Assessment of Demographic Factors Influencing Access to Loans for Women SMIEs in Nyalenda
Aaron Mwayi1, Samuel Jerry Odindo2, *, Dan O. Odindo2
1Department of Health Promotion, Faculty of Community Health and Development, Great Lakes University of Kisumu, Kisumu, Kenya
2Department of health system Management, Faculty of Community Health and Development, Great Lakes University of Kisumu, Kisumu, Kenya
Introduction: Most residents in Nyalenda work as informal traders and hence realize very low income. As a tool for poverty reduction, there is need for SMIE women to access loans as their major source of capital. This study was a descriptive, cross-sectional survey design which involved quantitative data collection methodologies. The findings were analyzed and used to determine demographic factors influencing access to loans for women SMIEs in Nyalenda. Methods: The study employed stratified random sampling and cluster sampling technique to come up with a sampling size of 399. For the quantitative data, frequencies were run using SPSS and Chi –square test was used to determine the association. Findings: In age category, 25-34 category recorded the highest of 158 women SMIEs (42.5%) while 55 -64 recorded 8 women SMIEs (2.2%). In relationship to household, spouses recorded 218 (58.6%) while granddaughters recorded 8(2.0%). In marital status, married monogamous recorded 180 (48.4%), while separated divorced recorded 8 (2.2%). In number of dependents, 1-4 recorded 148 (39.8%).Conclusion: Age distribution and education level has an association with ability to access loan.
Demographic, Economics, SMIEs, Loans for Women
Received: April 10, 2015
Accepted: April 27, 2015
Published online: July8, 2015
@ 2015 The Authors. Published by American Institute of Science. This Open Access article is under the CC BY-NC license. http://creativecommons.org/licenses/by-nc/4.0/
Although women operate more than half of the SMIEs in Kenya, studies have shown that their level of access to loans is low. Many studies have tried to establish the factors influencing this situation under certain contexts. This study was conducted to establish demographic factors that influence access to loans for women small-scale and micro-entrepreneurs (SMIEs) in Nyalenda peri - urban settlement in Western Kenya.
Peri - urban settlement areas have high concentrations of poverty and social and economic deprivation which includes broken families, unemployment, economic, physical as well as social exclusion.
The potential of women is greatly underdeveloped in many societies, yet women clearly play a very important role in many respects, including in the local economy1.
Previous studies have reported on the influence of gender on business performance and growth from western economies and have concluded that WOEs perform less well. It also appears that under-resourcing at start-up contributes to this situation1. There is consensus among practitioners, policy makers and entrepreneurs about the major impediments for the growth of women-owned SMEs1.
Access to finance is one of the most critical factors for market entry, growth and survival of SMEs. This access is further complicated for women-owned SMEs in developing countries and LDCs. Across the globe, access to finance is identified as the top most barrier to growth of WOEs. This access is important in the whole life cycle of a SME. Moreover, access to finance by women owned enterprises has a multiplier effect on the national economy in general and specifically on employment, GDP growth and poverty alleviation. The flip side of the financing issues is internal financial management of the SMEs, which also hinders growth of this vibrant sector. In many countries, there are innovative mechanisms of financing SMEs thanks to the active role of business associations and governments’ emphasis on SME economic growth1.
Kenya is a low income country with 56% of its population living below the poverty line6. This is despite a high population growth rate precipitated by the country's high fertility rate of about 4.86. The most common poverty lines for international comparisons are US$1 a day for low-income countries (in which category Kenya falls), US$2 for middle income countries, and US$4 for transition economies10.
The poor constitute the majority of the Kenyan population and the most affected are women. According to the 1997 Welfare Monitoring Survey (WMS), in urban areas, 49% of women registered overall poverty, while 38% were food poor. Some of the characteristics of the poor included large families of 6.4 compared to 4.6 for non-poor and high fertility rates for poor women. Other characteristics of the poor include physical disability, HIV/AIDS, orphans and street children7.
As the third largest urban centre in Kenya, Kisumu city has a population of 565,000 people according to estimates. Economic activities include sugar, fishery, agriculture, some tourism, and a large informal economy5. Kisumu district has many pockets of poverty in the city particularly slum settlements such as Obunga, Bandani, Nyalenda, Nyawita and Manyatta. People who live below the poverty line in the district are estimated to be 53% (267,310 people)6.
