What role does gender play in economic decisions? An interview with Dr. Helena Fornwagner

Andrew Gordon



May 2023

What role does gender play in economic decisions? An interview with Dr. Helena Fornwagner

Andrew Gordon



May 2023

How much of who we are and the choices we make is shaped by the gender we identify with, or our sex? This is a question that has fascinated people for generations and sparked many debates. As our society's views on gender and sexuality have evolved, understanding the impact of these factors on human behavior has become even more important and nuanced.

In a recent interview, researcher Dr. Helena Fornwagner discusses her recent paper that used an innovative online experiment with both transgender and cisgender participants. Their goal was twofold: first, to see whether gender or sex better accounted for variations in people's choices; and second, to test whether prompting people with masculine or feminine mindsets fundamentally changed their behavior.

You can read the full paper, published in Nature Scientific Reports, here.

Could you give us a little introduction about yourself, your background, and your area of expertise?

My name is Helena Fornwagner and I’m a lecturer at the University of Exeter. I'm originally from Austria, where I completed my PhD at the University of Innsbruck, in experimental economics. As a behavioral expert and experimental economist, I work towards answering questions using tools such as experiments.

I'm a keen observer and try to find questions that I believe are worth exploring from a scientific perspective, but also for the general public. In general, I'm interested in understanding what shapes our behavior, including incentives, emotions, or biological factors. Recently, I've been focusing on the latter, particularly gender and the biological component of it, i.e., biological sex. Today, we'll be discussing a study that explores this very topic.

We’re talking today about your paper regarding the impact of gender on economic decisions. I was wondering if you could start off by defining gender and how it is different from sex?

In our paper, we rely on definitions set by other scientific papers. Specifically, when we use the term "sex", we refer to the biological component assigned at birth by others. When we use the term "gender", we refer to a broader range of aspects including cultural, behavioral, social, and psychological aspects of being male or female. We acknowledge that there are different ideas and caution is necessary, but we use the definitions commonly used by others.

Your paper is specifically talking about gender differences in economic decisions. Why is it important to examine economic decisions in this context?

We make economic decisions every day. For example, when we spend money at the supermarket, invest in the stock market, or donate. Investigating what drives these decisions is very important. According to the behavioral and experimental literature, one aspect that influences what we decide is gender.

When we started this project, we realized that papers were often not very precise about the term "gender," especially in countries where there is only one word for gender and sex, such as German. Researchers were not clear about what they were measuring.

So we were interested in exploring the extent to which the biological sex can account for human behavior or other, gender-related aspects. In other words, this is looking a bit into the idea of whether it’s nature or nurture driving how we decide in economic domains. We were fortunate to have access to a remarkable and unique subject pool, i.e. cis- and transgender individuals, that allowed us to tease apart these two concepts and gain a better understanding.

Can you talk a little bit about how the study was designed? Who were the participants, and how did you approach measuring economic decisions?

Firstly, we adopted an innovative approach in our experimental study by recruiting transmen and transwomen alongside cismen and ciswomen. We utilized participants' self-identification of gender and sex and employed established scaling methods from psychological and medical science fields.

This approach offers the advantage of distinguishing between the influence of sex and gender on behavior based on having cis- and transgender participants. To clarify, let's consider an example: Generally, a ciswoman possesses a female sex and identifies with a feminine gender, whereas a transman has a female sex but identifies with a masculine gender. Consequently, any disparities observed in the behavior of these two groups of subjects may be attributed to differences in gender rather than sex.

We measured their degree of competitiveness, which reflected their willingness to enter tournaments. Additionally, we investigate their level of risk-taking by having them invest in a simple lottery, with more investment indicating greater risk-seeking behavior.

Finally, we looked into the participants' level of altruism by giving them an endowment and asking them to decide how much money they wanted to keep for themselves and how much they wanted to donate to different charities.

Our first goal was to analyze how the behavior of these groups differs across the three domains we measure (competitiveness, risk-taking, and altruism). Second, we were also interested in understanding whether priming a participant with typically male or female-gendered words affects behavior. For example, we wanted to see if we could change the behavior of cis-people more by priming than trans people.

To do this, we conducted a priming intervention before measuring competitiveness, risk, and altruism. The intervention involved a simple word search task that primed participants with neutral, male, or female words. Priming means nothing else than subconsciously activating a specific mindset. We then compared the behavior of the different subject groups depending on the priming they experienced.

So, our paper included (what we call) a correlational approach, which compared behavior across subject groups, and (what we coined as) a causal approach, which compared behavior within one subject group across different priming conditions.

It was interesting that you used Prolific filters to identify subjects, but then classified them according to survey data. How come?

I think it's always good to include an extra layer of security. So, use the filters, but double-check. Once they were in the study we asked them ‘what is your biological sex’ and ‘what is your gender’? We gave many different options for the gender question, including transgender.

In addition to that, we also measured gender on a continuous scale. We used a questionnaire called the Bem Sex-Role Inventory. Based on a participant’s responses, you are told how male or female they are and then you can also transfer that to different binary categories if you want.

The good news is that your filters on Prolific are very well-calibrated. Regardless of which approach we used, the categorization that we got from the Prolific filters did not really change.

Can you walk us through the main results?

It's a very quick answer, which is that regardless of the economic behavior we were looking at, we generally found that neither gender nor sex play a role in how people behave in our study. We took a number of steps to ensure the validity of these findings including pre-registering, power analyses, and robustness checks for all statistical analyses, still the result remained the same - we do not see that gender or sex within the sample we recruited on Prolific plays a role in behavior.

It came across in the paper that this was in contrast to what is typically shown. While this may not be specifically true for economic games, it is generally observed in literature that such differences exist. Do you have any hypothesis for why this was not the case in your study?

There is an anecdote I can share. I still run experiments in the lab and online, and it seems that behavior is not always consistent. For example, students may behave differently than non-students, particularly with respect to the domains we explore.

Fortunately, others have, e.g.,  written about competitive behavior and conducted amazing meta-analyses showing that, for example, there is a gender gap in competitiveness among students. Men generally compete more than women. However, the more non-students you sample, the less this gap appears.

So it could also be a bit of an age effect here - as we age and gain more experience, it's perhaps not surprising that we behave differently than when we were toddlers. However, based on this meta-analysis then, it seems that our results are consistent with other studies.

There is a parallel here between what you are discussing and some very famous social psychology research in prejudice work where they looked at the impact of category versus trait priming. I wonder if you think that, in the case of your priming, it would be valuable to try priming traits like "bold," "risky," "macho," and "brave" to prime masculine categories?

I have, with a different group of co-authors,  actually already published a related priming study in Nature Communications. That study focused on priming ‘power’, which is a trait that can be associated with being male. While I agree that your suggestion is very interesting, I am not sure if I would combine my research on gender identities with priming in the future. My main focus is on people with different gender identities and how they behave. If I had more funding, I would explore both areas.

What’s next for you in this area of research?

We have a number of studies ongoing and under review at the moment, so I can’t really discuss them at this point. But we are continuing in this theme of work for the present and hope to have more results to share soon!

Where can our readers stay up-to-date with your research?

The best place to keep up-to-date would be my website or by following me on Twitter (@HFornwagner).

This research was carried out using the Prolific platform and our reliable, highly engaged participants. Sign up today and conduct your best research with our powerful and flexible tools.

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