Evaluation of the Social Economic Welfare of Women Groups in Bunyala Sub County, Kenya ()
1. Introduction
In recent time, increasing attention of academic and political discussions has been directed at tackling poverty. Poverty has assumed a global phenomenon which is threatening the development efforts (of governments, non-governmental organisations and individuals) and survival of humanity. Africa, the world’s second-largest continent both in terms of landmass and population, stands as a paradox of untapped potential and enduring challenges. Amidst its breathtaking landscapes and cultural richness, it grapples with a persistent and pervasive issue: poverty (Barke, 2023).
In the past, monetary measures using the International Poverty Line (IPL) have revealed that Africa contributed the largest number of inhabitants living in poverty. On the other hand, non-monetary measures which have targeted inequality testing (using the Gini Coefficient) have provided conclusive evidence that inequality is dominant on the African continent especially Sub Saharan Africa (Ruzengwe, 2019). Despite the grim picture presented by the literature above, poverty levels have declined substantially since Africa’s growth resurgence starting in the 1990s. This progress was driven mainly by income growth, consistent with the global evidence. Nonetheless, inequality often played a complementary role in most of the countries and, in a small number of cases, it was the primary driver of changes in poverty (Fosu & Gafa, 2020).
In many developing countries, less than half the population has access to formal financial services. Lack of access to finance is often the critical mechanism for generating persistent income inequality, as well as slower economic growth (Beck, Kunt, & Honohan, 2009). This study therefore sought to evaluate the patterns of accessing loan services in Bunyala Sub County and also how this eventually affected their social economic welfare.
Bringing in the gender angle while discussing poverty issues brings to the fore the extent of marginalization of females. Kundu, George, Yesodharan (2022) explain further that generally, women are deprived of their rights and are usually refrained from decision-making in major areas of individual and family life. Irshad and Sudha (2022) propose the need for women empowerment, since it is a critical issue in today’s world, as it aims to increase economic, social, and political power.
Microfinance has long been associated with generating empowerment of women (Hossain, 2020). Globally, women’s economic empowerment has been identified as a prerequisite to enhancing their economic development participation. It accelerates economic development and improves women’s economic independence by creating job opportunities. Additionally, their social, educational and health standards both at the individual and family level improve. The government of Kenya introduced the Women Enterprise Fund to empower women economically through the provision of credit and business support services (Bosire, Wamue-Ngare, & Okong’o, 2022).
Chigunta (2017) explains further that women fund schemes emerged as means of guaranteeing access to women enterprise fund loans as a strategy that was responsive to social related realities. This was necessitated by many hindering factors associated with formal enterprise lending institutions whose products were highly collaterized. Research has shown that women’s empowerment has a great impact on the reduction of poverty in society (Wei et al., 2021). The question evaluated by this study was whether there was any significant impact of the WEF (Women Enterprise Fund) on beneficiaries located in Busia County, given that poverty was entrenched in the area. The Organisation for Economic Co-operation and Development (2022) highlighted a disturbing trend in Africa. Most women who owned micro enterprise projects were confronted with challenges in improving their projects and promoting the tradition of innovation. Tesfayohannes (2012) explained further that in Africa, most women who owned small micro enterprise project were confronted with challenges of enhancing their ventures and promoting innovation. This was associated with the existing environment faced with social cultural and technological factors creating challenges for the owners of small-scale projects. This led women to make tradeoffs between tradition and the kind of activities to undertake. For instance, most women in Tanzania tend to undertake enterprises in harmony with their traditional roles and which were least profitable.
Empowerment can be defined as a way of increasing welfare benefits, equality of access to resources such as educational opportunities, abolition, institutional gender discrimination, women mobilization and bargaining power. Impact evaluation studies routinely find that lending to women benefits their households. However, a number of them also find that this may not empower the women concerned (Garikipati, 2008). Studies have also shown that empowerment lessens violence against women. Empowerment fosters an entrepreneurial ecosystem among societies including vulnerable groups and is important to cultivate and foster entrepreneurial culture within organizations (Margaret, Anese, & Emmanuel, 2019).
