How to reduce the gender gap in graduate readings [*]

"The Gender Readings Gap in Political Science Graduate Training" by Heidi Hardt, Amy Erica Smith, Hannah June Kim, and Philippe Meister was recently published in the Journal of Politics and featured in a Monkey Cage blog post. The Kim Yi Dionne header for the Monkey Cage post indicated that:

Throughout academia, including in political science, women haven't achieved parity with men. As this series explores, implicit bias holds women back at every stage, from the readings professors assign to the student evaluations that influence promotions and pay, from journal publications to book awards.

The abstract to the JOP article indicates that "Introducing a unique data set of 88,673 citations from 905 PhD syllabi and reading lists, we find that only 19% of assigned readings have female first authors". This 19% for assigned readings is lower than the 21.5% of publications in the top three political science journals between 2000 and 2015 (bottom of page 2 of the JOP article). However, the 19% is based on assigned readings published at any time in history, including authors such as Plato and Sun Tzu. My analysis of the data for the article indicated that 22% of assigned readings have female first authors when the assigned readings are limited to assigned readings published between 2000 and 2015 inclusive. The top three publications benchmark therefore produces an estimate of the gender readings gap in political science graduate training for 2000 to 2015 publications that is less than one percent and trivially advantages women.

Figure 1 in the Hardt et al. JOP article reports percentages by subfield, with benchmarks for published top works, which I think are articles in top 10 journals; the first and third numeric columns in the table below are data reported in Figure 1. Using the benchmark for published top works, my analysis limiting the assigned readings to assigned readings published between 2000 to 2015 inclusive (the middle numeric column) produced a difference greater than 1% that disadvantaged female first authors for only one of the five subfields with benchmark data (comparative politics):

For an example topic most relevant to my work, the Hardt et al. Figure 1 gender gap for American politics is 3.41 percentage points (15.66 compared to 19.07), but falls to 0.61 percentage points (18.46 compared to 19.07) when the time frame of the assigned readings is set to the 2000-2015 time frame of the top publications benchmark. Invocation of an implicit bias that holds back women might be premature if the data indicate a gap of less than 1 percentage point in an analysis that does not include relevant control variables such as any gender gap in how "syllabus-worthy" publications are within the set of top publications. The 5.50 percentage point gender gap for comparative politics might be large enough to consider implicit bias in that subfield, but that's a localized concern.

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NOTES

1. [*] The post title alludes to this tweet.

2. The only first authors coded female before 1776 are Titus Livy and Sun Tzu (tab surname1 if female1==1 & year<1776).

3. Code below:

* Insert this command into the Hardt et al. do file after Line 11 ("use 'Hardt et al. JOP_Replication data.dta', clear"):
keep if year>=2000 & year<=2015

* Insert these commands into the Hardt et al. do file after new Line 124 ("tab1 gender1 if gender1 < 3 [aweight=wt] // THE TOPLINE RESULTS WE REPORT EXCLUDE THOSE 304 OBSERVATIONS"):
tab1 gender1 if gender1 < 3 [aweight=wt] // This should report 21.86%
tab1 gender1 if gender1 < 3 // This should report 22.20%

* Insert this command into the Hardt et al. do file before new Line 184 ("restore"):
tab topic mn

* Run the Hardt+et+al.+JOP_Replication+code-1.do file until and including new Line 126 ("tab1 gender1 if gender1 < 3 // This should report 22.20%"). These data indicate that, of first authors coded male or female, about 22% were female.

* Run new Line 127 to new Line 184 ("tab topic mn"). Line 184 should output data for the middle column in the table in this post. See the "benchmark_teelethelen" lines for data for the right column in the table.