At Emsi, we’re committed to providing clear, accurate labor market data to give you a better understanding of your regional economy. To do that, we’re always looking for ways to improve and update our data and the methodology behind it.
This week, we launched an updated methodology for collecting hires data, which is a crucial information set in our Job Postings Analytics(JPA)/Job Posting Competition report. The report, found in Analyst, Developer, and Talent Analyst, shows employer demand for specific skills based on job postings and advertisements, and includes data about companies looking for talent, specific job titles, and posting intensity (an indicator of how hard employers are working to fill a position).
What is hires data, and what is it for?
Hires data is meant to give context to posting counts. For example, the chart below shows job postings information for mechanical engineers in the state of Maryland. We can see that, while roughly 700 postings for this occupation are posted every month in the area of study, fewer than 200 people are actually hired. The hires number keeps users from making the understandable assumption that 700 postings per month in the region means that there are 700 vacancies for mechanical engineers.
Hires data comes from the Census Bureau’s Quarterly Workforce Indicators (QWI) dataset, and is reported by each business (businesses’ data is aggregated by industry by the time the data is published).
Hires are tracked by following Social Security numbers on business payrolls every quarter. A hire is counted whenever a Social Security number appears on a business’s payroll that was not present in the previous quarter. QWI hires gives a thorough accounting of quarterly hiring activity by businesses in the U.S. This hiring activity is a good background metric for JPA, since job postings attempt to measure demand for talent. Layering job postings over hires shows demand for talent as advertised by businesses alongside actual demand for talent in practice.
Emsi’s old methodology for occupation hires was to apply staffing patterns to industry hires, converting them to occupation hires. Staffing patterns show the occupational makeup for each industry. For example, this is a partial staffing pattern for Hospitals (NAICS 622):
Here we see that registered nurses make up 30.5% of the staff in the Hospital industry. The next occupation is nursing assistants (CNAs), which makes up 6.7% of Hospital staff.
Emsi’s old methodology took QWI hires figures for Hospitals and multiplied them by the percent makeup as listed in tables like the one above to produce occupation hires figures. The problem with this approach is that it assumes that 30% of an established hospital’s hires are for registered nurses, 6.7% are for CNAs, etc.
However, turnover rates are generally higher among lower-skilled, lower-paid occupations (such as CNAs), and lower among higher-skilled, higher-paying occupations (like RNs). Therefore, there may be many instances, especially in highly technical and skilled industries such as hospitals, where staffing pattern makeup does not accurately reflect hiring behavior. We decided to overhaul our methodology for calculating hires largely because of this flaw.
Our new methodology uses occupation growth and occupation replacement needs from the BLS to create a model hires figure for each occupation. These figures are tailored to each industry and region (growth is industry- and region-specific whereas replacement figures are national). Growth and replacement needs are summed to form occupational hires by industry. These hires are then converted to percentages, showing what percent of each industry’s hiring activity is focused on each occupation. These percentages are then applied to the QWI industry hires figures to break them out into occupation hires figures. Occupation hires from each industry are then summed (e.g. all RN hires from the Hospital, Offices of Physicians, and other industries are summed). The result is hires by occupation. Emsi occupational hires data is available down to the county level.
For a more detailed explanation of Emsi’s new Hires methodology, see our Knowledge Base article.
The most noteworthy changes with the new methodology are changes of magnitude. Many occupations gained or lost a lot of hires. Generally, lower-skilled, lower-paying occupations saw a gain in hires while higher-skilled, higher-paying occupations saw a decrease in hires. The order of occupations from most hires to least hires generally stayed the same although magnitudes changed.
Here are the 10 occupations that gained the most hires using the new methodology:
Here are the 10 occupations** that saw the biggest decrease in hires using the new methodology:
** This list includes cooks, restaurant. 77% of this occupation is employed by the Full-Service Restaurant industry. Since this industry employs 35% waitstaff and 19% cooks, restaurant, it is likely that much of the gain in hires for waitstaff (+464,000 hires) was taken from cooks, resulting in a loss of hires for cooks. Although cooks saw a sharp drop in the number of hires, they remain one of the top occupations in terms of hires (moving from ninth place in the old methodology to 13th place in the new).
Here are the 10 occupations with the greatest percent increase in hires between the old and new methodologies:
Here are the 10 occupations with the greatest percent decrease in hires between the old and new methodologies: