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

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.

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Fig. 1

Source: own calculations based on HBS, EU-Silc and SHIW data

Fig. 2

Source: own calculations based on EU-Silc data

Fig. 3

Source: own calculations based on HBS and NHBS data

Fig. 4
Fig. 5

Source: own calculations based on HBS and NHBS data

Fig. 6

Source: own calculations based on HBS and NHBS data

Fig. 7

Source: own calculations based on HBS and NHBS data

Notes

  1. 1.

    The willingness to overpass the limits of this type of measures has led Eurostat to produce indicators of material deprivation (Eurostat 2012a) and the multidimensional indicator of poverty or social exclusion (Eurostat 2016). However they are still considered as marginal or complementary estimates.

  2. 2.

    Some of these items have been discussed within the Interinstitutional Working Group on Poverty Estimates established by Italian National Institute of Statistics (https://www.istat.it/it/archivio/189524).

  3. 3.

    Elasticity accounts for the extent to which economic needs change with household size (Förster and Mira D’Ercole 2012).

  4. 4.

    The absolute term refers to the fact that the measure is independent on the distribution of the proxy variable; however, its definition is obviously dependent on (relative to) the reference context.

  5. 5.

    For a survey of the most used poverty indices and the axioms they satisfy, see Chakravarty (2009).

  6. 6.

    It should be emphasised that the SHIW by Bank of Italy, conducted by a professional interviewer network, shows a lower response rate (53.3% in 2014) (Bank of Italy 2015) and that several European HBSs (using again professional interviewer networks) show even lower levels of coverage (in 2010, the values varied between 5% in Belgium, 42% in Denmark, 51% in UK and Sweden to over 80% in Turkey and Romania) (Eurostat 2012b).

  7. 7.

    When the sample selection steps and weighting system arrangement had to be done, the information from the tax returns was not yet available and, therefore, the fiscal income was not used as a variable for either sample stratification or post-stratification. The fiscal information became available after 1 year and, at that moment, it was linked to HBS and NHBS data by individual fiscal codes (tax identification numbers).

  8. 8.

    It follows that the use of tax information in the sampling phase (for stratification) would not be of great utility, while it could be very useful in defining substitutions; likewise, a number of constraints could be added to the final calibration to comply with the tax income classes.

  9. 9.

    It must be underlined that some people are not properly represented in tax statistics, as the population not liable for taxation due to their low levels of income and tax evaders.

  10. 10.

    This analysis has been conducted as one of the activities of the already mentioned Interinstitutional Working Group on Poverty Estimates.

  11. 11.

    The consumption expenditure is the net of extraordinary maintenance expenses, premiums paid for life insurance and annuities, mortgage rate and repay loans; the household disposable income is derived as the sum of the income from employment, property income and transfers, net of taxes and social contributions.

  12. 12.

    HBS does not allow us to properly single out the component of own consumption from the total expenditure, whereas own consumption is not collected at all by the SHIW. Therefore, the own consumption is always included in the definition from the former survey, while it is always excluded from the latter survey.

  13. 13.

    It must be noted that the monthly imputed rent for the main accommodation ranges from 531 euros in EU-Silc to 576 euros in NHBS. Nevertheless, the NHBS data also include the imputed rent from the main accommodation appurtenances (garages, basements, etc.) and also from any second house.

  14. 14.

    The coefficients of this scale are as follows: 1 for a 1 member household, 1.9 for a 2 member household, 2.7 for a 3 member household, 3.5 for a 4 member households, 4.2 for a 5 member households, 5.0 for a 6 member households and 5.7 if the members are 7 or more.

  15. 15.

    The elasticities resulted equal to 0.53 for S1, 0.67 for S2, 0.73 for S3 and 0.87 for S4 (Betti et al. 2016).

  16. 16.

    The reference method corresponds to the calculation of poverty incidence on HBS data including imputed rent and goods produced for own consumption, using the traditional approach, S2 scale and poverty line at 60% of the mean of per-capita distribution.

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.

    Article  Google 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.

    Book  Google 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.

    Article  Google 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.

    Article  Google 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.

  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.

