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The Gender Math Gap and its Effects on Earnings

by Alessia Leibert
October 2022

Minnesota ranks fourth lowest in the nation for percentage of women in poverty1, and 12th highest for the percent of women with a Bachelor's degree or higher credential. Despite these accomplishments, female workers in Minnesota experience lower earnings compared to their male peers.

This study analyzes the role of math proficiency in contributing to gender pay gaps in Minnesota. Specifically, it compares female high school graduates to their male peers with the aim of identifying (1) if a gender math gap exists, (2) how the math gap influences educational choices in secondary and postsecondary school, (3) how the math gap contributes to gender wage disparities years after high school, and (4) implications of the findings.

The dataset consists of 142,909 individuals who graduated from a Minnesota public high school between June 2013 and June 2015. Since we know their high school experiences and labor market histories, we can identify the earliest points in life when gender wage gaps emerge and formulate hypotheses on how math proficiency contributes to their formation. This evidence can help to craft policy responses to address the problem.

Is There a Math Gap by Gender?

Standardized scores in the MCA2 math test administered in eighth grade show that a bigger share of girls scored below the median than boys, 51% versus 48% (Figure 1).

Figure 1

MCA math test score percentiles by gender

The gap in the same group of students is even larger in eleventh grade, with 51% of girls scoring below the median to 43% of boys, and 21% scoring above the seventy-fifth percentile versus 28% among boys3. This indicates a deterioration in math skills among girls relative to boys4.

Why does this gap matter? Scoring above the median in eighth grade math increases the likelihood of entering accelerated math paths, allowing students to take AP Statistics & Trigonometry and Calculus. Students who scored below the median in math missed out on these opportunities: 2% took Statistics & Trigonometry and only 1% took Calculus. Overall, boys are more likely to enroll in calculus than girls5.

The gender math gap is also evident in the subgroup of students who took the ACT exam6. ACT scores show that girls do well in reading but underperform boys in math and science, with a wider gap in math (Table 1).

Table 1 - ACT Scores by Subject and Gender

Gender Number of test-takers Avg. Science score Avg. Math score Avg. Reading score Avg. ACT composite score
Female 58,284 22.44 22.56 23.21 22.78
Male 47,995 23.81 24.07 23.27 23.40
Total in sample who took the ACT* 106,279 23.06 23.241 23.24 22.82
*This figure is lower than the original sample size because ACT scores were not reported for 22% of students.

These math trends continue in more recent cohorts7 and lend further evidence to the existence of a gender math gap among Minnesota high school graduates.

Math Gaps, Postsecondary Major, and Gender Pay Gaps

Just as eighth grade math scores influence high school course-taking patterns, ACT math scores influence the choice of postsecondary major. Table 2 documents gender imbalances by major as well as the relationship between major and average ACT math scores among completers of a Bachelor's degree. The table also displays earnings outcomes six years after high school in the Twin Cities Metro Area.

Majors and ACT match scores for Bachelor's Degree completers employed in Minnesota 6 years after high school, by gender

The main takeaways from this analysis are:

  • The overall average ACT math score among females was 23.5 versus 25.5 among males, a two point difference;
  • Average math scores vary considerably by major, suggesting that college readiness in math was more critical to success in some majors than others. STEM fields - where females are severely under-represented, with the only exception of Biology- had the highest average ACT math scores. In contrast, female-dominated majors had lower than average ACT math scores, such as Education (accounting for 11% of females versus of 4% of males), Psychology (10% females versus 3% of males), Journalism/media/communications (11% females versus 6% males), and Healthcare except RN (5% females versus 1% males);
  • The overall earnings ratio between females and males in the Twin Cities Metro for the selected cohorts was 89%. This means that, even after controlling for education level and region of employment, females earned 89% of the wage earned by their male peers. This gap shrinks significantly, and often disappears entirely, when results are broken down by major. Differences in math proficiency might have contributed to steering females away from majors requiring higher math skills, resulting in the gender math gap taking a big bite out of women's earnings;
  • Registered Nursing graduates had surprisingly high average ACT scores for a female-dominated field, probably because a strong math foundation is needed for admission to and completion of four-year RN programs. Math preparedness is more important to success in healthcare careers 8 than girls might realize;
  • Females are fairly equally represented in science-related STEM majors like Biology and Physical Sciences. Recall from Table 1 that ACT scores in science among females were not as far apart from males' as were math scores, signaling a stronger interest for the "S" in STEM among females.

