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Effective and Scalable Programs to Facilitate Labor Market Transitions for Women in Technology

Author

Listed:
  • Athey, Susan

    (Stanford U)

  • Palikot, Emil

    (Stanford U)

Abstract
We describe the design, implementation, and evaluation of a low-cost and scalable program that supports women in Poland in transitioning into jobs in the information technology sector. This program, called "Challenges," helps participants develop portfolios that demonstrate capability for relevant jobs. We conduct two independent evaluations, one focusing on the Challenges program and another on a one-to-one mentoring program. We exploit the fact that both programs were oversubscribed to randomize access among applicants and measure the impact of the programs on the probability of finding a job in the technology sector within four months. We estimate that the mentoring program increases the probability of finding a job in technology by 13 percentage points and the Challenges program by 9 percentage points. The benefit of Challenges can be compared to the program cost of approximately $15 per person. Next, we show that treatment effects vary with individual characteristics, and we estimate gains from optimally assigning applicants across the two programs. We find that optimal assignment increases participants' average probability of finding a job in technology by approximately 13% compared to random assignment. Finally, we analyze the counterfactual impact of expanding the available spots in Challenges from 15% to 50% of applicants, while assigning applicants to programs using the proposed targeting rule. Considering the entire applicant pool as the baseline, this generates a 30% increase in technology sector jobs.

Suggested Citation

  • Athey, Susan & Palikot, Emil, 2022. "Effective and Scalable Programs to Facilitate Labor Market Transitions for Women in Technology," Research Papers 4063, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:4063
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    References listed on IDEAS

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    Cited by:

    1. Cordier, J.; & Salvi, I.; & Steinbeck, V.; & Geissler, A.; & Vogel, J.;, 2023. "Is rapid recovery always the best recovery? - Developing a machine learning approach for optimal assignment rules under capacity constraints for knee replacement patients," Health, Econometrics and Data Group (HEDG) Working Papers 23/08, HEDG, c/o Department of Economics, University of York.
    2. Athey, Susan & Palikot, Emil, 2024. "The Value of Non-traditional Credentials in the Labor Market," Research Papers 4189, Stanford University, Graduate School of Business.
    3. Frank Germann & Stephen J. Anderson & Pradeep K. Chintagunta & Naufel Vilcassim, 2024. "Frontiers: Breaking the Glass Ceiling: Empowering Female Entrepreneurs Through Female Mentors," Marketing Science, INFORMS, vol. 43(2), pages 244-253, March.

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