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Electronic Commerce Research
Erschienen in: Electronic Commerce Research 4/2020

24.10.2018

Smartphone use and income growth in rural China: empirical results and policy implications

verfasst von: Wanglin Ma, R. Quentin Grafton, Alan Renwick

Erschienen in: Electronic Commerce Research | Ausgabe 4/2020

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Abstract

The diffusion of mobile information and communication technologies (ICTs) has important implications for rural economic development. Many studies have investigated the potential contributions of mobile ICTs to agricultural production and poverty reduction, but have failed to consider the wider income effects of the use of updated ICTs, such as smartphones. Our findings, based on household-level survey data from rural China and an endogenous switching regression model, indicate that gender, farmers’ education, farm size, and off-farm work participation are the main drivers of smartphone use. Further, we find that smartphone use increases farm income, off-farm income and household income substantially and there is a statistically significant difference in the income effects between male and female users of smartphones. Possible policy interventions from our findings include: (1) support to increase use of smartphones by households headed by women; and (2) a ‘win–win’ approach to rural development that includes improved hard (roads) and soft (education) infrastructure and encompasses the increased use of smartphones so as to increase both off-farm employment opportunities and farm and off-farm incomes.
Vorheriger Artikel Content shared between banks and users on the social ecosystem: an inductive exploratory inquiry
Nächster Artikel When to launch a sales promotion for online fashion products? An empirical study

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Fußnoten
1
A mobile phone can only be used for voice communication and text/SMS, while a smartphone has a touch-screen, provides Internet access and enables the installation of software applications (“APPs”) such as an iPhone or an Android [15].
 
2
The advanced communication services include remote video communication, online payment, weather report, email trading, web browsing, shopping, travel booking, financial transfer, photography, healthcare, entertainment and social networks.
 
3
For example, using a propensity score matching (PSM) method, Kirui et al. [19] found that the use of mobile phone-based money transfer services exerts a positive and significant impact on farm income in Kenya. In their recent study on Thailand, Vietnam, Laos and Cambodia, using an endogenous treatment effects (ETE) model, Hübler and Hartje [20] found a positive nexus between smartphone ownership and household income. While these studies are valuable, they have methodological drawbacks. In the case of the PSM approach it is dependent on the conditional independent assumption, which states that the unobservable error in the treatment equation is uncorrelated to the potential outcomes, while the weakness of the ETE approach is that it fails to estimate the average effect of the treatment on the treated.
 
4
For the purpose of robustness check and comparison, we also present the results estimated from an inverse probability weighted estimator with regression adjustment and a propensity score matching model.
 
5
Two strategies are used to test the validity of the instrument variable. First, we run a probit model for smartphone use and three OLS regression models, respectively, for farm income, off-farm income and household income with inclusion of the social network variable. The results, which are available on request, show that the social network variable has a statistically significant impact on smartphone use while it has no statistically significant impact on the three income outcomes. Second, a Pearson correlation analysis was used, which shows that the social network variable has a statistically significant correlation with smartphone use while it has insignificant correlations with income indicators. These findings support the decision to employ the social network variable as a valid instrument.
 
6
In the present study, 318 smartphone users are identified, and the remaining are considered as non-users of smartphones. Among those who did not use smartphones (175 households), the survey results show that 170 of them used traditional mobile phones and the rest (only 5 households) did not use either a traditional mobile phone or a smartphone.
 
7
For the purpose of comparison, and as a check on the robustness of our findings, the results estimated using the IPWRA and PSM methods are presented in the Appendix.
 
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Metadaten
Titel
Smartphone use and income growth in rural China: empirical results and policy implications
verfasst von
Wanglin Ma
R. Quentin Grafton
Alan Renwick
Publikationsdatum
24.10.2018
Verlag
Springer US
Erschienen in
Electronic Commerce Research / Ausgabe 4/2020
Print ISSN: 1389-5753
Elektronische ISSN: 1572-9362
DOI
https://doi.org/10.1007/s10660-018-9323-x

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