Impact of E-Commerce Adoption on Farmers Participation in

Article

Impact of E-Commerce Adoption on Farmersrsquo Participation inthe Digital Financial Market Evidence from Rural China

Lanlan Su 1 Yanling Peng 2 Rong Kong 3 and Qiu Chen 1

Citation Su L Peng Y Kong R

Chen Q Impact of E-Commerce

Adoption on Farmersrsquo Participation

in the Digital Financial Market

Evidence from Rural China J Theor

Appl Electron Commer Res 2021 16

1434ndash1457 httpsdoiorg103390

jtaer16050081

Academic Editor Steve Worthington

Received 20 March 2021

Accepted 27 April 2021

Published 30 April 2021

Publisherrsquos Note MDPI stays neutral

with regard to jurisdictional claims in

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iations

Copyright copy 2021 by the authors

Licensee MDPI Basel Switzerland

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license (https

creativecommonsorglicensesby

1 China Center for Agricultural Policy (CCAP) School of Advanced Agricultural Sciences Peking UniversityBeijing 100871 China lanlansuccappkueducn (LS) qchenccappkueducn (QC)

2 Institute of Regional Economy and Finance Sichuan Agricultural University Chengdu 611130 Chinajxyanlingsaueducn

3 School of Economics and Management Northwest Agriculture and Forestry UniversityYangling 712100 China

Correspondence kr1996nwsuafeducn

Abstract Although the increasing adoption of digital finance in recent years has exerted a wide-ranging influence on farmersrsquo consumption and production activities many farmers in China stillseriously suffer from digital financial exclusion Few studies have documented the different impactsof e-commerce adoption characterized by online purchases and sales on farmersrsquo participation in thedigital financial market measured by their engagement in digital payments digital wealth manage-ment and digital credit in rural China Using survey data from 832 entrepreneurial households inrural China we contribute to the literature by confirming that both online purchases and sales have arobust significant and positive impact on farmersrsquo participation in the digital financial market andthat this impact on digital wealth management is successively larger than that on digital paymentsand digital credit with the propensity score matching (PSM) method and instrument variable (IV)approach employed We further discover that the impact of online purchases and sales on farmersrsquoparticipation in the digital financial market is significantly mediated by digital financial literacyMoreover the impact of online purchases and sales on farmersrsquo participation in the digital financialmarket is larger for those with high education levels pursuing skills training running new agricul-tural operation entities (ie family farms professional cooperatives) and engaging in agriculturalentrepreneurship Our findings suggest that more effective measures to enhance adoption ratesof online purchases and sales innovation in rural market-oriented digital financial products andservices systematic training for farmers in e-commerce skills as well as digital financial literacy anddifferentiated support measures for different groups of farmers to reduce the gap are urgently neededin China

Keywords e-commerce adoption online purchases online sales digital finance digital financialliteracy

1 Introduction

With the emergence and rapid development of digital technology a fundamentalchange has occurred around the world in the financial services industry especially interms of the manner in which people engage in the financial market Digital financestemming from the effective integration of digital technology and finance has gone throughimpressive development since 2013 in China [1] The accelerating development of digitalfinance has gradually exerted a wide-ranging influence on peoplersquos consumption andproduction activities in China [2] this influence on rural residents in developing countriesunder a transition economy has recently aroused increasing concern among scholars aswell as policy-makers [3] In this context the Chinese central government has given greatpolicy priority to bolster digital finance in rural areas [2] In spite of the increasing adoptionrate of digital finance in recent years many farmers in China still seriously suffer from

J Theor Appl Electron Commer Res 2021 16 1434ndash1457 httpsdoiorg103390jtaer16050081 httpswwwmdpicomjournaljtaer

J Theor Appl Electron Commer Res 2021 16 1435

digital financial exclusion [4] especially in terms of adopting the products and servicesof digital wealth management and digital credit [56] Widespread exclusion from digitalfinance which intensifies the Matthew effect of the rural financial market has become aconsiderable barrier against the expansion of inclusive finance in rural areas in the digitaleconomy era [7]

Little attention has yet been paid to the differences in the adoption of multiple digitalfinancial products among rural residents brought by e-commerce adoption Consumersrsquodemographic and socio-economic characteristics and the regional financial environmenthave been the primary focal points of previous research associated with their exclusionfrom individual digital financial products or services [8ndash10] These discussions above havebeen mainly restricted to the traditional offline transaction scenes in the massesrsquo dailyproduction and life Indeed for rural residents the changes arising from using digitalpayments digital wealth management and digital credit have been increasingly prominentin the era of e-commerce [3] but the impact of e-commerce adoption on farmersrsquo usage ofmultiple digital financial products has been rarely discussed in existing studies

Nowadays the boom of e-commerce is widespread in rural areas in China and a rangeof revenues brought about by e-commerce adoption has triggered much attention fromscholars [11ndash13] It is obvious that innovations in digital finance like online payment tech-nology provide indispensable support for the effective functioning of e-commerce [414] Italso can be seen that the diffusion of e-commerce inevitably involves many financial issueswith regard to payment transactions credit accessibility and capital management [15]However few studies have explored whether the e-commerce adoption for both onlineproduct sellers and purchasers can effectively relieve their exclusion from and increasetheir participation in the digital financial market particularly in regard to digital wealthmanagement and digital credit Quantifying the causal relationship between farmersrsquoadoption of e-commerce and digital finance empirically faces challenges caused by omittedunobservable variables sample self-selection and reverse causality with cross-sectiondata used Therefore with the propensity score matching (PSM) method and instrumentvariable (IV) approach employed to identify the causality and address endogeneity biaswe investigate whether e-commerce adoption by farmers drives their usage of multipledigital financial products How does such an impact takes place and which groups offarmers divided by their characteristics (eg education levels skills training experienceoccupation type) are most affected

A comprehensive and adequate assessment of the impact of e-commerce adoptionon farmersrsquo engaging in the digital financial market would not be accomplished withoutdistinguishing the difference between online purchases and online sales [16] Previousstudies have primarily observed the effects of consumersrsquo online purchases which includea lower degree of information asymmetry less time and labor costs for transactions andmore economic benefits for the purchasers [1718] Meanwhile some studies have high-lighted the influence of producersrsquo online sales which involve wider sales channels agreater sales volume and more sales flexibility for the sellers [19] In view of the distinctdifferences between online purchases and online sales in essence [20] the difference be-tween their impacts on the use of multiple digital financial products or services cannot beignored Additionally due to the fact that purchasing the means of production and sellingproducts are the two most critical and common economic activities infiltrating farmersrsquo en-trepreneurship it therefore makes more sense to concentrate on the relationship betweenentrepreneurial farmersrsquo e-commerce adoption and their usage of multiple digital financialproducts [21] A farmer was defined as an entrepreneur if he or she established and wasengaged in one of these activities small handicraft operations enterprise operations farmoperations cooperative management business services etc

Indeed the use of digital financial products and services might be directly or indirectlyrelated to transactions on e-commerce platforms Digital payment services such as WeChatAlipay and Bestpay have become the main tools for farmersrsquo online transactions andconsumption [3] Moreover digital wealth management resources like Yursquoe Bao and apps

about insurance securities and funds are effective management tools that benefit farmersrsquocapital reserves liquidity management and property appreciation [2] Digital peer to peer(P2P) lending providers for instance Jing Dong Dai and Wang Nong Dai and consumerloan products like Ant Huabei and Jingdong Baitia are becoming increasingly popular inChina as they provide important capital for farmersrsquo production and operations [22] JingDong Dai and Jingdong Baitiao are provided by Jingdong Finance Wang Nong Dai andAnt Huabei are provided by Alipay Studies have suggested that conducting transactionsonline can facilitate the accumulation of internet knowledge and financial literacy forpurchasers [23] which benefits for individualsrsquo financial market participation [2124]Therefore we assume that digital financial literacy reflecting a comprehensive level ofconsciousness knowledge and ability related to digital finance in individualsrsquo humancapital would be a potential pathway through which e-commerce adoption stimulatefarmersrsquo usage of digital financial products

Our study aimed to ascertain the impact of e-commerce adoption on rural residentsrsquoparticipation in the digital financial market and explore possible pathways for enhancingthe inclusion of digital finance which would provide useful suggestions for other develop-ing countries In general our study contributed to the existing research in the followingways First rather than discussing general internet users and individual supplier anddemander sides of online products we concentrated on rural entrepreneurial farmers andemphasized both sellersrsquo and purchasersrsquo adoption of e-commerce and participation in thedigital financial market We elucidated the general and differential influence mechanism ofe-commerce adoption characterized by online purchases and sales on farmersrsquo usage ofmultiple digital financial products Second taking potential endogeneity problems causedby sample self-selection bias omitted unobservable variables and reverse causality intoconsideration we employed both the PSM method and IV approach to empirically dissectthe different impacts of e-commerce adoption on farmersrsquo usage of digital payments digitalwealth management and digital credit Third we shed light on the mediation effects ofdigital financial literacy on the relationship between online purchases as well as onlinesales on farmersrsquo usage of different digital financial products and services using mediationmodel The heterogeneous effects across individual and family characteristics of farmerswere taken into account

The rest of this paper is organized as follows Section 2 presents a review of previousliterature on the topic as well as the hypotheses to be tested Section 3 introduces theempirical strategies employed in the research Section 4 describes the data and mainvariables considered Section 5 presents and discusses the empirical results and is followedby the conclusions and implications in Section 6

2 Literature Review and Hypothesis21 Development of the Definition of Digital Finance

European and North American countries adopted the concepts of e-banking ande-finance early [1525] which laid the foundations for Chinese scholars to put forward theconcept of digital finance (internet finance) Few scholars and policy-makers have clearlydistinguished between the concepts of digital finance and internet finance Although noconsensus has been reached on the definition of digital finance scholarsrsquo opinions on itscore elements and basic attributes have become increasingly clear in China For instanceresearch by Xie and Zou conceptualized internet finance as a third kind of financing patterndifferent from the other two kindsmdashindirect financing from commercial banks and directfinancing from the capital market [26] Wu further regarded internet finance as a newfinancial pattern built on the basis of internet thinking internet platforms and cloud dataintegration [27] This formed a mainstream definition that was recognized by most scholarsin China The Institute of Digital Finance at Peking University championed the conceptof digital finance and constructed an inclusive digital finance index in which paymentsloans insurance investments and credit are conceptually taken into account The DigitalInclusive Financial Index (2011ndash2018) was compiled by a research group at the Institute of

