSH 33,37 8,09 2,85 26,42 12,14 6,45 11,33 11,48 6702,69 2,31 0,80 0,36 0,69 0,52 0,80 0,60 0,61 174,13 10: GJIROKASTËR 19,34 4,24 1,39 28,69 11,04 2,88 13,41 13,58 8727,08 1,63 0,50 0,21 0,64 0,52 0,40 0,62 0,63 235,40 11: HAS 44,15 12,31 4,77 28,62 13,74 10,80 13,27 13,73 6080,12 3,23 1,37 0,69 0,77 0,70 1,51 0,74 0,80 234,04 12: KAVAJË 26,93 6,59 2,35 29,92 12,37 4,91 14,61 14,87 7896,19 2,45 0,82 0,36 0,97 0,51 0,74 0,95 0,98 269,72 13: KOLONJË 25,87 5,84 1,94 26,65 11,25 4,27 11,52 11,64 7473,21 2,25 0,74 0,31 0,63 0,59 0,64 0,56 0,56 206,36 14: KORÇË 24,92 5,66 1,90 28,11 11,39 4,10 12,82 13,09 7820,45 1,57 0,51 0,21 0,59 0,37 0,44 0,55 0,58 157,24 15: KRUJË 32,01 7,66 2,65 27,49 11,86 5,98 12,24 12,65 6975,91 2,21 0,79 0,35 0,49 0,49 0,76 0,46 0,52 188,69 16: KUÇOVË 24,95 5,42 1,76 25,76 10,90 3,94 10,75 10,90 7413,14 2,34 0,66 0,26 0,49 0,45 0,58 0,42 0,42 231,29 17: KUKËS 35,19 8,93 3,24 28,26 12,61 7,21 12,94 13,30 6781,61 2,74 0,99 0,45 0,61 0,50 1,00 0,57 0,60 233,81 18: KURBIN 35,15 9,51 3,68 28,92 13,78 7,76 13,74 13,80 6822,25 2,80 1,07 0,52 0,77 0,60 1,08 0,76 0,77 253,73 19: LEZHË 35,54 9,75 3,79 33,78 13,79 7,93 18,79 20,93 7453,34 2,92 1,20 0,60 1,71 0,70 1,21 2,10 3,74 321,63 20: LIBRAZHD 35,73 9,00 3,25 27,28 12,56 7,33 12,08 12,33 6605,86 2,56 0,90 0,40 0,61 0,46 0,92 0,55 0,60 206,39 21: LUSHNJË 32,73 8,10 2,89 29,11 12,33 6,40 13,73 14,29 7116,74 2,98 1,08 0,48 0,91 0,56 1,04 0,87 0,98 237,18 22: MALËSI E MADHE 35,48 8,85 3,16 27,95 12,31 7,16 12,62 13,03 6729,40
16 3,10 1,12 0,50 0,62 0,58 1,11 0,58 0,62 247,74 23: MALLKASTËR 31,49 7,63 2,69 27,50 12,13 5,98 12,28 12,55 7013,71 3,43 1,14 0,48 0,96 0,59 1,08 0,85 0,91 272,26 24: MAT 42,52 11,14 4,09 27,33 12,77 9,61 12,03 12,43 6114,73 3,04 1,31 0,63 0,64 0,63 1,38 0,58 0,59 192,34 Head District count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 25: MIRDITË 39,78 10,13 3,65 27,41 12,46 8,52 12,09 12,45 6334,79 2,67 1,09 0,51 0,64 0,57 1,12 0,59 0,59 184,64 26: PEQUIN 37,59 9,11 3,17 25,95 11,91 7,53 10,87 11,19 6336,37 2,98 1,08 0,47 0,59 0,51 1,09 0,51 0,53 201,47 27: PËRMET 23,79 5,32 1,76 26,89 11,17 3,80 11,75 11,86 7745,22 1,99 0,64 0,26 0,59 0,55 0,53 0,53 0,54 191,00 28: POGRADEC 32,29 7,84 2,75 27,75 12,07 6,15 12,46 12,75 6983,39 2,47 0,90 0,40 0,67 0,53 0,86 0,61 0,64 182,77 29: PUKË 47,82 13,38 5,14 28,67 13,50 12,05 13,21 13,88 5866,92 3,41 1,63 0,83 0,74 0,73 1,79 0,69 0,77 222,45 30: SARANDË 14,17 3,13 1,05 33,09 11,36 2,01 18,14 19,33 10853,06 1,50 0,44 0,18 2,24 0,61 0,32 2,59 3,63 659,96 31: SKAPRAR 27,78 6,04 1,94 25,39 10,80 4,52 10,40 10,59 7082,92 2,27 0,67 0,27 0,46 0,46 0,62 0,39 0,40 198,16 32: SHKODËR 30,50 7,51 2,67 28,62 12,32 5,78 13,30 13,54 7282,87 1,65 0,62 0,29 0,53 0,44 0,58 0,51 0,51 142,63 33: TEPELENË 25,62 5,69 1,87 26,75 11,09 4,16 11,58 11,74 7519,78 2,12 0,67 0,28 0,55 0,51 0,59 0,49 0,50 190,96 34: TIRANË 21,82 5,01 1,71 30,06 11,65 3,52 14,74 15,26 8561,76 1,67 0,53 0,22 0,55 0,44 0,44 0,56 0,61 197,29 35: TROPOJË 29,55 7,20 2,55 28,12 12,22 5,51 12,86 13,03 7302,37 2,50 0,86 0,38 0,72 0,55 0,80 0,67 0,70 240,94 36: VLORË 17,33 3,89 1,31 32,38 11,50 2,60 17,31 18,77 9840,06 1,67 0,48 0,19 1,41 0,40 0,37 1,65 2,93 422,78
17
Table 8: Poverty and inequality indices by Mini-Municipality of Tirana City (%) Head Mini municipality count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 1 17,31 3,98 1,38 29,10 11,88 2,67 13,97 14,09 9144,96 1,31 0,44 0,21 0,58 0,74 0,35 0,61 0,60 200,88 2 16,23 3,69 1,26 30,84 11,68 2,43 15,65 15,96 9805,39 1,33 0,45 0,20 0,72 0,67 0,35 0,77 0,82 224,98 3 15,08 3,25 1,07 28,22 11,14 2,12 13,09 13,29 9311,47 1,24 0,37 0,16 0,60 0,63 0,28 0,59 0,61 217,67 4 20,63 4,72 1,62 27,88 11,72 3,29 12,77 12,86 8329,17 1,58 0,52 0,24 0,59 0,69 0,44 0,59 0,56 193,88 5 11,94 2,55 0,84 31,13 11,09 1,60 15,91 16,32 10853,22 1,04 0,30 0,14 0,76 0,77 0,22 0,79 0,89 265,61 6 24,19 5,60 1,93 27,57 11,76 4,06 12,44 12,68 7777,56 1,93 0,64 0,29 0,65 0,64 0,57 0,63 0,61 196,62 7 15,19 3,42 1,17 29,91 11,73 2,25 14,75 15,06 9717,14 1,30 0,42 0,20 0,66 0,79 0,33 0,69 0,73 224,60 8 16,22 3,63 1,23 29,46 11,56 2,41 14,28 14,51 9427,05 1,35 0,44 0,20 0,62 0,80 0,34 0,65 0,65 218,92 9 15,58 3,58 1,25 30,61 11,98 2,37 15,46 15,70 9856,68 1,26 0,42 0,20 0,69 0,79 0,33 0,74 0,77 227,08 10 9,13 1,75 0,53 29,33 9,87 1,05 14,05 14,45 11093,21 1,11 0,28 0,11 0,75 0,74 0,19 0,73 0,85 291,49 11 29,43 7,25 2,61 28,30 12,50 5,57 13,10 13,40 7304,17 2,28 0,87 0,42 0,72 0,77 0,82 0,73 0,71 185,96
18 Table 7: Poverty and inequality indices by Communes. Head Code count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 1 0,20 0,04 0,01 0,26 0,11 0,03 0,11 0,11 8085,93 2 0,28 0,06 0,02 0,24 0,11 0,05 0,10 0,10 6874,80 3 0,44 0,11 0,04 0,24 0,12 0,10 0,10 0,10 5801,79 4 0,34 0,08 0,03 0,25 0,11 0,06 0,10 0,10 6456,59 5 0,33 0,08 0,03 0,25 0,11 0,06 0,10 0,10 6551,29 6 0,28 0,06 0,02 0,24 0,11 0,05 0,10 0,10 6953,27 7 0,37 0,08 0,03 0,24 0,11 0,07 0,09 0,10 6252,91 8 0,46 0,12 0,04 0,24 0,12 0,11 0,09 0,10 5605,69 9 0,38 0,09 0,03 0,24 0,11 0,07 0,09 0,10 6155,58 10 0,26 0,06 0,02 0,25 0,11 0,04 0,10 0,11 7226,95 11 0,31 0,07 0,02 0,26 0,11 0,05 0,11 0,11 6850,02 12 0,43 0,11 0,04 0,25 0,12 0,10 0,10 0,10 5826,61 13 0,45 0,13 0,05 0,26 0,14 0,11 0,11 0,11 5717,47 14 0,52 0,15 0,06 0,26 0,14 0,14 0,11 0,11 5342,43 15 0,66 0,22 0,09 0,26 0,16 0,23 0,11 0,11 4508,18 16 0,52 0,15 0,06 0,26 0,14 0,14 0,11 0,11 5338,24 17 0,62 0,21 0,09 0,27 0,16 0,21 0,12 