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November 10, 2021
Compositional Distortions to a Measure of Wage Growth during the Pandemic
Measures of year-over-year growth in wages (or hourly earnings) used in economic analysis often tell a fairly consistent story. For example, chart 1 makes it apparent that wage growth was generally higher heading into the 2007–09 recession than heading out of it and that wage growth stayed low for the first half of the 2010s before trending up moderately over the second half of the decade. However, with the onset of the COVID-19 pandemic, growth in average hourly earnings from the US Bureau of Labor Statistics' (BLS) establishment survey (the blue line in the chart) deviated substantially from the other two series depicted.
The leisure and hospitality industry provides a useful illustration of why the establishment survey measure of hourly earnings growth spiked in March and April of last year. In February 2020, average hourly earnings for production and nonsupervisory workers in leisure and hospitality were 40 percent lower than they were for all private nonfarm payroll workers. And although the leisure and hospitality industry accounted for just under 14 percent of private nonfarm production and nonsupervisory jobs in February 2020, it accounted for nearly 40 percent of the lost private production and nonsupervisory jobs in the subsequent two months. The 4.5 percentage point increase from February 2020 to April 2020 in the blue line in chart 1 falls by 1.9 percentage points if we remove leisure and hospitality from the calculation.
The August 2020 FRBSF Economic Letter—aptly titled "The Illusion of Wage Growth"—by Erin E. Crust, Mary C. Daly, and Bart Hobijn shows that restricting the sample to people employed in the second quarters of 2019 and 2020 reduced growth in median usual weekly earnings over that period by nearly 8 percentage points from the published rate of 10.4 percent. The Atlanta Fed's Wage Growth Tracker, which uses the same type of restriction, and the Employment Cost Index (ECI), which controls for employment share changes among industries and occupations , were not subject to the illusion of wage growth shown by the blue line in chart 1.
Unfortunately, the adjustments used in the Wage Growth Tracker and the ECI are not feasible with the establishment survey measure of hourly earnings because that measure is constructed solely from the information in each month's employment report. As an alternative, Goldman Sachs provides an adjustment for what it terms the composition bias in the establishment survey measure. This adjustment keeps hours worked fixed at their year-ago level in the wage calculation using industry-level data.
I've written an appendix that provides the details of a related approach for calculating a composition-adjustment term from the monthly establishment survey data. Besides adjusting for industry composition, this approach also adjusts for types of workers: production and nonsupervisory workers versus nonproduction/supervisory employees. The appendix also shows that adjusting for worker type and industry rather than industry alone materially affects the composition-adjusted measure of average hourly earnings for April 2020. It also shows that—unlike measures from the BLS and the San Francisco Fed, which control for educational attainment—the measure of labor composition (sometimes called labor quality ) constructed with only establishment survey data has not trended up much since the mid-2000s.
The basic intuition underlying the approach described in the appendix is that, apart from some trivial rounding error, the BLS measure of aggregate weekly payrolls is equal to the product of average hourly earnings and aggregate weekly hours worked. So, in much the same way that we can express nominal gross domestic product (GDP) as the product of real chain-weighted GDP and a GDP price deflator, aggregate weekly payrolls can also be decomposed as the product of composition-adjusted measures of wages and hours worked. This approach maintains the equality with aggregate payrolls since the composition adjustments to hourly earnings and hours worked offset each other exactly.
Chart 2 shows the results of adjusting for changes in both industry and worker type for measures of average hourly earnings growth and aggregate hours worked during the pandemic. Adjusting for composition makes average hourly earnings growth during the pandemic more like the ECI and Wage Growth Tracker measures, but, nevertheless, some important differences exist. Unlike the composition-adjusted measure of nominal wage growth, the ECI and Wage Growth Tracker measures languished in the second half of 2020 and surged in their most recent readings. Composition-adjusted hourly earnings grew 1.1 percent from March 2020 to April 2020, which is less than the 4.6 percent spike in the unadjusted measure but still strong enough to suggest that the adjustments made here still miss some meaningful changes in worker composition in the earliest months of the pandemic.
As you look at this chart, note that the adjustment is constructed using wage and hours data for 253 industry groups, all but 10 of which are further split into production and nonsupervisory and nonproduction/supervisory employee groups.
The right panel in chart 2 shows private nonfarm payroll employment alongside the standard measures of aggregate hours worked and a measure adjusted for industry and worker-type composition. In October, private nonfarm payroll employment, hours worked, and composition-adjusted hours worked were 2.5, 1.7, and 1.2 percentage points, respectively, below their February 2020 levels.
