International Journal of Business and Risk Management
ISSN (Print): ISSN Pending ISSN (Online): ISSN Pending Website: https://www.sciepub.com/journal/ijbrm Editor-in-chief: Abdelkader Derbali
Open Access
Journal Browser
Go
International Journal of Business and Risk Management. 2024, 5(1), 1-10
DOI: 10.12691/ijbrm-5-1-1
Open AccessArticle

Moderating Effects of Extraneous Shocks on the Relationship Between Default Determinants and Agribusiness Loan Default Rate in Agricultural Finance Corporation, Mount Kenya Region

M’Muruku Salesio Miriti1, , Mwirigi Rael Nkatha2 and Gathungu Geofrey Kingori3

1Department of AGEC, AGBM & AGED, Chuka University, Chuka, Kenya

2Department of Plant Sciences, Chuka University, Chuka, Kenya

3Department of Business Administration, Chuka University, Chuka, Kenya

Pub. Date: July 04, 2024

Cite this paper:
M’Muruku Salesio Miriti, Mwirigi Rael Nkatha and Gathungu Geofrey Kingori. Moderating Effects of Extraneous Shocks on the Relationship Between Default Determinants and Agribusiness Loan Default Rate in Agricultural Finance Corporation, Mount Kenya Region. International Journal of Business and Risk Management. 2024; 5(1):1-10. doi: 10.12691/ijbrm-5-1-1

Abstract

Moderating the indicators of default in agribusiness loans presents shifts in the performance of credit markets. Mount Kenya region of Agricultural Finance Corporation (AFC) registered a high default rate of 20.33% in repayment of agribusiness loans, attributable to the moderator effect, inter alia. The comparison of 20.33% with a 10% benchmark set by Central Bank of Kenya for all types of loans is unfavourable. This study aimed at analysing the effects of the moderator (extraneous shocks) on the relationship between the borrower-lender determinants of default and the Agribusiness Loan Default Rate (ALDR), in AFC Mount Kenya region. Using a descriptive research design in the region with 11 branches and a population of 3,002 agribusiness borrowers, 300 respondents was drawn as sample, using a systematic random sampling technique with an interval of ten borrowers. Primary data was collected using a structured questionnaire and analysed using Statistical Package for Social Sciences (SPSS V.27). Use of stepwise econometric regression model on relationship between the borrower-lender indicators and ALDR revealed as follows: borrower’s socioeconomic indicators and the extraneous shocks, with introduction of the moderator, there was 2.8% increase in the prediction power; for enterprise decision making indicators there was 0.1% increase in ALDR; and in lender behavioural characteristics indicators, a 3.7% increase in the prediction power was registered. The ANOVA Analysis on the overall significance indicated the existence of a statistically and significant interaction between respective indicators and the extraneous shocks on the ALDR (F=37.988, F=29.659, F=72.092 and F=56.887; p-value=0.00<0.05). The study contributes to the existing body of knowledge in risk management and agricultural finance by accentuating the far-reaching threats of extraneous shocks in constraining the sustainable production process, thus ALDR. The study recommends that it is strategically imperative to close the risk gaps facing the farming communities thus: players to adopt coping strategies- interventionist policies pointing to partnerships for training, facilitating access to effective technologies and subsidizing relevant agricultural insurance to affordability; besides, policy directions should encourage proactive engagement in good agricultural practices and ubiquitous risk mitigation; borrowers should be proactive and alert by providing for contingencies and eventually absorbing agro-risks; penultimately, lenders should be collaboratively engaged in supervised lending to ensure timely interventions and individualized involvement of borrowers, thus compatible with contemporary agribusiness realities; ultimately, credit stakeholders should craft advocacy and inclusion mechanisms, aiming to cushion the societies from incipient distresses and devastating constraints.

Keywords:
Agribusiness loan default rate moderator extraneous shocks repayment

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  Oxfarm. (2021). What is the difference between agriculture and agribusiness? http://oxfarm.co.ke/agri_biz-insights/what-is-the-difference-between-agriculture -and- agribusiness/19-08-21.
 
[2]  Mulunga, B. (2021). Why Agribusiness Should be part of Kenya’s Economic Agenda. https:// www.kenyacic.org/ 2021/08/why-agribusiness-should-be-part-of-kenyas- economic-agenda/.
 
