Higher employment rates mean higher wages for low-wage workers

Low-wage jobs pay better when the local labor market is tight. This is because an employer who can’t easily replace one worker with another will tend to pay current workers more to incentivize them to stay and also will tend to invest more in equipment and training that make workers more productive and grows the economy.

One way to see this relationship is to look at what percent of people living in an area have a job and the wages near the bottom of the area’s wage distribution. For example, for workers between the ages of 25 and 54 in the ~100 largest metro areas, a one percentage point increase in the share of the age group with a job results in $0.13 an hour in additional wages for the first decile full-time wage earner in the metro area, equivalent to more than $200 extra per worker per year (in November 2018 dollars).

Employment rates (the share of the age group with a job) have been rapidly increasing since 2012 and show no sign of slowing (and perhaps even show signs of accelerating, as higher wages pull more people off the sidelines). If the trend is allowed to continue until the employment rate returns to its late 1990s rate, the employment-rate-related real wage boost for low-wage full-time workers would be between $500 and $600 a year.

cbsa_p10wage_epop

Data source

This relationship has been pointed out countless times, for example by Dean Baker, but it worth reiterating, using the latest data. I’ve calculated these figures from the Current Population Survey public use microdata, using the latest two years of monthly data (December 2016-November 2018). The results are stored here in csv file, for those curious about which dot is which metro area. I’ve also added the union membership rate and the unemployment rate for the area to the file. Wages are in November 2018 dollars, adjusted for inflation within the 2-year period by the regional CPI-U. The largest metro areas are the 97 center-based statistical areas (CBSAs) with at least 300 valid wage observations during the 2-year period. The python code that generates the results is available here. I use a set of programs called the bd CPS to standardize the CPS data from 1994 to present, which are available as a GitHub repo.

 

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bd CPS version 0.2 released

Version 0.2 of my personal Current Population Survey partial extract code and documentation is uploaded to GitHub. A few new variables were added, and several errors were fixed.

If anyone makes use of this code and finds an error, please let me know!

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.

educ_ltu

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.

age_ltu

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.

jobfinding

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.

Fig1

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.

Fig2

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.

Economist as a plumber with a body camera (EPBC)

Economists could do more to show their work and to maintain their results.

Economists aren’t particularly transparent in their day-to-day activities, but what they do is very important. Because economics offers little possibility for laboratory-style experiments or hard-science precision, there is a perverse opportunity for conclusions to be reached before data are collected. This is dangerous and would be less likely if economists 1) reported what they do more frequently and more clearly, and 2) shifted some of their responsibilities toward maintenance of existing policy or past results.

Esther Duflo described the potential for a shift in the role economists play in society. Rather than being biased architects designing massive social policies, economists can be the people who are responsible for maintaining social systems and keeping them running smoothly from month-to-month, without any gaps or leaks. Economists can be more like plumbers.

The worst possible way to implement this shift in responsibility would be to hire a separate group of economists to fiddle with policy or day-trade things like the stock of unemployed. Instead, a practical way to do this is for existing (and particularly newly-minted) economists to “wear body cameras”, reporting more of their day-to-day work on personal or work websites or blogs, or on twitter, or GitHub.

Since poverty is deadly and bad policy causes people suffering and death, economists, and others involved in crafting public policy, yield a lethal weapon. If they were in-effect “recorded”, the way these well-paid individuals practice their trade would likely change–both in what results are presented and also in what tasks are undertaken. And if it turns out that economists are already completely honest and unbiased, then “body cameras” would massively boost the field’s credibility.

There are now many free, open-source, and well-documented tools, like python and R, for working with public economic data and contributing to analysis. Plus, there is enough space on the cloud to share other iterations that aren’t presented in a final set of regression coefficients, for example. It would also be helpful for more economists to share the code that produces their results, so that others can extend or modify it. Increasingly, technology is making it possible for economists to show more of their work.

Economists as plumbers with body cameras, in practice, also means following up very frequently on past work and on how existing systems are performing for all people (not just the aggregated/synthetic statistical “person”). For example, the economists who justify liberalization of US trade policy could be the ones responsible for resolving the local effects from the related factory closures. Economists pushing cuts to social services in response to debt levels could report monthly on what their policy does to both the debt level and the poverty rate. In essence, economists could do more checking-up on what they’ve done in the past and, if necessary, clean up after themselves.

The EPBC therefore has two goals: 1) encouraging the showing of how results were obtained, and 2) more frequently revisiting past results. To contribute to achieving this, I’m publishing a series of jupyter notebooks that show my recent attempts at working with public economic data using python. For future projects and blog posts, I’ll link to the new notebooks, and use the tag EPBC (for economists as plumber with body camera). I’m not sure if this will work out, or be sustainable, but its an interesting idea and worth a try.