0,12 4725,32 18 0,62 0,20 0,09 0,27 0,16 0,21 0,12 0,12 4726,64 19 0,59 0,20 0,09 0,29 0,16 0,20 0,14 0,14 4977,95 20 0,54 0,16 0,06 0,26 0,14 0,15 0,11 0,12 5252,99 21 0,17 0,04 0,01 0,36 0,11 0,03 0,22 0,28 10360,88 22 0,09 0,02 0,01 0,42 0,14 0,01 0,33 0,29 23561,25 23 0,00 0,00 0,00 0,36 0,32 0,00 0,22 0,21 34123,17 24 0,16 0,03 0,01 0,27 0,10 0,02 0,12 0,13 8909,13 25 0,13 0,02 0,01 0,26 0,10 0,02 0,11 0,11 9211,16 26 0,32 0,08 0,03 0,26 0,12 0,06 0,11 0,12 6862,91 27 0,35 0,08 0,03 0,26 0,11 0,07 0,11 0,11 6511,61 28 0,36 0,09 0,03 0,26 0,12 0,07 0,11 0,11 6517,60 29 0,30 0,07 0,02 0,26 0,11 0,05 0,11 0,12 7059,18 30 0,34 0,08 0,03 0,27 0,12 0,07 0,12 0,12 6700,08 31 0,46 0,12 0,05 0,27 0,13 0,11 0,12 0,12 5813,24 32 0,25 0,05 0,02 0,25 0,10 0,04 0,10 0,10 7301,18 33 0,48 0,14 0,05 0,27 0,14 0,13 0,12 0,12 5638,13 34 0,28 0,06 0,02 0,28 0,11 0,05 0,13 0,14 7533,39 35 0,31 0,07 0,02 0,26 0,11 0,06 0,11 0,12 6905,97 36 0,33 0,08 0,03 0,26 0,12 0,06 0,11 0,11 6740,70 37 0,47 0,13 0,05 0,27 0,14 0,12 0,12 0,12 5724,71 38 0,12 0,02 0,01 0,26 0,10 0,01 0,11 0,11 9446,17 39 0,41 0,11 0,04 0,28 0,13 0,09 0,12 0,13 6216,71 40 0,54 0,16 0,06 0,26 0,14 0,15 0,11 0,11 5228,58 41 0,35 0,09 0,03 0,26 0,12 0,07 0,11 0,12 6537,66 42 0,35 0,09 0,03 0,26 0,12 0,07 0,11 0,11 6551,20 43 0,44 0,12 0,05 0,29 0,13 0,11 0,13 0,14 6143,12 44 0,53 0,15 0,06 0,25 0,13 0,14 0,10 0,10 5270,85 45 0,17 0,04 0,02 0,31 0,14 0,03 0,16 0,16 9700,71 46 0,38 0,09 0,03 0,26 0,12 0,08 0,11 0,11 6278,01
19 47 0,41 0,11 0,04 0,27 0,13 0,09 0,12 0,12 6169,68 48 0,38 0,10 0,04 0,29 0,13 0,08 0,14 0,14 6665,45 49 0,24 0,05 0,02 0,28 0,11 0,04 0,13 0,13 7917,37 50 0,35 0,09 0,03 0,28 0,13 0,07 0,13 0,13 6775,94 Head Code count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 51 0,34 0,09 0,03 0,28 0,13 0,07 0,12 0,13 6771,01 52 0,10 0,02 0,01 0,28 0,10 0,01 0,13 0,13 10602,66 53 0,40 0,11 0,04 0,29 0,14 0,10 0,14 0,14 6387,09 54 0,12 0,02 0,01 0,27 0,09 0,01 0,12 0,12 9724,07 55 0,31 0,07 0,02 0,25 0,11 0,05 0,10 0,10 6741,96 56 0,31 0,07 0,02 0,26 0,11 0,05 0,11 0,11 6911,52 57 0,26 0,06 0,02 0,26 0,11 0,04 0,11 0,11 7310,15 58 0,19 0,04 0,01 0,27 0,11 0,03 0,12 0,12 8360,47 59 0,26 0,05 0,02 0,24 0,10 0,04 0,09 0,09 7125,28 60 0,37 0,09 0,03 0,26 0,12 0,07 0,11 0,11 6395,36 61 0,37 0,09 0,03 0,25 0,12 0,07 0,10 0,11 6345,37 62 0,47 0,12 0,05 0,26 0,13 0,11 0,11 0,11 5727,26 63 0,30 0,07 0,02 0,26 0,11 0,05 0,11 0,11 6880,82 64 0,48 0,13 0,05 0,26 0,13 0,12 0,11 0,11 5655,98 65 0,35 0,08 0,03 0,25 0,11 0,06 0,10 0,10 6448,47 66 0,33 0,07 0,02 0,24 0,11 0,06 0,10 0,10 6516,86 67 0,34 0,08 0,03 0,25 0,11 0,06 0,10 0,10 6540,90 68 0,50 0,13 0,05 0,25 0,13 0,12 0,10 0,11 5470,94 69 0,55 0,16 0,06 0,26 0,13 0,15 0,11 0,11 5231,86 70 0,37 0,09 0,03 0,24 0,12 0,07 0,10 0,10 6200,20 71 0,37 0,09 0,03 0,26 0,12 0,07 0,11 0,11 6384,36 72 0,23 0,04 0,01 0,23 0,09 0,03 0,09 0,09 7268,82 73 0,39 0,09 0,03 0,24 0,12 0,08 0,09 0,10 6111,65 74 0,30 0,06 0,02 0,25 0,11 0,05 0,10 0,10 6875,68 75 0,37 0,09 0,03 0,26 0,12 0,07 0,11 0,11 6383,90 76 0,42 0,10 0,04 0,24 0,12 0,09 0,09 0,10 5899,98 77 0,42 0,11 0,04 0,27 0,12 0,09 0,11 0,12 6138,21 78 0,34 0,08 0,03 0,25 0,12 0,06 0,10 0,10 6504,06 79 0,37 0,09 0,03 0,26 0,13 0,08 0,11 0,11 6349,38 80 0,09 0,02 0,01 0,28 0,10 0,01 0,13 0,13 11063,48 81 0,40 0,10 0,04 0,25 0,12 0,08 0,10 0,10 6068,46 82 0,39 0,09 0,03 0,23 0,11 0,08 0,08 0,08 5965,08 83 0,38 0,09 0,03 0,24 0,12 0,08 0,09 0,09 6106,91 84 0,40 0,10 0,03 0,24 0,12 0,08 0,10 0,10 5984,62 85 0,36 0,09 0,03 0,27 0,13 0,07 0,12 0,12 6519,97 86 0,32 0,07 0,02 0,26 0,11 0,06 0,11 0,11 6872,49 87 0,37 0,09 0,03 0,25 0,12 0,07 0,10 0,10 6290,61 88 0,20 0,04 0,01 0,26 0,11 0,03 0,11 0,12 8199,27 89 0,38 0,09 0,03 0,25 0,12 0,08 0,10 0,10 6224,17 90 0,29 0,07 0,02 0,26 0,11 0,05 0,11 0,11 7046,16 91 0,25 0,06 0,02 0,27 0,12 0,04 0,12 0,12 7664,64 92 0,43 0,11 0,04 0,25 0,13 0,10 0,10 0,10 5832,03 93 0,25 0,06 0,02 0,26 0,11 0,04 0,11 0,11 7418,71
20 94 0,38 0,09 0,03 0,24 0,12 0,08 0,09 0,09 6121,87 95 0,18 0,04 0,01 0,25 0,10 0,03 0,10 0,10 8071,77 96 0,39 0,10 0,04 0,25 0,12 0,08 0,10 0,10 6118,26 97 0,51 0,14 0,05 0,24 0,13 0,13 0,09 0,09 5295,72 98 0,49 0,13 0,05 0,25 0,13 0,12 0,10 0,11 5505,41 99 0,33 0,08 0,03 0,26 0,12 0,06 0,11 0,11 6715,96 100 0,34 0,08 0,03 0,25 0,11 0,06 0,10 0,10 6604,49 101 0,41 0,10 0,04 0,23 0,12 0,09 0,09 0,09 5863,43 Head Code count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 102 0,40 0,10 0,03 0,25 0,12 0,08 0,10 0,10 6127,99 103 0,40 0,10 0,03 0,24 0,12 0,08 0,09 0,09 5925,73 104 0,47 0,12 0,04 0,24 0,13 0,11 0,09 0,09 5518,34 105 0,13 0,02 0,01 0,27 0,09 0,02 0,12 0,12 9536,60 106 0,21 0,05 0,01 0,30 0,11 0,03 0,15 0,16 8825,42 107 0,29 0,07 0,02 0,27 0,12 0,05 0,12 0,12 7201,40 108 0,29 0,07 0,02 0,27 0,11 0,05 0,12 0,12 7218,00 109 0,10 0,02 0,01 0,27 0,10 0,01 0,12 0,12 10220,12 110 0,26 0,06 0,02 0,27 0,11 0,04 0,12 0,12 7569,07 111 0,16 0,03 0,01 0,28 0,10 0,02 0,12 0,13 8917,48 112 0,27 0,06 0,02 0,27 0,11 0,05 0,12 0,12 7391,35 113 0,17 0,03 0,01 0,27 0,11 0,02 0,11 0,11 8911,66 114 0,21 0,05 0,01 0,28 0,10 0,03 0,12 0,13 8254,42 115 0,17 0,03 0,01 0,28 0,10 0,02 0,13 0,13 8998,28 116 0,18 0,04 0,01 0,28 0,10 0,02 0,12 0,13 8841,98 117 0,23 0,05 0,02 0,28 0,11 0,04 0,13 0,13 8248,85 118 0,53 0,16 0,06 0,27 0,14 0,15 0,12 0,12 5395,02 119 0,41 0,11 0,04 0,27 0,13 0,09 0,12 0,12 6163,59 120 0,62 0,19 0,08 0,25 0,14 0,19 0,10 0,11 4810,19 121 0,37 0,10 0,04 