Relationship between economic growth and public expenditure

Volume 2, Number 4, October – December’ 2013 ISSN (P):2279-0918, (O):2279-0926
International Journal of Entrepreneurship & Business Environment Perspectives © Pezzottaite Journals. 627 | P a g e
RELATIONSHIP BETWEEN ECONOMIC GROWTH AND PUBLIC EXPENDITURE
THROUGH WAGNER’S LAW: AN ANALYTICAL STUDY IN INDIAN PERSPECTIVE
Dr. Dhiresh Kulshrestha5
ABSTRACT
Every government also tries to avoid the condition of fiscal deficit and to control their Public Expenditure and Revenue. In
this order, fiscal policy is the center point of development, which is the fundamental instrument to control the trade cycle in
the economy. Fiscal policy has considered a center stage in policymaking.
A striking feature of public expenditure in India is its continuous increase since independence. The Indian Government fiscal
policy is in the center of the debates that is related to expenditure and revenue pattern of the government.
Fiscal Policy should control their public expenditure and invest it in a proper direction so that faster, more inclusive and
sustainable growth might be achieved according to the strategy of 12th five-year plan. However, in real, in India, many times
continuously increase in fiscal deficit with reduction in growth (GDP) has observed.
KEYWORDS
GDP, Economic Growth, Public Expenditure, Government Expenditure, Real Per-Capita Government
Expenditure etc.
INTRODUCTION
Public expenditure incurred by public authorities like central, state and local governments to satisfy the collective social wants of
the people is known as public expenditure. Public expenditure is required to promote rapid economic development, trade and
commerce, agricultural and industrial sectors, rural development, balanced regional growth, full-employment and maintain price
stability, mineral resources like coal and oil, socio-economic overheads eg. Roadways, railways, power etc. and to ensure an
equitable distribution of the resources.
The first question comes in the mind, Does the Indian economy Support Wagner’s Law in present scenario?
In this paper discussed about the six versions of Wagner’s Law, these versions (from 1961-1980).
Six versions of Wagner’s law
Different versions of Wager’s hypothesis have been empirically investigated in functional form since the 1960s as shown below:
The earliest and simplest version of this law was given by Peacock and Wiseman in1961 by using the following double log
equation from which the elasticity of estimates was derived. According to them, growth in Real Government expenditure (RGE)
is dependent upon the growth in real GDP. Therefore, this function shows that Government Expenditure (GE) is a function of
Gross Domestic Product (GDP).
Where, LGE is log of Government Expenditure,
LGDP is log of Gross Domestic Product (GDP),
a1 is Intercept (constant),
b1 is Coefficients of LGDP.
The equation shows the positive relationship between LGE (dependent variable) and LGDP (independent variable), i.e. total
government expenditure increases due to an increment in gross domestic product.
Further, Gupta in 1967 used different model to test the validity of Wagner's law with the effect of increase in Population (P).
According to him, growth in Real per-capita government expenditure (RGE/P) is dependent upon the growth in real GDP per
capita (RGDP/P).
5
Associate Professor, Department of Economics, Central University of Haryana, Haryana, India,
drdhireshkulshrestha@gmail.com

In addition, in 1968, Goff man introduced the following absolute version of the law:
According to him growth in real government expenditure (RGE) is depend upon the growth in real GDP per capita (GDP/P).
Further, in 1969, Pryor gave a similar explanation of this law by using government consumption expenditure (GCE) instead of
total government expenditure as a positively dependent variable upon gross domestic product (GDP):
In all the above versions (Peacock & Wiseman, Gupta, Goff man and Pryor) Wagner’s law holds true in case the value of slope
coefficient (b) i.e. elasticity is more than unity.
All above versions as reviewed by Timm (1961) are in absolute sense and thus he concluded with the opinion that Wagner had a
relative growth in mind. Therefore, the Wagner’s law should be interpreted in a relative sense. Thus, Musgrave (1969) has
explained the growth in public expenditure in the relative sense by using the following relation:
This states that the share of nominal government expenditures in nominal GDP (NGE/ NGDP) depends upon the real GDP per
capita (RGDP/P).
Further, Mann in 1980 also interpreted the law in relative sense by using the following equation:
This presents that the share of nominal government expenditure in nominal GDP (NGE/NGDP) depend upon the real GDP. Thus
in the case of both the versions (Musgrave and Mann) Wagner’s law holds true in case the value of slope coefficient (b) exceeds
zero i.e., the elasticity is greater than zero. However, there are no objective criteria to decide which of the six versions is most
appropriate. Therefore, all the six versions of Wagner’s law tests in many studies of different developed and developing countries.
Six Versions of Wagner’s law
The Regression form of all the six versions of Wagner’s law is presented in the following table:
Sources: Verma, S. and R. Arora (2010), Does the Indian economy Support Wagner’s Law?
An Econometric Analysis, Eurasian Journal of Business and Economics, 3 (5), pp. 77-91
Some selected variables of Economic Growth and Public Expenditure were taken based on the above-mentioned six versions of
Wagner’s Law.
This Paper is also measure the applicability of six versions of Wagner’s Law in India in a selected time from 1970-71 to 2011-12.
Wagner’s Law talks about the relationship between Economic Growth and Public Expenditure, so there is need to know about the
Public expenditure pattern and Economic growth in India.
Table-1: Regression Form of Six Versions of Wagner's Law
S. No. Versions Regression Equation
1
Peacock-Wiseman
(1961)
LGE = a + bLGDP + u
2 Gupta (1967) L (GE/P) = a + bL (GDP/P) + u
3 Goff man (1968) LGE = a + bL (GDP/P) + u
4 Pryor (1969) LGCE = a + bLGDP + u
5 Musgrave (1969) L (NGE/NGDP) = a + bL (GDP/P) + u
6 Mann (1980) L (NGE/NGDP) = a + bLGDP + u

LITERATURE REVIEW
Singh and Sahni (1984) determined the directions and patterns of causality between national income and public expenditure as
well as in its various components during 1950-51 to 1980-1981. The data have been drawn from various official publications of
the government of India: central statistical organization, basic statistics relating to the India economy 1950-51 to 1978-79 for
public expenditure for ministry of finance (1982-83), economic survey for annual issues for national income, RBI report on
currency and finance annual issues for public expenditure by function. For the purpose of the study of the utilized Granger; Sims
framework, Darwin’s test II F- statistics and R square. The results of this empirical study have shown that the causality between
the national income and public expenditure was neither Wagnerian (Y-X) not Keynesian (x-y).
