Using a large data set of performance goals employed in executive incentive contracts, we find that a disproportionately large number of firms exceed their goals by a small margin as compared to the number that fall short of the goal by a similar margin. This asymmetry is particularly acute for earnings goals, when compensation is contingent on a single goal, when the pay-performance relationship around the goal is concave-shaped, and for grants with non-equity-based payouts. Firms that exceed their compensation target by a small margin are more likely to beat the target the next period and CEOs of firms that miss their targets are more likely to experience a forced turnover. Firms that just exceed their Earnings Per Share (EPS) goals have higher abnormal accruals and lower Research and Development (R&D) expenditures, and firms that just exceed their profit goals have lower Selling, General and Administrative (SG&A) expenditures. Overall, our results highlight some of the costs of linking managerial compensation to specific compensation targets.

Many researchers use the G-index or E-index to measure firms’ takeover defenses. Others argue that these indices are not related to firms’ takeover likelihoods. We find that, unlike their raw values, the instrumented versions of these indices are significantly and negatively related to acquisition likelihood. The difference between the raw and instrumented results indicates that the G-index and E-index include an endogenous component and highlights the importance of accounting for endogeneity in tests that use takeover indices to measure takeover deterrence. We provide data on new instruments that researchers can use to address these issues.Received April 13, 2016; editorial decision October 14, 2016 by Editor David Dennis.

This paper provides novel evidence on the real and financial market effects of legal institutions. Our analysis exploits persistent and externally imposed differences in court enforcement that arose when the U.S. Congress assigned state courts to adjudicate contracts on a subset of Native American reservations. Using area-specific data on small business lending, we find that reservations assigned to state courts, which enforce contracts more predictably than tribal courts, have stronger credit markets. Moreover, the law-driven component of credit market development is associated with significantly higher per capita income, with stronger effects in sectors that depend more on external financing.

American Economic Association Universal Academic Questionnaire Summary Statistics by Charles E. Scott and John J. Siegfried. Published in volume 107, issue 5, pages 678-80 of American Economic Review, May 2017

Exchange rate models with uncertain and incomplete information predict that investors focus on a small set of fundamentals that changes frequently over time. We design a model selection rule that captures the current set of fundamentals that best predicts the exchange rate. Out-of-sample tests show that the forecasts made by this rule significantly beat a random walk for 5 out of 10 currencies. Furthermore, the currency forecasts generate meaningful investment profits. We demonstrate that the strong performance of the model selection rule is driven by time-varying weights attached to a small set of fundamentals, in line with theory.

While neoclassical theory emphasizes the impact of trade on wage inequality between occupations and sectors, more recent theories of firm heterogeneity point to the impact of trade on wage dispersion within occupations and sectors. Using linked employer–employee data for Brazil, we show that much of overall wage inequality arises within sector–occupations and for workers with similar observable characteristics; this within component is driven by wage dispersion between firms; and wage dispersion between firms is related to firm employment size and trade participation. We then extend the heterogenous-firm model of trade and inequality from Helpman et al. (2010) and estimate it with Brazilian data. We show that the estimated model provides a close approximation to the observed distribution of wages and employment. We use the estimated model to undertake counterfactuals, in which we find sizable effects of trade on wage inequality.

We consider the dynamic factor model where the loading matrix, the dynamic factors, and the disturbances are treated as latent stochastic processes. We present empirical Bayes methods that enable the shrinkagebased estimation of the loadings and factors. We investigate the methods in a large Monte Carlo study where we evaluate the finite sample properties of the empirical Bayes methods for quadratic loss functions. Finally, we present and discuss the results of an empirical study concerning the forecasting of U.S. macroeconomic time series using our empirical Bayes methods.

In this paper, we empirically estimate the costs of delay in the FDIC's closures of 433 commercial banks between 2007 and 2014 based upon a counterfactual closure regime. We find that the costs of delay could have been as high as 18.5 billion, or 37% of the FDIC's estimated costs of closure of 49.8 billion. We think that these findings call for a more aggressive stance by bank regulators with respect to the provisions for loan losses and write-downs of banks’ non-performing assets. More aggressive (and earlier) provisions and write-downs, or adoption of a capital ratio that penalizes nonperforming loans, would allow the concept of “prompt corrective action” (PCA) to play the role that it was meant to play in reducing FDIC losses from insolvent banks.

Many experiments have found that participants take more investment risk if they see returns less frequently, see portfolio-level returns (rather than each individual asset's returns), or see long-horizon (rather than one-year) historical return distributions. In contrast, we find that such information aggregation treatments do not affect total equity investment when we make the investment environment more realistic than in prior experiments. Previously documented aggregation effects are not robust to changes in the risky asset's return distribution or the introduction of a multi-day delay between portfolio choice and return realizations.

We examine the financial conditions of dealers that participated in two of the Federal Reserve's lender-of-last-resort (LOLR) facilities—the Term Securities Lending Facility (TSLF) and the Primary Dealer Credit Facility (PDCF)—that provided liquidity against a range of assets during 2008–2009. Dealers with lower equity returns and greater leverage prior to borrowing from the facilities were more likely to participate in the programs, borrow more, and, in the case of the TSLF, at higher bidding rates. Dealers with less liquid collateral on their balance sheets before the facilities were introduced also tended to borrow more. The results suggest that both financial performance and balance sheet liquidity play a role in LOLR utilization.