On the predictors of loan utilization and delinquency among microfinance borrowers in Zimbabwe: A Poisson regression approach

Chamboko, Richard; Guvuriro, Sevias Article On the predictors of loan utilization and delinquency among microfinance borrowers in Zimbabwe: A Poisson regression approach Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Chamboko, Richard; Guvuriro, Sevias (2022) : On the predictors of loan utilization and delinquency among microfinance borrowers in Zimbabwe: A Poisson regression approach, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-13, https://doi.org/10.1080/23322039.2022.2111799 This Version is available at: https://hdl.handle.net/10419/303756 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 On the predictors of loan utilization and delinquency among microfinance borrowers in Zimbabwe: A Poisson regression approach Richard Chamboko & Sevias Guvuriro To cite this article: Richard Chamboko & Sevias Guvuriro (2022) On the predictors of loan utilization and delinquency among microfinance borrowers in Zimbabwe: A Poisson regression approach, Cogent Economics & Finance, 10:1, 2111799, DOI: 10.1080/23322039.2022.2111799 To link to this article: https://doi.org/10.1080/23322039.2022.2111799 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 16 Aug 2022. Submit your article to this journal Article views: 3187 View related articles View Crossmark data Citing articles: 5 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE On the predictors of loan utilization and delinquency among microfinance borrowers in Zimbabwe: A Poisson regression approach Richard Chamboko 1,2 * and Sevias Guvuriro 3 Abstract: Microfinance institutions (MFIs) are a prominent financial inclusion initiative in many developing countries. In Zimbabwe, however, less is known about microfinance borrowers, determinants of loan utilisation and borrowers’ repayment behaviour. Demonstrating that MFIs serve those who are economically marginalised and traditionally excluded from the formal financial system is useful in a country where most of the economic activities are in the informal sector. This study investigated the factors associated with the utilisation of microfinance loans and delinquency among microfinance borrowers using the Poisson, logit and the zerotruncated Poisson regression models on 6165 unique borrowers in Zimbabwe. The study findings revealed that microfinance loans were significantly more likely to be accessed by low-income individuals, who took small loans with relatively high instalments. Women were less likely to access microfinance loans, and reliable borrowers were more likely to access repeat loans. The level of income, number of previous loans and loan terms explained the delinquency among borrowers. Largely, the findings suggest that microfinance in Zimbabwe serves the needs of the lowincome group. However, policies that seek to improve access to credit for women and youth remain a priority. Subjects: Statistics for Business, Finance & Economics; Microeconomics; Econometrics; Banking; Credit & Credit Institutions; Financial Services Industry Keywords: Credit; microfinance institutions; delinquency; Zimbabwe; poisson regression JEL Classification: E51; G21; G23; G29 1. Introduction Access to formal credit remains low in developing countries and more so in Sub-Saharan Africa (SSA) where only 7% of adults have borrowed from a formal financial institution (Demirguc-Kunt et al., 2018). Low-income earners and micro, small and medium enterprises (MSMEs) in need of finance to start or expand their businesses largely remain credit constrained (IFC, 2020). To alleviate such access to finance challenges, many financial inclusion initiatives were proffered across the world (Girón et al., 2021). Many governments, donors and development agencies adopted financial inclusion as a development policy tool (Akeju, 2022; Chamboko & Guvuriro, 2021; Emara & El Said, 2021; Kim, 2022; Msulwa et al., 2021; Ozili, 2020). Through various policy agents, many governments promoted the establishment of microfinance institutions (MFIs) to afford financial access to those who are economically marginalised and excluded from the formal financial system (Abrar et al., 2021; Hermes & Lensink, 2011; Sane & Thomas, 2013). Chamboko & Guvuriro, Cogent Economics & Finance (2022), 10: 2111799 https://doi.org/10.1080/23322039.2022.2111799 Page 1 of 13 Received: 12 December 2021 