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Estimating the eect of retirement on mental health via panel discontinuity designs

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  • Fe, Eduardo
  • Hollingsworth, Bruce

Abstract

This article explores the potential effects of retirement on mental health and health care utilisation isolating sources of potential heterogeneity in treatment effect. To estimate the effects of retirement, we devise a new identifying strategy that combines kink and regression discontinuity designs with panel data methods. Our method is then applied to the British Household Panel Survey, a rich representative longitudinal survey. It is found that retirement has a small impact on primary care use, but overall has little effect on mental health.

Suggested Citation

  • Fe, Eduardo & Hollingsworth, Bruce, 2012. "Estimating the eect of retirement on mental health via panel discontinuity designs," MPRA Paper 38162, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:38162
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    Cited by:

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    3. Sahlgren, Gabriel H., 2012. "Work ‘til You Drop: Short- and Longer-Term Health Effects of Retirement in Europe," Working Paper Series 928, Research Institute of Industrial Economics.
    4. Ganong, Peter & Jäger, Simon, 2014. "A Permutation Test and Estimation Alternatives for the Regression Kink Design," IZA Discussion Papers 8282, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Discontinuity design; weak identification; retirement; health; kink regression; regression discontinuity;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • J26 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Retirement; Retirement Policies
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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