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.

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.

We investigate the role of intellectual property rights protection in cross-border merger and acquisition (M&A) activity. We document a significant increase in inbound cross-border M&As after a country implements reforms that strengthen local intellectual property rights. Importantly, we find that intellectual property rights have an impact on merger activity only in industries that are more intellectual capital-intensive, and when the target country has weaker intellectual property rights protection than the acquirer country. We also find that synergy gains in cross-border M&As are positively related to reforms of intellectual property rights. These results are consistent with the notion that acquirers are concerned about the local protection of intellectual capital when considering foreign acquisitions, and care the most when the target firm is in an industry that uses intellectual property intensely and in a country that has lesser-developed protections that their own country does.

Performance share plans are an increasingly important component of executive compensation. They are equity-based, long-term incentive plans where the number of shares to be awarded is a quasi-linear function of a performance result over a fixed time period. We derive closed-form formulas for the value of a performance share plan when the performance measure is: (1) a non-traded measure following an Arithmetic Brownian Motion (e.g., earnings per share), (2) a non-traded measure following a Geometric Brownian Motion (e.g., revenue), or (3) a rank-order tournament of traded asset returns that are following Arithmetic Brownian Motions (e.g., percentile of ranked stock returns). Then we empirically test our valuation formulas. We find that our valuation formulas are more accurate for performance share plans based on earnings per share when forecasting using analyst consensus prior to the grant date. We also find that the efficiency of our valuation model greatly depends on the method used to forecast future firm performance. The policy implication is that FASB should consider requiring that grant date fair value be estimated using valuation formulas such as ours.