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July 1, 2014
By: Jonathan Rothwell
This report uses a unique database from the labor market
information company Burning Glass and other sources to analyze the skill requirements
and the advertisement duration time for millions of job openings. It reaches
the following conclusions:
These job openings data provide new evidence that,
post-recession, STEM skills, particularly those associated with high levels of
educational attainment, are in high demand among employers. Meanwhile, job
seekers possessing neither STEM knowledge nor higher education face
extraordinary levels of competition for a scarce number of jobs. Governments at
all levels, educators, training organizations, and civic leaders can utilize
job vacancy data to better understand the opportunities available to workers and
the specific skills required of them. Improving educational and training
opportunities to acquire STEM knowledge should be part of any strategy to help unemployed
or low-wage workers improve their earnings and employability.
Brookings is a non-profit institution and the chart above was created using Highsoft software under a non-commercial license, the terms of which can be found here. Please note that Highsoft software is not free for commercial use.
Workers with skills in science, technology, engineering, and
mathematics (STEM) play a hugely important role in driving innovation and
economic growth.1
As economies have developed in wealth and complexity since the industrial
revolution, scientists, engineers, and other STEM workers have grown in
relative importance as a share of the labor market, and STEM skills are widely
needed across a wide variety of blue-collar, craft, and professional
Likewise, the rewards for both education and STEM skills have grown relative to
other positions, and workers in STEM today are among the highest paid.3
This report addresses a set of issues of increasing interest
to policy makers, educators, businesses, and workers of all ages: What skills
are most in demand and most likely to lead to a rewarding career? Is there a
shortage of workers possessing the skills used in STEM occupations (i.e., STEM
skills)? What skills and bodies of knowledge should schools and post-secondary
institutions offer to teach students in order to provide them the best chances
of economic success?
The results show that skills common to STEM occupations are
in short supply relative to demand and are valued more by employers. Moreover,
companies located in regions with low unemployment rates for STEM workers have
greater difficulty filling their openings, all else being equal. It follows
that increased training in STEM fields like computer science and medicine will
ease hiring for employers and lead to high-paying career paths for workers.
While this is certainly not the first report to highlight
the economic advantages that accrue to those with STEM skills, the data and
analysis here are novel and useful in a few ways. First, unlike most reports on
the STEM workforce or skill-shortage issues more broadly, this report draws on
a database (developed by the labor market information company Burning Glass) that
allows one to measure the duration of job vacancy advertisements—a measure of
hiring difficulty. There has never been a nationwide analysis of vacancy
duration for the United States by occupation. The closest such study came from
a 1964 survey conducted by the United States Employment Service of unfilled
openings posted at local government employment centers in 78 metropolitan
areas.4 Among
the few recent studies that have looked at vacancy duration, none have examined
differences by occupation, let alone skill level, geography, company,
educational requirements, or status as a STEM worker, as this report does. This
report also draws upon a uniquely large sample size: 1.1 million advertisements
were used to calculate summary statistics for the first quarter of 2013, and
3.3 million were used in the formal statistical analysis, across 52,000 companies.
These make up the full universe of job advertisements listed on company
websites, not a survey, which would be subject to sampling and response bias. Finally,
each vacancy is listed on a company website, not a job board, and examining
only these listings insures that duplication is not an issue and there are no
differences across companies in advertisements costs.
In short, the technological breakthroughs in "big data,"
such as those provided by Burning Glass and used here, mean that no previous job
vacancy database has ever been assembled that can so thoroughly test labor
market theories related to job search, skill mismatch, and shortage.
Even with these advantages, no database is perfect. Job
openings posted online tend to require higher levels of skill and education
than openings that are not advertised online, and the interpretation of
advertisement duration is complicated by the fact that low-skilled, low-wage,
high-turnover positions are often advertised continuously (see the appendix for
Understanding Job Vacancies
in the Context of Long-Run Versus Short-Run Skill Shortage
Over the last three decades, the earnings of college
graduates have soared far above the earnings of those with just a high school
diploma. Leading labor economists have argued that this trend represents a
long-standing shortage (or inadequate growth in supply) of college-educated
workers in the United States relative to demand.5 As
it happens, the earnings for STEM knowledge have also increased at any given
level of education and experience. In 1970, workers earned 12 percent higher
earnings if they worked in an occupation that was significantly more STEM
oriented (scoring one standard deviation higher). By 2012, that premium was up
to 21 percent.6
The Great Recession obviated long-run shortage problems but
only temporarily. As the economy recovered, signs of hiring difficulty for STEM
workers returned. For young graduates with STEM degrees, census data show
rising relative salaries from 2009 to 2012, the only years with field-of-degree
Meanwhile, job vacancy data for the United States in early 2014 reveal five job
openings for every unemployed computer worker, 3.3 for every unemployed health care
practitioner, and 1.7 and 1.1 for those in architectural/engineering and
science occupations, respectively. By contrast, there were just 0.7 openings for
those in legal occupations, 0.2 for production workers, and 0.1 for
construction workers.8
In order for businesses to succeed, they need to match the
right employee to the right position. Accordingly, businesses invest heavily in
searching for the right employees, through methods such as advertising their vacancies
and thoroughly evaluating candidates. According to one survey, the average U.S.
company spends $3,500 per new hire in job advertising costs and human resources
staff, or $124 billion per year.9
Employer surveys report large costs from hiring difficulty, including
overworked current staff, lower quality output, loss in revenue, and delays in product
Meanwhile, previous studies estimate that the average job
vacancy lasts approximately 20 days.10
For companies, lengthy searches are a necessary if expensive cost of matching.
Each day a job goes unfilled costs the enterprise the value of that potential
worker, and this cost is higher for higher-paying and highly skilled positions,
even as the cost of hiring a skilled worker who is the "wrong fit" is also
higher. Employer surveys report large costs from hiring difficulty, including
overworked current staff, lower quality output, loss in revenue, and delays in product
In short, companies have a strong incentive to fill vacancies as quickly as
possible, but first they must identify qualified and willing candidates.
Job matching is an ocean of economic activity. Across the
United States, 4.7 million workers are hired in a typical month.12
That churn dwarfs the number of net new jobs created, which has averaged just
160,000 per month since 2010.13
Economists have a well-developed theory of job search to guide their thinking
on labor market matching and its macroeconomic consequences, and the three
leading theorists in this field were awarded the Nobel Prize in 2010.14
In this framework, hiring is determined by both supply and demand, not just the
number of vacancies—a measure of demand. In particular, when there are many
unemployed workers looking for work, such as during a recession, jobs vacancies
are filled quickly, leading to many new hires for a given level of vacancies.
Likewise, when unemployment falls, as in the recovery from the recession, vacancies
become harder to fill.15
Yet, job openings data do not garner the same level of
attention from researchers and the media as do other measures like GDP growth,
job growth, and the unemployment rate. The reason may be the lack of detailed
data that are collected and reported by the government. The Bureau of Labor
Statistics' Job Openings and Labor Turnover Survey (JOLTS) does not provide any
information on openings by occupation or even detailed industry. Nor does it
provide regional or even state-level information. It is probably not very
helpful to a laid-off auto worker in Michigan to know that there is a job
opening somewhere in the Midwest in the manufacturing sector, but that is the
most detailed information provided by the underfunded JOLTS program. The
pioneering work cited above has relied on private-sector data sources, such as
that provided by the Conference Board.16
The analysis here confirms the broad theoretical predictions
of "search theory"—that hiring difficulty is affected by both supply and demand—and
provides new insight into the characteristics of jobs that take long periods to
fill. This report goes beyond previous studies by considering a much larger and
richer set of factors that influence job duration—including the local labor
market, the value of specific skills required to do the job, and the company
doing the recruiting.
After summarizing previous research and describing the data,
this paper will analyze trends in hiring difficulties by skill, education, and occupation,
with a focus on STEM occupations and the skills common to them. The results
show that there is a national relative shortage of workers with STEM skills, and
the shortage is roughly as severe now as before the recession. Generally, more
valuable STEM skills—measured by salary offers—are more difficult to fill. The
problem is particularly acute in specific regions with few STEM workers or few
unemployed STEM workers. These results imply that enhanced STEM training and
education would have enormous benefits for individuals, firms, and regions.
Summary of Previous
Studies of Shortages and Vacancies
The formal economic analysis of job vacancies as a sign of
shortage issues goes back at least to the 1950s. Kenneth Arrow and William
Capron argued that unfilled vacancies represent a "dynamic shortage" that will
persist until wages rise sufficiently to attract enough workers to acquire the
necessary skills.17
While the theory implies shortages will only be temporary, long-term shortages
can persist in reality because financial incentives have a very weak effect on
the learning of children, as recent experimental evidence demonstrates.18
Empirical research on job vacancies has been limited by a
dearth of data, but a few published articles shed light on how vacancy data can
be used to discern skill shortage. In the 1960s, economists analyzed openings
data collected from government staffing agencies and found that highly skilled
positions took longer to fill and that employers cited a lack of qualified
candidates as the most common explanation for why jobs were hard to fill. Consistent
with the claims of employers, the studies also documented fewer job applicants
per vacancy for more skilled positions. This gap, moreover, varied widely
across regions, suggesting that skill mismatches have an important geographic
More recent work confirms that job vacancies requiring more
education, training, or experience take longer to fill.20
Economists have interpreted long vacancy periods as synonymous with shortage,
after adjusting for job characteristics that would make the open position
unattractive, such as low wages.21 Openings
for higher-skilled positions receive fewer initial applications, prolonging the
duration of the vacancy.22
There is a strong correlation between firm reports of shortage and the duration
of vacancies.23
Shortages, in this sense, can be attributed to firm factors—such as rapidly
increasing demand—or regional labor market factors—such as the presence of few
skilled workers and low unemployment rates.24
Shortages are particularly common in firms using advanced technologies,
suggesting that skill shortages will persist unless there is continued
advancement in the attainment of technical skills.25
Other evidence shows that raising wages may not be sufficient to fully address
shortages. Firms that pay high relative wages often report shortages.26
Finally, skill shortages appear to negatively affect firm productivity and
To summarize, there is small but consistent academic literature establishing
the usefulness of job vacancy data to interpret skill shortages and other labor
market trends.
