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

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Notes

  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.

References

  1. Anselin, L. (1992). Spatial econometrics: Methods and models. Boston: Kluwer.

    Google Scholar 

  2. Atkinson, A., Rainwater, L., & Smeeding, T. (1994). Income distribution in OECD countries: Evidence from luxembourg income study, Tech. Rep. vol. 18 of Social Policy Studies, OECD.

  3. Banerjee, S., Carlin, B., & Gelfand, A. (2004). Hierarchical modeling and analysis for spatial data. UK: Chapman & Hall.

    Google Scholar 

  4. Bank, A. D. (2007). Research study on poverty-specific purchasing power parities for selected countries in Asia and the Pacific, Tech. rep., 2005 International Comparison Program in Asia and the Pacific.

  5. Battese, G., Harter, R., & Fuller, W. (1988). An error-components model for prediction of county crop areas using survey and satellite data. Journal of the American Statistical Association, 83, 28–36.

    Article  Google Scholar 

  6. Betti, G., & Lemmi, A. (2014). Introduction. In G. Betti & A. Lemmi (Eds.), Poverty and social exclusion: New methods of analysis (pp. 1–6). London: Routledge.

    Google Scholar 

  7. Brandolini, A., & Saraceno, C. (2007). Introduzione. In A. Brandolini, & C. Saraceno (Eds.), Povertà e Benessere. Una geografia delle disuguaglianze in Italia (pp. 167–195). Bologna: Il Mulino.

  8. Chambers, R., & Pratesi, M. (2013). Small area methodology in poverty mapping: An introductory overview. In G. Betti & A. Lemmi (Eds.), Poverty and social exclusion: New methods of analysis (pp. 213–223). London: Routledge.

    Google Scholar 

  9. Chambers, R., & Tzavidis, N. (2006). M-quantile models for small area estimation. Biometrika, 93(2), 255–68.

    Article  Google Scholar 

  10. Cressie, N. (1991). Small-area prediction of undercount using the general linear model. Tech. Rep. In Proceeding of the statistical symposium 90: Measurement and improvement of data quality, Statistics Canada.

  11. Deaton, A. (2006). Purchasing power parity exchange rates for the poor: Using household surveys to construct ppps. Tech. Rep. Princeton: Princeton University.

    Google Scholar 

  12. Deaton, A. (2010). Price indexes, inequality, and the measurement of world poverty. American Economic Review, 100, 5–34.

    Article  Google Scholar 

  13. Deville, J., & Sandal, C. (1992). Calibration estimators in survey sampling. Journal of the American Statistical Association, 87(418), 377–382.

    Article  Google Scholar 

  14. Dupriez, O. (2007). Building a household consumption database for the calculation of poverty PPPs, Tech. rep. Washington: World Bank.

  15. Eurostat. (2014). Living conditions in Europe, Tech. rep. Eurostat.

  16. Fabrizi, E., Giusti, C., Salvati, N., & Tzavidis, N. (2014). Mapping average equivalized income using robust small area methods. Papers in Regional Science, 93(3), 685–701.

    Article  Google Scholar 

  17. FAO (2015). Spatial disaggregation and small-area estimation methods for agricultural surveys: Solutions and perspectives, Tech. Rep. Technical Report Series GO-07-2015, Global Strategy—Improving Agricultural and Rural Statistics. http://www.gsars.org/wp-content/uploads/2015/09/TR-Spatial-DisaggregationSmall-Area-Estimation-for-Ag-Surveys-210915.pdf. Accessed 01 December 2015.

  18. Fay, R., & Herriot, R. (1979). Estimates of income for small places: An application of James–Stein procedures to census data. Journal of the American Statistical Association, 74, 269–277.

    Article  Google Scholar 

  19. Ferrante, C., Occhiobello, R., & Polidoro, F. (2014). State of play of ISTAT project for compiling sub-national PPPs in Italy, Tech. Rep. Paper presented at the Workshop on Inter-Country and Intra-Country Comparisons of Prices and Standards of Living, September 1–3, Arezzo, Italy (provisional draft).

  20. Giusti, C., Marchetti, S., Pratesi, M., & Salvati, N. (2012). Robust small area estimation and oversampling in the estimation of poverty indicators. Survey Research Methods, 6(3), 155–163.

    Google Scholar 

  21. Giusti, C., Tzavidis, N., Pratesi, M., & Salvati, N. (2014). Resistance to outliers of M-quantile and robust random effects small area models. Communications in Statistics-Simulation and Computation, 43(3), 549–568.

    Article  Google Scholar 

  22. Guio, A. C. (2005a). Material deprivation in the EU, Tech. rep. Luxembourg: Eurostat.

  23. Hagenaars, A., de Vos, K., & Zaidi, M. (1994). Poverty statistics in the late 1980s: Research based on micro-data. Office for Official Publications of the European Communities.

  24. Henderson, C. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics, 31, 423–447

    Article  Google Scholar 

  25. ICP-TAG. (2010). Papers on sub-national PPPs based on integration with CPIs, Tech. rep. Papers presented at the 2nd ICP technical advisory group meeting, Washington DC, February 17–19. http://documents.worldbank.org/curated/en/2010/02/20224682/sub-national-ppps-based-integration-cpis-research-project-draft-proposal. Accessed 01 December 2015.