Digital Finance at Peking University httpsjszx6pkueducndocs2019-0720190724210323477869pdf accessed on 20 April 2021

Following existing studies we define farmersrsquo participation in the digital financialmarket as the utilization of digital financial products and services (such as online pay-ments online credit and online wealth management) in their daily lives production andoperation activities It is worth noting that digital finance has not changed the nature ofthe financial industry and there are four main functions of consumer finance paymentrisk management savings or investment and credit [28] Hence we focus on three of theseaspects for farmers participating in the digital financial market (digital payment digitalwealth management and digital credit) in the context of rural China

22 Nexus between Online Purchases and Participation in the Digital Financial Market

In recent years consumers in China have shown an increasing interest in purchasingproducts through e-commerce platforms The differences between online purchases andoffline situations in terms of perceived search costs price sensitivity and response flexibilityare the main reasons why consumers demonstrate higher purchase motivation in virtualmarkets [1729]

The related existing studies have revealed that using the internet for buying activitieshad a positive effect on purchasersrsquo acceptance of internet banking services [8] which isjust one form of digital payment service in China A certain amount of liquid capital byproducers is needed for online purchases due to the fact that purchases of raw materialsproduction and sales often taken place at different times [30] The urgent demands forfunds can directly prompt farmers to apply for quick loans through P2P platforms or use in-ternet consumer loans for installment consumption [22] Likewise a long-term investmentreserve fund by producers is essential for bulk purchases [30] thereby stimulating farmersrsquodemands for flexible and diverse digital wealth management products In addition theherding effect of social networks arising from online transactions would effectively helpmotivate farmersrsquo participation in the digital financial market [31] As mentioned aboveonline purchases has been found to be positively related to individualsrsquo financial liter-acy [23] which would affect their management of personal finance [2432] credit demandand accessibility [21] Therefore the following hypotheses are proposed here

Hypothesis 1 Online purchases can increase farmersrsquo participation in the digital financial marketmeasured by their participation in digital payments digital wealth management and digital credit

Hypothesis 2 Online purchases can affect farmersrsquo participation in the digital financial marketthrough their digital financial literacy

23 Nexus between Online Sales and Participation in the Digital Financial Market

Nowadays there are mainly two kinds of online sales in China friend circle sales andwebsite sales Friend circle sales aim at acquaintance and derivative networks based onWeChat QQ Weibo and other social platforms (eg Tiktok Kwai) while website sales relyon Tmall Jingdong Taobao and other professional e-commerce websites In China WeChatis a mobile instant messenger application based on social relationships and was launchedin 2010 by the Tencent Company QQ is the largest and most used online service portal inChina and was founded in November 1998 by the Tencent Company Weibo is a Chinesemicroblogging website which was launched by Sina Corporation in August 2009 Tiktokand Kwai are popular short video entertainment platforms Friend circle sales integratewords pictures and videos into a marketing platform and carry out mass informationtransmission based on acquaintance networks and their derivatives to maximize the saleof products [3334] Additionally the establishment of shopping guide websites helps tofully display the products sales information and gather target consumers [35] which cancontinuously increase e-marketing flexibility and effectiveness [36]

Previous studies have preliminary confirmed that using the internet for selling ac-tivities has had a positive effect on sellersrsquo acceptance of internet banking services [8]which is just one kind of digital payment service in China It has been suggested thatsellersrsquo engaging constantly in online sales activities increases their operating income andprofit [19] which would enhance individualsrsquo adoption of online wealth managementproducts and services [10] Moreover a certain amount of working capital for operators isneeded for an increase in online sales scale [37] especially in the sales of seasonal agricul-tural products For e-commerce households access to credit would relieve their liquidityconstraints ensure orderly operations and accelerate agricultural transformation [638]In addition it has been argued that financial literacy is positively related to individualsrsquoadoption of financial management and demand for credits [212439] The increase in onlinesales would effectively promote the precipitation and accumulation of internet knowledgefinancial literacy and improve online social relationships for the sellers [33] This wouldbe helpful for further reinforcing individualsrsquo trust in digital finance and inspiring theirenthusiasm for accepting digital financial products or services [31] Therefore the followinghypotheses are proposed in this study

Hypothesis 3 Online sales can enhance farmersrsquo participation in the digital financial marketmeasured by their participation in digital payments digital wealth management and digital credit

Hypothesis 4 Online sales can influence farmersrsquo participation in the digital financial marketthrough their digital financial literacy

Based on the above literature review and hypothesis the conceptual framework isschematically depicted in Figure 1

JTAER 2021 2 FOR PEER REVIEW 5

carry out mass information transmission based on acquaintance networks and their de-rivatives to maximize the sale of products [3334] Additionally the establishment of shop-ping guide websites helps to fully display the products sales information and gather target consumers [35] which can continuously increase e-marketing flexibility and effectiveness [36]

Previous studies have preliminary confirmed that using the internet for selling activ-ities has had a positive effect on sellersrsquo acceptance of internet banking services [8] which is just one kind of digital payment service in China It has been suggested that sellersrsquo engaging constantly in online sales activities increases their operating income and profit [19] which would enhance individualsrsquo adoption of online wealth management products and services [10] Moreover a certain amount of working capital for operators is needed for an increase in online sales scale [37] especially in the sales of seasonal agricultural products For e-commerce households access to credit would relieve their liquidity con-straints ensure orderly operations and accelerate agricultural transformation [638] In addition it has been argued that financial literacy is positively related to individualsrsquo adoption of financial management and demand for credits [212439] The increase in online sales would effectively promote the precipitation and accumulation of internet knowledge financial literacy and improve online social relationships for the sellers [33] This would be helpful for further reinforcing individualsrsquo trust in digital finance and in-spiring their enthusiasm for accepting digital financial products or services [31] There-fore the following hypotheses are proposed in this study Hypothesis 3 Online sales can enhance farmersrsquo participation in the digital financial market measured by their participation in digital payments digital wealth management and digital credit Hypothesis 4 Online sales can influence farmersrsquo participation in the digital financial mar-ket through their digital financial literacy

Based on the above literature review and hypothesis the conceptual framework is schematically depicted in Figure 1

Figure 1 The conceptual framework

3 Model Specification 31 Modelling the Adoption Decision of E-Commerce

According to the random utility decision model proposed by Becerril and Abdulai individualsrsquo decision about whether to adopt internet technology depends on the utility the individual expects to derive from this adoption [40] Farmer adoption occurs when the expected utility of using internet technology ( iU1 ) is greater than the utility without using

internet technology ( iU0 ) ie iU1 - iU0 gt 0 The difference between the utility with and

without e-commerce adoption may be denoted as a latent variable iA so that 0 gtiA

indicates that the utility with e-commerce adoption exceeds the utility without adoption

3 Model Specification31 Modelling the Adoption Decision of E-Commerce

According to the random utility decision model proposed by Becerril and Abdulaiindividualsrsquo decision about whether to adopt internet technology depends on the utilitythe individual expects to derive from this adoption [40] Farmer adoption occurs whenthe expected utility of using internet technology (U1i) is greater than the utility withoutusing internet technology (U0i) ie U1i minus U0i gt 0 The difference between the utilitywith and without e-commerce adoption may be denoted as a latent variable Alowasti so thatAlowasti gt 0 indicates that the utility with e-commerce adoption exceeds the utility withoutadoption Therefore Alowasti is not observable but can be expressed as a function of the observedcharacteristics and attributes denoted as Zi in a latent variable model as follows

Alowastki = Φ(Zi) + microi (1)

1 i f Alowastki gt 0

0 i f Alowastki lt= 0(2)

In Equations (1) and (2) Alowastki is a binary indicator variable that equals 1 if a farmeradopts online purchases (k = 1) or sales (k = 2) and is otherwise zero Zi is a vector ofan all exogenous explanatory variable including individual characteristics householdcharacteristics and village characteristics microi is a random disturbance item assumed to benormally distributed

32 Modelling the Impacts of E-Commerce Adoption on Engaging in Digital Financial Market

According to the theory of behavioral finance an individualrsquos participation decisionin the financial market arises from their pursuing the maximum expected utility basedon bounded rationality [2841] It is believed that farmersrsquo adoption of new technologylike e-commerce would exert direct or indirect impacts on the measurement and com-parison of the costs benefits and risks of participating in the traditional and new-typefinancial market

To measure the impact of online purchases and sales on farmersrsquo participation in thedigital financial market we construct the baseline model as follows

Flowastmi = X1iβ1i + δ1 Aki + ε1i (3)

In Equation (3) Flowastmi is a dummy variable that equals 1 if the farmer i participatedin the digital financial market m = 1 2 3 respectively denotes farmersrsquo participation indigital payments digital wealth management and digital credit X1i is the control variablevector that affects the participation decisions of the farmer i in the digital financial marketas shown in Table 1 Alowastki represents the adoption of online purchases (k = 1) or onlinesales (k = 2) of the farmer i ε1i is a random disturbance item The adoption of onlinepurchases or sales could be endogenous variables in Equation (3) mainly due to omittedunobservable variables reverse causality and sample self-selection problems [42]

Table 1 Definition and summary of variables

Variables DefinitionFull Sample

Mean SD

Digital payments =1 if the respondent used it =0 otherwise 075 043

Digital wealth management =1 if the respondent used it =0 otherwise 023 042Digital credit =1 if the respondent used it =0 otherwise 010 031

Online purchases =1 if the respondent purchased raw materials machinery and other meansof production online =0 otherwise 023 042

Online sales =1 if the respondent sold products online =0 otherwise 036 048

Digital financial literacy The total score of each respondent for the six measurement items related todigital finance 243 172

Gender =1 if the respondent is male =0 otherwise 078 042

Age Age of respondent (unit year) 4449 922

Education Respondent years of education (unit year) 894 334

Risk propensity =1 extremely unpreferred =2 relatively unpreferred =3 neutral =4relatively preferred =5 extremely preferred 248 109

Internet learning ability =1 very bad =2 relatively bad =3 neutral =4 relatively good =5 very good 335 136

Skills training =1 if the respondent participated in training related to business skills (eginternet usage) =0 otherwise 053 049

Information access =1 if the respondent often obtains information from their circle of friendsvia social platforms =0 otherwise 057 049

Online time per week The average time spent online per week (unit hour) 1453 1370

Annual network fee The average network fee per year (unit hundred RMB) 728 851

Table 1 Cont

Mean SD

Number of WeChat friends The number of WeChat friends in frequent contact with the respondent(unit hundred people) 068 153

Financial network =1 if relatives or friends worked at banks or credit cooperatives=0 otherwise 018 038

