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Introduction

Until
end of nineteenth century, economists believed that, the importance and needs
of government intervention is reducing by increasing of nation’s growth as Karl
Marx & Adam Smith (as classics approach) suggested a negative correlation
between public expenditure (government intervention) and economic growth (Henrekson
1990). In the late nineteenth century Adolph Wagner introduced his well knowing
proposition which also known as Wagner law.

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In
the latest years of 19th century the Germen economist Adolph Wagner
publishes his first book in 1883 which called Economics of Finance and
the second one in 1893 called the Basics of Political Economy, in both
he introduced his proposition. He said there is a positive relationship between
economic activities and government expenditure.(Peimoz donlec.2009).

He
observed that economic development in countries undergoing industrialization
was being accompanied by a growing public activity relative to the economy.
Thus, he made as study of the economic history of European countries such as
United Kingdom and Germany as well as United State and Japan (MAGABLEH, 2006)
in base of which he proposed his hypothesis as follow:

“Historically there exists a clear tendency for an expansion of
public

activity together with the progress of the economy…” (Biehl, 1998)

The
relationship and causality between government expenditures and economic growth
has been an enduring issue in public economics, theatrically and empirically.
There are two approach of this issue, first one is the Wagnerian
approach which believes that an increase in economic growths cause increase in
the public expenditure. And the second one is Keynesian approach which
states that public expenditure causes economic growth, that we could find
empirical studies for both of them.( P. SRINIVASAN,2013) as we are investigating the validity of
Wagner law in this paper so we will not talk about Keynesians.

According
to Henrekson (1993) there are three reasons for increase of government
intervening in the Wagner law. First, industrialization and urbanization
would lead the substitution of public activity to the privet activity. Second,
the growth or increase in real income would attract or facilitate the relative
expansion of incomes elastics (cultural and welfare) expenditure where public
producers were more efficient than privet. And finally developments and Technological
advancement require governments to take the managements of natural monopolies
in order to increase efficiency of the economy.(Hal?c?o?lu, 2003)

Wagner
law has been investigated empirically in deferent frameworks and with few
exceptions it received strong support for most countries, thus, this paper
investigated the validity of Wagner law for industrial G7 countries via using
advance econometric technique of co-integration.

To
investigate the Wagner law this paper organized as follows, including the
present introduction section, section 2 provides the empirical literature of
testing Wagner law, the section 3 describes Econometric Methodology used in the
paper. Section 4 presents the empirical result regarding the validity of Wagner
law for industrial or G7 countries. The final section of this study presents
the conclusion for this paper.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

REVIEW
OF LITERATURE

Wagner
(1883), in his first book and his second book(1893) exposed his properties
which, the first time expressed by him, unlike classic economist’s that
believes, importance of government intervention is reducing by increasing of
nation’s growth and suggests of  a
negative correlation between public expenditure (government intervention) and
economic growth, he indicate his hypothesis that, there is a positive
intendancy from economic growth to government expenditure, the hypothesis was
based on historical study on economy of European industrial countries such as
Germany and United Kingdom as well as U.S.A and Japan in which the result of
study illustrate the relationship between economic growth and government
expenditure.

Wagner
has been indicated his hypothesis theoretically and the interpretations of his
hypothesis in functional forms become Controversial, therefore several version
of the functional form of the law have been introduced. The first formulated
form was used by Peacock – Wiseman (1961), the second version of formulated
form was introduced by Gupta (1967) which is known as Gupta’s version of law
too. The third one belongs to Goffman (1968), the forth version of Wagner low
is linked to Pryor (1968), the fifth one referred to Musgrave (1969), and the 6th
one formulated by Mann (1980). The most common formulated form of Wagner law as
we mentioned is as follow;

No

version

Date of modification

Regression equation form

1

Peacock – Wiseman

1961

2

Gupta

1967

3

Goffman

1968

4

Pryor

1968

5

Musgrave

1969

6

Mann

1980

 

 

 

 

 

Where
GE stand for total government expendatur, GDP for Gross domestic product, GCE
for government consumption expenditure and, p stands for population.

