Skip to main content

Advertisement

SpringerLink
Log in

Smartphone-based information acquisition and wheat farm performance: insights from a doubly robust IPWRA estimator

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

This study examines the impact of smartphone-based information acquisition on crop yields, net returns, return on investment (ROI), and production costs, using survey data collected from 558 wheat farmers in rural China. We employ a double-robust inverse probability weighted regression adjustment estimator to address the potential selection bias issue. The results reveal that information acquisition via smartphones significantly increases wheat yields, net returns, and ROI by 7%, 31%, and 39%, respectively, and it has a negative but insignificant impact on production costs. These results largely echo the results estimated from propensity score matching and endogenous switching regression models. In general, our findings suggest that policy interventions targeting to boost farm economic performance should consider distributing agricultural production and marketing information via smartphones.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Ideally, smartphone-based information acquisition is expected to reduce production costs. It is worth to mention here that smartphone-based information acquisition might increase production costs if it induces farmers to use expensive inputs such as drought-tolerant wheat varieties, organic fertilizers and green pesticides.

  2. As rightly pointed by an anonymous reviewer, the crop price might have no variations if the marketing price of crops for the whole market is fixed at a certain time. Therefore, the crop price is discussed in Sect. 2.1 in consideration of the theoretical framework generalization, but it is not considered as an outcome variable in our empirical analysis.

  3. Although IPWRA estimator has advantages over PSM model, RA estimator, and IPW estimator, it cannot fully address the selection bias. In fact, IPWRA estimator only mitigates selection bias arising from observed factors [51, 65].

References

  1. Nakasone, E., Torero, M., & Minten, B. (2014). The power of information: The ICT revolution in agricultural development. Annual Review of Resource Economics, 6(1), 533–550. https://doi.org/10.1146/annurev-resource-100913-012714

    Article  Google Scholar 

  2. Nakasone, E., & Torero, M. (2016). A text message away: ICTs as a tool to improve food security. Agricultural Economics, 47(S1), 49–59. https://doi.org/10.1111/agec.12314

    Article  Google Scholar 

  3. Krell, N. T., Giroux, S. A., Guido, Z., Hannah, C., Lopus, S. E., Caylor, K. K., & Evans, T. P. (2020). Smallholder farmers’ use of mobile phone services in central Kenya. Climate and Development. https://doi.org/10.1080/17565529.2020.1748847

    Article  Google Scholar 

  4. Omulo, G., & Kumeh, E. M. (2020). Farmer-to-farmer digital network as a strategy to strengthen agricultural performance in Kenya: A research note on ‘Wefarm’ platform. Technological Forecasting and Social Change, 158, 120120. https://doi.org/10.1016/j.techfore.2020.120120

    Article  Google Scholar 

  5. Haile, M. G., Wossen, T., & Kalkuhl, M. (2019). Access to information, price expectations and welfare: The role of mobile phone adoption in Ethiopia. Technological Forecasting and Social Change, 145, 82–92. https://doi.org/10.1016/j.techfore.2019.04.017

    Article  Google Scholar 

  6. Birthal, P. S., Kumar, S., Negi, D. S., & Roy, D. (2015). The impacts of information on returns from farming: Evidence from a nationally representative farm survey in India. Agricultural Economics (United Kingdom), 46(4), 549–561. https://doi.org/10.1111/agec.12181

    Article  Google Scholar 

  7. 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. https://doi.org/10.1111/1477-9552.12194

    Article  Google Scholar 

  8. Shiferaw, B., Kebede, T., Kassie, M., & Fisher, M. (2015). Market imperfections, access to information and technology adoption in Uganda: Challenges of overcoming multiple constraints. Agricultural Economics (United Kingdom), 46(4), 475–488. https://doi.org/10.1111/agec.12175

    Article  Google Scholar 

  9. World Bank. (2018). Information and communications for development 2018: Data-driven development. World Bank.

    Book  Google Scholar 

  10. Camacho, A., & Conover, E. (2019). The impact of receiving SMS price and weather information on small scale farmers in Colombia. World Development, 123, 104596. https://doi.org/10.1016/j.worlddev.2019.06.020

