Nature | News Feature

Inequality quantified: Mind the gender gap

Despite improvements, female scientists continue to face discrimination, unequal pay and funding disparities.

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INTERACTIVE: Science's gender gap

Female scientists have made steady gains in recent decades but they face persistent career challenges. US universities and colleges employ far more male scientists than female ones and men earn significantly more in science occupations.

Gender breakdown by field of study for US scientists and engineers with PhDs employed in academia

  • Male

  • Female

Adjust scale:

Data source: National Science Foundation http://www.nsf.gov/statistics/seind12/append/c5/at05-17.pdf

Median annual salary of US scientists and engineers employed full time in 2008

Data source: National Science Foundation Table 9-16 http://www.nsf.gov/statistics/wmpd/2013/tables.cfm

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US science, engineering and health doctorate holders employed in all positions in academia, by sex and degree field 1973–2008 (thousands).
Gender and field 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2006 2008
Both sexes, all fields 118.0 134.1 145.5 155.3 167.1 176.1 190.2 195.9 206.6 210.6 213.8 217.5 232.5 240.2 245.5 259.5 272.8 272.8
Both sexes, Physical sciences 25.5 27.5 29.2 28.8 30.0 29.9 32.2 32.8 33.6 33.7 35.0 35.7 37.5 38.7 38.6 39.9 39.6 39.3
Both sexes, Mathematics 9.7 11.0 11.7 12.2 12.4 12.9 13.6 13.8 14.5 15.2 15.5 14.6 15.6 15.2 14.9 16.7 17.4 17.4
Both sexes, Computer sciences 0 0 0 0.1 0.3 0.5 0.8 1.1 1.5 2.0 2.5 3.1 3.3 3.7 3.8 5.2 5.8 6.9
Both sexes, Life sciences 34.9 39.4 42.6 47.0 51.3 54.9 58.7 61.3 64.8 66.9 68.2 71.6 77.3 81.9 84.3 90.0 95.5 94.0
Both sexes, Psychology 12.2 14.8 16.2 17.7 20.1 21.0 23.1 23.7 25.0 25.2 25.0 26.1 27.3 29.0 30.4 31.8 35.0 34.3
Both sexes, Social sciences
Both sexes, Engineering 12.4 13.4 14.8 15.8 16.1 18.1 19.9 21.2 22.9 22.8 23.1 23.8 26.6 25.5 26.6 27.8 29.6 29.8
Male, all fields 107.2 120.3 129.0 136.0 144.0 149.8 159.2 162.0 168.0 168.7 166.9 165.1 173.3 175.8 175.0 180.7 182.7 179.4
Male, Physical sciences 24.0 25.9 27.4 26.9 27.8 27.7 29.8 30.0 30.5 30.8 31.4 31.4 32.4 33.4 32.8 33.7 32.3 31.7
Male, Mathematics 9.0 10.3 10.8 11.3 11.3 11.8 12.3 12.5 13.0 13.9 13.7 12.8 13.5 12.9 12.6 13.8 14.1 13.7
Male, Computer sciences 0 0 0 0.1 0.3 0.4 0.7 0.9 1.3 1.6 2.1 2.5 2.6 2.9 2.9 4.3 4.5 5.5
Male, Life sciences 30.8 34.3 36.6 40.1 42.9 44.5 46.7 47.9 49.5 50.1 49.4 50.1 52.6 55.1 54.9 56.6 57.9 55.3
Male, Psychology 10.0 11.8 12.6 13.5 14.9 15.1 16.0 16.2 16.5 16.0 14.7 14.7 15.4 15.6 15.7 15.6 16.0 15.5
Male, Social sciences
Male, Engineering 12.3 13.3 14.7 15.7 15.9 17.8 19.5 20.6 22.2 21.8 22.1 22.3 24.8 23.4 24.3 24.8 26.0 25.8
Female, all fields 10.7 13.8 16.5 19.4 23.1 26.5 31.1 34.0 38.7 41.9 46.9 52.4 59.2 64.4 70.5 78.7 90.1 93.4
Female, Physical sciences 1.4 1.6 1.7 1.9 2.1 2.2 2.5 2.8 3.1 3.0 3.6 4.4 5.1 5.3 5.8 6.1 7.3 7.7
Female, Mathematics 0.6 0.8 0.9 0.9 1.1 1.1 1.3 1.4 1.5 1.4 1.7 1.8 2.1 2.2 2.3 2.9 3.3 3.8
Female, Computer sciences 0 0 0 0.0 0.0 0.1 0.1 0.1 0.2 0.4 0.5 0.6 0.7 0.8 0.8 1.0 1.2 1.4
Female, Life sciences 4.0 5.1 6.0 6.9 8.4 10.3 12.1 13.3 15.3 16.8 18.8 21.5 24.7 26.7 29.4 33.4 37.6 38.8
Female, Psychology 2.2 3.0 3.6 4.3 5.2 5.9 7.1 7.6 8.5 9.2 10.3 11.5 11.9 13.4 14.7 16.2 19.0 18.8
Female, Social sciences
Female, Engineering 0.1 0.1 0.1 0.2 0.2 0.3 0.4 0.6 0.7 1.0 1.1 1.5 1.7 2.1 2.3 3.0 3.6 4.0
US science, engineering and health doctorate holders employed as full-time senior faculty members in academia, by sex and degree field 1973–2008 (thousands).
Gender and field 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2006 2008
Both sexes, all fields 74.0 84.3 90.7 97.2 107.3 115.6 119.7 127.3 131.0 133.0 128.6 127.3 131.9 136.7 136.9 142.0 139.4 144.9