As
we mentioned before the Wagner law has been tested for many countries using
time series and cross sectional data sets, the empirical results, apart few
exceptions strongly supported the Wagner law (Hal?c?o?lu. 2003).

The empirical investigation after Adolph Wagner, with
using time series data has been started by Peacock and Wiseman
(1961),Musgrave(1969), Michas (1975),Pryor(1968),Mann(1980) and Ram 1987) in
which the researchers found strong support for the Wagner’s law but in all those
investigations data have assumed stationary in the same order, that after using
time series methods the results changed for example Henrekson (1993) has reinvestigated
the validity of Wagner law in the case of Sweden and couldn’t fount support of
the law, Papapertrou and Handryiannis (1995) have failed to find a positive long
run relationship(support of Wagner law) in case of Greece.

Ferda Hal?c?o?lu (2003) have been tested the validity of Wagner law in the case
of Turkey for the period of 1960 – 2000 using modern time – series econometric techniques,
the result of his study do not support the validity of Wagner law for turkey. In
the same case but for period of 1960 – 2006 özlem Tasseven tested the validity of Wagner law for turkey again that the result
was the same, he couldn’t found support of Wagner law for turkey, but Raif Cergibosan
and Emre Çevik and Caner Demir (2017), become succeed to find support of Wagner
law for turkey but in the period of 1960 – 2015.

According
to research which was done by Bharat R. Kolluri, Michael J. Panik & Mahmoud
S. Wahab (2000), the Wagner law holds for industrial countries (G7) for the
period of 1960 – 1993. Saten Kumar, Don Webber and Scott Fargher (2009) tested the
Wagner law for New Zealand , Wijeveera,Albet and Garis ton(2009) for Kingdom of
Saudi Arabia. Primož Dolenc (2009) for SLOVENIA. Clement A. U. Ighodaro and Dickson
E. Oriakhi (2010) for Nigeria. Satish Verma and Rahul Arora(2010). Mosayeb
Pahlavani1, Davoud Abed and Farshid Pourshabi, (2011), in case of Iran for
period of 1960–2008. P.Srinivasan (2013) in case of India for the period of
1973 – 2012 . And, Jan Kuckuck (2012) in the case of Five Western European
Countries. All this studies found support of wagner law for the period of their
researches.

Abdur
Rauf ,Dr. Abdul Qayum and Prof Dr. Khair-uz Zaman (2012) have tested the
validity of Wagner law for Pakistan but the result of investigation doesn’t support
the validity of wagner for Pakistan in the period of 1979-2009. Andrew Phiri
(2016) has investigated the relation between economic growth and government expenditure
in which the result  doesn’t support the
validity of Wagner law for South Africa.

This
paper adopts the formulation which was initially used by Pryor (1968) in which
the government consumption expenditure and gross domestic product have used as
variables.

                                                                         

Where
 stands for constant term, LN (RGCE) stands for
logarithmic form of real government consumptions expenditure, LN (RGDP) stands
for logarithmic form of real gross domestic products and u stands for classical
regression error. For validity of Wagner’s law,  is expected to be greater than zero.

 

Econometric
Methodology

The  of GCE (Government Consumption Expenditure),
GDP (Gross Domestic Product) and GDP deflator for this paper collected from
World Bank; data bank and used as the real and logarithmic form to achieve the
most reliable results. 

To
test or investigate the validity of Wagner law for the industrial countries
(G7), this paper adopts the formulation which was initially used by Pryor
(1968) in which the government consumption expenditure and gross domestic
product have used as variables which formulated as follows:

                                                                              
(7)

 

Where
 stands for constant term, LN (RGCE) stands for
logarithmic form of real government consumptions expenditure, LN (RGDP) stands
for logarithmic form of real gross domestic products and u stands for classical
regression error. For validity of Wagner’s law,  is expected to be greater than zero.  In order to prevent any spurious relationship,
the time-series properties of the variables have been analyzed before any
estimation.

