Research
Primary field: Microeconometrics, Causal Inference.
Secondary fields: Applied Microeconomics.
Publications
The Cost of Gendered Attitudes on a Female Candidate: Evidence from Google Trends (with Raphael Corbi). Economics Letters, Volume 196, November 2020
Abstract: How much can negative attitudes towards women affect voting for a female candidate on a major election? We measure gender animus by calculating a proxy based on Google search queries that include gender-charged language. Such approach likely elicits socially sensitive attitudes by limiting the concern of social censoring, circumventing usual difficulties associated with survey-based measurements. We compare the proxy to Hillary Clinton's vote share in the presidential election of 2016, controlling for the vote share of the previous Democratic presidential candidate, Barack Obama. Our results indicate that a one standard deviation increase in our proxy is associated with a 2 percentage points relative loss for Hillary and suggest that online-based observable behavior can be useful for measuring different kinds of hard-to-measure social attitudes.
Working Papers
You can find me talking briefly about the paper here!
Abstract: Difference-in-Differences (DiD) is a popular method used to evaluate the effect of a treatment that exploits variation in treatment status that comes from the exposure to a shock, usually in the form of a policy change. When there is imperfect compliance towards the shock, the usual DiD estimand fails to recover relevant causal parameters. This article presents an identification strategy in DiD settings with imperfect compliance that identifies Marginal treatment effects (MTE). We show how to combine and modify standard instrumental variables (IV) and DiD assumptions to identify treatment effects in DiD settings where individuals enter into treatment with at least partial knowledge of their unobservable gains. We propose two estimators for the MTE that are consistent under different assumptions regarding the functional form of potential outcomes and prove their asymptotic normality. Furthermore, we derive an estimator for the local average treatment effect (LATE) that is robust to misspecification of the MTE model. We assert the desirable finite-sample properties through simulation studies of a linear MTE model. Finally, we use our results to investigate heterogeneity in the returns to primary education attendance in Indonesia. We find a pattern of reserve selection gains in the returns of primary education. Individuals with a higher distaste for enrolling in primary education have positive returns, while individuals with a lower distaste may have a negative return to primary education.
Work in Progress
Instrumental Variables with Multiple Time Periods (Job Market Paper).
Abstract: The instrumental variables (IV) method has been widely studied in cross-sectional settings. However, applications of IV methods with sequential endogenous non-absorbing treatments in panel data are pervasive in applied research. When past treatments affect current potential outcomes and assignment is serially correlated, the standard methods used by the applied researcher are no longer valid. This paper proposes the nonparametric identification of dynamic causal effects in a potential outcomes framework in which potential outcomes depend on the treatment path taken by a unit through time and each IV instruments its contemporary treatment. I provide a nonparametric estimator that is unbiased over the randomization distribution and derive its finite population limiting distribution as the sample size increase. A discrete choice model can be combined with the potential outcomes framework in order to identify dynamic versions of the Marginal Treatment Effect. Monte Carlo Simulations assert the desirable finite-sample properties of the estimators. An application of the estimator shows that there is substantial time-varying heterogeneity on the effects of law enforcement on illegal deforestation, but the effects are not persistent through time.
Difference-in-Discontinuities: Estimation, Inference and Validity Tests (with Stephanie Shinoki and Cristine Pinto). Draft coming soon.
Abstract: This paper sets out to explore the econometric theory behind the newly developed difference-in-discontinuities design. Despite its increasing use in causal inference research, there are currently limited studies of its underlying principles and properties. The method combines elements of regression discontinuity and difference-in-differences, allowing researchers to eliminate the effects of potential confounders at the discontinuity threshold and account for changes in the larger environment. We formalize the difference-in-discontinuity theory by stating the identification assumptions under which the difference-in-discontinuities estimate is valid and propose two nonparametric estimators, deriving their asymptotic properties. Monte Carlo simulation studies show that the estimators have good performance in finite samples. Finally, we revisit a difference-in-discontinuities paper that studies the effects of relaxing fiscal rules on public financial outcomes in Italian municipalities. The results show that the proposed estimator exhibits substantially smaller confidence intervals for the estimated effects.