Nonresponse Bias in Inequality Measurement: Cross‐Country Analysis Using Luxembourg Income Study Surveys

Hlásny, Vladimír

doi:10.1111/ssqu.12762

Cited by 18 publications

(7 citation statements)

References 49 publications

(105 reference statements)

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“…The unit non-response rate was slightly below 4 percent in 1977 and rose to above 8 percent around 2007 and then above 14 percent by 2018. This has likely affected the representativeness of the top of the income distribution disproportionately, because non-response rates are positively associated with income level Ravallion 2006, and2007;and Hlasny, 2020).…”

Section: Surveys Data Versus Tax Data: Comparison Of Income Shares Of...mentioning

confidence: 99%

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Drawing a Line: Comparing the Estimation of Top Incomes between Tax Data and Household Survey Data

et al. 2022

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The paper uses the flexibility of household survey data to align their income categories and recipient units with the income categories and units found in data produced by tax authorities. Our analyses, based on a standardized definition of fiscal income, allow us to locate, for top-income groups, the sources of discrepancy. We find, using the cases of the United States, Germany, and France, that the results from survey-based and tax data correspond extremely well (in terms of total income, mean income, composition of income, and income shares) above the 90 th percentile and up to the top 1 percent of the distribution. Information about income composition, available in the US, allows us to investigate the determinants of this gap in the US. About three-fourths of the tax/survey gap is due to differences in nonlabor incomes, especially self-employment (business) income. The gap itself may be due to tax-induced re-classification of income from corporate to personal or/and to lower ability of surveys to capture top 1 percent incomes.

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Section: Surveys Data Versus Tax Data: Comparison Of Income Shares Of...mentioning

confidence: 99%

“…Attempts have been made to adjust for differences due to behavioral (non-response) and sampling factors. See, e.g.,Piketty, Saez, and Zucman (2018),Hlasny andVerme (2018), andBourguignon (2018).…”

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confidence: 99%

Drawing a Line: Comparing the Estimation of Top Incomes between Tax Data and Household Survey Data

et al. 2022

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Section: Tax Data Survey Datamentioning

confidence: 99%

Drawing a Line: Comparing the Estimation of Top Incomes Between Tax Data and Household Survey Data

Yonzan¹,

Milanović²,

Morelli³

et al. 2020

Preprint

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The paper uses the flexibility of household survey data to align their income categories and recipient units with the income categories and units found in data produced by tax authorities. Our analyses, based on a standardized definition of fiscal income, allow us to locate, for top-income groups, the sources of discrepancy. We find, using the cases of the United States, Germany, and France, that the results from survey-based and tax data correspond extremely well (in terms of total income, mean income, composition of income, and income shares) above the 90th percentile and up to the top 1 percent of the distribution. Information about income composition, available in the US, allows us to investigate the determinants of this gap in the US. About three-fourths of the tax/survey gap is due to differences in non-labor incomes, especially self-employment (business) income. The gap itself may be due to tax-induced re-classification of income from corporate to personal or/and to lower ability of surveys to capture top 1 percent incomes. (Stone Center Working Paper Series)

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“…Income variables typically behave differently at the tails of the distribution [44]. In particular, while the generalised beta distribution of the second kind seems to be a suitable model for the bulk of the income distribution [45], incomes above the 95th percentile seem to be better represented by a Pareto distribution [46].…”

Section: Other Classes Of Bivariate Lorenz Surfacesmentioning

confidence: 99%

Lorenz Surfaces Based on the Sarmanov–Lee Distribution with Applications to Multidimensional Inequality in Well-Being

Sarabia

Jordá

2020

Mathematics

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The purpose of this paper is to derive analytic expressions for the multivariate Lorenz surface for a relevant type of models based on the class of distributions with given marginals described by Sarmanov and Lee. The expression of the bivariate Lorenz surface can be conveniently interpreted as the convex linear combination of products of classical and concentrated univariate Lorenz curves. Thus, the generalized Gini index associated with this surface is expressed as a function of marginal Gini indices and concentration indices. This measure is additively decomposable in two factors, corresponding to inequality within and between variables. We present different parametric models using several marginal distributions including the classical Beta, the GB1, the Gamma, the lognormal distributions and others. We illustrate the use of these models to measure multidimensional inequality using data on two dimensions of well-being, wealth and health, in five developing countries.

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Nonresponse Bias in Inequality Measurement: Cross‐Country Analysis Using Luxembourg Income Study Surveys

Cited by 18 publications

References 49 publications

Drawing a Line: Comparing the Estimation of Top Incomes between Tax Data and Household Survey Data

Drawing a Line: Comparing the Estimation of Top Incomes between Tax Data and Household Survey Data

Drawing a Line: Comparing the Estimation of Top Incomes Between Tax Data and Household Survey Data

Lorenz Surfaces Based on the Sarmanov–Lee Distribution with Applications to Multidimensional Inequality in Well-Being

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