metadata: authors: 0: author: Luo,Xubei
1: author: Niu,Chiyu
count: China
docna: 0: docna: E-Commerce Participation and Household
            Income Growth in Taobao Villages
docty: Policy Research Working Paper
subtopic: Educational Sciences,Inequality,Hydrology,Agricultural Economics,Labor Markets
teratopic: Education,Poverty Reduction,Water Resources,Agriculture,Social Protections and Labor
abstracts: cdata!: E-commerce has developed rapidly in
            China, and Taobao Villages, which are villages significantly
            engaged in e-commerce, are prospering in rural areas.
            E-commerce is fostering entrepreneurship and creating
            flexible and inclusive employment opportunities, including
            for women and youth. This paper examines the role of
            e-commerce participation in household income growth, drawing
            from a survey of representative Taobao Villages in 2017. The
            paper presents three main findings. First, e-commerce
            participation is not random: participation is higher among
            the households with younger household heads, with secondary
            education, particularly those with technical and vocational
            education, urban work experience, and knowledge of
            e-commerce. Second, e-commerce participation is associated
            with higher household income, with some indications that
            participation has a strong positive effect on household
            incomes. Third, e-commerce appears to yield benefits that
            are broadly shared among participants in an equitable way in
            Taobao Villages.
geo_reg_key: 517198,517191,119225
repnme: repnme: E-Commerce Participation and Household
            Income Growth in Taobao Villages
display_title: E-Commerce Participation and
            Household Income Growth in Taobao Villages
listing_relative_url: /research/2019/04/30973652/e-commerce-participation-household-income-growth-taobao-villages
topicv3: Water m1326522 137,Land Tenure m1326631 888,Poverty m1326687 695,Education m1327048 470,Jobs m1503931 2670
publishtoextweb_dt: 2019-04-10T00:00:00Z
historic_topic: Education,Poverty Reduction,Water Resources,Agriculture,Social Protections and Labor
no_of_pages: 31
author_keys: 0: author_key: 677436
1: author_key: 1388573
model_extractions: Policy Research Working Paper 8811 E-Commerce Participation and Household Income Growth in Taobao Villages _Xubei Luo Chiyu Niu_ Poverty and Equity Global Practice April 2019
Policy Research Working Paper 8811

# **Abstract**

E-commerce has developed rapidly in China, and Taobao Villages, which are villages significantly engaged in e-commerce, are prospering in rural areas. E-commerce is fostering entrepreneurship and creating flexible and inclusive employment opportunities, including for women and youth. This paper examines the role of e-commerce participation in household income growth, drawing from a survey of representative Taobao Villages in 2017. The paper presents three main findings. First, e-commerce participation is not random: participation is higher among the households with younger household heads, with secondary education, particularly those with technical and vocational education, urban work experience, and knowledge of e-commerce. Second, e-commerce participation is associated with higher household income, with some indications that participation has a strong positive effect on household incomes. Third, e-commerce appears to yield benefits that are broadly shared among participants in an equitable way in Taobao Villages.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at xluo@worldbank.org and cniu@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **E-Commerce Participation and Household Income Growth in Taobao Villages**

Xubei Luo and Chiyu Niu<sup>∗</sup> JEL codes: L8, O15, P25, L16 Key words: E-commerce, income growth, rural economy, industrial clustering

> ∗ The authors thank Shaohua Chen, Samuel Freije-Rodriguez, John Giles, Bert Hofman, Deepak Mishra, Ambar Narayan, Hoon Sahib Soh, Xuejiao Xu, and Salman Zaidi for valuable comments and discussion; Zhengwei Jiang, Zhenzhong Sheng, Ruidong Zhang from Alibaba Group for suggestions and for sharing the Alibaba online data for the research; Ramiro Flores Cruz, Hua Ding, Tao Kong, Qiang Li, Juan Munoz, Jinjie Wang, Yunbo Zhou, the entire team of the Institute of Social Science Survey of Peking University, China, and all survey interviewers for collaboration in the Taobao Village Survey; and all participants in the Taobao Village Survey for sharing their information and experience. The views expressed here are those of the authors and do not reflect those of the World Bank, its Executive Directors, or the countries they represent.
# **1. Introduction**

E-commerce has developed rapidly in China. From early 2000 to 2016, total e-commerce sales boomed, rising from zero to US$750 billion, making China the world’s largest e-commerce market with a global share of over 40 percent (Woetzel et al. 2017). The growth in e-commerce is expected to continue. According to a recent report by Goldman Sachs (2017), China’s e-commerce market will be US$1.7 trillion by 2020, more than twice its size in 2016. As a modern model of conducting business, e-commerce in China has also been revolutionizing the traditional way of buying and selling in brick-and-mortar stores, bringing convenience to consumers and increasing competition among producers. For example, the penetration rate of e-commerce – the proportion of e-commerce sales of total retail sales – surpassed that of the United States in 2013 and is now 15 percent (Zhang and Chen 2019).

While e-commerce is more developed in urban areas – nearly three-quarters of online stores and internet users are in urban areas – the development of online sales has grown sharply in rural areas. Online retail transactions in rural areas have grown much faster than the national average. Rural online retail transactions increased by 153 percent, from 353 billion yuan in 2015 to 895 billion yuan in 2016, compared to a growth rate of 26 percent for national online retail transactions (Ministry of Commerce of the People’s Republic of China 2016). The rural share of total online retail transactions also climbed from 9 percent in 2015 to 17 percent in 2016.<sup>1</sup> The development of e-commerce in rural areas shows strong signs of clustering. E-tailers with frequent and large amounts of online transactions have emerged in villages. The number of Taobao Villages, defined by AliResearch as administrative village with e-tailers clustering and total annual e-commerce transaction volume of more than RMB 10 million (about US$1.5 million), as well as at least 10 percent of village households actively engaging in e-commerce or at least 100 active online shops operated by villagers, primarily with the use of Taobao.com and Tmall.com marketplace – increased from 212 in 2014 to 2,118 in 2017 and to 3,202 in 2018 (Figure 1). Most Taobao Villages are in coastal areas and show significant trends of clustering (Map 1). By 2018, most of the 3,202 Taobao Villages were in the eastern region of China, while more than 100 Taobao Villages were in the central and western regions and 13 were in the northeastern region. Development of Taobao Villages shows a trend of clustering, and new Taobao Villages tend to emerge next to existing Taobao Villages. In 2018, 70 percent of Taobao Villages are in Zhejiang, Guangdong, and Jiangsu provinces; 69 percent of new Taobao Villages are in 76 existing Taobao Village Clusters.<sup>2</sup> The number of Taobao Towns, defined as town or districts with more than three Taobao Villages, increased from 17 in 2014 to 242 in 2017, and to 363 in 2018. According to Ali Research (Nanjing University and AliResearch 2018), the clustering trend results from a developed industrial base in old Taobao Villages, attractiveness from e-commerce success in existing nearby Taobao Villages, and the rapid development of an e-commerce service ecosystem. The number of active online shops in Taobao Villages increased from 70,000 in 2014 to 660,000 in 2018 (Figure

> 1 Calculated based on data from the Ministry of Commerce of the People’s Republic of China, “E-Commerce in China.” Total rural online retail transactions refers to the sum of online retail sales transactions from e-commerce enterprises (including individuals) operated in the administrative regions at the county level or below (excluding city districts) (http://dzsws.mofcom.gov.cn/article/ztxx/ndbg/201706/20170602591881.shtml).

