Measuring Earnings from Work

Despite the ongoing pandemic, the US Bureau of Labor Statistics report that a typical full-time worker earns the equivalent of more than $52,000 a year. This post discusses earnings from wages and salaries and presents two broader measures that are designed to include people with little or no earnings.

As a starting point for thinking about a person’s earnings: survey data show that wages tend to increase with age. The typical older worker has more work experience and is paid more than the typical younger worker. Among full-time wage and salary workers, earnings increase steadily from $25,000 per year at age 16 to more than $60,000 per year around age 45, then plateau and remain near their peak until the social security retirement age (see chart 1). 

Measuring the earnings of full-time workers, however, does not tell us about people who are either working part-time, unemployed, or work-limited in another way. Typical earnings are much lower if we include the earnings of all people in the survey rather than just those who are currently working full-time. The median wage falls from $52,500 for current full-time workers to $15,290 for anyone in the survey aged 16 or older. Broken down by age, the broader measure shows earnings are zero for a typical 16 year-old, around $30,000 for the typical 25 year-old, and peak at around $42,000 for a typical person in their mid 40s (see chart 2). Median earnings past retirement age are zero.

By including people working part-time as well as people with zero earnings, the second chart reverses a major result from the first chart. Specifically, during the pandemic many people lost jobs or became part-time and therefore are removed from the 2020 measurement of full-time workers’ wages. Since jobs lost during the pandemic were more-often below the 2019 median wage, removing these jobs from a wage measurement makes the new median wage of the remaining jobs higher. In other words, the chart 1 result showing that the wage of the typical full-time worker is higher in 2020 than it was in 2019 is because the group of people being measured has changed and the typical worker is a different person. Measuring the same group of people in both 2019 and 2020 shows that earnings have instead fallen substantially during the pandemic. The reduction in earnings is confirmed by data on total wage and salary income.

Importantly, despite far worse economic conditions in 2020 than in 2019, chart 2 shows that several features of the relationship between earnings and age persist across both years. As one persistent feature, in both 2019 and 2020 the typical person who has reached retirement age has stopped working and has zero earnings. The US does a reasonably good job of providing welfare payments to people who reach retirement age, through Social Security, which facilitates retirement.

A second persistent feature is that children have no earnings in either year. With some exceptions, including newspaper delivery and family businesses, wage labor from children under the age 16 is uncommon and it is not measured in the survey. Also, many teenagers above the age of 15 are not working because of school.

Lastly, some portion of working-age adults are work-limited at any given point in time. The work-limited include people with an injury, disability, or illness, as well as students, and people with additional responsibilities at home such as caregivers and new parents. US rules around welfare of children and persons with disabilities are more strict and payments are meager, if available at all, relative to the help retirees receive from Social Security. One consequence of a work-focused welfare system is that many who become work-limited are expected to rely on the earnings of their family members to get by.

Family Earnings

Living with family is the way many people, including children, receive resources. Living together allows people to pool their income, share costs, and use resources like a car or a kitchen more efficiently. Economists sometimes calculate a concept called “equivalized” family earnings by adjusting the total earnings of a family for the size of the family (see footnote of chart 3 for details). The result is a per person measure of family wage and salary earnings that accounts for the efficiency of the size of a person’s family. Typical equivalized family earnings are $21,620 per person per year in 2020. 

By age, median equivalized family earnings are around $32,000 for newborns, $40,000 for 20-year olds, more than $50,000 for 50-year olds, and zero for those age 70 or older (see chart 3). The drop in family earnings starting at age 60 is from the combination of retirement and from people in these age groups being less likely to live with someone else who works.

Interestingly, the decrease in median earnings from 2019 to 2020 is less severe in the family earnings measure from chart 3 than in the personal earnings measure from chart 2. The resilience of equivalized family earnings to the recession is due in part to more people moving in with family. In 2020, a larger share of people utilize the resource efficiency of living with others, which is captured by an equivalized measure such as family earnings but not by the personal earnings measure.

Families with No Earnings

While the above sections focus on the typical person, measured as the median or middle member of the group, the effects of the COVID-19 recession have fallen more heavily on people nearer to the bottom of the wage distribution, an effect already evidenced in chart 1. One way to see how job losses hurt families is to look at the share of people whose family have no earnings. During a recession, the share without earnings is larger because there are fewer jobs. In 2020, 37.8 percent of people have no family earnings in the survey week, compared to 35.4 percent in 2019. By age, around 15 percent of newborns have no family earnings, compared to less than 10 percent of 45 year-olds and more than half of 70-year olds (see chart 4). 

