the earnings which include both measurement error and transitory fluctuations in earnings. Following Tansel and Bircan (2006), I use total family expenditure as a proxy for family income because total expenditure represents permanent income better than current income. Moreover, there are fewer errors in measuring total expenditure than in measuring income. I also use family income itself as a robustness check. I estimate the following equation: where _Edu_ expispt_ is household education spending of household _i_ in industry _s_ and province _p_ at time _t_ . � _ln_ ( _Total_ expipt_ ) (as a proxy for the family income) is the fitted value of total household expenditure derived from the the first stage equation given by: The vector _Xispt′_<sup>is a set of family specific characteristics that are correlated with both educational spending and</sup> income e.g., parents’ education. Since education spending and total household expenditure are both in logarithmic form, _ξ_ denotes elasticity.

Table 5 presents the maximum likelihood estimation results of Eq (4) and Eq (5) (the unconditional marginal effects). I find that income elasticity is significantly greater than one (3.284). Thus, as total expenditure decreases, education spending decreases more rapidly. The F-statistic in the corresponding first-stage regression is far above the critical value, indicating that the used instrument is relevant.

Table 5: Income Elasticity of Education Spending

|Dependent variable: Ln(Household Spending on Education)|
|---|
|2SLS<br>Robustness Check|
|Variables<br>IV: Ln(Expenditure)<br>IV: Ln(Income)|
|IV<br>3.284<sup>_∗∗_</sup><br>2.049<sup>_∗∗_</sup>|
|(1.374)<br>(0.850)|
|_First stage_|
|IV: Treat _×_ Post2007<br>-0.111<sup>_∗∗∗_</sup><br>-0.149<sup>_∗∗∗_</sup>|
|F-stat<br>353.74<br>553.24|
|R-squared<br>0.316<br>0.419|
|Observations<br>20,731<br>20,731|

_Source_ : Iranian Households Income and Expenditures Surveys (HIES). Note: Table presents estimated coefficients of Eq (4) and (5). Dependent variable is Ln(Household Spending on Education). Since education spending and total household expenditure are both in logarithmic form, the estimated coefficient ( _ξ_ ) denotes elasticity. I also use family income itself as a robustness check. Additional controls include household size, head’s age, and head’s education. Heteroskedasticityconsistent 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.

This negative effect on education spending is large compared to studies that find the income elasticity of education spending (Acar et al. (2016); Huy (2012); Qian and Smyth (2011)). While the results of these studies are different across countries, levels of family income, and other household characteristics such as parents’ occupation, most of these studies find that the income elasticity of education spending is significantly less than one implying that education is a necessity item. For those groups of households that education is a luxury good, income elasticity is less than two. I find an income elasticity of more than three. Using family income, the estimated elasticity of 25