  In order to
test the relationship between government consumption expenditure and gross
domestic product, the Granger co-integration has been utilized. The most
important  condition in order to test
Granger co-integration is the stationarity, which means for investigation of
co-integration the variables should be stationary in their level or differenced
forms (in the level I(0) or in the first difference I(1)). To check the
stationarity of variable a general from of ADF form of regression formulated as
follows:

 

                                                      (8)

 

Where  stands for tirst differenced deries of X, T
stands for trend and  is a white noise residual.

The hypothesis of unit root (non-stationary) is
tasted by setting the null  hypothesis.
Mostly variables are not stationary at their level, then we should investigate
the stationarity of the variables in the some order (in their level of first
difference are prefer), but if the data don’t become stationary at the first
difference I(1) the further differences navt longer five a unique long-run
solution(Serious and hall.2017). Once the data founded to be stationary in the
first difference, we can run a co-integration test.

Basically there are 2 approaches to test the
long run relationship between time series: first one is   Egle & Granger (1987) and the other one
is Johansen & Juselius (1990, 1992). The Johansen approach is based on VECM
which is a VAR represented model. The general VAR model with a lag length (p)
for Johansen approach formulated as follow:

     (9)

Where  stands for (mx1) vector of first difference
stationarity I(1), stands for (Sx1) vector of level stationarity I(0),
  stands for unknown parameters and stands
for error term. The hypothesis that  has a reduced rank ()
is tested using the trace and ?-MAX (maximum eigenvalues) test statistic. Once
co-integration found in time series-data, there must exist a bi-directional or
uni-directional causality between variables (Hal?c?o?lu,
2003).

A general Granger causality approach based on
VAR model formulated as follow:

                                                        
(10)

Where, the variable assumed to be stationary in
the some order and the imposed restrictions ()
has been tested using Wald F test.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Empirical
results

The ADF and ERS-Point Optimal unit
root has been tested to examine the stationarity order of variables, for time
series and equation (Pryor 1968) which revealed in the table (1) in logarithmic
form of data in order of level and first difference, stationarity.

Table1. Unit Root Tests

 

Exogenous: Constant, Linear Trend

Lag Length: (Automatic – based on AIC)

LEVELS I(0)

FIRST DIFFERENCE I(1)

COUNTRY

VARIABLE

 

t-Statistic

  Prob.

t-Statistic

  Prob.*

ITALY

LRGC

-2.980335

0.1489

-4.803901**

0.0003

LRGDP

-2.753564

0.2214

-4.850754**

0.0003

CANADA

LRGC

-2.960087

0.1545

-3.147864**

0.0300

LRGDP

-2.300378

0.4251

-4.563567**

0.0006

FRANCE

LRGC

-3.28271

0.0822

-4.92519**

0.0002

LRGDP

-3.096013

0.1196

-4.836165**

0.0003

GERMANY

LRGC

-3.460757

0.0562

-4.698262**

0.0004

LRGDP

-3.096013

0.1196

-4.836165**

0.0003

JAPAN

LRGC

-2.016054

0.5769

-4.723558**

0.0004

LRGDP

-1.701315

0.7343

-4.76733**

0.0003

UNITED KINGDOM

LRGC

-2.769568

0.2156

-4.451348**

0.0009

LRGDP

-3.59637**

0.0414

-4.89532**

0.0003

UNITED STATE

LRGC

-0.214817

 

-3.169386**

 

LRGDP

-1.640828

 

-4.856978**

 

ü  ** Rejection of unit root hypothesis, based on McKinnon’s critical value,
at 5%

ü  The lag selection based on AIC value.

ü  The unit root hypothesis for united stat variables tested under
equation of ERS-Piont Optimal unit root test.

ü  I(0) stationary at the level

ü  I(1) stationary at the firs difference

According
to the table (…….) all variables are appear to be stationary in their first
difference I(1).

The
lag lengths have been selected on the basis of AIC value which revealed in the
(annx…….) for the further process of Johnsen maximum likelihood co-integration
test, the table (2) revealed the result for Johansen and Juselius
co-integration.

