| dc.description.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. | en_US |
