INTRODUCTION:

The relationship between government expenditure and economic growth has received considerable attention over the last four decades. Over a century ago, German fiscal theorist Adolph Wagner proposed his well-known proposition, famous as the Wagner’s Law of increasing state activity. The law states that as the economic activity grows, there is a tendency for government activities to increase. Wagner’s hypothesis suggests a functional cause and effect relationship between the growth of an economy and the relative growth of public sector.

Wagner has identified several reasons for the increased size of the public sector relative to a country’s level of economic development (Bird, 1971). First, as industrialization progresses, there is a parallel increase in urbanization. This requires higher expenditure towards administrative and regulatory functions by the state; thereby effecting a substitution of public sector activity for private sector activity. Second, economic growth encourages relative expansion of income-elastic public expenditures on education, cultural, welfare and other socio-economic services. Third, there is a need for the state to provide funds for large-scale investments and to meet the technological requirements of an industrialized society. Wagner’s law is viewed as a long-term phenomenon that is generally expected to hold true for countries in the early stages of development.

Opposing the Wagner’s law is the Keynesian hypothesis. In the Keynesian view, public expenditure is an exogenous factor and a policy instrument for increasing national income. Any attempt to promote growth can be successful by a careful manipulation of public expenditures to stimulate output growth. Therefore, causality runs from public expenditure to national income. In contrast, Wagner considered public expenditure as endogenous to the growth of national income. Wagner’s hypothesis postulates a long-run elasticity of public spending with respect to national income as greater than one. The law expects public expenditure to grow in tandem with national income. Thereby, suggesting that the direction of causal flow runs from economic progress to government expenditure.

The objective of this research endeavor is to determine the nature, magnitude and direction of causal relationship between public expenditure and economic growth in India. There are some issues that need to be answered. Does Wagner’s law hold true in the Indian case? Whether national income causes public expenditure to rise or is it public expenditure that leads to rise in national income? The study attempts to explore these questions. In order to examine the long-run relationship between public expenditure and national income, recent econometric techniques have been employed in the study. The Engle-Granger cointegration analysis is used for exploring the long-run relationship between the variables under study. Should a long-run relationship exist between the variables under observation, the Vector Error Correction Model [VECM] is constructed to test for Granger causality and determine the direction of causal effect between government expenditure and national income.

The present research paper has been divided into five sections. Section one is the current introductory section that lays down the theoretical foundation. Section two presents the different formulations of Wagner’s law and covers the literature review. Section three elaborates upon the methodology used in the analysis. Section four reports and discusses the empirical results. Section five concludes the study.

2. Wagner’s Law Versions and Literature Review:

The Wagner’s law of ‘increasing state activities’ is a subject of debate among researchers. Literature reveals that there are six different interpretations of the Wagner’s hypothesis for defining the relationship between ‘economic progress’ and ‘the growth of state activity’.

The six most popular versions of the Wagner’s law can be stated as follows:

1. Peacock-Wiseman ‘Traditional’ Version [1961]

E = f (GDP)

2. Gupta [1967]/Michas [1975] Version

E/P = f (GDP/P)

3. Pryor Version [1968]

C = f (GDP)

4. Goffman Version [1968]

E = f (GDP/P)

5. Musgrave Version [1969]

E/GDP = f (GDP/P)

6. Modified Peacock-Wiseman ‘Share’ Version [Mann, 1980]

E/GDP = f (GDP)

where,

E = Government Expenditure and/or overall public sector outlays

P = Population

C = Government Consumption Expenditure

GDP = Gross Domestic Product

The above mentioned specifications were traditionally expressed mathematically in a log-linear form; regressing the expenditure variable [dependent variable] on the growth variable [independent variable]. The validity of Wagner’s law requires that the elasticity of government expenditure with respect to national income or GDP should be positive and greater than unity, thereby implying that government expenditure increases faster than economic growth. In general, a number of studies have been interested in exploring the elasticity of public expenditure with respect to a country’s level of economic development for testing the reliability of Wagner’s law. Other researchers have been keen on determining the direction of causal relationship between government expenditure and economic growth. The scope of the present study is to examine the causal relationship between government expenditure and national income for India in the long-run using two important formulations of the Wagner’s hypothesis, namely the ‘Peacock-Wiseman Traditional Version (1961)’ and the ‘Gupta (1967)/Michas Version (1975)’.