2. Problem Statement
The Woman Enterprise Fund is a semi-autonomous government agency in the Ministry of State and public-service established in 2007. The fund was meant to provide accessible and affordable credit to support women in starting or expand businesses for creating wealth and employment. Through this venture, the Government of Kenya sought to ensure that resources reached excluded women and served as a commitment to achieve empowerment. Subsequently, uptake of women enterprise fund in Bunyala Sub-County stands at 78%. Despite the uptake of the funds by various women groups the poverty index in the study area stands averagely at 64%. This research attempted to establish the point of disconnect, since on one hand, there seems to be a high uptake of the fund, yet the poverty index is still quite high.
3. Research Methodology
3.1. Study Area
This study was conducted in Bunyala Sub County of Busia County. This Sub County borders Samia to the north, Siaya to the East, Bondo to the south and Lake Victoria to the west. It covers an area of 185.6 kms with an estimated population of 66,568. It has the following four administrative units: Bunyala central ward, Bunyala south, Bunyala west and Bunyala North. The County is covered by River Sio, Nzoia, Yala among other tributaries leading to Lake Victoria (Busia County Integrated Development Plan, 2023-2027).
3.2. Target Population and Sampling
The study targeted a total population of 98 women groups that benefited from Women Enterprise Fund and who were registered with the Department of Social Development. Additionally, the research also targeted 3 women enterprise fund officers. The officers acted as the key informants of the study. The sample size was derived from Morgan and Krejcie table for sample size determination. The list of women group members who were registered with the women enterprise fund was provided by WEF officers. Stratified sampling was used to determine the distribution of the groups across the four administrative units in the study area. Random sampling was then used by researchers to identify chairpersons of the respective groups. A total sample of 78 groups was selected randomly from the administrative units.
3.3. Research Instruments
Data was collected using questionnaires, and key informant interviews. Questionnaires were preferred as the main instrument of data collection because of ease of administration.
3.4. Data Analysis
The quantitative data was presented using frequency tables and graphs. To ascertain the influence of uptake of women enterprise fund on household welfare of beneficiaries. The data was analyzed by using descriptive methods and thematic data analysis. The researcher collected both secondary data and primary data for purposes of data analysis.
4. Results and Discussions
4.1. Gender Distribution
The table below shows distribution of gender across the four administrative units Researcher used female and male as the main respondents during data analysis.
Findings as demonstrated in Table 1 show that 68.6% represented females across the 4 administrative units and 31.4% represented males. Bunyala West had the highest gender distribution of females, representing 41.1% across the Sub County. Bunyala South had 24.4%, Bunyala North 14.0% and Bunyala Central 11.6%. The findings show that the groups accessing the women fund had a significant portion of the male gender. This finding shows an application of a third representation required by the Kenyan government.
Table 1. Gender distribution across the sub-county.
Administrative Unit |
Gender |
Total |
Female |
Male |
Central |
4.7% |
7% |
11.6% |
North |
8.1% |
5.8% |
14% |
South |
14% |
10.5% |
24.4% |
West |
41.9% |
8.1% |
50% |
Total |
68.6% |
31.4% |
100% |
4.2. Age vs Gender of Respondents
The responses on age and gender are shown in Table 2. This covers between the age brackets of 15 years to 88 years of age.
Table 2. Age vs Gender of the respondents.
Age |
Gender |
Total |
Female |
Male |
15 |
0.0% |
1.2% |
1.2% |
19 - 28 |
11.9% |
1.2% |
13.1% |
29 - 38 |
21% |
9.3% |
30.3% |
39 - 48 |
23% |
14% |
37% |
49 - 58 |
4.5% |
4.7% |
9.2% |
59 - 68 |
7% |
1% |
8% |
Total |
68.6% |
31.4% |
100% |
The data shows that the majority of respondents were between the age bracket of 39 - 48 representing 37%. The bulk of this category was females who represented 23% when compared to the males who were 14%. Females in the age bracket of 29 - 38 years represented 21%. This category also represented the second-highest percentage of the male gender. It was interesting to note that 1.2% of the members were below 18 years. Given that the official age to borrow money in Kenya is 18, this phenomenon revealed that some groups had underage members.
