Regional Occupation Employment

A good estimate of occupation employment at the local geographic level is a critical piece of labor data. How is such an estimate made in JobsEQ, and what are the advantages of using these data in comparison to retrieving an occupation estimate straight from the Bureau of Labor Statistics (BLS)?

Highlights from the New Occupation Employment Projections

Every two years, the Bureau of Labor Statistics (BLS) updates their employment growth projections for both occupations and industries. We looked at highlights of the industry changes last month. Here we look at highlights and implications of the occupation forecasts.

Every one of the major occupation groups has slower job growth forecast compared to the previous. Healthcare occupations are still expected to grow the fastest—this includes healthcare support (+2.1% average annualized rate, 2014-2024) and healthcare practitioners and technical occupations (+1.5% AAR). The computer and mathematical group as well as personal care and service occupations have the next-highest growth projections at 1.2% per year. Construction and extraction occupations are still expected to see above-average job growth (+1.0% AAR), though this is twice as slow compared to the prior forecast. Two groups are expected to see declines from 2014-2024: production and farming, fishing, and forestry occupations.

Among the healthcare occupations, occupational therapy and physical therapist assistants and aides are expected to continue to be among the fastest growing (+3.4% AAR 2014-2024). Home health aides are also expected to see employment expand quickly (+3.3% AAR 2014-2024), though a bit slower than in the prior estimates (+4.0% AAR 2012-2022). Among notable changes, job growth expectations for pharmacy occupations have been lowered. Growth for pharmacists is now expected to be a below-average 0.3% AAR in 2014-2024 compared to prior expectations of 1.4% AAR growth in 2012-2022. Pharmacy technician jobs are projected to expand 0.9% AAR in 2014-2024 compared to 1.8% AAR expectations in 2012-2022. Registered nurses (RNs) and licensed practical and licensed vocational nurses (LPNs) are both projected to see 1.5% AAR growth 2014-2024—this represents just a slight slowing for RNs (+1.8% AAR in 2012-2022), but more of a lowered expectation for LPNs (+2.2% AAR in 2012-2022).

Within the computer occupation group, employment growth for web developers is projected to be the fastest (+2.4% AAR 2014-2024) in addition to being upgraded from prior expectations (+1.9% AAR 2012-2022). Information security analyst jobs are still expected to grow briskly (+1.7% AAR 2014-2024) though not quite as hot as previously forecast (+3.2% AAR 2012-2022). Computer programmer jobs were previously expected to grow at a slower-than-average pace (+0.8% AAR 2012-2022), but that occupation is now expected to contract (-0.8% AAR 2014-2024).

While production occupation employment overall is expected to decline, some production occupations are projected to expand. Food processing workers, for example, are expected to see modest growth (+0.3% AAR 2014-2024), the same expectation as in the prior forecast. Woodworkers were expected to expand in the 2012-2022 forecast, but are projected to see a slight annualized 0.1% decline in 2014-2024. Printing workers are expected to see employment declines (-1.5% AAR 2014-2024) at a quicker pace compared to the last projection (-0.5% 2012-2022).

Previously on this site we’ve talked about the hollowing out of the middle class and illustrated what that looked like over the past ten years. With the new BLS projections, we look at the next ten years and see no relief from this trend. The twenty percent of jobs with the highest wages are forecast to expand faster than average (+9.9% over the next ten years) and the twenty percent of jobs with the lowest wages are expected to grow faster than average (+6.8% over the same period). That leaves the middle sixty percent getting pinched—growing at a below-average 5.2%. Bottom line: ten years from now, if these forecasts play out, the middle class will be relatively smaller than it is today.

For job-seekers using the new BLS forecasts to gauge career prospects, it is important to note that job growth expectations should be considered along with replacement needs—openings resulting from retirements and movement between occupations—to get a full picture of expected employment opportunities. Regional factors also play a large role in job opportunities, and regional demand can be gauged with the help of a local labor market tool such as JobsEQ®.

