does measure of financial development matter ... - arf india

Does Measure of Financial Development Matterfor Economic Growth in India?

Naliniprava Tripathy

Professor (Finance), Dean Research, Indian Institute of Management ShillongMayurbhanj Complex, Nongthymmai, Shillong, 793014, Meghalaya, IndiaEmail: [email protected], [email protected]: 15 February 2019; Revised: 22 March 2019; Accepted: 15 April 2019; Publication: 30 May 2019

Abstract: The present paper examines the impact of financial development on economicgrowth in Indiaby using AutoRegressive Distributed Lag (ARDL) model and theGeneralized Method of Moment (GMM) model over a period of 15 years from June2003 to February 2018. The results of the study indicates that financial developmenthas a positive and significant impact on economic progression both in short run andlong run in India. The CUSUM test confirms the longrun stability relationship amongthem. Therefore, the study proposed that financial sector deepening should push aheadto augment economic growth of India.

Key words: Financial development, Economic growth, ARDL Cointegration Test, GMMModel, CUSUM Test

JEL Classification: C23, C58, E44, E51,

1. Introduction

The link between economic and financial development has been a topic ofinterest for academicians, researchers, and policymakers for the last threedecades. Financial progress is essential to economic growth, and economicprogress necessitates an efficient sound financial system. A developedfinancial system is critical for the country to exploit resources efficiently.Supply leading hypothesis postulated that financial development has apositive impact on economic growth (King and Levine, 1993: Patrick, 1966)and argued that financial sector development resulted in inflow of funds,increased savings, resourceful allocation of investments, plummeting thetransaction costs, managing risk, improves the efficacy of financialinstitutions; promote financial innovation and favorable regulatoryenvironment. Eventually, all these factors contribute to the overalldevelopment of an economy. Rajan et al. (1998) and Habibullah et al. (2006)also supported this hypothesis. Schumpeter (1911) pointed out that thebanking system is the crucial factor for economic growth due to its role inthe allocation of savings, the encouragement of innovation, and funding ofproductive investments. Levine (2005) advocated that financial institutionsand markets mobilizing savings from a large number of investors,

Journal of Quantitative Finance and Economics. 2019, 1, 1 ARF INDIAAcademic Open Access Publishingwww.arfjournals.com

68 Journal of Quantitative Finance and Economics. 2019, 1, 1

monitoring expenditure and carrying out corporate governance, processinginvestment projects for apportionment of savings for productive uses,diversifying liquidity and reducing the intertemporal risk for fosteringsavings and investment decision of economic growth.

On the contrary, the demand following hypothesis argued that economicgrowth causes financial development and growth of the financial sectorincreases with the result of the demand created by the economicdevelopment of a country. Financial development responds to economicgrowth (Robinson, 1952; Gurley & Shaw, 1955; Goldsmith, 1969; Patrick,1966). Odhiambo (2008) established demand following hypothesisempirically. The feedback hypothesis preached by Lewis (1955) expoundedthat financial and economic growth are causally related, and supportedboth supply leading and demand following hypotheses (Demetriades &Hussein, 1996; Greenwood & Smith, 1997). So far, whether financialdevelopment causes economic progress or economic growth causes financialadvancement is not yet resolved. Researchers found mixed empiricalevidence.

Further, the concurrent financial crises increased the importance ofresearch to find out the association between economic and financialdevelopment. To fill this gap, a modest attempt has been made to relookthe long run relationship between economic and financial progress in Indiaby using AutoRegressive Distributed Lag (ARDL) model and GeneralizedMethod of Moment (GMM) model. The rest of this paper planned as follows:the next section describes the literature review, section three introduce themethodology and data used. Section, four presents the empirical results.The concluding observations are shown in the final section.

