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Abstract The paper considers formally the mapping from distortions in the allocations of resources across firms to aggregate productivity. TFP gaps are characterized as the integral of a strictly concave function with respect to an employment-weighted measure of distortions. Size related distortions are shown to correspond to a mean preserving spread of this measure, explaining the stronger effects on TFP found in the literature. In general, the effect of correlation between distortions and productivity is shown to be ambiguous; conditions are given to determine its sign. An empirical lower bound on distortions based on size distribution of firms is derived and analyzed, revealing that substantial rank reversals in firm size are necessary for distortions to explain large TFP gaps. The effect of curvature on the impact and measurement of distortions is also considered.
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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.
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