Disentangling the impact of mean reversion in estimating policy response with dynamic panels

Dependence Modeling. 2022. Vol. 10. No. 1. P. 58–86.

Besstremyannaya G., Golovan S.

This article accounts for multivariate dependence of the variable of policy interest in dynamic panel data models by disentangling the two sources of intertemporal dependence: one from the effect of the policy variable and the other from mean reversion. In a situation where intensity of the policy varies over time, we estimate the unconditional mean in the autoregressive process as a function of the agent's characteristics and the policy intensity. Comparison of the fitted values of the unconditional mean under different values of the policy intensity enables identification of the policy effect cleared of mean reversion. The approach is relevant for measuring the effect of reforms, which use an intertemporal incentive where intensity of the reform varies over time. The empirical part of the article assesses the effect of hospital financing reform based on incentive contracts, related to the observed quality of services at Medicare hospitals in 2013-2019. We find a direct association between prior quality and quality improvement owing to the reform. Our result reassesses a stylized fact in the literature, which asserts that a pay-for-performance incentive leads to greater improvements at hospitals with lower baseline quality. © 2022 Galina Besstremyannaya and Sergei Golovan, published by De Gruyter.

Keywords: incentive contract, hospitals, policy evaluation, autoregressive process, intertemporal optimization

Publication based on the results of: Macroeconomic policy: from stagnation to sustainable economic growth (2022)