Highlights from the New Industry Employment Projections

Every two years, the Bureau of Labor Statistics updates their long-term growth projections for industry and occupation employment. Their latest was published earlier this month and includes many notable changes.

The biggest change, perhaps, is in the forecast for overall employment growth. For the 2012-2022 period, job growth overall was expected to average 1.0% per year—equivalent to about 1.6 million jobs per year. For 2014-2024, however, job growth is expected to be an average annualized 0.6%, or little under 1.0 million per year. Contributing to this slower projection is the aging baby boom generation which is moving into retirement and out of the labor force.

In the industry projections, nearly all of the sectors that had been projected to expand in 2012-2022 are expected to continue to grow but more slowly. The health care and social assistance sector is forecast to grow the quickest, at a 1.9% average annualized rate (AAR). Construction is projected to expand at a 1.2% pace, much slower than the 2.6% rate projected for 2012-2022. Professional and business services as well as educational services are projected to have above-average 0.9% annualized growth for 2014-2024.

Nonagriculture self-employment is the only group to have an increase in its rate of expected growth, from 0.6% AAR in 2012-2022 to 0.7% AAR for 2014-2024. This is another projection influenced by shifting demographics as older workers, in general, are more likely to be self-employed.

While health care industries are expected to see quick job growth in 2014-2024—especially home health care services (+4.8% AAR) and outpatient care centers (+4.1% AAR)—projections for social assistance job growth were muted. Child day care services (+0.7% AAR) and individual and family services (+1.3% AAR) are both projected to expand employment, but at significantly slower paces compared with 2012-2022 expectations.

Within the education sector, the private postsecondary schools are forecast to have slower employment growth. Private junior colleges, colleges, universities, and professional schools were projected to have 2.2% annualized average job growth in 2012-2022, but that forecast was cut to 1.2% per year for 2014-2024. This slowdown shouldn’t be a surprise in the light of the decline in college-aged population.

Within manufacturing, wood product is expected to contract employment at an annualized pace of 0.2% in 2014-2024, much slower than prior expectations of an average pace of 1.4% job growth for 2012-2022. Manufacturing industries that are expected to expand employment in 2014-2024 include architectural and structural metals (+0.3% AAR); agriculture, construction, and mining machinery (+0.5% AAR); medical equipment and supplies (+0.1% AAR); and beverage manufacturing (+0.3% AAR).

The professional, scientific, and technical services sector is expected to continue to grow at an above-average pace. Fast-growing industries in this sector are projected to include computer systems design and related services (+2.1% AAR 2014-2024); management, scientific, and technical consulting services (+2.4% AAR); and architectural, engineering, and related services (+0.8% AAR).

As mentioned in the opening, the BLS also updated their occupation projections. We’ll go into highlights from those in my next blog post.

Major Change Coming to Replacement Rates

A heads-up if you use occupation replacement rates in your work: new rates are being developed and are planned to be released in two years…and it looks like they will be very, very different. On average, over four times larger.

Right—four times larger; that is not a misprint.

Occupation replacement rates are developed by the Bureau of Labor Statistics (BLS) to describe the number of workers who leave their occupation and need to be replaced by new entrants into the occupation. The rates describe demand due to workers leaving the workforce (such as retiring) plus those moving from one occupation to another. Replacement demand is important in the fields of workforce development and education because—along with growth demand—it helps gauge the future training needs for specific occupations.

When publishing replacement estimates, the BLS includes the caution that the replacement needs are “underestimated” due to limitations in the methodology used for calculating these rates. In turn, in our JobsEQ system which uses these BLS rates, we reference the same caveat.

The Bureau of Labor Statistics has developed a new method that they believe is a more accurate measure for this type of occupation demand. Accompanying this change, the BLS plans to change terminology from “replacement rate” to “separation rate,” partially to help highlight the change in methodology. Regardless of the name change, the BLS emphasizes that the “new method is designed to measure the same concept as the old methodology: workers who leave their occupation and need to be replaced by new entrants into the occupation.”[1]

While the new data have not yet been officially released, the Bureau of Labor Statistics has posted some “experimental” results for comparison purposes. Even though the rates for some occupations change only slightly—dentists, for example—the new rates for most occupations are substantially larger. As one example, the estimated replacement demand for machinists (SOC 51-4041) increases roughly by a factor of four when put in terms of “occupational separations.” Using the old method, replacement needs for machinists during the period from 2012 through 2022 were estimated at about 91,000 workers; using the new method, separations for machinists in the same period are estimated to be over 392,000.

