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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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February 14, 2023

Real Wage Growth: A View from the Wage Growth Tracker

The Atlanta Fed's Wage Growth Tracker (WGT) measure of year-over-year nominal wage growth has been elevated during the last couple of years. It was 6.1 percent in December 2022. Although this level is down from its high of 6.7 percent in June and July of last year, it is much higher than the 3.6 percent average seen in 2019, before the COVID-19 pandemic. At the same time, inflation has also been high. For example, the consumer price index (CPI) increased 6.5 percent from December 2021 to December 2022. As a result, the real or inflation-adjusted WGT for December 2022 was −0.4 percent.

To see how the WGT has performed relative to inflation over time, chart 1 shows the time series of the Wage Growth Tracker (the solid green line) and the 12-month rate of change in the headline CPI (the solid orange line) since 1998.

As you can see, the WGT, in terms of median wage growth, has been below the average rate of inflation for most of 2021 and 2022. Prior to that period, the last time the real WGT had been negative was during 2011—a short period when CPI inflation reached 4 percent while the WGT was hovering around 2 percent.

The fact that the real WGT is negative tells us that less than 50 percent of the people in the WGT sample had real wage increases, relative to the CPI. But as you can also see in chart 1, at any point in time the distribution of wage growth across individuals varies widely. For example, during 2022, around 25 percent of the sample reported nominal wage growth of more than 18 percent, while another 25 percent reported nominal wage growth at or below zero. As a result of this wide dispersion of wage growth across people, a significant minority of people experience real wage gains even when inflation is elevated, just as a significant minority experience real wage declines even when inflation is low. Chart 2 shows the percentage of the nominal wage growth sample that was above the prevailing rate of inflation in each quarter.

For example, about 57 percent of the WGT sample had positive real wage gains during 2019, whereas during 2022, only 45 percent of people had positive real wage growth. Put another way, despite higher median nominal wage growth, the share of people with positive real wage growth between 2019 and 2022 due to higher inflation fell by 12 percentage points.

As a simple counterfactual, if the rate of inflation had stayed at its 2019 average level of 1.8 percent, then close to 63 percent of people would have had positive real nominal wage growth in 2022. That is, a tight labor market without high inflation would have resulted in a 6 percentage point gain in the share of workers experiencing real wage growth relative to 2019, rather than a 12 percent decline.

One thing to keep in mind when interpreting chart 2 is that it implicitly assigns the same CPI inflation to everyone's wage growth within a period. Recall that the CPI is an index of price changes for a representative basket of goods and services, but such a basket is unlikely to fully capture the cost-of-living experience for any individual. (The Atlanta Fed's myCPI tool will give you an idea of how CPI inflation varies among broad groups of people. Unfortunately, users cannot map myCPI inflation data to the individual-level data used to construct the WGT.)

Not surprisingly, groups of workers with the highest (lowest) median wage growth are those with the highest (lowest) share of positive real wage growth. The table depicts these differences, comparing outcomes for 2019 with those for 2022 for a selection of worker types.

Table 1 of 1: Percentage of Wage Growth Observations above the Rate of Inflation

For example, in 2022, 60 percent of workers aged 16−24 had positive real wage growth versus 65 percent in 2019. However, the 5 percentage point decline in the share of people aged 16−24 with positive real wage growth is much smaller than the decline in the share of workers aged 25 and older who had positive real wage growth. In particular, the share of people 55 and older who saw positive real wage growth declined by 15 percentage points.

Some workers have seen their nominal hourly wage increase proportionately more than others because of the tight labor market during the last couple of years, which has blunted the impact of higher inflation on those workers. Older workers, as well as people staying in the same job, have seen the largest increase in the share of real wage losses in 2022 relative to before the COVID-19 pandemic. That said, nominal wage growth across individuals varies a lot, even within age and job mobility categories. Explaining that variation remains a challenge for economists who often attribute it factors such as differences in productivity growth at the individual and firm level. Your own wage growth experience might not look like that of your neighbors or your colleagues, and it might not resemble that of the person with median wage growth either. The median wage growth is a useful guide to shifts in the distribution of wage growth over time, but it doesn't fully capture the breadth of wage growth experiences across individuals.

