  We describe the design, implementation, and evaluation of a low-cost
(approximately $15 per person) and scalable program, called Challenges, aimed
at aiding women in Poland transition to technology-sector jobs. This program
helps participants develop portfolios demonstrating job-relevant competencies.
We conduct two independent evaluations, one of the Challenges program and the
other of a traditional mentoring program -- Mentoring -- where experienced tech
professionals work individually with mentees to support them in their job
search. Exploiting the fact that both programs were oversubscribed, we
randomized admissions and measured their impact on the probability of finding a
job in the technology sector. We estimate that Mentoring increases the
probability of finding a technology job within four months from 29% to 42% and
Challenges from 20% to 29%, and the treatment effects do not attenuate over 12
months. Since both programs are capacity constrained in practice (only 28% of
applicants can be accommodated), we evaluate the effectiveness of several
alternative prioritization rules based on applicant characteristics. We find
that a policy that selects applicants based on their predicted treatment
effects increases the average treatment effect across the two programs to 22
percentage points. We further analyze how alternative prioritization rules
compare to the selection that mentors used. We find that mentors selected
applicants who were more likely to get a tech job even without participating in
the program, and the treatment effect for applicants with similar
characteristics to those selected by mentors is about half of the effect
attainable when participants are prioritized optimally.
