Advertisement

Social Indicators Research

, Volume 141, Issue 1, pp 299–330 | Cite as

Methodological Choices and Data Quality Issues for Official Poverty Measures: Evidences from Italy

  • Achille Lemmi
  • Donatella Grassi
  • Alessandra Masi
  • Nicoletta Pannuzi
  • Andrea RegoliEmail author
Article
  • 141 Downloads

Abstract

The plurality of the official poverty estimates in Italy covers both absolute and relative approaches, ranges from consumption to income-based measures, follows different methodologies and uses several data sources. We can therefore expect that each measure gives a somewhat different picture of poverty, in its level as well as in its change across subgroups of the population. This paper investigates the effect of methodological choices together with the effect of different data quality aspects on the official poverty estimates. Usually, methodological issues attract much attention both in literature and empirical studies. However, the results of the sensitivity analysis suggest that more specific attention should be paid to data quality issues and to the definition of the variables. Our main conclusion is that an improvement in the quality as well as the inclusion of some items in the definition of the variable may result in large changes in poverty indicators. This finding signals that the data quality aspects have a higher impact on poverty estimates than some methodological issues.

Keywords

Poverty measures Consumption expenditure Income Sensitivity analysis Italy 

Notes

Acknowledgements

We would like to thank three anonymous reviewers for their helpful comments and suggestions. The opinions expressed in this paper solely represent those of the authors and do not necessarily reflect the official viewpoint of Istat.

