in oil and energy industries in characteristics that are thought to be related to the potential for children’s education. In fact, the pre-treatment trends of outcome variables (family income and education outcomes) are not parallel for these two groups. Also, there is no single unaffected industry that provides a comparison for the affected industries. Hence, I use the SCM to find a combination of industries not (or less) affected by the sanctions as a synthetic control group and estimate the counterfactual for treated group. I consider all other industries but financial, real estate, and administrative and support service industries in the donor pool (11 industries),<sup>35</sup> though I check the sensitivity of results using different selected donor industries in section 6 (Table S4).

Weights are determined to maximize the similarity between the synthetic control and the treated households in terms of matching variables. For the main analysis, following Botosaru and Ferman (2019),<sup>36</sup> I consider only pretreatment family income as the matching variable. As a robustness check, I include observed covariates (parent’s education, employment status, age, etc.) and find similar results (Table S4). In particular, I find that algorithms that minimize the distance between the treated units and the synthetic control put small and ignorable variable weights for covariates if more pre-treatment outcomes are included. I also use a modified SC estimator by Ferman and Pinto (2021) and demean the data using information from the pre-treatment period because the values of pretreatment family income for the affected industries do not fall inside the convex hull of the corresponding values for the donor pool and can yield an imperfect fit. Thus, I construct the synthetic control using the demeaned data. Since there is more than one treated unit, based on Abadie et al. (2010), I aggregate the treated units into a single unit (pooled SCM). Pooled SCM can yield poor unit-specific fits. Thus, I check the fit for each treated unit and find a good fit mostly because the characteristics of households in affected units are similar (Figure S3). Moreover, following Ben-Michael et al. (2021), I use the partially pooled SCM as a robustness check and find similar results.

The optimal weights are positive for three industries information, education, and health with values 0.148, 0.169, and 0.683, respectively and take value zero for the other potential controls in the donor pool. Two features of these industries protect them from the sanctions. First, these industries are heavily regulated by the government. Therefore, their wages and employment size are little responsive to the market conditions.<sup>37</sup> Second, these industries are not dependent on trade, thus making them unaffected by the changes in the exchange rate due to trade

> 35Since the synthetic control is supposed to reproduce the outcome variables for treated industries in the absence of the sanctions, I discard financial, real estate, and administrative and support service industries from the donor pool. As explained above, some firms in the financial sector that owned or control by either IRGC or Setad are targeted by the sanctions, but in HIES only the job sector is observable not the firm. Thus, I exclude the financial industry. I also exclude households in real estate and administrative and support service industries from the donor pool because, as explained above, these households have a large difference in their characteristics relative to treated households which makes them unsuitable controls. Based on Abadie (2021), while the SCM puts small weights for dissimilar units, it is still important to limit the donor pool to units with similar characteristics to the affected unit to avoid interpolation biases. Also, these households were indirectly affected by the sanctions, though with a lag. Moreover, I exclude industries for which data are not consistently available in the HIES: arts, entertainment and recreation; activities of households as employers; undifferentiated goods- and services-producing activities of households for own use; professional, scientific and technical activities; activities of extraterritorial organizations and bodies.

> 36Botosaru and Ferman (2019) shows as long as there is a perfect match on pre-treatment outcomes, a perfect match on covariates is not required. Ben-Michael et al. (2021) and Doudchenko and Imbens (2016) also use only the lagged outcomes as matching variables and show including covariates has no effects on synthetic control.

> 37Those working in the information, Education, and health (the control groups) are mostly public sector employees for whom the minimum wage and the minimum percentage change in wage rates are determined by the Supreme Labor Council (in the Ministry of Labor and Social Welfare). The Supreme Labor Council adjusts wage rates in the public sector based on the expected inflation rate. Since the 2006 inflation rate was 12%, the Supreme Labor Council announced a minimum 10% increase in the wage rate of public workers assuming a 10% expected inflation rate in 2007. The actual inflation rate in 2007 was 18%. Figure 2 show a reduction in the real income of the control group in 2007. Thus, the Supreme Labor Council increased the wage rates at higher rates the following years. Overall, the real income of workers in the control group remains constant. The government financed this increase in the wage rate of workers in the public sector by reducing military spending (Source: Government Budget documents). For treated groups (oil & gas, and energy industries) there is no such inflation adjustment.

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