There is a growing literature body which examines the connections between financial status and economic growth. The aim of this paper is to examine the mechanism through which this positive connection is realized. The methodology is based on a pool data regression with dynamic of real GDP as dependent variable and some key variables of the financial sector. The main output of our study consists in the thesis that the financial status matter for the economic growth.finance, growth, cost of capital, yield

Dima, Bogdan

Milos, Laura Raisa

Pirtea, Marilen

English

Research Papers in Economics

The 3
rd European DAAAM International Young Researchers´ and Scientists´ Conference 
25-28th November 2009, Vienna, Austria 
 
 
A  EMPIRICAL A ALYSIS OF THE I TERLI KAGES BETWEE  FI A CIAL 
SECTOR A D ECO OMIC GROWTH
 
PIRTEA, G[abriel], M[arilen]; DIMA, B[ogdan] & MILOS, L[aura], R[aisa] 
 
Abstract: There is a growing literature body which examines 
the connections between financial status and economic growth. 
The aim of this paper is to examine the mechanism through 
which this positive connection is realized. The methodology is 
based on a pool data regression with dynamic of real GDP as 
dependent  variable  and  some  key  variables  of  the  financial 
sector. The main output of our study consists in the thesis that 
the financial status matter for the economic growth.   
Key- words: finance, growth, cost of capital, yield 
 
1.  I TRODUCTIO  
 
We consider that one of the most significant transmission 
channel between finance and growth is represented by the cost 
of  borrowed  financial  resources.  Financing  costs  are  an 
important  determinant  in  firms´  decisions  to  undertake 
investment projects. Higher interest rates, for example, reduce 
the  profitability  of  an  investment  project  because  of  higher 
financing costs. Therefore, lower the probability of the project 
being undertaken. 
Having access to a wider and diversified range of financial 
resources, the companies can realize their investment projects, 
adding value to their company and leading to economic growth. 
Therefore, in this paper the authors aim at finding new evidence 
of the mechanism through which the economic growth can be 
achieved.  Section  2  reviews  a  part  of  the  relevant  literature 
concerning  this  problem.  Section  3  provides  the  analytical 
framework. Section 4 offers the empirical framework. The last 
section  is  dedicated  to  some  conclusions  and  suggestions 
regarding potential further research. 
 
2.   LITERATURE REVIEW 
 
Levine  (1991)  is  among  the  first  authors  that  propose 
models  of  endogenous  growth  that  identify  the  mechanisms 
through  which  the  financial  system  influence  the  long-run 
growth  of  an  economy.  Levine’s  innovation  was  to  consider 
financial  services  as  affecting  economic  growth  through  five 
main channels: savings mobilization, resource allocation, risk 
management,  managerial  monitoring,  trade  facilitation.  By 
considering  the  functions  of  the  financial  sector  in  a 
comprehensive  manner,  Levine  is  able  to  demonstrate  a 
significant  role  for  financial  markets  that  was  not  present  in 
earlier models that used a narrower definition. Also, he states 
that industries and firms that rely on external financing tend to 
grow faster in countries with well developed financial systems 
than countries with poorly functioning financial systems. 
The  positive  association  between  the  degree  of 
development  of  the  financial  system  and  economical  growth 
was  largely  analyzed  also  by  Demirguc-Kunt  (2006)  and 
Levine and King (1993). They get to the conclusion that this 
correlation  stays  significant  even  when  other  factors  of 
influence  are  taken  into  consideration. Moreover,  they  prove 
that regarding a country with a developing financial system, the 
degree of financial development is correlated not only with the 
current  growth,  but  also  with  the  future  economical  growth. 
Their model identifies the innovation (including the financial 
one)  as  engine  for  economic  growth.  The  financial  markets 
have  the  role  of  discriminate  between  different  investment 
projects according to their efficiency potential. In this way, it is 
assured  the  function of  efficient  allocation.  This  is  the  main 
reason why, an economy with an efficient financial system will 
experiment  a  higher  rate  of  productivity  (Demetriades  and 
Hussein, 1996). These show that, on the case of some countries 
like Zair or Mexico, if the volume of loans as percent of GDP 
would  have  increased,  respectively  the  value  traded  on  the 
capital markets as percent of GDP would have increased in the 
considered period of time, then the economic growth, measured 
as GDP per capita, would have increased as well. 
If the nature of financing the economic growth manner is a 
key  variable  for  the  economic  growth,  there  are  still  some 
important methodological issues concerning the evaluation of 
the financing cost. Thus, the purpose of our study is to take into 
account  the  different  components  of  the  borrowed  financial 
resources,  in  order  to  examine  at  the  level  of  the  European 
Union countries, their impact on the economic growth. 
                                    
