The Nexus of Financial Sector Development and Economic Development in Zimbabwe: 1980-2022.

The Nexus of Financial Sector Development and Economic Development in Zimbabwe: 1980-2022.

Lawrence Dumisani Nyathi¹, Wilfred Petegumbo²

¹National University of Science and Technology, Lecturer-Department of Banking & Economic Sciences, Faculty of Business & Economic Sciences, Zimbabwe.

²National University of Science and Technology, Graduating Candidate-Department of Banking & Economic Sciences, Faculty of Business & Economic Sciences, Zimbabwe.

DOI: https://doi.org/10.51244/IJRSI.2024.1109073

Received: 20 September 2024; Accepted: 25 September 2024; Published: 11 October 2024

ABSTRACT

Economic literature has shown that financial development has a positive effect on economic growth. Since 1980, the financial sector has faced numerous challenges albeit the importance of the sector towards economic development. This study examines the nexus between financial sector development and economic development. The study data covered the period 1980-2022 using an econometric analysis applying the Autoregressive Distributed Lag (ARDL) regression model to assess the relationship between finance sector development and economic development. The research findings indicate that bank deposits have a statistically negative effect on economic development. Broad money (M2) is statistically significant beneficial effect on economic development over long term. The results also reveal that in the long run government spending has a negative effect on economic development. Long term economic development was negatively correlated with private investment. In conclusion, ther study underscores the need to address strengthen the financial sector so as to enable economic development. Therefore, the paper recommends that the government of Zimbabwe should effectively enact policies that promote trade liberalisation, job creation and similar pursuits in order to stimulate development in turn strengthen the financial sector.

Keywords: Financial Sector Development, Economic Development, Zimbabwe, Africa.

INTRODUCTION

Financial economics literature as show that financial sector development has a positive effect on economic growth. Song et al (2021) investigates the relationship among corruption, economic growth and financial sector development in 142 countries using panel cointegration and panel error correction models from 2002 to 2016. The results confirm that between GDP, COR and BM, a long-term cointegration relationship exist in the full sample and the sub-samples of developing countries. Moreover, in the full sample and in developing countries subsample, the panel FMOLS estimations indicate that economic growth has a positive effect to financial development, whereas corruption has a negative effect. The VECM shows the causal relationships exist between economic growth to financial development and corruption to financial development in the long run.

Wang et al (2021) applied the ARDL-PMG model exploring the long-run and short-run impacts of economic growth and financial development on renewable energy consumption based on the panel data during period 1997–2017 at national and regional levels of China. The long-run relationships indicate that for China as a whole and western China, economic growth stimulates renewable energy consumption while financial development impacts it negatively. However, the short-run relationships show that economic growth and financial development influence renewable energy consumption negatively and positively, respectively.

Ibrahim & Acquah (2020) in their study of 45 African countries over a period 1980 to 2016, applied a panel Granger Non-Causality and revealed that there are causal linkages between (i) financial sector development and economic growth, and (ii) FDI and financial sector development. Furthermore, Osei et al (2020) emphasizes that the financial sector is beneficial for economic growth. While on the same note, Mhadhbi, Terzi, & Bouchrika (2020) revisit the Granger causal relationship between banking sector development and economic growth for forty developing countries in the period 1970–2012. In order to capture the different aspects of banking sector development, we use two banking sector development indices and apply a panel bootstrap approach to Granger causality testing which takes into account cross-sectional dependence and heterogeneity issues. The empirical results show limited support for the supply-leading, demand-following and complementarity hypotheses, but provide evidence for a causal relationship between banking sector development and economic growth in twenty-five countries.

Olaniyi & Oladeji (2020) in their study to examine the moderating role of institutional quality in the finance-growth nexus in Kenya for the period 1986–2015 adopt a fully modified ordinary least square (FMOLS) estimator to take care of endogeneity problem inherent in the relationship among the variables while error-correction version of autoregressive distributed lag (ARDL) technique is adopted to take care of short- and long-run effects. The research outputs reveal that in the short-run institutions and finance complement and reinforce each other to enhance growth, while institutional framework in Kenya constitutes a drain that leaks out the growth benefits of financial development in the long run.

