The Dynamics of Monetarists Versus Keynesians Perspectives and Their Role in Economic Growth of Pakistan

Abstract

The study intends to investigate a short-run and a long-run causality among money, income, and prices in the Keynesian and Monetarists framework. This study emphasizes the importance of unrecorded money, which exists alongside legal monetary assets and plays a dual function in determining economic prosperity. The underground economy, which is a hidden component of aggregate economic activity, is determined using Tanzi's monetary approach (Tanzi, 1980). This research uses a time series of annual data from 1990 to 2019 for this purpose. The data is extracted from the World Bank database for the monetary and development indicators. The study keeping in view the trending nature in data follows a unit root testing followed by the Autoregressive Distributive Lag Model (ARDL) to assess the long and short-run dynamics of causality among the variables. In both the pricing and income equations, the study finds a significant level link among the variables; however, there is no evidence of the presence of a level association in the money equation. The short-run causal relationship provides evidence of bi-directional causation between the supply of money and national income. The outcome of this study advise that though the view point of both the Monetarist and Keynesian school holds in both short and long run, however, in Pakistan only the Monetarists' role of money supply and income holds in Pakistan. This evidence would be of precise interest to the policy-makers.

1. Introduction

Money, income, and the price level are the most important macroeconomic variables; each of these variables has a substantial impact on a country’s economic prosperity. Money’s function in determining national income and price level is a long-standing controversy in economic research. Theoretically, it is unclear whether money causes changes in national income or whether income has a significant impact on changing money stocks in the economy.

The Monetarist school and the Keynesian school are the two different schools of thinking. Both have opposing viewpoints on the role of monetary assets in a country. Monetarists argue that money is a key component that affects national income and price levels without causing a reaction, whereas income is a non-reacting factor. Cash, contrary to that, does not have any role in determining the state income and price level in the economy, according to Keynesians; slightly, this is true.

This demonstrates how national income influences the demand for money and how variations in money stocks are influenced by it. As a result, it’s safe to assume that income does not affect money. Taken together, Monetarist theorizes that the causation goes after cash to national income and the price level. Keynesians, on the other hand, believe that causation runs from national income to cash and price levels. In this debate, empirical evidence is inconclusive.

For the first time, Sims (1972) investigated this relationship in the context of the United States of America. He discovered unidirectional causation from money supply to state national income, as desired by the Monetarists, and disproved the Keynesians’ argument that Granger causality flows from national income to cash. Brillembourg and Khan (1979) used a comparably long dataset to explore the nexus in supply of money and the aggregate level of national income of the United States of America. Sims (1972) found one-way causality flows from money supply to national income and consumer prices, and this study result fully supports his findings.

“It is the noteworthy notion of a long-term link among the three crucial macroeconomic factors, such as money supply (MS), national income (GDP), and inflation proxied by consumer price index (CPI), that has been investigated views on the economy of Sudan. The short-run direction of function is examined in this article to determine the relationship between the three variables. Several academics have used co-integration approaches to study the long term equilibrium relationship between money demand functions. For example, MacDonald and Taylor (1992) and Arize (1994)”investigated the stability of the US M1 or M2 definitions of money demand function using the methodology proposed by Granger (1988) and Johansen (1988) and concluded that the process of adjustment toward balance is compatible.

In Pakistan, Ali et al. (1986) researched the relationship between prices and money supply and discovered that the Quantity Theory of Money (QTM) can be used to forecast prices. Jones and Khilji (1988) used monthly data on other channels of money (M1 and M2), price level, Consumer Price Index (CPI), and Wholesale Price Index (WPI) during the period 1973 to 1985 to investigate the causal connection correctly. Husain and Mahmood (1998) used the Granger test to show that money lags have a significant impact on the WPI and the CPI and that the money on money measures are reversed without any feedback effect. Using the Sims technique and quarterly data from 1972 to 1981, Khan and Siddiqui (1990) discovered unidirectional causality for CPI from M1 but bidirectional causality for CPI from M2.

