Where do men and women economists publish?

We all know that economics is a male-dominated discipline on average. But how does the representation of women look across different journals? Armed with Academic Sequitur* article metadata (going back to around 2000), I determined the genders of 82% of all authors in the data and calculated the prevalence of male authors by journal for 50 top-ranked journals in economics.** To see how things have changed over time, I also repeated this exercise with articles that were published in 2018-2019.

Just to set some expectations: in the gender-matched dataset, 82% of author-article observations are male (80% when restricted to 2018-2019). So if a journal has, say, 75% male authors, it’s doing better than average. With that, here are the top 10 male-dominated journals, ranked by share of male authors over the entire data period.*** To be super-duper scientific, 95 percent confidence intervals are also shown, and I added a vertical line at 82.1% for easy benchmarking to the average.

So three of the top five journals (Econometrica, QJE, and ReStud) have also been the three most male-dominated journals, at least historically, with 90%, 89%, and 88% male authors, respectively. A fourth (Journal of Political Economy) also barely made the top ten, with 87% male authors. These numbers also illustrate that there’s not much difference between the #1 and #10 male-dominated journal.

Encouragingly, there are some improvements as well. The share of male authors in QJE was almost 9 percentage points lower in 2018-2019 compared to the whole sample period. JPE‘s share decreased by 7 percentage points, putting these journals in the top 5 most improved. If ranked based on 2018-2019 shares, Econometrica would be #6, ReStud would be #11, QJE would be #24, and JPE would be #28, just barely in the bottom half.

The Journal of Finance, by contrast, has taken a small but statistically significant step backwards, with a 3 percentage point increase in the share of male authors. If ranked by the 2018-2019 male ratio, it would be number 1.

Here are the least male-dominated journals (rank 41-50). Economics of Education Review and JHR are both about 66% male. Surprisingly, both applied AEJs are in the least male-dominated group (AEJ: Applied is 71% male; AEJ: Policy is 74%). This may be because they are newer, though it is worth noting that their overall average is below the 2018-2019 average of 80%.

Here’s the rest of the pack. First, here are journals ranked 31-40 on the male-dominated scale (i.e., next 10 least male-dominated), ordered by share male in the overall sample. AER and ReStat are in this group, with 80% and 81% male, respectively. Thus, AER has historically been an outlier among the top five on this dimension (using 2018-2019 shares, it would rank #19, right in the middle of the other top five journals).

Here’s rank 21-30, all in the low-to-mid 80’s.

And here’s rank 11-20. AER: Insights is 84% male. The other two AEJs are in this group, with males representing about 85% of all author-article observations.

These patterns do not necessarily reflect discrimination: the representation of women in a particular field will obviously make a difference here (as evidenced by the positions of macro and theory journals). I leave it up to you, the reader, to interpret the numbers.****

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* Academic Sequitur is a tool I developed to help researchers keep up with new literature. You tell us what you want to follow, we send you weekly (or daily!) emails with article abstracts matching your criteria.

** Close to 1.5 percent of the initial observations are dropped because only the initials of the author are available. About 16.5 percent of the observations cannot be mapped to a name for which the gender is known. This includes a lot of Chinese names, for which it is very difficult to determine gender, according to my brief internet research. Names which can be both male and female are assigned a gender based on the relative probability of the name being male.

*** Each observation in the sample is an article-author, so those who publish in a journal multiple times will contribute relatively more to its average. Each coefficient is from a journal-specific regression. Confidence intervals are based on heteroskedasticity-robust standard errors.

**** If you want the numbers underlying these graphs, you can download the csv file here.

Machine learning in economics

Machine learning seems to be everywhere in economics these days. I wondered – has these been a gradual trend or is this a sudden explosion? So I again turned to Academic Sequitur data. This time, I decided to stick to NBER working papers as my data source, largely because they lead journal publications by a few years. I looked for the following terms in the abstract or title: “machine learning”, “lasso”, “neural net”, “deep learning”, and “random forest”. The graph below shows the percent and number of NBER working papers that meet these criteria over time (on the left and right y-axis, respectively).

An explosion indeed! Virtually no paper abstract/titles mention anything machine-learning related in the abstract in 2000-2014. Then we have a respectable five papers in 2015, one paper in 2016, followed by 15 papers in 2017, 22 papers in 2018, and five papers so far this year. As a percentage of total papers, the machine learning papers are small, however, making up at most 1.5% of total papers. Whether the numbers stagnate or keep skyrocketing remains to be seen!

And in case you’re wondering, the prize for the first NBER working paper to utilize machine learning goes to…”Demand Estimation with Machine Learning and Model Combination” by Patrick Bajari, Denis Nekipelov, Stephen Ryan, and Miaoyu Yang, issued in February of 2015.

Update: here’s how the graph would look if you also counted “big data” as indicating machine learning. Prize for first NBER paper to mention “big data” goes to “The Data Revolution and Economic Analysis” by Liran Einav and Jonathan Levin, issued in May 2013.

Forthcoming (if this post is popular): published papers utilizing machine learning!