Both sexes, Physical sciences 15.2 17.1 18.0 18.8 19.7 20.2 20.8 21.5 21.4 21.2 20.6 20.0 20.5 20.7 20.7 21.3 20.2 20.7
Both sexes, Mathematics 5.9 6.9 7.6 8.3 9.1 9.7 10.0 10.5 10.9 11.8 11.5 10.6 10.8 10.8 10.2 11.2 10.7 11.1
Both sexes, Computer sciences 0 0 0 0.0 0.0 0.1 0.1 0.3 0.4 0.9 0.9 1.7 1.7 2.1 2.4 2.9 3.2 3.8
Both sexes, Life sciences 21.0 23.4 24.6 27.0 29.6 32.6 33.7 35.8 36.4 37.4 35.8 37.2 38.3 40.6 41.2 43.4 43.7 44.4
Both sexes, Psychology 7.3 8.7 9.1 9.9 11.7 12.8 13.5 14.3 15.0 15.3 14.3 14.5 15.3 15.6 15.9 15.8 16.1 16.9
Both sexes, Social sciences
Both sexes, Engineering 8.7 9.7 10.7 11.6 12.4 13.7 13.9 15.3 15.9 15.8 15.7 15.3 16.6 16.6 16.8 16.8 16.2 17.3
Male, all fields 69.7 78.9 84.7 90.2 98.7 104.9 107.4 113.2 115.2 115.5 110.3 107.0 109.4 110.6 108.3 109.7 104.6 106.1
Male, Physical sciences 14.7 16.6 17.4 18.1 19.0 19.4 20.0 20.6 20.3 20.3 19.5 18.8 18.9 19.0 18.6 18.9 17.6 17.6
Male, Mathematics 5.6 6.5 7.2 7.9 8.6 9.1 9.3 9.8 10.0 10.8 10.5 9.8 10.0 9.7 9.1 9.9 9.2 9.2
Male, Computer sciences 0 0 0 0.0 0.0 0.1 0.1 0.3 0.4 0.8 0.8 1.4 1.3 1.6 2.0 2.4 2.6 3.2
Male, Life sciences 19.5 21.6 22.7 24.8 26.9 29.1 29.4 31.0 31.0 31.4 29.3 29.3 30.0 31.1 30.4 30.9 30.4 29.9
Male, Psychology 6.4 7.6 7.8 8.4 9.7 10.5 10.8 11.2 11.5 11.3 10.2 10.1 10.7 10.3 10.3 9.5 9.1 9.3
Male, Social sciences
Male, Engineering 8.7 9.7 10.7 11.5 12.2 13.6 13.7 15.1 15.7 15.4 15.3 14.8 16.1 15.8 15.9 15.7 15.0 16.0
Female, all fields 4.3 5.4 6.0 7.0 8.6 10.7 12.4 14.0 15.8 17.6 18.3 20.3 22.5 26.1 28.6 32.3 34.8 38.8
Female, Physical sciences 0.5 0.5 0.6 0.7 0.7 0.8 0.9 1.0 1.1 0.9 1.1 1.2 1.5 1.9 2.1 2.4 2.7 3.1
Female, Mathematics 0.3 0.4 0.4 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.0 0.8 0.8 1.1 1.1 1.3 1.4 1.9
Female, Computer sciences 0 0 0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.3 0.3 0.5 0.4 0.5 0.6 0.6
Female, Life sciences 1.5 1.8 1.9 2.2 2.7 3.5 4.3 4.8 5.4 6.1 6.5 7.8 8.3 9.5 10.8 12.5 13.3 14.5
Female, Psychology 0.8 1.1 1.2 1.4 2.0 2.4 2.7 3.1 3.5 4.0 4.1 4.4 4.6 5.4 5.6 6.3 7.0 7.6
Female, Social sciences
Female, Engineering 0.0 0.0 0.0 0.1 0.1 0.2 0.2 0.2 0.2 0.4 0.3 0.5 0.5 0.8 0.9 1.1 1.2 1.3
US science, engineering and health doctorate holders employed as full-time junior faculty members in academia, by sex and degree field 1973–2008 (thousands).
Gender and field 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2006 2008
Both sexes, all fields 29.3 32.1 34.9 34.0 34.6 32.8 37.2 37.2 38.7 40.1 43.8 44.0 46.4 47.4 50.5 53.0 57.7 54.6
Both sexes, Physical sciences 5.6 5.2 5.7 4.7 4.6 4.0 4.6 4.7 4.8 5.0 5.2 5.6 6.1 6.6 6.7 7.2 7.5 6.9
Both sexes, Mathematics 3.3 3.5 3.3 3.1 2.6 2.5 2.7 2.4 2.6 2.4 3.2 2.4 2.8 2.4 2.3 3.0 3.4 3.4
Both sexes, Computer sciences 0 0 0 0.1 0.2 0.3 0.6 0.6 0.9 1.0 1.4 1.2 1.3 1.2 0.9 1.3 1.7 1.9
Both sexes, Life sciences 8.5 9.7 10.3 10.3 11.3 10.8 11.9 12.3 12.8 13.7 15.0 15.6 16.9 17.5 19.2 20.3 20.8 20.4
Both sexes, Psychology 3.6 4.2 4.8 4.4 4.8 4.5 5.0 4.9 5.2 5.4 5.2 5.5 5.5 6.2 6.6 6.3 7.6 6.4
Both sexes, Social sciences
Both sexes, Engineering 2.6 2.5 2.7 2.8 2.3 2.7 4.0 4.0 4.3 4.3 4.5 4.8 5.0 4.5 5.1 5.5 6.1 5.2
Male, all fields 26.0 27.5 28.9 27.3 27.1 25.2 27.8 27.2 27.6 28.1 29.7 28.5 29.5 30.1 31.0 31.5 33.3 31.7
Male, Physical sciences 5.2 4.9 5.2 4.3 4.1 3.5 3.9 4.1 4.1 4.1 4.2 4.1 4.5 5.1 5.1 5.5 5.6 5.0
Male, Mathematics 3.1 3.2 2.9 2.7 2.2 2.2 2.3 2.0 2.2 2.2 2.7 2.0 2.2 1.7 1.7 2.1 2.4 2.4
Male, Computer sciences 0 0 0 0.1 0.2 0.3 0.5 0.5 0.8 0.8 1.1 1.0 1.0 0.9 0.7 1.0 1.3 1.4
Male, Life sciences 7.5 8.1 8.4 8.1 8.9 8.1 8.5 8.5 8.4 8.8 9.5 9.5 9.8 10.4 11.0 11.2 11.1 10.8
Male, Psychology 2.7 3.0 3.3 2.8 3.0 2.6 2.7 2.7 2.9 3.0 2.3 2.4 2.1 2.7 2.7 2.4 2.8 2.4
Male, Social sciences
Male, Engineering 2.6 2.4 2.7 2.7 2.2 2.6 3.8 3.8 4.0 3.9 4.0 4.1 4.4 3.9 4.3 4.5 4.8 3.9
