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.

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.