Credit allocation, risk management and loan portfolio performance of MFIs—A case of Ugandan firms

Ssekiziyivu, Bob; Mwesigwa, Rogers; Joseph, Mayengo; Nabeta, Isaac Nkote Article Credit allocation, risk management and loan portfolio performance of MFIs—A case of Ugandan firms Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Ssekiziyivu, Bob; Mwesigwa, Rogers; Joseph, Mayengo; Nabeta, Isaac Nkote (2017) : Credit allocation, risk management and loan portfolio performance of MFIs—A case of Ugandan firms, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 4, https://doi.org/10.1080/23311975.2017.1374921 This Version is available at: https://hdl.handle.net/10419/206007 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. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. 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/ Ssekiziyivu et al., Cogent Business & Management (2017), 4: 1374921 https://doi.org/10.1080/23311975.2017.1374921 BANKING & FINANCE | RESEARCH ARTICLE Credit allocation, risk management and loan portfolio performance of MFIs—A case of Ugandan firms Bob Ssekiziyivu 1 *, Rogers Mwesigwa 1 , Mayengo Joseph 1 and Isaac Nkote Nabeta 2 Abstract:Purpose: The purpose of this study was to establish examine the relationship between credit allocation, risk management and loan portfolio performance of MFIs in Uganda. Design/methodology/approach: A cross-sectional research design was adopted which involved descriptive, correlation and regression approaches. Data were analysed through SPSS. Simple random sampling was used to select a sample of 40 MFIs from the population of 45 in Kampala and Wakiso districts. Findings: Results indicated that credit allocation and risk management had a significant relationship with loan portfolio performance. Results from the regression analysis showed that credit allocation and risk management significantly predicted 23.9% of loan portfolio performance. Practical implications: It was recommended that managers of the MFIs should conduct pre-disbursement trainings through workshops and seminars for all successful loan applicants which would enable them on how to utilize the loan facilities acquired which will eventually reduce on *Corresponding author: Bob Ssekiziyivu, Department of Business Administration, Makerere University Business School, Kampala, Uganda E-mail: [email protected] Reviewing editor: Oluwafemi Oyemomi, Northumbria University, UK Additional information is available at the end of the article ABOUT THE AUTHORS Bob Ssekiziyivu holds master and bachelor’s degrees in Business Administration from Makerere University and a postgraduate Diploma in Business Education from Makerere University Business School. He is currently studying CPA from Institute of Certified Public Accountants of Uganda (ICPAU). His research area is microfinance management. Rogers Mwesigwa is a PhD student at Makerere University. He holds an MBA and Bachelor of Human resource Management at Makerere University. He also holds a CPA certificate from Institute of Certified Public Accountants of Uganda (ICPAU). His research interests are in areas project management and sustainability and microfinance. Mayengo Joseph holds master and bachelor’s degrees in Business Administration from Makerere University. His research interest is in strategic management and financial management. Isaac Nkote Nabeta holds a PhD in finance and he is currently the dean, Faculty of Commerce at Makerere University Business School. His research area is Finance management. PUBLIC INTEREST STATEMENT Access to credit can play a pivotal role in economic growth. Banks and lending institutions provide the services that allow people to save and invest available assets and resources, which further supports and strengthens economic activity. Within economically poor communities, MFIs provide the credit access and financial services needed to develop income-earning businesses. However, in Uganda, the continued deterioration of loan portfolio performance among MFIs prompted the government to take several measures to improve on their performance but none of the measures have yielded positive results. Thus, this paper helps government, clients and MFIs to well understand issues of credit allocation, risk management and loan portfolio performance. Pre-disbursement training is recommended for all successful loan applicants for efficient loan allocation and management. This can be done through sensitization workshops for borrowers so to increase their financial literacy and utilization of the acquired loan facilities and this will eventually reduce on the default rates to improve the portfolio management in Ugandan MFIs. Received: 28 June 2017 Accepted: 29 August 2017 First Published: 04 September 2017 © 2017 