3.  THE A ALYTICAL FRAMEWORK 
 
The  analytical  framework  is  represented  by  a  Two-Stage 
Least Square (TSLS) regression between the dynamic of real 
GDP and the relevant explanatory variables with the inclusion 
of some cross-section random effects and also of some period 
specific  instrumental  variables.  This  could  be  seen  as  an 
appropriate  technique  when  some  of  the  right-hand  side 
variables are correlated with the error terms, and there is neither 
heteroskedasticity,  nor  contemporaneous  correlation  in  the 
residuals.  The  general  specification  of  the  regression  model 
looks like: 
                           it t i t it it X y ε γ δ β α + + + + =
'
(1) 
 
where  y is  the  dynamic  of  real  GDP, 










=
bm
mm
div
X   is  the 
vector  of  explanatory  variables  (div represents  the  dividend 
yield  for  each  country,  obtained  as  a  ratio  between  the 
distributed dividends by the companies that compose the main 
index, if possible and the market capitalization ,  mm the 3-
month money market rate for each country and  bmthe yield 
on current 10 years Government bonds, for each country) with 
t β   coefficients  that  are  period  specific,  α   is  an  overall 
constant,  while  the  t i γ δ ,   represent  cross-section  or  period 
specific effects. 
The  random  effects  specifications  assume  that  the 
corresponding  effects  i δ   and  t γ are  realizations  of 
independent  random  variables  with  mean  zero  and  finite 
variance.  Most  importantly,  the  random  effects  specification assumes that the effect is uncorrelated with the idiosyncratic 
residual. 
The estimation of the covariance matrix for the composite 
error  formed  by  the  effects  and  the  residual  (e.g., 
it t i it v ε γ δ + + =     in  the  two-way  random  effects 
specification),  uses  the  quadratic  unbiased  estimators  (QUE) 
from Swamy Arora method. This estimator uses residuals from 
the within (fixed effect) and between (means) regressions. The 
list of instrumental variables includes the lagged values of the 
dependent and explanatory ones: 
 
[ ] 1 1 1 1 − − − − = t t t t t bm mm div gdp I STRUME T  (2) 
                                              
The  structure  of  the  correlations  between  residuals  is 
described as a Period Heteroskedasticity and Serial Correlation 
(Period SUR) one. This class of covariance structures allows 
for  arbitrary  period  serial  correlation  and  period 
heteroskedasticity  between  the  residuals  for  a  given  cross-
section, but restricts residuals in different cross-sections to be 
uncorrelated. Accordingly, it is assumed that: 
 
( )
( ) ) 3 ( 0 X |
X |
*
*
=
=
i jt is
st i it is
E
E
ε ε
σ ε ε
    
                 
for  all  t s j i , , ,   with  j i ≠ .  It  should  be  noticed  that  the 
heteroskedasticity and serial correlation does not vary  across 
cross-sectionsi .  Using  the  cross-section  specific  residual 
vectors, one may rewrite this assumption as: 
     
( ) ) 4 (
* ' X | T i i i E   = ε ε
                     
for all i  with  
 
) 5 (
...
...
...
2 1
2 22 21
1 12 11
















=  
TT T T
T
T
T
σ σ σ
σ σ σ
σ σ σ
 
This  specification  involves  covariances  across  periods 
within  a  given  cross-section,  as  in  seemingly  unrelated 
regressions with period specific equations. Such a framework 
allows for multiple interactions between the involved variables 
without imposing to rigid hypothesis about the nature of such 
interlinkages.  
 
