Predictors of broad money supply : an evidence of Zimbabwe's volatile liquidity

Abstract

Zimbabwe’s currency once had a value stronger than the United Sates Dollar (USD) and was rated one of the strongest currencies in the world. However, rampant inflation and economic collapse led to the devaluation of the Zimbabwean dollar (Dzingirai & Tambudzai, 2014). This study sought to deepen the understanding of Zimbabwe’s money supply (M3) crisis and identify the significant predictors of the crisis. It aimed to develop a predictive M3 model based on the multiple linear regression method. The study sought to investigate how the following macroeconomic variables impacted M3: The government marginal propensity to spend (GMPS), tax revenue growth (TRG), growth domestic product growth rate (GDPGR), unemployment rate (U), gross domestic product marginal propensity to spend (GDPMPS), liquidity credits growth (LCG), producer price index (PPI) and consumer price index(CPI), foreign direct

investment (FDI) and direct domestic investments (DI) were considered probable predictors of the M3. Based on monetary theories, time-series data of the Zimbabwe economy was studied from 1980 to 2017. The data revealed that the liquidity credits and the gross domestic product marginal propensity to consume (GDP MPC) of the country were the significant predictors of the broad M3. LCG (p = 0.000) and GDP MPC (p= 0.021) in the model explain 57.4% of the variance in M3 (adjusted R 2 = 57.4%). LCG accounted for 50%, while GDP MPC accounted for an additional 7.4%. The best predictor model was specified as follows M3= -3.045 + 0.833 Log (LCG) + 0.300 Log (GDP MPC) + e. The equation suggests that for every unit increase in LCG, there is a corresponding increase in the M3 by 0.8333 holds other factors constant. Also, a unit increase in the Gross Domestic Product-Marginal Propensity to Consume (GDP MPC), there is a 0.3-unit increase in the M3. The findings are significant for central bankers, financial institutions, governments, and households. These findings help Zimbabwean policymakers to establish complementary monetary and fiscal policies that will control the LCG as this has been identified as the most significant predictor of the M3. The central Bank and financial institutions would have to come up with credit risk strategies that lower broad M3 growth. The study recommends that future studies be conducted using other statistical methods like logistic regression and structural equation modelling to identify the other factors that would significantly predict broad M3 as this study managed to explain 57.4% variability.