2. Literature Review

Cheng (1999) applied the Granger causality test and cointegration test toexamine the empirical relationship between financial and economic progressin Korea and Taiwan. The result showed that economic and financialdevelopment depended on each other. Darret (1999) also used bothcointegration test and Granger causality test to document the relationshipbetween economic and financial progress in the middle east countries,which included Saudi Arabia, Turkey and the United Arab Emirates (UAE).The study found financial development is a leading indicator of economicgrowth in three countries and reveals the stable relationship betweeneconomic and financial development. Gan (2006) explored the associationbetween macroeconomic factors and the New Zealand stock market,discovered that the New Zealand stock market is not a leading indicatorfor changes in macroeconomic variables. Empirical evidence found no

Does Measure of Financial Development Matter for Economic Growth in India? 69

causality exists between financial and economic development. Kenourgiosand Samitas (2007) finding a positive relationship between finance andeconomic growth for Poland and indicates that credit to the private sectoris one of the powerful forces of longrun growth. AbuBader and AbuQarn(2008) found a causal relationship occurs between financial and economicprogression in Egypt, Morocco, and Tunisia respectively. Humpe andMacmillan (2009) studied the longterm relationship between stock marketmovements and macroeconomic variables in the United States of Americaand Japan indicating US stock prices influenced positively by industrialproduction while negatively by inflation. In Japan, stock prices positivelyintegrated with industrial production whereas negatively related to themoney supply, the rate of inflation. Regmi (2012) used Johansen and Juselius,and VECM Granger causality method to estimate the impact of financialdevelopment on economic growth. The study used stock marketcapitalization as a proxy for financial development. The study establishedthe positive contribution of financial development on economic growth.Bittencourt (2012) explored the association between financial and economicprogress of four Latin American countries. The study suggested thesignificance and impact of financial progress on economic growth. Hsuehet al. (2013) examined the causal relationship between financial andeconomic development among ten Asian countries. The study found thedirection of causality between finance and growth is sensitive to the financialdevelopment indicators. Narayan and Narayan (2013) found evidence thatneither the financial sector nor the banking sector contributes to economicgrowth for the Middle Eastern countries. Zouheir Abida et al. (2015)explored the causal relationship between financial and economic growthin Tunisia, Morocco, and Egypt by using the Generalized Method of Moment(GMM) model. The study found the presence of a healthy positiverelationship between financial and economic progress. Dilek Durusu et al.(2017) determine the contribution of the credit market and stock marketdevelopment on the economic growth of 40 selected countries by using theSolow–Swan growth model. The findings implied that financialdevelopment plays a significant role in economic growth for the majorityof sample countries. Vassiki and Richard (2017) examined the relationshipbetween financial and economic growth in the Ivory Coast by using aCommon Component Analysis (CCA). The study found the unidirectionalcausal link between economic and financial development.

The paper endeavors to discover the long run association betweeneconomic and financial development of India by using the ARDL model.Secondly, the study used Generalized Method of Moments (GMM) tocorroborate the relationship. Thirdly, CUSUM test employed to check the


longrun stability of the relationship between them.The hypothesisformulated as follows: H0: Financial Development does not have asignificant impact on Economic Growth, H1: Financial Development doeshave a significant impact on Economic Growth

3. Data and Methodology

The time series monthly data has taken for the study from June 2003 toFebruary 2018. Financial progression involves the interface of manyactivities and institutions. Both banks and stock markets are playing asignificant role in India for financial development. Financial developmentcaptured through two perspectives: financial depth and financial efficiency.Financial depth measured through savings and deposits. The banks play adominant and useful role in promoting financial development by mobilizingthe financial resources of the public and makes them available for investmentin productive enterprises. Bank credit increases the speed of the process ofeconomic development in the country by providing a loan to the industriesin time. On the other hand, economic growth encourages credit expansionthrough its demand for financial services. The relationship between thebank and economic growth is of practical significance in policymaking.

The financial efficiency is the ability to perform as a principal role oftransforming deposits to credits. (Asongu 2012). An improvement in theperformance of the stock market is an indication of the growth of the overalleconomy. The development of the stock market facilitates channelizingsavings into productive investments, which lead to an increase in economicgrowth. Usually, stock market growth examined with market capitalizationand BSE Sensex. BSE Sensex included the top 30 companies according tomarket capitalization approach and considered as an indicator of the growthof the overall stock market. BSE offered various services to stock marketparticipants and reached to the investors around the globe.