Chart 4 can help to illustrate the relationship between poverty, age, and welfare. By definition, families without earnings will be in poverty unless they receive sufficient income from welfare or from private assets. Because the elderly are far less likely to have family earnings from work, the age group relies heavily on other sources of income to avoid poverty. For elderly people without much private wealth, Social Security payments facilitate independent living and result in much less poverty for elderly people and their families. That is, social insurance makes up for a lack of family earnings and reduces elderly poverty to the same rate or lower as the overall population. Other countries go further than the US and apply this same technique to other groups of people who do not have earnings, such as children. Including people who do not have earnings when measuring earnings can be useful for thinking about how the US should treat someone when they are work-limited.


Adding texture to the jobs report

The monthly jobs report shows a widely-cited spike in unemployment, starting in April 2020, as a result of the COVID-19 pandemic. It’s worth illustrating that employment effects go far beyond what is measured by unemployment, and additional analysis of household survey data give a more complete picture. The large number of people affected support a case for broad eligibility for government financial support.


The age 16 or older population can be divided into three categories: those who are employed in any amount, those who are not employed but are on temporary layoff or are looking for work (the unemployed), and those who are out of the labor force. The out of the labor force group includes students, caregivers, persons with disabilities, and the elderly, as well as those who have given up looking for work but would like a job.

Screenshot from 2020-08-16 20-44-39

Because the three categories in the chart above sum to 100% in any give month, a change in one category must be offset by a change in another category. For example, during a recession (shaded gray area) the employed share of the population falls as people become unemployed and leave the labor force.During the great recession in 2008-09 the job losses were protracted. From October 2006 to October 2007, the employed share of the age 16+ population fell by 0.6 percentage point. From October 2007 to October 2008, the employed share fell by 1.0 percentage point, and from October 2008 to October 2009 it fell by another 3.2 percentage points. In contrast, the 2020 recession started very abruptly. The March report covered the week before many places had shut down, but the April 2020 report showed a 9.3 percentage point drop in the employed share of the age 16+ population from April 2019. Each subsequent month, with the most recent available being July, showed a smaller one-year rate of change than the previous month.Screenshot from 2020-08-16 20-45-00To get a bigger sample size and an overview of the April 2020 onward data, the following sections combine the monthly survey data for April through July 2020 and compare it to the corresponding combined survey data covering April to July 2019. The average decrease in the employed share of those 16 or older was 7.2 percentage points, with three-quarters of that shift moving into unemployment (a 5.2 percentage point one-year change) and one-quarter leaving the labor force (2.0 percentage point one-year change).Screenshot from 2020-08-16 20-45-21

Overall Change by Age

Data show the effects in labor force status are disproportionate to young people. Among three-year age groups used for these calculations, the age 19-21 group was the hardest hit, with a decrease in the age group employment rate of 16.8 percentage points in April through July 2020 compared to the same four months the previous year. The age 19-21 group was also disproportionately likely to leave the labor force, with a 7.2 percentage point one-year change in the share of the population that is not in the labor force, compared to a 9.6 percentage point change in the share that is unemployed. The effect was similar but less pronounced for those ages 16-18, 22-24, and 25-27.Screenshot from 2020-08-16 20-45-34Next we look at changes within each of the three categories discussed above.

Changes within types of employment

Shifts within the employment category are important. For example, some full-time workers have their hours cut but are still employed, and some people lose a second job. Additionally, the Bureau of Labor Statistics reported that some people who should probably should have been classified as unemployed were instead described as “employed, but not at work” in the data, particularly in April 2020. In these three examples the person is classified as employed and wouldn’t show up in the previous chart.Therefore, the next chart divides those who are employed into four nonoverlapping categories: multiple jobholders (people who have more than one job), those fully-employed (defined as working 35 or more hours or voluntarily working fewer) and at work during the reference week, those working fewer than 35 hours but who want more hours (part-time for economic reasons), and those who are employed but not at work. The one-year change in each category shows that the employment effect is substantially larger when considering the deterioration within employment categories.Most age groups saw a decrease in the “fully-employed, at work” category that was larger than the overall change in employed defined in the previous sections. This is because of the reduction in second and third jobs, the increase in people not at work, and the shift of millions of people to part-time status, despite their preference for full-time work. The reduction in multiple jobholding rates was highest among those age 22-24 (1.9 percentage point decrease). The age 49-51 group had the biggest increase in the share of the age group that works part-time but wants to work full-time, with a 3.4 percentage point increase.Screenshot from 2020-08-16 21-45-08

Changes in Unemployment by Age

A bright spot in the four months of historically bad data is that most of the unemployed are on layoff. Unlike other recent recessions, the reduction in economic activity was a decision made for public health purposes. Arguably, the US did not do enough or move quickly enough in reducing employment and non-essential economic activity, causing the country to have many more cases and preventable deaths than western Europe.It is tough to know whether a job loss will actually be temporary, but the expectation during the early months of the shutdown was that people stay home and that policymakers provide enough income to keep most families financially stable without them going out and looking for work. The data show that people very disproportionately did report their unemployment as a layoff. That said, there was still an increase in the share of the age 16+ population that is actively looking for work. Those in their 20s are disproportionately likely to be looking for work rather than on temporary layoff, with the peak being for those 22-24, who have an increase of 1.8 percentage points in 2020 from the previous year rate of looking for work.Screenshot from 2020-08-16 20-46-05

