Get access Abstract This study examines how changes in state corporate tax rates affect corporate social responsibility (CSR) performance among U.S. firms. Using staggered state-level tax reforms and a difference-in-differences (DiD) design, we identify an asymmetric causal effect: tax cuts significantly enhance CSR performance by reducing concerns, whereas tax increases only marginally weaken CSR strengths. Drawing primarily on signaling theory, complemented by slack resource and stakeholder perspectives, we argue that tax cuts expand financial slack, enabling firms to use CSR as a positive signal of financial strength, long-term orientation, and responsible use of tax savings. In contrast, firms avoid cutting CSR significantly after tax hikes to prevent negative signaling. In support of the theories, our heterogeneity analyses show that these effects are stronger among financially constrained firms and are concentrated in material CSR issues that are financially relevant to investors. A domain-level analysis further reveals that tax increases reduce environmental strengths, while tax cuts lower concerns related to employee relations, diversity, and environmental practices. These findings highlight how tax policy shapes CSR through its impact on financial flexibility and stakeholder expectation, offering implications for corporate strategy and public policy.

We show that online listing prices closely approximate transaction prices across Europe. Our validation uses the IWH European Real Estate Index (EREI), a near-real-time database of monthly purchase prices, rents, listing counts, and time on market for 1,154 NUTS-3 regions in 16 countries since April 2024. With publication lags as short as one month, validated listing prices offer a timely, comparable complement to official house price statistics. Purchase price levels correlate between 0.94 and 0.97 with transaction prices in the five most populous markets, while calibration slopes range from 0.95 to 1.00. Aggregate listing prices exceed benchmark transaction prices by 13% to 22%.

This paper examines how initial public offerings (IPOs) affect firms' internal organization. We find that IPO firms become more hierarchical and standardized organizations, characterized by additional layers, more managers, smaller control spans, and larger administrative functions. These changes occur mostly in preparation for the IPO and can be only partially explained by growth. IPO firms with greater human capital risk experience larger hierarchical changes. Hierarchical changes help firms standardize employee roles and formalize internal processes. Our results suggest that firms reorganize to reduce their dependence on key individuals' human capital when transitioning to public markets.

Predicting IPO first-day returns is inherently challenging due to the wide range of contributing factors, each with distinct statistical properties. We assess the performance of several machine learning (ML) techniques and identify XGBoost as the most statistically effective model for forecasting first-day returns. Using a comprehensive set of 863 pre-IPO variables, our high-performing predictive model accurately estimates both the direction and magnitude of IPO first-day returns. The most influential predictors include underwriter agency measures, price revision, and the free-float fraction. Using a rolling-window predictive approach, the model demonstrates substantial practical value, generating approximately $300 billion in gains from IPOs with positive first-day returns and avoiding more than $22 billion in losses from those with negative returns over the 2000–2016 period.