> 2 A Taobao Village Cluster is defined as a cluster with more than 10 neighboring Taobao Villages.

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<!-- Start of picture text --> ¢<br><!-- End of picture text -->
sector and point out that besides establishing farmer organizations (Hazell et al. 2010 and Markelova et al. 2009) and promoting contract farming between smallholders and agribusiness firms (Abebe et al. 2013), e-commerce can serve as an efficient alternative to enhance market access for smallholders.

A fast-growing body of case studies on e-commerce in rural China focuses on Taobao Villages. Numerous cases show the prosperity of Taobao Villages and that people gain wealth and have better lives through participating in e-commerce. Case studies, such as Shaji in Jiangsu province and Caoxian in Shandong province, show many young and talented people, including women, have returned to their hometown in rural areas, earning income similar to or higher than they were as migrant workers in the cities and at the same time enjoying family life with their elderly and children. Many have become leaders of e-commerce in their home villages and are role models for their fellow villagers. Case studies in Mengjin in Henan province show people are enriched by access to new markets through online platforms for traditional cultural products such as peony painting and Tang tri-color ceramics. Case studies in Xifeng in Guizhou province show households received higher farmgate price for kiwis and therefore have an incentive to increase the production through online sales to domestic as well as European markets. Many cases, including poverty-stricken counties in remote and mountainous areas, show that access to an online market allows people in rural areas to enjoy the convenience, variety, and similarly low prices that are enjoyed by people in big cities.<sup>3</sup> Many case studies aim to understand the formation of Taobao Villages and their spatial patterns. Zeng et al. (2015) examine two of the first Taobao Villages, Dongfeng village in Jiangsu province and Junpu village in Guangdong province, and highlight a five-step process for the formation of Taobao Villages: introduction of Taobao projects, primary diffusion, accelerated diffusion, collective cooperation, and vertical agglomeration. They emphasize the prerequisites for the healthy development of Taobao Villages include two factors, one is the identification of a promising e-commerce project which can naturally trigger diffusion, and the other is decent local infrastructure, such as internet, transportation, and logistics. Qi et al. (2018) analyze the determinants of the formation of Taobao Villages using county-level panel data from the statistical yearbooks of Zhejiang and Jiangsu provinces for the years 2012–2015. They find that from the array of county-level characteristics, villages with more educated populations are more likely to become Taobao Villages. The Chinese Taobao Village development report (Nanjing University and AliResearch 2018) examines the patterns of formation of the Taobao Villages and their evolution, drawing from extensive fieldwork. The report shows that, while most Taobao Villages on the coast developed spontaneously, often led by a couple of first-mover return migrants and followed by fellow villages, government support, often through subsidized service provision by experienced e-commerce service firms, has become a major force for the incubation of inland Taobao Villages.

> 3 See more details in AliResearch (2017), _Taobao Villages._

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To our knowledge, however, there is little empirical evidence of the impact of e-commerce on household income in the rural areas and the distribution of the benefits of e-commerce participation due to data limitation. The most comprehensive study on the welfare implications of e-commerce in China is by Couture et al. (2017). The authors conducted a randomized control trial in eight counties in three provinces, Anhui, Henan, and Guizhou, to evaluate the effect of Alibaba’s Rural Taobao Program.<sup>4</sup> Their results show that e-commerce expansion through the program reduces the cost of living for certain groups in the rural population, but produces no significant income gains for the average rural producers one year after the establishment of the program. The results are consistent with observations in our fieldwork. At the early stage of the Rural Taobao Program, Alibaba mainly focused on cultivating the online shopping culture by subsidizing the logistics to increase the variety and convenience of local online purchases, while the increase in online sales, if any, in particular in places where the industrial base was weak and access to markets was limited, would take more time to materialize. However, it remains to be seen whether e- commerce development, particularly in areas such as Taobao Villages with clusters of e-tailers, has an impact on household income.

In the literature, if we broaden the scope from e-commerce to the internet in general, several papers document the effect of the internet on income and the labor market. Hjort and Poulsen (2017) show that the internet has large positive effects on employment rates and average incomes by studying how the arrival of submarine fiber optic internet cables in Africa affected labor market outcomes. Their results show that, through firm entry, productivity, and export, the probability of being employed in a skilled position increases substantially, but the probability of holding an unskilled job is unaffected. Houngbonon and Liang (2017) look at the effect on income and inequality within townships from household broadband internet adoption in France. They find that between 2009 and 2013, broadband adoption contributed 34 percent of the rise in average income and 80 percent of the fall in the Gini index of income inequality, and the gain is greater for the bottom income decile groups.

Growth, jobs, and services are the most important returns to digital investments (World Bank 2016). Digital platforms can create instant business opportunities for entrepreneurs, thereby creating jobs (World Bank 2019). E-commerce development has the potential to increase incomes and help upgrade the supply chains in rural areas, by lowering the costs of trade and increasing market access and eventually improving services by using big data to tailor production to consumer demand and by leveraging the benefit of digital technology to improve product quality. In 2014, the government launched the rural e-commerce demonstration program to put the poverty relief and rural vitalization strategy into practice using e-commerce development as an instrument, paying attention to the central and western regions, especially the poorest and most underdeveloped counties. In the same year, Alibaba Group initiated its Rural Taobao Program to build an e-

> 4 In this project, in collaboration with the local governments, Alibaba subsidized transportation costs, hired local helpers, and installed village service terminals to help villagers with online transactions.

5
commerce service system reaching 1,000 counties and 100,000 villages to encourage e-commerce activities in rural areas.<sup>5</sup> In this paper, we aim to fill the gap in the literature to study the association of e-commerce participation and household income growth in Taobao Villages, where e-commerce development is more advanced in rural China and economies of scale can potentially play a role. The paper also provides policy suggestions to strengthen the role of e-commerce in inclusive growth. This study will draw on the data from the Taobao Village Survey, with information at the village, household, and individual levels, to examine the role of e-commerce participation. To our knowledge, this study will be the first to draw on systematic data in rural China, where engagement in e-commerce is strong. It has three objectives: 1) examine the factors that determine the participation in e- commerce; 2) compare with the counterfactual (without e-commerce participation) to estimate the direct effect of e-commerce on household income, and 3) investigate the distribution of the benefits of e-commerce participation among households of different characteristics and its inclusiveness.

The paper is structured as follows: section 2 provides a broad picture of the Taobao Villages, drawing on the Taobao Village Survey; section 3 presents the research methodology of propensity score matching; section 4 discusses the empirical results related to the role of e- commerce participation on household income growth in Taobao Villages and for different groups of the population; and section 5 concludes.

