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.


Poverty and age

Mismatches between the timing in life of labor income and major expenses cause unnecessary poverty in the US. Labor income (earnings from working) is most common between the ages of 25 and 54 and tends to be highest in amount for people in their 40s and 50s. In contrast, education expenses accrue disproportionately to young adults, and healthcare expenses are higher for the elderly. As another example, new parents tend to have lower wages than parents of teenagers while childcare expenses are very high in the first few years of a child’s life. These mismatches between the timing of income and expenses create financial stress and poverty.

One way to see this is to look at poverty by age:

Screenshot from 2019-12-27 19-39-38

The blue area shows the dollar amount of poverty reduced by the combined welfare programs and tax credits. The green area shows the amount of poverty remaining after taxes, welfare, and medical, work, and childcare expenses. The large blue area on the right of each graph shows that market income leaves enormous poverty for the elderly, but that this poverty is reduced to below-average rates by programs such as social security, supplemental security, and medicare. These programs are absolutely critical to poverty reduction but still leave some elderly in poverty.

Two age groups that stand out as facing higher-than-average levels of poverty by disposable income are young adults (students and new parents disproportionately) and those before social security retirement/medicare eligibility age. These groups correspond to the bump in disposable income poverty (green area) around age 20 and again around age 60. These age groups are less likely to work relative to 25 to 54 year olds and therefore have less labor income. Unlike 25 to 54 year olds, those who are younger or older have expenses higher than their income, hence the poverty caused by the mismatch in the timing of income and expenses.

Technical note:

Total poverty is defined as the dollar amount of resources that people are below their Census-defined “poverty threshold”. In the case of market income it is the total amount of money that would need to be given to people for their before-tax labor and capital income to equal their poverty threshold, according to the Supplemental Poverty Measure (SPM). Total poverty by disposable income corresponds with the SPM-poverty-rate-based amount of poverty.

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, it still isn’t getting labor income to half of people. Reaching 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 making the child tax credit fully refundable, is the most effective way to 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).

Choropleth with matplotlib and basemap

When I was using excel to make all of my graphs, the choropleth map was out of reach and particularly alluring. Later, using Stata, I figured out how to make choropleth maps, but the results were never quite right. Now, much later, excel has tools to make these maps easily, and so do you, even if you don’t have a modern copy of excel, by using python!

What follows is an example that maps 2017 GDP growth by Norwegian county.

First, GDP growth data is collected from the statistics office website. Next, shapefiles that match the regions in the data (NO_Fylker_pol_latlng) are downloaded and, using an online tool, simplified to 10% (because Norway has an extremely complex coastline). The simplified shapefiles are saved in a folder called shapefiles.

Python imports:

# Import packages 
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
from matplotlib.colors import Normalize
from mpl_toolkits.basemap import Basemap as Basemap

Data from the Stats Office as dictionary d:

# data for choropleth
d = {'Østfold': 2.5,
 'Akershus': 2.5,
 'Oslo': 2.8,
 'Hedmark': 2.2,
 'Oppland': 2.3,
 'Buskerud': 2.0,
 'Vestfold': 2.1,
 'Telemark': 0.7,
 'Aust-Agder': 2.5,
 'Vest-Agder': 0.9,
 'Rogaland': -0.4,
 'Hordaland': 1.2,
 'Sogn og Fjordane': 1.6,
 'Møre og Romsdal': 0.7,
 'Sør-Trøndelag': 2.9,
 'Nord-Trøndelag': 2.9,
 'Nordland': 1.9,
 'Troms': 2.1,
 'Finnmark': 2.0}

Code creates the map:

# Create map with lcc projection and boundaries that tightly frame Norway
m = Basemap(llcrnrlon=5, llcrnrlat=57, urcrnrlon=33, urcrnrlat=71,
            projection='lcc', lat_1=57, lon_0=15)

fig = plt.figure(figsize=(8, 16))

m.drawmapboundary()   # Create space for drawing county shapes

# read shapefiles using latin-1 encoding and call shape data "no_co"
m.readshapefile('shapefiles/NO_Fylker_pol_latlng', 'no_co', 

ax = plt.gca()   # Call the current plot area "ax"
ax.axis('off')   # Turn off border on outer edge of map

