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D Y N A M I C  E C O N O M E T R I C  M O D E L S  
DOI: http://dx.doi.org/10.12775/DEM.2018.001  Vol. 18 (2018) 5−34 

Submitted March 10, 2018 ISSN (online) 2450-7067 
Accepted June 15, 2018 ISSN (print) 1234-3862 

Shravani Sharma and Supran Kumar* 

Dynamics of Financial Development  
and Economic Growth: Panel Data Analysis  

for Selected Indian States 

A b s t r a c t. With the help of standard refined panel analysis techniques the present study 
analysed the dynamics of causal relationship between financial development and economic 
growth for selected Indian states. Mainly focusing on banking level indicators the present 
attempt measured the extent of financial development in the selected Indian states. Three 
major econometric techniques including panel unit root tests, cointegration tests and finally 
the panel error correction model have been implemented for identifying the relationship be-
tween variables. Firstly, the series was tested for cross-sectional independence and then 
checked for the presence of unit roots. The results of both first and second generation unit 
root indicated an integration of order one for all the variables and a long-run relationship 
between financial development and respective economic growth indicators was confirmed by 
the Pedorni’s and Westerlund’s cointegration tests. The results of the present study empha-
sized on the critical role of credit provided by banks in the process of long run economic 
growth across states. Apart from this the results of the study highlighted a very relevant fact 
that the Indian economy has a lot of scope in harvesting the less financially developed areas 
of the states which can run rapidly on the greeny path of dynamic and sharp long term sus-
tainable economic growth.  

K e y w o r d s: causality; economic growth; financial development; panel data; unit root.  

J E L Classification: G21; C23; O40.   

                                                
* Correspondence to: Shravani Sharma, Faculty of Management, School of Business, Shri 

Mata Vaishno Devi University, Katra, Jammu & Kashmir (India), E-mail: shravanishar-
ma06@gmail.com; Supran Kumar, Faculty of Management,  School of Business, Faculty of 
Management, Shri Mata Vaishno Devi University, Katra, Jammu & Kashmir (India) E-mail: 
suparn329@yahoo.co.in. 



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Introduction 
      The pace of provincial and national economic growth of an economy is 
primarily depicted by positive and continuous transformation in the level of 
production of goods and services. The continuous evolvement in the produc-
tion of goods and services directly depends upon the capability of an econo-
my in efficient utilization of its available physical and human resources. 
Moreover, effective aggregation of physical and human capital is basically 
catalysed and promoted by productive financial intermediation (Fitzgerald, 
2006). This accumulation requires regular mobilization of foreign and do-
mestic savings by discovering the productive projects, meanwhile also keep-
ing proper checks on the working of these projects (Goldsmith, 1969) and 
here, it is pertinent to mention that the monitoring of a project is facilitated 
by an effective and sound financial system.  
 Financial system is considered as the mainstay of an economy. Effective 
and conducive financial system is instrumental in providing sound and pro-
gressive business environment. However, endowment of this environment as 
well as financial infrastructure is indeed a crucial challenge faced by a na-
tion. It is suggested that countries which do well in terms of financial devel-
opment for constant period of time are equally productive on the front of 
reduction of their poverty level and generation of better infrastructure as well 
(Barro, 1996). Thus, financial development entails for the establishment and 
development of financial institutions which consequently promote growth 
and investment process. Available literature on financial development and 
economic growth specifies that apart from managing the savings of individ-
uals as well as groups, credit allocation in financial system plays a very 
prominent role since it is considered as the key for rational and inclusive 
economic development. Modern concept of economic growth suggests that 
rational allocation of capital investment increases the effectiveness of finan-
cial institutions which in turn promotes emergence and growth of an econo-
my (Yang and Yi, 2007).  
 Financial development is considered as one of the major policy areas for 
enhancing the economic efficiency and simultaneously economic growth of 
an economy. In the present context, financial system is one of such inputs 
which propel the economic growth of a country with its slow accumulation. 
There are variety of ways through which the financial system can affect the 
economic growth of any economy. Here, it is worth mentioning that a more 
developed financial system accesses and thus, in turn utilizes correct infor-
mation about the borrowers of funds (Boyd and Prescott, 1986). Moreover, 
a more developed and sound financial system will also provide more reliable 
database regarding the prospective clients to deal with and thus, will in turn 
help in channeling the resources to higher yielding projects. So, development 



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of financial intermediaries helps in increasing economic growth (Greenwood 
and Jovanovic, 1990). Different channels through which the financial inter-
mediaries diligently ascertain the authentication of the borrowers includes 
the mortgage system, interests rates, collateral requirements and so on (Ti-
wary and Thampy, 2015). These above mentioned channels not only make 
the utilization of funds effective but also create an environment for proper 
mobilisation of funds within an economy as well as for attracting funds from 
other economies as well (Boyd and Smith, 1998). The other prime channel 
through which the financial system can affect the growth of an economy is 
by offering more competitive and effective products. Hence, a financial sys-
tem which provides a proper system to effectively manage the high return 
projects directly helps in boosting the economic growth. Though, there is 
ample of indication that financial development plays a vital role in endorsing 
economic growth of the industrialized countries (Beck and Levine, 2004), 
but evidences are relatively mixed in case of developing or emerging econ-
omies. With uncertain market condition, it has become imperative to identify 
the channels of finance to economic growth as the aforesaid matters have 
important policy implications. Assuming financial development as an engine 
to economic growth policy makers should focus on the creation and promo-
tion of modern financial institutions including banks, non-banks, and stock 
markets in order to promote genuine and long-term economic growth.  
 However, on the other hand, there are studies (Patrick, 1966; Stern, 
1989; Ram, 1999; Akinboade, 2000; Favara, 2003; Majid and Mahrizal, 
2007; Demetriades and James, 2011; Nain and Kamaiah, 2014 and Kumar et 
al., 2015) which do not agree to this view and have rejected any causal role 
for financial development in the growth process. If this contrasting argument 
is the accurate depiction of reality, then all the policy work and efforts to 
encourage financial development would be premature and in fact will lead to 
uneconomical use of limited resources. Moreover, unnecessary emphasis on 
financial development will also turn away attention from other, perhaps 
more, urgent policy options to spur economic growth such as labour training 
and skill development programs to improve productivity, legal reforms to 
induce investment, and export promotion schemes etc. Apart from this, ma-
jority of the studies that deal with this concern consider economic growth at 
the aggregate level, where it is very simple to establish and explain a causal 
relationship between financial development and economic growth (Misra, 
2003, Acharya et al., 2009, Giri and Mohapatra, 2012, Nain and Kamaiah, 
2014 and Sharma and Bardhan, 2016). In Indian context, there have been 
dearth of studies that deal with the impact of domestic financial development 
on economic growth at regional and empirical level. To put it briefly, there is 
no universal consensus on the relationship between financial development 



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and economic growth. There are some mixed results regarding the nexus 
between financial development and economic growth. Thus, focusing on the 
development of the aforementioned issue the present study is an attempt in 
the direction to uncover the relationship between the financial development 
and economic growth with the help Indian context.   

