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.
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Notes
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].
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.
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.
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.
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.
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.
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.
References
Poushter, J. (2016). Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies. Pew Research Center, 1–5. http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/. Accessed 22 Feb 2016.
Lio, M., & Liu, M. C. (2006). ICT and agricultural productivity: Evidence from cross-country data. Agricultural Economics, 34(3), 221–228.
Ogutu, S. O., Okello, J. J., & Otieno, D. J. (2014). Impact of information and communication technology-based market information services on smallholder farm input use and productivity: The case of Kenya. World Development, 64, 311–321.
Fu, X., & Akter, S. (2016). The impact of mobile phone technology on agricultural extension services delivery: evidence from India. The Journal of Development Studies, 52(11), 1561–1576.
Aker, J., & Ksoll, C. (2016). Can mobile phones improve agricultural outcomes? Evidence from a randomized experiment in Niger. Food Policy, 60, 44–51.
Abdul-Salam, Y., & Phimister, E. (2017). Efficiency effects of access to information on small-scale agriculture: Empirical evidence from Uganda using stochastic frontier and IRT models. Journal of Agricultural Economics, 68(2), 494–517.
Tadesse, G., & Bahiigwa, G. (2015). Mobile phones and farmers’ marketing decisions in Ethiopia. World Development, 68, 296–307.
Muto, M., & Yamano, T. (2009). The impact of mobile phone coverage expansion on market participation: Panel data evidence from Uganda. World Development, 37(12), 1887–1896.
Zanello, G. (2012). Mobile phones and radios: Effects on transactions costs and market participation for households in Northern Ghana. Journal of Agricultural Economics, 63(3), 694–714.
Shimamoto, D., Yamada, H., & Gummert, M. (2015). Mobile phones and market information: Evidence from rural Cambodia. Food Policy, 57, 135–141.
de Silva, H., & Ratnadiwakara, D. (2008). Using ICT to reduce transaction costs in agriculture through better communication: A case-study from Sri Lanka. Colombo: LIRNEasia.
Sekabira, H., & Qaim, M. (2017). Mobile money, agricultural marketing, and off-farm income in Uganda. Agricultural Economics, 48(5), 597–611.
Fan, Q., & Salas Garcia, V. B. (2018). Information access and smallholder farmers’ market participation in Peru. Journal of Agricultural Economics, 69(2), 476–494.
Dammert, A. C., Galdo, J., & Galdo, V. (2013). Digital labor-market intermediation and job expectations: Evidence from a field experiment. Economics Letters, 120(1), 112–116.
Hartje, R., & Hübler, M. (2017). Smartphones support smart labour. Applied Economics Letters, 24(7), 467–471.
Acker, J. C., & Mbiti, I. M. (2010). Mobile phones and economic development in Africa. Journal of Economic Perspectives, 24(3), 207–232.
Lee, W. H., Miou, C. S., Kuan, Y. F., Hsieh, T. L., & Chou, C. M. (2018). A peer-to-peer transaction authentication platform for mobile commerce with semi-offline architecture. Electronic Commerce Research, 18(2), 413–431.
Park, M., Yoo, H., Kim, J., & Lee, J. (2018). Why do young people use fitness apps? Cognitive characteristics and app quality. Electronic Commerce Research. https://doi.org/10.1007/s10660-017-9282-7.
CNNIC. (2018). Statistical report on internet development in China. Beijing: China Internet Network Information Center.
Kirui, O. K., Okello, J. J., Nyikal, R. A., & Njiraini, G. W. (2013). Impact of mobile phone-based money transfer services in agriculture: Evidence from Kenya. Quarterly Journal of International Agriculture, 52(2), 141–162.
Poushter, J. (2017). China outpaces India in internet access, smartphone ownership. Pew Research Center. Retrieved from http://www.pewresearch.org/fact-tank/2017/03/16/china-outpaces-india-in-internet-access-smartphone-ownership/. Accessed 16 Mar 2017.
Sylvester, G. (2016). Use of mobile phones by the rural poor: Gender perspectives from selected Asian countries. Colombo: The Food and Agriculture Organization of the United Nations, LIRNEasia, and International Development Research Centre.
Sekabira, H., & Qaim, M. (2017). Can mobile phones improve gender equality and nutrion? Panel data evidence from farm households in Uganda. Food Policy, 73, 95–103.
Hübler, M., & Hartje, R. (2016). Are smartphones smart for economic development? Economics Letters, 141(March), 130–133.
Khanal, A. R., & Mishra, A. K. (2016). Financial performance of small farm business households: The role of internet. China Agricultural Economic Review, 8(4), 553–571.
Kiiza, B., & Pederson, G. (2012). ICT-based market information and adoption of agricultural seed technologies: Insights from Uganda. Telecommunications Policy, 36(4), 253–259.
Munyegera, G. K., & Matsumoto, T. (2016). Mobile money, remittances, and household welfare: Panel evidence from Rural Uganda. World Development, 79, 127–137.
Ma, W., Renwick, A., Nie, P., Tang, J., & Cai, R. (2018). Off-farm work, smartphone use and household income: Evidence from rural China. China Economic Review. https://doi.org/10.1016/j.chieco.2018.06.002.
Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). Cambridge, MA: MIT Press.
Wossen, T., Abdoulaye, T., Alene, A., Haile, M. G., Feleke, S., Olanrewaju, A., et al. (2017). Impacts of extension access and cooperative membership on technology adoption and household welfare. Journal of Rural Studies, 54, 223–233.
Ma, W., Renwick, A., & Bicknell, K. (2018). Higher Intensity, Higher Profit? Empirical Evidence from Dairy Farming in New Zealand. Journal of Agricultural Economics. https://doi.org/10.1111/1477-9552.12261.
Lokshin, M., & Sajaia, Z. (2004). Maximum likelihood estimation of endogenous switching regression models. The Stata Journal, 4, 282–289.
Ma, W., & Abdulai, A. (2016). Does cooperative membership improve household welfare? Evidence from apple farmers in China. Food Policy, 58, 94–102.
Khonje, M., Manda, J., Alene, A. D., & Kassie, M. (2015). Analysis of adoption and impacts of improved maize varieties in Eastern Zambia. World Development, 66, 695–706.
Wooldridge, J. M. (2015). Control function methods in applied econometrics. The Journal of Human Resources, 50(2), 420–445.
Phimister, E., & Roberts, D. (2006). The effect of off-farm work on the intensity of agricultural production. Environmental & Resource Economics, 34(4), 493–515.
Rathod, P., Chander, M., & Bardhan, D. (2016). Adoption status and influencing factors of mobile telephony in dairy sector: A study in four states of India. Agricultural Economics Research Review, 29(1), 15–26.
XINHUANET. (2018). Internet Plus Agriculture model to promote integrated rural development. Retrieved from http://www.xinhuanet.com/english/2018-06/27/c_137285120.htm. Accessed 27 June 2018.
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The authors gratefully acknowledge the financial support from the Faculty of Agribusiness and Commerce at Lincoln University within the Seed Fund Project (INT5056).
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Ma, W., Grafton, R.Q. & Renwick, A. Smartphone use and income growth in rural China: empirical results and policy implications. Electron Commer Res 20, 713–736 (2020). https://doi.org/10.1007/s10660-018-9323-x
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DOI: https://doi.org/10.1007/s10660-018-9323-x