the program or not, with the average outcomes of everyone assigned to the control group ( _i.e.,_ the nontrainees).

If instead one is interested in the effects among individuals who have actually completed the program, then one should consult the average treatment effects on the treated (ATT) results, which are based on actual program participation, rather than program assignment.

We present both intent-to-treat (ITT) and average treatment effects on the treated (ATT) estimates of the impact of the Employment Fund’s training program. In our view, the ITT estimates are more relevant from the policy maker’s perspective.<sup>35</sup> If one were to invest in scaling up the EF’s training programs to more Nepali youth using the same eligibility criteria, the ITT estimates indicate the level of impact one could expect to achieve _on those target youth_ . The ITT estimates account for the foreseeable uptake and dropout rates that one would expect if the program were expanded. The ATT estimates are relevant from a program implementer’s point of view, as they indicate how well the program worked for the participants who completed the course. They compare everyone who completed the training to everyone who did not, regardless of why they did not complete the training ( _e.g.,_ they were not offered a space, they were not eligible, or they dropped out or declined to join).<sup>36</sup> Because neither the direction nor the extent of bias can be determined precisely, and to address both the policy and programmatic perspectives, this report presents ITT results in Section VI. ATT estimates are available in a separate Annex upon request from the authors.

# **d. Heterogeneous Effects**

In seeking to examine the effects of the Employment Fund’s training program, it is important to keep in mind that different people within the intervention may respond differently to the same policy intervention—a possibility that researchers refer to as “treatment heterogeneity.” We might be concerned that people with particular social or demographic characteristics may fare differently than the average program participant.

Because treatment heterogeneity has important policy implications, we estimate heterogeneous treatment effects by estimating the impact both for the full sample of participants as well as for several subpopulations. For example, we may want to know how well the program works for the average participant or how well the program works specifically for women, young women, and different ethnic groups that are especially targeted by the EF. Different effects for various demographic groups can assist us in informing the policy debate regarding whether specialized investments are paying off or whether further strategies might be needed to ensure that various groups participate and benefit equally from the program. The AGEI sub-group ( _i.e.,_ young women aged 16-24), in particular, merits special attention because of the Employment Fund’s outreach activities towards this particular age group.

> 35 Conceptually, the ITT estimates are equal to or lower than the ATT estimates, since they include all the people to whom the program was targeted but chose not to participate, or who did not complete the program in the treatment group. ATT estimates may overestimate true program impact, since they represent the impact among a self-selected group of people who chose to take part and complete the program. It may be that those who are more motivated and capable are more likely to remain in the program, and the less capable drop out; on the other hand, it may be that the more capable decide that they don’t really need what the program provides.

> 36 Data on training program completion were obtained from the Employment Fund’s administrative records, and cross-checked with self-reported training program participation collected at follow-up.

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