This evidence shows that equalizing choice of postsecondary major might be unattainable without first equalizing math proficiency in 11th grade, which in turn stems from math proficiency in 8th grade. Waiting until 12th grade to try to equalize choice of postsecondary major is too late because paths toward career preparation begin to diverge by gender much sooner.

Math Gaps and Earnings by Region

Figure 2 shows that students employed in the Twin Cities Metro who scored at or above the median in 11th grade math, which we label "high-achieving" for simplicity (the solid blue and green lines) had higher earnings six years after high school than low-achieving students. The uptick in wage trajectories in the solid lines four years after graduation marks the point at which high-achieving students typically complete a postsecondary credential and enter better paying jobs than those held by low-achieving students, who are least likely to complete a credential.

Figure 2

Growth in Hourly Wages by Gender and Match Scores in 11th Grade

Math achievement had a similar impact on earnings between males and females, as shown by nearly parallel earnings trajectories leading to an almost identical wage gap after 6 years; high-achieving females earned 95% of the wage of their male counterparts while low-achieving females earned 94% of the wage of their male counterparts.

It is tempting to conclude from all of this evidence that math preparedness in 11th grade is critical to students' earnings. However, the math uber alles claim has some limits. It fails to explain, among other things, why males and females with lower-than-median math skills have such different earnings outcomes in Greater Minnesota (Figure 3).

Figure 3

Growth in hourly wages by gender and MCA math scores in 11th grade

Males scoring below the median (blue dotted line) experienced rapid wage growth immediately after high school and ended up with a large wage advantage over their female peers (green dotted line): $18.74 versus $16.10. This corresponds to a ratio of 86% between the earnings of females and those of males. Given that $18.85 is the family-sustaining wage9, it is clear that the transition from high school to career is far less successful for females with low math achievement. This result is concerning also because low-achieving females represent the majority - 59% - of females in Figure 3.

The high earnings of males with lower-than-median math skills are driven by students who took more than 100 hours of Career & Technical (CTE) courses in the skilled trades10, representing 40% of the group. CTE appears to have given these students marketable skills or work-based experiences that helped them overcome their early math gaps or compensate for them.

How Does the Math Gap Contribute to Gender Wage Disparities After Accounting for Other Characteristics?

Could the gaps illustrated in Figures 2 and 3 stem from differences in what men and women study in high school or in college? Or might they stem from differences in how men and women participate in the labor market after finishing their education? To answer these questions this analysis uses data on some of the most important factors known to influence job productivity and, therefore, earnings. The analysis distinguishes between factors that are fixed such as age or that emerge before entering the labor market (Table 3) and factors that develop through participation in the labor market (Table 4). For reasons of length, the tables display only variables with a statistically significant effect on earnings.

Table 3

Gender differences in demographics and educational characteristics among individuals employed in Minnesota 6 years after high school graduation, Cohorts 2013-2015, N= 101,120
- Number Female Male
Total 101,120 52,973 48,147
Percent - 52% 48%
White 79,902 52% 48%
Not white 21,218 54% 46%
Eligible for free or reduced-price lunch (1) 11,812 53% 47%
Ever spoken a language different from English at home 36,572 55% 45%
Ever attended an Area Learning Center, Alternative Learning Program, or Vocational Cooperative (2) 28,800 51% 49%
Location of high school and work
Attended high school in the Twin Cities Metro 54,829 53% 47%
Attended high school in Greater Minnesota 46,291 52% 48%
Employed in the Twin Cities Metro in Year 6 63,163 53% 47%
Employed in Greater Minnesota in Year 6 37,957 52% 49%
MCA reading and math proficiency
Scored at or above the median in 8th grade MCA reading 45,807 56% 44%
Scored at or above the median in 8th grade MCA math 44,520 50% 50%
Scored at or above the median in 11th grade MCA math 48,847 48% 52%
Advanced Placement and other rigorous course-taking in high school
Took any Advanced Placement (AP) exams 31,569 56% 45%
Took AP Statistics and Trigonometry 6,655 49% 51%
Passed AP calculus exam 6128 43% 57%
Took any early-college courses (CE or PSEO) (3) 33,580 60% 40%
Career & Technical Education (CTE) courses in high school, selected measures
Took CTE with a work-based learning component (e.g., internships) 3,524 51% 49%
Took any CTE courses related to Construction, Repair (e.g., HVAC), Engineering Technologies, and Precision Production (e.g., welding) 24,100 19% 81%
Took >=150 CTE hours in Business, except non-math related fields (4) 18,944 47% 53%
Took >=150 CTE hours in Fashion, Interior Design (FAID) and Photography 6495 73% 28%
Took >=150 CTE hours in Family and Consumer Sciences (FACS), including Early Childhood Education 5,735 72% 28%
Took >=150 CTE hours in Culinary Arts, Cosmetology, and Hospitality 4,979 79% 21%
Took >=150 CTE hours in Health Care 3,677 79% 21%
Postsecondary outcomes 7 years after high school
Never enrolled 10,303 37% 63%
Enrolled and left postsecondary school without a credential 28,492 46% 54%
Sub-baccalaureate certificate or Associate's degree 15,646 55% 45%
Bachelor's or above 44,438 59% 42%
Enrolled without completing and still in college 2,241 63% 37%
(1) This is an indicator of socio-economic status.
(2) Alternative Schools are choice-based programs whose mission is to serve students who have not found success in traditional schools.
(3) Concurrent Enrollment (CE) and Post-Secondary Enrollment Options (PSEO) allow students to earn college credits while enrolled in courses that are not part of their high school curriculum.
(4) Excluded categories are secretarial services, business communications, and hospitality management, which are among the least math-related business subjects.

