The United States, along with much of the world, is in the midst of an economic transition from manual to intellectual labor. The changing nature of work, including the automation of labor, is an important issue facing society, with implications not only for our standard of living, but also for socio-economic and political divisions.
With this backdrop in mind, at Microsoft Research we hope to augment the study of employment in the U.S. Traditionally, agencies like the Census Bureau and the Bureau of Labor Statistics use a variety of measurement techniques to monitor the number of new jobs, either positive or negative, across an exhaustive taxonomy of employment. These methods are accurate, but often lag actual trends, as they are predominately based on surveys done after the fact.
Given that a majority of people use internet search as part of their job search, in work presented at the CHI 2018 conference we explored whether we could use Bing searches, anonymized and aggregated, to augment these traditional approaches to surveying the shifting employment landscape. The use of internet search data provides several advantages, including the ability to update employment measures in near real-time, to measure at significant scale, both in terms of population size and geographies covered, and finally to understand intent to change jobs that may or may not be realized.
For the CHI research paper, we started by classifying almost a quarter of a billion job search queries from 2015-2016 into employment sectors, such as retail, transportation, technology, education, healthcare and so on. To do so, we first created very carefully curated dictionaries of job titles to roughly categorize searches that contain those job titles (for example, “nursing jobs in Seattle”) into one of the employment sectors (healthcare). We then used Word2Vec to find new job searches not in our dictionaries (for example, “rn jobs in Seattle”). The overall performance of the classifier was quite high, and some representative search query keywords can be found in the table below.
|Employment Sector||Example Job Search Query Keywords|
|Architecture / Engineering||entry level biomedical engineering jobs, auto-cad jobs|
|Art||voice acting careers, museum curator job|
|Business||vp of operations jobs, hr career|
|Construction||construction laborer jobs, welder jobs|
|Education||community college professor jobs, school district careers|
|Finance||financial banking jobs, mortgage lender careers|
|Food||bartender jobs, craigslist dishwasher jobs|
|Healthcare||surgical tech jobs, clinic jobs rn|
|Leisure||casino jobs, laundry jobs in hotel|
|Manufacturing||machine operator jobs, jobs in shopfitting|
|Retail||clothing store job applications online, retail career outlets|
|Science||psychology research associate jobs, jobs in r\&d|
|Technology||software architect career, sql dba jobs|
|Transportation||airport runway jobs, cdl jobs|
Building on the CHI paper, and using similar methods, this site provides an interactive look at job searches across the U.S. The map above () shows summaries of job searches from all U.S. counties in 2017 in the Engineering job sector. The intensity of the color indicates the percent of job queries from the county relative to searches for jobs in other sectors. Scanning the list of job sectors at the top right shows that healthcare and education are the two most common sectors for job searching, followed by business, transportation, and finance.
Selecting an individual county will pull up its job sector search profile. For instance, selectingin New York shows that Manhattan’s county is similar to the national average in terms of what jobs are searched for, but lower on education jobs, particularly with respect to finance job (12% compared to 8% nationally) searches, and is substantially higher than the national average in searches for business jobs (14% compared to 8% nationally). Nearby is more dominated by job searches for healthcare (31% compared to 28% nationally) with relatively fewer searches for architecture/engineering jobs (1% compared to 2% nationally).
High population counties like Seattle’s King county tend to have a distribution of searches over job sectors that are closer to that of the national average. Lower population counties are more likely to skew toward an individual job sector. Looking at job searches for, for instance, we see a broad trend toward the more of such searches in the Midwest and rustbelt, but also a few standout counties with smaller populations, such as (21% compared to 4% nationally) and (20% compared to 4% nationally).
We see similar patterns in other employment sectors. In the, for instance, we see high numbers of job searches in (21% compared to 4% nationally) and (14% comapred to 4% nationally), both homes to major ski resorts.
Importantly, we wanted to understand how job searches in the different employment sectors relate to various demographics.
What is the role of income and education level in job searching?
Jobs in sectors like business and finance and technology often pay more than retail or manufacturing jobs, for example. If we split counties into quartiles (top 25% and bottom 25% of counties) based on, we first notice that the majority of higher income counties (shown in green) contain urban centers. We also see that people in higher income counties do in fact search for significantly more jobs. However, in terms of what people search in the top and bottom counties in terms of income, while most of the differences were in the expected directions in wealthy counties, we do not see more searches for , , and jobs compared to poorer counties.
We do see more job sector differences are in counties with higher versus lower levels of, with people in higher education counties significantly more likely to search for and jobs, and those in lower education counties much more likely to search for , , and jobs. Transportation jobs searches from counties lower in educational attainment, for instance, constitute about 1.5% more of all job searches than do transportation job searches from counties higher in educational attainment.
Is there an urban advantage for job prospects?
Broadly speaking, high population counties correspond to urban environments that may offer more options for employment, particularly in “new economy” areas such as thesector. Our findings show significantly more searches for business, but we do not see significantly more searches for technology jobs in counties in the top quartile of population.
Somewhat surprisingly, we observe an urban advantage to searches in the. This also corresponds with simillar trends across both and demographic cuts, perahps reflecting a rise in food culture in urban environments. However, given higher percent of searches for high-paying job areas such as , this could also be a marker of rising inequality in cities.
More populated areas likely offer a wider variety of job opportunities. Indeed in the CHI 2018 paper we show that increases in population correlate with increases in the diversity of job types searched for. For example, looking at highly populated(Brooklyn), we see a profile of job searches that includes a healthy percentage of searches from all employment sectors. On the other hand, smaller population counties often skew toward a particular industry. For example, in , nearly a third of all searches were for transportation jobs.
In summary, we utilized jobs search queries on Bing to explore demand for employment at county scale in the United States. We show here that in 2017 people incounties tended to search more for higher paying (finance) jobs. While those in counties did search more for traditionally lower paying jobs, such as in transportation. Encouragingly we did see equivalent levels of searches for “new economy” jobs (technology) in those same lower income counties.
Please refer to our CHI 2018 paper for additional analyses, including a comparison of the jobs people are currently working according to BLS versus the jobs people are searching for, broken down by low and high poverty areas.
If you have comments, questions, or ideas about these data, contact us at firstname.lastname@example.org.