The reason for the large increase from the current replacement rates to the new separation rates, to quote the BLS, is that “the current method undercounts openings because it only accurately measures workers who follow a traditional career path—entering an occupation at a young age, working in the same occupation for many years, then retiring—which is not the case for many workers in most occupations.”[2] The new method is generally described as being “more robust and more statistically sound,”[3] the details of which can be found on the BLS website.

To be complete, there is another technical difference between replacement and separation rates in how each deals with declining occupations, but that change is not as impactful on the change in magnitude in the rates compared to the other methodological changes.

The bottom line is that even though the new official rates may not be available for another two years, this impending change highlights the need to keep in mind the caution about replacement rates: the current rates should be considered only as a minimum measure of training needs due to replacements.[4] It is a caveat of particular importance, given that the degree of underestimation may be severalfold.

Replacement and Separation Rate Comparison for Select OccupationsReplacement and Separation Rate Comparison for Select Occupations

[1] http://www.bls.gov/emp/ep_separations_faqs.htm

[2] Ibid.

[3] Ibid.

[4] For example, see the BLS description of that here: http://www.bls.gov/emp/ep_replacements.htm.

The Graying of America

Most people know that the percentage of older Americans is increasing dramatically. What’s less known to the average person is how that graying will impact different areas of the country.

The overall demographic shift is illustrated in the chart below that shows four age cohorts. The first three are each based on equal twenty-year spreads: people age 0 to 19, those who are age 20 to 39, and the age group 40 to 59. The fourth cohort is defined as those age 60 and older.

The age group 60 and up was roughly 40% smaller than the other cohorts in 2000. By 2010 it had begun to close the gap, but it was still about a third smaller than the other cohorts. By 2030, however, this 60+ age group will be nearly the largest cohort—surging from 57 million in 2010 to over 93 million in 2030.

U.S. Population by Age Cohorts

The reason for this increase is the aging of the baby boom generation along with the overall increase in life expectancy. The ramifications are many. The shift affects consumer spending patterns, health care needs, labor force mix, and so on.

The demographic shift will be manifest differently throughout the nation. The map below shows the age mix for every county in the nation as it transforms from 2010 through 2030. Each county is colored according to the cohort that is the largest during that given year (using the same cohorts from above).

A word about these data: the age mix data at the county level for 2010 are derived from the decennial census. The projections by age through 2030 at the national level follow forecasts from the Census Bureau. The county-level projections are produced by Chmura and can be accessed through the JobsEQ labor market system.

What do the data show? In 2010, fewer than 10% of the counties in the nation had demographic mixes where the 60+ age cohort was largest. By 2019, however, the 60+ cohort should be largest in over half of the counties. By 2027, this cohort is expected to be largest in three-quarters of the nation’s counties.

Areas of the country not expected to see the 60+ cohort become the largest during this timeframe include regions in Texas and California, various metropolitan areas, and counties with large college student populations—the latter of which stick out on the map among the light blue areas, as the age 20 to 39 cohort will remain the largest in these places.

The interaction of a shifting age mix and overall population growth also can create surprising effects. For example, the population of the Cincinnati metropolitan statistical area (MSA) is projected to grow an average annualized 0.5% from 2015 through 2030. This population growth, though, is being driven by expansion in the age 60+ cohort (+2.2% per year). The population age 0 to 59 in Cincinnati is expected to actually decline overall during this same period. And Cincinnati is not alone in this boat; other MSAs expected to see overall population growth but declines in population under age 60 are Philadelphia, Chicago, Memphis, Green Bay, Mobile, and many others.

