12

Panel Data Methods and Applications

to Health Economics

Andrew M. Jones

Abstract

Much of the empirical analysis done by health economists seeks to estimate the impact of specific
health policies, and the greatest challenge for successful applied work is to find appropriate sources
of variation to identify the treatment effects of interest. Estimation can be prone to selection bias
when the assignment to treatments is associated with the potential outcomes of the treatment.
Overcoming this bias requires variation in the assignment of treatments that is independent of the
outcomes. One source of independent variation comes from randomized controlled experiments.
But, in practice, most economic studies have to draw on non-experimental data. Many studies
seek to use variation across time and events that takes the form of a quasi-experimental design,
or “natural experiment,” that mimics the features of a genuine experiment. This chapter reviews
the data and methods that are used in applied health economics with a particular emphasis on
the use of panel data. The focus is on nonlinear models and methods that can accommodate
unobserved heterogeneity. These include conditional estimators, maximum simulated likelihood,
Bayesian MCMC, finite mixtures and copulas.

12.1 Introduction 558
12.2 Identification strategies: finding relevant variation 563
12.2.1 Randomized experiments 563
12.2.2 Natural experiments 565
12.2.2.1 Health shocks 565
12.2.2.2 Economic shocks 566
12.2.2.3 Educational reforms 569
12.2.2.4 Health policies and reforms 569
12.2.3 Natural controls 571
12.2.3.1 Families 571
12.2.3.2 Twin studies 572
12.2.3.3 Communities 573
12.2.4 Anti-tests 574
12.3 Data and measurement issues 575
12.3.1 Administrative data or sample surveys 575
12.3.1.1 Non-response and attrition 578

557