This paper is concerned with possible model misspecification in moment inequality models. Two issues are addressed. First, standard tests and confidence sets for the true parameter in the moment inequality literature are not robust to model misspecification in the sense that they exhibit spurious precision when the identified set is empty. This paper introduces tests and confidence sets that provide correct asymptotic inference for a pseudo-true parameter in such scenarios, and hence, do not suffer from spurious precision. Second, specification tests have relatively low power against a range of misspecified models. Thus, failure to reject the null of correct specification does not necessarily provide evidence of correct specification. That is, model specification tests are subject to the problem that absence of evidence is not evidence of absence. This paper develops new diagnostics for model misspecification in moment inequality models that do not suffer from this problem.

We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity. We consider a general model with group-level unconfoundedness and provide conditions under which aggregate balancing statistics—group-level averages of functions of treatments and covariates—are sufficient to eliminate differences between groups. Building on these results, we re-interpret commonly used linear fixed-effect regression estimators by writing them in the Mundlak form as linear regression estimators without fixed effects but including group averages. We use this representation to develop Generalized Mundlak Estimators that capture group differences through group averages of (functions of) the unit-level variables and adjust for these group differences in flexible and robust ways in the spirit of the modern causal literature.

Using a dataset covering 3 million commercial borrower financial statements, we document a substantial, nearly monotonic decline in banks’ use of attested financial statements (AFS) in lending over the past two decades. Two market forces help explain this trend. First, technological advances provide lenders with access to a growing array of borrower information sources that can substitute for AFS. Second, banks are increasingly competing with nonbank lenders that rely less on AFS in screening and monitoring. Our results illustrate how technology adoption and changes in credit market structure can render AFS less efficient than alternative information sources for screening and monitoring.

Economics publishing proceeds much more slowly than in the natural sciences, and more slowly than in the other social sciences and finance. It is relatively even slower at the extremes. Much of the lag, especially at the extremes, arises from authors’ dilatory behavior in revisions. Additional rounds of resubmissions at top economics journals are related to additional citations; but conditional on resubmission, the delays are unrelated to greater scholarly attention. We offer several proposals for speeding publication, including no-revision policies such as Economic Inquiry’s, the use of “cascading referee reports,” limits on authors’ time revising, and limits on editors waiting for dilatory referees.

We extend Jin and Myers’s (2006) model to derive the relation between stock price crash risk and operating leverage (i.e., the fraction of fixed costs in total costs). The model predicts that (1) firms’ operating leverage decreases as stock price crash risk increases and (2) the negative effect of crash risk on operating leverage is more pronounced when firms are closer to the crash threshold or when managers face higher costs of stock price crashes. We empirically test the model predictions using a large sample of manufacturing firms in the US and find consistent results. Further analysis shows that higher levels of crash risk lead to a less sticky cost behavior. In addition, crash risk–driven operating deleveraging effectively reduces stock return volatility and enhances operating performance in subsequent years. Collectively, our findings reveal that crash-prone firms adopt a more flexible cost structure to delay stock price crashes and mitigate adverse outcomes.

Few studies investigate whether ESG mitigates the harmful effects of changes in firms’ external environments. We evidence that ESG mitigated the impact of COVID-19 work-from-home and workplace prescriptions amongst several other pandemic-related government regulatory interventions, even when controlling for firm size. In a novel approach, we apply scrutiny of firms to restrict our cross-national sample to only firms with no cross-border trade, that is, explicitly domestically focused operational processes irrespective of the endpoint of corporate sales, enhancing methodological robustness. Consequently, we isolate an ESG effect. Results indicate the existence of a premium during the onset of each analysed national pandemic experience, particularly pronounced for those corporations that had achieved more substantiative ESG-based preparation and development before the onset of the COVID-19 pandemic.

This study draws on London’s long experience with air pollution in order to improve our understanding of the overall effects of pollution exposure and how and why these effects evolve as locations develop. I compare uniquely detailed new mortality data covering 1866 to 1965 to the timing of London’s famous fog events, which trapped emissions in the city. I show that air pollution was a major contributor to mortality in London over this period and that it interacted strongly with specific infectious diseases. As a consequence of this interaction, reductions in the infectious disease burden substantially altered the health costs of pollution.