Michelmore (2018); Blanden and Gregg (2004); Bleakley and Ferrie (2016); Coelli (2011); Hilger (2016); Løken (2010); Lovenheim (2011); Lovenheim and Reynolds (2013); Manoli and Turner (2018); Pan and Ost (2014)). My result is large compared to other studies. I find that a 15% decrease in family income is predicted to decrease college enrollments by 4.8 percentage points. The large effects estimated in this paper are expected because of the persistent shock and lack of adjustment possibilities to the shock. Acemoglu and Pischke (2001) find that a 10% increase in family income increases college enrollments by 1-1.4 percentage points. Other studies find even smaller effects. For example, Hilger (2016) finds a father’s layoff reduces children’s college enrollment by less than half of one percentage point, despite dramatically reducing current and future parental income (by 14% initially and 9% after five years). He explains that much of reduction in parental spending on education may be offset by greater financial aid. Such financial aids, e.g. college loans are not available to Iranian children.

**Back-of-the-envelope Calculation** I consider a simple back-of-the-envelope calculation to understand the economic significance of these results. Children growing up after the imposition of sanctions may have lower earnings throughout their adult lives. Sanctions can affect the lifetime income of the next generation through two channels: lower education levels and lower wage rates. To find the children’s earnings loss due to the sanctions, I compare the present value of future lifetime earnings of children with ( _Is_ ) and without the sanctions ( _Ins_ ).

where _wj_<sup>_H_</sup> and _wj_<sup>_C_arethepercentageofchildrenwithahighschoolorlessandthepercentageofchildrenwith</sup> a university degree, respectively. _T_ is the number of working years and _β_ is the discount rate (0.95). For this calculation, I focus on the primary working ages (30-54).<sup>46</sup> I do not observe _Income_<sup>_H_</sup> and _Income_<sup>_C_</sup> (real annual income at different ages for high school graduates and college graduates) because children who are affected by the sanctions are not yet old enough to directly measure their earnings. Children’s future annual income may be imputed from the information on children’s levels of education, using the relationship between earnings and education in observed data. I consider different scenarios for changes in their education and income.<sup>47</sup> Similarly, I calculate the present value of lifetime earnings of the current generation using the annual income before and after the implementation of the sanctions to find parents’ earnings loss.

The first exercise is to calculate what the expected magnitude of the children’s income would be if the sanctions had not been imposed. The more reasonable scenario is that after the sanctions were lifted children would not go back to school and the wage rates do not recover. The results of cohort analysis in section 7 (Table 6, Panel B) provide evidence for this assumption that affected children did not return to school. Also, this assumption that the wage rates do not recover after the sanctions are lifted, is reasonable considering the fact that average real income has increased by only 5% between 2013 and 2019. In this scenario, I assume without the sanctions, the next

> 46Since HIES is a cross-sectional survey, I observe single-year measures of the earnings. Such short-run measures of workers’ earnings may include both measurement error and transitory fluctuations in earnings. Thus, I select a period to observe the representativeworkers when their earnings are most likely to accurately reflect permanent earnings, ages 30-50 (the prime earnings years). _t_ = 0 is related to age 30 and _T_ = 24 is related to age 54.

> 47For example for future income I consider (i) median/average of (all/treated) workers’ income in the last year of sanctions (year 2013), and (ii) median/average of (all/treated) workers’ income before the sanctions (year 2006).

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