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Page 1 of 13 Bob Ssekiziyivu Page 2 of 13 Ssekiziyivu et al., Cogent Business & Management (2017), 4: 1374921 https://doi.org/10.1080/23311975.2017.1374921 the default rates. Originality/value: This is one of the few studies that focus on credit allocation, risk management and loan portfolio performance of MFIs within the context of Uganda. Subjects: Economics; Finance; Business, Management and Accounting Keywords: MFI; credit allocation; risk management and loan portfolio performance 1. Background Loan portfolio performance continues to attract attention of scholars and policy-makers due to the long reputable need for credible Microfinance institutions (MFIs). Some empirical evidence has shown that in most developing economies, MFIs have brought millions of citizens into cohesive financial institutions which are succeeding very well in providing financial services to its members for improving their standard of living (Collier, Katchova, & Skees, 2011; Kumar & Golait, 2009; Moti et al., 2012). In continuing with this service, Biekpe and Kiweu (2009) point out that loan portfolio performance of MFIs is critical. With issues of over-indebtedness emerging among microfinance customers. Microfinance institutions aim at maximizing the return to a portfolio while keeping the risk within acceptable bound (Van der Maas, 2006). This maximization requires a balancing of high repayment rates, low arrear rates, low default rates as well as low portfolio at risk. Unfortunately for Ugandan case, MFIs suffer from poor credit allocation strategies and weak risk management practices according to Association of Microfinance Institutions Uganda (AMFIU), report (2014). Loan portfolios are the major asset of MFIs and various studies have been undertaken as regards to, for example, loan portfolio performance (González-Vega, 2003; Kropp & Katchova, 2011; Qinlan & Izumida, 2013). On the other side, credit allocation has a responsibility of ensuring the distribution of loans to different portfolios. According to Mathur and Marcelin (2014), credit allocation is a process of how a bank divides its financial resources and other sources of credit to different processes, borrowers and projects. Overall, it is management’s goal to optimize credit allocation so that it generates as much wealth as possible for its shareholders. In a market with perfect information, there exists an equilibrium point where both demand and supply for loans are satisfied (Ciaian, Fałkowski, & Kancs, 2012). Furthermore, risk management enhances recovery rates of MFIs. Risk management is a cornerstone of prudent banking practice; undoubtedly all banks in the present-day volatile environment are facing a large number of risks such as credit risk, liquidity risk, foreign exchange risk, market risk and interest rate risk, among others risks which may threaten a bank’s survival and success (Beresford‐ Smith & Thompson, 2007). The continued deterioration of loan portfolio performance among MFIs in Uganda prompted the government to take several measures to improve on their performance but none of the measures have yielded positive results (Eriku, 2010). According to AMFIU (2014), MFIs in Uganda are customerfocused financial institutions offering relevant, accessible and affordable banking products and services in a refreshing manner with 514,214 borrowers and total loans of 612.5 million US dollars. However, MFIs loan loss grew from USh.114.6 billion to USh.243.1 billion between June 2013 and June 2014. The sector’s Non performing loans (NPLs) grew by USh.48.2 billion to reach USh.116 billion at the end of June 2014, thereby accounting for 22.8% of the total NPLs in MFIs (https://www. mixmarket.org). The above concerns suggest an important need for more empirical studies on the relationship between credit allocation, risk management and loan portfolio performance. It is therefore upon this background that prompted the researchers to examine the relationship between credit allocation and risk management on loan portfolio performance of MFIs in Kampala city and Wakiso district. Page 3 of 13 Ssekiziyivu et al., Cogent Business & Management (2017), 4: 1374921 https://doi.org/10.1080/23311975.2017.1374921 Although some MFIs in Uganda have strived to improve on their performance, most of them have been unsuccessful in achieving loan portfolio performance. The success of MFIs largely depends on the effectiveness of their credit management systems because these institutions generate most of their income from interest earned on loans extended to small and medium entrepreneurs. However, a number of them have reported poor loan portfolio performance. MFIs are faced with low repayment rates, high non-performing loans (NPLs), high arrears rates and high portfolio at risk. MFIs’ non-performing loans grew by USh.48.2 billion to reach USh.116 billion at the end of June 2014, thereby accounting for 22.8% of the total NPLs in the industry. Thus, in a view of the persistent unsatisfactory performance, credit allocation and risk management practices are questionable. 