Younsi & Bechtini (2020) in their study they aim to examine the causal relationships between economic growth, financial development, and income inequality in BRICS countries, namely, Brazil, Russia, India, China, and South Africa, using annual panel data covering the period 1990–2015.  The study provide evidence supporting Kuznets’ inverted U-shaped hypothesis on economic growth, financial sector and income inequality linkage.

Alexiou et al (2018) used a dynamic panel framework, covering 34 European and Commonwealth of Independent States economies for the period 1998–2014 and controls for the role of macroeconomic and institutional variables.  The results suggest, tentatively, that there has been a severance of the link between the financial sector and the real economy. The results, however, vary according to the level of economic development across the European and Commonwealth of Independent States economies. The effect of changes in investment expenditure, the money supply, wages, unit labour costs, and trade openness is found to be strong and in line with a priori expectations across all country samples.

Financial Sector Development in Zimbabwe

Due to macroeconomic imbalances and policy inconsistencies, the financial sector has faced many challenges over the past decades, which have led to pervasive collapse of the sector. Specifically, the Central Bank presided over quasi-fiscal activities, which subsequently fuelled hyperinflation, and posing a threat to financial intermediation. Ultimately, the general public lost confidence of the sector, (it was safer to keep your money under the pillow than in a bank). This led to further deterioration in the robustness of the financial system (Ndlovu, 2013).

Zimbabwe government inherited a strong financial system in 1980 from the colonial government. When the country got independent the financial sector was relatively small and dominated by foreign banks. The sector was pinned on controls of interest rates, foreign exchange, prices and credit allocation. The controls curbed inflation however they suppressed economic growth.

In 1990 the government of Zimbabwe adopted the Economic Structural Adjustment Program as recommended by World Bank. The policy entrenched privatisation, cutting public expenditure and financial liberalisation.  The financial sector was also liberalised during the same period, with entry requirement being eased. This was followed with more financial institutions and indigenous institutions entered the market too. Controls on foreign currency allocations, prices, credit allocations, prices and interest rates were removed. Due to poor implementation of the ESAP policy, the economy weakened in turn banks financed the deficit facing the economy. Inflation surged in the process because ESAP widened the deficits, job losses and industries closed, it failed to stabilise the economy as initially planned. Disastrous droughts in 1992 and again in 1995 had effects very similar to those experienced during the less serious droughts in the 1980s. The global recession in 1991/2 reduced raw material prices and export demand. At the same time the new ANC regime in South Africa cancelled its trade agreement with Zimbabwe. This led to high tariffs on exports, and the arrival of cheap subsidised South African goods just as Zimbabwe reduced its own tariffs. This contributed significantly to the de-industrialisation that occurred at the time Suzman (2003).

In 1996, government of Zimbabwe introduced the Zimbabwe Program for Economic and Social Transformation (ZIMPREST) which succeeded the ESAP policy. Sibanda and Makwata (2017) mentioned that the policy was meant to continue the unfinished business of ESAP i.e. parastatal reforms, financial sector reform, civil service reform etc. and aimed at overcoming the constraints to economic growth, employment creation and poverty alleviation as well as facilitating public and private savings and investment. However during the same period the government of Zimbabwe engaged into activities that shot down the objectives of ZIMPREST. The government of Zimbabwe chose to pay out Z$50 000 to a massive 60 000 ex-combatants of the nation’s liberation war. Furthermore, at the same time the government of Zimbabwe participated into the DRC war which was expensive also unbudgeted for too. Those massive off budget expenditures undermined confidence in Zimbabwe’s fiscal policy. On November 14, 1997, a day subsequently referred to as “Black Friday”, the Zimbabwe dollar lost 71.5% of its value against the United States dollar while the stock market subsequently crashed by 46% as investors scrambled out of the Zimbabwe dollar (Marawanyika, 2007 cited by Sibanda & Makwata, 2017).