Female, all fields 3.3 4.6 6.0 6.8 7.5 7.7 9.4 10.0 11.2 12.0 14.1 15.6 17.0 17.3 19.4 21.5 24.4 22.9
Female, Physical sciences 0.3 0.3 0.5 0.5 0.5 0.5 0.6 0.7 0.7 0.9 1.1 1.5 1.6 1.5 1.6 1.7 1.9 1.9
Female, Mathematics 0.2 0.3 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.3 0.5 0.5 0.6 0.7 0.6 0.9 1.1 1.0
Female, Computer sciences 0 0 0 0.0 0.0 0.0 0.1 0.1 0.1 0.2 0.3 0.2 0.3 0.2 0.2 0.3 0.4 0.5
Female, Life sciences 1.1 1.6 1.9 2.2 2.4 2.7 3.4 3.8 4.5 4.9 5.5 6.1 7.1 7.1 8.1 9.1 9.7 9.6
Female, Psychology 0.9 1.2 1.5 1.6 1.8 1.9 2.3 2.2 2.3 2.4 2.9 3.1 3.4 3.5 3.9 3.9 4.8 4.0
Female, Social sciences
Female, Engineering 0.0 0.0 0.0 0.1 0.1 0.1 0.2 0.2 0.3 0.4 0.5 0.7 0.6 0.5 0.8 1.0 1.3 1.3
US science, engineering and health doctorate holders employed in other full-time positions in academia, by sex and degree field 1973–2008 (thousands).
Gender and field 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2006 2008
Both sexes, all fields 7.6 8.3 8.8 11.4 12.6 13.4 18.1 16.4 19.2 20.2 22.2 23.9 26.4 29.3 31.7 35.4 36.7 39.9
Both sexes, Physical sciences 2.2 2.2 2.4 2.5 2.9 3.0 3.7 3.4 4.0 4.1 4.8 4.9 6.1 6.8 7.1 7.7 6.4 6.8
Both sexes, Mathematics 0.2 0.3 0.4 0.4 0.4 0.3 0.5 0.4 0.5 0.7 0.5 0.6 0.8 0.8 1.0 1.2 1.1 1.4
Both sexes, Computer sciences 0 0 0 0.0 0.0 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.6 0.6 0.8
Both sexes, Life sciences 2.5 2.4 2.8 3.9 4.0 4.6 6.2 6.0 6.7 7.2 7.7 8.4 8.4 9.5 10.5 12.2 13.8 14.5
Both sexes, Psychology 0.8 1.0 1.2 1.8 2.2 2.2 2.9 2.8 2.9 2.8 3.9 3.9 4.0 4.6 4.9 5.7 6.0 6.3
Both sexes, Social sciences
Both sexes, Engineering 0.8 0.9 0.8 0.9 1.1 1.1 1.5 1.1 1.5 1.8 1.5 2.1 3.1 3.0 3.4 3.6 3.1 4.4
Male, all fields 6.5 7.2 7.4 9.4 10.0 10.3 14.3 12.0 13.9 14.4 15.4 16.1 18.0 20.5 21.4 23.7 23.4 23.6
Male, Physical sciences 2.1 2.1 2.2 2.3 2.6 2.7 3.4 2.9 3.5 3.6 4.2 4.1 5.2 6.0 6.1 6.6 5.2 5.4
Male, Mathematics 0.1 0.2 0.4 0.2 0.3 0.2 0.4 0.5 0.4 0.5 0.4 0.4 0.7 0.8 0.8 0.8 0.8 1.0
Male, Computer sciences 0 0 0 0.0 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.2 0.2 0.6 0.5 0.7
Male, Life sciences 2.0 1.9 2.2 3.0 3.0 3.2 4.6 4.1 4.6 4.9 5.1 5.6 5.3 6.2 6.5 7.4 8.3 7.7
Male, Psychology 0.7 0.8 0.9 1.4 1.4 1.4 1.8 1.6 1.5 1.2 1.9 1.7 1.8 2.1 2.0 2.5 2.6 2.4
Male, Social sciences
Male, Engineering 0.8 0.9 0.8 0.9 1.1 1.0 1.5 1.0 1.4 1.7 1.5 2.0 2.7 2.6 3.1 3.1 2.7 3.7
Female, all fields 1.1 1.0 1.4 2.0 2.6 3.1 3.8 4.5 5.3 5.8 6.7 7.7 8.4 8.8 10.4 11.7 13.3 16.3
Female, Physical sciences 0.1 0.1 0.2 0.2 0.3 0.3 0.3 0.5 0.5 0.5 0.6 0.8 0.9 0.8 1.0 1.1 1.2 1.4
Female, Mathematics 0.0 0.0 0.1 0.0 0.1 0.1 0.1 0.1 0.1 0.0 0.0 0.1 0.2 0.1 0.1 0.3 0.3 0.4
Female, Computer sciences 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.1 0.1 0.0 0.1 0.1
Female, Life sciences 0.6 0.5 0.6 0.9 1.0 1.3 1.6 1.8 2.1 2.4 2.6 2.8 3.1 3.3 4.0 4.8 5.5 6.9
Female, Psychology 0.2 0.2 0.3 0.5 0.8 0.8 1.1 1.3 1.4 1.6 2.0 2.2 2.2 2.5 2.9 3.1 3.4 3.9
Female, Social sciences
Female, Engineering 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.1 0.3 0.3 0.4 0.5 0.4 0.7
US science, engineering and health doctorate holders employed in part-time positions in academia, by sex and degree field 1973–2008 (thousands).
Gender and field 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2006 2008
Both sexes, all fields 2.9 3.2 3.4 4.5 4.0 6.0 6.5 5.7 6.2 7.4 5.9 5.5 8.9 8.2 9.0 13.3 15.7 15.4
Both sexes, Physical sciences 0.7 0.7 0.7 0.8 0.7 1.1 1.1 0.8 0.7 1.2 1.0 0.9 1.2 1.0 1.0 1.1 1.5 1.6
Both sexes, Mathematics 0.1 0.2 0.2 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.5 0.7 0.7 0.6 0.9 1.2 1.0
Both sexes, Computer sciences 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.2 0.1 0.1
Both sexes, Life sciences 0.9 0.9 1.0 1.2 1.2 1.7 1.7 1.6 1.9 2.3 1.6 1.2 2.9 2.6 2.4 4.0 4.4 4.3
