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Abstract
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Before
This RCT aims to measure the impact of a 24-month STEM training initiative designed for first-generation women engineering students in India. Deployed nationwide by an Indian education start-up, the program employs a holistic strategy to overcome multifaceted barriers faced by women in STEM fields. By fostering a women-only environment, providing online accessibility, and emphasizing self-directed learning, the initiative seeks to address cultural, institutional, and psychological challenges hindering women's success in STEM. This study will evaluate two cohorts of the program: 2024-2026 and 2025-2027. The study, spanning 2023-2028, aims to evaluate the WE program's efficacy in enhancing participants' technical and higher-order skills, ultimately influencing their labor market outcomes once these women graduate college. The study will be conducted in India. The data collection will predominately be online.
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After
This RCT aims to measure the impact of a 24-month STEM training initiative designed for first-generation women engineering students in India. Deployed nationwide by an Indian education start-up, the program employs a holistic strategy to overcome multifaceted barriers faced by women in STEM fields. By fostering a women-only environment, providing online accessibility, and emphasizing self-directed learning, the initiative seeks to address institutional, and psychological challenges hindering women's success in STEM. This study will evaluate two cohorts of the program: 2024-2026 and 2025-2027. The study, spanning 2023-2028, aims to evaluate the WE program's efficacy in enhancing participants' technical and higher-order skills, ultimately influencing their labor market outcomes once these women graduate college. The study will be conducted in India. The data collection will predominately be online.
Update: the program for Cohort 2 has been put on hold.
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Last Published
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February 02, 2024 04:29 PM
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March 27, 2026 05:50 PM
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Intervention End Date
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December 31, 2025
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August 31, 2026
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Primary Outcomes (End Points)
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Technical skills (evaluated through a coding assessment, evaluating quality of code-writing, coding presentation, group projects)
College GPA,
Labor market outcomes (evaluted through mock interviews conducted by recruiters and labor market outcomes including earnings, etc.)
Confidence: self report and perceived (self report confidence measured through generalized self efficacy scale and perceived confidence measure through scale provided to interviewers in mock interview sessions)
Collaboration Skills (evaluated through self-report measures, reading the mind in the eyes test, and an adapted version of the lab-in-the-field experiment developed by Weidmann and Deming (2021))
Professional Network Measures (such as LinkedIn network growth)
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After
(1) Data analysis skills, measured via a screen-recorded coding assessment with trained rater evaluations; (2) Teamwork and collaboration, measured via survey-based workplace scenarios; (3) Career aspirations and orientation, measured via survey responses about post-college plans and career path preferences; (4) Professional development and labor market readiness, measured via LinkedIn profile data.
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Primary Outcomes (Explanation)
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Primary outcomes are organized into four pre-specified families. Family 1 (Data Analysis Skills): a summary composite index of data analysis competency constructed from trained rater evaluations of screen-recorded coding sessions, capturing ability to structure analytical tasks, work iteratively with data, exercise independent judgment, and communicate findings. Family 2 (Teamwork): measures of teamwork orientation and collaborative decision-making from survey responses to workplace scenarios, capturing whether the program shifts participants toward team-efficient versus self-reliant behaviors. Family 3 (Career Aspirations): measures of career plans and preferences from survey responses about post-college plans and career path rankings, capturing STEM orientation, role preferences, and willingness to pursue high-growth trajectories. Family 4 (LinkedIn): measures of professional development and labor market readiness from LinkedIn profile data, capturing investment in professional identity, signaling of technical competencies, and professional network development.
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Experimental Design (Public)
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The program is oversubscribed. For each cohort (2024 and 2025), we will randomize 400 participants who reach the final phase of the selection process to be part of the 24-month online learning experiential program. We will randomly select 200 participants to be part of the program and the other 200 to serve as the comparison group. We will do this for two cohorts. In total, we will randomize 800 participants across two cohorts.
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After
We evaluate the sixth cohort (2024–2026) of a national online training program for women engineers in India. The program is highly oversubscribed, with the number of students reaching the final selection stage being double the program's intake. We leverage this oversubscription to implement a randomized evaluation. After baseline surveys and skill assessments, 450 applicants from the final round were randomly assigned to treatment (N=225, selected into the program) and control (N=225, not selected). Endline data collection is scheduled for March–June 2026 and includes an online survey (~15 min), an individual coding assessment with screen recording (~45 min), and LinkedIn profile data.
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Planned Number of Clusters
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800 (pooled across two cohorts)
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450 individuals (not clustered)
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Planned Number of Observations
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800 (pooled across two cohorts)
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450 students
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Sample size (or number of clusters) by treatment arms
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400 participants in each arms (treatment = 400, comparison = 400)
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225 students treatment (selected into the program), 225 students control (not selected)
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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We will set alpha at 0.05 and power at 80%. With 400 individuals in each treatment arm (pooled across 2 cohorts), we can detect a Minimal Detectable Effect (MDE) of 0.15
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With 225 students per arm (N=450), 80% power, and a 5% significance level, the MDE without controls is 0.26 SD. Baseline measures include coding ability, technical score, aptitude score, English proficiency, IQ (Raven's Progressive Matrices), EQ (Reading the Mind in the Eyes), confidence, and Big Five personality traits. Accounting for an 85% response rate and baseline controls (estimated R² of 0.30–0.40 for skill-based outcomes using lagged measures), the MDE is approximately 0.22–0.23 SD for coding assessment outcomes and 0.25 SD for survey-based outcomes.
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Secondary Outcomes (End Points)
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Problem-solving and critical thinking (measured through Sudoku puzzles and Logical Reasoning Questions)
Grit (adapted from the Alan et al. (2019))
Big five personality traits
IQ Scores (Using Raven's Advanced Matrices Test)
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Whether participants have a LinkedIn profile; marriage expectations and timing; secondary career plans; process measures from the coding assessment.
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Secondary Outcomes (Explanation)
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Secondary outcomes include: whether participants report having a LinkedIn account, certainty and preferred timing of marriage, secondary career plans, and coding assessment process measures (including skill ratings, patterns of AI tool usage, and time allocation across tasks).
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