2. Literature review 2.1. Portfolio performance of MFIs Microfinance is defined as the provision of financial services to low-income clients, including consumers and the self-employed, who traditionally lack access to banking and related services (González-Vega, 2008). Microfinance is a place for the poor and near poor clients to get access to a high quality financial service, which include not just credit but also savings, insurance and fund transfer. According to Ledger wood, Microfinance is a provision of a broad range of financial services such as savings, credit, insurance and payment services to the poor or low-income group who are excluded from the normal banking sectors. A series of theoretical literature on microfinance has proposed numerous models to explain how the joint liability approach works before and after the loans’ disbursement (Brehanu & Fufa, 2008; Armendariz & Morduch, 2005). Before the disbursement of loans, most lenders face the problem of adverse selection. It is expected that MFIs must have sufficient information about incomes, repayment capacities and creditworthiness of borrowers, and that they will use this information to make decisions. However, after the loan disbursements, the MFIs may be confronted with a moral hazard and enforcement problems. Further, available literature shows that threatening not to refinance defaulters or offering larger loans to borrowers who repay their debts, creates an incentive for peer monitoring, peer pressure and intra-group help among the borrowers (Armendariz & Morduch, 2005; Ogawa, Parker, Singh, & Thacker, 2009). Microfinance Institutions in Uganda have tried to exploit the benefits of diversification. However, there has been no method for actually measuring the amount of diversification in a debt portfolio. Thus it should not come as a surprise that there have been many unexpected default events in MFIs portfolios in the last ten years (Bank Finland Plc’s Annual Report & Corporate Governance Report’s, 2013). Portfolio analysis has been applied successfully in a variety of areas of finance, notably to equity portfolios. The analysis shows the amount of risk reduction achievable through diversification. Bernile, Cumming, and Lyandres (2007) hypothesize that MFIs have increased focus on the effects of increased bank management practices on portfolio size and subsequent effects on portfolio success rates (González-Vega, 2008; Obamuyi, 2009). Lending to lower income group raises many debates among practitioners and academicians. The poor are usually excluded from credit facilities because of many reasons which include insufficient collateral to support their loans, high transaction costs, unstable income, lower literacy and high monitoring costs. 2.2. Credit allocation The role of the financial system is to allocate the bank’s savings to its highest value uses (Wurgler, 2000), including new firms (Kimutai & Ambrose, 2013). Consequently, how well firms in the financial sector, especially banks and MFIs, are governed affects not just those firms, but the efficiency of capital allocation across the entire economy. Credit allocation happens when lenders, in spite of having sufficient funds, do not offer loans to all applicants who are able to pay the prevailing interest rates or the non-price element of a loan contract such as collateral requirement (Ke, Wang, & Chan, 2011). Page 4 of 13 Ssekiziyivu et al., Cogent Business & Management (2017), 4: 1374921 https://doi.org/10.1080/23311975.2017.1374921 Average loan sizes are typically small and the institutional environment is underdeveloped, making adequate screening and thorough enforcement efforts immensely costly per unit of credit. One particular problematic characteristic of credit markets in developing countries is that they usually lack private or public mechanisms which assist lenders in sharing information about current borrowers and new loan applicants. 