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Gender-Based Occupational Segregation and Sex Differences in Sensory, Motor, and Spatial Aptitudes

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Demography

Abstract

Research on sex differences in humans documents gender differences in sensory, motor, and spatial aptitudes. These aptitudes, as captured by Dictionary of Occupational Titles (DOT) codes, predict the occupational choices of men and women in the directions indicated by this research. We simulate that eliminating selection on these skills reduces the Duncan index of gender-based occupational segregation by 20 % to 23 % in 1970 and 2012, respectively. Eliminating selection on DOT variables capturing other accounts of this segregation has a smaller impact.

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Notes

  1. The Duncan index, which we define shortly, ranges between 0 and 1 and is interpreted to indicate the proportion of women or men who would need to change occupations to produce a similar occupational distribution of men and women.

  2. Levanon and Grusky (2016) examined a number of the occupational characteristics examined here aggregated into composite measures.

  3. See also Croson and Gneezy (2009) and Eckel and Grossman (2008) on risk aversion; Gneezy et al. (2003), Niederle and Vesterlund (2007), and Cotton et al. (2013) on competition; and Deleire and Levy (2004), Leeth and Ruser (2006), Bonin et al. (2007), and Grazier and Sloane (2008) on earnings and mortality risk.

  4. We do not use the DOT physical demand codes of kneeling, climbing, balancing, stooping, crouching, crawling, talking, and reaching. We also exclude the aptitude form perception (the ability to perceive pertinent detail in pictures and graphs), the physical demands accommodation (the adjustment of the eye to bring things into focus), and field of vision.

  5. We convert information on the number of fatalities for each occupation in 2012 to a mortality rate using employment information. The earliest data on fatalities are from 1992. Because these data are provided for different occupational codes, however, we can match them to only 431 occupations (as opposed to the 468 used in our main analysis) in the 2000 census. The results are similar if we use the 1992 fatalities information for our 1970 analysis.

  6. O*NET, produced by the U.S. Department of Labor, has supplanted the DOT in recent years and provides many similar measures of occupational requirements as well as some additional characteristics. For time flexibility, we use Goldin’s (2014) proposed measures. The structure variable is reverse-coded in the O*NET, with higher values indicating more freedom for the worker to determine tasks, priorities, and goals.

  7. The occupational prestige score is based on data from the 1989 General Social Survey, in which respondents were asked to rank occupations on scale of social standing from 1 to 9. The initial scores were based on the 1990 census occupational coding. We obtain the measures for the 2000 census occupational coding using data from IPUMS (Ruggles et al. 2015).

  8. The exception is for physical strength, which is available in both years.

  9. This procedure produces occupation shares mj and fj that do not sum to 1. We rescale so that the shares do sum to 1 by allowing the total number of men and women in the labor market to change. In practice, the changes in the total size of the labor force are fairly small: approximately 3 % for men and 1 % for women.

  10. In Table 1, we report results for all 505 census occupation categories. In the analysis of skills, we use the 468 occupations that can be matched to DOT codes. This makes very little difference: the Duncan index for our 468 occupations is 0.644 in 1970 (the same as for the full set of occupations) and 0.508 in 2012 (vs. 0.506 for all occupations).

  11. Our definition of STEM jobs is from Beede et al. (2011).

  12. Weekly hours are provided in intervals in the 1970 census data. We use the midpoint of each interval to impute weekly hours.

  13. We calculate the log odds ratio separately for the age groups 18–24, 25–34, and 35–64 and estimate the pooled regression testing for interaction effects between dummy variables for the younger age groups and the DOT aptitude measures. The estimates of these interactions are uniformly statistically insignificant. We also pool the 1970 and 2012 data and estimate a pseudo-panel model, with occupation fixed effects for the limited number of aptitudes coded separately in the 1977 and 1991 data. Slightly more than one-half of the estimates from this specification are of the expected sign, but less than one-half are statistically significant because the standard errors are generally much larger (one statistically significant estimate is of the wrong sign). The temporal variation of the DOT codes in this analysis could be due to sub-occupational compositional changes, and only 9 of the 28 aptitudes/skills in Table 2 are available for this analysis.

