An Integrated Database to Measure Living Standards

Open access

Abstract

This study generates an integrated database to measure living standards in Italy using propensity score matching. We follow the recommendations of the Commission on the Measurement of Economic Performance and Social Progress proposing that income, consumption of market goods and nonmarket activities, and wealth, rather than production, should be evaluated jointly in order to appropriately measure material welfare. Our integrated database is similar in design to the one built for the United States by the Levy Economics Institute to measure the multiple dimensions of well-being. In the United States, as is the case for Italy and most European countries, the state does not maintain a unified database to measure household economic well-being, and data sources about income and employment surveys and other surveys on wealth and the use of time have to be statistically matched. The measure of well-being is therefore the result of a multidimensional evaluation process no longer associated with a single indicator, as is usually the case when measuring gross domestic product. The estimation of individual and social welfare, multidimensional poverty and inequality does require an integrated living standard database where information about consumption, income, time use and subjective well-being are jointly available. With this objective in mind, we combine information available in four different surveys: the European Union Statistics on Income and Living Conditions Survey, the Household Budget Survey, the Time Use Survey, and the Household Conditions and Social Capital Survey. We perform three different statistical matching procedures to link the relevant dimensions of living standards contained in each survey and report both the statistical and economic tests carried out to evaluate the quality of the procedure at a high level of detail.

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  • AlbayrakÖ. and T. Masterson. 2017. Quality of Statistical Match of Household Budget Survey and SILC for Turkey. Levy Economics Institute (Working Paper no. 885). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2924849 (accessed October 2018).

  • Alkire S. and J. Foster. 2011. “Counting and Multidimensional Poverty Measurement.” Journal of Public Economics 95(7–8): 476–487. Doi: http://dx.doi.org/10.1016/j.jpubeco.2010.11.006.

  • Andridge R.R. and R.J.A. Little. 2010. “A Review of Hot Deck Imputation for Survey Non-response.” International Statistical Review 78(1): 40–64. Doi: http://dx.doi.org/10.1111/j.1751-5823.2010.00103.x.

  • Attanasio O. E. Hurst and L. Pistaferri. 2015. “The Evolution of Income Consumption and Leisure Inequality in the US 1980–2010.” In Improving the Measurement of Consumer Expenditures edited by C.D. Carroll T.F. Crossley and J. Sabelhaus 100–140. University of Chicago Press.

  • Augurzy B. and C.M. Schmidt. 2001. The Propensity Score: A Means to an End IZA Discussion Paper No. 271. Available at: https://ssrn.com/abstract=270919 (accessed September 2015).

  • Austin P.C. N. Jembere and M. Chiu. 2018. “Propensity Score Matching and Complex Surveys.” Statistical Methods in Medical Research 27(4): 1240–1257. Doi: http://dx.doi.org/10.1177/0962280216658920.

  • Black D.A. and J.A. Smith. 2004. “How Robust is the Evidence on the Effects of College Quality? Evidence from Matching.” Journal of Econometrics 121: 99–124. Doi: http://dx.doi.org/10.1016/j.jeconom.2003.10.006.

  • Blundell R. and I. Preston. 1995. “Income Expenditure and the Living Standards of UK Households.” Fiscal Studies 16(3): 40–54. Doi: https://doi.org/10.1111/j.1475-5890.1995.tb00226.x.

  • Brandolini A. S. Magri and T. Smeeding. 2010. “Asset-based Measurement of Poverty.” Journal of Policy Analysis and Management 29(2): 267–284. Doi: https://doi.org/10.1002/pam.20491.

  • Brewer M. and C. O’Dea. 2012. Measuring Living Standards with Income and Consumption: Evidence from the UK. Institute for Social and Economic Research University of Essex and Institute for Fiscal Studies (Working Paper n. 2012-05). Available at: https://www.iser.essex.ac.uk/research/publications/working-papers/iser/2012-05.pdf (accessed April 2015).

  • Caiumi A. and F. Perali. 2015. “Who Bears the Full Cost of Children? Evidence From a Collective Demand System.” Empirical Economics 49: 33–64. Doi: http://dx.doi.org/10.1007/s00181-014-0854-2.

  • Caliendo M. and S. Kopeinig. 2008. “Some Practical Guidance for the Implementation of Propensity Score Matching.” Journal of Economic Surveys 22(1): 31–72. Doi: https://doi.org/10.1111/j.1467-6419.2007.00527.x.

  • Chen J. and J. Shao. 2000. “Nearest Neighbor Imputation for Survey Data.” Journal of Official Statistics 16(2): 113–131. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/nearest-neighbor-imputation-for-survey-data.pdf (accessed April 2015).

