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Is the United States Entering a Period of Higher Growth in Labor Productivity?

Image generated by GTP-4o illustrating labor productivity

Several articles in the business press have discussed the recent increases in labor productivity. For instance, this article appeared in this morning’s Wall Street Journal (a subscription may be required).

The most widely used measure of labor productivity is output per hour of work in the nonfarm business sector. The BLS calculates output in the nonfarm business sector by subtracting from GDP production in the agricultural, government, and nonprofit sectors. (The definitions used by the Bureau of Labor Statistics (BLS) in estimating labor productivity are discussed in the “Technical Notes” that appear at the end of the BLS’s quarterly “Productivity and Costs” releases.) The blue line in the following figure shows the annual growth rate in labor productivity in the nonfarm business sector as measured by the percentage change from the same quarter in the previous year. The green line shows labor productivity growth in manufacturing.

As the figure shows, both labor productivity growth in the nonfarm business sector and labor productivity growth in manufacturing are volatile. The business press has focused on the growth of productivity in the nonfarm business sector during the period from the third quarter of 2023 through the third quarter of 2024. During this time, labor productivity has grown at an average annual rate of 2.5 percent. That growth rate is notably higher than the growth rate that many economists are expecting over the next 10 years. For instance, the Congressional Budget Office (CBO) has forecast that labor productivity will grow at an average annual rate of only 1.6 percent over the period from 2025 to 2034.

The CBO forecasts that the total numbers of hours worked in the economy will grow at an average annual rate of 0.5 percent. Combining that estimate with a 2.5 percent annual rate of growth of labor productivity results in output per person—a measure of the standard of living—increasing by 34 percent by 2034. If labor productivity increases at a rate of only 1.6 percent, then output per person will have increased by only 23 percent by 2034.

The standard of living of the average person in United States increasing 11 percent more would make a noticeable difference in people’s lives by allowing them to consume and save more. Higher rates of labor productivity growth leading to a faster growth rate of income and output would also increase the federal government’s tax revenues, helping to decrease federal budget deficits that are currently forecast to be historically large. (We discuss the components of long-run economic growth in Macroeconomics, Chapter 16, Section 16.7; Economics, Chapter 26, Section 26.7, and the economics of long-run growth in Macroeconomics, Chapter 11; Economics, Chapter 21.)

Can the recent growth rates in labor productivity be maintained over the next 10 years? There is an historical precedent. Labor productivity in the nonfarm business sector grew at an average annual rate of 2.6 percent between 1950 and 1973. But growth rates that high have proven difficult to achieve in more recent years. For instance, from 2008 to 2023, labor productivity grew at an average annual rate of only 1.5 percent. (We discuss the debate over future growth rates in Macroeconomics, Chapter 11, Section 11.3; Economics, Chapter 21, Section 21.3.)

The Wall Street Journal article we cited earlier provides an overview of some of the factors that may account for the recent increase in labor productivity growth rates. The 2020 Covid pandemic may have led to some increases in labor productivity. Workers who temporarily or permanently lost their jobs as businesses closed during the height of the pandemic may have found new jobs that better matched their skills, making them more productive. Similarly, businesses that were forced to operate with fewer workers, may have found ways to restore their previous levels of output with lower levels of employment. These changes may have led to one-time increases in labor productivity at some firms, but are unlikely to result in increased rates of labor productivity growth in the future.

Some businesses have used newly available generative artificial intelligence (AI) software to increase labor productivity by, for instance, using software to replace workers who previously produced marketing materials or responded to customer questions or complaints. It will take at least several years before generative AI software spreads throughout the economy, so it seems too early for it to have had a broad enough effect on the economy to be visible in the productivity data.

Note also that, as the green line in the figure above shows, manufacturing productivity has been lagging recently. From the third quarter of 2023 to the third quarter of 2024, labor productivity in manufacturing has increased at an annual average rate of only 0.4 percent. This slowdown is surprising given that over the long run productivity in manufacturing has typically increased faster than has productivity in the overall economy. It seems unlikely that labor productivity in the overall economy can sustain its recent growth rates if labor productivity growth in manufacturing continues to lag.

Finally, the productivity data are subject to revision as better estimates of output and of hours worked become available. It’s possible that what appear to be rapid rates of productivity growth during the last five quarters may turn out to have been less rapid following data revisions.

So, while the recent increase in the growth rate of labor productivity is an encouraging sign of the strength of the U.S. economy, it’s too soon to tell whether we have entered a sustained period of higher productivity growth.

Will the United States Experience a Sustained Boom in the Growth Rate of Labor Productivity?

Blue Planet Studio/Shutterstock

Recent articles in the business press have discussed the possibility that the U.S. economy is entering a period of higher growth in labor productivity:

“Fed’s Goolsbee Says Strong Hiring Hints at Productivity Growth Burst” (link)

“US Productivity Is on the Upswing Again. Will AI Supercharge It?” (link)

“Can America Turn a Productivity Boomlet Into a Boom?” (link)

In Macroeconomics, Chapter 16, Section 16.7 (Economics, Chapter 26, Section 26.7), we highlighted the role of growth in labor productivity in explaining the growth rate of real GDP using the following equations. First, an identity:

Real GDP = Number of hours worked x (Real GDP/Number of hours worked),

where (Real GDP/Number of hours worked) is labor productivity.

