The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.
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August 30, 2017
Is Poor Health Hindering Economic Growth?
It is well known that poor health is bad for an individual's income, partially because it can lower the propensity to participate in the labor market. In fact, 5.4 percent of prime-age individuals (those 25–54 years old) reported being too sick or disabled to work in the second quarter of 2017. This is the most commonly cited reason prime-age men do not want a job, and for prime-age women, it is the second most often cited reason behind family responsibilities (see the chart). (Throughout this article, I use the measure "not wanting a job because of poor health or disability" as a proxy for serious health problems.)
In addition to being prevalent, the share of the prime-age population citing poor health or disability as the main reason for not wanting a job has increased significantly during the past two decades and tends to be higher among those with less education (see the chart).
Yet by some standards, the health of Americans is improving. For example, compared to two decades ago the average American is living two years longer, and the likelihood of dying from cancer or cardiovascular disease has fallen. These specific outcomes, however, may have more to do with improvements in the treatment of chronic disease (and the resulting reduction in mortality rates) than improvements in the incidence of health problems.
Another puzzle—which is perhaps also a clue—is the considerable variation across states in the rates of being too sick or disabled to work. For example, people living in Mississippi, Alabama, Kentucky, or West Virginia in 2016 were more than three times likelier to indicate being too sick or disabled to work than residents of Utah, North Dakota, Iowa, or Minnesota (see the maps below).
This cross-state variation is useful because it allows state-by-state comparisons of the prevalence of specific health problems. Among a list of more than 30 health indicators, the two factors that most correlate with the share of a state's population too sick or disabled to work were high blood pressure (a correlation of 0.86) and diabetes (a correlation of 0.83). Both of these conditions are associated with risk factors such as family history, race, inactivity, poor diet, and obesity. Both of these health issues have increased significantly on a national basis in recent years.
So how might poor health hinder economic growth? Health factors account for a significant part of the decline in labor force participation since at least the late 1990s. After controlling for demographic changes, the share of people too sick or disabled to work is about 1.6 percentage points higher today than it was two decades ago (see the interactive charts on our website). Other things equal, if this trend reversed itself during the next year, it could increase the workforce by up to 4 million people, and add around 2.6 percentage points to gross domestic product (calculated using our Labor Market Sliders).
Of course, such a sudden and large reversal in health is highly unlikely. Nonetheless, significant improvements to the health of the working-age population would help lessen the drag on growth of the labor supply coming from an aging population. Public policy efforts centered on both prevention and treatment of work-impeding health conditions could play an important role in bolstering the nation's workforce.
April 11, 2017
Going to School on Labor Force Participation
In the aftermath of the Great Recession, labor force attachment declined. However, that pattern has been reversing itself lately. In particular, the labor force participation rate (LFPR) of the prime-age (25 to 54 years old) population, the core segment of the workforce, has been moving higher since late 2015. While this is good news, the prime-age LFPR remains well below prerecession levels, meaning that there are more than two million fewer prime-age people participating in the labor force. What factors have contributed to that decline? Where did those people go?
The Atlanta Fed LFP dynamics web page has an interactive tool that allows users to drill down into the drivers of the change in LFPR. The tool breaks the change in LFPR into two parts. The first part is the effect of shifts in the share of the population in different age groups (we use five-year age groups). The second part is the change attributable to shifts in the rate of nonparticipation. Using a methodology described here, we can drill deeper into the second part to learn more about the reasons for not participating in the labor force.
The U.S. Census Bureau will make the first quarter 2017 microdata on the reasons for nonparticipation available in a few weeks, so the following chart shows a decomposition of the 1.8 percentage point decline in the prime-age LFPR (not seasonally adjusted) between the fourth quarters of 2007 and 2016.
In this chart, "residual" pertains to the part of the total change in the LFPR that is attributable to the simultaneous shifts in both age-group population shares and age-group participation rates. In the present case, the residual is zero.
Because we are examining changes in prime-age participation, and all age groups within prime-age have reasonably similar participation rates, a change in the composition of ages tends to have little impact on the overall prime-age LFPR. Instead, the decline is due to shifts in the nonparticipation rate within age groups (the orange bar). In particular, the decline could indicate an increased likelihood of being in school, having family responsibilities that prevent participation, being in the shadow labor force (wanting a job but not actively looking), and a disability or poor health.