Over the period 2000-2008, the economy collapsed due to populist policies pursued by the government. The government engaged into the Fast Track Land Reform Program (FTLRP) which destroyed the agriculture and subsequently led to massive deindustrialization. From then on it was a rollercoaster ride as the economy descended deeper into depression. Inflation surged to insurmountable figures 231 million percent by August 2008, (Zimstats, 2008). A number of financial institutions were curated, closed and others liquidated. The contribution of the financial sector to economic activity in this period was minimal (ZEPARU, 2013). After abandoning the zimdollar early 2009, Zimbabwe dollarised in February 2009. During the dollarisation period the financial sector experience numerous challenges. The major challenges included lack of affordable long term credit, weak confidence, the Central Bank and Government. Hence the financial sector contributed less towards economic growth during the same period.

This study explores the impact of financial sector development towards economic growth. From independence all various sectors of the economy has been supported by the vibrant financial sector for growth and production. Generally, the pillar of Zimbabwean economy is agriculture sector which thrives under good and sound financial support. Zimbabwe, formerly known as Rhodesia, was known as the breadbasket of Africa until 2000, exporting wheat, tobacco, and corn to the wider world, especially to other African nations (Schlesinger, 2023). The vibrancy of the sector required adequate funding to procure all the needs. Agriculture also in turn produces raw materials for industries and it becomes a value chain to feed and pump up production into the whole economy. The economy of Zimbabwe has passed under different economic cycles. Since 2018 the government of Zimbabwe has instituted reforms to open up and grow the economy. For any meaningful economic growth needs a financial support, hence the critical juncture that underpins that financial sector drives economic growth and development to attain the nation’s Vision 2030.

METHODOLOGY

This study uses time series data for Zimbabwe from 1980 to 2022 and applies the ARDL method to assess the impact of financial sector on economic growth. The study makes gross domestic product as the dependent variable while domestic credit to private sector, bank deposit, broad money, government expenditure and private investment are treated as explanatory variables.

Econometric Model and Methodology

The study adopted the Autoregressive Distributive Lag Model Approach (ARDL), of Pesaran et al. (2001) drawing from theoretical and empirical models of Pesaran et al. (1996), Pesaran (1997), Pesaran & Shin (1995 and 1998), Pesaran et al. (2001), and Nayaran (2005) subsequently modified it under the situation of small sample size data (30-80 observations). The Autoregressive Distributive Lag (ARDL) model is employed as the estimate technique in this investigation because this model has the benefit of permitting variables which become stationary prior differencing (I (0)) and after the first differentiation (I (1)), but rejecting variables that becomes stationary following the second differentiation (I (2)). Furthermore, this strategy may be utilized regardless of whether the variable is fractionally co-integrated, me (1), or me (0) (Pesaran 1997 as cited by Puatwoe & Piabuo, 2017). Moreover, Puatwoe & Piabuo (2017) demonstrate that ARDL may be translated linearly to generate the error correction term (ECT), which combines short-run corrections with long-run equilibrium devoid of losing long-run information.

The model is stated as follows:

Where: α = constant, εt = error term, while β1, β2, β3, β4, β5, are coefficients.

The econometric model is presented as follows with an aim to evaluate to the short and long run nexus between financial sector and economic development.

Variable and Data

The study focuses on the short and long term nexus of Zimbabwe’s financial sector and economic development using the following indicators shown as variables (Table 1). The study made use of time series data from 1980 to 2022 in Zimbabwe.

Table 1: Variable Description

Variables	Variable type	Source
GDP Growth	Dependent variable	World Data Indicators 2022
Broad Money (M2)	Independent variable	World Data Indicators 2022
Domestic Credit to Private sector	Independent variable	World Data Indicators 2022
Bank deposits	Independent variable	World Data Indicators 2022
Private investments	Control variable	World Data Indicators 2022
Government expenditure	Control variable	World Data Indicators 2022

Model Assumptions and Diagnostic Tests

According to Kramer et al. (1985), a conventional regression output requires the addition of several specification checks. To determine whether the data gathered and used is reliable and objective, a number of tests were run in order to test for econometrics a priori postulations. These include the unit root which is performed on the time series to evaluate the integration order of the variables using the Augmented Dickey-Fuller (ADF), while the co-integration test will be tested using the Johansen test which is used to validate the long-run association between financial sector development and economic growth which is the F-bound tests, the ARDL test, and normality tests.

