_24_ to consider new investments or, perhaps, the act of submission itself notionally committed school owners to a course of action. We can show neither of these channels is salient by drawing on three separate sources of (proposed and actual) school investments: (a) pre-treatment proposed investment questions from the baseline survey; (b) investment plan data; and (c) investments as reported in the follow-up surveys. First, the correlation in proposed investments between (a) and (b) is high, suggesting that simply asking schools about investment plans is unlikely to explain our treatment e↵ects since (a) is asked of both treatment and control schools and (a) provides similar information to (b). Second, it also does not seem that (b) was particularly binding as the correlation between investments in (b) and (c) is low. Schools do not seem to have treated the business plan as a commitment device; instead, owners appeared to have finalized school investments after disbursement. Thus, it is unlikely that the submission of investment plans induced the kinds of large e↵ects we document here, and even less likely that it induced di↵erential e↵ects between the treatment arms.

_Bank Account.—_ In order to receive the grant funds, school owners had to open a one-time use bank account with our banking partner. This begs the question: Could this account opening have driven the e↵ects we observe? In our sample, 73% of school owner households already had bank accounts at baseline and this fraction is balanced across treatment arms. Further, in Appendix Table E7, we use an interaction between treatment and baseline bank account availability to check whether our pattern of treatment e↵ects is driven by previously unbanked households. We detect no statistically significant di↵erential impact by baseline bank account status.

_Village level Resources.—_ Given our design preference for school level comparisons, the grant amount was the same for all schools regardless of treatment arm. Therefore, grant per capita in a _L_ village is necessarily always lower than in a _H_ village, holding constant village size. To investigate whether this di↵erence in overall resource availability at the village level can explain our results, we use baseline variation in village size to additionally control for the per-capita grant size in each village. If per-capita grant size is an omitted variable that is correlated with treatment saturation and driving our results, we should find that the additional inclusion of this variable drives the di↵erence in our treatment coefficients to zero. We therefore replicate our base specifications including per-capita grant size as an additional control in Appendix Table E8, columns 1-3. We find that the qualitative pattern of our core results on enrollment, fees and test scores is unchanged. _L_<sup>_t_</sup> schools see higher enrollment on average, while _H_ schools experience higher fees and test scores on average. While we lose precision in the _H_ arm, we cannot reject that these coefficients are identical to our base specification. This suggests that alternative explanations based on equilibrium e↵ects from an school or spend the money on previously planned investments, thereby e↵ectively using the grant for personal uses. They could also propose changes to their plans at any time during the disbursement.