World Bank
Small area estimates of income poverty in Croatia: methodological report IntroductionThe At-Risk-of-Poverty (AROP) rate indicates the percentage of individuals within a country who live on less than 60 percent of the median national equivalized disposable income after social transfers. It is one of the main indicators derived from the European Union Statistics on Income and Living Conditions Survey (EU-SILC). In Croatia the EU-SILC is representative at the NUTS-1 level as well as at the NUTS-2. The National at-risk-of poverty rate for 2012 in Croatia is 20.4 percent. While regional poverty rates are considerably different between Continental and Adriatic Croatia, 22 and 17 percent respectively. Nevertheless it is possible that poverty levels within NUTS-2 spatial units, differ considerably. Figure 1: EU-SILC poverty map at level of representativenessPoverty figures at lower levels of aggregation (for example NUTS-3, LAU-1, or LAU-2) for Croatia are not possible with the EU-SILC. Geographical levels at which direct estimates lack the required precision are referred to as small areas (Guadarrama et al., 2015). Small area estimation (SAE) methods are those which seek to overcome the lack of precision. SAE methods achieve this by incorporating data sources with larger coverage. These methods present a way to circumvent the low representativeness of household survey methods by taking advantage of larger coverage surveys such as a census. In practice household surveys provide a satisfactory measure of welfare but possess low coverage, while the census has the coverage but lacks a suitable welfare measure. SAE methods take advantage of the best attributes of each data source in order to obtain welfare measures at levels of aggregation below those of the household survey’s representativeness. The use of SAE methods provides estimates of higher precision for small areas than those obtained using a household survey alone. Higher precision of welfare for smaller areas allows policy makers to better target assistance and interventions to the most disadvantaged communities.The Census of Population, Households and Dwellings of 2011 for the Republic of Croatia when combined with the 2012 EU-SILC facilitates the estimation of welfare at the household level. This makes obtaining poverty rates for areas below those of the EU-SILC’s representativeness possible. The small area estimation methodology used to obtain the estimates follows the one proposed by Elbers, Lanjouw, and Lanjouw (ELL) (2003). The methodology is perhaps the most widely used for small area estimation, and has been applied to develop poverty maps in numerous countries across the globe. Through the application of the analysis predicted poverty rates at the NUTS-3, as well as at the LAU-2 levels are obtained.Modeling approachThe ELL method is conducted in 2 stages. The first stage consists in fitting a welfare model using the 2012 EU-SILC data via ordinary least squares (OLS), and correcting for various shortcomings of this approach to arrive at generalized least squares estimates (GLS). It should be noted that the variables included in the welfare model of the 2012 EU-SILC must be restricted to those variables that are also found on the 2011 Census. This allows us to generate the welfare distribution for any sub-population in the 2011 Census, conditional on the sub-population’s observed characteristics (ELL, 2002). After correcting for shortcomings, the estimated regression parameters, standard errors, and variance components from the EU-SILC model provide the necessary inputs for the second phase of the analysis. The second stage of the poverty mapping exercise consists in using the estimated parameters from the first stage, and applying these to the 2011 Census data in order to predict welfare at the household level. Finally, the predicted welfare measure is converted into a poverty indicator which is then aggregated in order to obtain poverty measures at the desired level of aggregation (NUTS-2, NUTS-3, or LAU-2).Before fitting the welfare model, a comparison between the observable household characteristics from the EU-SILC and the Census is necessary. The purpose of the comparison is to ensure that variables have similar distributions, and that these have similar definitions across data sources. Because the exercise consists in predicting welfare in the census data using parameters obtained from EU-SILC observed characteristics, it is imperative that the observed characteristics across surveys are comparable.The next step in the ELL methodology consists in estimating a log adult equivalized household income model which is estimated via OLS. The transformation to log income is done because income tends to not be symmetrically distributed (graph 1), taking the logarithm of income is done to make the data more symmetrical. Figure 2: Adult equivalized income and natural logarithm of equivalized incomeThe household income model is:lnych=X'ch β+uch ( SEQ Equation \* ARABIC 1)where ych is the adult equivalized income of household h in municipality c, Xch are the household and locality characteristics, and uch is the residual. In the specified model the use of Households within a same municipality are usually not independent from one another and the following specification is used to account for this:uch= ηc+εch ( SEQ Equation \* ARABIC 2)where η and ε are assumed to be independent from each other and uncorrelated with the observables, Xch. Households in the same location share the same η, and it is expected that Euch2=ση2+σε2 the larger the variance of η the less precise the estimates of welfare will be when the spatial correlation of the residuals is ignored.The estimation of ση2 and σε2 is done utilizing Henderson’s method III (Henderson, 1953). In the case where the variance of the household specific error, σε2, is assumed to vary across households a parametric form of heteroscedasticity is assumed and modeled as:lnεch2A-εch2=Z'chα+rch ( SEQ Equation \* ARABIC 3)where A=1.05max?(εch2). Making use of these estimates it is possible to obtain an estimate for σε,ch2. The existence of the variance parameters require a re-estimation of the welfare model given that the OLS assumptions are unlikely to hold. The variance covariance matrix utilized for the GLS estimates is household cluster specific, and where the interrelatedness between households within a cluster is also allowed.Once GLS estimates are obtained it is possible to move on to the second stage of the exercise. Small area estimates of welfare (and standard errors) are obtained by applying the parameter and error estimates from the survey to the census data. In order to do this we must simulate welfare. Since poverty indices are based on non-linear forms of log adult equivalized income simulations are ideally suited for obtaining estimates of these measures. A value of log income per adult equivalent ych for each household is simulated making use of the β, η, and the ε parameters from the first stage, where each simulation r is equal to:yrch=exp(X'chβr+ηcr+εchr) ( SEQ Equation \* ARABIC 4)For each simulation a set of βr are drawn from bootstrapped versions of the EU-SILC sample. On the other hand for the location and household disturbance terms we obtain their variance parameters, σε,ch2rand ση2r, from the rth bootstrapped version of the EU-SILC. ηcr and εchr are thus drawn from a normal distribution assuming mean zero and variances equal to σε,ch2rand ση2r, respectively. If we define f(yrch) as a function that maps the estimated adult equivalized income measure to a poverty measure such as the at-risk of poverty head-count-rate (FGT 0) then the estimated mean poverty rate for a municipality c is equal to:FGT0c=1Rr=1Rh=1H fyrchwch ( SEQ Equation \* ARABIC 5)where wch is the population expansion factor (number of household members in household h divided by the total population of Croatia in the census). An alternative for the estimation of η is to use the information from the survey, Empirical-Best estimation (EB). The best estimate available to us of η, for a particular