RESULTS AND DISCUSSIONS

Descriptive statistics

Table 2: Description of basic statistic of each variable

	GDP	DOMESTIC CREDIT	BANK_DEP	M2	PRIVATE INVESTMENT	GORV_EXP
Mean	2.298266	18.01504	29.62304	17.46710	14.01275	16.76197
Median	2.099029	17.05000	1.380627	14.37926	14.65389	17.91626
Maximum	21.45206	84.81000	342.1160	151.5489	23.72906	27.48708
Minimum	-17.66895	0.000000	0.014875	0.030000	1.525177	2.047121
Std. Dev.	8.140982	14.10394	68.90232	26.68410	5.597113	5.304390
Skewness	-0.209604	2.727491	3.371090	3.203574	-0.511931	-0.994851
Kurtosis	3.328228	13.49430	13.93556	16.08836	2.498210	4.124903
Jarque-Bera	0.507881	250.6310	295.7028	380.4725	2.329321	9.360243
Probability	0.775738	0.000000	0.000000	0.000000	0.312029	0.009278
Sum	98.82542	774.6468	1273.791	751.0851	602.5482	720.7645
Sum Sq. Dev.	2783.574	8354.686	199396.2	29905.74	1315.762	1181.735
Observations	43	43	43	43	43	43

Source: (Eviews 10, 2024)

The descriptive statistics provide valuable preliminary insights into the relationships between financial sector development indicators (such as domestic credit, bank deposits, and money supply) and economic growth. The difference between the maximum and minimum values of each variable indicates that the time series data for the variables fluctuate dramatically. JB values of all variables is generally large with small p values. Meanwhile the data has TO Kurtosis are greater than 3 and the absolute value of skewness is greater than 0, indicating that the data is characterized by a spiky, thick-tailed distribution.

Multicollinearity Test

Correlation is a statistical measurement of the relationship between two variables. In the study, correlation was used to determine the relationships among the study variables and also predict the contribution of the study variables to economic growth.

Table 3: Correlation Matrix

	GDP	Domestic Credit	Bank Dep	M2	Private Investment	Gov Exp
GDP	1.000000
Domestic Credit	-0.386232	1.000000
Bank Dep	-0.301143	0.052608	1.000000
M2	-0.406551	0.787881	0.269596	1.000000
Private Investment	0.501540	-0.342955	-0.664021	-0.667702	1.000000
Gov Exp	0.243154	0.086148	-0.469755	-0.105035	0.323014	1.000000

Source: Eviews 10

The results of the correlation analysis is shown in the table above, which confirmed some level of correlation between the variables with 0.787881 as the highest correlation. According to the rule of thumb, we reject null hypothesis only if the correlation between variables is above 0.80. We fail to reject the null hypothesis (H0) and conclude that there exists no multicollinearity among the variables.

Optimal choice of lag

Moving on, the lag length test is conducted to determine the optimal lag length which is very important for the selected model.

Table 4: Optimal lag length test

Lags	AIC	SIC	HQC
1	42.59146	44.38299	43.23424
2	42.31340	45.64052	43.50714
3	39.86052	44.72324	41.60522
4	36.73641*	43.13473*	39.03207*

Source: Researcher’s own compilation from EViews 10

As illustrated in the table above selects lag order 4 for all the study variables, implying the optimal lag order of the model. Moreover, the Akaike Information Criterion (AIC) employed also indicates that lag 4 is the optimal choice.