Although all these factors put downward pressure on participation, an important countervailing influence is that the education level of the population has been rising over time, and participation tends to increase with more education. In 2007, 41.0 percent of the prime-age population had a college degree, and they had an 88.3 percent participation rate versus 79.5 percent participation for those without a degree. By the end of 2016, the fraction with a degree had increased to 47.3 percent, and that cohort's participation rate had declined 1 percentage point, to 87.3 percent, versus a drop of 3.5 percentage points, to 76.0 percent, for those without a degree.
To see the importance of rising education on participation, the following chart shows the decomposition of the 1.8 percentage point decline in prime-age LFPR based on education-group population shares (degree and nondegree) instead of age-group shares.
In this chart, "residual" indicates the part of the total change in LFPR due to the simultaneous shifts in both education-group population shares and education-group participation rates.
As the chart shows, the shift in the education distribution of the prime-age population from 2007 to 2016 by itself would have increased the prime-age participation rate by about 0.7 percentage points (the green bar). Conversely, if education levels had not increased then the participation rate would have decreased by even more than it actually did. The nonparticipation effect would be larger for most nonparticipation reasons and especially for reasons of disability or poor health (−0.8 percentage points versus −0.5 percentage points). See the charts and analysis in the "health problems" section of the Labor Force Dynamics web page for more information on health-related nonparticipation by education.
Despite some partial reversal over the last year and a half, the prime-age LFPR is still lower than it had been prior to the recession. However, the decline in participation could have been even larger if the education level of the population had not also increased. Rising education is associated with a lower incidence of nonparticipation than otherwise would be the case, and it's principally associated with less nonparticipation attributable to disability or poor health. While researchers agree on the positive association between education and health, pinning down the specific reasons for this remains somewhat elusive. Factors such as income, informational, and occupational differences—as well as public policy choices—all play a role. Recent research by Nobel laureate Angus Deaton and Anne Case suggests that both education and racial differences are important considerations—emphasizing the sharply rising incidence of health problems among middle-age, white families with lower levels of education—and this Washington Post article highlights rising disability rates in rural America.
January 07, 2016
What Occupational Projections Say about Entry-Level Skill Demand
On December 8, 2015, the U.S. Bureau of Labor Statistics (BLS) released its latest projections of labor force needs facing the U.S. economy from now until 2024.
Every two years, the BLS undertakes an extensive assessment of worker demand based on a number of factors: projected growth in the overall economy, dynamics of economic growth (such as which industries are growing fastest), labor force demographics (for example, the aging of the labor force), and expected changes in the labor force participation rate. Total worker demand includes both the number of workers needed to meet economic growth as well as the number of workers needed to replace current workers expected to retire.
A number of observations about these projections have already been identified. For example: overall employment growth will be slower, health care jobs will continue growing, and computer programmer jobs will lose ground.
In addition to the number of workers that will be in demand in different occupations in the U.S. economy, the BLS reports the skills that are needed for entry into those occupations—skills pertaining to both education levels and on-the-job training. As I perused this report, I was surprised at how much attention the press pays to the growth in high-skilled jobs at the expense of attention paid to those occupations requiring less skill but actually employ the greatest number of workers.
To be clear, the BLS does not project the educational requirements that will be needed for entering each occupation in 2024. It merely reports the most common education, training, and experience requirements needed to enter each occupation in the base year (in this case, 2014). Also it's important to note that these estimates of education needed to enter an occupation do not necessarily (and almost surely do not) match the average education of workers in that occupation at any given time, as those averages will reflect workers of many different ages and experience. The BLS gives a detailed description of how it identifies the entry-level educational and training requirements for each occupation. With those caveats in mind, let's take a look at the current distribution of jobs across the most common educational requirement for entering occupations.
The chart below tells us that, together, the typical entry-level requirement of a high school degree or less corresponds to nearly 64 percent of all jobs in the U.S. economy in 2014, while those typically requiring at least a bachelor's degree for entry represent 25.6 percent of jobs. The projected distribution of jobs in 2024 looks nearly identical: entry-level requirements (based on 2014 assessments) for 63 percent of all jobs requiring a high school degree or less and 26.2 percent requiring a bachelor's degree or more.
