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December 1, 2022
Labor Supply, Wages, and Inequality Conference: Day 2 Overview
The second day of the Atlanta Fed Center for Human Capital Studies's recent conference on labor supply, wages, and inequality switched the focus from labor supply to wage setting. The day was kicked off by Christina Patterson, who presented her paper "National Wage Setting ," coauthored by Jonathon Hazell and Heather Sarsons. This research explores how large, multi-establishment firms, which are increasingly dominating local labor markets, set wages across space. Benchmark models suggest that firms would vary wages across space because of local differences in productivity, cost of living, and competition, resulting in variation across regions.
The authors use data from the job market analytics firm Burning Glass Technologies about posted job-level wages for online vacancies between 2010 and 2019, along with a survey of human resource managers and executives, self-reported wages from payscale.com (a compensation data site), and firm employment visa application data. Their findings suggest that a large minority of firms set wages nationally and adopt pay structures that do not differ geographically. The two most important reasons given by firms is management simplicity and the importance of nominal comparisons to workers.
The national wage setting is associated with 3 to 5 percent lower profits for firms, but evidence suggests that national wage setting reduces earnings inequality without negatively affecting employment. However, this reduced inequality holds primarily for low-wage regions. National wage setting is also associated with increased regional wage rigidity.
The second paper of the day, "Industries, Mega Firms, and Increasing Inequality," presented by John Haltiwanger and coauthored by Henry R. Hyatt and James R. Spletzer, provided a broader lens through which we can view earnings inequality, which has drastically increased over the past decades. The existing empirical studies have shown that most of this inequality increase came from the rising differences in earnings between firms. Using comprehensive matched employer-employee data from the Longitudinal Employer-Household Dynamics database at the US Census Bureau, the authors show that the rising between-firms earnings dispersion is almost entirely accounted for by the increasing earnings dispersion between industries.
Increasing dispersion among industries operates at the two tails of the income distribution and is almost entirely accounted for by just 30 four-digit NAICS industries (as defined by the Census Bureau's classification system) The employment share of low-paying industries—such as restaurants and other eating places as well as general merchandise and grocery stores—has increased substantially, while real, inflation-adjusted wages in those industries fell. As a result, the left tail of the income distribution has fallen farther behind. On the other hand, the employment share of high-pay industries—such as software publishers, computer system design, information services, and management of companies—increased and was accompanied by large growth in those industries' average pay, leading to even higher relative income of the right tail of the income distribution.
Underlying these changes are worker-industry sorting and segregation patterns. Over time, workers with less education are more likely to end up working in low-paying industries, while more educated workers are more likely to cluster in the high-paying industries. These results suggest important changes have occurred in how lowest- and highest-paying firms restructure and organize themselves. These trends are also likely to be a by-product of recent technological innovations, led largely by firms and workers in industries with high pay. Though these innovations led to hefty rewards for high-skill workers, they also facilitated the scalability and expansion of mega-firms at the bottom of low-pay service industries. During the pandemic, workers in these low-pay industries have seen significant wage gains, but it remains to be seen if these recent changes will affect future inequality.
The day's third paper, "The Distributional Impact of the Minimum Wage in the Short and Long Run ," was presented by Elena Pastorina and coauthored by Erik Hurst, Patrick Kehoe, and Thomas Winberry. Their research continues the focus on wages by developing a framework to explore the impact of a $15 minimum wage, which would be a substantial increase in the current minimum wage and would be binding for 40 percent of workers without a college degree. The framework incorporates a large degree of worker heterogeneity within education groups, monopsony power (or considerable employer hegemony) in the labor markets, and putty-clay frictions (that allow for differing short- and long-run impacts of changes in the minimum wage).
Their results suggest that increases in the minimum wage are beneficial in the short run as they increase the welfare of the target group—low-income, noncollege workers making close to the initial minimum wage—with no large employment effects. However, the authors find that in the long run, firms will reoptimize their capital investment to better fit the changed relative prices of capital and labor. Thus, this group's employment, income, and welfare will eventually decline.