Similarly, Trading Volume considered as an indicator of stock marketdevelopment since stock prices need volume to move. The high volatilityof stock prices arises because of volume volatility and trading activities.Bank credit, bank deposit, BSE Sensex, BSE volume of trades and BSE marketcapitalization are used as a proxy for financial development.

The Index of Industrial Production (IIP) measures the changes in thevolume of production of industrial products and the growth rate of industrygroups. It shows the status of industrial activity such as industrial activityhas increased, decreased or remained the same. Indian IIP mainly focuses onthe Manufacturing, Mining, and Electricity sector with the most significantweight given to the Manufacturing sector, i.e., 75 percent. It also focuses onusebased industries, namely Basic Goods, Capital Goods, and Intermediate


Goods. The objective of IIP is to capture the direction and the trend ofindustrial production of the country, which in turn has an impact on thepolicy decision making. The IIP took as a proxy for real economic growth.

The study used two control variables such as inflation and export toimport ratio to observe the strength of the association between financialdevelopment and economic growth. Inflation is one of the critical parameterconcerning policy decisions of the Indian Government. Change in inflationinfluences economic policies to tighten that lead to increasing the nominalriskfree rate and affects the bank. The efficiency of the financial sector getsworse due to the high rate of inflation through financial market frictionsand slows the economic performance down. The inflation is taken as a proxyfor the Consumer Price Index.

The ratio of export to import defines the trade balance of a country. Itmeasured by dividing the value of export of goods and services by theamount of import of products and services. If imports more than exports, itcan distort the country’s balance of trade and undervalue their currency.The currency value, in turn, is one of the most significant elements of anation’s economic performance.

The financial development variable and economic growth variablescalculated as continuously compounded return using the given formula:

�

�1

ln tt

t

PR

P (3.1)

To check the multicollinearity amongst the variables, the correlationsbetween the explanatory variables examined.

To test the presence of panel unit roots, Levin et al.’s t (2002), Breitung’st (2000), Hadri’s Z (2000), Im et al.’s W (2003), Madala and Wu’s ADFFisher�2 (1999) and PP–Fisher’s �2 statistics.

3.1. AutoRegressive Distributed Lag (ADRL) Model

ADRL model developed by Pesaran and Pesaran (1997), Pesaran and Shin(1999) and Pesaran et al. (2001). It extensively accepted that AutoRegressiveDistributed Lag (ARDL) (Pesaran, 1997; Pesaran et al., 2000) approach issuperior to conventional cointegration techniques because of its versatility,i.e., the method can use irrespective of whether the underlying regressorsare pure I (0) or I (1) or mutually cointegrated. Thus, it avoids the potentialbias associated with unit roots and cointegration tests.

Under this method, if the variables cointegrated, there exists a stablelongrun relationship between them. The model is comparatively moreefficient with the restricted sample data size. Considering the advantages,the bounds test of cointegration within the ARDL used in the present paper.


The ARDL model involves estimating the following model:

� ��

��

�

� � � � � � � � �

� � � � � � � �

��

� �

� �

0 1 21 0

3 4 1 10 0

2 1

ln ln ln

ln ln ln

p q

t i t i i t ji j

r s

i i tt k t lk l

t t

IIP IIP FD

exporttoimport ratio INF IIP

lnFD

�o is the drift

, and �

t the white noise error term assumed serially uncorrelated.

� is the first difference operator, IIP denotes proxy for real economic growth,FD signifies financial development variables, EI ratiorepresents export toimport ratio, INF connotes inflation, p, qdenotethe lag length. �1i, �2irepresents shortrun dynamicswhile �1 and �2 represents longruncoefficients

Ftest conducted for the joint significance of the coefficients to test thelongrun relationship between variables. The null hypothesis in equationH

0: �1 = �

2 = 0 suggesting the absence of longrun relationship and an

alternative hypothesis of cointegration H1: �1 � �

2 � 0

Pesaran et al. (2001) formed two sets of critical variables. It assumed byone set that all variables are I (0), and another set assumes that all variablesare I (1). If the Fstatistic value is more than the upper critical value of thebound, the null hypothesis can be rejected indicating longrun cointegrationrelationship. When the FStatistic value is less than the lower critical valueof the bound, the null hypothesis of no cointegration accepted. The resultis inconclusive if the statistic value is in between the lower and upper criticalvalues