New agricultural operationentities

=1 if engaging in family farms professional cooperatives agriculturalenterprises etc =0 otherwise 032 047

Entrepreneurship industry =1 if the respondent was engaged in non-agricultural entrepreneurship=0 otherwise 039 049

Distance to the nearest town Distance from village to the nearest town (unit km) 506 445

Formal financial institutionstatus Number of formal financial institutions near the village in the same town 214 114

Taobao shops =1 if there were Taobao shops in the village =0 otherwise 029 045

Shaanxi =1 if from Shaanxi province =0 otherwise 038 049Ningxia =1 if from Ningxia province =0 otherwise 036 048

Notes The measurement items of digital financial literacy were as follows (i) deposit interest rate (ldquoIs the current deposit rate of Yursquoe Baohigher than that of banksrdquo No = 0 Yes = 1 Donrsquot know = 2) (ii) loan interest rate (ldquoIn general is the loan interest rate on P2P lendingplatforms (such as Renren loans Jingdong loans and Yilong loans) higher than that of banks within the same periodrdquo No = 0 Yes = 1Donrsquot know = 2) (iii) mortgage and guarantee requirements (ldquoIn general is collateral or a guarantor required for using consumer loans one-commerce platforms like Taobao Jingdong and Vipshoprdquo No = 0 Yes = 1 Donrsquot know = 2) (iv) credit (ldquoWould someonersquos bad creditrecord on Platform A affect their obtaining loans from Platform Brdquo No = 0 Yes = 1 Donrsquot know = 2) (v) transaction cost (ldquoDo we need topay a handling fee if using WeChat or Alipay to make balance withdrawals beyond a certain limitrdquo No = 0 Yes = 1 Donrsquot know = 2) and(vi) financial security (ldquoDo we need to use a password card U shield transaction code from SMS or other financial security tools whenusing online bankingrdquo No = 0 Yes = 1 Donrsquot know = 2) Respondentsrsquo risk propensity was obtained by asking their willingness to investin the following projects not any risk low risk and low return general risk and general return relatively high risk and high return andhigh risk and high return

33 PSM Method

Considering that whether farmers choose to adopt internet technology depends ontheir own internal and external conditions their decisions about the e-commerce adoption(Aki) may be affected by certain unobservable factors which are also related to the outcomevariable (Fmi) resulting in a correlation between Aki and εi Therefore there may beestimation bias due to sample self-selection problems if a binary probit or logit model isemployed for the estimation of Equation (3) Given that there are no strict requirements forfunction form assumptions parameter constraints an error term distribution or exogenousexplanatory variables in the PSM method this method has an obvious strength in dealingwith sample self-selection [43] Due to the limitation that PSM estimation cannot take theinfluence arising from unobservable factors into consideration the Rosenbaum boundsensitivity analysis is employed to test the robustness of the estimation results

According to the counterfactual analysis framework proposed by Rosenbaum andRubin [44] we define the average treatment effect on the treated group (ATT) as follows

ATT = E(Fmi|Aki = 1)minus E(Fni|Aki = 1) = E(Fmi minus Fni|Aki = 1) (4)

In Equation (4) Fmi denotes the participation decision of the farmer i in the digitalfinancial market when farmers adopted online purchases (online sales) and Fni denotes theparticipation decision of the farmer i in the digital financial market when farmers did notadopt online purchases (online sales) ATT measured the net impact of online purchases(online sales) on farmersrsquo participation in the digital financial market that is the differencebetween the probability of their participating in the digital financial market under thecondition of adopting versus non-adopting online purchases (online sales) AdditionallyE(Fmi|Aki = 1) is the actual result that can be directly observed while E(Fni|Aki = 1) is

the counterfactual result that cannot be directly observed but can be constructed by thepropensity score matching method Following the research of Rosenbaum and Rubin thereare different matching algorithms in the PSM and the trade-offsmdashin terms of bias andefficiencymdashof various methods are inconsistent [4445] If the estimation results of differentmatching methods are similar this indicates that they are robust

34 Instrument Variable Estimation

In order to address the potential endogeneity of online purchases and sales caused byomitted unobservable variables and reverse causality we used the samplersquos participationproportion in online purchases or sales from the same town (excluding the respondent)as a possible valid instrumental variable (IV) For the IV to be valid it had to correlatewith the endogenous variable and not affect the dependent variable through other mecha-nisms [42] Farmersrsquo participation proportion in online purchases or sales from the sametown (excluding the respondent) are undeniably related to the respondentsrsquo behavior aboutonline purchases or sales with highly similarity in their social and economic characteristicsRegarding the exogeneity restriction the respondentsrsquo decisions about the usage of digitalfinance might not be affected by the participation proportion in online purchases or sales ofothers from the same town therefore the two requirements correlation and exogeneity fora valid instrument were likely to be satisfied To check the endogeneity of online purchasesor sales DurbinndashWundashHausman (DWH) tests were conducted by introducing an IV

35 Mediation Model

According to the hierarchical regression model proposed by Baron and Kenny [46]we assigned the regression models of e-commerce adoption to usage of digital finance(see Equation (3)) e-commerce adoption to the digital financial literacy and e-commerceadoption and the digital financial literacy to farmersrsquo usage of digital finance The last twoequations were expressed as follows

DFLi = X2iβ2i + δ2 Aki + ε2i (5)

Flowastmi = X3iβ3i + δ3 Aki + γDFLi + ε3i (6)

where Fmi indicates the participation decisions in the digital financial market DFLi is thedigital financial literacy of the respondent i Aki represents the adoption of online purchases(k = 1) or sales (k = 2) of the farmer i δ2 and δ3 are the parameters to be estimated ε2i andε3i are the random disturbance terms

The mediation effect test procedures included the following steps (1) test the signif-icance of δ1 in Equation (3) if δ1 is significant continue to test otherwise stop the test(2) test the significance of δ2 in Equation (5) and γ in Equation (6) if at least one is notsignificant the Sobel test (step (4)) is needed to conduct further assessment If δ2 and γare significant conduct step (3) test whether δ3 in Equation (6) is significant if it is notsignificant it means that DFLi is a complete intermediary variable but if it is significantand δ3 lt δ1 DFLi is a partial intermediary variable (4) If the statistic z of Sobel test issignificant there exists a mediation effect [46] The Stata 150 software and the proceduresgmediationado were used for the Sobel test in the mediation effect test with which theupdated estimation command can be obtained In general the Sobel test has a better testeffect than stepwise regression but it also has the limitation of requiring the samplingdistribution to be normal In the future studies we will use the bootstrap method with abetter testing effect to obtain more convincing results

4 Data and Variables41 Data Source and Descriptive Statistics

Our data were based on a questionnaire survey conducted through face-to-face inter-views in 2018 in rural China Multi-stage cluster sampling procedures were performedFirst three provinces in the eastern and western parts of China were selected by taking the

development of e-commerce and digital inclusive finance into consideration Second ninecounties or districts in the selected provinces were selected by considering their develop-ment of e-commerce geographical environment and economic status We selected threerepresentative counties (Fuping Pingluo and Qingzhou) with higher levels of e-commerceactivities three representative counties (Nanzheng Tongxin and Shen) with average levelsand three counties (Gaoling Shapotou and Yinan) with poor levels Fuping Nanzhengand Gaoling are located in Shaanxi Shapotou Tongxin and Pingluo are located in Ningxiaand Shen Qingzhou and Yinan are located in Shandong Third three or four representa-tive towns reflecting different economic levels were extracted from each selected countyor district From these two or three representative villages were selected based on thesame principle Fourth 15 to 20 rural households (mainly the decision-makers in economicactivities) were selected from each selected village to interview Prior to conducting thesurvey we scouted various regions and tried to consult local town and village leaders thestaff of financial institutions and the farmers

Our research group selected 2000 farmers for interview However a small number offarmers directly refused to answer our questions or just finished part of the questions result-ing in many vacancies in the questionnaire items After excluding the above observationswith much missing data or outliers we obtained 1947 valid questionnaires coming from 105villages in 36 towns nine counties (or districts) and three provinces The representativenessof our sample is described as follows firstly according to the Report of Peking UniversityDigital Inclusive Finance Index (2011~2018) Shandong Shaanxi and Ningxia provinceswere the representative provinces with high average and low development level of digitalinclusive finance in China respectively In addition the 42nd Statistical Report on the De-velopment of Chinarsquos Internet showed that as of June 2018 the proportion of using digitalpayment for Chinese Internet users increased to 78 while the utilization rate of digitalwealth management increased to 21 The 42nd Statistical Report on the Development ofChinarsquos Internet can be accessed at httpwwwcacgovcn2018-0820c_1123296882htmaccessed on 20 April 2021 The proportions of using digital payment and digital wealthmanagement in our sample were 75 and 23 respectively which were in line withthe results of the above report Secondly Shaanxi and Ningxia were the representa-tive provinces of the growth-oriented e-commerce development mode (relatively fastgrowth rate but low level) while Shandong was the representative province of the rel-atively mature e-commerce development mode (relatively high level but slow growthrate) (The 2018 Report of Chinarsquos E-commerce Development Index can be accessed athttpwwwlifangwangnetdetailphpaid=145 accessed on 20 April 2021) Thirdly oursample set covers agricultural ecosystems varying with different geographic environmentssuch as the Guanzhong Plain Mountainous Area of Southern Shaanxi Loess Plateau andNorth China Plain which means the use of e-commerce and digital finance by farmersand entrepreneurial activities of farmers may present regional differences Based on theaforementioned reasons our sample can fairly reflect good representativeness at the na-tional level As mentioned previously considering that production and sale activities arethe two most critical and common economic activities for farmersrsquo entrepreneurship [21]we focus on the online purchases and sales of entrepreneurial farmers rather than generalfarmers We divided the sample into entrepreneurial farmers and non-entrepreneurialfarmers Agricultural and non-agricultural entrepreneurship were included in farmersrsquoentrepreneurial activities Agricultural entrepreneurship refers to agro-economic activitiessuch as scale management or start-ups of new businesses or new organizations (eg fam-ily farms professional cooperatives and enterprises) in traditional agriculture such asplantations aquaculture forestry and fisheries Non-agricultural entrepreneurship refersto the establishment of businesses in industrial sectors such as processing manufactur-ing and construction or engagement in non-agricultural economic activities in serviceindustries such as specialized services for agricultural production retailing wholesalingaccommodation transportation housekeeping culture and entertainment or medical andhealth services Thus an entrepreneurial sample of 832 was used for analysis The samples

from Shaanxi Ningxia and Shandong accounted for 3792 3623 and 2585 of the fullentrepreneurial sample respectively