The authors go on to show that the Earned Income Tax Credit (EITC), which is based on income and number of children, is more effective in improving the welfare of low-wage workers than merely increasing the minimum wage. However, they find that combining a modest increase in the minimum wage with the EITC improves welfare more than either program does alone.
The fourth and final paper of the second day of the conference was "Labor Market Fluidity and Human Capital Accumulation," by Niklas Engbom. Using panel data for 23 countries, Engbom finds a large degree of heterogeneity in labor market fluidity—specifically, job-to-job mobility across countries. He finds that mobility in highly fluid markets is about 2.5 times higher than in countries with low fluidity, and that higher fluidity is associated with higher real wage growth over a person's lifetime.
Engbom also documents that on-the-job training is more prevalent in countries that exhibit high fluidity and proposes a mechanism to explain the positive correlation among fluidity, wages, and training in which workers in highly fluid markets are able to accumulate more on-the-job skills and have higher productivity, resulting in higher wages.
The amount of labor market fluidity can also change over time, and Engbom notes that fluidity in the United States—while higher than many other countries—has declined significantly during the last 40 years. Engbom connects this secular decline to the flattening of worker lifetime wage profiles and estimates that reduced fluidity accounts for about half of this flattening.
One implication of this line of research is that there are potentially significant benefits to reducing barriers to job creation and allowing greater worker reallocation across jobs. Lower labor market fluidity reduces wage growth and human capital accumulation because it becomes harder for people to find jobs that fully utilize their skills, and it also discourages human capital accumulation.
That paper concluded the Atlanta Fed Center for Human Capital Studies's conference on labor supply, wages, and inequality. Next year's conference is already in the planning stages, so stay tuned for details.
November 30, 2022
Labor Supply, Wages, and Inequality Conference: Day 1 Overview
The Atlanta Fed's Center for Human Capital Studies held its annual employment conference in person this year. The conference, held October 13–14, was organized by Melinda Pitts, the center director, and two center advisers, Richard Rogerson of Princeton University and Robert Shimer of the University of Chicago. The conference's title was "Labor Supply, Wages, and Inequality," and the agenda and links to the eight papers presented can be found here. This Policy Hub: Macroblog post summarizes the four papers presented on day one of the conference. The next post will look at the four papers presented on the second day.
Raphael Bostic, president and CEO of the Atlanta Fed, opened the conference. His welcoming remarks addressed policy makers' desire to understand the changing labor market, mentioning the work done by researchers at the Atlanta Fed and encouraging the economists in the room to continue doing policy-relevant research to better inform decision makers. His welcome was followed by the first session, which featured two papers related to how the COVID-19 pandemic altered individuals' labor-supply decisions.
The first paper presented was "Has the Willingness to Work Fallen during the Covid Pandemic? ," by R. Jason Faberman, Andreas I. Mueller, and Ayșegül Șahin, and presented by Faberman. The answer to the question their title poses is "yes": desired hours fell dramatically during the pandemic and have not recovered to prepandemic levels. Using data from the US Census Bureau's Current Population Survey and the New York Fed's Survey of Consumer Expectations, the authors find that the decline was most pronounced among those with less than a college education, those whose current or most recent jobs posed more significant COVID exposure risk, and those not working or working only part-time.
An important implication of the results reported in this paper is that while the unemployment rate is again near historic lows, the labor market might be even tighter than the unemployment rate is making it appear. In other words, by adding together the desired hours of those working and not working, the potential labor supply has fallen farther than either the unemployment rate or the labor force participation rate, compared to prepandemic levels. As a result, the difficulty employers are having finding workers, or getting workers to work more hours, might not ease any time soon.
Another broader consideration is whether this decline in desired hours is a temporary blip or a fundamental shift in preferences. The latter would hold implications on several fronts: for potential growth in an economy fueled by labor; for the way policymakers might define full employment, when employment of those "wanting" work leaves a significant amount of labor resources on the sideline; and for discussion of what incentives might be brought to bear on reversing the shift in preferences. This paper joins a growing body of literature showing that the impact of this pandemic on individual behavior has been dramatic and unprecedented. Additionally, the decline in desired hours of work could prove to have lasting and profound implications for future economic growth.
