E-commerce adoption and Rural Sustainable Livelihood Development, The Case of Smallholders in China’s Agro-Food Sector

Yi Cai PhD Candidate, College of Economics and Management, Hubei Rural Development Research Center, Huazhong Agricultural University Email address: [email protected]

Chunping Xia* Professor, College of Economics and Management, Hubei Rural Development Research Center, Huazhong Agricultural University Email address: [email protected]

Cuicui Wang PhD Candidate, College of Economics and Management, Hubei Rural Development Research Center, Huazhong Agricultural University Email address: [email protected]

Scott Loveridge Professor, Department of Agricultural, Food and Resource Economics Michigan State University Email address: [email protected]

Selected Paper prepared for presentation at the 2019 Agricultural & Applied Economics Association Annual Meeting, Atlanta, GA, July 21 – July 23

Copyright 2019 by Yi Cai, Chunping Xia, Cuicui Wang, Scott Loveridge. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. E-commerce adoption and Rural Sustainable Livelihood Development,

The Case of Smallholders in China’s Agro-Food Sector

Abstract: We investigate the impact of e-commerce adoption on smallholder livelihood in China’s agro-food sector. Based on a sustainable livelihood framework, as well as a survey-based data set that allows us to correct selection biases by using Propensity Score Matching (PSM), we find that the most common approach for smallholders to participate in agro-food e-value chains is to cooperate with local e-tailers and become a e-tailer supplier. Moreover, e-tailer suppliers have more livelihood assets and are in better external e-commerce conditions than traditional suppliers that are not in the e-value chain. The PSM results further suggest that agro-food e-value chain participation has a positive and robust impact on improving smallholders’ agricultural income, while reducing their not marketed portion. We also find evidence about the digital divide between heterogeneous e-tailer suppliers. Results imply that policies should encourage and facilitate smallholder participation in agro-food e-value chains, and further promote smallholder self-learning to achieve long-term livelihood improvement. Finally, we advocate a balanced point among rural e-commerce development, rural socio-economic development, and sustainable livelihood development for smallholders in underdeveloped regions must be sought.

Key words: agro-food e-value chain, participation effect, smallholders, sustainable livelihood, rural China.

1. Introduction

Connecting smallholders with consumer markets efficiently is an ongoing challenge in developing markets. Modern Information and Communications Technologies (ICTs) can increase smallholder welfare from agricultural marketing ventures by offering them timely and accurate information to reduce information and transactions costs that lead to inefficiencies and weaken their bargaining position (Goyal 2010, Courtois and Subervie 2014,

Jensen 2007). With ICTs, the problem of information asymmetry between agro-food producers and consumers can also be alleviated (Jensen 2010, Aker 2010, Just et al. 2002,

Aker 2011). Furthermore, the internet can provide rural households with learning and employment opportunities (Akca, Sayili, and Esengun 2007, Hollifield and Donnermeyer

2003).

E-commerce involves purchasing and selling of goods and services, or the transmitting of funds or data, over an online network. E-commerce adoption by smallholders and small and medium-sized enterprises (SMEs) has a positive impact on improving rural residents’ livelihood and revitalizing rural China (Guihang, Qian, and Guangfan 2014, Zhang et al.

2018, Lin, Xie, and Lv 2016). Evidence also suggests rural e-commerce development reshapes the natural landscape (e.g. building warehouses and roads) and social-economic structure (Leong et al. 2016). As the result of e-commerce deployment for Chinese rural development, there are increasingly more rural successful e-commerce practices, which create more jobs and opportunities for rural households (AliResearch 2015). Defined by

Alibaba, a Taobao village refers to a village where at least 10 percent of its residents operate online stores with annual sales of at least 10 million Yuan in total (USD 1.6 million). Most of the Taobao villages are in southeastern China and engage in processing industries such as garment, footwear, furniture, and pottery business (AliResearch 2014).

The impact of mobile and internet adoption on farmers' marketing strategies and incomes, rural industrial competitiveness, and poverty alleviation has long been discussed by scholars around the world (Muto and Yamano 2009, Harwit 2004, Futch and McIntosh 2009, Burrell and Oreglia 2015, Bayes, Von Braun, and Akhter 1999, Aker and Fafchamps 2014, Abraham

2006, Jensen 2007). But until recently, literature focusing on e-commerce, especially the formation and impact of Taobao villages have been not published (Zhang et al. 2018, Qi,

Zheng, and Guo 2019, Lin, Xie, and Lv 2016, Leong et al. 2015). Evidence on Chinese rural e-commerce adopters’ welfare improvement from the consumption side has been found

(Couture et al. 2018, Fan et al. 2018, Luo, Wang, and Zhang 2019). However, to the best of our knowledge, research focusing on the impact of rural e-commerce on the agro-food production sector is scarce, especially with respect to smallholders. Moreover, from a micro-level perspective, the socio-economic transformation of rural community is literally based on the transformation of smallholders (Scoones 1998, Reardon, Delgado, and Matlon

1992). In other words, it depends upon a change in smallholder livelihood strategies to achieve and maintain sustainable livelihoods (Goldman 2000). In addition, it should be noted that the household’s livelihood strategy is a result of a combination of factors (Dorward et al.

2003). It is not only constrained by the livelihood assets owned by the household, but also limited by the external conditions such as market conditions, institutional support, social public service, and culture. All of these challenges coalesce into a sustainable livelihood framework, which is a practical framework for analyzing a concept of sustainable livelihood

(DFID 1999). As e-commerce adoption requires more skill than dialing mobile phone and surfing the internet, we first explore the most common strategy for small farmers in China’s underdeveloped regions to benefit from e-commerce. What is the connection between smallholders’ livelihood strategy on agro-food e-value chain participation and their livelihood assets and external e-commerce conditions? With regard to the impact of ICTs adoption, what is the average effect of e-commerce adoption on smallholders? In addition, digital divide refers to the acquisition, use or impact of any uneven distribution of ICTs between different groups (Norris 2001). The digital divide between small farmers will inevitably arise because many small farmers lack certain capital, skills or knowledge and therefore cannot fully exploit the newly introduced ICTs (Van Dijk 2012, Bach, Shaffer, and Wolfson 2013, Qiu et al. 2016). Evidence about the digital divide between rural e-tailers in Taobao village has been found (Guo et al. 2017, Luo and Niu 2019). Empirical study also indicates that Chinese rural online shoppers tend to be younger, richer, live closer to e-commerce terminal and village center (Couture et al. 2018). From this perspective, it is important to explore whether heterogeneous small agro-food producers differentially benefit from the same e-commerce adoption.

In this study, we integrate smallholder’s e-commerce adoption and sustainable livelihood analyses in an exploration of the rural development implications for China. To evaluate the impact of e-commerce adoption and further explore the potential digital divide between smallholders, we also need a proper counterfactual that follows statistical comparisons between groups of households that are alike in all relevant pre-treatment characteristics except e-commerce adoption. Using a cross-sectional household survey from rural China, we address the selection bias and isolate the causal effect of e-commerce adoption by employing

Propensity Score Matching (PSM). The aims of the paper are twofold: (1) to contribute to a better understanding of the implications of e-commerce adoption for smallholders integrated into agro-food e-value chains; and (2) to contribute to a broader theoretical and methodological discussion on the need to incorporate rural development and poverty reduction into ICT research (Zheng et al. 2018, Walsham 2012).