  20. Brandolini, A., Magri, S., & Smeeding, T. (2010). Asset-based measurement of poverty. Journal of Policy Analysis and Management, 29(2), 267–284.

    Article  Google 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.

    Article  Google 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.

  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.

  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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

  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.

  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.

    Article  Google 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.

    Article  Google 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. (2015b). Improving data quality for the next HBS round. Doc. LC/143/15/EN. June 2015. https://www.circabc.europa.eu/webdav/CircaBC/ESTAT/hbs/Library/working_groups/Working%20Group%202015/LC143–15EN_Improving_Data_Quality_For_The_Next_HBS_Round.pdf.

  45. 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.

  46. 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.

  47. 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.

  48. 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 

  49. 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.

    Article  Google Scholar 

  50. 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/.

  51. Hagenaars, A. (1987). A class of poverty indices. International Economic Review, 28(3), 583–607.

    Article  Google Scholar 

  52. 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 

  53. 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.

    Article  Google Scholar 

  54. 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.

    Article  Google Scholar 

  55. 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.

  56. 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.

  57. 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.

  58. 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.

  59. Istat. (2016a). Poverty in Italy year 2015. Households economic conditions and disparities. Press release. http://www.istat.it/en/archive/189215.

  60. Istat. (2016b). Income and living conditions year 2015. Households economic conditions and disparities. Press release http://www.istat.it/en/archive/193757.

  61. Istat. (2016c). The homeless. Press release. http://www.istat.it/en/archive/186791.

  62. Jenkins, S. P., & Van Kerm, P. (2014). The relationship between EU indicators of persistent and current poverty. Social Indicators Research, 116(2), 611–638.

    Article  Google Scholar 

  63. Kanbur, R., & Tuomala, M. (2016). Groupings and the gains from targeting. Research in Economics, 70, 53–63.

    Article  Google Scholar 

  64. 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 

  65. 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 

  66. 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).

  67. 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.

  68. 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.

  69. 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.

  70. 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.

    Article  Google Scholar 

  71. 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.

  72. Nelson, K. (2012). Counteracting material deprivation: The role of social assistance in Europe. Journal of European Social Policy, 22(2), 148–163.

    Article  Google Scholar 

  73. Nolan, B., & Whelan, C. T. (2011). Poverty and deprivation in Europe. Oxford: Oxford University Press.

    Book  Google Scholar 

  74. 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.

    Article  Google Scholar 

  75. 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.

  76. Orshansky, M. (1963). Children of the poor. Social Security Bulletin, 26(7), 3–13.

    Google Scholar 

  77. 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.

    Article  Google Scholar 

  78. 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.

  79. Pratesi, M. (Ed.). (2016). Analysis of poverty data by small area estimation. Hoboken: Wiley. https://doi.org/10.1002/9781118814963.

    Google Scholar 

  80. Ravallion, M. (2016). The economics of poverty: History, measurement, and policy. Oxford: Oxford University Press.

    Book  Google Scholar 

  81. Rowntree, B. S. (1901). Poverty: A study of town life. London: Longman.

    Google Scholar 

  82. Sen, A. K. (1976). Poverty: An ordinal approach to measurement. Econometrica, 44, 219–231.

    Article  Google Scholar 

  83. 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.

  84. Townsend, P. (1979). Poverty in the United Kingdom: A survey of household resources and standards of living. Harmondsworth: Penguin Books.

    Google Scholar 

  85. 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 

  86. UNECE. (2011). Canberra group handbook on household income statistics (2nd ed.). Geneva: UNECE.

    Google Scholar 

  87. 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.

    Article  Google Scholar 

  88. Villar, A. (2017). Lectures on inequality, poverty and welfare. Lecture Notes in Economics and Mathematical Systems (Vol. 685). Berlin: Springer. https://doi.org/10.1007/978-3-319-45562-4

  89. 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 

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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.

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Lemmi, A., Grassi, D., Masi, A. et al. Methodological Choices and Data Quality Issues for Official Poverty Measures: Evidences from Italy. Soc Indic Res 141, 299–330 (2019). https://doi.org/10.1007/s11205-018-1841-3

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Keywords

  • Poverty measures
  • Consumption expenditure
  • Income
  • Sensitivity analysis
  • Italy