Despite this literature, however, a few scholars have
recently argued that skill shortages are created by bad human resource
practices or do not exist at all.
The first argument suggests that the adoption of flawed software
algorithms used to sort through resumes are excluding many qualified candidates
for skilled positions and thus leading firms to misdiagnose their recruitment
challenges as shortage.28
While probably true in some cases, it seems unlikely that human resource
practitioners are systematically sabotaging—accidentally or not—their companies'
profits by excluding thousands of qualified workers for trivial reasons.
Moreover, this software-based argument cannot explain why the duration of job
vacancies fell during the recession and has since increased, as will be shown
below. Nonetheless, the data analyzed below adjust for human resource practices
specific to companies to test if this theory explains differences in vacancy
duration across skill levels.
A related and more persuasive argument along these lines is
that firms are not training as much as they used to and are putting too much
pressure on workers to have the perfect skills before being hired. Systematic
evidence is not available on this point, but a recent global survey found that
only 10 percent of companies report offering additional training to current employees
to help overcome talent shortages.29
The second argument is that weak wage growth for engineering
and computer occupations are inconsistent with shortage claims, and that there
may even be a surplus of workers with computer and engineering skills.30 Yet, evidence does
not support these
Nominal and real earnings for these positions have increased significantly and
much faster than wages in other occupations at least since 2000, and even
further back using consistent definitions of occupations or skill.32
A more likely explanation for wage patterns in recent years is that the Great
Recession temporarily eliminated most shortage problems and reduced wages, but
the resumption of economic growth is once again tightening the labor market and
regenerating a long-run shortage. It often takes a few years for wages to
adjust to market conditions.33
Related to the second argument on whether a shortage exists,
a recent analysis of an employer survey in the manufacturing sector concluded
that a shortage of production workers was a problem only for a small minority
of manufacturing firms.34
Only a small percentage of firms in manufacturing (24 percent) report having
vacancies lasting three months or longer. Yet, that low percentage includes
firms with no vacancies, which by definition could not have a recruitment
problem. Considering only firms eligible to have a recruitment problem (i.e.,
those with vacancies), these data show that two-thirds of firms report having a
long-term unfilled vacancy. At the same time, these data show that most production
jobs do not require many years of formal education or technical training,
though the report did not examine vacancies for engineers or other professional
STEM occupations.
Hard-to-fill vacancies take longer to fill often because of a lack of qualified
candidates. Yet, there are also a large number of low-skilled occupations that take a long time to fill because employers
face high turnover and a constant need to recruit new employees.
Job vacancy information is widely used outside of academia. Indeed,
U.S. government labor market statisticians released a guidebook suggesting that
lengthy openings durations for given occupations can be interpreted, with some
caution, as indicating a shortage.35
Many U.S. state labor departments now conduct their own job vacancy surveys,
and some ask employers to report the duration of their job vacancies and which
occupations are hard to fill and why. The conclusions that follow from these
data are that hard-to-fill vacancies take longer to fill often because of a
lack of qualified candidates. Yet, there are also a large number of low-skilled
occupations that take a long time to fill because employers face high turnover
and a constant need to recruit new employees, and so the employers maintain
vacancy advertisements for long periods.36
Thus, the interpretation of ad duration can generally be taken to be synonymous
with hiring difficulty except for positions with very high turnover and
typically low wages.
The state-level data suggesting skill imbalances are consistent
with national surveys. According to Manpower, roughly half of U.S. companies
reported difficulty filling positions in 2011 and 2012 because of lack of skills,
and this number increased from just 14 percent 2010.37
In 2013, the share reporting difficulty fell somewhat to 39 percent, but that
remains above the global average and above what many European countries report.38
Moreover, companies report that many of the hardest-to-fill jobs require STEM skills—skilled
trades and technicians, information technology (IT) staff, financial analysts,
and engineers. Another survey of human resource managers found that roughly
half of large U.S. companies report a STEM shortage, to which the managers attribute
lower productivity and firm growth.39
Small businesses also report increased hiring difficulty.
Since 1986, the National Federation of Independent Business has surveyed small
businesses on a monthly basis. Its February 2014 report shows the share of
small businesses reporting at least one unfilled vacancy has returned to
pre-recession levels, and the share reporting a lack of qualified job
candidates increased from 25 percent in October 2009 to 40 percent in October
To analyze characteristics of skill shortage in 2013 generally
and at the most detailed level, this study uses data purchased from Burning
Glass, a Boston-based labor market information company founded in 1999. Burning
Glass aggregates job postings from approximately 15,000 Internet job boards (as
well as company websites) and codes and categorizes those openings and their
requirements to facilitate interpretation and analysis.
This study relies on a subset of Burning Glass data that
uses job postings advertised on company websites for companies located in
metropolitan areas. In all, the database contains 4.7 million job postings from
company websites, representing 25% of all Burning Glass ads for 2013 in
metropolitan areas. Fifty-two thousand different companies are represented. Unlike
the full Burning Glass database, this subset includes information on the date
the job vacancy was first posted and when it was taken down from the company's
website. The difference in days between when a job was first posted and when it
was taken down provides a measure of its duration
in days, which will form the basis for key measures used in this analysis.
Advertisement duration is not precisely the same concept as vacancy
duration. Employers advertise to attract applicants and obtain a pool of
suitable candidates. Then, during a selection period, they sift through that
pool and conduct interviews.41 It
is not clear at what point in this process they remove advertisements. Moreover,
an ad may be re-posted months later after an unsuccessful round of interviews. These
measurement issues are addressed more directly in the appendix.
Summary findings on vacancy duration are reported only for
the first quarter of 2013. The reason for limiting the analysis to this period is
that the data are cut off in November 2013. As a result, vacancy duration
artificially appears shorter for advertisements posted later in the year simply
because the ads have not been allowed as much time to last. For the more sophisticated econometric
analysis, which undergirds the summary findings, ads posted from January to
August (3.3 million in total) are utilized and the results are adjusted by the
quarter in which the ads were posted.
In addition to vacancy duration, Burning Glass provides many
other useful pieces of information about job openings. The data include
occupational codes, industry codes, company names, and metropolitan location.
Many ads also include information on educational requirements (55 percent of
total), experience requirements (52 percent of total), and, to a lesser extent,
salary (7 percent of total).
Summary tables and statistics for the duration of STEM and non-STEM
vacancies by educational requirements use the minimum educational requirements
listed on the advertisement collected by Burning Glass.
The more sophisticated econometric analysis takes a slightly
different approach. To avoid discarding observations with incomplete
information, the baseline econometric analysis imputes educational requirements
to detailed (six-digit) occupations using the most frequent educational level
of workers, as reported by the Occupational Information Network (O*NET), a
collection of detailed occupational-based surveys. Results are also shown using
only the Burning Glass educational requirements. Here, the average of minimum
and maximum educational requirements will be used. For example, an
advertisement showing a range of possible educational levels from a bachelor's
to a Ph.D. would receive a higher education score than one showing only a
Burning Glass also lists specific "skills" and certifications
required for the job as mentioned in the advertisement. This information allows
one to distinguish skill levels between jobs within the same occupation. For
example, one computer occupation may require fluency with Microsoft Windows,
while another requires fluency in Java script or another programming language.
These skills can be quantified by taking advantage of salary information
associated with each job and calculating a mean salary for each skill. For
example, if a skill—like Microsoft Powerpoint—appears in 5,000 ads and 100 list
salary information, the mean salary from those 100 ads is used to quantify the
value of the skill.42
Since Burning Glass provides data on vacancy duration
starting only for 2013 (at least presently), alternative job vacancy data
sources were used to analyze broad trends in hiring difficulty.
The Conference Board's Help Wanted Online (HWOL) data series
has comprehensive historic coverage of job openings advertised on websites and Internet
job boards starting in 2005. It distinguishes between job openings that are
newly posted in a calendar month and those that are re-posted (or unfilled)
from the previous month. In this report, the unfilled rate is the share unfilled from the last month divided by
the share of total postings last month; it is an indicator of hiring
difficulty. Brookings purchased HWOL's occupational-level openings data from
2005 to February 2012 for the 100 largest metropolitan areas. This report
focuses on the period just before the recession (2006-Q4) until the latest
available data. National summary-level openings data are available for free
from the Conference Board's website.
A Bias Toward Professional Occupations?