2.3. Risk management In the management of credit risk we thus have to deal with “true uncertainty” in the sense of Frank Knight (Voropaev, 2009) who was the first to distinguish between “risk” based on known probability measures and true uncertainty where the underlying statistical distributions are unknown. Knight’s ideas have been further developed by several authors over the years and in particular by Ben-Haim (2001) who has developed a quantitative formulation known as information-gap decision theory. Ben-Haim (2005), has recently applied this theory to the management of financial market risk. Beresford‐Smith and Thompson (2007) provides that the management of credit risk is now commonplace in most financial institutions where safeguards are needed to lower potential losses from defaults on loans and therefore quantitative methods for managing these and other risks are now required in most countries. Further, Al-Tamimi and Al-Mazrooei (2007) stipulates that all banks in the present-day volatile environment are facing a large number of risks such as credit risk, liquidity risk, foreign exchange risk, market risk and interest rate risk, among others and such risks may threaten a bank’s survival and success. In other words, banking is a business of risk and for this reason, efficient risk management is absolutely required. According to the consultative paper issued by the Basel committee on banking supervision (see Hassan and Sanchez (2009)); most banks’ loans are the largest and most obvious sources of credit risk. Banks are increasingly facing credit risk in various financial instruments other than loans, including acceptances, interbank transactions, trade financing, foreign exchange transactions, financial futures, swaps, bonds, equities, options, the extension of commitments and guarantees and the settlement of transactions (Rosenberg, Gonzalez, & Narain, 2009). 2.4. Credit allocation and loan portfolio performance Bank Finland Plc’s Annual Report and Corporate Governance Report’s (2009) stipulates that credit allocation is an important determinant of loan portfolio performance. Recent theories predict considerable impacts of credit allocation on loan portfolio performance and in this regard, most MFIs in developing countries face this problem of temporal credit allocation which can be prevailed over through effective systems in managing this process. When credit allocation is efficiently managed, MFIs will have a better performance (Oboh & Ekpebu, 2011). There are several characteristics common to these studies. First, almost all of the studies investigated the effects of banks’ capital adequacy ratio and ratio of non-performing loans to total loans on credit allocation (Bank Finland Plc’s Annual Report & Corporate Governance Report, 2013). Many of the studies obtained significantly positive effects with respect to the capital adequacy ratio and significantly negative effects with the non-performing loans ratio in relation to bank loans. In other words, the credit crunch hypothesis is supported by these studies (Qinlan and Izumida, 2013). It is true that, in these studies, the factors affecting demand for bank loans were partially controlled for; however, they only estimate the supply schedule of bank loans. One exception is Ohkusa’s study (2002), in which the level and growth rate of firms’ profits are shown to be important factors that influence the lending attitude of financial institutions. Given that it is not an easy task to thoroughly identify demand and supply with respect to bank loans, it would be preferable to include not only factors affecting bank loan supply but also those affecting demand for bank loans. Page 5 of 13 Ssekiziyivu et al., Cogent Business & Management (2017), 4: 1374921 https://doi.org/10.1080/23311975.2017.1374921 Secondly, most of these studies examine total bank loans, with the exception of Ogawa (2003). They divide loans into several components and pay special attention to the relationships between banks’ balance sheet conditions and bank loans to small firms. Ogawa’s study (2003) revealed evidence suggesting that the effect of the non-performing loans ratio is particularly strong for loans to small firms. Moreover, Ogawa also examined the effect of the non-performing loans ratio on bank loans by industry and reported its effect on loan portfolio performance. More formal empirical works estimate the supply equation of bank loans. The balance sheet variables commonly used in these studies are the ratio of non-performing loans to total loans, banks’ capital adequacy ratio and firms’ debt-asset ratio. Guido (2008) found that banks with a higher nonperforming loans ratio tend to increase loans to the construction industry while Giné & Karlan, 2010) also found that non-performing loans to the real estate industry exert a significantly positive effect on loans granted to this industry. Tsuru (2001) found that the capital adequacy ratio had significantly negative effects on loans made to the real estate industry. Heilig, Young, and Williams (2012) argued that banks with a lower capital adequacy ratio tend to increase real estate loans. The studies by Hibara (2002), Kobayashi , Saita, and Sekine (2002), and Peek and Rosengren (2005), are unique in that they used matched samples of individual