Abstract
AbstractCrowdfunded microloans are a suitable tool for financing basic economic activities in developing as well as developed countries, favouring female empowerment. Despite the loans being relatively small, the widespread use of this instrument merits analyzing the factors affecting the microloan. One of these factors is gender because microloans are an important tool to finance projects promoted by women in many developing countries where microfinance is widely diffused. This research aims to determine if the gender of crowdfunded micro-borrowers is related to the main features which define the conditions of a microloan: amount, term, number of lenders, length of time to contact with borrowers and repayment system. The methodology used is the multinomial logit regression. The sample used in this study has been obtained by applying sampling techniques to a extensive public database from Kiva. This provided information on microloans from 56 countries around the world. The results based on amount, term, repayment method and recruitment period indicate that women are the best borrowers. All these variables, except the term, are significant at a 5% level. These findings may be useful to improve financial inclusion and outreach, consistently with the Sustainable Development Goals. Future research is needed to assess how “green and pink” microfinance (with environmental strategies particularly favored by women) can attract more ESG-compliant crowdfunding resources.
Publisher
Springer Science and Business Media LLC
Subject
General Economics, Econometrics and Finance,General Psychology,General Social Sciences,General Arts and Humanities,General Business, Management and Accounting
Reference105 articles.
1. Abdullah S, Quayes S (2016) Do women borrowers augment financial performance of MFIs? Appl Econ 48:1–12
2. Agresti A (2002) Categorical data analysis (second edition). John Wiley & Sons, Inc. Hoboken, New Jersey
3. Agresti A (2015) Foundations of linear and generalized linear models. John Wiley & Sons, Inc, Hoboken, New Jersey
4. Agresti A, Franklin C (2013). Statistics. The art and science of learning from data (third edition). Pearson, Boston
5. Ali A, Jamaludin N, Othman ZH (2016) Modeling microfinance acceptance among social network women entrepreneurs. Int J Econ Finan Issue 6(S4):72–77
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献