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.
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April 21, 2023
Retirement and Its Impact on Labor Supply
As I enter my sixties, the notion of retirement crosses my mind more often—and apparently I'm not alone. By my estimation, there are about five million more retirees in early 2023 than there were immediately prior to the COVID-19 pandemic. Chart 1 shows this increase, with the blue line depicting the estimated number of people who are not in the labor force and say they are retired.
As a technical aside, the data in the chart are corrected for population estimates based on the 2020 decennial census. The revised data imply fewer retirees in the years before 2020 than the population estimates based on the 2010 decennial census indicate.
The chart clearly shows a jump in the number of retirees with the onset of the COVID-19 pandemic in early 2020. I find that, of the five-million increase in the number of retirees since 2019, roughly one million can be attributed to a change in retirement behavior during the COVID-19 pandemic. That is, if retirement rates for each sex-age combination had stayed the same as they were on average in 2019, there would have been about four million more retirees simply because of the aging population. (See this study by my colleagues at the Board of Governors for related analysis on this topic.)
Interestingly, the couple of years leading up to the COVID-19 pandemic was a period of rising participation rates across all ages, responding in part to the tight labor market conditions that existed at the time (see, for example, this study that was presented at the 2021 Jackson Hole symposium). These conditions helped suppress retirement rates by keeping people engaged in the labor force. To see how this affected retirement numbers, I ran an experiment that held age-sex retirement rates fixed at their average levels in 2012 (see chart 2). (I choose 2012 as being representative of the period recovering from the Great Recession of 2008–09.) My analysis, which also used the demographics of aging from 2012, would put the number of retirees close to where it actually is today. Chart 2 shows this experiment, where the black dashed line shows the estimated number of retirees based on 2012 retirement rates.
What are the labor supply implications of increased retirement? One thing to keep in mind is that rising retirement numbers don't translate to a commensurate decline in the size of the labor force, because many people who enter retirement were already out of the labor force prior to retirement. For example, someone who was at home taking care of household responsibilities, and thus not counted as a participant in the labor force, might say they are retired once their working spouse or partner retires. Similarly, someone who had been too sick or disabled to work but is now eligible for retirement benefits might say they are now retired although their health is unchanged. Changing from one nonparticipation category to another doesn't affect the number of labor force participants. Chart 3 shows the distribution of preretirement categories of new retirees. I've aggregated the data to an annual frequency to smooth out any seasonal fluctuations. The crosshatched colors identify preretirement activities that are outside the labor force, and the solid colors identify activities within the labor force.
Around 60 percent of women who moved into retirement during 2022 were not in the labor force immediately prior to retirement (the hatched area of the chart). The largest preretirement category for women was "family responsibilities" (28 percent). For men, about 40 percent were not in the labor force for several years prior to retirement, with "other reasons" being the largest nonparticipation category (20 percent). Also notice that part-time and full-time employment are about equally important preretirement characteristics for both men and women, but because more men than women are labor force participants, moving from employment to retirement is generally more common for men than for women.
The aging population is clearly a powerful influence on the labor force. A recent blog post by my colleagues at the New York Fed argues that the aging population is behind almost all the shortfall in the overall labor force participation rate relative to pre-COVID. Importantly, an aging population is likely to continue to pressure the labor force in coming years. A January 2023 study by the Congressional Budget Office projected that between 2023 and 2053 the number of people aged 65 and older will increase by 25 million (an average rate of 1.2 percent per year), whereas the prime working-age population (defined as those aged 25–54) will have increased by only eight million (an average rate of 0.2 percent per year). Moreover, immigration will increasingly drive projected prime-age population growth as US birth rates remain below the rate necessary for a generation to exactly replace itself.
Absent faster growth in the working-age population through increased immigration, boosting the growth rate of the labor supply will need higher labor force participation rates. Encouragingly, this is something that we have started to see in recent months among the prime-age population. For example, a tight labor market has helped to increase prime-age labor force participation, which has increased from 82.5 percent in March 2022 to 83.1 percent in March 2023 and is the same as its pre-COVID level. However, prime-age participation is still well below the 84 percent level seen in the early 2000s. For the older population, especially those over 65, participation rates remain well below pre-COVID levels (currently around 19 percent versus 21 percent pre-COVID), suggesting that, in the wake of the pandemic, the desire and willingness of older Americans to work might have permanently changed. That said, one of the lessons I have learned over my career studying labor markets is that while demographic changes are usually slow and reasonably predictable, behavioral changes can be fast and are much less easily anticipated. During the 1990s, reforms to social security and a shift away from defined-benefit pension plans had a significant impact on older workers' participation in the labor force. Lost retirement savings during the Great Recession also lifted participation behavior. In that regard, according to the Federal Reserve's Distributional Financial Accounts overview, the net worth of older American's is around 25 percent higher than it was in 2019 but is down by about 3 percent at the end of 2022 from a year earlier. So never say never.
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.
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 spreadsheet. 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 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 ). 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 spreadsheet 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.
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 ). 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.
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 series 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 here. 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:
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.
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