While certain characteristics are equally distributed among employed men and women in the dataset, others are not. Percentages of females higher than 52% (the overall share of females in the sample) signal that a certain characteristic was more prevalent among females. The characteristics with the greatest gender imbalance were the following:

  • MCA reading scores in 8th grade, with females being over-represented among students scoring at or above the median relative to their presence in the sample (56% compared to 52% females in the sample);
  • MCA math scores in 11th grade, with females being under-represented among scoring at or above the median relative to their presence in the sample (48% compared to 52% females in the sample);
  • Participation in AP exams, with females being over-represented (56%);
  • Passing the AP calculus exam, with females being very under-represented (43%);
  • Participation in early-college courses such as Concurrent Enrollment and Postsecondary Enrollment Options (PSEO), with females being extremely over-represented (60%);
  • Participation in CTE courses related to the trades - that is, Construction, Repair, Maintenance, Precision Production, and Engineering Technologies - with females being extremely under-represented (19%);
  • Participation in CTE courses related to business, with females being under-represented (47%); these courses often teach hands-on math, such as accounting and finance;
  • Participation in CTE courses related to FAID, FACS, Culinary Arts/Cosmetology/Hospitality with females being extremely over-represented (72% and above);
  • Formal schooling, with females being over-represented among completers of a sub-baccalaureate credential (55%) and even more over-represented among completers of a bachelor or higher credential (59%).

These results are further demonstration of how math proficiency, interest in math, or both can shape course choices. While girls don't shy away from taking academically challenging high school coursework such as AP, they were under-represented in the two AP courses focused on math. CTE course-taking patterns also suggest that males were more likely to participate in CTE courses that could have boosted their math skills. A stated goal of CTE is to embed STEM content in the curriculum to give students opportunities to meet math and science credits as an alternative to regular STEM courses, through hands-on learning experiences11. CTE courses in the trades, for example, include STEM content such as blueprint reading, drafting/CAD, trigonometry, computer programming, physics and production technologies. Previous studies12 have shown that manufacturing employers desperately seek candidates with these skills and often recruit directly from high school CTE programs in the skilled trades.

What about factors that emerge after entering the labor market? The biggest gender differences in workplace characteristics are in industry of employment (Table 4). The industries with the closest gender balance are Professional and Technical Services and Firm Headquarters.

Table 4

Gender differences in selected industry sectors of employment 6 years after high school graduation Cohorts 2013-2015
- Number Female Male
Total 101,120 52% 48%
Accommodation and Food Services 9,314 57% 43%
Agriculture, Mining, Utilities 950 22% 78%
Construction 5,937 11% 89%
Education 6,681 72% 29%
Firm Headquarters 2,948 52% 48%
Government 2,690 47% 53%
Hospitals and Clinics 10,156 83% 17%
Manufacturing 8,149 29% 71%
Nursing and Residential Care Facilities 4,793 81% 19%
Retail, Technical Products and Services* 2,545 26% 74%
Retail, All Other 9,643 54% 46%
Professional and Technical Services 6,789 50% 50%
Social Assistance 4,303 82% 18%
Transportation and Warehousing 2,665 25% 75%
Wholesale 3,678 29% 71%
*Representing Automotive/Motor Vehicle Dealers and Building Material Dealers. These retailers have higher skill requirements than other types of retail businesses where females are over-represented.