# Map values between -2 and 4 to colors in the rainbow_r colormap
cm = plt.cm.rainbow_r
norm = Normalize(-2, 4)

# For each county, select the face color and add shape to the map
for info, shape in zip(m.no_co_info, m.no_co):
    fc = cm(norm(d[info['NAVN']])) 
    ax.add_patch(Polygon(shape, fc=fc, ec='white', lw=0.5))
# Add title and colorbar legend
plt.title(f'Norway Real GDP Growth by County, 2017', fontsize=16)
cb = fig.colorbar(ax.imshow([np.array([-2, 4])], cm), shrink=0.25, pad=-0.3)


An economic revival policy that fits rural areas well

Paul Krugman’s recent opinion piece in the New York Times argues that eastern Germany and southern Italy show that the US can’t help it’s rural citizens. I’m not sure I follow his argument in the piece, but he’s right that there are several reasons to believe that most large-scale economic policy proposals do not work well in rural areas. There is, however, one policy that would work particularly well.

Large-scale economic revival policies that have garnered a lot of political attention include: antitrust (essentially breaking up large corporations), raising the federal minimum wage aggressively, and instituting a jobs guarantee. I see each as working much better in cities and suburbs than in rural areas.

Expanding and enforcing antitrust law would push back against firm monopoly and monopsony power. Studies show that the consolidation of industries has been bad for workers. In the example of hospitals, when there is only one company running all of the hospitals in a city, it becomes harder for medical care workers to quit a bad job and move to a better one. As a result, hospitals can pay workers less, offer fewer benefits, and use more contractors. The issue here is that in a rural area there’s likely only one hospital anyway (or one factory or one chicken processing plant, etc). So it wouldn’t be efficient to split the rural hospital in two. There just aren’t enough patients to justify double the number of MRI machines, administrators, and accountants.

With the federal minimum wage unchanged for nearly a decade, and currently set at a poverty rate of $7.25 for an hour of work, raising the federal minimum wage seems like a no-brainer. The pushback to a higher federal minimum wage often comes from employers in rural areas. Their argument is that many jobs in rural areas would not make sense if the minimum wage was, say, $15.00 an hour, so the increase would disproportionately create job loses in rural areas. Thankfully, many states and cities are already moving towards a $15.00 minimum wage without federal intervention. But in many rural areas, the current optimal minimum wage is probably quite a bit lower than $15.00, perhaps $10.00 or $11.00 (granted federal wage increases would be gradual, but the point is that the optimal minimum wage is generally considered to be lower in rural areas, though there are exceptions). This is because the marginal productivity of a larger share of workers in rural areas is less than $15.00 per hour, compared to workers in cities and suburbs.

The jobs guarantee elicits comparisons to the Civilian Conservation Corps, which was very active in rural parts of the US. The idea of the jobs guarantee would be to turn the unemployment office into the employment office and to offer a $15.00/hour job to anyone who wants one. For rural areas the jobs guarantee wage faces the same problem as the minimum wage proposal discussed above, but additionally, there are highly-localized pockets of deep despair and poverty in rural areas. For example, my parents live in a town of about 5,000 people that has lost more than 2,000 jobs to trade since the 1990s. Perhaps I am not being creative enough, but I’m not sure there’s enough work (without adding equipment, tools, supervision, or expertise) to support even 400 new federal jobs in the area. Vegetable gardens, picking up trash, and childcare might be able to accommodate a few hundred people, but the problem is so large and so localized that I worry about what several thousand federal workers would actually do all day in the tiny town. In the case of my parents’ town, it’s probably better to just give people the money and not worry about what they do all day than to have them carry rocks back and forth between two piles.

There is one policy that fits rural areas better than it fits anywhere else: increasing who gets ownership income.

