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Anne Lanham, Lillian Mwanri

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Microfinance and Income Levels of AIDS Affected Households: A Quasi-experimental Survey

Consolata Oloo Ngala

Department of Economics, Masinde Muliro University of Science and Technology P. O. Box 190-50100 KAKAMEGA, Kenya

Abstract: Questions have been raised as to whether Microfinance Institutions (MFIs) provide social and economic security to AIDS affected households by improving their income. It is feared that MF leads to welfare loss. It has become a micro-debt trap that impoverishes rather than improves income level of the affected poor households. The main objective of this study was to establish the effect of microfinance on incomes of AIDS affected households in Kakamega County, Kenya. This study was guided by the Social Capital Theory. Descriptive and correlation research designs were adopted for the study. A sample of 416 survey household respondents was purposively and randomly selected and data analyzed using both descriptive and inferential statistics. The study found that MF products and services marginally increased income level of AIDS affected households. Pearson’s correlation coefficient (r = 0.249, p < 0.01) illustrated that the number of times a household went for loan was significant and marginally positively related to the current level of household income. Further, a pooled Mann Whitney U test indicated that there was highly significant difference between mean rank income levels of affected households with and without MF (U = 16321, p = 0.000). The study found that MF services can be a very important tool in AIDS impact mitigation. They improve income levels of AIDS affected households. However, access to MF services is limited due to high interest rates and limited competition among MFIs. The government should consider creating an enabling environment to allow more MF players to improve competition which would lead to low cost of borrowing money. Second, MFIs should reduce their current lending rate of between 16-22 percent through exploiting opportunities that can reduce the cost of loan processing like in mobile money transfer. this. The government should come up with a set of rules and regulations to guide the operations of MFIs in the country as this sub-sector remains unregulated. A longitudinal study should be carried out to find out how income has changed overtime in response to MF services.

Keywords: microfinance, income levels, HIV and AIDS, affected households, quasi-experimental, survey

Citation: Consolata Oloo Ngala, Microfinance and Income Levels of AIDS Affected Households: A Quasi-experimental Survey. Journal of Finance and Economics, 5 (2). 50-60. January 2017. doi:10.12691/jfe-5-2-2

1. Introduction

Acquired Immune Deficiency Syndrome (AIDS) continues to put huge demands on the economy despite the tremendous interventions that have been put in place by governments, development partners, civil society and the private sector to prevent and mitigate its effects. Current statistics indicate that more than 36 million people have died of AIDS related causes UNAID [1]. According to UNAID, at the end of 2015 approximately 36.7 million people were living with HIV globally of which 2.6 million were children. UNAIDS report of 2016 also indicated that the annual number of new infections among adults (+15) has remained static at an estimated 1.9 million people. Further, of those affected and living with Human Immune Deficiency Syndrome (HIV), 40 percent do not know their HIV status and 62 percent can’t access Antiretroviral Treatment (ART) UNAID [1]. The statistics also show that 2/3 of young people are not properly informed about HIV and therefore can’t make informed decision about their sexual health as they lack knowledge to prevent themselves from HIV infection UNAID [1]. Sub-Saharan Africa being the most affected region had 25.8 million people living with HIV in 2015 and approximately 34 million people had died from AIDS related causes WHO, [2]. The impact of these barriers are said to be strongest in high prevalence settings, especially in Eastern and Southern Africa UNAID [1].

In Kenya, HIV epidemic is referred to as generalized- affecting all sections of the populations including children, young people and adults UNAID [1]. It is estimated that 1.5 million people are living with HIV and more than half (53 percent) are unaware of their HIV status UNAID [1]. It was also estimated that there were 78,000 new infections and 36,000 AIDs related deaths (UNAID [1]. The prevalence rate is 5.9 percent and only 59 percent adults are on ART UNAID [1]. In addition, according to the Ministry of Health (MoH), 29 percent of annual adult deaths, 20 percent maternal mortality and 15 percent death of children under five are by AIDS in Kenya (Republic of Kenya [3]). There is an estimated 260,000 couples in HIV sero-discordant Republic of Kenya [3]. Futher, stable and married couples account for 44 percent of new infections Republic of Kenya [3]. The epidemic is said to have negatively affected the economy by lowering per capita output by 4.1 percent in Kenya Republic of Kenya [3]. Kakamega County contributes 3.4 percent of people living with HIV in Kenya and is ranked 8th (Republic of Kenya [3]. By the end of 2015 a total of 50,844 people were living with HIV in the county, with 14 percent being young people aged 15-24 years Republic of Kenya [3]. Further estimates indicate that 916 adults and 177 children died of AIDS related conditions in 2015 Republic of Kenya [3]. Interestingly, less than 2/3 of men and women have comprehensive knowledge of HIV and AIDS in the County Republic of Kenya [3]. The impact of AIDS in Kakamega County is associated with increasing number of orphans, child and elderly headed households, child prostitution and child labour, and high dropout rate from schools, and decrease in agricultural productivity [4]. The County is home to about 11, 087 poor households with AIDS orphans Republic of Kenya [3]. This is a replica of statistics at the global level. These figures underscore actual and potential threats posed by AIDS to households’ socio-economic stability in most AIDS heavily affected households in Kakamega.