And because an equation in which variables are multiplied together is equal to an equation in which the growth rates of these variables are added together, we have:

Growth rate of real GDP = Growth rate of hours worked + Growth rate of labor productivity

From 1950 to 2023, real GDP grew at annual average rate of 3.1 percent. In recent years, real GDP has been growing more slowly. For example, it grew at a rate of only 2.0 percent from 2000 to 2023. In February 2024, the Congressional Budget Office (CBO) forecasts that real GDP would grow at 2.0 percent from 2024 to 2034. Although the difference between a growth rate of 3.1 percent and a growth rate of 2.0 percent may seem small, if real GDP were to return to growing at 3.1 percent per year, it would be $3.3 trillion larger in 2034 than if it grows at 2.0 percent per year. The additional $3.3 trillion in real GDP would result in higher incomes for U.S. residents and would make it easier for the federal government to reduce the size of the federal budget deficit and to better fund programs such as Social Security and Medicare. (We discuss the issues concerning the federal government’s budget deficit in this earlier blog post.)

Why has growth in real GDP slowed from a 3.1 percent rate to a 2.0 percent rate? The two expressions on the right-hand side of the equation for growth in real GDP—the growth in hours worked and the growth in labor productivity—have both slowed. Slowing population growth and a decline in the average number of hours worked per worker have resulted in the growth rate of hours worked to slow substantially from a rate of 2.0 percent per year from 1950 to 2023 to a forecast rate of only 0.4 percent per year from 2024 to 2034.

Falling birthrates explains most of the decline in population growth. Although lower birthrates have been partially offset by higher levels of immigration in recent years, it seems unlikely that birthrates will increase much even in the long run and levels of immigration also seem unlikely to increase substantially in the future. Therefore, for the growth rate of real GDP to increase significantly requires increases in the rate of growth of labor productivity.

The Bureau of Labor Statistics (BLS) publishes quarterly data on labor productivity. (Note that the BLS series is for labor productivity in the nonfarm business sector rather than for the whole economy. Output of the nonfarm business sector excludes output by government, nonprofit businesses, and households. Over long periods, growth in real GDP per hour worked and growth in real output of the nonfarm business sector per hour worked have similar trends.) The following figure is taken from the BLS report “Productivty and Costs,” which was released on February 1, 2024.

Note that the growth in labor productivity increased during the last three quarters of 2023, whether we measure the growth rate as the percentage change from the same quarter in the previous year or as growth in a particular quarter expressed as anual rate. It’s this increase in labor productivity during 2023 that has led to speculation that labor productivity might be entering a period of higher growth. The following figure shows labor productivity growth, measured as the percentage change from the same quarter in the previous year for the whole period from 1950 to 2023.

The figure indicates that labor productivity has fluctuated substantially over this period. We can note, in particular, productivity growth during two periods: First, from 2011 to 2018, labor productivity grew at the very slow rate of 0.9 percent per year. Some of this slowdown reflected the slow recovery of the U.S. economy from the Great Recession of 2007-2009, but the slowdown persisted long enough to cause concern that the U.S. economy might be entering a period of stagnation or very slow growth.

Second, from 2019 through 2023, labor productivity went through very large swings. Labor productivity experienced strong growth during 2019, then, as the Covid-19 pandemic began affecting the U.S. economy, labor productivity soared through the first half of 2021 before declining for five consecutive quarters from the first quarter of 2022 through the first quarter of 2023—the first time productivity had fallen for that long a period since the BLS first began collecting the data. Although these swings were particularly large, the figure shows that during and in the immediate aftermath of recessions labor productivity typically fluctuates dramatically. The reason for the fluctuations is that firms can be slow to lay workers off at the beginning of a recession—which causes labor productivity to fall—and slow to hire workers back during the beginning of an economy recovery—which causes labor productivity to rise.

Does the recent increase in labor productivity growth represent a trend? Labor productivity, measured as the percentage change since the same quarter in the previous year, was 2.7 percent during the fourth quarter of 2023—higher than in any quarter since the first quarter of 2021. Measured as the percentage change from the previous quarter at an annual rate, labor productivity grew at a very high average rate of 3.9 during the last three quarters of 2023. It’s this high rate that some observers are pointing to when they wonder whether growth in labor productivity is on an upward trend.

As with any other economic data, you should use caution in interpreting changes in labor productivity over a short period. The productivity data may be subject to large revisions as the two underlying series—real output and hours worked—are revised in coming months. In addition, it’s not clear why the growth rate of labor productivity would be increasing in the long run. The most common reasons advanced are: 1) the productivity gains from the increase in the number of people working from home since the pandemic, 2) businesses’ increased use of artificial intelligence (AI), and 3) potential efficiencies that businesses discovered as they were forced to operate with a shortage of workers during and after the pandemic.

