Using the Shapley Decomposition to Disentangle the Impact of Circumstances and Efforts on Health Inequality

Deutsch, Joseph; Alperin, María Noel Pi; Silber, Jacques

doi:10.1007/s11205-017-1690-5

Cited by 24 publications

(14 citation statements)

References 24 publications

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“…We use a Shapley‐Shorrocks decomposition to measure the contribution of the measured circumstance variables (C) to overall socioeconomic inequality (Davillas & Jones, 2020; Deutsch, et al., 2018; Shorrocks, 2013; Wendelspeiss Chávez Juárez & Soloaga, 2014). This is implemented by computing the inequality index for all permutations of the circumstances and then averaging the marginal contribution of each circumstance.…”

Section: Methodsmentioning

confidence: 99%

The first wave of the COVID‐19 pandemic and its impact on socioeconomic inequality in psychological distress in the UK

Davillas

Jones

2021

Health Economics

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We use data from the UK Household Longitudinal Study (UKHLS) to compare measures of socioeconomic inequality in psychological distress, measured by the General Health Questionnaire (GHQ), before (Waves 9 and the Interim 2019 Wave) and during the first wave of the COVID‐19 pandemic (April to July 2020). Based on a caseness measure, the prevalence of psychological distress increased from 18.5% to 27.7% between the 2019 Wave and April 2020 with some reversion to earlier levels in subsequent months. Also, there was a systematic increase in total inequality in the Likert GHQ‐12 score. However, measures of relative socioeconomic inequality have not increased. A Shapley‐Shorrocks decomposition analysis shows that during the peak of the first wave of the pandemic (April 2020) other socioeconomic factors declined in their share of socioeconomic inequality, while age and gender account for a larger share. The most notable increase is evident for younger women. The contribution of working in an industry related to the COVID‐19 response played a small role at Wave 9 and the Interim 2019 Wave, but more than tripled its share in April 2020. As the first wave of COVID‐19 progressed, the contribution of demographics declined from their peak level in April and chronic health conditions, housing conditions, and neighbourhood characteristics increased their contributions to socioeconomic inequality.

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Section: Methodsmentioning

confidence: 99%

The first wave of the COVID‐19 pandemic and its impact on socioeconomic inequality in psychological distress in the UK

Davillas

Jones

2021

Health Economics

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“…Early contributions to the IOp literature have focused mainly on income (see Ramos & Van de Gaer, 2016; Roemer & Trannoy, 2016 for reviews). More recently, a growing literature has addressed the measurement of IOp in other relevant dimensions of individual well‐being such as education (Ferreira & Gignoux, 2013) and health (e.g., Davillas & Jones, 2020; Deutsch, Alperin, & Silber, 2018; Garcia‐Gomez, Schokkaert, van Ourti, & Bago d'Uva, 2015; Jusot, Tubeuf, & Trannoy, 2013; Rosa Dias, 2009; Rosa Dias, 2010; Trannoy, Tubeuf, Jusot, & Devaux, 2010).…”

Section: Introductionmentioning

confidence: 99%

A latent class approach to inequity in health using biomarker data

2020

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We adopt an empirical approach to analyse, measure and decompose inequality of opportunity (IOp) in health, based on a latent class model. This addresses some of the limitations that affect earlier work in this literature concerning the definition of types, such as partial observability, the ad hoc selection of circumstances, the curse of dimensionality and unobserved type-specific heterogeneity that may lead to biased estimates of IOp. We apply our latent class approach to measure IOp in allostatic load, a composite measure of biomarker data. Using data from Understanding Society: The UK Household Longitudinal Study (UKHLS), we find that a latent class model with three latent types best fits the data, with the corresponding types characterised in terms of differences in their observed circumstances. Decomposition analysis shows that about two thirds of the total inequalities in allostatic load can be attributed to the direct and indirect contribution of circumstances and that the direct contribution of effort is small. Further analysis conditional on age-sex groups reveals that the relative (percentage) contribution of circumstances to the total inequalities remains mostly unaffected and the direct contribution of effort remains small. K E Y W O R D S biomarkers, decomposition analysis, equality of opportunity, finite mixture models, health equity, latent class models J E L C L A S S I F I C A T I O N

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“…As a robustness check, we also use the Shapley value approach, as in Shorrocks ( 2013) and Sastre and Trannoy (2002) (see, e.g., Deutsch et al, 2018, for a recent application). The procedure calculates marginal contributions of each component in all possible decompositions, and then averages them out.…”

Section: Decomposition Of Changes In the Income Distribution Over Timementioning

confidence: 99%

Income Inequality and Redistribution in Lithuania: The Role of Policy, Labor Market, Income, and Demographics

Černiauskas

Sologon

O’Donoghue

et al. 2021

Review of Income and Wealth

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We model the household disposable income distribution in Lithuania and explore the drivers of the increase in income inequality between 2007 and 2015. We quantify the contributions of four factors to changes in the disposable income distribution: (i) demographics; (ii) labor market structure; (iii) returns and prices; and (iv) tax-benefit system. Results show that the effects of the factors were substantial and reflected heterogeneous developments over two subperiods: changes in the tax and benefit system cushioned a rapid rise in market income inequality because of the global financial crisis during 2007-2011, but failed to do so during the subsequent years of economic expansion, when rising returns in the labor and capital markets significantly increased disposable income inequality. We also find that declining marriage rates contributed to the increase in income inequality in Lithuania.

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Using the Shapley Decomposition to Disentangle the Impact of Circumstances and Efforts on Health Inequality

Cited by 24 publications

References 24 publications

The first wave of the COVID‐19 pandemic and its impact on socioeconomic inequality in psychological distress in the UK

The first wave of the COVID‐19 pandemic and its impact on socioeconomic inequality in psychological distress in the UK

A latent class approach to inequity in health using biomarker data

Income Inequality and Redistribution in Lithuania: The Role of Policy, Labor Market, Income, and Demographics

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