This paper examines how political networks influence insider trading in China. Using biographical data to construct chairman–politician social networks, we find that firms with stronger political networks engage in significantly less insider trading. The effect is stronger for non-state-owned enterprises (non-SOEs) and for long-standing or high-ranking connections. The muted trading persists during periods when insiders possess valuable private information, including prior to M&A announcements and major policy events. The evidence suggests that personal political networks function as informal governance mechanisms that discipline managerial opportunism when formal governance through state ownership is absent.

Drawing on detailed career and biographical data of Chinese politicians, this study builds a dynamic social network for all political elites in China and examines the selection process of provincial-level politicians. Using regression and tree-based machine learning techniques and leveraging individuals’ global centrality within political networks, we unveil the relative importance of economic performance, political networks, and career trajectory in determining the selection of provincial leaders. Our findings highlight the critical role of network embeddedness.

Using the adoption of Zombie Property Law (ZL) across several US states, we show that increased lender accountability in the foreclosure process affects mortgage lending decisions and standards. Difference-in-differences estimations using a state border design show that ZL incentivizes lenders to screen mortgage applications more carefully: they deny more applications and impose higher interest rates on originated loans, especially risky loans. In turn, these loans exhibit higher ex-post performance. ZL also affects lender behavior after borrowers become distressed, causing them to strategically keep delinquent mortgages alive. Our findings inform the debate on policy responses to foreclosure crises.

This paper provides theory and evidence on micro-level pricing behavior needed to model an aggregate New Keynesian Phillips Curve. We start with individual firms that are heterogeneous in their production technology and in the demand curves they face. We estimate the parameters of supply and demand curves by utilizing prices and quantities of outputs and factor inputs of firms along with exogenous downstream demand instruments from global input-output and trade data. The research addresses model heterogeneity using a clustering method to classify firms according to their production technology and observed price pass-through. The results show that more productive firms exhibit a lower price response to changes in demand. We find that the aggregate price response to demand shocks will be smaller when more productive firms absorb a larger portion of demand shocks, which generally is the case. At the same time, our results imply that idiosyncratic shifts in demand to clusters of firms with more rapidly rising marginal cost curves, or cost shocks to clusters of firms with high pass-through, will result in a higher aggregate price response. Finally, this paper provides a framework to incorporate heterogeneous pricing behavior into an estimate of the slope of the aggregate Phillips Curve.