Local Comparisons of Small Area Estimates of Poverty: An Application Within the Tuscany Region in Italy


The aim of this paper is to highlight some key issues and challenges in the analysis of poverty at the local level using survey data. In the last years there was a worldwide increase in the demand for poverty and living conditions estimates at the local level, since these quantities can help in planning local policies aimed at decreasing poverty and social exclusion. In many countries various sample surveys on income and living conditions are currently conducted, but their sample size is not enough to obtain reliable estimates at local level. When this happens, small area estimation (SAE) methods can be used. In this paper, a SAE model is used to compute the mean household equivalised income and the head count ratio for the 57 Labor Local Systems of the Tuscany region in Italy for the year 2011. The caveats of the analysis of poverty at the local level using small area methods are many, and some are still not so well explored in the literature, starting from the definition of the target indicators to the relevant dimensions of their measurement. We suggest in this paper that together with the universally recognized multidimensional, longitudinal and local dimensions of poverty, a new dimension must be considered: the price dimension, which should take into account local purchasing power parities to correctly compare the poverty indicators based on income measures.

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  1. 1.

    The household income needs to be equalized to take into account the differences in household size. Several equivalence scales have been proposed. In the application presented in this paper we use the modified OECD scale (Hagenaars et al. 1994): according to this scale the equivalized household size is computed for each household giving a weight of 1.0 to the first adult, 0.5 to other persons aged 14 or more and 0.3 to each child aged \(<\)14.

  2. 2.

    EU-SILC is a cross-sectional and longitudinal sample survey, coordinated by Eurostat, with the aim of providing timely and comparable data on income, poverty, social exclusion and living conditions in the EU state members. In Italy, EU-SILC is conducted by ISTAT to produce estimates of the Italian population living conditions at national and regional level (NUTS-2). In the design of the EU-SILC survey, regions are planned domains for which estimates are published, while provinces (Local Administrative Units, LAU-1) and municipalities (LAU-2) are unplanned domains. The regional samples are based on a stratified two-stage sample design: in each province, municipalities are the Primary Sampling Units (PSUs), while households are the Secondary Sampling Units (SSUs). The PSUs are stratified according to administrative regions and population size; the SSUs are selected by means of systematic sampling in each PSU.

  3. 3.

    They are costless in the sense that they take full advantage of the existing survey data and of other auxiliary data, without requiring additional data collection processes and costs, as it is shown in the next section. Indeed, they require additional knowledge on the statistical methods and models to implement the SAE procedures. This knowledge in the economy of this study is given for acquired.

  4. 4.

    Data from the Population Census have been used as auxiliary information to estimate poverty indicators in several previous applications (Giusti et al. 2012; Fabrizi et al. 2014; Salvati et al. 2014). However, these applications were all characterized by a time lag between the survey and the census data: for example, Salvati et al. (2014) used EU-SILC 2008 data together with Population census 2001 data. The use of lagged census information may lead to bias small area estimators, since it is likely that the population characteristics rapidly change. In the present application we avoid this problem by using EU-SILC and census data both collected in 2011.

  5. 5.

    The methods used in this study to assure the spatial comparability of the basket of goods and of the consumption behavior were those adopted by the International Comparison Programme of the World Bank, www.worldbank.org.


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The opinions expressed in this article are solely those of the authors. Nevertheless the authors would like to thank Luigi Biggeri, Emeritus Professor of Economic Statistics, for the many and fruitful discussions on the role of PPPs in the study of poverty at local level. The research presented in this paper was developed in the framework of the European Commission FP7 project InGRID (Inclusive GRowth Research Infrastructure Diffusion, www.inclusivegrowth.eu) and in the framework of the University of Pisa PRA 2015 project CSRHR (Corporate Social Responsibility & Human Rights, http://csrhrproject.ec.unipi.it).

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Correspondence to Caterina Giusti.

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Giusti, C., Masserini, L. & Pratesi, M. Local Comparisons of Small Area Estimates of Poverty: An Application Within the Tuscany Region in Italy. Soc Indic Res 131, 235–254 (2017). https://doi.org/10.1007/s11205-015-1193-1

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  • Poverty mapping
  • Poverty line
  • Model-based estimates
  • Purchasing power parities