Table2.
Revealed that we couldn’t reject the null hypothesis (test indicates no co-integration at the 0.05 level) for Italy which means the test couldn’t fount long run
relationship between general government consumption and gross domestic product
in the case of Italy.

 

 

Table2.
Johansen and Juselius co-integration tests and results

NULL

ATRERNATIVE

?-MAX          STATISTIC

95%                
CRITICAL VALUE

Prob

TRACE            
STATISTIC

95%                
CRITICAL VALUE

Prob

ITALY

r = 0

r = 1

11.46592

14.2646

0.1324

14.35874*

15.49471

0.0736

r ? 1

r = 2

2.892822*

3.841466

0.089

2.892822*

3.841466

0.089

CANADA

r = 0

r = 1

15.25191**

14.2646

0.0348

16.46196**

15.49471

0.0357

r ? 1

r = 2

1.210054

3.841466

0.2713

1.210054

3.841466

0.2713

FRANCE

r = 0

r = 1

15.74199**

14.2646

0.029

18.72742**

15.49471

0.0157

r ? 1

r = 2

2.985433*

3.841466

0.084

2.985433*

3.841466

0.084

GERMANY

r = 0

r = 1

21.68438***

14.2646

0.0028

23.49657***

15.49471

0.0025

r ? 1

r = 2

1.812199

3.841466

0.1782

1.812199

3.841466

0.1782

JAPAN

r = 0

r = 1

15.13392**

14.2646

0.0364

18.21303**

15.49471

0.019

r ? 1

r = 2

3.079106

3.841466

0.0793

3.079106*

3.841466

0.0793

UNITED KINGDOM

r = 0

r = 1

29.74447***

14.2646

0.0001

29.75308***

15.49471

0.0002

r ? 1

r = 2

0.00861

3.841466

0.9257

0.00861

3.841466

0.9257

UNITED STATE

r = 0

r = 1

14.90903**

14.2646

0.0395

17.05504**

15.49471

0.0289

r ? 1

r = 2

2.146012m

3.841466

0.1429

2.146012

3.841466

0.1429

ü  ***, ** Rejection of null hypothesis at the levels of  1% 
and  5%

ü  MacKinnon-Haug-Michelis (1999) p-values                                                                        

The
finding of Johansen and Juselius co-integration tests indicate long run
relationship between general government consumption expenditures and gross
domestic products for all other countries of G7.

The VECM
should be run after the data co-integrated, in this section the paper has been
investigated the short run causality which known as Wald test, for variables.
The table 3, shows the results of Wald test.

Table.
3 Wald
Test: causality direction test based on VECM

Null
Hypothesis

 LRGDP does not Granger Cause LRGC

 LRGC does not Granger Cause LRGDP

 

Test
Statistic

Value

df

Probability

Value

df

Probability

ITALY

F-statistic

0.968151

(2,
38)

0.389

1.345279

(2,
38)

0.2726

Chi-square

1.936301

2

0.3798

2.690558

2

0.2605

CANADA

F-statistic

5.730893

(1,
41)

0.0213

0.233149

(1,
41)

0.6318

Chi-square

5.730893**

1

0.0167

0.233149

1

0.6292

FRANCE

F-statistic

2.824994

(9,
19)

0.0271

2.593979

(9,
17)

0.0433

Chi-square

25.42495***

9

0.0025

23.34581***

9

0.0055

GERMANY

F-statistic

2.369844

(9,
17)

0.06

2.665132

(9,
17)

0.0391

Chi-square

21.32859**

9

0.0113

23.98619***

9

0.0043

JAPAN

F-statistic

1.259222

(8,
20)

0.3179

0.361517

(2,
38)

0.699

Chi-square

10.07377

8

0.2599

0.723034

2

0.6966

UNITED KINGDOM

F-statistic

1.940436

(10,
14)

0.1185

1.799983

(10, 14)

0.1527

Chi-square

23.28523**

10

0.0254

17.99983

10

0.055

UNITED STATE

F-statistic

6.164333

(2,
41)

0.0046

2.266169

(2,
38)

0.1175

Chi-square

12.32867***

2

0.0021

4.532338

2

0.1037

ü  ***, ** Revealed rejection of the null hypothesis in the level 1%,
5%

ü  Df shows the lag length which selected based on AIC.

