term outcome of interest (e.g., empowerment, labor market participation, consumption per capita, etc.). A recent paper by Athey et al. (2019) shows a way to construct these 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.

For such an approach to be useful for our colleagues and client countries at the World Bank, constructing a “library of surrogate indices” has been proposed (see McKenzie, 2020). Such a library would be extremely useful, because if, over time, we have estimates of the relationship between a number of short- and medium-term outcomes and the long-term outcome(s) of interest obtained from different settings – from experiments and from observational longitudinal data – then we can collect the components of the surrogate index when evaluating different/competing interventions and compare their expected long-term cost-effectiveness. This is potentially a much more satisfactory way to assess the effectiveness of interventions than focusing on one or two intermediate outcomes. Such a process of coordinated evaluations would further build the ‘library’ of knowledge, improving our confidence in predicting the longerterm impacts of interventions during adolescence using short-to-medium-term outcome measures.

# **5.1.2. Reporting on Cost and Cost Structure of Policies and Interventions**

The reporting on the cost of policies and the cost structure of policies affecting adolescents is limited. However, knowing the cost and cost structure are vital for comparing the overall effectiveness of different policies and for understanding whether a policy is likely scalable. Fortunately, strides have been made in reporting and comparing costs of economic inclusion policies via the World Bank’s Partnership for <u>Economic Inclusion’s Costing Dashboard. Expanding this tool to more policies, in</u> particular the policies that affect adolescents, would be extremely beneficial for policy makers to determine the best policies for improving adolescent well-being. Once more costing data is available, we believe it would be highly valuable to categorize interventions targeted to adolescent girls based on their cost-effectiveness in a similar manner to the report from the Global Education Evidence Advisory Panel (2020): <mark>“Smart Buys: Cost-effective Approaches to Improve Global Learning Levels”.</mark>

# **5.2.Improving Targeting via Machine Learning Methods**

In addition to universal programs (e.g., universal free primary school, health care, etc.), governments want to offer policies that are targeted to certain groups of individuals (when budgets are limited, targeting programs can lead to substantial gains in social welfare - Hanna and Olken, 2018). Often, however, it is difficult for governments (especially governments in developing countries) to determine which individuals to target. Better targeting of programs can lead to much larger impacts on the outcomes of interest and is thus an important feature of program design. For example, many CCT programs only generate small (albeit statistically significant) effects on enrollment as many of the recipients are inframarginal. Conversely, Duflo et al. (2021), in the context of Ghana, find substantial gains in enrollment when targeting scholarships to students who gained admission to secondary school but did not immediately 19