There is a well-known bias in online posting data that gives disproportionate weight to professional
occupations.1 Many small retail businesses or restaurants do not advertise their job vacancies online, and
they may also believe that recruitment of any kind is not worth the cost, given the low wages and high turnover of most
of their employees. Many accept walk-in applications for jobs that are "always open." To gauge the size of this bias,
this paper compares traditional surveys of employers—conducted by mail or phone—to aggregated online job posting data. By
comparing the distribution of openings across major occupations, one can assess the degree of bias. From this, it seems
two major occupational groups—computer workers and managers—are strongly overrepresented in online advertisements, and
one major group—food preparation and serving occupations—is severely underrepresented. The bias in the composition of
occupations advertised online compared to those advertised in other formats makes it difficult to assess the true level
of opportunity for workers with various skills.
Yet, this compositional bias does not appear to extend to advertisement duration. Employers report longer recruitment
durations for the same sets of occupations as those that are posted for longer periods online. (See the appendix for
details.) Moreover, there is evidence that most employers post jobs online. A survey of employers in Northeastern Ohio
found that 89 percent posted job advertisements on an online job board.2 By contrast, another problem arises
with interpreting job-openings duration by skill or occupation: Many high-turnover, low-skill, entry-level jobs are
"always hiring," an effort by firms to ensure that there is an adequate work team at any one time. This issue will be
addressed in more detail below.
1 Jonathan Rothwell, "Education, Job Openings, and Unemployment in Metropolitan America" (Washington:
Brookings Institution, 2013).
2 ERC, "2013 ERC Hiring Trends & Practices Survey" (Mayfield Village, OH: 2013).
To understand and analyze the characteristics of job
openings, a number of other public data sources are merged with Burning Glass
data. To calculate the education, training, and skill requirements of
vacancies, occupational codes are matched to Bureau of Labor Statistics data and
O*NET. Previous work by Brookings classifies the skill level of occupations in
terms of STEM and other skills using the O*NET knowledge survey.43
This method is used here.
A STEM job is defined as one that requires an extraordinary
level of STEM knowledge in one or more core STEM knowledge domains.
Additionally, an overall STEM score is assigned to each occupation based on the
aggregation of knowledge scores in each STEM domain (i.e., science, computers,
engineering, and math). The same method is used to score occupations in other
knowledge domains with O*NET data, like law, English, and management. This
approach greatly expands the number and diversity of occupations that one may
deem STEM to include occupations like industrial designers, plumbers, computer
machine programmers, nurses, doctors, and accountants, which in most previous
studies would not be considered STEM jobs. Occupations that score highly on
this STEM measure in one or more STEM domains are referred to as requiring higher-level
STEM skills.
Unemployment rates by major and minor occupational groups by
metropolitan area are calculated from the 2012 American Community Survey using
the University of Minnesota's Integrated Public Use Microdata Series (IPUMS),
which can be used to analyze individual-level census records.44
The census asks unemployed workers to list their last occupation, provided they
have worked within the last five years. This information can be used to measure
the unemployment rate by occupation. Public use micro-geographies (called
PUMAs) were matched to metropolitan area geographies using online tools
provided by the Missouri Census Data Center. The final variable—the unemployment
rate by occupation by metropolitan area—is used to explain advertisement
duration by occupation.
Finally, the appendix discusses a formal econometric
analysis used to predict the duration of advertisements. To summarize the
approach, the regression analysis controls for job characteristics (including
skill, education, turnover, and experience requirements), firm characteristics
(all firm characteristics unchanged from January to August 2013), and regional
characteristics (including the number of workers and the regional unemployment
rate for those with the same occupation as the ad). In a limited subset of the
data, the analysis also adjusts for ad-specific requirements and the difference
in regional wages and ad-specific salary. The main results consistently show
that ads seeking more valuable skills and/or STEM skills, ads requiring higher
educational attainment, and ads running in regions with fewer workers and low
unemployment rates for the vacant occupation are all associated with longer vacancies.
Job Openings for STEM Positions Take Longer to Fill Than Openings in Other
Which jobs in the economy are hardest to fill? For which
skills is demand from employers high relative to supply from the U.S. labor
force? The duration of job advertisements is one way to examine these
questions, though to avoid errors of interpretation it is important to keep a
few things in mind.
As previous research documents, many job vacancies fill
almost immediately, or at least the advertisement is taken down almost
Within one day, 44 percent of U.S. job advertisements in the Burning Glass
database are taken down. In some cases, the removal may be due to internal
promotion, and the ad itself may represent a symbolic gesture intended to meet
human resource standards. It is not possible to know from these data, but ads
taken down within one day share many characteristics with ads taken down a few
days later.46
Fully half of all ads are taken down within one week. Previous research finds
that employers stop advertising once they have collected a sufficiently large
pool of applicants. Yet, for higher-skilled vacancies, employers often receive relatively
fewer applications, prolonging the vacancy.47
After an initially high probability of success in matching,
the odds of filling vacancies fall quickly. Just two-thirds of jobs are filled
within 33 days. One-fifth of vacancies are posted for at least 70 days. Almost
half of the 239,000 jobs posted for at least 70 days require high levels of
STEM knowledge. Another 10 percent are in non-STEM and high-turnover
occupations—like food preparation and health care support—and likely represent
constant recruitment effort rather than search difficulty.
A detailed analysis of these data show that the difficulty
of filling vacant positions increases as the level of STEM knowledge increases.
Likewise, higher levels of education, higher pay, and more valuable skills are
associated with longer duration times. Still, STEM knowledge has an effect on vacancy
duration that is independent of education, salary, company characteristics,
industry characteristics, and the regional labor market. Even after controlling
for all of these factors, STEM knowledge remains a highly significant predictor
of the duration of a vacancy. These results come from a regression analysis
discussed in detail in the appendix. The highlights of that analysis are
summarized in this and subsequent sections.
The highly uneven distribution of vacancies suggests that a
single metric (like the average) may not adequately capture the differences
across groups. This analysis starts by showing the median (the point at which
half of ads have a shorter duration and half have a duration as long or longer),
the mean (the total duration per ad), and the duration at the 80th percentile
(the point at which 80 percent of ads have a shorter duration and 20 percent have
a duration as long or longer).
Across various metrics of duration, jobs that require higher
levels of STEM skill are advertised for longer periods (Table 1). At the
median, STEM job postings last over twice as long as non-STEM jobs (11 vs. five
days). At the mean, the difference is proportionately smaller but still highly
significant (39 vs. 33 days). Higher educational requirements also prolong
advertising, and the effect is particularly pronounced in STEM occupations.
Table 1. Duration of Advertised Vacancies in Days by Level of STEM and Minimum Educational Requirements, 2013-Q1
The duration of advertising for a STEM occupation that
typically requires a graduate degree is 25 days at the median, 50 days average,
and 93 days at the 80th percentile. These search times are much longer than the
average for all non-STEM U.S. vacancies, for which the median is just five days,
the average is 33, and the 80th percentile is 64.
Focusing only on the median (column 1 of Table 1) shows that
STEM vacancies require longer advertising durations at every level of education
compared to non-STEM jobs. The differences are largest for STEM jobs for which
minimum education requirements are a Ph.D.: 25 days for STEM vs. 14 days for
non-STEM. At the master's level, the difference is 10 days: 21 vs. 11. At the
bachelor's and associate's degree levels the differences are five and seven days,
The patterns change somewhat when looking at the average and
the 80th percentile. Again, the STEM jobs requiring more education post the
longest, but using these metrics reveals that "blue collar" or sub-bachelor's-level
STEM jobs are harder to fill than even bachelor's-level non-STEM jobs. The mean
duration for associate's-level and high-school-level STEM jobs is 40 days, compared
to just 37 days for non-STEM jobs requiring a bachelor's degree and 31 days for
non-STEM jobs requiring an associate's degree. These results suggest that the
supply-demand imbalances at the middle education levels are very different for
those with STEM skills than those without. At these education levels, those
with STEM skills are at a distinct advantage over their non-STEM counterparts.
At the 80th percentile, sub-bachelor's STEM jobs are
advertised for four to five days longer than non-STEM bachelor's vacancies. Associate's-level
STEM openings are advertised for 23 days longer than their non-STEM
counterparts at the same education level. Meanwhile, non-STEM jobs requiring a
master's or Ph.D. are also advertised for long periods, suggesting that skill
shortage is not limited to STEM, even if it tends to be more severe for those
Aggregating the data to these broad categories misses some
of the important variation and diversity of hard-to-fill STEM jobs, especially
within the lower levels of education. Using the educational requirements
specifically requested in the advertisement shows the occupational groups with highly
skilled STEM jobs that are hard to fill at various levels of education.
At the high school level, STEM-intensive health care
practitioner occupations are among the most difficult-to-fill jobs, with the
most frequent vacancies in this category occurring for medical and lab
technicians. These occupations advertise for 61 days, on average.
Advertisements for blue-collar STEM occupations—i.e., those requiring
no more than a high school education—in arts, design, entertainment, sports,
and media occupations, and installation, maintenance, and repair occupations also
last long periods, an average of 42 and 40 days, respectively. STEM jobs in these
occupations include broadcast technicians, HVAC technicians, geothermal
technicians, and auto mechanics. Jobs in skilled-construction trades, such as
building inspectors, carpenters, sheet metal workers, plumbers, and pipefitters,
can also be difficult to fill at this level.