0,30 0,13 0,08 0,14 0,15 6754,13 122 0,32 0,08 0,03 0,27 0,12 0,06 0,12 0,12 6937,30 123 0,41 0,11 0,04 0,27 0,13 0,09 0,11 0,12 6133,17 124 0,37 0,10 0,04 0,27 0,13 0,08 0,12 0,13 6481,59 125 0,12 0,03 0,01 0,28 0,12 0,02 0,13 0,13 10272,36 126 0,36 0,09 0,03 0,26 0,12 0,07 0,11 0,11 6450,64 127 0,36 0,09 0,03 0,27 0,12 0,07 0,11 0,12 6511,59 128 0,34 0,08 0,03 0,28 0,12 0,07 0,12 0,13 6832,74 129 0,28 0,07 0,03 0,30 0,13 0,05 0,15 0,15 7863,14 130 0,41 0,10 0,04 0,24 0,12 0,09 0,10 0,10 5967,25 131 0,29 0,07 0,02 0,26 0,11 0,05 0,11 0,11 7068,08 132 0,36 0,09 0,03 0,24 0,12 0,07 0,10 0,10 6334,85 133 0,46 0,12 0,04 0,26 0,12 0,11 0,11 0,12 5834,00 134 0,15 0,03 0,01 0,25 0,09 0,02 0,10 0,10 8593,88 135 0,16 0,03 0,01 0,25 0,10 0,02 0,10 0,10 8287,96 136 0,39 0,09 0,03 0,25 0,11 0,08 0,10 0,10 6179,30 137 0,29 0,07 0,02 0,26 0,11 0,05 0,11 0,12 7176,71 138 0,36 0,09 0,03 0,25 0,12 0,07 0,10 0,10 6395,11 139 0,34 0,08 0,03 0,26 0,11 0,06 0,11 0,11 6714,37 140 0,25 0,06 0,02 0,28 0,11 0,04 0,12 0,13 7772,09
21 141 0,43 0,11 0,04 0,27 0,12 0,09 0,12 0,13 6038,14 142 0,14 0,03 0,01 0,27 0,11 0,02 0,12 0,12 9410,65 143 0,42 0,10 0,04 0,24 0,12 0,09 0,09 0,10 5889,57 144 0,27 0,06 0,02 0,26 0,11 0,05 0,11 0,11 7187,51 145 0,26 0,06 0,02 0,26 0,11 0,04 0,11 0,11 7463,65 146 0,29 0,06 0,02 0,25 0,11 0,05 0,10 0,11 7013,92 147 0,48 0,13 0,05 0,25 0,13 0,11 0,10 0,11 5603,59 148 0,35 0,08 0,03 0,26 0,12 0,07 0,11 0,12 6637,61 149 0,32 0,07 0,02 0,25 0,11 0,06 0,10 0,10 6630,17 150 0,32 0,07 0,02 0,26 0,11 0,06 0,11 0,11 6780,06 151 0,29 0,06 0,02 0,26 0,11 0,05 0,11 0,11 7155,95 152 0,32 0,07 0,02 0,25 0,11 0,06 0,10 0,10 6641,81 Head Code count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 153 0,33 0,08 0,03 0,26 0,11 0,06 0,11 0,11 6728,36 154 0,41 0,10 0,04 0,25 0,12 0,09 0,10 0,11 6118,74 155 0,39 0,10 0,03 0,26 0,12 0,08 0,11 0,11 6236,05 156 0,28 0,06 0,02 0,27 0,11 0,05 0,12 0,12 7409,41 157 0,47 0,12 0,05 0,25 0,13 0,11 0,10 0,11 5661,09 158 0,28 0,07 0,02 0,27 0,11 0,05 0,12 0,12 7296,75 159 0,39 0,10 0,03 0,27 0,12 0,08 0,12 0,12 6337,35 160 0,24 0,05 0,02 0,26 0,11 0,04 0,11 0,12 7602,60 161 0,37 0,09 0,03 0,28 0,12 0,08 0,13 0,13 6595,91 162 0,34 0,08 0,03 0,25 0,11 0,06 0,10 0,10 6472,60 163 0,21 0,04 0,01 0,26 0,11 0,03 0,11 0,11 7821,30 164 0,26 0,06 0,02 0,25 0,11 0,04 0,10 0,11 7305,93 165 0,73 0,24 0,10 0,23 0,15 0,26 0,09 0,09 4163,28 166 0,31 0,07 0,03 0,27 0,12 0,06 0,12 0,12 6993,97 167 0,51 0,14 0,05 0,26 0,13 0,13 0,11 0,11 5455,02 168 0,40 0,10 0,04 0,27 0,12 0,09 0,12 0,13 6341,12 169 0,21 0,05 0,02 0,27 0,11 0,03 0,12 0,12 8162,23 170 0,49 0,14 0,05 0,28 0,14 0,13 0,12 0,13 5673,17 171 0,47 0,12 0,05 0,27 0,13 0,11 0,12 0,12 5834,99 172 0,29 0,07 0,02 0,27 0,11 0,05 0,12 0,12 7157,55 173 0,34 0,08 0,03 0,26 0,12 0,06 0,11 0,12 6698,37 174 0,35 0,09 0,03 0,28 0,12 0,07 0,13 0,13 6820,53 175 0,34 0,08 0,03 0,27 0,12 0,07 0,12 0,12 6782,36 176 0,45 0,12 0,04 0,24 0,12 0,11 0,10 0,10 5672,59 177 0,51 0,14 0,06 0,25 0,13 0,13 0,10 0,11 5383,04 178 0,39 0,10 0,04 0,26 0,12 0,08 0,11 0,11 6256,21 179 0,53 0,14 0,05 0,25 0,13 0,14 0,10 0,10 5297,24 180 0,42 0,11 0,04 0,28 0,13 0,10 0,13 0,13 6184,58 181 0,30 0,08 0,03 0,29 0,14 0,06 0,14 0,14 7381,31 182 0,36 0,10 0,04 0,28 0,14 0,08 0,13 0,13 6719,15 183 0,40 0,11 0,04 0,28 0,14 0,09 0,13 0,13 6362,81 184 0,44 0,12 0,05 0,28 0,14 0,11 0,13 0,13 5967,80 185 0,40 0,10 0,04 0,27 0,13 0,09 0,12 0,12 6304,75 186 0,43 0,12 0,04 0,27 0,13 0,10 0,12 0,13 6055,12 187 0,32 0,08 0,03 0,29 0,12 0,06 0,13 0,14 7183,24
22 188 0,44 0,12 0,05 0,28 0,14 0,11 0,13 0,13 6053,71 189 0,15 0,04 0,01 0,31 0,14 0,03 0,16 0,16 10475,20 190 0,23 0,05 0,02 0,29 0,12 0,04 0,14 0,14 8251,68 191 0,49 0,14 0,06 0,29 0,14 0,13 0,13 0,14 5723,22 192 0,48 0,15 0,06 0,51 0,15 0,13 0,46 0,53 9935,73 193 0,47 0,13 0,05 0,27 0,14 0,12 0,12 0,12 5698,78 194 0,44 0,11 0,04 0,26 0,13 0,10 0,11 0,11 5868,17 195 0,13 0,03 0,01 0,27 0,10 0,02 0,12 0,12 9490,58 196 0,63 0,20 0,08 0,25 0,15 0,20 0,10 0,11 4690,44 197 0,47 0,13 0,05 0,26 0,13 0,11 0,11 0,11 5669,99 198 0,34 0,08 0,03 0,26 0,12 0,07 0,11 0,11 6564,53 199 0,29 0,06 0,02 0,25 0,11 0,05 0,10 0,10 6956,68 200 0,35 0,08 0,03 0,26 0,12 0,07 0,11 0,11 6547,13 201 0,33 0,08 0,03 0,26 0,12 0,06 0,11 0,11 6629,85 202 0,52 0,15 0,06 0,25 0,13 0,14 0,10 0,11 5346,01 203 0,36 0,09 0,03 0,26 0,12 0,07 0,11 0,11 6461,93 Head Code count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 204 0,34 0,08 0,03 0,25 0,11 0,06 0,10 0,10 6556,77 205 0,45 0,11 0,04 0,24 0,12 0,10 0,09 0,10 5693,78 206 0,35 0,08 0,03 0,24 0,11 0,07 0,09 0,09 6276,69 207 0,43 0,11 0,04 0,24 0,12 0,09 0,09 0,09 5814,29 208 0,32 0,08 0,03 0,28 0,12 0,06 0,12 0,13 6932,02 209 0,40 0,10 0,04 0,25 0,13 0,09 0,10 0,10 6027,17 210 0,44 0,11 0,04 0,26 0,13 0,10 0,11 0,11 5891,10 211 0,37 0,09 0,03 0,25 0,12 0,07 0,10 0,10 6262,15 212 0,41 0,11 0,04 0,28 0,13 0,10 0,12 0,13 6209,45 213 0,40 0,10 0,04 0,26 0,12 0,09 0,11 0,11 6090,09 214 0,44 0,11 0,04 0,24 0,12 0,10 0,09 0,09 5740,00 215 0,38 0,09 0,03 0,24 0,12 0,07 0,09 0,09 6103,02 216 0,38 0,09 0,03 0,25 0,12 0,08 0,10 0,11 6282,79 217 0,41 0,10 0,04 0,25 0,12 0,09 0,10 0,10 6012,77 218 0,15 0,03 0,01 0,29 0,12 0,02 0,14 0,14 9651,85 219 0,40 0,10 0,04 0,26 0,13 0,09 0,11 0,11 6187,58 220 0,36 0,09 0,03 0,28 0,12 0,07 0,13 0,14 6711,65 221 0,34 0,08 0,03 0,28 0,12 0,07 0,13 0,13 6832,05 222 0,34 0,08 0,03 0,27 0,12 0,07 0,12 0,12 6754,68 223 0,46 0,13 0,05 0,28 0,13 0,12 0,13 0,13 5895,14 224 0,28 0,07 0,02 0,28 0,12 0,05 0,12 0,13 7302,58 225 0,26 0,06 0,02 0,28 0,11 0,04 0,12 0,13 7595,30 226 0,44 0,11 0,04 0,26 0,13 0,10 0,11 0,12 5959,72 227 0,44 0,11 0,04 0,26 0,12 0,10 0,11 0,11 5906,71 228 0,15 0,03 0,01 0,27 0,11 0,02 0,12 0,12 9090,51 229 0,31 0,07 0,02 0,27 0,12 0,06 0,12 0,12 7020,72 230 0,38 0,09 0,03 0,24 0,12 0,08 0,10 0,10 6212,81 231 0,41 0,11 0,04 0,25 0,13 0,09 0,10 0,10 5977,51 232 0,35 0,09 0,03 0,26 0,13 0,07 0,11 0,11 6569,87 233 0,45 0,11 0,04 0,23 0,12 0,10 0,09 0,09 5677,08 234 0,33 0,08 0,03 0,25 0,12 0,06 0,10 0,10 6521,36
