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. 2018 Sep:61:93-110.
doi: 10.1016/j.jhealeco.2018.07.001. Epub 2018 Jul 23.

Deriving risk adjustment payment weights to maximize efficiency of health insurance markets

Timothy J Layton  1 , Thomas G McGuire  2 , Richard C van Kleef  3
Affiliations

Deriving risk adjustment payment weights to maximize efficiency of health insurance markets

Timothy J Layton et al. J Health Econ. 2018 Sep.

Abstract

Risk-adjustment is critical to the functioning of regulated health insurance markets. To date, estimation and evaluation of a risk-adjustment model has been based on statistical rather than economic objective functions. We develop a framework where the objective of risk-adjustment is to minimize the efficiency loss from service-level distortions due to adverse selection, and we use the framework to develop a welfare-grounded method for estimating risk-adjustment weights. We show that when the number of risk adjustor variables exceeds the number of decisions plans make about service allocations, incentives for service-level distortion can always be eliminated via a constrained least-squares regression. When the number of plan service-level allocation decisions exceeds the number of risk-adjusters, the optimal weights can be found by an OLS regression on a straightforward transformation of the data. We illustrate this method with the data used to estimate risk-adjustment payment weights in the Netherlands (N = 16.5 million).

Keywords: Adverse selection; Health insurance; Risk adjustment.

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Figures

Figure A1:
Figure A1:
Predictability and Predictiveness
Figure 1:
Figure 1:
Average Payments for Enrollees with Selected “Healthy” and “Sick” Indicators in Two Models
Figure 2:
Figure 2:
Equilibrium Service Level Allocation
Figure 3:
Figure 3:
Difference in Payments for Enrollees with Selected “Healthy” and “Sick” Indicators with and without Changing the Data
See this image and copyright information in PMC

References

    1. Bergquist S, Layton T, McGuire T and Rose S (2018), “Intervening on the Data to Improve Health Plan Payment Performance,” NBER Working Paper; 24491, April 2018.
    1. Bundorf MK, Levin JD and Mahoney N (2012) “Pricing and Welfare in Health Plan Choice,” American Economic Review 102(7): 3214–3248. - PubMed
    1. Carey C (2017) “Technological Change and Risk Adjustment: Benefit Design Incentives in Medicare Part D,” American Economic Journal: Economic Policy, 9(1):38–73.
    1. Cutler DM and Reber SJ (1998) “Paying for Health Insurance: The Tradeoff between Competition and Adverse Selection,” The Quarterly Journal of Economics 113(2): 433–466.
    1. Eijkenaar F, Van Kleef RC, Van Veen SHCM, Van Vliet RCJA Research for the Dutch risk equalization model of 2015: Estimation of risk equalization weights Research report (in Dutch), Erasmus University Rotterdam; 2014.

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