    Article  Google Scholar 

  11. Mariscal Avilés, J., Benítez Larghi, S., & Martínez Aguayo, M. A. (2016). The informational life of the poor: A study of digital access in three Mexican towns. Telecommunications Policy, 40(7), 661–672. https://doi.org/10.1016/j.telpol.2015.11.001

    Article  Google Scholar 

  12. 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. https://doi.org/10.22004/AG.ECON.173644

    Article  Google Scholar 

  13. Wyche, S., & Steinfield, C. (2016). Why don’t farmers use cell phones to access market prices? Technology affordances and barriers to market information services adoption in rural Kenya. Information Technology for Development, 22(2), 320–333. https://doi.org/10.1080/02681102.2015.1048184

    Article  Google Scholar 

  14. Van Campenhout, B. (2017). There is an app for that? The impact of community knowledge workers in Uganda. Information, Communication & Society, 20(4), 530–550. https://doi.org/10.1080/1369118X.2016.1200644

    Article  Google Scholar 

  15. Fan, Q., & Salas Garcia, V. B. (2018). Information access and smallholder farmers’ market participation in Peru. Journal of Agricultural Economics, 69(2), 476–494. https://doi.org/10.1111/1477-9552.12243

    Article  Google Scholar 

  16. Kiiza, B., & Pederson, G. (2012). ICT-based market information and adoption of agricultural seed technologies: Insights from Uganda. Telecommunications Policy, 36(4), 253–259. https://doi.org/10.1016/j.telpol.2012.01.001

    Article  Google Scholar 

  17. Van Campenhout, B., Spielman, D. J., & Lecoutere, E. (2021). Information and communication technologies to provide agricultural advice to smallholder farmers: Experimental evidence from Uganda. American Journal of Agricultural Economics, 103(1), 317–337. https://doi.org/10.1002/ajae.12089

    Article  Google Scholar 

  18. Carrión-Yaguana, V. D., Alwang, J., & Barrera, V. H. (2020). Promoting behavioral change using text messages: A case study of blackberry farmers in Ecuador. Journal of Agricultural and Applied Economics, 52(3), 398–419. https://doi.org/10.1017/aae.2020.7

    Article  Google Scholar 

  19. Cole, S. A., & Fernando, A. (2018). ‘Mobile’izing agricultural advice: Technology adoption, diffusion and sustainability. Working Paper. https://doi.org/10.2139/ssrn.2179008.

  20. 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. https://doi.org/10.1016/j.worlddev.2014.06.011

    Article  Google Scholar 

  21. Casaburi, L., Kremer, M., Mullainathan, S., & Ramrattan, R. (2014). Harnessing ICT to increase agricultural production: Evidence from Kenya. Harvard Business School Working Paper, September 2013 (pp. 1–33).

  22. Sekabira, H., & Qaim, M. (2017). Mobile money, agricultural marketing, and off-farm income in Uganda. Agricultural Economics (United Kingdom), 48(5), 597–611. https://doi.org/10.1111/agec.12360

    Article  Google Scholar 

  23. Okello, J. J., Kirui, O. K., & Gitonga, Z. M. (2020). Participation in ICT-based market information projects, smallholder farmers’ commercialisation, and agricultural income effects: Findings from Kenya. Development in Practice, 30(8), 1043–1057. https://doi.org/10.1080/09614524.2020.1754340

    Article  Google Scholar 

  24. Aker, J. C., Ghosh, I., & Burrell, J. (2016). The promise (and pitfalls) of ICT for agriculture initiatives. Agricultural Economics (United Kingdom), 47, 35–48. https://doi.org/10.1111/agec.12301

    Article  Google Scholar 

  25. Kabunga, N. S., Dubois, T., & Qaim, M. (2012). Heterogeneous information exposure and technology adoption: The case of tissue culture bananas in Kenya. Agricultural Economics (United Kingdom), 43(5), 473–486. https://doi.org/10.1111/j.1574-0862.2012.00597.x

