Updated October 2022
Most Americans believe that a rising tide should lift all boats—that as the economy expands, everybody should reap the rewards. This outcome can be guaranteed by smart and compassionate policy choices or subverted by policymakers choosing a different path. EPI’s Productivity–Pay Tracker shows the shift toward the latter: Since the late 1970s, our policy choices have led directly to a pronounced divergence between productivity and typical workers’ pay. It doesn’t have to be this way.
Productivity–Pay Tracker
Change 1979–2021:
Productivity
+64.6%
Hourly pay
+17.3%
Productivity has grown 3.7x as much as pay
What is productivity and why did pay and productivity once climb together?
Productivity measures how much total economywide income is generated (i.e., for workers, business owners, landlords, and everybody else together) in an average hour of work. As productivity grows and each hour of work generates more and more income over time, it creates the potential for improving living standards across the board.
In the figure above, pay is defined as the average compensation (wages and benefits) of production and nonsupervisory workers. The pay for this group is one appropriate benchmark for “typical worker pay” because production and nonsupervisory workers have made up roughly 80% of the U.S. workforce over the entire period shown in the figure and because the data for production and nonsupervisory workers exclude extremely highly paid managerial workers like CEOs and other corporate executives. As the figure shows, pay for these workers climbed together with productivity from 1948 until the late 1970s. But that didn’t happen by accident. It happened because specific policies were adopted with the intentional goal of spreading the benefits of growth broadly across income classes. When this intentional policy target was abandoned in the late 1970s and afterward, pay and productivity diverged. Relinking pay and productivity so that workers share in the fruits of their labor will require another pronounced shift in policy.
Throughout history, whether pay for most workers tracked economywide productivity growth depended entirely on policy decisions. In the first 30 years following the end of World War II, for example, specific historical circumstances convinced U.S. policymakers that they had better ensure growth was broadly shared. To achieve this, they instituted a number of policies that spread growth evenly across income classes.
Macroeconomic policymakers targeted “high-pressure” labor markets with sustained low unemployment, the federal minimum wage was increased rapidly and regularly, unionization rights were actively safeguarded by the federal government, top tax rates were high, and regulations barred anti-trust and many other anti-worker efforts by corporations, employers, and the financial services industry.
Over these decades, the pay (wages and benefits) of the vast majority of workers rose in lockstep with economywide productivity. This tight link between hourly pay and productivity was the primary way that typical Americans benefited from economic growth.
Of course, the economy during those decades had serious and terrible flaws. Black workers faced high levels of discrimination in nearly every market they participated in—with particular harm done through discrimination in housing, labor, and financial markets. U.S. immigration policy welcomed migrants from a select group of European countries but generally treated migrants from other countries as nothing but a source of potential cheap labor to be exploited. Women faced high barriers to finding steady and decent work. Policymakers often actively sought to keep the benefits of overall growth from reaching these groups and focused on boosting the prospects only of white men—and these efforts often succeeded.
And yet the broad benefits of class-based policies that led to tight labor markets, high and rising minimum wages, unionization, high tax rates, and pro-worker regulation were so powerful that they spilled over to also greatly benefit even workers who were not white men. For example, even in the face of government-sanctioned, race-based discrimination, the median Black–white earnings gap for men narrowed between the 1940s and 1970s.
What broke the link between pay and productivity?
Starting in the late 1970s policymakers began dismantling all the policy bulwarks helping to ensure that typical workers’ wages grew with productivity. Excess unemployment was tolerated to keep any chance of inflation in check. Raises in the federal minimum wage became smaller and rarer. Labor law failed to keep pace with growing employer hostility toward unions. Tax rates on top incomes were lowered. And anti-worker deregulatory pushes—from the deregulation of the trucking and airline industries to the retreat of anti-trust policy to the dismantling of financial regulations and more—succeeded again and again.
In essence, policy choices made to suppress wage growth prevented potential pay growth fueled by rising productivity from translating into actual pay growth for most workers. The result of this policy shift was the sharp divergence between productivity and typical workers’ pay shown in the graph.
From 1979 to 2020, net productivity rose 61.8%, while the hourly pay of typical workers grew far slower—increasing only 17.5% over four decades (after adjusting for inflation).
A closer look at the trend lines reveals another important piece of information. After 1979, productivity grew at a significantly slower pace relative to previous decades. But because pay growth for typical workers decelerated even more markedly, a large wedge between productivity and pay emerged. The growing gap amid slowing productivity growth tells us that the same set of policies that suppressed pay growth for the vast majority of workers over the last 40 years were also associated with a slowdown in overall economic growth. In short, economic growth became both slower and more radically unequal.