Once the longrun relationship among variables established, thenoptimal ARDL model used to compute the static shortterm spillover andlongterm spillover coefficients between economic growth and financialdevelopment. This enables to assess whether financial development leadsto economic growth or economic growth leads to financial growth. TheARDL model as follows:

� ��

��

� � � � � � � � �

� � � � � � � �

��

� �

� �

0 1 21 0

3 4 1 1

0 0

ln ln ln

ln ln

p q

t i t i i t ji j

r s

i i tt k t lk l

t

IIP IIP FD

export to import ratio INF ECT

The coefficients of error correction term capture the speed of adjustmenttowards longterm equilibrium. The coefficient must be negative and


statistically significant. The negative sign indicates that the dependentvariable changes back to its equilibrium following a shock in short run.

Due to structural changes in the Indian economy, it assumed thateconomic variables might be exposed to one or manifold operationaldisruptions. Thus, the study used the CUSUM test to check the stability ofthe long run relationship between financial and economic development.The CUSUM test is the stability test usually used to determine the recursiveresiduals of variables. The test investigates parameter instability if theaggregate sum goes outside the area between the two critical lines.

3.2. Generalized Method of Moment (GMM) model

The study used GMM model developed by Arellano and Bover (1995), toresolve the problems of serial correlation, heteroscedasticity, andendogeneity of explanatory variables. The explanatory variable maycorrelate with the error term, hence the explanatory variable used asinstruments to solve the problem of serial correlation. The reliability of GMMdepends upon the validity of the instruments used in the study. Hence, thestudy used Sargan Hansen J a test of overidentifying moment conditionsand serial correlation to prove or reject the null hypothesis for the validityof instruments. If it fails to reject the null hypothesis, it gives support to themodel. The study also uses AR (1), and AR (2) test to test the error term isnot serially correlated and if it fails to reject the null hypothesis, it back tothe model.

4. Empirical Analysis

The summary statistics of IIP, Inflation, Exchange rate, BSE Sensex, BSEmarket capitalisation, BSE volumes of trade, bank deposits, bank credits,import to export ratio are presented in Table 1.

Table 1: Descriptive Statistics

Variable Mean Median Standard Skewness Ex. Jarque ProbabilityDeviation Kurtosis Bera

IIP 0.004714 0.005470 0.022475 0.387444 4.761354 27.15399 0.000000

Inflation 0.005566 0.005672 0.007782 0.538127 6.162838 81.85372 0.000000

BSE SENSEX 0.012778 0.012487 0.065353 0.600339 5.725023 65.02743 0.000000

BSE Market 0.017051 0.021271 0.073827 0.666358 7.031175 132.1944 0.000000Cap

Volumes of 0.001651 0.003309 0.343407 0.213968 14.88559 1037.302 0.000000Trade

Bank Deposits 0.011941 0.008829 0.013351 0.945193 5.335516 72.04525 0.000000

Bank Credits 0.013747 0.011566 0.016992 0.786339 4.695290 39.21369 0.000000

Export to 0.000611 0.006879 0.104351 0.113369 3.750281 4.505098 0.055002Import Ratio


The table 1 shows the mean, standard deviation, skewness, excesskurtosis and JarqueBera estimates for the given variables. The JarqueBerastatistics for all the variables are significant and greater than zero, therefore,providing the evidence that all the series normally distributed. JarqueBerastatistics show that all the series are leptokurtic, exhibit nonnormality andindicate the presence of Heteroscedasticity.

The trend analysis of all the variables such as IIP, bank deposits, bankcredit, inflation, BSE Sensex, BSE market capitalization, BSE Turn over andexport to import ratio are presented in fig. 1. Figure 1 shows the trend ofeconomic and financial development.