2. Chinese rural e-commerce development and the impact on the livelihood of smallholders

Rural e-commerce development in rural China

Even though decades have passed since the introduction of e-commerce, only recently have many more smallholders in rural China gained access to the online market by selling agro-products directly to consumers via online e-commerce platforms (e.g., Taobao) and online chatting Apps (e.g., Wechat). The total amount of Chinese rural e-commerce transactions doubled in 2015 compared with 2014, reaching 353 billion Yuan (approximately

US$54 billion) (CCFA 2017). In the following year, the figure exceeded 467.5 billion Yuan

(approximately US$72 billion).

The driving force for Chinese rural e-commerce development comes from rural grassroots passion, e-commerce giants’ ambition, and government promotion (Li 2017). Research focusing on the evolution of the rural e-commerce ecosystem reveals that different roles emerge, adapt, and align over time through rural residents’ interactions with e-commerce

(Leong et al. 2016). The success of local e-commerce leaders and pioneers can be easily observed by their relatives and neighbors. It further raises the other residents’ awareness of e-commerce by demonstration and neighbor effects (Cui and Feng 2018). Eager to enhance their own income, farmers will imitate the production and marketing strategy of the one who earns more than themselves. With the recognition of the potential impact of e-commerce on rural industries and household livelihoods, both Chinese central and local governments launched a series of projects to promote rural e-commerce development. For example, the

Chinese central government launched a top-down Information and Communication

Technology for Development (ICT4D) project formally known as “E-commerce goes into rural areas” to promote e-commerce development in rural areas. Since the project began in

2014, 756 counties have been selected to serve as pilot areas until 2017, most of which are in underdeveloped regions in mid- and western China. A typical three-level “county, town, village” rural e-commerce service system has been widely accepted by local governments to meet the increasing demand of rural residents. On the other hand, e-commerce platform companies including Alibaba, China Post, Ganjie and Jingdong focus on grabbing rural e-commerce market share as e-commerce growth in urban China is slowing down after a boom during the past two decades. The active interaction between local government, e-tailer associations, e-commerce platform companies, and other stakeholders in e-business will help create a better e-commerce climate.

Impact on the livelihood of smallholders

Rural households often improve traditional livelihood strategies through agricultural intensification and rural livelihood diversification (Warren 2002). In assessing the livelihood outcomes and rural development implications of e-commerce adoption, a livelihoods approach offers valuable insights. A livelihood involves various assets, strategies, activities and other factors commonly required for living (Chambers and Conway 1992). Since the

1990s, various livelihood analysis frameworks have been developed. Work based on different research priorities by scholars and agencies such as the United Nations Development

Programme (UNDP), the UK Overseas Development Department (DFID), and International

Care (CARE) explore variations on the approach. The sustainable livelihoods analysis approach appeared in the mid-1980s (DFID 1999). It is defined based on the ability of a social unit to improve its assets in certain situations. Researchers apply the sustainable livelihoods analysis approach in the field of rural development (Ellis 2000, Toner 2003), ICT deployment (WorldBank 2005, Duncombe 2006, Wlokas 2011), poverty alleviation

(Erenstein, Hellin, and Chandna 2010, Ellis and Bahiigwa 2003), natural resource management (Chen et al. 2013, Hahn, Riederer, and Foster 2009, Perret 2014), and value chain participation (Challies and Murray 2011).

This study employs a sustainable livelihood framework to provide better understanding of the impact of agro-food e-value chain participation on small farmers’ agro-food production livelihood strategy. There are five critical actors, which include grassroots leaders, e-tailers, e-supply chain partners, third-party e-commerce service providers and institutional supporters identified by scholars within a rural e-commerce ecosystem (Leong et al. 2016). Generally, grassroots leaders and e-tailers are those rural elites, rural migrant workers returning from an urban area, individual investors, and college graduates who have computer skills and e-commerce experience that can manage an online business on their own. While for the rural residents who lack access to the internet or e-commerce skills, the most common way for them to participate in agro-food e-value chain is to produce, supply or distribute products that are sold via e-commerce. They are also defined as e-supply chain partners (Leong et al.

2016).

As economies grow, households shift away from traditional self-sufficiency goals and towards profit and income oriented decision making (Bowman and Zilberman 2013). Gaining and retaining access to a profitable agro-food value chain is proven to improve small farmers’ sustainable livelihood (Challies and Murray 2011). By participating into agro-food e-value chain, small farmers can connect with more customers and use internet marketing strategies to alleviate livelihood vulnerabilities including price volatility, poor market performance, and the limitation of seasonal factors on agriculture production and sales. For example, an online pre-sale strategy can extend sale time and take customized designed orders, thus making production more efficient. While households may be integrated into multiple supply chains, and engaged in a diverse range of on- and off-farm activities, it is argued that the approach and extent for them to participate in certain agro-food e-supply chains depends on the livelihood assets into which individuals are integrated. Social relations, institutions and organizations can also shape the household’s access to tangible and non-tangible livelihood assets (Challies and Murray 2011). The availability of, and ability to access, portfolios of assets, together with external conditions, such as public service, e-commerce climate and government support will further influences the potential outcome of their livelihood strategy.

Vulnerability Livelihood External context assets conditions N

· Public service · Price volatility H S · E-commerce · Poor market climate Influence Influence & Access · Seasonality · Government ... support ... F P

E-commerce Livelihood adoption outcomes

· Increase · E-tailer marginal profit

· E-supply chain · Increase sales partners · Extend sale period ...

Figure 1 Farmer e-supply chain participation and sustainable livelihood analysis

Note: N=Natural capital, H=Human capital, F=Financial capital, P=Physical capital, and S=Social capital

3. Methodology

Study area and sampling procedures

To fulfill our research goal, we use a cross-sectional household-level survey data set from rural China. A multistage stratified random sampling approach was used to collect the data in

2017-2018. First, the research team selected Macheng City, Enshi City and Jianshi County from Hubei province in mid China, Tiandong County and Tianyang County from Guangxi province in southwestern China, and Pingyi County from Shandong province in eastern

China. These six regions are the pilot areas of the “E-commerce goes into rural areas” project.