February 9, 2023

Population Control Adjustment's Impact on Labor Force Data: The 2023 Edition

Regular readers of Policy Hub: Macroblog will recall my description last year of smoothed labor force data, which reflect the latest population control adjustments by the US Bureau of Labor Statistics (BLS). I'm writing this post to let you know that I have updated those smoothed labor force data to incorporate the latest adjustments. You can find these smoothed series in this spreadsheetMicrosoft Excel file. As you may recall, each January, the BLS incorporates updated population estimates from the US Census Bureau into the data from the household survey used to construct important statistics such as the labor force participation (LFP) rate and unemployment rate. The BLS noted Adobe PDF file formatOff-site link that the majority of the overall population change incorporated into the latest adjustment reflected recent increased international migration, following a period of subdued international migration due to the pandemic, along with various methodological improvements.

The BLS does not revise historical data to incorporate the population control adjustments, a fact that could make comparisons of labor force data over time a bit misleading. However, the BLS does show the impact of the population adjustments for a selection of labor force series and population characteristics for December of the preceding year. To construct historical labor force series that are more comparable over time, I use those estimated impacts to implement 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.

More specifically, I first smoothed data for each year from 2012 to 2020 (2012 being the year when the 2010 census estimates were first incorporated). Then, I smoothed the data for 2012 to 2021 to account for the effects of the 2020 census population control adjustment introduced in January 2022. Finally, I smoothed the data for 2022 to reflect the latest population control adjustment introduced in January 2023. I applied this method separately to the statistics for which the BLS provides population adjustment impact estimates, and I adjusted the data using (where available) published seasonal adjustment factors. The linked spreadsheetMicrosoft Excel file contains smoothed estimates for the population, labor force, employment, labor force participation rate, and employment-to-population rate for selected population characteristics for 2012 through 2022.

As the following table shows, the effect of the latest population-control adjustment on broad age-group labor force participation rates for December 2022 was generally smaller than the impact of last year's adjustment on the December 2021 participation rates.

Table 01 of 01: Population Control Adjustment's Impact on the Labor Force Participation Rate Relative to the Published Estimate

For example, the BLS estimated that the population adjustment impact on the December 2021 LFP rate for the population aged 55 and older group was 0.7 percentage points, and this adjustment contributed to a 0.3 increase in the overall LFP rate relative to the published estimate. The BLS also estimated that the population-adjustment impact on the December 2021 LFP rate for the population aged 16–24 was −0.3 percentage points.

For December 2022, the BLS estimates that the LFP rate for the overall 55-and-older population would have been 0.1 percentage points lower than the published data indicated. Looking at the underlying population data, it appears that this adjustment resulted from an increase in the estimated size of the population aged 75 and older (up 1.5 percent, an unusually large amount, in the published population estimate between December 2022 and January 2023) and a decline in the population aged 65–69 (down 1.4 percent between December and January). At the other end of the age spectrum, the LFP rate for the 16–24 population would have been 0.5 percentage points higher than the published estimate. This adjustment seems to be the result of an increase in the estimated size of the size of the population aged 20–24 (the published estimate of the population aged 20–24 rose 5 percent between December and January, whereas the size of population aged 16–19 was essentially unchanged).

Chart 1, which compares the smoothed and published LFP rate for the population aged 16 and older, depicts the impact of smoothing on the historical data.

The latest population adjustments don't significantly affect the basic story of the overall LFP rate. This rate changed little over the course of 2022 and is still lower than it was pre-COVID, and the pre-COVID LFP rate was probably higher than the published data suggest. The biggest factor influencing the recent behavior of the overall LFP rate has been the lower participation by the population aged 55 and older, which reflects the combination of an aging population and a greater propensity of the older population to be retired than they were pre-COVID (see, for example, this recent study Adobe PDF file formatOff-site link). This drop in participation by the older population is evident in chart 2, which compares the smoothed and published LFP rates for the population aged 55 and older.

