# **5.1 Demand**

Of the 11,410 households who used their own well for cooking and drinking at baseline and who were offered an arsenic test, 2,829 (25%) bought a test under one of the selling schemes. To estimate the average treatment effect of selling schemes B and C relative to A, the following equation is estimated using a linear probability model: where _buysvh_ is equal to one if household _h_ in village _v_ and stratum _s_ bought a test at baseline and zero otherwise, _Bv_ and _Cv_ are village-specific indicator variables for the respective treatments, _Xsvh_ is a set of predetermined household and tube well characteristics, and _εsvh_ is an error term. To account for the stratified design, regressions also include strata fixed effects ( _δs_ ). Recall that treatment was stratified by the prevalence of unsafe wells based on BAMWSP data and by union. The coefficients of interest are _β_<sup>_B_</sup> and _β_<sup>_C_</sup> , which capture the causal impact on demand of selling schemes B and C, relative to A, respectively. All standard errors and statistical inference are robust to the presence of intra-village correlation of residuals.

Figure 2 shows graphically the simple comparison of take up rates across arms without the inclusion of controls or strata fixed effects. A first clear result is that neither the group sales nor the addition of the metal placard made any appreciable difference for demand. A second finding is that demand was overall quite low, with about one quarter of households purchasing the test in each of the three experimental arms. As in many earlier studies looking at demand for health-related preventive products, even a small fee led to low demand, despite the potentially vital information provided by the tests.<sup>15</sup> Table 2 displays the corresponding regression results. Column 1 shows see that, as expected, the small differences in demand between arms A, B and C are not statistically significant. Column 2 shows that the results are quite robust to the inclusion of controls. Because missing values in one or more of the controls lead to the loss of about 20% of observations, in column 3 the model is estimated without controls but including only the observations with complete observations used in column 2. In this case, _β_<sup>ˆ</sup><sup>_B_</sup> barely changes, consistent with the overall good balance between arms A and B suggested by the Imbens and Rubin approach. In contrast, _β_<sup>ˆ</sup><sup>_C_</sup> doubles in magnitude from 3 to 6 percent (s.e. 0.034) and it becomes significant, although only at the 10% level. Recall that Arm C appears to be different mostly because, on average and relative to the other arms, (a) household heads had better education and (b) the fraction of wells whose safety was unknown at the time of the test sales was higher and the fraction believed to be safe was lower. In column 2 it can be seen that low schooling predicts lower demand, while the higher prevalence of wells of unknown safety is positively associated

> 15The low demand also raises concerns on the sustainability of a test-for-fee selling scheme at this price. On an average work day, surveyors visited 25 well owners to offer As tests (with surveyor-specific averages ranging from 12 to 36 visits), but only sold six tests (with surveyor-specific averages ranging from 3.5 to 10.8 tests). Recall that the test price was chosen so that surveyors would earn a wage similar to that earned in the neighboring district of Araihazar for similar work by selling 15 tests a day. The low demand thus implies that the actual wage fell short of the expected one.

15