How to Get More Women to work in Tech

Dear students, review the attached article on efforts to get more women working in the tech industry. Consider the main points of the article and how the researchers were able to isolate the most significant factors. Finally, based on the outcome of the study, what do you think are the implications for the future of women in non-traditional occupations?

Note: Discussion responses should be a 5-7 sentence paragraph addressing questions asked within the writing prompt.

How to Get More Women to

Work in Tech

Role models and reassurance about potential success can impact women’s career decisions.

‘What made you decide to join your profession?’

When people are asked this question, they often

give answers such as: “I liked such-and-such subject

in school”, “I was inspired by so-and-so” or “It pays

well”. In most cases, there is an unspoken

assumption: They also believed that they had a

reasonably good shot at succeeding in this career.

According to the classic Roy model, individuals

choose their occupation based on a rational decision

process. They try to maximise their income, taking

into account their skills and relative rewards in

different sectors. It’s all about optimisation: People

go where they believe they will have the greatest

comparative advantage.

In the case of women, assumptions that certain

sectors do not welcome them may limit their

willingness to join. Consider the coding profession

in Peru, where women represent only 7 percent of

the workforce. Is this under-representation strictly a

matter of personal preference, or is it at least

partially due to stereotypes and social norms that

dissuade women from choosing this sector? Could

some women shun a career simply because they

don’t believe they could thrive in it?

In two field experiments in Latin America, we

observed that advertisements that included a female

role model and also corrected misperceptions about

women’s ability to pursue a career in tech

significantly increased the number of suitable female

applicants to a software coding bootcamp.

A bias-correcting intervention

We partnered with a non-profit organisation that

helps young women from low-income backgrounds

become digital coders via an intensive five-month

training programme. This international organisation

was interested in increasing application rates.

In our first experiment, which took place in Peru, we

created two versions of the programme registration

webpage. Both included basic information about the

training as well as the application form. However, in

one version, we added a message to correct biased

perceptions and beliefs about the role of women in

tech. This short message had three core

components:

1. It asserted that women can be successful in the

tech field.

2. It emphasised that the training gave access to a

network of women in the sector.

3. It told the story of a recent graduate (role model).

Though we did expect our intervention to have an

impact, we were stunned by the results.

Reassurance about career prospects more than

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doubled the number of applicants: 15 percent of

women exposed to our bias-correcting message

applied for training vs. only 7 percent of those who

visited the basic webpage.

In addition, when we compared the top 50

candidates in each group, those from our treatment

group garnered significantly higher scores on

cognitive and tech-specific ability tests

administered during the selection process.

What makes the needle move

We then ran a second experiment, this time in

Mexico, to figure out which of the three components

of our bias-correcting message resonated most with

applicants. To do this, we created four versions of

the training provider’s registration webpage. The

control one included the full message. In each of the

three other versions, we skipped one component:

One didn’t mention how women can be successful in

tech, one didn’t emphasise the existence of a peer

network and one didn’t feature a role model.

Based on this second experiment, we concluded that

the presence of a role model and the assertion that

women can be successful in tech are most important.

Their absence reduced applications by 23 and 18

percent, respectively, vs. the control group. (The

version that didn’t mention the peer network

yielded about the same application rate as the

control one.)

Deflecting self-segregation due to biases

By now most of us have heard that women earn 79

cents on the dollar compared to men. While it’s

true that women with full-time, year-round jobs

make less money than their male counterparts, most

of this gap isn’t due to straight-up sexism by

discriminatory employers. To a larger extent, it

reflects the fact that women tend to work in lower-

paid, female-dominated sectors.

Although not a panacea (and none exists), one way

to lessen the gender wage gap, over time, would be

to nudge more women towards better-paying

careers. The tech industry is only one example. Not

all women will be interested in such careers and

that’s fine. The idea is to ensure that gender norms

and self-segregation biases do not unnecessarily

steer away women, notably those who do show

interest and aptitude, from certain careers.

Our results indicate that in a male-dominated sector,

simple tweaks to the ‘selling message’ can

significantly impact women’s career decision

making. HR departments, policymakers and schools

may want to take note. Meanwhile, all of us should

cultivate an awareness of how social identity cues

and prescriptions can have subtle yet far-reaching

consequences on women’s behaviours.

Lucia Del Carpio is an Assistant Professor of

Economics at INSEAD.

Maria Guadalupe is an Associate Professor of

Economics and Political Science and the Academic

Director of the Randomized Control Trials (RCT)

Lab at INSEAD.

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