Generational Differences in American Occupations
It may seem obvious that fast food workers tend to be younger and clergymen tend to be older, but I was surprised that people over 65 years old made up a quarter of all school bus drivers. I hope bus insurance premiums are not too high. My main goal with this project was to discover interesting phenomena similar to that, and explore the less obvious relationships between occupation and age range.
Using Bureau of Labor Statistics estimates from the Current Population Survey, I looked for the ratio of workers between certain age ranges to the entire workforce for various jobs. I excluded occupations with below 75 thousand total workers and those with “other” in the title, due to gaps in the government’s data and the desire to focus on more commonly held jobs. For this set, jobs with the title “manager”, “administrator”, and “supervisors” were also excluded to focus on non-managerial employment.
Ages 20-24
0.088816 Avg. Proportion
Ages 25-34
0.241259 Avg. Proportion
Ages 35-44
0.227110 Avg. Proportion
Fast food and counter workers
0.351384
Recreation workers
0.326923
Waiters and waitresses
0.289474
Tellers
0.268398
Emergency medical technicians
0.254335
Food servers, nonrestaurant
0.235294
Veterinary technologists and technicians
0.227586
Animal caretakers
0.224551
Cashiers
0.221876
Dental assistants
0.221843
Veterinary technologists and technicians
0.413793
Substance abuse and behavioral disorder counselors
0.407692
Physician assistants
0.401099
Emergency medical technicians
0.387283
Bartenders
0.375556
Public relations specialists
0.368421
Biological scientists
0.361905
Software developers
0.357246
Software quality assurance analysts and testers
0.353659
Firefighters
0.348993
Eligibility interviewers, government programs
0.394737
Computer network architects
0.366906
Detectives and criminal investigators
0.364238
Medical scientists
0.362205
Nurse practitioners
0.350694
Human resources assistants, except payroll and timekeeping
0.344086
Claims adjusters, appraisers, examiners, and investigators
0.332461
Refuse and recyclable material collectors
0.326923
Speech-language pathologists
0.325472
Broadcast, sound, and lighting technicians
0.324786
Ages 45-54
0.192862 Avg. Proportion
Ages 55-64
0.159179 Avg. Proportion
Ages 65+
0.062728 Avg. Proportion
Editors
0.333333
Computer network architects
0.309353
Medical records specialists
0.303965
Dietitians and nutritionists
0.295775
Postsecondary teachers
0.290112
Manicurists and pedicurists
0.288026
Chemists and materials scientists
0.287356
Sewing machine operators
0.280702
Electrical, electronics, and electromechanical assemblers
0.278261
Detectives and criminal investigators
0.278146
Construction and building inspectors
0.317308
Bus drivers, school
0.298507
Stationary engineers and boiler operators
0.291667
Executive secretaries and administrative assistants
0.290598
Bus drivers, transit and intercity
0.287823
Laundry and dry-cleaning workers
0.270270
Title examiners, abstractors, and searchers
0.268817
Postal service mail carriers
0.268608
Secretaries and administrative assistants
0.266780
Machinists
0.263345
Bus drivers, school
0.263682
Bus drivers, transit and intercity
0.228782
Clergy
0.212658
Laundry and dry-cleaning workers
0.162162
Real estate brokers and sales agents
0.152559
Writers and authors
0.151751
Lawyers
0.138743
Bookkeeping, accounting, and auditing clerks
0.135338
Postsecondary teachers
0.135261
Construction and building inspectors
0.125000
There were many possible connections between changing social attitudes and the occupations chosen by the younger generations. I would have imagined substance and behavioral counselors to be older individuals with years of wisdom, but people under 35 make up almost half of the profession. I wonder if this reflects the increasing prominence of “therapy culture” among younger generations, with the rate of American adults actively seeing a therapist almost doubling since 2004.1 The high level of animal and biological related fields is also striking, and parallels my personal observations regarding the younger generation’s relationship to animals. This trend is observed in polling data. In 2006, 78-85% of pet owners considered their pet part of their family, while a 2023 poll from the same pollster puts the number at 97%.2,3
There was also an interesting distribution between the level of educational or experiential attainment needed for occupations among different age groups. The age range between 55-64 is composed of skilled blue-collar jobs and white-collar professions that do not necessitate a college degree, while the 45-54 and 65+ cohorts have a greater proportion of technical employment. I believe this is related to the potential for early retirement and enjoyment of work. People who are editors or computer network architects have the salaries to retire early, and people who are writers or postsecondary teachers might continue their profession past retirement age due to a passion for the profession. Title examiners and mail carriers might lack the salary to retire early and the passion to continue their work after Social Security benefits come. I also wonder if the effect of managing your own practice, relevant to real estate brokers and lawyers, helps explain their prevalence in the 65+ category.
Another aspect I wanted to explore was the relationship between the age cohort ratios and wages.
