The Impact of Interest Rate Spread and Money Supply (M2) on Economic Growth – A Study in 40 Selected Nations

The Impact of Interest Rate Spread and Money Supply (M2) on Economic Growth – A Study in 40 Selected Nations

Quy T. Vo^1,2,*, Mai. H. T. Tran^1,2

¹International University, Ho Chi Minh City, Vietnam

²Vietnam National University, Ho Chi Minh City, Vietnam

^*Corresponding author

DOI: https://dx.doi.org/10.47772/IJRISS.2023.70803

Received: 26 June 2023; Revised: 14 July 2023; Accepted: 17 July 2023; Published: 14 August 2023

ABSTRACT

The goal of this research study is to look at how the Interest Rate Spread and Money Supply (M2) affect Economic Growth in different countries. We evaluate the literature on conducting research to investigate the impact of independent variables and control variables on economic growth. The research spans 40 countries from 2001 to 2021 and is divided into three groups: Developed Countries, Developing Countries, and Emerging Countries. The findings confirmed that the Interest Rate Spread and Money Supply (M2) have a positive impact on Economic Growth. These results imply that expantaionary mometery policy supports economic growth. The positive relationship between interest rate spread and economic growth confirmed the the importance of the banking sector in the economy.

Keywords: Interest Rate Spread, Money Supply (M2), Economic Growth

INTRODUCTION

Most central banks have used monetary policy as a tool to support economic growth. Changing the money supply will affect the inflation rate and then interest rates in debts markets, both borrowing and lending rates, and finally cause prices to change. Therefore, changes in the money supply can affect the production of goods and services level in the economy. This is why monetary policy is a meaningful policy tool for achieving both inflation and growth objectives. The global economy has been experiencing the hard time: Russia’s invasion of Ukraine, inlation rate increasing in most economies and reaching the highest level for the decade. The central banks have applied an appropriate monetary policy to control inflation rate and gain expected economic growth rate. The situation motivated us to conduct the study to investigate the impact of money supply and interest rate spread on economic growth. The next section of the paper will be literature review, hypothesis development and research model, data processing, research findings and discussion and conclusion.

The neoclassical growth framework, and monetarist theory are theoretical frameworks providing insights into the connection between IRS, M2, and economic growth. However, there is still ambiguous agreement of the relationship between IRS, money supply (M2), and economic growth in previous studies. Obeng & Sakyi (2017) confirmed significantly impact of macroeconomic factors on IRS in the long run in Ghana. On other side, IRS impacts economic growth significantly and negatively both in the short and long term, according to Daniel, et al. (2022). Regarding the relationship between money supply (M2) and economic growth, according to Nyorekwa et. al., (2018), emerging nations with undeveloped financial systems have limited connectivity to the global market likely to have a weaker link between monetary policy and economic growth. However, Manouchehr & Ahmad (2011) confirmed a substantial correlation between M2 and gross domestic product in Iran. Most studies on the same research issue conducted in single economy, therefore the correlation between IRS, M2, and economic growth across various economies should be further investigated. This study tries to fill the gap by conducting the study with 40 countries in different groups of economies: emerging market economies, developing countries, and developed countries to provide meaningful empirical evidence to get insights of the relaionship.

LITERATURE REVIEW

The difference between lending rates and deposit rates of financial institutions is known as an interest rate spread. Monetarist theory introduced by Friedman (1968) highlights the importance of money supply as an important macroeconomic factor affecting the economic growth of a country. Monetarist theory states that changes in money supply have the greatest effect on both the pattern of the business cycle and the growth rate of the economy (Kenton, 2021).

Interest Rate Spread and economic growth

IRS is usually used as a measure of bank efficiency (Mohamed, Jalloh, & Yao, 2017). A considerable disparity between loan and deposit rates reflects a weak and ineffective financial sector that might limit economic growth and promote instability according to Cochrane (2013), High IRS, on the other hand, is one of the key drivers of delayed economic growth in developing economies (Leimbach, 2015).