23 235 0,32 0,08 0,03 0,24 0,12 0,06 0,10 0,10 6592,75 236 0,53 0,15 0,05 0,25 0,13 0,14 0,10 0,11 5338,84 237 0,41 0,10 0,04 0,28 0,12 0,09 0,13 0,14 6334,82 238 0,58 0,17 0,07 0,26 0,14 0,17 0,11 0,12 5080,88 239 0,45 0,11 0,04 0,25 0,12 0,10 0,10 0,11 5821,36 240 0,38 0,10 0,03 0,28 0,12 0,08 0,12 0,13 6498,66 241 0,48 0,13 0,05 0,26 0,13 0,11 0,11 0,11 5688,16 242 0,47 0,12 0,05 0,26 0,13 0,11 0,11 0,11 5693,69 243 0,23 0,05 0,02 0,26 0,11 0,03 0,11 0,11 7678,75 244 0,54 0,15 0,06 0,25 0,13 0,14 0,10 0,11 5259,78 245 0,43 0,11 0,04 0,25 0,12 0,09 0,10 0,11 5919,39 246 0,44 0,12 0,04 0,27 0,13 0,10 0,12 0,12 5976,96 247 0,56 0,16 0,06 0,26 0,14 0,16 0,11 0,11 5189,86 248 0,52 0,14 0,05 0,26 0,13 0,13 0,11 0,12 5459,11 249 0,45 0,12 0,04 0,26 0,12 0,11 0,11 0,11 5861,94 250 0,41 0,10 0,04 0,26 0,12 0,09 0,11 0,12 6189,72 251 0,47 0,13 0,05 0,27 0,13 0,11 0,11 0,12 5776,00 252 0,29 0,07 0,02 0,27 0,12 0,05 0,12 0,12 7262,65 253 0,36 0,09 0,03 0,26 0,12 0,07 0,11 0,11 6510,43 254 0,53 0,14 0,05 0,24 0,13 0,13 0,09 0,10 5264,60 Head Code count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 255 0,41 0,10 0,03 0,24 0,12 0,09 0,10 0,10 5958,55 256 0,41 0,10 0,04 0,25 0,12 0,09 0,10 0,10 5977,08 257 0,41 0,10 0,03 0,25 0,12 0,09 0,10 0,10 5981,74 258 0,27 0,06 0,02 0,26 0,11 0,05 0,11 0,12 7358,30 259 0,41 0,10 0,04 0,26 0,12 0,09 0,11 0,11 6100,42 260 0,39 0,09 0,03 0,26 0,12 0,08 0,11 0,11 6262,65 261 0,37 0,09 0,03 0,27 0,12 0,08 0,12 0,12 6593,30 262 0,21 0,05 0,02 0,27 0,11 0,03 0,12 0,12 8101,99 263 0,35 0,08 0,03 0,24 0,11 0,07 0,09 0,09 6287,96 264 0,21 0,04 0,01 0,25 0,10 0,03 0,11 0,11 7844,80 265 0,12 0,02 0,01 0,25 0,09 0,01 0,10 0,10 9225,05 266 0,26 0,05 0,02 0,25 0,11 0,04 0,10 0,10 7303,67 267 0,32 0,07 0,03 0,25 0,11 0,06 0,10 0,11 6744,07 268 0,39 0,10 0,03 0,25 0,12 0,08 0,10 0,11 6195,47 269 0,27 0,06 0,02 0,26 0,11 0,05 0,11 0,11 7260,48 270 0,33 0,08 0,03 0,26 0,11 0,06 0,11 0,11 6729,60 271 0,35 0,08 0,03 0,25 0,11 0,07 0,10 0,11 6512,91 272 0,47 0,13 0,05 0,26 0,13 0,11 0,11 0,11 5706,33 273 0,18 0,04 0,01 0,27 0,11 0,03 0,12 0,12 8505,24 274 0,51 0,14 0,05 0,25 0,13 0,13 0,10 0,10 5421,05 275 0,44 0,11 0,04 0,26 0,12 0,10 0,11 0,11 5897,20 276 0,35 0,08 0,03 0,26 0,12 0,07 0,11 0,11 6512,21 277 0,49 0,13 0,05 0,25 0,13 0,12 0,10 0,10 5447,20 278 0,52 0,14 0,05 0,27 0,13 0,13 0,11 0,12 5499,48 279 0,64 0,19 0,08 0,26 0,14 0,20 0,11 0,12 4793,78 280 0,45 0,12 0,04 0,25 0,13 0,10 0,11 0,11 5773,30 281 0,59 0,18 0,07 0,27 0,14 0,17 0,12 0,12 5028,69
24 282 0,57 0,17 0,07 0,29 0,14 0,17 0,13 0,14 5310,01 283 0,20 0,04 0,01 0,27 0,11 0,03 0,12 0,12 8232,14 284 0,52 0,14 0,05 0,26 0,13 0,14 0,11 0,12 5445,16 285 0,44 0,11 0,04 0,26 0,12 0,10 0,11 0,12 5989,37 286 0,49 0,14 0,05 0,30 0,14 0,13 0,14 0,15 5911,87 287 0,48 0,13 0,05 0,28 0,13 0,12 0,12 0,14 5818,08 288 0,02 0,00 0,00 0,34 0,21 0,00 0,21 0,20 25573,74 289 0,18 0,04 0,01 0,26 0,11 0,03 0,11 0,11 8421,19 290 0,25 0,06 0,02 0,27 0,11 0,04 0,11 0,12 7486,14 291 0,10 0,03 0,01 0,31 0,13 0,02 0,17 0,16 12275,36 292 0,09 0,02 0,01 0,29 0,11 0,01 0,14 0,13 11571,71 293 0,16 0,04 0,01 0,29 0,13 0,03 0,14 0,14 9694,45 294 0,25 0,06 0,02 0,27 0,11 0,04 0,12 0,12 7622,93 295 0,17 0,04 0,01 0,30 0,10 0,02 0,15 0,15 9479,22 296 0,28 0,06 0,02 0,25 0,11 0,05 0,10 0,10 7006,36 297 0,25 0,05 0,02 0,25 0,10 0,04 0,10 0,11 7439,85 298 0,32 0,07 0,02 0,25 0,11 0,06 0,10 0,10 6688,91 299 0,19 0,04 0,01 0,25 0,10 0,03 0,10 0,10 7936,33 300 0,37 0,08 0,03 0,23 0,10 0,07 0,09 0,09 6156,64 301 0,45 0,11 0,04 0,22 0,11 0,10 0,08 0,08 5622,95 302 0,20 0,04 0,01 0,25 0,10 0,03 0,10 0,10 7711,88 303 0,43 0,10 0,04 0,24 0,11 0,09 0,09 0,09 5843,59 304 0,32 0,07 0,02 0,25 0,11 0,06 0,10 0,10 6646,86 305 0,33 0,07 0,02 0,24 0,11 0,06 0,10 0,10 6543,52 Head Code count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 306 0,38 0,09 0,03 0,24 0,11 0,07 0,09 0,09 6114,84 307 0,38 0,10 0,03 0,27 0,12 0,08 0,12 0,12 6496,32 308 0,31 0,07 0,02 0,27 0,11 0,06 0,11 0,12 6924,93 309 0,30 0,07 0,02 0,28 0,11 0,05 0,13 0,13 7240,14 310 0,29 0,07 0,02 0,29 0,12 0,05 0,14 0,14 7488,39 311 0,44 0,11 0,04 0,26 0,13 0,10 0,11 0,11 5931,14 312 0,44 0,11 0,04 0,27 0,12 0,10 0,12 0,13 6040,29 313 0,38 0,10 0,03 0,27 0,12 0,08 0,12 0,12 6413,62 314 0,42 0,11 0,04 0,27 0,13 0,09 0,11 0,12 6108,89 315 0,61 0,19 0,08 0,28 0,14 0,19 0,13 0,14 5051,78 316 0,32 0,08 0,03 0,28 0,12 0,06 0,13 0,13 7007,30 317 0,67 0,21 0,09 0,28 0,14 0,22 0,13 0,15 4737,06 318 0,21 0,05 0,02 0,27 0,11 0,03 0,12 0,12 8199,50 319 0,45 0,12 0,04 0,27 0,12 0,10 0,12 0,13 6077,72 320 0,61 0,20 0,08 0,27 0,15 0,20 0,12 0,13 4923,35 321 0,32 0,08 0,03 0,29 0,12 0,06 0,14 0,14 7261,22 322 0,51 0,14 0,05 0,26 0,13 0,13 0,11 0,11 5477,34 323 0,30 0,07 0,02 0,28 0,11 0,05 0,13 0,13 7279,32 324 0,61 0,18 0,07 0,25 0,14 0,18 0,10 0,11 4918,02 325 0,46 0,12 0,04 0,24 0,12 0,10 0,10 0,10 5666,78 326 0,25 0,05 0,02 0,26 0,11 0,04 0,11 0,11 7443,10 327 0,33 0,08 0,03 0,26 0,11 0,06 0,11 0,11 6811,53 328 0,20 0,04 0,01 0,25 0,09 0,03 0,10 0,10 7945,35
