Publications
Publications
Delecourt, Solène. "The effect of relieving time constraints on the business performance of women-owned businesses: A field experiment". Research Policy, 54, no. 7, (2025): 105231.
Guilbeault, Douglas, Solène Delecourt, Tasker Hull, Bhargav Srinivasa Desikan, Mark Chu, and Ethan Nadler. "Online images amplify gender bias." Nature 626, no. 8001 (2024): 1049-1055.
Best Paper Prize at the 2022 International Conference on Computational Social Science
Semi-finalist at the Wharton People Analytics Competition, 2021
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Grace Hart, Chloe, Charlotte H. Townsend, and Solène Delecourt. "Who Believes Gender Research? How Readers’ Gender Shapes the Evaluation of Gender Research." Social Psychology Quarterly (2024): 01902725241234855.
Delecourt, Solène, and Anne Fitzpatrick. "Childcare matters: Female business owners and the baby-profit gap." Management Science 67, no. 7 (2021): 4455-4474.
Chatterji, Aaron, Solène Delecourt, Sharique Hasan, and Rembrand Koning. "When does advice impact startup performance?." Strategic Management Journal 40, no. 3 (2019): 331-356.
Age and Gender Distortion in Online Media and Large Language Models
Conditionally accepted at Nature
Policy reports, media coverage, and workplace interviews reveal that older women face a dual bias against both their gender and their age, negatively impacting not only their mental health, but also their professional status and opportunities. Yet strikingly little is known about the online prevalence of this dual bias and its broader impact. Here, we show that age-based gender bias pervades the internet through images, videos, and textual data, with effects on both people and artificial intelligence. First, we examine the age representation of women and men across 3,435 social categories (e.g., “doctor” or “friend”) in over 1.3 million recent images from Google, Wikipedia, IMDb, and Flickr, alongside thousands of YouTube videos. Across datasets and measurement strategies, women are consistently represented as younger than men. Next, we conducted a nationally representative, pre-registered experiment showing that searching for Google images of occupations amplifies bias in people’s beliefs about the age of women and men, as well as their preference for hiring younger women and older men. We then generalize beyond visual content by showing that women and men are represented as younger and older, respectively, in nine language models trained on billions of words from the internet. We conclude by showing how ChatGPT – a popular artificial intelligence algorithm trained on internet data – perpetuates age-based gender bias in workplace applications. When generating resumes, ChatGPT assumes women applicants are younger and less experienced than men, and it rates resumes from older applicants — and especially men — as higher quality. These findings capture the multimodal and multidimensional nature of gender bias online, revealing unique challenges and opportunities in the fight against gender inequality.
Douglas Guilbeault, Solène Delecourt, and Bhargav Srinivasa Desikan
The Uneven Impact of Generative AI on Entrepreneurial Performance
Revise & Resubmit at Management Science
Winner of the Wharton People Analytics Competition (2024)
Winner of the Best Paper Award by the STR Division of the Academy of Management (2024), Industry, Competition, and Strategic Entrepreneurship Track
Finalist and Second Place, SMS Annual Conference Best Paper Prize Competition (2024)
AOM best paper designation, STR Division (AOM 2024)
Nominated for the Carolyn B. Dexter Award (AOM 2024)
Nominated for Research Methods Paper Prize at the Strategic Management Society Annual Conference (2024)
Scalable and low-cost AI assistance has the potential to improve firm decision-making and economic performance. However, running a business involves a myriad of open-ended problems, making it difficult to know whether recent AI advances can help business owners make better decisions in real-world markets. In a field experiment with Kenyan entrepreneurs, we assessed the impact of AI advice on small business revenues and profits by randomizing access to a GPT-4-powered AI business assistant via WhatsApp. While we are unable to reject the null hypothesis that there is no average treatment effect, we find the treatment effect for entrepreneurs who were high-performing at baseline to be 0.27 standard deviations greater than for low performers. Subsample analyses show high performers benefited by just over 15% from the AI assistant, whereas low performers did about 8% worse. This increase in performance inequality does not stem from differences in the questions posed to or advice received from the AI, but from how entrepreneurs selected from and implemented the AI advice they received. More broadly, our findings demonstrate that generative AI is already capable of impacting—though in uneven and unexpected ways— real, open-ended, and unstructured business decisions.
Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz, Rembrand Koning
The Mixed Impacts of Gender Stereotypes in Face-to-Face Market Transactions: A Field Experiment among Microenterprises
Revise & Resubmit at Organization Science
Entrepreneurs often sell goods and services directly to customers in face-to-face transactions, particularly in developing countries. While existing literature suggests that gender stereotypes likely pattern customers’ behavior during in-person sales transactions, research offers divergent expectations about whether gender stereotypes would advantage or disadvantage female sellers relative to men. To reconcile these competing expectations, we introduce a stage-based theory of gender inequality in face-to-face transactions. Our theory suggests that gender stereotypes can disadvantage or benefit female sellers at different stages of customer-seller interactions. To test this theory, we conducted a novel field experiment in India, with research actors posing as sellers in outdoor food markets. The sellers in our experiment ran identical microenterprises and used standardized behavioral protocols. We gathered data about customers’ behavior at three critical stages of the sales process. We found that female sellers’ (dis)advantage ebbed and flowed across these stages: customers were more likely to approach women than men, requested more price discounts from women, and ultimately, purchased more from women. Our theoretical model and field experimental findings reveal how relative (dis)advantages can morph across transaction stages and cumulatively contribute to gendered differences in entrepreneurial experiences and performance.