The composition-adjustment factor (industry by production/supervisory worker employment type) as well as the associated measures of composition-adjusted hours worked and hourly earnings are available here . Future updates of this Excel file will also be available at this link.
March 23, 2021
Hourly and Weekly Perspectives on Wage Growth during the Pandemic
Despite record-setting job losses during the COVID-19 pandemic, median growth in the hourly rate of pay for those who stayed employed has held up remarkably well, which we can see in the Atlanta Fed's Wage Growth Tracker (see chart 1).
The Wage Growth Tracker compares individual hourly wages in the current month with what the same individual's hourly wage was 12 months earlier and calculates the change. The fact that the median wage growth has not slowed, despite the increase in unemployment, suggests that the pandemic's impact on the labor market has been quite unusual.
During the Great Recession, the slowing in median hourly wage growth coincided with a large increase in the share of workers reporting that their hourly rate of pay was unchanged from a year earlier. As chart 2 shows, the share of workers reporting zero change in their hourly rate of pay has ticked up a bit during the COVID-19 pandemic, but so far, what we see differs from observations we made during the Great Recession.
Why did the COVID-19 pandemic have a relatively smaller impact on median hourly wage growth compared to the Great Recession? One explanation is that the supply of unemployed job seekers far exceeded job vacancies in the earlier recession. That is, employers typically received many more applicants for each available position. As chart 3 shows, at the Great Recession's peak, there were 6.5 unemployed workers for each job posting and 5.7 unemployed not on temporary layoff for each job posting. I think unemployed workers not on temporary layoff is a more useful measure of unemployed job seekers because those on temporary layoff expect to be recalled by their employer and hence are not necessarily looking for another job. Contrast that with January 2021, when there were 1.5 unemployed workers for each opening and 1.1 unemployed workers not on temporary layoff for each job vacancy. In this sense, the labor demand and supply during the COVID-19 pandemic has been more in balance than during the Great Recession. Compared with the Great Recession, apart from the period during the initial lockdown, total vacancies by firms has scaled back relatively modestly during the pandemic while the number of workers looking for a job has increased by less.
Nonetheless, during both the Great Recession and the COVID-19 pandemic, many workers who remained employed have experienced an involuntary reduction in their work hours, which has dragged down workers' weekly paychecks even when their hourly rate of pay hasn't fallen. In February 2021, about 6.5 million workers were classified by the U.S. Bureau of Labor Statistics (BLS) as working part-time for economic reasons—almost 2 million more than in February 2020, just before the pandemic hit the U.S. economy. For this reason, I've constructed an alternate version of the Wage Growth Tracker, which shows the median growth of individual weekly earnings. This new measure uses the same data (from the Current Population Survey, jointly administered by the BLS and the U.S. Census Bureau) as the hourly earnings measure, and I show both series in chart 4 for comparison.
Generally, the two series move in tandem, with the weekly series slightly outpacing the hourly series during economic expansions as hours worked tend to rise. However, as we see here, during both the Great Recession and the COVID-19 pandemic, reduced hours worked each week lowered many workers' median growth in weekly earnings relative to hourly earnings.
As the economy recovers from the COVID-19 pandemic, watching both the hourly and weekly versions of the Wage Growth Tracker will be useful. As fewer worker face reduced hours, I expect to see median weekly wage growth recover and at least match the pace of hourly wage growth. A tighter labor market should result in higher wage growth on both an hourly and weekly basis. I'll write about the developments using new Wage Growth Tracker data we'll post soon, so check back.
Note: If you are interested in tracking the hourly and weekly versions of the Wage Growth Tracker you can do that here, or via the EconomyNow app, which also features several other Atlanta Fed data tools.
January 16, 2020
Do Higher Wages Mean Higher Standards of Living?
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A recent macroblog post used Atlanta Fed Wage Growth Tracker data to observe that the hourly wage of the lowest-paid workers has rebounded in recent years after declining for a decade. The chart below depicts this finding, showing the median hourly wage of the lowest-paid 25 percent of workers in the Tracker sample relative to the median for all workers.
Moreover, the post showed that this recovery was not just a story about states and localities increasing their minimum wages. It also appears that there has been a significant tightening in the labor market for unskilled or low-skilled jobs.
Taken at face value, this is good news for workers employed in low-wage jobs. But here's the rub: the median wage in the first quartile is still low—$11.50 in 2019, or 55 percent of the overall median wage. Moreover, these are hourly wages before taxes and transfers (we'll get back to this shortly). They don't represent what is happening to these workers' ability to make ends meet, which depends crucially on income after taxes and transfers.