References

  1. Aaberge, R., & Brandolini, A. (2014). Multidimensional Poverty and Inequality. In A. B. Atkinson & F. Bourguignon (Eds.), Handbook of income distribution (Vol. 2A). Oxford, North-Holland: Elsevier.Google Scholar
  2. Aizer, A., Eli, S., Ferrie, J., & Lleras-Muney, A. (2016). The long-run impact of cash transfers to poor families. The American Economic Review, 106(4), 935–971.  https://doi.org/10.1257/aer.20140529.CrossRefGoogle Scholar
  3. Alkire, S., & Foster, J. (2011). Understandings and misunderstandings of multidimensional poverty measurement. Oxford Poverty and Human Development Initiative (OPHI) working paper no. 43. http://www.3.qeh.ox.ac.uk/pdf/ophiwp/OPHIWP043.pdf.
  4. Alkire, S., Foster, J., Seth, S., Santos, M. E., Roche, J. M., & Ballon, P. (2015). Multidimensional poverty measurement and analysis. Oxford: Oxford University Press.CrossRefGoogle Scholar
  5. Alkire, S., & Samman, E. (2014). Mobilising the household data required to progress toward the SDGs. Oxford Poverty and Human Development Initiative (OPHI) working paper no.72. http://www.ophi.org.uk/wp-content/uploads/OPHIWP072.pdf.
  6. Anderson, G., Pittau, M. G., & Zelli, R. (2014). Poverty status probability: A new approach to measuring poverty and the progress of the poor. The Journal of Economic Inequality, 12(4), 469–488.  https://doi.org/10.1007/s10888-013-9264-5.CrossRefGoogle Scholar
  7. Angel, S., Heuberger, R., & Lamei, N. (2017). differences between household income from surveys and registers and how these affect the poverty headcount: Evidence from the austrian SILC. Social Indicators Research.  https://doi.org/10.1007/s11205-017-1672-7.Google Scholar
  8. Atkinson, A. B., & Bourguignon, F. (Eds.). (2014). Handbook of income distribution (Vol. 2A). Oxford, North-Holland: Elsevier.Google Scholar
  9. Atkinson, A. B., Guio, A., & Marlier, E. (Eds.). (2017). Monitoring social inclusion in Europe. Luxembourg: Publications Office of the European Union, Statistical Books Eurostat. https://www.ssrn.com/abstract=2981712.
  10. Balcázar, C. F., Ceriani, L., Olivieri, S., & Ranzani, M. (2017). Rent-imputation for welfare measurement: A review of methodologies and empirical findings. Review of Income and Wealth.  https://doi.org/10.1111/roiw.12312.Google Scholar
  11. Baldini, M., Peragine, V., & Silvestri, L. (2017). Quality of government and subjective poverty in Europe. CAPP Paper no. 149. https://155.185.68.2/campusone/web_dep/CappPaper/Capp_p149.pdf.
  12. Bank of Italy. (2014). Household Income and Wealth in 2012. Supplements to the Statistical Bulletin Sample Surveys. New series Number 5. https://www.bancaditalia.it/pubblicazioni/indagine-famiglie/bil-fam2012/index.html.
  13. Bank of Italy. (2015). Household income and wealth in 2014. Supplements to the statistical bulletin sample surveys. New series Number 64. https://www.bancaditalia.it/pubblicazioni/indagine-famiglie/bil-fam2014/index.html.
  14. Bavier, R. (2008). Reconciliation of income and consumption data in poverty measurement. Journal of Policy Analysis and Management, 27(1), 40–62.CrossRefGoogle Scholar
  15. Berthoud, R., Bryan, M. L., & Bardasi, E. (2004). The dynamics of deprivation: The relationship between income and material deprivation over time. Research Report no. 219. London: Department for Work and Pensions.Google Scholar
  16. Besharov, D. J., & Couch, K. A. (Eds.). (2012). Counting the poor: New Thinking about European poverty measures and lessons for the United States. Oxford: Oxford University Press.Google Scholar
  17. Betti, G., Cheli, B., Lemmi, A., & Verma, V. (2006). multidimensional and longitudinal poverty: An integrated fuzzy approach. In A. Lemmi & G. Betti (Eds.), Fuzzy set approach to multidimensional poverty measurement (pp. 111–137). Berlin: Springer.Google Scholar
  18. Betti, G., & Lemmi, A. (Eds.). (2013). Poverty and social exclusion: New methods of analysis. London, New York: Routledge.Google Scholar