Both sexes, Psychology 0.4 0.5 0.6 1.0 0.8 1.0 1.0 1.0 1.0 1.2 1.2 1.1 1.1 1.3 1.8 3.2 3.6 3.7
Both sexes, Social sciences
Both sexes, Engineering 0.1 0.1 0.1 0.3 0.2 0.4 0.3 0.3 0.5 0.5 0.5 0.4 0.3 0.4 0.4 0.8 1.1 0.8
Male, all fields 1.5 1.8 1.8 2.7 1.9 3.5 3.6 2.7 3.1 3.8 2.3 2.4 4.4 3.4 3.8 6.1 7.6 6.9
Male, Physical sciences 0.4 0.4 0.5 0.6 0.4 0.9 0.7 0.5 0.4 0.8 0.7 0.7 0.8 0.7 0.6 0.7 1.1 1.1
Male, Mathematics 0.1 0.1 0.2 0.2 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.3 0.3 0.4 0.3 0.6 0.9 0.6
Male, Computer sciences 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.1 0.1
Male, Life sciences 0.4 0.5 0.4 0.5 0.4 0.7 0.7 0.5 0.8 1.0 0.4 0.3 1.3 0.8 0.7 1.3 1.5 1.4
Male, Psychology 0.1 0.2 0.2 0.5 0.3 0.4 0.4 0.4 0.3 0.2 0.1 0.1 0.3 0.3 0.3 0.8 0.9 1.0
Male, Social sciences
Male, Engineering 0.1 0.1 0.1 0.3 0.2 0.3 0.3 0.3 0.5 0.5 0.4 0.3 0.2 0.2 0.4 0.7 1.0 0.6
Female, all fields 1.4 1.5 1.6 1.9 2.1 2.5 2.9 3.0 3.1 3.5 3.6 3.1 4.5 4.9 5.1 7.3 8.1 8.5
Female, Physical sciences 0.3 0.3 0.2 0.3 0.3 0.3 0.4 0.4 0.3 0.4 0.3 0.2 0.4 0.4 0.3 0.4 0.5 0.5
Female, Mathematics 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.4 0.3 0.3 0.2 0.3 0.4
Female, Computer sciences 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.1
Female, Life sciences 0.5 0.5 0.6 0.6 0.7 0.9 1.0 1.1 1.1 1.3 1.2 0.9 1.6 1.8 1.7 2.7 3.0 2.9
Female, Psychology 0.3 0.3 0.4 0.5 0.5 0.6 0.7 0.7 0.7 0.9 1.1 1.0 0.8 1.0 1.5 2.4 2.8 2.7
Female, Social sciences
Female, Engineering 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.3
US science, engineering and health doctorate holders employed as postdocs in academia, by sex and degree field 1973–2008 (thousands).
Gender and field 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2006 2008
Both sexes, all fields 4.2 6.2 7.6 8.1 8.5 8.3 8.7 9.3 11.5 9.9 13.3 16.8 18.9 18.5 17.5 15.7 23.3 18.0
Both sexes, Physical sciences 1.8 2.2 2.4 2.0 2.1 1.6 2.1 2.3 2.7 2.2 3.5 4.4 3.8 3.4 3.0 2.6 3.9 3.3
Both sexes, Mathematics 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.3 0.2 0.1 0.0 0.5 0.5 0.6 0.8 0.5 1.0 0.5
Both sexes, Computer sciences 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.0 0.1 0.1 0.2
Both sexes, Life sciences 1.9 3.0 3.9 4.8 5.2 5.1 5.3 5.4 6.8 6.3 8.2 9.1 10.8 11.6 11.0 10.0 12.8 10.5
Both sexes, Psychology 0.2 0.4 0.5 0.6 0.6 0.6 0.7 0.7 0.8 0.5 0.4 1.1 1.3 1.2 1.2 0.9 1.7 1.0
Both sexes, Social sciences
Both sexes, Engineering 0.2 0.3 0.4 0.3 0.2 0.3 0.2 0.5 0.6 0.5 1.0 1.2 1.7 1.1 0.9 1.1 3.0 2.1
Male, all fields 3.5 4.9 6.1 6.3 6.3 5.8 6.0 6.8 8.2 6.8 9.2 11.1 12.1 11.2 10.5 9.8 13.8 10.9
Male, Physical sciences 1.6 2.0 2.1 1.8 1.8 1.4 1.7 1.9 2.2 1.8 2.9 3.7 3.0 2.7 2.3 2.1 2.9 2.5
Male, Mathematics 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.0 0.0 0.3 0.3 0.5 0.6 0.3 0.8 0.4
Male, Computer sciences 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.1 0.1 0.2
Male, Life sciences 1.5 2.2 2.9 3.5 3.6 3.3 3.4 3.8 4.7 4.1 5.2 5.5 6.2 6.6 6.2 5.7 6.7 5.6
Male, Psychology 0.1 0.2 0.4 0.4 0.5 0.3 0.4 0.3 0.4 0.3 0.1 0.3 0.6 0.2 0.4 0.4 0.5 0.5
Male, Social sciences
Male, Engineering 0.2 0.3 0.4 0.3 0.2 0.3 0.2 0.5 0.5 0.4 0.9 1.0 1.4 0.9 0.7 0.9 2.4 1.7
Female, all fields 0.6 1.3 1.6 1.8 2.2 2.5 2.6 2.6 3.3 3.0 4.1 5.7 6.8 7.3 6.9 6.0 9.5 7.1
Female, Physical sciences 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.3 0.4 0.3 0.7 0.7 0.7 0.8 0.7 0.5 1.0 0.8
Female, Mathematics 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.1 0.2 0.2 0.2 0.1
Female, Computer sciences 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Female, Life sciences 0.4 0.8 1.1 1.2 1.6 1.8 1.8 1.8 2.2 2.3 3.0 3.7 4.6 5.1 4.7 4.3 6.1 4.9
Female, Psychology 0.0 0.1 0.1 0.2 0.2 0.2 0.3 0.4 0.5 0.3 0.3 0.7 0.8 0.9 0.8 0.5 1.1 0.6
Female, Social sciences
Female, Engineering 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.2 0.2 0.2 0.2 0.6 0.4

