Alex Xi He
Abstract: This paper shows that some managers pay higher wage premiums to their workers and these managers are targets of M&As. We use a manager-firm-worker matched dataset covering the population of Denmark from 1995 to 2011 and develop a novel framework to measure manager styles in wage-setting by tracking workers and managers across firms over time. We find that individual managers do matter for wages, and variation in manager fixed effects can explain a significant part of wage differences between firms. Establishments with high wage premiums due to generous managers are more likely to be acquired, and experience higher manager turnover and larger wage declines after acquisitions. Lower wages have little effect on firms’ productivity, and therefore represent a transfer from workers to shareholders. The replacement of high-paying managers accounts for almost all of the wage decline and about half the shareholder gains in all M&As, suggesting that rent extraction might be a major motive for merger transactions.
Abstract: This paper studies the effects of household liquidity constraints on individual labor market outcomes by exploiting a mortgage reform in Denmark in 1992, which for the first time allowed homeowners to borrow against housing equity for non-housing purposes. We find that following the reform, liquidity-constrained homeowners extracted housing equity, increased debt levels, and had higher earnings growth and lower employment rates. In contrast, the reform had small and opposite effects on the earnings and employment rates of homeowners with high liquid asset holdings. Consistent with models of job search with risk aversion, the option to borrow against housing equity allows liquidity-constrained individuals to search for high-wage jobs. The results imply that relaxing household liquidity constraints during recessions could potentially increase earnings and output in the longer run through labor market search.
3. "Artificial Intelligence, Firm Growth, and Industry Concentration." With Tania Babina, Anastassia Fedyk, and James Hodson. September 2020.
Abstract: Which firms invest in artificial intelligence (AI) technologies, and how do firms change after investing in AI? We provide a comprehensive picture of the use of AI technologies and its impact among US firms over the last decade, using a unique combination of job postings and individual-level employment profiles. We introduce a new measure of AI investments based on human capital and document that larger firms with higher sales, markups, and cash reserves tend to invest in AI more aggressively. Firms that invest more in AI experience faster growth in both sales and employment. The positive effect is concentrated among the largest firms, leading to a positive correlation between AI investments and industry concentration. However, increases in concentration are not accompanied by increased markups. AI enables the most productive firms to expand more, but is not associated with further increases in productivity. Our results are robust to instrumenting firm-level AI investments with local variation in industry-level AI investments, and we document consistent patterns across measures of AI using firms' demand for AI talent (job postings) and actual AI talent (resumes). Our findings support the view that new technologies such as AI increase the efficient scale of firms and contribute to the rise of superstar firms.
Abstract: Universities are an important source of new knowledge. U.S. universities have traditionally relied on federal government funding, but since 2000 the federal share has declined while the private industry share has increased. This paper offers the first causal comparison of federal and private university research funding, focusing on patenting and researcher career outcomes. We begin with unique data on grants from 22 universities, which include individual-level payments for everyone employed on all grants for each university-year. We combine this with patent and Census data, including national IRS W-2 histories. We instrument for an individual’s source of funding with government-wide R&D expenditure shocks within a narrow field of study. These funding supply changes yield a set of compliers who are pushed away from federal funding and into private funding. We find that a higher share of federal funding causes fewer but more general patents, much more high-tech entrepreneurship, a higher likelihood of remaining employed in academia, and a lower likelihood of joining an incumbent firm. Increasing the private share of funding has opposite effects for most outcomes. It appears that private funding leads to greater appropriation of intellectual property by incumbent firms.
5. "A Theory of Intermediated Investment with Hyperbolic Discounting Investors." With Feng Gao and Ping He, Journal of Economic Theory, 2018.
Abstract: Financial intermediaries may reduce welfare losses caused by hyperbolic discounting investors, who may liquidate their investment prematurely when the liquidation cost is low. In a competitive equilibrium, sophisticated investors are offered contracts with perfect commitment, and first best results are achieved; naïve investors are attracted by contracts that offer seemingly attractive returns in the long run but introduce discontinuous penalties for early withdrawal. If the investor types are private information, naïve investors withdraw early and cross-subsidize sophisticated investors. When a secondary market for long-term contracts opens for trading, financial intermediaries are compelled to offer contracts that have more flexible withdrawal options with linear schemes, and the welfare of naïve investors is improved. Arbitrage-free linear contracts allow for a unique term structure for interest rates that includes a premium for naïveté. Solvency requirements may limit competition for contracts and result in positive profits; banks that have capital are able to compete more aggressively, which improves investor welfare.
6. "Complementarity and Advantage in Competing Auctions of Skills." With John Kennes and Daniel le Maire. November 2018.
Abstract: We use a directed search model to develop estimation procedures for the identi cation of worker and rm rankings from labor market data. These methods allow for a general speci cation of production complementarities and the possibility that higher ranked workers are not more productive in all rms. We also o er conditions for a positive/negative assortative matching that incorporate the possibility of a stochastic job ladder with on-the-job search. Numerical simulations relate the implications of the model to the implications of xed e ect regressions and give further insights into the performance of our estimation procedures. Finally, we evaluate evidence for Denmark using our methods and we show that workers are highly sorted and that higher type workers are less productive than lower type workers while employed in lower type jobs.
Abstract: In this paper, I build a measure of technological distance between firms using the citation-based innovation network, which incorporates knowledge spillovers from upstream technological fields to downstream technological fields. I then use this measure to estimate the impact of technology spillovers using panel data on U.S. firms. I find that spillovers from firms innovating in upstream fields are quantitatively as important as spillovers from firms innovating in same fields. Consistent with the idea that firms innovate more when there is more past upstream innovation to build on, firms’ R&D investments respond positively to R&D investments of firms in upstream fields, but not to R&D investments of firms in downstream fields or in the same fields. Smaller firms on average operate in more upstream technological fields and generate more spillovers and higher social returns, which is contrary to the findings of previous research.