were taken: (i) delineation of the drainage network of the study area; (ii) connecting the areas of land (districts in India and grid cells in the Mekong) with the drainage network; (iii) identifying the areas of land which are being impacted by the water quality stations following the drainage path; and (iv) obtaining the distance between water quality station and each of the districts and grids. For tractability, this process was changed for the global analysis. Here, given the wide geographic range of monitoring stations across the world, tracing streamflows was deemed infeasible. Thus instead, gridcells were matched to the nearest monitoring station that is at a higher elevation than the gridcell itself. Since water can only flow from higher elevations to lower elevations, this still ensures water can only flow from the monitoring station into the gridcell, and not the other way around.

# 4. Main Results

Beginning with the Mekong analysis, results for estimating equation 1 are shown in figure 4a. A threshold of 100 millisiemens per meter (mS/m) is used to test for impacts. This threshold is where the Mekong River Commission and other sources estimate that one should start seeing impacts on irrigated crops (Kongmeng and Larsen 2014; Tanji and Kielen 2002). The figure plots of the coefficient of the binary variable indicating observations where electrical conductivity in the upstream monitoring station exceeds 100 mS/m. Each plotted coefficient is from a different regression where the restriction on cropland within each gridcell is varied from 30 percent, 75 percent, and 90 percent. Results show that when EC exceeds 100 mS/m, there is a reduction in agricultural productivity of 5.7‐8.2 percent. As the cropland requirements become more restrictive, the point estimate increases slightly, though it is not very sensitive. Figure 4b shows the similar results for the India data set. Like the Mekong region, productivity falls by 5.5‐6.6 percent when EC exceeds this threshold. This threshold is exceeded by approximately 5 percent of observations in both India and the Mekong River basin. Results using the GEMStat data are shown in figure 4c. Globally, it is found that when EC exceeds 100 mS/m, yields decline by 11.0 to 13.5 percent.

Next, we estimate a more flexible model. Figure 5 shows results from creating six bins of EC, each 40mS/m wide, with the 0‐40mS/m bin as the comparison group. The line graph displays the change in yields (left axis) due to a corresponding EC value shown on the x‐axis. Results show a nearly linear decline in yields as EC rises. Even when EC is in the lowest bin, at 40‐80 mS/m, the decline in yields is statistically detectable, and significant in magnitude at between 5.2 and 7.4 percent, relative to the omitted bin. Further, the regression is not sensitive to changing the cropland land use restriction, as all point estimates are within a tight range and all 95 percent confidence intervals overlap. The bar graphs display the percentage of observations falling into each of the buckets and correspond to the right‐axis. Across all 3 samples, non‐impacted observations (0‐40 mS/m) represent about 50‐60 percent of the sample. The next bin (40‐80 mS/m) is about 30 percent of the sample, and all subsequent bins are less than 10 percent.

# 5. Robustness Tests

In this section we present results from two additional specifications that have been tested to corroborate the main results presented in section 4. First, we show results where we vary the distance threshold to each upstream monitoring station. Then we show results where we control for other water quality parameters in the same regression.

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