“Academic literature indicates that there is a possible two- way relationship between health and economic growth as measured by gross domestic product (GDP)” Odrakiewicz [5]. It is also known from theories of economic development that health is a vital determinant of productivity of labour and hence it’s a measure of human capital. Studies have indicated that high poverty level is aggravated by illness associated with AIDS and that all efforts in tackling poverty are undermined by the AIDS scourge Mc Dough [6], Barnes [7]. Colleen [8] observed that households coping capacities in the context of AIDS infection became blurred as the health crisis continued. Productive members of the household grew sicker and needed hospitalization, costs and pressure escalated. This meant that households sold-off productive equipment and farm animals, borrowed from riskier sources (such as money-lenders), further reducing their engagement in business and farming. Some households fell into destitution, having to rely on charity for basic needs. In even more dire cases families were brokeup, and children were given to whoever could care for them. These findings demonstrate a vicious cycle of poverty that is likely to be caused by AIDS. AIDS Prevention and impact mitigation calls for a wholistic approach ranging from behaviour change, biomedical and structural: removal of economic barriers such as prohibitive costs to access medical services and even finance. In the year 2000, World Bank indicated that the impact of AIDS on the households could be mitigated by the introduction of a security scheme. This scheme could only be provided by governments and local financial institutions World Bank [9]. Microfinance institutions have taken up this role and since the declaration of the year 2005 as a Microcredit Year there has been an upsurge in the number of MFIs Kim and Watts [10], and Kenya is no exception.

Microfinance refers to a range of financial services to low income people, including credit, savings, insurance and money transfers Odell [11]. Other products and services include capacity building by training in business management and agricultural production. Proponents of Micro financing like Grameen Yunus were of the idea that the primary purpose of MF is to provide investment capital for microenterprise development to enable growth of income and assets Nagarajan [12]. Kumar [13] asserts that MF should be recognized as one of a series of measures with a disaster risk management strategy, and further, it opens up the possibility of expanding credit to the poor who would otherwise be regarded as ineligible to borrow especially from mainstream banks Saibel [14].

MF is, therefore, a collection of tools that service different types of clients around the world. MFIs offer credit facilities to clients in groups or as individuals. In group lending, credit is provided to support a group of homogeneous individuals who then distribute the loan among themselves. Individual group members then manage their own cash flow and apply it to whatever household priority they judge most important for their own welfare Watson and Dunford [15]. In Kakamega County and in the context of AIDS these groups are support groups which include: single women, youth, men, discordant couple, married couples and commercial sex workers support groups. The groups meet weekly to monitor loan repayments group guarantees and some even carry out table banking. This enhances social cohesion and improves financial management skills. MFIs also offer team loans to people running the same business. When one member becomes ill or dies, the other members can continues to operate the business and can complete loan repayments.

Individual lending is done to owners of existing business to provide loans for working capital and/or asset financing. This credit is given based on the business potential and availability of collaterals such as fixed asset charges. Credit insurance is another service that MFIs offers. This covers outstanding loans at the time of death and, as a result, protects the MFIs’ portfolios against non-repayment due to death. Apart from loans, some MFIs also offer integrative MF or “MF plus” defined as MF projects that focus on finance and other development issues such as linking credit to skill building and education Dunford [16].

Several projects and studies in Africa and beyond have demonstrated that financial assistance to poor households affected by AIDS have uplifted and helped cushion them against the ravages of AIDS. MF help smoothen consumption including purchase of nutritional diet, filling a resource and financial flow gap in payment of school fees and medical bill. It also acts as capital to start and reinvestment into small business, and in most AIDS affected households it has led to significant improvement in household assts, reduction in gender based violence and mproved household solidarity. Barnes et al., [17], Bishop et al., [18], Kim et al., [19],Sharman et al., [20] and Baired et al., [21].

