treated (TOT) (i.e., measuring a treatment effect on the population who actually _received or attended_ the treatment) generally results in higher effect sizes than focusing on the intention to treat (ITT) effect (i.e., the population who was in principle _assigned_ to treatment). However, ITT may be more relevant for policy (cf. Imbens and Wooldridge 2009: 15; Gertler et al. 2011: 73). Third, the delay between financial education treatment and measurement of the effect may negatively influence the effect size since effects of the intervention may decay over time (cf. Fernandes et al. 2014: 1867). Additionally, we control for the precision of effect size estimation by the inverse standard error (or the [squared] standard error, see supplemental appendix S3). All these variables are defined in table 2, which also provides descriptive statistics.

(ii) A core variable of financial education interventions is the _intensity of education_ (i.e., the number of hours taught). It is expected that higher intensity will support the effect. However, the timeframe over which the financial education intervention is delivered to the target group may also be of importance. We expect differences between high intensity and low intensity relative to the duration. Thus, we code the _hours of financial education per week_ (i.e., intensity per week) and the _duration_ of the intervention in weeks to investigate this issue.

(iii) The expectation regarding a possible relation between the _target group_ of education and effectiveness of financial education is as follows. Generally, learning is easier for younger people, younger people may be more open to new concepts and their baseline financial literacy scores are low (e.g., Lusardi and Mitchell 2014), meaning that the _age_ of the target group may be negatively related to the effect size of financial education. Second, a gender gap in financial literacy is treated as a stylized fact in the literature (cf. Lusardi and Mitchell 2014) which may also translate to gender differences in treatment effects. Thus we include the _percentage of women_ in the sample. Third, it is expected that the acquaintance of the target group with an educational environment may be helpful. As a proxy for such openness to education, we take the _income_ of the target group relative to the overall population. Fourth, we expect that the overall institutional level of education should support domain-specific educational efforts (Jappelli <u>2010). As a proxy for this potential relationship, we take a country’s</u> population mean _years of schooling_ as reported by the United Nations Development Program Human Development Reports. Additionally, we augment our data with _country-level financial literacy_ data from a 2015 global financial literacy survey (Klapper et al. <u>2015). We hypothesize that financial</u> education interventions may yield higher effects when the population baseline financial literacy is lower, indicating more room for improvement through education. Finally, as a control variable we code the country of intervention according to the World Bank country group classifications.

(iv) Regarding the _characteristics of the education program_ , it seems interesting whether the _channel_ (i.e., classroom, online, individual counseling, etc.) is important in explaining education effectiveness, since these formats come with different trainer to participant ratios and may rely on 14