4.3. Utilization of Money from Farming Venture
Within the study area, some respondents engaged in commercial farming. The researchers examined how the respondents utilized money from farming ventures. Responses included: engaging in business, buying clothes, paying debts, expenses, food, farm tools, paying WEF loans, medical and school fees. The results have been presented in Table 3.
Table 3. Utilization of money from farming.
How money from farming tasks used |
Total |
Business |
Clothes |
paying debts |
food |
farm tools |
paying WEF loan |
Medical |
Non farmers |
School fee |
1.2% |
4.7% |
2.3% |
23.2% |
3.5% |
2.4% |
2.3% |
56.9% |
3.5% |
100% |
More than half of the respondents (56.9%) of the respondents did not engage in farming activities. Amongst those who engaged in farming, expenses were majorly covered only either of the following: business expansion, buying clothes, paying debts, with expenses on food accounting for the highest percentage (20.9%). The findings also showed that only 2.3% utilized part of their money to pay WEF debts, while 1.2% invested monies from their farming venture. These results show that the bulk of the monies acquired was not invested but rather consumed.
The research findings concur with what Data4diets (2023) highlight that poorer and more vulnerable households spend a larger share of their income on food. The trend of expenditure within the study area gives a glimpse into the probable state of respondents in the study area.
4.4. Non-Agricultural Enterprises in the Study Area
Respondents also indicated that they engaged in non agricultural enterprises. Enterprises engaged in included: artisan, fishing, fish vendor, metal working, running a shop, tailoring. The results are presented in Table 4.
Table 4. Non-Agricultural enterprises engaged.
|
Enterprise 1 |
Enterprise 2 |
Total |
Artisan |
0.0% |
3.4% |
3.4% |
Fishing |
0.0% |
17.4% |
17.4% |
fish vendor |
0.0% |
1.2% |
1.2% |
metal working |
0.0% |
6% |
6% |
None |
0.0% |
17.5% |
22% |
Shop |
0.0% |
12.8% |
12.8% |
Tailoring |
1.2% |
36.0% |
37.2% |
The most popular economic activity was tailoring (36%), followed by fishing (17.4%). Amongst the respondents, only 1.2% had more than one business venture. Findings indicated that respondents engaged in various non-agricultural enterprises as follows: artisans were 3.4%, Fishing (17.4%), Fish vendors (1.2%), metal working (6%), None (22%), Shop (12.8%), Tailoring (36%). The results show that across the categories of people accessing loans, slightly less than a quarter (22%) were not gainfully employed. The question at hand would be that after accessing the loans and using the amounts in ventures which were not business related, how would they repay the amounts borrowed? On the other hand, three quarters of people accessing the loans had a business venture.
4.5. Main Source of Funds to Start Enterprise
This study investigated if there were other sources of funds other than WEF which group members had access to (Table 5). This included Banks, Groups, micro finance institution, NGO, table banking, WEF and Youth Fund. The table below presents the exact sources of funds pin pointed by the respondents.
Table 5. Main sources of funds.
|
|
Second source of funds |
Total |
Group |
Ngo |
One source of funds |
Table banking |
First source of funds |
Bank |
0.0% |
0.0% |
3.5% |
0.0% |
3.5% |
Group |
0.0% |
1.2% |
11.6% |
0.0% |
12.8% |
microfinance institution |
1.2% |
0.0% |
1.2% |
0.0% |
1.2% |
Ngo |
0.0% |
0.0% |
3.5% |
0.0% |
4.7% |
One source of funds |
0.0% |
0.0% |
11.6% |
0.0% |
11.6% |
Table banking |
0.0% |
0.0% |
51.2% |
0.0% |
51.2% |
Wef |
0.0% |
0.0% |
12.8% |
1.2% |
14.0% |
Youth fund |
0.0% |
0.0% |
1.2% |
0.0% |
1.2% |
Total |
|
1.2% |
1.2% |
96.5% |
1.2% |
100.0% |
Group members who accessed WEF was 12.8%. More than half of the group member used table banking (51.2%). Members who borrowed from micro finance institutions represented 1.2%, while people who got assistance from Non Government Organizations were 4.7%. People who accessed the youth fund were 1.2%. Amongst the respondents, there was a group which did not seem to have identified any source of funds (11.6%). These findings indicate that within the study area, most respondents got funds from one source when setting up their businesses. The Women Enterprise Fund, though present in the area was not yet fully exploited by members.