At the associate's degree level, the longest-advertised STEM
jobs include health care practitioners (like registered nurses), with an
average of 44 days; office support jobs (like statistical and database
technicians), with an average of 41 days; installation, maintenance, and repair
workers (e.g., supervisors and avionics, automotive, and telecommunications
technicians), with an average of 38 days; and computer workers (e.g., user
support specialists), with an average of 34 days.
At the bachelor's level, industrial and commercial design
occupations are among the STEM art and design occupations that advertise for
long periods—49 days on average. Engineering positions advertise 45 days, as do
education-related STEM positions. Computer occupations at the bachelor's
level—with software developers being the most common—advertise 43 days, as do various
managerial STEM positions including engineering managers, computer managers, and
industrial, financial, and loss-prevention managers.
At the master's level, STEM jobs in engineering advertise the
longest (61 days), followed by education (54), science (50), computers, (48),
health care (47), and management (41). Finally, at the Ph.D. or professional-degree
levels, vacancies last 58 to 45 days, on average, for jobs spread across
managerial positions (58 days), computer occupations (53 days), engineering
(52), science (51), and health care (45).
As implied in the above, the duration of job advertisements
differs widely across major occupational groups (Table 2). The median duration
of vacancies for health care practitioners and computer workers is 18 and 15
times longer, respectively, than for five large occupational groups including
construction, production, and office administration. Roughly 40 percent of
vacancies for health care practitioners and computer workers are advertised for
at least 30 days compared to 24 percent for office and administrative workers.
Table 2. Average Duration of Job Vacancy Advertisements Posted on Company Websites, by Major Occupation, 2013
Occupations with the lowest advertisement durations offer
low wages and have few educational requirements, making the pool of potential
workers larger. But even some high-paying jobs with high educational
requirements fill quickly. Legal occupations, for example, have a median advertising
duration of five days, reflecting particularly weak labor demand for lawyers
since the recession.48
It is also notable that most vacancies in science and social science are not as
hard to fill as those for engineers, computer workers, and health care workers,
suggesting variation in the market for different kinds of STEM skills.
If one were to plot out the number of vacancies that last
each day, the distribution would not be a "bell curve," which describes
variables like human height that cluster around a mean that can be considered
"normal." Rather, the distribution of vacancy durations is highly skewed, with
a very long tail. STEM occupations tend to require the longest advertisement
durations at each point in the distribution, but there is considerable
variation even within the same occupational groups.
For every major occupational group (Table 3), at least 20
percent of ads are taken down within one day (fourth column, "20 percentile of
ads"), but at the other extreme duration times extend out for every occupation.
Even for the easiest-to-fill occupation—office and administrative support—20
percent of vacancies ("80th percentile of ads") are advertised for at least 39
days (column 1). Still, the distribution or the share of jobs requiring very
long fill times varies widely between occupations. For life, physical, and
social science occupations and computer occupations, 20 percent of ads are
posted at least 77 and 76 days, respectively. That number is even higher—81 and
89—for architecture and health care occupations.
Table 3. Average Duration of Job Vacancy Advertisements Posted on Company Websites by Major Occupation and Quintile of
Duration in Days, 2013
Installation, maintenance, and repair occupations often
require STEM knowledge, acquired through on-the-job-training or post-secondary
education at a community college. At least 60 percent of these vacancies could
be regarded as easy to fill, but for 20 percent—perhaps those requiring very
specific skills or those advertised in places with a shortage—advertisements
are posted for at least 62 days.
An alternative concern with the interpretation of vacancies
advanced thus far is that company-specific practices (like human resource and
recruitment practices) may explain some of the variation in vacancy duration
across occupations. It may be that tech companies or other companies more
likely to hire STEM workers have common practices that delay matching workers
to job openings because of "pickiness" or formalized processes that subject
large number of candidates to interminable automated screening processes. If
such practices explained the longer duration of advertisements for STEM
positions, one would expect to see few differences in ad duration between
engineering and office administrative workers within the same company.
As it happens, even within the same business establishment
(defined here as the same company and regional location), STEM jobs take much
longer to fill than low-skilled positions. Health care, computer, science,
engineering, and management jobs take significantly longer to fill, on average,
relative to the average of all other vacancies within the same establishment
(Table 4). On the other hand, office and administrative positions, health care
support, and building maintenance jobs are filled much faster than other
occupations in the same establishment. Legal occupations also fill relatively
quickly. Overall, the average computer occupation takes 19 days longer to fill
than the average office administrative job within the same business
Table 4. Differences in Job Vacancy Advertisement Durations Within the Same Large Establishment for Ads Posted in
A few examples illustrate the differences across companies.
Google advertised over 100 vacancies for computer workers in the San Jose
metropolitan area in early 2013, and the ads were posted for an average of 97
days. In the same area, Google posted almost 100 ads for sales occupations, but
only for an average of 56 days. In between, management occupations lasted 79
days, on average. At Volvo, in the Greensboro, NC metropolitan area, ads for
architecture and engineering occupations lasted 79 days, on average, compared
to 57 days for installation, maintenance, and repair occupations, and just 22
and 21 days for business workers and production workers, respectively. Finally,
at Pfizer, in the New York metropolitan area, ads for science jobs were left up
for an average of 63 days compared to 13 days for jobs in sales and 12 days for
office administrative jobs.
Trends in Hiring Difficulty
Suggest Tightening Labor Market for STEM Workers
The above analysis establishes that STEM skills are
relatively more difficult to fill, suggesting a relative shortage of STEM
skills, but those findings do not prove that there is an "absolute" STEM
shortage. It may be the case there are no
skills that are in shortage. After all, the Great Recession had a wrenching
effect on unemployment and the demand for goods and services. It may be that
before the recession jobs were difficult to fill, but elevated unemployment and
depressed demand during the recovery period made jobs easy to fill, eliminating
any shortage. Analyzing trend data on hiring difficulty can shed light on these
issues. To capture hiring-difficulty trends, this report relies on the unfilled rate, the percentage of jobs
taking at least one month to fill, which is the only measure of hiring
difficulty available to us from the Conference Board data. If STEM jobs are as
difficult to fill now as before the recession, then the relatively high level of shortage looks more like an
absolutely high level of shortage, at
least as severe as before the recession.
National data on job openings from the Bureau of Labor
Statistics' JOLTS program show a clear increase in hiring difficulty since the
recession, but they provide no information on whether this increase varies by
occupation (see appendix). As it happens, the Conference Board's HWOL data
reveal that trends in hiring difficulty have played out very differently
according to the education and skill requirements of vacancies.
Hiring difficulty for STEM positions at higher levels of
education eased only slightly during the recession before becoming as difficult
as before by early 2012 (Table 5). This was the only category of workers to see
an increase in hiring difficulty from late 2006 to early 2012. The trend is
consistent with the idea that there is an absolute shortage, not just a
relative shortage, of highly educated STEM workers (defined as those with a
bachelor's degree or higher, but not necessarily those at the Ph.D. level).
Table 5. Percentage of Vacancies Reposted After One Month by Broad Occupational Group
Hiring for STEM jobs at mid-levels of education (those
requiring some college, perhaps a certification or an associate's degree)
remained easier in early 2012 than before the recession, but not by much
compared to jobs requiring only a high school diploma (STEM or not), as Table 5
shows. Meanwhile, hiring difficult for STEM jobs at high and mid-levels of
education consistently remained harder than for non-STEM jobs at high levels of
education. The easiest jobs to fill were those requiring only a high school
diploma and no significant STEM skills.
The gaps in hiring difficulty between occupations has expanded
in recent years. The broad trend across all of the 100 largest metropolitan
areas shows that demand for professional STEM occupations—health care
practitioners and computer workers, engineers, and scientists—was less
negatively affected by the recession and more positively bolstered by the
recovery, leading to increased hiring difficulty relative to before the
In the fourth quarter of 2006, jobs for health care
occupations went unfilled at a rate of 40.2 percent after one month; for
computer, engineering, and science occupations the unfilled rate was 39.5
percent (Table 6). These unfilled rates were only slightly higher than less-STEM-intensive
professional jobs (39.4 percent) and skilled blue-collar jobs in construction,
production, transportation and installation, maintenance, and repair (38.7
percent), and just four percentage points higher than the unfilled rate for
public service (e.g., education, protection, and social service) and less-skilled
service jobs.
Table 6. Percentage of Vacancies Reposted After One Month by Broad Occupational Group
This pattern changed dramatically once the recession hit. By
the third quarter of 2008, the unfilled rate plummeted 13 percentage points for
construction and production workers, 10 percentage points for low-skilled
service jobs, and almost 9 percentage points for public service jobs. The other
occupations saw a much smaller decrease in the unfilled rate (between 4 and 6
percentage points).
The gap in hiring difficulty between STEM and non-STEM jobs widened
still further during the initial years of the recovery. From 2008-Q3 to
2012-Q1, the unfilled rate increased 8 percentage points for health care
workers and 7 percentage points for other professional STEM workers. The
recovery for non-STEM professional occupations was comparatively lackluster at
3 percentage points for construction and production workers and low-skilled
service positions.49
Looking at occupations in greater detail (not shown in
table) reveals that computer occupations and health care practitioner
occupations had the largest increase in unfilled rates between 2006-Q4 and
2012-Q1 (3 and 6 percentage-point increases, respectively). Hiring difficulty
for social and other scientists and engineers increased slightly between
2006-Q4 and 2012-Q1, as the unfilled rates were 3 and 2 percentage points higher.
