Bye bye Ms. American Sci: Women and the leaky STEM pipeline

More than two-thirds of STEM jobs are held by men. In this paper, I provide a detailed analysis of the STEM pipeline from high school to mid-career in the United States, decomposing the gender gap in STEM into six stages. Women are lost from STEM before college, during college, and after college. Men are more likely to be STEM-ready before college, scoring higher on science tests and having taken more advanced math and science courses. This accounts for 35% of the overall gender gap in STEM careers. During college, men are far more likely than women to start in a STEM major, accounting for 26% of the gap. After college, male STEM graduates are more likely to enter STEM jobs, accounting for 41%. Men\u27s higher persistence in STEM majors is a smaller factor, while women attend college at higher rates than men, which works to reduce the final gender gap in STEM. The results show that there is no single stage to focus on in understanding the gender gap in STEM

Where the girls are: Examining and explaining the gender gap in the nursing major

The nursing major, at almost 90% female, has one of the largest gender gaps of any US college major. In trying to explain this gap, I sort through candidate explanations and show that nursing is an outlier on many dimensions. Relative to other majors, it has some of the slowest earnings growth, by far the highest occupational concentration, among the lowest penalties for part-time work, among the lowest unemployment rates, and the highest level of job tasks focused on helping others and interacting with people . These factors collectively account for most of nursing\u27s huge gender gap. While I cannot determine causal mechanisms, a major\u27s associated job tasks (especially helping others ) account for most (55%) of the variance in majors\u27 gender composition, while nursing\u27s low part-time penalty is also important. Earnings growth, earnings variance, and the share of women out of the labor force from the major are less important, and academic factors like course requirements and test scores are unrelated to major gender gaps

How bad is occupational coding error? A task-based approach

Studies of occupational choice and mobility are often plagued by rampant occupational coding error. Use of task-based occupation measures, such as O*Net, may mitigate the bias caused by coding error if the occupation is misclassified as an occupation similar to the true occupation. Measuring occupational changes in task space , I find that task-based measures reduce the problems of coding error, but only slightly. If one does not correct for coding error, one overestimates traditional occupational mobility rates by about 90%; using task-based measures, the overestimate of mobility is still 75%. I also show that when tasks are used as regressors and coding error is not corrected, estimates will be attenuated by 15%-20%. Task-based measures are a slight improvement over census occupation codes but are no panacea for dealing with coding error

Pre-market skills, occupational choice, and career progression

This paper develops a new empirical framework for analyzing occupational choice and career progression. I merge the NLSYs with O*Net and find that pre-market skills (primarily ASVAB test scores) predict the task content of the workers\u27 occupations. These measures account for 71 percent of the gender gap in science and engineering occupations. Career trajectories are similar across workers, so that initial differences in occupation persist over time. I then quantify the effect of layoffs on career trajectory and find that a layoff erases one-fourth of a worker\u27s total career increase in task content but this effect only lasts two years

Student performance in online health courses

The switch to remote classes disrupted higher education during the Covid-19 pandemic. Online courses have the potential to be especially disruptive in health fields, where more of the learning is hands-on and practice-based. Using detailed pre-Covid administrative data from a large, diverse public university, I study how online course delivery can impact student performance in these fields. While grades are similar on average in online courses as compared with in-person courses, grades are difficult to interpret and may not measure actual learning. I find that course pass rates–an outcome of real consequence for students–are 3.9 percentage points lower in online courses. This is especially true among Black and low-income students, for whom pass rates go down by 6.4 and 5.4 percentage points, respectively. The results suggest that the move to online courses may depress graduation rates in health fields, particularly among minority and lower-income students, leading to a less diverse healthcare workforce

Wages, Hours, and the School-to-Work Transition: The Consequences of Leaving School in a Recession for Less-Educated Men

Using the NLSY\u27s weekly work history data to precisely measure labor market outcomes and the school-to-work transition, I document severe but short-lived effects of leaving school in a recession for men with 9-12 years of education. I find significant effects of entry labor market conditions on wages, job quality, and the transition time from school to work. In contrast to published evidence on more educated workers, I also find large effects on work hours on both the extensive and the intensive margins. When workers leave high school in a recession, they take substantially longer to find a job, earn lower wages, and work fewer full-time weeks and more part-time weeks. A 4-point rise in the initial unemployment rate leads to an increase in the school-to-work transition time of 9 weeks, a 16% decline in year-one average wage, a 28% fall in hours worked in the first year, and a 45% decline in first-year earnings. However, effects of entry conditions are not persistent and are largely gone after the first year

The gender gap in college major: Revisiting the role of pre-college factors

This paper considers the importance of pre-college test scores in accounting for gender gaps in college major. Large gaps in major content exist: men are more likely to study math-, science-, and business-intensive fields, while women are more likely to study humanities-, social science-, and education-intensive fields. Previous research has found that gender differences in college preparation, typically measured by SAT scores, can account for only a small portion of these differences. Using a broader array of pre-college test scores (the ASVAB), I show that differences in college preparation can actually account for a large portion of most gender gaps in college major content, including 62% of the gap in science, 66% of the gap in humanities, and 47% of the gap in engineering. SAT scores explain less than half as much as the ASVAB scores, while noncognitive skill measures appear to explain none of the gaps in major. The gender gaps in test scores, particularly in science and mechanical fields, exist by the mid-teenage years and grow with age

Labor market returns to college major specificity

This paper develops a new approach to measuring human capital specificity, in the context of college majors, and estimates its labor market return over a worker\u27s life cycle. To measure specificity, we propose a novel method grounded in human capital theory: a Gini coefficient of earnings premia for a major across occupations. Our measure captures the notion of skill transferability across jobs. Education and nursing are the most specific majors, while philosophy and psychology are among the most general. Using data from the American Community Survey, we find that the most specific majors typically pay off the most, with an early-career earnings premium of about 5–6% over average majors (15-20% over the most general majors), driven by higher hourly wages. General majors lag far behind at every age. Despite their earnings advantage, graduates from specific majors are the least likely to hold managerial positions, with graduates from majors of average specificity being the most likely to do so. It may be that managerial positions require a mix of specific knowledge and broadly applicable skills

Major-occupation match quality:an empirical measure based on relative productivity

The match quality between a worker\u27s field of study in college and her occupation is an important labor market outcome. Yet this match quality is difficult to define and measure. We propose a new measure of major-occupation match quality based on relative productivity. A worker is well-matched if graduates from her major, working in her occupation, have high earnings relative to other major-occupation pairs. We show that some majors can be very well-matched or very badly matched (e.g. nursing), while others are never very well- or badly matched (e.g. humanities). Our measure has two desirable features: it is continuous, and it can be estimated in any data set including field of study, wage, and occupation