Data source: National Science Foundation http://www.nsf.gov/statistics/seind12/append/c5/at05-17.pdf

Median annual salary of scientists and engineers employed full time in all occupations, by highest degree, age and sex (2008).
Highest degree, occupation and age Female Male
All degrees, all ages 60000 84000
All degrees, 29 and younger 44000 53000
All degrees, 30 to 39 59000 79000
All degrees, 40 to 49 68000 95000
All degrees, 50 to 75 63000 91000
Bachelor’s, all ages 52000 75000
Bachelor’s, 29 and younger 40000 50000
Bachelor’s, 30 to 39 54000 73000
Bachelor’s, 40 to 49 60000 85000
Bachelor’s, 50 to 75 56000 80000
Master’s, all ages 63000 90000
Master’s, 29 and younger 49000 62000
Master’s, 30 to 39 60000 85000
Master’s, 40 to 49 71000 100000
Master’s, 50 to 75 66000 91000
Doctorate, all ages 77000 100000
Doctorate, 29 and younger 52000 75000
Doctorate, 30 to 39 69000 80000
Doctorate, 40 to 49 80000 100000
Doctorate, 50 to 75 86000 110000
Median annual salary of scientists and engineers employed full time in S&E occupations, by highest degree, age and sex (2008).
Highest degree, occupation and age Female Male
All degrees, all ages 70000 85000
All degrees, 29 and younger 50000 60000
All degrees, 30 to 39 70000 82000
All degrees, 40 to 49 80000 95000
All degrees, 50 to 75 78000 95000
Bachelor’s, all ages 68000 80000
Bachelor’s, 29 and younger 47000 56000
Bachelor’s, 30 to 39 70000 80000
Bachelor’s, 40 to 49 80000 90000
Bachelor’s, 50 to 75 71000 90000
Master’s, all ages 74000 92000
Master’s, 29 and younger 54000 68000
Master’s, 30 to 39 70000 89000
Master’s, 40 to 49 86000 100000
Master’s, 50 to 75 80000 98000
Doctorate, all ages 75000 96000
Doctorate, 29 and younger 49000 75000
Doctorate, 30 to 39 67000 80000
Doctorate, 40 to 49 78000 99000
Doctorate, 50 to 75 89000 105000
Median annual salary of scientists and engineers employed full time as scientists, by highest degree, age and sex (2008).
Highest degree, occupation and age Female Male
All degrees, all ages 70000 83000
All degrees, 29 and younger 45000 55000
All degrees, 30 to 39 66000 80000
All degrees, 40 to 49 80000 92000
All degrees, 50 to 75 75000 90000
Bachelor’s, all ages 65000 80000
Bachelor’s, 29 and younger 40000 53000
Bachelor’s, 30 to 39 67000 80000
Bachelor’s, 40 to 49 79000 89000
Bachelor’s, 50 to 75 70000 85000
Master’s, all ages 72000 90000
Master’s, 29 and younger 52000 65000
Master’s, 30 to 39 68000 87000
Master’s, 40 to 49 84000 100000
Master’s, 50 to 75 79000 89000
Doctorate, all ages 73000 91000
Doctorate, 29 and younger 43000 65000
Doctorate, 30 to 39 63000 72000
Doctorate, 40 to 49 76000 91000
Doctorate, 50 to 75 86000 100000
Median annual salary of scientists and engineers employed full time as biological life scientists, by highest degree, age and sex (2008).
Highest degree, occupation and age Female Male
All degrees, all ages 54000 65000
All degrees, 29 and younger 35000 30000
All degrees, 30 to 39 55000 56000
All degrees, 40 to 49 70000 73000
All degrees, 50 to 75 69000 82000
Bachelor’s, all ages 45000 50000
Bachelor’s, 29 and younger 30000 28000
Bachelor’s, 30 to 39 50000 54000
Bachelor’s, 40 to 49 52000 64000
Bachelor’s, 50 to 75 54000 56000
Master’s, all ages 58000 63000
Master’s, 29 and younger 48000 28000
Master’s, 30 to 39 56000 56000
Master’s, 40 to 49 69000 66000
Master’s, 50 to 75 71000 72000
Doctorate, all ages 70000 86000
Doctorate, 29 and younger 39000 60000
Doctorate, 30 to 39 55000 60000
Doctorate, 40 to 49 80000 86000
Doctorate, 50 to 75 92000 104000
Median annual salary of scientists and engineers employed full time as computer and information scientists, by highest degree, age and sex (2008).
Highest degree, occupation and age Female Male
All degrees, all ages 79000 86000
All degrees, 29 and younger 60000 61000
All degrees, 30 to 39 75000 85000
All degrees, 40 to 49 85000 96000
All degrees, 50 to 75 80000 91000
Bachelor’s, all ages 75000 82000
Bachelor’s, 29 and younger 56000 59000
Bachelor’s, 30 to 39 72000 82000
Bachelor’s, 40 to 49 80000 92000
Bachelor’s, 50 to 75 74000 88000
Master’s, all ages 84000 96000
Master’s, 29 and younger 66000 75000
Master’s, 30 to 39 80000 95000
Master’s, 40 to 49 89000 104000
Master’s, 50 to 75 92000 94000
Doctorate, all ages 95000 101000
Doctorate, 29 and younger 0 0
Doctorate, 30 to 39 100000 94000
Doctorate, 40 to 49 94000 103000
Doctorate, 50 to 75 95000 110000
Median annual salary of scientists and engineers employed full time as mathematical scientists, by highest degree, age and sex (2008).
Highest degree, occupation and age Female Male
All degrees, all ages 70000 80000
All degrees, 29 and younger 55000 49000
All degrees, 30 to 39 68000 74000
All degrees, 40 to 49 84000 90000
All degrees, 50 to 75 70000 91000
Bachelor’s, all ages 67000 61000
Bachelor’s, 29 and younger 57000 40000
Bachelor’s, 30 to 39 0 61000
Bachelor’s, 40 to 49 0 0
Bachelor’s, 50 to 75 0 98000
Master’s, all ages 73000 85000
Master’s, 29 and younger 53000 36000
Master’s, 30 to 39 70000 84000
Master’s, 40 to 49 83000 90000
Master’s, 50 to 75 70000 80000
Doctorate, all ages 80000 90000
Doctorate, 29 and younger 49000 82000
Doctorate, 30 to 39 80000 80000
Doctorate, 40 to 49 80000 97000
Doctorate, 50 to 75 93000 95000
Median annual salary of scientists and engineers employed full time physical scientists, by highest degree, age and sex (2008).
Highest degree, occupation and age Female Male
All degrees, all ages 60000 76000
All degrees, 29 and younger 38000 40000
All degrees, 30 to 39 59000 67000
All degrees, 40 to 49 70000 85000
All degrees, 50 to 75 74000 90000
Bachelor’s, all ages 55000 64000
Bachelor’s, 29 and younger 33000 40000
Bachelor’s, 30 to 39 60000 64000
Bachelor’s, 40 to 49 70000 70000
Bachelor’s, 50 to 75 69000 75000
Master’s, all ages 60000 80000
Master’s, 29 and younger 41000 29000
Master’s, 30 to 39 47000 63000
Master’s, 40 to 49 77000 94000
Master’s, 50 to 75 69000 98000
Doctorate, all ages 74000 93000
Doctorate, 29 and younger 55000 54000
Doctorate, 30 to 39 64000 76000
Doctorate, 40 to 49 80000 100000
Doctorate, 50 to 75 96000 104000
Median annual salary of scientists and engineers employed full time as psychologists, by highest degree, age and sex (2008).
Highest degree, occupation and age Female Male
All degrees, all ages 60000 69000
All degrees, 29 and younger 38000 33000
All degrees, 30 to 39 58000 55000
All degrees, 40 to 49 70000 70000
All degrees, 50 to 75 73000 80000
Bachelor’s, all ages 34000 0
Bachelor’s, 29 and younger 0 0
Bachelor’s, 30 to 39 0 0
Bachelor’s, 40 to 49 0 0
Bachelor’s, 50 to 75 0 0
Master’s, all ages 58000 58000
Master’s, 29 and younger 45000 0
Master’s, 30 to 39 55000 50000
Master’s, 40 to 49 67000 0
Master’s, 50 to 75 65000 63000
Doctorate, all ages 72000 85000
Doctorate, 29 and younger 0 0
Doctorate, 30 to 39 68000 60000
Doctorate, 40 to 49 72000 80000
Doctorate, 50 to 75 80000 99000
Median annual salary of scientists and engineers employed full time as engineers, by highest degree, age and sex (2008).
Highest degree, occupation and age Female Male
All degrees, all ages 77000 90000
All degrees, 29 and younger 61000 62000
All degrees, 30 to 39 78000 84000
All degrees, 40 to 49 90000 96000
All degrees, 50 to 75 97000 100000
Bachelor’s, all ages 72000 85000
Bachelor’s, 29 and younger 60000 60000
Bachelor’s, 30 to 39 75000 80000
Bachelor’s, 40 to 49 83000 93000
Bachelor’s, 50 to 75 94000 95000
Master’s, all ages 81000 96000
Master’s, 29 and younger 65000 70000
Master’s, 30 to 39 80000 90000
Master’s, 40 to 49 98000 104000
Master’s, 50 to 75 100000 102000
Doctorate, all ages 92000 106000
Doctorate, 29 and younger 75000 88000
Doctorate, 30 to 39 82000 95000
Doctorate, 40 to 49 109000 112000
Doctorate, 50 to 75 105000 120000
Median annual salary of scientists and engineers employed full time in non-S&E occupations, by highest degree, age and sex (2008).
Highest degree, occupation and age Female Male
All degrees, all ages 51000 78000
All degrees, 29 and younger 40000 47000
All degrees, 30 to 39 52000 72000
All degrees, 40 to 49 60000 92000
All degrees, 50 to 75 56000 82000
Bachelor’s, all ages 45000 67000
Bachelor’s, 29 and younger 37000 44000
Bachelor’s, 30 to 39 48000 65000
Bachelor’s, 40 to 49 50000 80000
Bachelor’s, 50 to 75 47000 68000
Master’s, all ages 57000 90000
Master’s, 29 and younger 45000 59000
Master’s, 30 to 39 54000 85000
Master’s, 40 to 49 65000 104000
Master’s, 50 to 75 62000 90000
Doctorate, all ages 80000 110000
Doctorate, 29 and younger 70000 0
Doctorate, 30 to 39 74000 80000
Doctorate, 40 to 49 85000 110000
Doctorate, 50 to 75 84000 119000