  19. Betti, G., Masi, A., & Regoli, A. (2016). Profili di disuguaglianza e povertà in Italia: un confronto tra stime da fonti ufficiali. Paper presented at the scientific meeting “La società italiana e le grandi crisi economiche 1929–2016, Rome, 25–26 November 2016.Google Scholar
  20. Brandolini, A., Magri, S., & Smeeding, T. (2010). Asset-based measurement of poverty. Journal of Policy Analysis and Management, 29(2), 267–284.CrossRefGoogle Scholar
  21. Buhmann, B., Rainwater, L., Schmaus, G., & Smeedinget, T. M. (1988). Equivalence Scales, well-being, inequality, and poverty: sensitivity estimates across ten countries using the Luxembourg Income Study (LIS) database. Review of Income and Wealth, 34(2), 115–142.CrossRefGoogle Scholar
  22. Butcher, K. F. (2017). Assessing the long-run benefits of transfers to low-income families. Working paper #26. Washington: Brooking Hutchins Center. https://www.brookings.edu/wp-content/uploads/2017/01/wp26_butcher_transfers_final.pdf.
  23. Carbonaro, G. (1985). Nota sulle scale di equivalenza, in Commissione di indagine sulla povertà e sull’emarginazione (Ed.), Primo rapporto sulla povertà in Italia (pp. 153–159). Roma, Istituto Poligrafico e Zecca dello Stato.Google Scholar
  24. Ceccarelli, C., & Cutillo, A. (2016). Representativeness of the 2014 NHBS and 2013 HBS samples in comparison to the universe of households residing in Italy using fiscal tax income data. Unpublished paper.Google Scholar
  25. Chakravarty, S.R. (2009). Inequality, Polarization and Poverty: Advances in Distributional Analysis, Economic Studies in Inequality, Social Exclusion and Well-Being, Springer.  https://doi.org/10.1007/978-0-387-79253-8 2.
  26. Chen, S., & Ravallion, M. (2013). More relatively-poor people in a less absolutely-poor world. Review of Income and Wealth, 59(1), 1–28.CrossRefGoogle Scholar
  27. Cifaldi, G., & Neri, A. (2013). Asking income and consumption questions in the same survey: what are the risks? Bank of Italy Working papers. Number 908. https://www.bancaditalia.it/pubblicazioni/temi-discussione/2013/2013-0908/en_tema_908.pdf.
  28. Citro, C. F., & Michael, R. T. (1995). Measuring poverty: A new approach. Washington, D.C.: National Academy Press.Google Scholar
  29. Consolini, P., & Donatiello, G. (2013). Improvements of data quality through the combined use of survey and administrative sources and micro simulation model. In M. Jäntti, V.-M. Törmälehto, & E. Marlier (Eds.), The use of registers in the context of EU–SILC: Challenges and opportunities (pp. 125–139). Luxembourg: Publications Office of the European Union.Google Scholar
  30. Coudouel, A., Hentschel, J. S., & Wodon, Q. T. (2002). Poverty measurement and analysis. In Klugman, J. (Ed.), Poverty reduction strategies paper (PRSP) sourcebook. , Washington, DC: The World Bank. http://www.documents.worldbank.org/curated/en/156931468138883186/pdf/2980000182131497813.pdf.
  31. Darvas, Z. (2017). Why is it so hard to reach the EU’s ‘poverty’ target?. Bruegel Policy Contribution Issue No. 1. https://www.bruegel.org/2017/01/why-is-it-so-hard-to-reach-the-eus-poverty-target/.
  32. De Vitiis, C., Falorsi, S., Inglese, F., Masi, A., Pannuzi, N., & Russo, M. (2014). A methodological approach based on indirect sampling to survey the homeless people. Rivista di statistica ufficiale, 16(1–2), 9–30.Google Scholar
  33. de Vos, K., & Garner, T. I. (1991). An evaluation of subjective poverty definitions: Comparing results from the U.S. and the Netherlands. Review of Income and Wealth, 37(3), 267–285.CrossRefGoogle Scholar
  34. de Vos, K., & Zaidi, M. A. (1997). Equivalence scale sensitivity of poverty statistics for the member states of the European community. Review of Income and Wealth, 43(3), 319–333.CrossRefGoogle Scholar
  35. Decerf, B. (2015). A new index combining the absolute and relative aspects of income poverty: theory and application. CORE Discussion Series (2015/50), 1–27.Google Scholar