municipality is that which comes from the survey (lnych-X'ch β=uch). Therefore making use of this information the estimates for the municipalities, cities, and districts of Zagreb that are present in the EU-SILC are tighter since more information is included into their drawing. For all locations that are not present in the EU-SILC, the use of EB makes no difference, since for these localities there is no additional information and thus their data generation process is still normal with mean zero and variance ση2r.Within the estimated measures there are three main sources of error: model error, error due to the disturbance, and due to computation error. These three sources of error, as noted by ELL (2003) are not correlated. The error in the welfare measure within a municipality due to the disturbance arises as a result of unobserved components of income within a particular locality. The smaller the population of the targeted municipality the larger this error will be, and thus limits the degree of disaggregation possible. The exact point at which this becomes unacceptable depends on how well the model fits the data.The model error depends entirely on the properties of the first stage estimators it is independent from the population size of the municipality. Within a given municipality the magnitude of this error component will also depend on how different the X variables are in that municipality from those of the EU-SILC data. Finally, computation error is due to the method used for computation. This error can be made as small as possible depending on computational resources at hand. Because often simulations are a finite number, the larger the number of simulations, the smaller the error due to computation will be.Data descriptionThe poverty mapping analysis requires two sources of data. In this instance the Croatian EU-SILC for 2012, and the Census of Population, Households and Dwellings of 2011 for the Republic of Croatia. The EU-SILC for 2012 is an ideal household survey for the SAE analysis because incomes reported in the 2012 EU-SILC correspond to 2011 calendar year, and thus are for the same time period as the census.Small area estimation is done under the assumption that the same underlying population is being captured by the survey and the census. This last assumption will be valid if both datasets are from the same time frame. Nevertheless, the inclusion or the use of datasets that are from differing time periods, or if the survey is not representative of the population, will break down this assumption. This last remark is more salient in instances where there have been considerable shocks in between the collection of the survey and the collection of the census (Bedi et al. 2007). EU-SILC 2012, CroatiaThe EU-SILC data is the EU reference source for comparative statistics on income and social exclusion. The 2012 EU-SILC for Croatia was made up of 5,853 households and is representative at the NUTS-2 level. The at risk of poverty threshold in Croatia for 2012 (income year 2011) is 24,000 HRK. Using this poverty threshold, the at-risk-of-poverty head count rate is 20.4 percent. The 2012 EU-SILC uses the 2001 Census as a sampling frame. The survey is performed as a stratified two-stage sample. The at-risk-of-poverty threshold is obtained by including all households, among these 2 have reported negative net disposable incomes. For purposes of the analysis done these households are no longer included. The households included in the EU-SILC dataset come from 370 municipalities. Finally, all municipalities with less than 3 households in the EU-SILC must be removed for the analysis. The final sample for the EU-SILC is made up of 5,618 households. Census of Population, Households and Dwellings 2011, Population by Sex and AgeThe 2011 Census for Croatia was provided by the Croatian Bureau of Statistics. The census includes key information on demographics of the household, education, labor force status, economic activity, occupation type, and labor status in main job. Along with these characteristics, the census also has information on the type of dwelling, the status of the dwelling, number of rooms in the dwelling, living area of the dwelling, and the construction year. Variable comparison between EU-SILC and CensusBecause small area methods require an estimation of a welfare model in the first stage which will then be applied to the census it is necessary that the choice of correlates matches across surveys. This not only requires variables to be similar, but requires that these have similar distributions. The selection of candidate variable is done in a two stage process:Comparison of questionnaires between the EU-SILC and the Census. The comparison yields a first set of candidate variables for the estimation. Candidate variables must come from similar questions. Comparison of the distribution of the candidate variables across datasets. The comparison is undertaken at the level of Republic of Croatia and at the NUTS-2 level. The comparability of the variables across surveys ensures that the welfare model from the 2012 EU-SILC can be applied to the Census such that reliable income estimates for the population can be derived.Making use of all variables that meet the above criteria several welfare models are estimated via OLS. Unlike most of econometrics, the purpose of the model is not to find any causal relationships but to find a model that best reflects the income level of a household. The income of a household is assumed to be a function of the number of household members present in the household, and the age composition of the household members. Additionally, income is assumed to be a function of the marital status of individuals aged 15 and over, their level of education, their occupation, and the sector in which they are employed in. In addition, and while likely not a determinant of income, we include a variable which reports the area of the dwelling in square meters. This variable is expected to have reasonable correlation with welfare. Finally, the use of location means of household level variables are included. This is done in order to explain the variation in welfare due to location as much as possible and thus improve precision of the welfare estimates.Table 1 contains a listing of the candidate variables for use in the model. The EU-SILC and the Census contain a comprehensive set of variables which match the criteria for modelling income at the household level. Both datasets contain information on the number of household members present in a household. Given that the sampling frame for the 2012 EU-SILC is the previous Census (Census of Population, Households and Dwellings 2001) it is not unexpected that the first moments of the EU-SILC and Census are somewhat different. Nevertheless, at the national level the means of the candidate variables match up considerably well. The mean values for the EU-SILC and for the Census are presented. The final choice of variables for the model is not only dependent upon how well the variables match up, but on how well they explain the variation of income. As the numbers on Table 1 illustrate, the two datasets match up quite well. The age groups, proportion of males, and household size are very close to one another, even at the statistical area level the variables are comparable with one another (Table 1A). Comparison between labor market variables also reveal that the datasets are close to each other with some differences arising in some of the occupations. Similarly these slight differences are also reflected at the regional level comparisons. Given that the differences that arise are not considerable all of the variables are valid candidates for the welfare model to be estimated in the next stage. Variables that are highly correlated are not included simultaneously. Keeping this in mind the selected model is the one which maximizes the adjusted R-squared of the model, but at the same time conforms to prior beliefs of how should the variable be related to income. Table 1: Population weighted candidate variable means in Census and EU-SILCVariable nameCensusEU-SILCMale0.4830.482Age [0,5)0.0500.045Age [5,15)0.1030.106Age [15,30)0.1860.186Age [30,65)0.4860.490Age [65+)0.1740.172Household size (Share of individuals living in household type)Households size of 10.0880.088Households size of 20.1830.183Households size of 30.2020.202Households size of 40.2480.247Households size of 50.1430.143Households size of 60.0760.073Household size of 7 or more0.0600.063Occupation (15+) (Share of individuals in households with at least one member)Manager0.0510.032Professionals0.1500.142Technicians0.1820.132Clerical support0.1290.118Service and sales0.2230.214Skilled agriculture0.0410.051Craft and trade0.1530.167Machine operators0.1120.117Elementary occupations0.0910.071Labor status, age 15-64 (Share of individuals in households with at least one member)Employed0.7420.724Retired0.4970.503Student0.2200.213Disabled0.0380.024Other0.7490.726Industry, age 15-64 (Share of individuals in households with at least one member)Agriculture, mining, and fishing0.0650.068Manufacturing0.1890.195Services and Sales0.6300.572Share of members with education in HH (age 15-64)Primary education0.0860.071Lower secondary0.1990.196Upper secondary0.5470.595Tertiary education0.1690.138Dwelling characteristicsSquare meters87.54288.942Model resultsThe initial welfare model corresponding to equation (1) is presented in column 1 of Table 2. The adjusted R-Squared for the model is (0.52) reflecting that the chosen model explains the variation on income well. In addition to the variables present in both the Census and EU-SILC, variable means for municipalities, cities, and districts of Zagreb are obtained from the Census and introduced to the model; these variables are introduced to improve precision by reducing the unexplained variation in income due to location. With the inclusion of these variables the ratio of the variance of η over the model’s MSE is 0.035. The low ratio illustrates the key role the variables play in improving precision of the estimates. Table 2: Weighted OLS & GLS estimates for Income model: 2012 EU-SILC?Coeff. WOLSCoeff. GLSIntercept8.4124***8.5379***No children under 5-0.104***-0.0781***No children between 5 and 15-0.1322***-0.1294***One child between 5 and 15-0.0795**-0.0834**No indiv. with lower secondary0.0433**0.045**No indiv. with primary0.2104***0.1671***One individual with primary0.11130.0943One person with tertiary education0.1123***0.0989***Two people with tertiary education0.1207***0.1299***1 member HH0.8795***0.9324***2 member HH0.7396***0.8062***3 member HH0.533***0.5899***4 member HH0.3815***0.4271***5 member HH0.1972***0.2414***6 member HH0.1801***0.2069***Nat. log Sq. M0.1091***0.0933***No married ind. In HH-0.1337***-0.134***Proportion of dwellings built 1990-20000.3398**0.3602**Proportion of dwellings with sewerage0.0967***0.0891***Proportion of HH with pension income1.0688***0.994***Municipal employment rates0.9721***0.9221***No ind. is a clerk-0.1071***-0.1107***No ind. is elementary teacher0.0743*0.0752**No ind. is a manager-0.2233***-0.224***No ind. is a professor-0.174***-0.1781***No ind. is a technician-0.1427***-0.1298***Northwest × no lower education0.0966***0.074**Northwest × 2p retired0.01010.0251Central East × lnM20.1009**0.1074***Central East × 2p workers-0.0755*-0.0819**Central Eastern-0.3389*-0.3659**Adriatic0.1142***0.1063***1 retiree0.2299***0.1921***2 retirees0.2733***0.2303***0 administrative workers0.085*0.0788**0 public employees-0.1317***-0.1248***1p working in HH0.5493***0.5428***2p working in HH0.3499***0.3463***3p working in HH0.1464***0.1529***Adjusted R-squared?0.52Ratio of variance of η over Mean Sq. error0.035?Number of observations5,618?5,618*, **, *** significant at the 10, 5, 1 percent level respectively. All households which have inconsistent labor information are removed.As noted in section 2, it is likely that income levels within a location are highly correlated and as a consequence E[uchuci|X]≠0. Additionally, error terms will likely have differing variances across observations (E[uch2|X]≠σ2). Due to these issues the model is re-estimated using Generalized Least Squares (GLS). The results for the GLS fitted model are presented in column 2 of Table 2.Equivalized income is positively correlated to household size. The omitted group is households with 7 or more individuals. Furthermore, equivalized income is negatively correlated to the absence of children in the household. Under the modified OECD scale, when comparing two households with equal household income, the household with lower adult equivalents will have greater adult equivalized income. Thus, all else equal, a household with 2 adults and a child will have greater adult equivalized income than one with 3 adults. Households with retirees also have greater equivalized incomes, this is most likely due to pensions being received by these individuals. After labor the most important source of labor income in Croatia is pension income.Education is also strongly correlated to equivalized income, households with members who have tertiary education have on average greater equivalized incomes. Also correlated to income is the presence of working members and most of the labor variables included are significantly correlated to equivalized income. Among these variables, the presence of working members have the greatest coefficients. Location, and location variable means are also correlated to equivalized income. Adult equivalized income is negatively correlated to being located in Central and Eastern Croatia as opposed to being in the Northwest. On the other hand residing in the Adriatic is positively and significantly correlated to adult equivalized income. In addition, equivalized income is positive and significantly correlated to localities with higher shares of households with pension incomes, households with sewerage, and dwellings built between 1990 and 2000.Poverty resultsThe coefficients estimated in the previous section provide the necessary inputs in order to estimate the first part of equation 4 (X'chβ) by combining coefficients with the Census variables. The vectors of disturbances for households are unknown, and must be estimated. As mentioned before, the error component is decomposed using Henderson’s method III, and the coefficients, β, are obtained by bootstrapped samples of the EU-SILC data. The model chosen is where η and ε are drawn from a normal distribution, with their respective variance structures. Finally, empirical best methods are chosen since these incorporate more information and are thus expected to provide a better fit.The clustering used for estimations is at the municipal, city, and districts of Zagreb level, the resulting poverty map aggregated to the NUTS-3 level is presented in Figure 3 and at the municipal, city, and districts of Zagreb level in Figure 4. The resulting poverty rates used for validation of the small area estimation undertaken are presented in Table 3. These compare the poverty rates obtained from the small area estimation to the direct estimates from the EU-SILC at the statistical area level. This provides support to the quality of the estimates obtained. Table 3: Poverty rates from EU-SILC and from poverty map exercise Statistical regionAROP EU-SILC EU-SILC95% CIPredicted95% CINorthwestern16.7%13.6%20.4%14.1%12.8%15.5%Central & Eastern29.1%26.2%32.2%28.0%25.7%30.2%Adriatic17.0%14.0%20.6%17.4%15.8%19.1%Total20.4%18.5%22.4%19.2%18.0%20.4%Note: Poverty line is at 24,000 HRK per adult equivalentResults at the NUTS-3 spatial unit level are presented in Table 4. These estimates illustrate the heterogeneity within the country. Within the Adriatic region poverty rates range from 11.9 to 25.2 percent, within Continental Croatia (composed of the Northwestern, and Central and Eastern statistical area) poverty ranges from 9.8 percent in Grad Zagreb, to 35.9 percent in Brodsko-posavska. Poverty levels within the Central and Eastern statistical area are considerably greater than the country average.At the municipal, city, and districts of Zagreb level further heterogeneity is revealed. In the Continental NUTS-2 region