Changes in Labor Force Participation by Age

Perhaps the most interesting chart, and the reason I initially looked into the topic, shows changes in categories of labor force non-participation (those out of the labor force). The source for this data asks people about their major activities and their reasons for not having a job and not looking for one. Those 16 or older can be out of the labor force because of school, a disability or illness, to care for home or others, because of retirement, or they can be discouraged because they do not believe work is available for them.The data show a mixture of effects, with the most prominent being an increase in the share who are discouraged. Some other effects are more age-specific. Young people are increasingly reporting school enrollment as their reason for non-participation, but also are disproportionately increasingly non-participants in the discouraged and other categories. There were also people age 22-45 who left caregiving roles, ostensibly to work or look for work. Additionally, the retired share of those 55 and older increased.Screenshot from 2020-08-16 20-46-23It’s worth noting here that age groups can partially offset each other in these data. For example, a portion of young people in college who would prefer to work are offset by people in their 20s and 30s who would go to school if they felt confident about leaving a job.Among those age 34 or older, there is actually a decrease in the share that report disability or illness as their reason for non-participation. It’s not clear what is happening here, though one possibility is that reductions in non-COVID-related medical procedures mean fewer people were sidelined temporarily by a surgery. Another possibility is that some portion of those who reported disability or illness found work or moved into other categories of non-participation or unemployment.

Summing up the categories

Far more people have labor market effects from the shutdown than those counted as unemployed. The unemployed average 19.5 million over the four month period and BLS reports 16.3 million unemployed in July. However, counting the people who are no longer fully employed plus those who are no longer multiple jobholders yields a total of 26.8 million people on average for the four month period.Screenshot from 2020-08-17 00-39-43The data also show that those age 16 to 24 are disproportionately affected. The last table includes only those in this combined age group. About one in seven in this group, about five million people, fell out of the fully employed category or the multiple jobholder category. About half of those are unemployed and the other half are underutilized or left the labor force.Screenshot from 2020-08-17 00-46-43

Teacher summer jobs

I’m curious about how jobs are changing for the roughly 5.5 million teachers in the US. Teaching doesn’t pay as well as jobs that require similar education, but teaching traditionally includes a summer break and retirement and healthcare benefits. This post looks at trends since 1997 in how teachers use their school summer break and shows that they are becoming more likely to work during the break.

Summer break

School summer breaks show up in data on the teaching share of total hours worked. In the school year, teaching comprises 3-3.5 percent of total work hours, compared to around one percent in the summer months.


Second jobs

Panel data show that the share of teachers working a different job in the summer is pretty stable, but that teachers are more likely to be at work as teachers over the summer months (June, July, and August) than they were in the past.


Earlier start to school year

The individual monthly data show an earlier start to the school year in many places (August rather than September) shortening the summer break for teachers. In August 2019, teaching comprised about 2.5 percent of total work hours, compared to 1.2 percent in August 1997.


How far can full employment get us?

Full employment–a version of the US economy where anyone who wants a job has one–is an important complement to welfare for poverty reduction, but ultimately not a substitute for welfare.

Why is full employment important?

Full employment means more labor income for people. A single person working full-time and full-year will have income above the poverty threshold, even if earning the minimum wage.

Typically full employment is also associated with less discrimination in hiring and with workers getting a stable share of output growth. Achieving full employment also better positions the US to provide for people who cannot or should not be working.

Are we at full employment?

The US is likely close–within a few million jobs–to full employment. Many economists had expected runaway inflation as the US unemployment rate fell below 5.5% but inflation has been well-contained. Unemployment is now around 3.5% which is near to historical lows.

What can’t full employment do?

People who live in income-sharing arrangements (for example a family) where the number of non-earners (for example: children, elderly, work-limited disabled, caregivers, students, etc) exceed the number of earners are at a major disadvantage, even under full employment, relative to those who live with more earners than non-earners. That is, a single mother of three kids will be relatively better off with full employment than without full employment, but is likely to struggle financially even with full employment.

The previous example is fairly intuitive, but what people don’t realize is just how few people actually work full-time and full-year, even in a near-to-full-employment economy.

In 2018, no part of the US had even 42% of its total population working full-time and full-year:

Screenshot from 2019-12-18 13-22-23

The map shows commuter zones–groups of counties that form local labor markets. Even with the economy near full employment in 2018, no part of the country has a majority of its population fully-employed. That is, even when the US is maximizing its number of workers, there are still large gaps in labor income over time and within any given point in time. And reaching the people not reached by labor income in full employment is precisely what welfare is for.

