setting to estimate the causal impact of intervention programs or collective behaviors. The seminal paper by Jalan and Ravallion (2003) uses PSM to estimate the effects of piped water on children’s diarrhea incidence and duration in India. With a cross-sectional data set, the authors match the households with and without piped water based on propensity scores estimated from observable household characteristics. They further stratify the cross-section by household income per capita and the highest education of a female family member, and then estimate the treatment effects with PSM in each subgroup. Imbens (2015) illustrates the application of PSM using non-experimental data with examples such as the lottery winnings effects on labor market outcomes and the job training program impact on subsequent earnings. Chiputwa et al. (2015) use PSM to identify the impact of food standard certification on poverty among coffee farmers in Uganda. Fischer and Qaim (2012) employ PSM to match farmers that form and do not form farmers groups to analyze the impact of farmer collective action on income in Kenya. Also using a cross-sectional household data set from Uganda, Kassie et al. (2011) use PSM to find that adopting improved groundnut varieties (technology) significantly increases crop income and reduces poverty. We follow the same methodology to study the role of e-commerce participation on household income growth in Taobao Villages.

# **_3.2 Logit model of participation_**

We start by estimating a logit model of participation decision on a set of observed characteristics. At the household level, we include the following variables:

1. _Gender of the household head_ . This is a dummy variable that equals one if the head is male and zero if female. E-commerce allows more flexible working hours and location (many people work at their own homes during periods they choose) and is often less manually demanding than traditional farming. This might make it especially suitable for female participation. We expect the coefficient to be negative.

2. _Age of the household head_ . As e-commerce is technology-intensive and new to rural areas, the younger generation may be more enthusiastic and better equipped to explore participation and find doing so easier than the older generation. We expect the coefficient to be negative.

3. _Marital status of the household head._ This dummy variable equals one if the head is married and zero otherwise. Having a spouse gives the household the potential to diversify its income sources and lower the risks to participating in any specific activities, including e-commerce. We expect the coefficient to be positive.

4. _Level of education of the household head_ . Education makes adopting e-commerce technology easier, but it also raises the opportunity cost of participation, because people with higher education also have better alternative labor opportunities. We expect the coefficient to be positive with inverse U shape.

5. _Health of the household head._ Being healthy is used as reference and being sick with the ability to work is differentiated from being sick without the ability to work. We expect the coefficient to be negative.

6. _Household head work experience in cities._ This is a dummy variable that equals one if the head has worked in cities before and zero otherwise. Urban working experience can 13