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

Susan Athey, Emil Palikot

TL;DR

This study evaluates two distinct interventions—Mentoring and Challenges—in Poland to facilitate women’s transitions into technology. Mentoring leverages networks through one-on-one mentorship, while Challenges builds credible skill signals via portfolio projects; both markedly increase technology employment within 12 months, by about $15.3$ and $11.3$ percentage points respectively, and exhibit different patterns of mechanism-driven heterogeneity. The authors develop a rigorous evaluation framework combining randomized design, heterogeneous treatment-effect analysis, and policy learning with AIPW and RMST-based metrics to demonstrate substantial gains from targeted program allocation, including an 86% improvement over random assignment at 15% capacity. The findings highlight the complementary nature of network expansion and credential signaling, supporting multi-path policy designs that tailor interventions to applicant characteristics to maximize impact at scale.

Abstract

We evaluate two interventions facilitating technology-sector transitions for women in Poland: Mentoring, focused on expanding professional networks, and Challenges, focused on building credible skill signals. Randomizing oversubscribed admissions, we find both programs substantially increase technology employment at twelve months - by 15 percentage points for Mentoring and 11 p.p. for Challenges. The distinct mechanisms through which the programs operate translate to heterogeneous treatment effects across geography, career stage, and baseline credentials. These differential effects create scope for improved allocation: algorithmic targeting across programs outperforms random assignment by 86% and experts' selection into Mentoring by 11%.

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

TL;DR

This study evaluates two distinct interventions—Mentoring and Challenges—in Poland to facilitate women’s transitions into technology. Mentoring leverages networks through one-on-one mentorship, while Challenges builds credible skill signals via portfolio projects; both markedly increase technology employment within 12 months, by about and percentage points respectively, and exhibit different patterns of mechanism-driven heterogeneity. The authors develop a rigorous evaluation framework combining randomized design, heterogeneous treatment-effect analysis, and policy learning with AIPW and RMST-based metrics to demonstrate substantial gains from targeted program allocation, including an 86% improvement over random assignment at 15% capacity. The findings highlight the complementary nature of network expansion and credential signaling, supporting multi-path policy designs that tailor interventions to applicant characteristics to maximize impact at scale.

Abstract

We evaluate two interventions facilitating technology-sector transitions for women in Poland: Mentoring, focused on expanding professional networks, and Challenges, focused on building credible skill signals. Randomizing oversubscribed admissions, we find both programs substantially increase technology employment at twelve months - by 15 percentage points for Mentoring and 11 p.p. for Challenges. The distinct mechanisms through which the programs operate translate to heterogeneous treatment effects across geography, career stage, and baseline credentials. These differential effects create scope for improved allocation: algorithmic targeting across programs outperforms random assignment by 86% and experts' selection into Mentoring by 11%.
Paper Structure (64 sections, 5 equations, 16 figures, 16 tables, 1 algorithm)

This paper contains 64 sections, 5 equations, 16 figures, 16 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Experimental Interventions and Theoretical Framework
  4. Context.
  5. Program Designs and Mechanisms
  6. Mentoring:
  7. Challenges:
  8. Theoretical Predictions and Empirical Strategy.
  9. Experimental Design and Data
  10. Mentoring.
  11. Challenges.
  12. Data
  13. Applicant Registration Survey.
  14. Mentors Registration Survey.
  15. LinkedIn Data.
  16. ...and 49 more sections

Figures (16)

  • Figure 1: An Example of Challenges Final Product.
  • Figure 2: Share of Applicants with a Tech Job per Group Over Time.
  • Figure 3: Characteristics of Groups Under Targeted Policy
  • Figure 4: Policy Value Across Capacity Levels
  • Figure 5: Covariate Balance Mentoring.
  • ...and 11 more figures