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Statistical Analysis on the Loan Repayment Efficiency and Its Impact on the Borrowers: a Case Study of Hawassa City, Ethiopia

Received: 10 September 2015     Accepted: 7 October 2015     Published: 26 November 2015
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Abstract

The major objective of this study is to study factors affecting loan repayment efficiency of borrowers and assess impact of efficient utilization of loan for the borrowers in Hawassa city in Ethiopia. Data used for this study was collected through a structured questionnaire. Classical and Bayesian logistic regression technique were used for data analysis. Factor analysis was used to reduce data and to incorporate the major determinants that the efficient utilization of loan have to the borrowers, whereas logistic regression is used to obtained factors affecting loan repayment performance of borrowers and it was extended to the Bayesian frame work using prior information that the parameter follows. Results of the classical binary logistic regression indicate that better repayment efficiency is associated with borrowers: sex, educational status, number of dependent family member, monthly income, loan size, additional source of income, motivation of repayment, time given for repayment, interest rate and screening mechanism when individuals apply for the loan. Also by using Bayesian logistic regression age, loan type, using loan for intended purpose and experience are significant in addition to significant predictors in classical logistic regression. From factor analysis, 27 factor used for impact assessment in which all the factor loaded highly in 7 significant factors like:-Benefit and obstacle related factor, capital effect, saving habit, expenditure, government spending, satisfaction level on the service and consumption change that has been seen after using loan. Thus, in order to improve repayment performance of borrowers, increasing loan size, training and giving some incentive in business areas, increasing awareness in different ways and studying factors which has significant impact on borrowers creditworthiness and giving solution to reduce that problems must be improved.

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 6)
DOI 10.11648/j.ajtas.20150406.28
Page(s) 562-575
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Loan Repayment Efficiency, Loan Impact, SMFI, Logistic Regression, Bayesian Logistic Regression, Multivariate Factor Analysis, Hawassa