42 Dependent Variable

The dependent variable was farmersrsquo participation in the digital financial marketwhich was mainly measured by digital payments digital wealth management and digitalcredit We asked every interviewee the following questions ldquoHave you used WeChatAlipay Tenpay Bestpay or other third-party payment software to conduct capital trans-actionsrdquo ldquoHave you participated in P2P platforms as an investor used Yursquoe Bao orinvested insurance securities funds etc through appsrdquo and ldquoHave you participatedin P2P platforms as a borrower used small loan products (Jing Dong Dai Wang NongDai etc) to obtain loan funds or used consumer loan products (Ant Huabei JingdongBaitiao Weipinhua etc) to realize consumption and pay by installmentrdquo According tothe answers to the above three questions we successively identified farmersrsquo participationbehaviors with respect to digital payments digital wealth management and digital credit

As reported in Table 1 75 of the respondents used digital payments 23 used digitalwealth management and 10 used digital credit Along with the popularity of WeChatand Alipay in rural China digital payments are increasingly widely used by farmersMeanwhile due to farmersrsquo perceived riskiness of the digital financial market and limitedknowledge reserve about digital finance [56] their participation rates in digital wealthmanagement and digital credit were generally low This is generally consistent with thestatistic that only 760 and 1110 households in China have access to credit and wealthmanagement products online respectively in Li Wu and Xiao [4] Indeed it is reasonablethat entrepreneurial farmersrsquo participation levels in the digital financial market would behigher than their counterparts due to more capital transactions wealth management andcredit demands for entrepreneurs

43 Treatment Variables

The treatment variables were online purchases and online sales in entrepreneurshipTo clearly identify farmersrsquo adoption of online purchases and online sales we askedthe respondents ldquoDid you use the internet for purchasing raw materials machineryand other means of production in your entrepreneurial activitiesrdquo ldquoDid you use circlesof friends on WeChat QQ and other social platforms (eg Weibo Tiktok Kwai) forselling products in your entrepreneurial activitiesrdquo and ldquoDid you use websites for sellingproducts in your entrepreneurial activitiesrdquo If the answer for the first question was ldquoyesrdquothe respondent was classified as an online purchaser If at least one of the answers to thelast two questions was ldquoyesrdquo the respondent was classified as an online seller As shownin Table 1 2347 of the sample took part in purchasing online while 3550 of the sampletook part in selling online As one statistic reported the number of e-commerce users inrural China in 2019 reached 230 million accounting for approximately 35 of the totalrural population (this can be obtained from the Report of China Rural E-commerce Markethttpwww100eccnzt2019ncdsbg (accessed on 20 April 2021)) This implied thatpurchases and sales online have become an important supplement to traditional offlinechannels Furthermore through the means comparison (see Table A1 of Appendix A) wecan preliminarily judge that those farmers who adopted online purchases or sales weremore inclined to take part in the digital financial market

44 Channel Variable

Farmersrsquo digital financial literacy was selected as the channel variable which affectedindividualsrsquo perceptions of and intentions towards the products and services in the digitalfinancial market Previous studies have mainly used items related to interest rates inflationand risk diversification to measure individualsrsquo financial literacy [213239] With referenceto the above research we measured farmersrsquo digital financial literacy using six questions(as reported in Table 1) which emphasized interest rates mortgage and guarantee re-

quirements credit transaction cost and financial security in the digital financial marketWe calculated the respondentsrsquo digital financial literacy using a scoring method based onwhether answers to each item were correct (ldquoYesrdquo for each item) or not with the correctanswer being given a score of 1 and other answers a 0 Using equal weights we thenobtained the digital financial literacy score for each respondent which ranged from 0 to 6As shown in Table 1 the average score for the digital financial literacy of respondents was243 indicating a low average level of financial literacy among rural residents in China [3]

45 Control Variables

Based on the existing literature [8ndash1020] and field experience we controlled for therespondentsrsquo individual characteristics (gender age education risk propensity inter-net learning ability skill training experience information access time spent online perweek) household characteristics (annual network fee number of WeChat friends financialnetwork new agricultural operation entities entrepreneurship industry) village charac-teristics (distance to the nearest town formal financial institution status Taobao shops(Alibaba Group in China has cooperated with local governments to establish Taobao storesand service stations in rural areas to promote online products dissemination to the coun-tryside as well as rural products access to the cities) and province dummies variables(Shaanxi Ningxia) Table 1 presents the detailed definitions and descriptive statistics of thecontrol variables

From Table 1 it is clear that 7822 of the respondents were male and had an averageage of 44 years Half of the respondents had only completed middle school while 3164 ofthem had attended high school or above In terms of skill training experience 5323 of therespondents had taken some kind of training courses on business skills in the past Withregard to risk propensity 1545 of the respondents were open to risk 5688 were riskaverse and 2766 were risk neutral Of the respondents 3056 reported poor internetlearning ability while 5326 believed that they could effectively learn and apply internetknowledge Moreover 5734 of the farmers often obtained economic information fromtheir friends through social platforms such as QQ WeChat and Weibo The respondentsrsquoaverage time spent online was 1453 h per week Overall the rural households surveyedpaid an annual 72788 RMB for network fees on average About 68 friends on averagewere in frequent contact with each respondent on WeChat Only 1816 of the householdshad relatives or friends working at banks or credit cooperatives Approximately 3210of the households were engaged in new agricultural operations such as family farmsprofessional cooperatives and agricultural enterprises Furthermore 3922 of householdsconducted entrepreneurship in a non-agricultural industry Regarding the village traits ofeach study village the average distance from the selected villages to the nearest town was506 km The average number of formal financial institutions in each town was two About2925 of the surveyed villages had Taobao shops established by individuals

5 Empirical Results and Discussion51 Determinants of the Adoption of Online Purchases and Sales

As reported in Table A2 of Appendix A gender education internet learning abilityskill training experience time spent online per week new agricultural operation entitiesand entrepreneurship industry positively affected farmersrsquo online purchases at differentsignificance levels while distance to the nearest town had a negative and significant impacton their online purchases The male participants were less skeptical about e-businessshowed less web apprehensiveness and were more satisfied with online shopping thantheir female counterparts [47] Additionally farmers with higher levels of educationgreater internet learning capabilities special training in business skills and more timespent online would seek more freedom of products and services choice and control in theironline shopping [1748] Additionally farmers engaging in new agricultural operationentities (ie engagement in family farms professional cooperatives) and non-agriculturalentrepreneurship showed a greater demand for various new products with high technical

content and low substitutability as well as fast and convenient transactions in onlinechannels

Moreover gender risk propensity internet learning ability skills training experienceinformation access time spent online per week annual network fee new agriculturaloperation entities entrepreneurship industry and the existence of Taobao shops in thevillage positively affected farmersrsquo online sales These findings are in line with the researchof Chitura et al [14] and Li et al [49] Moreover farmers engaging in new agricultural oper-ation entities involved in family farms and professional cooperatives and non-agriculturalentrepreneurship were more inclined to sell products online to receive wider productpromotions a greater market share and a higher sales profit

52 Impact of E-Commerce Adoption on Usage of Digital Finance521 The PSM Estimation Results

To ensure a good matching quality we conducted both a balanced hypothesis test anda common support hypothesis test As shown in Figure A1 of Appendix A the commonsupport interval of propensity scores for the online purchase adopters and non-adopterswas from 00038 to 08263 while that for the online sales participants and non-participantswas from 00528 to 08062 After matching with online purchases and online sales astreatment variables pseudo-R2 decreased significantly from 0159 and 0177 to 0004ndash0025and 0003ndash0031 respectively and LR values decreased significantly from 14330 and19117 to 214ndash1368 and 226ndash1530 respectively The joint significance test of explanatoryvariables changed from the 1 significance level before matching to the 10 level withoutsignificance the mean biases of explanatory variables decreased from 304 and 311 towithin 15 and the total bias reduced significantly All the above results indicated that thesample matching effectively balanced the differences of explanatory variable distributionbetween the treatment group and the control group and minimized the problem of sampleselection bias [44]

As shown in Table 2 both online purchases and online sales had a positive andsignificant impact on digital payments digital wealth management and digital creditfor farmers This finding is in line with Hojjati and Rabi [8] in that selling and buyingonline positively affects the adoption of internet banking which can be extend to farmersrsquoutilization decisions about digital wealth management and digital credit Specificallyfarmersrsquo online purchases increased the likelihood of participating in digital paymentsdigital wealth management and digital credit by an average of 716 897 and 667respectively Likewise farmersrsquo online sales increased the probability of participating indigital payments digital wealth management and digital credit by an average of 9281059 and 530 respectively Remarkably we found that the impact of online purchasesand sales on digital wealth management was larger than that on digital payments anddigital credit Moreover the impact of farmersrsquo online sales on digital payments and digitalwealth management was larger than that of online purchases on the contrary the impactof farmersrsquo online sales on their digital credit was smaller than that of online purchases

A possible explanation for this finding may be that wealth management behaviorstems from the traditional saving culture in China In fact the precautionary saving rate ofChinese households has been one of the highest in recent decades around the world withan imperfect social security system [50] The main means of online wealth managementfor farmers discussed in this article was Yursquoe Bao which is an effective tool for cashmanagement with value-added services This financial product has been popular in Chinasince it was launched in 2013 due to its advantages such as simple and clear process lowthreshold zero poundage and flexible use [310] The transaction and capital flow ofonline sales occur more frequently than that of online purchases which causes farmersrsquohigher dependence on digital wealth management and digital payments for online salesIn addition online purchases would stimulate farmersrsquo greater demand for digital creditthan online sales

Table 2 PSM estimation of the impact of e-commerce adoption on usage of digital finance

Matching MethodsOnline Purchases Online Sales

ATT AverageATTs ATT Average

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

Digital wealthmanagement

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

Notes standard errors in parentheses P lt 01 P lt 005 P lt 001 NNM denotes nearest neighbor matchingCM denotes caliper matching NNMC denotes nearest neighbor matching with a caliper KM denotes kernelmatching SM denotes spline matching and MM denotes Mahalanobis matching