In addition, Tiandong County was also nominated as the “Top 50 Agricultural Products E-Commerce in Impoverished Counties in 2017-2018” by AliResearch, which is the research department of the Chinese e-commerce giant Alibaba (Alibaba 2018). Second, considering the fact that only a small portion of rural households participate in e-value chains in underdeveloped areas, e-tailers that are specialized in e-business were randomly selected using the information provided by local bureaus. As mentioned in the previous section, there will be increasing need for e-tailers to source beyond their own field, hire workers for packaging and grading with the growth of e-commerce volume. However, because of the small e-business scale of local e-tailers, family members can execute functions such as packaging and grading. Thus, the interaction between e-tailers and small farmers is basically limited to agro-food product supply in our case. In addition, because of the lack of private vehicles and poor road conditions, roadside and town sales only contribute to a small portion of dispersed smallholder income. Most of the traded volume was sold to and the transactions were conducted with middlemen at the farm gate. In this regard, once we reached out to the e-tailer, small farmers from the same community were classified as e-tailer supplier or traditional supplier depending on their agro-food e-value chain participation. Then, within each group, farmers were chosen randomly.

As a result, we obtained 900 valid responses. However, the sample size of e-tailer is small

(only 84). The surveyed households are generally smallholders, and their primary occupation is agro-food production and marketing. In addition, due to the fact that most of the e-tailers interviewed are rural migrant workers who returned from an urban area, sole proprietors, and college graduates, and most of them have totally different characteristics in terms of livelihood assets compared with local smallholders, the following analysis will focus on comparing e-tailer suppliers (272 respondents) and traditional suppliers (533 respondents).

The e-tailer sample is small (N=84).

Livelihood asset and external e-commerce condition indicators design and measurement

(1) Indicators design

In terms of livelihood assets, human capital is essential for a diversified family livelihood (Sen

2001). It is commonly defined as the skills, knowledge, capabilities, and health of household member. Members of the household may be employed in different roles on- and off-farm, requiring different skills and abilities (Challies and Murray 2011). Furthermore, agro e-business provides various opportunities for all family members to participate (Zhang et al.

2018). For example, women and disabled can help with online store management while children and elders can provide assistance with packing (Lin, Xie, and Lv 2016). However, families with babies and elderly members may not be as productive as those with a smaller household (He et al. 2019). In our study case, we design indicators including highest academic degree of family member, family size, e-commerce talents and general health of family members to quantify household’s human capital. The description and measurement of the indicators are shown in table 1.

Since the vast majority of Chinese agro-food producers are small growers (Li, Su, and Liu

2016), land availability and quality, as well as irrigation capacity, are the constraining factors for agro-food production. A range of basic physical capital investments is necessary to establish and carry on agro-food production and marketing. Other than basic equipment, specialized tools may be required depending on the types of agro-food production a household pursues. Since most rural e-tailers set up a workshop in their own living room in the initial phase (Leong et al. 2016), it is necessary to possess rooms for packing, photographing, and storage. In addition, internet access requires a combination of equipment including modem, router, and computer, and internet quality with regard to speed and stability is dependent on network bandwidth. In this regard, we select indicators including farm size, farmer perception about farm land quality and irrigation to quantify household’s natural capital. Fixed assets for e-commerce and agriculture production, internet access and network bandwidth represent the household’s physical capital.

Smallholders depend on financial capital to take a new livelihood strategy, and maintain or upgrade livelihood strategies (Xu et al. 2018). Financial capital can also be converted into other capital, such as human capital by investing into children’s education. In this research, we try to acquire information on household’s income, loan access, and self-evaluation on financial condition. Even though most of the households interviewed had a range of on- and off-farm income sources, participation in the agro-food value chain generates a significant proportion of household income. Moreover, it was found in our survey that smallholders tend to underestimate their financial condition and suggest a strong resistance to the idea of borrowing money.

Social capital is represented by the relationships of trust and reciprocity among the social networks which the household depends (Kanji, MacGregor, and Tacoli 2005). At the neighborhood level, it is common for Chinese rural households to have constant interactions with neighbors. Smallholders usually face several complex production and marketing constraints such as high transaction costs of accessing input and output markets, unavailability of modern technologies, and poor access to credit services. To overcome such constraints, the formation of agricultural cooperatives has been promoted as a potential policy instrument, with the aim of boosting agricultural production in developing countries

(Chagwiza, Muradian, and Ruben 2016, Ma, Abdulai, and Goetz 2018, Ma et al. 2018). An e-tailers association can also promote information sharing between members. Furthermore, government-hired officials are also in charge of promoting of newly invented technology and market information. In this regard, two core indicators for social capital measurement in our study are whether the farmer has participated in a farmer’s co-op or e-tailer’s association and his/her trust of rural e-commerce promoter, village cadres, neighbor, and leading pioneers.

With regard to external e-commerce conditions, this study selects indicators from the perspectives of public service, government support, e-commerce climate, and agro-food industry to measure household external e-commerce conditions. Nowadays, Chinese local governments follow the central government’s instructions to invest and integrate a large amount of resources into rural e-commerce development. The Rural E-commerce Service

Center (RESC) is the essential component of the three-level “county, town, village” rural e-commerce service system (Cai et al. 2019). It removes both logistical and transactional barriers to e-commerce integration and thus provides villages with broader market access via e-commerce(Couture et al. 2018). The aim of a center is to facilitate local e-business by offering services such as logistics and delivery, packaging, marketing, website design, photography, customer service, etc. To ensure service access for every rural household, the plan is to establish an RESC in every available rural community. In addition to providing basic assistance, the center is also considered as a rural distribution center, since most of the logistic companies only deliver packages to the town distribution center due to poor road conditions and small package volume. E-commerce platform enterprises also actively cooperate with local government and participate in ICT4D projects such as RESC construction. From a regional perspective, institutional support from local governments on providing legitimacy for entrepreneurial risk taking and improving infrastructures is vital to break down the barriers for rural household entry and facilitate ongoing participation in e-value chains. Moreover, as the competition between telecommunication, delivery, and supply-chain service companies intensified, villagers who ventured into e-commerce could receive better offers for internet access, delivery, and e-supply chain services including online shop design and management, photography, and training. In addition, evidence suggests that village cadres with a college degree have a positive effect on e-commerce adoption in rural areas (CHFS and Ali-Research 2017). The specialty of Chinese rural e-commerce is the transparency of e-commerce activities (Leong et al. 2016). Since local e-tailers convert their living room to a workshop and transport their products by delivery service, e-commerce awareness of neighbors, relatives, and friends can be easily raised. Furthermore, the positive regional agricultural industry identity could add value to the agro e-business run by local enterprises and e-tailers.

As shown in table 1, the public service condition includes presence of a rural e-commerce service center, difficulty in solving e-commerce problems, and satisfaction with delivery service. The government support index includes the extent of government support for e-commerce development and e-commerce promotion. The e-commerce experience of relatives, friends and neighbors’ e-commerce participation, neighborhood security, and the existence of college-graduated cadre are the indicators for measuring the e-commerce climate condition. Finally, the agro-food industry condition is measured by the impact of local well-known e-commerce companies and recognized regional brands and products.

(2) Index measurement

Based on the discussion above, we design an index evaluation system to quantify household livelihood assets and external e-commerce conditions. Commonly used index weighting methods include expert scoring, principal component analysis, entropy method, and the combination of subjective and objective methods (e.g. expert scoring and entropy method).