This study provides unbiased evidence of the causal connection between income money, and prices by incorporating black money i.e., the un-reported economy in the total economic activities i.e. (reported + unreported). This study estimates black money of economy by applying the ARDL model which provides robust results compared to the simple OLS technique. This study also provides the long-run and short-run estimates of black money over the economy. Also, this study considers the most recent data available for Pakistan.

Following are the aims of the study.

1. To calculate the extent of black money in Pakistan.

2. To explore the short-run and long-run causal connection between money, income, and prices in Pakistan.

3. To find the direction of causality among the set of underlying variables.

1.1. Research Questions:

Research questions are as follows:

1. Is there any existence of a long-run or short-run association between money supply national income and inflation in Pakistan?

2. Is there any role of the excess money supply to boost economic prosperity in Pakistan?

2. Literature Review

2.1. The Black Economy

Looking at the literature on the black economy (unreported economic activity), it has been observed that the underground economy plays a significant role in the total economic activity, as documented by Tanzi (1980), Kemal and Qasim (2012); however, earlier studies do not consider unreported economic activity to be a part of the economy’s income, and exclude from the analysis assumingly, at the same time as explaining the casual connection between money, state income, and price level. Therefore, the inconclusive outcomes reported by earlier studies can be attributed to the exclusion of a key variable, the black economy, which is an important component of the total economic activity.

Unlike previous research, this study explores the short and long-term causation between money, national income, and price level in Pakistan taking into account both the reported and undescribed portions of overall economic activity. Incorporating a black economy can result in stronger and more neutral conclusions on money supply, national income, and price level causality, which can aid in understanding the differences between Keynesians and Monetarists. The findings of this research are also helpful in improving our understanding of the function of non-reporting economic activity in the dynamic structure of the economy. This study revaluates the causal connection between cash supply and value level in Pakistan utilizing late information on cash and costs and dealing with time arrangement properties.

2.2. Black Money in Pakistan

Various investigations have shown that black money is on the rise in Pakistan. Ahmed (2010), Yasmin and Rauf (2004), and Kemal and Qasim (2012), for example, discuss the elements that raise the volume of underground activities in Pakistan, which are derived from the country’s social, economic and political environment. Empirical outcomes of the documented statistics of economy in Pakistan are based on a well-known statistical method, such as Tanzi’s (1980) monetary approach, which looks at many variables around different parts of the economy to reduce the estimated amount of the informal economy in Pakistan. These indicators have supported the natural economy’s increasing level during the last three decades. Corruption, weak governance, unfriendly taxation policy, lengthy registration procedures, complicated institutional framework, time-consuming inspection practices, and knotty regulatory requirements are the main reasons causing economic agents not only to arrange their economic activities in more informal ways but also to remain in the informal sector in Pakistan.

3. Methodology

3.1. Variables and Data

This research employs a time series data for Pakistan that spans over 1990 to 2019. The data is taken from the World Development Indicators database of World Bank and some issues of Pakistan Economic Survey. Total Economic Activity is a dependent variable, while money supply (M2) and inflation as measured by the consumer price index (CPI) are exogenous factors.

3.2. The Model

This study uses a dynamic method to analyze the relationship between the variables under investigation, namely the Autoregressive Distributive Lag (ARDL) model proposed by Pesaran et al. (2001), to investigate the underlying link between factors. The ARDL model has the advantage of producing robust and reliable results for both long and short-haul linkages among the components.

The elements do not need to be included in a similar request for this process to work. It means that it offers results about a long-running causal link regardless of the regressors are stationary at I(0) or observe the unit root at I(1). ARDL is also suitable because it allows for the clarification of association as far as short-term and recently run items without sacrificing recently run data. The ARDL model for this exam entails determining the concomitant condition.

