## **3.4 Irrigation data & crop data**

We use data on irrigation statistics from the ICRISAT District Level Data to examine farmers’ responses to changes in the groundwater depth. The data provide information on areas irrigated by source: canals, tanks, tube wells, and other wells. We also access data on crop-wise irrigated areas for water-intensive crops.<sup>8</sup>

## **3.5 Soil moisture**

We use data on soil moisture from the European Space Agency’s Climate Change Initiative (Dorigo et al., 2017; Gruber et al., 2019).<sup>9</sup> This provides a gridded reanalysis product that uses satellite observation to estimate soil moisture (measured as m<sup>3</sup> of water per m<sup>3</sup> of soil). The ESA CCI SM algorithm combines active scatterometer data and the passive radiometer data to estimate the moisture content of the first few centimeters of the top soil. Environmental studies have shown soil moisture to be a good indicator of irrigation water use globally (Zohaib and Choi, 2020) and drought conditions in India (Modanesi et al., 2019). The data is provided on a 0.25<sup>_◦_</sup> _×_ 0.25<sup>_◦_</sup> grid. We aggregated to districts by averaging over all gridpoints within a district’s boundaries.

# **4 Empirical Approach**

In our primary empirical approach we take advantage of both the staggered roll-out of MNREGA and the heterogeneity in implementation to estimate a difference-in-differences model with a true control. Our approach is similar to that taken in García (2022) and relies on the fact that MNREGA implementation in non-Star states was limited compared to Star states to estimate the impact of MNREGA by comparing outcomes in Star and non-Star states after implementation.<sup>10</sup> This approach has the advantage of allowing for comparisons between Treated and Never Treated districts, which alleviates many of the most significant challenges with estimation in the staggered difference-in-differences approach (Goodman-Bacon, 2018; Callaway and Sant’Anna, 2021).

In Figure 1 we show the average number of water projects of various types completed in Star and non-Star states. Star states complete substantially more conservation, irrigation, and microirrigation projects than non-Star states.<sup>11</sup> They also complete slightly more drought mitigation projects and slightly fewer flood mitigation projects. Collectively Star states complete almost five times as many projects on average as non-Star states. These differences in the number of completed projects lend support to our choice to define treatment based on both the timing of MNREGA implementation and location in a Star state.

> 8Data available from ICRISAT: http://data.icrisat.org/dld/src/irrigation.html

> 9Data available here: https://esa-soilmoisture-cci.org/ We use data from ESA CCI SM v06.1

> 10In A1 we present results that use only the staggered roll-out of MNREGA for identification, an approach that is more standard in the literature on MNREGA. This approach has the disadvantage of not allowing for comparisons to a Never Treated control group because all districts eventually receive MNREGA. Broadly our results are consistent between the two approaches.

> 11Table A2 formally tests whether Star states complete more projects than non-Star states and confirms that they do.

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