To the best of our knowledge, there is no comprehensive study of e-commerce adoption at the household level. In this regard, this study employs the entropy method to eliminate the defect of artificial subjectivity. The basic idea of entropy weight theory is that the indicator is more important if the difference of the value among the evaluating objects is higher (Amiri, Rezaei, and Sohrabi 2014, Liu et al. 2016, Zhao et al. 2018). The weight calculated based on 805 respondets (272 e-tailer suppliers and 533 traditional suppliers) by using entropy method are shown in table 1. Table 1 Indicator description, measurement, and weight evaluation Indicator Description and measurement Weights 1= primary school or below，2= middle school，3= high school，4= college Highest academic degree of family member 0.293 degree or above Human Family size Family population 0.151 =Adults familiar with e-commerce*1+adults unfamiliar with capital E-commerce talents e-commerce*0.75+elderly people with limited labor*0.25+elderly people and 0.269 young child ability*0 Family general health Ranging from 1 to 5 0.287 Farm size Farm size /mu, 1mu=666m2 0.729 Natural Farmer perception of farm land quality Ranging from 1 to 5 0.156 capital Farmer perception of irrigation condition Ranging from 1 to 5 0.115 Fixed assets for e-commerce operation Ranging from 1 to 5 0.730 Physical Internet access and network bandwidth Ranging from 1 to 5 0.061 capital Fixed assets for agriculture production Ranging from 1 to 5 0.209 Household income Family total income in 2016 0.386 Financial Loan access 0=None，1= private lending or bank loan，2= private lending and bank loan 0.237 capital Financial condition Ranging from 1 to 5 0.377 Farmer’s co-op participation 0=non-participator，1= participator 0.472 Social E-tailers’ association participation 0=non-participator，1= participator 0.126 capital The degree of trust in village cadres, neighbor, and leading pioneers, ranging Trust 0.402 from 1-5 Rural e-commerce service center 0=no，1=yes 0.378 Public Difficulty in solving e-commerce problems Ranging from 1 to 5 0.313 service Delivery service Ranging from 1 to 5 0.308 Government Government support on e-commerce operation Ranging from 1 to 5 0.427 support Government e-commerce promotion Ranging from 1 to 5 0.573 The e-commerce experience of relatives and friends Ranging from 1 to 5 0.403 E-commerce Neighbors e-commerce participation Ranging from 1 to 5 0.354 climate Neighborhood security Ranging from 1 to 5 0.243 College graduated cadre 0=no, 1=yes 0.000 Industry Local well-known e-commerce enterprises Ranging from 1 to 5 0.515 foundation Regional recognized brand or product Ranging from 1 to 5 0.485 Self-efficacy Confidence in mastering e-commerce, ranging from 1 to 5 0.442 E-commerce Perceived usefulness Expectations on enhancing sales volume, reducing risk, etc., ranging from 1 to 5 0.209 awareness Independence Independent decision-making, ranging from 1 to 5 0.197 Risk awareness Awareness on the risk in e-business, ranging from 1 to 5 0.332 Note: No interviewee reported that there was college graduate cadre Between group comparisons

To illustrate the difference of the livelihood assets and external e-commerce conditions between e-tailer suppliers and traditional suppliers, we will use the rank order Wilcoxon Rank-Sum test and Kruskal–Wallis ANOVA test to support the following analysis.

PSM modeling for e-commerce adoption effect evaluation

PSM is a non-parametric estimation method (Imbens 2004). It is commonly applied to correct potential self-selection biases and estimate treatment effects by matching samples from treatment group and control group with similar observable characteristics (Caliendo and Kopeinig 2008).

In this research, farmers who participate in agro-food e-value chain are considered as the treatment group and those selling through traditional channel (e.g., layers of collectors, assemblers and middlemen) are categorized into the control group. The average treatment effect (ATE) is defined as the average difference in agricultural livelihood outcomes between treatment and control groups. Assume represents the outcome variables. and represent the treatment and control group, respectively.

(1)

Logit and Probit models can be used to analyze the e-commerce adoption decision of smallholders (Caliendo and Kopeinig 2008). Moreover, there are different PSM algorithms to find the comparison group. The aim of k-nearest neighbor algorithm is to find k closest observations in the control group to match each observation in the treatment group. Based on k-nearest neighbor matching, caliper matching employs a threshold to control the maximum distance of between target and matching observations. It is suggested that a suitable caliper size for caliper matching is one-fourth of the standard deviation of the propensity scores (Rosenbaum and Rubin

1985). All of the comparison members within a caliper are considered in radius matching. Similar to radius matching, kernel matching, local liner regression matching, and spline matching give larger weight to control observations with smaller distances. But the weighting functions are different. Ideally, all matching algorithms would produce the same result. However, the trade-off between bias and efficiency is associated with each algorithm in practice (Caliendo and Kopeinig 2008). In this regard, the average treatment effect on the treated group can be estimated as follows:

(2) where is a series of variables that not only determine the agricultural production and marketing outcome of the smallholder, but also directly impact the decision about agro-food e-value chain participation. is the probability of observing a household with in the sample.

To evaluate PSM matching quality, the variable distribution should be reviewed to ensure no systematically significant differences remain after matching procedure

(Rosenbaum and Rubin 1985, Smith and Todd 2005). Sufficient overlap and an area of common support between the treatment and control groups are required

(Rosenbaum and Rubin 1985, Caliendo and Kopeinig 2008). Moreover, Rosenbaum bounds should be calculated to resolve the concern on robustness and unmeasured bias (Rosenbaum 2002).

As for the outcome variables ( ) for PSM modeling, because certain agro-food value chain activities may draw resources such as labor or land away from other livelihood strategies (Maertens and Swinnen 2009, Miyata, Minot, and Hu 2009, Wang,

Moustier, and Loc 2014), it is logical to measure the agro-food e-value chain participation effect on household agricultural livelihood outcome by quantifying household agriculture gross income, per capital agriculture income, and not marketed portion.

Moreover, as discussed above, we need to control a set of observable household characteristics to achieve reliable PSM estimations. Males and females are different both physically and psychologically, older farmers tend to be more experienced in farming and coping with farm risks, and farmers with more years of education and being a village cadre could be more receptive to new farming techniques. Therefore, the impact of household head characteristics including gender, age, education, and whether if he/she is a village cadre should not be ignored. In addition, ICTs awareness is of great important on household’ ICTs adoption

(Venkatesh et al. 2003). The e-commerce awareness indicator was quantified by the entropy method based on the household’s response to self-efficacy, perceived usefulness, independence, and risk awareness of e-commerce usage (see table 1).

Finally, county dummies are included to control for heterogeneity in weather, culture and other effects that vary at the regional level.

4. Results

Agro-food unit price

Nowadays, Chinese agro-food value chains have become increasingly buyer-driven

(Alibaba 2018). Agro-products with regional identity can compete for niche markets. The potential revenue is huge for e-tailers who are capable of selling their

agro-products online. Moreover, they can also benefit from lower transactions costs.

