# **Employment Outcomes**

The first outcome variable to analyze is whether the person is employed or not, including those in wage and self-employment. Estimates from the five models are in table 3.2. All models, except for model 5, provide the ITT estimates using probit regressions. Model 1 takes only treatment status as an explanatory variable, while model 2 includes other explanatory variables such as gender, age, education level, and the division of residence. Further, model 3 looks at an interaction term between gender and treatment status. Previous employment status is the obvious predictor of current employment status. Therefore, model 4 adds the baseline survey’s employment status as an additional variable. Finally, model 5 uses the IV approach using an IV probit regression to estimate the _treatment on the treated_ effect.

**<u>Table 3.2 Impact of RPL on Whether the Person Is Employed or Not</u>**

||||Models<br>|||
|---|---|---|---|---|---|
|Variables|(1)<br>Treatment<br>only<br>|(2)<br>Standard<br>|(3)<br>With gender<br>interaction|(4)<br>With baseline<br>employment<br>|(5)<br>Instrumental<br>variable<br>|
|**In treatment**|0.182<sup>**</sup><br>(2.24)|0.236<sup>***</sup><br>(2.58)|0.038<br>(0.35)<br>|0.333<sup>***</sup><br>(1.48)|0.410<sup>***</sup><br>(3.40)|
|**In treatment * female**|||0.596<sup>***</sup>|||
|**(Interaction term)**|||(2.97)<br>|||
|Female||-0.749<sup>***</sup>|-1.060<sup>***</sup>|-0.598<sup>***</sup>|-0.595<sup>***</sup>|
|||(-7.13)<br>|(-7.23)<br>|(-5.22)<br>|(-5.17)<br>|
|Age||0.095<sup>***</sup><br>(8.29)|0.097<sup>***</sup><br>(8.43)|0.070<sup>***</sup><br>(6.35)|0.071<sup>***</sup><br>(6.37)|
|Living in a metropolitan area||0.005|-0.010|0.033|0.038|
|||(0.04)|(-0.08)|(0.28)|(0.32)|
|_Education: (base primary school)_||||||
|Secondary school||-0.330<sup>**</sup>|-0.336<sup>**</sup>|-0.288<sup>**</sup>|-0.288<sup>**</sup>|
|||(-2.45)|(-2.49)|(-2.03)|(-2.02)|
|Higher secondary school||-0.846<sup>***</sup>|-0.854<sup>***</sup>|-0.612<sup>***</sup>|-0.621<sup>***</sup>|
|||(-6.43)|(-6.48)|(-4.30)|(-4.35)|
|Degree or above||-0.614<sup>***</sup>|-0.614<sup>***</sup>|-0.471<sup>***</sup>|-0.483<sup>***</sup>|
|||(-3.76)|(-3.71)|(-2.73)|(-2.80)|
|Employed at baseline||||1.258<sup>***</sup><br>(11.50)|1.267<sup>***</sup><br>(11.52)|
|Observations|983|983|983|983|983|

_Source:_ Original table for this publication.

_Note:_ Y = 1 when the person is employed, including wage and self-employment; otherwise, Y = 0 (see equation 3.1). z-statistics in parentheses. The estimates are also controlled for geographical divisions of respondents. Model 5 is an IV probit regression using the instrumental variable to estimate a _treatment on the treated_ effect. *** p<0.01, ** p<0.05, * p<0.1.

The estimates show that those in the treatment group are more likely to be employed. The estimates are statistically significant. The estimated effects are even larger when the regressions include employment status at the baseline (model 4) or use the IV approach (model 5). Interestingly, model 3 with gender interaction terms shows the effect of RPL on employment is likely to be much more significant for females. With the gender interaction term, the statistical significance of the treatment on males vanishes, while females’ treatment effect is positive and statistically significant. This finding suggests that females are likely to benefit more than male participants from the RPL program, offsetting some disadvantages for females on employment. Overall, other things remaining the same, females are less likely to be employed. There is also a positive correlation between age and the likelihood of employment. The employment rate is not different between those receiving SoC and SoA certificates (details not shown), consistent with the project’s observations about the employers’ lack of distinction between the two certificates.

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