To be sure, the growth in higher-skill jobs far outpaces that for low- or middle-skill jobs. The number of jobs requiring a bachelor's degree or more for entry (in 2014) is expected to grow by 34 percent, whereas the number of jobs requiring less than a bachelor's degree is expected to grow by only 6 percent. This difference in growth rates reflects, in part, an expected continuation of the phenomenon of declining middle-skill jobs that my Atlanta Fed colleagues (and others) have discussed previously. Although labeled "middle-skill," entry into these occupations (such as office support and many manufacturing occupations) is not likely to require more than a high school degree.
However, even though the growth in low- and middle-skill jobs is expected to be slower than in higher-skill jobs, the total number of job openings based on predicted growth and replacement needs between 2014 and 2024 is expected to be nearly 32 million for jobs requiring less than a bachelor's degree for entry (based on 2014 assessments), with 30 million of those requiring only a high school degree or less. The total number of job openings requiring at least a bachelor's degree is expected to be about 12 million. In other words, the number of jobs requiring a high school degree or less in order to enter is twice as large as the number of jobs that require a college degree to enter.
The other side of this story, however, is that those jobs typically requiring less education at entry don't pay nearly as much as jobs requiring higher levels of education. The dollar figures in parentheses on the chart reflect the median annual salary of jobs with the different entry-level educational requirements. What we see is that while the majority of U.S. jobs require a high school degree or less at entry, those jobs pay less than half of what a job requiring at least a college degree pays.
So let's say a worker wants good job prospects (with a large number of job openings over the next decade), doesn't want to go to college, and wants to optimize chances for the highest salary possible. What is this worker to do? Fortunately, some of my colleagues at the Atlanta Fed, Cleveland Fed, and Philadelphia Fed have produced a report identifying what they call "opportunity occupations," which they define as those paying salaries higher than the geographically-adjusted national median for at least 70 percent of adults who have less than a college education. Some jobs among their top opportunity occupations are nurses, bookkeepers, first-line supervisors of retail workers, truck drivers, computer user support specialists, police officers, and electricians and workers in several other construction trades. Their report also identifies the U.S. metropolitan areas possessing a high share of opportunity occupations.Even though the share of jobs in the U.S. economy requiring less than a college degree at entry is getting smaller (very slowly), the largest number of jobs in the economy is, by far, jobs requiring less than a college degree at entry, and those jobs offer a wide range of options that pay above the national median wage.
June 30, 2014
The Implications of Flat or Declining Real Wages for Inequality
A recent Policy Note published by the Levy Economics Institute of Bard College shows that what we thought had been a decade of essentially flat real wages (since 2002) has actually been a decade of declining real wages. Replicating the second figure in that Policy Note, Chart 1 shows that holding experience (i.e., age) and education fixed at their levels in 1994, real wages per hour are at levels not seen since 1997. In other words, growth in experience and education within the workforce during the past decade has propped up wages.
The implication for inequality of this growth in education and experience was only touched on in the Policy Note that Levy published. In this post, we investigate more fully what contribution growth in educational attainment has made to the growth in wage inequality since 1994.
The Gini coefficient is a common statistic used to measure the degree of inequality in income or wages within a population. The Gini ranges between 0 and 100, with a value of zero reflecting perfect equality and a value of 100 reflecting perfect inequality. The Gini is preferred to other, simpler indices, like the 90/10 ratio, which is simply the income in the 90th percentile divided by the income in the 10th percentile, because the Gini captures information along the entire distribution rather than merely information in the tails.
Chart 2 plots the Gini coefficient calculated for the actual real hourly wage distribution in the United States in each year between 1994 and 2013 and for the counterfactual wage distribution, holding education and/or age fixed at their 1994 levels in order to assess how much changes in age and education over the same period account for growth in wage inequality. In 2013, the Gini coefficient for the actual real wage distribution is roughly 33, meaning that if two people were drawn at random from the wage distribution, the expected difference in their wages is equal to 66 percent of the average wage in the distribution. (You can read more about interpreting the Gini coefficient.) A higher Gini implies that, first, the expected wage gap between two people has increased, holding the average wage of the distribution constant; or, second, the average wage of the distribution has decreased, holding the expected wage gap constant; or, third, some combination of these two events.