The main causes of poverty in the district can be identified as environmental, economic, health and socio-cultural factors. Prevalence of HIV/AIDS stands at 38% and it is among the highest in the country. This problem has increased dependency ratio, drains resources in treatment and has increased the number of orphans and widows in the district6. Kisumu has a past history of vibrant textile industries, which had direct linkages with clothing enterprises. The collapsed Kisumu Cotton Mills (KICOMI) and Rift Valley Textiles (RIVATEX) in Eldoret and Ken Knit among others has lead to rapid growth of SMIE activities in the recent past due to the general collapse of manufacturing firms in Western region2. Women in Kisumu bear disproportionately large share of both domestic and agricultural work. Despite their large contribution to both household income and rural economy, women are faced with inhibitive cultural norms such as traditional divisions of labor, lack of access to land and property, wife inheritance, exclusion of women in decision-making and restriction on family inheritance6. Therefore, this study shall helped in establishing demographic factors influencing access to loans for women SMIEs in Nyalenda.
1.1. Significance of the Study
The study shall assist the government and financial institutions in identifying factors that are influencing access to loans for women SMIEs. The government and financial institutions shall consider these factors whenever they want to allocate funds inform of loans to different sub groups or whenever they are involved in a policy making exercise.
To establishing demographic factors influencing access to loans for women SMIEs in Nyalenda.
2.1. Study Design
The study was a descriptive, cross-sectional survey design. It involved qualitative and quantitative data collection methodologies. Quantitative data collection was done by use of a checklist and semi structured questionnaire. Qualitative data was collected through Key Informant Interviews (KII).
2.2. Study Site
The study was carried out in West Kolwa location, Winam division, Kisumu County in Kisumu Town East constituency. This study was carried out in eleven (11) sub units in two of the sub locations in West Kolwa location. The two sub locations were Nyalenda "A" and Nyalenda "B". Nyalenda "A" had a total of six sub units namely: Dago, Kanyakwar, Libeto, Wathari "A", Wathari "B", and Wathari "C". Nyalenda "B" had a total of five sub units namely: Wathari, Wasiko, Mbeya, Kasiwi, and Nyangiendo.
2.3. Study Population
The study population consisted of women of age 15 to 64 years who were SMIEs in Nyalenda slum settlement. The reason for using age 15 years as the minimum age is due to the fact that from literature review, some lending institutions did not require a loan applicant to produce a national identification card or be of a certain age in order to secure a loan.
2.4. Unit of Analysis
The unit of analysis targeted lactating mothers with infants less than 5 years old, and who had previously breastfed their baby during their age of six month below in Siaya County.
2.5. Sample Size Determination
Estimation of the number of women SMIEs in Nyalenda was based on the fact that a study done by African Development Bank found that 46% of SMIEs in Kenya are owned by women (ADB and ADF, 2004).
Therefore the following formula was used to determine the target population:
N= Target population
P= percentage of SMIEs owned by women in Kenya
X= total population of women of age 15 to 64 years in Kenya
Z= total population of females in Kenya
A= total population of females in Nyalenda "A" sub-location
B= total population of females in Nyalenda "B" sub-location
The target population was 5780.
The following formula was used to determine the sample size:
n= sample size
N= the target population
e= precision level
Therefore the sample size was 399.
2.6. Sampling Technique
The study employed stratified random sampling and cluster sampling. The study area (Nyalenda slum settlement), was divided into two strata: Nyalenda "A" and Nyalenda "B". Nyalenda "A" had six sub units namely: Dago, Kanyakwar, Libeto, Wathari "A", Wathari "B", and Wathari "C". The size of this stratum was determined using the formula below:
Enumerators formed clusters whereby one enumerator collected data in one sub unit; hence proportionally 220 was divided by 6.
Therefore each of the six enumerators collected data from 37 respondents.
Nyalenda "B" had five sub units namely: Wathari, Wasiko, Mbeya, Kasiwi, and Nyangiendo.
The size of this stratum was determined using the formula below:
Enumerators formed clusters whereby one enumerator collected data in one sub unit; hence proportionally 184 was divided by 5.
Therefore each of the five enumerators collected data from 37 respondents.
2.7. Sampling Interval
The formula below was used to calculate the sampling interval for each sub unit:
N= target population
n= sample size
k= sampling interval
The sampling interval for every sub unit was 15.
Questionnaires were administered per cluster, while KIIs were administered to representatives of four lending institutions in Kisumu East district.
