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Policy Hub: Macroblog provides concise commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues for a broad audience.

Authors for Policy Hub: Macroblog are Dave Altig, John Robertson, and other Atlanta Fed economists and researchers.

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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 RogersonOff-site link of Princeton University and Robert ShimerOff-site link 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? Adobe PDF file format," 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 Adobe PDF file formatOff-site link," 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 Adobe PDF file formatOff-site 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 Adobe PDF file formatOff-site link," 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 2022Off-site link 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 Adobe PDF file formatOff-site link, 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 Adobe PDF file formatOff-site link 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 Adobe PDF file formatOff-site link). 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 spreadsheetMicrosoft Excel file.

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.

Chart 01 of 06: Published versus Smoothed LFP Rate: Ages 16 and Older

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.

Chart 02 of 06: Published versus Smoothed LFP Rate: Ages 55 and Older

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.

Chart 03 of 06: Published versus Smoothed LFP Rate: Ages 16-24

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.

Chart 04 of 06: Published versus Smoothed LFP Rate: Ages 25-54

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.

Chart 05 of 06: Change in LFPR from January 2020 to January 2021

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.

Chart 06 of 06: Change in LFPR 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.

February 3, 2022

Rounded Wage Data and the Wage Growth Tracker

The US Census Bureau recently announced some changes it plans to make this year to the Current Population Survey Public Use File (CPS PUF). Here at the Atlanta Fed, we use data from the CPS PUF to construct the Wage Growth Tracker, and one of the planned changes will significantly affect the tracker. Specifically, a person's usual weekly or usual hourly earnings, which are unrounded currently, will be rounded.

The Wage Growth Tracker bases its results on the median, or middle, observation in the distribution of percent wage changes for a sample of individuals linked between the current month and the same month a year earlier. The Wage Growth Tracker time series has yielded useful insight into the rapidly shifting dynamics of the labor market in the wake of COVID-19, especially as compositional effects have distorted wage data. It's also helped economists and policymakers understand which income levels were seeing the greatest growth and that job switchers were finding the most wage gains.

How will the rounding of wage data affect the Wage Growth Tracker? The announced CPS PUF rounding rules vary by wage level and are different if the earnings are reported on an hourly or weekly basis. (You can see more details here Adobe PDF file formatOff-site link.) Most people in the CPS report earnings on an hourly basis, and most wage observations range from $10 to $99.99 an hour. Under the rounding rules those earnings will be rounded to the nearest dollar. So, for example, if someone reports making $14.50 an hour, that wage will be rounded to $15, while workers reporting a wage of $15.40 an hour will also have that wage rounded to $15.

One implication of the rounding rules is that it will make no wage change appear more common than it currently is. To illustrate, suppose someone's pay went from $14.50 to $15.40 an hour. The rounding rules would show no change in the person's wage (both would be recoded as $15) even though that person's actual wage increased by 6.2 percent. Chart 1 shows what happens to the proportion of zero wage changes if the rounding rules were applied to the CPS PUF earnings data used to construct the Wage Growth Tracker from 1998 to 2021.

Chart 1: Impact of Rounding on Fraction of Zero Wage Changes

As you can see, during the Great Recession, when labor demand was especially weak, about 17 percent of wage growth observations based on unrounded earnings data were zero. But if the rounding rules had been applied back then, more than 25 percent of wage growth observations would have been zero.

Obviously, this change is a big deal for the Wage Growth Tracker. When more than a quarter of the observations are zero near the middle of the wage change distribution, it is very likely that the median observation will also be zero. This effect is evident in chart 2, which compares the median Wage Growth Tracker series using unrounded earnings data with what it would have been if the rounded data had been used.

Chart 2: Impact of Rounding on the Wage Growth Tracker

Clearly, if the rounding rules had been in place in the past, the Wage Growth Tracker time-series would be a much less useful indicator of wage growth or labor market trends.

So, what to do? It turns out that the rounding rules don't affect all summary measures of wage growth as much as they affect the median measure. For example, as chart 3 shows, the mean—or average—wage growth comes out of the rounding changes essentially unaffected.

Chart 3: Impact of Rounding on Average Wage Growth

Unfortunately, not only is the average higher than the median, because wage growth varies greatly across individuals (the monthly sample standard deviation is typically around 25 percent), you can also see that both of the average wage growth series are much more variable month to month than the median series using unrounded data. Indeed, the robustness to variability in the underlying wage change data is a primary reason why the Atlanta Fed's Wage Growth Tracker is based on median rather than average wage growth.

But there is potential solution. Borrowing from the researchOff-site link on using trimmed means of price change data to construct measures of inflation that are robust to extreme price changes, I was able to construct a trimmed-mean wage growth series using the rounded data that has broadly similar properties to the (median) Wage Growth Tracker series constructed from unrounded data. Specifically, for each month's sample, I excluded the bottom 20 percent of wage growth observations (that is, the largest percent wage declines) and the top 25 percent (the largest percent increases) and computed the average of the remaining data. (Note that the trimming is asymmetric because more of the large wage changes tend to be increases than decreases, which is also why the average is higher than the median in the previous chart.)

Chart 4 shows the trimmed-mean series constructed using rounded earnings data, along with the (median) Wage Growth Tracker series that uses unrounded data. I would describe this trimmed-mean series as a reasonable (though not perfect) approximation of the Wage Growth Tracker series (something we could have used if we only had rounded earnings data in the past).

Chart 4: Approximating the Median Wage Growth of Unrounded Data

When the January 2022 CPS PUF data become available in February, we will produce the trimmed-mean version of the overall Wage Growth Tracker and add it to the Atlanta Fed's Wage Growth Tracker data set. We are currently exploring if a similar approach will produce useful alternatives to the Wage Growth Tracker for other ways to view the data, such as those for job switchers versus job stayers, or by average wage level. Watch this space for updates.