    Article  Google Scholar 

  26. Hübler, M., & Hartje, R. (2016). Are smartphones smart for economic development? Economics Letters, 141, 130–133. https://doi.org/10.1016/j.econlet.2016.02.001

    Article  Google Scholar 

  27. 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, 52, 80–94. https://doi.org/10.1016/j.chieco.2018.06.002

    Article  Google Scholar 

  28. Ma, W., Grafton, R. Q., & Renwick, A. (2020). Smartphone use and income growth in rural China: Empirical results and policy implications. Electronic Commerce Research, 20(4), 713–736. https://doi.org/10.1007/s10660-018-9323-x

    Article  Google Scholar 

  29. Kiberiti, B. S., Sanga, C. A., Mussa, M., Tumbo, S. D., Mlozi, M. R. S., & Haug, R. (2016). Farmers’ access and use of mobile phones for improving the coverage of agricultural extension service. International Journal of ICT Research in Africa and the Middle East, 5(1), 35–57. https://doi.org/10.4018/ijictrame.2016010103

    Article  Google Scholar 

  30. Liao, C. N., Chen, Y. J., & Tang, C. S. (2019). Information provision policies for improving farmer welfare in developing countries: Heterogeneous farmers and market selection. Manufacturing and Service Operations Management, 21(2), 254–270. https://doi.org/10.1287/msom.2016.0599

    Article  Google Scholar 

  31. Ma, W., & Abdulai, A. (2017). The economic impacts of agricultural cooperatives on smallholder farmers in rural China. Agribusiness, 33(4), 537–551. https://doi.org/10.1002/agr.21522

    Article  Google Scholar 

  32. Kleemann, L., Abdulai, A., & Buss, M. (2014). Certification and access to export markets: Adoption and return on investment of organic-certified pineapple farming in Ghana. World Development, 64, 79–92. https://doi.org/10.1016/j.worlddev.2014.05.005

    Article  Google Scholar 

  33. Liu, R., Gao, Z., Nayga, R. M., Shi, L., Oxley, L., & Ma, H. (2020). Can “green food” certification achieve both sustainable practices and economic benefits in a transitional economy? The case of kiwifruit growers in Henan Province, China. Agribusiness, 36(4), 675–692. https://doi.org/10.1002/agr.21641

    Article  Google Scholar 

  34. Ma, W., Renwick, A., & Bicknell, K. (2018). Higher intensity, higher profit? Empirical evidence from dairy farming in New Zealand. Journal of Agricultural Economics, 69(3), 739–755. https://doi.org/10.1111/1477-9552.12261

    Article  Google Scholar 

  35. Manda, J., Gardebroek, C., Kuntashula, E., & Alene, A. D. (2018). Impact of improved maize varieties on food security in Eastern Zambia: A doubly robust analysis. Review of Development Economics, 22(4), 1709–1728. https://doi.org/10.1111/rode.12516

    Article  Google Scholar 

  36. Adolwa, I. S., Schwarze, S., & Buerkert, A. (2019). Impacts of integrated soil fertility management on yield and household income: The case of Tamale (Ghana) and Kakamega (Kenya). Ecological Economics, 161, 186–192. https://doi.org/10.1016/j.ecolecon.2019.03.023

    Article  Google Scholar 

  37. Tambo, J. A., & Mockshell, J. (2018). Differential impacts of conservation agriculture technology options on household income in sub-saharan Africa. Ecological Economics, 151, 95–105. https://doi.org/10.1016/j.ecolecon.2018.05.005

    Article  Google Scholar 

  38. Walker, I., & Zhu, Y. (2018). University selectivity and the relative returns to higher education: Evidence from the UK. Labour Economics, 53, 230–249. https://doi.org/10.1016/j.labeco.2018.05.005

    Article  Google Scholar 

  39. Wossen, T., Abdoulaye, T., Alene, A., Haile, M. G., Feleke, S., Olanrewaju, A., & Manyong, V. (2017). Impacts of extension access and cooperative membership on technology adoption and household welfare. Journal of Rural Studies, 54, 223–233. https://doi.org/10.1016/j.jrurstud.2017.06.022