Adam Blandin followed with the presentation of his paper, "Work from Home Before and After the COVID-19 Outbreak ," coauthored with Alexander Bick and Karel Mertens. The authors designed the Real-Time Population Survey, a national labor market survey of adults aged 18–64 that ran from April 2020 through June 2021. The authors find that the share of the US population working from home (WFH) increased from 14 percent just before the pandemic to 40 percent early in the pandemic and still represented 25 percent of all employment as of June 2021. Working with custom survey questions and a structural model, the authors attempt to determine how much of the shift to WFH was a short-term substitution to an inferior form of production driven by the exigencies of the pandemic, as opposed to firms making a one-time investment to learn how to produce with remote workers. Specific survey questions found that more than 60 percent of workers who transitioned to WFH believed they could have always done their job remotely but were required to come in by their employer. Employing a structural model with endogenous wages (that is, wages based on a number of discrete factors) based, in part, on WFH status; a COVID-period in-person production penalty; and a one-time switching cost to remote work, the authors attribute much of the shift in work location to firms adopting remote work production. Combined with survey responses, the model suggests that remote work will persist long after COVID has waned.
The second session of the first day continued the theme of labor supply but shifted away from pandemic-specific research. Eric French presented "Labor Supply and the Pension-Contribution Link ," coauthored with Attila S. Lindner, Cormac O'Dea, and Tom A. Zawisza. Public pensions in the United States and many other are unfunded, pay-as-you go systems with benefits determined by a formula based on earnings history. Many governments have considered proposals to reform this formula, but a key concern is whether workers would respond to changes in their future pension benefits by adjusting their labor supply. To answer this question, the authors examined a change in the Polish pension system that altered the benefit for workers younger than 50 on January 1, 1999, with neither changes in benefits for older workers nor changes in the other plan characteristics. The original formula based benefits on the highest 10 years of salary growth, and the new system took into account every year of earnings.
Using a regression discontinuity design (RDD) and all tax returns linked to the Polish population registry, the authors estimate labor supply responses occurring between 2000 and 2002. This empirical design identifies the effects of the policy change by comparing individuals who were born only a few days apart and who face a very similar labor market and economic environment but are assigned to different pension plans. They found that the net return to work fell by an additional 5.2 percent in high-growth regions relative to low-growth regions. At the same time, the RDD allowed them to estimate that employment declines between regions differed by 2.29 percent. Taken together, these figures imply that the employment elasticity with respect to work incentives is 0.44.
This elasticity is in the range of estimates we typically see in the literature. However, one novel aspect of this paper lies in the fact that the research observes labor supply changes in response to changes in benefits to be received many years in the future, whereas most of the literature estimates the labor-supply response to the contemporaneous return to work. These results provide constructive evidence that individuals' labor supply responds in a forward-looking way to incentives in the pension formula, suggesting that tightening the link between contributions and benefits has the potential to alleviate labor supply distortions caused by payroll taxes.
Rather than focusing on how workers respond to external policy changes, the final paper of the day explored how an individual's risk preference and (over)confidence alter their job search behavior and labor market outcomes. Laura Pilossoph presented the last paper of the day, "Gender Differences in Job Search and the Earnings Gap: Evidence from the Field and Lab ," coauthored with Patricia Corté, Jessica Pan, Ernesto Reuben, and Basit Zafar.
The authors collected data on the employment search behavior of recent (2012–19) bachelor's graduates from the Questrom School of Business at Boston University. They collected data on the standard demographics involved in job search outcomes, including timing of acceptance and both accepted and rejected offers, job search expectations, and measures of risk. They found that, on average, women accepted jobs earlier in the search process than men did, the initial accepted salary was higher for men than for women, and the willingness to accept risk is higher for males. The authors then developed a job search model that incorporated gender differences in the levels of risk aversion and overoptimism about prospective job offers. The model predicts that if women are more risk-averse than men, then they will have lower reservation wages (the lowest wage at which someone would accept a given job) and search earlier. Likewise, if men are overconfident, then they will have a higher reservation wage. In other words, the decline in the reservation wage and increased job finding are derived from female risk aversion and male learning (that is, updating expectations about job offers) or having less optimism. Controlling for the measures of risk and overconfidence reduced the gender gap in wages by 37 percent.