25 329 0,35 0,08 0,03 0,25 0,11 0,07 0,10 0,10 6501,40 330 0,36 0,09 0,03 0,26 0,12 0,07 0,11 0,11 6485,60 331 0,20 0,04 0,01 0,25 0,10 0,03 0,10 0,10 7860,00 332 0,25 0,05 0,02 0,26 0,10 0,04 0,11 0,11 7484,69 333 0,25 0,05 0,02 0,26 0,11 0,04 0,11 0,11 7487,84 334 0,14 0,03 0,01 0,26 0,10 0,02 0,11 0,11 8937,85 335 0,40 0,09 0,03 0,24 0,12 0,08 0,10 0,10 6050,11 336 0,27 0,06 0,02 0,27 0,11 0,04 0,12 0,12 7398,74 337 0,36 0,09 0,03 0,27 0,12 0,07 0,12 0,12 6582,56 338 0,33 0,08 0,03 0,28 0,11 0,06 0,13 0,13 6981,78 339 0,25 0,06 0,02 0,27 0,11 0,04 0,12 0,12 7639,38 340 0,24 0,05 0,02 0,27 0,11 0,04 0,12 0,12 7753,83 341 0,33 0,08 0,03 0,27 0,11 0,06 0,12 0,13 6878,89 342 0,22 0,05 0,02 0,26 0,10 0,03 0,11 0,12 7870,29 343 0,34 0,08 0,03 0,26 0,11 0,06 0,11 0,12 6620,75 344 0,36 0,09 0,03 0,26 0,12 0,07 0,11 0,11 6477,21 345 0,30 0,07 0,02 0,28 0,12 0,06 0,13 0,13 7236,29 346 0,29 0,07 0,02 0,27 0,11 0,05 0,12 0,12 7177,72 347 0,42 0,10 0,03 0,25 0,11 0,09 0,10 0,11 5926,88 348 0,18 0,04 0,01 0,30 0,12 0,03 0,15 0,15 9273,48 349 0,35 0,08 0,03 0,26 0,11 0,07 0,11 0,12 6608,86 350 0,26 0,06 0,02 0,27 0,11 0,04 0,12 0,12 7486,25 351 0,43 0,11 0,04 0,26 0,12 0,10 0,11 0,11 5952,05 352 0,42 0,10 0,04 0,26 0,12 0,09 0,11 0,11 6089,77 353 0,42 0,11 0,04 0,27 0,13 0,10 0,12 0,12 6038,00 354 0,20 0,04 0,01 0,27 0,11 0,03 0,12 0,11 8293,84 355 0,27 0,07 0,02 0,28 0,12 0,05 0,13 0,13 7470,57 356 0,33 0,08 0,03 0,29 0,12 0,07 0,13 0,14 7035,47 Head Code count FGT(1) FGT(2) Gini Gini-poor Sen GE(0) GE(1) Con 357 0,38 0,10 0,03 0,27 0,13 0,08 0,12 0,12 6406,11 358 0,49 0,14 0,05 0,27 0,13 0,12 0,12 0,12 5678,21 359 0,27 0,06 0,02 0,28 0,12 0,05 0,13 0,13 7574,98 360 0,31 0,07 0,03 0,26 0,12 0,06 0,11 0,11 6930,77 361 0,29 0,07 0,02 0,28 0,12 0,05 0,13 0,13 7400,09 362 0,34 0,08 0,03 0,25 0,11 0,06 0,10 0,10 6523,93 363 0,05 0,01 0,01 0,43 0,14 0,01 0,32 0,32 23215,99 364 0,27 0,06 0,02 0,27 0,11 0,05 0,12 0,12 7436,69 365 0,24 0,05 0,02 0,27 0,11 0,04 0,12 0,12 7701,06 366 0,18 0,04 0,01 0,28 0,11 0,03 0,13 0,13 8645,20 367 0,25 0,05 0,02 0,34 0,11 0,04 0,19 0,23 8630,55 368 0,22 0,05 0,02 0,29 0,13 0,04 0,14 0,13 8367,59 369 0,27 0,06 0,02 0,25 0,11 0,05 0,10 0,10 7091,10 370 0,38 0,10 0,04 0,27 0,13 0,08 0,12 0,12 6362,93 371 0,26 0,05 0,02 0,25 0,11 0,04 0,10 0,10 7219,43 372 0,10 0,02 0,01 0,29 0,11 0,01 0,14 0,14 11226,31 373 0,29 0,07 0,02 0,26 0,11 0,05 0,11 0,11 7019,70 374 0,30 0,07 0,02 0,25 0,11 0,05 0,10 0,10 6775,92
26 27 Annex 1: Poverty and inequality maps
Per Capita Consumption by administrative disaggregation.
28
Poverty and inequality Maps by Prefectures.
29
30
31
Poverty and inequality Maps by Districts.
32
33
34
Poverty and inequality Maps by Communes.
35
36
37 Annex 2: Comparison between Census and LSMS sources
The main aim of this annex consists in comparing the common information collected by the census and the sample survey. The two sources of data have been fully analysed in order to identify the common concept and to construct the common variable to be compared. The original Census and LSMS variables have been transformed in order to get comparable variables. The Table A2 in this Annex reports the list of those common variables divided into three categories: a) Household dwelling conditions and presence of durable goods (23 variables). b) Household head characteristics (8 variables) c) 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) approach adopted by the imputation software (IVE-ware), and it is fully described in the Annex 4. The variables subjected to the imputation procedure were: a) Type of building b) Inhabited dwelling surface c) Highest level of education achieved by any member of the household. Each of the 38 variables distributions from the Census were compared with the corresponding distribution from the LSMS, with the weighted distribution from the LSMS and, for the above three variables, with the imputed LSMS distribution. A chi-square test was utilised for the comparisons. The distributions are reported in the attached excel file (Distributions.xls). The first decision to take lies in whether or not to consider the weights in the estimation of the linear regression model in the LSMS data set. Comparing the census distributions with the corresponding two in the LSMS (not weighted and weighted), in 21 cases the original distribution fits better than the weighted distribution. Moreover in the 17 cases where the weighted distribution fits better, for five variables the difference between the two distributions is so small that they can considered the same, whereas for the other two cases, the distributions are very different from the census one so that they cannot be included in the regression model. Taking into account all the above considerations it seems necessary NOT to consider the weights in the estimation of the linear regression models (see section 3).
38 The second decision to take lies in 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 get new distributions (mainly dummies) which were similar, as far as possible, to those in the census. Anyway, some variables have been excluded because they are not comparable at all. The list of new variables to be used as potential regressors in the estimation of the model is reported in Table A1.