# **2. Overview of Taobao Villages**

In recent years, villages with a large share of online sellers and a large share of GDP coming from e-commerce, or Taobao Villages, emerged and developed rapidly in rural China, particularly in the coastal region. This rapid growth demonstrates that the transition from physical to digital commerce, often perceived as a phenomenon of high-income countries in urban areas, can happen at a lower level of development and in rural areas as well.

# **_2.1. Taobao Village Survey data and summary statistics_**

This analysis uses the Taobao Village Survey, a collaboration between the World Bank, Alibaba Group, and Peking University, to examine the characteristics of the Taobao Villages and households. The survey, conducted between August and September 2018, is representative of the universe of 2,118 Taobao Villages in 2017. The survey uses a stratified random sampling design with villages as the primary sampling units, which are stratified by the gross merchandise value (GMV) of the villages’ online sales in the Alibaba platforms, including Taobao.com and Tmall.com. The secondary sampling units are the cell grids over the satellite image of the village maps, where a specific number of grids are randomly chosen according to the estimated number of households

> 5 Under the Rural Taobao Program ( 农村淘宝 ), Alibaba Group establishes thousands of e-commerce outposts across China in collaboration with regional governments and recruits local talent to serve the demand of the local communities. Alibaba Group’s rural strategy is supported by its various initiatives involved in rural e-commerce, such as the Specialties of China ( 特色中国 ) and agricultural resources ( 淘宝农资 ) channels on Taobao Marketplace, Taobao University, Tmall.com’s fresh produce channel ( 喵鲜生 ), and the Blue Stars program ( 满天星计划 ).

6
within each grid. The tertiary sampling units are households stratified by e-commerce participation status (whether the household owns an e-shop or not). The sample covers 1,371 households in 80 villages across eight provinces,<sup>6</sup> of which, 616 households own e-shops (e-households) and 755 do not (non-e-households). To control for income outliers, the top and bottom 3 percent of the income distribution is dropped. After dropping the observations with missing data, 453 e-households and 620 non-e-households in 80 villages are used in this analysis. The details of survey sampling design can be found in Appendix I.

# **_2.2 Characteristics of Taobao Villages_**

Rural areas with good infrastructure and good access to markets tend to be most conducive to e-commerce. All Taobao Villages have access to broadband internet and a mobile communication network. Over 50 percent of people in Taobao Villages use the internet, mostly through smartphones (Figure 3). People with higher education are more likely to use internet (Figure 4). The internet provides people with news, entertainment, online shopping, work, and education (Figure 5).

**Figure 3. Devices to use the internet**

<!-- Start of picture text --> Smartphone<br>Desktop<br>Laptop<br>PAD<br>0% 50% 100%<br>Percent of people using internet<br><!-- End of picture text -->

**Figure 4. Share of people using the internet, by education level**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Never been to Primary school Vocational<br>school or primary education education, college<br>school not degree, or<br>complete university degree<br>Use Internet Do not use Internet<br><!-- End of picture text -->

> 6 These provinces are Beijing, Hebei, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Yunnan.

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**Figure 5. Purpose to use the internet**

<!-- Start of picture text --> News<br>Entertainment<br>Online shopping<br>Learning<br>Work related to e-commerce business<br>Information other than news<br>Work not related to e-commerce business<br>0% 10% 20% 30% 40% 50% 60% 70%<br>Percent of people using internet<br><!-- End of picture text --> More than 90 percent of people in Taobao Villages perceived their social status as equal to or higher than it was with five years ago, and they believe they will have equal or even higher social status two years in the future. Compared with other households, e-households are 8 percent more likely to perceive an improvement in social status in the past five years and expect an improvement in the coming two years (Figure 6 and Figure 7).

**Figure 6. Self-rated social status compared to 5 years ago**

**Figure 7. Self-rated social status 2 years in the future compared to current status**

<!-- Start of picture text --> 100% 100%<br>80% 32% 80% 38%<br>52%<br>62%<br>60% 60%<br>40% 61% 40% 56%<br>43%<br>20% 20% 36%<br>0% 0%<br>E-households Non-E-households E-households Non-E-households<br>Lower social status Equal social status Lower social status Equal social status<br>Higher social status Higher social status<br><!-- End of picture text --> Taobao Villages have a higher share of migrants<sup>7</sup> than other villages in China (Table 1). While a typical rural village population is 5.3 percent migrants,<sup>8</sup> the Taobao Villages in our sample are 29 percent migrants, 19.6 percent come from other provinces. The causal relationship between the inflow of migrants and formation of Taobao Villages is unclear. It may be that villages with a large inflow of migrants, typically those with a strong industrial base, are more likely to become Taobao Villages. It may also be that a village where the business environment is more conducive for e-commerce (or commerce in general) attracts more migrants, who are more likely to be entrepreneurial and move there to start their own businesses or to seize employment opportunities. What is clear is the strong positive association of migrant inflows in Taobao Villages.

> 7 Immigrants are defined as individuals who live in this village but have their hukou, which is the population registration system, registered outside the village.

> 8 The numbers are derived from the Chinese Population Census in 2010. Total rural population is taken from module 1- 1c, and numbers of migrants by origin are taken from module 1-4c.

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**Table 1. Share of migration in an average village and a Taobao Village**

|**Percentage**|**of migrants**|**in all rural vil**|**lages**<br>|**Percentage of**|**migrants in T**|**aobao Villages**||
|---|---|---|---|---|---|---|---|
|**All**<br>**migrants**|**Different**<br>**village**<br>**same**<br>**county**|**Different**<br>**county same**<br>**province**|**Different**<br>**province**|**All migrants**|**Different**<br>**village same**<br>**county**|**Different**<br>**county same**<br>**province**|**Different**<br>**province**|
|5.3%|1.7%|1.4%|2.2%|29.0%|4.7%|4.7%|19.6%|

Note: Social registration ( _hukou_ ) information for all rural villages is derived from module 1-1c and 1-4c of China Population Census in 2010. Migrants are defined as individuals who live in this village but are registered outside of the village.

# **_2.3 Characteristics of e-households and non-e-households in Taobao Villages_**

E-commerce participation is not random. E-households and non-e-households differ in many dimensions. Table 2 shows the descriptive statistics for these two household types in the Taobao Villages. Comparing the characteristics of household heads, e-households are significantly younger than non-e-households with an average difference of about 10 years. E-household heads are better educated than those of the non-e-households. Specifically, non-e-household heads are about 12 percent more likely to have completed primary or junior high school education compared to e- household heads, but 6 percent, 10 percent, and 3 percent less likely to complete senior high school, college, and university education, respectively. E-household heads are healthier and less likely to be unable to work due to sickness. E-households are 16 percent less likely to be locally registered than non-e-households, but 13 percent more likely to come from another province. This observation confirms the finding on the village level illustrated in Table 1 that people tend to migrate to Taobao Villages to engage in e-commerce. E-household heads are 9 percent more likely to have working experience in urban areas and 31 percent more likely to have self-employed experiences than non-ehousehold heads. Among those who have urban experiences, e-household heads are 20 percent more likely to have learned about e-commerce than non-e-household heads. Measured by a constructed risk preference index,<sup>9</sup> e-household heads are significantly less risk-averse than non-e-households.

