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 unclear correlations between occupation and age range.
Using Bureau of Labor Statistics information, I looked for the ratio of workers between certain age ranges (20-24, 25-34, 45-54, 54-64, and 65+) 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 less niche 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 a 2006 Pew Research Center poll, where 78-85% of pet owners viewed their animal as family, as compared to their 2023 poll which 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 a bastion of skilled blue collar and white collar jobs 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 frequency in the 65+ category.
Another aspect I wanted to explore was the relationship between the age cohort ratios and wages. Individual earnings tend to peak at the 45 to 54 cohort and I was curious if these trends followed through on the occupational level.4 The data shows a complex picture.
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 decrease of 0.63 percentile points of wages for every 1 percentile decrease in the cohort proportion.
Wage vs 35-44 Proportion
Despite the fact that individual earnings tend to peak around 45 to 54, the 35 to 44 segment had the highest slope in the data set. 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 basically no 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 arguably the most interesting, being in between a negatively correlated younger cohort and a positively correlated older cohort. The low correlations coefficient (0.07) and slope (0.2891) reflect this ambiguous spot. 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 job ladders.
Wage vs 45-54 Proportion
The 45 to 54 cohort has a positive relationship between wage and age proportion, but is less significant than the previous cohort. 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.
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 “Chief Executives.” 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 a whopping 0.03, it is not very meaningful. 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.
Sources:
Demographic and wage data available from the Bureau of Labor Statistics at https://www.bls.gov/cps/cps_aa2023.html, specifcally 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/