To this point it’s difficult to evaluate the long-run effects of any of these factors. Wconomists and business managers haven’t yet reached a consensus on whether working from home increases or decreases productivity. (The debate is summarized in this National Bureau of Economic Research Working Paper, written by Jose Maria Barrero of Instituto Tecnologico Autonomo de Mexico, and Steven Davis and Nicholas Bloom of Stanford. You may need to access the paper through your university library.)

Many economists believe that AI is a general purpose technology (GPT), which means that it may have broad effects throughout the economy. But to this point, AI hasn’t been adopted widely enough to be a plausible cause of an increase in labor productivity. In addition, as Erik Brynjolfsson and Daniel Rock of MIT and Chad Syverson of the University of Chicago argue in this paper, the introduction of a GPT may initially cause productivity to fall as firms attempt to use an unfamiliar technology. The third reason—efficiency gains resulting from the pandemic—is to this point mainly anecdotal. There are many cases of businesses that discovered efficiencies during and immediately after Covid as they struggled to operate with a smaller workforce, but we don’t yet know whether these cases are sufficiently common to have had a noticeable effect on labor productivity.

So, we’re left with the conclusion that if the high labor productivity growth rates of 2023 can be maintained, the growth rate of real GDP will correspondingly increase more than most economists are expecting. But it’s too early to know whether recent high rates of labor productivty growth are sustainable.

The Effect on a Firm’s Costs of Using a Generative AI Program

Supports: Microeconomics, Chapter 11, Section 11.5; Economics, Chapter 11, Section 11.5; and Essentials of Economics, Chapter 8, Section 8.5

Photo from the Wall Street Journal.

Imani owns a firm that sells payroll services to companies in the Atlanta area. Her largest cost is for labor. She employs workers who use software to prepare payroll reports and to handle texts and calls from client firms. She decides to begin using a generative AI program, like ChatGPT, which is capable of quickly composing thorough answers to many questions and write computer code. She will use the program to write the additional computer code needed to adapt the payroll software to individual client’s needs and to respond to clients seeking advice on payroll questions. Once the AI program is in place, she will need only half as many workers. The number of additional workers she needs to hire for every 20 additional firms that buy her service will fall from 5 to 1. She will have to pay a flat monthly licensing fee for the AI program; the fee will not change with the number of firms she sells her services to. Imani determines that making these changes will reduce her total cost of providing services to her current 2,000 clients from $2,000,000 per month to $1,600,000 per month

In answering the following questions, assume that, apart from the number of workers, none of the other inputs—such as the size of her firm’s office, the number of computers, or other software—change as a result of her leasing the AI program.

a. Briefly explain whether each of the following statements about the cost situation at Imani’s firm after she begins using the AI program is correct or incorrect.

Her firm’s average total cost, average variable cost, and average fixed cost curves will shift down, while her firm’s marginal cost curve will shift up.
Her firm’s average total cost, average variable cost, average fixed cost and marginal cost curves will all shift up.
Her firm’s average total cost, average variable cost, and marginal cost curves will shift down, while her average fixed cost curve will shift up.
Her firm’s average total cost, average variable cost, average fixed cost, and marginal cost curves will all shift down.
Her firm’s average fixed cost curve will shift up, but her other cost curves will be unchanged.

b. Draw a graph illustrating your answer to part a. Be sure to show the original average total cost, average variable cost, average fixed cost, and marginal cost curves. Also show the shifts—if any—in the curves after Imani begins using the AI program.

Solving the Problem

Step 1: Review the chapter material. This problem requires you to understand definitions of costs, so you may want to review the sections “The Difference between Fixed Costs and Variable Costs,” “Marginal Costs,” and “Graphing Cost Curves”

Step 2: Answer part (a) by explaining whether each of the five listed statements is correct or incorrect. The cost of the AI program is fixed because it doesn’t change with the quantity of her services that Imani sells. Her firm will have greater fixed costs after licensing the AI program but she will have lower variable costs because she is able to produce the same level of output with fewer workers. Her marginal cost will also decline because she needs to hire fewer workers as the quantity of services she sells increases. We know that the average total cost per month of providing her service to 2,000 clients has decreased because we are given the information that it changed from ($2,000,000/2,000) = $1,000 to ($1,600,000/2,000) = $800.

This statement is incorrect because her average fixed cost curve will shift up as a result of her total fixed cost having increased by the amount of the AI program license and because her marginal cost curve will shift down, not up.
This statement is incorrect because all of her cost curves, except for average fixed cost, will shift down, not up.
This statement is correct because it describes the actual shifts in her cost curves.
This statement is incorrect because her average fixed cost curve will shift up, not down.
This statement is incorrect because her rather than being unaffected, her average total cost, average variable cost, and marginal cost curves will shift down.

Step 3: Answer part (b) by drawing the cost curves for Imani’s firm before and after she begins using the AI program. Your graph should look like the following, where the curves representing the firm’s costs before Imani begins leasing the AI program are in blue and the costs after leasing the program are in red.