The Impact of Top Incomes Biases on the Measurement of Inequality in the United States

Hlásny, Vladimír; Verme, Paolo

doi:10.1111/obes.12472

Cited by 9 publications

(5 citation statements)

References 57 publications

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“…One of the indicated factors was the share of labour in national income. Many researchers find the exploration of the links between labour's share in national income and income inequality intriguing, as evidenced by studies conducted by Erauskin (2020), Atkinson et al (2011), Petreski (2022, and Hlasny and Verme (2021). The relationship between the above-mentioned phenomena needs to be clarified, as it depends not only on the research period but also on the development of the economy, its structure, level of digitalisation, place in the global value chain, and implemented social and public policy.…”

Section: Discussionmentioning

confidence: 99%

“…As a result, there is an urgent need to delve deeper into these dynamics to formulate informed policies and interventions that can address emerging challenges and support more equitable and sustainable economic development (Piketty, 2014;Piketty & Zucman, 2014). Atkinson (2009) also points to the inequalities resulting from the diversification of the income structure in terms of their origin, the same as Wildowicz-Szumarska (2022), Hlasny and Verme (2021) and Jianu et al (2021).…”

Section: Literature Reviewmentioning

confidence: 99%

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Labour share and income inequalities in the European Union, taking into account the level of development of economies

Šoltés,

Gawrycka,

Szymczak

et al. 2023

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Research background: The relationship between labour share and income inequality is a complex and multifaceted problem. Despite ongoing discussions among economists, there is still no consensus on the direction of the relationship between labour share and income inequality. Purpose of the article: The article aims to assess the impact of labour share on income inequality, which is measured in three ways: the Gini index of gross income, the Gini index of market incomes, and the Gini index of household disposable income. Methods: Dynamic panel data models were applied to estimate the relationship between Gini coefficients and socio-economic indicators. The study investigated 25 European Union countries over the 2011–2021 period. Findings & value added: Despite the long convergence process of the EU economies, there is still great diversity in the labour share, social inequalities, and the interplay between these factors. The added value of this research is the indication of labour share impact on three Gini measures covering a diverse income spectrum (from labour and capital). Based on the research findings, hypothesis 1, claiming that the more developed the national economy, the lower the share of employment income, favouring capital gains, is confirmed. Hypothesis 2 (as the share of income from work increases, the Gini coefficient of gross incomes decreases) must be rejected. There is no significant relationship between labour share and the studied Gini measures in 'old' EU countries. In 'new' EU members, there is a reverse relationship than assumed in hypothesis 2. The growth of the Gini coefficient was influenced by the rise in labour share, which can be attributed to the diversity in economic structures.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Labour share and income inequalities in the European Union, taking into account the level of development of economies

Šoltés,

Gawrycka,

Szymczak

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The reweighting method has been used in Anand and Segal (2017), Hlasny and Verme (2017), Burkhauser et al . (2018), Hlasny and Verme (2018), Campos‐Vazquez and Lustig (2020), and Department for Work and Pensions (2015).…”

Section: Correction Methodsmentioning

confidence: 99%

Underreporting of Top Incomes and Inequality: A Comparison of Correction Methods using Simulations and Linked Survey and Tax Data

Flachaire

Lustig

Vigorito

2022

Review of Income and Wealth

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Household surveys do not capture incomes at the top of the distribution well. This yields biased inequality measures. We compare the performance of the reweighting and replacing methods to address top incomes underreporting in surveys using information from tax records. The biggest challenge is that the true threshold above which underreporting occurs is unknown. Relying on simulation, we construct a hypothetical true distribution and a "distorted" distribution that mimics an underreporting pattern found in a novel linked data for Uruguay. Our simulations show that if one chooses a threshold that is not close to the true one, corrected inequality measures may be significantly biased. Interestingly, the bias using the replacing method is less sensitive to the choice of threshold. We approach the threshold selection challenge in practice using the Uruguayan linked data. Our findings are analogous to the simulation exercise. These results, however, should not be considered a general assessment of the two methods.

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“…We follow Cowell and Flachaire (2007) in top coding the survey data, and we carefully test the stability of our main results. Our top coding of the raw CHIP data may lead to an underestimation of inequality indices (Hlasny & Verme, 2021). However, we find that the key parameters of our power‐law model and the corrected Gini coefficient remain stable when we top‐code the raw CHIP data by at least 0.4% in all 3 years (see Figure A1).…”

Section: Data and Variablementioning

confidence: 99%

Correction of China's income inequality for missing top incomes

Wan

2023

Review Development Economics

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After accounting for top incomes missing from the Chinese Household Income Project, this paper examines the income inequality trend in China during the 21st century. Our analysis, which involves fitting the upper tail of the income distribution to a power‐law model, reveals that the authoritative income survey data miss 0.68% of the population and 6.19% of aggregate income in 2018. Despite the most recent survey data providing better coverage of top incomes, our correction is still crucial. The raw survey data indicate a consistently increasing Gini coefficient between 2002 and 2018, but the corrected index starts to decline from 2013. Meanwhile, the revised top 1% income share increases from 7.01% in 2002 to 7.89% in 2013 and then slightly decreases to 7.64% in 2018, while the revised top 10% income share stabilises at around 33% throughout the period. Notably, China's revised top 10% and top 50% income shares in 2018 are close to those of the United Kingdom but are considerably lower than those of the United States.

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The Impact of Top Incomes Biases on the Measurement of Inequality in the United States

Cited by 9 publications

References 57 publications

Labour share and income inequalities in the European Union, taking into account the level of development of economies

Labour share and income inequalities in the European Union, taking into account the level of development of economies

Underreporting of Top Incomes and Inequality: A Comparison of Correction Methods using Simulations and Linked Survey and Tax Data

Correction of China's income inequality for missing top incomes

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