1. Literature Review 
 Significant research studies are available on the aspect of financial de-
velopment and economic growth and other related issues contributed by 
academicians, researchers and institutions. The writings on the relationship 
between financial development and economic growth can be traced from the 
pioneering work of Schumpeter (1911), Robinson (1952), McKinnon (1973), 
Shaw (1973) and Lucas (1988) etc. 
 Goldsmith (1969) using data of 35 countries for the period 1860–1963 
studied the relationship between financial development and economic 
growth and identified that financial development exerts a causal influence on 
economic growth. The study concluded with a positive correlation between 
economic growth and financial development. In a similar study Gregorio and 
Guidotti (1995) by highlighting the importance of the efficiency of the fi-
nancial system more specially the credit component instead of volume of 
investments uncovered the long-run relationship between the growth in fi-
nancial system and growth of the economy. The study revealed that efficient 
allocation of credit in the financial system plays a significant role in uplifting 
the economic condition of the Latin American countries. The reason for this 
was attributed towards the introduction of financial liberalization. By exam-
ining the casual relationship between the financial development and econom-
ic growth in Indian context, Bhattacharya and Sivasubramanian (2003) sug-
gested that there exists a one-way causal relationship from financial devel-
opment to economic growth.  
 By focusing on Egypt, Abu-bader and Abu-Qarn (2007) identified a bi-
directional relationship between financial development and economic 
growth. Furthermore, the study also uncovered that the relationship between 
the two is catalysed by increasing resources for investment and enhancing 
efficiency.  
 In another study, Hassan et al. (2011) provided substantiated evidence on 
the link of financial development and economic growth in various low and 
middle-income countries. In context of developing countries the study indi-
cated a positive relationship between financial development and economic 
growth. Similarly, Al-Malkawi et al. (2012) in an empirical study uncovered 
a negative and statistically significant relationship between financial devel-
opment and economic growth for United Arab Emirates. Lacth and Gurgul 



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(2012) by considering differnt aspects of financial development identified 
unique causality from stock market development to economic growth and 
from economic growth to banking sector development in poland. 
 Furthermore, focusing on significant parameters of financial develop-
ment various studies identified uni-directional causal relationship from credit 
provided by banks to the development of economic growth. Osman (2014) 
examined impact of private sector credit on the economic growth of Saudi 
Arabia using ARDL model and concluded that there is long-run as well as 
short-run relationship between private sector credit and economic growth of 
Saudi Arabia. The study suggested bank credit to private sector as an im-
portant contributor towards the economic growth of Saudi Arabia. Similarly, 
Emecheta and Ibe (2014) concluded a positive and statistically significant 
relationship between bank credit to private sector, broad money and econom-
ic growth in Nigeria. In a more recent study Korkmaz (2015) found a signif-
icant role of domestic credit provided by banking sector on economic growth 
for selected Europian economies. Furthermore, there are some studies in the 
recent literature which support the uni-directional causality hypothesis from 
economic growth to bank credit. In a study Onuorah and Ozurumba (2013) 
suggested a uni-directional relationship running from economic growth to 
credit provided by banks. Similarly, Obradovic and Grbic (2015) suggested 
that economic growth contributes to financial deepening. The study identi-
fied a uni-directional causality running from private enterprise credit to eco-
nomic growth of Serbia. In a more recent study Aydi and Aguir (2017) con-
sidering Southern Mediterrean countries indicated a strong positive relation-
ship between financial development and economic growth. The study fo-
cused more on the advancement and more innovation of banking sector. In 
a somewhat similar study Witkowska and Kompa (2017) identified strong 
relationship between change in the political envionment and the develop-
ment of the financial system in poland. Bist (2018) considering a panel of 16 
African countries highlighted the presence of long-run relationship between 
financial development and economic growth with a positive relationship 
from the indicators of financial development towards the economic growth. 
The study emphasized on extending of credit facility to the private sector in 
these countries for achieving more growth. In another study Younis and 
Bechtini (2018) uncovered one-way causality from financial development to 
inequality in income in BRICS countries. In another study Nyasha and 
Odhiambo (2018) illustrated the crucial role of financial development in 
accelarting economic growth in South Africa.  
 In Indian context considering 25 states, Mishra (2003) studied the rela-
tionship between financial credit provided by banks and the economic 
growth and indicated a positive relationship between the two. Furthermore, 



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using multivariate cointegration and error correction techniques for Indian 
economy, Kamat and Kamat (2007) suggested strong evidence in favour of 
finance-led growth hypothesis and suggested finance as a strong and leading 
indicator for the economic growth. Moreover, the results of the study also 
provided relevant evidence that improvement in the stock market enhances 
the infrastructural growth of the economy. On the similar lines, the study 
conducted by Singh (2008) indicated long-run as well as short-run relation-
ship between financial development and economic growth. In another at-
tempt to identify the relationship between financial development and eco-
nomic growth, Arora (2012) presented a multi-purpose and multi-
dimensional representation of the bank credit. The study proposed that the 
credit provided by banks acts as a base for growth, globalization, urbaniza-
tion, removing the inequalities between rural and urban areas, small and 
large borrowers and finally economic growth. The study subsequently, advo-
cated a broader role of credit, which is growth oriented as well as develop-
mental in nature. In another study, Acharya et al. (2009) investigated the 
relationship between the financial development and economic growth in 
different sets of Indian states including BIMARU and nine other states using 
the credit outstanding as an indicator of financial development and con-
firmed the relationship between financial development and growth in Indian 
states. Hye (2011) identified the relationship between financial development 
and economic growth by constructing an index of financial development 
including four financial variables. The findings of the study suggested that 
during most of the years the index negatively related with economic growth 
of the country. Giri and Mohapatra (2012) supported the supply leading hy-
pothesis and highlighted the importance of financial development for better 
growth in Indian context. In the similar context, Ray (2013) also highlighted 
the positive role of financial development in the economic growth of India 
during 1990–91 to 2010–11. However, using aggregate data, Nain and 
Kamaiah (2014) found no evidence of causality between financial develop-
ment and economic growth in India. Similarly, Kumar et al. (2015) em-
ployed Toda and Yamamoto Granger-causality tests for South Africa for 
data over the period 1971 to 2011 and confirmed the absence of causality 
between financial development and economic growth, thus indicating that 
these two variables evolve independently with each other. Similarly, Kumar 
and Chauhan (2015) did not find any evidence of causality between saving 
deposits with commercial bank and economic growth of India.  
 The existing literature on the subject matter centred on different varia-
bles, covering different countries/states, utilizing different econometric tech-
niques, considering different time frames, and hence, presented varied re-
sults too. Though, there are many studies in Indian context uncovering the 



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relationship between the two however, most of them either focused on few 
numbers of states or the time frame considered was inadequate. Therefore, 
testing the same relationship with larger data set will provide better and reli-
able results. Moreover, the literature focusing on the pooled data of states 
with empirical data is also very limited in Indian context. Thus, in this regard 
the present study contributes to the literature by providing evidence and 
hence, fulfilling the gap by undertaking panel data analysis of 23 Indian 
states over a period of 17 years. Hence, the present study is pursued in this 
direction to fill the existing gap. 

