Estimating the at Risk of Poverty Rate Before and After Social Transfers at Provincial Level in Italy

  • Caterina Giusti
  • Stefano Marchetti
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 227)


Considering the local areas where citizens live is fundamental to investigate deprivation and social exclusion, particularly in a period of increasing financial difficulties and reduction of public funding. In this work we estimate the at risk of poverty rate of Italian households before and after social transfers at provincial level. To obtain these estimates we use data coming from the EU-SILC 2013 survey and data coming from the population census and administrative archives in a small area estimation framework, since the design of EU-SILC survey does not allow for reliable direct estimation at provincial level. Our results, besides indicating the essential role of social transfers in the reduction of the at risk of poverty rate, allow a sub-national analysis of the phenomenon of interest that would be lost by using traditional statistical techniques.


Area level models Small area estimation EU-SILC 



This work is part of the activities of the PO.WE.R. research project, supported by the University of Pisa (PRA 2016).


  1. 1.
    Baldini, M., Gallo, G., Reverberi, M., Trapani, A.: Social transfers and poverty in Europe: comparing social exclusion and targeting across welfare regimes. Technical Report No. 91, Department of Economics “Marco Biagi”, University of Modena and Reggio Emilia—WP (2016)Google Scholar
  2. 2.
    Bowman, A., Hall, P., Prvan, T.: Bandwidth selection for the smoothing of distribution functions. Biometrika 85, 799–808 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Chandra, H., Salvati, N., Chambers, R.: A spatially nonstationary Fay-Herriot model for small area estimation. J. Surv. Stat. Methodol. 3(2), 109–135 (2015)CrossRefGoogle Scholar
  4. 4.
    Cressie, N.: Statistics for Spatial Data. Wiley, New York (1993)zbMATHGoogle Scholar
  5. 5.
    EC: Social protection performance monitor (SPPM)—methodological report by the indicators sub-group of the social protection committee. Technical Report. European Commission—Social Protection Committee (2012)Google Scholar
  6. 6.
    Fabrizi, E., Ferrante, M., Pacei, S.: A micro-econometric analysis of the antipoverty effect of social cash transfers in Italy. Rev. Income Wealth 60(2), 323–348 (2014)CrossRefGoogle Scholar
  7. 7.
    Fay, R., Herriot, R.: Estimation of income from small places: an application of James-Stein procedures to census data. J. Am. Stat. Assoc. 74, 269–77 (1979)CrossRefGoogle Scholar
  8. 8.
    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. (2016)Google Scholar
  9. 9.
    Giusti, C., Marchetti, S., Pratesi, M., Salvati, N.: Robust small area estimation and oversampling in the estimation of poverty indicators. Surv. Res. Methods 6(3), 155–163 (2012)Google Scholar
  10. 10.
    Hagenaars, A., De Vos, K., Zaidi, M.: Poverty statistics in the late 1980s: research based on micro-data. Technical Report. Luxembourg: Official Pubblication of the European Communities (1994)Google Scholar
  11. 11.
    Longford, N., Nicodemo, C.: The contribution of social transfers to the reduction of poverty. Technical Report. IZA Discussion Paper no. 5223 (2010)Google Scholar
  12. 12.
    Molina, I., Salvati, N., Pratesi, M.: Bootstrap for estimating the mse of the spatial EBLUP. Comput. Stat. 24, 441–458 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Rao, J.: Small Area Estimation. Wiley, New York (2003)CrossRefzbMATHGoogle Scholar
  14. 14.
    Zimmerman, D., Cressie, N.: Mean squared prediction error in the spatial linear model with estimated covariance parameters. Ann. Inst. Stat. Math. 44, 27–43 (1992)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Economics and ManagementPisaItaly

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