    Article  Google Scholar 

  40. CRSY. (2019). China rural statistical yearbook. China Statistics Press.

    Google Scholar 

  41. Zhang, L., & Fan, W. (2020). Rural homesteads withdrawal and urban housing market: A pilot study in China. Emerging Markets Finance and Trade, 56(1), 228–242. https://doi.org/10.1080/1540496X.2018.1556157

    Article  Google Scholar 

  42. Kaske, D., Mvena, Z. S. K., & Sife, A. S. (2018). Mobile phone usage for accessing agricultural information in southern Ethiopia. Journal of Agricultural & Food Information, 19(3), 284–298. https://doi.org/10.1080/10496505.2017.1371023

    Article  Google Scholar 

  43. Li, J., Ma, W., Renwick, A., & Zheng, H. (2020). The impact of access to irrigation on rural incomes and diversification: Evidence from China. China Agricultural Economic Review, 12(4), 705–725. https://doi.org/10.1108/CAER-09-2019-0172

    Article  Google Scholar 

  44. Martey, E., Etwire, P. M., & Kuwornu, J. K. M. (2020). Economic impacts of smallholder farmers’ adoption of drought-tolerant maize varieties. Land Use Policy, 94, 104524. https://doi.org/10.1016/j.landusepol.2020.104524

    Article  Google Scholar 

  45. Fu, X., & Akter, S. (2012). Impact of mobile telephone on the quailty and speed of agricultural extension services delivery: Evidence from rural e-services project in India. In International Association of Agricultural Economists (IAAE) 2012 triennial conference.

  46. McAfee, R. P. (1995). Multiproduct equilibrium price dispersion. Journal of Economic Theory, 67(1), 83–105. https://doi.org/10.1006/jeth.1995.1066

    Article  Google Scholar 

  47. Stigler, G. J. (1961). The economics of information. Journal of Political Economy, 69(3), 213–225. https://doi.org/10.1086/258464

    Article  Google Scholar 

  48. Hao, J., Bijman, J., Gardebroek, C., Heerink, N., Heijman, W., & Huo, X. (2018). Cooperative membership and farmers’ choice of marketing channels—Evidence from apple farmers in Shaanxi and Shandong Provinces, China. Food Policy, 74, 53–64. https://doi.org/10.1016/j.foodpol.2017.11.004

    Article  Google Scholar 

  49. Liu, Y., Ma, W., Renwick, A., & Fu, X. (2019). The role of agricultural cooperatives in serving as a marketing channel: Evidence from low-income regions of Sichuan province in China. International Food and Agribusiness Management Review, 22(2), 265–282. https://doi.org/10.22434/IFAMR2018.0058

    Article  Google Scholar 

  50. Takahashi, K., & Barrett, C. B. (2014). The system of rice intensification and its impacts on household income and child schooling: Evidence from rural Indonesia. American Journal of Agricultural Economics, 96(1), 269–289. https://doi.org/10.1093/ajae/aat086

    Article  Google Scholar 

  51. Kang, J. D. Y., & Schafer, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22(4), 523–539. https://doi.org/10.1214/07-STS227

    Article  Google Scholar 

  52. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press.

    Google Scholar 

  53. Zheng, H., Ma, W., & Li, G. (2020). Adoption of organic soil amendments and its impact on farm performance: Evidence from wheat farmers in China*. Australian Journal of Agricultural and Resource Economics. https://doi.org/10.1111/1467-8489.12406

    Article  Google Scholar 

  54. Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5–86. https://doi.org/10.1257/jel.47.1.5

    Article  Google Scholar 

  55. Stata Press. (2019). Stata treatment-effects reference manual: Potential outcomes/counterfactual outcomes release 16.

