the growing season).<sup>25</sup> All NDVI measures are then log transformed for the regressions to allow for an interpretable effect. We prefer the differenced measure because it effectively controls for non-crop vegetation (such as forest cover) by measuring the change in greenness from the planting period (when land is fallow) to the point in the season where crops are the most green.

We use additional likely correlates of agricultural production to validate the use of growing-season NDVI measures as a proxy for agricultural output at the village level (Table A1). Cross-sectional regressions with state fixed effects were run using log endline year (2011-2013 average) growing season change in NDVI (as described above) as the dependent variable. At the village level, these correlates are: cereal crop potential production measure (low input usage) from the FAO Global Agro-Ecological Zones (GAEZ) aggregated to the village level (log); share of village land area under any type of irrigation; and per capita annual consumption (described above). Additionally, panel NDVI data was regressed at the district level on agricultural output from the Planning Commission’s series of district domestic product data, across a consistent sample of districts.

# **A5 Consumption**

We combine data from 2012 SECC and the concurrent IHDS-II (2011-12) to impute villagelevel consumption measures following the methodology in Elbers et al. (2003). To do this, using IHDS data, we regress total household consumption on dummy variables that are equivalent to all asset and earnings information contained in the SECC.<sup>26</sup> We then use the coefficients to predict household-level consumption in the SECC microdata. This is used to generate consumption per capita at the individual level, which is in turn used to produce village level statistics for mean consumption per capita, per capita consumption at different village percentiles, and share of the population below the poverty line.<sup>27</sup> For the purpose of regressions, consumption variables are winsorized at the 1st and 99th percentiles, and log transformed. As outlined in Elbers et al. (2003), in order to get correct standard errors and p-values, we perform a double bootstrap, first in the IHDS regressions to generate 1,000 different asset coefficient vectors, and then over villages in our main sample.

The only earnings variable available at the village level comes from the SECC. It records monthly earnings of the highest earning member of the household, censored into three bins: 0 to 4,999 rupees, 5,000 to 9,999 rupees and 10,000+ rupees. As 85% of households report

> 25To reduce noise, we define our endline measure as the average of the measures for 2011, 2012 and 2013, and our baseline measures as the average of the measure for 2000, 2001 and 2002.

> 26These variables are roof material (grass, tile, slate, plastic, GI metal, brick, stone, and concrete), wall material (grass, mud, plastic, wood, brick, GI sheets, stone, and concrete), number of rooms, phone ownership (landline only, mobile only, and both landline and mobile), house ownership (owned), vehicle ownership (two wheeler and four wheeler), land ownership, kisan credit card, refrigerator, and highest individual income in household (between 5,000 and 10,000 rupees and more than 10,000 rupees).

> 27We use the official rural poverty line of INR 27/day from the Tendulkar Committee Report (Government of India, 2014).

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