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The dynamics of health insurance coverage: identifying trigger events for insurance loss and gain

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Abstract

By linking consecutive years of the 1996–2004 Current Population Survey (CPS), we create new estimates of annual transitions into and out of health insurance coverage. Using the matched CPS panel data, we explore the dynamic factors—including job loss, changes in hours or weeks worked, and movement between firm sizes—associated with health insurance loss and gain. Job loss is strongly associated with losing insurance, whereas becoming reemployed is only weakly associated with gaining insurance. Movement down (up) in employment size is associated with insurance loss (gain), but movement to employers with fewer than 10 employees is associated with especially high rates of loss. Changes in hours or weeks worked and employment type are also strongly associated with insurance transitions.

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Notes

  1. An extensive literature also examines the “job lock” phenomenon—that employees will stay longer in jobs that provide them with health insurance than jobs that do not. The magnitude of this effect has been estimated to vary substantially depending on the data, econometric specification, or subgroups examined (Madrian 1994; Kapur 1998; Gruber and Madrian 2001; Gilleskie and Lutz 2002).

  2. We estimate separate regressions for health insurance transitions allowing the effects to differ for insurance gain and loss, which is more flexible than a fixed effects regression for health insurance coverage. For comparison, however, we also estimate cross-sectional and fixed effects regressions for the probability of health insurance coverage and note the results below.

  3. Prior to matching years we remove the supplemental samples to the 2001–2004 ADFs, which are generally not re-interviewed in the following March.

  4. One drawback to the matched CPS data is that when households dissolve due to marital breakup, the CPS does not re-interview both marital partners. Thus, we cannot examine the relationship between insurance transitions and changes in marital status. We can, however, examine the relationship between spousal job changes and health insurance transitions for adults whose marriages remain intact.

  5. Age in the second survey year is allowed to be in the range from −1 to +3 from the first survey year.

  6. On the other hand, measurement error in reporting health insurance coverage across surveys is likely to overstate transition rates.

  7. The steady-state health insurance coverage rate is simply equal to G/(G+L), where G is the rate of gaining health insurance and L is the rate of losing health insurance.

  8. The CPS also provides information on types of health insurance coverage. We find that 93.1% of individuals with health insurance have private health insurance. Examining transitions between types of coverage, we also find that most movement from uninsurance to insurance appears to be largely to and from private insurance coverage.

  9. Similar to Czajka and Olsen (2000) we view these dynamic factors as “trigger events” instead of as truly exogenous determinants of health insurance transitions.

  10. The included measure of employer change is imputed from changes in employer type, employer size (at least 2 size categories) and major industry category, and from having multiple jobs in the second survey year because the CPS does not include a direct measure of employer changes. We find that 38.9% of the sample has an employer change using our imputed measure, which likely to overstate employer changes (see Fairlie and London 2005 for more discussion).

  11. We also estimate cross-sectional and fixed effects regressions for the probability of health insurance coverage (see Appendix 3). The estimates provide similar findings as those expected based on the results from the health insurance transition regressions. In particular, the signs of the coefficients generally align with the health insurance gain coefficients and are opposite in sign from the health insurance loss coefficients.

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Acknowledgments

The U.S. Department of Labor provided funds for this research. The views expressed here are solely the responsibility of the authors and should not be interpreted as reflecting the views of the funder. We would like to thank Carlos Dobkin, John Holahan, Matt Ruttledge, Donald Wittman and participants at the 2005 Annual Meetings of the Association for Public Policy Analysis and Management and the ERIU Conference at the University of Michigan for useful comments and suggestions. Oded Gurantz provided excellent research assistance.

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Authors and Affiliations

  1. University of California, Santa Cruz, CA, USA

    Robert W. Fairlie

  2. RAND, Santa Monica, CA, USA

    Robert W. Fairlie

  3. Stanford University, Stanford, CA, USA

    Rebecca A. London

Authors
  1. Robert W. Fairlie
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  2. Rebecca A. London
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Correspondence to Robert W. Fairlie.

Appendices

Appendices

Appendix 1 Match rates and false match rates (Current Population Survey, Matched Annual Demographic Surveys (1996–2004))
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Appendix 2 Comparison of analysis variables for original and matched samples (Current Population Survey, Annual Demographic Surveys (1996–2004))
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Appendix 3 Probit and linear regressions for probability of health insurance coverage—marginal effects (Current Population Survey, Matched Annual Demographic Surveys (1996–2004))
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Fairlie, R.W., London, R.A. The dynamics of health insurance coverage: identifying trigger events for insurance loss and gain. Health Serv Outcomes Res Method 8, 159–185 (2008). https://doi.org/10.1007/s10742-008-0033-z

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