If the fruits of economic growth are not going to workers, where are they going?
The growing wedge between productivity and typical workers’ pay is income going everywhere but the paychecks of the bottom 80% of workers. If it didn’t end up in paychecks of typical workers, where did all the income growth implied by the rising productivity line go? Two places, basically. It went into the salaries of highly paid corporate and professional employees. And it went into higher profits (i.e., toward returns to shareholders and other wealth owners). This concentration of wage income at the top (growing wage inequality) and the shift of income from labor overall and toward capital owners (the loss in labor’s share of income) are two of the key drivers of economic inequality overall since the late 1970s.
How can we fix the problem?
For future productivity gains to lead to robust wage growth and widely shared prosperity, we need to institute policies that firmly connect pay and productivity and build worker power. For a description of many promising policies, see EPI’s Policy Agenda—notably the sections on worker power, good jobs, and full employment. Without policy interventions, economic growth will continue to sputter, and the growth we do see will largely fail to lift typical workers’ wages.
Where can I learn more about the productivity–pay gap and how to close it?
A series of EPI reports over the last several years track wage trends and racial wage gaps and their relation to the productivity–pay disconnect. Two foundational papers explain in detail how we measure the productivity–pay gap and why broad-based wage growth is our central economic challenge.
Identifying the Levers Generating Wage Suppression and Wage Inequality | May 13, 2021
This paper estimates the effect of a range of discrete, identifiable policy changes on the gap between pay and productivity. These policy changes account for a sizable share of the gap.
State of Working America, Wages 2019: A Story of Slow, Uneven, and Unequal Wage Growth Over the Last 40 Years | February 20, 2020
This paper calculates key wage trends and wage gaps over the past 40 years, highlighting brief episodes of wage growth and why they occurred.
Black Workers’ Wages Have Been Harmed by Both Widening Racial Wage Gaps and the Widening Pay–Productivity Gap | October 25, 2016
This paper highlights how much higher Black workers’ wages would be if both racial wage gaps closed and median Black wages kept pace with economywide productivity growth. It finds that closing the productivity–pay gap for Black workers is essential to ensuring that Black workers secure their share of economic growth.
Understanding the Historic Divergence Between Productivity and a Typical Worker’s Pay: Why It Matters and Why It’s Real | September 2, 2015
This paper analyzes the productivity–pay disconnect and the factors behind it, and explains the measurement choices and data sources used to calculate the gap.
Raising America’s Pay: Why It’s Our Central Economic Policy Challenge | June 4, 2014
The paper argues that broad-based wage growth is the key to reversing the rise of income inequality, enhancing social mobility, reducing poverty, boosting middle-class incomes, and aiding asset-building and retirement security.
How to Raise Wages: Policies That Work and Policies That Don’t | March 19, 2015
This paper shows that wage stagnation is not inevitable: It is the direct result of public policy choices on behalf of those with the most power and wealth. Because wage stagnation was caused by policy, it can be alleviated by policy.
How does EPI construct the productivity–pay graph?
EPI makes a series of data choices to construct the indices of productivity and pay in the chart above. Our data choices reflect our end goal: to compare growth in the typical worker’s pay with the potential growth in living standards (consumption) that productivity growth represents.
In brief, we begin with a measure of labor productivity—economywide income divided by total hours worked in the economy. We measure productivity for the broad economy—not just the “nonfarm business sector”—by accessing nonpublic data sources that count outputs from farms, government agencies, and nonprofits. We adjust these calculations for depreciation, and then further for price inflation.
The pay measure starts with the average hourly wage of production and nonsupervisory workers in the private sector, who account for roughly 80% of private-sector workers and thus are a good proxy for the “typical” worker. We adjust this wage for inflation and add inflation-adjusted estimates for nonhealth benefits and health-specific inflation-adjusted estimates for health benefits to arrive at a measure of typical worker pay.
Methodology and data sources
This section describes how EPI constructs the figure tracking the growing gap between overall productivity growth and the pay of the vast majority of workers. Specifically, we detail how the productivity and pay indices being compared are created, and we briefly describe the economic logic and rationale behind the data choices we make. Readers can find a more detailed description of the methodology in our 2015 report, Understanding the Historic Divergence Between Productivity and a Typical Worker’s Pay.
Constructing the ‘effective’ productivity index
To construct our productivity index, we begin with a measure of inflation-adjusted (real) output per labor hour for the total economy, a nonpublished series calculated by the Bureau of Labor Statistics (BLS). Output is equivalent to gross domestic product (GDP), the market value of all final goods and services produced in the U.S.—which is equivalent to total income earned in the U.S. (except for statistical discrepancies).