3.3. Methodology

\(\Delta \operatorname{In} \mathrm{TEA}_{t}=\beta_{0}+\sum_{i=1}^{n} \beta_{1} \Delta \operatorname{In} \mathrm{TEA}_{t-i}+\sum_{i=0}^{n} \beta_{2} \Delta \operatorname{In} \mathrm{CPI}_{t-i} \\ \begin{aligned} \quad \qquad \qquad +\sum_{i=0}^{n} \beta_{3} \Delta \operatorname{In~MS}_{t-i}+\gamma_{1} \operatorname{In}^{i=1} \mathrm{TEA}_{t-1}+\gamma_{2} \operatorname{In~CPI}_{t-1} \\ +\gamma_{3} \operatorname{In~MS}_{t-1}+\varepsilon_{t} \end{aligned} \)       (1)

In the first equation, TEA, is the aggregate economic activity that is the total of stated national income i.e. GDP and unstated income (black money). In this study, CPI is used as a proxy for prices. MS shows the money supply and it is taken as broad money (M2). All the macroeconomic variables are in log run.

In the initial segment of the condition, β₁, β₂, and β₃ show the short run, while γ₁, γ₂, and γ₃ show the long-run association between the factors in the model. This model runs beneath the accompanying invalid and elective speculation.

H0: γ₁ = γ₂ = γ₃ = 0 (no long-run association exist).

H1: γ₁≠ γ₂ ≠ γ₃ ≠ 0 (long run-association exist).

The bound test is used to test the incorrect hypothesis that the underlying association hasn’t run in a long time. The determined F-statistic is compared to Pesaran et al. (2001) classified attributes (upper and lower bounds). Invalid speculation is dismissed if the F-statistic exceeds the higher basic worth, regardless of whether the request for factors is 0 or 1 or the other way around. The outcome is unknown if the F-measurement falls between the two boundaries. When the included request of the factors is known and each of the factors chases unit root at I, the choice is believed to be the premise of higher bound (1). The factor request is acknowledged as I(0), and the choice is entirely predicated on the lower bound. ARDL calculates (r + 1)^k the number of relapses to get the ideal slack extent of factors. “R” represents an extent of a slacks at the most extreme and “K” is the number of factors inside the model.

If the co-joining is discovered, the accompanying condition is evaluated. Regardless of whether we find the level relationship i.e. a cointegration. For this purpose, the following equation is estimated.

\(\begin{aligned} \operatorname{In}(\mathrm{TEA})_{t}=& \beta_{0}+\sum_{i=1}^{n} \beta_{1} \operatorname{In}(\mathrm{TEA})_{t-i}+\sum_{i=0}^{n} \beta_{2} \operatorname{In}(\mathrm{CPI})_{t-i} \\ &+\sum_{i=0}^{n} \beta s_{3} \operatorname{In}(\mathrm{MS})_{t-i}+\varepsilon_{t} \end{aligned}\)       (2)

The ‘error correction’ equation is empirically assessed to find the short-run association. ECM indicates the velocity of modification in the long term owing to trouble in the short term The ECM calculating equation can be written as showing in the following equation:

\(\begin{aligned} \Delta \operatorname{In}(\mathrm{TEA})_{t}=& \eta_{0}+\eta_{1}(\mathrm{ECM})_{t-1}+\sum_{i=1}^{n} \beta_{1} \Delta \operatorname{In}(\mathrm{TEA})_{t-i} \\ &+\sum_{i=0}^{n} \beta_{2} \Delta \operatorname{In}(\mathrm{CPI})_{t-i}+\sum_{i=0}^{n} \beta_{3} \Delta \operatorname{In}(\mathrm{MS})_{t-i}+\varepsilon_{t} \end{aligned}\)       (3)

In addition, ‘variance decompositions’ (VDCs) and ‘impulse response functions’ (IRFs) are used to assess the dynamic interaction and causality nexus among the variables. The VDCs show the forecast error, which is ascribed to its own and other variables’ innovation. As a result, we may use VDC to assess the importance of money supply, income, and price volatility in the economy. IRF, on the other hand, shows how one variable reacts to a one standard deviation change (1 S.D) in another variable. As a result, we may evaluate the model’s size, causation direction, and persistence of numerous elements.

