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Mind the wealth gap: a new allocation method to match micro and macro statistics for household wealth

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  • Michele Cantarella
  • Andrea Neri
  • Maria Giovanna Ranalli

Abstract

The financial and economic crisis recently experienced by many European countries has increased demand for timely, coherent and consistent distributional information for the household sector. In the Euro area, most of the NCBs collect such information through income and wealth surveys, which are often used to inform their decisions. These surveys, however, can often suffer from biases, usually caused by non-response and under-reporting behaviours, leading to a mismatch with macroeconomic aggregates. In this paper, we develop a novel allocation method which combines information from a power law (Pareto) model and imputation procedures so to address these issues simultaneously, when only limited external information is available. We provide two important contributions: first, we adjust the weights of observed survey households for non-response bias, then, we correct for measurement error. Finally, we produce distributional indicators for four Euro-Area countries.

Suggested Citation

  • Michele Cantarella & Andrea Neri & Maria Giovanna Ranalli, 2021. "Mind the wealth gap: a new allocation method to match micro and macro statistics for household wealth," Papers 2101.01085, arXiv.org, revised Jan 2021.
  • Handle: RePEc:arx:papers:2101.01085
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    1. Alvaredo, Facundo & Atkinson, Anthony B. & Morelli, Salvatore, 2018. "Top wealth shares in the UK over more than a century," Journal of Public Economics, Elsevier, vol. 162(C), pages 26-47.
    2. A. B. Atkinson, 2017. "Pareto and the Upper Tail of the Income Distribution in the UK: 1799 to the Present," Economica, London School of Economics and Political Science, vol. 84(334), pages 129-156, April.
    3. Andrea Colciago & Anna Samarina & Jakob de Haan, 2019. "Central Bank Policies And Income And Wealth Inequality: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 33(4), pages 1199-1231, September.
    4. Carsten Schröder & Charlotte Bartels & Markus M. Grabka & Martin Kroh & Rainer Siegers, 2018. "A Novel Sampling Strategy for Surveying High-Worth Individuals - An Application Using the Socio-Economic Panel," SOEPpapers on Multidisciplinary Panel Data Research 978, DIW Berlin, The German Socio-Economic Panel (SOEP).
    5. The Eurosystem Household Finance and Consumption Network, 2009. "Survey data on household finance and consumption - research summary and policy use," Occasional Paper Series 100, European Central Bank.
    6. Bertrand Garbinti & Jonathan Goupille-Lebret & Thomas Piketty, 0. "Accounting for Wealth-Inequality Dynamics: Methods, Estimates, and Simulations for France," Journal of the European Economic Association, European Economic Association, vol. 19(1), pages 620-663.
    7. Thomas Blanchet & Juliette Fournier & Thomas Piketty, 2022. "Generalized Pareto Curves: Theory and Applications," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(1), pages 263-288, March.
    8. Facundo Alvaredo & Emmanuel Saez, 2009. "Income and Wealth Concentration in Spain from a Historical and Fiscal Perspective," Journal of the European Economic Association, MIT Press, vol. 7(5), pages 1140-1167, September.
    9. Stefan Bach & Andreas Thiemann & Aline Zucco, 2019. "Looking for the missing rich: tracing the top tail of the wealth distribution," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(6), pages 1234-1258, December.
    10. Arrondel, Luc & Lamarche, Pierre & Savignac, Frédérique, 2019. "Does inequality matter for the consumption-wealth channel? Empirical evidence," European Economic Review, Elsevier, vol. 111(C), pages 139-165.
    11. Mumtaz, Haroon & Theophilopoulou, Angeliki, 2020. "Monetary policy and wealth inequality over the great recession in the UK. An empirical analysis," European Economic Review, Elsevier, vol. 130(C).
    12. Frémeaux, Nicolas & Leturcq, Marion, 2020. "Inequalities and the individualization of wealth," Journal of Public Economics, Elsevier, vol. 184(C).
    13. Paolo Acciari & Salvatore Morelli, 2020. "Wealth Transfers and Net Wealth at Death: Evidence from the Italian Inheritance Tax Records 1995–2016," NBER Chapters, in: Measuring Distribution and Mobility of Income and Wealth, pages 175-203, National Bureau of Economic Research, Inc.
    14. Garbinti, Bertrand & Goupille-Lebret, Jonathan & Piketty, Thomas, 2018. "Income inequality in France, 1900–2014: Evidence from Distributional National Accounts (DINA)," Journal of Public Economics, Elsevier, vol. 162(C), pages 63-77.
    15. Emmanuel Saez & Gabriel Zucman, 2016. "Editor's Choice Wealth Inequality in the United States since 1913: Evidence from Capitalized Income Tax Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(2), pages 519-578.
    16. Stephen P. Jenkins, 2017. "Pareto Models, Top Incomes and Recent Trends in UK Income Inequality," Economica, London School of Economics and Political Science, vol. 84(334), pages 261-289, April.
    17. Philip Vermeulen, 2016. "Estimating the Top Tail of the Wealth Distribution," American Economic Review, American Economic Association, vol. 106(5), pages 646-650, May.