[3]  Wanjira, K., Mburu, I., Nzuve, M., Makokha, S., Emongor, R. & Taracha, C. (2023) Impact of climate-smart maize varieties on household income among smallholder farmers in Kenya: The case of Embu County.
 
[4]  General, A. (2022). Report of the Auditor General on Agricultural Finance Corporation for the Year Ended 30 June 2021. Office of the Auditor General.
 
[5]  Seven, U. & Tumen, S. (2020). Agricultural credits and agricultural productivity: Cross-country evidence. The Singapore Economic Review, 65(supp01), 161-183.
 
[6]  Musembi, E. (2019). Demand for agricultural credit by rural smallholder farmers: a case of climate smart agriculture villages in Nyando basin, Kenya (Doctoral thesis).
 
[7]  Mutyasira, V., Hoag, D. & Pendell, D. (2018). The adoption of sustainable agricultural practices by smallholder farmers in Ethiopian highlands: An integrative approach. Cogent Food & Agriculture, 4(1), 1552439.
 
[8]  M'Muruku, S. M., Kingori, G. G., & Mwirigi, R. N. (2023). Effect of Borrower's Socio-Economic Profile on Agribusiness Loans Default Rate in Agricultural Finance Corporation, Mount Kenya Region. Muruku, S., Gathungu, G. & Mwirigi.
 
[9]  Nyebar, A., Obalade, A. & Muzindutsi, P. (2023). Effectiveness of Credit Risks Management Policies Used by Ghanaian Commercial Banks in Agricultural Financing. In Financial Sector Development in Ghana: Exploring Bank Stability, Financing Models, and Development Challenges for Sustainable Financial Markets (pp. 231-264). Cham: Springer International Publishing.
 
[10]  Paudel, P. (2022). Credit risk management in Nepalese cooperative societies. Credit Risk Management, ISBN: - 978-9937-0-5200-9.
 
[11]  Gatimu, E. (2022). Effect of Management Practices on Non-Performing Loans in Deposit Taking Savings and Credit Cooperatives in Kenya-Management Perspective (Doctoral dissertation, JKUAT-COHRED).
 
[12]  Adusei, C. (2017). Determinants of Agribusiness Entities Loan Default in the Tamale Metropolis of Ghana. European Journal of Accounting, Auditing and Finance Research Vol.5 No.3, pp.1- 20, March 2017.
 
[13]  Opa, V. & Tabe-Ebob, T. (2020). The Effects of Loan Default on Commercial Banks Profitability: Case Study BICEC Limbe, Cameroon.
 
[14]  Pohl, C., Schüler, G. & Schiereck, D. (2023). Borrower-and lender-specific determinants in the pricing of sustainability-linked loans. Journal of Cleaner Production, 385, 135652.
 
[15]  Rana, W., Gill, S. & Akram, I. (2023). Policy framework for contract farming: An alternate to Aarthi system in Pakistan, International Food Policy Research Institute (IFPRI).
 
[16]  Ali, D. & Deininger, K. (2022). Institutional determinants of large land-based investments’ performance in Zambia: Does title enhance productivity and structural transformation? World Development, 157, 105932.
 
[17]  Ramanujam, V. & Vidya, K. (2017). A Study on the credit repayment behaviour of borrowers. Int Res J Business and Manage, 10(8), 9-18.
 
[18]  Quaye, F., Nadolnyak, D. & Hartarska, V. (2017). Factors affecting farm loan delinquency in the Southeastern USA. Research in Applied Economics, 9(4).
 
[19]  Yu, L., Song, Y., Wu, H., & Shi, H. (2023). Credit Constraint, Interlinked Insurance and Credit Contract and Farmers’ Adoption of Innovative Seeds-Field Experiment of the Loess Plateau. Land, 12(2), 357.
 
[20]  Dohlman, E. (2020). Risk in agriculture. Economic Research Service US Department.
 
[21]  Naheed, S. (2023). An overview of the influence of climate change on food security and human health. life, 3, 15.
 
[22]  Fairchild, A. J., & McQuillin, S. D. (2010). Evaluating mediation and moderation effects in school psychology: A presentation of methods and review of current practice. Journal of school psychology, 48(1), 53-84.
 
[23]  Baron, R. & Kenny, D. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical.
 