Research by Atieno, Kadima, and Juma (2022) reveled that awareness significantly influenced success of table banking. This was due to the fact that group members were primarily interested in their own welfare. The most essential aspects of awareness strategy that are significant in enhancing wealth maximization in groups are: attraction of new members, regular group meeting, continued savings arrangements and liquid products.
Another research by Tallam (2016) indicated that low-income households notably encounter limited access to financial services from formal financial institutions to meet their working and investment needs, but also face high costs for transacting basic financial services through check cashers and other alternative financial service providers.
Given that most of the group members got access to funds through table banking in the study area, it can be assumed that the table banking helped hold the groups together. The findings also show that the group members, although registered by the Kenya Women Finance Trust as possible beneficiaries, the bulk of group members did not access the funds available from the government. Members preferred to borrow from each other and grow their internal funds. One reason highlighted by group members for this trend is that the interest from the table banking was significantly lower than that of the KWFT. Further, profits accruing from the table banking venture were shared by the group members.
4.6. Comparison of Housing Conditions before and after Joining Group
The researchers made comparisons of housing conditions before accessing the loans and after. Table 6 presents the results.
Table 6. Housing conditions.
Housing condition |
Now |
Before Loan |
permanent house |
46.5% |
15.1% |
semi-permanent house |
43.0% |
29.1% |
independent flat |
1.2% |
0% |
sharing house |
1.2% |
12.8% |
Hut |
8.1% |
43.0% |
Total |
100.0% |
100.0% |
Findings above demonstrated that 46.5% had permanent houses after acquiring funds as compared to before acquiring loans at 15.1%. Ownership of semi permanent houses went up to 43.0% after accessing the loans as opposed to 29.1% before. Those renting independent flats went up to 1.2% from 0%. Before accessing the loans, the respondents who shared a house were 12.8%. This percentage dropped significantly to 1.2%. Before accessing the loan, respondents who lived in huts represented 43%. After accessing the loan, this went down to 8.1%. These findings show that accessing the loan played a significant role in improvement of housing conditions.
4.7. Comparison of Tenure Status before and after Joining Group
The research made further comparisons on the tenure status of respondents. The results have been presented in Table 7.
The findings on tenure status revealed that 80.2% of respondents owned houses currently compared to 41.9% before acquiring loans. Currently, 15.1% lived in rented houses compared to 36% before acquiring the loan. It seems that a significant percentage put up their houses and moved from the rent set up. Before the loan, 18.6% were hosted by someone else who paid the rent. This percentage went down significantly after accessing the loan. The results as presented in Table 7 show that the housing status and tenure status increased steadily and indicated positive change after accessing the loans.
Table 7. Comparison of tenure status.
Tenure |
Current Status |
Before loan |
Owned |
80.2 |
41.9 |
Rented |
15.1 |
36.0 |
supplied free by employer |
2.3 |
3.5 |
supplied free (rent paid by someone else) |
2.3 |
18.6 |
|
100.0 |
100.0 |
4.8. Roof Construction Material Composition
The comparison of roof construction material was made after and before acquiring loans. The following variables used included thatch/straw, mud, cement and wood plank (Table 8).
Table 8. Roof construction materials.
Roof Material |
Currently |
Before loan |
thatch, straw |
4.6 |
27.9 |
Mud |
3.5 |
4.7 |
Wood planks |
0 |
1.2 |
iron sheets |
88.4 |
65.1 |
Roof Tiles |
3.5 |
1.2 |
Total |
100.0 |
100.0 |
Findings on materials used currently and before for Roof Construction were as follows before the fund intervention, 27.9% lived in a house having Thatch/straw as a covering. This percentage went down to 4.6% once the members accessed the funds. Houses made completely of mud accounted for 4.7% before fund disbursement. This went down to 3.5% after disbursement. Houses covered by wood planks accounted for 1.2% before the intervention and 0% after the disbursement of funds. Houses covered by iron sheets before the intervention accounted for 65.1% while the percentage went up to 88.4% after the disbursement of funds. The trend of responses showed that the respondents tended to improve their housing conditions once they accessed the funds.