Similar patterns were observed for management and business occupations.
Meanwhile, legal occupations saw unfilled rates plummet from 36 percent to 27
To summarize, the relatively high levels of difficulty in
filling job vacancies for computer and health care occupations appear to be as
acute after the recession as before—a period of low unemployment and tight
labor markets. Therefore, the contemporary relative shortage of workers in
these positions is especially severe. The market for scientists and engineers, as
well as professional workers in less-STEM-intensive fields, is also quite
tight, though hiring difficulty was not back to pre-recession levels as of
early 2012. Meanwhile, for legal occupations and most blue-collar or less-skilled
service occupations, hiring difficulty practically disappeared during the
recession and has not returned.
Given the striking differences across occupations, one may
wonder how these trends in hiring difficulty correspond to more conventional
measures of economic performance by occupation. It turns out that the overlap
is strong, especially for the hardest-to-fill occupations. Since the start of
the recession in 2007, no occupational category has added net jobs at a faster
rate than computer and mathematical occupations, which experienced 21 percent
growth and a net increase of 725,000 jobs.50
The next-fastest-growing category has been health care practitioner occupations,
which increased by 16 percent from a larger base and added 1.2 million jobs on
net. The slowest-growing occupations are still below their 2007 job totals.
These include construction and production occupations, which are off 23 percent
and 13 percent, with net decreases of 2.2 and 1.3 million jobs, respectively.
Office and administrative support occupations also saw a large job decrease
from 2007 to 2013, a loss of 1.7 million.
These employment growth changes are highly correlated with
the level and changes in unemployment since the recession. As of March 2014, the
national unemployment rate was 2.2 percentage points above its 2007 level of
4.6 percent, but unemployment rates for computer and health care practitioners stand
at just 0.7 and 0.9 percentage points above 2007 levels. Architectural and
engineering occupations, as well as life, physical, and social science
occupations did not fare as well, increasing by 1.4 and 2.0 percentage points,
which is still below the national average. Yet, unemployment was so low before
the recession that current unemployment is still only 3 and 4 percent for these
workers, which helps explain why job vacancies are difficult to fill despite a
net decrease in job growth from 2007 to 2014. Meanwhile, construction workers
still suffer from unemployment rates that are 4.2 percentage points above the
2007 level.
Specific High-Value Skills Requested by Employers and Common to STEM Occupations
Are Particularly Scarce Relative to Demand and Particularly Valuable to Employers
Skills vary both between and within occupations. Every job
is classified into an occupation according to various characteristics like
skill, task, and educational requirements. Yet, some occupations are more
uniform across industries, companies, and work teams (like cashiers or electricians),
while others differ widely in the specific skills required in different
settings. This is the case for many computer, health care, and management jobs,
as one can see by looking at the number of distinct skills requested by
employers in job advertisements (Table 7).
Table 7. The Value and Diversity of Skills Within Major Occupational Groups
In the first quarter of 2013, employers requested 1296
distinct skills for health care workers and 1272 distinct skills for computer
workers. For health care jobs, the most common skill requirements include
patient care, treatment planning, case management, Lotus Notes (a form of
collaboration software), clinical experience, medical coding, and
rehabilitation. For computer jobs, Oracle, Microsoft Windows, JAVA, SQL, SAP, quality
assurance and control, LINUX, and UNIX are frequently mentioned skill
requirements. Thus, for these sorts of jobs, one needs to have a very specific
skill to be qualified. It is not enough to have worked in the same occupation
for the requisite amount of time and to have obtained the requisite degree.
Skills matter.
Likewise, some skills have much greater value to employers
than others. The value of a distinct skill is measured by calculating the
average pay associated with each skill. For example, if two jobs in early 2013
mention "JAVA programming" as a required skill, and one of the advertisements
cites a salary of $100,000 and the other a salary of $50,000, then the average
value of the skill is $75,000.51
This value is a function of both the skill's relative
scarcity and its capacity to generate value for the firm. The skills common to
computer occupations are collectively the most valuable in the economy at
present, at least as reflected by job advertisements that report specific
salaries and are advertised on company websites. Architectural and engineering
skills are also highly valued, as are skills in other professional occupations.
Among the least-valued skills are those commonly found in building and grounds
cleaning, security, production, and personal care. For occupations with at
least 50 vacancies, the most valuable computer skills include data
visualization, Netcool, process management, natural language processing,
Mathematica, automation tools, wireless telecommunications, machine learning,
PIG, software as a service, iOS, game development, Android Software Development
Kit, and structured query reporter. Each of these skills is worth well over
$100,000 in average salary, as advertised in early 2013.
Not only are computer skills the most valuable, they are
advertised for the longest period of time, suggesting that they are the most
difficult to fill (Table 8). For each distinct skill, the data allow a
calculation of the median advertising duration in days, and that value is
averaged across all skills according to which occupation requested the skill
most frequently. The weighted average median duration for computer skills comes
to 22.7, just ahead of skills in the sciences. Engineering and health care skills
are also relatively difficult to fill, whereas skills in construction, legal,
office, and production are easy to fill, requiring about half as much time.
Table 8. The Duration of Advertisements for Distinct Skills by Most Frequent Major Occupational Group
Among particular skills requested in at least 500 different
job announcements in the first quarter of 2013, 434 distinct computer-related
skills are advertised for at least 30 days on average. Another 255 distinct
computer skills linger in company website ads for at least 40 days. There are
80 such skills related to engineering occupations and 20 related to science
Compare these to skills common to legal professions. Just
one skill with at least 500 vacancies—intellectual property—is advertised for
an average of 40 days. Since intellectual property lawyers often work for tech
companies to initiate or enforce patents, this is arguably the most STEM
intensive of all legal skills. Meanwhile, just eight skills sought in 500 or
more openings in office and administrative positions meet or exceed an average
duration of 40 days; these include qualitative analysis and policy review.
Among the most difficult-to-fill computer skills are many
unfamiliar to all but computer programmers and software experts (see Figure 5 for partial list). The hard-to-fill skills include Mathematica, PIG,
Apache Hadoop, OpenGL, NoSQL, Python, R, C++, Android, iOS, Biostatistics, Ruby
on Rails, and PERL. Ads with these skills linger on company websites for an
average of 50 days. At the opposite end of the spectrum are a smaller number of
computer skills that are relatively easy to fill. Among these, requiring 33 days
or fewer, are support skills, which include technical support, IT support, help
desk support, and telephone technical support. These are also skills associated
with lower advertised salaries.
Figure 5. Computer and Math Skills with the Longest Advertisment Durations with 500 or more advertisements in 2013-Q1
Source: Brookings analysis of data from Burning Glass
Engineering analysis, HAZOP, electronic design, Mathcad, circuit
design, chemical engineering, engineering software, and power distribution are
among the hardest-to-fill engineering skills, requiring at least 45 days of
advertising. Also high on the list are ads requiring the
"Six Sigma" engineering process management certifications, which post an
average of 43 days for black and green belts. The easier-to-fill skills include
manufacturing engineer, which may reflect a still-depressed manufacturing
sector relative to five to 10 years ago, and architecture-related skills like
LEED, permitting, and CAD, which may reflect the still weak commercial and residential
construction markets. These ads are posted for 36 or 37 days, on average.
For science and social science vacancies, the
hardest-to-fill skills include Stata, an analysis and programming tool commonly
used by economists; biologics development; proteins; cell biology; biotechnology;
and analytical chemistry (Figure 7). These skills are posted for at
least 40 days, on average. Among the easiest skills to fill are policy analysis
(32 days) and environmental studies (25 days).
Figure 7. Average Duration of Job Advertisements Requiring Specific Life and Social Science Skills
A high percentage of installation, maintenance, and repair occupations
require significant STEM knowledge in at least one field, and the occupations
cover a diverse array of skills (Figure 8). Among the hardest-to-fill
skills, each lasting at least 40 days, are plotters, a kind of printer used in
technical designs; sockets; computer hardware/hardware knowledge; engine repair;
and equipment inspection. Among those advertised for the shortest period are PC
support, masonry, carpentry, and plumbing repairs. The last three are common in
the hard-hit construction sector.
Figure 8. Average Duration of Job Advertisements Requiring Specific Installation and Repair Skills
The regional supply of workers in a given occupation affects the length
of vacancy advertisements
Hiring difficulty varies widely across U.S. regions. In
general, metropolitan areas in the South have somewhat longer median
advertisement duration times than those in the Northeast and West. The
difference is three days (eight versus five), but statistically significant.
The Midwest has greater variation with some states at either end of hiring
difficulty spectrum.52
The large metropolitan area with the longest median duration
time, at 21 days, is Detroit, followed by San Jose, CA and Washington (20 days),
and by New Haven, CT; Charleston, SC; Chicago; Grand Rapids, MI; San Francisco;
and Durham-Chapel Hill, NC (18 days). Those with a median duration of just one
day include Columbus, Toledo, and Youngstown, OH; Madison and Milwaukee, WI;
Akron, OH; Pittsburgh and Lancaster, PA; Boise, ID; Minneapolis; and Denver and
Colorado Springs, CO. The below map illustrates this variation using median ad
duration for all metropolitan areas.