0 means that data are unavailable.

Data source: National Science Foundation Table 9-16 http://www.nsf.gov/statistics/wmpd/2013/tables.cfm

As an aspiring engineer in the early 1970s, Lynne Kiorpes was easy to spot in her undergraduate classes. Among a sea of men, she and a handful of other women made easy targets for a particular professor at Northeastern University in Boston, Massachusetts. On the first day of class, “he looked around and said 'I see women in the classroom. I don't believe women have any business in engineering, and I'm going to personally see to it that you all fail'.”

He wasn't bluffing. All but one of the women in the class ultimately left engineering; Kiorpes went on to major in psychology.

Nature special: nature.com/women

Such blatant sexism is almost unthinkable today, says Kiorpes, now a neuroscientist at New York University. But Kiorpes, who runs several mentoring programmes for female students and postdoctoral fellows, says that subtle bias persists at most universities. And it drives some women out of science careers.

By almost any metric, women have made great gains in closing the scientific gender gap, but female scientists around the world continue to face major challenges. According to the US National Science Foundation, women earn about half the doctorates in science and engineering in the United States but comprise only 21% of full science professors and 5% of full engineering professors. And on average, they earn just 82% of what male scientists make in the United States — even less in Europe.

Scientific leaders say that they continue to struggle with ways to level the playing field and entice more women to enter and stay in science. “We are not drawing from our entire intellectual capital,” says Hannah Valantine, dean of leadership and diversity at the Stanford School of Medicine in California. “We've got to put on the accelerator to evoke social change.”

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One of the most persistent problems is that a disproportionate fraction of qualified women drop out of science careers in the very early stages (see 'Women in science'). A 2006 survey of chemistry doctoral students by the Royal Society of Chemistry in London, for example, found that more than 70% of first-year female students said that they planned a career in research; by their third year, only 37% had that goal, compared with 59% of males1.

