Data are used from the 1984–2016 Displaced Workers Surveys (DWS) to investigate the incidence and consequences of job loss, 1981–2015. These data show a record high rate of job loss in the Great Recession, with serious employment consequences for job losers, including very low rates of re-employment and difficulty finding full-time employment. The average reduction in weekly earnings for job losers making a full-time–full-time transition are relatively small, with a substantial minority reporting earning more on their new job than on the lost job. Most of the cost of job loss comes from difficulty finding new full-time employment.

This paper examines the interplay among bank liquidity creation (which incorporates all bank on- and off-balance sheet activities), monetary policy, and financial crises. We find that: (1) high liquidity creation (relative to trend) – particularly off-balance sheet liquidity creation – helps predict crises, controlling for other factors; (2) monetary policy has statistically significant, but economically minor effects on liquidity creation by small banks during normal times, and these effects are even weaker during financial crises; (3) monetary policy has very little effects on medium and large bank liquidity creation during both normal times and crises. These findings suggest that authorities may wish to monitor bank liquidity creation closely in order to predict and perhaps lessen the likelihood of financial crises. They might also consider other tools to control bank liquidity creation, such as capital and liquidity requirements.

We estimate the stock’s likelihood of extreme returns by measuring the extent to which the stock’s trades are correlated with market-wide and industry-wide trades during normal times, referred to as herding. We find that stocks whose trades herd most with aggregate-level trades experience most negative (positive) returns during market crashes (booms). While herding generates extreme returns in both sides, investors appear to demand compensation for the possibility of extreme low returns. This is the case even when we control for standard asset pricing variables and other tail risk proxies.

Extant literature shows that IPO first-day returns are correlated with market returns preceding the issue. We propose a rational explanation for this puzzling predictability by adding a public signal to Benveniste and Spindt (1989)’s information-based framework. A novel result of our model is that the compensation required by investors to truthfully reveal their information decreases with the public signal. This “incentive effect” receives strong empirical support in a sample of 6300 IPOs in 1983–2012. Controlling for the incentive effect, the positive relation between initial returns and pre-issue market returns disappears for top-tier underwriters, where the order book is held to be most informative, effectively resolving the predictability puzzle.

We argue that anomalies may experience prolonged decay after discovery and propose a Bayesian framework to study how that impacts portfolio decisions. Using the January effect and short-term index autocorrelations as examples of disappearing anomalies, we find that prolonged decay is empirically important, particularly for small-cap anomalies. Papers that document new anomalies without accounting for such decay may actually underestimate the original strength of the anomaly and imply an overstated level of the anomaly out of sample. We show that allowing for potential decay in the context of portfolio choice leads to out-of-sample outperformance relative to other approaches.

This paper uses experimental data from the Head Start Impact Study (HSIS) combined with nonexperimental data from the Early Childhood Longitudinal Study–Birth Cohort (ECLS-B) to study the performance of nonexperimental estimators for evaluating Head Start program impacts. The estimators studied include parametric cross-section and difference-in-differences regression estimators and nonparametric cross-section and difference-in-differences matching estimators. The estimators are used to generate program impacts on cognitive achievement test scores, child health measures, parenting behaviors, and parent labor market outcomes. Some of the estimators closely reproduce the experimental results, but a priori it would be difficult to know whether the estimator works well for any particular outcome. Pre-program exogeneity tests eliminate some outcomes and estimators with the worst biases, but estimators/outcomes with substantial biases pass the tests. The difference-in-differences matching estimator exhibits the best performance in terms of low bias values and capturing the pattern of statistically significant treatment effects. However, the variation in bias is greater across outcomes examined than across methods.

In this paper, we address the question of whether the gender of a firm’s leader affects the cost of bank funding faced by small and medium enterprises in Europe. Using a large sample of observations of non-financial firms, during the years 2009–2013, we empirically test for the presence of discrimination, comparing female-led and male-led firms. After controlling for a rich set of variables and addressing potential endogeneity, our results show that i) female-led enterprises are more likely to face worse price conditions for bank financing compared to their male-led counterparts and, ii) firms whose leadership changes from female to male are more likely to benefit from an improvement in interest rate levels. This evidence is robust to different model specifications and various methodological approaches. The existence of such bias in the credit markets highlights the need of policy measures addressing female-led businesses, thus reducing their bank financing burdens and enhancing their entrepreneurial opportunities.