Data reject the argument that welfare is not needed in full employment. Non-earners comprise a large share of every country’s demographic picture, even at full employment, and non-earners are unevenly distributed within families. This combination means poverty cannot be solved through labor income alone. Existing welfare programs in the US, like unemployment insurance, provide income for earners who lose their job; separately, there are welfare programs that provide income to non-earners regardless of the state of the economy, such as low-income, low-asset elderly reached through Supplemental Security Income. Other countries show that adopting additional programs, for example a child allowance, would effectively and immediately reduce poverty from its stubbornly high rate.

Who gets credit for the wage growth of low wage workers?

Last month, I pointed out 7% wage growth at the first decile (10th percentile). The CEA noted this same figure in a recent report, claiming credit for the uptick at the bottom of the wage distribution. My piece, in contrast, had assigned credit to the state, local, and company minimum wage increases. AEI has a piece that thanks both the long expansion and the minimum wage increases.

The AEI piece points to Atlanta Fed analysis showing that states without state minimum wage hikes have seen increases in the median wage of the first quartile (bottom 25%) of wage earners. This makes a strong case that state minimum wage increases only explain part of the story. But it’s worth testing the argument that if additional states had raised their minimum wage those states would have seen larger wage gains for low-wage workers. That is, by making the wrong decision the no- and low-minimum wage states fared worse, but despite making the wrong decision, they still fared reasonably well.

A test of my claim comes from comparing total hours worked in different wage groups and in two state groups: those that raised the state minimum wage from 2016 to 2019 and those that didn’t. To better capture total wage income, aggregate hours are used instead of number of workers. In the baseline data which cover 2016, slightly more than half (55.4%) of hours worked in the US were from people in the 27 states with minimum wage increases during 2016-2019. This is how the total hours worked are distributed by wage group and state group in 2016:


Notably, the share of hours that are paid the federal minimum wage or less is low; several times as many hours of work were paid between $7.26 and $12.00.

In the latest year of data, which ends November 2019, the story has changed:


The share of work hours paid less than $12 has fallen substantially since 2016. The effect is more pronounced in states that increased the minimum wage but also apparent in those that didn’t. As a result, states that didn’t raise the minimum wage now disproportionately have low wage work relative to the states that did.

It’s worth noting that total hours of work increased by five percent over the period. The concern that raising the minimum wage will cause job losses doesn’t show up in the data. The states that raised their minimum wage maintained a fairly constant share of total hours of work, claiming 55.2% of total hours over the past year.

Looking more closely at the data, the long economic expansion does deserve some credit for wage growth at the bottom of the distribution. Importantly, states that acted to raise minimum wages better shared the economic expansion with low wage workers.

Wage growth is in the pipeline

If you haven’t looked at the US wage distribution recently, you might be surprised to see nominal growth of 7.0 percent in 2019 Q3 and 6.7 percent in Q2 in first decile usual weekly earnings. My own calculations show an even stronger 8.7 percent year over year increase in October 2019. The increase in the first decile wage seems to be coming from state, local, and company increases to the minimum wage. It is also an indication that higher wages may soon be coming to the workers in the middle of the income distribution.

First, the data:

First decile wages and growth

The dark blue lines are the BLS quarterly data on first decile usual weekly earnings for full-time wage and salary earners. The light green lines (bd CPS) calculate the same series on a monthly basis.

The BLS and bd CPS series both show first decile wage growing at its strongest rate since the late 1990s. One conventional story here is that a tight labor market, as measured by a low unemployment rate, makes it harder for employers to find qualified replacements for employees who quit, which makes existing employees more likely to get raises. As a result, wage growth is usually strongest when the labor market is tight.

But there’s a catch. BLS uses the same sample to report nominal median wage growth of 3.6 percent in Q3 and 3.7 percent in Q2. BLS data on real average hourly earnings, which adjusts nominal earnings data for inflation, shows 1.2 percent real wage growth in October (nominal wage growth of 3.0 percent and inflation of 1.8 percent). This isn’t terrible, but it’s no seven percent. With the unemployment rate historically low, why isn’t median and average wage growth stronger?

Leaning on unemployment to fully explain wage growth is clearly not working. An alternative measure of whether a labor market is tight is the employment rate for people age 25-54. This measure is currently at it’s highest level since 2007 but is still the equivalent of more than a million jobs away from its late 1990s and early 2000s level. In other words, there’s evidence that the labor market is still over a million jobs away from full employment.

Maybe it’s better to ask instead why wages are growing so rapidly at the first decile and whether it means anything for wage growth more broadly. The data show that the number of full-time workers earning $400 per week or less has fallen to 7.5 million in the latest three months from 14.7 million in the same three months of 2014. The story here seems to be higher local and state minimum wages and higher company minimum wages at large employers. Several areas and employers moved the minimum wage to above $10 per hour ($400 for a 40-hour week) during the past five years.