Accepted: 06 August 2022 *Corresponding author: Richard Chamboko, International Finance Corporation, World Bank Group, Washington, DC, USA E-mail: [email protected] Reviewing editor: David McMillan, University of Stirling, Stirling, UK Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. These MFIs are often exempted from regulatory requirements or are subject to relaxed regulatory regimes (Sane & Thomas, 2013). By expanding access to finance, policy makers hypothesise that the financial tools (including credit) are poverty escape routes (Abrar et al., 2021; Ahmed & Hasan, 2009; Dunford, 2006; Littlefield et al., 2003; Msulwa et al., 2021). Women and youth are informally employed, and those residing in remote and rural areas are mostly economically marginalised and excluded from the formal financial system (Chamboko et al., 2018). Commercial banks find it costly to do business with these economic agents given their remoteness, fragmentation and the tininess of loans they typically need. Moreover, commercial banks consider extending credit to these economic agents as too risky due to lack of collateral, proof of residence and identity documents; proof of income and transaction history, as well as other information required to generate credit scores (IFC, 2020). It is in this context that MFIs are mostly established and promoted to reach out to these last mile clients with microfinance services. Notwithstanding the positive view about microfinance (provisioning of microcredit, microsavings, microinsurance and micropayments) and the social objectives that microfinance programmes seek to achieve, there has been longstanding doubts on the beneficiaries of these services and programmes (Hermes & Lensink, 2007; Quayes, 2021). Hulme and Mosley (1996), Scully (2004), and Simanowitz and Walter (2002) argued that microfinance services hardly reach the poor (or the poor are deliberately excluded) as they are deemed too risky. Exclusion criteria include the requirement to save with an MFI or having an already registered business before borrowing (Kirkpatrick & Maimbo, 2002; Mosley, 2001). Other critics also argued that the poor lack confidence and consider microfinance loans as too risky and hard to borrow from MFIs (Ciravegna, 2005). More recently, Churchill (2018), Churchill (2020) and Quayes (2021) questioned how MFIs balance between outreach depth (reaching to the poor) and sustainability. In Zimbabwe, MFIs have shown healthy growth in terms of number of institutions, their branches and assets (see, section 2). The Reserve Bank of Zimbabwe (RBZ) revealed that MFIs in the country had access to cheaper loans from the bank’s Microfinance Revolving Loan Facility for onward lending to advance the financial inclusion objectives (Reserve Bank of Zimbabwe, 2015–2020). However, there is no research evidence on the functioning of these MFIs, particularly looking at the determinants of loan utilization and repayment behaviour among the microfinance borrowers in this Southern African country. The absence of such studies extends beyond Zimbabwe into other Sub-Saharan African countries. Banna et al. (2022) suggests that the lack of studies on issues relating to microfinance borrowers and delinquency in developing countries is probably due to the lack of reliable data. It is important to ascertain whether the clients that the MFIs are serving are indeed the intended ones and to assess the factors that drive loan delinquency. Understanding these issues illuminates the success of MFIs as agents for financial inclusion in developing countries. Such a success can translate to the previously marginalised segment of society engaging in sustainable economic activities that may engender better welfare outcomes (Achugamonu et al., 2020). In the current study, we therefore seek to empirically determine the factors that predict the utilisation of microfinance loans and delinquency among microfinance borrowers in Zimbabwe. To achieve this, we use reliable data from a private credit bureau in Zimbabwe for loans extended by MFIs between 2013 and 2017. Insights from the study may be particularly unique given that the investigation is carried out in a country that has an ailing economy for a protracted period (Mazhazhate et al., 2020) and a large and growing informal sector (Dube & Casale, 2019). Lenders in developing countries’ environments may find the investigation of this nature useful from a