2. Research Methodology 

2.1. Sources of Data and Variables Used 
 There is significant advantage of using panel data or pooled data over 
cross-sectional and /or time-series data as the panel data increases the degree 
of freedom for estimation of parameters and hence facilitates the use of mul-
tivariate analysis techniques (Hassan et al., 2011). The present study utilized 
the state level data of Union of India for the period of 1997–1998 to 2015– 
–2016 and the data have been compiled from the various reports of Reserve 
Bank of India including Handbook of Statistics on Indian Economy, Basic 
Statistical Returns of Scheduled Commercial banks, Branch Banking statis-
tics for the aforesaid duration. A total of 23 states comprising Andhra Pra-
desh, Arunachal Pradesh, Assam, Bihar, Gujarat, Haryana, Himachal Pra-
desh, Jammu and Kashmir Karnataka, Kerala, Madhya Pradesh, Maharash-
tra, Manipur, Meghalaya, Nagaland, Orissa, Punjab, Rajasthan, Sikkim, 
Tamil Nadu, Tripura, Uttar Pradesh and West Bengal have been selected for 
the estimation of the relationship between financial development and eco-
nomic growth. Although, the study has tried to capture the overall scenario 
of the financial development and economic growth nexus by including all 
the major states, yet the union territories of India are kept out of the preview 
of the present study. 
 At present, India is a federation of 29 states and 6 union territories. For 
the purpose of the analysis, the present study has left out the state of Delhi 
and the six union territories, as these are smaller geographical units, working 
and process are different. Among the remaining 29 stat0.es, three states 
namely Uttaranchal, Jharkhand, and Chhattisgarh, and Telengana carved out 
of the states of Uttar Pradesh, Bihar, Madhya Pradesh and the state of An-
dhra Pradesh respectively, were formed in the year 2000 and later. For ana-
lysing the data for these four states two major techniques were available:  
(i) Merging the data of these newly formed states with their parent states and 
(ii) the other option to analyse these states would have been to split the data 



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12 

before 2000 between new states and their parent states. For this, data at dis-
trict level would be needed, which is not available in public domain for most 
of the variables used in the study. Thus, the present study has merged these 
newer states with their parent states for the purpose of research modelling 
estimation and consequent analysis.  
 In recent years, the structure of employment and income generation in 
the Indian economy has gone through some critical changes. India, a pri-
marily agrarian and rural economy, is the eleventh largest economy in the 
world in terms of nominal GDP and the fourth largest in the world in terms 
of purchasing power parity (IMF reports, 2011). India is currently the second 
fastest growing economy (after China), which registered 8.9 per cent growth 
during 2010 and after a slight slowdown, it is again reaching new heights.  
An attempt has been made to incorporate a comprehensive list of variables 
(refer Table 1), which reflects the general and informative results about dif-
ferent indicators of financial development. By considering some of the rele-
vant financial development indicators the present study attempted to identify 
the relationship of financial development and economic growth in Indian 
context. 
 The Table 1 provides the summary of different studies alongwith the 
nature of data taken, the indicators used, etc. In India scheduled commercial 
banks accounted for around 70 per cent of the total assets of the financial 
system (Inoue and Hamori, 2010) so the present study has focused on the 
banking sector as a measure of financial development. Banking sector devel-
opment is defined as the development in the quality, quantity and effective-
ness of the banking services (Pradhan et al., 2011). This procedure involves 
the interaction of many activities and cannot be measured by a single indica-
tor (Levine and Zervos, 1998; Rousseau and Wachtel, 2000; Beck and Lev-
ine, 2004; Naceur and Ghazouani, 2009). Most of the studies given in Table 
1 focused on banking level indicators and considering the above mentioned 
literature the present study has focused mainly on credit, deposits, number of 
branches and their per capita deposits and credits etc. measure as financial 
development indicators.  
 In the present endeavour, for carrying out the estimations, the per capita 
state domestic product (PCSDP) and state domestic product (SDP) have 
been used as the proxy for economic development. On the other hand, the 
financial variables (proxy of financial development) used in the study for 
state level data are – (1) the number of branches of banks (NOF) (2) The 
outstanding credit of all the scheduled commercial banks of the state to the 
different sectors(OC) (3) Per capita credit (PCC) (4) per capita deposit of all 
scheduled commercial banks of selected states(PCD) (5) population per of-
fice(PPO) (6) Number of debit accounts (DA) and (7) Number of credit ac-



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13 

counts (CA). The above mentioned indicators are primarily considered as the 
indicators of depth of the financial institution in a country thus, it is pre-
sumed that higher the depth higher will be the economic growth (Inoue and 
Hamori, 2010). These measures are widely accepted and frequently used in 
the finance-growth relationship literature (Kendall, 2012; Aghion et al., 
2007; Jayaratne and Strahan, 1996; King and Levine, 1993a). For making 
the data comparable all the values are taken in their respective logarithmic 
form. The use of multiple indicators for financial development helps in bet-
ter understanding of different aspects and processes of financial develop-
ment. Three major econometric techniques including panel unit root tests, 
cointegration tests and finally the panel error correction model have been 
implemented for identifying the relationship between variables. 

Table1. Different indicators of financial development 

S.No Studies Period Name/Number of 
Economies Type of Data Financial Development Indicators 

1 King and 
Levine 
(1993a, 
1993b) 

1960–
1989 

80 Cross-Section 
Data 

i) Liquid liabilities of financial sys-
tem divided by GDP 

ii) Ratio of bank credit divided by 
bank credit by central bank do-
mestic asset, 

iii) Ratio of credit allocated to pri-
vate enterprise to total domestic 
credit, 

iv) Credit to private sector to GDP. 
2 Gregorio and 

Guidotti 
(1995) 

1960–
1985 

80 Pooled Cross-
Section data 

i) Bank Credit 
ii) GDP 

3 Berthelemy 
and 

Varoudakis 
(1995) 

1960–
1985 

91 Panel Data i) Money supply 
ii) GDP 

4 Demetriades 
and Lunitel 

(1996) 

1961–
1981 

India Time-series iii) Bank deposit liabilities 
i) GDP 

5 Demetriades 
and Lunitel 

(1996a) 

1962–
1982 

Nepal Time-series 
Data 

i) Bank deposit liabilities 
ii) GDP 

6 Rajan and 
Zingales 
(1998) 

1980–
1990 

55 Panel Data i) The ratio of credit to private 
sector to GDP,  

ii) Accounting standards 
7 Levine and 

Zervos (1998) 
1976– 
1993 

41 Time-series 
Data 

i) Ratio of market capitalization to 
GDP,  

ii) Ratio of total value of trades to 
GDP, iii)turnover ratio 

 



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14 

Table1. Continued 

S.No Studies Period Name/Number of 
Economies Type of Data Financial Development Indicators 