The first message from Chart 2 is that—as has been documented numerous other places (here and here, for example)—inequality has been growing in the United States, which can be seen by the rising value of the Gini coefficient over time. The Gini coefficient’s 1.27-point rise means that between 1994 and 2013 the expected gap in wages between two randomly drawn workers has gotten two and a half (2 times 1.27, or 2.54) percentage points larger relative to the average wage in the distribution. Since the average real wage is higher in 2013 than in 1994, the implication is that the expected wage gap between two randomly drawn workers grew faster than the overall average wage grew. In other words, the tide rose, but not the same for all workers.
The second message from Chart 2 is that the aging of the workforce has contributed hardly anything to the growth in inequality over time: the Gini coefficient since 2009 for the wage distribution that holds age constant is essentially identical to the Gini coefficient for the actual wage distribution. However, the growth in education is another story.
In the absence of the growth in education during the same period, inequality would not have grown as much. The Gini coefficient for the actual real wage distribution in 2013 is 1.27 points higher than it was in 1994, whereas it's only 0.49 points higher for the wage distribution, holding education fixed. The implication is that growth in education has accounted for about 61 percent of the growth in inequality (as measured by the Gini coefficient) during this period.
Chart 3 shows the growth in education producing this result. The chart makes apparent the declines in the share of the workforce with less than a high school degree and the share with a high school degree, as is the increase in the shares of the workforce with college and graduate degrees.
There is little debate about whether income inequality has been rising in the United States for some time, and more dramatically recently. The degree to which education has exacerbated inequality or has the potential to reduce inequality, however, offers a more robust debate. We intend this post to add to the evidence that growing educational attainment has contributed to rising inequality. This assertion is not meant to imply that education has been the only source of the rise in inequality or that educational attainment is undesirable. The message is that growth in educational attainment is clearly associated with growing inequality, and understanding that association will be central to the understanding the overall growth in inequality in the United States.
By Julie L. Hotchkiss, a research economist and senior policy adviser at the Atlanta Fed, and
Fernando Rios-Avila, a research scholar at the Levy Economics Institute of Bard College
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January 17, 2014
What Accounts for the Decrease in the Labor Force Participation Rate?
Editor's note: Since this post was written, we have developed new tools for examining labor market trends. For a more detailed examination of factors affecting labor force participation rates, please visit our Labor Force Participation Dynamics web page, where you can create your own charts and download data.
Despite the addition of only 74,000 jobs to the economy in December, the unemployment rate dropped significantly—from 7 percent to 6.7 percent. The decline came mostly from a decrease in the labor force.
Since the recession began, the labor force participation rate (LFPR) has dropped from 66 percent to 63 percent. Many people have left the labor force because they are discouraged from applying (U.S. Bureau of Labor Statistics data indicate that a little under 1 million people fall into this category). But the primary drivers appear to be an increase in the number of people who are either retired, disabled/ill, or in school.
Certainly, the aging of the population accounts for much of the increase in the retired and disabled/ill categories. Still, there has been a lot of movement over the past few years in the reasons people cite for not participating in the labor force within age groups. Knowing the reasons why people have left (or delayed entering) the labor force can help us understand how much of the decline will likely halt once the economy picks back up and how much is permanent. (For more on this topic, see here, here, and here.)
The chart below shows the distribution of reasons in the fourth quarter of 2013. (Of the people not in the labor force, 1.6 percent indicate they want a job and give a reason for not being in the labor force. They are categorized here as "want a job" only.) Young people are not in the labor force mostly because they are in school. Individuals 25 to 50 years old who are not in the labor force are mostly taking care of their family or house. After age 50, disability or illness becomes the primary reason people do not want to work—until around age 60, when retirement begins to dominate.
How has this distribution changed over the past seven years? For simplicity, I've grouped people by age to show changes over time in the reasons people give for not being in the labor force. However, you can also see an interactive version of the same data without age buckets—and download the data—here.
Of the 12.6 million increase in individuals not in the labor force, about 2.3 million come from people ages 16 to 24, and of that subset, about 1.9 million can be attributed to an increase in school attendance (see the chart below). In particular, young people aged 19 to 24 are more likely to be in school now than before the recession. Among college-age people, those absent from the labor force because they are in school rose from 57 percent to 60 percent. Among people of high school age, the share not in the labor force because they are in school rose from 87 percent to 88 percent.
