# **<mark>5. Results––Impact estimates from 11 RCTs of Public Works Programs</mark>**

We now present the intent-to-treat (ITT) estimates of public works interventions on key outcomes. ITT estimates are based on the initial assignment to treatment status, regardless of whether individuals participated in the public works program. In most cases, governments or other parties that introduce programs can make them available to their target population, but they cannot mandate take-up, hence the ITT estimate is arguably the most policy-relevant parameter as it estimates the benefit of providing _access_ to a program. <mark>The ITT estimate is computed as the difference in average outcomes in the treatment group (randomly chosen public works participants, from within the population targeted by the program) and average outcomes in the control group (randomly chosen non-participants from within the same population).</mark><sup>16</sup> <mark>We report results separately for each outcome, study, and, when applicable, followup survey. In addition, we aggregate results across studies and follow-up surveys using a</mark> random effects REML model (like the approach in McKenzie, 2021). The meta-analysis takes as inputs the point estimates and standard errors from each study. Higher weight is given to studies that have smaller standard errors.

# **5.1 Employment**

Since public works programs offer cash in exchange of work, we first present their impacts on participants’ employment. In Figure 3, we document the estimated impacts on individuals’ employment from the different studies, along with the control mean for each case. The figure displays results by the timing of the survey with respect to program completion, with short-run impacts measured before individuals exit the program at the top (4 to 0 months before program completion), followed by impacts measured after individuals have exited the program in the medium run (from 1 to 66 months after program completion). Lastly, we also present estimates from a random-effects meta-analysis regression model that captures the short run, medium run, and overall impact of public works on employment.

Short-run treatment effects during the program are positive and significant in Comoros, Côte d’Ivoire, and Djibouti. In Lao PDR, they are positive but marginally not significant. The probability of participants being employed increases by 14 to 54.5 percentage points during the program, with an average effect of 29.3 percentage points (95 percent CI:10.8, 47.8) across the four studies. These effects correspond to very large increases in relative terms in Comoros (263 percent) and in Djibouti (256 percent), where small shares of the control group are otherwise employed (11.9 percent and 21.3 percent, respectively). The relative effects are much smaller in Côte d’Ivoire (16 percent), where

> 16 All cluster randomized controlled trials included in this review, except the study on the Egypt Community program, compare randomly chosen participants in treatment clusters to non-participants in control clusters. In some cases, the sampling strategy used to sample non-participants in control clusters might differ from the sampling strategy used to sample participants in treatment clusters (e.g., Leight and Mvukiyehe, 2022).

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