  36. Delle Fratte, C. & Lariccia, F. (2015). The impact of Administrative data on final estimates of It-Silc income variables. Paper presented at the London EU-Silc Best Practice Workshop, 16th and 17th September 2015.Google Scholar
  37. Dhongde, S., & Minoiu, C. (2013). Global Poverty Estimates: A Sensitivity Analysis. World Development, 44, 1–13.  https://doi.org/10.1016/j.worlddev.2012.12.010.CrossRefGoogle Scholar
  38. Dudel, C. (2017). Variance estimation for sensitivity analysis of poverty and inequality measures. Survey Research Methods, 11(1), 81–92.Google Scholar
  39. Ebert, U., & Moyes, P. (2017). Inequality and isoelastic equivalence scales: restrictions and implications. Social Choice and Welfare, 48(2), 295–326.CrossRefGoogle Scholar
  40. Eurostat. (2003). Household Budget Surveys in the EU. Methodology and recommendations for harmonisation. Methods and nomenclatures Series. http://www.ec.europa.eu/eurostat/ramon/statmanuals/files/KS-BF-03-003-__-N-EN.pdf.
  41. Eurostat. (2012a). Measuring material deprivation in the EU, Indicators for the whole population and child-specific indicators. Luxembourg: Publications Office of the European Union.https://www.ec.europa.eu/eurostat/documents/3888793/5853037/KS-RA-12-018-EN.PDF.
  42. Eurostat. (2012b). Household Budget Survey 2010 Wave. EU Quality report. https://www.ec.europa.eu/eurostat/web/household-budget-surveys/publications.
  43. Eurostat. (2015a). Methodological guidelines and description of EU-Silc target variables, DocSILC065 (2014 operation), May 2015. https://www.circabc.europa.eu/sd/a/2aa6257f-0e3c-4f1c-947f-76ae7b275cfe/DOCSILC065%20operation%202014%20VERSION%20reconciliated%20and%20early%20transmission%20October%202014.pdf.
  44. Eurostat. (2016). Smarter, greener, more inclusive indicators to support the Europe 2020 strategy. Publications Office of the European Union, Luxembourg.  https://doi.org/10.2785/571743. http://www.ec.europa.eu/eurostat/documents/3217494/7566774/KS-EZ-16-001-EN-N.pdf.
  45. Figari, F., Paulus, A., Sutherland, H., Tsakloglou, P., Verbist, G., & Zantomio, F. (2017). Removing homeownership bias in taxation: the distributional effects of including net imputed rent in taxable income. Fiscal Studies.  https://doi.org/10.1111/1475-5890.12105. http://www.onlinelibrary.wiley.com/doi/10.1111/1475-5890.12105/pdf.
  46. Fisher, J., Johnson D., Latner, J., Smeeding, T., & Thompson, J. (2016). Inequality and mobility using income, consumption, and wealth for the same individuals. National Poverty Center Working paper series #16-02. http://www.npc.umich.edu/publications/u/2016-02-npc-working-paper.pdf.
  47. Förster, M. F., & Mira D’Ercole, M. (2012). The OECD approach to measuring income distribution and poverty. In D. J. Besharov & K. A. Couch (Eds.), Counting the poor: New thinking about european poverty measures and lessons for the United States. Oxford: Oxford Scholarship.Google Scholar
  48. Garner, T. I., & Short, K. S. (2010). Identifying the poor: Poverty measurement for the U.S. from 1996 to 2005. Review of Income and Wealth, 56(2), 237–258.  https://doi.org/10.1111/j.1475-4991.2009.00374.x.CrossRefGoogle Scholar
  49. Grassi, D., Pannuzi, N., & Siciliani, I. (2010). New Measures Of Poverty: The Absolute And Extreme Poverties. In 45th Scientific Meeting of the Italian Statistical Society proceedings, University of Padua, June 29, 2010–July 1, 2010. http://www.new.sis-statistica.org/pubblicazioni/indice-articoli-pubblicati-negli-atti-rs/atti-della-xlv-riunione-scientifica-2010/.
  50. Hagenaars, A. (1987). A class of poverty indices. International Economic Review, 28(3), 583–607.CrossRefGoogle Scholar
  51. Hagenaars, A., de Vos, K., & Zaidi, M. A. (1994). Poverty statistics in the late 1980s: Research based on micro-data. Luxembourg: Office for Official Publications of the European Communities.Google Scholar
