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Losers and losers: Some demographics of medical malpractice tort reforms

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Abstract

Our research examines how recent reforms have affected a key aspect of patients’ implicit insurance present in medical malpractice torts. Specifically, we estimate how non-economic damages caps affected pre-trial settlement speed and settlement amounts. Maximum entropy (most likely) quantile regressions emphasize that the post-reform settlement effects most informative for policy evaluation differ greatly from OLS (mean) estimates and clarify the conclusion emerging. In particular, the effect of the tort reform here can best be thought of as a 25% tax on the asset value of settlements that exempts settlements involving infants. The social welfare effects of tort reform are less clear than the asset reduction effects due to likely health state dependent utility.

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Notes

  1. A parallel line of research examines economic differences in damage cap effects across insurance providers (Viscusi and Born 2005).

  2. The law also introduced mandated periodic payments for future medical expenses and procedural reforms on expert witness reports. It also broadened the definitions of volunteer and Good Samaritan to provide greater protection against providers serving in such roles and shortened the length of time after an incident that a suit could be filed. Of all the reforms, the cap on damages and the early offer reforms have the largest effects on the settlement process (Sloan and Chepke 2008; O’Connell and Viscusi 2007).

  3. The more risk averse actor accepting a smaller settlement appears in a more general case of two risk averse bargainers who go to an uncertain arbitrator if they cannot reach a settlement (Crawford 1982). For the first examination of liability settlement issues with risk aversion and empirical results see Viscusi (1988). For a general theoretical discussion of the economic welfare consequences of tort settlements (insurance) when utility is health state dependent see Shavell (1978).

  4. In Section 3.5 we check the robustness of our results to the length of the settlement window.

  5. Present value calculations use the average of the real interest rate on a 3-month T-bill over the time period of our sample.

  6. An additional benefit is that the maximum entropy quantile procedure implicitly solves for the quantile regression estimator with the smallest standard error (Bera et al. 2010).

  7. It is important to emphasize that the maximum entropy quantile model complements the conventional quantile model by taking the full spectrum of quantile regression results and establishing a point in the distribution where there is the most information content. It is merely a coincidence that the maximum entropy quantile and the median cap effect estimates are similar in percentage terms, which need not be the case in general.

  8. Results not tabulated are similar for settlement windows of 3.25 or 3.75 years.

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Author information

Authors and Affiliations

  1. Department of Economics, University of Colorado Denver, Denver, CO, 80217, USA

    Andrew I. Friedson

  2. Center for Policy Research and Department of Economics, Syracuse University, Syracuse, NY, 13244-1020, USA

    Thomas J. Kniesner

  3. Department of Economics, Claremont Graduate University, Claremont, CA, 91711, USA

    Thomas J. Kniesner

  4. IZA, Bonn, Germany

    Thomas J. Kniesner

  5. Maxwell School of Syracuse University, Syracuse, NY, 13244-1020, USA

    Thomas J. Kniesner

Authors
  1. Andrew I. Friedson
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  2. Thomas J. Kniesner
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Corresponding author

Correspondence to Thomas J. Kniesner.

Additional information

We thank Mary Santy for help with manuscript preparation, the editor and an anonymous referee for helpful comments on our research, and Jim Ziliak and Antonio Galvao Jr. for programming help with the maximum entropy quantile regression model.

Appendix: Asset value calculations

Appendix: Asset value calculations

Values in Table 5 were calculated using the following methods:

  • Column 2: Average of settlements within 3 years for given age group.

  • Column 3: Estimated policy effect from Table 2, set equal to zero if not significant.

  • Column 4: Estimated policy effect from Table 3, set equal to zero if insignificant, multiplied by estimated effect of cash demanded in Table 3.

  • Column 5: Average duration of case in pre-policy period.

  • Column 6: Column 4 multiplied by estimate from Table 4.

  • Column 7: Sum of columns 2, 3 and 4, adjusted for change in timing of payment in Table 6: (col2 + col3 + col4)/1.0141(col6/365)

  • Column 8: Divide column 7 by column 1. Multiply by negative 1.

Values in Table 7 were calculated using the following methods:

  • Column 2: Same as Table 5, Column 8.

  • Column 3: Uses median techniques, generated using same logic as Table 5. The differences are:

    • Column 2 of Table 5 uses the median settlement amount.

    • Columns 3 and 4 of Table 5 come from median.

  • Column 4: Uses MEQ techniques, generated using same logic as Table 5. The differences are:

    • Column 2 of Table 5 uses the MEQ settlement amount.

    • Columns 3 and 4 of Table 5 come from MEQ regressions (MEQs vary based on age group).

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Friedson, A.I., Kniesner, T.J. Losers and losers: Some demographics of medical malpractice tort reforms. J Risk Uncertain 45, 115–133 (2012). https://doi.org/10.1007/s11166-012-9152-6

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  • DOI: https://doi.org/10.1007/s11166-012-9152-6

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