Camilo Garcia-Jimeno

On Mis-measured Binary Regressors: New Results and Some Comments on the Literature

This paper studies the use of a discrete instrumental variable to identify the causal effect of a endogenous, mis-measured, binary treatment. We begin by showing that the only existing identification result for this case, which appears in Mahajan (2006), is incorrect. As such, identification in this model remains an open question. We begin by proving that the treatment effect is unidentified based on conditional first-moment information, regardless of the number of values that the instrument may take. We go on to derive a novel partial identification result based on conditional second moments that can be used to test for the presence of mis-classification and to construct simple and informative bounds for the treatment effect. In certain special cases, we can in fact obtain point identification of the treatment effect based on second moment information alone. When this is not possible, we show that adding conditional third moment information point identifies the treatment effect and the measurement error process.

Matching Pennies on the Campaign Trail: An Empirical Study of Senate Elections and Media Coverage

We study the strategic interaction between the media and Senate candidates during elections. While the media is instrumental for candidates to communicate with voters, candidates and media outlets have conflicting preferences over the contents of the reporting. In competitive electoral environments such as most US Senate races, this can lead to a strategic environment resembling a matching pennies game. Based on this observation, we develop a model of bipartisan races where media outlets report about candidates, and candidates make decisions on the type of constituencies to target with their statements along the campaign trail. We develop a methodology to classify news content as suggestive of the target audience of candidate speech, and show how data on media reports and poll results, together with the behavioral implications of the model, can be used to estimate its parameters. We implement this methodology on US Senatorial races for the period 1980-2012, and find that Democratic candidates have stronger incentives to target their messages towards turning out their core supporters than Republicans. We also find that the cost in swing-voter support from targeting core supporters is larger for Democrats than for Republicans. These effects balance each other, making media outlets willing to cover candidates from both parties at similar rates.

The Political Economy of Moral Conflict: An Empirical Study of Learning and Law Enforcement Under Prohibition

The U.S. Prohibition experience shows a remarkable policy reversal. In only 14 years, a drastic shift in public opinion required two constitutional amendments. I develop and estimate a model of endogenous law enforcement, determined by beliefs about the Prohibition‐crime nexus and alcohol‐related moral views. In turn, the policy outcomes shape subsequent learning about Prohibition enforcement costs. I estimate the model through maximum likelihood on Prohibition Era city‐level data on police enforcement, crime, and alcohol‐related legislation. The model can account for the variation in public opinion changes, and the heterogeneous responses of law enforcement and violence across cities. Results show that a 15% increase in the homicide rate can be attributed to Prohibition enforcement. The subsequent learning‐driven adjustment of local law enforcement allowed for the alcohol market to rebound to 60% of its pre‐Prohibition size. I conclude with counterfactual exercises exploring the welfare implications of policy learning, prior beliefs, preference polarization, and alternative political environments. Results illustrate the importance of incorporating the endogenous nature of law enforcement into our understanding of policy failure and policy success.

A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models, Second Version

The identification of causal effects in linear models relies, explicitly and implicitly, on the imposition of researcher beliefs along several dimensions. Assumptions about measurement error, regressor endogeneity, and instrument validity are three key components of any such empirical exercise. Although in practice researchers reason about these three dimensions independently, we show that measurement error, regressor endogeneity and instrument invalidity are mutually constrained by each other and the data in a manner that is only apparent by writing down the full identified set for the model. The nature of this set makes it clear that researcher beliefs over these objects cannot and indeed should not be independent: there are fewer degrees of freedom than parameters. By failing to take this into account, applied researchers both leave money on the table - by failing to incorporate relevant information in estimation - and more importantly risk reasoning to a contradiction by expressing mutually incompatible beliefs. We propose a Bayesian framework to help researchers elicit their beliefs, explicitly incorporate them into estimation and ensure that they are mutually coherent. We illustrate the practical usefulness of our method by applying it to several well-known papers from the empirical microeconomics literature.