firms and bank lenders’ transactions. By examining the relationship between the non-performing loans ratio and the debt-asset ratio, Hibara (2002), found that banks with higher non-performing loans ratios tended to use less restraint in making loans to firms with high debt-asset ratios. Kobayashi et al. (2002) showed that beyond a certain point of debt-asset ratio, a rise in the debtasset ratio corresponded with an increase in lending to firms in the construction and real estate industries. Peek and Rosengren (2005) offer the most comprehensive micro study. They demonstrated that Japanese banks increased loans to less profitable firms in the 1990s. Moreover, they found that this was especially so for banks with balance sheet deterioration and loans to affiliated firms. Since listed firms’ information constitutes the micro data, the evidence supporting the ever greening argument is confined to large firms, with no information concerning small or medium-sized firms included. It is worth noting that small or medium-sized firms are more dependent on bank loans, so investigation into how credit allocation is influenced by the balance sheet conditions of firms, as well as that of banks, is important. The benefit of examining micro data from firms and banks in the context of this study is that it enables us to make clear estimates of the effects of balance sheet conditions of firms and banks on credit allocation. However, we fail to obtain macro or industry level implications for credit allocations from micro level evidence. It is true that credit is constrained for some firms with heavy debts and is expanded for some firms with excessive debts, but micro level evidence is silent as to the aggregated consequences of credit allocations on the industry level. In the subsequent sections, we investigate how credit is allocated for small and medium-sized firms as well as large firms. 2.5. Risk management and loan portfolio performance As a lending institution, MFI is a risky business because it offers credit to the poor people without any collateral and the repayment of loans cannot be fully guaranteed. However, the repayment rate is the most important performance indicators of MFIs. Yang and Mwase (2012), finds that the failure of a large number of MFIs in many developing countries was due, among other things, to their inability to ensure good repayment rates among their borrowers. Venkiteshwaran (2014), also emphasizes that a high profit earned by MFIs cannot be used as the only indicator of self-sustainability of the institutions, since a high profit could be attained only in the short period. Attaining a high loan collection rate is a necessary condition for MFI to become self-sustainable in the long-run. Loan losses often have been the largest cost borne by the institutions and the principal cause of insolvency and illiquidity. Page 6 of 13 Ssekiziyivu et al., Cogent Business & Management (2017), 4: 1374921 https://doi.org/10.1080/23311975.2017.1374921 Measuring the effect of risk management on loan portfolio performance is instrumental to understanding the channels through which internal practices affect the lending process. Rehman, suggests that optimal application and commitment towards risk management result in an increased company’s perfomance, the financially well managed MFIs are operationally efficient. Risk management practices promote more strategic consideration of risk and its effective implementation can create a long-term competitive advantage (Nocco & Stulz, 2006). Luhmann (2005) argues that risk unlike danger and uncertainty, implies a domain for decision-making about the future. Therefore, risk management as a component of financial management practice creates an expectation of decidability and management of uncertainty and opportunity (Power, 2007). Indeed, certain risk management practices provide strategies that can influence a large number of customers to have a lasting preference for an MFI’s products. Thompson, Strickland, and Gamble (2009) are of the view that the adoption of risk management techniques may provide an organization with a sustainable competitive advantage over its rivals. 