UNDERSTANDING ONLINE JOB ADS DATA
producing supplemental estimates of
demand within detailed occupations,
industries, and geographies. It can show
the relative demand for different types of
skills and levels of education. The real-time
nature of job ads data also allows for the
early detection of labor demand trends,
which gives job seekers, employers, and
policymakers a forward-looking analytical
tool. Real-time labor market indicators
can be particularly useful in aligning
education and training curricula with
workforce needs in emerging or rapidly
changing industries, such as healthcare
and information technology.
Online job ads data show great promise,
especially in combination with other
educational and labor market data. In
its current state, however, it has several
limitations. The data are subject to
systematic errors introduced by how
employers utilize the Internet for their
talent search, the vendor data collection
processes, and the effectiveness of the
artiﬁcial intelligence used to collect and
piece out the information from the ads.
If left untreated, systematic errors can
undermine the predictive power of the
data and skew public policy decisions.
Another limitation is that, although there
are analyses that examine the role of online
job ads, a well-deﬁned relationship between
online job ads and traditional employment
data has not been established.
to our back-of-the-envelope calculation,
discussed in more detail later in the report,
between 60 and 70 percent of job openings
are currently posted online, the majority
for high-skilled white-collar occupations
that require at least a Bachelor’s degree.
There are differences in coverage from one
vendor to another based on their approach
used to collect online job ads.
coverage of job openings, however,
remains elusive even at this day and age of
Internet use, since not all job openings are
posted online. We estimate that 80 to 90
percent of openings that require at least
a Bachelor's degree get posted online.
By contrast, just 30 to 40 percent of
openings for candidates with some college
or an Associate’s degree, and only 40 to
60 percent of openings for high school
diploma holders appear online. It is critical
for job seekers, researchers, and decision
makers, then, to understand better the
strengths and limitations of this emerging
tool before relying on its predictive power.
For example, job seekers with some college
or an Associate’s degree who restrict their
job search efforts to online sources will see
only a fraction of the available employment
Burning Glass Technologies (BGT) is one of
the leading vendors of online job ads data.
BGT is at the forefront of improving this
quickly evolving data source; BGT browses
more than 15,000 job-related websites.
While our analyses are based on BGT data,
some of the limitations that we outline in this
report have external validity and may apply
to other data providers, such as Monster,
CareerBuilder, and Wanted Analytics.
because we have not explored competing
data sources to the same extent, we are
not able to discuss the limitations in other
sources or make comparisons between
We suspect that many of the
concerns addressed in this report will fade
over time as the country achieves universal
Internet access and employers increasingly
use the Internet to ﬁll job vacancies.
4. There is some research that explores the trends in employment, job openings, and job ads series and their lags,
yet the trends don’t show the strong consistency needed to establish a reliable relationship between series and
requires further research. See page 9 of this report and Upjohn Institute (http://www.upjohn.org/node/678).
5. Help Wanted Online (HWOL) has 28 percent more job ads than BLS's JOLTS data, the ofﬁcial data source of job openings.
However, we have not had the opportunity to analyze HWOL data.
6. We are grateful to BGT for its transparency and willingness to allow us to examine its data. Few vendors have been so
open and responsive about key issues such as ﬁeld consistency and reliability, de-duplicating ads, and geographic accuracy.
7. Using online job ads data from CareerBuilder Inc., Wright (2012) reports similar concerns.
8. For example, HWOL's data series includes seasonal adjustment to its ads data and this might make the series less volatile
than it would otherwise be.