Top

Empirical Economics

Published in:

01-03-2021

Are survey data underestimating the inequality of wealth?

Published in: Empirical Economics | Issue 2/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This paper studies households’ response behaviour in a wealth survey. We analyse how unit non-response and item non-response contribute to the estimated distribution of wealth. Our findings imply that wealth inequality is underestimated in the survey. The downward bias is originating from item non-response and not from unit non-response. Wealthier households are less likely to provide answers to wealth-related questions. As a result, the level of net wealth is underestimated and the top tail of its distribution is missing. Imputation can eliminate biases throughout most of the wealth distribution but does not recover the estimates in the top tail.

previous article Natural resources and income inequality in developed countries: synthetic control method evidence

next article On the long-run dynamics of income and wealth inequality

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Examples of wealth surveys are the Bank of Italy’s SHIW survey, the Federal Reserve Board’s SCF, the Bank of Spain’s EFF and the ECB’s HFCS.

There are actually only four income components that need to be imputed, because one of the income components, private pensions, had no missing observations. See “Appendix 1” Table 2 for the related statistics.

The following description of methods proceeds from the description of the commands “mi impute chained” and “mi impute pmm” in the Stata manual.

As imputed values are drawn from the observed data, this method also preserves the distribution of the observed data. We have tested alternative imputation methods such as the linear regression, but this gives a very similar distribution of the imputed data to that from predictive mean matching.

This weak link between net wealth and unit response is not caused by controlling for income deciles in the regressions, as the link is still weak when the income deciles are excluded from the regression.

The Estonian HFCS aims at oversampling the richest households. The oversampling is based on personal incomes of the survey contact people in the period prior to the survey (the income data are obtained from the registers). To test the sensitivity of our estimates to oversampling, we re-estimated the results shown in Tables 2 and 4 removing the oversampled contact persons randomly. This had no significant effect on the estimation results, and therefore the estimates based on this exercise are not reported.

Alvaredo F, Saez E (2010) Income and wealth concentration in Spain in a historical and fiscal perspective. In: Atkinson AB, Piketty T (eds) Top incomes global perspective. Oxford University Press, Oxford

Bach S, Thiemann A, Zucco A (2018) Looking for the missing rich: Tracing the top tail of the wealth distribution. DIW discussion paper no. 1717

Bover O (2011) The Spanish survey of household finance (EFF): description and methods of the 2008 wave. Documentos Ocasionales no 1103, Banco De España

Bricker J, Henriques A, Krimmel J, Sabelhaus J (2016) Measuring income and wealth at the top using administrative and survey data. Brook Pap Econ Act 2016:261–312CrossRef

Brzezinzki M, Salach K, Wronski M (2020) Wealth inequality in Central and Eastern Europe: evidence from household survey and rich lists’ data combined. Econ Transit Inst Change 28:637–660CrossRef

Chakraborty R, Waltl SR (2018) Missing the wealthy in the HFCS: micro problems with macro implications. European Central Bank, working paper series, 2163

Cowell FA, Van Kerm P (2015) Wealth inequality: A survey. J Econ Surv 29(4):671–710CrossRef

D’Alessio G, Faiella I (2002) Non-response behaviour in the Bank of Italy’s Survey of Household Income and Wealth. Banca D’Italia working paper no 462

Dell F, Piketty T, Saez E (2007) Income and wealth concentration in Switzerland over the 20th century. In: Atkinson AB, Piketty T (eds) Top incomes over the 20th century. Oxford University Press, Oxford

Gelman A, Hill J (2006) Data analysis using regression and multilevel/hierarchical models. Columbia University Press, New YorkCrossRef

HFCS (2013) The Eurosystem household finance and consumption survey: results from the first wave. ECB statistics paper series no 2

HFCS (2017) The household finance and consumption survey: methodological report for the second wave. ECB statistics paper series no 17

Johansson F, Klevmarken A (2007) Comparing register and survey wealth data. Department of Economics, Uppsala University, Uppsala

Kennickell AB, Woodburn RL (1999) Consistent weight design for the 1989 and 1995 SCFs, and the distribution of wealth. Rev Income Wealth 45(2):193–215CrossRef

Lundberg J, Waldenström D (2018) Wealth inequality in Sweden: what can we learn from capitalized income tax data? Rev Income Wealth 64(3):517–541CrossRef

Meriküll J, Rõõm T (2019) Estonian household finance and consumption survey: results from the 2017 wave. Bank of Estonia occasional paper no 1/2019

Neri A, Ranalli MG (2012) To misreport or not to report? The measurement of household financial wealth. Banca D’Italia working paper no 870

Osier G (2016) Unit non-response in household wealth surveys. ECB statistics paper series no 15

Pérez-Duarte S, Sánchez-Muñoz C, Törmälehto V-M (2010) Re-weighting to reduce unit non-response bias in household wealth surveys: a cross-country comparative perspective illustrated by a case study. In: Proceedings of the European conference on quality in official statistics

Piketty T (2014) Capital in the twenty-first century. The Belknap Press of Harvard University Press, CambridgeCrossRef

Roine J, Waldenström D (2009) Wealth concentration over the path of development: Sweden, 1873–2006. Scand J Econ 111(1):151–187CrossRef

Roine J, Waldenström D (2015) Long-run trends in the distribution of income and wealth. In: Atkinson AB, Bourgiognon F (eds) Handbook of income distribution, vol 2. North-Holland, Amsterdam

Vermeulen P (2016) Estimating the top tail of the wealth distribution. Am Econ Rev Pap Proc 106(5):646–650CrossRef

Vermeulen P (2018) How fat is the top tail of the wealth distribution? Rev Income Wealth 64(2):357–387CrossRef

Yan T, Curtin R (2010) The relation between unit nonresponse and item nonresponse: a response continuum perspective. Int J Public Opin Res 22(4):535–551CrossRef

Title: Are survey data underestimating the inequality of wealth?
Publication date: 01-03-2021
Published in: Empirical Economics / Issue 2/2022
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-021-02030-6

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 2/2022

Sector connectedness in the Chinese stock markets

Testing the binomial fixed effects logit model, with an application to female labour supply

The forecasting of consumer exchange-traded funds (ETFs) via grey relational analysis (GRA) and artificial neural network (ANN)

Explaining German outward FDI in the EU: a reassessment using Bayesian model averaging and GLM estimators

Unitary or collective households? A nonparametric rationality and separability test using detailed data on consumption expenditures and time use

Livestock production and income inequality in rural Vietnam