Figure 1: Time Series Plot of all the variables

Table 2: Correlation matrix and VIF

Variables IIP Infla BSE BSE BSE Bank Bank Export VIFtion Sensex Market VOL Deposits Credits to

Cap ImportRatio

IIP 1Inflation 0.0627 1 1.09BSE Sensex 0.0574 0.0304 1 1.25BSE Market Cap 0.0748 0.0576 0.9578 1 1.26BSE VOL of 0.1109 0.0425 0.0100 0.0086 1 1.06tradeBank Deposits 0.0119 0.0380 0.0220 0.0512 0.1710 1 1.72Bank Credits 0.0393 0.1986 0.0118 0.0493 0.2183 0.6075 1 1.95Export to 0.0137 0.1943 0.0093 0.0226 0.0619 0.0340 0.2801 1 1.19Import Ratio


Table 2 reveals the correlation between the various variables. A robustassociation is found between the growth of IIP and BSE Sensex, IIP andBSE market capitalization, and bank credits. The reason is due to increaseproduction and investment activity which usually financed by borrowingsfrom banks. Therefore, if industrial production and capital spending arerising, there is an increase in the banking sector growth. If industrialproduction increases, investors and stock markets become more optimisticbecause companies’ performance increases, which ultimately lead to anincrease in the country’s economic growth.

IIP is interrelated negatively with bank deposit, BSE volumes of trade,export to import ratio and inflation. The reason is that an increase in inflationleads to rising prices, declining the purchasing power of money, reducesconsumption and economic growth. Fluctuation in the exchange rate is anessential factor that affects economic performance, due to its impact onoutputs, imports, export prices, interest rate, and inflation rate. Depreciationof domestic currency is distressing industrial output since it leads to theincreased cost of imported raw materials lead to an increase in the cost ofproduction and increase the unit cost. Higher unit cost makes outputuncompetitive, which may ultimately lead to reducing the industrial outputof firms.

Similarly, inflation is related adversely to BSE Sensex, BSE marketcapitalization, BSE volumes of trade, bank deposits, bank credits and exportto import ratio. Inflation curbs consumer savings, spending, and corporateprofits. The negative relationship between inflation and bank depositsattributed to the fact that inflation increases the cost of living. People willneed more money for expenses, hence more withdrawals and lead to areduction in the level of deposits.

Stock market negatively related to bank deposits, bank credits andexport to import ratio. The results indicate that banking activities areaffecting the stock market growth, economic growth, and national’s currencyexchange rate.

To overcome the problem of multicollinearity, the Variance InflationFactor (VIF) is used. It indicates that VIF is less than eight, so nomulticollinearity problem arises among variables.

Since there are a large number of interrelated variables, PrincipalComponent Analysis used to reduce the dimensionality of a data set andmaximize the variance of a linear combination of variables.

Table 3 reveals the results of the Principal Component Analysis. Fromthe eigenvalues, the first PC shows 65.26 percent of the standardizedvariance; the other PC labels approximately 33.34 percent, the third PCexplains about 1.4 percent. BSE Sensex is the leading indicator that


demonstrates the variation of dependent variables. Hence, the studyrestricted to BSE Sensex as proxies for financial development.

Before cointegration analysis, it is essential to test the order of integrationof all variables whether they are stationary. Hence, the panel unit root testsperformed on the level data series.

Table 4: Panel Unit Root Tests

Statistic Prob.**

Levin, Lin & Chu t* 34.6879 0.0000Breitung tstat 16.1238 0.0000Hadri Zstat 4.64918 0.0000Im, Pesaran and Shin Wstat 33.3712 0.0000ADF Fisher Chisquare 581.963 0.0000PP Fisher Chisquare 573.654 0.0000

Levin, Lin & Chu, Breitung’s and Hadri’s unit root tests assumecommon unit root process and autocorrelation coefficients of thetested variables across cross sections are identical whereas IPS, ADFFisher �2 and PP–Fisher �2 tests based on individual unit root processassumption and autocorrelation coefficients vary across crosssections.It observed that all the variables have unit roots and are thusnonstationary.