Several experimental designs done to compare MF programme and non-programme households found that household income increase with increase in credit. Affected households tend to save in more ways due to financial management skills acquired in MF loaning schemes. Household members of MF that consumed between two to three meals a day had benefitted from MF during the seasonal famine while those that consumed between two to one meal a day saw their food security improve due to MF membership (Barnes [17], Sherer et al., [22], Nanoor [23], Da silva, [24] and Claudia [25])

However, other studies have pointed out that microfinance products, especially loan, has driven people into a debt trap Hulme, [26], Murdoch, [27]. MF products have indeed become a micro-debt that impoverish rather than improve income of the AIDS affected poor households. Pertinent questions have, therefore, been raised as to whether MF provides social and economic security by mitigating the severity of AIDS in affected households. The idea of this study is to establish the influence of microfinance services and products on income level of AIDS affected households. These inconsistencies in finding by various researchers warranted a confirmatory study especially in AIDS heavily affected areas such as Kakamega County in Kenya.

2. Theoretical Framework

MFIs eliminate the borrowing constraints that poor households in developing countries face. It is intended to increase households’ wealth and utility by enabling them to acquire some income for investment and consumption hence mitigating disasters such as AIDS and poverty in affected households. This study was guided by the Social Capital Theory by Bourdieu, [28], Joint Liability Theory by Ghatak and Guinane [29] and the Welfarist Approach to Micro Financing by Carroll, [30], Copestake [31]. Bourdieu [28] in his capital theory states that there are three capitals that members of the group gain as they make use of MF products and services. These capitals are economic, social and cultural. The fundamental one, according to the author is economic. To him, economic capital is measured by money which gives the recipient power to purchase, invest and consume. The economic capital also yields social and cultural capital. Social capital is enhanced by social networks that promote cohesion among members of the groups. Cultural capital on the other hand will promote investment in human capital in form of education and even accumulated knowledge. The effect of MF products can be both positive and negative. This theory therefore, underscores the possible implications of MF products and services provided to households through the Grameen Model.

Ghatak and Guinanne [29] in their Joint Liability Theory postulates that as MF institutions use group model to offer their products and services they are able to cushion themselves from any loss that is likely to emerge from members defaulting (the group acts as the collateral). The members will, through group pressure, discourage defaulting amongst themselves. In the same vein, they will repay the loan incase one of their group members is not able to repay.

On the other hand, the Welfarist Approach to MF proponents like Carroll [30], Copestake, [31] proposes that the performance of MFIs can be measured by evaluating the socio-economic outcome of their products and services on the households. In this study, the economic status of AIDS affected households after MF was evaluated by the variable households’ currently monthly income.

3. Data Description

The study used mainly primary data which was collected by use of structured questionnaire consisting of closed and open ended questions that gave baseline information on a wide range of issues. Information was gathered among AIDS affected households with and those without MF. Data captured include background information about the head of the household such as age, sex, marital status, employment status, education level and size of the household. This enabled the researcher to describe participants in the study. Further, information on household resources before and after the MF loan was also captured. It sought to gather information such as main source of household income, number of times they were given a loan, amount of credit obtained, reason for loan demand and how they spent the credit facility. The variables were categorized so that a respondent gave the range of the information required. Categories were used because there are some variables such as household income and age that are difficult to measure accurately Rutstein and Kiersten, [32]. Many households especially the poor will tend to understate their income as a mechanism of seeking for donations and other assistance from the government and Non Governmental Organization (NGOs). The rich on the other hand in fear of robbery will hide their income and wealth. AIDS affected households without MF questionnaire did not elicit information on number of loans advanced, amount advanced and number of times they got the loan. This was because they had not been given any loan. The data obtained by use of questionnaire were of categorical, ordinal and interval scale.

Focus group discussions (FGDs) were carried out to gather information on household life experiences, achievements and challenges related to micro financing in the context of AIDS pandemic. The study conducted two (2) FGDs with between 8 and 12 respondents from each stratum in each selected study area (sub-County). A total of fourteen (14) FGDs were conducted in the study area. Homogeneous FGD membership was used to enable revelation of critical cross-cutting issues as members had the same orientation and experiences with MF. It also eliminated fear and stigmatization that a respondent could have been subjected to when heterogeneous memberships were used. This information was used in triangulation with information obtained from questionnaires.

In-depth interviews (IIs) were used to collect data from credit officers, MFIs managers and the Coordinator of the AIDS support group umbrella body called Pamoja Positive Voices Network. It generated information that enabled the researcher to better understand micro financing, what drives demand for it, requirements for micro financing, approaches used in micro financing, successes and challenges and best ways to mitigate AIDS and other disasters using micro financing. A total of 20 respondents were engaged in an In-depth Interviews, one (1) credit officer and one (1) manager per MFI. For purposes of validation, data from questionnaire and those from FGDs and IIs (quantitative and qualitative approaches respectively) were compared and contrasted, through a process called triangulation.