The reason for this investigation is to rethink the causality between cash and pay and cash and costs in Pakistan. We utilize a generally longer informational index covering the period from 1990–2019. Additionally, we deal with the stochastic properties of factors utilized in exploration. Moreover, this investigation is not the same as the further past examinations since we have measured the effect of “black economy/underground economy/un-detailed economy” at the same time as a piece of announced public pay in the model. This particular variable is important as it contains a critical piece of public compensation that causes a push in expanding and cash supply. The existence of the un reported economy is liable for changes in the authority assessments of macroeconomic factors like national income generation, utilization, the inflation rate, and so forth.

For the estimation of black money estimation, this study follows the methodology of Tanzi (1980). Various studies follow Tanzi (1980) i.e Zulkhibri (2007). The money-related perspective demonstrates that the dark economy manifests itself in terms of cash interest, which can be used to evaluate the extent of the unreported economy in Pakistan.

Researchers have described various ways to calculate the extent of the black economy. Analyzing from a monetary point of view, the activities of the black economy that provide financial support throughout the use of cash or bonds are estimated. Indeed, even without taxes, the extent of the dark economy will be influenced by other criminal operations like betting, drug dealing, and pirating, which are generally done using cash, which prompts an increment in the underground economy. However, gathering information on these exercises is an obstruction to monitoring them. To such an extent that the dark economy will be trapped as money or bearer bonds through the currency demand function and the components affecting these elements, for example, interest rate, per capita pay, and so forth.

The issue of the dark economy and its techniques for assessment has for quite some time been the subject of extreme discussion in the economic literature. A few researchers have turned to coordinate strategies, while others have followed indirect methods to gauge the level of the dark economy. In this investigation, we have adopted a direct approach to estimating the black portion of GDP in the economy, known as the “monetary approach”. The monetary process of estimating the black economy shows that the black economy exists in terms of the demand for money, from which efforts are made to calculate approximately the magnitude of the black economy and the reduction of taxes. The essential thought in this methodology is to distinguish the demand for cash balance and therefore kill the impacts of changes in tax payments on this specific degree of money demand to finish. However, this technique is based on two key assumptions. First, underground activities or measures are a direct result of high tax rates, while, second, the currency is mostly used as a source of value for transactions and in the economy. Tanzi (1980) used this technique to estimate the extent of the underground economy of Pakistan.

This study is unalike from the previous research because the impact of the underground economy is considered to be a part of the legal reported national income (GDP) in the empirical model. It is probable that the existence of the black economy expressively influences macroeconomic factors like change in the price level, money demand in circulation, assets stocks, unemployment, wages rates, exchange rates, etc. To empirically assess the volume of the unreported economy in Pakistan, this study is following the “monetary approach” pioneered by Tanzi (1980). According to this approach by Tanzi, the model for currency demand is written as:

\(\begin{aligned} \left(\frac{\mathrm{CC}}{\mathrm{M} 2}\right)_{t}=& \lambda_{0}+\lambda_{1}\left(\frac{\mathrm{CC}}{\mathrm{M} 2}\right)_{t-1}+\lambda_{2}\left(\frac{T}{Y}\right)_{t} \\ &+\lambda_{3}(R)_{t}+\lambda_{4} Y g_{t}+\varepsilon_{t} \end{aligned}\)       (4)

where CC represents the currency flow in the economy at period t, Y denotes the national income i.e. GDP, M2 shows the extent of broad money which is composed of M1+demand deposit at the commercial banks, (T/Y) is representing the Tax to GDP ratio, shows the rate of growth per capita income, R is the rate of interest, and is the white noise error. After estimating the currency demand, the size of the unreported economy is assessed via tax evasion. Particularly, the currency demand function equation that is given in Equation (4) is initially estimated with the “tax” factor while this equation is re-estimated without the “tax” factor to find out the level of “legal money” flowing in the economy. The remaining process for estimating the magnitude of the black economy is described as follows:

\(\left[\left(\frac{C C}{M_{2}}\right)_{t}-\left(\frac{C C}{M_{2}}\right)_{w t}\right]=\lambda_{2}\left(\frac{T}{Y}\right)_{t}\)       (5)

Illegal Money (IM) = λ₂ × M₂       (6)

Legal Money (LM) = M2 (broad money) – IM (Illegal Money)       (7)

Velocity = GDP/LM       (8)

Black Economy (BE) = (IM) × Velocity of currency/Money       (9)

The sign of the lag term of the tax to GDP ratio term is expected to be positive in the equation of money demand. This is because of a reason that, with a rise in the general tax level, people have been involved in more illegal activities especially tax evasion, and this act of people is supported by the utilization of money in circulation. If a rate of interest on capital is high, it may increase the magnitude of the opportunity cost of currency holding, and hence the sign of real interest rate is anticipated to be negative. The expected sign of Yg would also be negative. This is predicted because, if there is economic progress in the country, the transactions on currency in hand would be substituted by other monetary instruments like credit cards or debit cards, etc. Conferring to Iqbal et al. (1998), a structural change of policies in Pakistan have raised the inflation and level of poverty in unconditional terms that result in an increase in the demand for funds and hence currency hoarding in the country.

4. Results and Discussion

4.1. Unit Root Test

In order to confirm that none of the variables is integrated of the order greater than 1, and the dependent-variable is integrated of order 1, two tests for checking the unit root are applied, viz., the augmented Dicky Fuller test (ADF) and Phillips Peron (PP). The projected outcome of ADF and PP tests for level and the first differenced series is presented in Table 1. The ADF and PP test-statistic is with time trend as well as without a linear time trend. Since the results of the ADF test are very sensitive to the lag length, this criterion is suggested by Campbell and Perron (1991) and is applied to choose the appropriate lag length. Explicitly, it starts with a maximum lag length of ‘m’ number of lags. Then it is based on standard student’s t-test and chronologically deletes the statistically non-significant lagsin the data until the last lag appears to be significant.

Table 1: Results for Unit Root

Note: All the variables included in this test are taken in log form. ***, **, and *represent the series is stationary at 1%, 5%, and 10% level of significance.

The result of the augmented Dicky Fuller (ADF) test shows that all variables are either I(0) or I(1) but none of them are integrated of order two. Money supply appears as non-stationary at a level when the ADF equation is estimated including a linear time trend. Though, it doesn’t mean that variables that appear stationary at levels (money supply) do not have any influence on the dependent variable i.e. GDP. A bound test for co-integration is applied for checking an association at level, regardless the variables are stationary at level of first difference. The PP test to check a unit root is also practiced checking the efficiency of the outcomes of the ADF test. The PP testifies the null hypothesis of stationary against the alternative hypothesis of non-stationary.

The PP test, like the ADF test, evaluates various data, notably PP(c) and PP (c + t) meaning with and without a linear temporal trend. The choice of a maximum lag length of 1 is justified by the presence of autocorrelation in annual time series data after lag one. The outcome of the ADF test is highly supported by the results of the PP test.

