Knowledge that Transforms

Constant elasticity of substitution (CES) demand for monopolistically competitive firm varieties is a standard tool for models in international trade and macroeconomics. Intervariety substitution in this model follows a simple share proportionality rule. In contrast, the standard tool kit in industrial organization (IO) estimates a system in which cross-elasticities depend on similarity in observable attributes. The gain in realism from the IO approach comes at the expense of requiring richer data and greater computational challenges. This paper uses the data generating process of Berry et al. (1995), BLP, who established the modern IO method, to simulate counterfactual trade policy experiments. We use the CES model as an approximation of the more complex underlying demand system and market structure. Although the CES model omits key elements of the data generating process, the errors are offsetting, allowing it to fit BLP-based predictions closely. For aggregate outcomes, it turns out that incorporating non-unitary pass-through matters more than fixing over-simplified substitution patterns.

We develop a general theory of omitted variable bias for a wide range of common causal parameters, including average treatment effects, average causal derivatives, and policy effects from covariate shifts. We show how plausibility judgments on the maximum explanatory power of omitted variables are sufficient to bound the bias, facilitating sensitivity analysis in otherwise complex models. Finally, we provide statistical inference methods that can leverage modern machine learning algorithms for estimation. These results allow empirical researchers to perform sensitivity analyses in a flexible class of machine-learned causal models using very simple tools. Empirical examples demonstrate the utility of our approach.

This paper provides a method to study quantile effects in discrete choice with social interactions. The method is based on a behavioral social interactions model from quantile preference in decision making and demonstrates peer effects on different quantiles of discrete outcomes. The peer effects parameters are estimated by a nested pseudoscore (NPS) approach, which is developed to tackle the computational burden pertaining to the social interactions model. Consistency and asymptotic normality are established for the proposed NPS estimator. We illustrate the finite sample performance of the model and the estimator by Monte Carlo experiments and an application of peer effects among students on exercise decisions, using the National Longitudinal Study of Adolescent Health dataset.

In 2001 a large and sustained supply shock halted a heroin epidemic in Australia. We use drug offenses to identify individual opioid users and examine how the shock affected their mortality risks and criminal activity over the next eight years. Initially, gains from fewer overdoses are offset by drug substitution and more crime, including homicides. Most adverse effects dissipate over time, whereas persistent mortality reductions save the lives of around one in 48 individuals in our sample. Our results demonstrate that reducing the supply of illicit opioids can lead to meaningful longer-term improvements, even when the short-term effects are ambiguous.

Several studies have linked rising insolvency rates to increasing inequality and argued that this might be explained by individuals’ desire to “keep up with the Joneses.” Using unique administrative register data on individual insolvencies in Sweden, I test whether the probability to become insolvent is related to one’s income distance relative to peers. Identification relies on area fixed effects, an extensive set of background characteristics, and varying the definition of the relevant reference group. I find that higher inequality increases the individual’s probability to become insolvent and that this effect is primarily driven by men.

Regulators often rely on regulated entities to self-monitor compliance, creating strategic incentives for endogenous monitoring. This paper builds a framework to detect whether local governments skip air pollution monitoring when they expect air quality to deteriorate. The core of our method tests whether the timing of monitor shutdowns coincides with the counties’ air quality alerts—public advisories based on local governments’ own pollution forecasts. Applying the method to a monitor in Jersey City, New Jersey, suspected of a deliberate shutdown during the 2013 “Bridgegate” traffic jam, we find a 33% reduction of this monitor's sampling rate on pollution-alert days. Building on large-scale inference tools, we then apply the method to test more than 1,300 monitors across the United States, finding fourteen metropolitan areas with clusters of monitors showing similar strategic behavior. We assess geometric imputation and remote-sensing technologies as potential solutions to deter future strategic monitoring.

Regulators of new products confront a trade-off between speeding a product to market and collecting additional product quality information. The FDA's Breakthrough Therapy Designation (BTD) provides an opportunity to understand if regulators can use new policy to innovate around this trade-off. We find that the BTD program shortened clinical development times by 23% and did not affect the ex post safety profile of drugs with the designation. The BTD program had the greatest impact on less experienced firms and reduced clinical trial design complexity. The results suggest that targeted regulatory innovation can shorten R&D periods without compromising product quality.

Gun violence is a major problem in the United States, and extensive prior work has shown that higher temperatures increase violent behavior. We consider whether restricting the concealed carry of firearms mitigates or exacerbates the effect of temperature on violence. We use two identification strategies that exploit daily variation in temperature and variation in gun control policies between and within states. We provide evidence that more-prohibitive concealed-carry laws attenuate the temperature-homicide relationship. Our findings are consistent with more-prohibitive policy regimes reducing the lethality of altercations.

Women are underrepresented in patenting. In this study, we consider differential responsiveness to rejection as a contributor to the gender gap in invention. Leveraging the prosecution histories of almost one million U.S. patent applications and the quasirandom assignment of applications to examiners, we show that women are 3.6–6.9 percentage points less likely to continue in the application process following an early-stage rejection. Conditional on applying for a patent, male-female disparities in the propensity to abandon applications account for more than half of the overall gender gap in issued patents. We provide suggestive evidence that institutional support can help reduce the attrition gap.

This paper provides novel evidence on the causal effect of high school Grade Point Average (GPA) on the human capital development and labor market trajectory of individuals. Causal identification is achieved by exploiting a unique feature of the Norwegian education system that produces exogenous variation in GPA among high school students. We find little effect on the number of completed years of higher education, but significant effects on the number and quality of higher education programs available to students after high school. Most importantly, we find persistent effects on students’ long-run labor market outcomes, most notably market wage.