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Comparing register and survey wealth data

Comparing register and survey wealth data. Fredrik Johansson and Anders Klevmarken Department of Economics Uppsala University. An ideal measure?. The measure on which the decision maker acts! Is the survey response such a measure? – Probably not!

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Comparing register and survey wealth data

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  1. Comparing register and survey wealth data Fredrik Johansson and Anders Klevmarken Department of Economics Uppsala University

  2. An ideal measure? • The measure on which the decision maker acts! • Is the survey response such a measure? – Probably not! • What about the market value of an asset? – Yes, perhaps if there is a well defined market value. • But, what is the market value of a house which is not put on the market?

  3. Less of nonresponse and measurement errors in register data? • Complete enumeration – but not always of the target population • Register data are collected for administrative purposes and not for statistical purposes • Self-interest in underreporting assets and over reporting liabilities • But in Sweden: Banks, insurance companies, brokers and housing associations report to the tax authorities for each single individual. • The market value of real estate is an estimate produced by Statistics Sweden

  4. The estimates of market values of real property in register data Error=estimate – true market value

  5. What is a measurement error? • Survey response – ”ideal” value • We will use the market value as of the last of December 2003 as the ideal value of a financial asset assuming that register data have no measurement errors. • For real property we recognize that there are measurement errors both in the survey and in the register measures. We will account for the measurement error in register data when ever possible.

  6. Assets included • Home equity (owner occupied house or condominium) • Other real estate • Bank holdings • Bonds • Stocks and shares • Mutual funds • Debts

  7. Data sources • Register data: LINDA 2002 sample size approx. 1.1 million individuals • Survey data: UU_RAND 2002, sample size 1431 individuals (households) aged 50+, response 893, subsample from LINDA SHARE_SE 2003, sample size 4700 households, response 2208, at least one household member 50+

  8. The very rich UU-RAND compared to LINDA 2002 SHARE_SE compared to LINDA 2003

  9. Measurement errors and the variance (inequality) of wealth

  10. Table 4. The relative importance of measurement errors in estimating the variance of an asset, by type of asset

  11. Wealth as a dependent variable

  12. Estimated regression slopes with measurement errors in the dependent variable; independent variable is age

  13. Estimated regression slopes with measurement errors in the dependent variable; independent variable if ”healthy”

  14. Wealth as explanatory variable

  15. If Y is error prone with the additive error ν then,

  16. Estimated regression slopes with measurement errors in the independent variable (gross wealth) /

  17. Multivariate regression with one error prone variable (gross wealth)

  18. Regressions of assets (y) on error prone gross wealth (W) and age (X)

  19. Conclusions • With the exception of the top 1% SHARE_SE does not underestimate the average level of wealth. The survey has rather a tendency to over estimate wealth. • At the top 1% the underestimate is due to selective nonresponse. Very, very rich people do not participate, while there is no tendency for those who participate to underreport. • The main problem in the survey is the large error variance and the negative correlation between errors and true values. • In our data the error variance ranges from almost 40% of the true variance (bank holdings) to almost 60% (stocks). • The correlation ranges from -0.17 (debts) to -0.52 (mutual funds)

  20. Conclusions cont. The consequences are: • No severe overestimates of inequality. • In regressions with error prone gross wealth as an explanatory variable the negative bias from the error variance is to a large extent compensated by the negative correlation between error and true value. The survey estimate of the marginal effect of gross wealth appears to have little bias. • In regressions with wealth as a dependent variable the correlation between the measurement errors and explanatory variables will bias the slope estimates. The sign of the bias depends on asset and explanatory variable.

  21. Conclusions cont. • Measurement errors in (our) wealth surveys do not have classical properties. • Compensating error properties give decent estimates of the inequality of wealth and of the marginal effect of wealth, • But approximately the right estimates for the wrong reason is a poor consolation! • We need to learn more to be able to compensate for the effects of errors in survey wealth measures and if possible design surveys such that measurement errors are in controle.

  22. SHARE_SE compared to LINDA 2003

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