Table3.
Revealed existence of bi-directional causality for France and Germany, uni-
directional causality for Canada, United Kingdom and United State and non-existence
of causality for Italy and Japan.

Table 4. Pairwise Granger
Causality Tests

Country

 Null Hypothesis:

Obs

F-Statistic

Prob.

ITALY

 LRGDP does not Granger Cause LRGC

 

45

0.26229

0.7706

 LRGC does not Granger Cause LRGDP

 

 

0.01933

0.9809

CANADA

 LRGDP does not Granger Cause LRGC

46

9.76236***

0.0032

 LRGC does not Granger Cause LRGDP

 

 

3.69879

0.0611

FRANCE

 LRGDP does not Granger Cause LRGC

38

2.82499**

0.0271

 LRGC does not Granger Cause LRGDP

 

 

3.48154**

0.0106

GERMANY

 LRGDP does not Granger Cause LRGC

38

2.73594**

0.031

 LRGC does not Granger Cause LRGDP

 

 

2.99441**

0.0211

JAPAN

 LRGDP does not Granger Cause LRGC

39

0.68947

0.6966

 LRGC does not Granger Cause LRGDP

 

 

0.88287

0.546

UNITED
KINGDOM

 LRGDP does not Granger Cause LRGC

46

5.47655**

0.024

 LRGC does not Granger Cause LRGDP

 

 

3.45902

0.0698

UNITED
STATE

 LRGDP does not Granger Cause LRGC

45

7.21193***

0.0021

 LRGC does not Granger Cause LRGDP

 

 

0.05103

0.9503

The paper has been used the Angle
Granger pairwise test as well that support the finding of Wald test in the
table 3.The result of pairwise test of Granger appeared in the table 4.

ü  ***, ** Revealed rejection of the null hypothesis in the level 1%,
5%

ü  Lag Length: (Automatic – based on AIC)

 

The
empirical results of this research strongly support the validity of Wagner law
for Canada, United Kingdom, United State, Germany and France but do not support
the validity of law for Italy and Japan for period of 1970-2016.

 

 

 

 

 

 

 

Conclusion

The validity
if Wagner law has been tested for G7 Industrial countries in this paper using
time- series data and econometrics modern techniques for the period of
1970-2016.

The
paper considered several specifications which commonly employed in the
literature, for empirical investigating of Wagner law in last dictates. In the
empirical section of this paper the ADF and ERS-Point Optimal unit root has
been tested to examine the level of stationarity for variables which indicate
all variables are stationary in their first differences.

The
lag lengths have been selected on the basis of AIC value which revealed in the (annx…….) for the
further process of  Johansen  maximum likelihood co integration test,  The finding of Johansen co-integration test
for  G7 industrial counties shows that
except Italy , there is a long run co-integration exist between Government
consumption and Gross domestic product in the period of 1970-2016 for Canada,
Germany, France, United kingdom, United states and Japan which the
normalization of coefficients also supports the finding but by running VECM
(Vector Error Correction Model), the finding of Error correction
coefficient(table:….) and Wald Test does not support the long run and short run
causality for Japans variables ,whereas 
it supports the long run and short run causality for other countries.

The
Granger’s causality test revealed a bi-directional causality for variables of
Germany and France and uni-directional causality for variables of Canada,
United Kingdom and United State and no causality for variables of Italy, but in case of Japan as the finding shows;
a positive long run relationship has been founded between government
consumptions and gross domestic products which supported the law but the
causality tests doesn’t support it.

Finally the paper found a strong support of Wagner
law for Canada, Germany, France, United Kingdom and United States, the finding
also shows that the Wagner law does not hold for Italy and Japan in the period
of 1970-2016.

 

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