4.9. Wall External Material Comparison for before and after Joining Group
The construction materials evaluated by the study included: mud and poles, timber, unburnt bricks with cements, burnt bricks with mud, burnt bricks with cement, cement blocks and stones (Table 9). This enabled the researchers to gauge the building materials at the disposal of beneficiaries before and after accessing loans.
Table 9. Wall external materials.
Material |
Currently |
Before loan |
mud and poles |
12.8 |
54.7 |
Timber |
5.8 |
8.1 |
unburnt bricks |
3.5 |
1.2 |
burnt bricks with mud |
18.6 |
14.0 |
burnt bricks with cement |
20.9 |
15.2 |
cement blocks |
32.6 |
5.8 |
Stone |
5.8 |
1.2 |
Total |
100.0 |
|
Before accessing the loan, more than half of the respondents (54.7%) lived in houses having an exterior wall of mud and poles. After accessing the loan, this percentage went down to 12.8%. A similar decline was also noted amongst those who had timber as an external material with 8.1% before the loan and 5.8% after accessing the loan. Most respondents seem to have put up houses having burnt bricks with mud (18.6%), burnt bricks with cement (20.9%) or cement block (32.6%). Whereby more than a third of the respondents houses had cement blocks for an external material.
4.10. Toilet Type Comparison
The study evaluated toilet type used by respondents. The variables under consideration included: covered pit latrine, covered pit latrine shared, ventilated pit latrines, uncovered pit latrine Flush toilet private, flush toilet shared and, bush. The results are presented in Table 10.
Table 10. Toilet type comparison.
|
Currently |
Before Loan |
covered pit latrine private |
70.9 |
10.5 |
covered pit latrine shared |
18.6 |
72.1 |
Ventilated Improved Pit latrine private |
2.3 |
2.3 |
uncovered pit latrine |
2.3 |
8.1 |
flush toilet private |
1.2 |
0 |
flush toilet shared |
3.5 |
2.3 |
Bush |
1.2 |
4.7 |
Total |
100.0 |
100.0 |
More than half of the respondents currently make use of the covered pit latrine-private (70.9%), or covered pit latrine shared (18.6%). It seems that most members used the covered shaped pit latrine before accessing the loan. After accessing the loan however, they upgraded to the private pit latrine (70.9%), or uncovered pit latrine (2.3%). Respondents who used the bush went down to 1.2% from 4.7%.
4.11. Lighting Source Comparison
The respondents used different types of lightning sources including electricity, Koroboi (tin lamp) or Solar (Table 11).
Table 11. Lighting source comparison.
Type |
Currently |
Before Loan |
Electricity |
59.3 |
20.9 |
Koroboi (Tin lamp) |
9.3 |
45.3 |
Solar |
31.4 |
30.2 |
Total |
100.0 |
100.0 |
There was a spike of members using electricity after accessing the loan (59.3%) while slightly more than a third used solar (31.4%). Before the loan, slightly less than half (45.3%) used tin lamps as a light source. This percentage however went down to 9.3% after accessing the loan.
5. Conclusion
The findings show that the social welfare of respondents improved after accessing loaning facilities. It was worth noting that only a small portion of the group members utilized the funds from the Women Enterprise Fund. Members preferred to use funds from table banking to improve their living conditions, such that there was an improvement in the social status but not necessarily in their economic status. The low poverty index also arose due to the fact that the group members could only access a limited amount of funds available from the table banking, as opposed to tapping into more funds available from the Women Enterprise Fund.
6. Recommendation
There is need to train groups in the study area on the importance of accessing funds from the Women Enterprise Fund so as to access a larger volume of money, rather than the ones available from the table banking. In this light, table banking can be the secondary source of funds, while Women Enterprise Fund becomes the primary source.