Factors Influencing the Psychological Well-Being of Women Entreprise Fund Beneficiaries in Njoro Sub-County, Kenya


The study determined the influence of credit counselling, household debt, and socio-demographic factors on the psychological well-being of the beneficiaries of Women Enterprise Fund (WEF) in Njoro sub-county of Nakuru county in Kenya. The study employed an ex-post-facto research design. The target population was 380 women in Njoro Sub-County who had received WEF loans. A stratified random sample of 190 respondents was selected, 95 women who were current in their loan repayment and 95 who were unable to repay their loans. Data were collected by the use of a structured questionnaire, the 42 item version of Ryff’s Psychological Well-Being Scales (Ryff, 1989) clustered in six dimensions: autonomy, environmental mastery, purpose in life, positive relations, personal growth, and self-acceptance was included. Descriptive and inferential statistics were used to analyse the data at 95% level of confidence. The psychological wellbeing for the women entrepreneurs was found to be (M = 3.91, SD = 0.72) on a scale of 1 to 6. Psychological wellbeing of WEF beneficiaries was positively influenced by credit counselling (β = 0.714, t = 15.08, p < 0.001) and age of household head (β = 0.174, t = 1.94, p < 0.050). Negative statistical significant influences were found between household debts (β = -0.391, t = -2.66, p = 0.008) and psychological wellbeing. Inclusion of credit counselling services in the WEF loaning procedures was recommended.

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Ndunda, L. , Mutinda, M. , Ngumi, O. and Gachohi, N. (2020) Factors Influencing the Psychological Well-Being of Women Entreprise Fund Beneficiaries in Njoro Sub-County, Kenya. Psychology, 11, 1729-1739. doi: 10.4236/psych.2020.1111109.

1. Introduction

Access to credit is a key element in poverty reduction programmes and empowers women economically ( Rathiranee, 2017). The economic empowerment of women has been the focus of World Bank gender mainstreaming programmes, which prioritised the development assistance to women ( World Bank, 2008). They identified gender equity and women empowerment as development objectives and means of promoting growth, reducing poverty and support of better governance. Financial availability and accessibility are cited in many studies as being one of the major barriers and constraints to financial growth ( ADB-OECD, 2014; Bold & Lee, 2015; Dupas, Keats, & Robinson, 2019; Mwobobia, 2012; World Bank, 2009). The setbacks include lack of start-up capital; lack of awareness of existing credit schemes; high-interest rates; lengthy and vigorous procedures for loan applications; lack of collateral security for finance. These factors have become a major barrier to the growth potential of businesses owned by women.

Women in Kenya have gained access to credit through the Women Enterprise Fund (WEF). The Fund was established by the Kenya Government in 2007 as a flagship project under the social pillar in vision 2030 to champion the realization of the Millennium Development Goals (MDGs) on poverty reduction and gender equity through women empowerment. The WEF was meant to increase resources to women to boost their enterprises and increase their incomes, enhance their livelihoods and uplift their wellbeing ( The Republic of Kenya, 2007). The credit from WEF is administered by different microfinance institutions located at the constituency level through existing women groups ( WEF, 2011). The fund by 2015 had disbursed a total of K.Shs. 3 billion to 614,414 women borrowers in all the 210 constituencies countrywide ( WEF, 2015). The loans from the fund earn an interest of 8% and the women groups act as security for the loans. In Molo constituency (which includes Njoro sub-county) with 189 women groups has received K.Shs 11,600,000 from the fund ( WEF, 2015). Njoro sub-county with 38 women groups has received more than K.Shs 2 million ( WEF, 2016).

The availability of credit through WEF has created new challenges that were not envisioned in the beginning, these include: heavy borrowing by the women as noted by Sambu (2013), that 87% of the women had more than one loan; low loan repayment rates, the defaulters were estimated at over 50% in Njoro sub-county ( WEF, 2016); mismanagement of the funds due to poor financial management skills ( Ijaza, Mwangi, & Ng’etich, 2014; Kiraka, Kobia, & Katwalo, 2013); lending procedures, collateral requirement, training, and agency policies ( Mole & Namusonge, 2016), family problems arising from the empowerment of the women ( Kiraka et al., 2013). These emerging situations are capable of causing significant emotional problems to the beneficiaries.