The findings from the field were replicated in a specially designed laboratory experiment that featured sequential job search. The lab experiment yielded very similar results, with the gender gap in wages reduced by 30 percent when accounting for risk preferences and overconfidence. The results from both analyses suggest that risk preferences place a significant role in the gender differential.
In tomorrow's post, we'll summarize the papers presented on day two of the conference.
October 21, 2022
Viewing the Wage Growth Tracker through the Lens of Wage Levels
One of the most popular features of the Atlanta Fed's Wage Growth Tracker is its depiction of median year-over-year wage growth of four different wage levels (wage quartiles). Unfortunately, the sample size of each quartile for a month is quite small, and thus the median wage growth for each quartile is noisy. For that reason, the Tracker shows changes by wage quartile only as a 12-month moving average. However, although the averaging smooths out a lot of the month-to-month noise in the series, it also means that the series have a substantial lag in showing wage growth changes across quartiles.
Instead, I have produced a cut of the wage growth data by wage level that can show a three-month moving average, which gives a better near-term picture of wage growth trends. The restriction, however, is that rather than using four wage groups, I put the average wage-level data (that is, the average of a person's reported wage in the current month and their reported wage a year earlier) into two groups: those whose average wage is above the median and those whose average wage is below the median. Essentially, I split the distribution of average wages in half.
Chart 1 plots the resulting three-month moving average of the two groups' median wage growth.
As you can see, median wage growth has been elevated since 2020 for workers across the wage distribution. But for workers in the bottom half of the wage distribution, median growth has been especially high during the last year. High wage growth for lower-paid workers aligns with numerous anecdotal reports suggesting that worker shortages since the pandemic have been especially acute in industries that pay below-average wages, such as leisure and hospitality.
Chart 1 allows another interesting observation: in the years leading up to the pandemic, the median wage growth of those in the lower half of the wage distribution was typically a bit above those in the upper part of the distribution. This was a period when the labor market was also tight, although much less so than it is today. Chart 2, which shows the sum of employment and job openings relative to the size of the available labor force, makes clear the divergence in the degree of overall labor market tightness today versus prior to the pandemic.
By this measure, though the gap has narrowed a bit in recent months, labor demand remains well above its supply, and this gap has been putting upward pressure on wages across the spectrum.
The Wage Growth Tracker series for the two wage groups is available now in the downloadable spreadsheet here and will be updated with October data after the Current Population Survey micro data for October is released, which usually occurs about a week after the US Bureau of Labor Statistics issues its labor report.
March 1, 2022
Assessing Recent Labor Market Improvement
The US Bureau of Labor Statistics' (BLS) labor report for January 2022 showed that the overall labor force participation (LFP) rate increased 0.3 percentage points from December's published level. This increase put the LFP rate at its highest level since the pandemic began and, taken at face value, might make you think that the labor supply problems that have plagued the recovery from the COVID-19 pandemic were easing.
However, it turns out that this jump in the LFP rate was entirely an artifact of the BLS incorporating population control adjustments into the January labor force data. These are independent estimates of the civilian noninstitutionalized population ages 16 and older used to make sure that labor force statistics computed from the Current Population Survey (CPS) accurately reflect the population and are incorporated into the CPS data each January. The latest adjustments are the first to use information from the 2020 decennial census , and showed that the US population was almost 1 million larger than the published estimate for December 2021. By itself, a jump in the size of the population isn't an issue for comparing LFP rates over time. But the new population adjustments also showed that the population was considerably younger than previously estimated (in particular, the share of the population aged 70 and older was smaller). This shift in the age distribution is important because a younger population generally means a higher rate of participation in the labor force.
The BLS does not revise the historical data when new population control adjustments are incorporated into labor statistics. But it did report that the population control adjustment would have lifted the December 2021 LFP rate for the population ages 16 and older by 0.3 percentage points if it had revised the December data. This increase is the same as the increase in the published LFP rate from December to January. In other words, the December-to-January increase in the published LFP rate didn't indicate an improvement in labor force participation at all.