2.8. Data Processing
Immediately after the data collection exercise, data was entered on daily basis by the data clerks. Consistency and completeness checks were done and data cleaned. Information from the questionnaires was electronically entered using Statistical Package for Social Sciences (SPSS) version 16 program. All the hard copies of all questionnaires were used for counter checking the information electronically entered. Each tool was entered individually and no merging was done during the entry.
2.9. Data Analysis
For the quantitative data, frequency distributions were calculated and the performance indicator was access to loans hence cross tabulations were done to determine which factors affected the women SMIEs who had secured loans. Pearson Chi-Square (P) values were used to test statistical significance in the relationship between two variables whereby a P value >0.05 confirmed that there was no significant association in the relationship between two variables, while a P value ≤0.05 confirmed that there was a significant association in the relationship between two variables.
Qualitative data from KIIs was analyzed according to emerging themes which were analyzed by selection of similar statements, definition of emerging concepts and finally the qualitative findings were synchronized with the quantitative findings.
3.1. Loan Secured by Demographic Factors
A significant association was established between the respondent’s age and loan secured (p=.011). The study showed that out of the 85 women SMIEs who were in the age bracket of between 35-44 years, 27.1% had secured loans. This was followed by those in the age category of between 25-34 years (158), of whom 20.9% had secured loans.
|Demographic Characteristic||n (%)|
|Age of Respondent|
|Age Category||n (%)|
|Relationship to HHH||n (%)|
|Marital status||n (%)|
|Married monogamous||180 (48.4)|
|Married polygamous||37 (9.9)|
|Number of dependants||n (%)|
The study found no significant association between women SMIEs relationship to HHH, marital status/type, and number of dependants with women SMIEs ability to secure loans as shown in table 3.2 below:
|Demographic Characteristics||Loan Secured||P-value|
|Yesn (%)||Non (%)|
|15-24||13 (14.1)||79 (85.9)|
|25-34||33 (20.9)||125 (79.1)|
|35-44||23 (27.1)||62 (72.9)|
|45-54||1 (3.4)||28 (96.6)|
|55-64||1 (12.5)||7 (87.5)|
|Total||71 (19.1)||301 (80.9)||0.01*|
|Relationship to HHH|
|Spouse||36 (16.5)||182 (83.5)|
|HHH||23 (22.5)||79 (77.5)|
|Daughter||10 (23.3)||33 (76.7)|
|Granddaughter||2 (25)||6 (75)|
|Total||71 (19.1)||301 (80.9)||0.622|
|Single||15 (17.2)||72 (82.8)|
|Married polygamous||9 (24.3)||28 (75.7)|
|Married monogamous||34 (18.9)||146 (81.1)|
|Separated/divorced||0 (0)||8 (100)|
|Widow||13 (21.7)||47 (78.3)|
|Total||71 (19.1)||301 (80.9)||0.558|
|Number of dependants|
|None||7 (17.9)||32 (82.1)|
|1-4||26 (17.6)||122 (82.4)|
|5-8||24 (22.6)||82 (77.4)|
|9-12||12 (25.5)||35 (74.5)|
|>12||2 (6.3)||30 (93.8)|
|Total||71 (19.1)||301 (80.9)||0.215|
3.2. Socio–Cultural Characteristics of Women SMIEs
The study established that majority of women SMIEs were members of welfare groups. The study established that about quarter of the women SMIEs were Catholics while about 15.6% were Anglicans. The study showed that the majority of women SMIEs had attained primary level class 5-8 education while 5.4% had not gone through any form of formal education. The socio-cultural characteristics are summarized in table 3.3 below:
|Socio-cultural characteristics||n (%)|
|Social support group membership|
|Welfare group||n (%)|
|Women’s group||118 (31.7)|
|Church group||105 (28.2)|
|Youth group||80 (21.5)|
|Education Level||n (%)|
|No education||20 (5.4)|
|Primary: 1-4||45 (12.1)|
|Primary: 5-8||143 (38.5)|
3.3. Loan Secured by Socio-Cultural Factors
A significant association was established between the respondent’s education level and loan secured (p=.000). The study established that of the 37 women SMIEs who had tertiary education, 45.9% had secured loans. This was followed by those who had no education 20, of whom 30% had secured loans.
