I’ve written about IPUMS before. It’s great. Among individual details are their occupations and industry of their occupation. That’s convenient because we can observe how technology spread across America by observing employment in those industries. We can also identify whether demographic subgroups differed or not by occupation. There’s plenty of ways to slice the data: sex, race, age, nativity, etc.
But what do we know about historical occupations and what they entailed? At first blush, we just have our intuition. But it turns out that we have more. There is a super boring 1949 report published by the Department of Labor called the “Dictionary of Occupational Titles”. The title says it all. But, the DOL published another report in 1956 that’s conceptually more interesting called “Estimates of Worker Trait Requirements for 4,000 Jobs as Defined in the Dictionary of Occupational Titles: An Alphabetical Index”. The report lists thousands of occupations and identifies typical worker aptitudes, worker temperaments, worker interests, worker physical capacities, and working conditions. Below is a sample of the how the table is organized:
The aptitudes are numbers that represent binned percentiles, and the physical capacities and working conditions are indicator variables. Unfortunately, the 1950 occupations and industry codes don’t match IPUMS. But, there are less than 200 industries. That’s just begging for digitization and a crosswalk by industry. And that’s what I did.*
Previously, we could observe a higher proportion of men than women in, say, steel fabrication and suspect that men have “some sort” of physical or mental comparative advantage related to steel fabrication. But what does that mean? The advantage of the DOL report is that we can say specifically what occupation and physical characteristics men systematically excel at and whether they characterize steel fabrication. But that’s just an example.
Say that, after controlling for other observables, there is an occupation-based earnings gap between men and women of 20%. If we control for aptitude or physical capacity characteristics, then we are controlling for the types of human capital that are associated with jobs that earn more or less.*
For example, consider a measure of verbal aptitude, the capacity to talk and hear, and loud and noisy working conditions. If men historically leaned into their comparative advantage of being physically stronger rather than pro-social communicators, then we’d see them work less in verbal occupations and more in noisy occupations (>80 decibels). For the 1910 IPUMS data that’s exactly what we see! Men participated more in industries of 3.6% lower verbal aptitude, were 2.1% less likely to be in industries with high requirements for talking and hearing, and were 9.6% more likely to work in noisy industries.
Remember that 20% earnings gap? If we include the above 3 industry variables and the gap shrinks, then that means that part of the earnings gap is due to human capital differences. Of course, all of the typical caveats about different gender roles, expectations, mentors, etc still apply. But once people are in the labor force, there portfolio of human capital determines their industry and has a consequent impact on their earnings.
Isn’t that cool?
*See also: Gray, Rowena. 2013. “Taking Technology to Task: The Skill Content of Technological Change in Early Twentieth Century United States.” Explorations in Economic History 50 (3): 351–67. https://doi.org/10.1016/j.eeh.2013.04.002.
** See: Pitt, Mark M., Mark R. Rosenzweig, and Mohammad Nazmul Hassan. 2012. “Human Capital Investment and the Gender Division of Labor in a Brawn-Based Economy.” The American Economic Review 102 (7): 3531–60.
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