As shown in table 2, only a few respondents are able use e-commerce and they obtain

higher online unit prices. Taking e-tailers’ offer is the most common way for small

farmers to participate in e-value chains in the survey areas. It should be noted that

Tiandong County and Tianyang County are geographically adjacent. Farmers’ on-farm

productive configurations and local public transport and telecommunications services

conditions are similar. In our analysis, we consider farmers from Tiandong County

and Tianyang County as a group. Farmers from Enshi city and Jianshi County are also

grouped for the same reason. Furthermore, table 2 also indicates that e-tailers enjoy a

higher unit price by selling products on e-commerce platforms. They also offer a

higher sourcing price to small growers than traditional middlemen. For example, in

our field survey we found that the price of golden peaches produced in Pingyi County

is 6-12 Yuan/500g on e-commerce platforms. E-tailer suppliers receive a 1-2

Yuan/500g offer from e-tailer for high quality peaches, which is twice as much as the

farm-gate price offered by traditional traders.

Table 2 Agro-food unit price in survey areas

Pingyi Macheng Tiandong/Tianyang Enshi/Jianshi

Peach Sesame Chestnut Peanut Mango Tea E-tailer 6-12 10-15 10 10-15 6-7 20-300 E-tailer supplier 0.6-2 6-7 6-7 3-5 4-5 5-15 Traditional supplier 0.4-0.8 5-6 5-6 2.5-3 2-4 5 Notes: unit price = Chinese Yuan/500g

Livelihood assets and external e-commerce conditions evaluation and comparison

(1) Whole sample

The final scores of the farmers’ livelihood assets and external e-commerce conditions are shown in Table 3. For the sake of simplicity, we further use logistics logarithmic

for index score normalization, where 푡 is the figure before normalization, and is

its final value:

(3) +푒−푡

As a whole, the subcomponents of livelihood asset and external e-commerce

condition distribute differently. As shown in Table 3, the median of natural, human,

physical, financial, and social capital of 805 samples are 0.971, 0.967, 0.928, 0.964,

and 0.870, respectively. In terms of external e-commerce conditions, the median of

public service, government support, e-commerce climate, and agro-food industry

conditions of 805 samples are 0.904, 0.929, 0.950, and 0.881, respectively.

Table 3 Value of farmer’s livelihood assets

Livelihood assets Sample indicator Natural Human physical Financial Social type capital capital capital capital capital Max 1.000 0.992 0.982 0.999 0.947 Whole Min 0.737 0.849 0.801 0.787 0.769 sample Median 0.971 0.967 0.928 0.964 0.870 Max 1.000 0.991 1.000 1.000 1.000 E-tailer Min 0.737 0.849 0.737 0.737 0.737 suppliers Median 0.976 0.971 0.976 0.976 0.976 Max 1.000 0.992 0.978 0.998 0.942 Traditional Min 0.757 0.889 0.801 0.787 0.769 suppliers Median 0.969 0.966 0.928 0.964 0.870 External e-commerce conditions Sample Statistical Public Government E-commerce Industry Number of type indicators service support climate foundation respondents Max 0.972 0.982 0.990 0.982 Whole Min 0.731 0.731 0.815 0.731 805 sample Median 0.904 0.929 0.950 0.881 Max 0.972 0.982 0.990 0.982 E-tailer Min 0.787 0.731 0.832 0.731 272 suppliers Median 0.928 0.929 0.968 0.881 Max 0.972 0.982 0.990 0.982 Traditional Min 0.731 0.731 0.815 0.731 533 suppliers Median 0.904 0.919 0.940 0.881

(2) Comparison between e-tailer suppliers and traditional suppliers

In terms of livelihood assets, table 3 shows that the score intervals for all of the

livelihood capitals are close between e-tailer suppliers and traditional suppliers. The

median of natural, physical, financial, and social capital of e-tailer suppliers are the

same, which is 0.976. It is higher than that of traditional suppliers. As for human

capital, the median of e-tailer suppliers is 0.971, while the figure for traditional

suppliers is 0.966. The Wilcoxon Rank-Sum test is used to test the difference in the

livelihood assets and external e-commerce conditions between e-tailer suppliers and

traditional suppliers since the data sets failed to pass the test for normality and

homogeneity of variances (see Appendix table 1) (Mann and Whitney 1947). Table 4

reports the Wilcoxon Rank-Sum test result, from which we can ascertain the

distribution of all livelihood capitals are significantly different between e-tailer

suppliers and traditional suppliers except for financial capital. With regard to external

e-commerce conditions, e-tailer suppliers reported high levels of public service,

government support, and e-commerce climate conditions. The median of those three

conditions are 0.928, 0.929, and 0.968, while the figures for traditional suppliers are

0.904, 0.919, and 0.940. Moreover, e-tailer suppliers and traditional suppliers have the

same median on agro-food industry condition, which is 0.881. As for the difference in

distribution, table 4 suggests that the difference among all of the external e-commerce

conditions between e-tailer suppliers and traditional suppliers are significant. Table 4 Results of Wilcoxon Rank-Sum test

Natural capital Financial capital Human capital Social capital physical capital P=0.000 P=0.455 P=0.000 P=0.000 P=0.000 Government E-commerce Industry Public service support climate foundation P=0.000 P=0.000 P=0.000 P=0.000

PSM estimation results

(1) Estimation of the propensity score

We use a Logit regression model to predict the probability of household agro-food e-value chain participation. The summary statistics for the variables discussed in section 3.4 are reported in Table 5 by group type. Table 5 shows that e-tailer suppliers’ agricultural livelihood outcomes are significantly better than that of traditional suppliers. Specifically, the average differences are 4.42 and 10.37 thousand Yuan with regard to per capita farm income and gross farm income. Furthermore, the not marketed portion of traditional farmers is 12.976%, while the figure for e-tailer suppliers is less than 10%. In terms of matching variables, we observe that except from household head’s gender, age, age square, whether or not a cadre, and financial capital, other variables are statistically different between e-tailer suppliers and traditional suppliers. E-tailer suppliers are better off with regard to household head educational level and e-commerce awareness, livelihood assets and external e-commerce conditions. Table 5 Summary statistics E-tailer suppliers Traditional suppliers Difference Normalized Standard Standard T-stat Average Average (%) Difference deviation deviation Per capita farm income 1.643 0.384 1.201 0.530 26.9 0.000 0.954 Outcome (10,000Yuan) variables Gross farm income (10,000Yuan) 3.486 1.647 2.449 1.528 29.8 0.000 0.653 Not marketed portion (%) 9.746 2.872 12.976 3.058 -33.1 0.000 -1.089 Gender (male=1) 0.680 0.467 0.689 0.464 -1.3 0.788 -0.018 Age 51.640 7.017 51.321 7.697 0.6 0.319 0.043 Age square 2715.721 723.990 2692.957 790.162 0.8 0.691 0.030 Education 2.143 0.738 1.859 0.669 13.3 0.001 0.403 Village cadre (yes=1) 0.051 0.221 0.019 0.136 62.7 0.026 0.178 E-commerce awareness 0.967 0.014 0.958 0.017 0.9 0.000 0.601 Natural capital 0.957 0.045 0.947 0.049 1.0 0.007 0.203 Matching Financial capital 0.957 0.031 0.956 0.029 0.1 0.662 0.003 variables Human capital 0.967 0.018 0.962 0.017 0.5 0.000 0.261 Social capital 0.894 0.028 0.866 0.027 3.1 0.000 1.020 Physical capital 0.937 0.033 0.927 0.036 1.1 0.000 0.274 Industry foundation 0.885 0.067 0.849 0.073 4.1 0.000 0.508 E-commerce climate 0.958 0.029 0.936 0.032 2.3 0.000 0.724 Public service 0.913 0.042 0.890 0.046 2.5 0.000 0.512 Government support 0.926 0.044 0.897 0.058 3.1 0.000 0.574 Note: County dummies are not reported. The Logit estimation results in table 6 indicate that household agro-food e-value chain participation is biased towards household head characteristics, livelihood assets, and external e-commerce conditions. Specifically, household heads that are older, with higher education and e-commerce awareness are more likely to participate in agro-food e-value chain. In terms of livelihood assets, respondents with more human, social, and physical capital and less financial capital are more inclined to cooperate with online merchants. Note that the natural capital endowment does not significantly impact household’s choice on e-value chain participation. A possible explanation is that e-tailers rely more on sourcing products from their neighbors, relatives, and friends to reduce transaction costs as the amount of online orders is small in the initial stage of e-commerce. While for those rural residents who possess sufficient financial capital, they may prefer to start up e-business and become e-tailers on their own.

