Economic productivity is important. Do you actually know what it means?

With labor productivity growth slowing down, let’s go back to the basics

Amanda Silver
Jan 12, 2020 · 6 min read
Image from Bureau of Labor Statistics

Whether you ascribe to the idea that we’re in the Second Machine Age or entering the Fourth Industrial Revolution, you cannot deny that the world around us is changing at a dizzying pace. Just looking at the products demoed at last week’s Consumer Electronics Show (CES), you’ll learn about AI enabled mirrors or wearable brainwave devices that can improve your focus. So with all this new technology, why is economic productivity actually slowing down?

Before we can begin to answer that question, we need to abandon all of our assumptions around the concept of productivity. By learning more about what productivity actually means and how it is measured, we might have a chance of starting to understand the larger social questions.

What is productivity?

The inputs, or factors of production, in this case could be the hours worked, raw materials used, and machines and equipment needed in the production process.

This is distinct from efficiency, which is concerned with increasing yield by reducing waste. You can think of efficient production as being an ingredient in productivity. They are certainly related concepts, but productivity is the one we are more concerned about when trying to understand how an economy is performing over time.

How we measure productivity

productivity measures of important economic magnitudes arose in the face of a theoretical tradition which denied them any relevance to economic structure or policy. (47)

So even if we have made progress since Sigler wrote those words, we must remember that the variables we select and assumptions we make will color our results, and therefore should be considered before we draw any conclusions around our findings.

Productivity measurement methods are either partial, meaning they look at one input factor, or multifactor (MFP), meaning they look at an aggregate of several factors. The differences are well outlined in Measuring Productivity, the manual created by OECD, which is used as the established standards across nations.

I encourage you to take a closer look at Measuring Productivity, which elaborates on the strengths and weaknesses of each measurement method

Within each productivity measure, analysts might be using either gross or value added methods. Paul Schreyer, the author of Measuring Productivity differentiates the two by noting:

When purchases of intermediate inputs are deducted from gross output, one obtains a measure of value added. In this sense, value added is a net measure. (24)

In other words, gross measures includes intermediate inputs (things like materials and capital) while value added subtracts them out. I highlight this because studies have found that the choice of method can result in a notable difference in the results at the industry level.

Understanding two common measures

  1. Average Labor Productivity (ALP)

Labor productivity is our attempt to understand the output of each person in the workforce. It is an incredibly valuable measurement because it is associated with rising in living standards. As workers produce more, this can increase their earnings, and the increased availability of goods can also lead to more affordable prices.

Can you believe we’ve gotten this far without mentioning gross domestic product? Well, a country’s GDP is the market value of everything it produces during a given period of time. Look back to our blue jeans example, and you might notice that GDP can correspond to output in the productivity equation. In one way of measuring labor productivity, we take GDP and divide by the number of hours worked. Occasionally the number of persons employed is used instead.

Actual economic models are much more complicated than the graphic above, but it should give you a sense of what we mean by workforce productivity

We can quickly note two limitations with this method: 1) GDP doesn’t do a very good job of capturing output in service and knowledge industries, as well as capturing the quality of output and 2) hours worked does not necessarily capture the quality of labor or skill composition of the workforce.

2. Total Factor Productivity (TFP)

Next we’ll look at Total Factor Productivity, which economist Moses Abramovitz referred to as, “the measure of our ignorance.” In the simple macroeconomic model below, you use the measured output (Y) and the function of inputs like capital (K) and labor (L), to get residual A, your TFP.

Economists typically weigh capital by 0.3 and labor by 0.7

Also referred to as the Solow residual, after Nobel Laureate Robert Solow, this metric is said to capture the influence of technology and efficient production that cannot be explained by the other inputs. In other words, if you can use the same amount of capital and labor as another country, but have higher output, then we interpret this as having a more efficient and innovative way of combining those inputs.

Two notable limitations with this method are 1) R&D and organizational capital are important inputs, but as noted by the OECD Future of Productivity report, accurate measurement of these factors is, “still a work in progress” 2) measurement error with capital or labor will end up embodied in the TFP metric.

Exploring productivity issues

There’s much more to learn when it comes to issues of economic productivity, far beyond the scope of this article. But with this newfound appreciation for the definition of productivity and the limitations in measurement, I invite you to continue exploring the following relevant topics:

The productivity paradox

  • The productivity paradox: why we’re getting more innovation but less growth (Vox, 2016)
  • The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox (David, 2010)

Decoupling of productivity and wages

Measurement issues with productivity

As the world around us continues to change, we must be prudent about understanding the way that we are measuring that change. Going back to the basics helps us build a more critical analysis, so I hope this article has helped you move forward with your learning with a more nuanced lens.

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Amanda Silver

Written by

Obsessed with people & process; advocate for labor dignity, voice & ownership. Subscribe to Workable for monthly news on changing work: https://bit.ly/2LAonT2

The Startup

Get smarter at building your thing. Follow to join The Startup’s +8 million monthly readers & +794K followers.

Amanda Silver

Written by

Obsessed with people & process; advocate for labor dignity, voice & ownership. Subscribe to Workable for monthly news on changing work: https://bit.ly/2LAonT2

The Startup

Get smarter at building your thing. Follow to join The Startup’s +8 million monthly readers & +794K followers.

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