The number of middle-aged workers not in the labor force rose by 1.8 million (or 11 percent), with four main factors driving the increase.* "Wants a Job" increased 546,000 (34 percent). The "In School" category increased 438,000 (a 38 percent rise). "Disability/Illness" rose 393,000 (an 8 percent rise), and 302,000 more people said they were retired (a 43 percent rise; see the chart below).
Among individuals aged 51 to 60, those not in the labor force increased by 1.6 million (or 16 percent). This increase came almost entirely from the number of people who are disabled or ill, which rose by 1.3 million (a 33 percent increase). Interestingly, the number of retired individuals actually fell by 305,000 between the fourth quarter of 2007 and the fourth quarter of 2010. Since then, the number of retired people within this age group has risen 183,000 but remains 122,000 lower than fourth-quarter 2007 levels. So it seems more people in this age group were delaying retirement instead of leaving early (see the chart below).
About 6.8 million of the 12.6 million increase in those not in the labor force came from the 61-and-over category. An additional 5.3 million (a 17 percent increase) are retired, and 1 million more (a 34 percent increase) are not in the labor force because they are disabled or ill. The other categories were little changed (see the chart below).
In total, the number of people not in the labor force rose by 12.6 million (16 percent) from the fourth quarter of 2007 to the fourth quarter of 2013. About 5.5 million more people (a 16 percent increase) are retired, 2.9 million (a 23 percent increase) are disabled or ill, and 2.5 million (a 19 percent increase) are in school. An additional 161,000 are taking care of their family or house, and an additional 99,000 are not in the labor force for other reasons. The fraction who say they want a job has risen the most (32 percent) but has contributed only 11 percent to the total change. The chart below shows the overall contributions by reason to the changes in labor force participation for all age groups since the onset of the recession.
What further changes can we anticipate? It's hard to say, as many moving parts are at play. Most people currently in school will be approaching the labor market upon graduation. But increased college and graduate school enrollment could augur a permanent shift in the portion of the population who are in school instead of the labor force. We can also expect continued downward pressure on the LFPR from retiring baby boomers as well as boomers who exit the labor force because of disability or illness.
Last, the portion of people who want a job has increased the most since the recession began, and is currently 1.4 million above its prerecession level. People in this category tend to have greater labor force attachment, making them more likely to shift into the labor force. In fact, the number of people in this category has already started to decrease—and is down 709,000 from the fourth quarter 2012.
My Atlanta Fed colleagues Julie Hotchkiss and Fernando Rios-Avila in their 2013 paper "Identifying Factors behind the Decline in the U.S. Labor Force Participation Rate," looked at a range of LFPR projections for 2015–17 based on different labor market assumptions. Depending on the future strength of the U.S. labor market, the projections are highly varying—ranging between a decline of 2.4 percentage points and an increase of 2 percentage points from the 2010–12 average of 64.1 percent. So far, more factors are pulling down the LFPR than pushing it up; the latest reading for December 2013 is already 1.3 percentage points below the 2010–12 average. At that pace, the Hotchkiss et al. lower-bound estimate will be reached before the end of 2014, unless the dynamics change as the economy further improves.
By Ellyn Terry, an economic policy analysis specialist in the research department of the Atlanta Fed
* I've chosen to break the "middle-age" grouping at age 50 instead of 54 because the probability of retiring has changed in different ways over the past few years for the 25- to 50-year-old group and the 51- to 60-year-old group. See the chart mentioned earlier for more detail.
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December 23, 2013
Goodwill to Man
By pure coincidence, two interviews with Pennsylvania State University professor Neil Wallace have been published in recent weeks. One is in the December issue of the Federal Reserve Bank of Minneapolis’ excellent Region magazine. The other, conducted by Chicago Fed economist Ed Nosal and yours truly, is slated for the journal Macroeconomic Dynamics and is now available as a Federal Reserve Bank of Chicago working paper.
If you have any interest at all in the history of monetary theory over the past 40 years or so, I highly recommend to you these conversations. As Ed and I note of Professor Wallace in our introductory comments, very few people have such a coherent view of their own intellectual history, and fewer still have lived that history in such a remarkably consequential period for their chosen field.