Map: Median Duration of Openings
What explains this variation? No one factor, certainly. As
the appendix shows, company characteristics, ad characteristics, and
metropolitan supply and demand factors all have an effect, not to mention
individual idiosyncrasies of resumes and hiring managers. Yet, broad patterns
are evident, with the strongest single explanatory variable being the average
value of skills advertised for the job opening.
Many of the metropolitan areas with the most valuable skills
demanded by employers are also those with the longest median duration times
(the correlation is 0.55 for large metro areas). The average skills mentioned
in a San Jose job announcement are the most valuable in the country—worth
$68,000 on average, using salary data from the entire country associated with
each skill advertised. Next is San Francisco at $63,500, followed closely by
Washington; Austin, TX; New York; Durham-Chapel Hill, NC; Seattle; Charlotte, NC;
Hartford, CT; Raleigh, NC; Boston; Bridgeport, CT; and San Diego—all with
averages above $60,000. At the low end, the average skills advertised are worth
less than $53,000 in metros such as Toledo and Lancaster, OH; Boise, ID;
Youngstown, OH; and McAllen, TX.
As mentioned above, STEM jobs tend to demand more valuable
skills. And so, despite the variation in ad duration times across metropolitan
areas, STEM jobs, especially those requiring more education, take longer to
fill consistently across metropolitan areas. In 82 percent of all metropolitan
areas and 91 of the 100 largest metropolitan areas, STEM jobs take longer to
fill, on average, than non-STEM jobs. In 82 of the top-100 metro areas, the
average STEM job is advertised for at least 30 days. Yet, different STEM
occupations, requiring different levels of education, are more or less
mismatched between supply and demand across metropolitan areas.
For STEM jobs requiring a bachelor's degree or higher, Fresno,
CA has the longest average duration, at 68 days. Health care practitioner jobs
in Fresno are advertised for 74 days on average, and the length is 71 and 108
days, respectively, for computer and life, physical, and social science jobs in
the area. In San Jose, it takes 59 days on average to fill a STEM job requiring
a bachelor's degree, and 54 days in San Francisco. In Seattle and Washington,
it takes 48 and 46 days, respectively. On the other extreme, in Des Moines, IA;
Columbus, OH; Syracuse, NY; and Knoxville, TN, it takes less than 30 days to
fill the average STEM vacancy for occupations that typically require a
bachelor's degree.
In other metropolitan areas, sub-bachelor's STEM jobs stand
out as hard to fill. In Stockton, CA, the average advertising duration of a job
requiring STEM knowledge and a mid-level of education (between some college and
an associate's degree) is 91; it is 85 in Omaha, NB; 69 in North Port, FL; 62
in Worcester, MA; and 60 in Providence, RI. Across these metros, a large
percentage of the hard-to-fill mid-level STEM jobs are in health care
occupations. By contrast, health care and other mid-level STEM jobs are
relatively easy to fill in places like Madison, WI and Pittsburgh. In other
areas, like Durham-Chapel Hill, Seattle, San Diego, and Detroit, installation,
maintenance, and repair vacancies—which are STEM intensive—endure for long
periods: 51,45, 44, and 43 days, respectively. However, in Denver and Hartford,
CT, it takes just 21 days to fill vacancies for these occupations.
Of course, there is no pure national labor market—a given
vacancy is not at all equally likely to be filled by a worker living in any
part of the country. In many ways, the capacity of a company to find the
workers it needs in a timely fashion depends on the time of the vacancy and location
of the job. Companies can draw workers—especially young workers who face lower
social costs from relocating—from around the country and even around the world.
At the same time, travel, information, and social costs mean that the
metropolitan area is the most relevant labor market for recruiting.
Empirically, it turns out that metropolitan-area location
has a large and important effect on how quickly a company can fill its
vacancies, and evidence discussed in the appendix suggests that the local
market is more important than the national market. The median duration of a job
opening increases as the unemployment rate for workers in the same occupation
decreases. That is, when unemployment is high for a given occupation in a given
metropolitan area, the local supply of workers willing and able to apply for
and accept a position is also high. Therefore, companies can easily fill their
vacancies, even for some highly skilled positions. At the same time, a larger
absolute number of even employed workers also helps a company fills its
vacancies faster because some percentage of employed workers will be willing to
seek a more attractive opportunity, especially if the pay is higher. These
results are robust to a number of competing interpretations, including company
differences, as the appendix discusses.
The relationship between local labor markets and vacancy
duration is clear from simple summary statistics. For workers in an occupation with
a regional unemployment rate of 10 percent or higher, the median ad duration is
7.4 days (Table 9). The duration increases to about nine days for workers with
a regional unemployment rate between 5 and 10 percent and jumps to roughly two
weeks once the unemployment rate falls under 5 percent. There is a similar
relationship between ad duration and the unemployment rate in terms of the
share of vacancies posted more than 30 days and the within-company differences
in average duration.
Table 9. Labor Supply Characteristics of Workers' Occupations by Level of Unemployment and Duration of Job Vacancies
in Metropolitan Areas
Another way to see the supply-demand mismatch for these
occupations is to examine the number of vacancies per worker. In the first
quarter of 2013, there were just 0.1 vacancies for every unemployed worker in
an occupation for which the regional unemployment rate was greater than 10
percent. Meanwhile, there were 1.4 vacancies per worker if the occupation's regional
unemployment rate was below 3 percent. Simply put, workers in
high-demand/low-unemployment occupations face much less competition in the
labor market than do those in low-demand/high-unemployment occupations. A
construction worker or food preparation worker living in a metropolitan area
with high unemployment has about the same odds of getting a job as an applicant
to an Ivy League university has of getting admitted.
A few caveats are worth noting in interpreting the ratio of
vacancies per unemployed worker. The total number of job vacancies listed here
is likely understated for all occupations, since these ads are taken only from
company websites and not job boards. Also, food preparation and serving
openings, a high unemployment occupation, are probably understated the most,
though not nearly enough to make up the differences here. In other words, the gaps
between groups are much too large to be explained by data limitations.
Finally, these results show that the dominant characteristic
of occupations with low regional unemployment rates and high numbers of job
vacancies is that they are in STEM fields. Over two-thirds—68 percent—of online
job advertisements for occupations for which the regional unemployment rates were
below 3 percent are for STEM occupations. When unemployment rates for STEM
workers are over 10 percent, just 15 percent of ads seek workers with STEM
expertise, and the jobs advertised tend to be for non-professional occupations
in areas such as construction or production.
A few metropolitan-level examples illustrate the statistical
link between regional occupational-specific unemployment rates and vacancy
duration. The national unemployment rate for computer and mathematical science
workers in 2012—the latest year with comparable metropolitan data—was 3.5
percent, but it varied considerably across large metropolitan areas.
In metropolitan San Jose and Boston, the unemployment rate
for computer and math workers was 3.5 and 3.1 percent, respectively, but in
metropolitan New York City and Las Vegas it was 5.1 and 9.2 percent. Partly as
a result, companies filled vacancies in early 2013 much faster in New York and
Las Vegas than in San Jose and Boston. The mean vacancy duration for computer
workers was 34 days in Las Vegas and 36 days in New York; in San Jose and
Boston, mean duration times dragged out to 61 and 43 days.
Examples from engineering occupations further illustrate the
trend. In metropolitan Dallas, the unemployment rate for architects and
engineers is 3.6 percent compared to 2.8 percent in Houston, and it takes 10
extra days to fill a vacancy for architects and engineers in Houston—51 versus
41 days. Likewise, in metropolitan Atlanta the unemployment rate for architects
and engineers is 7 percent, compared to just 1.8 percent in Raleigh, NC.
Vacancies for these workers are posted for 29 days in Atlanta compared to 54
days in Raleigh.
To some extent, these differences in ad duration time are attributable
to differences in the skill level required of the workers and other factors,
but a more detailed analysis—discussed in the appendix—shows that metropolitan
area unemployment rates explain some of the differences even accounting for
those skill-related factors. Metropolitan areas with larger numbers of workers
in a given occupation also tend to fill vacancies faster, all else being equal.
This report analyzes an extraordinary new database of
millions of job openings and their characteristics in terms of skills,
occupations, educational requirements, location, and advertising duration. To
the author's knowledge, this is by far the largest database of vacancy duration
to be analyzed, and this is the first national research database of vacancy
duration by skill level. In that respect, this report offers a powerful tool
for understanding the supply and demand for skills, yet it touches on only a
few of many research issues that the data can help address with new clarity and
The principle finding is that there is a relative shortage
of U.S. workers with STEM skills. In other words, STEM skills are in high
demand relative to supply, and the problem is especially acute in certain
metropolitan areas, where the average vacancy for STEM workers takes months to
fill. As a result, workers with STEM knowledge tend to readily find job
opportunities, even as large categories of workers with little education or
STEM skills compete over a relatively small number of jobs.
The extent to which there is an absolute shortage of STEM
skills is harder to determine. One might consider things like the actual number
of job vacancies by field and how it compares to the number of potential
workers with the right skills. This would involve estimating how frequently
employed workers in various fields would be able or willing to switch jobs. Alternatively,
and more directly, one could analyze data on the number of qualified applicants
for each STEM job, but such data do not currently exist.
