Opportunity Occupations Monitor
The Opportunity Occupations Monitor displays opportunity employment and its prevalence across labor markets. Opportunity employment is an estimate of the number and share of jobs accessible to workers without a bachelor's degree that pay more than the national median wage ($39,811 in 2019). The darker blue areas on the map below represent places with a higher proportion of opportunity employment, and therefore a better chance that a worker with less than a bachelor's degree earns a livable wage.
Please read these steps to utilize the information offered by the tool:
- Step 1: Select a geography—either states or metro areas—above the tool.
- Step 2: Select a state or metro area by checking its box to the left of the map, or by clicking on one or more states or metro areas on the map. A breakdown of the labor market of the chosen area appears below the map.
- Step 3: Select a year—between 2012 and 2019—to the left of the map.
- Step 4: Click on any part of the stacked bar to display a table of all occupations in the selected area, and their data, below. Click on the arrows to the right of the column titles above the occupation table to sort columns by ascending or descending order. Hovering over the data will display wage distributions and trends in employer educational requirements. Clicking on the table will open that occupation's O*NET web page for more information.
Frequently Asked Questions
How do you define "Lower-Wage Employment"?
This is an estimate of the number and share of jobs that pay less than the national median wage, adjusted for local cost of living differences.
How do you define "Opportunity Employment"?
This is an estimate of the number and share of jobs that require less than a bachelor's degree and pay at least the national median wage, adjusted for local cost of living differences.
How do you define "Higher-Wage Employment, Bachelor's Degree Required"?
This is an estimate of the number and share of jobs that require a bachelor's degree and pay at least the national median wage, adjusted for local cost of living differences.
Can workers without a bachelor's degree expect to earn the wages displayed in the tool?
The Opportunity Occupations Monitor displays a range of wages for each occupation-area-year combination, ranging from the 10th percentile through the 90th percentile wage earned by those employed in the occupation. However, these ranges are indicative of the wages earned by all workers, and the ranges are not broken down by educational attainment. Workers with a bachelor's degree or other advanced degrees likely earn wages higher in the distribution, on average, compared with similar workers without these degrees. As a result, the median wage for workers with a bachelor's degree or greater likely differs from the median wage for those without these degrees, even within an occupation.
Can a worker expect to earn the wages displayed in the tool?
The median wage represents the typical wage. By definition, 50 percent of workers earn above the median wage, and 50 percent of workers earn below the median wage. Users should look at the range of wages displayed in the tool to assess how wages vary within the occupation. Some occupations have higher earnings' variation than others. Many factors explain this variation, including the worker's experience, credentials, and employer.
Can workers obtain an opportunity occupation right out of school and without work experience?
The answer varies by occupation. Some opportunity occupations allow the worker to obtain employment after earning a credential. Others occupations may require a worker to have extensive experience and a credential to qualify for employment. More information on relevant or necessary credentials is provided by the U.S. Department of Labor's Occupational Information Network (O*NET).
Is the adjusted median wage the same as a self-sufficiency wage?
No, these are different concepts. A self-sufficiency wage means that a family earns enough income to meet basic expenses at an adequate level, without public or other supports. The self-sufficiency wage varies by family composition and the age of children.
About the Data
Burning Glass Technologies (BGT): The team used Burning Glass Technologies data from 2010 to 2019 to understand the level of education sought by employers filling open positions. BGT collects online job advertisements and populates a database that can be used for labor market research. We analyzed the occupations associated with the job ads, the metro areas and states of the employers, and the minimum level of education listed in the ads to calculate the share of jobs accessible to sub-baccalaureate workers in a given metro area for a given occupation. It is not known whether the lowest level of education mentioned by the employers is required or simply preferred.