targeting perspective to ensure that they reach out to the right clients with suitable loan products. In addition, the findings may shed light into the factors that need attention to mitigate credit losses. This paper may also provide useful insights on the role of MFIs on achieving governments’ financial inclusion objectives. Chamboko & Guvuriro, Cogent Economics & Finance (2022), 10: 2111799 https://doi.org/10.1080/23322039.2022.2111799 Page 2 of 13 The rest of the paper is structured as follows: Section 2 explores the trends on MFIs in Zimbabwe whilst section 3 provides literature review and hypotheses development. Section 4 describes the data and methods used in the study. Section 5 presents and discusses the results, and Section 6 concludes the paper. 2. MFIs trends in Zimbabwe During the past decade, Zimbabwe has seen a consistent growth on the number of MFIs operating in the country. Figure 1, extracted from the Reserve Bank of Zimbabwe (RBZ) Microfinance Industry Reports (2015–2020), shows that since 2009, there has been a steady increase in the number of MFIs. The figure grew from 95 MFIs in 2009 to 229 MFIs in December 2019. A decline in this number is seen in 2020 presumably due to the Corona Virus (Covid-19) pandemic. Similarly, branches of these MFIs grew significantly from only 106 in 2009 to 1,017 in 2019, before declining to 697 in 2020. Also, the client base grew from 58,325 in 2011 to 454,428 in 2019 before sliding down to 303,323 in 2020. Growth in branches and client base could also have been impacted by the Covid-19 pandemic. Most of the MFIs in Zimbabwe do not take deposits but provide credit only to their clients. Providing small loans to individuals and microentrepreneurs, usually excluded from commercial banks is the noticeable contribution offered by the MFIs in the country. By December 2020, there were only eight deposit-taking MFIs in the country, while the rest was credit-only MFIs. MFIs’ number and value of outstanding loans are shown in Figure 2. The number of outstanding loans steadily grew from 2013 to 2019 before substantially declining in 2020. Similarly, the value of 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 MFIs 95 114 146 150 143 147 152 185 183 205 229 198 Branches 106 118 132 278 334 473 571 639 682 750 1017 697 Clients 58325 96749 150188 205282 205940 290552 323286 293919 454428 303323 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 500000 0 200 400 600 800 1000 1200 Figure 1. MFIs, branch penetration and outreach. Source: RBZ Microfinance Industry Reports (2015–2020). 0 1,00,000 2,00,000 3,00,000 4,00,000 5,00,000 6,00,000 0 500 1000 1500 2000 2500 2013 2014 2015 2016 2017 2018 2019 2020 Total value of outstanding loans ($mill) Number of outstanding loans Figure 2. Growth in outstanding loans. Source: (RBZ) Microfinance Industry Reports (2015–2020). Chamboko & Guvuriro, Cogent Economics & Finance (2022), 10: 2111799 https://doi.org/10.1080/23322039.2022.2111799 Page 3 of 13 outstanding loans steadily grew between 2013 and 2019 and substantially rose in 2020, a situation which could possibly be explained by an increase in the ticket sizes on the advances. Statistics on the number of MFIs, their branches, clientele size, number and value of outstanding loans suggest that the Zimbabwean microfinance industry is growing. However, the Zimbabwe Association of Microfinance Institutions lamented high default rates and how that threatened the sustainability of the MFIs (The Sunday News, 2018). Identifying the predictors of microfinance loan utilization and loan delinquency is thus vital to support the growth of the microfinance industry. 3. Literature review and hypotheses development Access to credit as a conduit to facilitate upward social mobility is one of the pillars of financial inclusion as a developmental tool. With increased financial inclusion, the economically marginalised can accumulate human or physical capital and/or engage in entrepreneurial activities (Kling et al., 2022; Mehrotra & Yetman, 2015; Nimbrayan et al., 2018; Otioma et al., 2019; Van Hove & Dubus, 2019). However, financial market imperfections such as transaction costs and information asymmetries hinder commercial banks from serving this stratum of the population. In addition, MFIs whose primordial design was to afford financial access to this stratum of the population (Abdullah & Quayes, 2016; Abrar et al., 2021; Hermes & Lensink, 