The dependent variable for this study was the psychological wellbeing, this psychometric measure was used to gauge the psychological emotions of the loan beneficiaries. Psychological well-being refers to the individual’s evaluation of their universal human needs and effective functioning psychologically ( Ryff & Singer, 1996). It is a combination of feeling good and functioning effectively ( Huppert, 2009). The concept measures states of how well people perceive aspects of their functioning; for example, the extent to which they feel they are in control of their lives, having a sense of purpose (working towards valued goals), feeling what they do is meaningful and worthwhile and they have good relationships with others. This description is similar to the World Health Organization (WHO) definition of positive mental health as “a state of well-being in which the individual realizes his or her abilities, can cope with the normal stresses of life, can work productively and fruitfully, and can make a contribution to his or her community” ( WHO, 2001: p. 3).

Studies have shown that personality traits ( Steel, Schmidt, & Shultz, 2008), environmental features ( Mazzucchelli & Purcell, 2015) affect life situations of individual citizens and in turn their sense of Psychological well-being. Dolan, Peasgood, & White (2008) noted that higher levels of income and socioeconomic factors influenced psychological well-being positively. Psychological well-being has also been found to be influenced by life circumstances and external environmental influences such as marital status, culture ( Oishi & Schimmack, 2010) and income ( Diener, Ng, Harter, & Arora, 2010; Kahneman & Deaton, 2010). It is therefore possible to assess and predict outcomes of social change on the well-being of the people using the concept of psychological well-being. This study therefore aimed at assessing the factors influencing the psychological wellbeing of the WEF beneficiaries in Njoro Sub-County.

2. Method

2.1. Participants

The study was conducted within Njoro Sub-County in Nakuru County, Kenya. The ex-post-facto research design was used. Stratified random sampling was used to select 190 WEF beneficiaries, the sample was stratified into defaulters and non-defaulters each with 95 WEF beneficiaries selected at random.

2.2. Research Questionnaire

A structured questionnaire was used to collect data. The Carol Ryff’s Psychological Wellbeing Scale ( Ryff, 1989), the version with 42 items ( Abbott, Ploubidis, Huppert, Kuh, Wadsworth, & Croudace, 2006) was adopted to measure the psychological wellbeing of the participants.

Ryff’s Scales of Psychological Well-being ( Ryff, 1989) were designed to measure six theoretically motivated constructs of psychological well-being: autonomy—independence and self-determination; environmental mastery—the ability to manage one’s life; personal growth—being open to new experiences; positive relations with others—having satisfying high-quality relationships; purpose in life—believing that one’s life is meaningful, and self-acceptance—a positive attitude towards oneself and one’s past life. Many versions of the Ryff’s multidimensional psychological well-being scales have been developed and used in different studies. Abbott et al., (2006) summarizes these models, they identify versions with different numbers of items that have been applied in a variety of settings and samples. The original instrument included 120 items (20 per dimension) but shorter versions comprising 84 items (14 per dimension), 54 items (9 per dimension), 42 items (7 per dimension) and 18 items (3 per dimension) are now widely used. It is important to note that the overlap among items in the alternative versions of the Ryff scales is limited; for example, the 18-item version has only six items in common with the 42-item version, one item for each dimension. A very short measure of psychological wellbeing scale of 8 items named the flourishing scale has been developed ( Diener, 2009).