4. Methodology

This study adopted both qualitative and quantitative approaches with a quasi-experimental framework. The study compared information from AIDS affected household clients with MFIs (main/group 1) and those without MF (control/group 2). Those without MF were affected clients who had registered for MF but had not been granted (still on the waiting list) or accepted client “client-to-be”).This arrangement enabled control of selection bias and isolation of external factors by ensuring that both groups possessed inherent characteristics (entrepreneurship drive and ability) that were similar Kim et al., [19], Czura, [33]. Socio-economic characteristics of both the main and control groups were compared. Members of AIDS support groups in the community were used as target respondents in this study as they had accepted their health status, gone public and were living positively with AIDS. Both groups were drawn from the same study area which is largely rural. The differences between observed changes in the socio-economic and socio-political indicators in these households were hence assumed to be attributable to the contribution of MFI intervention programmes. The sampled households were required to recall accurately historical data concerning their situation before the first MFI product and thereafter (before and after approach). If a household was still financing a loan they had to show the contribution of those MFI services to their social and economic life. Credit officers and MFI managers were the next target population. This was because they were in charge of the programmes and knew exactly their clients’ characteristics and areas of residence. Specific reference in both the groups under study was head of the household (responsible Man, Woman or Child). Head of the household was defined as the key decision maker in the household whose authority was recognized by other members of the household KNBS and ICF Macro [34]. This authority often comes with socio-economic responsibility for the household. Household was defined as one or more persons who usually live and eat together, whether or not they are related by blood, marriage or adoption and recognize each other as members of the same household KNBS and ICF Macro [34]. It can also be defined as a person or group of people living in the same residence in which economic production, consumption, inheritance and child rearing are carried out in organized manner KNBS and ICF Macro [34].

This study used different sampling techniques to pick representative samples. Purposive sampling was to select the study area, Kakamega County. This area was selected because it is one of the counties in Kenya with high HIV prevalence of 4.0% (Republic of Kenya [3]) and hosts a number of MFIs. Kakamega County has seven (7) sub Counties KNBS and ICF Macro [34]. This gave the sum total population of Kakamega County from which representative samples of affected households were picked The study generated both quantitative and qualitative data which were first analyzed descriptively. Descriptive analysis was carried out to bring out differences in income distributions between with and without MF by demographic and socio-economic background characteristics of the affected households.

Further, a correlation analysis was done to explain the strength and significance of the relationship between MF services and current level of affected household income. Actual total amount of money that a household gets at the end of every month from their various sources of income was used to measure current monthly income. A four point scale was used on ascending order from the lowest income earning of below Ksh. 5,000 to above Ksh. 20,000 the highest income earning (refer to Table 1).

Finally, to be able to test the research null hypothesis (Ho): there is no significant difference in income levels of HIV and AIDS affected households with MF and without MF services, a pooled sample estimate for the entire county was subjected to Mann-Whitney U test to compare mean rank of income of the two groups of respondents. Mann-Whitney U test best fitted in the analysis because the study compared two groups of respondents with the income data that was positively skewed as confirmed by Kolmogorov-Smirnov test (p = 0.000 < 0.05). According to Fay and Proschan [35], Mann Whitney U test is used to calculate the probability of finding any difference in rank sums under the null hypothesis that the samples were drawn from population with the same distribution.

Limitations of the Study

The study was limited by unwillingness of respondents to answer certain questions which they considered sensitive and confidential and fear of stigmatization: to overcome the above two related limitations, researcher and the assistants were trained on simple counselling techniques to be able to persuade and draw respondents closer to remove suspicions and fear. Skillful rapport building, involving reassurance and respect was also employed. The focus group discussions were organised in local church grounds and other social places within the villages this made affected household respondents more relaxed and open to share their experiences because of the familiar environment. In addition the infected household heads could not be kept for long without food. Some of them excused themselves to go and take medicine. The researcher overcame these limitations by organising meetings in the mid- morning from 10-12 noon when the infected household heads have had some breakfast and are about to get into lunch.

Ethical Consideration

To ensure proper ethical consideration, the researcher sought for oral consent where only respondents willing to provide information voluntarily were interviewed. In addition, the principle of respondent confidentiality and privacy was adhered to throughout the study. All participants’ views were respected and treated with utmost confidence.. Further, a written consent to take respondents’ photos was sought. More importantly, the researcher sought permission and certification from the National Council for Science and Technology, Kenya (NCST) now called National Commission for Science Technology and Innovation, Kenya (NACOSTI) to carry out research.