2011; Sane & Thomas, 2013), are drifting to profit-making and commercialisation for sustainability reasons (Chikalipah, 2018a). In this section, we review the literature on the factors that influence access to and use of credit from MFIs as well as the repayment behaviour to explore the extent to which the marginalised benefit from financial inclusion initiatives. The first important factor that explains MFIs clients’ access and use of credit is gender. Aggarwal et al. (2015) reported that while the gender dimension to access and use of credit from MFIs varies internationally, MFIs generally have more women borrowers than men. Reed et al. (2015) reported in the 2015 Microcredit Report that 82% of the poorest clients served by the MFIs were women. Using the MIX database over the period 2001 to 2014, Hessou et al. (2021) showed that more than 60% of active MFI borrowers were women irrespective of whether the MFI has a deposit-taking status or not and whether it is profit oriented or not. Hemtanon and Gan (2020) reported that the Village Funds category of MFIs in Thailand targets low-income rural households mostly with female heads. Abdullah and Quayes (2016), however, showed that although most microcredit borrowers were female, recent trends show an increase in male representation, using a panel of 891 MFIs over a period of 10 years. There are two arguments why MFIs have more women borrowers than men. The first argument is that women are a component of the poorest strata of the population and thus fit in the MFIs’ initial goal (Aggarwal et al., 2015). The second argument is that women have greater social impact, are more trustworthy and lead to better loan portfolio quality and financial performance (Aggarwal et al., 2015). The age of the client is the second loan utilisation factor identified in empirical studies. Hemtanon and Gan (2020) reported that the Village Funds category of MFIs mostly serve the old household heads, while the “Savings Group Production” category of MFIs serves the young household heads. Sangwan and Nayak (2020) found that MFIs in India experience a higher loan demand from younger people compared to older people. Kodongo and Kendi (2013) shows that MFIs tend to avoid younger clients (below 30 years) arguing that most MFI loans are targeted for business purposes, yet youthful applicants are unlikely to have adequate business experience. The third factor relates to the clients’ level of income. As MFIs shift from operating as “not-for-profit” to profit-oriented organisations, the target is shifting from the poorest of the poor to salaried workers and micro-businesses in need of relatively large loan amounts (Chikalipah, 2018b). This is in harmony with commercialisation move reported earlier. The data we have for our study enable us to assess the influence of gender, age and income levels on MFIs loan utilisation in Zimbabwe. We also explore the significance of loan variables (loan amount, instalment size, loan term) as the data permit. Turning to loan repayment, Fadikpe et al.’s (2022) study in the Sub-Saharan Africa (SSA) shows that having more female borrowers is associated with better repayment rates. Similarly, Chikalipah (2018a) Chamboko & Guvuriro, Cogent Economics & Finance (2022), 10: 2111799 https://doi.org/10.1080/23322039.2022.2111799 Page 4 of 13 shows that women borrowers in the Microfinance industry in SSA are less risky. Earlier studies (e.g., Agier & Szafarz, 2013; Baklouti, 2013; D’espallier et al., 2013, 2011; Hulme et al., 1996; Schicks, 2014; Strøm et al., 2014; Todd, 1996) suggested that women in particular, and low-income earners in general, default less after borrowing from the MFIs, compared to men and high-income earners, respectively. These studies pointed out that better repayment behaviour by women could be due to them investing in types of business that allow easier repayment (D’espallier et al., 2011) or just being conservative (Todd, 1996). The conservative explanation links to Croson and Gneezy’s (2009) finding of women being risk averse. Earlier, Sharma and Zeller (1997) reported women’s risk averseness reflected by the less risky business activities they embark on. Another explanation that is suggested in the literature is that women have fewer credit opportunities than men and must therefore religiously repay their loans to ensure continued access to credit (Armendariz & Morduch, 2010). Contrary to the above, Sangwan