Unit Root Test results for 1980 to 2022

Table 5: Unit root test using Augmented Dickey Fuller test

Variable	T-statistic	Prob *	Decision
GDP	-4.025973	0.0002**	I(0)
Domestic credit to private sector (% of GDP)	-3.211912	0.0262**	I(0)
Bank Deposit	-2.308637	0.0219**	I(0)
M2 (% of GDP)	-3.735914	0.0069**	I(0)
Government Expenditure (% of GDP)	-6.686752	0.0000**	I(1)
Private Investment	-6.598871	0.0000**	I(1)

Source: (Eviews, 2024)

From table 5, the specific stationary of each variable can be observed.  All the variables are stationery at 5% GDP, Domestic credit to private sector, bank deposit and broad money are integrated to order zero while government expenditure and private investment are stable at first order difference which meets the cointegration test conditions.

Cointegration results

Table 6: F-bound test results

F-Bound Test	Null hypothesis: No levels relationship
Test statistic	Value	Significance	I(0)	I(1)
f-statistic	17.34773	5%	2.14	3.34
K	5

Source: (Eviews 10, 2024)

From table 6, the results reveal that there is a long-run relationship amongst the variables since F-statistic (17.35) is greater than the upper bound value of 3.34 at 5% level of significance. Therefore the results reveal that there is a long run relationship between finance sector and economic development in Zimbabwe.

Normality Test

Figure 1: Histogram-Normality test

Source: (Eviews 10, 2024)

Figure 1 above depicts that the data is normally distributed as the probability of 0.927680 is greater than 0.05 for the null hypothesis. We therefore conclude that data is normal and fail to reject the null hypothesis.

Stability Test

Using the CUSUM and CUSUM-squared tests at the five percent level of statistical significance, the robustness of the long- and short-term correlations between financial sector development and economic development was explored. The model is stable when the CUSUM line falls within the lines reflecting the significance level. In the figure below the blue line is within the 5% threshold of significance, hence there is a long- term stability between the variables in our model.

Figure 2: CUSUM

CUSUMQ

From the figure below, the CUSUM line sits in-between both lines of the threshold of significance at 5%. Therefore, it means the model is stable and so may be utilized for causality, bound test, as well as long-run correlation.

Figure 3: CUSUM of Squares

Long and Short Term Coefficient Estimation

Table 7: Results of long- and short-term coefficients of ARDL-ECM.

Variable	Coefficient	Standard Error	T-statistic	Probability
Domestic credit to private sector	0.13194	0.117710	1.21096	0.00242
Bank deposit	-0.138695	0.040653	-3.411648	0.0052
Broad money (M2)	0.280456	0.081458	3.443340	0.0049
Private invst	-0.009222	0.199641	-0.046195	0.0035
Govt expend	-0.043497	0.179138	-0.242815	0.8122
Short Run ECM Regression
ContiEq (-)*	-1.131188	0.093155	-12.14314	0.0000
R-squared	0.965706	Mean dependent var		0.126592
Adjusted R-squared	0.923343	S.D dependent var		8.767103
S.E of regression	2.427341	Akaike infor criterion		4.909327
Sum squared resid	100.1638	Schwarz criterion		5.847746

Source: (Eviews 10, 2024)

Table 7 reveals that the coefficient of money supply (M2) is positive and statistically significant suggesting that an increase in money supply by 1% is associated with an increase in GDP by 0.28% in the long run. This aligns with the idea that greater availability of money can facilitate economic activity and growth. Moreover, this finding supports the view that monetary aggregates reflecting financial deepening can foster economic expansion (Song et al (2021); Ibrahim & Acquaah, 2020). However this contradicts with the Zimbabwean scenario. Growth of money supply has fuelled inflation and instability hence regression of economic growth.  Bank deposit negatively significant towards economic growth with -0.138695. When banks have more deposits, spontaneously they lend to productive sectors of the economy increasing investment and potentially increasing long-run growth. According to IMF (2020) postulates that  higher levels of bank deposits as a percentage of GDP might restrain economic growth fuel exchange depreciation, leading to  inflationary pressures thus resulting inefficient allocation of capital. In Zimbabwe due to economic ill-discipline has results in large bank deposits which have failed to ignite economic growth and development due to inconsequential policies.