For households at the bottom of the income distribution, means-tested transfers can play an especially important role. Means-tested transfers—cash payments and in-kind benefits from federal, state, and local governments designed to assist individuals and families with low incomes and few assets to meet their basic living needs—represent about 70 percent of income before taxes and transfers for households in the bottom quintile of the income distribution, according to a recent report by the Congressional Budget Office. However, the size of the transfers tends to decrease as earnings increase, and they stop altogether when a worker exceeds income- and asset-eligibility thresholds.
The interaction between changes in earnings and various means-tested public assistance programs is an important public policy issue, and it is one that staff at the Atlanta Fed are studying. In a March 2019 macroblog post, David Altig and Laurence Kotlikoff reported that this interaction results in low-income households facing a higher median effective marginal tax rate than high-income households. For low-income households with children, this effect can be especially severe because the presence of children increases the value of programs such as the Supplemental Nutrition Assistance Program (or SNAP, formerly known as the food stamp program) and the likelihood of enrollment in additional programs such as federally subsidized child care. (You can read further research on the effective or implicit marginal tax rates of low-income households at Congressional Budget Office (2016), Romich and Hill (2018), and Chien and Macartney (2019).)
To illustrate the point, the Atlanta Fed team studied the case of a hypothetical single mother with two young children who works in a near-minimum-wage, full-time job and whose basic living expenses are helped by various transfer programs. One avenue to improving her family's standard of living is if she were to return to school and pursue a higher-paying career as a nurse. Over the long term, the net gains from education and career advancement are unambiguous. However, the Atlanta Fed's analysis shows that as long as her children still require care, the reduction in payments from various benefit programs could partially or even completely offset the gains. Look for an Atlanta Fed paper discussing this very real dilemma coming soon on the Bank's Economic Mobility and Resilience webpage.
What do findings like this mean for interpreting the Wage Growth Tracker's evidence that people in the bottom part of the wage distribution are experiencing relatively larger wage gains? Perhaps there is a bit less to celebrate than meets the eye. Around 46 percent of these individuals are in households with children. To the extent that they also participate in means-tested public assistance programs, the relative increase in their family's standard of living could be much less than the size of their pay raise would suggest.
December 16, 2019
Faster Wage Growth for the Lowest-Paid Workers
On November 25, Fed chair Jay Powell gave a speech titled "Building on the Gains from the Long Expansion," in which he observed that
Recent years' data paint a hopeful picture of more people in their prime years in the workforce and wages rising for low- and middle-income workers.
In making this point, Chair Powell used a cut of the Atlanta Fed's Wage Growth Tracker that looks at the median annual wage growth of workers in the lowest 25 percent of the wage distribution. As the following chart shows, the lowest-paid workers have been experiencing higher median wage growth (the blue line) in the last few years than workers overall (the green line). This reverses the pattern seen in the wake of the Great Recession, when median wage growth for lower-paid workers slowed by more than for workers overall.
The faster median wage growth for lower-wage workers shown in chart above has also translated into an increase in the relative median wage level of these workers. To see this, the following chart shows the median wage level for those in the lowest wage quartile relative to the median for all workers in the Wage Growth Tracker dataset.
The chart shows that for workers in lower-wage jobs, their relative median wage over the 2000s has deteriorated, and that erosion has reversed course only in the last few years. This reversal may reflect increasing tightness of the labor market for lower-wage jobs relative to other jobs over the last few years. The challenge of filling jobs requiring few skills is something we have been hearing about a lot recently from the businesses we talk to (for example, see here), and this sort of challenge could be behind higher wages for those workers. However, several state and local governments have increased the minimum wage in recent years, which would also push up the relative pay for those in the lowest-paid jobs.
Are the observations in the previous chart solely attributable to minimum wage increases? To get some idea, the next chart contrasts the relative median wage in states that increased their minimum wage at some point between 2014 and 2019 to those that did not. The blue line is the relative median wage of the lowest quartile in the 28 states that increased their minimum wage (23 states introduced new minimum wage levels, and five implemented increases legislated before 2014), and the green line is relative median wage for the states that did not increase their minimum wage.
We would expect to see a rise in the relative median wage in the states that raised their minimum wage, and indeed we do. For the group of states that increased minimum wages (the blue line), the relative median wage is now closer to that of states that did not increase their minimum wage (the green line). Interestingly, though, even in the "no increase" states, the relative median wage has improved, suggesting that the increased tightness of labor markets, or some other factor than hikes in state minimum wages, is playing a role in pushing up the pay for those in lower-wage jobs. Consistent with the message of Chair Powell's speech, the good news is that there is scope to continue to build on the gains from the long and ongoing expansion for workers at the bottom end of the wage distribution.
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