  56. Wooldridge, J. M. (2007). Inverse probability weighted estimation for general missing data problems. Journal of Econometrics, 141(2), 1281–1301. https://doi.org/10.1016/j.jeconom.2007.02.002

    Article  Google Scholar 

  57. Cattaneo, M. D. (2010). Efficient semiparametric estimation of multi-valued treatment effects under ignorability. Journal of Econometrics, 155(2), 138–154. https://doi.org/10.1016/j.jeconom.2009.09.023

    Article  Google Scholar 

  58. Raghunathan, K., Kannan, S., & Quisumbing, A. R. (2019). Can women’s self-help groups improve access to information, decision-making, and agricultural practices? The Indian case. Agricultural Economics (United Kingdom), 50(5), 567–580. https://doi.org/10.1111/agec.12510

    Article  Google Scholar 

  59. D’Amato, A., Giaccherini, M., & Zoli, M. (2019). The role of information sources and providers in shaping green behaviors. Evidence from Europe. Ecological Economics, 164, 106292. https://doi.org/10.1016/j.ecolecon.2019.04.004

    Article  Google Scholar 

  60. Larochelle, C., Alwang, J., Travis, E., Barrera, V. H., & Dominguez Andrade, J. M. (2019). Did you really get the message? Using text reminders to stimulate adoption of agricultural technologies. Journal of Development Studies, 55(4), 548–564. https://doi.org/10.1080/00220388.2017.1393522

    Article  Google Scholar 

  61. Imbens, G. W., & Rubin, D. B. (2015). Causal inference: For statistics, social, and biomedical sciences an introduction. Cambridge University Press. https://doi.org/10.1017/CBO9781139025751

    Book  Google Scholar 

  62. Diekmann, F., Loibl, C., & Batte, M. T. (2009). The economics of agricultural information: Factors affecting commercial farmers’ information strategies in ohio. Review of Agricultural Economics, 31(4), 853–872. https://doi.org/10.1111/j.1467-9353.2009.01470.x

    Article  Google Scholar 

  63. Handino, T. D., D’Haese, M., Demise, F., & Tamirat, M. (2019). De-commoditizing Ethiopian coffees after the establishment of the Ethiopian Commodity Exchange: An empirical investigation of smallholder coffee producers in Ethiopia. International Food and Agribusiness Management Review, 22(4), 499–518. https://doi.org/10.22434/IFAMR2018.0047

    Article  Google Scholar 

  64. Simtowe, F., Amondo, E., Marenya, P., Rahut, D., Sonder, K., & Erenstein, O. (2019). Impacts of drought-tolerant maize varieties on productivity, risk, and resource use: Evidence from Uganda. Land Use Policy, 88, 104091. https://doi.org/10.1016/j.landusepol.2019.104091

    Article  Google Scholar 

  65. Mano, Y., Takahashi, K., & Otsuka, K. (2020). Mechanization in land preparation and agricultural intensification: The case of rice farming in the Cote d’Ivoire. Agricultural Economics (United Kingdom), 51(6), 899–908. https://doi.org/10.1111/agec.12599

    Article  Google Scholar 

Download references

Funding

Hongyun Zheng acknowledges the financial support from the National Natural Sciences Foundation of China (Grant No. 71873050). Wanglin Ma acknowledges the funding support from the Chinese Academy of Agricultural Sciences for the Science and Technology Innovation Project (No. IFND2019-3) and the funding support from the National Agriculture Science Data Center for the Food Nutrition and Health project.

Author information

Authors and Affiliations

  1. College of Economics and Management, Huazhong Agricultural University, Wuhan, 430070, China

    Hongyun Zheng

  2. Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China

    Wanglin Ma

  3. Department of Global Value Chains and Trade, Faculty of Agribusiness and Commerce, Lincoln University, Christchurch, New Zealand

    Wanglin Ma

Authors
  1. Hongyun Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wanglin Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wanglin Ma.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 8, 9 and 10.

Table 8 Summary statistics of agricultural inputs
Full size table
Table 9 Average treatment effects of smartphone-based information acquisition on outcome variables using PSM and ESR models
Full size table
Table 10 Average treatment effects of smartphone-based information acquisition on agricultural inputs: IPWRA estimator
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, H., Ma, W. Smartphone-based information acquisition and wheat farm performance: insights from a doubly robust IPWRA estimator. Electron Commer Res 23, 633–658 (2023). https://doi.org/10.1007/s10660-021-09481-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-021-09481-0

Keywords

JEL Classifications

Search

Navigation