It has been claimed that gender differences in academic fields results from stereotypes about how difficult the fields are (Leslie, 2015). This is based on the finding that percetions of brilliance requirement of different fields correlated strongly with the gender distribution. It apparently did not dawn on the people making this claim that the stereotypes were probably very accurate: people who study physics really are smarter than those who study psychology. Indeed, that gender differences related to actual difficulties of fields, as measured by mean cognitive/scholastic scores by those studying them, was shown by Scott Alexander shortly after the study was published and Randy Olson a year before it was published. However, it was also shown in 2002, 13 years before ‘Perceptions of Brilliance’ in a not well known paper by Templer and Tomeo from 2002.

They collected combined GRE scores for 46 fields as well the the percent Male for the graduates of PHD programs. The data looks like this:

##                                      Program Mean Percent.male
## 1                      Physics and Astronomy 1903        87.59
## 2                       Material Engineering 1840        83.29
## 3                                Mathematics 1835        78.13
## 4                       Chemical Engineering 1820        84.44
## 5                                 Philosophy 1811        72.09
## 6                                  Economics 1795        75.95
## 7                          Other Engineering 1786        89.70
## 8                                  Chemistry 1764        71.87
## 9        Computer and Information Technology 1762        84.57
## 10                    Mechanical Engineering 1762        93.01
## 11     Electrical and Electronic Engineering 1760        90.54
## 12                         Civil Engineering 1718        89.40
## 13              Earth, Atmosphere and Marine 1704        75.43
## 14                        Biological Science 1688        59.33
## 15                    Industrial Engineering 1676        84.98
## 16                 Natural Science and Other 1666        59.56
## 17               English Language Literature 1665        35.53
## 18                          Bank and Linance 1662        82.81
## 19                                   History 1659        62.77
## 20              Anthropology and Archaeology 1654        46.29
## 21                         Political Science 1645        71.10
## 22      Arts — History, Theory and Criticism 1638        39.01
## 23                       Secondary Education 1623        37.50
## 24     Architecture and Environmental Design 1622        80.00
## 25                               Agriculture 1619        76.84
## 26               Foreign Language Literature 1612        29.94
## 27              Library and Archival Science 1574        31.11
## 28             Arts — Performance and Studio 1563        55.50
## 29                Curriculum and Instruction 1545        32.65
## 30                                Psychology 1536        37.78
## 31                                 Sociology 1523        49.25
## 32                Health and Medical Science 1509        41.48
## 33         Education Evaluation and Research 1505        28.57
## 34                            Communications 1505        51.04
## 35    Business Administration and Management 1480        74.93
## 36                      Other Social Science 1477        54.43
## 37                      Elementary Education 1475        20.88
## 38                                Accounting 1466        53.97
## 39                         Education — Other 1460        34.58
## 40                     Public Administration 1443        66.22
## 41                  Education Administration 1430        45.18
## 42                         Special Education 1410        17.53
## 43 Student Counseling and Personnel Services 1405        35.44
## 44                            Home Economics 1406        25.76
## 45                               Social Work 1385        30.50
## 46                 Early Childhood Education 1376         3.23

Plotting the two variables:

GG_scatter(d, "Mean", "Percent.male", case_names_vector = d$Program) +
  xlab("Mean GRE score (V+Q)") + ylab("Percent male PHD graduates") +
  scale_y_continuous(limits = c(0, 100)) + scale_x_continuous(limits = c(1300, 1950))

As might be expected, these findings are related to the median income of the academic majors too.