  • Coli A. F. Tartamella G. Sacco I. Faiella M. Scanu M. D’Orazio M. Di Zio I. Siciliani S. Colombini and A. Masi. 2005. La costruzione di un archivio di microdati sulle famiglie italiane ottenuto integrando l’indagine ISTAT sui consumi delle famiglie italiane e l’indagine Banca d’Italia sui bilanci delle famiglie italiane. Technical Report Working Group ISTAT-Bank of Italy Rome. Available at: https://www.istat.it/it/files/2018/07/2006_12-1.pdf (accessed April 2015).

  • Conti P.L. D. Marella and M. Scanu. 2012. “Uncertainty Analysis in Statistical Matching.” Journal of Official Statistics 28: 69–88.

  • Conti P.L. D. Marella and M. Scanu. 2016. “Statistical Matching Analysis for Complex Survey Data with Applications.” Journal of the American Statistical Association 111: 1715–1725. Doi: http://dx.doi.org/10.1080/01621459.2015.1112803.

  • Conti P.L. D. Marella and A. Neri. 2017. “Statistical Matching and Uncertainty Analysis in Combining Household Income and Expenditure Data.” Statistical Methods & Applications 26(3): 485–505. Doi: http://dx.doi.org/10.1007/s10260-016-0374-7.

  • Dehejia R.H. and S. Wahba. 1999. “Casual Effects in Nonexperimental Studies: Revaluating the Evaluation of Training Programs.” Journal of the American Statistical Association 94: 1053–1062. Doi: http://dx.doi.org/10.1080/01621459.1999.10473858.

  • Dehejia R.H. and S. Wahba. 2002. “Propensity Score Matching Methods for Non-Experimental Causal Studies.” Review of Economics and Statistics 84(1): 151–161. Doi: http://dx.doi.org/10.1162/003465302317331982.

  • D’Orazio M. M. Di Zio and M. Scanu. 2006a. Statistical Matching: Theory and Practice. New York: Wiley.

  • D’Orazio M. M. Di Zio and M. Scanu. 2006b. “Statistical Matching for Categorical Data: Displaying Uncertainty and Using Logical Constraints.” Journal of Official Statistics 28: 137–157. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/statistical-matching-for-categorical-data-displaying-uncertainty-and-using-logical-constraints.pdf (accessed September 2017).

  • D’Orazio M. M. Di Zio and M. Scan. 2009. Uncertainty Intervals for Non-Identifiable Parameters in Statistical Matching. 57th Session of the International Statistical Institute Durban (South Africa) August 2009. Available at: http://isi.cbs.nl/iamamember/CD8-Durban2009/A5%20Docs/0074.pdf (accessed April 2015).

  • D’Orazio M. M. Di Zio and M. Scanu. 2017. “The Use of Uncertainty to Choose Matching Variables.” In Statistical Matching edited by Ferraro et al. Soft Methods for Data Science. New York: Springer.

  • Donatiello G. M. D’Orazio D. Frattarola A. Rizzi M. Scanu and M. Spaziani. 2014. “Statistical Matching of Income and Consumption Expenditures.” International Journal of Economic Science Vol. III(3): 50–65. Available at: https://www.iises.net/download/Soubory/soubory-puvodni/pp50-65-ijoes-V3N3.pdf (accessed April 2015).

  • Foster J. J. Greer and E. Thorbecke. 1984. “A Class of Decomposable Poverty Measures.” Econometrica 52(3): 761–766. Doi: http://dx.doi.org/10.2307/1913475.

  • Garrido M.M. A.S. Kelly J. Paris K. Roza D.E. Meier R.S. Morrison and M.D. Aldridge. 2014. “Methods for Constructing and Assessing Propensity Score.” Health Services Research 49(5): 1701–1720. Doi: http://dx.doi.org/10.1111/1475-6773.12182.

  • Goldberger A. 1991. A Course in Econometrics. Cambridge Massachusetts: Harvard University Press.

  • ISTAT. 2013. Rapporto Bes 2013: il Benessere Equo e Sostenibile in Italia. Rome: ISTAT. Available at: https://www.istat.it/it/archivio/84348 (accessed August 2018).

  • ISTAT. 2014. Rapporto Bes 2014: il Benessere Equo e Sostenibile in Italia. Rome: ISTAT. Available at: https://www.istat.it/it/archivio/126613 (accessed August 2018).

  • Kiesl H. and S. Rässler. 2009. “How Useful Are Uncertainty Bounds? Some Recent Theory with an Application to Rubin’s Causal Model.” 57th Session of the International Statistical Institute Durban (South Africa) 16–22 August 2009. Available at: http://isi.cbs.nl/iamamember/CD8-Durban2009/A5%20Docs/0169.pdf (accessed April 2015).

