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 learn from both private sources and imperfectly informative stock market prices. We devise a novel calibration strategy that uses a combination of firm-level production and stock market data to pin down the information structure in the economy. Applying this methodology to data from the US, China, and India reveals substantial losses in productivity and output due to informational frictions - even when only one factor, namely capital, is subject to the friction. Our estimates for these losses range from 5-19% for productivity and 8-28% 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. Private learning plays a significant role in mitigating uncertainty and improving aggregate outcomes; learning from financial markets contributes little, even in the US.