et al. (2020) in agreement with Dorfleitner et al. (2017) show that higher-income households are less likely to be delinquent. The authors attributed this to the idea that higher-income individuals or households are likely to start and operate high return entrepreneurial activities with better cash flow and hence increase their chances to repay. With respect to the client’s age, earlier studies (e.g., Bhatt & Tang, 2002; Dunn & Kim, 1999; Kodongo & Kendi, 2013; Mokhtar et al., 2012) reported that age correlates negatively with the probability of loan default, suggesting that older borrowers are more responsible and disciplined compared to younger borrowers. However, Baklouti (2013) found a nonmonotone relationship and argued that younger borrowers default less as they have more independence compared to the middle-aged borrowers, while the older borrowers default less as they have, and over time become more risk averse, more knowledgeable and more responsible. Related to other variables in our data, empirical research has also reported on the relationship between credit risk and loan variables such as loan amount and duration as well as number of previous loans. Some studies show that the loan amount associates positively with repayment (e.g., Baklouti, 2013; Kodongo & Kendi, 2013; Mokhtar et al., 2012). However, other studies report the contrary (e.g., Baesens et al., 2011; Chikalipah, 2018a; Van Gool et al., 2012). Dinh and Kleimeier (2007) and Kočenda and Vojtek (2011) reported on the number of previous accorded loans associating with lower default probability, a result that could be due to lender–borrower relationship. However, Baklouti (2013) found that repeat borrowers default more than those who infrequently borrow. Kodongo and Kendi (2013) reported that repayment period or loan duration does significantly influence delinquency regardless of lending methodology (group or individual) or loan size. Given the growing number of MFIs in Zimbabwe, a country with an ailing economy and expanding informal sector, two hypotheses are proffered for this study: Hypothesis 1: MFI loan utilisation is concentrated among those who are in the poorest strata of the population (women, youth, and low-income earners) Hypothesis 2: Those in the poorest strata of the population (women, youth, and low-income earners) have a high propensity to repay their loans compared to other groups. 4. Methodology 4.1. Data The study uses data from a private credit bureau in Zimbabwe. The focus is on personal loans extended by MFIs between 2013 and 2017. The sample consists of 6,165 microfinance borrowers for the selected period. The variables of interest include the borrowers’ demographics (age, gender and income); loan variables (loan term, loan amount and instalments size); a behavioural variable (number of missed payments), and number of previous loan contracts. The outcome variable for the component that Chamboko & Guvuriro, Cogent Economics & Finance (2022), 10: 2111799 https://doi.org/10.1080/23322039.2022.2111799 Page 5 of 13 predicts the factors explaining loan utilization is the number of MFI loans an individual has accessed from different MFIs over a five-year period. It is, however, important to note that having taken many loans does not equate to better financial outcomes compared to taking fewer loans. Thus, the scope of the study ends on predicting those who are likely to have repeat loans and profile their characteristics and does not delve into the welfare outcomes of those individuals. On the drivers of loan delinquency, the outcome of interest is the number of missed payments. 4.2. Descriptive analysis Table 1 provides a summary of the descriptive statistics for the sample. Fifty-nine (59) percent of the borrowers were males. While one in 10 borrowers is of age above 55 years, about a quarter of the sample are below 35 years of age. Slightly above a third (35%) and slightly below a third (30%) are within the age ranges of 35 to 44 and 45 to 54 years, respectively. For the four income categories presented, each claims a reasonable share, ranging from 20% (for < US$250) to 28% (US$350-US$500). On average, borrowers took four loans with a minimum of one and a maximum of nine. The average loan term is 10 months and average instalment size of US$115. Loan delinquency ranges from 0 to 9, however, with a low average of 0.28 missed payments. 