5. Results and Discussion

5.1. Income Distribution by Background Factors of Respondents

5.1.1. Demographic Factors of Respondents

Sex

Looking at sex of household heads and their current level of income, majority of female 61.4% (123) compared to only 33.7% (67) of their male counterparts earned income below Ksh. 5,000 ($57.8) irrespective of MF loan status. However, the proportion of females with MF tended to be higher compared to their colleagues without MF in the higher income categories. This indicated that female households that had benefitted from MF had relatively higher income compared to those that were yet to benefit from MF (refer to Table 1). This finding confirms that of Borchgrevink et al., [36] in Tigrey regional state who concluded that MF program had a positive influence on client’s assets and income. In addition, the study found that the proportion of male headed households particularly those with MF were leading female headed households in higher income brackets in Kakamega County. Here, the patriarchal nature of most African communities (including the Luhya community in which this study was based) means that most of the time female headed households are likely to be those of single, widow or divorced women. These findings concur with that of Togba [37] in Cote d’Ivoire that found that 77.7 % of female headed households were poor as compared to only 56.75% of male headed households. Other studies (Katsushi and Azam, [38], Aikaeli, [39] equally established that households headed by female had lower income than those headed by male. They maintained that lower income is a representative of the economic disadvantages that women face.

Age

Comparing age of respondents and their current monthly income levels, those without MF were dominating in lowest income bracket of less than Ksh. 5,000 ($57.8) a month relative to those with MF. In the subsequent higher income brackets, those with MF were more in all age brackets as shown in Table 1. This was an indication that income level had no direct relationship with age.

Household size

Looking at the household size, the majority 57.8% (118) and 74.1% (146) of those with and without MF and who had 7-9 and 1-3 household sizes respectively, earned below Ksh. 5,000 ($57.8) refer to Table 1. However, it was evident that larger proportions of respondents with MF had relatively higher levels of income. For other cases, relating household size to monthly incomes, the study revealed mixed results, an indication that household size had no specific relationship with income levels of respondents. It could, however, be related to demand for MF such that as household size increased, participation in MF decreases. Having many dependants could be a deterrent to savings and accumulation of assets that can act as collateral for loans. Even though this study, focused on AIDS affected households under the Grameen Model where the group acts as collateral, still households were required to have their own complementary collaterals in the form of household goods, farm animals, and other assets to qualify for MF. A study done in Meru, Kenya by Omboi and Wangari [40] confirms these findings. They found that larger families are associated with poor and less educated people whose propensity to save and generate income is limited by the high consumption needs of the household. Their ability to accumulate assets which they can use as collaterals to borrow credit is therefore limited. On the contrary, a study by Nguyen [41] in Vietnam that found out that large household sizes had significant positive effect on credit participation. Silumba, [42], Shah et al., [43]) in Zambia and India respectively also found household size to be positively and significantly related to demand for MF.

Table 1. Percentage income distribution by demographic characteristics of respondents with and without MF

5.1.2. Socio-economic Factors

Education level

The majority of respondents without MF irrespective of their level of education were in the lowest income bracket of less than Ksh 5,000 ($57.8). This was reported in all income brackets. On the contrary, those with MF irrespective of their level of education, dominated in higher levels of income. These findings indicated that there was no direct link between education and income, but a positive relationship between access to MF and income. There was a weak positive correlation (r=0.235 p<0.05) between monthly income and education (refer to Table 3). Other scholars such as Kuznets [44], De Gregorie and Lee [45] and Barro [46] found a positive relationship between income and education. Cohen and Addison [47] also found that better education earns higher wages, experience less unemployment and work in more prestigious occupations than their less educated counterparts. There is always an assumed notion that education and income are collinear. This lack of direct link between education and income level of affected household heads in this study could be attributed to the increase in supply of job seekers from primary, secondary and tertiary and above level of education into the formal job market without corresponding vacancies to accommodate them. On the other hand opportunities for joining the informal sector also depend on the availability of capital. In this context, therefore, the amount of income one earns is not determined by the level of education. A study by Birciakova et al., [48] in Czech Republic confirms that there is a bigger possibility of getting into the risk of poverty for households with higher levels of education because of lack of job opportunities.

The other reason for this finding in Kakamega County could be that AIDS contributes to financial vulnerability of the affected households leading to low income irrespective of the level of education of the household head. But affected household respondents with MF reportedly earned more income compared to those without MF as indicated by the descriptive analyses (refer to Table 2). In the IIs and FDGs, discussants alluded to the fact that education level is not a factor in consideration for MF services. As long as one is an adult (18 years and above) and is a member of a group and had saved 15% of the amount applied for in the six months prior to loan application.