Perhaps my favorite part of our interview was the following, where Professor Wallace reveals how he thinks about teaching economics, and macroeconomics specifically (link added):
If we were to construct an economics curriculum, independent of where we’ve come from, then what would it look like? The first physics I ever saw was in high school... I can vaguely remember something about frictionless inclined planes, and stuff like that. So that is what a first physics course is; it is Newtonian mechanics. So what do we have in economics that is the analogue of Newtonian mechanics? I would say it is the Arrow-Debreu general competitive model. So that might be a starting point. At the undergraduate level, do we ever actually teach that model?
[Interviewers] That means that you would not talk about money in your first course.
That is right. Suppose we taught the Arrow-Debreu model. Then at the end we’d have to say that this model has certain shortcomings. First of all, the equilibrium concept is a little hokey. It’s not a game, which is to say there are no outcomes associated with other than equilibrium choices. And second, where do the prices come from? You’d want to point out that the prices in the Arrow-Debreu model are not the prices you see in the supermarket because there’s no one in the model writing down the prices. That might take you to strategic models of trade. You would also want to point out that there are a lot of serious things in the world that we think we see that aren’t in the model: unemployment, money, and [an interesting notion of] firms aren’t in the Arrow-Debreu model. What else? Investing in innovation, which is critical to growth, isn’t in that model. Neither is asymmetric information. The curriculum, after this grounding in the analogue of Newtonian mechanics, which is the Arrow-Debreu model, would go into these other things. It would talk about departures from that theory to deal with such things; and it would describe unsolved problems.
So that’s a vision of a curriculum. Where would macro be? One way to think about macro is in terms of substantive issues. From that point of view, most of us would say macro is about business cycles and growth. Viewed in terms of the curriculum I outlined, business cycles and growth would be among the areas that are not in the Arrow-Debreu model. You can talk about attempts to shove them in the model, and why they fall short, and what else you can do.
Of the many things that I have learned from Professor Wallace, this one comes back to me again and again: Talk about how to get the things in the model that are essential to dealing with the unsolved problems, honestly assess why they fall short, and explore what else you can do. To me, this is not only a message of good science. It is one of intellectual generosity, the currency of good citizenship.
I was recently asked whether I align with “freshwater” or “saltwater” economics (roughly, I guess, whether I think of myself as an Arrow-Debreu type or a New Keynesian type). There are many similar questions that come up. Are you a policy “hawk” or a policy “dove”? Do you believe in old monetarism (willing to write papers with reduced-form models of money demand) or new monetarism (requiring, for example, some explicit statement about the frictions, or deviations from Arrow-Debreu, that give rise to money’s existence)?
What I appreciate about the Wallace formulation is that it asks us to avoid thinking in these terms. There are problems to solve. The models that we bring to those problems are not true or false. They are all false, and we—in the academic world and in the policy world—are on a common journey to figure out what we are missing and what else we can do.
It is deeply misguided to treat models as if they are immutable truths. All good economists appreciate this intellectually. And yet there is an awful lot of energy wasted, especially in the blogosphere, on casting aspersions at those who are perceived to be seeking answers within other theoretical tribes.
Some problems are well-suited to Newtonian mechanics, some are not. Some amendments to Arrow-Debreu are useful; some are not. And what is well-suited or useful in some circumstances may well be ill-suited or even harmful in others. Perhaps if we all acknowledge that none of us knows which is which 100 percent of the time, we can make just a little more progress on all those unsolved problems in the coming year. At a minimum, we would air our disagreements with a lot more civility.
By Dave Altig, executive vice president and research director at the Atlanta Fed
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April 12, 2013
Higher Education: A Deflating Bubble?
There are at least two sides to every debate, but it’s becoming clearer by the day that the debate over the cost of higher education is being won by people like University of Tennessee law professor Glenn Reynolds.
A frequent writer and lecturer, and even more frequent blogger, Reynolds visited the Atlanta Fed recently to share his views with local community leaders. He reported that total student loan debt now stands at over $1 trillion—more than total credit card debt and auto loan debt combined. As these charts from the New York Fed show, the increase in total student debt over the past eight years is a result of greater numbers of students and families taking on educational debt as well as higher debt balances per student.