Mining, Utilities, Construction, Manufacturing, and Wholesale are among the most male-dominated industries in Minnesota. These industries tend to offer union wages, more job stability, and also on-the-job training13.

What part of the gender pay gap is attributable to math skills? Estimation results by region

Our last research question attempts to quantify the association between earnings outcomes and student characteristics in order to identify how much of the gender pay gap still remains after accounting for productivity-related factors, and what part of the gap is attributable to math skills. We use a linear regression technique of the form suggested by Mincer14 to measure the combined effect of all characteristics and identify differences in how each factor contributes to gender pay gaps in the Twin Cities Metro relative to Greater Minnesota. We ran nine regression models for each region using the same variables displayed in Table 3 and 4. Selected results for the Metro regressions are reported in Appendix.

Before interpreting the results, we need to mention that regression is a correlational analysis method and thus does not prove causal effects. In other words, finding that one characteristic is related to an "outcome" variable (e.g., that education level is related to earnings) does not mean that the characteristic is the cause of the outcome.

Figure 4 reports the results from 18 regression models. Each bar represents the effect of being female on earnings, holding other characteristics constant. Therefore each bar represents the gender wage gap, which is always negative because females earn less than males in every model. The length of the bars is measured in logs, which can be interpreted as percentages. The purpose of this analysis is to observe how the effect of being female varies as more information is added to the model, in order to isolate the contribution of each factor net of the characteristics controlled for in the previous steps. Whenever possible, variables were added to the model in chronological order. For example, AP and CTE coursework was added to the model before Concurrent Enrollment and PSEO, because students can take AP and CTE courses from ninth grade while CE and PSEO courses are available almost exclusively in eleventh and twelfth grade.

Figure 4

Gender Wage Gaps in the Twin Cities Metro versus Greater Minnesota

The initial raw gap (Model 1) is higher in Greater Minnesota at -8.8% compared to -6.7% in the Metro. That is, women working in Greater Minnesota were paid around 8.8% less on average than men. Model 2 adjusts the baseline estimate of the wage gap by taking into account the demographics and school characteristics listed on top of Table 3 with a few additional variables15. Accounting for these characteristics leaves the gender gap unchanged (size of the blue bar) because these characteristics are equally distributed across genders.

Model 3 adds controls for math achievement levels (measured in quartiles) in 8th and 11th grade, our metrics of interest. This has the effect of narrowing the gender wage gap in both regions (see dark grey bars), meaning that if females had the same math achievement level as males, gaps would be narrower. This result delivers the proof of an impact of math achievement on the gender wage gap. The effect is larger in the Metro, where the bar shrinks from -6.5% to -5.5% (see regression results in Appendix). This result makes sense because the Metro has a much higher concentration of STEM and STEM-related jobs than the rest of the state. MCA reading levels in 8th and 10th grade were also added to the model. Only 8th grade reading reached statistical significance, but the sign of the coefficient is negative, and its statistical significance disappears when other variables were added to the model (see Appendix). This does not mean that reading skills are not important, but that math scores are a better predictor of future earnings than reading scores.

Model 4 adds variables which control for participation in AP courses. The only AP courses that reach statistical significance are related to math: taking AP Statistics and Trigonometry, taking AP Calculus, and passing the AP Calculus exam16. This again confirms that math knowledge matters to future earnings.

Model 5 adds controls for participation in Career and Technical Education (CTE) courses during high school, including intensity of participation (as concentrators or not), participation in work-based learning such as internships, as well as detailed variables representing 16 fields of study where students took the most hours of CTE instruction. The results (purple bars) provide a big surprise: the gender pay gap decreases starkly. The biggest reduction in the gap is achieved when we add a variable representing whether a student took more than 100 hours of instruction in the skilled trades including Construction, Repair, Engineering/Engineering technologies, and Precision Production.

