Emil Palikot: Helping to Close the Tech Industry’s Gender Gap
The under-representation of women working in information technology (IT) fields has been a persistent pattern in the tech industry. Emil Palikot was excited that he may have found one way to address this. Having only recently moved to the Bay Area to take a position in the Golub Capital Social Impact Lab (GCSIL) at the Stanford Graduate School of Business, he came across an initiative, Dare IT, in his native Poland, that was seeing success helping women find employment in IT.
Palikot tells the story of how Aleksandra Bis and Natalie Piling, two women working in the IT field in Poland, came up with the idea for Dare IT in 2017. “They saw first-hand that it was very hard for women to break into IT,” said Palikot. “So they left their tech executive positions to focus full-time on creating this social impact organization.” Dare IT launched a program that matches senior professionals in the field with women who already have some tech skills but are looking for a way to find the right job or return to the field after a gap. Mentors help mentees network, build confidence, and boost skills that improve their employability. “They had thousands of applications and couldn’t keep up with the demand,” Palikot said. “I met with them to help design a randomized experiment to evaluate the success of their program.”
Palikot knew that these types of experiments were the bread and butter of GCSIL — and that this issue was important to the lab and its social sector partners. He brought his project idea to his supervisor, GCSIL Director and Economics of Technology Professor Susan Athey, to seek her support for moving forward. “I didn’t get the reaction I expected,” Palikot recalled, describing how Athey expanded his initial idea. “She said that we needed to find a more scalable solution. The challenge of finding good mentors was holding back Dare IT’s ability to meet the need. It was a different way of thinking big, a Californian approach to solving problems at scale.”
Palikot and Dare IT went back to the drawing board to develop a new approach: “We set up interviews with IT hiring managers in Poland and asked them why they weren’t hiring more women. They cited the lack of hands-on, practical experience and doubts about whether they could do the job.” They were also skeptical of online certificates and MOOCs (Massive Open Online Courses.)
So Dare IT created an online program called Challenges in which participants work on guided projects that allow them to signal their technical skills. Industry partners were happy to help develop these projects based on their recent business cases, including a UX design path where the goal was to develop a design of a mobile app. These guided projects become samples of high-quality end products that participants can describe during a job interview to showcase practical experience.
Palikot and his colleagues then conducted a set of randomized experiments to test the efficacy of the original and new approaches. “We found that Mentoring increased the chance of getting a job by over 40%, from 29% to 42% — a 13 percentage points overall improvement — and that the impact of Challenges was an increase from 20% to 29% probability of getting a job in the technology sector. But the Challenges program was much cheaper to implement, costing just $15 per person, and was very scalable.
They dug deeper to understand the differences in the effectiveness of these approaches. “Mentoring was better for more experienced people returning to the labor market — such as mothers — and women who were from smaller towns or who were transitioning between industries.” It was also beneficial for those who lacked connections to the sector, Palikot said. His analysis also helped them identify who was “not benefiting from the Dare IT program” and thus to guide Dare IT’s use of scarce resources. He sees the project as an ideal example of GCSIL’s mission “to help social impact organizations become more effective at what they do. Dare IT was already effective before we began working with them, but they didn’t know how to evaluate or scale their program. Now they are able to serve 1000s of people per quarter and know who to target and how best to use their scarce resources.”
Palikot had a diverse set of skills and experiences that helped prepare him for his work at GCSIL. After getting a master’s degree in Economics he worked in management consulting in Poland. “I got insights into how big business functions,” he recalled. “It felt like there was a big game being played among the consultants, the top management of the company and the boards. This got me interested in corporate governance and the incentives that shape how firms run.”
So he decided to get his PhD. “The best place to study this was Toulouse School of Economics,” he continued. “They had a number of big superstars in game theory, like Nobel Prize winner Jean Tirole. I started working on these theoretical questions but then drifted into more empirical work using big data. It was very exciting.”
At that time, new papers appeared on race and gender discrimination in online platforms. One showed that Black hosts on Airbnb were charging 12% less in New York City than an equivalent apartment owned by a white person, even though the guests were never likely to see the host. Palikot set out to replicate this study, looking at ride-sharing data in France where people could offer passengers a lift in their car for a drive between cities. He found that new Arab drivers on the platform earned 13–14% less on average than their French peers but that enabling reviews of drivers almost completely closed the earnings gap. “It was my first case of seeing how economics can make a positive impact in the real world.”
When the opportunity came up to join GCSIL, he was “over the moon,” recalled Palikot. “Susan is one of the biggest stars in this field of economics. I’m learning a lot from her and her distinctive way of looking at problems. And I enjoy the entrepreneurial spirit at Stanford.”
“What’s unique about the Golub Capital Social Impact Lab is being able to integrate cutting-edge tools from computer science and machine learning to address practical problems,” said Palikot. “We work with a lot of non-profits who have very thin budgets. It’s hard for them to hire the tech talent they need to do what we do. When we engage with them, they see the value of what we bring.” But it can sometimes be hard to know where to draw the line between doing the work needed for the experiment and providing broader technical assistance. “It can be complicated if you have a strong partnership. You believe in the organization doing good things and want to do all you can to help them be successful.”
He cited the example of a recent project with an Indian Ed Tech firm that had developed a free app to help children learn to read English. “It was built on sound educational principles and was often the best option for poor children to learn English. They had a catalog of stories. We created a personalized recommendation system a la what Netflix does for films. We tested to see if this made a difference and got amazing results: thanks to the recommendations, children would use the app longer, access more stories and other activities, and get higher scores on quizzes. And all we did was change the algorithm for the recommendations. It was a way to use tools that we know to help a lot of people.”
As he looks ahead at different career paths, he is attracted by the idea of bridging academia and industry. “Economists, particularly those who know AI and micro-economics, have very good outside options,” Palikot observed. “On the other hand, academics have the freedom to focus on topics that interest them most. I like to create real-world solutions in a principled way, but to still have the ability to generalize from what is learned. There are many extremely smart people in the private sector, but they focus on building value for their companies, with no incentive to extend innovations beyond.”