There is a massive literature on the relationship between public expenditure and economic growth across the globe. The studies are extensive and the results are nevertheless varied. The empirical findings in the literature on Wagner’s law have led to conclusions that are either contradictory or mixed. Among the studies that supported the existence of Wagner’s law are Wagner and Weber (1977), Abizadeh and Gray (1985), Courakis et.al (1993), Park (1996), Singh (1997), Thornton (1999), Islam (2001), Chang (2002), Iyare and Lorde (2004), Aregbeyen (2006), Sideris (2007), Ogbonna (2012), Menyah and Wolde-Rufael (2013), Adedokun and Olaniyi (2017). Lamartina and Zaghini (2011) used panel cointegration analysis for 23 OECD advanced economies for determining the relationship between government expenditures and economic growth for the period 1970-2006. They support the widespread applicability of Wagner’s law across their sample countries. Also, their study arrived at long-run income elasticity greater than one, strictly supporting the Wagner’s interpretation. They found the correlation to be higher in countries with lower per capita GDP. Bojanic (2013) examined Wagner’s law for nine different versions employing annual time series data for the period 1940-2010 for Bolivia. The study carries out the cointegration based causality test. The findings confirm bidirectional Granger causality between income and government expenditures in six versions of the law. The strong effect of income on various government expenditure indicators lends great support to Wagner’s proposition. Hallim (2018) investigated the validity of Wagner’s law in G7 countries for the period 1970-2016 using latest time series econometric techniques. Except for Japan and Italy, the study supports Wagner’s law for the rest five nations both in short-run and long-run.

On the other hand, studies by Singh and Sahni (1984), Ashworth (1994), Hondroyiannis and Papapetrou (1995), Demirbas (1999), Huang (2006), Babatunde (2011) found no support for Wagnerian hypothesis. Even studies such as Ram (1986), Afxentiou and Serletis (1996), Ansari et al (1997), Burney (2002), Wahab (2004), Kesavarajah (2012), Mohammadi and Rati (2015) rendered only limited support for Wagner’s law. Dogan and Tang (2006) determined causality between government expenditure and economic growth for five South East Asian countries – Indonesia, Malaysia, Phillipines, Singapore and Thailand. The Granger causality results do not support Wagner’s law for any of the five countries under study. Only for Phillipines, the existence of Keynesian hypothesis is indicated. Ighodaro and Oriakhi (2010) tested Wagner’s law for Nigeria for the period 1961-2007. Although the dependent and independent variables were found to share a long-run relationship, Wagner’s hypothesis failed to prevail for Nigeria rather Keynesian hypothesis was validated for all the estimations. Phiri (2016) examined nonlinearities in Wagner’s law for South Africa. The results indicate a non-linear relationship between government expenditure and output. The study found a long-run causality running from government spending to economic growth, validating Keynesian hypothesis in South Africa’s case.

Mixed support for Wagner’s law have been reported by studies as Holmes and Hutton (1990), Lin (1995), Payne and Ewing (1996), Arghyrou (1999), Jackson et al (1999), Chang et al (2004), Narayan et al (2006), Albert and Ton (2009), Keho (2016). Holmes and Hutton (1990) found unidirectional causality from national income to government expenditure for India using the parametric test but reverse causality was derived in case of non-parametric test. Chang et al (2004) examined five different versions of Wagner’s law for ten countries for the period 1951-1996. They lent a strong positive support for the causal effect from income to government expenditure for five countries; while no such relationship was found for the remaining countries in the sample. Narayan et al (2006) conducted a panel study across China’s provinces. They found less support for Wagner’s law for China as a whole and for the higher income eastern provinces. For the less developed lower income provinces, the results were mixed with almost unitary elasticity of government expenditure with respect to real income; against a long-run unidirectional Granger causality running from real GDP to real government expenditure.

The research results on Wagner’s law have been largely inconclusive and there seems to be no consensus on the direction of causal relationship between government expenditure and economic progress.

The issue “whether increasing government expenditures are the cause of economic growth or if economic growth is the cause of increasing government expenditures” is particularly important for developing countries where the public sector absorbs a relatively large share of society’s economic resources. The early studies on Wagner’s hypothesis adopted a simplistic approach towards the issue. They generally ignored the stationary properties of the data series and tested for value of income-elasticity coefficient using the traditional regression analysis. Unlike them, the recent empirical research works follow a cointegration based methodology, succeeded by tests of causality in some cases. Empirical tests of Wagner’s law have yielded results that differ considerably in terms of the countries under consideration, time period covered and methodology adopted. In order to confirm whether Wagner’s law is valid or not for India, it needs to be empirically investigated.