This result demonstrates two important points. First, CTE course-taking is a meaningful indicator of future career and/or educational choices, which ultimately affect earnings. Second, these choices impact the gender wage gap indicating that CTE gave male students more opportunities to prepare for gainful employment than females. In other words, gender differences in the way students sort into CTE courses favored males regardless of other characteristics controlled for in the model, including math proficiency. This means the most male-dominated CTE pathways boost earnings for students, while the most female-dominated CTE pathways do not. In our models only one female-dominated CTE pathway, healthcare, led to positive effects on earnings.

An additional implication of this result is that male dominance in well-paid jobs not requiring a college degree drives gender wage gaps more than math skills. This dominance begins in high school with boys taking CTE courses in the skilled trades, offering an alternative to college for students with lower-than-median math skills. This explains the results from Figure 3: females are more penalized than males in the Greater Minnesota labor market when their math achievement is low because they are excluded – or steer away from – opportunities for career preparation in the skilled trades.

We could conclude from Model 5 that if CTE course choices were equalized by gender the gender wage gap would be cut by almost half. This goal is immensely difficult to achieve because males' dominance of skilled trades jobs is rooted in social norms and in job requirements often based on physical strength and stamina. A more realistic policy goal would be to mitigate the gender divide in skilled trades CTE.

Model 6 adds controls for early-college courses such as Concurrent Enrollment and PSEO. Adding these variables increases the gender wage gap (see yellow bars) because girls are more likely to participate in these courses in hopes of completing a postsecondary credential faster and with less cost,17 and postsecondary educational attainment has the effect of increasing the gender wage gap.

Model 7 adds controls for postsecondary credential attainment. These variables increase the gender wage gap because females - as shown in Table 3 - hold higher levels of postsecondary education than males. In other words, if males had the same education level as females, wage gaps would be even higher.

Model 8 adds information on postsecondary major of completion or enrollment. Examples of majors with low returns are Psychology, Family and Consumer Sciences (including Early Childhood Education), and Social Work (see regression results in Appendix). In general, college majors and jobs that emphasize service to others are undervalued in labor markets, and they tend to be female-dominated. Choice of major drives earnings for both men and women but has a dramatically different effect on the gender pay gap in the two regions. The female coefficient (light green bar) in the Metro shrinks by a half, from -5.4% to -2.7% (as we expected given the results from Table 3), while the drop in Greater Minnesota is smaller. What seems to be driving gender wage gaps in the Metro is the fact that men disproportionately earn degrees in fields that are well compensated, while in Greater Minnesota it is more common for women to earn less than men even within the same major.

The final model, Model 9, adds information on 26 industry sectors of employment, producing a dramatic reduction in the gap in both regions. The darker green bar in Greater Minnesota shrinks from 6.6% to 2.9%, while in the Metro the gap is entirely wiped out, dropping to 0.2% and losing statistical significance. This means that, holding all other variables constant, gender segregation by industry is responsible for more than half of the gender pay gap in Greater Minnesota and all of the residual gap in the Metro.

The distinction between major and industry is an important one. While major is typically a voluntary choice, industry of employment is the result of job sorting mechanisms in the labor market that individual job seekers have less control over. Finding an association between industry and gender pay gaps, while simultaneously holding major constant, signals that women are less successful than men at entering jobs where their educational investments are fully rewarded. This is a problem of equal opportunities.

Why does the gender pay gap disappear in the Metro but not in Greater Minnesota? This result suggests that some important productivity characteristics related to gender and specific to Greater Minnesota were omitted from the models, or that gender discrimination in the Greater Minnesota labor market is driving up the wage gap. One likely omitted variable is occupation. For example, Construction firms in Greater Minnesota hire relatively more highway maintenance workers and earth drillers than construction firms in the Metro and might hire more males due to the physically strenuous and hazardous nature of these jobs. In contrast, the Metro has more knowledge-economy jobs, which are more gender-neutral and do not give males a productivity advantage.

The result that matters the most to our study is that the variables representing math scores in 8th and 11th grade are statistically significant in all models. This gives strength to the claim that gaps in math skills before and during high school influence post-high school earnings18. The R squared of Model 9 is .324 in Greater Minnesota and .323 in the Metro, meaning that observed characteristics were able to explain 32% (or one-third) of wage variation.

An important caveat to these results is that students' math achievement is also likely associated with characteristics not measured in this study, such as family support of education and other courses taken in high school. Future research should address these limitations by controlling for a broader range of courses taken in high school and by using estimation methods that can correct for omitted variable bias.