8 Rousseau and 
Wachtel 
(2000) 

1980–
1995 

47 Panel Data i) Liquid liabilities,  
ii) Stock Market capitalization, 
iii) Stock Market value traded 

9 Beck et al 
(2000) 

1960–
1995 

63 for cross-
sectional and 77 

for panel 

Cross-section 
and Panel 
Data 

i) Private credit to GDP 
ii) GDP 

10 Safdari et 
al.(2011) 

1975–
2008 

Iran Panel i) Trade to GDP ratio  
ii) The share of gross fixed capital 

formation to nominal GDP 
11 Sharma and 

Ranga (2014) 
2000–
2012 

India Time-series i) Saving deposits with commercial 
bank 

ii) GDP 
12 Kumar and 

Chauhan 
(2015) 

1975 
to 

2013 

India Time-series i) Saving deposits with commercial 
bank 

ii) GDP 
13 Sherawat and 

Giri(2015) 
1993–
2012 

India Panel Data i) Credit  as a share of state output 
ii) Deposit as a share of state out-

put 
iii) Number of scheduled commer-

cial bank branches 
14 Sharma and 

Bardhan 
(2016) 

1980–
2011 

India Panel Data i) Per capita deposits 
ii) Per capita credit 
iii) Per capita GDP 

2.2. Panel Unit Root Test 
 To identify the nature and pattern of data in the dataset under considera-
tion, a series of panel unit root test were employed in the present analysis. 
Otherwise problem with spurious regression could be faced. The literature 
on stationarity measurement has divided the unit root tests into first genera-
tion unit root test (Levin, Lin and Chu (LLC), Breitung, Hadri, Im Pesaran 
Shin test (IPS)) (Maddala and Wu, 1999; Breitung, 2000; Hadri, 2000; Levin 
et al, 2002 and Im et al., 2003) and second generation unit root tests 
(O’Connell; Breitung and Das; Moon and Perron, Bai and Ng and Pesaran 
developed by O’Connell, 1998; Breitung and Das Moon and Perron, 2004; 
Bai and Ng, 2004; Breitung and Das, 2005 and Pesaran, 2007) (Hurlin, 2004 
and Hu et al., 2006). The first generation unit root tests are in-turn divided 
into homogeneous model hypothesis tests (Levin, et al., 2002; Breitung, 
2000 and Hadri, 2000) and heterogeneous model hypothesis tests (Im et al., 
2003 and Maddala and Wu, 1999). Although, cross-sectional units in the 
present study are the states of India which more or less comes under homo-



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DYNAMIC ECONOMETRIC MODELS 18 (2018) 5–34 

15 

geneous model hypothesis, yet to reinforce the results in the present attempt 
tests from heterogeneous hypothesis model have also been implemented.  
 In case of IPS test, for analysing the presence of unit root, the model is 
specified as follows: 

Yit = βiyit-1+ ∑ ∅#$
%
$&' ∆yi t-j + Xitp+ϵit (1)                                                                                                                                

where i = 1,2,...,N are the number of cross-sections over period t = 1,2,…,Ti 
Here, y¡t stands for each variable under consideration in our model, β is auto-
regressive coefficient and ϵ is the error term.  The null and alternate hypoth-
esis of the respective model:  

H0 :  βi = 0, for i = 1, 2,…, N. (2)                                                 
This indicates that a unit root is present and thus, the model would be non-
stationary in this case.  

H1:  βi< 0, for i = 1, 2,…, N.  (3)                                                                                                     
IPS test uses different unit root test for all the cross-sections and the final 
test statistics is represented as 𝑡 ̅is the average of individual ADF statistics. 

𝑡̅=1/N ∑ (𝑡)%#,
$&'  

where tpi is the individual t-statistic for testing the null hypothesis. Under the 
null hypothesis of non-stationarity IPS show that the t-statistic follows as-
ymptotically a standard normal distribution. IPS provides simulated critical 
values for t for different number of cross-sections N, series length T. 
 On the other hand, LLC estimates the simple regression equation of dif-
ferenced concerned variable as follows: 

∆xit=∝+δxit-1∑ ∅ij ∆s
r=1 xit-j + uit (4)                                                                       

where, x is the variable under consideration, δ is the coefficient, u is the error 
term and i = 1, 2,…, N implying the number of cross-sections and  
t = 1,2,…, T. 
 The LLC unit root test has developed a procedure utilizing pooled cross-
section time-series data to test the null hypothesis that each individual time-
series contains a unit root. Under LLC the unit roots are tested using ADF 
regression 
 The LLC test involves that rejection of the null can occur even when 
only a small sub-set of series are stationary (Divino et al., 2009). Apart from 
this, the LLC test assumes that errors are independent across all cross-
sections (Banerjee, 1999). In the present study, comparison of the results 
from the above mentioned types of tests would enable overall comparison of 
cross-section results. 



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 Literature reveals that not considering the cross-sectional dependence 
across cross-section units may affect the sample behaviour of the panel unit 
root test which subsequently results to the incorrect decision in a unit root 
test (O’Connell, 1998, Banerjee 1999; Pesaran, 2007). In order to evade this 
under-performance of the unit root tests, the present study carried out second 
generation panel unit root test which takes care of cross-sectional depend-
ence and also allows for parameter heterogeneity and serial correlation be-
tween cross-section units in the panel (Pesaran, 2007). 
 Pesaran (2007) panel unit root test takes care of cross-sectional depend-
ence by augmenting the DF regression (Dicky and Fuller, 1979) with cross-
sectional mean and its lag. This test is known as cross-sectionally augmented 
Dicky–Fuller (CADF) test and is based on the following regression.  

∆yi, t = αi + ᵖi yi,t-1+ βoyt-1 + β1∆yt+ ϵi,t (5)                                                               
where yt is the average of y at time t for all N observations. The presence of 
lagged cross-sectional average and its first difference accounts for the cross-
sectional dependence through a factor structure. CADF regression is run for 
each cross-sectional unit and then average is taken over all the cross-sections 
(similar to Im et al. 2003) and the resulting test statistic is calculated as fol-
lows:    

CIPS(N,T)='
,
∑ 𝑡#(𝑁, 𝑇),
#&'  (6)                                                                                                                                                                   

where, ti(N, T) is the statistic obtained from individual CADF regression of 
ith cross-sectional unit and where CIPS stands for cross sectional augmented 
IPS (Im et al., 2003) unit root test) 

2.3. Panel Cointegration Tests 
 Long-run relationship between the variables after unit root is demon-
strated with panel cointegration tests. The present study implemented Ped-
roni (1999) for testing panel cointegration for the selected states of union of 
India. Pedroni (1999) cointegration test is a combination of seven statistics 
including Panel v-Statistic, Panel rho-Statistic, Panel PP-Statistic, Panel 
ADF-Statistic, Group rho-Statistic, Group PP-Statistic, Group ADF-Statistic. 
These statistics are estimated on the basis of cointegrating regression equa-
tion 

Yi, t = β0 + β 1i X 1i,t  + … + β Ni XNi,t + Ei,t ,  (7) 
t= 1,…,T; i = 1,…, S                                      

where, T is the number of observations over time, S denotes the number of 
individual members in the panel, and N is the number of independent varia-
bles. It is assumed here that the slope coefficients, β1i ,.., βNi and the member 



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17 

specific intercept β0 can vary across each cross-section. To compute the ap-
propriate panel cointegration test statistics, the cointegration regression in 
(7) is estimated by applying OLS for each cross-section (Orsal, 2007). 
 Secondly, the present study also utilised the four panel cointegration 
tests of Westerlund (2007) which having sufficient sample properties and 
high power relative to popular panel cointegration tests (e.g. Pedroni, 2004) 
and is also applicable in the situation of cross-section dependencies between 
the data.  