In terms of household characteristics, e-households have slightly more members than none-households and have younger age composition. Land ownership of the two household types is not significantly different, potentially because agricultural land is not essential capital for e- commerce business.

> 9 Risk preference of the household head is constructed from the answers to hypothetical survey questions that ask people to choose between receiving an amount of cash for certainty versus playing a game with uncertainty. The seven survey questions are formulated as follows: “Would you like to receive X yuan for certainty or receiving 2,000 yuan with 50% possibility and zero yuan with 50% possibility?” Value X takes values 400, 600, 800, 1000, 1200, 1400, and 1600. We construct a binary variable for each of these seven questions, which takes value one if the answer is to receive with certainty, and zero if the answer is to play the game. We calculate a proxy of risk premium from each question. For example, when X is 400, and the person would like to receive 400 yuan with certainty. This means that the person would like to pay at least 600 yuan to avoid playing the game with uncertainty, which further means that the risk premium in this case is at least 600 yuan. We then add up all positive and negative “risk premiums” from the seven questions and normalize it to a range from 0 to 1, with 0 being the most risk-loving and 1 being the most risk-averse.

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**Table 2. Summary statistics**

|**Household head characteristics**|E-households|Non-e-households|Difference|
|---|---|---|---|
|Age of HH head|41.020|51.450|-10.291***|
|If HH head is male|0.947|0.922|0.024|
|If HH head is married|0.905|0.910|-0.003|
|HH head’s highest education is...||||
|less than primary|0.060|0.189|-0.129***|
|primary school completed|0.150|0.271|-0.121***|
|middle school completed|0.361|0.323|0.038|
|high school completed|0.082|0.024|0.058***|
|vocational school completed|0.164|0.136|0.028|
|college completed|0.133|0.037|0.096***|
|university completed|0.051|0.019|0.031***|
|Health of HH head is...||||
|healthy|0.969|0.895|0.074***|
|sick but can work|0.015|0.052|-0.036***|
|sick and cannot work|0.015|0.053|-0.038***|
|Hukou status of HH head is...||||
|registered in current village|0.591|0.751|-0.160***|
|same county different village|0.064|0.055|0.009|
|same province different county|0.075|0.057|0.019|
|different province|0.270|0.137|0.132***|
|If HH head worked in urban areas before|0.469|0.380|0.090***|
|If HH head learned about e-commerce while in urban areas|0.265|0.066|0.199***|
|If HH head is self-employed|0.485|0.176|0.308***|
|Risk preference index (ranges 0-1; 0 means most risk-loving<br>and 1 means most risk-averse)|0.300|0.390|-0.095***|
|**Household characteristics**||||
|Household size|3.810|3.530|0.280***|
|Agricultural land (_Mu_)|2.690|2.640|0.062|
|Share of HH members age 15~35|0.503|0.290|0.212***|
|Share of HH members age 36~49|0.161|0.220|-0.059***|
|Share of HH members age 50~69|0.139|0.268|-0.130***|
|Number of observations<br>Note: Risk preference of the household head is constructed fr|453<br>om the answers t|620<br>o hypothetical survey q|1073<br>uestions that ask|

Note: Risk preference of the household head is constructed from the answers to hypothetical survey questions that ask people to choose between receiving an amount of cash for certainty versus playing a game with uncertainty. The seven survey questions are formulated as the following: “Would you like to receive X yuan for certainty or receiving 2000 yuan with 50% possibility and zero yuan with 50% possibility?” Value X takes values 400, 600, 800, 1000, 1200, 1400, and 1600. We construct a binary variable for each of these seven questions, which takes value one if the answer is to receive with certainty, and zero if the answer is to play the game. We calculate a proxy of risk premium from each question. For example, when X is 400, and the person would like to receive 400 yuan with certainty. This means that the person would like to pay at least 600 yuan to avoid playing the game with uncertainty, which further means that the risk premium in this case is at least 600 yuan. We then add up all positive and negative “risk premiums” from the seven questions and normalize it to a range from 0 to 1, with 0 being the most risk-loving and 1 being the most risk-averse. * p-value <0.1 ** p-value<0.05 *** p-value<0.01

# **3. Research methodology**

This section presents the methodology to examine the role of e-commerce participation in household income growth and the distributional aspects of the benefits of e-commerce participation. The descriptive statistics show the average income per capita of e-commerce households is higher than that of non-e-commerce households. Empirically, we aim to answer two questions: 1) Are more-wealthy/better-endowed households more likely to participate in e-commerce than less wealthy households? and 2) Do more-wealthy/better-endowed households benefit more from e- commerce participation in absolute and relative terms?

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If e-commerce participation is random across the population, one may use the following model to assess the causal impact of e-commerce participation on household income: (1) where is household income per capita of household i, ii ii is a constant term, ii takes value one if YY = αα+ γγPP + εε household _i_ participates in e-commerce and zero otherwise, gives the difference in average ii ii household income per capita between the e-households and non-e-households, YY αα PP contains both the observed and unobserved (to an econometrician) characteristics of household γγ _i_ . Under the ii εε assumption that e-commerce participation is random, is independent of . As a result, captures the average treatment effect on the treated (ATT) of e-commerce participation on ii ii PP εε γγ household per capita income.

However, our survey data are collected in an un-experimental setting, where e-commerce participation is not random but determined by household _i_ ’s choice based on . The dependence between and introduces selection bias in the estimate of . In other words, the choice of ii ii PP εε treatment and control groups simply based on e-commerce participation is far from scientifically ii ii PP εε γγ rigorous. In addition, the linearity assumption in equation (1) can be strong and could lead to serious bias if specified inaccurately (Imbens 2015).

To remediate the un-experimental setting, we use propensity score matching (PSM). Rosenbaum and Rubin (1983) developed the use of the propensity score—the probability of receiving treatment conditional on covariates—to reduce the dimensionality of the matching problem. Since then, PSM has been widely employed in empirical economic studies (Heckman et al. 1997, Dehejia and Wahba 1999, and Davies and Kim 2003), and many other fields where there is a treatment, a group of treated individuals, and a group of untreated individuals. After matching, PSM does not assume any parametric relationship between outcome variables and participation decisions to calculate the treatment effects, which is another reason PSM is more reliable than the model in equation (1).

# **_3.1 Assumptions and caveats_**

Identification using the PSM method rests on two assumptions: unconfoundedness and common support. The application of PSM and its results regarding the size of the causal effects are subject to several caveats.