Table 3: Principal Components Analysis

Eigenvalues: (Sum = 5, Average = 1)

Principle Component EigenValue % of Variance Cumulative%

1 1.957897 0.6526 0.65262 1.000183 0.3334 0.98603 0.041920 0.0140 1.0000

Eigenvectors (loadings)

Variables Factor Lodgings Communalities Factor score

BSE Sensex 0.707111 0.009071 0.707044BSE Market Capitalisation 0.707101 0.010534 0.707034BSE Turnover 0.001035 0.999903 0.013863

Table 5: Bounds Test for Co integration (Dependent variable ln IIP)

K FStatistic Level of I (0) – Lower I (1) – Uppersignificance Bound Bound

5 23.71408 1% 3.06 4.155% 2.39 3.38

10% 2.08 3

Note: *, ** and ***denote statistical significance at 1%, 5% and 10% respectively


Autoregressive distributed lag (ARDL) model or bounds testingapproach is estimated to test the presence of a longrun relationship betweenfinancial and economic growth.

Table 5 shows that Fstatistics value is 23.71408, whichis higher thanthe upper bound value at one percent level of significance. Hence, the nullhypothesis rejected indicating the existence of a longrun relationship (cointegration). Therefore, industrial production, which acts as proxies’ forreal economic growth, cointegrated with financial development suggestingthat if the financial development upsurges, it leads to economic growth.

Table 6: Long-run coefficients model

Variable Coefficient tStatistic Prob

BSE Sensex 0.054277 2.663205 0.0086*Bank deposit 0.092660 0.971243 0.3329Bank Credit 0.184948 2.200409 0.0292**Inflation 0.058334 0.274146 0.7843Export to Import Ratio 0.003667 0.096702 0.9231C 0.002960 1.585180 0.1150

Note: *, ** and ***denote statistical significance at 1%, 5% and 10% respectively

Table 6 presents the estimated longrun coefficients. It indicates thatthe stock market and bank credit are positively affecting economic growthwhile bank deposit, inflation, and export to import ratio negativelyinfluencing economic growth. Bank credits are positive and statisticallysignificant associated with economic growth in the long term. The estimatedcoefficients reveal that 1 percent upsurge in bank credit causes 18.49 percentincrease of economic growth indicating that an increase in bank creditincreases the demand for production of goods in the economy, which inturn leads to augmented borrowing, increased consumption and positiveimpact on the economy. The result shows that 1 percent increase in stockmarket growth leads to boost economic growth by 5.4 percent suggestingthat higher industrial production, in turn, implying higher demand, increasefirms’ sale and generate better profits. Hence, economic growth has a directimpact on the rise in stock prices and a positive effect on financialdevelopment.

However, 1 percent rise in bank deposits leads to a decrease in economicgrowth by 9.2 percent signifying that bank deposits move in the oppositedirection during the weakening of economic growth. Weaker economicgrowth leads to reduce shortterm interest rates. The result also exhibitsthat the economic growth related negatively to inflation and export to importratio. The table presents that 1 percent increase in inflation leads to a declinein economic growth by 5.8 percent implying that increasing inflation


decreasing the purchasing power of money, which reduces consumptionlevel and shrinkage the economic growth.

Similarly, the rise in the value of the exchange rate causes contractionof economic growth possibly due to the increasing inflation rate, interestrates, the weak domestic currency, and high import price. As a result, thedemand for imported goods reduced and shifting consumption to localproducts. The aggregate demand increases lead to demandpull inflation.Industrial producers witness an improvement in competitiveness and pushthem to reduce their costs, and it leads to inflation over the long run. Hence,depreciation on the exchange rate causes both costpush inflation anddemandpull inflation that affects economic growth. The result specifiesthat 1 percent increase in export to import in India lead to decline theeconomic development by 0.3 percent. This adverse effect is due to steadilyimports are more than the value of exports over the sample period. Exporthas a direct impact on domestic investment, but the influence is not able totransfer to economic growth due to increasing trends of imports.

4.1. Short run dynamics model

The study used Error Correction Model to determine the short run relationamong them based on ARDL approach.

Table 7: The Short Run impact of financial development on economic growth(Dependent Variable is IIP)

Variable Coefficient Std. Error tStatistic Prob.