2.4. Dynamic Panel Causality and Long-term Relationships 

 Pedronis’ heterogeneous panel cointegration tests are only able to speci-
fy whether the variables are cointegrated and if a long-run relationship exists 
between them. Since, the mentioned model does not indicate the direction of 
causality, the present study estimated the two-step panel-based Vector Error 
Correction Model (VECM) proposed by Engle and Granger (1987) and uses 
it to conduct causality test on the financial development and economic 
growth relationship for the specified data of the study.  
 To identify the cointegration relationship, consider a two variable system 
equation: 

Y it =β Xi t 

where, Y and X are two variables of interest, t is the time period and i is the 
number of cross sections. 
The corresponding panel VEC model is: 

∆Yit = β1j +∑ 𝛼'#3	
%
3&' ∆𝑌#673+∑ 𝜂'#3

9
3&' ∆𝑋#673 + λECT1it-1 (8) 

∆Xit = β2j +∑ 𝛼;#3	
%
3&' ∆𝑋#673+∑ 𝜂;#3

9
3&' ∆𝑌#673 + λECT2it-1 (9)                                       

where, Δ is a first-difference operator applied to the variables; p and q are 
the lag lengths; i represents state i in the panel (i = 1, 2…., N); t denotes the 
year in the panel (t = 1, 2, …., T); Y and X are the variable of interest; ECT is 
error correction term which is derived from the cointegration equation. 
 The present study estimated the long-run equilibrium relationship given 
by the lagged Error Correction Term (ECT), which is a measure of the extent 
by which the observed values in time t-1 deviate from the long-run equilibri-
um relationship and in case the variables are cointegrated any such deviation 
at time t-1 should make alterations in the values of the variables in the next 
time point in an effort to force the variables back to the long-run equilibrium 
relationship (Ageliki et al., 2013).  



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18 

3. Results and Discussion 
 To study the casual relationship among different indicators of financial 
development and economic growth, Panel VECM is implemented in the 
present study. However, before testing the extent and pattern of causality, 
the data-set is tested for cross-sectional dependence and stationarity. The 
present study examined the cross-sectional independence using Friedman 
test (Pala, 2016) having null hypothesis of no cross-section independence. 
The test is beneficial for the panel having small time period and large cross-
section. The test is a non-parametric in nature where Spearman’s rank corre-
lation coefficient is used to estimates the cross-sectional dependency for the 
estimates. 
 Table 2 reveals that Friedman test-statistic is highly significant for eco-
nomic growth and measures of financial development. Hence, evidence of 
cross-sectional dependence in the data-series indicates that Indian states for 
these relevant indicators are cross-sectionally dependent or correlated due to 
various observed and unobserved common factors (Sharma and Bardhan, 
2016; Mishra and Mishra, 2014). 

Table 2. Cross-sectional dependence test for panel data 
Test Statistics Probability 

Friedman test (chi-square) 260.96* 0.00 
Note: *: Statistically significant at 1 per cent level of significance Friedman’s test statistic showed an 
asymptotically χ2 distribution with T-1 degrees of freedom. 

 Further, to test the order of integration of the variables under considera-
tion literature provides two types of unit root tests. The first generation panel 
unit root tests are mainly based on the restrictive assumption of cross-
sectional independence (Levin et al., 2002 (LLC); Im et al., 2003 (IPS); 
Maddala and Wu, 1999; Breitung, 2000). Literature reveals that not consid-
ering the cross-sectional dependence across cross-section units may affect 
the sample behaviour of the panel unit root test which subsequently results to 
the incorrect decision in a unit root test (O’Connell, 1998, Banerjee 1999; 
Pesaran, 2007). In order to evade this under-performance of the unit root 
tests, the present study carried out second generation panel unit root test 
which takes care of cross-sectional dependence and also allows for parame-
ter heterogeneity and serial correlation between cross-section units in the 
panel (Pesaran, 2007). 
 Pesaran (2007) panel unit root test takes care of cross-sectional depend-
ence by augmenting the dicky fuller regression (Dicky and Fuller, 1979).  
 Here, Pesaran (2007) panel unit root test in the presence of cross-
sectional dependence fails to reject the null hypothesis of unit root at level 
for all the variables (Table 3). However, first differenced series of state level 



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per capita GDP, SDP and indicators of financial development are stationary 
except for population per office and outstanding credit.  

Table 3.  Second generation panel unit root test with cross-sectional dependence 
Variables Level First Difference Order of Integration 

SDP 1.95 –4.13* I(1) 
PCSDP 0.12 –4.24* I(1) 

POP 18.64 17.52 – 
CA 0.21 –2.80** I(1) 
DA 6.13 –3.17* I(1) 

NOF 2.92 –3.63** I(1) 
OC –0.05 –1.23 – 

PCC 3.50 –3.47* I(1) 
PCD 2.72 –4.27* I(1) 

Note: *: Statistically significant at 1 per cent level of significance; **: statistically significant at 5 per cent 
level of significance. 

 In order to check the robustness of the model, the present study also es-
timated the unit root using the first generation individual panel unit root test 
like Im, Pesaran, Sin; Fisher-ADF; Fisher-PP tests and common root test like 
Levin, Lin, Chu which propose the null hypothesis in favour of unit root too. 
The assumptions regarding the common unit root indicates that the tests are 
estimated using common autoregressive parameters for all the series includ-
ed in the panel data-set, while the individual unit root tests provide different 
autoregressive coefficients in individual series in the panel data-set. The 
most widely used panel unit root tests in the literature involve the Levin et 
al. (2002) and Im et al. (2003). Thus, for studying the robustness of the mod-
el, the present study utilised both type including common and individual unit 
root tests for analysis. Further, the lag length criterion for group or pool unit 
root test is the automatic lag length selection that entails the information 
matrix criterion based for the number of lag difference terms and the Newey-
West automatic bandwidth selection. The lag values used for the computa-
tion of the model were on the basis of the default values.  
 The null hypothesis for the panel unit root test is that the variables in-
volve the presence of unit root. The test statistics for all the variables used in 
the data-set are shown in Table 4. All the variables accepted unit root hy-
pothesis of non-stationary at levels except for LLC test in case of outstand-
ing credit. The statistically insignificant values for rest all the tests statistics 
(refer Table 4) in the level form suggest the presence of unit root and hence, 
confirm the data to be non-stationary.  

