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 female 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 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.

Answer to gig economy question depends on baseline

The US Bureau of Labor Statistics reported on Thursday that contingent and alternative work was occurring at about the same rate in May 2017 that it did in February 2001. Even more surprisingly, some categories of this type of work were actually occurring less frequently in 2017 than in 2005. Media coverage of the report noted the lack of growth in these categories of work and the surprise among those who expected confirmation of a growing gig economy. One take that has been missing, however, is the possibility that other changes to the labor market mean we should expect much less contingent and nonstandard work. Two indicators that nonstandard work has been falling come from regularly-published monthly data showing the long-term decline in self-employment and multiple job holding. Relative to this alternative baseline, the BLS report shows a labor market that has been changing in dangerous ways.


“Contingent” work is comprised of jobs that are not expected to last, even if there is no change to the economy. In a related concept, “alternative” work arrangements include independent contracting, on-call work, and working for a temp agency or contract company while assigned to one client’s place of business. The structure of these jobs varies but they all have a nonstandard employment relationship; independent contractors may provide their labor with less intermediation than the standard worker while contract company and temp agency employees operate through more labor market intermediaries. While only some alternative work arrangements are contingent, all of these categories might be associated with a less-smooth income stream.

Importantly, despite the popularity of treating “contingent”, “alternative”, and “gig” work as synonymous, BLS defines gig work very narrowly as only those jobs that include work found and paid for through a website or mobile app. BLS has not yet released any estimate of gig work, but plans to do so by September 30, in a Monthly Labor Review article. The gig work questions ask people whether they have done ANY gig work, whereas the contingent and alternative work questions focus on the main job.

Interpreting the survey results relative to an alternative baseline

With lots of discussion about Uber drivers, shortened job tenure, and the fissured workplace, analysts were expecting to see a growing share of US workers employed in contingent and alternative work arrangements. However, this expectation seems to forget major demographic changes since 2005. The overall employment rate has fallen since 2001, particularly among young people and students, who are traditionally disproportionately “contingent” workers. Simultaneously, people are now much more likely to have a college degree. Since contingent workers tend to be younger, and those with college degrees tend to have more stable employment, a baseline assumption about the labor force, based on demographics alone, would suggest that nonstandard work arrangements become less common, not more common.

Two indicators that show this changing baseline are the incidence of unincorporated self-employed workers and the rate of multiple jobholding. Both measures are proxies of contingent and alternative work and have fallen by a third or more since the first contingent worker supplement was conducted in February 1995.

Fewer unincorporated self-employed

It’s important to remember that the analysis released by BLS on Thursday came from a one-time supplement to a survey that is conducted every month. If, during the week before one of the monthly interviews, someone drove for Uber, provided labor through Task Rabbit, or worked one of many non-gig independent contractor jobs, as either their first or second job, they will show up as “self-employed, unincorporated.” As expected by demographics, the share of the population that is classified this way has fallen since 1995 for both those age 25 to 54 and for those age 55 or older (figure 1).


In 1995, 7.3 percent of the age 25 to 54 population was unincorporated self employed, compared to 6.3 percent in 2001, 6.2 percent in 2005, and 4.8 percent in the year ending May 2018. The share of those over age 55 who are self employed and unincorporated has also fallen, from 5.1 percent in 2005 to 4.3 percent over the past year.

Since my calculation above includes those who are self employed as their second job, it helps to rule out the possibility that the contingent and alternative workforce numbers are misleadingly low because they do not capture the second job. Additionally, using a moving annual average of the data helps to smooth out the volatility that is possible when looking at any single month of CPS data.

Falling multiple jobholder rate

A second indicator supporting a lower baseline for comparison of recent BLS estimates comes from the fall in the share of workers with more than one job (figure 2).


The share of employed with more than one job has fallen from 6.6 percent in 1995 to 5.3 percent in 2005 and to 4.7 percent in the year ending May 2018. Among women, the trend is less pronounced, with 6.4 percent of female jobholders reporting more than one job in 1995, compared to 5.7 percent in 2005, 5.6 percent in 2017, and 5.4 percent over the past year. Since some explanation of the lower-than-expected BLS estimates has focused on concerns over measurement, and specifically that second jobs are not captured, it is worth pointing out how much less common second jobs now are in the CPS, compared to the period when the earlier survey supplements were conducted.

Policy should focus on people not work

When adjusting for increased education, an aging workforce, and changes to overall employment rates, incidence of contingent and alternative work actually seems to have grown relative to its 2001 rate. In age and education, those who are currently working are more demographically similar to those who traditionally hold standard jobs, with the potential exception of some independent contractor jobs. When compared to a baseline where, all else equal, contingent and alternative work should actually have fallen over the past 12 years, it is concerning to see this type of work retain its share of the labor market.

One explanation for the relative persistence of nonstandard work is that many of the jobs that used to be temporary or unstable have been either outsourced, automated, or lost to the housing bubble, while jobs that used to be stable and standard are being destabilized. The influence that companies in places like Silicon Valley have over labor policy has grown, and these companies seem to be using their influence to try to make otherwise unprofitable ventures profitable, at the expense of workers. Partially as a result of corporate influence, and in the context of other changes in the US labor market, the main difference between the 2001 and 2017 contingent worker supplement results may be that the less stable jobs of today take a lot more student debt to get.

Perhaps more importantly, though, it is off-target to focus on the share of the economy currently claimed by contingent and alternative work. Financial statements already show that many of the new labor market intermediaries are not profitable. The focus should instead be on policy that tries to protect and improve job quality and to prevent gaps in unemployment insurance from needlessly putting people in poverty during the next economic downturn. The ACA, as one relevant policy example, may have been blessed by the 2017 survey data: health insurance among contingent and alternative workers is now much more common.