IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2022i1p529-d1018242.html
   My bibliography  Save this article

Does Agricultural Credit Input Promote Agricultural Green Total Factor Productivity? Evidence from Spatial Panel Data of 30 Provinces in China

Author

Listed:
  • Fuwei Wang

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

  • Lei Du

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

  • Minghua Tian

    (School of Economics and Management, Beijing Forestry University, Beijing 100083, China)

Abstract

Improving agricultural green total factor productivity is crucial to promoting high-quality agricultural development. This paper selects the panel data of 30 provinces in China from 2009 to 2020 and uses the super-efficiency SBM model with undesirable outputs to measure the agricultural green total factor productivity of all regions in China. On this basis, this paper uses the panel data fixed-effect model and spatial Durbin model to empirically discuss the impact of agricultural credit input on agricultural green total factor productivity and its spatial spillover effect. The main conclusions are as follows: First, from 2009 to 2020, the average values of agricultural green total factor productivity in national, eastern, central, and western regions are 0.8909, 0.9977, 0.9231, and 0.8068, respectively, and the agricultural green total factor productivity needs to be further improved. Second, the agricultural green total factor productivity presents a significant and positive spatial correlation, and the spatial distribution of agricultural green total factor productivity is not random and irregular. Third, agricultural credit input can significantly promote agricultural green total factor productivity in the local region, but it hinders the improvement of agricultural green total factor productivity in the adjacent regions. Fourth, the impact of agricultural credit input on the agricultural green total factor productivity and its spillover effect has a significant regional heterogeneity. This paper believes that paying attention to the spatial spillover effect of agricultural total factor productivity, optimizing the structure and scale of agricultural credit input, and formulating reasonable agricultural credit policies can improve agricultural green total factor productivity.

Suggested Citation

  • Fuwei Wang & Lei Du & Minghua Tian, 2022. "Does Agricultural Credit Input Promote Agricultural Green Total Factor Productivity? Evidence from Spatial Panel Data of 30 Provinces in China," IJERPH, MDPI, vol. 20(1), pages 1-22, December.
    • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:529-:d:1018242
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/1/529/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/1/529/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hu, Yue & Liu, Chang & Peng, Jiangang, 2021. "Financial inclusion and agricultural total factor productivity growth in China," Economic Modelling, Elsevier, vol. 96(C), pages 68-82.
    2. J. Paul Elhorst, 2014. "Spatial Panel Data Models," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 37-93, Springer.
    3. Yang Yang & Heng Ma & Guosong Wu, 2022. "Agricultural Green Total Factor Productivity under the Distortion of the Factor Market in China," Sustainability, MDPI, vol. 14(15), pages 1-15, July.
    4. Adjognon, Serge G. & Liverpool-Tasie, Lenis Saweda O. & Reardon, Thomas A., 2017. "Agricultural input credit in Sub-Saharan Africa: Telling myth from facts," Food Policy, Elsevier, vol. 67(C), pages 93-105.
    5. Li, Xingguang & Huo, Xuexi, 2021. "Impacts of land market policies on formal credit accessibility and agricultural net income: Evidence from China's apple growers," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    6. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107609464.
    7. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    8. Liping Zhu & Rui Shi & Lincheng Mi & Pu Liu & Guofeng Wang, 2022. "Spatial Distribution and Convergence of Agricultural Green Total Factor Productivity in China," IJERPH, MDPI, vol. 19(14), pages 1-16, July.
    9. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    10. Zhihai Yang & Dong Wang & Tianyi Du & Anlu Zhang & Yixiao Zhou, 2018. "Total-Factor Energy Efficiency in China’s Agricultural Sector: Trends, Disparities and Potentials," Energies, MDPI, vol. 11(4), pages 1-16, April.
    11. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    Full references (including those not matched with items on IDEAS)

