Photo depicting a construction worker building along a construction rail.
Features

Weekend Long Reads: Deconstructing Productivity

by Kevin Schofield

This weekend’s “long read” is an article published by Noah Smith, looking at the productivity of construction workers and how it’s measured.

The conventional wisdom is that for decades construction productivity has been in a slow decline, in sharp contrast to other intensive industries such as agriculture and manufacturing. This graph from the Economist sums up the picture well:

Chart depicting the gross value-added at constant prices per hour worked in the United States with a bright blue line representing agriculture industry, a yellow line representing wholesale and retail, a blue line representing manufacturing, a red line representing construction, and a dark blue line representing overall. All the lines demonstrate an increase, except for the red Construction line which decreases over time. The agriculture line represents the highest increase.
Chart depicting the gross value-added at constant prices per hour worked in the United States. Data sourced from McKinsey Global Institute, graph created by the Economist.

Smith points out that there are two problems with the conventional wisdom. First, it lumps all construction together; once you disaggregate it, there are some significant differences in productivity for different types of construction. For example, industrial construction productivity has increased, while transportation construction productivity has declined.

Chart depciting the index of labor productivity in highways and industrial construction, including subcontractor hours, from 2002 to 2016 with a dark blue line representing industrial building construction and a dark red line representing highways, streets, and bridges construction.
Chart depciting the index of labor productivity in highways and industrial construction, including subcontractor hours, from 2002 to 2016. Data sourced from the U.S. Bureau of Labor Statistics.

Similarly, multi-family home construction has become much more productive than single-family homes, suggesting that some kinds of buildings lend themselves more to the kinds of innovation and technological improvements that make workers more productive.

Chart depciting the index of labor productivity in single-family and multifamily new housing construction, including subcontractor hours, from 1987 to 2016 with the dark blue line representing single-family new housing construction and the red line representing multifamily new housing construction.
Chart depciting the index of labor productivity in single-family and multifamily new housing construction, including subcontractor hours, from 1987 to 2016. Data sourced from the U.S. Bureau of Labor Statistics.

Smith argues that part of the reason why construction productivity as a whole has languished for so long is that over time there has been a shift in the mix of construction projects in the U.S. toward more single-family homes and transportation projects, some of the least productive types of construction. But Smith also raises a second problem: He casts doubt on the way we measure productivity in construction, or more specifically the accuracy of the underlying data. He rightly points out that worker productivity doesn’t suddenly spike over the course of a few years and then regress just as quickly, so the jagged edges in the graphs above are strong arguments that the underlying data is unreliable. 

Smith’s article is a good reminder that it’s usually a good idea to reserve a fair amount of skepticism when confronted by the conventional wisdom — and that not all data is created equal.

What happened to construction productivity?

Kevin Schofield is a freelance writer and the founder of Seattle City Council Insight, a website providing independent news and analysis of the Seattle City Council and City Hall. He also co-hosts the “Seattle News, Views and Brews” podcast with Brian Callanan, and appears from time to time on Converge Media and KUOW’s Week in Review.

📸 Featured image is attributed to J J (under a Creative Commons, CC BY-NC-ND 2.0 license).

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