One can argue that this trend is not necessarily a bad thing. Education is an investment in human capital, and if those newly acquired skills are valued highly by employers, then going to college can be a positive net present value project, even with debt financing.
And wage data reveal that these skills are indeed valuable. As this Cleveland Fed article and chart show, the median wage for a worker with a bachelor’s degree was about 30 percent higher than that of a worker with only a high school diploma in the late 1970s and grew to more than 60 percent higher by the early 2000s. However, the data also show that over the last decade the value of a college degree measured by wages has stagnated.
And here begins the crux of Reynolds’s concern. The cost of attending college has continued to grow, and grow rapidly. Between the 2000–01 and 2010–11 academic school years, the cost of undergraduate tuition, room, and board rose 42 percent at public institutions and 31 percent at private not-for-profit institutions, after adjusting for inflation, according to the National Center for Education Statistics.
A stagnant wage premium with rising costs of attendance suggests that, at least on average, the value proposition of going to college is deteriorating. To make matters worse, Reynolds described students graduating with significant levels of student loan debt who often cannot find jobs that pay enough to cover the loan payments. Moreover, unlike credit card debt, student loans are not dischargeable in bankruptcy, meaning that there is no opportunity to get out from under the debt burden other than through full repayment. Reynolds told of individuals whose high levels of student debt are limiting their career choices, ability to obtain mortgages, and save for retirement. He even went on to say that student loans are affecting a much more personal market—the marriage market. After all, he says, “Who wants to marry someone with huge amounts of unpayable debt?”
Reynolds contends that ”something that can’t go on forever, won’t,” and he believes that seeing friends or family members having financial problems because of student loans is leading college students to become more cost conscious. Additionally, he notes that more and more of today’s students are focusing on majors that seem likely to offer a strong salary over time. The chart on 2009 enrollment and wage premiums by major show some support for that notion.
Large fractions of students are enrolled in majors with relatively higher wage premiums, including business and engineering, but there are also substantial enrollments in education, psychology, and the humanities. For Reynolds it is not so much about seeking out the highest-wage major; instead, his advice is, “Don’t go to a college that will require you to borrow a lot of money.”
What’s the endgame? Well, he expects that when the bubble bursts, there will be less “dumb money” to be gained, students will demand a higher return on investment, and schools will ultimately be forced to adapt. According to Reynolds, colleges have two different strategic choices: increase the value of the education for the current cost, or lower the cost of providing the current level of value. And he expects the most common response will be the latter, likely involving technology such as MOOCs (massive open online courses) and other innovations in teaching methods.
When any bubble bursts, there are some casualties. In this case, it may be that some colleges do not survive once market discipline has been unleashed. Given the statistics above, you might think that it would be the small liberal arts colleges that will suffer the most, but in this video, shot during the visit to Atlanta, Reynolds argues that these colleges may actually gain from the coming shakeout.
Reynolds indicated that there is change in the air, but it’s coming slowly. The bubble may not have burst, but he sees it deflating. He noted, “A lot of people hope it will pass. They’ll muddle through without dramatic changes. And frankly I hope they’re right. But I don’t think they are.”
By Paula Tkac, vice president and senior economist in the Atlanta Fed research department and
Michael Chriszt, vice president and community relations officer in the Atlanta Fed’s public affairs department
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- An Update on Labor Force Participation
- Another Look at the Wage Growth Tracker's Cyclicality
- GDPNow's Second Quarter Forecast: Is It Too High?
- November 2017
- October 2017
- September 2017
- August 2017
- July 2017
- May 2017
- April 2017
- March 2017
- February 2017
- January 2017
- Business Cycles
- Business Inflation Expectations
- Capital and Investment
- Capital Markets
- Data Releases
- Economic conditions
- Economic Growth and Development
- Exchange Rates and the Dollar
- Fed Funds Futures
- Federal Debt and Deficits
- Federal Reserve and Monetary Policy
- Financial System
- Fiscal Policy
- Health Care
- Inflation Expectations
- Interest Rates
- Labor Markets
- Latin America/South America
- Monetary Policy
- Money Markets
- Real Estate
- Saving, Capital, and Investment
- Small Business
- Social Security
- This, That, and the Other
- Trade Deficit
- Wage Growth