  



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20 

Table 4. First generation panel unit root tests 
Variables  LLC** IPS** ADF** PP** 

PCC Level 3.29 6.70 10.54 12.23 
First Difference –6.55* –10.95* 202.37* 2530.66* 

PCD Level 2.63 6.47 121.60* 366.04* 
First Difference –7.39* –16.37* 297.91* 3339.01* 

PS Level 7.33 6.21 15.66 14.91 
First Difference –5.91* –3.52* 80.48* 158.15* 

NOF Level 17.75 13.99 9.52 1.44 
First Difference 0.80 1.60 45.43 64.33 

OC Level –2.51* –0.60 38.42 35.52 
First Difference 19.01 3.44 16.51 223.74* 

DA Level 13.02 10.08 15.51 42.33 
First Difference –4.18* –6.03* 142.47* 1186.30* 

CA Level 4.34 7.63 5.48 5.18 
First Difference –2.67* –3.76* 92.39* 203.65* 

SDP Level –5.10* 1.78 32.13 218.95* 
First Difference –9.71* –8.12* 156.84* 523.58* 

PCSDP Level 2.96 3.19 19.78 99.67 
First Difference –11.98* –12.18* 222.09* 352.34* 

Note: *: Statistically significant at 1 per cent level of significance. Probabilities for Fisher tests are com-
puted using an asymptotic Chi-square distribution. All other tests assume asymptotic normality. Automat-
ic lag length selection based on SIC: 0 to 3. Newey-West automatic bandwidth selection and Bartlett 
kernel. 

 Again by analysing the data in the first difference, the statistically signif-
icant values of all the statistics for all variables (except for number of bank 
branches and outstanding credit) rejects the null of unit root and thus, sub-
stantiate the data to be stationary at the first difference. In other words, au-
thentication of  Levin, Lin and Chu t stat; Im, Pesaran and Shin, W-stat; 
ADF-Fisher, Chi-square; PP-Fisher, Chi-square statistics less than the criti-
cal value of 1 per cent level of significance authenticate the rejection of null 
hypothesis, thus, validating the absence of unit root in the first differenced 
variables except for number of bank branches and outstanding credit. These 
results imply that for the respective states the variables are integrated of 
order one I(1).  
 Comparison of the results of both the first generation unit root test with 
that of the second generation unit root provides consistent results for most of 
the indicators of financial development as well as of the economic growth. 
However, the variables number of bank branches and population per office 
are I(1) in one type of test but not in the other, so the present study proceeds 
with both number of bank branches as well as population covered by the 
banks for further analysis as deleting a variable for analysis is not appropri-
ate. However, outstanding credit is not taken into consideration for further 
analysis as the results of both first as well as the second generation unit root 



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21 

test substantiate that the variable is stationary and hence can’t be taken for 
further analysis. 
 The results of the panel unit root test suggest that the variables under 
consideration are non-stationary in their level forms and thus, the application 
of OLS/GLS or other techniques would yield inconsistent and biased results 
(Ramirez, 2006). Thus, it becomes necessary to move forward towards panel 
cointegration analysis to test the long-run relationship, if exists, between the 
variables. The panel cointegration test examines the presence of a long-run 
relationship between the variables (Ghali, 1998; and Basu et al., 2003). The 
cointegration test is estimated using the Pedroni (1999, 2004) as well as 
through Westerlund (2007) test which takes the cross-section dependence 
aspect also. The Pedroni (1999, 2004) test provides seven statistics and 
which are further grouped into two sub-dimensions: the “panel statistics” or 
“within dimension,” which corresponds to the unit root statistic against ho-
mogenous alternatives (Breitung and Pesaran, 2008); and the “group mean 
statistics” or “between dimension,” which involve the averaging of the indi-
vidually estimated autoregressive coefficients for each country, individually. 
The null hypothesis for all the seven Pedroni tests is that there is no cointe-
gration between variables involved in the model. Another important aspect 
of the Pedroni (1999, 2004) statistics is analysis of the test using critical 
value of −1.64. This means that a test statistic of less than −1.64 implies 
rejection of the null for all other tests except the v-statistic. For the panel-v, 
the critical value is 1.64. Thus, a test statistic greater than 1.64 indicates that 
the null hypothesis of no cointegration is rejected. According to Lund (2010) 
if the results indicate inconsistency, the panel ADF and group ADF should 
be implemented. The result of Pedroni’s cointegration between number of 
credit accounts and SDP of the respective states suggests that all the four 
within dimensions panel cointegration test for the respective states reject the 
null hypothesis of no cointegration between the variables and overall all the 
seven statistics too rejected the null hypothesis of no cointegration at 1 per 
cent level of significance (refer Table 5).  
 Similarly, for the second situation of number of debit accounts and SDP 
of the selected states suggests that all the seven statistics are statistically 
significant and thus, rejected the null hypothesis of no cointegration. Similar 
results are obtained both in case of population per office and number of bank 
branches as well.  In a similar way, the relationship of per capita credit and 
per capita SDP also reject the null hypothesis for all the Pedroni (2004) ma-
jor statistics. Likewise, the relationship between per capita deposits and per 
capita SDP too reveals that all the seven statistics are significantly rejecting 
the null of no cointegration. The panel ADF and group ADF show consistent 
results. The Pedroni (2000) test rejected the null hypothesis of no cointegra-



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22 

tion in the important tests like Panel variance test, panel ADF and group 
ADF thus, on the whole, the results of the panel cointegration explicate that 
cointegration lies in all the specified empirical models and the results clearly 
point to a statistically significant long-run relationship between SDP and the 
discussed financial variables in different models. 

Table 5.  First generation panel cointegration test  
Pedroni’s Cointegration Test  

Method 
SDP and CA 
Test Statis-
tic(p-value) 

SDP – DA 
Test Statis-
tic(p-value) 

SDP – Pop 
Test Statis-
tic(p-value) 

PCSDP – 
PCC Test 
Statistic(p-

value) 

PCSDP – 
PCDA Test 
Statistic(p-

value) 

SDP – 
NOF Test 

Statis-
tic(p-value 

Within dimension/Panel statistic  
Panel v-

statistic (+) 20.98* 20.97* 24.74* –1.52 1.35** 14.92* 
Panel ρ-
statistic –11.89* –13.22* –10.38* –8.30* –9.08* –12.71* 

Panel PP-
Statistic –7.35* –8.60* –4.55* –13.05* –14.04* –8.00* 

Panel ADF-
statistic –3.44* –3.93* –3.47* –8.85* –8.59* –9.86* 

Between dimensions/Group mean statistic  
Group ρ-
statistic –3.29* –3.74* –2.96* –5.02* –5.35* –3.64* 

Group PP-
Statistic –14.13* –14.92* –11.48* –16.05* –16.08* –15.26* 

Group ADF-
statistic –8.72* –8.14* –8.82* –12.31* –11.79* –11.48* 

Note: *: Statistically significant at 1 per cent level of significance, **: statistically significant at 10 per 
cent level of significance. The Pedroni (2004) statistics are one-sided tests with a critical value of −1.64 
(ko−1.64 implies rejection of the null), except the v-statistic that has a critical value of 1.64 (1.64 suggests 
rejection of the null); Selection of lags is based on Schwarz Information Criterion (SIC). Newey-West 
automatic bandwidth selection and Bartlett kernel. 