Furthermore, better conditions on agro-food industry, e-commerce climate, and government support have positive impact on households’ e-value chain participation.

Table 6 Estimation of the propensity score using Logit model.

Standard Coefficient Z-statistics error Gender (male=1) 0.493 0.212 2.32 Age 0.022** 0.014 1.57 Age square -0.249 0.120 -0.21 Education 0.310** 0.136 2.28 Village cadre (yes=1) 0.011 0.535 0.02 E-commerce awareness 32.062*** 7.369 4.35 Natural capital 3.059 2.100 1.46 Financial capital -9.014** 3.668 -2.46 Human capital 13.253** 6.098 2.17 Social capital 33.853*** 3.950 8.57 Physical capital 5.909** 2.761 2.14 Industry foundation 3.368** 1.703 1.98 E-commerce climate 14.009*** 3.416 4.10 Public service 3.906 2.681 1.46 Government support 9.714*** 2.135 4.55 County 1 (if Macheng) -0.596* 0.1312 -1.91 County 2 (if Enshi/Jianshi) 0.162 0.330 0.49 County 3 (if Tiandong/Tianyang) -0.274 0.339 -0.81 Constant -103.180*** 11.231 -9.19 Number of observation 805 Pseudo 푅2 0.304 LR 313.27*** Note: * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

(2) PSM results

The estimated average participation effect based on the PSM procedure is presented in table 7. The average participation effects among nine matching algorithms are similar in magnitude and statistically significant. Matching estimates show that e-commerce adoption has a positive effect on smallholder farmer’s agriculture livelihood outcomes, i.e., enhancing per capita agriculture income, gross agriculture income, and reducing not marketed portion. Bootstrap standard errors based on 500 replications are reported.

The result of the average estimated effect of nine PSM algorithms on household per capita farm income and gross farm income are 3.45 thousand Yuan and 10.37 thousand Yuan, respectively. Based on PSM results, the difference between e-tailer supplier and traditional supplier on these two indicators are 21.2% and 24.5%, which is lower than figures obtained from using descriptive analysis (26.9% and 29.8%).

Similarly, the results also suggest that an e-value chain participant is 3.115% lower than that of a non-participant with regard to not marketed portion. The figure is lower than that of straightforward comparison (3.23%) as well.

Table 7 Estimation of the average participation effect (ATT)

ATT Per capita Gross agriculture Not marketed (Std.Err.) agriculture income income portion

K-nearest neighbor 0.317*** 0.802*** -2.921*** matching, k=1 (0.063) (0.194) (0.428) K-nearest neighbor 0.345*** 0.870*** -3.232*** matching, k=4 (0.049) (0.160) (0.403) Caliper matching, k=4, 0.351*** 0.880*** -3.185*** caliper=0.07 (0.434) (0.158) (0.390) Radius matching, k=4, 0.339*** 0.809*** -3.049*** Radius =0.07 (0.036) (0.131) (0.344) Kernel matching, 0.356*** 0.833*** -3.136*** bandwidth=0.06 (0.040) (0.136) (0.336) Kernel matching, 0.341*** 0.809*** -3.077*** bandwidth=0.1 (0.039) (0.132) (0.312) Local liner regression 0.365*** 0.858*** -3.212*** matching, bandwidth=0.1 (0.043) (0.139) (0.329) Local liner regression 0.371*** 0.867*** -3.182*** matching, bandwidth=0.8 (0.043) (0.139) (.329) 0.324*** 0.794*** -3.040*** Spline matching (0.035) (0.139) (0.331) Average ATT 0.345 0.836 -3.115

Notes: (i) ̂ 푒 . (ii) Bootstrap standard errors based on 500 replications are reported. (iii) * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

As shown in table 8, lower pseudo-푅2, LR statistics, and bias indicate and higher P statistics are found after PSM matching. This indicates that there are no systematic and significant differences exist. To ensure the e-value chain participants and nonparticipants locate in the same domain, common support and balancing conditions are checked. Samples that fall outside common support interval have to be disregarded (13.3% of treated and 3.9% of untreated). It is acceptable because the

proportion of lost individuals is small (Caliendo and Kopeinig 2008).

Table 8 PSM quality indicators before and after matching

Pseudo LR P Mean Median

푅2 statistics statistics bias (%) bias (%)

Before matching 0.299 307.63 0.000 34.1 27.2 K-nearest neighbor matching, k=1 0.023 15.13 0.653 8.1 6.6 K-nearest neighbor matching, k=4 0.032 21.55 0.252 9.3 9.2 Caliper matching, k=4, caliper =0.07 0.028 18.74 0.408 8.9 8.8 Radius matching, k=4, Radius =0.07 0.023 15.01 0.661 8.3 8.5 Kernel matching, bandwidth=0.06 0.030 20.03 0.331 9.5 8.9 Kernel matching, bandwidth=0.1 0.024 16.06 0.588 8.6 8.4 Local liner regression matching, bandwidth =0.1 0.023 15.10 0.588 8.4 6.8 Local liner regression matching, bandwidth =0.8 0.023 15.13 0.653 8.1 6.6 Spline matching 0.023 15.13 0.653 8.1 6.6

The Rosenbaum bounds sensitivity analysis tests the potential effect of unobservable

factors. Since the test results are similar among nine matching algorithms, we provide

the kernel matching (bandwidth=0.06) test result for reference in table 9. The p values

represent the upper and lower bounds from the Wilcoxon signed rank test for

estimation of the average participation effect for each level of unobserved selection

bias (Mwangi, Markelova, and Meinzen-Dick 2012). As we can observe from table 9,

the participation effect remains significantly positive when the odds of participation

caused by unobserved covariate increase to 200 percent (i.e., Gamma=2). We can

conclude that the average participation effect estimated by using kernel matching

(bandwidth=0.06) PSM procedure in table 7 is a pure effect of agro-food e-value

participation.

