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. 2020 Oct;57(5):1853-1879.
doi: 10.1007/s13524-020-00902-7.

Early Determinants of Work Disability in an International Perspective

Axel Börsch-Supan  1   2   3 , Tabea Bucher-Koenen  4   5   6 , Felizia Hanemann  4   7
Affiliations

Early Determinants of Work Disability in an International Perspective

Axel Börsch-Supan et al. Demography. 2020 Oct.

Abstract

This study explores the interrelated roles of health and welfare state policies in the decision to take up disability insurance (DI) benefits due to work disability (WD), defined as the (partial) inability to engage in gainful employment as a result of physical or mental illness. We exploit the large international variation of health, self-reported WD, and the uptake of DI benefits in the United States and Europe using a harmonized data set with life history information assembled from SHARE, ELSA, and HRS. We find that the mismatch between WD and DI benefit receipt varies greatly across countries. Objective health explains a substantial share of the within-country variation in DI, but this is not the case for the variation across countries. Rather, most of the variation between countries and the mismatches are explained by differences in DI policies.

Keywords: Disability insurance; International comparisons; Life histories; Social security and public pensions; Work disability.

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Conflict of interest statement

The authors report no conflicts of interest.

Figures

Fig. 1
Fig. 1
WD and DI receipt in Europe and the United States among individuals aged 50–65. Source: Own calculations based on weighted data from SHARE Wave 5, ELSA Wave 6, and HRS Wave 11.
Fig. 2
Fig. 2
Generosity of DI systems over time and by countries: Austria (AT), Germany (DE), Sweden (SE), Netherlands (NL), Spain (ES), Italy (IT), France (FR), Denmark (DK), Switzerland (CH), Belgium (BE), Czech Republic (CZ), England (EN), and United States (USA). Source: Own calculation based on OECD (2003, 2010).
Fig. 3
Fig. 3
Variance decomposition for the probability of reporting WD and receiving DI benefits. Source: Own calculations based on weighted data from SHARE Wave 5, ELSA Wave 6, and HRS Wave 11. Based on linear regression models (N = 29,788).
Fig. 4
Fig. 4
Variance decomposition for the probability of reporting WD and receiving DI benefits. Source: Own calculations based on weighted data from SHARE Waves 3 and 5, and ELSA Waves 3 and 6. Based on linear regression models (N = 4,703).
Fig. 5
Fig. 5
WD and DI receipt (normalized). Source: Own calculations based on weighted data from SHARE Wave 5, ELSA Wave 6, and HRS Wave 11.
Fig. 6
Fig. 6
Match between predicted WD and DI receipt. Source: Own calculations based on weighted data from SHARE Wave 5, ELSA Wave 6, and HRS Wave 11.
Fig. 7
Fig. 7
Frequency of mismatches by country. Source: Own calculations based on weighted data from SHARE Wave 5, ELSA Wave 6, and HRS Wave 11.
Fig. 8
Fig. 8
Counterfactual simulation for WD. Source: Own calculations based on weighted data from SHARE Wave 5, ELSA Wave 6, and HRS Wave 11.
Fig. 9
Fig. 9
Counterfactual simulation for DI. Source: Own calculations based on weighted data from SHARE Wave 5, ELSA Wave 6, and HRS Wave 11.
See this image and copyright information in PMC

References

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