State dependence and heterogeneity in health using a bias-corrected fixed-effects estimator

This paper estimates a dynamic ordered probit model of self-assessed health with two fixed effects: one in the linear index equation and one in the cut-points. This robustly controls for heterogeneity in unobserved health status and in reporting behavior, although we cannot separate both sources of heterogeneity. We find important state dependence effects, and small but significant effects of income and other socioeconomic variables. Having dynamics and flexibly accounting for unobserved heterogeneity matters for those estimates. We also contribute to the bias correction literature in nonlinear panel models by comparing and applying two of the existing proposals to our model.