Employment Status

There was no respondent with MF who was reportedly unengaged and earned income above Ksh. 10,000 ($115.6). Formally employed with and without MF earned at least in all the other income brackets. However, only 38.1% (78) and 33.3% (66) of those informally employed without MF earned incomes below Ksh. 5,000 ($57.8) and between Ksh. 10,000 - 20,000 ($115.6 - $231.2) respectively. The greater majority 67.7% (135) of those respondents without MF was in informal employment and earned incomes below Ksh. 5,000 ($57.8). Those with MF dominated in almost all other subsequent higher income brackets. These findings could imply that MF services and products increased the level of income of those who were unengaged and informally/self employed. Khaleque [49] in Bangladesh found that MF directly induces self-employment activity or transfers available working days from the labour day activity and maximizes their economic gains such as a high income, savings among others (Iron out the contradictions in the above statements).

Marital status

Comparing marital status and current income levels, all those respondents without MF in all marital status categories were dominating in the lowest income bracket of below Ksh. 5,000 ($57.8) (refer to Table 2). All other subsequent income levels were dominated by those household respondents with MF in all the categories of marital status. This finding implied that MF service gave all the respondents of marital status with MF an upper hand by improving their income levels.

Table 2. Percentage income distribution by Socio-economic characteristics of respondents with and without MF

Sources of employment

Generally those respondents who were formally employed and had benefited from MF were the majority 33.3% (68) in middle and higher income brackets of Ksh 5,001 – Ksh.10,000 ($57.8-$115.6), Ksh 20,000 ($231.2) and above. Those without MF 33.3% (66) who were mostly formally employed earned lower incomes of below Ksh 5,000 ($57.8). There were observed variations also in other forms of employment among those with and without MF. The majority of those with MF and were in business, agriculture and other source of income were in the higher income brackets of Ksh. 5,001 ($57.8) and above. Those without MF in all sources of income on the other hand, were dominating in lower income brackets of Ksh. 5,000 ($57.8) and below. This revealed that MF was a contributing factor to income levels of affected households irrespective of forms/source of employment (refer to Table 2).

In recapitulation, engaging in MF services improved the level of income. A study by Sherer et al., [22] in Guatemala, Malawi and Thailand that evaluated financial outcome of an integrated microcredit and health education found improvement in household income ranging from 22% to 64% with small but significant gains in health knowledge in all the three countries.

Table 3. Correlation between background characteristics and Current monthly income of affected households with MF

5.2. Meeting Expenditure Gap

In multiple response, 55.7% (225) of respondents with and without MF indicated that they mostly meet their expenditure gap by sale of farm produce. This was because the greater 49.5% (200) of the respondents irrespective of loan status in Kakamega County were engaged in agriculture. When the respondents were asked in the interviews why they applied for MF, an overwhelming majority indicated that they did so because of inadequate incomes.

Table 4. Percentage distribution of ways of meeting expenditure gap by households with and without MF (multiple responses)

Notably, a good number 24.4% (50) and 20.5% (42) of respondents with MF also meet their expenditure gap by sale of assets and savings respectively. About 23.6% (47) and 11.1% (22) of household respondents without MF meet their expenditure gap by gifts and savings respectively. This implied that those respondents with MF had been able to acquire some assets hence rely on their sale to meet expenditure gap. Erulkher and Chong [50] in Nairobi, Kenya, working on a cash transfer (TRY) programme, found that cash transfer had a positive impact on income, savings and household assets of those who participated in the programme. These findings also concur with those of Gibbs [51] and Garikipati [52] Kabeer [53] all studies in Bangladesh, and Padma and Getachew [54] from Ethiopia. Affected households in Kakamega County could only acquire assets and save if they had a surplus of income. Discussants in FGDs reportedly gave first priority to food and then medical expenses and other necessary expenditures like school fees (if any) after which they could acquire assets and possibly save for investment. Savings being requisite for MF, every other household irrespective of MF status had at least some savings. However, after MF their savings grew. Discussants with MF pointed out that whenever they were contributing to repay the loan, they also contributed towards their share savings. This means regular savings through compulsory accounts without which loan could not be advanced. From the FGDs, it emerged that AIDS support groups met weekly to discuss issues that affect them. They also occasionally gave time during such meetings to MF credit officers to give them talks on financial and business management related issues. Such were the times when they repaid their loans and in addition gave out their share contributions. They pointed out that the amount of cash each gave out as savings varies from Ksh. 50 – Ksh.100 ($0.578 - $1.156) weekly. These savings entitled them to borrow as they were part of collaterals. Affected households were indeed trained to manage the impact of AIDS disaster by saving for the future. These study findings confirmed that of Nagarajan [12] in India which revealed that MF services had enabled households to minimize losses through skilled human resources and diligent management of portfolio and cash flows.

