Bridging the Tech Industry’s Gender Gap

Research on internalised gender biases by INSEAD Professors Lucia Del Carpio and Maria Guadalupe may help explain why more women don’t enter the tech field.

Although computing and technology are among the world’s fastest-growing industries, the gender divide in the tech sector is widening. In 1995, 37 percent of computer scientists were women, but that number has since dropped to 24 percent, according to research from Accenture and Girls Who Code. Unless this trend is reversed, within 10 years, it is expected to sink further to 22 percent.

To better understand the source of this problem, INSEAD Assistant Professor of Economics Lucia Del Carpio and Associate Professor of Economics and Political Science Maria Guadalupe are conducting new research on internalised gender biases. Their fieldwork in Latin America demonstrates that showing successful female role models dramatically increases the number of applications from women to coding training programmes. This suggests that shifting women’s perceptions of what they can accomplish is perhaps the first step in bringing gender equity to the industry.

We spoke with Del Carpio and Guadalupe about their research and how to create meaningful, global change in the tech sector.

Q. What drew you to this topic?

Del Carpio: We observed patterns of occupational segregation. There are certain jobs in which you only observe women, and there are certain jobs in which you only observe men. Technology is one of those. We were also worried about recent changes in the economy and the skills that are considered valuable.

“The digital economy is growing a lot, so we wondered: Why are more women not getting into the tech sector?”

Guadalupe: That was the big question. What are the barriers that preclude more women from entering a sector that’s clearly growing—especially when a lot of traditional jobs are dying? Is the fact that there are few women in the industry a barrier to more women entering the industry?


Q. How did the research begin?

Del Carpio: Our hypothesis was that there are some gender norms that affect how you make a decision. Perhaps people don’t choose what they like or where they can make the most money because they don’t feel they belong or have the ability to enter certain sectors. I am Peruvian, and I found a company in Peru that was training only women to code. We started working with them to try to understand how successful the programme was, the type of women they were training and the impact that these women had in the labour market. Almost everybody they trained gets a job—most receive a threefold increase in income—but, for some reason, they were not getting enough candidates. We thought this was the right opportunity.


Q. What did the study involve?

Guadalupe: The first experiment involved creating a message that de-biases women from the perception that they cannot be successful in the tech industry.

Del Carpio: They were selling the programme by saying it would teach you how to code so you can have a career in the tech sector with more income. Our intervention added a component that addressed exclusively the issue of women in the tech sector. It noted that the programme was designed for women because we believe women have the ability to succeed in this sector and can provide a perspective the sector needs. We also stated that they developed a network of women, so you won’t be alone. But the key component of the intervention was to provide a role model—a woman like them from a low-income background who had taken the programme and transformed her life.


Q. What were your findings?

Guadalupe: That basic message doubled our application rate. Women were twice as likely to apply for the training when they heard that information. This is a very powerful effect. In the follow-up experiment, we tried to see what information in that initial message women cared about. We found they responded to the message that women can have high returns and be successful in tech. The role model information had a big effect, too. When we showed prospective applicants models of women who did the training, application rates increased. However, telling women they would have a network of women when they graduated had no effect on application rates. So the fact that people say the reason women do not apply to tech is because they do not have a network of women afterwards does not seem to be borne out by the data.

Del Carpio:

“It was a very simple intervention, but it speaks to the strength of the barrier—how a simple message at the right moment can have a lot of impact.”

If you are able to address women’s concerns at the right time, you can completely change who is selecting the industry.

Guadalupe: We started in two cities in Peru and expanded to Mexico, and we kept getting the same result. Our research suggests that there’s some barrier that makes women not apply, but the reverse could be true if you thought about a female-dominated profession, such as nursing. The dominant stereotype in the industry has an effect on your likelihood to try to enter that industry because you doubt your success.


Q. How do you hope your results will be applied?

Guadalupe: If companies and educational institutions want to attract more women, they have to give examples of women being successful. Another important part is to create a gender-neutral environment. For example, if all the firm’s networking happens in the evening after work and women are more likely to have to go home and take care of children, that’s going to discriminate against women.

Del Carpio:

“We want to make people and organisations conscious about how they advertise their jobs or careers. It’s not neutral.”


If we are able to correct certain misperceptions at the right time, you can have much better choices for society. You can allow women to succeed in technology.

Editor’s note: Check out this INSEAD webinar featuring Professor Lucia Del Carpio for more information on her research.

Print Friendly, PDF & Email

Leave a Reply

Your email address will not be published. Required fields are marked *