# **_<u>Unconfoundedness</u>_**

The assumption of unconfoundedness states that: YY(0), YY(1) ⊥PP | XX

where ⊥ denotes independence, that is, given a set of observable covariates X that are not affected by treatment, potential outcomes are independent of treatment assignment.<sup>10</sup> This implies that all variables that influence treatment assignment and potential outcomes simultaneously must be

> 10 In plain English, unconfoundedness means participation is a random event among people after controlling for the set of observed characteristics.

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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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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
positively affect e-commerce participation because people are more likely to be exposed to e-commerce in urban areas. We expect the coefficient to be positive.

7. _Household head knowledge about e-commerce while working in cities_ . This is a dummy variable and interactive term with the variable household head work experience in cities. It equals one if the household head has worked in cities and has knowledge about e- commerce while working in cities and zero otherwise. Exposure to e-commerce can help people overcome the difficulties in e-commerce participation. We expect the coefficient to be positive.

8. _Household head is self-employed_ . This dummy variable equals one if the head is selfemployed and zero otherwise. Self-employment is often associated with entrepreneurship. We expect the coefficient to be positive.

9. _Risk preference of the household head_ . This is constructed from the answers to hypothetical survey questions that ask people to choose between receiving an amount of cash for certainty versus playing a game with uncertainty. The value of the constructed variable ranges from zero to one, with zero being least risk-averse and one being most risk-averse.<sup>13</sup> We expect the coefficient to be negative.

10. _Household size_ . Larger households might enjoy economies of scale and benefits from the division of labor within the household, both of which can relax time constraints for the potential entrepreneur to participate in e-commerce. We expect the coefficient to be positive.

11. _Agricultural land ownership (land size measured in mu)._ Land is the most important productive capital in rural areas. However, the effect of land ownership on e-commerce participation could be ambiguous: while people with more land tend to be richer and have fewer financial constraints to participating in e-commerce, land ownership also raises the opportunity costs of participating e-commerce.

12. _Age structure of household members._ We include three ratios of individuals of working age: the shares of household members aged 15 to 35 years, 36 to 49 years, and 50 to 69 years, omitting the share of household members age 70 and older and those younger than 15. We expect the coefficient to be positive for the younger cohorts.

At the village level, we include the following variables as control: 13. _Total population._ Population serves as a proxy for local market potential and labor supply. We expect the coefficient to be positive.

14. _Implementation of_ Dibao _policy in the village_ . _Dibao_ is one of the most important social security policies for the poor in rural China. It serves as proxy government support for poverty reduction. We expect the coefficient to be positive.

> 13 See footnote 9.

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15. _Piped water_ . Piped water is a proxy for local hard infrastructure development. We expect the coefficient to be positive.

16. _Distance between the village and nearest hospital in kilometers._ Distance to hospitals serves as a proxy for local soft infrastructure development. We expect the coefficient to be positive.

17. _Distance between the village and the closest logistic center in kilometers._ Distance to a logistic center is a proxy for e-commerce operational costs. We expect the coefficient to be negative.

18. _Distance between the village and the closest train station in kilometers_ . Distance to a train station is a proxy for market access. The farther away to train stations, the higher the transaction costs to sell products in traditional markets and the lower the opportunity costs of engaging in online sales. We expect the coefficient to be positive.

19. _Whether the village is located in the suburbs of a major city._ This is a dummy variable that equals one if the village is in the suburbs of a major city and zero otherwise. Being close to a big city means lower costs for migrating or commuting to the city for a job, which suggests higher opportunity costs to engage in e-commerce in the village. We expect the coefficient to be negative.

After obtaining the propensity scores, which are the predicted probabilities from the above model, a radius matching algorithm is performed to match the e-households with the non-ehouseholds based on their propensity scores. The default radius is 0.1, which means non-ehouseholds and e-households within a difference in propensity score of 0.1 are matched together. We then discard the unmatched as in Dehejia and Wahba (2002), because the unmatched observations failed the common support requirement. Finally, we use the matched e-households and non-e-households to calculate the ATT of e-commerce participation.

# **4. Empirical results**

In this section, we discuss the factors that condition the e-commerce participation at the household and village levels, examine the quality of the matching process, and present the results of the effect of e-commerce participation on household income growth.

# **_4.1 E-commerce participation_**

The e-commerce participation equation is estimated with a logit model to obtain the propensity scores used for matching. The marginal effects results are shown in Table 3. The education of the household head is positively related to e-commerce participation, but the positive and statistically significant marginal effects of education peak at technical school education and then level off. This result indicates that e-commerce participation requires a prerequisite level of education to get started, and technical school education seems a better fit for this requirement than general education. However, e-commerce is not attractive to graduates with university degrees, because these graduates tend to have higher opportunity costs in participating in e-commerce. Immigration from a different province than the Taobao Village is also positively and significantly 15
related to e-commerce participation, which corroborates the tabulation results in Table 3. Having knowledge of e-commerce through urban work experience (not just urban work experience) increases the probability of e-commerce participation. This finding coincides with the literature of technology adoption where knowledge is the first step to adopting, and the better a person understands the technology the more likely he or she is to adopt it (Feder and Slade 1984). Household heads who are less risk-averse are more likely to participate in e-commerce, as e- commerce is still relatively new to the rural areas and inherently more risky than traditional farming. Age, gender, marital status, and health of the household head are not significant predictors of e- commerce participation.

Regarding the household characteristics, households with more family members are more likely to participate in e-commerce, probably because they enjoy economies of scale and division of labor as well as risk diversification. Agricultural land ownership is not significant in explaining participation. Household composition in terms of ages matters for e-commerce participation. The larger the share of members ages 15 to 35, the more likely the participation. This result also indicates that it is easier for younger people to participate in e-commerce. Land, while it is a most important asset in rural areas, does not have a significant effect on e-commerce participation. This might be because land is not a direct capital or necessary condition in e-commerce operation. On the one hand, households with more land might be in a stronger financial position to participate in e-commerce; on the other hand, people without land might have lower opportunity costs to participate in e-commerce.

On the village level, people living in villages that are farther away from a train station are more likely to participate in e-commerce. As distance to train stations serves as a proxy for access to markets, the farther away people are to train stations, the higher their costs of accessing traditional markets. This result indicates that e-commerce reduces transaction costs by connecting sellers and buyers online and becomes a substitute for traditional markets. Similarly, people who live in villages that are in the suburbs of a big city are less likely to participate in e-commerce, either because they are closer to traditional markets or it is easier for them to engage in urban labor activities. Both proximity to public transportation and urban hubs are good proxies of market access. This suggests that e-commerce may substitute for traditional market access for Taobao Villagers. However, other local characteristics are not significant in determining e-commerce participation. For example, village population, which usually signifies local market potential, is not associated with e-commerce participation. This may be because e-commerce helps to connect e-tailers to consumers online, which attenuates the role of local demand. Social protection policies, such as _Dibao_ , and medical services indicators, such as proximity to hospital, are not significant determinants of e-commerce participation, potentially due to relatively high average level of social protection and services in Taobao Villages. Distance to a logistic center is not statistically significant, which might be because our samples are limited to Taobao Villages, which all have high levels of logistic service supply with little difference.

