This article shows that when a compensation peer firm experiences a significant failure in its say-on-pay (SOP) voting, the focal firm’s stock price is adversely affected, resulting in reduced CEO pay in the subsequent period. This pay-reduction effect is amplified when the board is more powerful, when proxy advisors express concerns about CEO pay, and when the compensation consultant lacks quality. Directors who react to the price drop and cut the CEO’s pay receive higher votes in future director elections, implying a market feedback effect for directors of the focal firm triggered by their peers’ SOP voting failure.

This paper examines how firms adjust the number and types of workers they hire in response to increased biodiversity transition risk. Using the adoption of the Key Biodiversity Areas Standard of 2016 as a source of variation that increases the risk of future land-use restrictions, we find that firms reduce job postings in affected areas and reallocate labor to less exposed regions. This effect is concentrated among firms that make negative impacts on biodiversity. Cuts are stronger among production roles, while hiring in green and adaptive occupations increases. The effect is not driven by changes in capital investment or workers’ labor supply decisions. Our findings contribute to the ongoing debate on the costs and benefits of biodiversity conservation policies and their implications for labor market outcomes.

We review an empirical literature that studies how political polarization affects financial decisions. We first discuss the degree of partisan segregation in finance and corporate America, the mechanisms through which partisanship may influence financial decisions, and the available data sources used to infer individuals’ partisan leanings. We then describe and discuss the empirical evidence. Our review suggests an economically large and often growing partisan gap in the financial decisions of households, corporate executives, and financial intermediaries. Partisan alignment between individuals explains team and financial relationship formation, with initial evidence suggesting that high levels of partisan homogeneity may be associated with economic costs. We conclude by proposing several promising directions for future research.

This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in mean-squared prediction error relative to a random walk benchmark can be achieved in real time for forecast horizons of up to two years. A particularly promising model is a six-variable Bayesian vector autoregressive model that includes the fundamental determinants of the supply and demand for natural gas. To capture real-time data constraints of these and other predictor variables, we assemble a rich database of historical vintages from multiple sources. We also compare our model-based forecasts to readily available model-free forecasts provided by experts and futures markets. Given that no single forecasting method dominates all others, we explore the usefulness of pooling forecasts and find that combining forecasts from individual models selected in real time based on their most recent performance delivers the most accurate forecasts.