3. METHODOLOGY:

This study explores the existence or absence of Wagner’s law in case of India using the Granger causality technique. For this purpose, a three step procedure is applied to time series data of government expenditure (E), gross domestic product (G) and population (P); for India. The test procedure is carried out for two models of Wagner’s law: ‘Peacock-Wiseman Traditional Version’ and the ‘Gupta/Michas Version’. The Peacock-Wiseman model expresses total government expenditure as a function of gross domestic product; while the Gupta/Michas model states per capita government expenditure as a function of per capita gross domestic product. In the first step, the time series data or variables are examined for stationarity or unit roots with the help of Augmented Dickey-Fuller (ADF) test, and their order of integration is determined. Next, the hypothesis of a long-run relationship between the bivariate models is tested using the cointegration analysis (employing Augmented Engle-Granger or AEG test) for the resultant stationary data. As existence of cointegration between variables implies confirmed causality in at least one direction, Granger causality tests (using Vector Error Correction Mechanism-VECM) would be adopted for the cointegrated models to determine the direction of cause and effect relationship. The testing of causality dynamics between the country’s economic progress and its state activities may lead to four possibilities: (i) unidirectional causality from national income to government expenditure; (ii) unidirectional causality from government expenditure to income; (iii) bidirectional causal relationship between income and government expenditure; and (iv) no causal relationship between the variables. It is important to note that in the absence of cointegration or long-run relationship between variables, standard Granger causality test may be adopted for analyzing the short-run linkages between stationary or I(0) data series.

The study is carried out for a long period of nearly half a century from 1970-71 to 2016-17. Between the two models to be considered for analysis, there are four data sets defined as: E is the total government expenditure; G is gross domestic product representing economic progress of the country measured as GDP at current market prices; E/P is the ratio of total government expenditure to total population or per capita government expenditure; G/P is the ratio of gross domestic product to total population or per capita income. All variables used in the study are annual time series data expressed in nominal terms measured in crore (Base: 2004-05); and converted into natural logarithms. The data have been obtained from various issues of Handbook of Statistics on Indian Economy published by RBI.

4. EMPIRICAL ANALYSIS:

The two interpretations of Wagner’s law – ‘Peacock-Wiseman Traditional Version’ and ‘Gupta/Michas Version’ undergo the cointegration based Granger causality test for determining whether Wagner’s law or Keynesian hypothesis is valid in the Indian case. The analysis has been carried out stepwise as discussed in the methodology and the results are reported in the following order:

4.1 Unit Root Test

4.2 Cointegration Test

4.3 Granger Causality Test - VECM/Wald’s F Test

4.1 Unit Root Test:

First, univariate time series analysis is performed with the idea of confirming the stationarity of the variable series involved in causality testing of the issue whether higher national income causes higher government expenditure; or higher government expenditure causes national income to rise; is the causal relationship two ways; or may be none exists at all. The variables are tested for unit roots in levels and successively for differenced variables, as the case may be. The ADF test results for unit roots determine whether variables are stationary or not and if not then at what level do they become stationary. The ADF test equation has a constant, trend and one-period lag of the dependent variable. The selection of lag length to be used in the ADF test equation is the one that minimizes the problem of autocorrelation. As rightly stated by Charemza and Deadman (1992) that “the practical rule for establishing the value of [m] is that it should be relatively small in order to save degrees of freedom, but large enough not to allow for the existence of autocorrelation in e_t”. The unit root test results are reported in Table 1.

Table: 1 Unit Root Test

Augmented Dickey-Fuller Test with a Constant, Trend, Lag=1 Time Period: 1970-71 to 2016-17

Variables	ADF Test Statistic^@		Order of Integration
Variables	Level	First Difference	Order of Integration
Government Expenditure
E E/P	-0.82 -1.17	-4.34^* -4.25^*	I [1] I [1]
*National Income*
G	-2.57	-4.79^*	I [1]
G/P	-2.42	-4.10^**	I [1]
	Mackinnon Critical Values: 1% = -4.17 5% = -3.51

@ Significance is based on Mackinnon critical values for rejection of hypothesis of a unit root.

* = Significant at 1%

** = Significant at 5%

Inference:

All the four time series variables are non-stationary at levels. Non-stationarity can be rejected for the variables upon differencing once. Therefore, all the data series considered for the analysis are integrated of the order one, or I(1). The government expenditure and national income variables as paired in the two formulations of Wagner’s law; precisely the Peacock-Wiseman traditional version (E–G) and Gupta/Michas version (E/P–G/P) are integrated of the same order. As cointegration test can be carried out for only the pair of variables that are integrated of the same order, the above pairs can be used further for determining the cointegrating regressions.

4.2 Cointegration Test:

After establishing the order of integration of variables on the basis of unit root test results, cointegration test is conducted between the pair of stationary variables for the models having the same order of integration. To be able to proceed with the cointegration test, first we need to estimate the linear cointegrating regression using OLS method and derive the residual series for cointegration testing. If the residual has a unit root lower than the variable pair under study, the variable pair for the respective model is said to be cointegrated. The residual based unit root test or AEG test equation is carried out with one-period lag of dependent residual variable, but has no drift or trend term as it was already included in the ADF function for the unit root test of time series data. The cointegrating regressions are presented in Table 2 and the results for cointegration test for residuals (unit root test for residuals) are reported in Table 3.

