<u>Table 1: Treatment Efects on Labor Market Outcomes</u>

||(1)<br>Employed|(2)<br>Hours<sup>c</sup>|(3)<br>Earnings<sup>c</sup>|(4)<br>Hourly wage<sup>c</sup>|(5)<br>Written contract|
|---|---|---|---|---|---|
|Treatment|0.052|0.201|0.338|0.197|0.020|
||(0.012)|(0.052)|(0.074)|(0.040)|(0.010)|
|Mean outcome|0.309|8.85|159.3|9.84|0.120|
|Mean outcome for employed||28.85|518.3|32.28|0.392|
|# observations|6607|6598|6589|6574|6575|
|# clusters|84|84|84|84|84|

Coefficients are from regressing each outcome on a vector of treatment assignments, randomization block fixed effects, and prespecified baseline covariates (measured skills, self-reported skills, education, age, gender, employment, discount rate, risk aversion). Heteroskedasticity-robust standard errors shown in parentheses, clustering by treatment date. Mean outcome is for the control group. All outcomes use a 7-day recall period. Outcomes marked with<sup>c</sup> use the inverse hyperbolic sine transformation for the treatment effects but the control group means are reported in levels. All monetary figures are reported in South Africa Rands. 1 Rand _≈_ USD 0.167 in purchasing power parity terms. The sample sizes differ across columns due to item non-response, mostly from respondents reporting that they don’t know the answer.

treatment effects as percentage changes. We assign zeros to job characteristics for non-working respondents (e.g. earnings, hours) and to search measures for non-searching respondents (e.g. number of applications submitted) to avoid sample selection. We thus analyze treatment effects on realized outcomes, rather than latent outcomes that may be non-zero for the non-employed or non-searching. We also estimate quantile treatment effects on selected outcomes, which allows us to focus on the distribution of outcomes for only employed candidates. The estimating equations and variable definitions are prespecified. We report treatment effects on some outcomes that are not prespecified but note when we do so. In Appendix D, we show that our results are robust to prespecified adjustments for multiple testing and omitting the prespecified covariates **X** _id_ .

# **3.3 Certification Improves Labor Market Outcomes**

The first main effect of certification is to increase employment. Current employment rises by 5.2 percentage points from a control group mean of 30.1 percentage points (Table 1, column 1). We also ask about employment in each calendar month between treatment and endline and show in Table D.10 that certification increases employment in every month between treatment and follow-up.

Certification increases average weekly hours worked by 20% (column 2). We code hours worked as zero for non-employed candidates. So the treatment effect on hours may reflect two effects: an extensive margin effect if treatment increases the employment rate and an intensive margin effect if treatment increases the hours that employed candidates work. We adapt a decomposition proposed by Attanasio et al. (2011) to identify these two effects (details in Appendix C). We define the extensive margin effect as the treatment effect on employment multiplied by mean hours worked for employed control group candidates. Intuitively, this is the rise in hours we would see if treatment 14