100%(1)1 out of 1 people found this document helpful
This preview shows page 70 - 73 out of 91 pages.
assets owned by women entrepreneurs was higher for continuing businesses (75%) than forstarting businesses (25%) Table 4.28: Ownership of Collateral Ownership of collateral By women entrepreneurs 15% By Male family members 85% 58Figure 4.3: Deterrents for Taking Credit Offers85% %10 3% 2% 0 % % 10% 20 30 % 40 % 50 % 60 % 70 % 80 % 90% High interest ratesDo not need creditNot ready to takeOther reasons
CHAPTER FIVESUMMARY, CONCLUSIONS AND RECOMMENDATIONS5.0 INTRODUCTION The chapter describes the summary of the findings, recommendations, and suggestions for furtherresearch. 5.1 SUMMARY OF FINDINGS From the findings it was evident that trained women entrepreneurs had better chances ofaccessing business credit from MFIs. From the analysis of responses from Managers of MFIs, thereasons that were stated for this is that trained women were better able to keep financial recordsconcerning their businesses develop business plans and project proposal than untrained women.59
Approximately 70% of trained women entrepreneurs who applied for credit were able to accesscredit while 50% of untrained women entrepreneurs accessed credit from MFIs. These findings concur with the findings of Kimuyu (1999) in his research on Enterprise Structureand performance in Kenya. He finds that there is a possible positive correlation between anentrepreneur‘s education level and ability to pursue profitable entrepreneurship, understand andfamiliarize with the workings of credit/loan arrangements and finally successfully manage loans.In emphasizing this point, International Labour Organization (2006) states that lower educationlevels puts women entrepreneurs in Kenya at a disadvantage compared to men. While gender gapin primary education in Kenya has decreased in recent years, the gap remains high at secondaryand tertiary education levels. Lower education does not emphasize entrepreneurship skills. Itdecreases the chances that women will have the knowledge needed to excel in business, andthereby contribute to the country‘s overall economic growth. Findings indicate that income levels affect the level of credit that can be advanced to womenentrepreneurs. This can be attributed to the measurement of ability to pay using level of incomeby credit raters, MFIs and other issuers of credit. The findings of Namusonge (2006) address theeffect of low income on access to credit by women owned enterprises in Kenya. It states thataccess to credit has eventually become a detrimental factor to advancing their small scale businessenterprises as most businesses owned by the rural poor women are poorly managed, have lowincome and are mostly deemed not credit worthy by financial institutions. It continues to note thatthese women who own small business enterprises also are most reluctant to take credit as it is anexpensive option to improving their business. They fear taking the risk associated with credits.