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# **Table 3. Participation equation estimation**

|**Variable**<br>**E-commer**|**ceparticipation(logit marginal effects)**|
|---|---|
|**Household head characteristics**||
|Age of HH head|-0.001|
|If HH head is male|-0.076|
|If HH head is married|0.022|
|HH head’s highest education(reference is less thanprimaryeducation)||
|Primaryschool completed|0.073|
|Middle school completed|0.128***|
|High school completed|0.135**|
|Technical school completed|0.297***|
|College completed|0.262***|
|Universitycompleted|0.109|
|Health of HH head(reference is healthy)||
|Sick but can work|-0.083|
|Sick and cannot work|-0.007|
|Hukou status of HH head(reference is registered in current village)||
|Same countydifferent village|-0.01|
|Sameprovince different county|0.024|
|Differentprovince|0.090**|
|If HH head worked in urban areas before|-0.04|
|If HH head learned about e-commerce while in urban areas|0.190***|
|If HH head is self-employed|0.186***|
|**Household characteristics**||
|Household size|0.058***|
|Agricultural land(Mu)|-0.002|
|Share of HH members age 15~35|0.346***|
|Share of HH members age 36~49|0.048|
|Share of HH members age 50~69|-0.03|
|Risk preference index (ranges 0-1; 0 means most risk-loving and|-0.203***|
|1 means most risk-averse)||
|**Village characteristics**||
|Population in the village|0.001|
|Whether have Dibaopolicy|0.009|
|Distance to the nearest hospital|0.003|
|Distance to the nearest logistic center|0.001|
|Distance to the nearest train station|0.219***|
|Whether havepiped water|0.086|
|Whether in the suburbs of a bigcity|-0.078***|
|Number of observations|1073|
|Note: Risk preference of the household head is constructed from the answers to<br>choose between receiving an amount of cash for certainty versus playing a game<br>formulated as the following: “Would you like to receive X yuan for certainty or<br>yuan with 50% possibility?” Value X takes values 400, 600, 800, 1000, 1200, 14<br>each of these seven questions, which takes value one if the answer is to receive<br>game. We calculate a proxy of risk premium from each question. For example,<br>400 yuan with certainty. This means that the person would like to pay at least 60<br>which further means that the risk premium in this case is at least 600 yuan. We t<br>premiums” from the seven questions and normalize it to a range from 0 to 1, wit<br>risk-averse. * p-value <0.1 ** p-value<0.05 *** p-value<0.01|hypothetical survey questions that ask people to<br>with uncertainty. The seven survey questions are<br>receiving 2000 yuan with 50% possibility and zero<br>00, and 1600. We construct a binary variable for<br>with certainty, and zero if the answer is to play the<br>when X is 400, and the person would like to receive<br>0 yuan to avoid playing the game with uncertainty,<br>hen add up all positive and negative “risk<br>h 0 being the most risk-loving and 1 being the most|

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<!-- Start of picture text --> “ Propensity score for e-households<br>ar<br>3>o7<br>is)<br>no<br>02 4 6 8 1<br>Probability of participating e-commerce<br><!-- End of picture text --> <!-- Start of picture text --> Propensity score for non-e-households<br>o<br>N<br><<br>a5<br>0 2 4 6 8 1<br>Probability of participating in e-commerce<br><!-- End of picture text -->
to derive the tests, the differences in the blocks are not significantly different from zero. This means that the matched households have similar characteristics even if they differ by e-commerce participation. In other words, the common support assumption is satisfied. Details of the balancing tests can be found in Appendix II Table A1.

# **_4.3 Role of e-commerce participation on income growth (PSM results)_**

# _4.3.1 Full sample_

After the radius matching and balancing procedure, 515 of 620 observations in the control group are matched with all 453 observations in the treatment group. As shown in Table 5, the estimated average treatment effect on the treated, which is an estimate of e-commerce direct effect on income in Taobao Villages, is 35,388 yuan. Compared to the mean income in the control group, which is 35,019 yuan, this result is equivalent to an income growth of 101 percent for the full sample.

# **Table 5. PSM results**

||**Control mean**|**E-commerce**<br>**effect**|**T-stat**|**Growth**<br>**rate**|
|---|---|---|---|---|
|Full sample|35018.9|35388.2|10.2|101.1%|
|Wealth quartile 1|26642.9|25360.3|2.8|95.2%|
|Wealth quartile 2|27427.0|26996.1|3.8|98.4%|
|Wealth quartile 3|34957.3|33944.4|4.9|97.1%|
|Wealthquartile 4|57801.7|32906.3|3.3|56.9%|
|Head education, primary|28259.2|28236.9|3.9|99.9%|
|Head education, middle school|33908.9|31735.8|5.0|93.6%|
|Head education,high school|39011.6|28946.6|1.9|74.2%|
|Head education,technical school|54367.1|45777.1|3.0|84.2%|
|Knew e-commerce|48461.3|35820.9|3.1|73.9%|
|No urban experience or did not hear about e-<br>commerce while in urban|33205.6|29294.6|7.1|88.2%|
|Agequartile 1,mean age 29|46054.0|32914.8|3.4|71.5%|
|Agequartile 2, mean age 40|38653.5|30434.9|3.9|78.7%|
|Age quartile 3, mean age 52|30761.9|29404.0|4.9|95.6%|
|Agequartile 4,mean age 66|25608.8|26346.8|2.6|102.9%|

Note: The wealth index is constructed from principal component analysis of the household’s ownership of automobiles, trucks, motorcycles, tractors, personal computers, laptops, air conditioners, televisions, cellphones, and dwellings.

However, one should use caution when interpreting this result as the estimated direct effect of e-commerce participation on household income growth represents mainly an upper bound while the indirect effect of e-commerce development on the local economy in Taobao Villages is likely to be positive (for the reasons discussed in the previous section). In addition, when defining e- households, we do not take into account the number of years that households have been in e- business or have experience in conducting e-commerce. In other words, we do not differentiate those that have been operating e-shops for a long time from those that just opened recently. Within the e-households, there are variations in e-commerce experience, as shown in Figure 9. The mean years of experience, defined as the mean number of years between the year of opening the first e- shop until 2018 for the e-households is 4.6 years. In other words, the estimated average direct effect 19
<!-- Start of picture text --> 1<br>!<br>81<br>}Mean years of experience in e-commerce<br>1<br>1<br>!<br>&1<br>!<br>> 1<br>2I<br>®S !<br>og 1<br>a1<br>1<br>!<br>!<br>f=}Nt1<br>1<br>1<br>!<br>|<br>oO<br>123 4 5 6 7 8 9 10 11 12 13 14 15 16 17<br>Years of experience in e-commerce till 2018 for e-households<br><!-- End of picture text -->
rural areas (Zheng 2007), the result here further confirms that e-commerce appears to yield benefits broadly shared among participants in an equitable way within the Taobao Villages.