4.3. Empirical strategy On determining the factors associated with microfinance loan utilisation, the outcome of interest is the number of loans accessed by individual borrowers over a 5 year-study period. Thus, the Table 1. Descriptive statistics Variable: Percentage Sample size (n) Gender Male 59.1 6165 Female 40.9 Total 100.0 Age (years) <35 24.2 6165 35–44 35.3 45–54 30.4 55+ 10.0 Total 100.0 Income (US$) <250 20.0 6165 250–350 24.5 350–500 28.5 500+ 27.0 Total 100.0 Loan Amount (US$) <500 19.0 6165 500–749 21.1 750–999 19.8 1000–1499 20.7 1500+ 19.4 Total 100.0 Variable: Mean (S.D) [Min-Max] Sample size (n) Number of loans per borrower 4.18 (2.385) 1–9 6165 Loan Term in months 10.13 (3.416) 3–60 6165 Instalment Size 115.58 (112.592) 10–1308 6165 Number of missed payments 0.28 (1.505) 0–9 6165 Note: S.D = standard deviation, Min = minimum, Max = Maximum. Chamboko & Guvuriro, Cogent Economics & Finance (2022), 10: 2111799 https://doi.org/10.1080/23322039.2022.2111799 Page 6 of 13 dependent variable (number of loans), yi is discrete and non-negative and can be regarded as count data and assumed to follow a Poisson distribution such that the expectation of yi is assumed to be λi. The count data model formulation is as follows: In λi ð Þ ¼ xiβþεi where xi is the vector of explanatory variables, β is a vector of coefficients associated with xi and εi is the error term. Given that yi is count data, the probability of yi conditional to εi is expressed as follows: P yijεi ð Þ ¼ exp λi ð Þλiyi yi! To ascertain the drivers of loan delinquency and the depth therefore, the analysis was implemented in two steps. The first step treated the outcome as binary, where borrowers were categorised into zero (no missed payments), and 1 for those who missed one or more payments during the five-year period. To model this outcome, the logit model was implemented, and this is mathematically expressed as: logit πi ð Þ ¼ x0 iβ Where xi is a set of covariates and β represents the vector of regression coefficients. For the second step which models the depth of delinquency among those who missed payments, the outcome of interest was the number of missed payments during the five-year period. In this case, the outcome was also counted (excluding zero); hence, a zero-truncated Poisson regression model was implemented (an extension of the Poisson regression model). 5. Results and discussion The results presented in Table 2 show that women were significantly less likely to take microfinance loans when compared to males [marginal effects (ME) = −0.2; p < 0.10]. The age of the borrower was not an important factor with regard to loan utilisation. Compared to those whose incomes were above US$500, borrowers with lower incomes were significantly more likely to take repeat loans [with ME for those whose incomes were below US$250 as 1.01 (p < 0.01), incomes between US$250 and US $349 as 0.90 (p < 0.01), and those with incomes between US$350 to US$499 as 0.83 (p < 0.01)]. The results also show that those who took smaller loans were significantly more likely to take repeat loans compared to those who took loans of higher values. Compared to those who took a loan amount of more than US$1500, the ME of repeat borrowing for a loan amount of less than US$500 was 0.75 (p < 0.01), a loan amount between US$500 and US$749 has a ME of 0.49 (p < 0.01), and a loan amount between US$1000 and US$1499 has a ME of 0.46 (p < 0.01). Borrowers who had loans on longer terms were more likely to repeat borrowing [ME = 0.04; p < 0.05] and so were those with higher instalment sizes [ME = 0.0009; p < 0.01]. Those borrowers with higher incidence of missing payment were significantly less likely to have repeat loans [ME = −0.23; p < 0.01]. The results reported on gender and age of the borrower are in line with studies in other countries. A study by Chamboko et al. (2021) in Democratic Republic of Congo and by Milana and Ashta (2020) that compares some developing and developed countries, reported that women and youth continue to have limited access to credit. The results do not support the development thesis postured in support of microfinance programmes. Thus, the hypothesis that MFIs target women and youth as segments that are excluded from traditional financial systems dominated by commercial banks does not hold for the Zimbabwean data. Chamboko & Guvuriro, Cogent Economics & Finance (2022), 10: 2111799 https://doi.org/10.1080/23322039.2022.2111799 Page 7 of 13