Productivity Dispersion and Measurement Error

• Author: Terje Skjerpen,Thomas von Brasch,Diana‐Cristina Iancu
• DOI: http://doi.org/10.1111/roiw.12455
• Published date: 01 December 2020
• Date: 01 December 2020

© 2019 International Association for Research in Income and Wealth

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PRODUCTIVITY DISPERSION AND MEASUREMENT ERROR

by Thomas von brasch, Diana-crisTina iancu and Terje skjerpen*

Statistics Norway

Several reasons have been put forward to explain the high dispersion of productivity across estab-

lishments: quality of management, different input usage and market distortions, to name but a few.

Although it is acknowledged that a sizable portion of productivity dispersion may also be due to meas-

urement error, little research has been devoted to identifying how much they contribute. We outline a

novel procedure for identifying the role of measurement error in explaining the empirical dispersion of

productivity across establishments. The starting point of our framework is the errors-in-variable model

consisting of a measurement equation and a structural equation for latent productivity. We estimate the

variance of the measurement error and subsequently estimate the variance of the latent productivity

variable, which is not contaminated by measurement error. Using Norwegian data on the manufacture

of food products, we find that about one percent of the measured dispersion stems from measurement

error.

JEL Codes: C23, J24, L11

Keywords: establishment performance, labor productivity, measurement errors, productivity dispersion

1. inTroDucTion

It is widely accepted that the dispersion of productivity across establishments

and industries is high. Dispersion is commonly measured by means of the standard

deviation across establishments, where the productivity of each establishment is

measured relative to a reference point, such as the mean productivity level at a

given point in time. Using this procedure, it is typically found that the standard

deviation across establishments is large and lies in the range of 30 to 100 percent;

see Bartelsman and Wolf (2018).

Several reasons have been put forward to explain this high productivity dis-

persion: noisy selection (Jovanovic, 1982), sunk cost of entry (Hopenhayn, 1992),

quality of management (Bloom and Van Reenen, 2010), different input usage, as

the intensity of R&D or other intangible capital (Crepon et al., 1998), product sub-

stitutability (Syverson, 2004), product market rivalry (Bloom et al., 2013), market

distortions (Hsieh and Klenow, 2009), skill-biased technical change and technolog-

ical adoption (Dunne et al., 2004) and innovation dynamics (Foster et al., 2018), to

name but a few. Although it is acknowledged that the high productivity dispersion

may also be due to measurement error, little research has been devoted to identify-

ing how much they contribute.

Note: We are grateful for helpful comments from two anonymous referees and the editor.

Correspondence to Terje Skjerpen, Statistisk sentralbyrå, Forskningsavdelingen, PO Box 2633 St.

Hanshaugen, NO-0131 Oslo, Norway. (terje.skjerpen@ssb.no)

Review of Income and Wealth

Series 66, Number 4, December 2020

DOI: 10.1111/roiw.12455

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