Accounting for Anticipation Effects: An Application to Medical Malpractice Tort Reform

Abstract

, and three anonymous referees for helpful comments. Anup Malani thanks the Samuel J. Kersten Faculty Fund at the University of Chicago for funding. Julian Reif thanks the National Science Foundation for financial support. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Testing the Validity of the Single Interrupted Time Series Design

“…The diamonds within squares represent points that are identical in either potential outcome. A frequent cause of this phenomenon is referred to as "anticipation effects," wherein knowledge of the impending intervention leads to a change in behavior different from what would otherwise have occurred [56]. Similar issues can arise through history, selection, and instrumentation.…”

Does Autonomy Matter in State Owned Enterprises? Evidence from Performance Contracts in India

“…We term such possible run‐up as the preparation effect and estimate for each relevant sample, two specifications, one without, and one with such preparation to illustrate the point. Evidence of such preparation effects has been well documented in studies focusing on measurement of policy changes (Brown et al., ; Malani & Reif, ). To incorporate preparation effects we create three dummy variables, mou_prep1 , mou_prep2 and mou_prep3 , each of which represents a particular year before the enterprise signed the MOU contract.…”

Announcement effects of health policy reforms: evidence from the abolition of Austria’s baby bonus

“…This estimate is slightly smaller than the estimate obtained by the baseline model, but it is well within the estimated range of estimates from panel A above (i.e., the estimates are not significantly different from each other). 16 The results presented in the subsequent columns show estimates of the residual number of births in the hypothetical scenario that the policy change happened one, 2 or 3 years earlier than it actually did. It is immediately evident that none of the placebo regressions yields a residual that is statistically different from zero, underlining our argument that the announced abolition of the baby bonus increased fertility in the short-run.…”

“…It is also evident that anticipatory behavior should be factored in when evaluating specific health policy reforms. In fact, ignoring announcement effects may lead to misleading conclusions regarding the impact of the policy reform under study [5,16]. Moreover, we may be especially concerned about potential negative health effects if individuals anticipate even small changes in financial incentives, but are unable to foresee all potential health effects resulting from specific behavioral changes.…”

“…Specifically, we run the following regression:€ b t ¼ a þ c m þ e t ,where € b t denotes the de-trended number of monthly births and c m denotes the inclusion of a full set of monthly dummies 16. It is actually quite intuitive that the estimate based on the detrended number of births is smaller because the filter fits the time trend using all observations-including the extra births in December 1996.…”