certain pockets of high poverty levels are detected, particularly in the Central and Eastern statistical region. In the Adriatic region some municipalities with higher poverty rates are also observed. The results of the poverty map suggest an overall spatial clustering of poverty; this is further analyzed in section 6, where basic analysis of the spatial association is undertaken.Figure 3: Poverty Map for Croatia (NUTS-3 poverty headcount)Finally, the distribution of the Republic of Croatia’s population that is at-risk-of-poverty is illustrated in Figure 5. The County with the lowest concentration of poor is in the Adriatic region, Li?ko-senjska. The county is one of the least populated in the country, and although it has an at-risk-of-poverty rate which is close to 20 percent it has the fewest poor. On the other hand Grad-Zagreb which is the least poor county in the Republic of Croatia with an at-risk-of-poverty rate close to 10 percent has the third highest concentration of the country’s poor. Figure 4: Poverty Map for the Republic of Croatia (poverty headcount for municipalities, cities, and districts of Zagreb)Table 4: County level poverty estimates ?EU-SILC direct estimates?H3-EB Model predictionStatistical AreaAROP95% CINUTS-3 (counties)PopulationAROP95% CINorthwestern16.7%13.6%20.4%Zagreba?ka 311,918 16.7%13.9%19.5%Krapinsko-zagorska 129,393 18.8%15.9%21.7%Vara?dinska 170,380 17.1%14.6%19.7%Koprivni?ko-kri?eva?ka 112,540 20.3%17.4%23.3%Me?imurska 110,888 20.8%17.5%24.0%Grad Zagreb 772,340 9.8%8.0%11.6%Central & Eastern29.1%26.2%32.2%Sisa?ko-moslava?ka 168,534 23.7%19.6%27.8%Karlova?ka 125,722 23.2%19.4%27.1%Bjelovarsko-bilogorska 117,420 20.0%15.6%24.5%Viroviti?ko-podravska 83,129 33.4%28.7%38.2%Po?e?ko-slavonska 75,912 26.5%21.1%31.9%Brodsko-posavska 154,863 35.9%31.6%40.1%Osje?ko-baranjska 297,230 28.0%24.8%31.1%Vukovarsko-srijemska 174,324 31.9%28.4%35.3%Adriatic17.0%14.0%20.6%Primorsko-goranska 290,446 11.9%10.0%13.8%Li?ko-senjska 49,766 19.8%15.7%24.0%Zadarska 167,029 25.2%20.9%29.5%?ibensko-kninska 107,345 24.7%20.7%28.8%Splitsko-dalmatinska 445,049 19.5%16.9%22.0%Istarska 204,025 11.9%9.6%14.1%Dubrova?ko-neretvanska 118,707 14.5%11.3%17.8%Republic of Croatia20.4%18.5%22.4%? 4,186,960 19.2%18.0%20.4%Note: Poverty line is at 24,000 HRK per adult equivalentFigure 5: Distribution of the poor by NUTS-3 spatial units for the Republic of CroatiaThe use of poverty mapsLocal indicators of spatial association of povertyUsing the poverty map output we seek to determine if there is a pattern to how poverty rates of municipalities, cities, and districts of Zagreb are distributed within the Republic of Croatia. When analyzing geographical data it is assumed that things that are closer are more related to things that are farther away (Tobler, 1970). This supposes that two municipalities that are closer together will be more alike than municipalities which are farther away.As noted in Section 5 and in Figure 4, there appears to be some spatial clustering in the results from the poverty maps. In fact the Central and Eastern statistical area seems to be lagging behind the Adriatic and Northwest. This illustrates a divergence within the Continental NUTS-2 region. Poverty rates in Central and Eastern regions are considerably greater than the rest of the country, and the region appears to be a hotspot for poverty. Furthermore, there appears to be a clear demarcation of low versus high poverty areas. Insofar as determining if there is in fact spatial correlation we rely on Global Moran’s I as well as Local Moran’s I statistic. In order to obtain undertake analysis of spatial association it is necessary to establish a degree of spatial proximity between the locations in Croatia. In order to do this, a spatial weights matrix is used, which relies on the row-standardized inverse distances between the center of the municipalities and the surrounding municipalities. This ensures that nearer neighbors have a greater influence on the analyzed outcomes, in this instance poverty rates. The presence of spatial association is confirmed by a global Moran’s I index of 0.52 which is significant at the 1 percent level. Local Moran’s I can aide in identifying which localities have a statistically significant relationship with its neighbors. Spatial autocorrelation facilitates the identification of high poverty areas noted in the map presented in Figure 4 (particularly in the Central and Eastern statistical area within the Continental NUTS-2), as well as low poverty areas (around Zagreb and the surrounding areas of Istarska). These results bring to light the challenges that arise for regional development, and add a new layer to the discussion.Figure 6 presents the results for the Global and Local Moran’s I statistics. The significant Global Moran’s I of 0.52 suggest that there is spatial autocorrelation. Additionally, the map illustrates regions which are significantly different from their neighbors, and regions which are high-poverty areas and low poverty areas. All colored areas show a significant relationship to their neighbors. Those locations marked as “High – High” (“Low-Low”) are areas where poverty is significantly greater (lower) than the neighborhood’s poverty and are greater (lower) than the average poverty among municipalities, cities and districts of Zagreb.A cluster of high poverty is clearly delineated in the Eastern Central statistical area (Figure 6 and 7). In Zagreb and surrounding areas a cluster of low poverty is highlighted, the same holds true for the north of the Adriatic region. Municipalities, cities, and/or districts of Zagreb marked as low-high outliers and the high-low outliers are particularly of interest. While poverty may be high (low) in particular areas, there are some municipalities that have a significantly lower (higher) level of poverty than its surroundings. These are mostly observed in the Adriatic and Eastern Central areas.The hot spot analysis in Figure 8, brings to light a demarcation and separation between regions. This was also evident in the results from the OLS and GLS (see Table 2). All three statistical areas are different. Independently from the NUTS-2 classification which aggregates the Northwestern statistical area and the Eastern and Central statistical area, when it comes to welfare these areas are considerably different. Figure 6: Poverty Map for the Republic of Croatia (Spatial association of headcount poverty)Figure 7: Poverty Map for the Republic of Croatia: hot spot analysis (Getis-Ord Gi)Concluding remarksDirect poverty estimates from the EU-SILC are only reliable at the statistical area level, and thus at the NUTS-2 level. This complicates the analysis of poverty at more disaggregated levels since the reliability of direct estimates are questionable. Data from the Census of Population, Households and Dwellings 2011 coupled with small area estimation techniques aide policy makers overcome the lack of precision at lower geographical levels. The results from the poverty mapping exercise, coupled with spatial analysis reveal the heterogeneity of poverty in Croatia. Results from spatial analysis reveal that there is a cluster of high poverty in the Central and Eastern region of Croatia. There is a clear poverty demarcation in the country, where the Central and Eastern part of the country is clearly doing worse than the rest of the country. Results also reveal that while the Continental NUTS-2 spatial unit, may seem poorer than the Adriatic, the result is mainly driven by the aggregation of the two statistical regions (Northwest, and the Central and Eastern statistical regions).The use of the poverty map in order to assist in the guidance of resource allocation can help policy makers achieve considerable gains in poverty reduction. Additionally, the visual format of the maps is simple to understand which makes it easy for the population at large to take notice of where their community stands compared to the rest of the country. Moreover, because the maps are based on established data sets, these are objective. As a consequence the maps may help prevent subjective decision making. Given the mentioned uses of the poverty maps these are valuable component of the policy maker’s tool kit when trying to decide where limited funds can be distributed among the population which needs assistance. ReferencesBaric, M., & Williams, C. (2015). Tackling the undeclared economy in Croatia. South-Eastern Europe Journal of Economics, 11(1).Bedi, T., Coudouel, A., & Simler, K. (Eds.). (2007). More than a pretty picture: using poverty maps to design better policies and interventions. World Bank Publications.Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2002). Micro-level estimation of welfare. World Bank Policy Research Working Paper, (2911)Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro–level estimation of poverty and inequality. Econometrica, 71(1), 355-364.Elbers, C., Fujii, T., Lanjouw, P., ?zler, B., & Yin, W. (2007). Poverty alleviation through geographic targeting: How much does disaggregation help?. Journal of Development Economics, 83(1), 198-213.Guadarrama, M., Molina, I., & Rao, J. N. K. (2016). A Comparison of Small Area Estimation Methods for Poverty Mapping. STATISTICS IN TRANSITION new series and SURVEY METHODOLOGY, 41.Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic geography, 46(sup1), 234-240.Van der Weide, R. (2014). GLS estimation and empirical bayes prediction for linear mixed models with Heteroskedasticity and sampling weights: a background study for the POVMAP project. World Bank Policy Research Working Paper, (7028).AppendixMathematical appendixThe discussion below presents the methodology detailed by ELL (2002 and 2003). Interested reader should refer to these documents for the full discussion.From the estimation of equation 1 we obtain the residuals u?ch , and by defining u?c. as the weighted average of u?ch for a specific cluster we can obtain e?ch :The variance of the location effect (ηc) is given by:where u.. = Cwcuc. (where the wc represents the cluster’s weight) and:where ec.=hechnc (nc is the number of households in the cluster). The parametric from of heteroscedasticity is presented as:This is simplified by setting B=0 and A=1.05max?(ech2), which leads to the simpler form that can be estimated via regular OLS:By defining B=exp?(Zchα) and using the delta method the household specific variance for ech is equal to:The use of ση2 and σε2 allows us to get the variance covariance matrix used for the OLS estimates:The estimates for the GLS detailed by ELL (2003) are:andIn response to criticisms of the methodology an extensive revision was made to the methods, including the addition of empirical best estimation, by Van der Weide (2014). For a detailed discussion on the EB approach and the other changes implemented readers are guided towards Van der Weide (2014).The revisions include an improved GLS estimator: and a new variance covariance matrix:These are the estimates used for the second stage of the estimation (detailed in the methods section).Poverty mapping softwareOne of the most common small area methods used for poverty mapping was proposed by Elbers, Lanjouw, and Lanjouw (2003). This methodology has been widely adopted by the World Bank and has been applied in numerous poverty maps conducted by the institution. In its e?orts to make the implementation of the ELL methodology as simple as possible, the World Bank created a software package that could be easily used by anyone. The software, PovMap (Zhao, 2006), has proven to be an invaluable resource for the World Bank as well as for many statistical agencies seeking to create their own poverty maps. The software is freely available and has a graphical user interface which simplifies its use.Poverty map results produced in this document have all made use of the PovMap software. The PovMap software can be downloaded, free of charge, at tables and graphsTable 1A: Population weighted candidate variable means in Census and EU-SILC at the Statistical Area levelsNorthwestCentral & EasternAdriaticVariable nameCensusEU-SILCCensusEU-SILCCensusEU-SILCMale0.47770.47710.48430.48320.48730.4870Age [0,5)0.05150.04420.04760.05120.04830.0400Age [5,15)0.10210.10790.10820.10500.09920.1059Age [15,30)0.18720.18730.18970.18970.18170.1817Age [30,65)0.49370.49640.47640.48010.48990.4920Age [65+)0.16550.16420.17820.17400.18100.1805Household size (Share of individuals living in household type)Households size of 10.0860.0870.0860.0870.0880.090Households size of 20.1750.1730.1810.1830.1950.196Households size of 30.2000.1990.1890.1890.2150.217Households size of 40.2430.2440.2370.2380.2600.257Households size of 50.1440.1430.1540.1470.1330.140Households size of 60.0830.0890.0850.0810.0610.046Household size of 7 or more0.0700.0650.0670.0740.0470.053Occupation (15-64) (Share of individuals in households with at least one member)Manager0.0660.0320.0310.0150.0520.048Professionals0.1880.1730.1070.1030.1450.140Technicians0.2140.1510.1400.0950.1830.140Clerical support0.1500.1290.1030.0720.1270.145Service and sales0.2200.1920.1920.1870.2540.263Skilled agriculture0.0350.0370.0640.1060.0250.021Craft and trade0.1690.2020.1450.1510.1400.141Machine operators0.1220.1350.1180.1120.0930.099Elementary occs.0.0900.0670.1030.0690.0810.080Labor status, age 15-64 (Share of individuals in households with at least one member)Employed0.7930.7620.6890.6710.7320.727Retired0.4970.5130.5150.5270.4920.470Student0.2230.2260.2200.1920.2210.216Disabled0.0360.0160.0520.0450.0300.016Other0.7270.7250.7940.7540.7450.703Industry, age 15-64 (Share of individuals in households with at least one member)Agriculture, mining, and fishing0.0520.0470.1120.1300.0410.040Manufacturing0.2250.2410.1910.1770.1470.158Services and Sales0.6840.6050.5320.4690.6550.624Share of members with education in HH (age 15-64)Primary education0.0750.0670.1070.0740.0810.074Lower secondary0.1840.1950.2630.2520.1620.149Upper secondary0.5360.5690.5210.5800.5780.639Tertiary education0.2060.1700.1100.0930.1790.139Dwelling characteristicsSquare meters90.71187.12092.52395.29683.18785.564Table A2: Alpha model ?Coeff.Std Err.1 Retiree-0.2663**0.1066No service sector workers0.3921***0.14071 working person-0.289**0.1472 working persons-0.2543**0.1208Constant-5.5976***0.1786Adj. R20.0019Observations2,229?Figure A1: NUTS 3 Poverty estimates and 95% confidence intervalsFigure A2: Poverty in the districts of Zagreb Table 3A: Poverty indicators by LAU-2Location Population Head count povertyStd. Err. Head count povertyPoverty Gap Std. Err. Poverty GapPoverty Gap Sq.Std. Err. Poverty Gap Sq.Share of poorDonji Grad 35,609 6.901.601.600.400.500.200.30Gornji Grad-Medve?èak 29,750 5.501.801.200.400.400.200.20Trnje 41,021 7.301.601.700.400.600.200.30Maksimir 47,362 7.502.401.700.600.600.200.40Pe??enica-?itnjak 55,057 16.003.204.401.001.800.401.00Novi Zagreb-istok 58,052 6.601.701.400.400.500.200.40Novi Zagreb-zapad 56,647 10.402.302.500.600.900.300.70Tre?njevka-sjever 54,197 9.902.602.400.700.900.300.60Tre?njevka-jug 65,555 6.801.701.500.400.500.200.50?rnomerec 37,577 6.802.201.500.600.500.200.30Gornja Dubrava 60,882 16.103.904.201.201.700.501.10Donja Dubrava 35,871 16.303.504.301.101.800.500.70Stenjevec 50,678 8.702.202.100.600.800.200.50Podsused-Vrapèe 44,580 6.801.401.500.400.500.100.30Podsljeme 18,858 4.901.501.100.400.400.100.10Sesvete 68,924 12.706.803.302.001.300.901.00Brezovica 11,720 12.303.802.901.101.100.400.20Grad Zagreb 772,340 9.800.902.400.300.900.108.60Andrija?evci 4,020 37.508.9011.103.204.801.600.20Antunovac 3,610 21.307.805.702.502.301.100.10Babina Greda 3,516 42.6010.9013.104.205.702.100.20Bakar 8,211 16.004.804.001.401.500.600.10Bale - Valle 1,125 13.804.803.301.301.200.500.00Barban 2,688 10.705.802.501.700.900.700.00Barilovi? 