Here are the first three EPBC notebooks (python 3.6):

Is Amazon killing Walmart?

US exports by trading partner

Efficiently reading fixed-width files like the CPS public use microdata

 

 

 

 

 

 

 

 

 

Tech sector index nearing all-time highs

An important measure of the strength of the tech sector has been climbing rapidly over the past two years and surpassed its turn of the millennium level in June. This doesn’t mean there is another tech bubble, but investors should take heed. 

From 1995 to 2000, millions of US households purchased personal computers and used them to connect to the internet for the first time. It was apparent at the time that new technologies, often from government origins, were bringing massive changes. Private investment poured into the tech sector, including to many companies that had never generated a profit and even some without revenue. Money coming into the sector began to overflow and pour back out in the form of lavish executive spending and absurd demonstrations of wealth. When the speculative “dot-com” bubble burst, people lost jobs and retirement savings.

The tech pulse index, produced by the Federal Reserve Bank of San Francisco, tracks what is going on in the tech sector by looking at relevant changes in investment, employment, industrial production, and consumer spending. The index nearly tripled from 1995 to its peak in October 2000, growing at an average annualized rate of 18.7 percent. By the bubble’s trough in 2003, the measure had fallen to nearly half of its peak. From the 2003 trough until the recent uptick in 2016 the index increased at an average annualized rate of 2.9 percent.

tech_pulse

The tech pulse has been on a tear since 2016. From May 2016 to the latest data, covering July 2018, the index has increased at an average annualized rate of 11.4 percent. In June, the index passed its January 2000 level, and it is currently about 13 percent below the all-time high achieved at the peak of the bubble.

There are many similarities between the current tech boom and the dot-com bubble, such as: absurd demonstrations of wealth, the failure to convert money coming in into new productive assets, a backdrop of rising interests rates, and highly-valued companies not generating profit. In some cases, valuations are so high that it seems people are betting not necessarily on tech companies ability to create game-changing technology, which is already priced in, but on their ability to either force competitors to fail or to absorb competitors until market share allows for monopoly prices and monopsony wages. In some cases, the competitor is a public good, such as local and regional public transportation in the case of Uber and Telsa, respectively. Once our public asset is taken over or goes broke and stops operating, the private alternative can raise its prices and allow its quality to deteriorate.

In the case of Amazon, which currently generates a profit mostly through its web services division, investors seem to be betting that eventually the company will have enough power to raise prices without consumers being able (or willing) to shop elsewhere. It’s also worth noting that Amazon got where it is by not paying sales tax. Apple and Alphabet (Google), which already generate massive amounts of profit, still benefit excessively from not paying taxes.

While I don’t know whether the current tech boom is a bubble, and I lean towards thinking it is not, it’s always good to be cautious and to pay attention to growth rates, like those captured in the very clever tech pulse index (here’s info on the methodology). In general, investors should be wary about buying into companies that are already expensive and where the remaining upside requires the alignment of several events in the unknowable future.

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.

Definitions

“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).

seu

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).

mjh

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.

 

 

Sawing off the top of a step ladder

Policymakers may have accidentally demonstrated that they have the skills needed to curb the pay of the top earners in a profession. Unfortunately, these problem solving skills were not applied to CEOs, who regularly get paid a thousand times what their middle-paid worker makes. Instead, some state lawmakers have tackled the non-existent problem of teachers making too much money.

In 2016, public school teachers in the states with recent strikes or large scale protests (West Virginia, Oklahoma, Arizona, Kentucky, North Carolina, and Colorado) are paid about three dollars per hour less than other teachers nationwide. This is not new. After adjusting for prices, the gap was about the same in 2006. What has changed is teacher pay near the “top”. In 2006 a public school teacher making $40.93 per hour (in 2016 dollars) makes more than 90 percent of public school teachers nationwide, while it takes an hourly wage of $37.95 to make more than 90 percent of teachers in states with strikes and protests. The nationwide 90th percentile wage ticked up to $43.27 by 2016, but fell to $34.98 for the states that have subsequently organized strikes and protests.

teacherpay

The top half of the public school teacher wage distribution is already low compared to other occupations. But after state efforts to curb teacher pay, 90 percent of the public school teachers in states with strikes and protests make less than $35 an hour, and more than a quarter of the group is paid less than $16 an hour. These hourly wage figures are based on annual earnings in the March CPS, so they already account for differences in weeks worked per year and hours worked per week, and are therefore more comparable to the hourly wages of other occupations. Using these wage values, teachers, relative to other occupations, have the additional burden of being paid, on average, for fewer hours per year, making it harder for them to make ends meet.

Policymakers should reverse the misguided efforts to cut teacher pay. If anything, teachers should be better rewarded for their contribution to society. It shouldn’t take strikes and protests to make this point.