Ownership income is the main explanation for why the super-rich are so damn rich. Bill Gates, while known for Microsoft (which benefited massively from intellectual property protections), actually owes much of his wealth to diversified investments (getting paid for owning things other than Microsoft). Rich people own an enormous portion of the country’s stocks, bonds, and real estate, and these investments offer a return that equates to an increasingly large share of national income. With labor income, which has also seen the rich (CEOs, doctors, lawyers) pull away from everyone else, time is an equalizer. Everyone gets the same amount of time (24 hours per day). But when it comes to ownership income, whoever owns the most and the best gets the most money. The majority of people don’t own anything that offers a return (to keep with the analogy, they have 0 hours per day), while the wealthiest people own tens of billions of dollars of productive assets (they have millions of hours per day).

This is not a natural outcome but the direct result of US rules, which means it doesn’t have to be that way. A combination of two policy proposals would result in a very effective way to improve quality of life for people, that works particularly well in rural areas. First, Dean Baker proposed a scrip tax that would replace the US corporate income tax with the ownership of non-voting shares (say 25%) of each company. The idea here is that corporations do everything they can to avoid paying taxes, and usually are pretty good at it. But if the treasury owns a portion of the shares of those companies, then it eliminates the need to tax them. When the company does well and makes more money so does the treasury. The scrip tax could be used to create Matt Bruenig’s proposed social wealth fund (SWF). Essentially think of this as an index fund that is owned by every person in the US equally. Each person would receive dividend payments from this fund, just like rich people do now. For a family of four, this dividend would be several thousands of dollars a year that they would get the same way rich people get their money, just by existing in a system designed to benefit them. **In his report, Matt discusses several ways to pay for the SWF; I’m personally drawn to the funding idea from Dean, which is why I combine the two ideas in this blog post.**

Why would expanding ownership income work for rural areas? First, fewer people in rural areas have ownership income now, compared to cities and suburbs. We can see this in both the Survey of Consumer Finance and the Current Population Survey. Ownership income is rare in metro areas (cities and suburbs), but is even more rare in rural areas. That said, the scrip tax/SWF would not be a transfer from cities and suburbs to rural areas, but a transfer from the ultra rich to everyone else. There are very rich people everywhere, including in rural areas.

Second, rural areas face a problem with diversity of industry. On an aggregate level, rural areas have all sorts of industries, just as cities and suburbs do. But on a local level, there is often one dominant industry, an outcome that is much less common in cities and suburbs. For example, my parents town made nylon starting in the 1930s. This was the industry that employed pretty much everyone, directly or indirectly. The main street was “Nylon Boulevard,” and the town called itself “the nylon capital of the world.” When nylon production was moved to Mexico (and then subsequently Asia and Brazil), there was no other industry for the former nylon workers to move into. The people with the means to leave did, but many people were tied to their homes (now much more likely to have an underwater mortgage and much harder to sell) and families and were subjected to a downward spiral that continues today. Had the nylon factory been in a city, the workers could have stayed in their homes but more easily switched jobs, simply because there are so many more industries in a densely populated local area (there are counter-examples here, like Youngstown, OH, but on average this is the case).

With the scrip tax and social wealth fund, 25% of those nylon-producing assets would have been owned by the fund, while the nylon workers who own shares of the fund would own a tiny portion of every industry. Thus, the result is a diversification of assets. If one geographic area or one industry gets hit, everyone in the US loses a few pennies in the value of their share of the fund, but the local area’s total set of assets are at least slightly more diversified. Their shares of the fund are much more stable relative to their local economy. Certainly the job losses would still devastate the area, but at least one source of income would be largely unchanged (or even increase, as the outsourcing resulted in higher profits for the DuPont corporation).

Lastly, prices are 12 percent lower in rural areas compared to non-rural areas. This is one of the reasons the minimum wage proposals don’t work as well in rural areas, but it is also the reason that ownership income expansion works particularly well in rural areas. Since each person in the US would own the same portion of the fund, each person, no matter where they live, would get the same amount in their dividend payment. However, this dividend payment would buy more goods and services in rural areas than it would in cities and suburbs. The difference in prices is partially because land values differ so much between rural areas and cities/suburbs. An acre in southwest Delaware is never going to be worth as much as an acre in downtown Seattle.