D(IIP) 0.141546 0.073666 1.921460 0.0565**

D(BSE Sensex) 0.010054 0.017592 0.571487 0.5685

D(Bank Deposits) 0.111585 0.104337 1.069465 0.2865

D(Bank Credits) 0.255803 0.100136 2.554546 0.0116*

D(Inflation) 0.367630 0.194372 1.891370 0.0604***

D(Export To Import Ratio) 0.014550 0.014002 1.039164 0.3003

CointEq(1) 1.598371 0.121184 13.189643 0.0000*

Diagnostic Tests

Rsquared 0.742653

Adjusted Rsquared 0.717908

Akaike info criterion 4.903833

Schwarz criterion 4.611043

HannanQuinn criter. 4.785041

DurbinWatson stat 2.058212

Ramsey RESET Test 2.143554(0.0336)

Heteroskedasticity Test 9.397667(0.8558)

Note: ***, **, and * shows significance at 10%, 5% and 1%, respectively.


The shortrun dynamics of the ECM version of the ARDL Modelpresented in Table 7. The results confirm that the error term is negative andsignificant at 1 percent level indicating the presence of a shortrunrelationship between finance and economic growth. The error correctionterm is 1.59 suggesting that 15.9 percent of errors of economic growth ofthe previous year corrected within the current year and speed of adjustmentstowards longrun equilibrium is breakneck.

The table 7 demonstrates the insignificant relationship betweeneconomic growth and bank deposits in the short run. The reason mayattribute to higher economic activities that require more money for workingcapital. Export to Import ratio and inflation is having a negative relationshipwith economic growth in the short run whereas stock market positivelyinfluences the economic progress in the short run. The bank credit has apositive and significant impact on economic progression in the short run.The reason is that bank provides credit to various productive sectors of theeconomy and uplift the priority sector, which raises income and providesemployment to the people of lower income group and improving economicactivities of the nation.

The coefficient of BSE Sensex shows positive sign but statisticallyinsignificant indicating a 1percent increase in stock market developmentincreasing the economic growth by 1 percent in the short run. Similarly,inflation and export to import ratio are negative, and it indicates that ifIndia’s inflation and export to import ratio increase by 1 percent, economicgrowth decrease by 36.76 percent. Inflation is statistically significant at 5percent level of significance.

Error correction statistically significant at 1 percent level signifyingunidirectionally causal relation running from financial development toeconomic growth. Hence, it suggested that financial development leads toeconomic advancement and supports the supplyleading hypothesis. Theentire diagnostic test endorses the short run results.

Table 8: BreuschGodfrey Serial Correlation LM Test

Fstatistic 1.089304 Prob. F(2,154) 0.3390Obs*Rsquared 2.399307 Prob. ChiSquare(2) 0.3013

BreuschGodfrey Serial Correlation LM Test establishes about no serialcorrelation found and the model used in the study is good as per table8.The results of diagnostic tests display that the prediction errors from themodel are normally distributed, and there is no problem ofheteroskedasticity. Therefore, it suggests that the long run model welldesigned.


Table 9: Impact of Financial on Economic Development GMM estimates(Dependent Variable IIP)

Variable Coefficient tStatistic Prob

BSE Sensex 0.051532 1.996843 0.0474*

Bank deposit 0.099037 0.618989 0.5368

Bank Credit 0.180683 1.694481 0.0920*

Inflation 0.088991 0.488198 0.6260

Export to Import Ratio 0.027650 1.412349 0.1597

C 0.003279 1.555097 0.1218

Rsquared 0.175037

AR(1) test 0.0000

AR(2) test 0.0002

Pvalue Sargan test 0.312935

It is seen from table 9 that IIP is having a positive statistically significantrelationship with BSE Sensex and bank credit. Therefore, it indicates thatrising industrial production not only enhances companies’ performance,which augments the stock market growth but also mounting the bankingsector growth since production activity are financing by banks. The resultsalso exhibit that Bank deposits, export to import ratio and inflationnegatively related to IIP. The reason is that high inflation makes investmentsless desirable, and exports become more expensive which affect the balanceof payments. As a result, economic growth decreases. The Sargan HansenJtest pvalue is 0.312, which prove the hypothesis that instruments variablesare not correlated with the set of residuals. It cannot reject the hypothesis.AR (1) and AR (2) test with associated pvalue is accepted in order andestablished that there is no serial autocorrelation in the second order in theerror terms.

