Loan growth and risk: evidence from microfinance institutions in Africa

Doctoral Thesis


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Microfinance markets in Sub-Saharan Africa (SSA) have experienced remarkable growth, particularly after the early 2000s. Since microfinance institutions (MFIs) provide financial services such as loans, savings and insurance to poor clients who face exclusion from formal financial institutions, they are considered as one of the most prolific tools to alleviate poverty and achieve financial inclusion in developing countries. These institutions are of particular importance in SSA, given that the region has the highest poverty levels in the world and the highest levels of financial exclusion. However, in recent years the fast loan growth of MFIs has been accompanied increasingly by loan delinquencies which threaten the financial health of these institutions. This is a major concern for policymakers, regulators and practitioners given the developmental importance of microfinance in the region. Despite the pivotal role of microfinance, there is only a very limited number of studies that either investigate the underlying reasons for the fast growth of MFIs or that identify the determinants of credit risk in MFIs in this particular region of Africa. Motivated by both the remarkable loan growth and the rising credit risk that MFIs experienced and the fact that SSA has been neglected in the relevant literature, this thesis provides evidence from the region on the factors that contribute to MFIs’ growth, the determinants of MFIs’ credit risk as well as the factors that influence access to MFIs credit. The latter pays particular attention to the effect of mobile financial services (MFS) on borrowing from MFIs, an aspect that has been ignored in previous scholarly work. Furthermore, the thesis overcomes the limitations of previous studies that employed static regressions, which are limited in dealing with panel endogeneity bias, by focusing on the dynamic aspects of loan growth and credit risk. The thesis is structured around three related studies that are presented in three chapters, namely Chapter 2, Chapter 3 and Chapter 4. The purpose of the second chapter is to identify the factors that explain variations in loan growth in the region’s MFIs. This is an important issue as high loan growth may pose significant stability risks in the microfinance sector via a deterioration in portfolio quality. The chapter applies two-step system generalised method of moments estimators on data for 34 countries in SSA over the period 2004 - 2014. The results show that loan growth is higher in MFIs that have lower risk exposure, higher capital asset ratios and already recording high growth. Similarly, loan growth is higher in countries with better economic prospects, and in those with sound private sector policies and regulations. Against expectations, loan growth is faster in countries with poor legal rights of borrowers and lenders. Credit risk in microfinance institutions in SSA has been rising, and the financial health of these institutions remains an issue of concern. Hence, Chapter 3 examines the factors that explain variations in credit risk in MFIs in the region. Similarly, the chapter employs a system GMM approach on data for 34 countries in SSA over the period 2004 – 2014. Results suggest that the main predictors of credit risk in SSA are lagged credit risk, loan growth, provisions for loan impairment, GDP per capita growth and ease of getting credit. In addition, the study identifies threshold effects in the relationship between credit risk and loan growth. Credit risk falls with loan growth until a trough at 36.8% when this relationship is reversed. On the regional scale, comparisons suggest that credit risk is most persistent in East Asia and the Pacific but least persistent in SSA. Relatively few scholarly works have analysed the influence of mobile financial services (MFS) on access to credit. Chapter 4 aims to identify the factors that explain the differences in the propensity to use loans from MFIs in Kenya, paying particular attention to the effects of mobile money (M-money), mobile banking (M-banking) and mobile credit (M-credit). Kenya is an interesting case study because the country outperforms other SSA countries in terms of financial and digital inclusion. The study applies a probit model using FinAccess cross sectional data that was collected in 2013 (N=6112) and 2015 (N=8665). After addressing endogeneity concerns in the data, the 2013 results suggest that the factors that make a significant difference in the likelihood of using MFI credit include income, gender and type of cluster. An important observation is that non-poor users of M-money are more likely to use microcredit. The 2015 results show that the likelihood of using MFI credit is lower among those using M-banking and M-credit as well as among males and married persons. However, higher income, being educated, higher household size and being located in a rural cluster are associated with a higher propensity to use MFI credit. In addition, the results suggest a Ushaped relationship between age and the probability to use MFI credit. Similarly, the negative relationship between the likelihood of using MFI credit and using M-banking and M-credit suggests that the introduction of MFS in the financial sector has resulted in the migration of clients from microfinance products towards mobile-based financial services. In terms of policy, two recommendations stand out. Firstly, since dynamics matter for both loan growth and credit risk, credit management strategies that incorporate past risk and loan performance are likely to be more effective. Secondly, the evident trade-offs between loan growth and credit risk confirm the fact that modest loan growth is not the source of instability within the region’s microfinance sector. However, the presence of threshold effects suggests that MFIs should determine the turning points for lending growth because excessive growth in loans can be perilous to the existence of the institution itself, and the sector by extension.