5.3. Microfinance Services and Level of Income

Comparing number of loans and current level of income, the study revealed that income rose with increase in the number of loans. Majority 63.4% (130) of household respondents in the lowest income bracket of Ksh. 5,000 ($57.8) and below had only taken loan once. Whereas a moderate percentage 27.3% (56) of those who reportedly earned Ksh. 20,000 ($231.2) and above, and had taken loans over four times.

Table 5. Percentage number of loans and level of income by respondent with MF

Pearsons correlation coefficient (r) showed that the number of times a household went for the loan was positively and significantly (r = 0.249, p < 0.01) related to the current level of income (Refer to Table 6). This implied that as the number of loans increased household income also increased. Nanoor [23] in Ghana explored the impact of MF on household’s income and found that household’s income increased with an increase in credit (MF).

Table 6. Correlation between components of MF and current level of income

Pearsons correlation analysis also showed that amount of money taken as last loan was significant and marginally positively correlated (r = 0.326, p < 0.01) to income level of the affected households. Business advisory and training on health related issues were negatively and weakly correlated to income level at r = -0.156, p < 0.05 and r = -0.118, p < 0.1 respectively. Only training on food production was insignificantly related to the level of income (Table 6). These findings indicated that as the number of loans and amount of money taken as loan increased, current income levels also increased, though marginally. The mixed findings in income and trainings on food production and business advisory could perhaps partly be due to financial diversion as only 33.2% (68) of the households invested the whole amount of loan money in business to raise their levels of income. The majority 53.7% (110) of affected households invested only part of the loan money in business while 13.2% (27) invested all in non-business ventures.

5.4. Level of Income and Extent of Loan Investment

When the level of income was correlated with the extent of loan investment, the majority 49.1% (101) of those respondents who were earning Ksh. 5,000 ($57.8) and below invested part of their loan money in business. About 32.4% (66) of those earning between Ksh. 5,001- Ksh. 10,000 ($57.8 - $115.6) invested their entire loan monies in business. Only 18.5% (38) of those households who earned between Ksh.10,001-Ksh.20,000 ($115.6-$231.2) invested their entire loan in non-business. This trend was realized in subsequent higher income levels as shown in Table 7.

Table 7. Extent of loan investment and level of income of respondent among those with MF

These findings suggested that as the level of income rose more of the loan monies were not invested in business but in non-business activities that could not realize quick returns. These ventures included education, food consumption and health among others. This finding is in agreement with that of Banerjee et al., [55] in Hyderabab India who found that only 30 % of the loan money is used in starting a business and the rest are used for consumption and repaying the loan among other things. Maia et al., [56] did a meta-analysis of literature on micro credit and income and concluded that micro-credit has very small or no effect on the income of their participants because they use their loans to meet their basic needs first instead of self-employment that may increase their income. Findings from this study implied that in the context of high poverty levels and AIDS scourge, the affected households may not be borrowing to invest but to meet their consumption gap. An assessment of the impact of HIV and AIDS in Kenya by National AIDS Control Council revealed that affected households are at risk of being pushed into poverty as a result of HIV and AIDS Republic of Kenya [57]. In such situations, the probability of AIDS affected households investing loan money in business and thereby increasing household income is unlikely. Consuming instead of investing could lead to reduced ability to repay loans. This further leads to a cycle of borrowing and greater debt. Group discussions with MF clients pointed out that they hawked loans from one MF to the other. This enabled them to avoid defaulting and its consequences. They further stated that MFIs charged interest rates of between 16 percent and 22 percent per annum which are too high for them. For example, if one had a loan of Ksh 20,000 ($200) he/she will be required to pay Ksh 600 ($6.00) per week (Ksh 500 ($5.00) for the loan and Ksh 100 ($ 1.00) as weekly share contribution and Ksh 334 ($3.34) interest per month). This translates to a total of Ksh 2,734 ($27.34) per month. Discussants maintained that these amounts of monies were not easy to come-by especially from small scale businesses that they operated. Therefore resorted to loan hawking and those who were unable to loan hawk saw their household assets being taken by auctioneers hired by the MFIs. Interviews with MFIs managers revealed that there was no government policy that guides MFIs operation in this critical sector. Loan recovery, they pointed out, only depends on Auctioneers Act Cap 526 of 1999. This act according to MFIs is not the real law and can sometimes be challenged in court. In addition, government regulations overprotect the clients of MFIs. This finding is in agreement with that of Nasir [58] in India where he found out that 55% of MFIs agree that due to weak law and legislation they are not able to make their loan collection system as effective as they want to. MFIs managers reported that they were at times forced to pay medical bill for their sick clients so that they can go back to work in their businesses to repay the loan. A number of sick clients used the loan to seek medication and later found it difficult to repay. This confirmed findings by Barnes et al., [59] that loans are a burden when the client of MFIs is seriously ill or has to take care of a person who is chronically ill. MFIs managers further pointed out that quite a number of MF clients hawk loans without the knowledge of MFI credit officers. They reported that some of their clients were not open enough to tell credit officers that they were financing loans from other MFIs. Others diversified the use of loan fund. They reportedly didn’t use the whole amount for the purpose for which it was intended. This presented a challenge to MF clients when it comes to repaying their weekly and even monthly contributions. In Ethiopia, MFIs reported seeing more clients with multiple loans either through the MF or holding additional loans from other institutions, money lenders or relatives. Kassaye et al., [60].