Table A1: Common constructed variables in the Census and the LSMS. Concept Variable Categories 1 Type of building Plastered 1=plastered, 0=otherwise 2 Main construction material Bricks_stone 1=bricks or stones, 0=otherwise 3 Elevator Lift 1=present, 0=otherwise 4 Time of construction Time44 1=before 1945, 0=otherwise 5 Time4560 1=1945-1960, 0=otherwise 6 Time6180 1=1961-1980, 0=otherwise 7 Time8190 1=1981-1990, 0=otherwise 8 Time91 1=after 1990, 0=otherwise 9 Number of dwellings in the building Dewll1 1=1 dwelling, 0=otherwise 10 Number of rooms Rooms 11 Inhabiting surface Surf1 1=less than 40mq, 0=otherwise Surf2 1=40-69mq, 0=otherwise Surf3 1=more than 69mq, 0=otherwise 12 Water supply for the dwelling Water 1=inside, 0=otherwise 13 Toilet facilities Wc 1=inside, 0=otherwise 14 Ownership of the dwelling Own 1=owner, 0=otherwise 15 Agricultural land Agricul 1=yes, 0=otherwise 16 Black and white or colour TV TV 1=yes, 0=otherwise 17 Parabolic Parabolic 1=yes, 0=otherwise 18 Refrigerator Refrigerator 1=yes, 0=otherwise 19 Washing machine Wash 1=yes, 0=otherwise 20 Electric or gas heater Heater 1=yes, 0=otherwise 21 Conditioning air Air 1=yes, 0=otherwise 22 Computer Computer 1=yes, 0=otherwise 23 Car Car 1=yes, 0=otherwise 24 Household head gender HH_female 1=female, 0=otherwise 25 Household head age HH_age 26 Migration Migr89 1=Immigrated before 1990, 0=otherwise 1=Immigrated since 1990, 0=otherwise 27 Migr90 28 Household head marital status HH_mstatus 1=married, 0=otherwise 29 Household head employment status HH_work 1=employed, 0=otherwise 30 Spouse age SP_age 0 if not present 31 Spouse economic status SP_work 1=employed, 0=otherwise 32 Household size HHsize 33 Eldest son age CH1_age 0 if not present
39 34 Children aged 0-5 Child5 35 Children aged 6-15 Child6_15 36 Highest level of education achieved by High_edu1 1=up to lower secondary, 0=otherwise any member of the household 1=Upper secondary, 0=otherwise 37 High_edu2 1=University or more, 0=otherwise 38 High_edu3 39 Number of non working members NO_work
The construction of the two variables describing the migration phenomenon (migr89 and migr90 for the household holder) needs to be explained, since we lose some information from the LSMS source. According to the three questions in the census questionnaire, it is possible to identify the district of birth, the district of residence in 1989 and the present district; it is therefore possible to describe four different pattern for each household member: (0 0 1, 0 1 1, 1 0 1, 1 1 1) where 1 stands for “resident in the actual district” at each of three periods, and 0 stand for “otherwise”. Since we are here interested in the immigration phenomenon, we can define the person as immigrant before the year 1989 for the second path, while we can define the person as immigrant from 1990 for the first and the third path. Clearly for the third path, the person has also emigrated before the year 1989. Of course we cannot observe “double” migration movements occurring within the two non observed bands (birth-1989 and 1990-today).
Graph A1: Migration: information from the LSMS.
[1] Were you born here?
YES NO
[2] [3] Have you always lived here? Did you move after 1990?
YES NO YES NO
Path (1) [3] Path (4) Path (5) Did you move after 1990? Category 111 Category 001 Category 011 migr89 = 0 migr89 = 0 migr89 = 1 migr90 = 0 migr90 = 1 migr90 = 0
YES NO
Path (2) Path (3)
Category 101 Category 111
40 migr89 = 0 migr89 = 0 migr90 = 1 migr90 = 0
Path (3) would have generated migr89 = 1, but this path is not comparable with the Census, so it is assumed migr89 = 0.
Comparing this information to that from the LSMS we can observe some differences in the definition of the concepts that must be taken into account. The migration phenomenon in the LSMS survey is observed thought three linked questions described in Graph A1. Here it is possible to describe five different paths, one more than the information obtained from the census. Here we get path three obtained, which corresponds to individuals born in the present district, not having lived continuously in this district, but not moving to this district after the year 1990: it means that they have experienced a “double” migration between the birth and the year 1990. For let the variable migr89 to be comparable between the two sources we must drop this immigration before the year 1990.
Table A2: The common variables in the LSMS and Census CENSUS HOUSEHOLD SURVEY HH Dwelling Conditions and Presence of Durable Goods 1 Building character (Section “Building”, Q. 2, variable Building outside appearance? (Module 3A, Part a, “Character”) Q.3, variable “m3a_q03”) 4 categories should be grouped into 2 as follows 3 categories should be grouped into 2 as follows (1,2):1 = plastered, (3,4):2 = not plastered (1,2):1 = plastered, (3):2 = not plastered
2 Main construction material (Section “Building”, Q. 3, What is the major construction material of the variable “Material”) external walls of building? (Module 3A, Q. 2, 4 categories: Pre-fabricated, Bricks or stones, wood, variable “m3a_q02) other 6 categories should be grouped into 4 as follows (2:1) =Pre-fabricated, (1:2) = Bricks or stones, (3:3) = wood, (4,5,6):4 = other 3 Has the building an elevator? (Section “Building”, Q. Does the dwelling have the following? .... elevator 4, variable “Elevator”) (Module 3A, Q. 11, variable “m3a_q11_7) Convert category 2 into category 0 in order to get the Dummy variable (1 – if has, 0 – if has not) following: Dummy variable (1 – if has, 0 – if has not) 4-5 Time of construction (section Building, Q. 5, variable Time of construction of the dwelling (Module 3A, Q. “Construct” and “Construct_year”). 5), 5 categories: before 1945, 1945-60, 1961-80, 5 categories: before 1945, 1945-60, 1961-80, 1981- 1981-1990, after 1990, variable “m3a_q5a”; 1990, after 1990, and if after 1990, specify the year. if after 1990, specify the year, variable “m3a_q5b”. 6 Number of dwellings in the building (Section Dwelling type (Module 3A, Q1, variable “m3a_q01”). “Building”, Q. 7, variable dwellings). 4 categories, the last one can not be compared: 6 categories should be grouped into 3 as follow: (1:1) single family house, building with up to 15 = 1 dwelling; (2,3,4,5):2 = 2-15 dwellings, (6:3) = 16 apartments, building with more than 15 apartments, dwellings and more. other (specify). 7 Number of rooms (Section “Dwelling”, Q3, variable Number of rooms, excluding kitchen, balconies and “Rooms”) corridor (Module 3A, Q8, variable “m3a_q08”).
8 Number of work rooms (Section “Dwelling”, Q3, Number of rooms used for business, (Module 3A, Q9, variable “Rooms_work”) variable “m3a_q09”). 9 Does the dwelling has a room only for cooking? Does the dwelling have the following? .... separate (Section “Dwelling”, Q4, variable “kitchen”). kitchen (Module 3A, Q. 11, variable “m3a_q11_1)
41 10 What is the inhabited surface? (Section “Dwelling”, What is the area of you dwelling? (Module 3A, Q7, Q5, variable “inhabiting”). variable “m3a_q07”) 5 categories: less than 40m2, 40-69 m2, 70-99 m2, 100- 6 categories: less than 40m2, 40-69 m2, 70-99 m2, 130 m2, more than 130 m2. 100-130 m2, more than 130 m2, don’t know-not sure. The last category should be imputed. 11 Water supply for the dwelling (Section “Dwelling”, Main source of water used. (Module 3B, Q1, variable Q6, variable “Water”. “m3b_q01”). 7 categories: running water inside the 4 categories: inside the dwelling, outside the dwelling, dwelling, running water outside the dwelling, water well or water tank, not supplied with water, should be truck, public tap, spring or well, river, lake, pond or grouped as follows: (1:1) = water inside, (2,3,4,:2) = similar, other (specify), should be grouped as follows: other. (1:1) = water inside, (2,3,4,5,6,7,:2) = other. 12 Toilet facility. (Section “Dwelling”, Q7, variable Kind of toilet. (Module 3A, Q10, variable “m3a_q10). “WC”). 5 categories: one WC inside, two or more WC inside, 5 categories: one WC inside, two or more WC inside, WC outside with piping, WC outside without piping, WC outside with piping, WC outside without piping, other (specify). no WC. It is expected that “other” is “no WC”: 13 Principal heating. (Section “Dwelling”, Q8, variable Does the dwelling have central heating? “heating”). (Module 3B, Q12, variable “m3b_q12”) 3 categories: central heating, individual heating, no If no check Q16 to calculated individual heating or form of heating. none (Module 3B, Q16, variable m3b_q16).
Material for heating. (Section “Household”, Q2, What other sources of heating does your household variables heating_1-heating_6). mainly use? (Modulo 3B, Q16, variable “m3b_q16”). A set of six dummies has been crated to describe the 7 categories: electricity, wood, gas, oil-petrol, coal, use of each kind of material, hence the result is not a none-no heating, other (specify), distribution. Because of this reason the census and the should be grouped into 6 as follows: (2:1) = wood, LSMS variables are not comparable. (1:2) = electricity, (3:3) = gas, (4:4) = oil-petrol-etc, (5:5) = coal, (6,7:6) = none 14 Ownership of the dwelling. (Section “household”, Q1, What is the ownership of the building? (Module 3A, variable “Ownership”) Q13, variable “m3a_q13”). 3 categories: owner, renter, other. 6 categories should be grouped into 4 as follows: If no owner, who is the owner of the dwelling? (1,2):1 = owner, (3:2) = renting from a private (Section “household”, Q1, variable not_owner) individual, (4:3) = renting from the state, (5, 6):4 5 categories: another person or family, private =other. building enterprise, old regime owners, public housing entity, other. The combination of the previous variables created a new variable with 4 categories: 1= owner, 2= renting from another person or family, renting from private building enterprises, renting from old regime owners, renting from others, 3= renting from public housing entities, 4= other, should be grouped as follows: (1:1) = owner , (2.1, 2.2, 2.3, 2.5:2) = renting from a private individual, (2.4:3) = renting from the state, (3:4) =other. 15 Agricultural land (Section “household”, Q3, variable Did you cultivate in the last year any land owned to agricul_land). Do you own agricultural land? Dummy the household? (Module 12, Q01, variable m12_q01) variable (1 – if has, 0 – if has not) 16-23 Household equipment (Section Household, Q4, (Module 3C, Q02, variable m3c_q02). variables tv parabolic refrigerator wash heater_elect Black and white and colour TV have been merged heater_gas air computer car) into one variable, TV. Electric and gas heater have been merged into one variable. Household (HH) head characteristics 24 Gender (Section “PI Individual Questionnaire, Q1, Gender. (Module 1, Q2, variable “m1_q02”). variable sex, where referent_person = 1). 2 categories: Male , Female. 2 categories: Male , Female.