  26. ISTAT. (2008). L’indagine europea sui redditi e le condizioni di vita delle famiglie (EU-SILC), Tech. Rep. Metodi e Norme n.37, ISTAT.

  27. ISTAT. (2009). La misura della povertà assoluta, Tech. Rep. Metodi e Norme n.39, ISTAT.

  28. ISTAT. (2010). La differenza nel livello dei prezzi al consumo tra i capoluoghi delle regioni italiane, anno 2009, Tech. rep., ISTAT.

  29. ISTAT. (2014). La misura dell’inflazione per classi di spesa delle famiglie, Tech. Rep., Statistiche Flash, ISTAT.

  30. Jiang, J., & Lahiri, P. (2006). Mixed model prediction and small area estimation. Test, 15, 1–96.

    Article  Google Scholar 

  31. Lelkes, O., & Gasior, K. (2008). Social inclusion and income distribution in the European Union, Tech. Rep., Applica Final Report, European Commission.

  32. Lelkes, O., & Gasior, K. (2011). Income poverty in the EU. Situation in 2007 and trends (based on EU-SILC 2005–2008), Tech. Rep., Policy brief January 2011, European Centre.

  33. Marlier, E., Cantillon, B., Nolan, B., Van Den Bosch, K., & Van Rie, T. (2012). Developing and learning from EU measures of social inclusion. In D. J. Besharov & K. A. Couch (Eds.), Counting the poor: New thinking about European poverty measures and lessons for the United States (pp. 299–342). New York: Oxford.

    Google Scholar 

  34. Molina, I., & Rao, J. N. K. (2010). Small area estimation of poverty indicators. Canadian Journal of Statistics, 38(3), 369–385.

    Article  Google Scholar 

  35. Molina, I., Salvati, N., & Pratesi, M. (2009). Bootstrap for estimating the MSE of the spatial EBLUP. Computational Statistics, 24, 441–458.

    Article  Google Scholar 

  36. OECD. (2011). Compendium of OECD well-being indicators, Tech. rep., OECD.

  37. Petrucci, A., & Salvati, N. (2006). Small area estimation for spatial correlation in watershed erosion assessment. Journal of the Agricultural, Biological, and Environmental Statistics, 11, 169–182.

    Article  Google Scholar 

  38. Prasad, N., & Rao, J. (1990). The estimation of the mean squared error of small area estimators. Journal of the American Statistical Association, 85, 163–171.

    Article  Google Scholar 

  39. Pratesi, M. (2016). Introduction on measuring poverty at local level using SAE methods. In M. Pratesi (Ed.), Analysis of poverty by small area estimation methods (pp. 2–7). New York: Wiley.

    Google Scholar 

  40. Pratesi, M., Giusti, C., & Marchetti, S. (2012). Small area estimation of poverty indicators. In C. Davino & L. Fabbris (Eds.), Survey data collection and integration (pp. 89–101). New York: Springer.

    Google Scholar 

  41. Pratesi, M., & Salvati, N. (2008). Small area estimation: The EBLUP estimator based on spatially correlated random area effects. Statistical Methods and Applications, 17, 113–141.

    Article  Google Scholar 

  42. Pratesi, M., & Salvati, N. (2009). Small area estimation in the presence of correlated random area effects. Journal of Official Statistics, 25, 37–53.

    Google Scholar 

  43. Rao, J. N. K. (2003). Small area estimation. New York: Wiley.

    Google Scholar 

  44. Saei, A., & Chambers, R. (2005). Small area estimation under linear and generalized linear mixed models with time and area effects, Tech. Rep. WP M03/15, Southampton Statistical Sciences Research Institute.

  45. Salvati, N. (2004). Small area estimation by spatial models: The spatial empirical best linear unbiased prediction (spatial EBLUP), Tech. Rep. Working Paper No. 2004/04, University of Florence.

  46. Salvati, N., Giusti, C., & Pratesi, M. (2014). The use of spatial information for the estimation of poverty indicators at the small area level. In G. Betti & A. Lemmi (Eds.), Poverty and social exclusion: New methods of analysis (pp. 261–282). New York: Wiley.

    Google Scholar 

  47. Salvati, N., Tzavidis, N., Pratesi, M., & Chambers, R. (2012). Small area estimation via M-quantile geographically weighted regression. Test, 21, 1–28.

    Article  Google Scholar 

  48. Singh, B., Shukla, G., & Kundu, D. (2005). Spatio-temporal models in small area estimation. Survey Methodology, 31, 183–195.

    Google Scholar 

  49. Tobler, W. (1970). A computer movie simulation urban growth in the Detroit region. Economic Geography, 46, 234–240.

    Article  Google Scholar 

  50. Tzavidis, N., & Marchetti, S. (2016). Robust domain estimation of income-based inequality indicators. In M. Pratesi (Ed.), Analysis of poverty by small area estimation methods (pp. 33–56). New York: Wiley.

    Google Scholar 

  51. Walker, R., & Ashworth, K. (1994). Poverty dynamics: Issues and examples. Avebury.

  52. Weziak-Bialowolska, D., & Dijkstra, L. (2014). Monitoring multidimensional poverty in the regions of the European Union, Tech. rep., European Commission—Joint Research Centre.

Download references

Acknowledgments

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).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Caterina Giusti.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

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