522 The IV Estimation Results

The IV estimation results of online purchases and sales on farmersrsquo usage of digitalfinance are shown in Table 3 The t values of IV were all significant at the 1 level andthe first-stage F-statistics in the IV probit regression were 2727 and 4660 suggestingthat the IV was a strong instrument since it exceeded the conventional ldquorule of thumbrdquoof 10 for an F-statistic [42] The DWH endogeneity tests all rejected the null hypothesisthat online purchases or sales were exogenous at the 10 significance level The resultsindicated that online purchases or sales had a positive and significant impact on farmersrsquoadoption of digital payments digital wealth management and digital credit at least atthe 10 level Additionally the positive impact of online purchases or sales on farmersrsquoadoption of digital wealth management was larger than that impact on digital paymentsand digital credit which was ignored in the existing studies [568] Hence we concludedthat our main aforementioned conclusions were still confirmed with mitigating potentialendogeneity of online purchases and sales

Table 3 IV estimation of the impact of e-commerce adoption on digital finance usage

VariablesDigital Payments Digital Wealth Management Digital Credit

(1) (2) (3)

Online purchases 00763 (00462) 00983 (00208) 00585 (00289)Control variables fixed Yes Yes YesWald X2 15144 16235 14554 F-value of first stage 2727 2727 2727 t value of IV 522 522 522 DWH endogenous test 358 422 538 Observations 832 832 832

Online sales 00789 (00137) 01029 (00268) 00534 (00323)Control variables fixed Yes Yes YesWald X2 16402 17728 16306 F-value of first stage 4660 4660 4660 t value of IV 683 683 683 DWH endogenous test 310 329 517 Observations 832 832 832

Notes standard errors in parentheses P lt 01 P lt 005 P lt 001 The samplersquos participation proportion in online purchases or salesfrom the same town (excluding the respondent) was selected as an IV

53 Robustness Checks531 Rosenbaum Bound Sensitivity Analysis

Since the influence arising from unobservable factors was not considered in the PSMestimation we used the sensitivity analysis of treatment effect to do the robustness testWhen the gamma coefficient which refers to the effect of ignored and unobservable factorsis close to one and the existing conclusion is no longer significant the PSM estimationresult is not robust however when the gamma coefficient is larger than one (usually closeto two) and the existing conclusion becomes no longer significant the result is relativelyreliable [45] Through the ATT sensitivity analysis results (the details are not reportedhere due to the space limitations) we clearly found that when the gamma coefficientincreased to 38 and 42 respectively the impacts of online purchases and sales on farmersrsquoparticipation in the digital financial market were no longer significant Therefore theconclusions of our study were robust

532 Superposition Effect

For further robustness testing we took both online purchases and online sales as thetreatment variable As shown in Table A3 of Appendix A the average treatment effectsof both online purchases and online sales on the use of digital payments digital wealthmanagement and digital credit were significantly positivemdashat least at the 10 significancelevelmdashand the average ATTs were 00776 00890 and 00712 respectively Those farmerswho have more experience in online purchases and sales were inclined to have strongerdemands for and more adoption of digital financial products and services [8] In brief theimpact of both online purchases and sales on farmersrsquo usage of digital wealth managementwas successively greater than that on digital payments and digital credit which was neverdiscussed in the previous studies [8] Therefore the main conclusions above were robust

54 Potential Impact Pathways

Table 4 summarizes the outcomes of the mediation effect test of digital financialliteracy According to the hierarchical regression model proposed by Baron and Kenny [46]we successively estimated the effect of online purchases and sales on participation in thedigital financial market (Columns 1 to 3) the effect of online purchases and sales on digitalfinancial literacy (Column 4) and the effect of online purchases and sales on participationin the digital financial market with digital financial literacy introduced (Columns 5 to 7)

Table 4 The mediation effect of digital financial literacy

VariablesDigital

PaymentsDigital WealthManagement

DigitalCredit

DigitalFinancialLiteracy

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

Online purchases 00822 (00426)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

Digital financialliteracy

00581 (00098)

00726 (00093)

00422 (00078)

Control variablesfixed Yes Yes Yes Yes Yes Yes Yes

LR X2FWald X2 35917 20528 14822 2241 25202 20556 15812 Pseduo R2R2 050 023 027 039F-value of first stage 3511 3511 3511 t value of IV 635 635 635 DWHendogenous test 1212 1056 986

Online sales 01028 (00302)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

LR X2FWald X2 31263 20672 14450 2118 25843 20801 15924 Pseudo R2R2 033 023 026 038F-value of first stage 3511 3511 3511 t value of IV 635 635 635 DWHendogenous test 1402 1255 1037

Observations 832 832 832 832 832 832 832

Notes marginal effects were reported outside the parentheses F-value and R2 only used for Column (4) with OLS estimation employedThe control variables were the same as in Table 1

Considering that digital financial literacy could be an endogenous variable due tomeasurement errors omitted unobservable variables and reverse causality we use IVapproach to address potential endogeneity of digital financial literacy Following Bucher-Koenen and Lusardi [51] we used the average level of digital financial literacy in therespondentsrsquo village (excluding the respondent) as a potentially valid instrumental variable(IV) On the one hand farmersrsquo average level of digital financial literacy in the same village(excluding the respondent) was clearly related to the respondentsrsquo digital financial literacydue to a high similarity for respondents coming from the same village in terms of socialand economic characteristics On the other hand farmersrsquo usage of digital finance mightnot have been affected by the average digital financial literacy of others coming from theirvillages therefore the two requirements correlation and exogeneity for a valid instrumentwere likely to be satisfied [42]

As shown in Columns (1) to (4) online purchases positively and significantly affectedfarmersrsquo digital payments digital wealth management digital credit and digital financialliteracy at the 10 1 5 and 1 levels with magnitudes of 00822 00910 00485 and04987 respectively Likewise online sales were positively and significantly associated withfarmersrsquo digital payments digital wealth management digital credit and digital financialliteracy at the 1 1 10 and 1 levels with magnitudes of 01028 01151 00634and 04264 respectively As presented in Columns (5) to (7) all the DWH endogeneitytests rejected the null hypothesis that digital financial literacy was exogenous at the 1significance level The first-stage F-statistic in the IV probit regression was 3511 with at-value of 635 verifying that the IV was a strong instrument [42] The results indicatedthat online purchases had a positive and significant impact on farmersrsquo participation in

digital payments digital wealth management and digital credit with magnitudes of 0073800531 and 00337 respectively Likewise online sales had a positive and significant impacton farmersrsquo participation in digital payments digital wealth management and digitalcredit with magnitudes of 00912 00665 and 00342 respectively A comparison of themarginal effects from only introducing online purchases or sales in Columns (1) to (3)and those from simultaneously introducing online purchases or sales and digital financialliteracy in Columns (5) to (7) show that the former effects are larger With reference to themediation effect test procedures put forward by Baron and Kenny [46] we concluded thatboth online purchases and sales could affect farmersrsquo participation in the digital financialmarket through the partial mediation effect of digital financial literacy Our findingsexpanded the existing research on the relationship between e-commerce adoption andindividualsrsquo financial literacy [23] and the relationship between e-commerce adoption andonline banking service utilization [8] Furthermore the Sobel test showed that 52743629 and 5856 respectively of the impacts between online purchases and farmersrsquoparticipation in digital payment digital wealth management and digital credit weremediated by digital financial literacy Similarly 2762 2584 and 2966 respectivelyof the impacts between online sales and farmersrsquo participation in digital payment digitalwealth management and digital credit were mediated by digital financial literacy Thesefindings confirmed that farmersrsquo participation practice of both online purchase and onlinesales benefits for the accumulation of digital financial literacy [23] and then promotes theirrational usage of digital financial products and services

55 Heterogeneous Treatment Effects

Farmersrsquo education level skills training experience engaging in new agricultural oper-ation entities (eg family farms professional cooperatives) and entrepreneurial industries(agricultural and non-agricultural entrepreneurship) all have potential impacts on theiradoption of e-commerce [1749] and participation in the digital financial market [89] Thusthey are regarded as group variables to explore the heterogeneous treatment effects acrossdifferent groups The results are displayed in Table 5

As reported in Columns (1) and (2) for farmers with a high level of education onlinepurchases or sales adoption could increase the propensity of using digital payments digitalwealth management and digital credit at least at a 10 significance level while onlythe impact of online sales on digital payments was significant at a 10 level for farmerswith a low education level As shown in Columns (3) and (4) for those farmers withskills training experience online purchases could facilitate their involvement in digitalwealth management and digital credit at the 5 and 10 significance levels respectivelyonline sales could lead to a high probability of using digital payments and digital wealthmanagement at the 10 and 1 significance levels respectively As displayed in Columns(5) and (6) for the new agricultural operation entities online purchases could promote theiruse of wealth management and access to credit through the internet at the 5 significancelevel respectively online sales could lead to a high probability of using digital paymentsand digital wealth management at the 10 and 1 significance levels respectively Asreported in Columns (7) and (8) both online purchases and online sales showed a significantimpact on participation in the digital financial market for farmers engaging in agriculturalentrepreneurship

Table 5 Heterogeneity results of the impact of e-commerce adoption on usage of digital finance

Treatment Variables Dependent Variables

Education Levels Skills Training Experience New AgriculturalOperation Entities Entrepreneurship Industry

(1) (2) (3) (4) (5) (6) (7) (8)

Low High No Yes No Yes Non-Agriculture Agriculture

Onlinepurchases

Digital payments 00163(00564)

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

Digital wealth management 00824(00561)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

Digital credit 00507(00398)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

Online salesDigital payments 00842

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Notes standard errors in parentheses P lt 01 P lt 005 P lt 001 Low level of education is defined as middle school and below (nine years of education and below) high level of education is defined ashigh school and above (ten years of education and above) New agricultural operation entities refers to involvement in family farms professional cooperatives and agricultural enterprises

Consequently the group comparisons indicated that the marginal effects of onlinepurchases and sales on farmersrsquo participation in the digital financial market were gener-ally larger among farmers who had higher education levels more skills training expe-rience were running new agricultural operation entities and were pursuing agricultureentrepreneurship Reasons for such could be that higher education levels lead to a betterunderstanding of the costs benefits and risks of digital financial products and a betterability to make good use of them [25] Moreover possessing business-related skills trainingwould enhance farmersrsquo abilities to choose and adopt digital financial products and ser-vices thus increasing their trust in digital finance [52] In addition there are more capitaltransactions liquidity management credit demand and higher levels of risk tolerance andinternet usage for new agricultural operation entities compared with traditional agricul-tural operators [38] Agricultural entrepreneurs are more familiar with and show moredependence on the production and sales activities in the field of agriculture [53] whichmakes it relatively easier for them to participate in the digital financial market related tothe agricultural field

