the fiscal cost of expanding a policy (inclusive of fiscal externalities resulting from the expansion), (b) the beneficiaries’ willingness to pay for the policy expansion, and (c) the relative weight society places on giving an extra dollar to the set of beneficiaries. Given our focus is comparing policies targeted to adolescent girls in poor settings, it is unlikely that (c) will vary greatly across policies (within a given country). Thus, we will focus on why we cannot estimate (a) and (b). First, few papers considered in this review report on the cost of implementing their policy. Thus, we cannot estimate (a). We hope that reporting on cost will improve given this recent unifying framework of Hendren and Sprung-Keyser (2020) and the development of program costing tools, such as the World Bank’s <u>Partnership for Economic Inclusion Costing Dashboard. Second, in order to ascertain the willingness to pay for</u> policies, we typically need to know how a policy affects lifetime well-being.<sup>34</sup> To estimate this, we need to understand the long-term impacts of policies (and be able to translate these long-term impacts into monetary values). However, most papers estimate the impact of policies on short-term outcomes. This is entirely reasonable – we need to know whether a policy is working soon after implementation – not 60 years down the road. Thus, we need a way to translate these short-term changes into changes in long-term outcomes. This, we believe, is a knowledge gap which we discuss in more detail in section 5.<sup>35</sup>

# **-** **<u>Appendix C: Scaling Up Programs</u>**

Many of the interventions that we consider in this review and use to inform our summaries in Table 1 are small and are often implemented by non-governmental organizations. While conducting small interventions is beneficial when trying to learn if an intervention is successful (e.g., keeping programs small reduces risks, keeps budgets small, takes less time, and can be more closely monitored), our ability to extrapolate causal effects from small interventions to scaled-up versions is limited for a number of reasons. Muralidharan and Niehaus (2017) and Cull and McKenzie (2020) note that (1) small study samples may not be representative of the population the government wants to target; (2) implementation may become increasingly difficult as a program grows (e.g., monitoring and oversight may become increasingly hard) – this is particularly true if a government becomes involved in the implementation due to (potential) poor bureaucratic management, capture by vested interests, and low state capacity; (3) small interventions may not capture spillover effects, such as general equilibrium effects; and (4) interventions may become prohibitively expensive as they get scaled up – this is of particular concern for very intensive small-scale programs.

One may then ask, “how can a government go about successfully scaling-up small-scale interventions,” and, based on this, “which interventions discussed above lend themselves to be more readily scaledup?” Andrews et al. (2021) provide guidance on the first part to this question by investigating successful large-scale economic inclusion programs in many different settings. They note the following findings regarding successful scale-up:

> 34 For policies that simply deliver cash or provide (inframarginal) market goods in-kind, calculating the willingness to pay is simply equal to the monetary size of the transfer. However, calculating the willingness to pay for other types of policies is more difficult; see Hendren and Sprung-Keyser (2020) for examples on how to do this.

> 35 A recent paper by Athey et al. (2019) shows a way to construct ‘surrogate indices’ for the long-term outcome of interest using experimental or observational data, so that interventions can be evaluated in the shorter-run to form a good idea about their long-term effectiveness. We discuss this approach in Section 5.

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