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. 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.
    2. 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.
    3. 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.
    4. J. Paul Elhorst, 2014. "Spatial Panel Data Models," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 37-93, Springer.
    5. 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.
    6. 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.
    7. 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.
    8. 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 9781107029514, November.
    9. 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.
    10. 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).
    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)

    Citations

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


    Cited by:

    1. Lingui Qin & Yan Zhang & Yige Wang & Xinning Pan & Zhe Xu, 2024. "Research on the Impact of Digital Green Finance on Agricultural Green Total Factor Productivity: Evidence from China," Agriculture, MDPI, vol. 14(7), pages 1-23, July.

    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. 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).
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Thanh Ngo & Kan Wai Hong Tsui, 2022. "Estimating the confidence intervals for DEA efficiency scores of Asia-Pacific airlines," Operational Research, Springer, vol. 22(4), pages 3411-3434, September.
    9. Richard Adjei Dwumfour & Eric Fosu Oteng-Abayie & Emmanuel Kwasi Mensah, 2022. "Bank efficiency and the bank lending channel: new evidence," Empirical Economics, Springer, vol. 63(3), pages 1489-1542, September.
    10. Wanglin Ma & Kathryn Bicknell & Alan Renwick, 2019. "Feed use intensification and technical efficiency of dairy farms in New Zealand," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(1), pages 20-38, January.
    11. Sebastian Lakner & Thelma Brenes‐Muñoz & Bernhard Brümmer, 2017. "Technical Efficiency in Chilean Agribusiness Industry: A Metafrontier Approach," Agribusiness, John Wiley & Sons, Ltd., vol. 33(3), pages 302-323, June.
    12. Rendao Ye & Yue Qi & Wenyan Zhu, 2023. "Impact of Agricultural Industrial Agglomeration on Agricultural Environmental Efficiency in China: A Spatial Econometric Analysis," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    13. Skevas, Ioannis, 2020. "Inference in the spatial autoregressive efficiency model with an application to Dutch dairy farms," European Journal of Operational Research, Elsevier, vol. 283(1), pages 356-364.
    14. Aparicio, Juan & Zofío, José L., 2023. "Decomposing profit change: Konüs, Bennet and Luenberger indicators," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    15. Alem, Habtamu, 2020. "Performance of the Norwegian dairy farms: A dynamic stochastic approach," Research in Economics, Elsevier, vol. 74(3), pages 263-271.
    16. Nguyen, Hoa-Thi-Minh & Do, Huong & Kompas, Tom, 2021. "Economic efficiency versus social equity: The productivity challenge for rice production in a ‘greying’ rural Vietnam," World Development, Elsevier, vol. 148(C).
    17. Matthew J. Higgins & Donald J. Lacombe & Briana S. Stenard & Andrew T. Young, 2021. "Evaluating the effects of Small Business Administration lending on growth," Small Business Economics, Springer, vol. 57(1), pages 23-45, June.
    18. Parmeter, Christopher F., 2021. "Is it MOLS or COLS?," Efficiency Series Papers 2021/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    19. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    20. Yingying Shi, 2024. "Corruption, technical efficiency and total factor productivity growth: empirical evidence from China," Economic Change and Restructuring, Springer, vol. 57(6), pages 1-24, December.

    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.