Solène Delecourt, Laura Doering and Odyssia Ng
Equalizing business characteristics can close the gender gap in micro-business performance
Revise & Resubmit at the Strategic Management Journal
Women-owned businesses earn less than men-owned businesses worldwide, particularly among small firms in developing countries. One potential explanation for this gap is that female business owners may generate lower returns for a given business compared to their male counterparts. If there is indeed a gender gap in business returns, then the gender performance gap could be attributed to two potential factors: differences in the owner's human capital, or female-led businesses facing constraints in market demand. To test whether women have lower returns than men, we conducted a pre-registered field experiment where we equalized business characteristics for men and women, including inventory, hours, setup, and location. Specifically, we created our own businesses in real markets. We set up pairs of market stalls in three different markets and supplied them with identical goods. We then recruited experienced sellers from other local markets and randomized them to our stalls. Prior to our experiment, we found that men earned at least 50% more than women. But men and women also differed in various characteristics, such as women having lower levels of education and operating smaller businesses. Our experimental results demonstrate that equalizing business characteristics reduces the gender gap in business performance to zero. This indicates that the gender gap in business performance is not solely rooted in differences in returns. Instead, our findings suggest that men and women have the potential to achieve equal returns on business resources.
Solène Delecourt and Odyssia Ng (World Bank)
Firm location, childcare, and the gender profit gap
Revise & Resubmit at the Strategic Management Journal
Gender inequality is ubiquitous, including among small business owners in developing countries, which constitute the majority of businesses worldwide. To understand inequality in business performance, research has primarily focused on improving the performance of existing firms by providing capital, advice, or training. These strategies often increase the performance of male-, but not female-owned, firms. In this paper, we explore whether gender differences in a business' initial conditions can help explain this puzzle. If men and women start different kinds of businesses, and those initial choices affect profitability but are hard to change ex-post (are ``sticky"), this may limit potential profitability. To answer this question, we use rich quantitative data from a representative sample of 3,077 businesses in Kenya. In this context, women make 47% fewer profits than men. We find that women tend to locate their businesses in less profitable locations. While firm profits grow with the distance from home, women are more likely to locate their businesses close to home. Using census data of all firms in the study area, we find that male entrepreneurs locate in places with less competition. Male business owners are over five times more likely to be a monopolist in their sector within a specific radius than women. Though sector and location are important for profits, the majority of owners do not update these initial decisions after founding their business: in the two years preceding our study, only 7% of entrepreneurs changed either sector or location. Our findings suggest that these sticky conditions are more binding for women with greater childcare responsibilities than for men or other women. Our results point to the importance of initial conditions in perpetuating the gender gap among small firms in developing countries.
Solène Delecourt, Anne Fitzpatrick, Layna Lowe, Anya Marchenko
Global Evidence on Gender Gaps and Generative AI
Generative AI has the potential to transform productivity and reduce inequality, but only if used broadly. In this paper, we show that recently identified gender gaps in AI use are nearly universal. Synthesizing evidence from 16 studies that surveyed 100,000 individuals across 26 countries, along with new data on the gender of AI platform users, we show that the AI gender gap is present in nearly all regions, sectors, and occupations. Using data from two studies that offered participants the chance to use AI tools, we then show that even when the opportunity for men and women to access AI is equalized, women are still less likely to use AI. Our findings underscore the critical need for targeted interventions that go beyond access to address the structural and behavioral barriers that have resulted in a global gender gap in AI use.
Nicholas G. Otis, Solène Delecourt, Katelyn Cranney, Rembrand Koning
Who wants to work on which ideas? Evidence from the market for startup talent
Nominated for Best Conference Paper Award at the Strategic Management Society Annual Conference 2023
Nominated for Research Methods Paper Prize at the Strategic Management Society Annual Conference 2023
Startups employ few women, especially at their earliest stages. An underappreciated implication of this demographic inequality is that startup ideas targeting women may struggle to attract the talent needed for venture success. If talent prefers to work on ideas that resonate with their backgrounds, or if workers feel uncomfortable working on ideas focused on other demographic groups, then a lack of workforce diversity may have an unequal impact on which ideas succeed. We test this thesis using a field experiment and observational data on the universe of high-tech startup workers in the U.S. In our experiment, we randomize the gender-focus of startups ideas, for example changing an e-bike startup to specifically focus on female commuters. Results from our (in-progress) field experiment show that shifting a non-female-focused startup idea to be female-focused reduces the probability a candidate applies to the job by 15 percentage points. This effect size is equivalent to reducing a job’s posted annual salary by $30,000. Concealing the gender focus of an already female-focused startup idea has equivalent but opposite effect. These effects are rooted in the choices of male job seekers; the minority of women who look for startup jobs on our job-search platform are more likely to apply to female-focused ideas. We then turn to our observational analysis covering the near-universe of U.S. startups in PitchBook. We use word-embedding models to measure how much an idea focuses on women. We find that female-focused ideas attract 25% fewer employees, that those employees have on average 30% less work experience, and that these gaps are driven by the fact that 80% of startup employees are men. Finally, building on models of bias in entrepreneurial experimentation, we find that these gaps only hold for early-stage firms, female-focused ventures that raise substantial capital exhibit no talent gap. Our findings illustrate how heterogeneity in beliefs about which early-stage ideas are "worthy of working on" can impact the rate and direction of innovation.
Solène Delecourt, Rem Koning, and Sahiba Chopra