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Impacts of Distance Education on Agricultural Performance and Household Income: Micro-Evidence from Peri-Urban Districts in Beijing

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  • Jianxin Guo

    (Institute of Agricultural Information and Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Songqing Jin

    (China Academy for Rural Development, Zhejiang University, Hangzhou 310058, China
    Department of Agricultural, Food, and Resource Economics, Michigan State University, East Lansing, MI 48824, USA)

  • Lei Chen

    (Institute of Agricultural Information and Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Jichun Zhao

    (Institute of Agricultural Information and Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

Abstract

Information communication technology (ICT) has changed the traditional agricultural extension service mode worldwide. This paper examines the effects of the Rural Distance Education Project (RDEP) on the household income, agricultural productivity, and off-farm employment of farmers in peri-urban areas in Beijing. Using the survey data of 783 randomly selected farm households from 54 villages in three Beijing peri-urban districts in 2014, and the propensity score matching method (PSM), we find that the RDEP has a significant and positive effect on agricultural productivity and input use. Meanwhile, the program’s effects are heterogeneous across districts and households. For example, the RDEP has significant impacts on several outcome indicators, such as agricultural labor productivity (at a 5% level of significance), agricultural land productivity (at a 10% level), and input use intensity (at a 1% level) in Tongzhou (an agriculturally more important district, with a more intensive RDEP usage), but none of these effects is significant in Pinggu district. Furthermore, the RDEP is found to have bigger, and statistically more significant effects, for households with junior high school education than for those with either lower or higher than junior high school education. Furthermore, the RDEP is more effective for households with more assets than those with fewer assets. These results point toward the importance of using a rural distance education program as an effective extension service, and the need to take community and individual characteristics into account in the implementation and design of future programs.

Suggested Citation

  • Jianxin Guo & Songqing Jin & Lei Chen & Jichun Zhao, 2018. "Impacts of Distance Education on Agricultural Performance and Household Income: Micro-Evidence from Peri-Urban Districts in Beijing," Sustainability, MDPI, vol. 10(11), pages 1-19, October.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:3945-:d:179196
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    1. Shimamoto, Daichi & Yamada, Hiroyuki & Gummert, Martin, 2015. "Mobile phones and market information: Evidence from rural Cambodia," Food Policy, Elsevier, vol. 57(C), pages 135-141.
    2. Aker, Jenny C. & Ksoll, Christopher, 2016. "Can mobile phones improve agricultural outcomes? Evidence from a randomized experiment in Niger," Food Policy, Elsevier, vol. 60(C), pages 44-51.
    3. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    4. Abadie, Alberto & Imbens, Guido W., 2011. "Bias-Corrected Matching Estimators for Average Treatment Effects," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 1-11.
    5. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    6. Muto, Megumi & Yamano, Takashi, 2009. "The Impact of Mobile Phone Coverage Expansion on Market Participation: Panel Data Evidence from Uganda," World Development, Elsevier, vol. 37(12), pages 1887-1896, December.
    7. Tadesse, Getaw & Bahiigwa, Godfrey, 2015. "Mobile Phones and Farmers’ Marketing Decisions in Ethiopia," World Development, Elsevier, vol. 68(C), pages 296-307.
    8. Ellen Verhofstadt & Miet Maertens, 2014. "Smallholder cooperatives and agricultural performance in Rwanda: do organizational differences matter?," Agricultural Economics, International Association of Agricultural Economists, vol. 45(S1), pages 39-52, November.
    9. Ogutu, Sylvester Ochieng & Okello, Julius Juma & Otieno, David Jakinda, 2014. "Impact of Information and Communication Technology-Based Market Information Services on Smallholder Farm Input Use and Productivity: The Case of Kenya," World Development, Elsevier, vol. 64(C), pages 311-321.
    10. Robert Jensen, 2007. "The Digital Provide: Information (Technology), Market Performance, and Welfare in the South Indian Fisheries Sector," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(3), pages 879-924.
    11. Uwe Deichmann & Aparajita Goyal & Deepak Mishra, 2016. "Will digital technologies transform agriculture in developing countries?," Agricultural Economics, International Association of Agricultural Economists, vol. 47(S1), pages 21-33, November.
    12. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    13. Thia Hennessy & Doris Läpple & Brian Moran, 2016. "The Digital Divide in Farming: A Problem of Access or Engagement?," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 38(3), pages 474-491.
    14. Esther Duflo & Michael Kremer & Jonathan Robinson, 2011. "Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya," American Economic Review, American Economic Association, vol. 101(6), pages 2350-2390, October.
    15. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    16. Jock R. Anderson, 2004. "Agricultural Extension: Good Intentions and Hard Realities," The World Bank Research Observer, World Bank, vol. 19(1), pages 41-60.
    17. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    18. Aparajita Goyal, 2010. "Information, Direct Access to Farmers, and Rural Market Performance in Central India," American Economic Journal: Applied Economics, American Economic Association, vol. 2(3), pages 22-45, July.
    19. Becerril, Javier & Abdulai, Awudu, 2010. "The Impact of Improved Maize Varieties on Poverty in Mexico: A Propensity Score-Matching Approach," World Development, Elsevier, vol. 38(7), pages 1024-1035, July.
    20. Labonne, Julien & Chase, Robert S., 2009. "The power of information : the impact of mobile phones on farmers'welfare in the Philippines," Policy Research Working Paper Series 4996, The World Bank.
    21. Cole, Shawn A. & Fernando, A. Nilesh, 2013. "The Value of Advice: Evidence from Mobile Phone-Based Agricultural Extension," 2013 Fourth International Conference, September 22-25, 2013, Hammamet, Tunisia 160520, African Association of Agricultural Economists (AAAE).
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