observed by the researcher. Clearly, we cannot fully ensure that our survey has captured all variables that influence the e-commerce participation decision.

Therefore, the estimated magnitude of income increase due to direct e-commerce participation could be upwardly biased for three reasons. First, e-commerce engagement is higher in Taobao Villages than elsewhere and the contribution of e-commerce participation is therefore likely to be higher than in average rural areas in China. Second, the counterfactual earnings of e- households are earnings of other productive activities in the village, which are likely to be lower than the would-be income of the e-commerce participants who have migrated to other areas.<sup>11</sup> Third, the counterfactual earnings of e-commerce households will be downward biased due to the missing information of the unobservables and the contribution of e-commerce participation in household income will be upward biased: (a) The effects of important individual unobservables (such as rate of time preference, openness to new experience, entrepreneurial ability or orientation, and personal drive) cannot be collected in a standard survey and cannot be included in the estimates of counterfactual earnings; and (b) Most of these unobserved qualities are likely associated with higher income whether or not an individual is engaged in e-commerce. Meanwhile, e-commerce development may contribute to income increase indirectly through backward or forward linkages and create demand for related offline industries in the ecosystem. For example, new industries and jobs are brought to Taobao Villages to support e-commerce development. People in the villages might not open online shops but can improve earnings by providing services such as express delivery, specialized website design, and online-shop modeling. Such jobs would not be available if not for e-commerce development. Therefore, the results need to be interpreted with caution. Given the data constraints, we nonetheless consider this the best feasible way to examine the role of e-commerce participation on income growth in Taobao Villages.

# **_<u>Common support</u>_**

0 < P P P P P PP(PP= 1|XX) < 1

The assumption of common support states that It ensures that persons with the same X values have a positive probability of being both participants and nonparticipants (Heckman et al. 1999). In other words, X values will not be perfect predictors of e-commerce participation.<sup>12</sup> This assumption can be checked by looking at whether the matching variables are balanced across the treatment and control groups. Specifically, t-tests on household and village characteristics between the matched treatment and control observations are provided in the results section.

Critiques of PSM are usually based on violations of the unconfoundedness assumption in empirical settings. However, to the extent that observational surveys can capture most of the factors that influence treatment status, PSM remains one of the workhorses in impact evaluation literature. Several studies used PSM to analyze development issues in a cross-sectional and non-experimental

> 11 We observed a high share of returned migrants in Taobao Villages. In the absence of the opportunity to participate in e-commerce, some of the individuals/households that currently reside in Taobao Villages would have migrated in pursuit of better employment opportunities.

> 12 In plain English, common support means no attributes should perfectly predict participation. For example, if all participants are males, then being a male perfectly predicts participation, common support fails. Instead, if we observe both males and females as participants, common support is satisfied.

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