Whether the absolute STEM shortage is mild or severe,
important consequences follow from even a relative shortage. Without major
changes in training or education policy and practice, the relative shortage of
STEM workers will likely play out by enlarging the already sizable long-term
gap in lifetime earnings and unemployment rates between STEM and non-STEM
workers, exacerbating income inequality generally and inequality across
racial/ethnic groups and gender more specifically.
At the same time, as the economy continues to recover from
the Great Recession, opportunities for low-skill workers will also expand,
lowering unemployment and reinforcing growth. That prediction is consistent
with previous Brookings research emphasizing the importance of cyclical demand
in explaining changes in unemployment.53
Still, macroeconomic recovery will not erase the large differences in earnings
or unemployment rates across occupations, which have favored STEM workers for a
number of years across cycles of recessions and booms.55 These
efforts cut across political party lines, levels of government, and sectors at
a time when the nation's leaders can seem to agree on little else. A less
conventional conclusion from that research is that there is little relative
effort at the federal level to train incumbent workers or educate young workers
for sub-bachelor's degree STEM careers. The findings from this study are
consistent with the implication that STEM careers at the post-secondary but
sub-bachelor's level (e.g., nurses; technicians; and installation, maintenance,
and repair workers) are in high relative demand.
There are exceptions, however. Blue collar STEM jobs in
construction and production jobs are relatively easy to fill nationally. This
exception can be explained by the extraordinary number layoffs that hit the
construction and manufacturing sectors during the recession, combined with
long-term competition with developing countries with respect to manufacturing.
There are regional pockets of hiring difficulty for these occupations, but
nationally, on average, they are easier to fill than most non-STEM jobs.
At another level, this report presents a new and powerful tool to examine relative supply and demand imbalances across
occupations and
skills. A handful of states are currently conducting similar analyses (see box),
but their reliance on either survey data or alternative data sources limits the
details that can be extracted for research or policy purposes. For students and
educators, the detailed information on the value and labor market demand for
specific skills could prove useful for motivation or funding. Moreover, regional-level
statistics can be used to assess strengths and weaknesses in the area's
workforce pipeline. The goal of all of these efforts should be to expand
opportunities for people to pursue and enter rewarding careers, many of which
have large social benefits.
State Initiatives to Understand and Address STEM Labor Demand
Labor market data has the potential to improve education and training practices, but in order to maximize the benefits
states need detailed data and a policy framework that connects data to funding allocations.
On the data gathering side, Washington and Oregon state governments conduct what are perhaps the most detailed state-
specific vacancy surveys and include explicit questions about hiring difficulty and duration by occupation. Florida's
Department of Economic Opportunity does not collect ad duration data, but it does use Conference Board data to track
growth in STEM vacancies posted online, and it combines these data with detailed administrative records from colleges and
universities to estimate supply-demand mismatches.
1 State of North Carolina, http://www.governor.state.nc.us/newsroom/press-releases/20140410/governor-pat-mccrory-proposes-higher-
2 Pennsylvania Higher Education Assistance Agency, Pennsylvania Targeted Industry Program, available at http://www.governor.state.nc.us/newsroom/press-releases/20140410/governor-pat-mccrory-proposes-higher-
investment-high-demand (June 2014).
3 State University System of Florida, Board of Governors, Performance Based Funding Model, available at http://www.flbog.edu/about/budget/performance_funding.ph
p (June 2014).
4 Mary Wright, "Aligning Career Training With Employer Demand" (New York: Jobs for the Future, 2014).
1 Jonathan Rothwell et al., "Patenting Prosperity:
Invention and Economic Performance in the United States and Its Metropolitan
Areas" (Washington: Brookings Institution, 2013); Committee on Prospering in
the Global Economy of the 21st Century, et al., Rising Above the Gathering Storm: Energizing and Employing America for
a Brighter Economic Future (Washington: National Academies Press, 2007);
Members of the 2005 "Rising Above the Gathering Storm" Committee, Rising Above the Gathering Storm,
Revisited: Rapidly Approaching Category 5 (Washington: National Academies
Press, 2010).
Jonathan Rothwell, "The Hidden STEM Economy" (Washington: Brookings
Institution, 2013).
Economics and Statistics
Administration, "STEM: Good Jobs Now and for the Future" (Washington: U.S.
Department of Commerce, 2011); Anthony P. Carnevale, Nicole Smith, and
Michelle Melton, "STEM: Science, Engineering, Technology, and Mathematics"
(Washington: Georgetown University Center for Education and the Workforce,
2011); Todd Gabe, "Knowledge and Earnings," Journal
of Regional Science 49 (3) (2009): 439-57; Paolo Buonanno and Dario
Pozzoli, "Early Labor Market Returns to College Subject," Labour 23 (4) (2009): 559–88 ; Jin Hwa Jung and Kang-
Shik Choi,
"Technological Change and Returns to Education: The Implications for the
S&E Labor Market, Global Economic
Review 38 (2) (2009): 161-84; Peter Arcidiacono, "Ability Sorting and the
Returns to College Major," Journal of Econometrics 121 (1-2) (2004):
Vladimir Chavrid and Harold Kuptzin, "Employment Service Operating Data as a
Measure of Job Vacancies," in The
Measurement and Interpretation of Job Vacancies (New York: Columbia
University Press, 1966), available at http://www.nber.org/chapters/c1610
5 Claudia Goldin and Lawrence Katz, The Race Between Education and Technology (Cambridge,
MA: Harvard University Press, 2008); Clifford
Winston, Robert W. Crandall, and Vikram Maheshri, First Thing We Do, Let's
Deregulate All the Lawyers (Washington: Brookings Institution,
2011); Daron Acemoglu and David Autor, "Skills, Tasks and Technologies:
Implications for Employment and Earnings," Working Paper No. 16082 (Cambridge,
MA: National Bureau of Economic Research, 2010); Philip Oreopoulos and Uros
Petronijevic, "Making College Worth It: A Review of Research on the Returns to
Higher Education," Working Paper No. 19053 (Cambridge, MA: National Bureau of
Economics Research, 2013).
A standard deviation in STEM knowledge is the difference between a medical
scientist and an engineering technician, an engineering technician and a
computer-support specialist, a computer-support specialist and a truck driver,
and a truck driver and a security guard.
Engineering and computer science majors both experienced 2.9 percent growth in
nominal earnings from 2009 to 2012, the highest among all fields and just ahead
of other STEM fields: health care sciences and biology and life sciences.
Meanwhile, salaries for undergraduate law majors fell 8.1 percent. This sample
was 21- to 30-year-olds with bachelor's degrees. The American Community Survey
data were analyzed via IPUMS, which allows researchers access to microdata.
Data from Conference Board Help Wanted Online Series, press release (http://www.conference-
9 That figure includes at least some part of human
resource staff salaries and comes from Karen O'Leonard, "The Talent Acquisition
Factbook 2011" (Oakland, CA: Bersin & Associates, 2011). Estimates from the United Kingdom are
comparable at about $2,700 per hire for entry-level college graduates; CFE
Research, "The AGR Graduate Recruitment Survey 2013" (Leicester, U.K., 2013).
10 Steven J. Davis, R. Jason Faberman, and John C.
Haltiwanger, "The Establishment-Level Behavior of Vacancies and Hiring," The Quarterly Journal of Economics
(2013) 128 (2): 581-622.Considering that there are roughly 4 million job
vacancies in a given month and the average worker is paid $46,000 per year, a
back-of-the-envelope estimate suggests that unfilled vacancies cost employers
$6 billion per year in direct lost value. Assuming that workers in the same
company complement each other, it is likely that missing workers will also
degrade the value of their colleagues.
Careerbuilder, "Companies Losing Money to the Skills Gap, According to
CareerBuilder Study," available at http://www.careerbuilder.com/sh
(March 2014).
Brookings analysis of Bureau of Labor Statistics' Job Openings and Labor
Turnover Survey (JOLTS).
For example, in December 2013, 4.4 million workers were hired, but only 113,000
net jobs were created. Net job changes come from the Bureau of Labor
Statistics' Current Employment Survey.
14 Dale Mortensen and Christopher Pissarides, "New
Developments in Models of Search in the Labor Market," Handbook of Labor Economics, 3, edited by O. Ashenfelter
and D.
Card (Elsevier, 1999); Olivier Jean Blanchard and Peter Diamond, "The Beveridge
Curve," Brookings Papers on Economic
Activity 1 (1989): 1-76.
Blanchard and Diamond, "The Beveridge Curve."
16 Ibid. See also Katharine G. Abraham and Michael
Wachter, "Help-Wanted Advertising, Job Vacancies, and Unemployment," Brookings
Papers on Economic Activity (1987) 1: 207-248.
Kenneth J. Arrow and William M. Capron, "Dynamic Shortages and Price Rises: The
Engineer-Scientist," Quarterly Journal of
Economics 73 (2) (1959): 292-308.
18 Roland G. Fryer, Jr. "Financial
Incentives and Student Achievement: Evidence from Randomized Trials," The Quarterly Journal of Economics
(2011) 126 (4): 1755-1798.
19 Chavrid and Kuptzin, "Employment Service Operating
Data as a Measure of Job Vacancies."
20 John M. Barron, Mark C. Berger, and Dan A. Black,
"Employer Search, Training, and Vacancy Duration," Economic Inquiry 35 (1997): 167-92.
