Three distinct data sets were used to measure surface water quality. These data sets are from the Mekong River Basin Commission (MRC), the Central Water Commission (CWC) of India, and GEMStat of the United Nations Global Environmental Monitoring system for freshwater (UNGEMs). Table 2 summarizes these data sets. The MRC data span four countries (Cambodia, Lao PDR, Thailand, and Vietnam) and cover the main tributaries of the Mekong River. Of the 121 stations with data on EC, 23 are in Cambodia and Lao PDR, 21 are in Thailand, and 54 are in Vietnam (figure 1).

In India, the Central Water Commission or CWC (under the umbrella of the Ministry of Water Resources) maintains a network of 375 river monitoring stations throughout India that cover almost all major river basins in the country. Water samples are taken at specific time intervals (monthly or quarterly), and then sent to regional laboratories for analysis.

Finally, the GEMStat data from UNGEMS are used for the global analysis. These data are collected by the United National Environmental Programme (UNEP) and self‐reported by participating countries. GEMStat contains over 3 million observations for 224 water quality parameters in 71 countries. EC is one of the most documented pollutants in GEMstats with 167,914 observations of EC in 1,719 stations and 71 countries. As with the Indian CWC data, frequency of observations varies significantly across countries and monitoring stations. Some monitoring stations have multiple observations per month while others have seasonal or annual observations (e.g., one station in Japan has 1,721 observations of EC only). For all the data sets, observations at a single monitoring station are collapsed at the annual level. This leaves us with a panel data set of year/station observations.

# 2.2 Agricultural Productivity and Land Use

To measure changes in agricultural productivity, we use a satellite‐based estimate of net primary production (NPP). NPP is linearly related to the amount of solar energy that plants absorb over a growing season (Running _et al_ . 2004). There is substantial evidence that there is a strong positive correlation between satellite‐derived estimates of NPP and crop yields (Lobell _et al_ . 2002; Lu and Zhuang, 2010; Tum and Gunther 2011), and for this reason it is a frequently used proxy of yields in the economics literature (Strobl and Strobl, 2011; Blanc and Strobl, 2013; Blanc and Strobl, 2014; Zaveri, Russ, and Damania 2019).<sup>2</sup> Our time‐varying NPP data come from the moderate resolution imaging spectroradiometer (MODIS), whose data start in 2000. We use the annual MOD17A3 measures from 2000‐2013 generated by the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana (Zhao _et al_ ., 2005) which corrects for cloud contamination prevalent in MODIS land products.<sup>3</sup>

> 2 A closely related measure to NPP is the normalized difference vegetation index (NDVI). NDVI when combined with growing season data of different crops can also provide a measure of plant health and physical productivity that is directly related to NPP. For instance, MODIS‐NPP is determined using NDVI along with other factors (Running et al. 2004), and in general, NDVI is considered a good predictor for NPP. However, without knowing the time‐varying distribution of crops underlying the global land cover data, we cannot accurately estimate the corresponding growing season data and therefore, cannot measure the maximum NDVI during the growing season.

> 3 The improved MOD17 by the NTSG is a post‐reprocessed data set that corrects for cloud‐contamination in NASA’s MOD17 data set.

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