**Finally, we find that social role models significantly shape and reinforce individuals' selfbeliefs of efficacy through their example.** Gender differences in perceived occupational selfefficacy have been observed, with male youths having a strong sense of self-efficacy for both traditional male-dominated and female-dominated occupations. In contrast, female youths tend to have a weaker sense of self-efficacy in occupations dominated by men, including STEM-related jobs (Bandura, et al., 2001). Math interest and science self-efficacy are identified as crucial predictors of STEM career aspirations, particularly among male youths (Mau & Li 2017). Additionally, there is evidence of greater gender differences in STEM learning experiences, parental involvement, and STEM self-efficacy among students from collectivist cultures compared to those from individualist cultures (Mau, et al. 2020). Early interest in STEM subjects at the beginning of high school is another significant factor predicting STEM career interest at the end of high school (Sadler, et al. 2012).

**Table 1. Youths’ aspirations to complete university degree or higher, marginal effects from logit regressions**

|**Main variables**|**Pooled sample**|**Ethiopia**|**Malawi**|**Nigeria**|
|---|---|---|---|---|
|**Characteristics of theyouths**|||||
|Youth is female|-0.079***<br>(0.02)|-0.113***<br>(0.038)|-0.0004<br>(0.034)|-0.101***<br>(0.032)|
|Primary education|0.190***<br>(0.0400)|0.291***<br>(0.0754)|0.234***<br>(0.0395)|0.0814<br>(0.0824)|
|Secondary education|0.352***|0.393***|0.269***|0.320***|
||(0.0348)|(0.0652)|(0.0440)|(0.0732)|
|Tertiary education|0.358***|0.481***|-|0.290***|
||(0.0431)|(0.0651)|(-)|(0.0754)|
|Started working <14 years|-0.119***<br>(0.0275)|-0.175**<br>(0.0845)|-0.188***<br>(0.0449)|-0.0977***<br>(0.0335)|
|Started working 15-18 years|-0.092***<br>(0.024)|-0.133**<br>(0.054)|-0.171***<br>(0.045)|-0.05<br>(0.034)|
|Started working 19-22 years|-0.037<br>(0.0354)|-0.083<br>(0.065)|-0.151***<br>(0.053)|0.022<br>(0.057)|
|Ideal job is STEM related work|0.096***|0.236***|0.089*|0.042|
||(0.029)|(0.061)|(0.047)|(0.032)|
|Oldest youth|-0.017*|0.015|-0.011|-0.022|
||(0.01)|(0.019)|(0.014)|(0.014)|
|**Characteristics of the adult head of the household**|||||
|Primary education|0.07***|0.077|0.02|0.056*|
||(0.021)|(0.054)|(0.04)|(0.032)|
|Tertiary education|0.047|0.208***|-0.024|-0.018|
||(0.057)|(0.063)|(0.154)|(0.061)|
|Wage job employee|0.086***<br>(0.029)|0.091<br>(0.057)|0.024<br>(0.044)|0.108**<br>(0.044)|
|Observations|2,613|831|852|916|

Note. Results are presented in margins and clustered standard errors in parentheses. Individual-level survey weights used. Statistical significance at: *** p<0.01, ** p<0.05, * p<0.1. Enumeration areas fixed effect included. The variable ‘Started working [age]’ captures the age of the first job, but includes youths who came back to study or are NEETs after having worked for a while. In Malawi, we excluded 14 observations from the regression analysis because they had completed tertiary education, which resulted in perfect collinearity with the dependent variable. This happened because all the youths who had completed tertiary education in Malawi expressed aspirations to pursue a university degree or higher. Likewise, for 18