Psychometric assessment of the Ryff’s psychological well-being scales have been done ( Springer & Hauser, 2006; Ryff & Singer, 2006). Abbott et al., (2006) undertook a psychometric assessment of the 42 item version of the psychological well-being and concluded that the addition of two method factors to reflect positive and negative item content improved the model. Abbott, Ploubidis, Huppert, Kuh, & Croudace (2010) assessed the precision of measurement of the 42 items Ryff’s psychological well-being scale and found an inter-factor correlation of (>0.80), which was described as sufficiently high. The construct (predictive) of the psychological wellbeing correlated well with the severity of depression measure.

2.3. Data Analysis

The data were analysed using descriptive and inferential statistics in the Statistical Package for the Social Sciences (IBM SPSS version 26) at a 95% level of significance.

3. Results

3.1. Demographics

The characteristics of the respondents are shown in Table 1.

The majority (84.7%) of the participants had attained eight (8) years of formal schooling and only 4.7% were illiterate. The majority (93.2%) of the participants were below 50 years, as shown in Table 1.

3.2. Psychological Well-Being of WEF Beneficiaries

The mean scores for the six dimensions of Ryff’s psychological wellbeing for the WEF beneficiaries, standard deviation and Cronbach’s alpha are shown in Table 2.

The psychological well-being of the WEF beneficiaries was found to be (grand mean score 3.91) on a scale of 1 to 6.

3.3. Influence of Credit Counselling on the Psychological Well-Being of WEF BeneficiariesUnits

A comparison of the psychological wellbeing of the women who had undergone

Table 1. Characteristics of the participants.

Table 2. Mean scores for the different dimensions of the psychological wellbeing.

credit counselling and the ones that had not been counselled revealed that the counselled group had a higher value (mean score 4.52) compared to (mean score 3.45), these results were found to be statistically significant (t = −15.08, df 188, p < 0.001) shown in Table 3.

The relationship between the independent variable credit counselling and the dependent variable psychological wellbeing was determined using the bivariate linear regression and the results are given in Table 4.

The results of the simple linear regression (Table 4) show that there is a statistically significant relationship (β = 0.740, t = 15.08, p < 0.001) between credit counselling and psychological wellbeing. The women that had received credit counselling were current in their loan repayment and their psychological wellbeing was higher than the group that did not receive counselling.

3.4. Influence of Household Debt on the Psychological Well-Being of WEF Beneficiaries

Simple linear regression was used to assess the influence and the results as shown in Table 5.

Household debt was operationalised as the number of unpaid loans the household had. It was found that the group had between 1 and 6 loans, and 74.4% of the households had more than 2 loans. Household debts had a negative influence (β = −0.391, t = −2.66, p = 0.008) on the psychological wellbeing (Table 5). Households with many loans were found to have low psychological well-being.

3.5. Influence of Household Socio-Demographic Factors on the Psychological Well-Being of WEF Beneficiaries

The influence between the three household socio-demographic variables (age,

Table 3. Mean scores for the counselled and un-counselled group.

(t- = 15.08, df = 188, p < 0.001).

Table 4. Regression Coefficients for Credit Counselling and Psychological Wellbeing

R2 = 0.545, F (1, 188) = 227.4, p < 0.001.

Table 5. Regression coefficients for household debt and psychological wellbeing.

R2 = 0.705, F (1, 188) = 7.103, p = 0.008.

Table 6. Regression Coefficients for the Socio-demographic Factors and Psychological Wellbeing.

R2 = 0.20, (F (3, 128) = 1.904, p = 0.032.

household number and formal education level) was determined by the use of multiple linear regression and the results are in Table 6.

Influence of three beneficiaries’ socio-demographic factors on the psychological wellbeing was determined using multiple linear regression (Table 6). The factors include age, level of formal education and household number. Age had a statistically significant influence (β = 0.174, t = 1.94, p = 0.050) on the psychological wellbeing, while education level (β = 118, t = 1.31, p = 0.192) and household number (β = 066, t = 0.73, p = 0.466) were found to have no statistically significant influence on the psychological wellbeing of women entrepreneurs.