Chart 02 of 02: Labor Force Participation for Those 55 and Older

Similar to last year, the population control adjustment didn't affect the LFP rate for the overall 25–54 (prime) age group. As chart 3 shows, there is no difference between the smoothed and published prime-age LFP rates, and they have been fluctuating during the last year at close to their pre-COVID levels.

Later this year, the Census Bureau will publish updated monthly population estimates and projections for 2022 and 2023 for individual ages that will allow more careful adjustments to LFP rates for finer age groups than the BLS provided. In the meantime, I hope you find the smoothed labor force seriesMicrosoft Excel file useful.

January 9, 2023

The Wage Growth Tracker with Rounded Wage Data: The Final Plan

On December 15, 2022, the US Census Bureau released its final plan for improving disclosure avoidance procedures for the Current Population Survey Public Use Files (CPS PUF), and that plan is available hereOff-site link. As you may recall, we here at the Atlanta Fed have been keenly interested in the proposed changes because we actively use the public use files to produce statistics such as the Wage Growth Tracker.

Part of the plan to avoid disclosure of individuals in the CPS-PUF is to round the CPS PUF earnings data. Previously, I have written about how the initial proposed rounding rules for hourly and weekly wages would have harmed the reliability of the Wage Growth Tracker (see here and here). In this Policy Hub: Macroblog post, I take a look at how the final plan for rounding wages would affect the Wage Growth Tracker.

The following table summarizes the Census Bureau's final rounding rules for hourly and weekly wages in the CPS PUF, with the prior proposed values shown in parentheses if they differ from the final values:

Table 01 of 01: Final Wage Rounding Plan for 2023 Data

As you can see in the table, the final rounding rules are less restrictive than the prior proposal released in July 2022. In particular, the Census Bureau modified its proposal by raising the upper boundaries of the rounding for hourly wages. It also updated the weekly rounding to better align with the hourly wage rounding rules, assuming a traditional 40-hour work week, along the lines I had suggested here.

The following chart shows the published Wage Growth Tracker based on unrounded data (orange line), and what it would have been if the final rounding plan had been in place (blue line).

If you have difficulty seeing any difference between the two lines, it's because they differ very little. The rounded wage data would have had very little impact on the Wage Growth Tracker statistic. I believe this outcome is a win for the collaborative process that the Census Bureau employed when developing this final plan, which included sharing information about the proposals and gathering suggestions for revision from the user community.

The Census Bureau plan includes one other change that will also directly affect the Wage Growth Tracker data. The Wage Growth Tracker excludes wage observations that have been topcoded. (Topcoding helps preserve the anonymity of the highest wage earners in the sample under study by replacing their actual wage with a topcode value.) The Census Bureau is introducing a dynamic topcode that will apply to the top 3 percent of earnings reported each month. This method will replace the current one, which applies fixed-dollar topcode thresholds to the wage data. For weekly earnings, the static threshold is currently $2,884.61 ($150,000 a year) and results in the potential exclusion of about 5.5 percent of the wage data that could have gone into the Wage Growth Tracker statistics. The new dynamic topcode will result in fewer cases being topcoded and thereby modestly expand the sample size used to compute the Wage Growth Tracker. However, if the highest wages are mostly people with relatively low wage growth (because, for example, they are late in their careers), then the calculated median wage growth could be a bit lower than it would have been. For that reason, at least initially, we plan to maintain a parallel set of Wage Growth Tracker data that continue to implement the static topcoding to see if we note any systematic differences arising from the dynamic topcode.

The changes to the CPS PUF will be implemented with the release of the January 2023 data in early February. I will report here on what we learn about the impact of the switch to dynamic topcoding, but users of the Wage Growth Tracker data can be confident that the switch to the rounding of the underlying wage data will have minimal impact.

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 Adobe PDF file formatOff-site link," 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.comOff-site link (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 InequalityOff-site link," 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 DynamicsOff-site link 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 Adobe PDF file formatOff-site link," 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 AccumulationOff-site link," 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.