Higher minimum wages could eventually translate into higher median wages. For example, the first decile and median wage tend to move together:

First decile and median wage growth

In the period of faster wage growth and tighter labor markets during the late 1990s, first decile wage growth accelerated and median wage growth accelerated shortly after. If employment trends continue, it seems reasonable to expect an increase in the median wage of five percent or more in 2020.

But the current period is not the late 1990s. Business fixed investment and labor productivity growth are both particularly weak now compared to then. Negative net capital investment and increasing payrolls suggest an aggregate situation akin to fewer tools per worker, which casts doubt on forecasts of sustained real wage growth.

Technical Note

Unlike the first two charts, I use some additional steps to smooth out the third chart comparing median and first decile wage growth. I calculate the wage in each month as the wage in the previous three months combined, so October 2019 is based on combined microdata from August, September, and October 2019. Additionally, I use the Census X13as program to seasonally adjust the results. The growth rate is based on this resultant seasonally-adjusted nominal wage.

In both sets of charts, I replicate the BLS process of taking a “binned median” to reduce how much the data reflect breaks around certain round numbers (like $400 per month).

Six southern US metro areas: part 5 – reasons for labor force non-participation

Part five in the series of blog posts on six mid-sized southern US cities looks at why people are not participating in the labor force (not employed and not looking for work).

The six areas of interest are: the Chattanooga-Cleveland-Dalton, TN-GA combined statistical area, the Greenville-Anderson-Spartanburg, SC combined statistical area, the Asheville, NC center-based statistical area, the Johnson City-Kingsport-Bristol, TN-VA combined statistical area, the Huntsville, AL center-based statistical area, and the Knoxville, TN center-based statistical area. See the first post in the series for more background.

The source for these results is 24 months of aggregated Current Population Survey microdata, covering January to December in 2017 and 2018. Because I am using the CPS, it’s important to remember that all blog posts in the series look only at civilian, non-institutionalized people. The US has a large military and a very very large prison population, but neither are included in the survey results or in this analysis.

Employed or caring for house/family

As discussed in the previous post, the primary explanation for differences between employment rates of men and women is that women are more likely to do unpaid work in the home, such as caring for children or elderly relatives. The share of men and women who are primarily “working” (either employed or caring for house or family) is pretty much identical, with a few exceptions.


Among people age 16-64 in the US as a whole, men (76.9%) are slightly more likely to be employed or in a care role. The story is the same in five of the six areas of interest, though the gap between men and women is a bit larger in each of the five, compared to the US as a whole. The biggest gap between men and women is in Huntsville, where 77.7% of men and 73.4% of women are employed or in care roles. In Asheville, women are slightly more likely than men to be employed or in care roles.

In two areas, Johnson City-Kingsport-Bristol and Chattanooga-Cleveland-Dalton, men and women are both quite a bit less likely to be employed or in care roles than the US average. In contrast, in the Knoxville area, the opposite is true; both men and women are more likely to be employed or in care roles than those in the US as a whole.

Other reasons for non-participation

Beyond care roles, there are large differences between the areas in the other reasons for non-participation in the labor force (people who are not employed or looking for work). Other reasons for non-participation include people that are in school (more likely among 16-24 year olds) and that are retired (more likely among 55-64 year olds). A substantial portion of the population is also not employed primarily because of disability or illness. Some are also discouraged, meaning they want a job but have stopped looking for work because they do not believe one is available for them.


In the US as a whole, about six percent of men and women are not in the labor force due to disability. A smaller share of the population, about 4.5%, are in this category in Asheville. Likewise, women in Huntsville are slightly less likely to be out of the labor force due to disability or illness, compared to the US a whole. In Knoxville, the share is near the US average. In contrast, the share of men in the Chattanooga area (9.7%), women (8.9%) and men (8.3%) in the Greenville area, and men in the Johnson City-Kingsport-Bristol area (8.6%) who are not in the labor force due to disability or illness is well above the US average. The most striking instance is among women in Johnson City-Kingsport-Bristol, where 12.1% of the age 16-64 population is not in the labor force because of disability or illness.

If someone can explain why non-participation from disability is twice the national average for women in Johnson City-Kingsport-Bristol, please leave a comment. My speculation would be something like factory closures. Physically demanding jobs, like factory work, can lead to disability or illness. When factories close, people are left without job opportunities (often the factory is the main employer in the area), and with the scars of the their former job, thus are more likely to leave the labor force and more likely to be disabled.

Another major reason for non-participation is retirement. Among 16-64 year olds, women are more likely to be retired in the US as a whole and in each of the six areas. Partially this is because women make up a larger share of 55-64 year olds. In Asheville, where the population is older than the US average, the retired share of the age 16-64 population is much higher than in the US as a whole or in the other five areas of interest.