MFI managers reported that because of rampant loan use diversification they have assisted many widows and supported orphans from poor background to go back to school. They indicated that they often wrote cheques to schools directly and did not give out cash to guardians of orphaned children. MFIs have, therefore, invested and promoted human and social capital.

5.5. Mann-Whitney U Test for Differences in mean rank of Incomes of Affected Households with and without MF

To further examine the influence of MF on income levels of affected households, Mann-Whitney U test was run on data for sub-Counties and a pooled estimate for the County. This was to test the hypothesis; Ho: there is no statistically significant difference in mean rank of incomes of affected household with and without MF. The following results were obtained as shown in Table 8.

Table 8. Comparison of mean rank of household income for sub-Counties and Pooled estimate for the County

Findings from U test showed that there was a statistically significant difference between income of affected households with MF and without MF services in Kakamega Central sub-County (U = 601; p = 0.035). This could be interpreted to mean that affected households with MF services had higher income levels compared to those without MF. The z-values calculated (-2.104) when compared with the critical z value (+_1.645) gave enough evidence to reject the null hypothesis. Results from Kakamega North sub-County also showed that there was a highly significant difference between income levels of households with MF as compared to those without MF (U = 104; p = 0.000). The z calculated value (-4.463) when compared with the z critical value (+_1.645) showed that the null hypothesis should be rejected. However, findings from Kakamega East, Kakamega South, Mumias, Lugari and Butere sub-Counties indicated that the mean rank of households with MF were slightly higher compared to those without MF. These differences though statistically insignificant, were an indicator that MF services were a contributing factor to rising income levels.

A pooled Mann Whitney U test done (for the entire County) showed that there was highly statistically significant difference between mean rank income levels of affected households with and without (U = 16321, p = 0.000). Those households with MF services had higher income levels as compared to those without MF services. The z calculated value of (-3.886) when compared to the z critical value (+-1.645) also gave ample evidence for the null hypothesis to be rejected and the alternative accepted, an indication that MF has actually impacted positively on AIDS affected households’ income.

6. Conclusions

The study concluded that MF services marginally increased income levels of AIDS affected households. It was affirmed that MF services were positive and marginally correlated to current level of income of AIDS affected households. In addition, there were high statistically significant differences in mean rank of income between households with and without MF. Affected households with MF had comparatively higher incomes than those without MF. They were often able to acquire assets and even make savings. However, loan use diversification in non-business activities made AIDS affected households lose their acquired assets in loan recoveries. MFIs offered loans at high interest rates leading to loan hawking by MFI clients in order to repay the loan and that there is no policy to guide operations of MFIs in Kenya.

MF is thus deemed a very important tool in AIDS impact mitigation. They improve income levels of AIDS affected households. However, access to MF services is limited due to high interest rates and limited competition among MFIs. The government should consider creating an enabling environment to allow more MF players to improve competition which would lead to low cost of borrowing money. Second, MFIs should reduce their current lending rate of between 16-22 percent through exploiting opportunities that can reduce the cost of loan processing like in mobile money transfer. The government should also come up with a set of rules and regulations to guide the operations of MFIs in the country as this sub-sector remains unregulated. A longitudinal study should be carried out to find out how income has changed overtime in response to MF services.

Acknowledgements

My sincere thanks to Mr. Robert Amakobe Shinali coordinator Pamoja Positive Voices Network for linkages to all support groups in Kakamega County and to all chairpersons of support groups who sacrificed their time and allowed me to meet members of their groups that formed my respondents. I would also like to thank Microfinance Institution managers and credit officers who formed key informants in this study and responded to my research questions.

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