whose harvest can be easily mechanized, (b) where combines were present to facilitate the hiring of these combines in place of manual labor, and (c) where, as I show in the model described in Appendix 4, farms tended to be larger.

This expected variation in the impact of MNREGA provides an opportunity to test the hypothesis that it was increases in mechanization that linked MNREGA to increases in agricultural fires. To do so, I construct an index of mechanization for each district. The construction of this index is driven by the features of my model and the relationship between fire use and mechanization in Indian agriculture as described in the existing literature (e.g. among others Jain et al. (2014) and Bhuvaneshwari et al. (2019)). I follow Asher and Novosad (2018) and use an index rather than testing multiple measures of mechanization to limit concerns of multiple hypothesis testing.<sup>23</sup> To construct the index, I consider the district averages of the following variables in the years prior to 2006: the share of agricultural land in both marginal and medium or large holdings (less than 1 HA or more than 4 HA), the number of combines in 2006, and the production areas of wheat, rice, and sugarcane (the ability to mechanize varies across crops in India (Solomon, 2016)).<sup>24</sup> To make the index, I turn these averages into Z scores. Each district receives a Z score for each individual measure. I then add them together to determine an index measuring the likelihood of mechanization in a given district. Higher index scores indicate a higher predicted level of mechanization. In the appendix, I show that this score is positively correlated with the best available measure of post-MNREGA mechanization – the number of combines in a district in 2011. I then divide districts into quartiles based on their mechanization index score and run my difference-in-differences regression from equation 1 on districts in each quartile.<sup>25</sup> I find in the full, nationwide sample, districts with the highest mechanization scores have sub-

> 23For details of each component of the index, as well as heterogeneity results by the individual components, see Appendix 3.8.

> 24Proposition 2 of my model indicates the impact of MNREGA on agricultural fires will vary across districts based on the median farm size, _AD_ . Specifically, given a distribution of _A_ that is single-peaked and where the threshold farm size for adoption of mechanization ( _A_<sup>ˆ</sup> ) is above _AD_ districts with initially higher _AD_ will see a larger increase in fires after the implementation of MNREGA. Figure A2 indicates that the average distribution of farm sizes across India does appear to be single-peaked. Further, Foster and Rosenzweig (2011) report that in the pre-MNREGA period (the late 1990s) fewer than 10% of farms were mechanized, suggesting a level of _A_<sup>ˆ</sup> substantially above _AD_ .

> 25Li (2017) suggests that areas with higher land concentrations, an important piece of my mechanization score, had lower pre-MNREGA agricultural wages than other districts, which suggests my mechanization score is not simply capturing districts with high wages prior to MNREGA. Using, imperfect, wage data from ICRISAT I find that the correlation between the Z score of district wages in 2005 and the mechanization index is -0.11 ( _p_ = 0 _._ 71). I directly measure the correlation between the Z score of per capita GDP and my mechanization score and find that it is -0.07 ( _p_ = 0 _._ 45) which suggests that I am also not simply identifying wealthier districts.

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