People who are not employed because they are in school make up about seven percent of the age 16-64 US population. This rate is pretty consistent across the six areas, with two exceptions. Women in Huntsville are more likely to be in school and not employed, and women in the Johnson City-Kingsport-Bristol area are less likely to be in school and not employed.

Finally, about two percent of the US age 16-64 population wants a job but has given up looking for one because they do not believe one is available for them. This could be because there is a lack of jobs for people with their skills, or because of discrimination. The discouraged worker share of the population is near or slightly less than the US average in five of the six areas, with the one exception being for women in the Chattanooga area, who are slightly more likely than women nationwide to be discouraged.

The next blog post in the series will look at reasons for unemployment, who is unemployed, and duration of unemployment. The jupyter notebook used in the analysis above can be found here.

Six southern US metro areas: part 4 – labor force status

Part four in the series looks at whether people are working, looking for working, or doing something else, in each of six mid-sized metro areas around the southern portion of the Appalachian mountains. Since economic cycles influence whether people want jobs and whether they can find them, this post also looks at how the labor force status has changed for men as the overall US economy improved from 2015 to 2018.

The six areas of interest are: the Chattanooga-Cleveland-Dalton, TN-GA combined statistical area, the Greenville-Anderson-Spartanburg, SC combined statistical area, the Asheville, NC center-based statistical area, the Johnson City-Kingsport-Bristol, TN-VA combined statistical area, the Huntsville, AL center-based statistical area, and the Knoxville, TN center-based statistical area. See the first post in the series for more background.

The source for these results is 12 months of aggregated CPS microdata, covering January to December 2018. The rate of change data compares the 2018 results with those from January to December 2015.

Employed, unemployed, or “not in the labor force”

This section compares the six areas in terms of whether men and women age 16 to 64 are employed, unemployed (meaning they don’t have a job and are actively trying to find one), or are “not in the labor force.” Not in the labor force, which I will generally refer to as “non-participation,” can be for any reason, such as discouragement (want a job but have given up hope of finding one), disability or illness, school, taking care of family, retirement, or for other, unspecified, reasons.

In 2018, the share of each area’s age 16-64 population that is employed, unemployed, and not in the labor force is similar to the US as a whole. Overall, in the US, 2.6% of women and 3.2% of men are unemployed (this is not the unemployment rate, but rather the unemployed share of the population). Among the six metro areas of interest, the unemployed share of women ranges from 0.8% in Knoxville to 4.3% in the Chattanooga area. The unemployed share of men ranges from 1.2% in the Johnson City-Kingsport-Bristol area to 3.3% in the Greenville area.


In the US as a whole, 76.1% of 16 to 64 year-old men are employed, compared to 65.5% of women. The main reason for the gap is that women are more likely to be doing unpaid work at home, like taking care of children or elderly relatives. The employed share of men in the six areas ranges from 73.8% in Asheville to 78.1% in the Greenville area and Huntsville. The employed share of women ranges from 59.8% in Huntsville to 68.1% in Knoxville. Huntsville has the largest gap between men’s and women’s employment.

Finally, for those age 16-64 in the US as a whole, 31.8% of women and 20.8% of men are not in the labor force. Again, the patterns are similar in all of the six areas. The non-participating share of women ranges from 29.9% in Asheville to 36.9% in Huntsville. Among men, the non-participating share ranges from 18.6% in the Greenville area to 23.8% in the Johnson City-Kingsport-Bristol area.

Change in labor force status

While the labor force status in each of the six areas of interest doesn’t differ too much in 2018 from the US as a whole, there have been some pretty big changes within some of the areas since 2015, which are discussed in this section.

From 2015 to 2018 there was general improvement in the overall US economy, but seemingly even more substantial improvement in the local economies of most the six areas. This is evidenced by the change in the share of the each area’s population that was employed in 2018 compared to 2015. For the US as a whole, the age 16-64 employment share increased by 2.1 percentage points for women and 1.9 percentage points for men. In contrast, only women in the Greenville area (0.5pp) and men in Asheville (1.6pp) had an employment share increase that was smaller than the US average.



Among the six areas of interest, the largest change in employment share was for women in Knoxville. The employed share of this group increased by 7.3 percentage points, to 68.1% in 2018 from 60.8% in 2015. This is a massive increase in a relatively short period of time. Among men, the largest increase was 6.1 percentage points in the Greenville area, where the employed share of 16-64 year olds increased to 78.1% in 2018 from 72% in 2015.

In addition to men in Greenville and women in Knoxville, other groups with at least a four percentage point increase in the employed share of the population include women in Huntsville (4.8pp), men (5.5pp) and women (4.1pp) in Johnson City-Kingsport-Bristol, and men in Knoxville (4.3pp).

