Working Paper: NBER ID: w20340
Authors: Joel M. David; Hugo A. Hopenhayn; Venky Venkateswaran
Abstract: 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.
Keywords: Information Misallocation; Aggregate Productivity; Firm Dynamics
JEL Codes: E44; O11; O16; O47
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Imperfect information (D83) | Misallocation of resources (D61) |
| Misallocation of resources (D61) | Reduced aggregate productivity (O49) |
| Imperfect information (D83) | Reduced aggregate output (E23) |
| Residual uncertainty (D89) | Extent of misallocation (D61) |
| Volatility of fundamental shocks (E32) | Residual uncertainty (D89) |
| Quality of information available to firms (L15) | Residual uncertainty (D89) |
| Informational frictions (D89) | Dispersion in marginal revenue products of capital (D29) |
| Informational frictions (D89) | Productivity losses (J17) |
| Informational frictions (D89) | Output losses (G33) |