Wage vs 20-24 Proportion
As expected, occupations with a higher proportion of employees aged 20 to 24 tend to pay less. Many of these position titles are not considered desirable careers. This age range has the most significant correlation coefficient in the set at 0.4, with an expected 0.63 decrease in wage percentile per a 1 point increase in the cohort’s share.
Wage vs 35-44 Proportion
Although individual earnings tend to peak around 45 to 54, the 35 to 44 segment had the highest slope in the data set.4 An increase in one percentile for the age category corresponded to a 0.53 increase in wage percentile, with a relatively high correlation coefficient of 0.27. When looking at the top 25 jobs associated with this variable, there are many high-end medical, corporate analyst, and engineering positions listed which help explain the relationship.
Wage vs 55-64 Proportion
There is no meaningful relationship between having a higher proportion of 55 to 64 year olds and wages. My hypothesis is that the highest salaried employees start to retire or reduce hours at this age, while most workers still need to work full time, yet have had the time to move up the ranks professionally. This explains why there is a cluster of the highest age cohort proportioned jobs between the wage percentiles of 30% to 70%.
Wage vs 25-34 Proportion
The 25 to 34 cohort is in a transitory period in many people’s careers. The low correlations coefficient (0.07) and slope (0.2891) reflect this ambiguous phase. As suggested by its highest proportion list, workers in this spot have the competitive advantage of using their recent education for emerging technical fields and their physical ability for frontline blue-collar work. However, they have not yet had the time to climb corporate ladders.
Wage vs 45-54 Proportion
The 45 to 54 cohort has a positive relationship between wage and age proportion, but its correlation coefficient is low. A one percentile increase in age proportion leads to a 0.36 percentile in wage proportion, with a correlation coefficient of 0.12. I was surprised this range did not have a higher correlation, but there was a surprising lack of high-paying corporate jobs when looking at other correlated jobs outside the list. Even when accounting for the absence of managerial professions, there is little difference.
Wage vs 65+ Proportion
For the final cohort, there is also no correlation between wage and age. The distribution on graph is striking, the highest proportion occupations for this cohort have an even balance across wage percentiles. People who need to work for material needs do so, and those who built up a career and love their profession continue to do so. Maybe some work to escape boredom even if they dislike their jobs.
What if you add managerial positions?
Surprisingly, very little changed in the regression models when occupations with manager, administrator, and supervisors were included. I even added back the “Chief Executives” title. The largest effect was with the 25-34 age cohort, whose slope dropped from 0.28 to 0.19. However, since the correlation coefficient was 0.03, this change is rather anticlimactic. I was expecting this inclusion of managers to positively affect the older cohorts, since frequencies of almost all managerial titles were associated with older age groups. Despite their higher wages, managers do not make up enough of the workforce to skew the numbers too heavily.
The jobs with a high proportion of young or old workers usually have intuitive explanations for their age demographics. Nobody is surprised that waiters tend to be younger and collegiate professors tend to be older. It was the ones in the middle that were unusual. What makes people in their late 30s be welfare eligibility interviewers or people in their early 50s be construction inspectors at twice the rate of other jobs? I was unable to test for statistical significance when incorporating both age and occupational variables as it would have required a reweighting of the baseline data, which is beyond the scope of the project. There is a chance these high values are statistical anomalies, but they could reflect a disinterest in younger people in working those positions or difficult pathways to achieve those positions.
I also believed the relationship between wage and age proportion to be very interesting, with some unexpected results. The most shocking was the 65+ group, where there was no observed correlation between the variables. I speculated that people working at that age do so because they love their work or they really need the money, but that would lend itself to a more bimodal distribution. It was also interesting that the highest positive correlation was in the 35-44 group rather than the 44-54 group, when individual earnings are supposed to peak. A few reasons I think it could include the decade more of experience and earnings making up for differences in base wage between occupations, differences in wage variance within occupations, and the increased incentive of younger workers to join growing occupations. I asked ChatGPT and it suggested human capital accumulation as a possible answer. Although older workers ostensibly have more career experience and knowledge, certain tech related work may be better suited towards younger workers with more relevant educational experiences.
Sources:
Demographic and wage data available from the Bureau of Labor Statistics at https://www.bls.gov/cps/cps_aa2023.html, specifically used 11b and 39.
1: https://news.gallup.com/poll/467303/americans-reported-mental-health-new-low-seek-help.aspx
2: https://www.pewresearch.org/social-trends/2006/03/07/gauging-family-intimacy/
3: https://www.pewresearch.org/short-reads/2023/07/07/about-half-us-of-pet-owners-say-their-pets-are-as-much-a-part-of-their-family-as-a-human-member/
4: https://www.sofi.com/learn/content/average-income-by-age/
© Peter Derrah 2025. All rights reserved.