Placebo Reforms

Imagine that you have been appointed as the chief of police in a certain district. You want the public to remember you as someone who brought down crime levels. As you enter the role, you face a decision whether to implement a large-scale police reform. Although you believe that the reform will lower crime in the long run, you realize that due to short-run fluctuations, things might get worse before they get better. You are concerned that the good effects will be noticeable only after you step down and thus attributed to your successor, while you will take the blame for the short-run downturn. The chief’s predicament is shared by many expert decision-makers. A surgeon benefits when a patient attributes his recovery to an operation performed by the surgeon himself. CEOs get credit when the company’s performance improves shortly after a major acquisition decision. And politicians benefit when GDP growth is perceived as a consequence of their own economic reforms. How do such concerns affect decision-makers’ actions, especially when they realize that their successors will face a similar dilemma? To address this question, I construct a stylized dynamic model of strategic reform choices, in which an infinite sequence of policymakers (PMs) monitor the stochastic evolution of an economic variable x. Each PM moves once, and chooses an action that may affect the continuation process. One action is interpreted as a “default” or “inaction,” whereas all other actions are interpreted as “active reforms” or “interventions.” The sole objective of each PM is to maximize his public evaluation. Specifically, PMs would like the public to attribute good (bad) outcomes to their own (other PMs’) actions. Public evaluation takes place at all periods and each PM employs a constant discount factor to weigh all future evaluation periods. The PM chooses to intervene only if the discounted credit he expects to get is strictly positive. The public’s attribution rule is a crucial component of the model. I assume that the rule departs from conventional “rational expectations.” The motivation for this departure is that in the class of situations I am interested in, evaluators arguably lack the experts’ degree of sophistication, and often rely on intuitive heuristics for drawing links between actions and consequences. This is not an informational asymmetry in the usual sense, but rather an asymmetry in the level of understanding of a stochastic environment.