    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. Markose Chekol Zewdie & Michele Moretti & Daregot Berihun Tenessa & Zemen Ayalew Ayele & Jan Nyssen & Enyew Adgo Tsegaye & Amare Sewnet Minale & Steven Van Passel, 2021. "Agricultural Technical Efficiency of Smallholder Farmers in Ethiopia: A Stochastic Frontier Approach," Land, MDPI, vol. 10(3), pages 1-17, March.
    2. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    3. Ke Liu & Yurong Qiao & Qian Zhou, 2021. "Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR," IJERPH, MDPI, vol. 18(8), pages 1-22, April.
    4. Yakun Wang & Jingli Jiang & Dongqing Wang & Xinshang You, 2022. "Can Mechanization Promote Green Agricultural Production? An Empirical Analysis of Maize Production in China," Sustainability, MDPI, vol. 15(1), pages 1-24, December.
    5. Antony Andrews & Omphile Temoso & Sean Kimpton, 2021. "Persistent and Transient Inefficiency of Australian States and Territories in Providing Public Hospital Services: An Application of Bayesian Stochastic Finite Mixture Frontier Analysis," Economic Papers, The Economic Society of Australia, vol. 40(2), pages 104-115, June.
    6. Ajayi, V. & Pollitt, M., 2022. "Changing times: Incentive regulation, corporate reorganisations, and productivity in the Great Britain’s gas networks," Cambridge Working Papers in Economics 2254, Faculty of Economics, University of Cambridge.
    7. Muktar Geleto & Mohammed Essa, 2022. "Analysis of Red Pepper Production Risk Adjusted Technical Efficiency: The Case Of Lanfuro District In Siltie Zone, Southern Ethiopia," International Journal of Business and Management, International Institute of Social and Economic Sciences, vol. 10(1), pages 30-58, May.
    8. Sabrina Auci & Laura Castellucci & Manuela Coromaldi, 2021. "How does public spending affect technical efficiency? Some evidence from 15 European countries," Bulletin of Economic Research, Wiley Blackwell, vol. 73(1), pages 108-130, January.
    9. Wang, Xiuli, 2023. "Exploring the role of resource industry dependence and green finance in green development efficiency in the context of post-Covid-19 period," Resources Policy, Elsevier, vol. 85(PB).
    10. Rocha, Jr., Adauto B. & Fulginiti, Lilyan E. & Perrin, Richard K. & Walters, Cory G., 2022. "What is the value of crop insurance for Nebraskan farmers?," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322529, Agricultural and Applied Economics Association.
    11. Magambo, Isaiah & Dikgang, Johane & Gelo, Dambala & Tregenna, Fiona, 2021. "Environmental and Technical Efficiency in Large Gold Mines in Developing Countries," MPRA Paper 108068, University Library of Munich, Germany.
    12. Roland Banya & Nicholas Biekpe, 2018. "Banking efficiency and its determinants in selected frontier african markets," Economic Change and Restructuring, Springer, vol. 51(1), pages 69-95, February.
    13. Kodjo Adandohoin, 2021. "Tax transition in developing countries: do value added tax and excises really work?," International Economics and Economic Policy, Springer, vol. 18(2), pages 379-424, May.
    14. Nikolskiy, Ilya & Furmanov, Kirill, 2023. "Assessing the accuracy of efficiency rankings obtained from a stochastic frontier model," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 71, pages 128-142.
    15. Tommaso Agasisti & Sabine Gralka, 2019. "The transient and persistent efficiency of Italian and German universities: a stochastic frontier analysis," Applied Economics, Taylor & Francis Journals, vol. 51(46), pages 5012-5030, October.
    16. MAIMOUNA DIAKITE & Jean-François BRUN, 2016. "Tax Potential and Tax Effort: An Empirical Estimation for Non-Resource Tax Revenue and VAT’s Revenue," EcoMod2016 9537, EcoMod.
    17. Sabine Gralka, 2018. "Persistent inefficiency in the higher education sector: evidence from Germany," Education Economics, Taylor & Francis Journals, vol. 26(4), pages 373-392, July.
    18. Cheng, Xiaomei & Bjørndal, Endre & Lien, Gudbrand & Bjørndal, Mette, 2015. "Productivity Development for Norwegian Electricity Distribution Companies 2004-2013," Discussion Papers 2015/27, Norwegian School of Economics, Department of Business and Management Science.
    19. Xuelan Li & Rui Guan, 2023. "How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?," IJERPH, MDPI, vol. 20(2), pages 1-23, January.
    20. Ablam Estel APETI & Bao-We-Wal BAMBE & Jean Louis COMBES, 2022. "On the Macroeconomic Effects of Fiscal Reforms : Fiscal Rules and Public Expenditure Efficiency," LEO Working Papers / DR LEO 2985, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

    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:jijerp:v:20:y:2022:i:1:p:529-:d:1018242. 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.