While there are certainly details to be worked out for any large-scale economic policy, it is silly to suggest that the failure of Germany and Italy prove there’s no policy that would help US rural areas. Norway and Alaska offer a counter-example that would work well.

Six southern US metro areas: part 6 – unemployment

My sixth blog post on mid-sized cities near the southern section of the Appalachian mountains looks at unemployment (people who do not have a job but are actively trying to find one).

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 36 months of aggregated Current Population Survey microdata, covering January 2016 to December 2018.

Unemployment rate

In a previous post, I calculated the unemployed share of the population. This post looks at the unemployment rate, which is the unemployed share of the labor force. Over the three-year period from 2016 to 2018, the US unemployment rate averaged 4.4%. The unemployment rate varied in the six areas of interest from 2.9% in Asheville to 5.1% in Huntsville. The unemployment rate in this three-year period averages 3.5% in Knoxville, 3.9% in the Greenville-Anderson-Spartanburg area, 4.0% in the Johnson City-Kingsport-Bristol area, and 4.6% in the Chattanooga-Cleveland-Dalton area.

BLS publishes high frequency estimates of unemployment in the Local Area Unemployment Statistics report. I’ve used the multi-year averages instead, to allow analysis of why people are unemployed and for how long they have been looking for a job.

Reason for unemployment

Unemployment can be grouped into four categories, based on what people were doing before they became unemployed: 1) people who quit a job and are looking for a new one (job leavers), 2) people who lost a job and are looking for a new one (job losers), 3) people who are looking for their first job (new entrants), and 4) people who were previously not in the labor force (for example: disabled or ill, taking care of family, retired, or had simply given up hope of finding work) and are now looking for work again.

From 2016-18, nearly half of US unemployment was because people had lost a job. This “job loser” category can be the most painful, as it is perhaps the least voluntary. The person had a job, and presumably wanted to keep it, but could not, and now they are trying to find a replacement job. In contrast, the re-entrant category, which makes up 1.3% of the US labor force, can be a positive indication. From 2001 to 2014, the US labor market was particularly poor for many people, and, as a result, many decided to stop looking for work. But over the past few years, the labor market has improved, and new jobs are pulling people off of the sidelines and encouraging them to look for work again. These people show up as “re-entrants”. Job leavers can also be an indication of a strong labor market. When people are confident that they can find a better job, they are more likely to leave their current job.

In four of the six areas of interest, the unemployment rate is below the US average for the “right” reasons. That is, people are less likely to be job losers or unemployed new entrants. In Knoxville and the Johnson City-Kingsport-Bristol area, more than half of the unemployed are job leavers and re-entrants. Re-entrants are also disproportionately common in the Greenville-Anderson-Spartanburg area.

The job loser share of the labor force is at or below the national average in all six areas, and particularly low in Knoxville and Asheville. The new entrant share of the labor force is above average in Huntsville and Chattanooga. Job leavers are more common in Chattanooga and Johnson City.


Duration of Unemployment

Another important determinant of whether unemployment is particularly painful is the duration of unemployment. If people are unemployed for a short time, they may be able to rely on unemployment benefits and, in some cases, their personal savings, to survive. However, a long period of unemployment can devastate savings, exceed the period where unemployment benefits are allowed, affect mental health, and even create a situation where people begin to lose their skills.

Long-term unemployed, measured as those whose unemployment has lasted 27 weeks or more, makes up one percent of the US labor force. The long-term unemployment rate is the same in Johnson City-Kingsport-Bristol and Knoxville. In contrast, it is more common in Huntsville (1.8%) and less common in the Greenville area (0.7%), Chattanooga (0.5%), and Asheville (0.3%).

Short-term unemployment (lasting a month or less) makes up 1.4% of the US labor force, 1.5% in the Greenville area, 1.1% in Knoxville, and 1.3% in Huntsville. The unemployed populations in both Huntsville and Knoxville are more likely to be long-term unemployed than the US average.


The next blog post in the series will look at which industries and occupations employ people in the six areas. The jupyter notebook used to create the analysis above is here.

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.