# 3 Findings on Impacts of the RPL Program

## Overall Outcomes

First, we provide descriptive statistics of outcome variables at the end-line by treatment status in table 3.1. On most outcome variables, the mean is similar in the treatment and control groups. The exceptions are on variables indicating whether the person is employed, especially on the variable of being wage employed, and the extent to which the respondents agree on the statement, “I believe that in the long run, hard work brings a better life.” Both variables are significant at the 5 percent significance level. Unconditional on other characteristics, the treatment group applicants are most likely to be employed at the end-line. They are more likely to agree to the statement on hard work bringing a better life, suggesting that the Recognition of Prior Learning (RPL) program certification may have motivated them to work harder.

**-** **<u>Table 3.1 Comparison of End line Characteristics between Treatment and Control Groups</u>**

||All<br>(n=983)|Treatment<br>(n=4 93)|Control<br>(n=490)|Difference|
|---|---|---|---|---|
|**Outcome variables**|Mean|Mean|Mean|(T-C)|
|_Employment and earnings status_|||||
|Employed|0.61|0.65|0.58|0.07<sup>**</sup><br>|
|Wage employed|0.43|0.46|0.39|0.07<sup>**</sup>|
|Self-employed|0.19|0.19|0.18|0.00|
|Unemployed|0.07|0.07|0.07|0.00|
|Studying or unavailable for work|0.31|0.28|0.35|-0.07<sup>**</sup>|
|Current wage earnings (monthly)|11,436|11,551|11,303|248<br>|
|Expected earning in five years|31,742|32,422|31,059|1,363<sup>*</sup>|
|_Confidence and aspirations: opinion on a scale of 1–10_|||||
|Mastered the technical skills needed for my work|8.70|8.69|8.72|-0.03|
|Have adequate soft skills|8.94|8.96|8.92|0.04|
|Can achieve career aspirations|9.25|9.27|9.22|0.06|
|Confident I can work through challenges|9.24|9.28|9.19|0.09|
|Willing to take risks for achieving better at work|9.25|9.25|9.25|0.00<br>|
|In the long run, hard work brings a better life|9.64|9.69|9.58|0.11<sup>**</sup>|

_Source:_ Original table for this publication. *** p<0.01, ** p<0.05, * p<0.1.

## Impact on Economic Outcomes

We consider next the regression analyses for key outcomes of interest. Since the randomization was largely followed, we use a simple regression specification to estimate the program impact: where Y1 is the outcome of interest (employment status and earnings) at the end-line; T is a binary variable that specifies whether the person is in a treatment group; X is a set of individual-level characteristics such as age, gender, and level of education; u is the error term; and β0, β1, and γ are the coefficients to be estimated. The coefficient β1 in the equation is of particular interest. If there is indeed a positive impact of the RPL assessment, the estimate for β1 should be statistically significantly positive. As discussed earlier, the study tries to estimate _intention to treat_ (ITT) effects; therefore, T = 1 if the person is in the treatment group regardless of actual participation in the RPL assessment. In some cases where feasible, the study also attempts to estimate the _treatment on the treated_ effects using the instrumental variable (IV) approach.

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