2,967 23.908.606.602.802.701.300.10Ba?ka 1,658 12.604.902.901.401.000.600.00Ba?ka Voda 2,773 21.606.305.701.902.200.800.10Bebrina 3,185 40.3010.7012.404.305.502.200.10Bedekov?ina 7,759 20.005.505.301.702.100.800.20Bednja 3,954 31.607.309.302.704.001.300.10Beli Manastir 9,459 32.506.4010.502.604.801.400.30Belica 3,150 12.305.102.901.301.000.500.00Beli??e 10,509 36.2010.2011.604.005.302.100.40Benkovac 10,934 42.308.6013.203.505.801.800.50Berek 1,437 39.9010.5013.104.206.102.200.10Beretinec 2,117 18.307.504.402.101.700.900.00Bibinje 3,969 30.308.508.503.003.501.500.10Bilje 5,590 23.006.406.502.102.701.000.10Biograd Na Moru 5,501 17.006.304.301.901.600.800.10Bizovac 4,456 23.007.006.002.202.401.000.10Bjelovar 39,061 15.805.004.201.601.700.700.70Blato 3,460 6.003.101.100.700.400.200.00Bogdanovci 1,877 24.208.406.302.702.401.200.10Bol 1,576 16.505.904.001.601.500.700.00Borovo 4,857 41.807.8013.003.305.801.800.20Bosiljevo 1,253 24.706.307.002.202.901.100.00Bo?njaci 3,748 43.009.9014.204.406.502.400.20Brckovljani 6,432 26.207.207.402.403.101.100.20Brdovec 11,048 13.704.003.301.101.200.400.20Brestovac 3,691 40.2011.6012.204.505.202.200.20Breznica 2,188 27.709.407.603.103.101.400.10Brinje 3,180 33.307.309.702.704.101.400.10Brod Moravice 849 20.305.607.002.103.501.200.00Brodski Stupnik 2,950 47.2015.1015.406.606.903.500.20Brtonigla - Verteneglio 1,622 14.605.903.301.501.200.600.00Budin??ina 2,390 36.1010.7010.503.904.401.900.10Buje - Buie 5,102 10.704.402.501.200.900.500.10Buzet 6,048 6.903.401.500.900.500.300.00Cerna 4,489 37.308.0011.203.104.801.500.20Cernik 3,562 40.109.4012.403.905.402.000.20Cerovlje 1,650 12.205.502.701.301.000.500.00Cestica 5,504 34.906.9010.702.404.901.200.20Cetingrad 1,921 32.1011.009.404.203.902.100.10Cista Provo 2,310 42.4011.4013.104.705.702.400.10Civljane 226 64.0013.3022.507.0010.604.000.00Cres 2,777 10.704.602.401.200.800.500.00Crikvenica 10,947 13.002.803.100.801.200.300.20Crnac 1,445 41.808.8012.803.705.501.900.10?abar 3,748 4.703.700.900.900.300.300.00?a?inci 2,758 37.908.8011.303.304.801.600.10?a?avica 1,983 33.9010.609.703.804.001.800.10?aglin 2,363 46.309.8015.204.406.902.400.10?akovec 26,422 17.203.105.301.002.500.500.50?avle 7,071 12.204.102.901.101.000.500.10?azma 7,926 13.204.203.201.101.200.400.10?eminac 2,780 27.406.807.302.202.901.000.10?epin 11,299 19.506.505.102.002.000.900.30Darda 6,746 45.508.4016.003.707.802.100.30Daruvar 11,482 10.803.402.500.900.900.300.10Davor 2,967 33.7010.209.603.703.901.800.10Delnice 5,747 12.903.703.401.101.400.400.10Desini? 2,604 26.409.307.002.902.801.300.10De?anovac 2,706 37.8013.9011.305.804.903.000.10Dicmo 2,753 29.908.508.503.003.501.400.10Dobrinj 2,051 14.005.303.201.501.100.600.00Doma?inec 2,217 24.707.607.402.503.301.200.10Brela 1,698 14.505.303.501.501.300.600.00Donja Dubrava 1,895 17.606.204.301.801.600.800.00Donja Stubica 5,375 15.005.103.701.401.400.600.10Donja Vo?a 2,392 44.607.2014.303.006.401.600.10Donji Andrijevci 3,666 32.307.709.502.904.001.400.10Donji Kraljevec 4,527 12.904.803.001.301.100.500.10Donji Kukuruzari 1,634 61.208.8021.905.0010.503.000.10Donji Lapac 2,028 47.2011.7015.705.307.202.900.10Martijanec 3,788 16.606.603.901.801.400.800.10Donji Miholjac 9,275 29.305.708.201.903.400.900.30Mu? 3,838 25.507.106.602.302.501.000.10Prolo?ac 3,491 38.308.7011.703.405.101.700.20Donji Vidovec 1,378 21.106.006.101.902.600.900.00Dragani? 2,665 23.106.707.002.303.101.100.10Dra? 2,681 47.9010.4016.104.707.502.600.10Drenovci 4,969 44.608.9014.604.006.602.100.30Drenje 2,592 51.6010.8017.304.908.002.700.20Drni? 7,422 22.806.205.902.102.300.900.20Drnje 1,832 19.205.805.901.902.701.000.00Dubrava 5,023 31.809.608.803.403.501.600.20Dubrovnik 41,417 7.802.301.800.600.600.200.40Duga Resa 11,120 19.007.004.902.301.901.000.20Dugi Rat 6,982 26.007.107.102.302.801.000.20Dugo Selo 17,201 16.804.904.301.501.700.600.30Dvor 5,478 45.208.1014.803.706.702.000.30?akovo 26,790 30.206.008.702.103.701.000.90?elekovec 1,490 18.705.404.901.701.900.800.00?ulovac 3,171 43.5012.4014.105.106.502.700.20?ur?enovac 6,598 36.507.0010.802.504.601.200.30?ur?evac 8,090 23.905.307.701.903.601.000.20?urmanec 4,150 17.806.904.202.001.500.800.10Erdut 7,108 48.3011.7016.005.207.302.800.40Ernestinovo 2,064 14.406.003.301.601.100.600.00Ervenik 1,098 62.8011.0022.706.0010.803.500.10Farka?evac 1,889 30.9011.309.404.104.202.000.10Ferdinandovac 1,739 22.409.206.302.902.601.400.00Feri?anci 2,093 39.009.1012.103.705.301.900.10Fu?ine 1,570 10.404.202.301.100.800.400.00Gar?in 4,729 41.7010.3013.304.105.902.100.20Gare?nica 10,258 26.705.707.902.003.401.000.30Generalski Stol 2,586 23.907.106.102.102.400.900.10Glina 8,757 28.106.308.102.203.401.100.30Gola 2,389 22.906.806.002.002.400.900.10Gori?an 2,777 17.805.404.301.501.600.600.10Gorjani 1,564 40.1011.0012.104.205.202.000.10Gornja Stubica 5,258 23.306.706.002.002.300.900.10Gornji Bogi?evci 1,957 52.607.5018.703.709.002.200.10Gornji Kneginec 5,252 20.706.105.301.802.000.700.10Gospi? 12,320 14.103.603.501.001.300.400.20Gra?ac 4,661 43.408.4013.803.606.101.800.20Gra?i??e 1,416 11.504.702.601.200.900.500.00Gradac 3,237 25.809.007.303.103.001.500.10Gradec 3,601 25.707.807.102.602.901.200.10Gradina 3,799 55.609.2019.204.609.002.600.20Gradi?te 2,627 34.208.0010.003.004.201.500.10Gro?njan - Grisignana 733 19.105.404.601.601.700.700.00Grubi?no Polje 6,383 19.404.205.301.302.100.600.10Gundinci 2,013 58.5011.4020.505.809.703.300.10Gunja 3,637 60.308.2023.204.5011.802.700.20Hercegovac 2,378 15.906.204.001.801.500.800.00Hlebine 1,271 23.206.906.602.302.901.100.00Hra??ina 1,535 22.106.805.302.001.900.800.00Hrvace 3,595 39.6010.8011.804.205.002.100.20Hrvatska Dubica 2,070 47.608.1015.603.607.002.000.10Hrvatska Kostajnica 2,734 27.407.807.402.702.901.300.10Brezni?ki Hum 1,314 25.009.206.702.902.601.300.00Hum Na Sutli 4,851 11.805.702.801.601.000.700.10Hvar 4,218 12.104.002.801.001.000.400.10Ilok 6,500 19.305.805.001.801.900.800.10Imotski 10,671 39.209.2012.703.805.702.000.50Ivanec 13,447 16.903.204.200.901.600.400.30Ivani?-Grad 14,292 20.604.405.601.402.300.600.30Ivankovo 7,762 36.706.9010.502.604.401.200.30Ivanska 2,908 24.508.407.002.703.001.300.10Jakovlje 3,813 15.005.403.601.501.300.600.10Jak?i? 3,986 26.707.507.502.603.101.200.10Jal?abet 3,120 23.406.506.202.002.500.900.10Jarmina 2,440 31.109.808.503.303.401.500.10Jasenice 1,395 25.609.006.602.802.501.200.00Jasenovac 1,987 34.4010.1010.003.704.101.800.10Jastrebarsko 15,625 13.103.903.201.101.200.400.20Jelenje 5,277 19.206.004.701.701.800.700.10Jelsa 3,556 16.106.904.002.101.500.900.10Josipdol 3,723 30.008.809.103.104.101.500.10Kali 1,628 18.909.004.502.801.601.200.00Kanfanar 1,541 8.103.601.800.900.600.400.00Kapela 2,939 37.5010.2011.504.005.002.000.10Kaptol 3,446 40.2010.0012.704.005.602.000.20Karlobag 915 25.9010.307.003.702.801.700.00Karlovac 54,120 18.002.804.800.901.900.401.10Kastav 10,346 9.203.402.100.900.700.300.10Ka?tela 38,044 20.305.205.201.602.000.700.90Kijevo 415 24.408.405.902.502.101.000.00Kistanje 3,429 74.808.6032.506.4017.804.400.30Klakar 2,251 29.608.308.102.903.301.400.10Klana 1,966 9.704.002.201.000.800.400.00Klanjec 2,911 8.904.002.001.000.700.400.00Klenovnik 2,006 20.307.205.202.202.000.900.00Klin?a Sela 5,108 14.506.303.501.801.300.700.10Klis 4,738 23.105.206.001.602.300.700.10Klo?tar Ivani? 5,990 27.507.707.702.703.201.300.20Klo?tar Podravski 3,200 41.008.3015.403.708.002.100.10Kne?evi Vinogradi 4,517 41.509.1013.303.806.002.000.20Knin 15,011 42.707.7014.003.406.301.800.70Komi?a 1,519 16.305.403.901.501.400.600.00Konavle 8,549 10.404.602.401.200.900.500.10Kon?anica 2,340 11.206.202.701.701.000.700.00Konj??ina 3,658 18.608.004.802.501.801.100.10Koprivnica 29,930 14.702.303.800.701.500.300.50Koprivni?ki Bregi 2,270 20.504.905.201.502.000.700.10Koprivni?ki Ivanec 1,972 19.707.605.002.301.901.000.00Kor?ula 5,585 12.705.702.901.601.100.600.10Ko?ka 3,889 34.808.4010.303.204.401.600.20Kotoriba 3,080 25.805.709.402.204.801.300.10Kraljevec Na Sutli 1,727 10.304.202.101.000.700.400.00Kraljevica 4,490 11.503.902.601.000.900.400.10Krapina 12,105 13.003.903.101.001.200.400.20Krapinske Toplice 5,249 14.005.603.501.601.300.700.10Kri? 6,794 26.906.207.302.002.900.900.20Kri?evci 20,631 15.104.603.701.301.400.600.40Krk 5,951 10.505.202.301.300.800.500.10Krnjak 1,826 48.2010.5016.204.807.502.700.10Kr?an 2,913 15.905.404.001.601.500.700.10Kula Norinska 1,608 37.709.6011.603.805.102.000.10Kutina 22,337 19.704.005.501.302.300.600.50Kutjevo 6,165 30.708.508.703.003.601.400.20Labin 11,497 6.703.101.400.800.500.300.10Lani??e 328 17.806.904.002.001.400.900.00Lasinja 1,612 15.006.603.801.901.500.800.00Lastovo 792 16.507.204.002.101.500.900.00Legrad 2,185 11.804.603.001.301.100.500.00Lekenik 5,885 22.906.206.101.902.500.900.20Lepoglava 7,437 22.706.406.102.102.401.000.20Levanjska Varo? 1,016 60.509.5023.405.6011.903.600.10Lipik 6,002 22.506.406.102.102.400.900.20Lipovljani 3,450 17.506.304.301.801.600.800.10Li?ane Ostrovi?ke 686 32.3010.009.703.904.202.000.00Li?njan - Lisignano 3,806 14.104.603.401.301.300.500.10Lobor 2,818 25.506.106.601.902.500.800.10Lokve 1,004 15.605.403.601.501.300.600.00Lovas 1,207 15.707.503.802.101.400.900.00Lovinac 995 13.206.303.301.801.300.800.00Lovran 4,033 9.503.802.201.000.800.400.00Lovre? 