Misallocation
‘How costly are markups?,’ Midrigan, V. (with D. Yu), (2019).
We study the welfare costs of markups in a dynamic model with heterogeneous firms and endogenously variable markups. We find that the welfare costs of markups are large. We decompose the costs of markups into three channels: (i) an aggregate markup that acts like a uniform output tax, (ii) misallocation of factors of production, and (iii) an inefficiently low rate of entry. We find that the aggregate markup accounts for about three-quarters of the costs, misallocation accounts for about one-quarter, and the costs due to inefficient entry are negligible. We evaluate simple policies aimed at reducing the costs of markups. Subsidizing entry is not an effective tool in our model: while more competition reduces individual firms’ markups it also reallocates market shares towards larger firms and the net effect is that the aggregate markup hardly changes. Size-dependent policies aimed at reducing concentration can reduce the aggregate markup but have the side-effect of greatly increasing misallocation and reducing aggregate productivity.
‘Accounting for Plant-Level Misallocation,’ Midrigan, V. (with D. Yu), (2009).
We use panel data for Korean Manufacturing plants to document substantial dispersion in the average product of capital, three times greater than dispersion in the average product of other factors. If one interprets this as evidence of misallocation (dispersion in the marginal product of capital), aggregate productivity losses are substantial, about 40 percent: We evaluate the ability of a model of industry dynamics in which firms face non-convex capital adjustment costs, financing frictions, and uninsurable investment risk to account for the dispersion in the marginal product of capital. We show that the frictions necessary to reconcile the model’s predictions with the data are large and account for the bulk of within-plant time-series variance in the average product of capital. They are incapable, however, of sustaining the large and persistent differences in the marginal product of capital in the cross-section and thus account for a small fraction (less than 10%) of the misallocation in the data.
‘Measuring Cross-Country Differences in Misallocation,’ Rotemberg, M. (with K. White), (2017).
We describe differences between the commonly used cleaned version of the U.S. Census of Manufactures and what establishments themselves report. Following the methodology of Hsieh and Klenow (2009), we show that several editing strategies, including industry analysts’ manual edits, dramatically lower measured losses in the U.S. data: from around 371% in the collected data to 62% in the Census-cleaned data. Many of these types of edits are infeasible in non-U.S. datasets. We therefore reanalyze the iconic Hsieh and Klenow (2009) result using common data cleaning strategies for the U.S. and for India: a standard trimming-outliers approach and a new Bayesian approach for editing and imputation. Under both methods, there is little evidence that measured misallocation is significantly higher in India than in the United States.
‘Are We Undercounting Reallocation’s Contribution to Growth?,’ Rotemberg, M. (with M.Nishida, A. Petrin, and K. White), (2015).
There has been a strong surge in aggregate productivity growth in India since 1990, following significant economic reforms. Three recent studies have used two distinct methodologies to decompose the sources of growth, and all conclude that it has been driven by within-plant increases in technical efficiency and not between-plant reallocation of inputs. Given the nature of the reforms, where many barriers to input reallocation were removed, this finding has surprised researchers and been dubbed “India’s Mysterious Manufacturing Miracle.” In this paper, we show that the methodologies used may artificially understate the extent of reallocation. One approach, using growth in value added, counts all reallocation growth arising from the movement of intermediate inputs as technical efficiency growth. The second approach, using the OlleyPakes decomposition, uses estimates of plant-level total factor productivity (TFP) as a proxy for the marginal product of inputs. However, in equilibrium, TFP and the marginal product of inputs are unrelated. Using microdata on manufacturing from five countries – India, the U.S., Chile, Colombia, and Slovenia – we show that both approaches significantly understate the true role of reallocation in economic growth. In particular, reallocation of materials is responsible for over half of aggregate Indian manufacturing productivity growth since 2000, substantially larger than either the contribution of primary inputs or the change in the covariance of productivity and size.