Conclusions and Policy Implications

This study documents the gender math gap and examines whether it translates into differences in post-high school earnings outcomes between males and females. The main findings are summarized below:

  • A gender math gap exists in 8th grade and widens in 11th grade;
  • Math proficiency in 8th grade is closely related to students' high school and postsecondary course selection, such as the likelihood of taking math-related AP courses and enrolling in a STEM postsecondary major;
  • The large gender imbalance in choice of AP and Career and Technical Education (CTE) courses suggests that lower readiness in math, or negative attitudes towards math, might have pushed females towards careers and postsecondary majors with lower labor market value than those chosen by males. Therefore, waiting until 12th grade to try to equalize choice of postsecondary major is too late, because paths toward career preparation begin to diverge by gender much sooner;
  • College-readiness in math, measured by ACT math scores, influences the likelihood of completing a Bachelor's in high-paying fields such as STEM and Registered Nursing;
  • Males, thanks primarily to CTE programs in the skilled trades, come very close to obtaining a family-sustaining wage by age 24 even when they score below the median in 11th grade math and even without attaining a postsecondary credential. In contrast, females have to rely more heavily on postsecondary education19, which often hinges upon college-readiness in math;
  • Math scores in 8th and 11th grade are a strong predictor of future earnings and contribute to the gender wage gap. However, other factors have an even greater impact on the gap. The biggest contributors to the gender wage gap in Greater Minnesota are gender differences in CTE course-taking and industry segregation by gender, while the biggest contributors in the Metro are gender differences in postsecondary major and industry segregation by gender;
  • The gender pay gap in the Metro is entirely eliminated when we control for productivity characteristics, while in Greater Minnesota 2.8% of the gap remains unexplained. This result suggests that the Metro labor market is more favorable to working women than the Greater Minnesota labor market.

These findings have strong equity implications. Since the transition from high school to career is harder for females, in part because they are less likely to access job skills training in high school and instead wait until postsecondary school, they are more likely to take on a financial burden. Every time a category of workers has to shoulder the cost of skills training more than another category (e.g., females versus males) inequalities will inevitably emerge in the labor market. A category can be defined by gender, race, age, disability, or any other characteristic.

What are the policy implications of these findings? The 9 models depicted in Figure 4 help pinpoint the critical factors that contribute the most to the formation of gender wage gaps. This information can be used to formulate policies to address gender wage gaps at the time in life when these factors emerge. Here is a summary of factors and their policy implications:

  • Factor 1: Gender math gaps (Model 3). Since math gaps feed into the overall gender pay gap, policies aimed at improving math skills among girls in middle and high school to catch up with boys could be valuable;
  • Factor 2: Males' dominance in high-value CTE courses (Model 5). Breaking this dominance requires rebalancing the gender composition of CTE programs. Achieving this goal requires multiple interventions. First, improving career counseling services to promote skilled trades CTE programs to female students and their parents. Second, improving the learning environment for girls, for example by creating girls-only welding classrooms to help girls imagine themselves in these careers and boost their self-confidence. Third, expand CTE curriculum towards emerging careers, outside the traditional skilled trades, that don't require a four-year degree and are gender-neutral. Examples include robotics technologies, cybersecurity, artificial intelligence, data science, or supply chain management. Such updated curricula would benefit all students and improve the alignment between CTE and the skills sought by employers. Although a good foundation in math is helpful to succeed in these courses, it should not be a requirement. Instead, the curriculum should be designed to give students with diverse math abilities equal opportunities for STEM-related contextual learning20;
  • Factor 3: Females have to rely on postsecondary school to match the earnings of males because they have fewer direct paths from high school to financially viable careers (Model 6 and 7). This is a consequence of systemic factors, such as social norms pushing girls towards careers requiring college and low wages in female-dominated industries. To serve the needs of girls better, CTE should invest more in secondary-to-postsecondary educational pathways and dual enrollment opportunities;
  • Factor 4: Over-representation of males in high-pay postsecondary majors (Model 8). Fixing the gender math gap can help rebalance the gender composition of postsecondary majors only if accompanied by other policies or initiatives to help girls improve their expectations of success in these careers, challenging the stereotypical gender roles that push them towards low-wage occupations and industries. Awarding scholarships or partial tuition reimbursement to females who pursue high-demand, non-traditional majors would also contribute to rebalance the gender composition of these programs and females' representation in related occupations;
  • Factor 5: Over-representation of males in high-pay industries (Model 9). This factor, larger than any other, lies squarely in the responsibility of employers. Policies aimed at strengthening the enforcement of equal pay laws are essential to address this issue, as well as initiatives and support aimed at helping employers identify and remove gender bias in hiring and promotion.