1,691 35.109.8010.503.804.501.900.10Ludbreg 8,223 10.704.202.601.101.000.500.10Luka? 3,568 41.306.9012.802.705.601.400.20Lupoglav 918 13.706.203.101.601.100.600.00Ljube??ica 1,837 21.806.205.601.902.200.800.00Ma?e 2,511 30.608.008.202.803.301.300.10Makarska 13,684 11.603.402.801.001.100.400.20Mala Subotica 5,274 24.804.609.401.805.001.100.10Mali Bukovec 2,185 21.407.105.802.202.401.000.10Mali Lo?inj 7,916 14.704.503.401.201.200.500.10Malinska-Duba?nica 3,050 13.405.203.101.401.100.600.00Mar?ana 4,199 13.704.003.301.101.200.500.10Marija Bistrica 5,889 18.304.804.601.401.700.600.10Marijanci 2,358 28.608.107.502.502.901.100.10Marina 4,496 24.005.906.201.902.400.800.10Martinska Ves 3,393 26.307.507.102.502.801.100.10Maru?evec 6,275 15.004.303.701.101.400.500.10Matulji 11,121 11.104.102.601.101.000.500.10Medulin 6,374 6.203.201.400.800.500.300.00Metkovi? 15,956 29.007.208.402.503.501.200.50Mihovljan 1,921 35.008.1010.103.004.201.400.10Mikleu? 1,449 47.6010.3015.404.606.902.500.10Milna 1,022 14.506.303.401.801.200.700.00Mljet 1,061 20.106.405.302.102.100.900.00Molve 2,147 23.708.106.102.502.401.100.10Podravska Moslavina 1,153 35.109.4010.203.404.301.600.00Mo??eni?ka Draga 1,526 10.104.302.301.100.800.400.00Motovun - Montona 916 19.606.905.102.101.900.900.00Mrkopalj 1,205 12.805.502.901.401.000.600.00Mursko-Sredi??e 6,209 24.907.007.902.403.701.200.20Na?ice 15,912 24.305.807.001.903.000.900.40Nedeli??e 11,700 23.904.108.401.504.200.800.30Nere?i??a 845 13.805.803.001.501.000.500.00Netreti? 2,791 22.207.305.702.202.200.900.10Nin 2,710 23.006.906.002.402.301.100.10Nova Bukovica 1,769 50.509.7017.004.507.802.500.10Nova Gradi?ka 13,880 26.706.107.902.103.401.000.40Nova Kapela 4,108 35.209.7010.003.504.001.700.20Nova Ra?a 3,391 20.207.205.202.102.000.900.10Novalja 3,613 16.205.303.801.401.400.600.10Novi Marof 13,103 14.203.803.401.001.300.400.20Novi Vinodolski 4,976 13.904.303.401.201.300.500.10Novigrad - Cittanova 4,145 9.303.502.100.900.700.400.00Novigrad Podravski 2,758 32.907.5010.102.704.601.300.10Novska 13,404 25.207.807.102.702.901.300.40Nu?tar 5,486 25.006.907.002.302.901.000.20Nijemci 4,643 38.3012.3011.804.805.202.400.20Obrovac 4,254 43.709.3014.504.106.702.300.20Ogulin 13,687 19.605.305.201.602.100.700.30Promina 1,048 27.209.706.903.102.601.300.00Oku?ani 3,362 63.1010.9024.006.6012.104.200.20Omi? 14,654 27.106.707.502.303.001.000.50Omi?alj 2,973 14.004.903.701.501.500.700.00Opatija 11,369 12.404.002.901.101.100.400.20Oprisavci 2,481 24.707.306.502.202.601.000.10Oprtalj - Portole 850 19.307.805.002.401.901.000.00Opuzen 3,133 18.606.504.702.001.800.900.10Orahovica 5,090 25.406.706.902.302.801.000.10Orebi? 4,031 9.005.002.001.300.700.500.00Oriovac 5,719 33.507.809.802.904.201.400.20Biskupija 1,688 56.7011.4018.905.608.503.100.10Oroslavje 6,039 14.204.003.501.101.300.500.10Osijek 105,841 18.303.204.901.001.900.402.20Oto?ac 9,516 17.304.004.501.201.800.500.20Otok 5,401 41.7011.5012.904.705.702.400.30Ozalj 6,537 27.0010.407.403.303.001.500.20Pag 3,802 11.304.602.501.200.900.400.00Pako?tane 4,090 39.9010.5012.504.405.502.300.20Pakrac 8,345 24.105.906.602.002.600.900.20Pa?man 2,069 29.009.607.803.303.101.500.10Pazin 8,570 18.4010.204.603.001.801.300.20Peru?i? 2,636 25.008.307.002.802.901.300.10Peteranec 2,648 29.506.7010.102.505.001.300.10Petlovac 2,350 45.709.0014.603.906.502.000.10Petrijanec 4,695 24.107.208.402.504.301.400.10Petrijevci 2,761 30.208.308.502.803.501.300.10Petrinja 23,896 19.004.505.101.502.000.700.50Petrovsko 2,643 25.208.006.702.402.701.100.10Pi?an 1,805 12.605.402.801.400.900.500.00Pisarovina 3,661 10.404.702.401.200.900.500.00Pitoma?a 9,782 40.806.2013.502.506.301.400.50Pla?ki 2,057 52.4010.2017.104.807.702.600.10Pleternica 11,115 28.708.108.002.903.201.300.40Plo?e 9,776 21.006.205.502.002.100.900.20Podbablje 4,679 35.306.7010.902.604.801.300.20Podcrkavlje 2,544 33.808.3010.203.204.401.600.10Podgora 2,505 25.106.706.802.202.701.000.10Podgora? 2,834 53.809.1019.404.209.702.400.20Podstrana 8,932 11.403.402.800.901.100.400.10Podturen 3,810 29.208.308.802.704.001.300.10Pojezerje 896 38.0011.7010.904.404.502.100.00Pola?a 1,452 31.509.308.703.303.501.500.10Poli?nik 4,454 29.608.808.003.003.101.300.10Popovac 2,044 43.009.5014.004.306.302.300.10Popova?a 11,394 25.706.007.702.103.401.000.30Pore? - Parenzo 16,438 11.503.502.801.001.000.400.20Posedarje 3,565 32.508.709.203.103.801.400.10Postira 1,542 11.804.402.701.201.000.500.00Po?ega 25,406 18.803.804.901.201.900.500.50Pregrada 6,485 24.706.506.302.002.400.800.20Preko 3,339 17.405.904.101.701.500.700.10Prelog 7,638 14.604.603.501.301.300.500.10Preseka 1,413 11.805.502.501.300.800.500.00Primo?ten 2,794 18.405.804.401.701.600.700.10Pu?i??a 2,144 14.905.003.501.301.200.500.00Pula - Pola 55,918 11.202.002.600.500.900.200.70Punat 1,907 10.504.302.301.100.800.400.00Punitovci 1,750 36.609.5010.403.404.301.600.10Pu??a 2,615 13.405.303.301.501.300.600.00Rab 7,942 15.206.103.601.701.300.700.10Radoboj 3,339 25.306.006.601.802.500.800.10Rakovica 2,368 23.008.206.102.602.301.200.10Rasinja 3,171 40.507.0013.102.806.001.400.10Ra?a 3,074 14.904.903.501.401.300.500.10Ravna Gora 2,426 8.104.001.701.000.500.400.00Ra?anac 2,900 32.7010.109.203.603.801.700.10Re?etari 4,653 52.9017.1018.808.809.005.200.30Rijeka 125,857 10.901.502.600.400.900.201.60Rovinj 13,942 12.904.003.001.101.100.500.20Rovi??e 4,749 30.206.708.902.303.901.100.20Rugvica 7,661 25.307.106.902.202.801.000.20Ru?i? 1,559 22.608.405.602.602.101.100.00Saborsko 626 33.6012.7010.104.804.302.400.00Sali 1,672 14.005.903.001.601.000.600.00Samobor 37,186 13.903.603.401.001.300.400.60Satnica ?akova?ka 2,082 44.7010.7014.104.506.302.400.10Seget 4,787 26.007.306.902.302.701.000.10Selca 1,786 17.805.704.301.701.600.700.00Selnica 2,885 26.106.106.902.002.700.900.10Semeljci 4,219 44.209.8015.204.207.302.300.20Senj 7,095 13.503.703.201.001.100.400.10Sibinj 6,815 35.909.2010.603.604.501.800.30Sinj 24,471 24.307.706.702.602.701.200.70Sira? 2,201 23.408.606.102.702.401.200.10Sisak 46,762 17.003.704.501.201.800.500.90Skrad 1,054 8.604.701.701.100.500.400.00Skradin 3,701 25.007.306.702.402.601.100.10Slatina 13,529 25.905.307.401.803.100.900.40Slavonski Brod 57,296 30.304.409.101.604.000.802.00Slavonski ?amac 2,112 41.5010.1013.304.205.902.200.10Slivno 1,906 22.807.506.002.202.401.000.00Slunj 5,012 36.009.3010.703.604.501.800.20Smokvica 874 8.003.701.600.900.500.300.00Sokolovac 3,346 34.009.0010.103.404.301.700.10Solin 23,670 12.004.002.901.101.100.400.30Sopje 2,242 49.5011.9015.705.406.902.900.10Split 173,163 13.401.803.300.501.200.202.60Sra?inec 4,689 18.505.904.801.701.900.700.10Stankovci 1,982 31.9010.008.603.503.401.600.10Stara Gradi?ka 1,349 42.1011.2013.204.605.802.400.10Stari Grad 2,744 15.606.203.701.801.300.700.00Stari Jankovci 4,322 40.909.4012.803.805.601.900.20Stari Mikanovci 2,864 38.1011.7011.704.805.102.500.10Starigrad 1,869 29.308.108.002.803.101.300.10Staro Petrovo Selo 5,090 47.408.7015.703.907.202.100.30Ston 2,287 24.908.806.803.002.701.400.10Strizivojna 2,494 42.007.9012.903.005.601.500.10Stubi?ke Toplice 2,736 14.105.203.501.501.300.600.00Su?uraj 458 21.408.505.002.501.801.000.00Suhopolje 6,477 36.0010.5011.504.305.102.200.30Suko?an 4,533 31.807.908.802.803.601.300.20Sunja 5,709 44.509.8014.304.306.402.300.30Supetar 3,997 12.605.002.901.401.100.500.10Sveti Filip I Jakov 4,434 30.707.308.702.503.601.200.20Sveti Ivan Zelina 15,623 19.904.905.101.502.000.700.40Sveti Kri? Za?retje 6,037 19.405.504.801.701.800.700.10Sveti Lovre? 