high school after lifting the sanctions. Following Duflo (2001), I also use a cohort DID strategy and estimate the effects on each cohort using the following equation and observations from the last year of the sanctions (2013): where _cohortil_ is a dummy that indicates whether individual _i_ belongs to cohort _l_ (early-exposed or fully-exposed) and _ρc_ is a vector of cohort fixed effects. As in the main analysis, _Treati_ is a dummy for households in treated industries. _Yispc_ is the quantity of education (enrollment, completing different levels of education, and years of schooling). Each _γl_ can be interpreted as an estimate of the effect on a given cohort (the not-exposed cohort serves as the baseline).

Table 6: Heterogeneous Effect on Enrollment rates and Years of Education by Exposure

||Enrollment in|High Scho|ol two|Colleg|e|Years of|
|---|---|---|---|---|---|---|
||grade 1-9|Ever Attending|Completing|Ever Attending|Completing|Education|
|Panel A|||||||
|Sample:|||||||
|Early Exposed Treated: Age in 2007|1-5|10-12|12-15|12-15|12-15|10-15|
|Fully Exposed Treated: Age in 2007|6-15|13-16|16-20|16-20|16-20|16-20|
|Early-Exposed|0.058|-0.007|-0.008|-0.017<sup>_∗∗∗_</sup>|-0.003|-0.265<sup>_∗∗∗_</sup>|
||(0.063)|(0.007)|(0.012)|(0.005)|(0.002)|(0.052)|
|Fully-Exposed|0.019|-0.007|-0.037|-0.061<sup>_∗∗∗_</sup>|-0.033<sup>_∗∗∗_</sup>|-0.353<sup>_∗∗∗_</sup>|
||(0.015)|(0.007)|(0.033)|(0.010)|(0.007)|(0.078)|
|Prob _>_ chi2|0.235|0.988|0.414|0.073|0.039|0.609|
|Panel B: Cohort analysis (2013, 2019)|||||||
|2013: Early-Exposed (_γ_1)|0.069|-0.021|-0.151|-0.013|-0.072|-0.229<sup>_∗∗_</sup>|
||(0.046)|(0.021)|(0.103)|(0.057)|(0.058)|(0.095)|
|2013: Fully-Exposed (_γ_2)|0.045|-0.022|-0.152<sup>_∗_</sup>|-0.171<sup>_∗∗∗_</sup>|-0.244<sup>_∗∗∗_</sup>|-0.543<sup>_∗∗∗_</sup>|
||(0.047)|(0.018)|(0.077)|(0.048)|(0.079)|(0.120)|
|2013: Prob _>_ F|0.230|0.899|0.996|0.009|0.031|0.020|
|2019: Early-Exposed (_γ_1)|0.063|-0.003|-0.010|-0.024|-0.171<sup>_∗∗∗_</sup>|-0.583<sup>_∗_</sup>|
||(0.041)|(0.064)|(0.048)|(0.047)|(0.061)|(0.396)|
|2019: Fully-Exposed (_γ_2)|0.042|-0.066|-0.029|-0.242<sup>_∗∗∗_</sup>|-0.216<sup>_∗∗∗_</sup>|-0.672<sup>_∗∗_</sup>|
||(0.050)|(0.071)|(0.040)|(0.050)|(0.070)|(0.327)|
|2019: Prob _>_ F|0.558|0.248|0.747|0.000|0.500|0.784|
|Panel C: Placebo Test|||||||
|Sample:|||||||
|Fake Treated: Age in 2007|16-22|21-24|21-24|25-30|25-30|25-30|
|Not Exposed: Age in 2007|23-26|25-30|25-30|31-35|31-35|31-35|
|Fake Treated _×_ Treated Industries|0.003|0.002|0.015|0.012|0.016|0.121|
||(0.006)|(0.005)|(0.042)|(0.018)|(0.019)|(0.078)|

_Source_ : Iranian Households Income and Expenditures Surveys (HIES). Note: The table presents the heterogeneous effects by exposure to the sanctions. Panel A presents estimated coefficients of _T reat×cohort_ ( _γ_ 1 in equation (6)) using observations from years 2013 and 2019. The dependent variable for attending any level of education is an indicator for whether an individual ever enrolls in a specified education level (including students and graduates). The dependent variable for completing high school and college is an indicator for whether an individual graduated. Panel B presents estimated coefficients of _T reat × cohort_ ( _γ_ 1 in equation (6). Panel C shows the results of a placebo test considering fake treatment cohorts. Heteroskedasticity-consistent standard errors accounting for clustering at the province and industry level in parentheses. p-values are calculated using wild bootstrap randomization inference (WBRI). Treated: oil and gas, energy supply industries. Synthetic control: information (0.148), education (0.169), and health (0.683) industries.<sup>_∗_</sup> Significant at 10% level;<sup>_∗∗_</sup> significant at 5% level;<sup>_∗∗∗_</sup> significant at 1% level.

Table 6 Panel A and B present the effects on the quantity of education (enrollment, completing different levels 30