  14. We estimate the models reported in Table 2 using the shrinkage estimator least absolute shrinkage and selection operator (LASSO). For 1970, the estimates from this method are 0 for color vision, manual dexterity, and motor coordination. For 2012, the estimates are 0 for color vision, near acuity, hearing, manual dexterity, motor coordination, and eye-hand-foot coordination. For both years, the estimates for noise, feeling, handling, and the spatial measures are mostly modestly smaller than in Table 2. These results are available from the authors on request.

  15. As another test, we investigated whether simply adding a control for height diminishes the correlation of the log odds of employment and the DOT feeling variable. We calculated the average male height by occupation using the 1990–1994 NHIS and entered this as an additional control in regressions specification reported in Table 2. The results, reported elsewhere (Baker and Cornelson 2016a), indicated that adding this measure of height diminishes somewhat the correlation between the log odds ratio and the feeling variable, but also does not have a substantive effect on the correlation between the log odds and our measures of cognitive and noncognitive skills.

  16. The NHIS does not provide detailed occupational coding after 1994.

  17. The uniform conditioning property implies that every alternative randomly sampled from the choice set has some positive probability of being observed. McFadden (1978:544) gave several examples of selection procedures that satisfy this property; our selection procedure corresponds to his example C-1.

  18. Importantly, the correlation of height with an attribute does not necessarily imply that the average height difference between males and females rationalizes the sex selection that we see in the data. On one hand, a dimension of the sense of touch is hypothesized to be mechanically related to finger size, and males and females of similar finger size have been found to have similar senses of touch. On the other, although taller men and women select into jobs that require better hearing, and research indicates that stature is positively related to hearing for each sex, females (as we noted earlier) have more acute hearing in some dimensions despite their smaller stature.

  19. For these regressions, we switch to the 1990 census occupational codes, which is the coding available in the NHIS. Because 472 occupations have nonmissing log odds in the NHIS data, we limit analysis to these occupations.

  20. We also examine the implications of selection on these skills for the gender wage gap. Holding wages and overall employment in an occupation fixed, we examine the effect on male and female average wages of eliminating differential gender selection on observable occupational attributes. In many cases, skill-based occupational segregation favors women in terms of compensation. Eliminating selection on physical strength or people/things orientation substantially increases the gender wage gap in both years, and eliminating selection on sensory or motor skills increases the wage gap in 2012 (yet decreases it in 1970). In contrast, eliminating selection on cognitive skills (particularly math), spatial skills, and measures of occupational risk leads to a lower gender wage gap in both years, but particularly in 2012. These results underline that the significance of the segregation of males and females in employment for the gender gap in pay is dependent on the relative prices of these skills at a particular time and place. These results are available from the authors on request.

  21. For example, the DOT aptitudes Finger Dexterity (a fine motor skill) and Eye-Hand-Foot Coordination (a gross motor skill) are used to represent routine and nonroutine manual skills, respectively, in the Autor et al. (2003) taxonomy of tasks. Tables 2–4 show that these skills have strong relationships with the log odds of male employment, particularly when used in isolation.

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Acknowledgments

We gratefully acknowledge the research support of the Social Sciences and Humanities Research Council (#410-2011-0724) and a Canada Research Chair at the University of Toronto. Fran Blau kindly provided the occupational crosswalk for the 2000 census occupational coding. We thank the referees for helpful comments as well as Dwayne Benjamin, Diane Halpern, and Gary Solon for their input on an early draft. We also thank seminar participants at UBC–Kelowna, UBC–Vancouver, and the WOLFE workshop at the University of York.

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  1. Department of Economics, University of Toronto, 150 St. George Street, Toronto, Ontario, M5S 3G7, Canada

    Michael Baker

  2. National Bureau of Economic Research, Cambridge, MA, USA

    Michael Baker

  3. Department of Economics, University of Notre Dame, 3051 Jenkins Nanovic Hall, Notre Dame, IN, 46556, USA

    Kirsten Cornelson

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Baker, M., Cornelson, K. Gender-Based Occupational Segregation and Sex Differences in Sensory, Motor, and Spatial Aptitudes. Demography 55, 1749–1775 (2018). https://doi.org/10.1007/s13524-018-0706-3

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