Information, Misallocation and Aggregate Productivity
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 US, China, and India reveals substantial losses in productivity and output due to the informational friction. Our estimates for these losses range from 7-10% for productivity and 10-14% for output in China and India, and are smaller, though still significant, in the US. 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 US.
We thank Jaroslav Borovicka, Virgiliu Midrigan, Pete Klenow and Laura Veldkamp for their helpful comments, Andy Atkeson, Yongs Shin, Jennifer La'O, Ben Moll and Bernard Dumas for their insightful discussions of earlier versions, Cynthia Yang for excellent research assistance, and many seminar and conference participants. David gratefully acknowledges financial support from the Center for Applied Financial Economics at USC. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Joel M. David & Hugo A. Hopenhayn & Venky Venkateswaran, 2016. "Information, Misallocation, and Aggregate Productivity," The Quarterly Journal of Economics, vol 131(2), pages 943-1005. citation courtesy of