While on the same note, domestic credit was statistically significant with a positive coefficient of 0.13 % as well statistically significant indicates a potentially positive association with GDP. Puatwoe & Piabuo, (2017) emphasizes the importance of credit availability in stimulating economic growth. So, we cannot draw a strong conclusion about the impact of domestic credit on economic growth based on this analysis. Zimbabwe has relied for domestic credit to finance financial needs for the economic as international capital has been stifled by sanctions. Private investment and government expenditure have got negative coefficients which basically means that they have got a negative relationship with economic growth in the long-run. This suggests that in this model, private investment and government expenditure do not exert significant influence on GDP growth, contrasting with studies that emphasize the role of public and private investment in driving economic activity (Beck et al., 2019).

The model has a coefficient of 96.6% as indicated by the R² (0.965706) which is greater than 50%. This clearly shows that the model used is a good fit. Also, it means that 96.6% of variation in economic growth is explained by the variation in the independent variables. Moreover, the reliability of coefficients was tested using the adjusted R² which is 92.3% in this case meaning this is a good model as illustrated in the table below. Moreover, the regression model was estimated using the case 1 of no constant and trend since they were found not statistically significant. Also, the maximum number of lags for this model is 4 as determined by the optimal lag length test.

According to Puatwoe & Piabuo (2017), the rule for the existence of a short-run link between development of the financial sector and growth in the economy is that the coefficient of the error correction term should be negative and it should be significant. Therefore, the above results has an error correction term of -1.13 which implies an oscillatory convergence (Table 5). Also, the results suggests that increases in domestic credit in the past two to three periods led to increases in GDP growth in the current period as indicated by positive coefficients for lagged changes (D(DOMESTIC_CREDIT) with lags -2 and -3). Moving on, the results for bank deposits are mixed. While a positive coefficient for D (BANK_DEP (-2)) suggests a delayed positive impact, which is an indication that the overall short-run effect seems weak. Positive coefficients for D (M2) indicate that increases in money supply lead to higher GDP growth in the current and following periods. The negative coefficients for lagged changes (M2 (-1) to M2 (-3)) suggest a dampening effect over time.

CONCLUSION AND RECOMMENDATION

The aim of the study was to analyse the long run and short run relationship between financial sector development and economic development. The study used data from 1980 to 2022 using the ARDL approach for empirical analysis. In the long run, economic development is positively related to domestic loans, which enable growth of the productive sectors. Over time, bank deposits have a statistically negative effect on economic growth. Furthermore, as anticipated, broad money (M2) has a statistically significant beneficial effect on economic growth over the long term. In the long-run regression model indicates that government spending has a negative effect on economic growth. Long-term economic development was negatively correlated with private investment. Government of Zimbabwe needs to address macroeconomic imbalances and ease of doing business reforms for financial sector expansion to production and economic development.

Therefore, the study makes the following recommendations;

• The positive long-run impact of M2 suggests that central bank could consider policies that promote a stable and growing money supply, such as setting inflation targets and using open market operations.
• The positive impact of Bank Deposits indicates that policies promoting financial inclusion and access to banking services could be beneficial for long-run economic growth. Therefore, the policy makers should promote access to bank accounts for all citizens, particularly those in underserved communities. This can be done through simplified account opening procedures and mobile banking initiatives.
• Since bank deposits, and broad money (M2) proved to have a significant impact on economic growth in Zimbabwe, the Government should foster a conducive environment for financial sector development. Firstly, there should be financial literacy in which the government educate the public on the benefits of saving and investing. This empowers individuals to make informed financial decisions and participate in the financial system. Also, it has to maintain low inflation and controlled interest rates. Stable economic conditions encourage people to save and invest for the future. Last but not least the Zimbabwean government can encourage saving through tax breaks for interest earned or contributions to retirement accounts.
• Finally, the findings suggested that Zimbabwe’s financial system could be weak, leading to poor financial efficiency. Since the development of financial systems is a secondary outcome of economic growth, this may imply that governmental efforts should move towards trade liberalization, job creation, and similar pursuits in order to stimulate economic growth. Promoting investments and removing obstacles for foreign investments are examples of measures that might fall under this category.

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