42 25 Age (Section “PI Individual Questionnaire, Q2, Age (Module 1, Q5, variable “m1_q05y”» where variable Age has been constructed from Q2, where m1_q03 = 1). referent_person = 1). 26-27 Migration Were you born in this municipality? Place of birth (Section “PI Individual Questionnaire, (Module 2, Q1, variable “m2_q01”, Q3, variable id_district_birth where referent_person = 1), 3 categories: in Albania=district or town-village, Module 2, Q2, variable “m2_q02”, Abroad. Place of residence on 1 April 1989 (Q4, variable Module 2, Q3, variable “m2_q03”, id_district_res89 where referent_person = 1). 3 categories: in Albania=district or town-village, where m1_q03 = 1). Abroad. Place of residence at census moment (Q6, where “at the same place where you reside” variable id_district where referent_person = 1). 4 categories: in Albania=at the same place where you reside, other district or other town-village, abroad. If abroad, the reason of the absence: 4 categories, Studies, Work, in an institutional household, other-not stated. 28 Marital status (Section “PI Individual Questionnaire, Marital status. (Module 1, Q6, variable “m1_q06 Q7, variable marital_status). where m1_q03 = 1). 4 categories: Single, Married, Widowed, Divorced. 5 categories: Married, Divorced/separated, Living If no single it is also asked month and year of last together, Widow/er, Single marriage. to be grouped as follows: (3,5:1) = Single, (1:2) = Married, (4:3) = Widowed , (2:4) = Divorced. 29 Household head Highest diploma obtained (Section Highest diploma attained (Module 4B, Q6, variable “PI Individual Questionnaire, Q10, variable hh_edu “m4b_q06” where m1_q03 = 1). where referent_person = 1). 8 categories: None, Primary 4 years, Primary 8 years, 8 categories: No diploma, 4 years school (elementary), Secondary general, Vocational 2 years, Vocational 8 years school (lower secondary), Upper Secondary- 4/5 years, University, Post-graduate Vocational (2 years), Upper Secondary-General (4 to be grouped as follow: (1:1) = No diploma, (2:2) = 4 years), Upper Secondary –Technical (4 years), years school (elementary), (3:3) =8 years school University, Post-University. (lower secondary) , (5:4) = Upper Secondary- If university degree, specify which one. Vocational (2 years), (4:5) = Upper Secondary- General (4 years), (6:6) = Upper Secondary– Technical (4 years), (7:7) = University, (8:8) = Post- University. 30 Employment Status (Section PI Individual (Module 7A, Q5, variables m7A_q02 m7A_q03 Questionnaire, Q12, variable hh_work where m7A_q04 which is summarized in variable m7A_q05 referent_person = 1). where m1_q03 = 1). 9 categories: Employed, Unemployed-looking for a new job, Unemployed-looking for the first job, Housekeeper, Student, Retired, In compulsory military service, Not employed-not looking for a job, Other inactive (handicapped, etc.) are grouped as follows: (1:1) = employed , (2,3,4,5,6,7,8,9 :2) = other. Dummy variable: work. 31 Type of employment (Section PI Individual (Module 7c, question 6, variable m7c_q06) Questionnaire, Q15, variable Employment). 4 categories: 4 categories: Employee, Employer, Own account an employee of someone who is not a member of your worker, Contribution family worker. household, a paid worker in a household farm or nonfarm business enterprise, an employer, a worker on own account or unpaid worker in a household farm or nonfarm business enterprise, are grouped as follows : (1:1) = Employee, (3:2) = Employer, (4:3) = Own
43 account worker, (2:4) = Contribution family worker. Household (HH) socio-demographic characteristics 32 Age spouse (Section “PI Individual Questionnaire, Age of spouse (Module 1, Q5, variable “m1_q05y” Q2, variable Age has been constructed from Q2, where m1_q03 = 2). where referent_person = 2). 33 Economic status Spouse Economic status spouse (Module 7A, Q5, variable Employment Status (Section PI Individual m7A_q05 where m1_q03 = 2) Questionnaire, Q12, variable hh_work where referent_person = 2). 9 categories: Employed, Unemployed-looking for a new job, Unemployed-looking for the first job, Housekeeper, Student, Retired, In compulsory military service, Not employed-not looking for a job, Other inactive (handicapped, etc.) are grouped as follows: (1:1) = employed , (2,3,4,5,6,7,8,9 :2) = other. Dummy variable: work. 34 Household size Household size Variable numcomp was constructed in the data set.
35 Eldest son age Age (Module 1, Q5, variable “m1_q05y »» where (Section “PI Individual Questionnaire, Q2, variable m1_q03 = 3). Ch1_Age has been constructed from Q2, where referent_person = 4 ). 36 Number of children 0-5 Number of children 0-5 (Section “PI Individual Questionnaire, Q2, variable (Module 1, Q5, variable “m1_q05y » , variable child5 child5 has been constructed from Q2, where has been constructed from Q5, where m1_q03 = 3). referent_person = 4 ). 37 Number of children 6-15 Number of children 6-15 (Section “PI Individual Questionnaire, Q2, variable (Module 1, Q5, variable “m1_q05y » , variable child6_15 has been constructed from Q2, where child6_15 has been constructed from Q5, where referent_person = 4 ). m1_q03 = 3). 38 Highest level of education achieved by any member of Highest diploma attained (Module 4B, Q6, variable the household “m4b_q06”). Variable (Section “PI Individual Questionnaire, Q10, 8 categories: None, Primary 4 years, Primary 8 years, variable high_edu). Secondary general, Vocational 2 years, Vocational 8 categories: No diploma, 4 years school (elementary), 4/5 years, University, Post-graduate 8 years school (lower secondary), Upper Secondary- to be grouped as follow: (1:1) = No diploma, (2:2) = 4 Vocational (2 years), Upper Secondary-General (4 years school (elementary), (3:3) =8 years school years), Upper Secondary –Technical (4 years), (lower secondary) , (5:4) = Upper Secondary- University, Post-University. Vocational (2 years), (4:5) = Upper Secondary- If university degree, specify which one. General (4 years), (6:6) = Upper Secondary– Technical (4 years), (7:7) = University, (8:8) = Post- University.
44 Annex 3: The imputation procedures
The imputation procedures are based on the “sequential regression multivariate imputation” (SRMI) approach adopted by the imputation software (IVE-ware). The method proposed by the authors of the software (Raghunathan, Lepkowski, Van Hoewyk, and Solenberger, 2001) constructs the imputed values by fitting a sequence of regression models and drawing values from the corresponding predictive distribution, under the hypothesis of Missing at Random (MAR) mechanism, infinite sample size and simple random sampling. The procedure is a variant of the estimation-maximisation (EM) algorithm and follows a bayesian paradigm. The sequential multivariate model used made for more complete imputation of the variables, while at the same time safeguarding their variance and their inter-correlation.5 A brief outline of the approach may be described as follows: ° Initially, the variables are divided into two types: auxiliary variables used to impute the others, and target variables which are the subject of the imputation. In the initial stages the auxiliary variables are those relating to the demographic characteristics (sex, age) and to labour force characteristics. ° The auxiliary (exogenous) variables are supposed to be available for all cases. If not, some ad hoc procedures are used to perform the necessary imputations. The objective of this is not to impute 'final' values of these variable as such, but to provide a basis for their use in the imputation of the target (income) variables. ° The target variables are arranged in a sequence, starting with those with the smallest proportion of (or with no) missing values. (Alternatively, the ordering can be in terms of decreasing explanatory power of the variables.) Going down in sequence, each target variable is imputed using all the variables above it, for which all information is available (or has been previously imputed), as auxiliary variables in the multivariate regression. The model is as follows. With U as the matrix containing variables with no missing data (including as a result previous imputation), and Y1, Y2...Yk are variables with increasing rates of missing data, the sequence of imputations is determined by the following factorisation: [Y1¦U] [Y2¦U, Y1] ...[Yk¦U, Y1, ..., Yk-1 ] where [Y¦X] is the conditional joint distribution of Y where x is known.
5 The EM procedure implemented by Eurostat was based on the work of a team at the Institute of Social Research (ISR) of the University of Michigan.