2.6. Credit allocation and risk management Merton-type models, also referred to as structural models, such as Portfolio Manager (Becker & Milbourn, 2011) and Credit Metrics, have become a standard choice for financial institutions’ credit risk economic capital frameworks. In these models, default correlations between different borrowers are modelled using a set of common systematic risk factors associated with the state of economy. Computationally heavy Monte Carlo simulations are usually used for calculations of portfolio-wide risk measures as well as credit allocation to sub-portfolios and/or individual exposures. However, simulation-based risk allocation on exposure level suffers from Monte Carlo noise and is especially demanding in terms of computer power/time. Unfortunately, most of the research on credit allocation techniques focuses on “advanced” risk measures like value at risk (VaR) and expected shortfall (ES), leaving variance–covariance-based allocation approach aside. Despite the shortcomings of this approach, this (old-fashioned) allocation method still remains the allocation method of choice for many financial institutions. Yet, no efficient analytical solution has been reported so far. A brute force approach consists of calculations of all pairwise correlations in the portfolio and the amount of such calculations is quadratic in the number of loans in the portfolio. This quadratic complexity of the calculations makes such an approach impractical for big portfolios. In this article, a variance–covariance-based analytical credit allocation technique is proposed. The proposed approach is applicable to fully featured Gaussian multi-factor Merton-type models, is suitable for virtually any portfolio size and composition and is remarkably accurate and fast. The main advantage of the proposed technique is that the underlying algorithm is of linear complexity in portfolio size (Al-shibly, 2013). 2.7. Research methodology The study was carried out using a cross-sectional research design where a population of 45 registered MFIs in Kampala city and Wakiso district (AMFIU 2014) and a sample of 42 was chosen according to Krejcie and Morgan (1970). The unit of analysis was the microfinance institutions and the unit of enquiry was the MFI officers who are General Managers (GM), Branch Managers, risk managers and supervisors. A list of officers was obtained from the Human resource officers where random numbers were assigned to each officer. In order to fulfil the objectives of the study, the researchers selected a sample of respondents who were considered sufficiently experienced on the study variables especially from the point of view of their work experience. The appropriate respondents were purposively selected, based on their knowledge of the information required, accessibility, experience and willingness to participate in the study. Though four officers per MFI were targeted, the number of respondents from the firms ranged between three and four officers. To address variations in firm responses, a minimum of three officers were considered for analysis. The decision to accept a minimum of three senior staff per firm is based on Baer and Frese (2003). Page 7 of 13 Ssekiziyivu et al., Cogent Business & Management (2017), 4: 1374921 https://doi.org/10.1080/23311975.2017.1374921 2.8. Data source, data collection instrument, validity and reliability Primary data were obtained from officers of registered operating MFIs by use of the questionnaire. The respondents were requested to indicate the extent of their agreement with a series of questions on a five-point Likert scale. The instrument was pre-tested through a pilot study to get rid of any possible errors to ensure its validity and reliability. Research assistants were hired, but guided to ease the data collection process. Validity of the questionnaire was established using Content validity Index (CVI) to determine the relevance of the questions in measuring the variables (Campbell & Stanley, 1966). In order to test and improve the validity of the questionnaire, the researchers availed the first draft of the instruments to experienced researchers for constructive criticism and then later to the supervisors. These were requested to look at the items and check on language clarity, relevancy, comprehensiveness of content and the length of the instruments. Thereafter, the researchers made the necessary adjustments in respect to the comments. The researchers went ahead to calculate a Content Validity Index (CVI). The questionnaire was given to experts who rated the questions by providing their comments and the researcher used their judgements to change some of the items in the questionnaire. In order to establish the reliability of the instrument, the researchers conducted a pilot study. Using the results of the pilot study, the reliability of the instruments was computed using the Cronbach’s Alpha Coefficient. Results in Table 1 indicate that the research instrument contained items relevant to the study. This is because the content validity index for all the variables was above the acceptable threshold of 0.7 according to George and Mallery (2003) and Gliem and Gliem (2003). The Cronbach’s alpha coefficients of all the variables of over 0.7 implied that the instrument could be relied on to produce consistent results as commended by George and Mallery (2003). 