    18. Paiella, Monica, 2007. "Does wealth affect consumption? Evidence for Italy," Journal of Macroeconomics, Elsevier, vol. 29(1), pages 189-205, March.
    19. Xavier Gabaix & Rustam Ibragimov, 2011. "Rank - 1 / 2: A Simple Way to Improve the OLS Estimation of Tail Exponents," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 24-39, January.
    20. Luigi Guiso & Monica Paiella & Ignazio Visco, 2005. "Do capital gains affect consumption? Estimates of wealth effects from Italian households� behavior," Temi di discussione (Economic working papers) 555, Bank of Italy, Economic Research and International Relations Area.
    21. Philip Vermeulen, 2018. "How Fat is the Top Tail of the Wealth Distribution?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 64(2), pages 357-387, June.
    22. Christelis, Dimitris & Georgarakos, Dimitris & Jappelli, Tullio & Pistaferri, Luigi & Rooij, Maarten van, 2021. "Heterogeneous wealth effects," European Economic Review, Elsevier, vol. 137(C).
    23. Ampudia, Miguel & van Vlokhoven, Has & Żochowski, Dawid, 2016. "Financial fragility of euro area households," Journal of Financial Stability, Elsevier, vol. 27(C), pages 250-262.
    24. Schröder, Carsten & Bartels, Charlotte & Grabka, Markus M. & König, Johannes & Kroh, Martin & Siegers, Rainer, 2020. "A Novel Sampling Strategy for Surveying High Net‐Worth Individuals – A Pretest Application Using the Socio‐Economic Panel," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 66(4), pages 825-849.
    25. Giovanni D'Alessio & Andrea Neri, 2015. "Income and wealth sample estimates consistent with macro aggregates: some experiments," Questioni di Economia e Finanza (Occasional Papers) 272, Bank of Italy, Economic Research and International Relations Area.
    26. M. Giovanna Ranalli & Andrea Neri, 2011. "To misreport or not to report?, The case of the Italian survey on household income and wealth," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 12(2), pages 281-300, October.
    27. Thomas Blanchet & Ignacio Flores & Marc Morgan, 2022. "The weight of the rich: improving surveys using tax data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 119-150, March.
    28. Sofie R. Waltl & Robin Chakraborty, 2022. "Missing the wealthy in the HFCS: micro problems with macro implications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 169-203, March.
    29. Arthur B kennickell, 2008. "The role of over-sampling of the wealthy in the survey of consumer finances," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The IFC's contribution to the 56th ISI Session, Lisbon, August 2007, volume 28, pages 403-408, Bank for International Settlements.
    30. Arthur B. Kennickell, 2019. "The tail that wags: differences in effective right tail coverage and estimates of wealth inequality," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(4), pages 443-459, December.
    31. Coibion, Olivier & Gorodnichenko, Yuriy & Kueng, Lorenz & Silvia, John, 2017. "Innocent Bystanders? Monetary policy and inequality," Journal of Monetary Economics, Elsevier, vol. 88(C), pages 70-89.
    32. Casiraghi, Marco & Gaiotti, Eugenio & Rodano, Lisa & Secchi, Alessandro, 2018. "A “reverse Robin Hood”? The distributional implications of non-standard monetary policy for Italian households," Journal of International Money and Finance, Elsevier, vol. 85(C), pages 215-235.
    33. Valentina Michelangeli & Cristiana Rampazzi, 2016. "Indicators of financial vulnerability: a household level study," Questioni di Economia e Finanza (Occasional Papers) 369, Bank of Italy, Economic Research and International Relations Area.
    34. repec:mpr:mprres:4780 is not listed on IDEAS
    35. Bertrand Garbinti & Jonathan Goupille-Lebret & Thomas Piketty, 2021. "Accounting for Wealth-Inequality Dynamics: Methods, Estimates, and Simulations for France," Journal of the European Economic Association, European Economic Association, vol. 19(1), pages 620-663.
    36. repec:mpr:mprres:4937 is not listed on IDEAS
    37. Aban, Inmaculada B. & Meerschaert, Mark M. & Panorska, Anna K., 2006. "Parameter Estimation for the Truncated Pareto Distribution," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 270-277, March.
    38. Chakraborty Robin & Kavonius Ilja Kristian & Pérez-Duarte Sébastien & Vermeulen Philip, 2019. "Is the Top Tail of the Wealth Distribution the Missing Link between the Household Finance and Consumption Survey and National Accounts?," Journal of Official Statistics, Sciendo, vol. 35(1), pages 31-65, March.
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    Cited by:

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    2. Riccardo De Bonis & Matteo Piazza, 2021. "A silent revolution. How central bank statistics have changed in the last 25 years," PSL Quarterly Review, Economia civile, vol. 74(299), pages 347-371.
    3. Kennickell, Arthur B., 2021. "Chasing the Tail: A Generalized Pareto Distribution Approach to Estimating Wealth Inequality," SocArXiv u3zs2, Center for Open Science.
    4. Engel, Janina & Riera, Pau Gayà & Grilli, Joseph & Sola, Pierre, 2022. "Developing reconciled quarterly distributional national wealth – insight into inequality and wealth structures," Working Paper Series 2687, European Central Bank.

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    More about this item

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • N3 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy

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