45 The form of regression depends on the nature of Y, such as a generalised linear regression for continuous variables (as in the case of income amounts), a logistical regression for binary variables, etc. ° Once a variable with missing values has been imputed, it is moved from the second set to the first, i.e. used as an auxiliary variable in imputation of the next variable in the list. ° After all variables in the list have been dealt with as above, the process is started again with the first variable in the target set, but this time using all the other variables as predictors, using for each the given or the most recently imputed value is used. The process is performed for each variable in turn, and is repeated iteratively. The LSMS data set presented missing values in four variables of interest: a) Type of building b) Inhabited dwelling surface c) Highest level of education achieved by head of household d) Highest level of education achieved by any member of the household. Variable c) and d) showed the same value for more than 70% of the cases in the sample, so we have decided to take into account only the highest level of education in the household in order to avoid quasi-correlation problems in the imputation procedure and in the estimation of the consumption model in section 3. Table A3 reports the SAS-IVEware program and result of the imputation procedure. The eight missing values in the type of building have been imputed maintaining the original distribution of the variables. Five missing values out seven for the inhabited surface of the dwelling (in classes) were imputed into the larger class (more than 130 mq) underlining the difficulty of respondents in estimating the surface of a large dwelling. Finally, forty eight missing values out of sixty in the highest level of education in the household were imputed into the lower classes, showing a sort of “shame” felt by the respondents in declaring their low level of education achieved.
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Table A3: The SAS-IVEware imputation program and result. IVEware Setup Checker, Thu Jan 02 13:21:44 2003 1
Setup listing:
DATAIN albania.LSMS; DATAOUT albania.LSMSimp; DEFAULT CATEGORICAL; CONTINUOUS logcon roomspp child5 ch6_15 hhsize elder hh_age hh_age2 sp_age no_work; TRANSFER hhid stratum; ITERATIONS 5; SEED 479013; RUN;
IVEware Iterative Imputation Procedure, Thu Jan 02 13:33:09 2003 1
Variable Observed Imputed Double counted hhsize 3599 0 0 child5 3599 0 0 ch6_15 3599 0 0 elder 3599 0 0 hh_age 3599 0 0 hh_work 3599 0 0 migr89 3599 0 0 migr90 3599 0 0 sp_age 3599 0 0 sp_work 3599 0 0 high_edu 3537 62 0 no_work 3599 0 0 m3a_q03 3591 8 0 m3a_q07 3592 7 0 dwell1 3599 0 0 brick 3599 0 0 time44 3599 0 0 time4560 3599 0 0 time6180 3599 0 0 time8190 3599 0 0 roomspp 3599 0 0 surf1 3599 0 0 surf2 3599 0 0 waterin 3599 0 0 wcinside 3599 0 0 own 3599 0 0 TV 3599 0 0 parab 3599 0 0 fridge 3599 0 0 wash 3599 0 0 heater 3599 0 0 car 3599 0 0 agrland 3599 0 0 hh_fem 3599 0 0 hh_age2 3599 0 0 logcon 3599 0 0
Variable hhsize Observed Imputed Combined Number 3599 3599 Minimum 1 1 Maximum 15 15 Mean 4.32398 4.32398 Std Dev 1.80914 1.80914
Variable child5 Observed Imputed Combined Number 3599 3599 Minimum 0 0 Maximum 5 5 Mean 0.464296 0.464296
47 Std Dev 0.737912 0.737912
IVEware Iterative Imputation Procedure, Thu Jan 02 13:33:09 2003 2
Variable ch6_15 Observed Imputed Combined Number 3599 3599 Minimum 0 0 Maximum 6 6 Mean 0.822729 0.822729 Std Dev 0.996506 0.996506
Variable elder Observed Imputed Combined Number 3599 3599 Minimum 0 0 Maximum 4 4 Mean 0.542651 0.542651 Std Dev 0.750987 0.750987
Variable hh_age Observed Imputed Combined Number 3599 3599 Minimum 15 15 Maximum 98 98 Mean 50.7877 50.7877 Std Dev 13.969 13.969
Variable hh_work Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 1401 38.93 1401 38.93 1 2198 61.07 2198 61.07 Total 3599 100.00 3599 100.00
Variable migr89 Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 2742 76.19 2742 76.19 1 857 23.81 857 23.81 Total 3599 100.00 3599 100.00
Variable migr90 Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 3124 86.80 3124 86.80 1 475 13.20 475 13.20 Total 3599 100.00 3599 100.00
Variable sp_age Observed Imputed Combined Number 3599 3599 Minimum 0 0 Maximum 84 84 Mean 37.3857 37.3857 Std Dev 20.206 20.206 IVEware Iterative Imputation Procedure, Thu Jan 02 13:33:09 2003 3
Variable sp_work Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 2182 60.63 2182 60.63 1 1417 39.37 1417 39.37 Total 3599 100.00 3599 100.00
Variable high_edu Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 11 0.31 0 0.00 11 0.31 1 135 3.82 14 22.58 149 4.14 2 1253 35.43 26 41.94 1279 35.54 3 666 18.83 8 12.90 674 18.73 4 122 3.45 0 0.00 122 3.39
48 5 804 22.73 11 17.74 815 22.65 6 511 14.45 3 4.84 514 14.28 7 35 0.99 0 0.00 35 0.97 Total 3537 100.00 62 100.00 3599 100.00
Variable no_work Observed Imputed Combined Number 3599 3599 Minimum 0 0 Maximum 12 12 Mean 2.8105 2.8105 Std Dev 1.62976 1.62976
Variable m3a_q03 Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 1 1571 43.75 2 25.00 1573 43.71 2 722 20.11 2 25.00 724 20.12 3 1298 36.15 4 50.00 1302 36.18 Total 3591 100.00 8 100.00 3599 100.00
Variable m3a_q07 Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 1 487 13.56 1 14.29 488 13.56 2 1442 40.14 0 0.00 1442 40.07 3 1173 32.66 0 0.00 1173 32.59 4 365 10.16 1 14.29 366 10.17 5 125 3.48 5 71.43 130 3.61 Total 3592 100.00 7 100.00 3599 100.00
Variable dwell1 Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 1323 36.76 1323 36.76
1 2276 63.24 2276 63.24 Total 3599 100.00 3599 100.00 IVEware Iterative Imputation Procedure, Thu Jan 02 13:33:09 2003 4
Variable brick Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 515 14.31 515 14.31 1 3084 85.69 3084 85.69 Total 3599 100.00 3599 100.00
Variable time44 Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 3329 92.50 3329 92.50 1 270 7.50 270 7.50 Total 3599 100.00 3599 100.00
Variable time4560 Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 3159 87.77 3159 87.77 1 440 12.23 440 12.23 Total 3599 100.00 3599 100.00
Variable time6180 Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 2404 66.80 2404 66.80 1 1195 33.20 1195 33.20 Total 3599 100.00 3599 100.00
Variable time8190 Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent
49 0 2714 75.41 2714 75.41 1 885 24.59 885 24.59 Total 3599 100.00 3599 100.00
Variable roomspp Observed Imputed Combined Number 3599 3599 Minimum 0.0833333 0.0833333 Maximum 8 8 Mean 0.677459 0.677459 Std Dev 0.495535 0.495535
Variable surf1 Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 3112 86.47 3112 86.47 1 487 13.53 487 13.53 Total 3599 100.00 3599 100.00
IVEware Iterative Imputation Procedure, Thu Jan 02 13:33:09 2003 5
Variable surf2 Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 2157 59.93 2157 59.93 1 1442 40.07 1442 40.07 Total 3599 100.00 3599 100.00
Variable waterin Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 1488 41.34 1488 41.34 1 2111 58.66 2111 58.66 Total 3599 100.00 3599 100.00
Variable wcinside Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 1171 32.54 1171 32.54 1 2428 67.46 2428 67.46 Total 3599 100.00 3599 100.00
Variable own Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 265 7.36 265 7.36 1 3334 92.64 3334 92.64 Total 3599 100.00 3599 100.00
Variable TV Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 177 4.92 177 4.92 1 3422 95.08 3422 95.08 Total 3599 100.00 3599 100.00
Variable parab Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 2819 78.33 2819 78.33 1 780 21.67 780 21.67 Total 3599 100.00 3599 100.00
Variable fridge Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 629 17.48 629 17.48 1 2970 82.52 2970 82.52 Total 3599 100.00 3599 100.00
Variable wash Observed Imputed Combined
50 Code Frequency Percent Frequency Percent Frequency Percent 0 1681 46.71 1681 46.71
IVEware Iterative Imputation Procedure, Thu Jan 02 13:33:09 2003 6
Variable wash Observed Imputed Combined 1 1918 53.29 1918 53.29 Total 3599 100.00 3599 100.00
Variable heater Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 1465 40.71 1465 40.71 1 2134 59.29 2134 59.29 Total 3599 100.00 3599 100.00
Variable car Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 3243 90.11 3243 90.11 1 356 9.89 356 9.89 Total 3599 100.00 3599 100.00
Variable agrland Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 2023 56.21 2023 56.21 1 1576 43.79 1576 43.79 Total 3599 100.00 3599 100.00
Variable hh_fem Observed Imputed Combined Code Frequency Percent Frequency Percent Frequency Percent 0 3140 87.25 3140 87.25 1 459 12.75 459 12.75 Total 3599 100.00 3599 100.00
Variable hh_age2 Observed Imputed Combined Number 3599 3599 Minimum 225 225 Maximum 9604 9604 Mean 2774.47 2774.47 Std Dev 1494.59 1494.59
Variable logcon Observed Imputed Combined Number 3599 3599 Minimum 7.30399 7.30399 Maximum 11.1479 11.1479 Mean 8.95251 8.95251 Std Dev 0.531571 0.531571
Bibliography
INSTAT (2000), General Census of Agricultural Holdings 1998. INSTAT (2002), The Population of Albania in 2001. Raghunathan T. E., Lepkowski J., Van Voewyk J., 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.
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