We analyze the supply-side disruptions associated with COVID-19. We find that sectors in which a higher fraction of the workforce is not able to work remotely experienced greater declines in employment and expected revenue growth, worse stock market performance, and higher likelihood of default. The stock market overweights low-exposure industries. Thus, our findings cast light on the disconnect between stock market indices and aggregate outcomes. We combine these ex ante heterogeneous industry exposures with daily financial market data to create a stock return portfolio that tracks news about the supply-side disruptions resulting from the pandemic.

The view that empirical strategies in economics should be transparent and credible now goes almost without saying. By revealing for whom particular instrumental variables (IV) estimates are valid, the local average treatment effects (LATE) framework helped make this so. This lecture uses empirical examples, mostly involving effects of charter and exam school attendance, to illustrate the value of the LATE framework for causal inference. LATE distinguishes independence conditions satisfied by random assignment from more controversial exclusion restrictions. A surprising exclusion restriction is shown to explain why enrollment at Chicago exam schools reduces student achievement. I also make two broader points: IV exclusion restrictions formalize commitment to clear and consistent explanations of reduced‐form causal effects; the credibility revolution in applied econometrics owes at least as much to compelling empirical analyses as to methodological insights.

We examine how community banks respond to liquidity shocks created by natural disasters. We address community banks’ responses to liquidity shocks due to their focused geographic and economic presence, which coincide with their communities’ exposure to the disasters and the ability of the local banks to meet their needs. We find that community banks respond to liquidity shocks by managing their balance sheet, rather than any single balance sheet account. In particular, we find that they respond to the liquidity needs of their communities by increasing loans as deposits are withdrawn.

This paper introduces a stylized model to capture distinctive features of waiting list allocation mechanisms. First, agents choose among items with associated expected wait times. Waiting times serve a similar role to that of monetary prices in directing agents' choices and rationing items. Second, the expected wait for an item is endogenously determined and randomly fluctuates over time. We evaluate welfare under these endogenously determined waiting times and find that waiting time fluctuations lead to misallocation and welfare loss. A simple randomized assignment policy can reduce misallocation and increase welfare.

I show index funds are more likely to oppose management on contentious management sponsored proposals at firms held only by their family's index funds than on proposals at firms co-held by their family's active funds. Additionally, shareholder proposals garner a greater level of support by index funds when the firm's shares are not simultaneously held by a fund's same-family active funds. Consistent with “locked-in” motives to monitor, these results imply index funds participate as more engaged voters when same-family active funds avoid holding positions in a firm.

We price the risky component of specialness spreads—identified by their deviations from the expected auction cycle—within a dynamic term structure model estimated using daily prices of all outstanding Treasury securities and corresponding special collateral (SC) repo rates. This allows us to derive a time-varying SC risk premium that we quantitatively link to various price anomalies, such as the on-the-run premium. The SC risk premium explains about 80% of the on-the-run premium and a substantial share of other Treasury price anomalies, suggesting that unexpected fluctuations in the specialness spreads of recently issued nominal Treasury securities are a common risk factor.

This study explores whether NielsenIQ scanner data from U.S. retailers contain incremental information about the GAAP revenue of corresponding manufacturers. Using retail product/store/week data from 2006 to 2018, we construct a measure of aggregated consumer purchases at the manufacturer/quarter level, and find that it strongly predicts GAAP revenues. In addition, analyst forecasts of revenues have predictable errors, which implies that analysts do not fully incorporate the information in consumer purchases. Exploring investment implications, we find that hedge portfolios that buy (sell) stocks of firms with high (low) abnormal consumer purchases generate annualized returns on the magnitude of 14%–19%, depending on specification. Overall, these findings suggest that scanner data on consumer purchases provide an information edge over GAAP revenue, shedding light on the benefits of using transactional data.

Elementary portfolio theory implies that environmentalists optimally hold more shares of polluting firms than non-environmentalists, and that polluting firms attract more investment capital than otherwise identical non-polluting firms through a hedging channel. Pigouvian taxation can reverse the aggregate investment results, but environmentalists still overweight polluters. We introduce countervailing motives for environmentalists to underweight polluters, comparing the implications when environmentalists coordinate to internalize pollution, or have nonpecuniary disutility from holding polluter stock. With nonpecuniary disutility, introducing a green derivative may dramatically alter who invests most in polluters, but has no impact on aggregate pollution.

The utility of machine learning (ML) for regression-based causal inference is illustrated by using lasso to select control variables for estimates of college characteristics’ wage effects. Post-double-selection lasso offers a path to data-driven sensitivity analysis. ML also seems useful for an instrumental variables (IV) first stage, since two-stage least squares (2SLS) bias reflects overfitting. While ML-based instrument selection can improve on 2SLS, split-sample IV and limited information maximum likelihood do better. Finally, we use ML to choose IV controls. Here, ML creates artificial exclusion restrictions, generating spurious findings. On balance, ML seems ill-suited to IV applications in labor economics.