Knowledge that Transforms

Abstract How do import tariffs affect employment? We develop an empirical strategy to identify the effects of tariffs using a difference-in-differences strategy, comparing antidumping (AD) investigations resulting in AD tariffs to those not resulting in AD tariffs. We find that an AD tariff decreases imports and increases employment in the protected sector. Moreover, employment in downstream firms decreases, while upstream firms are unaffected because the protected sector sources inputs abroad. Using a model to quantify the aggregate effects, we find that the Brazilian AD policy increased employment by 0.06% at a welfare loss of 2.4%.

Abstract After remaining flat for decades, labor productivity at U.S. restaurants surged 15% during the COVID pandemic. The surge has persisted. We explore this using mobile phone data tracking visits and spending at 100,000 limited-service restaurants. It cannot be explained by scale economies, market power, or mechanical consequences of COVID demand fluctuations. However, restaurants’ productivity growth is strongly correlated with the share of their customers visiting for 10 minutes or less, which rose considerably during COVID and persisted. This restaurant-level relationship between labor productivity and customer dwell time is large enough to explain most of the aggregate productivity increase.

Abstract We use administrative data to measure sibling spillovers on academic performance before and after the introduction of Free Secondary Education (FSE) in Tanzania. Prior to FSE, students whose older siblings narrowly passed the secondary school entrance exam were less likely to go to secondary school themselves; with FSE, the effect became positive. A triple-differences analysis, using geographic variation in FSE exposure, shows that FSE caused the reversal. Mechanism analyses suggest that changes in parental investments were a more likely channel for this reversal than direct sibling interactions. By alleviating financial constraints, FSE allowed households to invest in more children.

Abstract No. Elite institutions offer high salaries because they hire the most valued faculty. Moreover, in contrast to the broader labor market, faculty are equally likely to move up and down the prestige ladder, and they increase their salary either way. We speculate that these facts reflect the visible nature of faculty productivity and the sporadic nature of academic job openings.

Abstract This paper develops a semiparametric sieve approach to estimate impulse response functions of nonlinear time series models within a broad class of structural autoregressive specifications. A two-step procedure flexibly captures nonlinearities without imposing fixed parametric forms. We establish uniform estimation guarantees and propose an iterative algorithm that makes impulse response computation straightforward. Simulation results show robustness to misspecification with only modest efficiency losses. In an application to U.S. monetary policy, we find larger GDP responses to interest rate hikes than in linear models. We also examine oil supply news shocks of varying magnitudes to assess limitations when analyzing large shocks.

Abstract How do trade shocks affect welfare and inequality when human capital is endogenous? Using India’s IT boom and internal migration data, I document that IT employment and engineering enrollment increased with exports, especially in regions with larger college-age populations. I develop a spatial model featuring higher education choice and differential migration costs for college versus work. The IT boom increased welfare by 2.39%, but without educational mobility, gains would be 25% lower and regional inequality 1.5 times larger. Removing endogenous education further reduces gains by over one-third. Education policies like national scholarships could substantially reduce regional inequality from trade shocks.

Abstract We propose a restrictiveness measure for economic models based on how well they fit predefined synthetic data. This measure, together with a measure for how well the model fits real data, outlines a Pareto frontier, where models that rule out more regularities, yet capture the regularities that are present in real data, are preferred. To illustrate our approach, we evaluate the restrictiveness of models in two laboratory settings—certainty equivalents and initial play—and one field setting—takeup of microfinance in Indian villages. The restrictiveness measure reveals insights about each, including that some economic models with only a few parameters are very flexible.

Abstract Why do some individuals evade taxes while others do not? We study this question using administrative tax records from Uruguay linked to a tailored survey of taxpayers. Using third-party reports, we measure individual income under-reporting as an indicator of evasion. We then examine how three factors predict who evades: social preferences (e.g., honesty measured through incentivized laboratory games), peers (e.g., the behavior of current and former coworkers), and economic factors (e.g., the marginal tax rate). We find that social preferences have little power to predict evasion, while economic factors matter more and peer behavior is the strongest predictor.

Abstract This paper theoretically and empirically examines the role of information in pharmaceutical detailing (promotional interactions between drug representatives and physicians). We start with a theoretical framework in which pharmaceutical firms target detailing visits to physicians who learn about drug quality and prescribe accordingly. We derive several predictions about the role of information in these visits, which we then test empirically using prescriptions and pharmaceutical detailing visit data. We find limited empirical support of learning as the dominant mechanism, though cannot rule it out completely. We conclude with discussing alternative models that may be more consistent with the observed empirical patterns.

Abstract Standard estimators in information provision experiments place more weight on individuals who update their beliefs more in response to new information. This paper shows that, in practice, these individuals who update the most have the weakest causal e!ects of beliefs on outcomes. Standard estimators therefore understate these causal e!ects. I propose an alternative local least squares (LLS) estimator that recovers a representative unweighted average e!ect in a broad class of learning rate models that generalize Bayesian updating. In five of six recent studies, estimates of the e!ects of beliefs on outcomes increase. In two, they more than double.