Accounting for depreciation
To account for the fact that depreciation of the existing capital stock (the plant, equipment, and buildings used to produce goods and services) will reduce future income growth if it’s not replaced, we calculate a measure of net productivity growth that fully replaces capital depreciation. To do this, we multiply the output-per-hour measure from BLS by the ratio of net to gross domestic product. Measures of net and gross domestic product are provided by the National Income and Product Accounts (NIPA) of the Bureau of Economic Analysis (BEA). Both measures can be found in Table 1.7.6.
Estimating how much of productivity growth is ‘effective’ for boosting households’ living standards
We make further adjustments to account for the fact that the measure of net productivity growth obtained by making the calculations above is adjusted for inflation in economic output, not inflation in the goods and services consumed by households. In the jargon, an output deflator is being used for the productivity index. But we want to compare worker pay growth with the potential growth in living standards (consumption) that productivity growth represents, so we need to know how much growth in this productivity index translates into potential growth in consumption. This is achieved by constructing a measure of net productivity that is deflated by the consumer price index (CPI) rather than the output deflator. To make this adjustment, we reverse the effects of the output deflator, calculating a nominal measure of net productivity using the deflator for net domestic product found in Table 1.7.4 from the BEA NIPA. We then deflate this nominal series by the consumer price index research series (CPI-U-RS) calculated by the BLS. The result is an index of productivity growth that is “effective” in boosting households’ consumption possibilities.
Constructing the pay index
We start with a measure of average hourly wages for production and nonsupervisory workers in the private sector (shorthanded as nonsupervisory hereafter). In some contexts we have used median wages to describe pay for “typical” workers, but these are derived from the Current Population Survey (CPS), which only goes back to 1973. Since we want to go back further in time than this to compare pay and productivity, we use the hourly earnings of production and nonsupervisory workers instead. The Bureau of Labor Statistics (BLS) includes only private-sector workers in their calculations of hourly pay for this group. But because production and nonsupervisory workers account for roughly 80% of private-sector workers, and they largely do not include extremely well-paid corporate executives, their wages are a good proxy for what a typical worker earns. Hourly wages for all production and nonsupervisory workers are available from 1964 on. For earlier years, we use the trend in hourly earnings of production and nonsupervisory workers in the goods-producing sectors and assume that the hourly pay of all production and nonsupervisory workers followed that trend before 1964.
Accounting for the benefits that make up part of compensation
To account for health care coverage and other benefits that workers receive in addition to wages and salaries, we use data from Table 7.8 (“Supplements to Wages and Salaries by Type”) from the BEA NIPA and combine it with data from NIPA Table 2.1 (“Personal Income and Its Disposition”). From these two sources, we get a measure of total economywide wages and salaries, employer payments for employee health insurance, and all other nonhealth, nonwage compensation. Both wages and salaries and nonhealth benefits are deflated with CPI-U-RS to get real measures. For employer payments for employee health insurance, we construct a health-specific deflator consistent with the CPI-U-RS. To do this, we use the ratio of the price index for health goods and services from Table 2.3.4 from the BEA NIPA relative to the overall price index for personal consumption expenditures (PCE) from the same table. This ratio gives us a relative measure of how much faster health-related goods and services have risen since 1948 than overall inflation. We multiply this ratio of health-specific prices to overall prices to the CPI-U-RS to get an estimate of price inflation in health care that is on a consistent basis with the CPI-U-RS. We then deflate employer payments to employee health insurance by this health-specific deflator. Combining the inflation-adjusted measures of wages and salaries, employer payments for employee health insurance, and all other benefits gives us a measure of total compensation. We then calculate the ratio of total compensation to wages and salaries to estimate a “multiplier” that we can apply to nonsupervisory hourly wages to estimate total hourly compensation (pay).
How does EPI’s productivity–pay graph compare with other versions?
EPI’s productivity–pay graph helps answer a very important question: Do typical workers in the United States share in the benefits of economic growth? The big and growing gap between productivity and pay growth answers that with a resounding “no.” But in order to see this clearly, all of the adjustments we have made to the data are necessary. Failing to understand the role of these data adjustments in highlighting the failure of productivity growth to redound to the benefit of typical workers has led many to focus on inappropriate measures of both productivity and pay, and this has led them to come to wrong conclusions.