Money Supply (M2) and economic growth

Chaitip et. al., (2015) employed the Pooled Mean Group Estimator (PMGE) to investigate the link between money supply and economic development in ASEAN Economic Cooperation (AEC) countries between 1995 and 2013. Their findings revealed that the money supply support economic growth. Inam (2017) investigated the impact of monetary policy on Nigeria’s economic development from 1970 to 2012 by using the Ordinary Least Squares (OLS) approach and the Granger Causality test. The analysis showed a statistically negligible positive correlation between the money supply and economic growth. Aslam (2016) investigated the influence of the money supply on the Sri Lankan economy from 1959 to 2013. The findings revealed a considerable positive effect of the money supply on economic growth at a 1% level of significance.

Hypothesis development and research model

Based on the theories and empirical evidence presented above, we propose the following research hypotheses:

H1: Interest rate spread has a positive influence on economic growth in the selected countries.

H2: Money supply (M2) has a positive influence on economic growth in the selected countries.

To test above research hypotheses, we proposed the research model as below:

α_i: The regression intercept.

β_i: The regression slopes.

ε_i,t: The random error of the listed country i at the end of quarter t

Table 1 – Variables description

Data processing

Data is yearly data point collected from 2001 to 2021. The research sample consists of 40 countries listing in Appendix 1 with 840 observations. The Generalized Least Squares (GLS) regression model was used to test research hypotheses. Table 2 presents statistical despription of all variables in the research model. Table 3 shows the result of the autocorrelation test.

Statistical description

Table 2 – Descriptive statistics

Correlation testing

Table 3 shows the correlation coefficient between variables in the research model. The test confirmed that the autocorrelation phenominon does not exist in the data set because all the correlation coefficient values are lower than 0.4.

Table 3 – Correlation matrix

Multicollinearity test with Variance Inflation Factor (VIF)

To get the effective regression coefficient, a multicollinearity test was conducted and resulted in VIF value lower than 3. The Multicollinearity Test results indicate a mean Variance Inflation Factor (VIF) of 1.12. This suggests that there is a moderate level of correlation among the independent variables in the regression model. While a VIF between 1 and 5 suggests some degree of multicollinearity, it is not severe enough to warrant immediate corrective measures. The relatively low VIF value indicates that the independent variables in the model are not highly correlated with each other. This is a positive finding as it ensures that the variables provide unique and independent information to the regression analysis. The absence of severe multicollinearity enhances the reliability of the estimated coefficients and allows for more accurate interpretation and inference.

Table 4 – VIF test for multicollinearity

RESEARCH FINDINGS

Table 5 presents the result of hypothesis testing by applying GLS (Generalized Least Squares) regression model. The GLS regression model provides reliable coefficient estimates with statistically significant relationships between the GDP growth rate and the independent variables with p-values lower than 0.05. The results confirmed that H1 and H2 are accepted. In other words, interest rate spread and money supply are explaining factors of the GDP growth rate.

Table 5 – The testing result for the hypothesis

In addition, the coefficient estimates for the GDP growth rate variable with IRS, M2 growth rate, gross domestic savings, and foreign direct investment variables have t-statistics greater than 1.96 providing empirical evidence of a significant relationship between these variables in the research model. The coefficient estimate for the GDP growth rate and inflation rate variable has a t-statistic below 1.96 suggesting that the relationship is not statistically significant.

CONCLUSIONS AND RECOMMENDATIONS

This study conducted aims to investigate the impact of interest rate spread and money supply (M2) on  economic growth across various countries. By utilizing the GLS regression model, valuable insights into the associations between these variables were gained. The analysis of the data revealed a statistically significant relationship between the IRS, money supply (M2), and economic growth in the selected 40 countries. These findings further validate the substantial influence of interest rate spread and money supply (M2) on economic growth across different countries, providing empirical evidence of their important role in driving economic growth dynamics. The study’s findings imply that the central banks may use IRS and M2 as tools to gain expected economic growth rate.

In conclusion, this study provided empirical evidence of the impact of interest rate spread and money supply (M2) on economic growth in various countries. The findings substantiate the importance of these factors in shaping economic performance. The indings also provide recommendations to policymakers, investors, and financial analysts in making well-informed decisions That Foster Sustainable Economic Growth And Stability./.