 Since, the variables in our study are cross-sectionally dependent (Table 
2) and Pedroni’s test of cointegration assume cross-sectional independency 
so the study moved further with analysis of the so called second generation 
panel cointegration tests that allow for cross-sectional dependence. Table 6 
shows that two of the four Westerlund’s panel cointegration tests; that are 
one each for the panel (Pτ) and group mean statistics (Gα), confirmed the 
presence of cointegration. Hence, the above mentioned tests reinforce the 
results and highlight the presence of cointegration among the variables taken 
into consideration. 
  



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3.1. Panel Vector Error Correction Model (VECM) 
 The causality test is conducted using VECM technique if the variables 
are integrated to the same order as well as cointegrated (Moudatsou and 
Kyrkilis, 2011; Fowowe, 2011; Emirmahmutoglu and Kose, 2011). The 
question of long-run causal relationship between financial development and 
economic growth is now examined more thoroughly with the use of panel 
vector error correction models. Defining the lagged residuals, the dynamic 
error correction models are estimated for the variable sets differently. The 
estimated results are presented in Tables 1.7. The short-run causality is 
estimated by the statistical significance level of the coefficients of the first 
differenced variables while the long-run causality is determined by the 
statistical significance of the respective error correction term (ECT) values 
using t-tests (Minija, 2012).  
 In the first case (SDP-CA) the statistical significant value of the  
t-statistics of SDP for the relation CA=f(SDP) suggests that in short run the 
economic growth helps in the advancement of credit in the financial system. 
However, the statistically insignificant value of the ECT values for the same 
relation does not provide any evidence of the long run relationship from SDP 
to number of credit accounts. Considering the other relation where 
SDP=f(CA) suggests that although, in short run credit do not have 
a significant impact on the economic growth, but the statistically significant 
ECT value (Kamal, 2015 and Bhanumati and Azhagaiah, 2014) illustrates 
that higher credit allocation in the long run in turn helps in boasting the 
growth of the economy. The estimated coefficient of the residual from the 
VECM test (ECT) with statistically significant value and a negative sign 
confirms the long-run equilibrium relation between the independent and 
dependent variable at 1 per cent level of significance. Here, the speed of 
adjustment implies that 2.5 per cent of the disturbance in the short-run will 
be corrected each year. The results are in line with the previous study of 
Sharma and Bardhan (2016) thus, this evidence emphasize on the critical 
role of bank credit in the process of long run economic growth across states. 
It has been suggested that in the absence of developed financial markets 
small and medium-sized industries which are considered the backbone of an 
economy, in particular, the economies very often fail to expand due to the 
dearth of funds. Further, expansion of credit not only supports industrial 
activities but also helps in generation of more physical and human capital 
which are also essential elements of economic growth. Since, small firms are 
generally considered labour intensive and are supposed to generate more 
employment opportunities so for a typical developing economy such as 
India, providing better credit facility has a positive implications on economic 
growth (Sharma and Bardhan, 2016). This also highlights that credit and 



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24 

economic growth affect each other directly thus, suggesting a key indicator 
for enhancing the regional as well as national economic growth. Similarly, in 
case of SDP-DA the statistical significant value of ECT for the relation 
DA=f(SDP) suggests that in long run, economic growth does provide 
a strong evidence of causality towards the financial development, though the 
statistically insignificant values for SDP for the same relation suggests that 
in the short run there is no impact of this variable on  deposits as a whole. 
Considering the other relationship SDP=f(DA) the results indicate that 
accumulation of deposit in the short run enhances economic growth in the 
long run thus, reinforce the results obtained in Table 5 and 6. The above 
findings verify that deposits are crucial indicator and are significantly 
beneficial to the economic growth of the Indian states. Higher deposits with 
the intermediaries means channelling of surplus funds to the deficit units of 
the economy hence, transforming deposits into loans and other advances 
(Naira, 2016). The results are in line with the earlier studies of Bhanumurty 
and Singh (2013) and Sehrawat and Giri (2015) which suggest a bi-
directional causality and emphasis that economic growth has a critical role in 
financial development and that financial development leads to further 
development of economic growth of an economy. From the results of this 
relation it can be inferred that policy makers should emphasis on the 
measures which enhance the economic growth but they also focus on the 
development of banking system by considering the long term perspective of 
the mutual causality between the two which will ultimately benefit the 
economy as a whole.   

Table 6. Second generation panel cointegration test  
Westerlund Cointegration Test  

Method 
SDP and CA 
Test Statistic 

(p-value) 

SDP – DA 
Test Statistic 

(p-value) 

SDP – Pop 
Test Statistic 

(p-value) 

PCSDP – 
PCCA Test 
Statistic (p-

value) 

PCSDP – 
PCDA Test 
Statistic (p-

value) 

SDP – NOF 
Test Statistic 

(p-value) 

Gτ –1.78* –1.94* –1.49* –1.72* –2.36* –1.74* 
Gα –4.82 –4.97** –4.52 –4.65 –10.62* –4.51 
Pτ –8.18* –7.84* –8.84* –7.09* –9.17* –10.65* 
Pα –16.74* –14.91* –14.86* –5.42* –8.53* –17.19* 

Note: *: Statistically significant at 1 per cent level of significance, **: statistically significant at 10 per 
cent level of significance, Selection of lags is based on Akie Information Criterion (AIC) and Schwarz 
Information Criterion (SIC).  

 Overall the above two findings suggests that it is essential to expand the 
business and dealings of banks by augmenting the credit and deposits as it 
decides the degree of financial accessibility and in turn promotes the 
economic growth in the long run. 



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 Further, the statistically significant value of coefficients of SDP in next 
scenario of PPO=f(SDP) points towards an important short-run relationship 
suggesting that with the increase in economic growth, the population served 
by the banks will also increases. With the increase in SDP of the state the 
circulation of the money will increase and hence, more people will utilize the 
services of the bank. Moreover, considering the broader perspective higher 
population covered under the banking system will slowly and steadily 
enhance the economic growth in the long run. This is indicated by the 
statistically significant coefficient of ECT in the SDP= f(POP).  
 Further, the statistically significant value of coefficient of SDP in next 
scenario of NOF=f(SDP) points towards an important short-run relationship 
illustrating that with the increase in economic growth, the number of 
branches within a state will increase and hence, the population served by the 
banks. Ardic and Damar (2006) considered it as the indicator of financial 
depth and suggested that with the increase in number of branches the 
domestic competition increases and thus, results in the better financial 
services and in-turn helps in the long run growth. 
 The short run dynamics of per capita credit and economic growth 
presented in Table 7 suggest that a short-run increase of bank credit inturn 
induces an increase in economic growth in the selected Indian states. 
However in the long-run, the magnitude of per capita credit coefficient is 
quite small and insignificant indicating that per capita credit partially 
determines the magnitude of real economic growth in the short-run only. 
This can be inferred that the growth of bank credit influences per capita SDP 
of the selected Indian states, but the reverse is not necessarily true. In other 
words, it can be argued that economic growth in Indian states is caused by 
the expansion and improvement of the banking system in the long-run. 
 A significant relationship between the another proxy of financial 
development, the bank accounts (deposits) per person suggests that with 1 
per cent increase in the deposits the economic growth will increase with the 
same amount. Moreover, the estimated coefficient of the residual from the 
VECM test (ECT) with statistically significant value and a negative sign 
confirms the presence of long-run equilibrium relation between the 
independent and dependent variable at 5 per cent level of significance. The 
speed of adjustment implies that 1.6 per cent of the disturbance in the short-
run will be corrected each year. However, the results of the study suggested 
that there is no significant relationship from economic growth to per capita 
SDP of the states in short run or in long run.   