3. Findings 3.1. Demography Results from the analysis indicate that out of the 139 respondents, 57.6% of the respondents were males and 42.4% were females. Of the 139, 55.4% were aged between 25 and 35 years, followed by those aged between 36 and 45 years accounting for 33.1%, those below the age of 25 years accounted for 10.1% and lastly were those aged between 46 and 55 years accounting for 1.4%. A question was asked to find out the level of education of the respondents, and 54% were found to have attained a bachelor’s degree, 35% a master’s degree, and 11% were found to have attained certificate/Diploma. In terms of positions held by the respondents, the position of branch manager accounted for 27.3%, supervisors 26.6%, general manager 24.5% and risk manager 21.6%. Experience wise 47.5% of the respondents had spent less than 5 years in the organization, 45.3% have spent between 6 and 10 years, 5.8% have spent 11 and 15 years and 1.4% had spent 16 and 20 years. In terms of firm financing, 45% were found to use equity and loans as a form of finance, 25% use equity capital, 17.5% use donations, and 12.5% use loans only as a form of financing. Further in terms of number of years of operation, 41% were found to have existed between 6 and 10 years, 29.5% between 11 and 15 years, 19.4 % of the MFIs had been in existence for more than 16 years and 10.1% had existed for less than 5 years. In terms of capital size in shillings, 40.3% of the MFIs had capital of 1.5 Billion shillings and above, 22.3% had between 1 and 1.5 Billion shillings, Table 1. Validity and reliability results Source: Primary data. Variable Content validity index Cronbach’s alpha Number of items Credit allocation 0.813 0.701 14 Risk management 0.802 0.748 19 Loan portfolio performance 0.941 0.721 18 Page 8 of 13 Ssekiziyivu et al., Cogent Business & Management (2017), 4: 1374921 https://doi.org/10.1080/23311975.2017.1374921 25.9% between 500 Million to 1 Billion shillings and 11.5 were found to have less than 500 Million shillings. Lastly, as far as branches were concerned, majority of the MFIs accounting for 50.4% had less than 5 branches, 20.9 % ranged between 6 and 10 branches, 13.7 % between 11 and 15 branches, 7.2 % between 16 and 20 branches and 7.9 % of the MFIs had more than 20 branches. 3.2. Correlation analysis Pearson product moment correlation method was used to generate the measure of the magnitude and direction of the relationship between the study variables. The results that were obtained are presented in Table 2. Results in Table 2 showed a positive significant relationship between credit allocation and risk Management (r = 0.426, p < 0.01). This implied that higher levels of credit Allocation through collateral requirement, amount applied for, and amount approved are associated with risk Management in regard to assessment, control, prioritization, and design procedures. Results further showed a positive significant relationship between credit allocation and loan portfolio performance (r = 0.395, p < 0.01). This implied that higher levels of credit allocation through collateral requirement, amount applied for, and amount approved are associated with loan portfolio performance in regard to arrear rates, portfolio at risk, and repayment Rates. Also, there was a positive significant relationship between risk management and loan Portfolio (r = 0.446, p < 0.01). This implied that higher levels of risk management through assessment, control, prioritization, and design procedures are associated with loan portfolio in regard to arrear rates, portfolio at risk and repayment Rates. 3.3. Regression analysis In order to establish the extent to which the variance in loan portfolio performance of MFIs is explained by credit allocation and risk management, regression analysis was carried out. Regression analysis also helped to establish the significance of each of the independent variables on the variance in the loan portfolio performance of MFIs. The results that were obtained are presented in Table 3 below. The results in Table 3 above reveal that credit allocation and risk management had significant effect on loan portfolio performance, thus (β = 0.240, p < 0.01) and (β = 0.326, p < 0.01), respectively, Table 2. Correlation results **Correlation is significant at the 0.01 level (2-tailed).  1 2 3 Credit allocation (1) 1.00 Risk management (2) 0.426** 1.00 Loan portfolio performance(3) 0.395** 0.446** 1.00 Table 3. Regression results Model Unstandardized coefficients Standardized coefficients tSig. BStd. error β (Constant) −1.006E-013 0.029 0.000 1.000 Credit allocation 0.240 0.079 0.251 3.055 0.003 Risk management 0.326 0.079 0.339 4.134 0.000 a. Loan portfolio R = 0.500 F statistic = 22.698 R 2 = 0.250 Sig. = 0.000 Adjusted R 2 = 0.239