The study also estimated the average marginal effects<sup>7</sup> based on the same set of binary regression models to give more intuitively understandable estimates. Table 3.3 shows the estimated change in the probability of being employed for a unit change in the key variables. The models without the gender interaction term (models 1, 2, and 4) indicate that being in the treatment group would increase the chance of employment by around 7 to 8 percentage points. The IV model (model 5) shows a slightly larger effect of nearly 10 percentage points. These are by no means insignificant employment probability improvements, since only about 60 percent of the sampled applicants were in employment at the baseline. Finally, the gender interaction term in model 4 shows a very positive impact of treatment on female employment—nearly an 18 percentage point increase in employment probability for the female treatment group compared to the male control group. Nevertheless, being female is associated with about a 22 percentage point decrease in employment likelihood, practically overriding the effect of RPL vis-à-vis their male counterparts. Being employed at the baseline would improve employment probability by 39 percentage points, which is not surprising.

**<u>Table 3.3 Average Marginal Effect Estimates</u>**

||||<br>|Models<br>||
|---|---|---|---|---|---|
|Variables|(1)<br>Treatment<br>only|(2)<br>Standard|(3)<br>With gender<br>interaction<sup>a,*</sup>|(4)<br>With baseline<br>employment|(5)<br>Instrumental<br>variable|
|**In treatment**|0.070<sup>**</sup>|0.068<sup>***</sup>|0.175<sup>***</sup>|0.080<sup>***</sup>|0.097<sup>***</sup>|
||(2.24)|(2.57)||(3.42)|(3.43)|
|Female||-0.225<sup>***</sup>|-0.225<sup>***</sup>|-0.153<sup>***</sup>|-0.151<sup>***</sup>|
|||(-7.15)|(-7.28)|(-5.05)|(-5.00)|
|Employed at baseline||||0.391<sup>***</sup><br>(10.81)|0.390<sup>***</sup><br>(10.88)|
|Observations|983|983|983|983|983|

_Source:_ Original table for this publication. _Note:_ Z-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

a The treatment effect estimate in model 3 is the average marginal effect of the gender interaction term and a change in the probability of employment when female-and-treatment is compared to the base outcome of male-and-control.

# **Transition Analysis**

Next, we take a more descriptive and dynamic look at the effects of RPL on employment through the lens of labor market transition to examine and unpack how workers in the sample moved from one employment status to another between the baseline and end-line surveys.

First, one would notice that the overall employment share has fallen by 2 percentage points by the end-line. The overall percentage of the employed was 63 percent at the baseline and 61 percent at the end-line. The decrease is sharper for the control group. Such a general decline in employment share can most likely be attributable to how the project invited and screened potential RPL applicants. The project solicited applications through public media such as newspapers and notice boards at the assessment center; however, it also conducted aggressive dissemination through local companies. Inviting applications through local companies would naturally increase the applications from employed workers.

Furthermore, when the project screened the applicants, it prioritized applicants who have work experience. This screening criterion may well have contributed to having more applicants currently working. In the end,

> 7 The average marginal effect is an effect on the probability of outcome turning 1. It is a change in the probability when a variable changes by one unit while keeping all other variables constant.

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