The Social Security “full benefit retirement age” is currently 66 for people reaching that age today, and that will rise to 67 for people reaching that age in 2030. Despite this forestalling in retirement age, the number of people in the nation at full retirement age will increase substantially. From 12% at full retirement age in 2010, by 2030 in the United States approximately 18% will be at full retirement age.

This shift is shown among the largest metropolitan areas below. Despite variety in current mix and growth rates, a dramatic increase in the percentage of population at full retirement age is coming across the board. 

Top 50 MSAs by % Population at Full-Retirement Age
MSA 2010 (% age 66+) 2030 (% age 67+)
New York-Newark-Jersey City, NY-NJ-PA MSA 12% 18%
Los Angeles-Long Beach-Anaheim, CA MSA 10% 16%
Chicago-Naperville-Elgin, IL-IN-WI MSA 11% 17%
Dallas-Fort Worth-Arlington, TX MSA 8% 13%
Houston-The Woodlands-Sugar Land, TX MSA 8% 13%
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA 12% 19%
Washington-Arlington-Alexandria, DC-VA-MD-WV MSA 9% 14%
Miami-Fort Lauderdale-West Palm Beach, FL MSA 15% 20%
Atlanta-Sandy Springs-Roswell, GA MSA 8% 14%
Boston-Cambridge-Newton, MA-NH MSA 12% 18%
San Francisco-Oakland-Hayward, CA MSA 12% 17%
Phoenix-Mesa-Scottsdale, AZ MSA 12% 17%
Riverside-San Bernardino-Ontario, CA MSA 10% 15%
Detroit-Warren-Dearborn, MI MSA 12% 19%
Seattle-Tacoma-Bellevue, WA MSA 10% 16%
Minneapolis-St. Paul-Bloomington, MN-WI MSA 10% 16%
San Diego-Carlsbad, CA MSA 11% 16%
Tampa-St. Petersburg-Clearwater, FL MSA 16% 22%
St. Louis, MO-IL MSA 13% 19%
Baltimore-Columbia-Towson, MD MSA 12% 18%
Denver-Aurora-Lakewood, CO MSA 9% 15%
Charlotte-Concord-Gastonia, NC-SC MSA 10% 16%
Pittsburgh, PA MSA 16% 24%
Portland-Vancouver-Hillsboro, OR-WA MSA 11% 17%
San Antonio-New Braunfels, TX MSA 10% 15%
Orlando-Kissimmee-Sanford, FL MSA 12% 17%
Sacramento--Roseville--Arden-Arcade, CA MSA 11% 17%
Cincinnati, OH-KY-IN MSA 11% 18%
Kansas City, MO-KS MSA 11% 17%
Las Vegas-Henderson-Paradise, NV MSA 11% 15%
Cleveland-Elyria, OH MSA 14% 22%
Columbus, OH MSA 10% 16%
Indianapolis-Carmel-Anderson, IN MSA 10% 16%
San Jose-Sunnyvale-Santa Clara, CA MSA 10% 15%
Austin-Round Rock, TX MSA 8% 13%
Nashville-Davidson--Murfreesboro--Franklin, TN MSA 10% 16%
Virginia Beach-Norfolk-Newport News, VA-NC MSA 11% 17%
Providence-Warwick, RI-MA MSA 13% 21%
Milwaukee-Waukesha-West Allis, WI MSA 12% 18%
Jacksonville, FL MSA 11% 18%
Memphis, TN-MS-AR MSA 10% 16%
Oklahoma City, OK MSA 11% 16%
Louisville/Jefferson County, KY-IN MSA 12% 19%
Richmond, VA MSA 11% 18%
New Orleans-Metairie, LA MSA 11% 18%
Raleigh, NC MSA 8% 14%
Hartford-West Hartford-East Hartford, CT MSA 13% 20%
Salt Lake City, UT MSA 8% 13%
Birmingham-Hoover, AL MSA 12% 19%
Buffalo-Cheektowaga-Niagara Falls, NY MSA 15% 22%
Source: Chmura Economics & Analytics, JobsEQ

Research support was provided by Allison Magee and Asim Timalsina