  



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26 

Table 7. Panel VECM estimation 

Dependent Variable Coeff t-Stat Dependent Variable Coeff t-Stat Direction   of 
Causality 

SDP SR CA 0.107 0.650 CA SR SDP –0.009*** –0.135 CA<–>SDP LR ECT –0.025* –2.661 LR ECT –0.001 –0.423 
SDP SR DA 0.002** 0.051 DA SR SDP –0.429 –2.336 SDP<–>DA LR ECT –0.038 –0.878 LR ECT –0.446* –6.784 

PCSDP SR PCC 0.008* 1.826 PCC SR PCSDP 1.752 3.142 PCC–>PCSDP LR ECT 0.014 3.252 LR ECT –0.004 –0.378 
PCSDP SR PCD 0.010* 0.951 PCD SR PCSDP 1.056 3.928 PCD–>PCSDP LR ECT 0.016** 3.075 LR ECT 0.006 0.437 

SDP SR POP –0.132 –1.050 POP SR SDP 0.017** 0.410 POP<–>SDP LR ECT –0.038* –3.240 LR ECT –0.012 –0.190 
SDP SR NOF 0.560 0.345 NOF SR SDP –0.26** –1.527   SDP<–>NOF LR ECT –0.162* –3.288 LR ECT –0.031* –4.462 

Note: *: statistically significant at 1 per cent level of significance, **: statistically significant at 5 per cent 
level of significance, ***: statistically significant at 10 per cent level of significance; SR: Short-run 
relationship, LR: Long-run relationship 

 The results in Table 7 overall suggests a bi-directional causal 
relationship suggesting that credit is a crucial factor of production, and in-
creasing proper credit allocation in the long-run will enhance the economic 
growth so there is a need to develop an advance current credit allocation 
process. So, focus increasing the number of business units of the banks in 
less developed areas will benefit the economy directly. Thus, overall it is 
highlighted that policies which affect financial development in short-run or 
in long-run are likely to have an effect on economic growth and vice-versa. 

Conclusions and Policy Implications 
 With the help of standard refined panel analysis techniques including 
panel unit root, panel cointegration and panel error correction model for 
a sample of 23 states of India over the period of 1997–1998 to 2015–2016, 
the present study analysed the dynamics of causal relationship between fi-
nancial development and economic growth in the sampled states of the coun-
try. The results of the study are robust as the present study have utilised both 
the first generation and well as the recently developed second generation unit 
root as well as cointegration tests. Firstly, the series was tested for cross-
sectional dependence, and the statistically significant values of cross-
correlation data provided evidence in support of cross-sectional dependency. 
Further, the results of both first and second generation unit root as well as 
cointegration test that account for cross-sectional dependence were used to 
examine the data, and the results indicated an integration of order one for all 
the variables (except for few as mentioned above). The results suggested the 



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27 

presence of cointegration and causality between financial development in-
puts and economic growth. The Pedorni’s and Westerlund’s cointegration 
test confirm the long-run relationship between financial development and 
respective economic growth indicators.  
Policy implications of the present empirical results are presented as follows.  
In the present case, a cyclic relationship exits, financial market develops 
more in terms of credit allocation as a consequence of economic growth 
which in turn feedbacks a stimulant to real growth. Hence, policies which 
emphasize more on the financial development and economic growth need to 
be developed. In the developing economy like India, the financial sector is 
mainly dominated by commercial banks. Hence, to speed up the financial 
development of such economies, stringent efforts should be directed towards 
improvements in the banking sector with easy access to loans.  
 The analysis reveals that there exists a positive relationship between the 
number of business units, the population served by the bank and economic 
growth suggesting that higher the number of bank branches higher will be 
the population served by the branches and hence, more will be the level of 
economic growth. These two significant indicators highlight a very relevant 
fact that the Indian economy has a lot of scope in harvesting the less finan-
cially developed geographical areas of the state which can run rapidly on the 
greeny path of dynamic and sharp long term sustainable economic growth. 
So, the government needs to provide additional consideration to the banking 
development in less developed states so as to facilitate economic growth as 
well as social development and thus, minimize regional disparities in devel-
opment and economic prosperity at regional and macro level. The banks 
need to support and assist opening of more bank branches in different states 
so that the commercial banks should increase the business potential by ac-
celerating both deposits and advances to improve the business and the state 
government should improve the climate for investment through better gov-
ernance so that the banking activities for developmental schemes are accel-
erated.  
 The bi-directional relationship between credit and economic growth and 
in short-run the uni-directional causality from per capita credit suggests that 
the banking system in Indian economy has a lot of scope of flourishing. Any 
expansion of the domestic credit provided by the banking sector will pro-
mote economic growth of the Indian economy and vice-versa. Because the 
financial sectors in India is still developing, deeper and more efficient finan-
cial markets are needed to improve the levels of economic development. 
Policies should aim at enhancing financial development and economic 
growth across states rather than concentrating on limited states which, in 
turn, possibly will help in growth convergence. Moreover, this has a signifi-



Shravani Sharma and Supran Kumar  

DYNAMIC ECONOMETRIC MODELS 18 (2018) 5–34 

28 

cant policy implication that a well-planned financial policy for promoting 
the development of domestic financial markets encompassing the banking 
sector is a crucial growth strategy for developing economy like India. 
 Identification of promising business ventures by keeping an eye on the 
upcoming market trends will not only help the banks to earn better and safe 
returns by reducing their non-performing assets but will also enhance the 
economic growth of our country. Like, there are special credit schemes for 
small and medium enterprises but it is important to allocate more bank credit 
to such endeavours as experience of emerging economies like China shows 
that these enterprises contribute around 90 per cent to the GDP and are also 
important for employment generation and poverty alleviation.  
 The two important aspect of the economic growth of Indian economy are 
agriculture and the industrial sector. Policy makers should make some im-
portant and strict decisions to reduce and rationally distribute loans to the 
non-performing agricultural and industrial sector.  
 Finally, on the basis of results obtained from the present study it should 
be recognized that economic growth itself may have the potential to promote 
further banking-sector development and hence, bring about additional eco-
nomic prosperity through an interactive feedback effect. So, policy makers 
should pursue policies that attract lucrative investments in the country which 
will not only help in generating better avenues but also will create a more 
competitive environment which in-turn will benefit the economic growth of 
the country as well as the financial sector.  
 Further, it is pertinent to mention that the nature of data and the model-
ling method used in the present study uncovered some important aspect of 
the relationship between financial development and economic growth. How-
ever, extending the present study by capturing more aspect of financial de-
velopment could provide some more interesting results on the aforemen-
tioned aspect.  

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