Abstract :

Public works are one of the most popular safety net and employment policy instruments in the developing world, despite limited evidence on their effectiveness and optimal design features. This paper presents results on contemporaneous and post-program impacts from a public works intervention in Côte d’Ivoire. The program provided 7 months of temporary employment in road maintenance to urban youths. Participants self-selected to apply for the public works jobs, which paid the formal minimum wage and were randomized among applicants. Randomized sub-sets of beneficiaries also received complementary training on basic entrepreneurship or job search skills. During the program, results show limited contemporaneous impacts of public works on the level of employment, but a shift in the composition of employment towards the better-paid public works wage jobs. A year after the end of the program, there are no lasting impacts on the level or composition of employment, although positive impacts are observed on earnings through higher productivity in non-agricultural self-employment. Large heterogeneity in impacts are found, particularly during the program. Results from machine learning techniques suggest potential trade-offs between maximizing contemporaneous and post-program impacts. Traditional heterogeneity analysis shows that a range of practical targeting mechanisms perform as well as the machine learning benchmark, leading to stronger contemporaneous and post-program benefits without sharp trade-offs. Overall, departing from
self-targeting based on the formal minimum wage would lead to strong improvements in program cost-effectiveness.