21 M. J. Andrews, S. Bradley, D. Stott, and R. Upward,
"Successful Employer Search? An Empirical Analysis of Vacancy Duration Using
Micro Data," Economica 75 (2008): 455-80;
Jonathan Haskel and Christopher Martin, "The Causes of Skill Shortages in
Britain," Oxford Economic Papers 45 (4)
(1993): 573-88; Andrea Weber, "Vacancy Durations: A Model of Employer Search," Applied Economics 32 (2000): 1069-
Weber, "Vacancy Durations."
23 Haskel and Martin, "The Causes of Skill Shortages in
25 Jonathan Haskel and Christopher Martin, "Technology,
Wages, and Skill Shortages: Evidence From UK Micro," Oxford Economic Papers, 53 (4) (2001): 642-58.
26 Penny Mok, Geoff Mason, Philip Stevens, and Jason
Timmins, "A Good Worker Is Hard to Find: Skills Shortages in New Zealand Firms,"
New Zealand Ministry of Economic Development, Occasional Paper 12/05 (2012).
27 Philip Andrew Stevens, "Skill Shortages and Firms' Employment
Behavior," Labour Economics 14 (2)
(2007): 231-49; Jonathan Haskel and Christopher Martin, "Do Skill Shortages
Reduce Productivity: Theory and Evidence From the United Kingdom," Economic
Journal 103 (1993): 386-94.
Peter Cappelli, "Why Good People Can't Get Jobs: The Skills Gap and What
Companies Can Do About It" (Wharton Digital Press, 2012).
ManpowerGroup, "2013 Talent Shortage Survey Research Results" (http://www.manpowergroup.us/campaigns/talent-shortage-
30 Hal Salzman, Daniel Kuehn, and B. Lindsay Lowell,
"Guestworkers in the High-Skill U.S. Labor Market: An Analysis of Supply, Employment,
and Wage Trends" (Washington: Economic Policy Institute, 2013); Michael S.
Teitelbaum, Falling Behind? Boom, Bust
& the Global Race for Scientific Talent (Princeton, NJ: Princeton
University Press, 2014).
31 Robert D. Atkinson and Luke A. Stewart, " The Real
Story on Guestworkers in the High-Skill U.S. Labor Market" (Washington:
Information Technology and Innovation Foundation, 2013).
32 "Information Technology and Innovation Foundation
Debate: Is There a STEM Worker Shortage?" Washington DC, March 12, 2014 (http://www.itif.org/events/itif-debate-there-stem-worker-shortage).
For example, the Great Recession began in late 2007, according to the National
Bureau of Economic Research, but wages grew throughout 2008 and peaked in 2009,
even as unemployment increased quickly. Despite job growth and falling
unemployment from 2010 to 2013, wages fell during this period. Finally, wages
look to be growing over the year ending in the first quarter of 2014 for the
first time since 2008, according to data from the Current Population Survey.
Meanwhile, median wages for computer workers and other professional STEM
occupations were significantly higher in 2013 than in 2000, despite the
recession and no wage growth for the median worker.
Paul Osterman and Andrew Weaver, "Why Claims of Skill Shortage in Manufacturing
Are Overblown" (Washington: Economic Policy Institute, 2014).
35 A national working group of experts summarized the
interpretation of unfilled duration data: "Statistics on 'time-open' can be
considered an indicator of hiring difficulty. In fields that are well-supplied
with qualified, interested workers, job vacancies can be filled quickly. Where
shortages exist, job vacancies are likely to remain vacant and open for hire
longer"; National JVS Workgroup (Bureau of Labor Statistics, Employment and
Training Administration, State Labor Market Information Offices), "About JVS:
How-To Guide for States Implementing a Job Vacancy Survey," National JVS
Workgroup (2002) (http://www.jvsinfo.org/downloadFiles/aboutjvs.pdf).
36 Colorado
Department of Labor and Employment, "Mesa County Job Vacancy Survey 2005"
(2005); Idaho Department of Labor, "Idaho Job Vacancy Survey Report,
Spring 2010" (2010); Oklahoma Employment Security Commission, "Oklahoma Job
Vacancy Survey: 1st Half 2006" (2006); Minnesota
Department of Economic Security, "Help Wanted 2000 Report: Twin Cities Job Vacancy Survey" (Minneapolis: Metropolitan
Investment Board, 2000).
37 ManpowerGroup, "2012 Talent Shortage Survey Results"
ManpowerGroup, "2013 Talent Shortage Survey Research Results" (http://www.manpowergroup.us/campaigns/talent-shortage-
[xxxix] International Communications Research, "The Bayer
Facts of Science Education XVI: U.S. STEM Workforce Shortage—Myth or Reality?
Fortune 1000 Talent Recruiters on the Debate" (Media, PA: ICR, 2013).
40 William C. Dunkelberg and Holly Wade, "NFIB Small
Business Economic Trends" (Washington: National Federation of Independent
Business, 2014).
Jan van Ours and Geert Ridder, "Vacancy Duration: Search or Selection?" Oxford Bulletin of Economics and
55 (2) (1993): 187-98; Weber, "Vacancy Durations."
There does not appear to be any bias in using the company website-only ads for
skill salaries. Comparing the Burning Glass duration database, which uses only
company websites, to the Burning Glass full database of openings, there is a
very high correlation between the number of openings per skill (0.92) and a
fairly high correlation between the average salaries advertised with each skill
(0.51). This is across 24,123 different skills. The salary correlation
increases with the number of observations. For skills with at least 50
vacancies, the correlation is 0.69, across 7,924 skills.
Rothwell, "The Hidden STEM Economy."
44 Steven Ruggles,
J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and
Matthew Sobek, Integrated Public Use Microdata Series: Version 5.0
[Machine-readable database]. Minneapolis: University of Minnesota, 2010.
Andrews et al., "Successful Employer Search?"
An analysis of these single-day ads reveals that they are somewhat less likely
to be in highly skilled, STEM-intensive, and highly educated occupations. They
are more likely in large companies, but less likely in companies with high
average skill or STEM requirements for their vacancies. They are most common in
the public sector—in which 62 percent of ads are posted for one day or less.
Transportation and construction sectors have single-day advertisement rates of
55 and 56 percent, which are the second and third highest. Single-day
advertisement rates are lowest in information, professional services, and retail,
with rates of 37, 36, and 33 percent, respectively.
47 Weber, "Vacancy Durations."
According to data collected annually from legal associations, students
graduating from law school faced much lower starting salaries in 2012 compared
to 2008 (a 15 percent decline in nominal salaries) and a much lower percentage
are practicing law (a fall of 10 percentage points). Data from the National
Association for Law Placement.
49 Even among production workers, unfilled rate trends
vary distinctly. The unfilled rate for construction workers fell from over 36
percent in 2006-Q4 to 20 percent in 2008-Q3 and 16 percent in early 2012.
Installation, maintenance, and repair occupations were just as easy to fill in
2012-Q1 as 2008-Q3. By contrast, the manufacturing sector rebound made
production and transportation jobs harder to fill, with the latter experiencing
the sharpest rebound—from 25 percent to 31 percent. Yet, the unfilled rate for
transportation vacancies was still down eight percentage points from 2006-Q4,
when it was 39 percent.
Bureau of
Labor Statistics, Current Population Survey. 2014 data are for March, unemployed
persons by occupation and sex: http://www.bls.gov/cps/aa2007/aat25.txt (2007);
http://www.bls.gov/web/empsit/cpseea30.htm (2014); employment http://www.bls.gov/cps/aa2007/aat39.txt
http://www.bls.gov/web/empsit/cpseea19.htm (2014)
This is a very simple approach in that it does not make any adjustments for
differences in occupation, education, experience, company, or the combination
of skills requested. It merely asks: What is the average pay associated with
each skill? Additional complications may be addressed in future research.
52 The states (and federal districts) with the longest
median duration times include Vermont, Delaware (both with 20 days), Michigan,
the District of Columbia, Illinois, Rhode Island, Connecticut, Maryland,
Virginia, New York, Kentucky, Oregon, and North Carolina, each with over 12
days for the median vacancy. By contrast, nine states have median vacancies of
just one day: Idaho, Ohio, Colorado, Montana, Wisconsin, Alaska, Minnesota,
Maine, and South Dakota. Using the average number of days, Michigan and
California have the highest durations at 47 days each. South Dakota has the
shortest at 18 days.
Jonathan Rothwell, "Education, Job Openings, and Unemployment in Metropolitan
America" (Washington: Brookings Institution, 2012).
54 The underlying cause of those earnings differences
are complex, but three explanations can broadly account for why professional
jobs tend to earn such high wages: (1) the high level of skill and therefore
productivity of high-wage earners; (2) differences in industry profits,
competitiveness, and regulation—which may reward skilled workers in the
financial and health care sectors disproportionately; and (3) the relative
scarcity of skills, which pushes up the price.
Rothwell, "The Hidden STEM Economy."
Workers with skills in science, technology, engineering, and mathematics (STEM) play a hugely important role in driving innovation and economic growth. Jonathan Rothwell analyzes job skill requirements and how long it takes to fill openings, finding that skills common to STEM occupations are in short supply relative to demand. Meanwhile, job seekers possessing neither STEM knowledge nor higher education face extraordinary levels of competition for a scarce number of jobs.
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