肯尼亚纳库鲁县小额金融机构借款人贷款偿还的决定因素

IF 0.7 Q4 BUSINESS, FINANCE
Wafula Nathan Wamalwa
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引用次数: 3

摘要

发展中国家的许多金融机构提供储蓄和信贷等金融服务,以援助包括农民在内的若干小农企业。这一努力符合旨在到2015年将贫困人口减少50%的千年发展目标。然而,金融机构增加信贷量以刺激减贫目标的可持续性和连续性取决于还款率。然而,许多金融机构未能有效地开展这些服务,可能是因为以前的预付款贷款违约。本研究采用描述性设计。这种设计的目的是提供事务的描述。研究的主要目的是确定纳库鲁县小额金融机构借款人贷款偿还的决定因素。具体目标是确定借款人的教育水平、收入水平和年龄作为纳库鲁县微型金融机构贷款偿还的决定因素的重要性。目标人群为590名小额金融机构的贷款借款人和员工,每一层采用分层随机抽样的方式抽取10%的样本,再从借款人和员工名单中简单随机抽取被调查者,使人口中的每一个成员都有平等和独立的机会被选为被调查者,也是最简单、最方便、无偏见的选择方法。采用问卷调查法收集横截面数据。数据分析采用线性多元回归模型量化决定因素,描述性统计采用频率表和百分比饼图进行分析。结果表明,教育程度、收入水平和年龄负向是贷款偿还的显著决定因素。该研究建议,应鼓励收入和教育水平较低的借款人贷款,因为他们与贷款偿还的关系比其他借款人要大。来自样本的研究还建议,应该认真对待年轻人,因为他们也与偿还贷款有关。尽管如此,大多数mfl仍在继续经营。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determinants of Loan Repayment by Borrowers from Micro-Financial Institutions in Nakuru County Kenya
Many financial institutions in developing countries provide financial services such as saving and Credit to aid several smallholder enterprises including farmers. This is an effort in line with the Millennium development goals which seeks to reduce poverty by 50% by the year 2015. However, the sustainability and continuity of the financial institutions to increase the volume of credit to stimulate the poverty reduction goal depends on the repayment rates. However many of the financial institutions have not been able to carry out these services effectively perhaps because of loan defaults of previous advances. The study used descriptive type of design. This is the design whose purpose is to provide a description of affairs as they are. The main objective of the research was to identify the determinant of loan repayment by borrowers in micro financial institutions in Nakuru County. Specific objectives were to determine the significance of level of education, level of income and age of borrowers as a determinant of loan repayment in Micro Financial Institutions in Nakuru County. The target population comprised a total of 590 loan borrowers and employees of Micro Financial institutions out of which a sample of 10%was picked using stratified random sampling on each stratum out of which a simple random was done to pick the respondent from the list of borrowers and employees, which enable every member of the population have an equal and independent chance of being selected as respondents and also simplest, most convenient and bias free selection method. The cross sectional data was collected by use of questionnaire. The data was analyzed using linear multiple regression model which quantified the determinants while the descriptive statistics was analyzed by use of frequency tables and percentages pie charts. The results showed that education level, income level and age negative were significant determinants of loan repayment. The study recommends that borrowers with low levels of income and education should be encouraged to take up loan since they are associated with loan repayment than their counterparts respectively. The study from the sample also recommends that youths should be taken serious because they are also associated with loan repayment. Nevertheless Most MFLs were going concern.
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