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.

Climate policy design must balance emissions mitigation with concerns for fairness, particularly as climate change disproportionately affects the poorest households within and across countries. Integrated Assessment Models used for global climate policy evaluation have so far typically not considered inequality effects within countries. To fill this gap, we develop a global Integrated Assessment Model representing national economies and subnational income, mitigation cost, and climate damage distribution and assess a range of climate policy schemes with varying levels of effort sharing across countries and households. The schemes are consistent with limiting temperature increases to 2 °C and account for the possibility to use carbon tax revenues to address distributional effects within and between countries. We find that carbon taxation with redistribution improves global welfare and reduces inequality, with the most substantial gains achieved under uniform taxation paired with global per capita transfers. A Loss and Damage mechanism offers significant welfare improvements in vulnerable countries while requiring only a modest share of global carbon revenues in the medium term. The poorest households within all countries may benefit from the transfer scheme, in particular when some redistribution is made at the country level. Our findings underscore the potential for climate policy to advance both environmental and social goals, provided revenue recycling mechanisms are effectively implemented. In particular, they demonstrate the feasibility of a welfare improving global climate policy involving limited international redistribution.

This paper examines how supplier IPO events affect their key customers’ cost of debt. The evidence reveals that average loan spreads for customers increase by roughly 20% (23.7 basis points) following suppliers’ IPO events. This negative spillover effect is more pronounced when suppliers make significant relationship-specific investments (high switching cost), when suppliers face less concentrated customer bases, or when customers face more concentrated supplier bases. Our results show that customers receive less favourable trade terms and are forced to pay more for inputs after their suppliers go public, all of which increase customers’ operational costs, risk and subsequent borrowing costs. Furthermore, we document that customer loan contracts become significantly more restrictive after a supplier's IPO. Finally, we find that the observed negative spillover effect is also present in customers’ access to the public bond market.

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.