Sahoo (2001) studied and analyzed through his paper about the Wagner’s hypothesis for India from 1970-71 to 1998-99. The
researcher used causality co-integration and Error Correction Mechanism (ECM) in the presence of a structural break using
advanced unit root tests. Identifying the structural breaks in different times, the study finds support for long-run equilibrium
relationship between public expenditure and economic growth. The ECM revealed a bi-directional causality in absolute terms
supporting both Wagner’s hypothesis and Keynesian view. However, the ECM supports only Wagner’s hypothesis in per capita
terms.
The present study deals with only two variables viz. Public Expenditure (Et) and Gross Domestic Product (Gt). A simple two stage
Engle- Granger (E-g) co-integration procedure is applied for testing the long- run relationship. Both Et and Gt data were expressed
in real term. The data had been compiled from Handbook of Statistics on Indian Economy of RBI. Both Et and Gt support co-
integration of the two variables. However, the ECM results suggest that the GDP has powerful long and short run effects on
public expenditure. There is a positive impact of GDP on public expenditure. This study supports the evidence of Wagner’s Law
but it also shows a weaker evidence of Keynesian hypothesis in India Economy according to this period. According to the finding
of this paper, Wagner’s hypothesis can be used for India that economic growth is a causative factor for increase in Government
Expenditure.
Chandra (2004) tested econometrically whether the government actually succeeded in acting as an engine of growth in India by
using the growth of real government size and GDP growth. To measure the government size he used three proxies, namely Real
Government Consumption (RGC), Real Government Investment (RGI) and Real Government Expenditure (RGE) whereas RGE
was defined as the sum of RGC and RGI. The data have been taken from national account statistic (NAS), government of India.
To test three prepositions: did total government-spending act as an engine of growth? Did government investment act as engine
of growth? Did government consumption act as engine of growth? He employed augmented Dickey Fuller (ADF) test, residual
based test and Granger causality test. The analyst found that the government did not act as an engine of growth either in the short
or in the long run in India during 1950-96.
Ghorbani and Zarea (2009) examined Wagner’s Law by using Iran’s time Series data from the period of 1960-2000. For
estimating it, they used some test like Engel- Granger co-integration test, which showed that GNP, government-expenditure and
government consumption expenditure were not co-integrated. They have investigated six versions of Wagner’s Law like (Goff
man 1968, Gupta 1967, Mann 1980, Musgrave 1969, Peacock and Wiseman 1961, Pryor 1969). On the time series data, they used
co-integration analyses, ECM mechanisms and causality test, which made it possible to investigate this long- run relationship
between government expenditure and GNP. The data used in this study included Gross national Production (GNP), Total
Government Expenditure (GE), Government Consumption Expenditure (GCE) and population (P) for the period 1960-2000 of
Iran’s economy. These data are presented by the Central Bank of the Islamic Republic of Iran and World Bank. There were
several test applied on such type of data like Engle and Granger test; Augmented Dickey- Fuller (ADF) unit root tests to each
series and their first differences to determine the stationary of each individual series.
According to results of unit root tests for residual series, null hypothesis of non- stationary is not rejected for each of the six
versions. The elasticity of the real income in all equations is found to be positive and bigger in comparison to non-proportional
versions. Based on this elasticity Wagner’s Law will be accepted, but because variables are not co-integrated, these results are not
reliable. All 6 versions of Wagner’s Law indicate lack of a long run relationship between public expenditure and GNP in Iran
economy. Results of Granger Causality test are different among developed and developing countries. According to results of this
study, for this duration of 1960-2000, Wagner’s Law is accepted in Iran economy. Therefore, along this period government
expenditure growth is a natural result of economic growth when increasing of government size causes increase in GNP.
Therefore, this cycle of government size and economic growth is continuous.
Verma and Arora (2010) examined the validity of Wagner’s Law in India over the period 1950-2008 in their study. Six versions
(Peacock-Wiseman, Gupta, Goff man, Pryor, Musgrave, Mann) of Wagner’s hypothesis given by different economist have been
estimated which support the existence of long-run relationship between economic growth and public expenditure. Two structural
breaks have also been given to test the impact of structural changes in Indian economy on the growth of public expenditure. It has
been found that the first structural break given for mid- liberalization period caused insignificant changes in the growth elasticity
of public expenditure. However, the observed change in the elasticity due to the second phase of intensive liberalization is
statistically significant.

FOCUS OF THE STUDY
The study is based on to fulfill the various points as following:
• To examine the relationship between public expenditure and economic growth.
• To introduction about the Wagner’s Law and applicability of six versions of Wagner’s hypothesis.
• To examine the applicability of six versions of Wagner’s hypothesis.
• To discuss the Result about the relationship, this is used for the analysis of the same.
HYPOTHESES OF THE STUDY
• Growth does not responsible for change in Public Expenditure.
• Wagner’s Law is not applicable in India.
RESEARCH METHODOLOGY
The study was exploratory in nature. It was based on Secondary data, which is on behalf of the RBI data from 1970-71 to 2011-
12. This is an Empirical study and based on secondary data. In literature, I found different opinions of the researchers about the
relationship between Economic Growth and Public expenditure in India as well as outside India. The present study examined the
relationship between Economic Growth and Public Expenditure in Indian Perspective at the selected duration.
The Sample Design
The present study is based on secondary data that covers the period from 1970-71 to 2011-12. Time series annually data for 42
observations has been utilized in this study.
