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Beyond BLS

November 2019

Why lifetime earnings vary across different occupation categories

Lisa N. Huynh

Economic models have often “equated workers’ occupations with their skill levels.” Occupations can also signify approximate wages and socioeconomic standing. For instance, doctors have a higher salary and are perceived to be “better off” than janitors. In “Occupational mobility and lifetime earnings” (Federal Reserve Bank of St. Louis Review, third quarter of 2019), Youngseok Shin and C.Y. Kelvin Yuen discuss the link between occupations and wages across 22 occupation categories. The authors find several components attribute to the disparity in lifetime earnings: differences in average wages for workers across occupation categories, the numbers of jobs held, and education–gender groups.

If everyone within the same occupation category is assumed to have the same wage, average wages between each occupation category still vary substantially. Shin and Yuen discover that among the 22 occupations observed, legal occupations had the highest average wages—almost 3 times higher than the wages for food preparation and serving occupations, the group with the lowest average wages. This contrast highlights the concept of “between-occupation” inequality: the wage gap that exists among different occupations. However, because individuals may often switch industries and occupations (especially the younger population) during their working lives, the occupation category of 25-year-old workers cannot fully predict their lifetime earnings.

Lifetime earnings correspond with how long each person remains employed during his or her career. Looking at the “job-switching rate,” the authors find that 54 percent of job switchers remain within the same occupation category. Shin and Yuen identify the effects of occupational mobility on average wages by examining the gross flow rate of each category. Gross flow rate refers to the number of workers leaving and entering each occupation category—higher gross flow rates suggest higher turnover within the occupation category. The authors find that gross flow rates are negatively correlated with average wages across occupational categories; occupations with higher wages have less turnover. This negative correlation indicates that high-paying occupations require “special skills and occupational-specific human capital” that take more time to cultivate. Yet, workers with higher paying occupations often stay unemployed longer compared with those with lower paying occupations.

The authors also examine the influence that gender and educational attainment may have on wages. They observe that across every occupation, overall average wages are higher among individuals with a bachelor’s degree. This “college premium” remains true even after they consider gender. Another important observation they discover is that among men and women at the same level of education, men have higher average wages than women. Men are also employed for longer periods than women, which further amplifies the differences in lifetime earnings.

Since these findings may not be the only factors that affect potential lifetime earnings, additional circumstances among different occupation groups should also be considered for identifying income disparities within the economy.

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