ing the _twowayfeweights_ Stata package from De Chaisemartin and D’Haultfoeuille (2020b). The results show that 57% of the ATTs have positive weights, while 43% have negative weights. The negative weights sum up to -0.54. Also, the TWFE estimate has a standard deviation of 0.006, meaning that the TWFE estimate and ATTs may be of opposite sign if the standard deviation of the treatment effect across the district _×_ year observations is equal to 0.006. Thus, the possibility of bias in the TWFE estimates in panel A cannot be overemphasized.

I, therefore, proceed to estimate the effects of mobile penetration on economic activities using the De Chaisemartin and D’Haultfoeuille (2020a) TWFE estimator that is robust to heterogeneous treatment effect.<sup>17</sup> The results as shown in panel B of Table 1, are qualitatively similar to the estimates in panel A. Quantitatively, there are some differences in the parameter estimates, plausibly due to heterogeneity in the treatment effects across groups. Nonetheless, the C&D estimator confirms a positive association between mobile penetration and local economic growth. Further, in Table A2 in the online Appendix, I replicate the results in Table 1 using a binary of treatment defined as 1 for districts with at least 10% coverage rate and 0 otherwise. The TWFE estimates in both tables are largely consistent: an indication that the results in this paper are not sensitive to the measure of mobile network coverage.

It is noteworthy to emphasize that both the De Chaisemartin and D’Haultfoeuille (2020a) and the conventional TWFE estimators rely on the common trends assumption. To assess the plausibility of this assumption, I provide three placebo estimates: DID<sup>_pl_,1</sup> , DID<sup>_pl_,2</sup> , and DID<sup>_pl_,3</sup> as shown in panel C. These estimates compare changes in the outcome variable of districts changing their treatment status and those not changing their status at the respective time periods before the change. For instance, DID<sup>_pl_,1</sup> compares the growth in nightlight intensity for districts changing their treatment (mobile connectivity) status and those not changing their status one year before the change. Similarly, DID<sup>_pl_,2</sup> and DID<sup>_pl_,2</sup> compare the growth in nightlight intensity for districts changing their treatment (mobile connectivity) status and those not changing their status two and three years, respectively, before the change. As shown in panel B, the placebo estimates are mostly negative and statistically significant in periods 2 and 3. That is, districts start experiencing differential negative pre-trend two and three years before connectivity. This provides suggestive evidence that even the De Chaisemartin and D’Haultfoeuille (2020a) estimates are likely to be biased and not represent the causal impact of mobile connectivity due to the presence of pre-trends.

It is possible that the pre-trends are likely to arise from the nature of expansion in mobile network connectivity, as the expansion is less likely to be randomly designed, motivated by market potential or policy requirements. To further explore the pre-trends assumption, I follow the

> 17Using the STATA package did_multiplegt.

10