The study covers selected independent variables GDP, the indicator of income denoted by LY, Per capita income (LYP) and
dependent variables Government expenditure (LGE), Real expenditure (LRE), real per capita expenditure (LREP), nominal per
capita expenditure (LNEY), Government final consumption expenditure (LGFCE). The data has been collected from Hand book
of Statistic on Indian Economy 2011-12 and annual report of (2000-01) published by Reserve Bank of India (RBI).
Tools used for Data Analysis
The data is analyzed by the:
 Crucial to check the stationary of the data by Unit Root Test Both Augmented Dickey Fuller (ADF) and Phillips
Perron (PP) tests are applied to the Level form and First Difference in logarithms term of the series form two models:
Intercept and Trend and Intercept.
 At first stage, the study checks the integration order of the series; Co- integration method is used to find out the
relationship between the variables of six versions of Wagner’s law in Indian perspective.
 In order to test other restrictions on the co-integrating vector, Johansen defines the two matrices α and β both of
dimension (nr) where r is the rank of Π. The properties of α and β are such that:
Π = α β
 Estimation of Co-integrating Vector and Coefficients of Error Correction:
It may be noted that β is the matrix of co-integrating parameters and α is the matrix of the speed of adjustment
parameters. Due to cross equation restrictions, it is not possible to estimate α and β using ordinary least squares.
However, maximum likelihood method, it is possible to (a) estimate VECM model as given in equation (4). (b)
Determine the rank of Π, (c) use the most significant co-integrating vectors to form β, and (d) select such that Π = α β.
 Used the Granger Causality Test:
The simple Granger Causality test (Granger, 1986) is as follows
In Yt = β01i In Yt-1 + 2i In GEt-1 + et …………………. (11)
In GEt = α01i In Yt-1 + 2i In GEt-1 µt …………………. (12)
Where,
In Yt is the natural logarithm of Gross Nation Income
In GEt is the natural logarithm of real Total government expenditure, and are white noise error terms.

The null hypothesis for equation (11) is that In Y does not Granger cause In GE. This hypothesis will be rejected if the
coefficients of the lagged Ys (Summation of β2 as a group) are found to be jointly significant (different from zero). The Null
hypothesis for equation (10) is that In GE does not Granger cause In Y. This hypothesis would be rejected if the coefficient of the
lagged GEs (Summation of α2i as a group) were found to be jointly significant. In both of these null hypotheses are rejected, and
then a bi-directional relationship is said to exist between the two variables (Government expenditure (G) and National Income
(Y).
ANALYSIS AND REPRESENTATION
Economic Growth
Economic Growth refers to increase over time in a country’s real output of goods and services- or more appropriately product per
capita.
Economic Development = Economic Growth + Structural Changes.
In the study, economic growth has been measured in term of GDP.
Public Expenditure and Economic Growth in India through Wagner’s Law
Wagner’s law shows the relationship between public expenditure and economic growth. For this purpose, this study checks the
spending pattern of central and state government in India. This study shows the trend of Revenue expenditure, Capital expenditure
and Real Gross Domestic Product in absolute and in percentage form in table 1.1 which is given below:
Table-1.1
Trends in Total Expenditure of Central and State Governments (in Billion)
Year
Total Revenue
Expenditure of
Central and State
Government
Total Capital
Expenditure of
Central and State
Government
Combined
Expenditure of
Central and State
Government
(2+3)
Gross Domestic
Product
(GDP)
1 2 3 4 5
1970-71 65.2 (60.38) 42.78 (39.62) 107.98 [1.68] 6443.89
1975-76 139.45 (61.54) 87.15 (38.46) 226.6 [3.05] 7430.85
1980-81 292.18 (64.31) 162.14 (35.69) 454.32 [5.24] 8663.4
1985-86 666.94 (68.38) 308.39 (31.62) 975.33 [8.75] 11141.33
1990-91 1452.92 (73.98) 510.94 (26.02) 1963.86 [13.20] 14876.15
1995-96 2829.88 (80.19) 699.2 (19.81) 3529.08 [18.52] 19058.99
2000-01 5656.64 (85.01) 997.63 (14.99) 6654.27 [26.05] 25540.04
2005-06 8774.1 (82.20) 1900.1 (17.80) 10674.2 [30.13] 35432.44
2010-11 20339.73 (83.58) 3997.07 (16.42) 24336.8 [46.47] 52368.23
Notes: Figure in Parenthesis of type ( ) represent the percentage of Total Central Government Expenditure and of type [ ]
represent the percentage of GDP.
Sources: Author's Elaboration from Handbook of Statistics on Indian Economy, Reserve Bank of India (RBI)
The visual inspection of Table 1.1 provides the trends in Revenue and capital expenditure of the Indian public sector. The share of
the revenue expenditure to the total expenditure of the Government of India has increased from 60.38 percent in 1970-71 to 83.58
percent in 2010-11. Consequently, the share of the capital expenditure to total expenditure has decreased from 39.62 percent in
1970-71 to 16.42 percent in 2010-11. It has been observed that capital expenditure started declining continuously from 1970
onwards and its share in total expenditure has fallen from 39.62 percent to 16.42 percent, which is not a healthy trend for a
developing country like India (Pethe and Lalvani, 1999). Further, the share of over all public expenditure to GDP has increased
from 1.68per cent in 1970-71 to 46.47 percent in 2010-11. In sum, the given increase in the share of public expenditure to GDP
has been attributable only to increase in the share of revenue expenditure. The major reasons behind an increase in the revenue
expenditure of the Central government are defense expenditure, administrative expenditure, subsidies, grants-in-aid to states and
expenditure on social and economic services. Thus, such a phenomenal increase in the government expenditure over the years
corroborates the expansion of public sector in India with economic growth.
Empirical Analysis - Unit Root Test
In order to analyze the impact of economic growth on government expenditure or applicability of Wagner’s law in India, the study
utilizes the annually time series data from 1970-71 to 2011-12. In empirical study LRE, LY, LREP, LYP, LRGFCE and LNEY
stand for LNGE, LGDP, LN (GE/P), LN (GDP/P), LNGCE and LN (NGE/NGDP) respectively.

