Author: Andrew Berger-Gross
How difficult is it to find a job in the region where you live? Will you be able to obtain employment in your particular field? How readily can employers recruit job applicants with relevant experience?
The answers to all of these questions can be found by consulting data on the supply and demand for labor in your region. In this installment we build on some of our earlier research in order to examine labor supply and demand conditions within each of North Carolina’s eight “Prosperity Zone” regions.1 2
The first thing we want to know when analyzing a region is the degree of slack in its overall labor market. A labor market is said to be “slack” when there are a large number of jobseekers for each available position—or, in other words, a high ratio of labor supply to labor demand. We say the labor market is “tight” when there are only a small number of jobseekers for each available position.
It is more difficult for jobseekers to get hired in a slack labor market because they have to compete with so many other job applicants. Employers have an easier time finding talent in a slack labor market because they have access to a large pool of jobseekers. On the other hand, when a labor market is tight, it is easier for jobseekers to find work, but more difficult for employers to find applicants.
The Western region of North Carolina, which includes Asheville, has the tightest overall labor market in the state with only 2.5 unemployed per job opening during the most recent period for which we have data (2013-2015). The Northeast region, which includes Greenville, has the slackest overall labor market with 5.8 unemployed per job opening. It follows that, in general, it is easiest to find a job in the Western region and hardest in the Northeast region.
We might also want to know whether the particular type of work available in each region is a “good match” for those seeking employment in the area. A region can be said to be occupationally “mismatched” when there is a gap between the needs of employers and the skills and experience offered by jobseekers. This mismatch makes it more difficult for jobseekers to find appropriate work and more difficult for employers to find appropriate job candidates.
The Northwest, which includes Hickory, has the least mismatched regional labor market in our state, while the Northeast has the greatest degree of mismatch. As illustrated on the first graph, both of these regions have very slack labor markets. What does it mean when two regions have similar levels of slack, but different levels of mismatch?
We can illustrate this distinction by looking at the markets for particular types of work (occupations) within each of these regions.
First, for the sake of simplicity, let’s group these types of work into broad occupational categories. Here we group major occupations into four categories based on the degree to which they involve “manual or cognitive” and “routine or non-routine” tasks. A manual job typically requires the application of physical skills, whereas a cognitive job entails intellectual skills. A routine job typically involves a limited set of rule-based tasks, whereas a non-routine job provides workers with a greater degree of discretion over how they perform their tasks.3 As a shorthand, we refer to these occupational categories as “knowledge jobs”, “service jobs”, “office and sales jobs”, and “blue collar jobs”, as follows:
Now let’s return to the example discussed above. The Northwest region and the Northeast region both have similar levels of overall labor market slack—5.4 and 5.8 unemployed per job opening, respectively. However, the Northwest’s regional labor market has more occupational mismatch than the Northeast.
Occupational mismatch can be thought of as a gap between the degree of labor market slack in each of a region’s occupation-specific labor markets. In the Northwest, these occupational markets range from 5.2 unemployed per job opening in the market for office and sales jobs to 5.8 in the market for service jobs—a very narrow gap between occupational markets, indicating very little mismatch. A job seeker in any of these occupational markets would face similar odds of finding a job.
Meanwhile, occupations in the Northeast range from 4.2 unemployed per job opening in the market for knowledge jobs to 8.5 in the market for blue collar jobs—a wider spread, indicating a relatively high degree of mismatch. A job seeker in the Northeast’s market for knowledge jobs would find it much easier to find a job than a blue collar worker.
The data on regional labor market slack and occupational mismatch presented in this article have important implications for our state’s workforce development system:
A high degree of occupational mismatch—as seen in the Northeast region—is a signal that many of the region’s unemployed would best be served by transitioning to a new career in a higher-demand field. The state’s workforce system can be particularly valuable in helping facilitate this transition.
But mismatch must be considered in tandem with the overall labor market slack in a region. The Northeast has a very slack regional labor market, with a surplus of unemployed jobseekers in all occupational categories. Even if the workforce system prepared displaced workers in the Northeast for knowledge jobs, these jobseekers would still face competition from 4.2 unemployed knowledge workers per job opening. While mismatch creates some opportunities for retraining in the Northeast, the fundamental problem faced by this region, as with the Northwest, is that there are too many unemployed workers and too few job opportunities.
Data sources cited in this article are derived from surveys and administrative records and are subject to sampling and non-sampling error. Any mistakes in data management, analysis, or presentation are the author’s.
Please see our previous article for a technical explanation of the methods used in this research.
1 Throughout this installment, we use an improved measure of labor demand that is described in detail in our previous article on the subject. This measure corrects some of the shortcomings of “real-time” labor demand data and adjusts for the relative efficiency of the job matching process in different fields.
2 In this article we use substate unemployment-by-occupation data from the Census Bureau’s American Community Survey (ACS). The Census Bureau provides ACS microdata at the Public Use Microdata Area (PUMA) level. In sparsely-populated sections of the state, these PUMAs cover multiple counties. The Prosperity Zone-level data reported here include data for all PUMAs that have at least one county within each region. As a result, the Prosperity Zone data reported here include some North Carolina counties outside the officially designated Prosperity Zone areas.
3Daron Acemoglu and David Autor group occupations in this manner in their canonical work on job polarization. In this article we follow recent work by Aysegul Sahin et al. in classifying 2-digit SOC occupations into four groups that broadly reflect Acemoglu and Autor’s typology. We have included “plain English” nicknames for these categories that, although imperfect, generally correspond to types of work that would be recognizable to a general audience. In addition to simplifying our analysis, there are substantive reasons for grouping occupational markets by broad category (such as the transferability of skills within these categories) as well as statistical considerations (such as a larger sample size and improved reliability).