Although equalizing math preparedness may seem like a drop in the bucket compared to the daunting systemic forces that produce gender pay gaps, it would improve women's chances to prepare for gainful employment while in high school or completing a credential in financially viable fields. Therefore, it deserves to be part of a workforce equity agenda.

About the data

This research uses data on students who graduated from a Minnesota public high school merged with postsecondary student records and Minnesota Unemployment Insurance wage records from the Statewide Longitudinal Education Data System (SLEDS). All students who graduated from high school between 2013 and 2015 are included except GED completers, individuals who graduated after age 20, and students who received special education services in 11th and 12th grade. Finally, since the study's main goal is to measure the role of course-taking behavior on outcomes, we only included students who had a record in each grade (9th, 10th, 11th, and 12th) to avoid bias in the measurement of course-taking patterns.

Appendix: OLS Regressions Controlling for Different Sets of Explanatory Variables

Dependent Variable: Log of Real Hourly Wages 6 Years After High School Graduation, Twin Cities Metro. N= 62,815 Model 2: Gender & other demographics Model 3: 8th/11th grade math Model 4: Statistics & Calculus Model 5: CTE courses Model 6/7: PSEO, CE & PS education level Model 8: PS Major Model 9: Industry
Gender Female -0.0647*** -0.0550*** -0.0542*** -0.0366*** -0.0536*** -0.0271*** -0.0024
MCA score quartiles in 8th/11th grade (reference: first quartiles) 25th-49th percentile in 8th grade math (second quartile) - 0.0162*** 0.0173*** 0.01718*** 0.0126* 0.0119** 0.0111**
50th-75th pctile in 8th grade math - 0.0216*** 0.0214*** 0.0219*** 0.0130*** 0.0112* 0.0118**
>75th pctile in 8th grade math - 0.0521*** 0.0360*** 0.0375*** 0.0264*** 0.0223*** 0.0238***
25-49th pctile in 11th grade math - 0.0226*** 0.0234*** 0.0238*** 0.0097*** 0.0116** 0.0079*
50-75th pctile in 11th grade math - 0.0491*** 0.0463*** 0.0480*** 0.0196*** 0.0179*** 0.0127**
>75th pctile in 11th grade math - 0.0960*** 0.0726*** 0.0772*** 0.0394*** 0.0323*** 0.0246***
>=50th pctile (median and above) in 8th grade reading - -0.0097** -0.0124*** -0.0076* -0.0157*** -0.0070* -0.0038
AP Took AP Statistics and Trig. - - 0.0416*** 0.0436*** 0.0369*** 0.0284*** 0.0243***
Passed AP Calculus exam - - 0.0547*** 0.0546*** 0.0572*** 0.0415*** 0.0114*
CTE courses Took CTE with work-based learning - - - 0.0393*** 0.0402*** 0.0391*** 0.0332***
Took >=100 hours in skilled trades - - - 0.0556*** 0.0631*** 0.0512*** 0.0301***
Took >=100 hours in auto-repair - - - 0.0390*** 0.0621*** 0.0503*** 0.0463***
Took >=100 hours in healthcare - - - 0.0491*** 0.0597*** 0.0383*** 0.0314***
CE & PSEO Took PSEO courses - - - - 0.0627*** 0.0555*** 0.0479***
Took Concurrent Enrollment - - - - 0.0137*** 0.0139*** 0.0120***
Postsecondary education level (reference: not enrolled) Enrolled and dropped out within 6 years from high school exit - - - - -0.0389*** -0.0593*** -0.0219***
completed a credential below Bachelor's - - - - 0.0407*** -0.0208*** -0.0249***
Completed a BA or graduate certif. - - - - 0.1046*** 0.0926*** 0.0687***
Completed a Master's degree - - - - 0.0764*** 0.0653*** 0.0719***
Did not complete and still in school - - - - -0.0693*** -0.0919*** -0.0534***
Major of enrollment or completion (reference: not enrolled or no major) Registered Nursing - - - - - 0.3412*** 0.3305***
IT - - - - - 0.1550*** 0.1212***
Engineering/Engineering technology - - - - - 0.1635*** 0.1094***
Family and consumer sciences - - - - - -0.0934*** -0.0274***
Journalism, media and communications - - - - - -0.0384*** -0.0319***
Psychology - - - - - -0.1293*** -0.0925***
Construction - - - - - 0.0547*** 0.0547***
Repair and transportation - - - - - 0.1296*** 0.1296***
Precision production - - - - - 0.0598*** 0.0598***
Industry (reference: retail) Agriculture, Mining, and Utilities - - - - - - 0.2818***
Construction - - - - - - 0.4039***
Advanced manufacturing - - - - - - 0.2154***
Wholesale - - - - - - 0.2382***
Nursing and residential care fac. - - - - - - 0.0351***
Social assistance - - - - - - -0.0238***
- Fixed effects (cohort, race, disability status, high school characteristics) Yes Yes Yes Yes Yes Yes Yes
- Adjusted R square 0.080 0.093 0.098 0.105 0.137 0.216 0.323
***Coefficients are statistically significant at the p <=0.001 level. ** Statistically significant at the <=0.01 level. *Statistically significant at the <=0.05 level.