Perhaps most interestingly, and as pointed out recently by economists Matthew Boesler and Elise Gould, much of the recent change in employment seems to be coming from people who were previously not looking for work. That is, while unemployment has clearly fallen, so has non-participation. This is a very important development because unemployment is already low by historical standards, but non-participation is still high by historical standards.

In the US as a whole, non-participation explains 0.8 percentage points of the 1.9 percentage point increase in employment among men, and 1.3 percentage points of the 2.1 percentage point increase in employment among women. In contrast, it over-explains employment growth among women in the Chattanooga-Cleveland-Dalton area, who have higher rates of unemployment in addition to higher rates of employment.

Notably, non-participation in Knoxville fell by 5 percentage points for women and by 3.7 percentage points for men, over the four year period. In the Greenville area, non-participation fell by 4.9 percentage points for men but actually increased by 1.1 percentage points for women. Non-participation also increased slightly among women in Asheville. Non-participation fell by 3.4 percentage points for women in Johnson City-Kingsport-Bristol.

The next post in the series will look specifically at reasons for non-participation. The jupyter notebook used in this analysis is here.

EDIT: The original version of this post used person weights, instead of the composite weights. The text and graphics above are updated to use composite weights.

Possible pieces of the long-term unemployment puzzle

Top US economists recently shared insights and data on the increase in the share of unemployed who are long-term unemployed. While unemployment is currently low, about one-fifth of those who are without a job and looking for one have been doing so for more than six months. As Martha Gimbel pointed out, in “1969 (the last time the unemployment rate was at 3.7%), [long-term unemployed] were 4.7% of the unemployed.” In other words, long-term unemployed used to be much more rare during periods of low unemployment.

Abigail Wozniak added that “more [long-term unemployment] is a clear trend,” citing research done for the CEA that gives insight into the trend. It’s very hard to argue, in light of this data, that something structural is not happening here.

I’m hoping to add a few points to the discussion, the first of which I have not seen made. To do so, I focus on the period from 1998 to present, which has a more stable women’s labor force participation rate than the period from 1969 to 1997. In 1998, the long-term share of unemployed was 14.1 percent, compared to 21.6 percent in the 12 months ending November 2018. The source for the following calculations and charts is basic monthly CPS microdata.

Educational specialization

In 1998, the highest level of education for 46 percent of the US labor force was a high school degree or less, while about 25 percent had a bachelor’s degree or more (the rest had either some college but no degree, or an associate degree). In contrast, the most recent twelve months of data show 35 percent of the workforce with a high school degree or less and 37 percent with a bachelor’s degree or more. One result of this long-term structural change, almost by definition, is increased specialization in the educational training of the workforce.

Educational specialization may mean less fungibility in employment. To illustrate this point in a highly exaggerated way, it may take an unemployed person with a BA in Mesopotamian history longer to find a job in the field than it does for someone with a GED who quit at Wendy’s. This explanation may be particularly relevant during a period with a tight labor market and a growing economy, when an unemployed person with advanced education is less desperate to take the first available job and when job opportunities are much more plentiful for those with less education.

We can see this point by looking at the long-term share of unemployed by their highest level of educational attainment (figure 1). Among unemployed with a bachelor’s degree, 23.8 percent are long-term unemployed (27 weeks or more), compared to 18.7 percent for those without a high school degree.

Figure 1.


So there are two things going on here. First, the advanced-education share of the overall labor force has increased. Second, more-educated unemployed people are more likely to be long-term unemployed. The effect of combining these two is a higher long-term share of all unemployment.

This first point is further substantiated because people with advanced education have become relatively more likely to be unemployed. During 1998, the unemployment rate for college grads was 2.0 percent, compared to 2.5 percent over the past year, and the unemployment rate for those without a high school degree was 10.5 percent, compared to 8.1 percent over the past year.

Age and unemployment duration

But education is only a small part of the story. This is obvious because the long-term unemployed share of every educational group has increased over the same 20-year period. A second explanation, which was covered by Gray Kimbrough in the original twitter discussion, is the age composition of the workforce. The same “specialization” story may apply to age.

If we look at the long-term share of unemployed by age, we see that older unemployed people are more likely to be long-term unemployed than younger unemployed people (figure 2). Among unemployed people age 21 to 25, for example, 16.7 percent have been unemployed for 27 weeks or longer, compared to 30.6 percent for unemployed people age 56 to 60.

Figure 2.


Again, like the education story, the composition of the labor force is shifting towards those more likely to be long-term unemployed. In 1998, people age 56 to 60 made up 5.5 percent of the labor force, compared to 9.4 percent today. The age 21-25 share of the labor force has been relatively stable over the period, declining slightly from 10.1 percent in 1998 to 10.0 percent over the last year. Likewise, unemployment rates by age further support the argument. In 1998, the unemployment rate for those age 56 to 60 was 2.6 percent, compared to 2.9 percent today. The 1998 unemployment rate for those age 21 to 25 was 7.0 percent, compared to 6.2 percent today.