Table 9 Rosenbaum bounds sensitivity tests based on kernel matching (bw=0.06)

Agriculture livelihood Level of hidden Upper bound Lower bound outcomes bias (Gamma) -critical -critical 1 0.000 0.000 Per capita agriculture 1.5 0.000 0.000 income’s ATT 2 0.000 0.000 1 0.000 0.000 Gross agriculture 1.5 0.000 0.000 income’s ATT 2 0.005 0.000 1 0.000 0.000 Not marketed 1.5 0.000 0.000 portion’s ATT 2 0.000 0.000

In addition, it should be pointed out that the code “psmatch2” in STATA used in this study provides rich approaches to perform PSM matching, but its biggest drawback is that the standard errors are not correct even with bootstrap (Abadie and Imbens 2008).

An official command “teffects psmatch” which can provide correct standard errors has been carried out recently (Abadie and Imbens 2016). However, it can only be used for the k-nearest neighbor matching. In this regard, we provide k-nearest neighbor matching (k=1 and k=4) results based on “teffects psmatch” command for reference

(see Appendix table 2).

(3) PSM results across different categories of household characteristics

To provide empirical evidence for the e-value chain participation impact on heterogeneous households, we further divide our simple into “greater than the median” and “lower than the median” groups with regard to household head characteristics, livelihood assets, and external e-commerce conditions.

The only household head characteristics will be discussed in the following analysis is e-commerce awareness based on matching performance (e.g., common support condition). Due to the similarity of PSM results among different PSM matching algorithms, table 10 shows the PSM results across different categories of household head e-commerce awareness based on kernel matching (bandwidth=0.06). We can find that smallholders with higher e-commerce awareness have larger improvement on per capita agriculture income and increase in commercial portion after participating in agro-food e-value chain.

Table 10 PSM results across different categories of household head e-commerce

awareness

ATT Per capita agriculture Gross agriculture Not marketed (Std. Err.) income income portion Greater than 0.351*** 0.794*** -3.530*** median (0.056) (0.244) (0.495) Lower than 0.331*** 0.795*** -2.562*** median (0.046) (0.178) (0.469) Notes: (i) PSM result based on Kernel matching (bw=0.06). (ii) Bootstrap standard errors based on 500 replications are reported. (iii) * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

Table 11 shows the PSM results across different categories of household livelihood assets. The results indicate that smallholder farmers with more livelihood assets benefit more from e-value chain participation. The advantage on different types of livelihood capital may lead to diverse livelihood outcome improvement. In particular, e-value chain participation helps more with regard to gross agriculture income for small farmers who are better off on natural and human capital. A possible explanation is that the natural and human capitals are directly related to agricultural output.

Farmers can benefit more from e-commerce adoption by offering more products.

Moreover, households with more social and human capital enjoy a larger increase in

terms of commercial portion. It is possibly due to the fact that e-tailers prefer to

source products from close neighbors, friends, relatives, and someone who can help

them with e-commerce operation.

Table 11 PSM results across different categories of livelihood assets

ATT Per capita Gross agriculture Not marketed (Std. Err.) agriculture income income portion

Greater 0.392*** 1.080*** -3.245*** Natural than median (0.062) (0.282) (0.515) capital Lower 0.281*** 0.375*** -3.084*** than median (0.066) (0.149) (0.556) Greater 0.328*** 0.876*** -2.895*** Financial than median (0.032) (0.229) (0.507) capital Lower 0.394*** 0.744*** -3.706*** than median (0.083) (0.186) (0.477) Greater 0.357*** 0.675*** -3.011*** Physical than median (0.045) (0.181) (0.445) capital Lower 0.271*** 0.779*** -3.018*** than median (0.048) (0.227) (0.537) Greater 0.374*** 0.772*** -3.475*** Social than median (0.064) (0.212) (0.496) capital Lower 0.402*** 0.745*** -2.659*** than median (0.036) (0.237) (0.447) Greater 0.343*** 0.893*** -3.457*** Human than median (0.036) (0.245) (0.560) capital Lower 0.302*** 0.459*** -2.595*** than median (0.066) (0.168) (0.484) Notes: (i) PSM result based on Kernel matching (bw=0.06). (ii) Bootstrap standard errors based on 500 replications are reported. (iii) * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

Once we stratify the sample by the external e-commerce conditions (Table 12), we

find that the participation effect increases with better conditions on e-commerce

climate. However it is interesting to note that e-tailer suppliers with a lower agro-food

industry foundation condition reported a higher reduction on not marketed portion. A

possible explanation is that regions with better industry foundation have already

developed a more efficient market and stable supply chain. Given such market

arrangements, households do not have to produce a range of goods and services for

their own consumption. Furthermore, the result also suggests a greater improvement

of agricultural livelihood outcomes for e-tailer suppliers in poor public service and

government support conditions. It could be the case that e-tailers and his suppliers in

those circumstances are facing less pressure from selling homogeneous primary

product.

Table 12 PSM results across different categories of external e-commerce conditions

ATT Per capita Gross agriculture Not marketed (Std. Err.) agriculture income income portion Greater 0.288*** 0.654*** -2.769*** Public than median (0.036) (0.186) (0.476) service Lower 0.364*** 0.940*** -2.733*** than median (0.042) (0.218) (0.487) Greater 0.421*** 1.171*** -3.200*** E-commerce than median (0.064) (0.216) (0.465) climate Lower 0.302*** 0.250 -2.336*** than median (0.043) (0.194) (0.464) Industry Greater 0.369*** 0.868*** -1.987*** foundation than median (0.050) (0.197) (0.440) Lower 0.265*** 0.708*** -4.200*** than median (0.089) (0.266) (0.545) Greater 0.277*** 0.738*** -3.305*** Government than median (0.038) (0.161) (0.446) support Lower 0.390*** 1.042*** -1.848*** than median (0.060) (0.244) (0.535) Notes: (i) PSM result based on Kernel matching (bw=0.06). (ii) Bootstrap standard errors based on 500 replications are reported. (iii) * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

For the same reason discussed in previous section, we also provide k-nearest neighbor

matching (k=4) results based on “teffects psmatch” command for reference (see

Appendix table 3).