  52. Hansen, K., & Kneale, D. (2013). Does how you measure income make a difference to measuring poverty? Evidence from the UK. Social Indicators Research, 110(3), 1119–1140.  https://doi.org/10.1007/s11205-011-9976-5.CrossRefGoogle Scholar
  53. Haveman, R., Blank, R., Moffitt, R., Smeeding, T., & Wallace, G. (2015). The war on poverty: measurement, trends and policy. Journal of Policy Analysis and Management, 34(3), 593–638.CrossRefGoogle Scholar
  54. Household Finance and Consumption Network. (2016). The Household Finance and Consumption Survey: methodological report for the second wave. ECB Statistical paper series no. 17. https://www.ecb.europa.eu/pub/pdf/scpsps/ecbsp17.en.pdf.
  55. Istat. (2009). La misura della povertà assoluta. In D. Grassi, & N. Pannuzi (Eds), Argomenti Istat series, n. 24. http://www3.istat.it/dati/catalogo/20090422_00/misura_della_poverta_assoluta.pdf.
  56. Istat. (2014). La ricerca nazionale sulla condizione delle persone senza dimora in Italia. In A. Masi & N. Pannuzi (Eds.), Metodi Istat series. http://www.istat.it/it/archivio/127256.
  57. Istat. (2015). La nuova indagine sulle spese per consumi in Italia. Grassi, D., & Pannuzi, N. (Eds.), Metodi Istat series. https://www.istat.it/it/archivio/182165.
  58. Istat. (2016a). Poverty in Italy year 2015. Households economic conditions and disparities. Press release. http://www.istat.it/en/archive/189215.
  59. Istat. (2016b). Income and living conditions year 2015. Households economic conditions and disparities. Press release http://www.istat.it/en/archive/193757.
  60. Istat. (2016c). The homeless. Press release. http://www.istat.it/en/archive/186791.
  61. Jenkins, S. P., & Van Kerm, P. (2014). The relationship between EU indicators of persistent and current poverty. Social Indicators Research, 116(2), 611–638.CrossRefGoogle Scholar
  62. Kanbur, R., & Tuomala, M. (2016). Groupings and the gains from targeting. Research in Economics, 70, 53–63.CrossRefGoogle Scholar
  63. Kuypers, S., & Marx, I. (2016). Estimation of joint income-wealth poverty: A sensitivity analysis. Social Indicators Research.  https://doi.org/10.1007/s11205-016-1529-5.Google Scholar
  64. Martinez, R., & Navarro, C. (2016). Has the great recession changed the deprivation profile of low in-come groups? Evidence from Spain. Review of Public Economics, 218(3), 79–104.Google Scholar
  65. Marx, I., Nolan, B., & Olivera, J. (2015). The welfare state and antipoverty policy in rich countries, In A. B. Atkinson & F. Bourguignon (Eds.), Handbook of Income Distribution (Vol 2).Google Scholar
  66. McGuinness, F. (2016). Poverty in the UK: Statistics. Briefing paper Number 7096, 16 June 2016, House of commons library. http://www.dera.ioe.ac.uk/29290/1/SN07096.pdf.
  67. Meyer, B. D., & Sullivan. J. X. (2010). Further results on measuring the well-being of the poor using income and consumption. Working paper, 07.19. The Harris school of public policy studies the University of Chicago. http://www.harris.uchicago.edu/sites/default/files/working-papers/wp_07_19.pdf.
  68. Meyer, B. D., & Mittag, N. (2017). Using linked survey and administrative data to better measure income: Implications for poverty, program effectiveness and holes in the safety net. Discussion paper series, no. 10943. IZA-Institute of Labor Economics. https://www.iza.org/dp10943.pdf.
  69. Meyer, B. D., & Sullivan, J. X. (2012). Identifying the disadvantaged: Official Poverty, consumption poverty, and the new supplemental poverty measure. Journal of Economic Perspectives, 26(3), 111–136.  https://doi.org/10.1257/jep.26.3.111.CrossRefGoogle Scholar
  70. Morduch, J., & Siwicki, J. (2017). In and out of poverty: poverty spells and income volatility in the U.S. financial diaries. US Financial Diaries Project/FAI, New York University. https://www.wagner.nyu.edu/files/faculty/publications/In%20and%20Out%20of%20Poverty%20-%20Morduch%20and%20Siwicki%20-%20June%202017.pdf.