6 Conclusions and Implications

Few studies have documented the impacts of e-commerce adoption measured byonline purchases and sales when explaining farmersrsquo digital financial exclusion [56] Ourresults provide a new perspective on the relationship between the e-commerce adoptionfor both online products demanders and suppliers and their participation in the digitalfinancial market in rural areas Using survey data on 832 entrepreneurial farmers in ruralChina we reveal that both online purchases and online sales have a significant and positiveimpact on farmersrsquo participation in the digital financial market This effect on the usageof digital wealth management is successively larger than that on the usage of digitalpayments and digital credit Moreover the effect of online purchases and sales on farmersrsquoparticipation in the digital financial market could be consistently mediated by their digitalfinancial literacy The results further provide strong evidence for that the impact of onlinepurchases and sales on farmersrsquo participation in the digital financial market is greaterfor those with high education levels pursuing skills training running new agriculturaloperation entities and engaging in agricultural entrepreneurship

Our study indicates that the driving role of e-commerce adoption both for onlineproducts suppliers and demanders on their engagement in the digital financial marketshould not be ignored in the digital economy era Our findings also contribute to theliterature by elucidating the differential influence of e-commerce adoption characterizedby online purchases and sales on farmersrsquo usage of multiple digital financial productsrepresented by digital payments digital wealth management and digital credit In additionour discussion on the impact path of e-commerce adoption on the participation in digitalfinancial market solicits further attention to farmersrsquo digital financial literacy and otherpossible pathways The heterogeneous effect of e-commerce adoption for different groupssuggests that farmersrsquo participation gap in the digital financial market would be widenedcaused by e-commerce adoption

This study provides beneficial practical implications as follows First our studysuggests that both online purchases and online sales significantly increased farmersrsquo partici-pation in the digital financial market In order to relieve farmersrsquo digital financial exclusiongovernments in China are expected to take more effective measures to enhance adoptionrates of online purchases and sales technology in particular for entrepreneurial farmersProfessional and systematic digital education relating to online purchases online salesonline management and other related content for different types of farmers is urgentlyneeded for further enhancing their adaptability to new changes in the era of e-commerceAdditionally the establishment of information support systems such as internet infrastruc-ture logistics facilities and e-commerce service platforms should be constantly optimizedOn the basis of this the construction of e-commerce demonstration regions and Taobaovillages should be continuously promoted

Second our findings also demonstrate that the impact of e-commerce adoption onfarmersrsquo usage of various types of digital financial products is different due to the differ-ence in digital financial demands Local financial institutions and the internet financialindustry in China should actively strengthen the related investigation on different farmersrsquorequirements of digital financial market participation It is an increasing trend amongrural areas to accelerate market-oriented financial reforms and strengthen function-basedregulation to foster the healthy and inclusive development of the digital financial marketThe innovative design of digital financial products and services related to investmentwealth management credit and relevant intelligent terminals should be strengthened foraccelerating the digital financial inclusion in rural areas

Third more attention should be paid to the mediation role of digital financial literacyin the relationship between farmersrsquo online purchases and sales and their participationin the digital financial market Government departments financial institutions schoolsand social education resources in China should be actively encouraged to strengthentheir systematic training of farmersrsquo financial knowledge especially in the form of digitalfinancial literacy Moreover farmersrsquo trust levels in the digital finance should be enhancedby promoting their overall digital financial literacy

Fourth our study further reveals that the effects of online purchases and sales onfarmersrsquo adoption of digital payment digital wealth management and digital credit varyacross individual and family characteristics The differences among farmers regardingeducation levels production and operation types and organizational forms should be fullyconsidered when optimizing the supply of digital financial products and services Moreeffective measures should be taken to encourage more farmers to actively participate inskills training expand their operation scale and engage in new types of agricultural busi-ness entities In order to reduce the gap in the usage of digital finance among farmers withdifferent characteristics more assistance should be given to the disadvantaged farmers

Limitations still exist in this study which drives us to pay attention to the improvementin aspects of sampling empirical data methods and impact mechanism in our futureresearch First our study only used the survey data of farmers from three provincesof China for empirical analysis and was a lack of the survey of provinces from centralChina which weakened the generalization of the research conclusions to a certain extentTo generalize our findings we would conduct survey in central provinces and expandthe sample to other provinces Second the cross-section data we used cannot capturethe changes of participation decision in the digital financial market before and after e-commerce adoption so there are obvious limitations in causality identification Hence wewould try to establish panel data through longitudinal survey for further study Third wejust tested the mediation effect of digital financial literacy when exploring the mechanismby e-commerce adoption affects farmersrsquo participation in the digital financial market Infuture studies we would discuss other pathways such as online social networks incomeand financial demand to extend our study

It is worth noting that the epidemic of COVID-19 has a great impact on Chinarsquosagricultural production sales capital liquidity and the sustainability of small and microenterprises in a certain period but it also accelerates the digital transformation of thewhole agricultural industry chain [54] Since agriculture is the basic industry of Chinarsquosnational economy and has the characteristics of weak industry vulnerable to naturalrisks and market risks the improvement of the adoption rate of farmersrsquo e-commerceand participation degree of digital financial market will help to continuously improvethe elasticity and efficiency of agricultural production In the context of fighting againstCOVID-19 more and more farmers especially entrepreneurial farmers are actively usingonline purchase and sales technology to reduce information asymmetry and ensure theorderly operation of production and operation This will also further promote farmersrsquoparticipation in the financial market from offline to online Therefore in further studies itwould be meaningful to explore the impact of COVID-19 on farmersrsquo e-commerce adoptionand participation in the digital financial market

Author Contributions Conceptualization LS methodology YP software YP validation LS andYP formal analysis LS investigation LS and RK resources RK data curation LS writingmdashoriginal draft preparation LS and YP writingmdashreview and editing LS YP and QC supervisionRK project administration RK and LS funding acquisition RK and LS All authors have readand agreed to the published version of the manuscript

Funding This work was supported by National Natural Science Foundation of China (NSFC) (grantnumbers 71773094 and 71903141) China Postdoctoral Science Foundation Project (grant number2020M680246) Humanities and Social Science Fund of Ministry of Education of China (grant number18YJC790125) and Natural Science Foundation Research Project of Shaanxi Province (grant number2020JQ-281)

Data Availability Statement The datasets analyzed during the current study are not publicly avail-able due to institutional restrictions but are available from the corresponding author on reasonablerequest

Acknowledgments LL contributed to the paper design data collection and writing YL con-tributed to the data analysis paper review and revision RK contributed to the paper revisions QCcontributed to the language editing

Conflicts of Interest The authors declare no conflict of interest

Appendix ATable A1 Definition and summary of variables(cont)

VariablesOnline Purchases Online Sales

Treatment Control T-Test Treatment Control T-Test

Digital payments 090 (030) 071 (045) 019 091 (028) 066 (047) 025 Digital wealth management 041 (049) 017 (038) 024 038 (048) 014 (035) 024 Digital credit 019 (040) 008 (026) 011 017 (037) 007 (025) 010 Digital financial literacy 349 (013) 211 (008) 138 320 (011) 202 (008) 118 Gender 086 (035) 075 (043) 011 081 (039) 076 (043) 005 Age 4204 (901) 4524 (915) minus320 4197 (889) 4587 (910) 390 Education 1043 (318) 848 (325) 195 992 (330) 840 (324) 152 Risk propensity 262 (108) 244 (109) 018 267 (110) 238 (107) 029 Internet learning ability 389 (115) 319 (137) 070 386 (115) 307 (138) 079 Skills training experience 069 (046) 048 (050) 021 063 (048) 048 (050) 015 Information access 073 (045) 053 (050) 020 078 (042) 046 (050) 032 Time online 1979 (1582) 1292 (1256) 687 1944 (1654) 1182 (1096) 762 Annual network fee 959 (905) 657 (822) 30205 960 (864) 600 (722) 35964 Number of WeChat friends 121 (213) 052 (189) 6894 111 (250) 045 (251) 6662 Financial network 023 (042) 016 (037) 007 023 (042) 015 (036) 008 New agricultural operation entities 047 (050) 028 (045) 019 042 (049) 027 (044) 015 Entrepreneurship industry 051 (050) 036 (048) 015 050 (050) 033 (047) 017 Distance to town 484 (358) 576 (648) minus092 502 (358) 512 (569) minus010Formal financial institution status 218 (112) 213 (115) 005 210 (210) 216 (119) minus006Taobao shops 029 (045) 026 (044) 003 034 (047) 026 (044) 008 Shaanxi 044 (050) 036 (048) 008 043 (050) 035 (048) 008 Ningxia 029 (046) 038 (049) minus009 030 (046) 040 (049) minus010 Observations 195 637 295 537

Notes mean values outside the parentheses with standard deviations in parentheses P lt 01 P lt 005 P lt 001

Table A2 Determinants of adoption of online purchases and sales

Variables Online Purchases Online Sales

Gender 04443 (01441) 02190 (01279)Age minus00048 (00063) minus00081 (00059)Education 00570 (00185) 00141 (00170)Risk propensity minus00045 (00502) 00756 (00458)Internet learning ability 00786 (00469) 00970 (00436)Skills training experience 03054 (01187) 02566 (01103)Information access 01199 (01200) 05131 (01109)Online time per week 00134 (00040) 00141 (00042)Annual network fee 00001 (00001) 00002 (00001)Number of WeChat friends 00003 (00002) 00003 (00002)Financial network 00529 (01341) 00949 (01286)New agricultural operation entities 02581 (01260) 02138 (01183)Entrepreneurship industry 04153 (01143) 03930 (01080)Distance to the nearest town minus00337 (00151) 00048 (00116)Formal financial institution status 00364 (00482) 00028 (00450)Taobao shops minus01260 (01313) 02867 (01213)Shaanxi 00370 (01534) 01051 (01429)Ningxia minus00570 (01603) 00588 (01477)Observations 832 832LR X2 14330 19117

Notes standard errors in parentheses P lt 01 P lt 005 P lt 001 Shandong was taken as the control group

Table A3 The superposition effect of both online purchases and sales

Methods Treated Controls ATT Average ATTs

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

Notes standard errors in parentheses P lt 01 P lt 005 P lt 001 NNM denotes nearest neighbor matching CM denotes calipermatching NNMC denotes nearest neighbor matching with a caliper KM denotes kernel matching SM denotes spline matching and MMdenotes Mahalanobis matching

Notes standard errors in parentheses P lt 01 P lt 005 P lt 001 NNM denotes nearest neighbor matching CM denotes caliper matching NNMC denotes nearest neighbor matching with a caliper KM denotes kernel matching SM denotes spline matching and MM denotes Mahalanobis matching

