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Determinants of loan demand in agriculture: empirical evidence from Germany

Author

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  • Wilm Fecke
  • Jan-Henning Feil
  • Oliver Musshoff

Abstract

Purpose - The purpose of this paper is to empirically investigate the influencing factors of loan demand in agriculture. With the structural changes that agriculture is undergoing and the accordingly higher financing requirements and volumes, the analysis of loan demand in agriculture is of particular interest. Design/methodology/approach - Detailed actual loan data at farm level, which is provided by a major German development bank for the agricultural sector, is used for the analysis. The data set covers the period from 2010 to 2014 and consists of 68,430 observations. Due to the data structure, an ordinary least square regression is conducted with the loan amount as the dependent variable. Many explanatory variables are included, such as the interest rate, the intended use of the loan, grace periods, the gross value added (GVA) and the business climate index for agriculture. Findings - Amongst others, the authors find that interest rate, GVA, grace periods and farmers’ business expectations have significant effects on the loan demand in agriculture. According to the results, the interest rate has a significant negative effect, whereas the granted grace periods, the GVA in agriculture and farmers’ business expectations have significant positive effects on the loan demand. Originality/value - This paper investigates the determinants of loan demand in agriculture in a developed country by using unique and comprehensive data at loan and farm level. Amongst others, elasticities of loan demand in agriculture are determined.

Suggested Citation

  • Wilm Fecke & Jan-Henning Feil & Oliver Musshoff, 2016. "Determinants of loan demand in agriculture: empirical evidence from Germany," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 76(4), pages 462-476, November.
    • Handle: RePEc:eme:afrpps:afr-05-2016-0042
    DOI: 10.1108/AFR-05-2016-0042
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    Citations

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    Cited by:

    1. Ifft, Jennifer E. & Kuhns, Ryan & Patrick, Kevin T., 2017. "Predicting Credit Demand with ARMS: A Machine Learning Approach," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258134, Agricultural and Applied Economics Association.
    2. Olena Serhiienko & Maryna Tatar & Lidiya Guryanova & Olena Shapran & Mykhailo Bril, 2023. "Improvement of Financial Instruments of the Agricultural Sector and Food Security Efficiency Increasing," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 115-142.
    3. Geoffroy Enjolras & Philippe Madiès, 2019. "The determinants of loan acceptance: a case study of French farms," Economics Bulletin, AccessEcon, vol. 39(1), pages 358-371.
    4. Tim Ölkers & Oliver Mußhoff, 2024. "Exploring the role of interest rates, macroeconomic environment, agricultural cycle, and gender on loan demand in the agricultural sector: Evidence from Mali," Agribusiness, John Wiley & Sons, Ltd., vol. 40(2), pages 484-512, April.
    5. Steele C. West & Amin W. Mugera & Ross S. Kingwell, 2021. "Drivers of farm business capital structure and its speed of adjustment: evidence from Western Australia’s Wheatbelt," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(2), pages 391-412, April.
    6. Spiegel, Alisa & Severini, Simone & Britz, Wolfgang & Coletta, Attilio, 2020. "Step-by-step development of a model simulating returns on farm from investments: the example of hazelnut plantation in Italy: The example of hazelnut plantation in Italy," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 9(1), April.

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