4. THE EMPIRICAL FRAMEWORK 
 
In order to implement the methodology, we considered a 
sample  of  data  belonging  to  16  countries,  members  of  the 
European  Union  (Greece,  Spain,  Italy,  Hungary,  Germany, 
Netherlands,  Belgium,  Portugal,  France,  Ireland,  Slovenia, 
Great Britain, Luxemburg, Sweden, Poland and Austria). Data 
was provided from Eurostat (2009) and World Federation of 
Exchanges (2009) and the time span is between 2004 and 2007. 
 The main results are presented in the below table. Despite 
some unit roots common processes at the level of the residuals 
(suggested especially by the Hadri Z stat test) the quality of the 
model could be overall considered as satisfactory. 
 
Dependent Variable: The dynamic of the real GDP 
Method: Pooled IV/Two-stage EGLS (Cross-section random effects) 
Cross-sections included: 16 
Total pool (unbalanced) observations: 42 
Swamy Arora estimator of component variances 
Period SUR (PCSE) standard errors & covariance (no degree of 
freedom correction) 
Variable  Coefficient 
Std. 
Error  t Statistic  Prob.   
C  673.4907  279.8444  2.406662  0.0220 
DIV—2005  21.77603  19.74610  1.102801  0.2783 
DIV—2006  58.63235  25.41840  2.306689  0.0277 
DIV—2007  99.60585  36.69249  2.714611  0.0106 
MM—2005  -78.40737  35.64972  -2.199383  0.0352 
MM—2006  -20.02118  33.53529  -0.597018  0.5547 
MM—2007  49.30112  57.57215  0.856336  0.3982 
BM—2005  44.34550  71.77164  0.617869  0.5410 
BM—2006  -12.54052  61.97273  -0.202356  0.8409 
BM—2007  -91.53098  48.93977  -1.870278  0.0706 
Effects Specification 
Cross section random S.D. / Rho  823.1883  0.9985 
Idiosyncratic random S.D. / Rho  32.24111  0.0015 
Weighted Statistics 
R squared  0.432588  Mean dependent var  16.03920 
Adjusted R 
squared  0.273003     S.D. dependent var  42.43189 
S.E. of regression  36.17916     Sum squared resid  41885.82 
Durbin Watson 
stat  2.289885     Instrument rank  13.00000 
Tab. 1. The TSLS estimation between the dynamic of real GDP 
and the considered financial variables 
 
5. CO CLUSIO S A D FURTHER RESEARCH 
 
The advanced analysis provided some empirical support for 
the  thesis  of  a  positive  connection  between  a  lower  cost  of 
borrowed resources and the economic growth (despite the fact 
that such a connection seems not to be stable enough over the 
considered period). 
As  a  potential  further  research,  there  can  be  taken  into 
consideration  a  broader  range  of  countries  belonging  to 
European  Union.  Likewise,  a  distinction  between  developed 
and developing countries, as far as concerns the current state of 
the  financial  system  must  be  done,  in  order  to  have  a  more 
accurate analysis. Moreover, the sample can be enlarged, using 
a larger time span.  
 
6. REFERE CES 
 
Demetriades,  P.  &  Hussein,  K.  (1996).  Does  financial 
development cause economic growth? Time-series evidence 
from  16  countries,  Journal  of  Development  Economics, 
Elsevier, vol.51(2), pg. 387-411 
Demirguc-Kunt,  A.  (2006).    Finance  and  economic 
development:  policy  choices  for  developing  countries, 
Policy Research Working Paper Series 3955, The World 
Bank 
Levine, R. (1991). Stock markets, growth and tax policy, The 
Journal of Finance, 46, pg.1445-65 
Levine, R. & King, R. (1993). Finance and growth: Schumpeter 
might be right, Quarterly Journal of Economics, MIT Press, 
vol. 108, issue 3, pg. 717-37 
***(2009)  http://epp.eurostat.ec.europa.eu-  Eurostat,  Accesed 
on: 2009 02 28  
***(2009) http://www.world-exchanges.org/-World Federation 
of Exchanges , Accesed on: 2009 02 28 

An empirical analysis of the interlinkages between financial sector and economic growth

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