To ensure sufficient observations to calculate the sub-baccalaureate share, we combined three years of BGT data to calculate shares where we found at least 100 job ads per occupation and area combination. For instance, 2012 estimates are based on job ads between 2010 and 2012, 2013 estimates are based on job ads between 2011 and 2013, and so forth. Where this approach yielded insufficient observations, we calculated the average sub-baccalaureate share for each occupation-area combination across all years (2010–17), and we also calculated this share for each occupation across the entire United States for these years. We then divided the states or metro areas into three groups based on the ratio of that area-occupation sub-baccalaureate share across all years with the national share across all years. An area where the ratio is below 0.95 is considered "least accessible to sub-baccalaureate workers." An area where the ratio is between 0.95 and 1.05 is considered "average accessibility to sub-baccalaureate workers." And an area where the ratio is greater than 1.05 is considered "most accessible to sub-baccalaureate workers." Finally, we use the average sub-baccalaureate share across an area's group in cases where we had fewer than 100 observations for a certain occupation.
U.S. Bureau of Labor Statistics (BLS) Occupational Employment Statistics (OES): The team accessed information on total employment and 10th, 25th, 50th, 75th, and 90th percentile wages by occupation for both states and metro areas between 2012 and 2019. OES data are updated annually. Note that information on some occupations in specific geographic areas is missing, so that total employment and median wage numbers do not reflect 100 percent of occupation data in a given year.
The metro area names and map displayed in the tool reflect the Office of Management and Budget’s (OMB) 2013 metro area definitions. Between 2012 and 2014, however, the composition and names of certain metro areas differed. While the tool displays the newer metro area’s name and geographic outline, the data displayed for those years represent the original metro area composition. This affects 64 metro areas in the tool: Anniston-Oxford-Jacksonville, AL (formerly Anniston-Oxford, AL); Atlanta-Sandy Springs-Roswell, GA (formerly Atlanta-Sandy Springs-Marietta, GA); Austin-Round Rock, TX (formerly Austin-Round Rock-San Marcos, TX); Bakersfield, CA (formerly Bakersfield-Delano, CA); Baltimore-Columbia-Towson, MD (formerly Baltimore-Towson, MD); Bend-Redmond, OR (formerly Bend, OR); Boise City, ID (formerly Boise City-Nampa, ID); Boston-Cambridge-Nashua, MA-NH (formerly Boston-Cambridge-Quincy, MA-NH); Buffalo-Cheektowaga-Niagara Falls, NY (formerly Buffalo-Niagara Falls, NY); Cape Girardeau, MO-IL (formerly Cape Girardeau-Jackson, MO-IL); Charleston-North Charleston, SC (formerly Charleston-North Charleston-Summerville, SC); Charlotte-Concord-Gastonia, NC-SC (formerly Charlotte-Gastonia-Rock Hill, NC-SC); Chicago-Naperville-Elgin, IL-IN-WI (formerly Chicago-Joliet-Naperville, IL-IN-WI); Cincinnati, OH-KY-IN (formerly Cincinnati-Middletown, OH-KY-IN); Cleveland-Elyria, OH (formerly Cleveland-Elyria-Mentor, OH); Denver-Aurora-Lakewood, CO (formerly Denver-Aurora-Broomfield, CO); Detroit-Warren-Dearborn, MI (formerly Detroit-Warren-Livonia, MI); Elizabethtown-Fort Knox, KY (formerly Elizabethtown, KY); Eugene, OR (formerly Eugene-Springfield, OR); Fort Collins, CO (formerly Fort Collins-Loveland, CO); Greenville-Anderson-Mauldin, SC (formerly Greenville-Mauldin-Easley, SC); Gulfport-Biloxi-Pascagoula, MS (formerly Gulfport-Biloxi, MS); Hinesville, GA (formerly Hinesville-Fort Stewart, GA); Houma-Thibodaux, LA (formerly Houma-Bayou Cane-Thibodaux, LA); Houston-The Woodlands-Sugar Land, TX (formerly Houston-Sugar Land-Baytown, TX); Indianapolis-Carmel-Anderson, IN (formerly Indianapolis-Carmel, IN); Janesville-Beloit, WI (formerly Janesville, WI); Kankakee, IL (formerly Kankakee-Bradley, IL); Kennewick-Richland, WA (formerly Kennewick-Pasco-Richland, WA); Killeen-Temple, TX (formerly Killeen-Temple-Fort Hood, TX); La Crosse-Onalaska, WI-MN (formerly La Crosse, WI-MN); Las Vegas-Henderson-Paradise, NV (formerly Las Vegas-Paradise, NV); Leominster-Gardner, MA (formerly Leominster-Fitchburg-Gardner, MA); Madera, CA (formerly Madera-Chowchilla, CA); Miami-Fort Lauderdale-West Palm Beach, FL (formerly Miami-Fort Lauderdale-Pompano Beach, FL); Muskegon, MI (formerly Muskegon-Norton Shores, MI); Myrtle Beach-Conway-North Myrtle Beach, SC-NC (formerly Myrtle Beach-North Myrtle Beach-Conway, SC); Naples-Immokalee-Marco Island, FL (formerly Naples-Marco Island, FL); New Orleans-Metairie, LA (formerly New Orleans-Metairie-Kenner, LA); New York-Newark-Jersey City, NY-NJ-PA (formerly New York-Northern New Jersey-Long Island, NY-NJ-PA); North Port-Sarasota-Bradenton, FL (formerly North Port-Bradenton-Sarasota, FL); Norwich-New London-Westerly, CT-RI (formerly Norwich-New London, CT-RI); Olympia-Tumwater, WA (formerly Olympia, WA); Panama City, FL (formerly Panama City-Lynn Haven-Panama City Beach, FL); Parkersburg-Vienna, WV (formerly Parkersburg-Marietta-Vienna, WV-OH); Phoenix-Mesa-Scottsdale, AZ (formerly Phoenix-Mesa-Glendale, AZ); Portland-South Portland, ME (formerly Portland-South Portland-Biddeford, ME); Providence-Warwick, RI-MA (formerly Providence-Fall River-Warwick, RI-MA); Reno, NV (formerly Reno-Sparks, NV); Sacramento-Roseville-Arden-Arcade, CA (formerly Sacramento--Arden-Arcade--Roseville, CA); Saginaw, MI (formerly Saginaw-Saginaw Township North, MI); Salisbury, MD-DE (formerly Salisbury, MD); San Diego-Carlsbad, CA (formerly San Diego-Carlsbad-San Marcos, CA); San Francisco-Oakland-Hayward, CA (formerly San Francisco-Oakland-Fremont, CA); San Luis Obispo-Paso Robles-Arroyo Grande, CA (formerly San Luis Obispo-Paso Robles, CA); Santa Rosa, CA (formerly Santa Rosa-Petaluma, CA); Scranton-Wilkes-Barre-Hazleton, PA (formerly Scranton-Wilkes-Barre, PA); Spokane-Spokane Valley, WA (formerly Spokane, WA); Stockton-Lodi, CA (formerly Stockton, CA); Texarkana, TX-AR (formerly Texarkana-Texarkana, TX-AR); Trenton, NJ (formerly Trenton-Ewing, NJ); Vineland-Bridgeton, NJ (formerly Vineland-Millville-Bridgeton, NJ); and Wenatchee, WA (formerly Wenatchee-East Wenatchee, WA).
U.S. Bureau of Labor Statistics (BLS) Employment Projections (2018–28): The team retrieved information on entry-level education requirements and the projected change in employment by occupation. Employment projections are updated every two years. The 2018–28 projections are located on the BLS website (see link above). For 63 occupations that required a "doctoral or professional degree," we assumed this educational requirement superseded the data obtained from Burning Glass Technologies' job ads.
