Thinking Like a Gender Economist 101

Do women face discrimination in job interviews, for reasons related to their gender? Do men actually earn more, for the same amount and quality of work? Do people face career consequences for stepping outside of traditional gender norms? Does having more women in decision-making positions affect the success of companies?

Gender economics is a field of study that tries to answer questions such as these through a greater understanding of how gender influences economic outcomes. However broad the questions may be, one common theme is the difficulty of arriving at a definitive answer. With this in mind, this article aims to serve as an introduction for those who have little or no prior exposure to applied economic research, and are curious to learn more about how economists investigate such complex questions. 

How do gender economists study these issues?

Applied economic research uses data and statistical methods to investigate relationships between social and economic phenomena. In gender economics, such a relationship could for example be whether women are discriminated against in the workplace, holding all other factors constant.

Imagine that we want to investigate, specifically, whether being a woman has any effect on the promotions one receives. We want to exclude countless other things that can affect the same outcome: quality of work, circumstances of the company, economic conditions of that year, employee’s race, age, experience, etc. To do this, we can imagine and construct a hypothetical scenario to evaluate; a parallel universe in which these exact same workers are not women, so as to compare the promotion rates amongst the two alternatives.

This is exactly the type of issue that an applied economist has training for, and a set of tools that are very handy to investigate the problem, For example, an economist named Heather Sarsons recently studied [1] whether a person’s gender influences the way others interpret information about their ability. In order to do this, she obtained data on medical referrals in the US and compared the referrals men and women surgeons receive after they perform particularly successful or unsuccessful surgeries.

To compare, she chose equally experienced men and women surgeons that performed similar operations on similar patients equally well. Precisely because she made sure to compare surgeons that were as similar as possible on important dimensions, it is statistically plausible to assume that the difference in referral rates she is left with is the result of how men and women are judged differently. [2]

How do gender economists think through relationships?

Another issue that an economist deals with is trying to understand in what way closely related issues link to one another. Recently a study[3] raised the point that countries led by women, such as Germany, New Zealand, Taiwan and Norway, are among those with the most effective Covid-19 responses. Could there be a connection, in particular, a set of explanations that can be traced back to specific characteristics of women leaders? Importantly, could women leaders have caused a better response in these countries?

There are certainly many possibilities: studies suggest that women tend to be more risk-averse[4], and this could have resulted in a different set of policy responses under conditions of uncertainty. It is also possible that countries that are better prepared for such crises and respond more efficiently, also have the social and economic infrastructure to elect women to serve in positions of high political power.

Even though both narratives are consistent with the phenomena observed (namely that some countries have been better at crisis response, and that they also happened to be governed by women), they tell different stories. One tells us we should perhaps put more women in positions of high-level decision making because they have a different attitude compared to men; the other tells us that well-functioning bureaucracies might result in higher women representation in important decision-making positions.

While both of these claims might be true to an extent, they represent two different ways of thinking about the same problem, and have different implications. Therefore, economists spend time approaching the same issue from many different angles, imagining different realities and seeing if the available data plausibly supports these arguments.

How do gender economists put forward recommendations?

Drawing on insights obtained from previous research, many solutions are put forward to address the identified problems. Researchers then often need to be able to provide advice to policymakers and organisations on how to navigate key issues, and what consequences one might face as a result of an intervention.

For example, we might hypothesise that mentoring women graduate students in early stages of their careers will help them be more successful: it’s certainly reasonable to assume so, but we might also be curious about the extent to which it helps, and compare it with the cost of achieving the same result through other interventions. Or we might have conflicting hypotheses about the direction the effect will take: if we frame women-political leaders in a certain way compared to men, are people more or less likely to vote for them?

Many gender economists test the outcomes of such interventions by randomly allocating participants of a study into two groups, only one of which will undergo an intervention. This method, called a randomised controlled trial, resolves the issue of finding a counterfactual scenario, hence simplifying the heavy lifting that a statistical toolkit has to do. This methodology has become vastly popular: readers might recall the names of 2019 Nobel prize winners in Economics, Abhijit Banerjee, Esther Duflo and Michael Kremer, who won the prize for their contributions in broadening the use of the method in economics.

Ultimately, combating gender inequality starts with what is identified as a key issue and understanding the sources of inequality. Going further, policy recommendations should be based on evidence on their effectiveness in resolving the issues. Being able to systematically think through some of the potential scenarios and channels with a gender economist’s toolkit, we are better able to observe and understand how gender influences economic phenomena and produce the rigorous evidence that guides policy.

[1] Sarsons, Heather (2017), “Interpreting Signals in the Labor Market: Evidence from Medical Referrals”, Working Paper.

[2] For the curious reader: Sarsons found out that referrals to the surgeon drop by 24% after a bad outcome when the surgeon is female compared with only a slight stagnation in referrals when the surgeon is male. Following an unanticipated successful surgery, however, referrals to male surgeons increased more compared to referrals to female surgeons.

[3] Garikipati, Supriya and Kambhampati, Uma (2020), “Leading the Fight Against the Pandemic: Does Gender ‘Really’ Matter?”. Available at SSRN:

[4] van Staveren, Irene (2014), “The Lehman Sisters Hypothesis”, Cambridge Journal of Economics, Vol. 38 No. 5.

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