Contemporaneous and Post-Program Impacts of a Public Works Program : Evidence from Cate d'Ivoire. / Marianne Bertrand.

Format:

Book

Government document

Author/Creator:

Bertrand, Marianne.

Contributor:

Bertrand, Marianne.

Crapon, Bruno.

Marguerie, Alicia.

Premand, Patrick.

Series:

Other papers

World Bank e-Library.

Language:

English

Subjects (All):

Employment and Unemployment.

Skills Development and Labor Force Training.

Social Protections and Labor.

Local Subjects:

Employment and Unemployment.

Skills Development and Labor Force Training.

Social Protections and Labor.

Other Title:

Contemporaneous and Post-Program Impacts of a Public Works Program

Place of Publication:

Washington, D.C. : The World Bank, 2017.

System Details:

data file

Summary:

Public works are one of the most popular safety net and employment policy instruments in thedeveloping world, despite limited evidence on their effectiveness and optimal design features.This paper presents results on contemporaneous and post-program impacts from a public worksintervention in Cate d'Ivoire. The program provided 7 months of temporary employment inroad 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. Randomizedsub-sets of beneficiaries also received complementary training on basic entrepreneurship or jobsearch skills. During the program, results show limited contemporaneous impacts of publicworks on the level of employment, but a shift in the composition of employment towards thebetter-paid public works wage jobs. A year after the end of the program, there are no lastingimpacts on the level or composition of employment, although positive impacts are observed onearnings through higher productivity in non-agricultural self-employment. Large heterogeneityin impacts are found, particularly during the program. Results from machine learningtechniques suggest potential trade-offs between maximizing contemporaneous and postprogramimpacts. Traditional heterogeneity analysis shows that a range of practical targetingmechanisms perform as well as the machine learning benchmark, leading to strongercontemporaneous and post-program benefits without sharp trade-offs. Overall, departing fromself-targeting based on the formal minimum wage would lead to strong improvements inprogram cost-effectiveness.