1,014 10.104.902.101.200.700.500.00Sveta Nedelja 2,880 8.604.801.901.200.600.500.00Sveti Petar U ?umi 1,052 8.104.401.601.000.500.400.00Svetvin?enat 2,184 13.205.403.401.601.300.700.00Sveta Nedelja 17,785 11.005.002.601.300.900.500.20Sveti ?ur? 3,763 27.207.907.802.603.401.200.10Sveti Ilija 3,357 15.506.303.801.801.400.700.10Sveti Ivan ?abno 5,086 21.207.305.302.102.000.900.10Sveti Juraj Na Bregu 4,909 31.9013.209.104.703.802.200.20Sveti Martin Na Muri 2,586 21.405.005.501.502.100.600.10Sveti Petar Orehovec 4,449 12.505.302.801.301.000.500.10?estanovac 1,849 38.7010.5011.504.104.802.000.10?ibenik 45,426 13.903.003.400.901.200.400.70?kabrnja 1,770 23.908.106.402.602.601.200.00?olta 1,668 20.407.605.002.301.800.900.00?pi?i? Bukovica 4,171 41.908.6013.203.505.901.800.20?tefanje 1,988 23.608.107.402.903.401.500.10?trigova 2,526 24.906.806.702.102.701.000.10Tinjan 1,660 11.304.902.601.300.900.500.00Tisno 3,089 22.807.505.702.302.100.900.10Plitvi?ka Jezera 4,299 15.405.203.701.501.400.600.10Tompojevci 1,523 37.4010.7011.004.204.602.100.10Topusko 2,956 23.707.406.702.602.701.200.10Tordinci 2,004 33.5010.309.603.704.001.700.10Tovarnik 2,736 26.107.807.202.602.901.200.10Trilj 8,801 42.308.4013.003.405.601.700.40Trnava 1,568 53.7010.8018.505.008.802.800.10Trnovec Bartolove?ki 6,470 11.704.102.701.100.900.400.10Trogir 12,784 20.105.605.101.702.000.700.30Trpinja 5,386 41.608.4012.803.405.601.800.30Tuhelj 1,973 18.205.504.401.601.700.600.00Udbina 1,791 23.909.206.102.902.301.200.00Umag 13,383 13.004.003.101.101.200.400.20Une?i? 1,637 24.108.005.902.402.101.000.00Valpovo 11,216 21.505.305.701.702.300.800.30Vara?din 45,378 10.202.702.400.700.900.300.50Vara?dinske Toplice 6,316 17.306.204.301.801.600.800.10Vela Luka 4,059 13.005.503.001.501.100.600.10Velika 5,393 34.808.0010.403.104.501.500.20Velika Kopanica 3,258 47.9010.5015.404.606.902.400.20Velika Ludina 2,614 27.008.007.802.703.301.300.10Velika Pisanica 1,775 11.304.902.501.200.800.400.00Veliki Gr?evac 2,808 18.407.104.902.101.900.900.10Veliko Trgovi??e 4,856 26.908.707.202.802.801.300.10Veliko Trojstvo 2,687 29.908.208.402.703.401.200.10Vidovec 5,325 16.605.504.001.501.500.600.10Viljevo 2,038 61.1010.4022.305.2011.003.000.10Vinica 3,336 15.905.503.901.601.500.700.10Vinkovci 34,453 21.503.105.901.002.400.500.80Vinodolska Op?ina 3,539 13.804.103.201.101.200.400.10Vir 2,972 26.608.507.202.802.901.300.10Virje 4,451 30.907.809.002.803.801.400.20Virovitica 20,924 18.204.304.701.301.800.600.40Vis 1,842 14.905.803.401.601.200.700.00Visoko 1,498 35.307.909.402.703.601.300.10Vi?kovci 1,885 36.7013.8011.705.705.303.000.10Vi?kovo 14,235 12.203.802.901.001.100.400.20Vi?njan - Visignano 2,261 11.804.702.601.200.900.500.00Vi?inada - Visinada 1,146 10.804.802.401.200.800.500.00Vo?in 2,274 74.308.4031.206.0016.704.100.20Vodice 8,784 24.604.906.501.602.500.700.20Vodnjan - Dignano 5,943 23.907.106.702.302.801.100.20Vojni? 4,524 57.209.4020.504.909.902.900.30Vrati?inec 1,953 20.207.004.802.001.700.800.00Vrbanja 3,815 34.408.709.803.104.001.500.10Vrbje 2,162 60.709.5022.105.0010.802.900.10Vrbnik 1,244 9.004.702.001.200.700.500.00Vrbovec 14,406 22.405.406.001.702.400.800.40Vrbovsko 5,025 17.605.604.501.701.700.700.10Gvozd 2,889 42.109.8012.804.205.502.100.10Vrgorac 6,336 34.107.9010.102.904.301.400.20Vrhovine 1,378 57.5010.1020.305.209.603.000.10Vrlika 1,968 15.805.603.901.601.400.700.00Vrpolje 3,457 41.609.7013.104.005.802.000.20Vrsar - Orsera 2,152 9.804.302.201.100.800.400.00Vuka 1,145 29.408.708.002.903.201.300.00Vukovar 26,975 25.805.107.201.802.900.800.80Zabok 8,938 12.605.003.101.401.100.600.10Zadar 73,680 19.603.805.101.202.000.501.60Zagorska Sela 990 12.507.102.801.800.900.700.00Zagvozd 1,186 30.708.408.502.903.401.400.00Za?ablje 720 38.609.2012.503.905.602.100.00Zdenci 1,869 44.909.9013.904.006.002.000.10Zemunik Donji 1,885 19.806.905.002.101.900.900.00Zlatar 6,014 20.105.005.201.502.000.700.10Zlatar Bistrica 2,562 13.404.103.301.101.200.400.00Zmijavci 2,038 29.108.408.002.803.201.300.10?akanje 1,856 13.104.903.101.301.100.500.00?minj 3,462 7.904.101.701.000.600.400.00Kra?i? 2,511 21.307.005.502.202.101.000.10?upanja 11,622 34.709.7011.003.905.002.100.50Otok 6,218 35.9010.9010.704.204.502.100.30Rakovec 1,238 15.507.603.502.101.200.800.00Novigrad 2,365 25.805.806.801.802.700.800.10Kostrena 4,152 10.704.102.601.100.900.500.10Marija Gorica 2,214 16.906.104.401.801.700.800.00?umberak 830 24.407.106.002.102.300.900.00Velika Gorica 62,711 13.803.903.501.101.300.501.00Orle 1,924 28.106.808.102.303.501.100.10Zapre?i? 24,935 10.303.102.500.800.900.300.30Pokupsko 2,210 40.508.9012.603.505.601.800.10Kravarsko 1,966 34.209.009.903.304.101.600.10Bistra 6,389 15.306.503.701.801.400.800.10Luka 1,323 20.106.705.102.002.000.900.00Dubravica 1,425 18.806.504.802.001.900.900.00Bedenica 1,424 17.707.704.302.301.601.000.00Stupnik 3,652 12.105.203.001.501.200.600.10Jesenje 1,512 21.507.905.402.402.001.000.00Kumrovec 1,587 16.205.604.001.601.500.700.00Novi Golubovec 971 31.9010.009.003.503.701.600.00Majur 1,185 33.908.8010.003.304.201.600.00Ribnik 473 18.408.404.402.601.601.100.00Tounj 1,143 38.809.8011.603.905.002.000.10Veliki Bukovec 1,411 22.608.306.102.602.501.200.00Kalinovac 1,596 13.304.903.401.501.300.600.00Kalnik 1,351 28.808.608.202.903.401.400.00Novo Virje 1,169 18.407.604.302.101.600.800.00Severin 873 21.208.705.402.602.101.100.00?androvac 1,742 14.405.003.701.501.500.700.00Velika Trnovitica 1,356 27.508.307.902.903.301.400.00Zrinski Topolovac 861 27.008.407.702.703.301.300.00Bukovlje 3,018 34.807.6010.502.804.501.300.10Dragali? 1,340 30.309.608.903.503.801.700.00Gornja Vrba 2,478 34.508.7010.103.204.201.600.10Sikirevci 2,461 41.6011.3012.304.305.202.100.10Galovac 1,226 25.308.606.602.702.501.200.00Kukljica 686 16.207.303.902.201.400.900.00Povljana 756 17.007.004.102.001.500.800.00Privlaka 2,211 25.108.706.702.702.601.200.10Tkon 754 27.908.707.502.903.001.300.00Donja Moti?ina 1,637 42.7011.9012.905.005.502.500.10Magadenovac 1,904 26.6010.907.603.603.201.700.10Vladislavci 1,836 40.209.5011.903.505.001.700.10Pirovac 1,850 26.607.407.002.502.701.100.10Rogoznica 2,339 31.108.508.903.003.701.500.10Privlaka 2,754 33.609.609.603.404.001.600.10Vo?inci 1,931 34.809.209.903.304.101.500.10Dugopolje 3,439 24.808.606.302.602.401.100.10Le?evica 577 34.109.709.803.604.001.800.00Lokvi?i?i 783 50.808.8016.304.207.202.300.00Okrug 3,326 26.706.407.302.102.901.000.10Prgomet 665 14.406.103.401.801.200.700.00Primorski Dolac 769 19.307.304.802.101.700.900.00Runovi?i 2,373 28.509.608.403.503.601.800.10Sutivan 800 11.604.902.501.300.800.500.00Tu?epi 1,925 20.207.005.402.302.101.000.00Zadvarje 250 15.005.903.801.701.400.800.00Karojba 1,427 12.904.602.901.201.000.500.00Ka?telir-Labinci - Castelliere-S. Domenica 1,463 17.306.804.302.001.600.900.00Dubrova?ko Primorje 2,081 11.304.502.701.201.000.500.00Janjina 544 8.104.301.701.100.500.400.00Lumbarda 1,211 11.405.702.601.400.900.600.00Trpanj 705 13.206.503.001.701.000.700.00?upa Dubrova?ka 8,056 10.904.702.501.200.900.400.10Dekanovec 735 18.407.104.502.001.600.800.00Gornji Mihaljevec 1,911 24.908.306.502.702.501.200.10Orehovica 2,478 39.907.0016.303.508.902.300.10Strahoninec 2,653 10.304.702.301.200.800.500.00Sveta Marija 2,284 11.204.602.401.200.800.400.00?enkovec 2,795 6.803.801.500.900.500.400.00Jagodnjak 1,969 62.209.4024.305.6012.603.500.10Marku?ica 2,524 49.308.9016.704.007.702.100.10Negoslavci 1,370 40.2011.2012.304.305.302.200.10?odolovci 1,598 31.8010.309.303.803.901.800.10Podravske Sesvete 1,616 20.406.205.301.902.100.800.00Murter - Kornati 2,040 20.806.805.202.101.900.900.00Gornja Rijeka 1,753 22.407.805.402.202.000.900.00Fa?ana - Fasana 3,491 11.504.102.701.101.000.400.00Pribislavec 3,096 32.006.1013.102.707.201.700.10Bilice 2,255 18.206.904.702.101.800.900.00Kolan 789 10.104.802.101.200.700.400.00Kamanje 855 17.006.303.901.701.400.700.00Lopar 1,233 22.707.606.002.402.301.100.00Vrsi 2,036 26.108.406.602.602.501.100.10Tribunj 1,534 19.007.004.502.001.600.800.00?titar 2,049 41.8010.7012.604.305.302.100.10Funtana - Fontane 907 15.505.903.701.601.400.600.00Tar-Vabriga - Torre-Abrega 1,982 9.103.602.200.900.800.400.00 ................
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