Technical note: The box plot above shows the average (green diamond), median (white line), 90th, 75, 25th, and 10th percentile hourly wage. Public school teachers are identified as persons in occupations 2200 to 2340 who are employed by the government. The hourly wage variable is generated as the ERN-VAL / (WKSWORK * HRSWK). 

Can a tight labor market pull young people back to full-time work?

From 2001 to 2013, college enrollment in the US increased rapidly. Young people delayed the full-time work portion of their lives, ostensibly to seek skills for an evolving set of jobs. Trends since 2014, however, suggest that the increase in education is not an entirely structural change. Over the past few years, a tighter labor market has been leading more young people to full-time work, suggesting that a weak labor market, rather than concern over a changing set of jobs, was pushing some young people to enroll from 2001 to 2013.

The Current Population Survey shows that from 1994 to 2001 young people were increasingly taking full-time jobs. By March 2001, an annual average of 40.5 percent of people age 19 to 21, and 61.5 percent of people age 22 to 24, were employed full-time. Following the recession of 2001, the trend reversed completely. By March 2013, an annual average of only 23.3 percent of those age 19 to 21 had full time jobs, and only 46.0 percent for those age 22 to 24.

fte_by_age

While data show a long-term decrease in full-time employment among younger people, the same data also show two signs that a weak labor market plays a role. First, the full-time employed share of both age groups has increased steadily since 2014. As of the year ending in March 2018, 28.3 percent of those age 19 to 21, and 52.2 percent of those age 22 to 24, are employed full-time.

Second, if education were driving the change in youth employment then summer youth employment would increase relative to school-year employment. Since most US schools offer students a summer break, the share of young people employed full-time during June, July, and August is much higher than at any other point during the year. If young people are opting for school because of abundant attractive new jobs, then summer break might provide an increasing chance for full-time employment for those in school during the rest of the year. However, summer full-time employment has actually fallen relative to school-year employment, suggesting the opposite, that young people are having a harder time obtaining summer jobs compared to the period from the mid 1990s through 2001.

The weak labor market therefore plays a role in encouraging school enrollment; those who are able to do so can wait out a poor set of job opportunities while gaining new skills. It’s important to note, however, that the tighter labor market since 2014 seems to have slowed, but not reversed, the previously rapidly increasing share of young people who are not in the labor force, and do not want a job, because they are enrolled in school.

nilf_by_age

In 1994, for about half of those age 19 to 21 who are not in the labor force (meaning they are not employed and have not been looking for work) the reason was school enrollment. The rest are often staying home to take care of family, disabled and unable to work, or sometimes believe that there is no job available. Since then, school enrollment has rapidly increased labor market non-participation. By the latest data, covering the year ending in March 2018, more than two thirds of non-participation is due to school enrollment. Among those age 22 to 24, the rate climbs from about one third in 1994 to about one half in the latest year of data. School-related non-participation growth among the slightly older cohort has largely stalled since 2013, while growth has slowed some among the younger group.

Much of the overall drop in full-time employment among young people comes from school enrollment. At least some portion was motivated by the particularly poor quality of the labor market during the years from 2001 to 2013, with students hoping to emerge more competitive and ideally at a time when job applicants have more bargaining power. It remains to be seen how much the Fed will allow the labor market to tighten  and how full-time employment and school enrollment will respond among younger people.

College degree boom and a changing labor market baseline

The March FOMC meeting minutes, released today, noted that increased education is changing the unemployment baseline:

A few participants warned against inferring too much from comparisons of the current low level of the unemployment rate with historical benchmarks, arguing that the much higher levels of education of today’s workforce—and the lower average unemployment rate of more highly educated workers than less educated workers—suggested that the U.S. economy might be able to sustain lower unemployment rates than was the case in the 1950s or 1960s.

It’s worth reiterating the Fed point with another labor market example. From the CPS, the median weekly earnings of women between the age of 25 and 54 increased steadily from 1997 to 2003 and again from 2014 to present.

uwe

But women with a college degree in this age group did not see the more recent increase. Instead, their earnings are nearly $50 per week below the 2003 peak, after adjusting for inflation.

uwe_ba

The same wage series for those with less than a bachelor’s degree and those with advanced degrees also peak around 2003. The increase in education is what explains overall median wage growth. Because college educated workers earn more than workers without a college degree–and because so many more people have obtained college degrees compared to 2006–median wages for the entire group increase.

The increase in the share of this group with a college degree is so astounding that the population of 25-54 year old women without at least a bachelor’s degree has been falling rapidly since 2007.

pop_ltba

Recent overall wage growth is not driven by an unsustainable unemployment rate, but by families and individuals sacrificing time and money for a decreasing reward.