REFERENCES

1. Aslam, A. L. (2016, 03). Impact of Money Supply on Sri Lankan Economy: An Econometric Analysis. International Letters of Social and Humanistic Sciences, 67, 11-17. doi:10.18052/www.scipress.com/ILSHS.67.11
2. Chaitip, P., Chokethaworn, K., Chaiboonsri, C., & Khounkhalax, M. (2015, 12). Money Supply Influencing on Economic Growth-wide Phenomena of AEC Open Region. Procedia Economics and Finance, 24, 108-115. doi:10.1016/S2212-5671(15)00626-7
3. Cochrane, J. H. (2013, 05). Finance: Function Matters, Not Size. Journal of Economic Perspectives, 27, 29-50. doi:https://www.aeaweb.org/articles?id=10.1257/jep.27.2.29
4. Daniel, B., Paul, H., & Edmond, A. (2022). The Effect Of Interest Rate Spread On Economic Growth: Ghana’s Perspective. International Journal of Business and Management Review, 1-23.
5. Friedman, M. (1968). The Role of Monetary Policy. The American Economic Review, 58, 1-17.
6. Inam, U. S. (2017). Monetary Policy And Economic Growth In Nigeria: Evidence From Nigeria. Advances in Social Sciences Research Journal. doi:https://doi.org/10.14738/assrj.46.2806
7. Kenton, W. (2021, 03 21). Monetarist Theory: Economic Theory of Money Supply. From Investopedia: https://www.investopedia.com/terms/m/monetaristtheory.asp
8. Leimbach, M. a. (2015, 03). Future growth patterns of world regions – A GDP scenario approach. Global Environmental Change, 42. doi:10.1016/j.gloenvcha.2015.02.005
9. Manouchehr, N., & Ahmad, J. S. (2011). The Impact of Monetary Policy on Economic Growth in Iran. Middle-East Journal of Scientific Research, 740-743.
10. Mohamed, Jalloh, & Yao, G. (2017, 01). Financial Deepening, Interest Rate Spread and Economic Growth: New Evidence from Sub-Sahara Africa. International Journal of Business, Economics and Management, 4, 52-64. doi:10.18488/journal.62.2017.43.52.64
11. Nyorekwa, T., Enock, Odhiambo, & Nicholas. (2018, 05). Monetary Policy and Economic Growth: A Review of International Literature. Journal of Central Banking Theory and Practice, 7, 123-237. doi:10.2478/jcbtp-2018-0015
12. Obeng, S., & Sakyi, D. (2017, 03). Macroeconomic Determinants of Interest Rate Spreads in Ghana. African Journal of Economic and Management Studies, 8, 76-88. doi:10.1108/AJEMS-12-2015-0143

APPENDIX

Appendix 1 – List Of Countries

Appendix 2 – Stationarity Test of GDP Growth Rate Variable

Levin-Lin-Chu unit-root test for GDP

————————————

Ho: Panels contain unit roots               Number of panels  =     40

Ha: Panels are stationary                   Number of periods =     21

AR parameter: Common                        Asymptotics: N/T -> 0

Panel means:  Included

Time trend:   Not included

ADF regressions: 1 lag

LR variance:     Bartlett kernel, 8.00 lags average (chosen by LLC)

—————————————————————————–

                    Statistic      p-value

—————————————————————————–

 Unadjusted t       -15.5172

 Adjusted t*         -3.9807        0.0000

—————————————————————————–

Appendix 3 – Stationarity Test of Interest Rate Spread Variable

Levin-Lin-Chu unit-root test for IRS

————————————

Ho: Panels contain unit roots               Number of panels  =     40

Ha: Panels are stationary                   Number of periods =     21

AR parameter: Common                        Asymptotics: N/T -> 0

Panel means:  Included

Time trend:   Not included

ADF regressions: 1 lag

LR variance:     Bartlett kernel, 8.00 lags average (chosen by LLC)

