Research 

Abstract: High-technology IPOs lead to greater inequality among workers with different skills and gentrification in local neighborhoods. Using spatial causal inference and withdrawn IPOs as counterfactuals, I document positive wage effects for incumbent workers but displacement of low-skilled workers, mainly due to rising housing prices and rents. Both excess liquidity and agglomeration brought by high-technology IPOs contribute to the differentials. Further, I also develop a discrete-choice spatial equilibrium model that demonstrates that welfare changes diverge for high- and low-skilled workers, with an average IPO increasing the productivity of surrounding high-skilled workers by 20%.

Abstract:  This paper reveals a tradeoff in innovation outputs among high-technology clusters: while agglomeration enhances productivity through knowledge spillovers, competing for scarce talent distorts firms’ innovation strategies. Applying deep learning techniques to the entries of S&P 500 high-technology firms, we demonstrate that new entrants significantly alter incumbent innovation portfolios. Following firm entry, incumbents increase patent production by 7.5% and economic value by 3–4%, but they simultaneously experience a 55% reduction in citations and diminished scientific importance. This shift represents a fundamental reallocation from long-term scientific innovation to commercially focused, incremental improvements with higher immediate deployability but reduced scientific contribution. Dynamic citation analysis confirms treated patents receive more immediate citations but exhibit substantially lower long-term impact, reflecting changed R&D investment horizons. We identify labor market competition as the causal mechanism through tests exploiting variation in the labor market tightness and adoption of non-compete agreements. Our theoretical model with heterogeneous patent types demonstrates that the race for talents creates a wedge between profit-maximizing innovation strategies and social welfare optimization. New entrants choose excessive local hiring rates that maximize firm value but sub-optimize knowledge production.

Abstract:  We examine the neighborhood-level consequences of the Duty to Serve (DTS) program, a federal mandate requiring Government-Sponsored Enterprises (GSEs) to increase mortgage purchases in underserved rural areas. The program successfully shifted GSE purchasing toward a greater share of small-balance mortgages. This expansion of credit for affordable housing, however, triggered significant neighborhood sorting. While promoting homeownership, the policy induced an out-migration of high-skilled residents and an in-migration of low-skilled residents. This demographic shift led to a counterintuitive decrease in average housing prices and rents, masking price appreciation at the lower end of the market. Our findings suggest that while DTS achieved its primary goal of expanding credit, it also inadvertently fostered economic segregation in targeted communities.

Abstract:  Shareholder proposals targeting dual-class firms receive far less voting support due to the superior voting rights of insiders. Anticipating extremely low passing rates, managers in dual-class firms advance more proposals to the voting stage. Proposals being voted on are a positive signal that attracts public attention and enhances reputation. Hence, shareholder sponsors target dual-class firms to exploit higher voting-stage probability when they expect substantial reputational gains, which explains the puzzle of dual-class proposal submission. Our model and empirical evidence fit this story nicely and reveal its heterogeneity by proposal and sponsor types. Further exploration shows that the reputation surge following dual-class proposal voting alters the pattern of the ensuing single-class proposals by the same sponsor but does not translate into significant stock market reactions.