Many experts say that a big factor driving this trend is the lack of role models in the upper divisions of academia, which have been slow to change. The Royal Society of Chemistry has found, for instance, that female chemistry students are more likely than males to express low self-confidence and to report dissatisfaction with mentorship2. Female students “conclude consciously and unconsciously that these careers are not for them because they don't see people like them”, suggests Valantine. “That effect is very, very powerful — this sense of not belonging.”

The attrition continues at later stages. In biology, for example, women comprised 36% of assistant professors and only 27% of tenure candidates in a 2010 study by the US National Research Council3. “We're not talking about a lack of talent here. Part of the story is that women leave earlier. In a sense, they give up on an academic career,” says Curt Rice, vice-president of research and development at the University of Tromsø in Norway, who has studied gender equality in US and European universities.

Family values

Many of the UK chemistry students viewed research as an all-consuming endeavour that was incompatible with raising a family. Meeting the demanding schedule of academic research can seem daunting for both mothers and fathers. But family choices seem to weigh more heavily on the career goals of women.

SOurces: NSF/Ref. 12 (graduate); ref. 4 (postgraduate); NSF/WebCASPAR (early career); Ref. 3/ref. 12

Law professor Mary Ann Mason at the University of California, Berkeley, and her colleagues have found4 that male and female postdocs without children are equally likely to decide against research careers, each leaving at a rate of about 20%. But female postdocs who become parents or plan to have children abandon research careers up to twice as often as men in similar circumstances.

“The plan to have children in the future, or already having them, is responsible for an enormous drop-off in the women who apply for tenure-track jobs,” says Wendy Williams, a psychologist at Cornell University in Ithaca, New York. Furthermore, women who do become faculty members in astronomy, physics and biology tend to have fewer children than their male colleagues — 1.2 versus 1.5, on average — and also have fewer children than they desire5.

In response to these concerns, many universities have taken steps to establish family-friendly policies such as providing child-care assistance and extending tenure clocks for new parents. Shirley Tilghman, president of Princeton University in New Jersey, believes that such initiatives provide crucial support for women, but that other solutions are still needed. “I don't think there's a single obstacle,” she says. “I think there's a whole series of phenomena that add up.”

Live issue

At Yale University in New Haven, Connecticut, microbiologist Jo Handelsman is one of many researchers who think that gender discrimination continues to be a significant part of the problem. In a much-talked-about experiment last year6, her team showed that science faculty members of both sexes exhibit unconscious biases against women. Handelsman's group asked 127 professors of biology, chemistry and physics at 6 US universities to evaluate the CVs of two fictitious college students for a job as a laboratory manager. The professors said they would offer the student named Jennifer US$3,730 less per year than the one named John, even though the CVs were identical. The scientists also reported a greater willingness to mentor John than Jennifer. “If you extrapolate that to all the interactions that faculty have with students, it becomes very frightening,” says Handelsman.

Her findings match well with the results of a survey7 done in 2010 by the American Association for the Advancement of Science. Of the 1,300 or so people who responded, 52% of women said that they had encountered gender bias during their careers, compared with just 2% of men.

Still, other concrete evidence of bias is hard to find. Some measures show female scientists outperforming male rivals in landing interviews and job offers early in their careers. The National Research Council study3 showed that women accounted for 19% of the interview pool and received 32% of job offers for tenure-track electrical-engineering positions. Women fared just as well as men in tenure evaluations, but female assistant professors in many disciplines seemed less likely to reach tenure consideration compared with men.

Women face even more daunting odds in Spain. Men are 2.5 times more likely to rise to the rank of full professor than female colleagues with comparable age, experience and publication records8.

SOURCES: NIH; NSF/SESTAT

Disparities can also be found in grant funding in some countries. In one frequently cited study9, Christine Wennerås and Agnes Wold at the University of Gothenburg in Sweden found in 1997 that female applicants for postdoctoral fellowships had to score 2.5 times higher on an index of publication impact to be judged the same as men.

Several groups, such as the UK Medical Research Council and biomedical research charity the Wellcome Trust, have since investigated their grant programmes and found negligible or very subtle effects of gender10. The Canadian Medical Research Council found no differences in success rate in most of its research grant programmes, but reported lower success rates for women in some training grants11. In the United States, women are slightly more successful than men in obtaining grants from the National Science Foundation, but the trend is reversed for the National Institutes of Health (NIH). The NIH also gives women smaller awards on average (see 'The funding gap').

Information provided to Nature by the NIH through a Freedom of Information Act request indicates that the percentage of women on review panels has improved marginally over the past decade, from 25% in 2003 to 30% in 2012. Those figures roughly parallel the percentage of women applying for and receiving grants in that time.

Pay problems

The inequalities also extend to salaries. In the European Union, female scientists earned on average between 25% and 40% less than male scientists in the public sector in 2006 (ref. 12). Although the average pay gap is smaller in the United States, the disparity is particularly large in physics and astronomy, where women earn 40% less than men.

For young academic scientists, however, those differences may be fading. The National Research Council found an 8% pay gap at the level of full science and engineering professors but no significant differences among junior faculty members3. Some experts argue, however, that the salary gap may reflect other continued trends, such as the fact that a disproportionate share of women move into non-tenure positions or faculty jobs at lower-status universities.

Tilghman says that Princeton and many other universities have grown increasingly conscious of the need to track and rectify gender gaps in salary and other institutional support. “Absolutely, it needs eternal vigilance,” she says. “But we're in a much better place.”

Journal name:
Nature
Volume:
495,
Pages:
22–24
Date published:
()
DOI:
doi:10.1038/495022a

References

  1. Royal Society of Chemistry Change of Heart (RSC, 2008).

  2. Newsome, J. L. The Chemistry PhD: The Impact on Women's Retention (RSC, 2008).

  3. National Research Council Gender Differences at Critical Transitions in the Careers of Science, Engineering, and Mathematics Faculty (National Academies, 2010).

  4. Goulden, M., Frasch, K. & Mason, M. A. Staying Competitive (Center for American Progress, 2009).

  5. Ecklund, E. H. & Lincoln, A. E. PLoS ONE 6, e22590 (2011).

  6. Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J. & Handelsman, J. Proc. Natl Acad. Sci. USA 109, 1647416479 (2012).