4.2. Testing for Level Relationship

The bounds test proxied by the Wald test is employed for testing the relationship at the level among the undelaying variables in ARDL regardless of the variables are I(0) or I(1). Though, these tests are delicate to the lag order in the model. It is, therefore, important that optimal lag must be selected for an un-restricted conditional error correction model (UCECM) to get robust estimates. This study, employs a modified Akaike information criterion (MAIC), the optimal lag order as 1, is therefore selected for all the models. Table 2 shows the estimated F-statistic to check the incidence of the long-term relationship among the variables. All the selected equations have restricted trends and un-restricted intercepts. Panel-I of the table encircles the outcomes by considering TEA as a dependent variable. This panel also represents the critical values of upper and lower bound (6.34 and 7.33) at a 5% level of significance. Since the estimated F-value (7.52) is greater compared to the upper bound value, showing the existence of a long-term association among the TEA, and the selected exogenous variables. Panel-II of the Table 2 presents the outcome when money supply is considered as a dependent variable. F-statistic (7.40) is exceeding the lower-bound and upper-bound critical values (6.34 and 7.38) at a 5% level of significance, also showing the subsistence of a long-run relationship between the selected variables. Panel- III of the table shows the results when CPI is taken as the dependent variable. The F-stat i.e. (7.85) is exceeding the upper bound value of (7.38) at a 5% significance level. This represents that there exists a long-run association between the CPI and the other underlying explanatory variables.

Table 2: Bounds Test Results (F-test) for Level Relationship

After validation of the underlying explanatory variables has a noteworthy levels effect on total economic activities (TEA) and the country’s price level (CPI); the ARDL framework suggested by Pesaran et al. (2001) is employed to empirically assess the effects of the levels. In precise, the (TEA) equation is estimated by considering a maximum lag length equal to one as the TEA, MS, and CPI are cointegrated at this particular lag length. Though, to get more robust results for the long-run along with the short-term effects, this study employs a Schwarz and Bayesian criterion (SBC) to seek a more parsimonious model.

The long-term evaluations of the ARDL-UCECM model are presented in Table 3. The panel A, of Table 3, provides the empirical estimates of s long-term coefficients when TEA, is consider as a dependent variable in the equation. The estimated long-run estimates of money supply (coefficient = 0.836; p-value = 0.000) suggest that supply of money is positively and significantly associated to aggregate economic activity. The positive effect of money supply suggests that economic activities in-country increase with an increase in the quantity of money supply. This outcome is in line with the Monetarists, that are of the view that money supply has an important role in creating income in the economy. The direct effect of money supply on national income is also in line with the findings of Husain and Mahmood (1998), which also provide footprints of the long-run association between money supply and national income proxied by GDP; yet, the findings of this long-run relationship between money and national income is unlike the outcome of many previous studies that did not find any noteworthy role of money in increasing national income of the country, though they took the care of liberalization measures. It is, therefore, can be stated that money circulation has a significant effect on income in the long-term, in particular when one incorporates the unreported income i.e. black economy as well while assessing economic activities in the country. The estimated coefficient of CPI (–0.12) is negative and appears significant (p-value = 0.002). This implies that prices have a significant impact on income in the long term.

Table 3: Long-Term Estimates of ARDL

Panel-II of Table 3 shows the long-term coefficient of the effects of total economic activity and the underlying explanatory variables. The results represent that MS is significantly and directly related to inflation, whereas, there is no substantial relationship between the national economic activities and inflation in the long term. These results suggest that other things being considered constant, the higher the level of money supply, the higher will be the inflation in the economy in the long run. The evidence of a long-term role of money supply on the price level is consistent with the earlier studies like Jones and Khilji (1988), Khan and Siddiqui (1990), Ahmed (2010), and Abbas and Husain (2006).

Panel-III of Table 3 shows the positive and statistically significant association between inflation and money supply in the long run. The results also suggest a positive long-run relationship between total economic activities and money supply. These results empower the idea of the Monetarist school of thought.

4.3. Testing the Long Run Stability

To test the stability of the estimated ARDL model, the cumulative sum (CUSUM) test was applied. The outcome of this test is represented in Figures 1(a) and 1(b). The ‘CUSUM’ and ‘CUSUMSQ’ tests for stability, when CPI is considering as a dependent factor are revealed in Figure 1(a) appropriately. The plot of the stability tests, while TEA is considered as a dependent variable is revealed in Figure 1(b). The estimated line graph located within the upper critical bound values and lower critical bounds values, confirming a long-run stability of the estimated model. It can be seen that the estimated line of cumulative sum lies in between the critical boundaries at the 5 percent level of significance. Therefore, the outcomes of the CUSUM test show the stability of the estimated model in the long-run time period. Stability diagnosis test tells whether this model is stable or not use CUSUM test through stability diagnosis.