  • Krug E.G. L.L. Dahlberg J.A. Mercy A.B. Zwi and R. Lozano. 2002. World Report on Violence and Health. Geneva: World Health Organization. Available at: https://www.who.int/violence_injury_prevention/violence/world_report/en/ (accessed April 2015).

  • Kum H. and T.N. Masterson. 2010. “Statistical Matching Using Propensity Score: Theory and Application to the Analysis of the Distribution of Income and Wealth.” Journal of Economic and Social Measurement 35: 177–196.

  • Lechner M. 2008. “A Note on the Common Support Problem in Applied Evaluation Studies.” Annales d’Economie et de Statistique 91/92: 217–235. Doi: http://dx.doi.org/10.2307/27917246.

  • Lee W.S. 2013. “Propensity Score Matching and Variations on the Balancing Test.” Empirical Economics 44(1): 47–80. Doi: http://dx.doi.org/10.1007/s00181-011-0481-0.

  • Leulescu A. and M. Agafitei. 2013. Statistical Matching: a Model Based Approach for Data Integration. Luxembourg: Eurostat European Commission. Available at: https://ec.europa.eu/eurostat/documents/3888793/5855821/KS-RA-13-020-EN.PDF/477dd541-92ee-4259-95d4-1c42fcf2ef34?version=1.0 (accessed April 2015).

  • Masterson T. 2010. Quality of Match for Statistical Matches Used in the 1999 and 2005 LIMEW Estimates for Canada. Levy Economics Institute (Working Paper n. 615). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1670765 (accessed April 2015).

  • Masterson T. 2014. Quality of Statistical Match and Employment Simulations Used in the Estimation of the Levy Institute Measure of Time and Income Poverty (LIMTIP) for South Korea 2009. Levy Economics Institute (Working Paper n. 793). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2416850 (accessed April 2015).

  • Menon M. R. Pendakur and F. Perali. 2015. “All in the Family: How Do Social Capital and Material Wellbeing Affect Relational Wellbeing?” Social Indicators Research 124(3): 889–910. Doi: https://doi.org/10.1007/s11205-014-0816-2.

  • Meyer B. and J. Sullivan. 2011. “Further Results on Measuring the Well-being of the Poor Using Income and Consumption.” Canadian Journal of Economics 44(1): 52–87. Doi: http://dx.doi.org/10.1111/j.1540-5982.2010.01623.x.

  • Mittag N. 2013. Imputations: Benefits Risks and a Method for Missing Data. Harris School of Public Policy University of Chicago. Available at: http://home.cerge-ei.cz/mittag/papers/Imputations.pdf (accessed April 2015).

  • Perali F. 2003. The Behavioral and Welfare Analysis of Consumption. Dordrecht: Kluwer Academic Publishers.

  • Perali F. 2008. “The Second Engel Law: Is it a Paradox?” European Economic Review 52(8): 1353–1377. Doi: http://dx.doi.org/10.1016/j.euroecorev.2008.01.005.

  • Poissonnier A. and D. Roy. 2017. “Household Satellite Accounts for France. Methodological Issues on the Assessment of Domestic Production.” Review of Income and Wealth 63(2): 353–368. Doi: http://dx.doi.org/10.1111/roiw.12216.

  • Rässler S. 2002. Statistical matching: A Frequentist Theory Practical Applications and Alternative Bayesian Approaches. New York: Lecture Notes in Statistics 168 Springer.

  • Rässler S. 2004. “Data Fusion: Identification Problems Validity and Multiple Imputation.” Austrian Journal of Statistics 33: 153–171. Available at: https://www.researchgate.net/publication/228528513_Data_fusion_Identification_problems_validity_and_multiple_imputation (accessed April 2015).

  • Renssen R.H. 1998. “Use of Statistical Matching Techniques in Calibration Estimation.” Survey Methodology 24: 171–183. Available at: https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19980024354 (accessed August 2018).

  • Ridgeway G. S.A. Kovalchik B.A. Griffin and M.U. Kabeto. 2015. “Propensity Score Analysis with Survey Weighted Data.” Journal of Causal Inference 3(2): 237–249. Doi: http://dx.doi.org/10.1515/jci-2014-0039.

  • Rios-Avila F. 2014. Quality of Match for Statistical Matches Using the American Time Use Survey 2010 the Survey of Consumer Finances 2010 and the Annual Social and Economic Supplement 2011. Levy Economics Institute (Working Paper no. 798). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2432153 (accessed April 2015).

  • Rios-Avila F. 2015. Quality of Match for Statistical Matches Using the Consumer Expenditure Survey 2011 and Annual Social Economic Supplement 2011. Levy Economics Institute (Working Paper n. 830). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2554089 (accessed October 2015).

