Micro and Macro Drivers of Credit Risk: The Case of Zimbabwean Banking Industry (2009-2013)

There was troublesome development in non-performing loans since the inception of multiple-currency regime in Zimbabwe. The study investigated determinants of nonperforming loans in Zimbabwe using a panel of eight (8) banks. Using decomposed monthly data from 2009 to 2013, a combination of static and dynamic panel regression models were applied. Findings revealed that non-performing loans are influenced by microeconomic, macroeconomic and political factors. Results supported quiet life hypothesis. Based on this hypothesis, we identified that interest rates had strong positive nexus with non-performing loans and that they create a platform that threatens realization of the financial inclusion objective in Zimbabwe due to recurring cycles in Non-performing loans (NPLs).Interestingly both dynamic models managed to capture influence of Government of National Unity (GNU) on NPLs in Zimbabwe. We found negative association between GNU and NPLs and results were in line with our expectations. Negative connection means that GNU had a potential to reduce non-performing loans in banks. Capital adequacy and loan-to-deposit variables have significant influence on credit risk, although the Loan-to-deposits ratio (LTD) variable was only significant in two static models. Macroeconomic factors have influence on non-performing loans and they include unemployment rate, inflations rate and real GDP growth rate. According to study results, real GDP growth rate is significant in dynamic models only. Ability of dynamic models to detect GNU and real GDP growth rate undoubtedly proved how robust and superior dynamic models are over static models. Overall, we found systematic risk to be the major driver of credit risk than idiosyncratic risk. To promote financial inclusion in Zimbabwe, we recommended that banks should review interest rates downwards to levels between 5.78 - 8.1% to reduce borrower default rate by between 20-50%, update credit policies periodically to detect changes in customers’ characteristics as well as improving capital adequate ratios which will discourage moral hazard in banks. There is need for banks to shift their credit culture to values driven culture. Current profit and market share driven credit cultures compromises banks’ assets quality in the long-run.