Other organizations attempting to prepare graphs similar to EPI’s pay–productivity graph have used indices that can be downloaded directly from publicly available BLS data on productivity and employer costs (for wages and benefits) by major sector. The common alternative approach is to use the nonfarm business sector productivity data to plot the real output per hour and real hourly compensation indices (the NFB series includes productivity and unit labor costs as well as the output, hours, and compensation data on which those ratios are calculated). This approach produces a graph (Figure A) that at first glance looks a lot like EPI’s productivity–pay graph.
But Figure A here, constructed from BLS’s NFB productivity and costs data, does not tell one nearly as much about the dynamics of the U.S. economy and inequality in recent decades. Specifically, in this graph, there is a gap between productivity and pay but the overwhelming share of it is a statistical artifact of using different price deflators for the two series.
Below, we walk through how this chart can be adjusted in steps to make the figure that shows the inequality driving the wedge between productivity and pay.
Productivity adjustments
Our first adjustment to the NFB-based graph is to broaden our productivity measure from the nonfarm business sector to encompass the entire economy. This measure of economywide productivity is calculated by using the unpublished (but available upon request) total economy productivity (TEP) series tracked by the BLS. Because sectors outside the nonfarm business sector (government, in particular) tend to see slower productivity growth, this first adjustment (Figure B) slightly reduces the pace of productivity growth since 1948.
Our second adjustment to the productivity data (Figure C) corrects for the influence of depreciation—converting the series from a gross measure of productivity to a net measure. We do this by multiplying the TEP productivity index in each year by the ratio of gross to net domestic product.
Our final adjustment to productivity is to replace the output deflator used to inflation-adjust the BLS productivity measures with a consumption deflator. This last step isolates the growth in productivity that is “effective” in boosting the living standards of U.S. families. To do this, we retrieve nominal values of productivity by reflating the productivity index by the net domestic product deflator. We then deflate this nominal productivity series by the CPI-U-RS, a commonly used price deflator. The results are shown in Figure D.
Pay adjustments
The first adjustment we make to the NFB pay series (or average real hourly compensation) is to replace it with a measure of hourly earnings of production and nonsupervisory workers. We are less interested in what has happened to average pay than what has happened to pay for the vast majority of workers—and, as noted earlier, production/nonsupervisory workers make up roughly 80% of all private-sector workers. Rising inequality drives a large wedge between growth in average pay and pay for the vast majority and, by definition, one can never fully examine what has happened to trends in inequality by looking at averages (huge gains at the top pull the average upward). Figure E shows that the effect of this adjustment is dramatic. The measure of compensation per hour from NFB productivity data shows growth of 147% cumulatively between 1979 and 2019. The measure of hourly earnings (not including nonwage benefits) per hour of nonsupervisory workers shows cumulative growth of just 113% over the same period.
The much more modest rise of typical workers’ earnings relative to average pay highlights an important finding about the drivers of the growing wedge between pay and productivity: It is mostly driven by growing inequality among wage earners (greater gaps between earners at different points in the wage distribution). This is in contrast with inequality arising when more and more of income generated economywide accrues to the owners of capital and less and less to the labor force overall. While there has been a significant shift of income away from labor and toward capital in recent decades, this shift has had a far smaller effect on the productivity–pay gap than has rising inequality within wage earners. This can be seen in the figures below—the gap between average hourly compensation and the effective total economy productivity line is far smaller than the gap between nonsupervisory hourly wages and effective total economy productivity. Wage inequality has grown radically since 1979. For example, between 1979 and 2019, the top 1% of households essentially doubled the share of total wages they extract (see data in this spreadsheet. Probably the starkest representation of this radical growth in wage inequality can be seen in the stratospheric rise in the ratio of the pay of CEOs and other top corporate executives relative to typical workers in their industry.
Our second pay adjustment accounts for trends in nonwage compensation. If the value of health care and other nonwage benefits provided by employers grew more rapidly than wages, then looking only at hourly earnings could disguise how rising productivity may have boosted living standards for the vast majority. As Figure F shows, once we account for the value of benefits (including the effect of rapid inflation in health care), one sees that the overall trends are not materially changed. Perhaps surprisingly, the era that saw measures of total compensation rise noticeably faster than wages is the era before 1979.
How much of the productivity–pay gap is driven by inequality?
Beginning with the August 2021 update, the entire gap in EPI’s productivity–pay figure is associated with rising inequality—inequality among wage earners and the rising share of overall income going to owners of capital rather than to workers for their labor. However, since researchers and analysts may still be interested in factors that account for various parts of the wedge between our measure of pay and other measures of productivity, we decompose these gaps further.
