When countries change most favored nation (MFN) tariffs, partners that trade on MFN terms experience plausibly exogenous tariff changes. Using this variation, we estimate the trade elasticity at short and long horizons with local projections. We find that the elasticity of tariff-exclusive trade flows is −0.76 in the short run, and approximately −2 in the long run. Our long-run estimates are smaller than typical in the literature, and it takes 7 to10 years to converge to the long run, implying that (i) the welfare gains from trade are high and (ii) there are substantial convexities in the costs of adjusting exports. (JEL C51, F13, F14)

ABSTRACT Using data on the registration of clinical trials and the disclosure of trial results, we examine how firms respond to peer disclosures. We find that firms are less likely to disclose their own trial results if the results of a larger number of closely related trials are disclosed by their peers. This relation is stronger if the firms face higher competition (as measured by the number of competing trials). It is weaker if the firms are further along in their research than the peers (as measured by the trials’ phase) and if the peers’ disclosures convey more negative news (as measured by the firms’ stock price reaction). We also find that firms are more likely to abandon ongoing trials if a larger number of peers disclose the results of closely related trials. Additional tests suggest that this real effects channel does not drive the impact on the firms’ disclosure decisions. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M4.

ABSTRACT We study whether firms avoid financial disclosures to preserve their owners' financial privacy. We find that firms named after their owner, for whom firm disclosure would more directly expose owner information, are more opaque. Eponymous owners prefer firm opacity when disclosure exposes sensitive owner information with social stigma, in rural and anticapitalist areas, and in insider-oriented settings with high secrecy and distrust. When firms are forced to disclose, eponymous owners more frequently change their firms' names, and new firms are less frequently named after their founding owners. These findings indicate that owner-level privacy concerns dampen firm-level disclosure incentives. Data Availability: The data used in this study are available from public sources listed in the paper. JEL Classifications: D82; L51; M41.

We construct a model of an exchange economy in which agents trade assets contingent on an observable signal, the probability of which depends on public opinion. The agents in our model are replaced occasionally, and each person updates beliefs in response to observed outcomes. We show that the distribution of the observed signal is described by a quasi-nonergodic process and that people continue to disagree with each other forever. These disagreements generate large wealth inequalities that arise from the multiplicative nature of wealth dynamics, which makes successful bold bets highly profitable.

We compare how in-kind food assistance and an electronic voucher-based program affect the delivery of aid in practice. The Government of Indonesia randomized across 105 districts the transition from in-kind rice to approximately equivalent electronic vouchers redeemable for rice and eggs at a network of private agents. Targeted households received 46 percent more assistance in voucher areas. For the bottom 15 percent of households at baseline, poverty fell 20 percent. Voucher recipients received higher-quality rice, and increased consumption of eggs. The results suggest moving from a manual in-kind to electronic voucher-based program reduced poverty through increased adherence to program design. (JEL H53, I18, I32, I38, O12)

ABSTRACT We investigate (1) how well a machine learning algorithm can predict one-year ahead effective tax rates (ETRs) and (2) which items in the financial statements and notes are most useful for these predictions. We compare our machine-generated ETR predictions with those from ETRs implied by analysts’ earnings forecasts and find the algorithm’s predictions are less biased, more precise, and explain more of the variance in future ETRs. We then use Explainable AI (based on Shapley values) to measure the usefulness of each disclosure item in the algorithm’s predictions. We find that while some tax-related items are useful, others offer minimal value. Using the machine learning algorithm’s use of information as a benchmark, we then further use Shapley values to examine which information is underweighted or overweighted by analysts. Overall, our results help inform standard setters on the relevance of certain tax disclosures in achieving the objective of predicting future ETRs. JEL Classifications: G17.

We use a unique combination of register and survey data from Amsterdam to investigate the performance of school assignment mechanisms in practice. We find that deferred acceptance (DA) results in higher mean welfare than the adaptive Boston mechanism. This is due to students making strategic mistakes. The welfare gain of a switch from actual Boston to DA is over 90% of the welfare difference between actual Boston and optimal (proxy) Boston. Disadvantaged and lower-ability students would benefit most from such a switch.