Therefore, it is necessary to examine all the variables for their stationary. To check the stationarity of the data series, the study
employs the Augmented Dickey Fuller (ADF) and Phillips Perron (PP) tests. The results of unit root test (ADF and PP) tests are
exhibited in table A. The ADF and PP tests are performed for the two models; intercept as well as trend and intercept. Both
models are performed on the level as well as first difference of the series. The table A for ADF and PP indicates that all the
variables are non- stationary in level form for the intercept model except LY (log of GDP) and LYP (log of per capita income) at
one percent level of significance but all the variables are non- stationary in level form for the trend and intercept model. Whereas,
all the variables are in the stationary in first difference for both Intercept and Trend & Intercept models. So now, co- integration
model can be applied on the given data series.
The result of the unit root tests are presented in table A. The table shows that all the variables except LY and LYP are integrated
of order one I (1), which LY and LYP are tested to be integrated zero, at one percent level. When the variables were tested in 1st
difference all the variables are found to be integrated of order zero and hence are stationary. However, before conducting the
statistical tests for stationary, we also graphed all the variables in their levels and 1st
differences. It is seen that the graphs of
variables in levels exhibit non-constant variances while graphs in 1st
differences exhibit constant variances and are indicative of
the series being stationary. The two graphs are exhibited in figures A and B.
Table-A: Unit Root Test on Variables
Variables Model
ADF Test Statistic PP Test Statistic
Level First Difference Level First Difference
LY
Intercept
3.6288* -5.9202* 4.7799 -5.9670*
(-3.6009) (-3.6056) (-3.6009) (-3.6056)
Trend &
Intercept
-1.3404 -8.0356* -1.2679 -9.5266*
(-4.1985) (-4.2050) (-4.1985) (-4.2050)
LE
Intercept
-1.1061 -6.6453* -1.4959 -6.6877*
(-3.6009) (-3.6056) (-3.6009) (-3.6056)
Trend &
Intercept
-1.9285 -6.6390* -1.8825 -6.7094*
(-4.1985) (-4.2050) (-4.1985) (-4.2050)
LRE
Intercept
0.2215 -6.4856* 0.5950 -6.5217*
(-3.6009) (-3.6056) (-3.6009) (-3.6056)
Trend &
Intercept
-2.4582 -6.5259* -2.5727 -6.5810*
(-4.1985) (-4.2050) (-4.1985) (-4.2050)
LREP
Intercept
0.3342 -6.6409* 0.8441 -6.6577*
(-3.6009) (-3.6056) (-3.6009) (-3.6056)
Trend &
Intercept
-2.1776 -6.7717* -2.2454 -6.8465*
(-4.1985) (-4.2050) (-4.1985) (-4.2050)
LGFCE
Intercept
-3.3698 -7.0467* -3.3271 -14.6487*
(-3.6009) (-3.6056) (-3.6009) (-3.6056)
Trend &
Intercept
-3.330243 -6.957378* -3.236324 -14.34461*
(-4.1985) (-4.2050) (-4.1985) (-4.2050)
LRGFCE
Intercept
-1.330596 -7.277697* -1.046543 -13.07536*
(-3.6009) (-3.6056) (-3.6009) (-3.6056)
Trend &
Intercept
-3.254529 -7.205677* -3.246688 -14.23947*
(-4.1985) (-4.2050) (-4.1985) (-4.2050)
LYP
Intercept
4.274679* -5.287147* 5.323949 -5.367194*
(-3.6009) (-3.6056) (-3.6009) (-3.6056)
Trend &
Intercept
-0.957948 -7.854949* -0.957948 -9.2049*
(-4.1985) (-4.2050) (-4.1985) (-4.2050)
LNEY
Intercept
-2.929392 -6.061557* -2.94964 -6.27485*
(-3.6009) (-3.6056) (-3.6009) (-3.6056)
Trend &
Intercept
-2.560413 -4.715949* -2.476065 -6.566164*
(-4.1985) (-4.2050) (-4.1985) (-4.2050)
LEP
Intercept
-0.643789 -6.683824* -0.860661 -6.776689*
(-3.6009) (-3.6056) (-3.6009) (-3.6056)
Trend &
Intercept
-2.178472 -6.607156* -2.152848 -6.714455*
(-4.1985) (-4.2050) (-4.1985) (-4.2050)
Note: * indicates significant at 1 percent level. Brackets ( ) contain critical values.
Sources: Data Analysis

Figure-A: Graphs for Macroeconomic Variables (Levels)

Figure-B: Graphs for Macroeconomic Variables (1st
Difference)

Co-integration Test
The stationary behaviour of the series fulfills the criteria of estimating the co-integration model. In the co-integration, study
utilized the Johansen co-integration methodology. This technique is more robust in the case of more than two variables. To test
the existence of co-integrating Vectors for the six versions of Wagner’s law (Peacock & Wiseman, Gupta, Goffman, Pryor,
Musgrave and Mann), Johansson Trace and max Statistics have been used. In general, there can be up to two Co-integrating
vectors for all versions. The test results have been presented in table B. The table shows that in the case of Peacock- Wiseman the
null hypothesis is rejected as value of trace statistic (50.08) is greater than critical value (42.92) but it is Co- integrating for order
one, here the it is accepted because trace statistic is (21.19) and critical value is (25.87) at five percent level of significance where
Eigen value is 0.34 its max is 16.86.The Peacock &Wiseman version shows that Government Expenditure (GE) is a function of
Gross Domestic Product (GDP).