1Source: WalletHub 2021

2Minnesota Comprehensive Assessment (MCA). Given that a new baseline for 8th grade math was set in 2011, we used the median and 75th percentile in each cohort to determine the three categories displayed in Figure 1.

3The categories displayed in Figure 1 were calculated based on quartile distributions of MCA scores in 8th and 11th grade by graduation year cohort included in this study. See "About the data" for detailed information on sample inclusion criteria.

4If, instead of score quartiles, we had used MDE's proficiency categories (does not meet proficiency, meets proficiency, exceeds proficiency) the widening of the math gap between boys and girls in 11th grade would have been even more pronounced.

5 In our dataset, 13% of boys took the AP Calculus exam versus 10% of girls.

6TheACT test is the most widely administered college entrance exam in the U.S.


8Although some experts treat all healthcare occupations and corresponding training programs as STEM, this study does not.

9According to the cost of living tool, a family-sustaining wage in Minnesota in 2020 was $18.85. This benchmark is based on the most typical family structure in Minnesota of one fulltime worker, one part-time worker, and one child.

10As documented in this study, males who participated in CTE as concentrators and focused in the trades (Construction, Repair, Precision Production, Engineering/Engineering Technologies) had higher wages compared to other students regardless of differences in postsecondary education level.

11CTE courses may provide an opportunity for 9th-12th grade students to develop a deeper understanding of mathematics and science through hands-on learning. CTE programs are offered in 81% of Minnesota high schools.

12Source: 2019 Minnesota Hiring Difficulties survey, Table 2 in

13Students who participated in CTE in the skilled trades – and were overwhelmingly male – were working in these industries the year after high school graduation at significantly higher rates than other students (23% versus 15%) regardless of their 11th grade math achievement. This contributes to the excellent wage outcomes of males with low math achievement in Figure 3.

14See Jacob Mincer, Schooling, Experience, and Earnings, Columbia University Press, 1974.

15Additional variables include race; disability status and disability type; graduation cohort; 8th grade attendance; English Learner status; having graduated from a charter or International Baccalaureate school; having ever attended an online school; Planning Region of schooling and of employment.

16Some students register for the AP calculus exam but obtain a grade lower than 3, which means failing. Gender disparities in Calculus course enrollment improved over time but still persist. In 2021, 51.2% of males took Calculus (while males represented 50.7% of total enrollment) versus 48.8% of females (while females represented 49.2% of total enrollment). Source:

17If we could control for CE and PSEO course subjects we would likely see no change, or even a reduction, in the gender wage gap.

18To clarify this result, we added to the model an interaction between gender and a dummy representing whether students scored at or above the median in 11th grade math. The interaction is positive and significant in Greater Minnesota, meaning that the wage gap is smaller when women are proficient in math, and math achievement gives a greater boost to the earnings of females than of males. This is the most complete explanation of gender wage gaps shown in Figure 3: males with low math achievement were able to find well-paying jobs out of high school even controlling for other variables. This suggests that math achievement matters more to the earnings of females in the Greater Minnesota labor market.

19As demonstrated in a previous study, none of the pathways pursued by female CTE concentrators led to family-sustaining wages without a postsecondary credential. See Table 4 in Leibert, Females in Career and Technical Education, MN Economic Trends March 2022

20Embedding STEM content across its curriculum is already a stated goal of CTE, but historically it has been implemented unevenly across female-dominated and male-dominated course offerings.

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