Women empowerment through microfinance: Twenty year historical data analysis of selected microfinance institutions in NCR and CALABARZON
The research is aimed to determine whether women empowerment can be quantified and manifested by using only secondary data from three microfinance institutions in the NCR and Region IV-A from the period of 1999 to 2018. Secondary data alone was tested since most studies that the researchers encounte...
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Main Authors: | , , , |
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Format: | text |
Language: | English |
Published: |
Animo Repository
2021
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Subjects: | |
Online Access: | https://animorepository.dlsu.edu.ph/etdb_finman/8 https://animorepository.dlsu.edu.ph/context/etdb_finman/article/1012/viewcontent/Bala4.pdf |
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Institution: | De La Salle University |
Language: | English |
Summary: | The research is aimed to determine whether women empowerment can be quantified and manifested by using only secondary data from three microfinance institutions in the NCR and Region IV-A from the period of 1999 to 2018. Secondary data alone was tested since most studies that the researchers encountered approached the topic using a combination of primary and secondary data. Panel regression was used to give the viewers a rough visualization of the data that was obtained from the MIX market and STL decomposition using Loess was used in this study to be able to fill in the missing values in the data by representing it with a dot. In order to measure women empowerment by the use of secondary data, the retention rate was used to be a proxy variable for women empowerment, and panel data regression analysis using fixed effects was used to determine the significant variables. Results show that there are 3 significant variables which are Loans Outstanding Enterprise, Delinquency (1 Month), and Delinquency (More than 6 Months). Also, it shows that the 3 variables are negatively correlated with the retention rate. This paper would conclude that measuring women empowerment through microfinance is not a black and white approach since there are other factors that can possibly affect the results itself such as data insufficiency. |
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