—————————————————————————–

                    Statistic      p-value

—————————————————————————–

 Unadjusted t       -12.1698

 Adjusted t*         -4.9932        0.0000

—————————————————————————–

Appendix 4- Stationarity Test of M2 (Money Supply) Growth Rate Variable

Levin-Lin-Chu unit-root test for M2

———————————–

Ho: Panels contain unit roots               Number of panels  =     40

Ha: Panels are stationary                   Number of periods =     21

AR parameter: Common                        Asymptotics: N/T -> 0

Panel means:  Included

Time trend:   Not included

ADF regressions: 1 lag

LR variance:     Bartlett kernel, 8.00 lags average (chosen by LLC)

—————————————————————————–

                    Statistic      p-value

—————————————————————————–

 Unadjusted t       -18.0416

 Adjusted t*          1.5356        0.9377

—————————————————————————–

Appendix 5 – Stationarity Test of Gross Domestic Savings Variable

Levin-Lin-Chu unit-root test for SAV

————————————

Ho: Panels contain unit roots               Number of panels  =     40

Ha: Panels are stationary                   Number of periods =     21

AR parameter: Common                        Asymptotics: N/T -> 0

Panel means:  Included

Time trend:   Not included

ADF regressions: 1 lag

LR variance:     Bartlett kernel, 8.00 lags average (chosen by LLC)

—————————————————————————–

                    Statistic      p-value

—————————————————————————–

 Unadjusted t       -10.6618

 Adjusted t*         -2.9478        0.0016

—————————————————————————–

Appendix 6 – Stationarity Test of Inflation Rate Variable

Levin-Lin-Chu unit-root test for INF

————————————

Ho: Panels contain unit roots               Number of panels  =     40

Ha: Panels are stationary                   Number of periods =     21

AR parameter: Common                        Asymptotics: N/T -> 0

Panel means:  Included

Time trend:   Not included

ADF regressions: 1 lag

LR variance:     Bartlett kernel, 8.00 lags average (chosen by LLC)

—————————————————————————–

                    Statistic      p-value

—————————————————————————–

 Unadjusted t       -14.8588

 Adjusted t*         -4.2883        0.0000

—————————————————————————–

Appendix 7 – Stationarity Test of Total Exports Variable

Levin-Lin-Chu unit-root test for EXP

————————————

Ho: Panels contain unit roots               Number of panels  =     40

Ha: Panels are stationary                   Number of periods =     21

AR parameter: Common                        Asymptotics: N/T -> 0

Panel means:  Included

Time trend:   Not included

ADF regressions: 1 lag

LR variance:     Bartlett kernel, 8.00 lags average (chosen by LLC)

—————————————————————————–

                    Statistic      p-value

—————————————————————————–

 Unadjusted t        -8.3767

 Adjusted t*         -2.7073        0.0034

—————————————————————————–

Appendix 8 – Stationarity Test of Foreign Direct Investment Variable

Levin-Lin-Chu unit-root test for FDI

————————————

Ho: Panels contain unit roots               Number of panels  =     40

Ha: Panels are stationary                   Number of periods =     21

AR parameter: Common                        Asymptotics: N/T -> 0

Panel means:  Included

Time trend:   Not included

ADF regressions: 1 lag

LR variance:     Bartlett kernel, 8.00 lags average (chosen by LLC)

—————————————————————————–

Statistic      p-value

—————————————————————————–

 Unadjusted t       -15.3111

 Adjusted t*         -7.1352        0.0000

—————————————————————————–

Appendix 9 – Pooled OLS Regression Model

Source |       SS       df       MS              Number of obs =     840

————-+——————————           F(  6,   833) =    4.71

Model |   427.19149     6  71.1985817           Prob > F      =  0.0001

Residual |  12604.7411   833   15.131742           R-squared     =  0.0328

————-+——————————           Adj R-squared =  0.0258

Total |  13031.9325   839  15.5326967           Root MSE      =    3.89

—————————————————————————–

GDP |     Coef.        Std. Err.        t     P>|t|                     Beta

————-+—————————————————————

IRS |   .0315285   .0220843     1.43   0.154             .0499782

M2 |   .2878275   .1836052     1.57   0.117               .0537318

SAV |   .0584744   .0123408     4.74   0.000          .1739913

INF |  -.0104223   .0152648    -0.68   0.495          -.0235788

EXP |   -.006975   .0070504    -0.99   0.323           -.0384087

FDI |   .0352121   .0217631     1.62   0.106                .059239

 _cons |   2.015293   .3840908     5.25   0.000                        .

