Public works programs are often used to address the social challenges of unemployment, underemployment, and poverty by offering temporary employment for the creation of public goods, such as roads or infrastructure. Such workfare programs have theoretical advantages over cash-transfer programs, including provision to more disadvantaged recipients who would self-identify because of their willingness to work, as well as potential long-run benefits that accrue via work experience.
To assess the practical effects of these theoretical promises, the authors study labor-intensive public works programs in Sub-Saharan Africa that were adopted in response to such shocks as economic downturns, climatic shocks, or episodes of violent conflicts, and that offer public employment as a stabilization instrument. In doing so, the authors make two important contributions: They analyze both the contemporaneous and post-program impacts of a randomized public work program on participants’ employment, earnings and behaviors; and they leverage machine learning techniques to study the heterogeneity of program impacts, which is key to assessing whether departing from self-targeting would improve program effectiveness.
This second contribution is key because it suggests that improvements in self-targeting or targeting are first-order program design questions. Given the estimated distribution of individual program impacts, the authors show that a lower offered wage (and the subsequent change in self-targeting) was unlikely to improve program performance. In contrast, a range of practical targeting mechanisms perform as well as the machine learning benchmark, leading to stronger impacts during the program without reductions in post-program impacts.
The authors examine a program implemented by the Côte d’Ivoire government in the aftermath of a post-electoral crisis in 2010/2011. Funded by an emergency loan from the World Bank, the stated objective was to improve access to temporary employment opportunities among low-skilled, young (18-30) men and women in urban or semi-urban areas who were unemployed or underemployed, as well as to develop their skills through work experience and complementary training. Participants were remunerated at the statutory minimum daily wage.
All young men and women in the required age range and residing in one of 16 urban localities in Côte d’Ivoire were eligible to apply to the program. Because the number of applicants outstripped supply in each locality, fair access was based on a public lottery, allowing for a robust causal evaluation of the impacts of the program. In addition, randomized subsets of participants were also offered such benefits as entrepreneur and job-search training. Surveys of the treatment and control groups occurred at baseline, during the program (4 to 5 months after the program had started), and 12 to 15 months after the program ended.
The authors’ findings include the following:
- Impacts on employment are limited to shifts in the composition of employment towards the public works wage jobs during the program, with no lasting post-program impacts on the likelihood or composition of employment.
- Public works increase earnings during the program, but post-program impacts on earnings are limited.
- Savings and psychological well-being improve both during and (to a lesser extent) post-program. However, the authors find no long-lasting effects on work habits and behaviors, despite improvements during the program.
Finally, impacts on earnings remain substantially below program costs even under improved targeting. All things considered, should public work programs be deprioritized in favor of welfare programs with more efficient targeting procedures and lower implementation costs? Not necessarily. The authors stress that their analysis does not take into account all possible benefits of the program, both for the beneficiaries themselves but also for non-beneficiaries. For example, they observe lasting effects on psychological well-being and savings among beneficiaries that are not included in the cost-benefit ratios; they acknowledge the likelihood of other positive externalities associated with the program, such as a reduction in crime or illegal activities due to an incapacitation effect; and the authors do not quantify the societal value of the upgraded infrastructure.