and _β_<sup>ˆ</sup><sup>_C_</sup> = 0 _._ 46 (95% C.I. [0 _._ 231 _,_ 0 _._ 696]). The difference in switching rates between B and C is substantively important (19 percentage points) but is not estimated precisely (p-value=0.163). The estimated differences become smaller but remain large and significant when baseline controls are included (column 4). For both arms B and C the coefficients are almost identical (column 5) when the model does not include controls but it uses only the complete observations used in the regression with controls in column 4. This suggests that the impacts are not substantively biased by differences in the level of observed confounders.

Contrary to expectations, unsafe higher levels of arsenic do _not_ predict more switching. Using 100 as the omitted category, dummies for the arsenic level being equal to 200, 300 or 500/1000 ppb are actually _negative_ and in some case very large and statistically significant. This finding is consistent with most households gauging safety primarily in a binary way, an unfortunate possibility given that in reality arsenic health risk is to first order proportional to arsenic concentration.<sup>17</sup> Figure 4 shows indeed that overall switching rates were well approximated by a step function with a jump at 100 ppb. Note also that very high arsenic levels were not rare in the sample: although less than 30% of tested wells were unsafe, more than half of these had arsenic levels above 100 ppb.

The estimates in column 6 also include as regressor a dummy for the (endogenous) presence of a safe well within 50 meters, where neighboring well is defined as safe when it was identified as such by the research team. Recall that, at baseline, very few wells could be identified as safe by visible signs such as placards or paint on the well spout. In this model some observations are lost due to implausible entries for the geo-location of some wells. Controls are also included for the total number of wells in a 50-meter radius, and the dummy for safe wells is interacted with the treatment indicators. Among owners of unsafe wells in arm A, as expected, having a safe alternative nearby increases switching. The coefficient is large (27 percentage points) and significant at the 1% level. In group B, this association is weaker, given that the interaction (= _−_ 0 _._ 19) is _negative_ and its magnitude is about two-thirds of that observed in arm A, although it is estimated imprecisely and is thus not significant at standard levels. This is consistent with informal group agreements leading some households to share wells with other members, with less concern of geographical distance, something which may have happened if geographical proximity was a poor proxy for sorting into the same risk-sharing group. This remains, however, a conjecture, given that the data do now allow determining with certainty if the well being used at endline belonged to a group member. The interaction between distance to a safe well and the treatment C dummy is again negative (= _−_ 0 _._ 08) but smaller and not significant at standard levels.

Of the 217 ‘switchers’, almost all (214) listed safety concerns as the primary reason for their decision. Although the arsenic level in the new source of drinking water for switchers cannot be determined, about one third of these (79/217) had switched to a different well which was itself perceived by the respondent as being unsafe, while 88 had switched to a well reported as being safe, and the remaining 50 households did not know the status of the well. In principle even a switch to an unsafe well, if the new well is _safer_ , can reduce exposure to arsenic, but this finding suggests that in the study

> 17In a RCT carried out in 2008 in the neighboring Araihazar sub-district, Bennear et al. (2013) showed that attempts to highlight the existence of such gradient did not increase switching, with some evidence that it actually _decreased_ it.

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