—————————————————————————–

Appendix 10 – Heteroscedasticity Test for Pooled OLS Regression Model

White’s test for Ho: homoskedasticity

         against Ha: unrestricted heteroskedasticity

         chi2(27)     =    101.43

         Prob > chi2  =    0.0000

Cameron & Trivedi’s decomposition of IM-test

—————————————————

              Source |       chi2     df      p

———————+—————————–

  Heteroskedasticity |     101.43     27    0.0000

            Skewness |      40.14      6    0.0000

            Kurtosis |       6.81      1    0.0090

———————+—————————–

               Total |     148.38     34    0.0000

—————————————————

Appendix 11 – FEM Regression Model

Fixed-effects (within) regression               Number of obs      =       840

Group variable: country1                        Number of groups   =        40

R-sq:  within  = 0.0862                         Obs per group: min =        21

       between = 0.0075                                        avg =      21.0

       overall = 0.0204                                        max =        21

                                                F(6,794)           =     12.49

corr(u_i, Xb)  = -0.7023                        Prob > F           =    0.0000

—————————————————————————–

         GDP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————

         IRS |    .148236    .050407     2.94   0.003     .0492892    .2471828

          M2 |   .1308447   .1658913     0.79   0.431    -.1947926     .456482

         SAV |   .1503282   .0320581     4.69   0.000     .0873994    .2132569

         INF |  -.0439006   .0147292    -2.98   0.003    -.0728133   -.0149878

         EXP |   .0570777   .0176556     3.23   0.001     .0224205    .0917349

         FDI |   .0341773    .021153     1.62   0.107    -.0073452    .0756998

       _cons |  -2.667113   .7882859    -3.38   0.001    -4.214483   -1.119742

————-+—————————————————————

 Appendix 12 – REM Regression Model

Random-effects GLS regression                   Number of obs      =       840

Group variable: country1                        Number of groups   =        40

R-sq:  within  = 0.0794                         Obs per group: min =        21

       between = 0.0122                                        avg =      21.0

       overall = 0.0260                                        max =        21

                                                Wald chi2(6)       =     45.46

corr(u_i, X)   = 0 (assumed)                    Prob > chi2        =    0.0000

—————————————————————————–

         GDP |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————

         IRS |   .0991902   .0354093     2.80   0.005     .0297893    .1685911

          M2 |   .1713046   .1682697     1.02   0.309    -.1584979    .5011071

         SAV |   .0909694   .0206885     4.40   0.000     .0504207    .1315181

         INF |  -.0354344   .0147232    -2.41   0.016    -.0642913   -.0065775

         EXP |   .0165845   .0114161     1.45   0.146    -.0057905    .0389596

         FDI |   .0340258   .0211225     1.61   0.107    -.0073735    .0754252

       _cons |   .2256012   .6265673     0.36   0.719    -1.002448     1.45365

————-+—————————————————————

Appendix 13 – The Hausman Test

                 —- Coefficients —-

             |      (b)          (B)            (b-B)     sqrt(diag(V_b-V_B))

             |       re           fe         Difference          S.E.

————-+—————————————————————

         IRS |    .0991902      .148236       -.0490458               .

          M2 |    .1713046     .1308447        .0404599        .0281915

         SAV |    .0909694     .1503282       -.0593588               .

         INF |   -.0354344    -.0439006        .0084661               .

         EXP |    .0165845     .0570777       -.0404932               .

         FDI |    .0340258     .0341773       -.0001514               .

—————————————————————————–

                           b = consistent under Ho and Ha; obtained from xtreg

            B = inconsistent under Ha, efficient under Ho; obtained from xtreg

Appendix 14 – The Autocorrelation Test

Wooldridge test for autocorrelation in panel data

H0: no first-order autocorrelation

F(  1,      39) =      0.599

Prob > F        =      0.4438

Appendix 15 – The Heteroscedasticity Test For FEM Regression Model

Modified Wald test for groupwise heteroskedasticity

in fixed effect regression model

H0: sigma(i)^2 = sigma^2 for all i

chi2 (40)  =     842.41

Prob>chi2 =      0.000