  71. Nelson, K. (2012). Counteracting material deprivation: The role of social assistance in Europe. Journal of European Social Policy, 22(2), 148–163.CrossRefGoogle Scholar
  72. Nolan, B., & Whelan, C. T. (2011). Poverty and deprivation in Europe. Oxford: Oxford University Press.CrossRefGoogle Scholar
  73. Notten, G. (2016). How poverty indicators confound poverty reduction evaluations: The targeting performance of income transfers in Europe. Social Indicators Research, 127(3), 1039–1056.  https://doi.org/10.1007/s11205-015-0996-4.CrossRefGoogle Scholar
  74. Notten, G., & Mendelson, M. (2016). Using low income and material deprivation to monitor poverty reduction. Caledon Institute of Social Policy, 28 July 2016. http://www.caledoninst.org/Publications/PDF/1103ENG.pdf.
  75. Orshansky, M. (1963). Children of the poor. Social Security Bulletin, 26(7), 3–13.Google Scholar
  76. Pac, J., Nam, J., Waldfogel, J., & Wimer, C. (2017). Young child poverty in the United States: Analyzing trends in poverty and the role of anti-poverty programs using the supplemental poverty measure. Children and Youth Services Review, 74, 35–49.  https://doi.org/10.1016/j.childyouth.2017.01.022.CrossRefGoogle Scholar
  77. Pilkauskas, N., Campbell, C. & Wimer, C. (2016). Giving unto others: private financial transfers and hardship among families with children. National Poverty Center Working Paper Series #16-03. https://www.npc.umich.edu/publications/u/2016-03-npc-working-paper.pdf.
  78. Pratesi, M. (Ed.). (2016). Analysis of poverty data by small area estimation. Hoboken: Wiley.  https://doi.org/10.1002/9781118814963.Google Scholar
  79. Ravallion, M. (2016). The economics of poverty: History, measurement, and policy. Oxford: Oxford University Press.CrossRefGoogle Scholar
  80. Rowntree, B. S. (1901). Poverty: A study of town life. London: Longman.Google Scholar
  81. Sen, A. K. (1976). Poverty: An ordinal approach to measurement. Econometrica, 44, 219–231.CrossRefGoogle Scholar
  82. Serafino, P., & Tonkin, R. (2017). Statistical matching of European union statistics on income and living conditions (EU-SILC) and the household budget survey. Eurostat, Statistical working papers, Population and social conditions.  https://doi.org/10.2785/933460, http://www.ec.europa.eu/eurostat/documents/3888793/7882299/KS-TC-16-026-EN-N.pdf/3587dc1b-9f29-42cb-b0f9-0dfa21a47d41.
  83. Townsend, P. (1979). Poverty in the United Kingdom: A survey of household resources and standards of living. Harmondsworth: Penguin Books.Google Scholar
  84. Tran, V. Q., Alkire, S., & Klasen, S. (2015). Static and dynamic disparities between monetary and multidimensional poverty measurement: Evidence from Vietnam. In T. I. Garner & K. S. Short (Eds.), Measurement of poverty, deprivation, and economic mobility, book series: research on economic inequality, volume 23, 249–281. Bingley: Emerald Group Publishing Limited.Google Scholar
  85. UNECE. (2011). Canberra group handbook on household income statistics (2nd ed.). Geneva: UNECE.Google Scholar
  86. Verma, V., Betti, G., & Gagliardi, F. (2017). Fuzzy measures of longitudinal poverty in a comparative perspective. Social Indicators Research, 130(2), 435–454.  https://doi.org/10.1007/s11205-015-1194-0.CrossRefGoogle Scholar
  87. Villar, A. (2017). Lectures on inequality, poverty and welfare. Lecture Notes in Economics and Mathematical Systems (Vol. 685). Berlin: Springer. https://doi.org/10.1007/978-3-319-45562-4Google Scholar
  88. Weisbrod, B. A., & Hansen, W. L. (1968). An income-net worth approach to measuring economic welfare. American Economic Review, 58(5), 1315–1329.Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Tuscan Universities Research Centre ‘Camilo Dagum’SienaItaly
  2. 2.Italian National Institute of StatisticsRomeItaly
  3. 3.University of Naples ParthenopeNaplesItaly

Personalised recommendations