**Figure 10. Stratification results by wealth**

<!-- Start of picture text --> 120% 40000<br>95% 98% 97%<br>100%<br>30000<br>80%<br>57%<br>60% 20000<br>33,944 32,906<br>40% 25,360 26,996<br>10000<br>20%<br>0% 0<br>Wealth Wealth Wealth Wealth<br>quartile 1 quartile 2 quartile 3 quartile 4<br>control mean<br>Growth rate of income relative to Absolute income increase in RMB<br><!-- End of picture text -->

# _4.3.3 Stratification by education level of household head_

Rural e-commerce has made markets easier to access for rural residents, but it is still technology-intensive, and it may be hard for rural residents with limited education to fully take advantage of its benefits. To explore this hypothesis, we stratify the full sample into four subgroups according to the educational level of household head. The results in Figure 11 show that all education levels attain positive gains from e-commerce. Ranging from having completed primary school or less, middle school, high school, and technical school, the e-commerce effects are 28,237 yuan, 31,736 yuan, 28,947 yuan, and 45,777 yuan, respectively. Having completed technical school has the highest absolute gains from e-commerce among all educational levels. Excluding technical school education, having more general education does not seem to increase the gains from e-commerce.

This finding suggests that holding other observable factors equal, having vocational training is better than general education in facilitating e-commerce success. In other words, skills attained from vocational training could be more relevant to e-commerce than general education. For example, regular high school curriculums usually do not include management, business operation, or any technical skills, instead, they include liberal arts and science classes that may not be directly related to e-commerce. However, technical training in vocational schools can be targeted on specific skills. For example, curriculums in vocational schools can include web page design and retailing that are helpful to e-commerce operations. Additionally, vocational training, such as gardening, cooking, and tailoring, can give e-shop owners the practical skills and knowledge needed to understand the markets for the products they may produce and sell through e-commerce. In the Taobao Village Survey, data also show that e-commerce training is one of the top three supports e-tailers need from the government. These pieces of evidence, while far from conclusive, suggest that collective action from the villagers or government support in designing complementary training programs can go a long way to promote further e-commerce development.

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Although primary and middle school education are associated with lower absolute gains from e-commerce than high school and vocational school education, the relative gains from them tell a different story. The mean income for primary, middle, high, and vocational school groups that do not participate in e-commerce is 28,259 yuan, 33,908 yuan, 39,011 yuan, and 54,367 yuan, respectively, which monotonically increase with education attainment. In other words, without e- commerce, those with only primary education or less would find it difficult to catch up with those in the village who had a high school education. As shown by the growth rates comparison, household heads with primary education attained 100 percent increase compared to their counterfactual, but the same number is only 74 percent for those with high school education. For those who had constraints in obtaining education, e-commerce may have the potential to help them narrow the income gaps with the rest.

**Figure 11. Stratification by education of household head**

<!-- Start of picture text --> 120% 50000<br>100%<br>94%<br>100%<br>84% 40000<br>74%<br>80%<br>30000<br>60%<br>45,777<br>20000<br>40% 31,736<br>28,237 28,947<br>10000<br>20%<br>0% 0<br>Head edu, Head edu, Head edu, Head edu,<br>primary or middle high school technical<br>less school school<br>RMB<br>control mean<br>Absolute income increase in<br>Growth rate of income relative to<br><!-- End of picture text --> Note: Small sample for household heads with college, university or above education.

# _4.3.4 Stratification by age of household head_

The full sample is divided evenly into four age quartiles, and the results are presented in Figure 12. The mean age of the household head in the first quartile is 29, 40 in the second quartile, 52 in the third quartile, and 66 in the fourth quartile. The absolute gains from e-commerce participation decrease with age, but the growth rates due to e-commerce participation increase with age. These results show that although the younger generation is more likely to participate in e- commerce, the older generation, if they do participate, benefits significantly perhaps drawing from their experience. This seems to suggest that e-commerce participation can potentially be inclusive for different age groups. With rapid aging, largely due to the outmigration of the prime age population, supporting e-commerce participation can be one way to provide income opportunities for the left-behind older segment of the labor force.

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**Figure 12. Stratification results by age of household head**

<!-- Start of picture text --> 120% 35000<br>103%<br>96%<br>100% 30000<br>79%<br>25000<br>80% 71%<br>20000<br>60%<br>32,915<br>30,435 29,404 15000<br>26,347<br>40%<br>10000<br>20% 5000<br>0% 0<br>Age quartile Age quartile Age quartile Age quartile<br>1, mean age 2, mean age 3, mean age 4, mean age<br>29 40 52 66<br>control mean<br>Growth rate of income relative to Absolute income increase in RMB<br><!-- End of picture text -->

# _4.3.5 Stratification by knowledge and work experience of household head_

The effect of e-commerce on income is estimated in a subgroup where people have learned about e-commerce while working in an urban area, and in a subgroup where people have no urban work experience or did not hear about e-commerce while working in an urban area. As shown in Figure 13, the absolute gain from e-commerce in the former subgroup is 35,821 yuan, which is about 6,000 yuan higher than for the latter group. However, the growth rate in the latter group is higher than the former one. This means that having urban work experience with knowledge that is specific to e-commerce helps to facilitate e-commerce success, but people with no knowledge at all about e-commerce (which often means people with other differences compared with those who have the knowledge) can achieve considerable income growth as well. This result also sheds light on the issue of return migrants in rural areas. The opportunity to participate in e-commerce in their hometown might offer high return to the intangible skills they have obtained when they migrate. E-commerce as an alternative livelihood option provides additional incentive for the rural-to-urban migrants to return to rural areas.

**Figure 13. Stratification results by knowledge and work experience of household head**

<!-- Start of picture text --> 100% 88% 40000<br>74%<br>80%<br>30000<br>60%<br>20000<br>35,821<br>40%<br>29,295<br>10000<br>20%<br>0% 0<br>Knew e-commerce while No urban experience or<br>working in urban areas did not hear about e-<br>commerce while in urban<br>RMB<br>control mean<br>Growth rate of income relative to Absolute income increase in<br><!-- End of picture text --> 23
# **5. Conclusion**

E-commerce has developed rapidly in rural China. As the penetration rate in rural areas is just 40 percent (Woetzel et al. 2017), there is still ample room to increase further. However, more studies are needed to understand whether and how e-commerce can help improve the livelihoods and production efficiency in rural areas. Using a nationally representative sample of Taobao Villages, we have detailed the characteristics of Taobao Villages and the households living there, as well as the pattern of e-commerce participation and its effect on income distribution across different stratified groups.

The empirical results show that: 1) e-commerce participation is not random, participation is higher among households with younger household heads, with secondary education (particularly those with technical and vocational education), urban work experience, and knowledge of e- commerce; 2) e-commerce participation is associated with higher household income, with some indications that participation has a strong positive effect on household incomes; and 3) e-commerce benefits among participants seem to be inclusive in Taobao Villages: among the households engaging in e-commerce, while the wealthy gain more in absolute terms, the less wealthy experience higher income growth in relative terms. While younger individuals with technical training, knowledge of e-commerce, and migrant experience are likely to have higher income increase in absolute terms through e-commerce participation, those older, with less education, knowledge, or migrant experience have the potential to benefit more in relative terms.

