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Policy Hub: Macroblog provides concise commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues for a broad audience.

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January 7, 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.

Chart 1: Actual and Fixed Real Wages, 1994-2013

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.

Chart 2: Wage Distribution Gini Coefficients over Time

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.

Chart 3: Distribution of the Workforce across Educational Status

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.

Photo of Jessica DillBy 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

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.

Photo of Ellyn TerryBy 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.


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.

Happy holidays.

David Altig By Dave Altig, executive vice president and research director at the Atlanta Fed