Knowledge that Transforms

I partially identify the marginal treatment effect (MTE) when the treatment is misclassified. I explore two restrictions, allowing for dependence between the instrument and the misclassification decision. If the signs of the propensity scores’ derivatives are equal, I identify the MTE sign. If those derivatives are similar, I bound the MTE. To illustrate, I analyze the impact of alternative sentences (fines and community service versus no punishment) on recidivism in Brazil, where court appeals processes generate misclassification. The estimated misclassification bias may be as large as 10% of the largest possible MTE, and the bounds contain the correctly estimated MTE.

In markets with asymmetric information between sellers and buyers, feedback mechanisms are important to increase market efficiency and reduce the informational disadvantage of buyers. Feedback mechanisms might work because of self-selection of more trustworthy sellers into markets with such mechanisms or because of reputational concerns of sellers. We show in a field experiment how to disentangle self-selection from reputation effects. Based on 476 taxi rides with four different types of taxis, we find strong evidence for reputation effects but little support for self-selection effects. We discuss policy implications of our findings.

We provide the first estimates of intergenerational income mobility using tax data for a large developing country, namely Brazil. We measure formal income from tax and payroll data, and we train machine learning models on census and survey data to predict informal income. We quantify the estimation bias resulting from income imputation and other sources of measurement error, and show that such bias remains negligible in our context. A 10 percentile increase in parental income rank is associated on average with a 5.5 percentile increase in child income rank, with considerable variation across sociodemographic groups and geographical areas.

Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an identification problem under unobserved confounding — when selected and unselected units differ in unobserved ways that affect outcomes. We propose a framework for robust design and evaluation of predictive algorithms that bounds how much outcomes may differ between selected and unselected units with the same observed characteristics. These bounds formalize common empirical strategies including proxy outcomes and instrumental variables. Our estimators work across bounding strategies and performance measures such as conditional likelihoods, mean squared error, and true/false positive rates. Using administrative data from a large Australian financial institution, we show that varying confounding assumptions substantially affects credit risk predictions and fairness evaluations across income groups.

This paper estimates the size of the media multiplier, an easily generalizable model-based measure of how far media coverage magnifies the economic response to shocks. We combine monthly aggregated and anonymized credit card activity data from 114 card-issuing countries in 5 destination countries with a large corpus of news coverage in issuing countries reporting on violent events in the destinations. To define and quantify the media multiplier, we estimate a model in which latent beliefs, shaped by either events or news coverage, drive card activity. According to the model, media coverage can more than triple the economic impact of an event. We document, through our model, that this effect is highly heterogeneous and depends on the broader media representation of countries in each other’s news. We speculate about the role of the media in driving international travel patterns.

This paper describes how to reparametrize low-dimensional factor models with one or two factors to fit weak identification theory developed for generalized method of moments models. Some identification-robust tests, here called “plug-in” tests, require a reparametrization to distinguish weakly identified parameters from strongly identified parameters. The reparametrizations in this paper make plug-in tests available for subvector hypotheses in low-dimensional factor models with one or two factors. Simulations show that the plug-in tests are less conservative than identification-robust tests that use the original parametrization. An empirical application to a factor model of parental investments in children is included.

We report the results of two experiments designed to better understand the mechanisms driving decision making under ambiguity. We elicit individual preferences over different sources of uncertainty, entailing different degrees of complexity, from subjects with different sophistication levels. We show that (1) ambiguity aversion is robust to sophistication, but the strong relationship previously reported between attitudes toward ambiguity and compound risk is not and (2) Ellsberg ambiguity attitude can be partly explained by attitudes toward complexity for less sophisticated subjects only. Overall, regardless of the subject’s sophistication level, the main driver of Ellsberg ambiguity attitude is a specific treatment of unknown probabilities.

Urban forests are ubiquitous, yet their impacts and values remain largely unknown. We study a massive urban afforestation policy in Beijing that planted 1/3 of a million acres of greenery in less than a decade. We conduct a remote-sensing audit of the program, finding that it contributes to a substantial greening up of the city. This causes significant downwind air quality improvement, reducing average PM2.5 concentration at city population hubs by 4.2%. Rapid vegetation growth unexpectedly led to a 7.4% increase in pollen exposure. Analysis of medical claims data shows that increased aeroallergens triggered emergency room visits, mirroring pollution effects though much less severe. Monetized net health benefits of the program amount to 1.5% of the city’s GDP. Urban forests are only partially capitalized in housing values, with buyers mainly appreciating proximity to green spaces but not the air quality improvements they bring.

This study examines the impact of school choice on academic achievement. I use differences in the number of schools across similar Romanian towns, generating variation in school choice for local students, who compete for seats via test scores. I find that more school choice results in increased sorting of students by admission scores across different schools. Sorting widens achievement gaps between high- and low-admission score students. High-scorers having access to better teachers and peer effects are the primary factors explaining these widening gaps. Last, between-school competition via school choice does not increase average achievement levels.

Migrants shape market access: They reduce international trade frictions and they affect the geographical location of demand. This article incorporates both effects in a model of inter- and intranational trade and migration calibrated to U.S. states. It estimates the elasticity of exports and imports to migrants and shows that reducing U.S. migrant population shares to 1980s levels would increase import (export) trade costs by 7% (2.5%) and decrease U.S. natives’ real wages by more than 2%. States with higher exposure to migrant consumer demand than to migrant labor competition would suffer more, as would states with higher export and import exposure.