Due to data limitations, our analysis only estimates the direct effect of e-commerce participation on household income growth in Taobao Villages, where e-commerce is most developed in rural China. To further understand the effect of e-commerce on rural China, a panel data set including villages that significantly engage in e-commerce and those that do not is required. More research is needed to 1) quantify the impact of e-commerce on income growth in rural areas, including in the non-Taobao Villages (areas where e-commerce is less developed), to understand the difference between Taobao Villages and non-Taobao Villages and to identify the potential channels through which e-commerce can be stimulated in broader rural areas to support rural vitalization; and 2) understand the direct and indirect impact of e-commerce development on the local economy, as well as the broader impact on the traditional brick-and-mortar shops and therefore the entire economy.

E-commerce has tremendous potential for stimulating growth, creating jobs, and reducing poverty. The challenges are translating this potential into reality. Developing e-commerce requires more than connecting people to the internet. Infrastructure and logistics, skills and entrepreneurship, as well as a conducive business environments are crucial. In Taobao Villages, investments in technical and vocational training, as well as tailored technical training on e-commerce, seem to help improve e-commerce participation and realize gains. More research is needed to distill lessons and identify the policy levers to support e-commerce development in rural areas and to enhance its impact on poverty reduction and inclusive growth.

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# **Appendix I. Sampling design of Taobao Village Survey**

In 2017, 2,118 Taobao Villages were reported to exist in China. The Taobao Village Survey uses a stratified sampling design to obtain a nationally representative sample of 100 villages. The 2,118 villages are first ranked according to their annual sales, then divided into five strata, and within each stratum 20 villages were selected randomly. Specifically, the strata are defined as follows:

- Stratum 1 includes the 20 Taobao Villages with the highest annual sales. All 20 villages in this stratum are selected with certainty.

- Stratum 2 includes 80 villages that rank from the 21st to the 100th Taobao Villages in the annual sales ranking. Within these 80 villages, 20 villages are selected randomly.

- Stratum 3 includes the next 200 villages in the annual sales ranking. Twenty villages are selected randomly, each with probability of 0.1.

- Stratum 4 includes the next 500 villages in the annual sales ranking. Twenty villages are selected randomly, each with probability of 0.04.

- Stratum 5 includes the remaining 1,318 villages. Twenty villages are selected, each with probability of 20/1318.

After the first round of listing, due to refusal of interviews and inability to get in contact, 14 villages (8 in stratum 2, 3 in stratum 3, 2 in stratum 4, 1 in stratum 1) are further selected into the sample as replacements. At the end, 80 villages complete the interviews, 12 villages from stratum 1, 18 from stratum 2, 18 from stratum 3, 14 from stratum 4, and 18 from stratum 5.

The area of each selected village was then divided into a grid with equal sizes and estimated number of households within each grid. For large villages, we randomly select grids to do households listing. For small villages, all households are listed. Ten e-households and 10 none-households are randomly selected from the listing.

Sampling weights are calculated as the inverse of the product of non-response rate and the probability of being selected from the population.

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# **Appendix II. Balancing tests**

# **Table A1. Balancing tests on characteristics of e-households and non-e-households**

||Mean differ|ence betwe|en the matc|hed e- and n|on-e-househ|olds|
|---|---|---|---|---|---|---|
|**Household head characteristics**|Block 2|Block 3|Block 4|Block 5|Block 6|Block 7|
|Age of HH head|-1.20|1.52|0.61|0.57|-1.32|-3.83|
|If HH head is male|-0.12|-0.03|0.03|-0.03|0.00|-0.01|
|If HH head is married|-0.08|-0.03|0.02|0.02|-0.03|0.00|
|HH head’s highest education is...|||||||
|less thanprimary|0.25|-0.31|-0.05|0.01|-0.04|0.03|
|primaryschool completed|0.08|0.24|0.04|-0.05|-0.09|0.01|
|middle school completed|-0.22|0.02|-0.05|0.02|0.09|0.10|
|high school completed|0.00|0.00|0.00|0.02|0.10|0.13|
|vocational school completed|-0.12|0.04|0.06|-0.07|-0.06|-0.18|
|college completed|0.00|0.02|0.00|0.04|0.01|0.20|
|universitycompleted|0.00|0.00|0.01|0.03|-0.01|-0.30|
|Health of HH head is...|||||||
|healthy|-0.07|0.09|0.02|-0.02|-0.02|0.00|
|sick but can work|-0.10|-0.10|-0.02|0.02|0.02|0.00|
|sick and cannot work|0.17|0.02|0.01|0.00|0.00|0.00|
|Hukou status of HH head is...|||||||
|registered in current village|0.13|-0.15|-0.06|0.05|0.06|-0.21|
|same county different village|-0.02|0.00|0.00|0.02|-0.05|-0.03|
|same province different county|-0.05|-0.04|0.06|-0.01|-0.05|-0.05|
|different province|-0.07|0.19|0.01|-0.06|0.03|0.28|
|If HH head worked in urban areas before|-0.02|0.18|0.10|-0.02|-0.04|0.05|
|If HH head learned about e-commerce while in urban|||||||
|areas|0.00|0.00|0.03|0.04|0.00|0.17|
|If HH head is self-employed|-0.12|-0.18|0.19|0.19|0.34|0.35|
|**Household characteristics**|||||||
|Household size|-0.30|-0.14|0.04|-0.26|-0.25|0.69|
|Agricultural land (Mu)|-1.17|-1.75|0.90|-0.94|0.36|0.29|
|Share of HH members age 15~35|0.08|0.05|-0.01|0.00|0.04|-0.06|
|Share of HH members age 36~49|0.13|-0.05|-0.01|-0.03|-0.04|0.03|
|Share of HH members age 50~69|-0.07|0.04|0.02|0.01|-0.01|0.00|
|Risk preference index (ranges 0-1; 0 means most risk-<br>loving and 1 means most risk-averse)|-0.08|0.00|-0.03|0.00|0.01|0.07|
|**Village characteristics**|||||||
|Population in the village|-3400.00|-114.50|-258.66|-422.36|2104.72|-387.82|
|Whether have Dibaopolicy|0.02|-0.08|0.01|-0.01|0.00|-0.02|
|Distance to the nearest train station|-1.39|-1.50|0.97|0.15|-0.48|-0.76|
|Distance to the nearest logistic center|-0.11|-0.03|-0.03|0.03|0.02|-0.05|
|<br>Whether have piped water|0.02|0.00|0.01|-0.03|0.00|0.09|
|Whether in the suburbs of a big city|-0.17|0.07|-0.01|-0.03|0.04|0.11|
|Number of observations|64|65|258|276|196|86|

. * p-value <0.1 ** p-value<0.05 *** p-value<0.01 29