Marcos Vera-Hernández
Emla Fitzsimons
José V. Rodríguez Mora
Tomasa Rodrigo
Álvaro Ortiz
Stephen Hansen
Vasco Carvalho
Gergely Buda
Gabriel Zucman
Anders Jensen
Matthew Fisher-Post
José-Alberto Guerra
Myra Mohnen
Christopher Timmins
Ignacio Sarmiento-Barbieri
Peter Christensen
Linda Wu
Gaurav Khatri
Julián Costas-Fernández
Eleonora Patacchini
Jorgen Harris
Marco Battaglini
Ricardo Fernholz
Alberto Bisin
Jess Benhabib
Cian Ruane
Pete Klenow
Mark Bils
Peter Hull
Will Dobbie
David Arnold
Eric Zwick
Owen Zidar
Matt Smith
Ansgar Walther
Tarun Ramadorai
Paul Goldsmith-Pinkham
Andreas Fuster
Ellora Derenoncourt
Golvine de Rochambeau
Vinayak Iyer
Jonas Hjort
Elena Simintzi
Paige Ouimet
Holger Mueller
Pablo Garriga
Gabriel Ulyssea
Costas Meghir
Pinelopi Koujianou Goldberg
Rafael Dix-Carneiro
Alessandro Toppeta
Áureo de Paula
Orazio Attanasio
Seth Zimmerman
Joseph Price
Valerie Michelman
Camille Semelet
Anne Brockmeyer
Pierre Bachas
Santiago Pérez
Elisa Jácome
Leah Boustan
Ran Abramitzky
Jesse Rothstein
Jeffrey T. Denning
Sandra Black
Wei Cui
Mathieu Leduc
Philippe Jehiel
Shivam Gujral
Suraj Sridhar
Attila Lindner
Arindrajit Dube
Pascual Restrepo
Łukasz Rachel
Benjamin Moll
Kirill Borusyak
Michael McMahon
Frederic Malherbe
Gabor Pinter
Angus Foulis
Saleem Bahaj
Stone Centre
Phil Thornton
James Baggaley
Xavier Jaravel
Richard Blundell
Parama Chaudhury
Dani Rodrik
Alan Olivi
Vincent Sterk
Davide Melcangi
Enrico Miglino
Fabian Kosse
Daniel Wilhelm
Azeem M. Shaikh
Joseph Romano
Magne Mogstad
Suresh Naidu
Ilyana Kuziemko

Misallocation or mismeasurement?

What is this research about and why did you do it?

A determinant of aggregate productivity differences, both across countries and within countries over time, is how well resources such as capital and labour are allocated across firms. The importance of such resource misallocation for aggregate productivity is often inferred from the dispersion in average revenue products (revenues over inputs) in firm-level data. Measurement error, for example due to omitted or double-counted revenue or inputs, implies that measured gaps in average products do not reflect true gaps in marginal products. This could thereby amplify dispersion in measured average products and lead researchers to overstate the magnitude of misallocation.

How did you answer this question?

We propose a methodology to correct estimates of misallocation for measurement error in revenue and inputs. Our approach exploits how revenue growth is less sensitive to input growth when a plant’s average products are overstated by measurement error. The key assumption is that the measurement errors are orthogonal to (i.e., uncorrelated with) the true marginal products. We apply our methodology to data on Indian manufacturing plants from 1985 to 2013 and U.S. Census data on manufacturing plants from 1978 to 2013.

Data sources are the Indian ASI and U.S. LRD. The India sub-figure shows uncorrected and corrected allocative efficiency for years 1985 to 2013. Average uncorrected allocative efficiency is 47.7% while average corrected allocative efficiency is 53.4%. The U.S. sub-figure show uncorrected and corrected allocative efficiency for years 1978 to 2013. Average uncorrected allocative efficiency is 47.6% while average corrected allocative efficiency is 67.4%.

What did you find?

We find that our correction lowers the potential gains from reallocation by 20% in India, but that measurement error is even more severe in the U.S. On average, our correction lowers the potential gains from reallocation in the U.S. by 60% between 1978 and 2013. Strikingly, measured revenue productivity dispersion in the U.S. exhibits a sharp upward trend, seemingly implying that misallocation increased dramatically from 1978 to 2013. However, we find that rising measurement error in U.S. plant-level data accounts for most of this trend, and that corrected allocative efficiency declined by considerably less.

What implications does this have for the research on wealth concentration or economic inequality?

Many policy choices involve trade-offs between social goals, such as reducing inequality and economic efficiency. For example, flexible labor laws may make it easier for firms to hire and fire workers but may not be desirable for workers seeking job security. Our methodology shows that the estimated efficiency gains from reallocation can be overstated due to measurement error and provides researchers a set of tools to estimate these more accurately.

What are the next steps in your agenda?

Future research should further decompose dispersion in average products into distortions which result in misallocation and can be addressed by policy, and other factors which don’t reflect true misallocation, such as measurement error or unavoidable adjustment costs or transportation costs.


This paper can be cited as follows: Bils, M., Klenow, P. J., and Ruane, C. (2021) "Misallocation or mismeasurement?" Journal of Monetary Economics, 124 (Supplement), pp. S39-S56.

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