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Can a Narrow Focus on Labor Productivity Block the Big Manufacturing Picture?

Manufacturers understand the impact of capital investment on labor productivity, and how science and engineering continuously improve the technologies in which they are investing. Economists, however, seem to lack an effective way to measure and understand these dynamics, or at least struggle to explain the meaning of productivity to the public.

The Interpretation Problem


It’s the Bureau of Labor Statistics (BLS) labor productivity report that always hits the business news, but because it does not include the effects of capital on productivity, people tend to think higher productivity and global competitiveness are driven solely by the workforce.


To see how machine tools, automation, and manufacturing knowledge increase labor productivity, you have to turn to the “multifactor” productivity report also produced by the BLS. Unfortunately, it is complicated. It uses terms like “capital intensity” or “intellectual property” indicators that don’t resonate with many people.


Even if the multifactor report better explains productivity than the labor productivity report, economists aren’t satisfied that it really measures or analyzes the right things.


A Better Solution for Measuring Labor Productivity


The Manufacturers’ Alliance for Productivity and Innovation (MAPI) Foundation set out to do better but faced some obstacles. The analysts started with the current BLS measures of labor productivity. They also used BLS data on capital investment per worker — capital intensity. Innovation is another factor they included since it’s a driver of productivity, but because it’s hard to measure, patents were selected as an approximation.


Having an educated workforce is also associated with productivity, but there isn’t an ideal metric for this either. The analysts settled on the number of people with college degrees as a proportion of the workforce. They wanted to measure cross-industry impacts, such as that of the computer industry on all others, and came up with some interesting correlations.


Overall, their new analysis supported the original hypothesis:


Innovation and capital investment drive productivity in manufacturing. The supply of educated labor affects the labor productivity share of multifactor productivity growth. Furthermore, productivity changes in some industry subsectors affect improvement in others. And ultimately, multiple factors have dynamics that affect each other.


From their analysis, the authors recommended a three-pronged approach to productivity growth, rather than a focus on one single factor.


  1. The development of regional industry clusters for these advantages: logistics between OEMs and tiers of suppliers, shared resources like research, and a workforce with industry specific skills.
  2. The support of the above industry clusters from new and revitalized regional R&D centers with a technology transfer mission.
  3. The integration of industry-relevant educational programs in schools to supply the educated workforce.


The analysts additionally encourage continued study of these productivity factors and an improved body of data. The author said they could not look at all factors that need to be considered if productivity growth is to be really understood. Economists need to continue to look for other drivers, starting with the effect of management behavior on productivity.


It is also important to understand that economists are not the only people who need to look more closely at multifactor productivity. Policymakers need to use it to make decisions about investment in R&D, resource development, and education. The public also needs a more comprehensible view of multifactor productivity dynamics. The business press could play a big role in clarifying it for them.


Ultimately, when people can see that many factors contribute to higher productivity and global competitiveness, they can support investment and policy that more surely benefit manufacturing.


Karen Wilhelm has worked in the manufacturing industry for 25 years, and blogs at Lean Reflections, which has been named as one of the top ten lean blogs on the web.

    Some opinions expressed in this article may be those of a contributing author and not necessarily Gray.

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