Further, Gupta’s version which tells about that growth in real per capita government expenditure (GE/P) is dependent upon the
growth in real GDP per capita (GDP/P) its co-integration results show that the null hypothesis for no co-integrating vector is
rejected as value of trace statistic (51.71) is greater than critical value (42.92) at five percent level of significance but it is Co-
integrating for order one, here it is accepted because trace statistic is (21.88) and critical value is (25.87) where Eigen value is
0.35 its max is 17.25.Moreover, Goff man’s version shows that growth in total government expenditure (GE) is depend upon the
growth in real GDP per capita (GDP/P) and the results show that null hypothesis for no co-integrating vector is rejected but it is
accepted for order one where trace statistic is (22.63) and critical value is (25.87)where Eigen value is 0.36 its max is 17.88.But in
case of Pryor version which shows that government consumption expenditure is a function of Gross Domestic Product (GDP),
there is some different situation because in this version null hypothesis for co-integrating vector is accepted as value of trace
statistic is (36.80) less than critical value (42.92) so here co-integrating vector for order zero where Eigen value is 0.41 its max is
20.91.Moreover, Musgrave version which states that the share of nominal government expenditures in nominal GDP (NGE/
NGDP) depends upon the real GDP per capita (GDP/P) and Mann version which presents that the share of nominal government
expenditure in nominal GDP depends upon the real GDP, both these Versions are like as Peacock & Wiseman, Gupta and Goff
man versions null hypothesis for no co-integrating vector is rejected but it is accept for order one where trace statistic is less than
from critical value at order one. Further, apply Vector Error Correction Model on all the versions of Wagner’s law except Pryor.
Table-B: Johansen Co-integration Test Trace Statistic
Sr.
No.
Variables
Null
Hypothesis
Alternative
Hypothesis
Eigen
value
Max-
Eigen
Trace
Statistic
Critical
Value
(0.5%)
Prob.
1
LRE,LY & DV r=0 r>0 0.5144 28.8970 50.0831 42.9153 0.0082
(Peacock & Wiseman) r≤1 r>1 0.3440 16.8609 21.1861 25.8721 0.1717
2
LREP,LYP & DV r=0 r>0 0.5257 29.8364 51.7136 42.9153 0.0053
(Gupta) r≤1 r>1 0.3504 17.2548 21.8773 25.8721 0.1451
3
LRE, LYP & DV r=0 r>0 0.5350 30.6278 53.2603 42.9153 0.0034
(Goff man) r≤1 r>1 0.3605 17.8811 22.6325 25.8721 0.1201
r≤2 r>2 0.1120 4.7514 4.7514 12.5180 0.6324
4
LRGFCE, LY & DV
(Pryor)
r=0 r>0 0.4071 20.9124 36.8022 42.9153 0.1785
r≤1 r>1 0.2606 12.0792 15.8898 25.8721 0.5014
r≤2 r>2 0.0909 3.8107 3.8107 12.5180 0.7693
5
LNEY, LYP & DV
(Musgrave)
r=0 r>0 0.5342 30.5634 51.0749 42.9153 0.0063
r≤1 r>1 0.3293 15.9757 20.5115 25.8721 0.2011
r≤2 r>2 0.1072 4.5358 4.5358 12.5180 0.6638
6
LNEY, LY & DV r=0 r>0 0.5158 29.0072 47.9577 42.9153 0.0145
(Mann) r≤1 r>1 0.3064 14.6318 18.9505 25.8721 0.2837
r≤2 r>2 0.1023 4.3187 4.3187 12.5180 0.6957
Note: * indicates Co integration equation at 5% level of significance.
Long-run of Vector Error Correction Model
The Vector Error Correction Model cannot apply if there is no co- integration. Hence, this test has been applied on remaining five
versions of Wagner’s law to check the relationship between expenditure and growth.
The results of this test is divide into two parts one to check long-run relationship and other to check short run relationship. The
behaviour of Long-run relationship between expenditure and economic growth for all the five versions of Wagner’s hypothesis is
presented in table C.
The estimates of the table show that in the case of Peacock-Wiseman version, the LRE positively depends on LRY it is found that
Z value is 2.26 this is more than tabulated value at five percent level (1.96), it rejects the null hypothesis. Therefore, it is statically

significant at five percent critical value. The coefficient value is 0.37, which shows that one percent change in independent
variable (LRY) there is 0.37 percent change will be in the dependent variable in the long run. The Standard Error term is -0.1630
percent, which shows the speed of adjustment in a year among the variables. Therefore, the peacock-Wiseman version shows sign
of applicability in the long run. The estimates of the table show that in the case of Gupta version, it is found that Z value is 2.93 so
it is statically significant at five percent critical value. The coefficient value is 0.28, which shows that one percent change in
independent variable (LYP) there is 0.37 percent change will be in the dependent variable in the long run. The Standard Error
term is -0.09 percent, which shows the speed of adjustment in a year among the variables. Therefore, the Gupta version also
shows sign of applicability in the long run. The estimates of the table show that in the case of Goffman version, it is found that Z
value is -1.30 so it is statically insignificant at five percent critical value. Therefore, the Goffman version shows negative sign of
applicability in the long run. The estimates of the table show that in the case of R.A. and P.B. Musgrave version, it is found that Z
value is -6.20 so it is statically significant at five percent critical value. The coefficient value is -0.64, which shows that one
percent change in independent variable (LYP) there is -0.64 percent change will be in the dependent variable in the long run it
may be due to price fluctuation with the passes of time. The variables will adjust from -0.09 this speed in a year. Therefore, the
Musgrave version also shows applicability in the long-run with negative sign. The estimates of the table show Mann version also
like Musgrave version, Z value is -7.39 so it is statically significant at five percent critical value. The coefficient value is -0.81,
which shows that one percent change in independent variable (LYP) there is -0.81 percent change will be in the dependent
variable in the long run. The Standard Error term is -0.11 percent, which shows the speed of adjustment in a year among the
variables. Therefore, the Musgrave version also shows the applicability in the long- run with negative sign.
All in all it is clear that all these five versions of Wagner's Law except Goffman (where Z-value is less than table value for five
percent level of significant) are statically significant and shows that the sign of applicability in the long-run.
