U.S. laws provide explicit whistleblower protections and direct regulators to adopt ambitious programs to reward whistleblowing. However, there is limited evidence on whether employee whistleblowing deters financial misreporting and tax aggressiveness. Using a sample of employee whistleblower cases obtained from the U.S. government, I provide evidence that firms subject to whistleblowing allegations exhibit significant decreases in financial misreporting and tax aggressiveness, compared with control firms. I find that this deterrent effect persists for at least two years beyond the year of the allegation. JEL Classifications: H25; M41; M48. Data Availability: All data are available from public sources identified in the paper.

Extant research suggests that conditional conservatism reduces information asymmetry between a firm and its shareholders as well as its debtholders. However, there is little evidence on whether conditional conservatism reduces information asymmetry differentially for shareholders and debtholders. We use the setting of a firm's choice between equity versus debt when it seeks a significant amount of external financing to examine this research question. We find that when firms raise a significant amount of external financing, the use of equity (versus debt) increases with the level of conservatism. We also find that the reduction in cost of equity associated with conservatism is greater for equity issuers than for debt issuers, but find no such difference when we examine cost of debt. In addition, we find that the positive effect of conservatism on the choice of equity issuance (versus debt issuance) is accentuated when the information asymmetry between the firm and its shareholders is more severe. Overall, our results suggest that conservatism reduces information asymmetry more between firms and shareholders than between firms and debtholders.

We present evidence consistent with markets failing to properly price information in seasonal earnings patterns. Firms with historically larger earnings in one quarter of the year (“positive seasonality quarters”) have higher returns when those earnings are usually announced. Analysts have more positive forecast errors in positive seasonality quarters, consistent with the returns being driven by mistaken earnings estimates. We show that investors appear to overweight recent lower earnings following positive seasonality quarters, leading to pessimistic forecasts in the subsequent positive seasonality quarter. The returns are not explained by risk-based explanations, firm-specific information, increased volume, or idiosyncratic volatility.

We present CoMargin, a new methodology to estimate collateral requirements in derivatives central counterparties (CCPs). CoMargin depends on both the tail risk of a given market participant and its interdependence with other participants. Our approach internalizes trading externalities and enhances the stability of CCPs, thus reducing systemic risk concerns. We assess our methodology using proprietary data from the Canadian Derivatives Clearing Corporation that include daily observations of the actual trading positions of all of its members from 2003 to 2011. We show that CoMargin outperforms existing margining systems by stabilizing the probability and minimizing the shortfall of simultaneous margin-exceeding losses.

Economics journals with reproducibility policies are cited more often than others. For the minority of journals with a mandatory and enforced policy, this is significant when controlling for time and journal effects. To cope with the large variety of software used and to develop standards for replicability, joint efforts of journals could ensure each empirical study is published with data, code, and instructions on how to use them together. Individual reviewers could take initiative by asking for replicable empirical results. The American Journal of Political Science sets an example by having all empirical studies externally check for replicability prior to publication.

We consider forecasting with uncertainty about the choice of predictor variables. The researcher wants to select a model, estimate the parameters, and use this for forecasting. We investigate the dis-tributional properties of a number of different schemes for model choice and parameter estimation: in-sample model selection using the Akaike information criterion, out-of-sample model selection, and splitting the data into subsamples for model selection and parameter estimation. Using a weak-predictor local asymptotic scheme, we provide a representation result that facilitates comparison of the distributional properties of the procedures and their associated forecast risks. We develop a sim-ulation procedure that improves the accuracy of the out-of-sample and split-sample methods uni-formly over the local parameter space. We also examine how bootstrap aggregation (bagging) affects the local asymptotic risk of the estimators and their associated forecasts. Numerically, we find that for many values of the local parameter, the out-of-sample and split-sample schemes perform poorly if implemented in the conventional way. But they perform well, if implemented in conjunction with our risk-reduction method or bagging.

Using a difference-in-differences approach, we study how intellectual property right (IPR) protection affects innovation in China in the years around the privatizations of state-owned enterprises (SOEs). Innovation increases after SOE privatizations, and this increase is larger in cities with strong IPR protection. Our results support theoretical arguments that IPR protection strengthens firms’ incentives to innovate and that private sector firms are more sensitive to IPR protection than SOEs. Received June 17, 2015; editorial decision November 23, 2016 by Editor Andrew Karolyi.

This paper examines the aftermath of postwar financial crises in advanced countries. We construct a new semiannual series on financial distress in 24 OECD countries for the period 1967–2012. The series is based on assessments of the health of countries' financial systems from a consistent, real-time narrative source, and classifies financial distress on a relatively fine scale. We find that the average decline in output following a financial crisis is statistically significant and persistent, but only moderate in size. More important, we find that the average decline is sensitive to the specification and sample, and that the aftermath of crises is highly variable across major episodes. A simple forecasting exercise suggests that one important driver of the variation is the severity and persistence of financial distress itself. At the same time, we find little evidence of nonlinearities in the relationship between financial distress and the aftermaths of crises.