  • Rios-Avila F. 2016. Quality of Match for Statistical Matches Used in the Development of the Levy Institute Measure of Time and Consumption Poverty (LIMTCP) for Ghana and Tanzania. Levy Economics Institute (Working Paper n. 873). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2838475 (accessed September 2017).

  • Rodgers W.L. 1984. “An Evaluation of Statistical Matching.” Journal of Business & Economic Statistics 2: 91–102. Doi: http://dx.doi.org/10.2307/1391358.

  • Rosenbaum P.R. and D.B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Casual Effects.” Biometrika 70: 41–55. Doi: http://dx.doi.org/10.1093/biomet/70.1.41.

  • Rosenbaum P.R. and D.B. Rubin. 1985. “Constructing a Control Group Using Multivariate Matched Sampling Methods that Incorporate the Propensity Score.” The American Statistician 39: 33–38. Doi: http://dx.doi.org/10.2307/2683903.

  • Rubin D.B. 1986. “Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations.” Journal of Business Economics and Statistics 4: 87–94. Doi: http://dx.doi.org/10.2307/1391390.

  • Rubin D.B. and N. Schenker. 1986. “Multiple Imputation for Interval Estimation from Simple Random Samples with Ignorable Nonresponse.” Journal of the American Statistical Association 81: 366–374. Doi: http://dx.doi.org/10.1080/01621459.1986.10478280.

  • Sharpe A. A. Murray B. Evans and E. Hazell. 2011. The Levy Institute Measure of Economic Well-Being: Estimates for Canada 1999 and 2005. Levy Economics Institute (Working Paper n. 680). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1895637&rec=1&srcabs=1670765&alg=7&pos=5 (accessed August 2018).

  • Singh A.C. H. Mantel M. Kinack and G. Rowe. 1990. On Methods of Statistical Matching with and Without Auxiliary Information. Methodology Branch Statistics Canada (Technical Report SSMD-90-016E). Available at: http://publications.gc.ca/collections/collection_2017/statcan/11-613/CS11-613-90-16-eng.pdf (accessed April 2015).

  • Singh A.C. H. Mantel M. Kinack and G. Rowe. 1993. “Statistical Matching: Use of Auxiliary Information as an Alternative to the Conditional Independence Assumption.” Survey Methodology 19: 59–79. Available at: https://www150.statcan.gc.ca/n1/pub/12-001-x/1993001/article/14475-eng.pdf (accessed April 2015).

  • Sisto A. 2006. Propensity Score Matching: un’Applicazione per la Creazione di un Database Integrato ISTAT-Banca d’Italia. Dipartimento di Politiche Pubbliche e Scelte Collettive – POLIS Università del Piemonte Orientale (Working Paper n. 63). Available at: http://polis.unipmn.it/pubbl/RePEc/uca/ucapdv/sisto63.pdf (accessed April 2015).

  • Slesnick D. 1993. “Gaining Ground: Poverty in the Postwar United States.” Journal of Political Economy 101: 1–38. Doi: http://dx.doi.org/10.1086/261864.

  • Smith J. and P. Todd. 2005. “Rejoinder.” Journal of Econometrics 125: 365–375. Doi: http://dx.doi.org/10.1016/j.jeconom.2004.04.013.

  • Stiglitz E. A. Sen and J.P. Fitoussi. 2010. Report by the Commission on the Measurement of Economic Performance and Social Progress Paris. Available at: http://www.stiglitz-sen-fitoussi.fr/en/index.htm (Accessed August 2018).

  • Tedeschi S. and E. Pisano. 2013. Data Fusion Between Bank of Italy-SHIW and ISTAT-HBS. Munich Personal RePEc Archive (Working Paper n. 51253). Available at: https://mpra.ub.uni-muenchen.de/51253/ (accessed April 2015).

  • Webber D. and R. Tonkin. 2013. Statistical Matching of EU-SILC and the Household Budget Survey to Compare Poverty Estimates using Income Expenditures and Material Deprivation. EUROSTAT Methodologies & Working papers European Union. Available at: https://ec.europa.eu/eurostat/documents/3888793/5857145/KS-RA-13-007-EN.PDF/37d4ffcc-e9fc-42bc-8d4f-fc89c65ff6b1 (accessed April 2015).

  • Wolff E.N. and A. Zacharias. 2003. The Levy Institute Measure of Economic Well-Being. The Levy Economics Institute (Working Paper n. 372). Available at: http://www.levyinstitute.org/pubs/wp/372.pdf (accessed April 2015).

  • Wolff E.N. A. Zacharias T. Masterson S. Eren A. Sharpe and E. Hazell. 2012. A Comparison of Inequality and Living Standards in Canada and the United States Using an Expanded Measure of Economic Well-Being”. Levy Economics Institute (Working Paper n. 703). Available at: http://www.levyinstitute.org/pubs/wp_703.pdf (accessed April 2015).

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