Table-C: Long Run
S. No. Versions
Dependent
Variable
Independent
Variables
Coefficients S. Error Z- Values
1
Peacock-
Wiseman
LRE
LRY 0.3689* -0.1630 2.2626
DV -0.1889 -0.0207 -9.1419
Trend 0.0540 -0.0038 14.1511
Constant 5.8576
2 Gupta LREP
LYP 0.2770* -0.0944 2.9349
DV -0.2617 -0.0348 -7.5159
Trend 0.0422 -0.0036 11.6111
Constant 5.1707
3 Goffman LRE
LYP -0.1229 -0.0946 -1.2990
DV 0.2459 -0.0350 -7.0184
Trend 0.0669 0.0037 18.2982
Constant 5.9533
4
R. A. Musgrave
and P. B.
Musgrave
LNEY
LYP -0.6359* -0.1025 -6.2031
DV -0.2611 -0.0374 -6.9739
Trend 0.0403 -0.0039 10.2909
Constant 4.2723
5 Mann LNEY
LY -0.8052* -0.1090 -7.3889
DV -0.2413 -0.0340 -7.0969
Trend 0.0600 -0.0061 9.8828
Constant 5.3490
Note: * Stands for significant for 5% level
Short run of Vector Error Correction Model
The behaviour of the short run relationship between expenditure and economic growth for all the five versions of Wagner’s
hypothesis has been shown in Table D. The short run table shows the combination of three variables in each version. A common
variable is dummy variable. The co-integration equation1 has been checked out at five percent level of significance.
The estimates of the table show that in the case of Peacock-Wiseman version, Z value is -2.3 this is more than tabulated value at
five percent level (1.96), it rejects the null hypothesis. So it is statically significant at five percent critical value the coefficient of
past error term is negative value is -1.15 and error correction term is 0.49, this result as per the validity of Co-integration model
means it is supporting that past error term should bear negative coefficient value. Similar type results are found for all the reaming
four versions where the coefficient value is statistically significant with negative sign. These results indicate that any type of
disequilibrium in the past is corrected very fast for long run equilibrium. The results of this table show the sign of applicability of
five versions of Wagner's law in the short run.

Table-D: Short Run
S. No. Versions Variables Coefficients S. Error Z- Values
1
Peacock and
Wiseman
Co-integrating
Equation
D(LRE) -1.1478* 0.4966 -2.3115
D(LRY) 0.1979 0.1386 1.4276
D(LRE(-1)) -0.2604* 0.1056 -2.4650
D(LRY(-1)) 0.7645 0.9295 0.8225
DV(LRE) 0.1121 0.1124 0.9974
DV(LRY) -0.0514 0.0314 -1.6374
C(LRE) -0.1021 0.1163 -0.8775
C(LRY) 0.0651* 0.0325 2.0033
2 Gupta
Co-integrating
Equation
D(LREP) -0.8811* 0.1824 -4.8319
D(LYP) -0.3008* 0.1030 -2.9204
D(LREP(-1)) -0.0156 0.0921 -0.1698
D(LYP(-1)) -0.8511* 0.3366 -2.5286
DV(LREP) 0.0623 0.0552 1.1284
DV(LYP) 0.0308 0.0312 0.9887
C(LREP) 0.0691* 0.0152 4.5338
C(LYP) 0.0394* 0.0086 4.5781
3 Goffman
Co-integrating
Equation
D(LRE) -0.7945* -0.1801 -4.4110
D(LYP) -0.3280* -0.0975 -3.3656
D(LRE(-1)) -0.0244 -0.0893 -0.2737
D(LYP(-1)) -0.9057* -0.3331 -2.7193
DV(LRE) 0.1058 -0.0619 1.7100
DV(LYP) 0.0326 -0.0335 0.9737
C(LRE) 0.0843* -0.0163 5.1689
C(LYP) 0.0386* -0.0088 4.3746
4
R.A. and P. B.
Musgrave
Co-integrating
Equation
D(LNEY) -0.4324* 0.1946 -2.2221
D(LYP) -0.3276* 0.0944 -3.4713
D(LNEY(-1)) 0.0027 0.0891 0.0305
D(LYP(-1)) -0.5151 0.3274 -1.5732
DV(LNEY) 0.0612 0.0684 0.8951
DV(LYP) 0.0327 0.0332 0.9868
C(LNEY) 0.0300 0.0182 1.6438
C(LYP) 0.0457* 0.0088 5.1655
5 Mann
Co-integrating
Equation
D(LNEY) -0.5396* 0.2044 -2.6406
D(LY) -0.3267* 0.0979 -3.3361
D(LNEY(-1)) -0.0067 0.0888 -0.0751
D(LY(-1)) -0.4158 0.3136 -1.3257
DV(LNEY) 0.0824 0.0679 1.2127
DV(LY) 0.0211 0.0325 0.6478
C(LNEY) 0.0379 0.0259 1.4646
C(LY) 0.0668* 0.0124 5.3888
Note: * Stands for significant for 5% level
Granger Causality Test
In table E Pair wise Granger Causality Test shows that the null hypothesis Xt does not cause Yt (i=0) is rejected if the computed
value of F-statistic exceeds the tabulated value at a specified level of significance. The causality tests performed by application of
E-views software are given in table of Ganger Causality Test.
After having a look on statistics the results of the table it is clear that the null hypothesis which is 'LY does not Granger Cause
LRE' is significantly rejected i. e. LY significantly Granger causes LRE at 2.6 percent level while second row of the table reports
that the null hypothesis 'LRE does not Granger Cause LY' is insignificantly accepted. Taking together both rows of the table the
results reveal that in the case of Wiseman-Peacock version, there is uni-directional relationship between LRE and LY. The second
row of the table shows that LRE does not Granger causes LY, which cannot be rejected due to non-significance result, as is
evidence in second row of the table.
All the results of the table shows that some variables are Granger cause to other variables like, LY Granger Cause LREP, LYP
Granger Cause LREP and, LY Granger Cause LRGFCE, LYP Granger Cause LRGFCE, Remaining related variables of Granger

cause does not cause to inverse variables because the null hypothesis is rejected due to significance value of probability, so
alternative hypothesis is accepted and the variables are caused in a single side or there are unidirectional relationship among them.
