Vaidyanathan (“Venky”) Venkateswaran is an assistant professor of economics at New York University Stern School of Business and joined the Federal Reserve Bank of Minneapolis as a research economist in 2018. Prior to joining NYU Stern, Venky worked as an assistant professor of economics at Pennsylvania State University, and earlier as vice president of fixed income research at Lehman Brothers. He received a Ph.D. in economics from the University of California, Los Angeles, and an M.B.A. from the Indian Institute of Management, Ahmedabad. He is the recipient of several fellowships and awards, including the Departmental Excellence Award at the University of California, Los Angeles, in 2007–8 and 2008–9. His research focuses on the role of information in macroeconomics and finance, and his work has been published in the Journal of Political Economy and the Quarterly Journal of Economics.
The Tail that Keeps the Riskless Rate Low
Screening and Adverse Selection in Frictional Markets
We incorporate a search-theoretic model of imperfect competition into a standard model of asymmetric information with unrestricted contracts. We characterize the unique equilibrium, and use our characterization to explore the interaction between adverse selection, screening, and imperfect competition. We show that the relationship between an agent’s type, the quantity he trades, and the price he pays is jointly determined by the severity of adverse selection and the concentration of market power. Therefore, quantifying the effects of adverse selection requires controlling for market structure. We also show that increasing competition and reducing informational asymmetries can decrease welfare.
We propose a theory linking imperfect information to resource misallocation and hence to aggregate productivity and output. In our setup, firms look to a variety of noisy information sources when making input decisions. We devise a novel empirical strategy that uses a combination of firm-level production and stock market data to pin down the information structure in the economy. Even when only capital is chosen under imperfect information, applying this methodology to data from the United States, China, and India reveals substantial losses in productivity and output due to the informational friction. Our estimates for these losses range from 7% to 10% for productivity and 10% to 14% for output in China and India, and are smaller, though still significant, in the United States. Losses are substantially higher when labor decisions are also made under imperfect information. We find that firms turn primarily to internal sources for information; learning from financial markets contributes little, even in the United States.
Nominal price adjustment is studied in an environment with firm-specific and aggregate shocks to economic fundamentals and incomplete, dispersed information. Firms update their expectations about fundamentals based on their own cash flows (revenues and wages). We show that in a model with realistic levels of product-level price dispersion, the firms’ inference about aggregate shocks is very gradual, yet in the aggregate prices adjust rapidly in response to aggregate nominal shocks. When an aggregate shock occurs, firms mistakenly attribute it to firm-specific shocks, but adjust prices nevertheless, since the exact nature of the shock matters little for their optimal pricing decision.