To confirm this, the study used a CUSUM stability test to verify thestability of longrun association between financial and economicprogression. If the CUSUM line lies in between the lines of the level ofsignificance, the model is stable. It observed from Fig. 2 that the plot ofCUSUM line stay within the critical 5 percent bound and does not exceedthe critical boundaries for IIP, bank deposits, bank credits, BSE Sensex,inflation and export to import ratio suggesting that the longrun stabilityrelationship exists between economic and financial development.

5. Concluding Observations and Policy Implication

The study examined the relationships between the financial and economicprogression of India from June 2003 to February 2018. The timeseries dataused in the study comprised of monthly observations of IIP, inflation, bankcredits, bank deposits, BSE Sensex, BSE market capitalizations BSE volume


of trades and export to import ratio. IIP indicator represents economicdevelopment whereas BSE Sensex, bank credit and bank deposits used as aproxy for financial growth. Inflation and import to the export ratio used ascontrol variables.

The study found a robust association between the growth of IIP withBSE Sensex, BSE market capitalization and bank credits. IIP correlatednegatively with inflation, BSE volume of trades, bank deposits, and exportto import ratio. In addition, a negative correlation found between BSESensex, bank deposits, bank credits, BSE volume of trade and export toimport ratio. Further inflation negatively associated with BSE Sensex, BSEmarket capitalization, BSE volumes of trade, bank deposits, bank creditsand export to import ratio. The study signposted that inflation curbsconsumer savings, spending and affects corporate profits. In the long run,inflation rates change likely to change the exchange rates. If the exchangerate high, the prices of imported goods will rise, and it contributes to higherinflation, which also raises interest rates. When a country’s interest ratesrise, money and investments will tend to flow to that country, driving upthe value of its currency. Therefore, the regulators need to streamline themacroeconomic factors to strengthening the stability to promote itsdevelopment.

ARDL cointegration tests employed to examine the longruncointegration between financial and economic growth. The study found alongrun relationship between financial and economic growth. The studyalso observed a healthy relationship between the growth of IIP and BSESensex, bank credits. Economic growth adversely correlated with bankdeposits, inflation and export to import ratio. This undesirable effect foundbecause the value of imports is more than the cost of exports. The positiverelationship between financial and economic growth also found by using

Figure 2: CUSUM Stability Test


GMM model. The findings of the study indicate that financial developmentdelivers an essential plausible mechanism for the longrun economic growthof India and supporting the supplyleading hypothesis. The CUSUM testalso confirms the longrun stability relationship exists between economicand financial development.

Financial deepening and financial deregulation are vital for a countryto achieve its economic growth. Financial deepening plays a significantrole in transferring created funds to the real sector. If the majority of theseestablished funds transferred to the real industry, economic growth willincrease. If savings accumulated and using them correctly, it will providean important contribution to the development of the economy. Increasingsavings will also support capital accumulation and the demand for goodsand services produced in the economy will increase with an increase inbank credits. Hence, the regulator needs to make policies that favor morecredit allocation in the economy. In the banking sector, while ensuring fullaccess to credit, steps need to take to reduce the risk of payment crisis. It isalso equally important to improve the technology and innovation that canbe applied in credit selection, evaluation, monitoring and controlling ofcredit risk.

Further measures for the probability of full and timely repayment ofdomestic and foreign debt need to be taken. Policymakers are obligatory toenhance macroeconomic stability to increase investor confidence. Asignificant implication of findings is that the economic and financialdevelopment in India deserves a healthy calibrated policy response forbringing out reformation in the banking sector to enhance more economicgrowth. Since the integration of economic and financial development is animportant implication for international portfolio diversification, it is a topicof interest for practitioners’, researchers and policymakers today. This studyhas a significant impact on global and institutional investors to make betterinvestment decisions. However, future research can be carried out byincluding other indicators such as government expenditure, a longtermbond that is also influencing the financial and economic growth.

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