As the growth variables are causing the expenditure’s variable, which is the theory of Wagner’s law. Therefore, results of causing
indicating the applicability of Wagner’s Law in India.
Table-E: Pair wise Granger Causality Tests
Null Hypothesis Observation F-Statistic Probability
LY does not Granger Cause LRE 41 5.3863 0.02576
LRE does not Granger Cause LY 0.01287 0.91029
LY does not Granger Cause LREP 41 8.11678 0.00704
LREP does not Granger Cause LY 0.45418 0.50444
LYP does not Granger Cause LREP 41 5.56738 0.02354
LREP does not Granger Cause LYP 0.27586 0.60248
LY does not Granger Cause LGRFCE 41 6.93919 0.01213
LGRFCE does not Granger Cause LY 0.02795 0.86811
LYP does not Granger Cause LGRFCE 41 4.9724 0.03174
LGRFCE does not Granger Cause LYP 0.24126 0.62613
RESULT AND DISCUSSION
The focus of this study is to check out the relationship between economic growth and public expenditure in India that was the law
given by Wagner:
 This paper examines out the applicability of six versions of Wagner’s law in India during 1970-71 to 2011-12 by
different econometric techniques. For this purpose, some variables of growth and public expenditure are selected on the
bases of six versions of Wagner’s Law. For analysis, the data unit root test is used like Augmented Dickey Fuller (ADF)
and Phillips Perron (PP) test to check the stationary of the data. In ADF and PP test, all the variables become non-
stationary at level of intercept except LY and LYP, which becomes stationary at intercept.
 Then in trend and intercept all, the variables become non-stationary at level form. Finally, all the data for variables
become stationary at first difference on intercept and trend and intercept in both the models. After that Johansen co-
integration test is used to test the existence of co-integrating vectors for the six versions of Wagner’s Law.
 As per the results of co-integration model five versions like Peacock-Wiseman, Gupta, Goff man, Musgrave, and Mann
shows that the null hypothesis is rejected and is significant at five percent level, but it shows only one level of co-
integration between the variables. However, the result of Pryor is the opposite of the five versions of Wagner’s Law.
 This version indicates the relationship between LRGFCE and LY variables and Pryor theory is not accepted as per the
statistically results and not fulfilling the hypothesis of having relationship between these two variables.
 After getting result on co-integration, the study checked the Vector Error Correction Model only on five versions of
Wagner’s Law except Pryor as it was not accepted. The test result of Vector Error Correction Model (VECM) in long-
run shows that Peacock and Wiseman, Gupta Musgrave and Mann versions are statistically significant at 5% level.
 The remaining version of Goff man is statistically insignificant at five percent level. The results of Musgrave and Mann
versions are statically significant with negative sign; it may be due to increase in prices. The all over result shows that
mostly versions become statistically significant at five percent level showing consistency to the theory in the long run.
In the short run Vector Error Correction Model (VECM) all the five versions like Peacock-Wiseman, Gupta, Goff man,
Musgrave, and Mann become statistically significant at five percent level showing consistency to the theory in the short
run. All five Versions supporting the Wagner’s law in short run.
 Finally to identify the cause direction of Granger Causality Test has been applied which shows some variables out of the
table because they do not effect each other and out of them several variables occur in uni-direction like LY Granger
cause LRE, LY Granger Cause LREP, LYP Granger Cause LREP and, LY Granger Cause LRGFCE, LYP Granger
Cause LRGFCE Therefore, the result is showing unidirectional relationship in the above mentioned variables.

CONCLUSION
On the basis of the statistics results conducted by the study it is indicating the relationship between Economic Growth and Public
Expenditure shows consistency in long run as well as short run and the result supported the Wagner’s theory in India during
selected time period.
 The study developed to analyses the relationship between Economic Growth and Public expenditure in Indian
perspective. This paper also talks about the usefulness of the relationship between Public expenditure and Economic
Growth for the fiscal policy.
 As the growth variables are causing the various types of expenditure variables, which are used in the theory of
Wagner’s law. Therefore, results of causing indicating the applicability of Wagner’s Law in India.
REFERENCES
1. Akitoby, B. B., Gupta, Clements S., & Inchanste, G. (2006). Public spending, Voracity and Wagner’s law in developing
countries. European Journal Economy, 22, 908-924.
2. Bhat K. S. et al. (1991). Causality between Public Expenditure and National Income in India. Margin, 23(4), 333-341.
3. Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over
time. Journal of the Royal Statistical Society, 37, 149-163.
4. Burney, N. A. (2002). Wagner's hypothesis: Evidence from Kuwait using co- integration tests. Applied Economics, 34,
49-57.
5. Chletsos, M., & Kollias, C. (1997). Testing Wagner's law using disaggregated public expenditure data in the case of
Greece: 1958-93. Applied Economics, 29, 371-377.
6. Courakis, A. S. et al (1993). Public Expenditure Growth in Greece and Portugal: Wagner’s Law and Beyond. Applied
Economics, 25, 125-134.
7. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimates for autoregressive time-series with a unit root.
Journal of the American Statistical Association, 74, 427-431.
8. Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing.
Econometrics, 55(2), 251-276.
9. Fan, S., Hazell, P., & Thorat, S. (2009). Governmen spending, growth and poverty in rural India. American Journal of
Agricultural Economics, 82(4), 1038-1051.
10. Gandhi, V. P. (1971). Wagner's law of public expenditure: Do recent cross-section studies confirm it?, Public Finance,
26, 44-56.
11. Goffman, I. J. (1968). On the Empirical Testing of Wagner’s Law: A Technical Note. Public Finance, 23(3), 359–364.
12. Granger, C.W. J., & Newbold, P. (1974). Spurious Regressions in Econometrics. Journal of Econometrics, 2(2), 111-
120.
