IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i13p7143-d582093.html
   My bibliography  Save this article

Empirical Study on the Effects of Technology Training on the Forest-Related Income of Rural Poverty-Stricken Households—Based on the PSM Method

Author

Listed:
  • Rong Zhao

    (Research Institute of Forest Policy and Information, Chinese Academy of Forestry, Beijing 100091, China)

  • Xiaolu Qiu

    (Research Institute of Forest Policy and Information, Chinese Academy of Forestry, Beijing 100091, China)

  • Shaozhi Chen

    (Research Institute of Forest Policy and Information, Chinese Academy of Forestry, Beijing 100091, China)

Abstract

The implementation of technology training is essential to promote the commercialization of research achievements, and plays a crucial role in poverty alleviation in China. Based on the microcosmic survey data of farmers in four poverty-stricken counties officially assisted by National Forestry and Grassland Administration, the effects of technology training on forest-related income of rural poverty-stricken households is analyzed by using Propensity Score Matching (PSM) method. The study found that after eliminating the deviation from the self-selection and the endogenous issues, the forestry technology training has increased the total forest-related family income and forestry production and operation income by 3.09 times and 2.82 times, respectively. The effect of technology training on income increase is remarkable. Besides, the behavior of poor farmers participating in forestry technology training is significantly affected by the following factors, such as gender, age, family size, managed forestland area, whether they held forest tenure/equity certificate, whether they joined forestry professional cooperatives, and whether they cooperated with forestry enterprises. In order to further improve the effect of technology in poverty alleviation, the following policy recommendations are proposed, including: (1) to encourage poverty-stricken households to actively participate in forestry technology training; (2) to establish a diversified system of forestry technology training; and (3) to ensure the training content is based on the actual needs of the poor.

Suggested Citation

  • Rong Zhao & Xiaolu Qiu & Shaozhi Chen, 2021. "Empirical Study on the Effects of Technology Training on the Forest-Related Income of Rural Poverty-Stricken Households—Based on the PSM Method," Sustainability, MDPI, vol. 13(13), pages 1-12, June.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7143-:d:582093
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/13/7143/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/13/7143/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yao Pan & Stephen C Smith & Munshi Sulaiman, 2018. "Agricultural Extension and Technology Adoption for Food Security: Evidence from Uganda," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(4), pages 1012-1031.
    2. Fernandez-Cornejo, Jorge & Hendricks, Chad & Mishra, Ashok K., 2005. "Technology Adoption and Off-Farm Household Income: The Case of Herbicide-Tolerant Soybeans," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 37(3), pages 1-15, December.
    3. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    4. Zemo, Kahsay Haile & Termansen, Mette, 2018. "Farmers’ willingness to participate in collective biogas investment: A discrete choice experiment study," Resource and Energy Economics, Elsevier, vol. 52(C), pages 87-101.
    5. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    6. Schreinemachers, Pepijn & Wu, Mei-huey & Uddin, Md. Nasir & Ahmad, Shahabuddin & Hanson, Peter, 2016. "Farmer training in off-season vegetables: Effects on income and pesticide use in Bangladesh," Food Policy, Elsevier, vol. 61(C), pages 132-140.
    7. He, Xu & Sakurai, Takeshi, 2019. "Transferability of Green Revolution in Sub-Saharan Africa: Impact Assessment of Rice Production Technology Training in Northern Ghana," Japanese Journal of Agricultural Economics (formerly Japanese Journal of Rural Economics), Agricultural Economics Society of Japan (AESJ), vol. 21.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rong Zhao & Tianyu Jia & He Li, 2023. "Could the Sloping Land Conversion Program Promote Farmers’ Income in Rocky Desertification Areas?—Evidence from China," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    2. Yuewen Huo & Songlin Ye & Zhou Wu & Fusuo Zhang & Guohua Mi, 2022. "Barriers to the Development of Agricultural Mechanization in the North and Northeast China Plains: A Farmer Survey," Agriculture, MDPI, vol. 12(2), pages 1-14, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    2. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    3. Rajeev Dehejia, 2013. "The Porous Dialectic: Experimental and Non-Experimental Methods in Development Economics," WIDER Working Paper Series wp-2013-011, World Institute for Development Economic Research (UNU-WIDER).
    4. Kölling, Arnd, 2013. "Wirtschaftsförderung, Produktivität und betriebliche Arbeitsnachfrage - Eine Kausalanalyse mit Betriebspaneldaten -," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79843, Verein für Socialpolitik / German Economic Association.
    5. Iacus, Stefano M. & Porro, Giuseppe, 2007. "Missing data imputation, matching and other applications of random recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 773-789, October.
    6. Sascha O. Becker & Marco Caliendo, 2007. "Sensitivity analysis for average treatment effects," Stata Journal, StataCorp LP, vol. 7(1), pages 71-83, February.
    7. Islam, Asadul & Nguyen, Chau & Smyth, Russell, 2015. "Does microfinance change informal lending in village economies? Evidence from Bangladesh," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 141-156.
    8. Aki Kangasharju, 2007. "Do Wage Subsidies Increase Employment in Subsidized Firms?," Economica, London School of Economics and Political Science, vol. 74(293), pages 51-67, February.
    9. Korir, Lilian & Rizov, Marian & Ruto, Eric, 2020. "Food security in Kenya: Insights from a household food demand model," Economic Modelling, Elsevier, vol. 92(C), pages 99-108.
    10. Frölich, Markus & Michaelowa, Katharina, 2004. "Peer effects and textbooks in primary education: Evidence from francophone sub-Saharan Africa," HWWA Discussion Papers 311, Hamburg Institute of International Economics (HWWA).
    11. Polyakov, Maksym & Iftekhar, Md Sayed & Fogarty, James & Buurman, Joost, 2022. "Renewal of waterways in a dense city creates value for residents," Ecological Economics, Elsevier, vol. 199(C).
    12. Hujer, Reinhard & Thomsen, Stephan L., 2010. "How do the employment effects of job creation schemes differ with respect to the foregoing unemployment duration?," Labour Economics, Elsevier, vol. 17(1), pages 38-51, January.
    13. Tatsuo Ushijima, 2016. "Diversification, Organization, and Value of the Firm," Financial Management, Financial Management Association International, vol. 45(2), pages 467-499, May.
    14. Juan Díaz & Miguel Jaramillo, 2006. "An Evaluation of the Peruvian "Youth Labor Training Program"-PROJOVEN," OVE Working Papers 1006, Inter-American Development Bank, Office of Evaluation and Oversight (OVE).
    15. Ravallion, Martin & Galasso, Emanuela & Lazo, Teodoro & Philipp, Ernesto, 2001. "Do workfare participants recover quickly from retrenchment?," Policy Research Working Paper Series 2672, The World Bank.
    16. Aydemir, Abdurrahman B. & Kırdar, Murat G., 2017. "Quasi-experimental impact estimates of immigrant labor supply shocks: The role of treatment and comparison group matching and relative skill composition," European Economic Review, Elsevier, vol. 98(C), pages 282-315.
    17. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand," NBER Technical Working Papers 0330, National Bureau of Economic Research, Inc.
    18. Paweł Strawiński, 2013. "Controlling for overlap in matching," Working Papers 2013-10, Faculty of Economic Sciences, University of Warsaw.
    19. Romi Kher & Shu Yang & Scott L. Newbert, 2023. "Accelerating emergence: the causal (but contextual) effect of social impact accelerators on nascent for-profit social ventures," Small Business Economics, Springer, vol. 61(1), pages 389-413, June.
    20. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7143-:d:582093. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.