4. Discussions

4.1. Credit Counselling and Psychological Wellbeing

The psychological wellbeing of the women who had received credit counselling was found to be higher than the one for the women who were not counselled. Credit counselling was found to influence psychological wellbeing positively, this implied that the women beneficiaries of WEF who had received counselling tended to have higher scores of psychological wellbeing. This study finding agrees with other studies that many people are not well informed on credit acquisition and repayment and most financial decisions made are reactive rather than proactive ( Anigbogu, Onugu, Onyeugbo, & Okoli, 2014; Mgbebu & Achike, 2017; Nwafor, Agu, Anigbogu, & Umebali, 2018; Nmadu, Bako, & Baba, 2013). Credit counselling assists with specific financial services and questions related to credit ( Siekei et al., 2013), this allows the loan beneficiaries to be more confident concerning the investment and repayment of the loan.

4.2. Household Debt and Psychological Wellbeing

The negative relationship between debts and psychological well-being found in this study agrees with a large body of literature describing the association between various indicators of financial distress and psychological ill-health (Bridges & Disney, 2010; Taylor et al., 2007; Weich & Lewis, 1998). A longitudinal study was undertaken by Xiang, Tan, Kang, Zhang, & Zhu, (2019) concluded that debt and financial stress cause negative psychological wellbeing to occur. Choi (2009) stated that it is difficult to imagine that one would naturally have happiness when they are unable to pay their bills and debts. Not having the money to meet one’s obligations causes individuals to experience emotional havoc and stress on the psyche. Most people when confronted with the problem of increasing debt respond by borrowing more money to meet their obligations, and this in turn causes more strain as the inability to meet obligations becomes more obvious. In their study, Dobbie & Song (2015) found that individuals in debt reported higher symptoms of lack of sleep, change in eating habits, fluctuating weight and difficulty in concentrating at work and home. All these symptoms cause changes and stress on the psyche. It is common for individuals in debt to feel overwhelmed and despair at the possibility of never getting their finances in order again.

4.3. Household Socio-Demographic and Psychological Wellbeing

Age was found to have a statistically significant relationship with debt, this is in line with the findings of Moffat (2005) that age and loan defaulting were related statistically. In particular, young individuals were found to have a higher loan default rate compared to older participants. As people get older, they become more averse to taking risks, which equals to taking loans. They are more focused on saving and unwilling to start any new ventures hence the low default rate at older ages (Moffat, 2005). On the other hand, women with more education are likely to take loans. Such women are more willing to take risks in terms of entrepreneurship and have confidence concerning possible payment options. Women with assets and a strong social circle are more likely to access larger loans, while the less educated focus on small loans ( Chaudhuri, 2010). Educated women join groups with the main aim of taking large loans for business ventures and growth ( Chaudhuri, 2010).

Finally, the more the people living within a household, the more likely that the women will access and default on loans ( Chaudhuri, 2010). Access to loans is often with the main aim of meeting household demands. Income is low. Women are more inclined to take risks into entrepreneurship, gathering capital through loans in an attempt to meet the many needs of the family. However, as Chaudhuri (2010) point out, again because of the high number of people living within the household, the loan is rarely used for investment rather the monies may be used to cater for household needs such as healthcare and education. The result is that there is no extra income from which to draw payments for the loans which then leads to default.

5. Conclusion

This study has examined how credit counselling, household debt and socio-demographic factors influence the psychological wellbeing of WEF beneficiaries. The findings show that credit counselling, household debt, and age influence the psychological well-being of the WEF beneficiaries. The institutions dealing with credit provision should endeavour to provide credit counselling to the clients before giving them loans to ensure that they clearly understand the terms and implications of non-payment of the loans. Credit counselling will enable the beneficiaries to properly plan for the investment of the funds so that they can generate profits which can be used in the repayment of the loans and in enhancing the wellbeing of the loan beneficiaries.


The authors acknowledge WEF beneficiaries in Njoro sub-county for willing to share their personal information and the WEF institution in identifying the beneficiaries.

Conflicts of Interest

The authors declare no conflicts of interest.


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