Clearly the latest population control adjustments complicate comparison of 2022 LFP rates to earlier periods. To construct historical LFP rate series that are more comparable over time, we implemented a simple smoothing method the BLS used previously to account for annual population control adjustments (described here ). This method essentially distributes the level shifts that result from the population control adjustments back over the relevant historical period for each series. To account for the effects of adjustments (made between the decennial census) to the census 2010 population base that were made in January 2013–January 2021, we first smoothed data for January 2012 to December 2020. Then we smoothed the data for January 2012 to December 2021 to account for the effects of the 2020 census population control adjustment introduced in January 2022. We applied the method separately labor force participation rates for the population ages 16 and older, as well as populations ages 16–24, 25–54, and 55 and older. You can see these series in this spreadsheet.
Chart 1 plots the published and smoothed seasonally adjusted LFP rate series for the population aged 16 and older. Notice that the smoothing method results in a gradually increasing upward shift to the LFP rate over the 10-year period, culminating with the December 2021 smoothed LFP rate 0.3 percentage points higher than the published LFP rate.
The upward shift is even greater for the population aged 55 and older shown in chart 2. Recall that a significant part of the population adjustment was a reduction in the size of the population aged 70 and older. Given that this age group has a lower LFP rate than those aged 55 to 69, the composition shift pushed the LFP rate higher for the 55 and older population overall. The BLS estimated that the population adjustment impact on the December 2021 LFP rate for this population group was 0.7 percentage points.
For the population aged 16–24, the smoothed series shown in chart 3 is lower than the published series through December 2021. This is because the population adjustments revealed that the population aged 16–19 was larger than previously estimated. Because the 16–19 age group has a lower LFP rate than those aged 20–24, the smoothed LFP rate series is lower than the published series. The BLS estimated that the population adjustment impact on the December 2021 LFP rate for this population group was −0.3 percentage points.
Finally, for the prime-age population (25–54), the smoothed series shown in chart 4 is identical to the published series. The population adjustments had no effect on the LFP rate for this age group.
It is important to consider how the difference between the published and smoothed series may alter one's assessment of labor market dynamics surrounding the pandemic-induced recession and subsequent recovery. Chart 5 shows that for the initial year of the pandemic, measured here as the change in the LFP rates from January 2020 to January 2021, the published LFP rate data (blue bars) and the smoothed estimates (green bars) tell a very similar story for all age groups: LFP rates declined between 1.5 percentage points and 2.0 percentage points for all age groups, with no material difference in the size of the change between the smoothed and published estimates within each age group.
However, the story is much different for the period between January 2021 and January 2022. Chart 6 depicts this difference, showing that the published LFP rate for the population aged 16 and over increased by nearly 0.8 percentage points over that 12-month period. In contrast, the corresponding smoothed estimate increased by only 0.5 percentage points. The discrepancy is even greater for the population aged 55 and older. For that age group, the published LFP rate increased by 0.8 percentage points, whereas the smoothed LFP rate shows an increase of less than 0.2 percentage points during the past year. That is, the smoothed data suggest the increase was less than one quarter as large as the increase in the published data. For the population aged 16–24, the smoothed LFP rate increased by more than the published series (0.7 percentage points versus 0.5 percentage points). Finally, for the population aged 25–54, there is essentially no difference between the change in the published and smoothed estimates. Both increased by close to 0.9 percentage points from January 2021 to January 2022.
To sum up, smoothing the labor force data to account for the annual population adjustments like that described here provides a way to allow LFP rates in 2022 to be compared to prior years. These estimates show that the recovery from January 2021 to January 2022 in the overall LFP rate and the rate for the population aged 55 and older is more modest than the published data would imply, while the recovery for the population aged 16-24 is better than implied by the published estimates.
Going forward, the published labor force data for January 2022 will be comparable with data for other months in 2022 since they are all based on the same population control adjustments. In January 2023, the BLS will likely incorporate new population adjustments that use additional 2020 census information. Hopefully those adjustments will be less eventful. Stay tuned as we discuss future data.
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