620 Panel Data Methods
Notes
- This chapter uses the “treatment-outcome” terminology that is commonplace in the eval-
uation literature. In practice many treatments are broad policy reforms associated with the
financing and delivery of health care rather than specific clinical interventions. Treatment
effects are defined here in terms of a binary treatment with just two regimes – the treated
and the controls. In practice there may be multiple treatments and varying intensities of
treatment. - In health economics, latent class models (LCMs) have typically been applied in the con-
text of nonlinear regression models, to allow for the role of unobserved heterogeneity in
the relationship between an observed outcome and a set of regressors. In the statistics
literature, LCMs are more commonly applied in the context of latent structural variables
and a set of observed indicators, such that the indicators are orthogonal conditional on
class membership. - Comparisons of treatment effects estimated using randomized experiments with those
estimated by matching methods in the labor economics literature cast considerable doubt
on the validity of such ignorability conditions and hence on the matching approach (e.g.,
Agodini and Dynarski, 2004; LaLonde, 1986; Smith and Todd, 2001). - Bertrandet al.(2004) highlight the risk of making misleading inferences using the standard
DD estimator if there is serial correlation in the outcomes and the standard errors are not
adjusted to take account of it.
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