This is the second in a series of blog posts about the potential impacts of automation in rural Minnesota. The first can be found here.
Part 2: Rural Vulnerability: A View from Job Sectors
Our first post defined the automation revolution and touched on its current impact in mining and agriculture. Part two widens the focus to examine employment across job sectors in West Central Minnesota in order to gauge how automation may impact the overall employment picture.
Specifically, we look at the distribution of folks employed in large job sectors and, using that information, determine whether rural areas are more vulnerable than the state as a whole. Greater Minnesota is a large and diverse area. So in order to make a comparison manageable, industry employment from the Quarterly Census of Employment and Wages (QCEW) for the West Central economic regions 4 and 6W was combined using the Department of Employment and Economic Development’s Regional Profile reports and online data from 2016. These two areas cover nearly all of the West Central region, from Yellow Medicine County in the South up to Clay and Becker counties in the North and collectively report the distribution of 105,089 jobs.
Table 1 below shows the distribution of West Central jobs in several important employment sectors.¹ The West Central region has more than the statewide average in several areas that involve jobs with a high percentage of potentially automated tasks, including manufacturing (14.2% vs. 11.2%), retail trade (12.9% vs. 10.6%), as well as agriculture and related services (2.74% vs. 0.72%). Wholesale trade (4.9% vs. 4.7%) and accommodation and food services (8.2% vs. 8.1%) are about the same as the state as a whole. Further, the region has a lower percentage of people working in areas where skills are harder to automate. West Central Minnesota lags sharply behind in professional & technical services (1.9% vs. 5.6%) and management (0.7% vs. 2.8%), though it mostly maintains parity in arts, entertainment, and recreation (1.6% vs. 1.8%).
But the employment picture is not all negative. Greater Minnesota employs more than the state average in many sectors with lower automation potential, such as healthcare, education, public administration, and construction. Moreover, job growth in many of these areas has increased over the past 10 years, and DEED predicts that, for the entire Northwest region, the health care and social assistance area, as well as construction, will grow significantly, 16.5% and 14.2% respectively, in the years ahead. ²
This snapshot of job sectors presents a somewhat mixed view of the prospects for job loss due to automation. Several regional sectors vulnerable to automation employ more than state average, and others considered more resistant to automation employ fewer people than the state average. Yet, about 40% of jobs are in four areas with low automation potential.
Additionally, the data aren’t telling the whole picture They leave out nuances about the various jobs in each sector, and each will have a mix of jobs with different amounts of tasks that can be automated. Additionally, these data only represent 2016, and the balance of employment between these sectors fluctuates from year to year, though majors sectors of employment tend to be fairly stable. Given these potential limits, another set of statistics, DEED’s Occupational Employment Statistics (OES), is provided in the appendix below; the configuration of job sectors and method of the OES are different, but the picture or under and over-represented sectors is largely similar.
Regardless of the percentages of West Central employees working in more vulnerable job sectors, what ultimately matters is whether firms, governments and organizations have the incentive to make investments to automate various jobs tasks. One factor that stands out clearly in Table 1 is how much less West Central employees make compared to the state average. The next post will discuss wages as a factor behind automation, as well as other key incentives, to suggest that rural Minnesota is likely to automate at a slower rate than the rest of the state.
Feel free to share your thoughts in the comments, and look out for the final part of this series in the next few weeks!
¹ The full table for the regions can be found at https://mn.gov/deed/assets/2016_rp_edr4_nov_tcm1045-133254.pdf and https://mn.gov/deed/assets/2016_rp_edr6W_nov_tcm1045-133259.pdf
² DEED, Labor Market Projections, 2014-2024
Appendix: Occupational Employment Statistics for Regions 4 & 6W, 1st Qtr 2016
Minnesota’s DEED offers an alternative measure of employment, Occupational Employment Statistics, that provide a slightly different categorization of job sectors (e.g., “office and administrative support” does not fit the QCEW categorization and OES captures “transportation and material moving”, not “transportation and warehousing”). Whereas QCEW data is a quarterly count of employment based on reports by all firms submitting Unemployment Insurance information, OES is a semi-annual survey conducted cooperatively by the U.S. Bureau of Labor Statistics and state agencies that gathers information, in the case of Minnesota, from about 6000 employer participants. OES job figures differ from the QCEW in that OES excludes self-employed, workers in unincorporated firms, contract workers and workers in multiple jobs. This configuration leads to dramatically undercounting agriculture jobs (see below) and about 5% fewer total jobs reported. Thus QCEW data was used for the analysis above.
Roger Rose is Center for Small Towns Director and an associate Political Science professor for the University of Minnesota – Morris.