[24]  Chowdhury, H., Malik, I., Sun, H., & Ali, S. (2022). Natural Disasters and Corporate Default Risk. The University of Queensland Brisbane, Queensland, Australia.
 
[25]  Chi, Q., & Li, W. (2017). Economic policy uncertainty, credit risks and banks’ lending decisions: Evidence from Chinese commercial banks. China journal of accounting research, 10(1), 33-50.
 
[26]  Idris, I. T., & Nayan, S. (2016). The moderating role of loan monitoring on the relationship between macroeconomic variables and non-performing loans in association of Southeast Asian nations countries. International Journal of Economics and Financial Issues, 6(2), 402-408.
 
[27]  Mutlu, Ü., & Özer, G. (2021). The moderator effect of financial literacy on the relationship between locus of control and financial behaviour. Kybernetes, 51(3), 1114-1126.
 
[28]  Shatnawi, S., Hanefah, M., & Eldaia, M. (2019). Moderating effect of enterprise risk management on the relationship between board structures and corporate performance. International Journal of Entrepreneurship and Management Practices, 2(6), 01-15.
 
[29]  Zebal, M. & Goodwin, D. (2011). Market Orientation in a Developing Nation-Antecedents, Consequences and the Moderating Effect of Environmental Factors. Marketing Bulletin, 22.
 
[30]  Shehata, M., Abdeljawad, M., Mazouz, A., Aldossary, K., Alsaeed, Y. & Noureldin Sayed, M. (2021). The moderating role of perceived risks in the relationship between financial knowledge and the intention to invest in Saudi Arabian stock market. International Journal of Financial Studies, 9(1), 9.
 
[31]  Fahlenbrach, R., Rageth, K., & Stulz, M. (2021). How valuable is financial flexibility when revenue stops? Evidence from the COVID-19 crisis. The Review of Financial Studies, 34(11), 5474-5521.
 
[32]  Simiyu, N. R. (2018). Project management practices and performance of agricultural projects by community-based organizations in Bungoma county, Kenya (Doctoral dissertation, Doctoral dissertation, Doctoral dissertation, Doctoral Thesis, Kenyatta University, Nairobi, Kenya).
 
[33]  Simple Maps. (2021). Kenya cities database, Pareto software, llc. © 2010-2021. https:// simplemaps.com/ data/ke-cities.
 
[34]  Agricultural Finance Corporation (AFC). (2022). Agricultural Finance Corporation branches, 31st December, 2021. http:// www.agrifinance.org/ branches.
 
[35]  Sirisilla, S. (2023). Bridging the Gap: Overcome these 7 flaws in descriptive research design, https:// www.enago.com/ academy/descriptive-research-design/.
 
[36]  McCombes, S. (2019). How to create a research design. Retrieved from Scribbr: https:// www. scribbr. com/ research-process/research-design.
 
[37]  Mphaka, P. L. (2017). Strategies for Reducing Microfinance Loan Default in Low-Income Markets (Doctoral dissertation, Walden University).
 
[38]  Daniel, W. & Cross, C. (2018). Biostatistics: a foundation for analysis in the health sciences. Wiley. ISBN 978-1-118-30279-8 (cloth).
 
[39]  Snyder, R. D., Ord, J. K., Koehler, A. B., McLaren, K. R., & Beaumont, A. N. (2017). Forecasting compositional time series: A state space approach. International Journal of Forecasting, 33(2), 502-512.
 
[40]  Vaske, J., Beaman, J. & Sponarski, C. (2017). Rethinking internal consistency in Cronbach's alpha. Leisure sciences, 39(2), 163-173.
 
[41]  Cronbach, M. & Hedge, R. (2001). Construct validity in psychological tests.
 
[42]  George, D. & Mallery, P. (2019). IBM SPSS Statistics 25 Step by Step (15th ed.). New York and London: Routledge.
 
[43]  George, D., & Mallery, P. (2018). IBM SPSS Statistics 25 Step by Step.
 
[44]  Hair Jr., J., Black, W., Babin, B. & Anderson, R. (2010). Multivariate Data Analysis: A Global Perspective. 7th Edition, Pearson Education, Upper Saddle River.
 
[45]  Zeyang, W. (2021). A stepwise regression analysis of the risk of corporate debt default. In 2021 International Conference on Economic Development and Business Culture (ICEDBC 2021) (pp. 1-6). Atlantis Press.