6. Conclusions and policy implications

The paper is an original contribution to the few available studies investigating the

e-commerce adoption and its impact on small farmers using robust econometric

methods with correction of selection biases. Our field survey and empirical results

show that, firstly, cooperating with e-tailers to become an e-tailer supplier is the most

common way for small farmers to participate in agro-food e-value chains. The farm

gate price offered by e-tailers is higher than that of traditional middlemen. Secondly,

e-tailer suppliers possess more livelihood assets and are in better external e-commerce

conditions compared with traditional suppliers who do not participate in agro-food

e-value chain. Moreover, the distribution of livelihood assets and external

e-commerce conditions are also significantly different between e-tailer suppliers and

traditional suppliers. Thirdly, the agro-food e-value chain participation has a positive

and robust impact on small farmers’ agricultural livelihood outcomes through

enhancing agriculture income and increasing commercial portion. In addition, the

agro-food e-value chain participation effect appears to be overestimated if the selectivity bias is not appropriately addressed. Finally, the potential gains of e-commerce adoption are higher for small famers with higher e-commerce awareness, more natural and human resources, and better e-commerce climate and agro-food industry conditions.

In terms of policy recommendations, ICT4D projects should be implemented to shrink the infrastructure gaps between regions and urban and rural areas from a broader perspective. Considering the fact that many smallholders are not fully aware and cannot take advantage of e-commerce, preferential policies could be offered to attract e-commerce enterprise and entrepreneurs to start e-businesses in underserved areas.

The demonstration and neighborhood effect is huge because of the transparency of rural e-commerce activity (Leong et al. 2016). The imitation and replication behavior of local smallholders and government support could help transform the village to a rural ecommerce cluster (Qi, Zheng, and Guo 2019). The pros of rural ecommerce cluster, i.e., Taobao village is that local SMEs and e-tailers may become more efficient, innovative and productive and hence more competitive through specialization (Zhang et al. 2018). Furthermore, e-commerce service industries such as micro-finance, website design, photography, and customer service will emerge and thrive, which all facilitate e-tailers operations. Small farmers will be able to find more off-farm opportunities to generate higher incomes from more diverse sources.

However, most of e-tailers in the same area are selling similar or even identical goods.

It creates intense competition and drives down the price for the goods and bids up wages and other input costs (Qi, Zheng, and Guo 2019, Lin, Xie, and Lv 2016). As a result, the entire industry could become less profitable (Fleisher et al. 2010).

Furthermore, value-added services including online marketing, website and graphic design, and photography could also help diversify local products and build up brand, and thus increase revenue.

To bridge the digital divide and enhance the impact of e-commerce adoption on resource-limited small farmers, it would be beneficial to provide them with funding, material, and technology guidance by integrating ICT4D projects with other developmental projects, such as rural revitalization, poverty alleviation, micro-finance, and agricultural technology promotion. Furthermore, encouraging farmers to participate in the industry with regional identity and competitive advantage may help them secure a sustainable livelihood (Cai and Xia 2018). From our field survey, even though Macheng city promoted the local brand “Macheng Impression” which includes a list of special local agricultural and sideline products that is suitable for selling online, most of the local farmers responded that they know little about the list and their on-farm productive configurations were also found to vary from it. In this regard, startup guidance for online sales should be offered to e-commerce entrepreneurs.

However, small producers’ bargaining power will not improve if they do not have the capital, skills or knowledge required to take advantage of ICTs. In this regard, education and skills development systems should be upgrade to prepare smallholders for the opportunities in information era from long-term perspective. Household can either become an e-tailer, product supplier, or engage in activities, such as packaging, photographing, and delivery to participate in agro-food e-value chains.

In the near future, smallholders are about to face a more complex situation where traditional middlemen are gradually marginalized from the agro-food value chain because they cannot match the farm-gate price offered by e-tailers. In this regard, institutional support remains indispensable for small farmers to gain and retain

e-value chain access. Finally, we advocate a balanced point among rural e-commerce

development, rural socio-economic development, and sustainable livelihood

development for smallholders in underdeveloped regions must be sought.

Acknowledgements

This study was supported by National Social Science Fund of China (grant numbers

17BJY136)

Appendix

Table 1 Test for normality and homogeneity of variances

Normality Homogeneity Normality Homogeneity P-value P-value test of variance test test of variance test Natural T 0.000 Agro-food T 0.000 0.118 0.074 capital E 0.000 industry E 0.000 Financial T 0.000 E-commerce T 0.000 0.481 0.085 capital E 0.000 climate E 0.000 Human T 0.000 Public T 0.000 0.213 0.037 capital E 0.000 service E 0.000 Social T 0.000 Government T 0.000 0.504 0.000 capital E 0.000 support E 0.000 Physical T 0.000 0.000 capital E 0.000 Note: T=Traditional supplier, E= E-tailer supplier

Table 2 Estimation of the average participation effect (ATT) based on “teffects

psmatch” command

ATT Per capita agriculture Gross agriculture Not marketed (Std. Err.) income income portion K-nearest neighbor 0.354*** 0.747*** -3.122*** matching, k=1 (0.034) (0.105) (0.343) K-nearest neighbor 0.355*** 0.790*** -3.093*** matching, k=4 (0.025) (0.099) (0.249) Note: (i) * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

Table 3 PSM results across different categories of household characteristics based on

“teffects psmatch” command

ATT Per capita Gross agriculture Not marketed (Std. Err.) agriculture income income portion Greater than 0.320*** 0.700*** -3.350*** E-commerc median (0.040) (0.064) (0. 262) e awareness Lower than 0.360*** 0.749*** -2.619*** median (0.038) (0.254) (0.333) Greater than 0.418*** 1.069*** -3.238*** Natural median (0.020) (0.166) (0.333) capital Lower than 0.259*** 0.450*** -2.881*** median (0.038) (0.080) (0.150) Greater than 0.375*** 0.959*** -3.022*** Financial median (0.037) (0.089) (0.307) capital Lower than 0.301*** 0.488*** -3.137*** median (0.044) (0.073) (0.185) Greater than 0.391*** 0.914*** -2.825*** Physical median (0.057) (0.232) (0.403) capital Lower than 0.290*** 0.662** -3.148*** median (0.037) (0.273) (0.291) Greater than 0.329*** 0.707*** -3.337*** Social median (0.023) (0.152) (0.205) capital Lower than 0.358*** 0.622** -2.512*** median (0.044) (0.289) (0.332) Greater than 0.398*** 1.267*** -3.755*** Human median (0.035) (0.269) (0.518) capital Lower than 0.291*** 0.426*** -2.351*** median (0.040) (0.099) (0.343) Greater than 0.312*** 0.930*** -2.949*** Public median (0.038) (0.232) (0.285) service Lower than 0.296*** 0.603*** -2.050*** median (0.032) (0.170) (0.419) Greater than 0.349*** 0.990*** -2.923*** E-commerc median (0.049) (0.140) (0.355) e climate Lower than 0.354*** 0.614 -3.446*** median (0.075) (0.459) (0.611) Greater than 0.425*** 0.816*** -2.176*** Industry median (0.039) (0.100) (0.409) foundation Lower than 0.275*** 0.806*** -4.007*** median (0.056) (0.138) (0.379) Greater than 0.347*** 1.049*** -2.664*** Governmen median (0.068) (0.253) (0.554) t support Lower than 0.308*** 0.751*** -3.697*** median (0.036) (0.041) (0.318) Notes: (i) K-nearest neighbor matching, k=4. (ii) * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

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