  7. Cell Associates Barriers for Women Scientists Survey Report (AAAS, 2010).

  8. Women and Science Unit White Paper on the Position of Women in Science in Spain (UMYC, 2011).

  9. Wennerås, C. & Wold, A. Nature 387, 341343 (1997).

  10. Grant, J., Burden, S. & Breen, G. Nature 390, 438 (1997).

  11. Friesen, H. G. Nature 391, 326 (1998).

  12. European Commission. She Figures 2009 (European Communities, 2009).

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Affiliations

  1. Helen Shen is an intern with Nature in Washington DC.

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  1. Avatar for Cathy Kessel
    Cathy Kessel

    Raquel Perales: Of course I don't know which articles you're reading or which country you're in but part of the explanation may be that attention often gets directed to lows and highs. With respect to percentages of women earning PhDs in the US, at about 30% mathematics and physics are between the lows (engineering and computer science) and the highs (psychology and biology).

    I've listed some sources of statistics on women in STEM in the US here

    The American Mathematical Society gives detailed annual reports on degrees granted from US institutions. The one for 2013 is here

  2. Avatar for Raquel Perales
    Raquel Perales

    I am a PhD math student. I have read some articles like this one and always wonder why female mathematicians are never or rarely mentioned. Is there not enough data about us?

  3. Avatar for Kathleen Taylor
    Kathleen Taylor

    It's not just science, it's science writing too. As an author of books published by a top university press, I shouldn't lack confidence, but it's hard not to waver when all the top science writers seem to be men. I can't help wondering if my books would have sold better if the name on the cover had been 'K.E.' Taylor rather than the clearly female 'Kathleen'. I chose the latter from sheer obstinacy, and because the status quo's never going to change if women keep taking the rational, self-interested option of making themselves seem more like men.

    That's the trouble: it's in researchers' interests to keep their heads down and not rock the system, whether they're male or female.

    How do we incentivise people to change the stereotypes? That needs the media, teachers, politicians and so on, as well as scientists.

  4. Avatar for Richard Monastersky
    Richard Monastersky

    Those interested in checking out the data used for the NIH grant graphic can find them at these links:Research grant size by gende

    Research grant numbers by gender

    Success rates for research project grants

    It is important to note that the charts for research project grants and research grants overlap in the categories the cover but they are not identical.

    For all awards, here is the gender split in award size

    Note that in all categories, women do not receive as much as men.

    All these charts come from the NIH Data Book

    The most recent NSF data on awards can be found in this report

  5. Avatar for James T. Dwyer
    James T. Dwyer

    There's no question that women have been discriminated against in the past, and most likely are still, at least to some extent, in the present. Technically, however, simply quantifying inequities does not provide indisputable evidence of discrimination. A complete quantitative analysis would require that pay differences be normalized by some reliable measure of job performance &#8211 of value to the employer. Likewise, the number and value of research grants awarded should be normalized by the number of applications and, ideally some independent measure of their quality.

    I realize this is an exceedingly sensitive issue &#8211 I apologize in advance to all those whose sensibilities I've offended &#8211 my intentions are purely technical. BTW, I'm a retired information systems analyst, concerned about my very capable granddaughters' future opportunities.

  6. Avatar for Irene Newton
    Irene Newton

    It would be great to see the raw data behind these graphs - for example, one nagging question I have is whether or not the median and the mean reveal the same trends.

  7. Avatar for Vivien Zapf
    Vivien Zapf

    Juggling family and work need not be a sacrifice. When my children arrived I switched to part time. I thought at the time that my career would be permanently ruined. But by getting off useless committees, removing junk-work from my schedule, delegating and becoming more efficient, I found myself producing just as much science half-time as I had been full-time. When I returned to full-time I was twice as efficient.
    Nobody can work 80 hours a day for their entire life. And most people working those hours are not very efficient. And recall that there is a reason sabbaticals are built into the academic career. We all need time to step back, take a break, re-evaluate, and spending time with our children can provide just this opportunity.

  8. Avatar for Cathy Kessel
    Cathy Kessel

    The article above says:

    "'The plan to have children in the future, or already having them, is responsible for an enormous drop-off in the women who apply for tenure-track jobs,' says Wendy Williams, a psychologist at Cornell University in Ithaca, New York."

    It's helpful to know that, for Professor Williams, "tenure-track" generally seems to mean what many others call "tenured <em>or</em> tenure-track."

    For example, in an article published last year, she and a co-author note that

    "the percentage of female assistant professors in many STEM fields tracks closely with the proportion of recent PhDs in these fields (Nelson and Brammer, 2010)."

    See p. 22 of Valla & Williams, 2012 in Journal of Women and Minorities in Science and Engineering, 18(1), http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3430517/. Nelson and Brammer's statistics concern positions at the top 100 US departments which were tenured or tenure-track.

    It's also helpful to know that the 2010 National Research Council study concerned Research 1 universities and that it was not a longitudinal study. The report says:

    "In biology and chemistry, the differences were statistically significant. In biology, 27 percent of the faculty considered for tenure were women, while women represented 36 percent of the assistant professor pool. In chemistry those numbers were 15 percent and 22 percent, respectively. This difference may suggest that female assistant professors were more likely than men to leave before being considered for tenure. It might also reflect the increased hiring of female assistant professors in recent years (compared with hiring 6 to 8 years ago). Note, however, that the probability of representation in the tenure pool in a cross-sectional study such as this is completely confounded with time." (pp. 148â&#x80&#x93149)

  9. Avatar for Tina I
    Tina I

    As a women in science, I have read so many depressing statistics on women in science, but few of these compilations really address whether there is true bias.

    So I did an experiment :-)

    I am at a medical school where grant success is important for salary recovery and is the main factor in promotion and tenure.

    In year 1, I submitted grants under my full name, and my first name is undoubtedly not gender neutral.

    In year 2, I submitted grants using only my initials and my last name. My success rate went up 5-fold. This is an experiment with n=1, but I didn't want to repeat it.

    However, in year 3, the university adopted an electronic grants system that (unbeknownst to me) automatically used my full first name again. In this blind study, my success rate went down 5-fold exactly coincident with changing back to a female name on the cover page of the application.

    In year 4, I changed my name with the university to have it only be my initials, thus frustrating the automated system. My success rate went back up 5-fold.

    I am the same applicant. The outcome only differed when the reviewers knew that I am female. While the replicates are low, I do not plan to repeat the experiment again...

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