13. Gujarati, D., & Sangeetha. (2007). Basic Econometrics. New Delhi: Tata McGraw-Hill.
14. Gupta, S. P. (1967). Public Expenditure and Economic Growth: A Time Series Analysis. Public Finance, 22(4), 423-
461.
15. Keynes, J. M. (1936). General Theory of Employment, Interest and Money. New York. NY: Harcourt, Brace and
Company. USA..
16. Michas, N. A. (1975). Wagner's law of public expenditures: What is appropriate measurement for a valid test?, Public
Finance, 30, 77-84.
17. Wagner, R. E., & Weber, W. E. (1977). Wagner’s Law, Fiscal Institutions and the Growth of Government. National
Tax Journal, 30, 59-68.

Appendix
Log Values
 Year LY LE LRE LREP LGFCE LRGFCE LGEY LYP LNEY LEP DV
 1970-71 8.68 4.68 7.29 7.90 2.46 5.07 -4.09 9.30 -1.48 5.30 0
 1971-72 8.69 4.87 7.43 8.02 2.76 5.31 -3.91 9.28 -1.36 5.46 0
 1972-73 8.69 5.02 7.47 8.04 1.85 4.30 -3.76 9.26 -1.31 5.59 0
 1973-74 8.73 5.10 7.39 7.93 2.26 4.55 -3.71 9.28 -1.43 5.65 0
 1974-75 8.75 5.22 7.35 7.88 3.06 5.19 -3.60 9.27 -1.47 5.75 0
 1975-76 8.83 5.42 7.57 8.07 2.88 5.03 -3.49 9.33 -1.34 5.92 0
 1976-77 8.84 5.54 7.63 8.11 2.40 4.49 -3.39 9.32 -1.30 6.02 0
 1977-78 8.92 5.66 7.70 8.15 1.90 3.94 -3.34 9.37 -1.30 6.12 0
 1978-79 8.97 5.84 7.85 8.29 2.40 4.41 -3.21 9.40 -1.20 6.28 0
 1979-80 8.92 5.91 7.78 8.18 2.71 4.57 -3.09 9.33 -1.23 6.32 0
 1980-81 8.99 6.12 7.87 8.26 2.78 4.53 -2.95 9.37 -1.19 6.51 0
 1981-82 9.04 6.22 7.88 8.24 2.84 4.50 -2.90 9.41 -1.25 6.59 0
 1982-83 9.07 6.39 7.96 8.31 2.90 4.48 -2.77 9.41 -1.19 6.73 0
 1983-84 9.14 6.54 8.03 8.36 2.74 4.24 -2.69 9.47 -1.20 6.86 0
 1984-85 9.18 6.73 8.14 8.45 2.71 4.12 -2.54 9.49 -1.12 7.03 0
 1985-86 9.22 6.88 8.23 8.51 2.95 4.30 -2.44 9.51 -1.09 7.16 0
 1986-87 9.27 7.04 8.33 8.59 2.90 4.19 -2.32 9.53 -1.04 7.30 0
 1987-88 9.30 7.16 8.35 8.59 2.86 4.05 -2.25 9.54 -1.06 7.39 0
 1988-89 9.40 7.29 8.40 8.62 2.74 3.85 -2.21 9.61 -1.09 7.50 0
 1989-90 9.46 7.44 8.47 8.67 2.67 3.71 -2.12 9.65 -1.08 7.63 0
 1990-91 9.51 7.58 8.51 8.69 2.64 3.57 -2.02 9.68 -1.09 7.76 0
 1991-92 9.52 7.69 8.50 8.65 2.55 3.36 -1.92 9.68 -1.12 7.85 1
 1992-93 9.58 7.79 8.51 8.64 2.58 3.30 -1.88 9.71 -1.16 7.93 1
 1993-94 9.63 7.92 8.54 8.66 2.77 3.40 -1.80 9.74 -1.17 8.04 1
 1994-95 9.69 8.07 8.60 8.69 2.42 2.95 -1.71 9.79 -1.18 8.16 1
 1995-96 9.76 8.17 8.61 8.68 2.92 3.36 -1.69 9.84 -1.25 8.24 1
 1996-97 9.84 8.29 8.66 8.72 2.60 2.96 -1.63 9.90 -1.27 8.35 1
 1997-98 9.88 8.43 8.73 8.77 2.91 3.21 -1.54 9.92 -1.24 8.46 1
 1998-99 9.95 8.60 8.82 8.84 3.17 3.40 -1.43 9.96 -1.20 8.61 1
 1999-00 10.02 8.71 8.91 8.91 2.69 2.89 -1.40 10.02 -1.20 8.71 1
 2000-01 10.06 8.80 8.97 8.95 1.73 1.89 -1.34 10.04 -1.18 8.78 1
 2001-02 10.12 8.90 9.03 8.99 1.87 2.01 -1.30 10.08 -1.17 8.86 1
 2002-03 10.15 9.02 9.11 9.06 1.27 1.37 -1.22 10.10 -1.12 8.96 1
 2003-04 10.23 9.20 9.25 9.18 2.04 2.10 -1.12 10.16 -1.06 9.13 1
 2004-05 10.30 9.26 9.26 9.18 2.21 2.21 -1.13 10.21 -1.13 9.18 1
 2005-06 10.39 9.28 9.23 9.13 2.59 2.55 -1.20 10.29 -1.24 9.17 1
 2006-07 10.48 9.43 9.32 9.21 2.34 2.24 -1.14 10.37 -1.24 9.31 1
 2007-08 10.57 9.59 9.43 9.30 2.75 2.59 -1.07 10.44 -1.23 9.46 1
 2008-09 10.64 9.78 9.54 9.39 2.99 2.75 -0.92 10.49 -1.16 9.64 1
 2009-10 10.72 9.92 9.62 9.47 3.25 2.95 -0.85 10.56 -1.15 9.77 1
 2010-11 10.80 10.10 9.72 9.55 2.87 2.49 -0.77 10.63 -1.15 9.93 1
 2011-12 10.86 10.21 9.75 9.56 2.62 2.16 -0.73 10.68 -1.18 10.02
1
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