The importance of geographic access for the impact of microfinance

Abstract

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Microfinance and Inequality: Case of Indonesia

“…It shows that variable L.BPR Loans/Cap in the column (1) and (3) consistently shows a negative coefficient, statistically significant at 5% level on the Gini ratio, indicating that a higher loan by BPR could reduce the income disparity. Contrary to the study of Hulme and Mosley (1997), our results support the study of Angelucci, Karlan, andZinman (2003) Imai et al (2012), Miled and Rejeb (2015) Bangoura et al (2016) Alimukhamedova, Filer, and Hanousek (2017, that microfinance might reduce income inequality, that access to finance for the poor is important factors and BPR, in this case, BPR in Indonesia has a contribution in providing access to finance for the poor. BPR, as a micro bank operating in a particular geographic area, has the advantage of possessing soft information of the borrowers (Meslier-Crouzille, Nys, and Sauviat 2012).…”

“…Their research shows that financial development increased the income share of the poorest quintile growth. Some research explores the relationship between microfinance and poverty in different countries, such as Mexico (Angelucci, Karlan, and Zinman 2003), Uzbekistan (Alimukhamedova, Filer, and Hanousek 2017), and Malaysia (Al-Mamun et al 2012). The results reveal that the microfinance institutions have a positive impact on access to durable spending, credit, and the yield of income-generating activities, significantly.…”

The Effectiveness of Microfinance Program on Women’s Empowerment: Location as Moderator

“…Regarding women's accessibility to microfinance, the geographical location of microfinance institutions has been discovered to play a pivotal role. More specifically, longer distances to the institution cause more challenges for households in seeking microfinance, thus impeding the process of improving their livelihoods (Alimukhamedova, Filer & Hanousek, 2017). Furthermore, location also has an impact on the entrepreneurial activities carried out by business owners.…”

Modeling customer satisfaction in microfinance sector: A fuzzy Bayesian networks approach

“…That would help them determine her/his ability and willingness to use the money as agreed and to place the appropriate conditions, such as the amount of loan and the repayment terms. Alimukhamedova et al 18 suggest that unfavorable geographical conditions can hamper the outreach of MFIs in isolated areas. The difficulty of gathering high-quality information about applicants in remote areas may also push the MFIs to adopt more restrictive loan conditions.…”