Figure A1 Propensity score density function of adopters and non-adopters of online purchases and sales

References 1 Liao GK Yao DQ Hu ZH The spatial effect of the efficiency of regional financial resource allocation from the perspective

of internet finance Evidence from Chinese provinces Emerg Mark Financ Trade 2020 56 1ndash13 2 Lei Y Policy Discussion of Internet Finance in China BOFIT Policy Brief Institute for Economies in Transition Bank of

Finland 25 November 2014 Available online

httpsheldahelsinkifibofbitstreamhandle12345678913462bpb13145b15dpdfsequence=1ampisAllowed=y (accessed on

20 April 2021) 3 Ren B Y Li LY Zhao HM Zhou YB The financial exclusion in the development of digital finance A study based on

survey data in the JingJinJi rural area Singap Econ Rev 2018 63 65ndash82 4 Li J Wu Y Xiao JJ The impact of digital finance on household consumption Evidence from China Econ Model 2020 86

317ndash326 5 Lenka SK Barik R Has expansion of mobile phone and internet use spurred financial inclusion in the SAARC countries

Financ Innov 2018 4 1ndash19 6 Turvey CG Xiong X Financial inclusion financial education and e-commerce in rural China Agribusiness 2017 33 279ndash285 7 Zhang Q Posso A Thinking inside the Box A closer look at financial inclusion and household income J Dev Stud 2019 55

1616ndash1631 8 Hojjati SN Rabi AR Effects of Iranian online behavior on the acceptance of internet banking J Asian Bus Stud 2013 7 123ndash

139 9 Mu HL Lee YC Examining the influencing factors of third-party mobile payment adoption A comparative study of Alipay

and WeChat pay J Inform Syst 2017 26 257ndash294 10 Ren JZ Sun H Acceptance behavior of internet wealth management based on user risk perception The case of Alibabarsquos

Yuebao In Proceedings of the 2017 International Conference on Management Engineering Software Engineering and Service Sciences Wuhan China 14ndash16 January 2017

11 Lele U Goswami S The fourth industrial revolution agricultural and rural innovation and implications for public policy and investments A case of India Agric Econ 2017 48 87ndash100

12 Qi JQ Zheng XY Guo HD The formation of Taobao villages in China China Econ Rev 2019 53 106ndash127 13 Liu M Zhang Q Gao S Huang JK The spatial aggregation of rural e-commerce in China An empirical investigation into

Taobao Villages J Rural Stud 2020 80 403-417 14 Chitura T Mupemhi S Dube T Bolongkikit J Barriers to electronic commerce adoption in small and medium enterprises

A critical literature review J Int Bank Commer 2008 13 1ndash13 15 Malhotra R Malhotra DK The impact of internet and e-commerce on the evolving business models in the financial services

industry Int J Electron Bus 2006 4 56ndash82

References1 Liao GK Yao DQ Hu ZH The spatial effect of the efficiency of regional financial resource allocation from the perspective of

internet finance Evidence from Chinese provinces Emerg Mark Financ Trade 2020 56 1ndash13 [CrossRef]2 Lei Y Policy Discussion of Internet Finance in China BOFIT Policy Brief Institute for Economies in Transition Bank of Finland

25 November 2014 Available online httpsheldahelsinkifibofbitstreamhandle12345678913462bpb13145b15dpdfsequence=1ampisAllowed=y (accessed on 20 April 2021)

3 Ren BY Li LY Zhao HM Zhou YB The financial exclusion in the development of digital finance A study based on surveydata in the JingJinJi rural area Singap Econ Rev 2018 63 65ndash82 [CrossRef]

4 Li J Wu Y Xiao JJ The impact of digital finance on household consumption Evidence from China Econ Model 2020 86317ndash326 [CrossRef]

5 Lenka SK Barik R Has expansion of mobile phone and internet use spurred financial inclusion in the SAARC countriesFinanc Innov 2018 4 1ndash19 [CrossRef]

6 Turvey CG Xiong X Financial inclusion financial education and e-commerce in rural China Agribusiness 2017 33 279ndash285[CrossRef]

7 Zhang Q Posso A Thinking inside the Box A closer look at financial inclusion and household income J Dev Stud 2019 551616ndash1631 [CrossRef]

8 Hojjati SN Rabi AR Effects of Iranian online behavior on the acceptance of internet banking J Asian Bus Stud 2013 7123ndash139 [CrossRef]

9 Mu HL Lee YC Examining the influencing factors of third-party mobile payment adoption A comparative study of Alipayand WeChat pay J Inform Syst 2017 26 257ndash294

10 Ren JZ Sun H Acceptance behavior of internet wealth management based on user risk perception The case of AlibabarsquosYuebao In Proceedings of the 2017 International Conference on Management Engineering Software Engineering and ServiceSciences Wuhan China 14ndash16 January 2017

11 Lele U Goswami S The fourth industrial revolution agricultural and rural innovation and implications for public policy andinvestments A case of India Agric Econ 2017 48 87ndash100 [CrossRef]

12 Qi JQ Zheng XY Guo HD The formation of Taobao villages in China China Econ Rev 2019 53 106ndash127 [CrossRef]13 Liu M Zhang Q Gao S Huang JK The spatial aggregation of rural e-commerce in China An empirical investigation into

Taobao Villages J Rural Stud 2020 80 403ndash417 [CrossRef]14 Chitura T Mupemhi S Dube T Bolongkikit J Barriers to electronic commerce adoption in small and medium enterprises A

critical literature review J Int Bank Commer 2008 13 1ndash1315 Malhotra R Malhotra DK The impact of internet and e-commerce on the evolving business models in the financial services

industry Int J Electron Bus 2006 4 56ndash82 [CrossRef]16 Patel VB Asthana AK Patel KJ Patel KM A study on adoption of e-commerce practices among the Indian farmers with

specific reference to north Gujarat region Inter J Commer Bus Manag 2016 9 1ndash7 [CrossRef]17 Chiu YP Lo SK Hsieh AY Hwang YJ Exploring why people spend more time shopping online than in offline stores

Comput Hum Behav 2019 95 24ndash30 [CrossRef]18 Wu LY Chen KY Chen PY Cheng SL Perceived value transaction cost and repurchase-intention in online shopping A

relational exchange perspective J Bus Res 2014 67 2768ndash2776 [CrossRef]19 Baourakis G Kourgiantakis M Migdalas A The impact of e-commerce on agro-food marketing The case of agricultural

cooperatives firms and consumers in Crete Br Food J 2002 104 580ndash590 [CrossRef]

20 Treiblmaier H Pinterits A Floh A Success factors of internet payment systems Inter J Electron Bus 2008 6 369ndash385[CrossRef]

21 Xu NN Shi JY Rong Z Yuan Y Financial literacy and formal credit accessibility Evidence from informal businesses inChina Financ Res Lett 2020 36 101327 [CrossRef]

22 Lee S Evaluation of mobile application in userrsquos perspective Case of P2P lending apps in fintech industry KSII Trans InternetInf Syst 2017 11 1105ndash1117

23 Lam LT Lam MK The association between financial literacy and problematic internet shopping in a multinational sampleAddict Behav Rep 2017 6 123ndash127 [CrossRef] [PubMed]

24 Navickas M Gudaitis T Krajnakova E Influence of financial literacy on management of personal finances in a younghousehold Bus Theory Pract 2014 15 32ndash40 [CrossRef]

25 Kolodinsky JM Hogarth JM Hilgert MA The adoption of electronic banking technologies by US consumers Inter J BankMark 2004 22 238ndash259 [CrossRef]

26 Xie P Zou CW Studies on the mode of internet finance J Financ Res 2012 12 11ndash2227 Wu XQ Internet finance The logic of growth Financ Trade Econ 2015 2 5ndash1528 Tufano P Consumer finance Annu Rev Financ Econ 2009 1 227ndash247 [CrossRef]29 Lo SK Hsieh AY Chiu YP Why expect lower prices online Empirical examination in online and store-based retailers Inter

J Electron Commer Stud 2014 5 27ndash37 [CrossRef]30 Patnaik BCM Sethy PK An empirical study on NPAs in working capital loan of Gramya banks TRANS Asian J Mark Manag

Res 2013 2 1ndash1331 Lee E Lee B Herding behavior in online P2P lending An empirical investigation Electron Commer Res Appl 2012 11 495ndash503

[CrossRef]32 Van Rooij M Lusardi A Alessie R Financial literacy and stock market participation J Financ Econ 2011 101 449ndash472

[CrossRef]33 Yang S Chen SX Li B The role of business and friendships on WeChat business An emerging business model in China J

Glob Mark 2016 29 174ndash187 [CrossRef]34 Lv ZP Jin Y Huang JH How do sellers use live chat to influence consumer purchase decisions in China Electron Commer

Res Appl 2018 28 102ndash113 [CrossRef]35 Bai B Law R Wen I The impact of website quality on customer satisfaction and purchase intentions Evidence from Chinese

online visitors Int J Hosp Manag 2008 27 391ndash402 [CrossRef]36 Rahimnia F Hassanzadeh JF The impact of website content dimension and e-trust on e-marketing effectiveness The case of

Iranian commercial saffron corporations Inf Manag 2013 50 240ndash247 [CrossRef]37 Panda A The status of working capital and its relationship with sales Int J Commer Manag 2012 22 36ndash52 [CrossRef]38 Hu L Lopez RA Zeng Y The impact of credit constraints on the performance of Chinese agricultural wholesalers Appl Econ

2019 51 3864ndash3875 [CrossRef]39 Miller M Godfrey N Leacutevesque B Stark E The Case for Financial Literacy in Developing Countries Promoting Access to Finance by

Empowering Consumers World Bank DFID OECD and CGAP Joint Note Washington DC USA 200940 Becerril J Abdulai A The impact of improved maize varieties on poverty in Mexico A propensity score-matching approach

World Dev 2010 38 1024ndash1035 [CrossRef]41 Morgan PJ Long TQ Financial literacy financial inclusion and savings behavior in Laos J Asian Econ 2020 68 101197

[CrossRef]42 Staiger D Stock JH Instrumental Variables Regression with Weak Instruments Econometrica 1997 65 557ndash586 [CrossRef]43 Heckman JJ Vytlacil EJ Econometric evaluation of social programs part II Using the marginal treatment effect to organize

alternative econometric estimators to evaluate social programs and to forecast their effects in new environments In Handbook ofEconometrics Elsevier Amsterdam The Netherlands 2007 pp 4875ndash5143