U.S. Bureau of Economic Analysis (BEA): The team used the regional price parity for each metro area and state between 2012 and 2018 in order to adjust the OES wages for cost of living differences. When the estimates were created, regional price parity data were available only through 2017, so 2017 regional price parity figures were applied to 2018 OES data.
U.S. Census Bureau American Community Survey (ACS) Five-Year Estimates from IPUMS: The team retrieved American Community Survey data from IPUMS (2008–12, 2009–13, 2010–14, 2011–15, 2012–16, 2013–17, and 2014–18 five-year estimates) in order to estimate the median weekly hours worked for each occupation in each year. We then used these estimates to calculate annual median wages using the OES data, where these data indicated the use of annual wages was not appropriate.
More Information about Opportunity Occupations
The following papers and infographics provide in-depth analyses of opportunity occupations. Our tool transforms this research into an interactive way to understand the changing nature of opportunity occupations and to access the most up-to-date information about specific occupations across geographic areas. The Federal Reserve Bank of Cleveland has also compiled a list of projects related to Opportunity Occupations.
Date: June 2020
About: This report suggests that job training and hiring practices that emphasize transferrable skills could help employers fill in-demand or hard-to-fill positions and provide new career opportunities for workers who have been laid-off, furloughed, or simply want a career change. Transitions connecting the most similar occupations would represent an average annual increase in wages of nearly $15,000 (or 49 percent).
Date: April 2019
About: This report finds that opportunity employment—defined as employment accessible to workers without a bachelor’s degree and typically paying above the national annual median wage, adjusted for regional differences in consumer prices—accounts for 21.6 percent of total employment in the metro areas analyzed. The report also illustrates how the local mix of occupations, employers’ educational expectations, and the cost of living combine to expand or limit local opportunity relative to national conditions.
Date: April 2019
About: This file includes one-page fact sheets describing the 10 largest opportunity occupations in each of the 121 metro areas analyzed.
Identifying Opportunity Occupations in the Southeast
Date: March 2019
About: The infographics depict opportunity occupations, jobs that don't require a bachelor's degree and pay at least the national annual median wage. The infographics identify top opportunity occupations and labor market outcomes by educational attainment in six southeastern states.
Date: October 2017
Author: Kyle Fee (Cleveland Fed)
About: This report takes a deep dive into the registered nurse (RN) labor market, using online job posting data to gain a better understanding of how much education employers prefer when hiring. It finds employer educational preferences for RNs—once trending toward more education—have been trending toward less education since 2014.
Date: January 2017
About: To ensure more low- and moderate-income families can enjoy pathways into prosperity, it is crucial to identify the economic opportunities available to non-college-educated or middle-skill workers. Using level of education requested by employers in online job advertisements, the authors explore why employer preferences for bachelor's degrees for the most prevalent opportunity occupations vary significantly between metropolitan areas.
Date: September 2015
Author: Keith Wardrip (Philadelphia Fed)
About: An extension of research from Identifying Opportunity Occupations in the Nation's Largest Metropolitan Economies, this report explores the degree to which the economies of 11 metropolitan statistical areas (MSAs) in Pennsylvania, New Jersey, and Delaware include opportunity occupations, or occupations characterized by above-average pay for workers without a bachelor's degree.
Date: September 2015
About: Opportunity occupations are jobs generally accessible to workers without a bachelor's degree that pay at least the national annual median wage, adjusted for local cost of living differences. Focusing on the 100 largest U.S. metropolitan areas, the authors identify the most prevalent opportunity occupations in these economies, highlight differences across metropolitan areas, and explore how employer preferences for education affect access to decent-paying jobs.
The research on opportunity employment is a joint initiative by the Federal Reserve Banks of Atlanta, Cleveland, and Philadelphia. Other researchers engaged in this work are Kyle Fee and Lisa Nelson at the Federal Reserve Bank of Cleveland, and Keith Wardrip at the Federal Reserve Bank of Philadelphia. Ashley Bozarth also made significant contributions to prior versions of the tool.