Figure 1: Graph of the CUSUM(a) and Square of CUSUM(b)

4.4. Short-Run Dynamics of ARDL

To check the short-run association among the underlying factors, an error in a selected ARDL-UCECM model is employed for the income and price equations, whereas in the case of the money supply Simple VAR model, this study did not find any significant co-integration. The outcomes of the table show that the error term ECT(–1) shows the speed of adjustment towards the long-term convergence equilibrium. The negative sign and statistical significance of the error correction term’s coefficient values are regarded as indicators of the model’s stability in restoring long-run equilibrium.

Panel-I of Table 4 shows the results by considering ΔMS as a dependent variable. CPI showed negative and significant outcomes of the short-run association among the variables. In the short run inflation cause to increase in the money supply as p = 0.05. These results are in line with the monetary school of thought. ECT shows the convergence of the system towards equilibrium in case of any external shock to the economy.

Table 4: Estimates of Error Correction Model

Panel-II of Table 4 shows the results when ∆TEA is considered as a dependent variable. The outcome shows that MS is positively and significantly, related to income in the short term by employing p < 0.10. Further, the outcome embarks that ECT is statistically significant as p < 0.10, showing the converging behavior of the system towards the equilibrium at the rate annual rate of 80%. These results are in line to Bilquees et al. (2012), who also recognized a strong relationship showing the money and agregate economic activities in Pakistan.

Panel-III of the Table shows the results of the short-run dynamics when D(CPI) is taken as a dependent variable. The table further shows a p < 0.05 for money supply that proves this variable as responsible for inflation in the short run. The results also suggest that the higher the inflation in the earlier period, the higher the rate of inflation would be in the current period. The system is also converging towards equilibrium at the adjustment rate of 22% per year as the ECT term is negative and statistically significan.

4.5. Causality Test

In order to examine the short-term causation between national income, prices, and MS The Granger Causality Test is employed in this study. The results are shown in Table 5’s Panel-I. MS and TEA do not cause each other. There is unidirectional causality between inflation and TEA, which suggests there is causality. In the United States, Brillembourg and Khan (1979) discovered uni-directional causality that extends from money to income and prices. Prices, on the other hand, are neither isolated nor paired with the MS cause (in the sense of Granger) for total economic activity.

Table 5: Granger Test Estimates

In the case of MS and income, there is no causality between the factors. Williams et al. (1976) prove the existence of one-way causation between money and prices. The findings of this study indicate that only pricing Granger induces MS in the short run.

Table 5 shows that there is one-way causation between CPI and TEA (income) in the short run, but no two-way causality between MS and TEA in the short-run (income). Granger causality analysis, on the other hand, reveals that there is one-way causation in the short run between CPI and the TEA (income), with CPI as the dependent variable, and that there is no two-way causality between MS and CPI. Similar results were reported when MS was used as a dependent variable, with unidirectional causality between CPI and MS but no bidirectional causation between TEA and MS.

5. Conclusion

According to the findings of this study, black money plays a significant role in Pakistan’s economy, accounting for over 30% of total money circulation. According to the preceding calculation, there is a significant link between the money supply and inflation in Pakistan’s economy. This is true of the monetary phenomenon of money and income nexus, but the role of the money supply in enhancing economic development is also critical, so we suggest that money is a major component that affects economic growth in Pakistan when compared to a level that causes hyperinflation in the country. Hence, we find that the Monetarist belief that an increase in the money supply causes an increase in economic prosperity is prevalent in Pakistan. This concept is based on a real market mechanism in which funds are constantly required to encourage growth by progressing toward industrialization.