Beyond the story of older workers being relatively more specialized, part of the relationship between age and long-term unemployment may be coming from older workers being pushed out of jobs. For more on this topic, please read the fascinating deep dive by ProPublica.

Estimating the compositional effect from age and education

While evidence suggests that more education and older workers explain some of the increase in the long-term share of unemployment, I’m not convinced it can fully explain the 7.5 percentage point increase in the measure over the last 20 years. Re-weighting the last 12 months of CPS labor force observations to match the education and age composition of the 1998 labor force shows that 2.2 percentage points of the 7.5 percentage point increase in long-term unemployment can be explained by age and education. The majority of the increase is therefore not explained by age and education.

One business cycle with two troughs

As a last thought on the issue, measures such as the prime-age employment-to-population ratio show that the US labor market is still recovering from the recession of 2001. A long, jobless recovery creates a situation where people who might otherwise be in the labor force and short-term unemployed are instead in other categories, such as caring for elder relatives, disabled, or in school. If this is the case, it would increase the long-term share of unemployment over the period since 2001.

An alternative way to look at long-term unemployment is to consider the share of unemployed people (age 25 to 54 shown) who are employed one year later. The trend for this indicator (figure 3) suggests that if the US economy is allowed to run hot for a few more years, it may reverse the portion of long-term unemployment effects that are cyclical and not structural.

Figure 3.


I hope the above helps to clarify or guide some thinking on the important topic, even if it can only explain one-third of the puzzle.

Student debt explains why wage growth hasn’t caused inflation

The Fed has been raising interest rates because of concern that rising wages will lead to inflation. The problem with this argument is that while wages are rising, inflation, which is about 2%, is coming from higher world energy prices and from housing shortages, not from broadly higher demand for goods or services. Importantly, household survey data offers the following explanation for how wages could be going up without causing inflation: household investment in education is the cause of higher wages. Data show that, from a macro perspective, the only people who got a raise are the ones with more education than their peers in previous decades. Importantly, these are the same people who can’t spend as much as their peers from previous decades because they borrowed to pay for the education.

Perhaps the easiest way to see the relationship between education and higher wages is to compare growth in the overall median real wage to growth in the median real wage for workers in three educational subgroups (figure 1). From 2000 to October 2018 median real weekly wages increased by 3.6 percent overall, but fell by more than 1.5 percent in each educational subgroup. The median worker with a bachelor’s degree or more earns 2.4 percent less in the latest data than the group’s median worker did in 2000. For the group with a high school degree or less the median wage fell by 1.6 percent. For workers with some college but no degree or with an associate degree, the median wage fell by a whopping 7.7 percent.


Since the three educational subgroups make up 100% of the total, the logical explanation for this outcome is an upward shift in the educational composition of the workforce. That is, workers have much more education in 2018 than they did in 2000. Since people with more education tend to earn more money, the result of a more educated workforce is a higher paid workforce.

While people are generally aware that education levels have risen in the US, they may be surprised to see the extent to which this is true over the past 20 years, or how rapidly the trend accelerated in response to the great recession. In 2000, 40 percent of the full-time age 25-54 workforce had a high school degree or less, compared to 30.7 percent in the year ending October 2018 (figure 2a). Likewise, in 2000, 31.5 percent of the workforce had a bachelor’s degree or more, compared to 43 percent in 2018. The some college or associate degree group remained relatively stable in size, but shifted in it’s wage distribution towards lower wage jobs.

By comparing how wages are distributed among those in each educational group between 2000 and the latest year of data, we can see that the jobs added in each educational group tend to be lower wage (not entirely, but disproportionately), while the jobs lost by each group tend to be higher wage (figure 2b). This suggests that education levels have risen faster than the corresponding availability of good new jobs.


One interpretation of what may be driving these results is that households, in general, were aware that real wages were falling during the relatively weak labor market from 2001 to present. Some households responded to the weak set of job opportunities by investing in education. These households generally received higher wages if they were able to get a degree and then translate the credential into a better job.

The educational investment was very expensive and so those higher wages are not likely to translate into more spending in the same way that wage growth from higher productivity and a tight labor market would. The families that were able to make this investment have income that is less disposable because they now have student loan debt.

To clarify this point, imagine that the labor market had remained tight from 2001 to present and that we never had massive outsourcing of jobs or the great recession. Workers in this case could demand higher wages, and get them, without spending a fortune on education.

When workers’ wage increases do not come with debt attached, the new wage money is much easier to spend on additional goods and services, which could drive up prices. However, if wage increases come with debt attached, as they do in this recent case, then the relationship between higher wages and higher prices breaks down. As a result, the Fed really should worry less about a theoretical wage-price spiral and instead focus on the possibility of achieving full employment before the next recession.