44 Rosenbaum PR Rubin DB Constructing a control group using multivariate matched sampling methods that incorporate thepropensity score Am Stat 1985 39 33ndash38

45 Rosenbaum PR Rubin DB The central role of the propensity score in observational studies for causal effects Biometrika 198370 41ndash55 [CrossRef]

46 Baron RM Kenny DA The moderator-mediator variable distinction in social psychological research Conceptual strategicand statistical considerations J Pers Soc Psychol 1986 51 1173ndash1182 [CrossRef] [PubMed]

47 Rodgers S Harris MA Gender and e-commerce An exploratory study J Advert Res 2003 43 322ndash329 [CrossRef]48 Kim JB An empirical study on consumer first purchase intention in online shopping Integrating initial trust and TAM Electron

Commer Res 2012 12 125ndash150 [CrossRef]49 Li XL Troutt MD Brandyberry AA Wang T Decision factors for the adoption and continued use of online direct sales

channels among SMEs J Assoc Inf Syst 2011 12 1ndash31 [CrossRef]50 Modigliani F Cao SL The Chinese saving puzzle and the life-cycle hypothesis J Econ Lit 2004 42 145ndash170 [CrossRef]51 Bucher-Koenen T Lusardi A Financial Literacy and Retirement Planning in Germany J Pension Econ Financ 2011 10 565ndash584

[CrossRef]

52 Poon WC Yong GFD Lam WHP An insight into the attributes influencing the acceptance of internet banking Theconsumersrsquo perspective Int J Serv Stand 2009 5 81ndash94 [CrossRef]

53 Sekyi S Abu BM Nkegbe PK Effects of farm credit access on agricultural commercialization in Ghana Empirical evidencefrom the northern Savannah ecological zone Afr Dev Rev 2020 32 150ndash162 [CrossRef]

54 Huang JK Impacts of COVID-19 on agriculture and rural poverty in China J Integr Agr 2020 19 2849ndash2853 [CrossRef]

Introduction

Literature Review and Hypothesis

Development of the Definition of Digital Finance

Nexus between Online Purchases and Participation in the Digital Financial Market

Nexus between Online Sales and Participation in the Digital Financial Market

Model Specification

Modelling the Adoption Decision of E-Commerce

Modelling the Impacts of E-Commerce Adoption on Engaging in Digital Financial Market

PSM Method

Instrument Variable Estimation

Mediation Model

Data and Variables

Data Source and Descriptive Statistics

Dependent Variable

Treatment Variables

Channel Variable

Control Variables

Empirical Results and Discussion

Determinants of the Adoption of Online Purchases and Sales

Impact of E-Commerce Adoption on Usage of Digital Finance

The PSM Estimation Results

The IV Estimation Results

Robustness Checks

Rosenbaum Bound Sensitivity Analysis

Superposition Effect

Potential Impact Pathways

Heterogeneous Treatment Effects

Conclusions and Implications

References

digital financial exclusion [4] especially in terms of adopting the products and servicesof digital wealth management and digital credit [56] Widespread exclusion from digitalfinance which intensifies the Matthew effect of the rural financial market has become aconsiderable barrier against the expansion of inclusive finance in rural areas in the digitaleconomy era [7]

Little attention has yet been paid to the differences in the adoption of multiple digitalfinancial products among rural residents brought by e-commerce adoption Consumersrsquodemographic and socio-economic characteristics and the regional financial environmenthave been the primary focal points of previous research associated with their exclusionfrom individual digital financial products or services [8ndash10] These discussions above havebeen mainly restricted to the traditional offline transaction scenes in the massesrsquo dailyproduction and life Indeed for rural residents the changes arising from using digitalpayments digital wealth management and digital credit have been increasingly prominentin the era of e-commerce [3] but the impact of e-commerce adoption on farmersrsquo usage ofmultiple digital financial products has been rarely discussed in existing studies

Nowadays the boom of e-commerce is widespread in rural areas in China and a rangeof revenues brought about by e-commerce adoption has triggered much attention fromscholars [11ndash13] It is obvious that innovations in digital finance like online payment tech-nology provide indispensable support for the effective functioning of e-commerce [414] Italso can be seen that the diffusion of e-commerce inevitably involves many financial issueswith regard to payment transactions credit accessibility and capital management [15]However few studies have explored whether the e-commerce adoption for both onlineproduct sellers and purchasers can effectively relieve their exclusion from and increasetheir participation in the digital financial market particularly in regard to digital wealthmanagement and digital credit Quantifying the causal relationship between farmersrsquoadoption of e-commerce and digital finance empirically faces challenges caused by omittedunobservable variables sample self-selection and reverse causality with cross-sectiondata used Therefore with the propensity score matching (PSM) method and instrumentvariable (IV) approach employed to identify the causality and address endogeneity biaswe investigate whether e-commerce adoption by farmers drives their usage of multipledigital financial products How does such an impact takes place and which groups offarmers divided by their characteristics (eg education levels skills training experienceoccupation type) are most affected

A comprehensive and adequate assessment of the impact of e-commerce adoptionon farmersrsquo engaging in the digital financial market would not be accomplished withoutdistinguishing the difference between online purchases and online sales [16] Previousstudies have primarily observed the effects of consumersrsquo online purchases which includea lower degree of information asymmetry less time and labor costs for transactions andmore economic benefits for the purchasers [1718] Meanwhile some studies have high-lighted the influence of producersrsquo online sales which involve wider sales channels agreater sales volume and more sales flexibility for the sellers [19] In view of the distinctdifferences between online purchases and online sales in essence [20] the difference be-tween their impacts on the use of multiple digital financial products or services cannot beignored Additionally due to the fact that purchasing the means of production and sellingproducts are the two most critical and common economic activities infiltrating farmersrsquo en-trepreneurship it therefore makes more sense to concentrate on the relationship betweenentrepreneurial farmersrsquo e-commerce adoption and their usage of multiple digital financialproducts [21] A farmer was defined as an entrepreneur if he or she established and wasengaged in one of these activities small handicraft operations enterprise operations farmoperations cooperative management business services etc

Indeed the use of digital financial products and services might be directly or indirectlyrelated to transactions on e-commerce platforms Digital payment services such as WeChatAlipay and Bestpay have become the main tools for farmersrsquo online transactions andconsumption [3] Moreover digital wealth management resources like Yursquoe Bao and apps

Mean SD

Table 1 Cont

Mean SD

33 PSM Method

35 Mediation Model

44 Channel Variable

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Mean SD

Table 1 Cont

Mean SD

33 PSM Method

35 Mediation Model

44 Channel Variable

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Mean SD

Table 1 Cont

Mean SD

33 PSM Method

35 Mediation Model

44 Channel Variable

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Mean SD

Table 1 Cont

Mean SD

33 PSM Method

35 Mediation Model

44 Channel Variable

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Mean SD

Table 1 Cont

Mean SD

33 PSM Method

35 Mediation Model

44 Channel Variable

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Table 1 Cont

Mean SD

33 PSM Method

35 Mediation Model

44 Channel Variable

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

35 Mediation Model

44 Channel Variable

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

44 Channel Variable

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

44 Channel Variable

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Digital payments

NNM 00573 (00344)

00892 (00331)

00928CM 00902

(00391)00918 (00335)

NNMC 00895 (00374)

00877 (00332)

KM 00689 (00370)

00995 (00328)

SM 00568 (00304)

00900 (00309)

MM 00670 (00365)

00986 (00352)

NNM 00875 (00451)

01090 (00382)

01059CM 00982

(00453)01007 (00373)

NNMC 00875 (00474)

00997 (00382)

KM 00821 (00436)

01035 (00368)

SM 00789 (00468)

01034 (00449)

MM 01040 (00443)

01196 (00432)

Digital credit

NNM 00677 (00349)

00498 (00288)

00530CM 00710

(00350)00526 (00279)

NNMC 00737 (00366)

00494 (00288)

KM 00611 (00338)

00509 (00276)

SM 00650 (00354)

00574 (00293)

MM 00618 (00350)

00578 (00314)

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

(1) (2) (3)

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

VariablesDigital

DigitalCredit

DigitalPayments

DigitalWealth

Management

DigitalCredit

(1) (2) (3) (4) (5) (6) (7)

00910 (00322)

00485 (00209)

04987 (01422)

00738 (00435)

00531 (00213)

00337 (00181)

00581 (00098)

00726 (00093)

00422 (00078)

01151 (00268)

00634 (00346)

04264 (01289)

00912 (00350)

00665 (00285)

00342 (00201)

00426 (00084)

00731 (00091)

00415 (00076)

Observations 832 832 832 832 832 832 832

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

(1) (2) (3) (4) (5) (6) (7) (8)

Onlinepurchases

00812 (00444)

00367(00614)

00551(00432)

00732(00462)

00371(00520)

00440(00470)

00732 (00432)

00867 (00511)

00947(00678)

01113 (00562)

00681(00746)

01216 (00545)

00218(00716)

01747 (00587)

01007 (00590)

00309(00526)

00687 (00414)

00261(00446)

01124 (00511)

00212(00556)

00847 (00470)

(00454)01123 (00606)

00388(00552)

00989 (00515)

00730 (00431)

01239 (00645)

00620(00658)

01331 (00464)

01322 (00443)

00443(00566)

01693 (00571)

00724(00756)

01715 (00454)

00405(00893)

00856 (00410)

00505 (00299)

00105(00420)

00571(00397)

00184(00463)

00473(00368)

01005(00663)

00856 (00410)

Observations 566 266 391 441 566 266 324 508

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Digital payments

NNM 09357 08378 00980 (00350)

CM 09328 08571 00757 (00353)NNMC 09328 08535 00793 (00343)KM 09357 08697 00660 (00383)SM 09362 08675 00687 (00316)MM 09362 08582 00780 (00387)

NNM 04286 03378 00908 (00531)

CM 04254 03423 00831 (00499)NNMC 04254 03345 00909 (00525)KM 04286 03455 00831 (00502)SM 04255 03429 00826 (00498)MM 04256 03221 01035 (00547)

Digital credit

NNM 02000 01153 00847 (00411)

CM 01940 01278 00662 (00388)NNMC 01940 01218 00722 (00405)KM 02000 01291 00709 (00340)SM 01986 01319 00667 (00390)MM 01986 01324 00662 (00344)

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

[CrossRef]

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Introduction




Model Specification



PSM Method


Mediation Model

Data and Variables


Dependent Variable

Treatment Variables

Channel Variable

Control Variables






Robustness Checks






References

Documents

Impact of E-Commerce Adoption on Farmers Participation in