Measuring knowledge intensity in manufacturing industries: a new approach

ABSTRACT

This article presents the methodology and results for measuring the knowledge intensity of manufacturing industries based on the knowledge-related characteristics of their workforce. The measures link information from the Occupational Information Network project (from the US Department of Labor) and the Occupational Employment Statistics (from the Bureau of Labor Statistics) to produce industry-specific knowledge intensity measures. The new measures overcome the main issues posed by the industry-average R&D share in total sales variable, often used as a proxy for knowledge intensity in the literature.

KEYWORDS:

• Tacit knowledge
• knowledge intensity
• R&D
• measurement

JEL CLASSIFICATION:

• J24
• L60
• O32

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

¹ Others in the management and strategy literature have referred to this type of knowledge as ‘sticky information’ (e.g., Von Hippel 1994; Szulanski 1996, 2002).

² Data from 2011, downloadable from ftp://ftp.bls.gov/pub/special.requests/oes/oesm11in4.zip.

³ O*NET is the successor of the US Department of Labor’ s Dictionary of Occupational Titles (DOT). I use the O*NET database version 17, downloadable from http://www.onetcenter.org/download/database?d=db_17_0.zip. Costinot et. al. (2011) also use O*NET to create an industry level measure of task routineness for 77 sectors. Keller and Yeaple (2013) also present results making use of knowledge intensity variables constructed with O*NET in the web appendix.

⁴ It also correlate positively with other less popular measures that could proxy for knowledge intensity or complexity. The correlation coefficient with the share of non-production workers in total employment, from the NBER-CES Manufacturing Industry Database (Becker, Gray, and Marvakov 2013), is 0.68. Similarly, the correlation coefficient with the Product Complexity Index, developed by Hausmann et al. (2014), is 0.49. These correlations were computed using SIC 4 digits codes.

⁵ According to the Skewness/Kurtosis test for normality, we cannot reject the hypothesis that these measures are normally distributed.

⁶ Bahar (2017) uses these measures to show that knowledge transmission plays a role in the proximity-concentration hypothesis. In particular, he shows that knowledge intensive industries are less likely to be replicated abroad by multinational corporations, and when they do, they are often located geographically closer to the headquarters unless they are in the same time zone.