The prevalence of pay based on risky firm outcomes for nonexecutive workers presents a puzzling departure from conventional contract theory, which predicts insurance provision by the firm. When workers at the same firm compete against each other for promotions, the optimal contract features pay based on firm outcomes as insurance against promotion risk. The model's predictions are consistent with many observed phenomena, such as performance‐based vesting and overvaluation of equity pay by nonexecutive workers. It also generates novel predictions linking a firm's hierarchy to its workers' pay structure.

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Studying China's credit market using a structural default model that integrates credit risk, liquidity, and bailout, we document improved price discovery and a deepening divide between state‐owned enterprises (SOEs) and non‐SOEs. Amidst liquidity deterioration, the presence of government bailout helps alleviate the heightened liquidity‐driven default, making SOE bonds more valuable and widening the SOE premium. Meanwhile, the increased importance of government support makes SOEs more sensitive to bailout, while the heightened default risk increases non‐SOEs' sensitivity to credit quality. Examining the real impact, we find severe performance deteriorations of non‐SOEs relative to SOEs, reversing the long‐standing trend of non‐SOEs outperforming SOEs.

We examine the timing of returns around the publication of anomaly trading signals. Using a database that captures when information is first publicly released, we show that anomaly returns are concentrated in the first month after information release dates, and these returns decay soon thereafter. We also show that the academic convention of forming portfolios in June underestimates predictability because it uses stale information, which makes some anomalies appear insignificant. In contrast, we show many anomalies do predict returns if portfolios are formed immediately after information releases. Finally, we develop guidance on forming portfolios without using stale information.

Process automation reduces racial disparities in credit access by enabling smaller loans, broadening banks' geographic reach, and removing human biases from decision making. We document these findings in the context of the Paycheck Protection Program (PPP), where private lenders faced no credit risk but decided which firms to serve. Black‐owned firms obtained PPP loans primarily from automated fintech lenders, especially in areas with high racial animus. After traditional banks automated their loan processing procedures, their PPP lending to Black‐owned firms increased. Our findings cannot be fully explained by racial differences in loan application behaviors, preexisting banking relationships, firm performance, or fraud rates.

Policymakers frequently use guarantees to mitigate panic‐based runs in the financial system. We analyze a binary‐action coordination game under the global games framework and propose a novel intervention program that screens investors based on their heterogeneous beliefs about the system's stability. The program only attracts investors who are at the margin of running, and their participation boosts all investors' confidence in the financial system. Compared with government guarantee programs, our proposed program is as effective at mitigating runs but features two advantages: it costs less to implement and it is robust to moral hazard.

Examining the contractual disclosures during the sale of private‐label residential mortgage‐backed securities before the 2008 financial crisis, we find that textual contents in the risk‐factor section predict subsequent losses and yet were not reflected in pricing. Insurance companies, especially life insurers and insurers with low regulatory capital ratios, are more exposed to textual risks. Consistent with issuers hedging litigation risks with disclosure, we find that textual contents are associated with second‐lien underreporting and preissuance written communications. Overall, we find that investors neglected risks in the purportedly safe assets before the crisis.

Unlike traditional firm production, gig economy workers provide their own physical capital. As a consequence, the low‐income households for whom gig economy opportunities are most valuable often borrow to participate. In the context of ride share, difference‐in‐difference analysis reveals increased vehicle purchases, borrowing, utilization, and employment around entry, but financially constrained individuals cannot participate. To assess the equilibrium importance of financing, I build and estimate a structural model of the gig economy. Access to finance proves critical for the gig economy's growth: without finance, equilibrium quantities would be 40% lower and prices 90% higher, and only higher‐income households could participate as drivers.