The study found no significant association between women SMIEs social support group membership and religion with women SMIEs ability to secure loans as shown in table 3.4.below:
|Socio-cultural Characteristics||Loan Secured||P-value|
|Yesn (%)||Non (%)|
|Social support group membership|
|Welfare group||24 (20.3)||94 (79.7)|
|Women’s group||22 (21)||83 (79)|
|Church group||14 (17.5)||66 (82.5)|
|Youth group||8 (21.6)||29 (78.4)|
|Others||3 (9.4)||29 (90.6)|
|Total||71 (19.1)||301 (80.9)||0.628|
|Pentecostal||20 (22.7)||68 (77.3)|
|SDA||17 (25)||51 (75)|
|Catholic||14 (15.4)||77 (84.6)|
|Anglican||6 (10.3)||52 (89.7)|
|Others||14 (20.9)||53 (79.1)|
|Total||71 (19.1)||301 (80.9)||0.191|
|Tertiary||17 (45.9)||20 (54.1)|
|No education||6 (30)||14 (70)|
|Secondary||32 (25.2)||95 (74.8)|
|Primary: 5-8||14 (9.8)||129 (90.2)|
|Primary: 1-4||2 (4.4)||43 (95.6)|
|Total||71 (19.1)||301 (80.9)||0.000*|
The findings of this study are discussed as per the objectives outlined in chapter one and according to results from previous studies. Generalizations are made from the results revealed by tables, graphs and information that were obtained from KIIs.
4.2. Demographic Factors Influencing Access to Loans for Women
The study established that more than half of the women SMIEs were between 15-34 years of age. The mean age was 31.5 years while the median age was 29.5 years. A significant association was established between the respondent’s age and loan secured whereby about a quarter of the women SMIEs who were in the age bracket of between 35-44 years had secured loans. This was followed by those in the age category of 25-34 years, of whom 20.9% had secured loans. This finding is in agreement with a previous study by Bardasicet al., (2008) which revealed that women entrepreneurs tend to be young, and this is an encouraging sign that access into entrepreneurship may be easier for younger cohorts of women. Overall 23.4% of the women SMIEs were single. This is supported by a finding in Nigeria where single women SMIEs was found to make up about 21.4% of Nigeria’s informal sector (Soetan, 1995). This is significant because it suggests that women may not find it easy to combine both family and enterprise responsibilities (Bardasicet al., 2008).The study further revealed that majority of the women SMIEs had between 1-8 dependants. This is supported by the fact that one of the characteristics of the poor in Kenya is large families of 6.4 members compared to 4.6 for non-poor and high fertility rates for poor women (RoK, 2001). However the study found no significant association between women SMIEs relationship to HHH, marital status/type, and number of dependants with women SMIEs ability to secure loans.
4.3. Socio–Cultural Factors Influencing Access to Loans for Women
The study established that majority of women SMIEs were either members of welfare SMIEs groups or women groups. This finding agrees with a report by Atieno (2009) which stated that women SMIEs in Kenya tend to maintain small homogeneous and cohesive social networks suited to reducing information asymmetries and hence supporting informal credit and risk sharing arrangements. Smaller social networks may have a marginal effect on enterprise productivity, while it is the innovation/economic networks that have a large and significant impact. The building of social capital is one reason for joining associations since it enables women SMIEs to make contacts that they hope to help them in future.
The findings of the current study established that education level influenced the ability to secure loans for women SMIEs. About 45.9% of the women SMIEs who had tertiary education had secured loans. This was followed by those who had attained secondary school education of whom about 25.2% had secured loans. This finding is similar to a report by Inter-parliamentary, (1998) which revealed that one of the constraints women SMIEs face in accessing loans is lack of skills due to lower levels of literacy and formal education. According to a report by the UN, (2001) women entrepreneurs have low educational levels and less professional experience. They lack management skills and competencies in finance and accounting, which are key to improving access to finance. Furthermore, due to social and educational factors, they fear complicated bank procedures and lack confidence to deal with lending institutions and effectively convey their business proposals.
The study found no significant association between women SMIEs social support group membership and religion with women SMIEs ability to secure loans.
5. Conclusion and Recommendation
Age distribution has an association with ability to access loan. This is attributed to the fact that women entrepreneurs tend to be young,
Education level has an association with ability to access loan. This is attributed to the fact that education level influenced the ability to secure loans for women SMIEs.
1. The government need to allocate more loan for young women entrepreneur.
2. Programs engaged in women empowerment through education need to be supported more so that women can have ability to secure loan.