References
[1] Abebe M. (2011). Determinants of Credit Repayment and Fertilizer Use by Cooperative Members in Ada District: East Shoa Zone, Oromia Region; M.Sc. Thesis, Haramaya University.
[2] AbrehamGebeyehu (2002). Loan repayment and its Determinants in Small-Scale Enterprises Financing in Ethiopia: A Case of Private Borrowers AroundZeway Area, M. Sc. Thesis, Addis Abeba University.
[3] Afrin, S. (2008). Multivariate Model of Micro Credit and Rural Women Entrepreneurship Development in Bangladesh: Bangladesh International Journal of Business and Management.
[4] Besag, J. (2001). Markov Chain Monte Carlo for Statistical Inference, University of Washington: USA Center for Statistics and the Social Sciences.
[5] Grimm, L. G. and Yarnold, P. R. (19195). Reading and Understanding Multivariate Statistics: Washington D.C. Am. Psychol.
[6] Huang, X. (2010). Bayesian Logistic Regression Model for Siting Biomass: Using Facilities.
[7] Meyer, J. B. (2002). Madness to the Method: Empirically Assessing Small Business Lending Under the Community Reinvestment. Urban Law Review, 34 (1), 1-38.
[8] Micha'elAddisu (2006). Micro-Finance Repayment Problems in the Informal Sector in Addis Ababa: Ethiopian Journal of Business & Development, Volume 2.
[9] Oke, J. T. O. Adeyemo, R. and Agbonlahor, M. U., (2007). An Empirical Analysis of Microcredit Repayment in Southwestern Nigeria, Humanity & Social Sciences Journal 2 (1): 63-74, ISSN 1818-4960.
[10] Okorie, A. (1986). Major Determinants of Agricultural Smallholder Loan Repayment in a Developing Economy: Empirical Evidences from Ondo State, Nigeria.
[11] Okorie, A. (1986). Major Determinants of Agricultural Smallholder Loan Repayment in a Developing Economy: Empirical Evidence from Ondo State Nigeria.
[12] Okorji, E. C. and Mejeha, W. (1993). The Formal Agricultural Loans in Nigeria: The Demand for Loans and Delinquency Problems of Smallholders Farmers in the Owerri Agricultural Zone of Imo State. Int. J. Trop. Agric., 1:1-13.
[13] Olagunju, F. I. and Adeyemo, R. (2007). Determinants of Repayment Decision Among Small Holder Farmers in Southwestern Nigeria, MedwellPakistan Journal of Social Sciences.
[14] Oluwansunmi, A. A. and Aloa, A. (1999). The Role of Credit in the Transformation of Traditional Agriculture: The Nigerian Experience. Nig. J. Econ. Soc. Studies.
[15] Padma, M. and Getachew, A. (2005). Women Economic Empowerment and Microfinance: A Review on Exercises of Awassa Women Clients.
[16] Pitt, M. M. and Khandker, S. R. (1998). The Impact of Group Based Credit Programs on Poor Households in Bangladesh: Does the Gender of Participants Matter?, Journal of Political Economy, Vol. 06, No. 5.
[17] Podgurky, D., (2000). Determinants of Microcredit Loans Repayment Problem Among Microfinance Borrowers in Malaysia.
[18] Pollil, G.and Obuobie, J. (2007).Microfinance Default Rates in Ghana: Evidence from Individual-Liability Credit Contracts.
[19] Roslan, A. H. and Karim, A. Z. A. (2009). Determinants of Microcredit Repayment in Malaysia: A Case of Agrobank. Humanity Soc. Sci. Journal; Volume 4: P.45-52.
[20] SalieAyalew (2007). Empirical Impact Assessment of Business Development Service on Micro and Small Enterprises in Towns of Amhara National Regional State: M.Sc. Thesis in Addis Ababa University.
[21] Sean, M. O. and David, B. D. (2007). Bayesian Multivariate Logistic Regression: Biostatistics Branch, National Institute of Environmental Health Sciences.
[22] Steiner, M. and Tym, C. (2005).Multivariate Analysis of Student Loan Defaulters at the University of South Florida: TG Research and Analytical Services.
[23] Steiner, M., and Teszler N. (2003). The Characteristics Associated with Student Loan Default at Texas: A and M University.
[24] Stiglitz, J. E. and Weiss, A. (1981). Credit Rationing in Markets with Imperfect Information, The American Economic Review, Volume 71, Issue 3, pp 393-410.
[25] Sydney, S. (2007). A Bayesian Approach to Variable Selection in Logistic Regression with Application to Predicting Earnings Direction, Ron Bird and Anthony Hall School of Finance and Economics University of Technology.
[26] Tedeschi, G. A. (2006). “Here today, gone tomorrow: Can dynamic incentives make Microfinance More Flexible?”, Journal of Development Economics.
[27] Tesfaye, G. B. (2009). Econometric Analyses of Microfinance Credit Group Formation, Contractual Risks and Welfare in Northern Ethiopia: PhD Thesis, Wageningen University, Netherlands.
[28] Volkwein, N. H. (1995). The Efficiency of Microfinance in Vietnam: Evidence from Ngo schemes in the North and the Central Regions.
[29] Woo, B. H. (2009). Women and Repayment in Microfinance: Institute of Research for Development in France, University of Agder, Norway Working Paper.
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  • APA Style

    Yonas Shuke Kitawa, Nigatu Degu Terye. (2015). Statistical Analysis on the Loan Repayment Efficiency and Its Impact on the Borrowers: a Case Study of Hawassa City, Ethiopia. American Journal of Theoretical and Applied Statistics, 4(6), 562-575. https://doi.org/10.11648/j.ajtas.20150406.28

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    ACS Style

    Yonas Shuke Kitawa; Nigatu Degu Terye. Statistical Analysis on the Loan Repayment Efficiency and Its Impact on the Borrowers: a Case Study of Hawassa City, Ethiopia. Am. J. Theor. Appl. Stat. 2015, 4(6), 562-575. doi: 10.11648/j.ajtas.20150406.28

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    AMA Style

    Yonas Shuke Kitawa, Nigatu Degu Terye. Statistical Analysis on the Loan Repayment Efficiency and Its Impact on the Borrowers: a Case Study of Hawassa City, Ethiopia. Am J Theor Appl Stat. 2015;4(6):562-575. doi: 10.11648/j.ajtas.20150406.28

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  • @article{10.11648/j.ajtas.20150406.28,
      author = {Yonas Shuke Kitawa and Nigatu Degu Terye},
      title = {Statistical Analysis on the Loan Repayment Efficiency and Its Impact on the Borrowers: a Case Study of Hawassa City, Ethiopia},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {4},
      number = {6},
      pages = {562-575},
      doi = {10.11648/j.ajtas.20150406.28},
      url = {https://doi.org/10.11648/j.ajtas.20150406.28},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150406.28},
      abstract = {The major objective of this study is to study factors affecting loan repayment efficiency of borrowers and assess impact of efficient utilization of loan for the borrowers in Hawassa city in Ethiopia. Data used for this study was collected through a structured questionnaire. Classical and Bayesian logistic regression technique were used for data analysis. Factor analysis was used to reduce data and to incorporate the major determinants that the efficient utilization of loan have to the borrowers, whereas logistic regression is used to obtained factors affecting loan repayment performance of borrowers and it was extended to the Bayesian frame work using prior information that the parameter follows. Results of the classical binary logistic regression indicate that better repayment efficiency is associated with borrowers: sex, educational status, number of dependent family member, monthly income, loan size, additional source of income, motivation of repayment, time given for repayment, interest rate and screening mechanism when individuals apply for the loan. Also by using Bayesian logistic regression age, loan type, using loan for intended purpose and experience are significant in addition to significant predictors in classical logistic regression. From factor analysis, 27 factor used for impact assessment in which all the factor loaded highly in 7 significant factors like:-Benefit and obstacle related factor, capital effect, saving habit, expenditure, government spending, satisfaction level on the service and consumption change that has been seen after using loan. Thus, in order to improve repayment performance of borrowers, increasing loan size, training and giving some incentive in business areas, increasing awareness in different ways and studying factors which has significant impact on borrowers creditworthiness and giving solution to reduce that problems must be improved.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - Statistical Analysis on the Loan Repayment Efficiency and Its Impact on the Borrowers: a Case Study of Hawassa City, Ethiopia
    AU  - Yonas Shuke Kitawa
    AU  - Nigatu Degu Terye
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    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
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    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20150406.28
    AB  - The major objective of this study is to study factors affecting loan repayment efficiency of borrowers and assess impact of efficient utilization of loan for the borrowers in Hawassa city in Ethiopia. Data used for this study was collected through a structured questionnaire. Classical and Bayesian logistic regression technique were used for data analysis. Factor analysis was used to reduce data and to incorporate the major determinants that the efficient utilization of loan have to the borrowers, whereas logistic regression is used to obtained factors affecting loan repayment performance of borrowers and it was extended to the Bayesian frame work using prior information that the parameter follows. Results of the classical binary logistic regression indicate that better repayment efficiency is associated with borrowers: sex, educational status, number of dependent family member, monthly income, loan size, additional source of income, motivation of repayment, time given for repayment, interest rate and screening mechanism when individuals apply for the loan. Also by using Bayesian logistic regression age, loan type, using loan for intended purpose and experience are significant in addition to significant predictors in classical logistic regression. From factor analysis, 27 factor used for impact assessment in which all the factor loaded highly in 7 significant factors like:-Benefit and obstacle related factor, capital effect, saving habit, expenditure, government spending, satisfaction level on the service and consumption change that has been seen after using loan. Thus, in order to improve repayment performance of borrowers, increasing loan size, training and giving some incentive in business areas, increasing awareness in different ways and studying factors which has significant impact on borrowers creditworthiness and giving solution to reduce that problems must be improved.
    VL  - 4
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Author Information
  • Department of Statistics, Hawassa University, Hawassa, Ethiopia

  • Department of Statistics, Hawassa University, Hawassa, Ethiopia

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