Table: 2Cointegrating Regressions Time Period: 1970-71 to 2016-17

Method: Ordinary Least Squares Included Observations: 47

Peacock-Wiseman Traditional Version

E = -0.78 + 0.99 G

[16.29] [83.64] t-statistic Prob = 0.000

R²

0.995

Adj. R²

0.995

D-W

0.327

S.E.

0.053

F-statistic

9383.373

Prob = 0.000

Gupta/Michas Version

E/P = -0.78 + 0.99 G/P

[12.79] [96.87] t-statistic Prob = 0.000

R²

0.994

Adj. R² 0.993

D-W

0.296

S.E.

0.052

F-statistic

9996.069

Prob = 0.000

Inference:

As the bivariate cointegrating regressions in Table 2 are in double log functional forms, the coefficients of national income variables or explanatory variables yield the income-elasticity of public expenditure. According to Wagner, the elasticity of government expenditure with respect to national income should be significantly positive and greater than one; for the law to hold true and applicable. In the Indian case, the results here reveal that for both the interpretations of Wagner’s hypothesis, the income elasticity is almost equal to unity implying a significantly positive proportionate impact of government expenditure on national income in total terms as well as in terms of per capita. In that sense, Wagner’s law does not completely hold true for India, although the nature of relationship looks quite robust.

Inference:

The AEG test for the residuals of the two models stated for testing Wagner’s hypothesis in Table 3 yield conflicting results. The residual ECT01 for the Peacock-Wiseman model is integrated of the order zero [I(0)] which is of a lower order than that of the total government expenditure (E) and national income (G) [I(1)]. Hence, E and G are cointegrated and tend to share a long-run stable relationship. The ADF test statistic for the residual ECT 02 of the Gupta/Michas model is I(1) implying absence of cointegration or any long-run relationship between per capita government expenditure (E/P) and per capita income (G/P). For the Peacock-Wiseman model, Granger causality tests using VECM are employed further since existence of cointegration between total government expenditure and gross domestic product assures the event of causality happening in at least one direction. The lack of long-run relationship between variables in the Gupta/Michas model requires that this non-cointegrated model be proceeded with standard Wald’s F test for causality testing of I(0) variables.

4.3 Granger Causality Test:

Once the long-run relationship between the variables has been established, Granger causality tests are employed for determining the direction of causal linkage. Vector Error Correction Model [VECM] is estimated for the cointegrated model using stationary expenditure and income variables. A lagged error correction term [ECT] obtained from the respective cointegrating regression is augmented in standard causality regressions to arrive at the VECM [for empirical applications and details refer Granger (1986, 1988a, 1988b), and Oxley (1993)]. Alternative Granger causality models were estimated using 1, 2 and 3 period lags for the regressors. Considering the AIC value, explanatory power of regressors, DW statistic and overall model significance represented by the F-statistic; the model with a one-period lag was found to be most appropriate and therefore reported for interpretation of results. Also, it is important to note here that for annual time series data, studies largely consider a lag of one year for analysis. The VECM based Granger causality test determines the direction of short-run and long-run causality for the Peacock-Wiseman model, which is presented in Tables 4.1 and 4.2. The causal relationship for the Gupta/Michas model is established on the basis of Wald’s F test for Granger causality and reported in Table 5.

Table: 4.1 Granger Causality Test - VECM [G to E]

Dependent Variable: ∆E
Method: Ordinary Least Squares
Time Period: 1970-71 to 2016-17
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.017	0.017	1.034	0.307
∆E_-1	0.243	0.157	1.548	0.129
∆G_-1	0.424	0.239	1.774	0.084
ECT _-1	-0.086	0.078	-1.107	0.275
R-squared :	0.121	F-statistic :		1.889
Adjusted R-squared :	0.057	Prob[F-statistic] :		0.147
D-W statistic :	1.981	AIC:		-4.525

Inference:

The explanatory variable ∆G as well as the coefficient of error correction term are insignificant at 5% level, indicating absence of any causal impact from national income to government expenditure. Hence, G does not Granger cause E either in short-run or long-run.

Table: 4.2 Granger Causality Test - VECM [E to G]

Dependent Variable: ∆G
Method: Ordinary Least Squares
Time Period: 1970-71 to 2016-17
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.042	0.009	4.633	0.000
∆G_-1	0.356	0.131	2.718	0.009
∆E_-1	-0.134	0.086	-1.559	0.127
ECT _-1	0.099	0.043	2.308	0.026
R-squared :	0.261	F-statistic :		4.818
Adjusted R-squared :	0.207	Prob[F-statistic] :		0.006
Durbin-Watson stat :	1.666	AIC:		-5.726

Inference:

The coefficient of explanatory variable ∆E is insignificant but the adjustment parameter or coefficient for the error correction term is significantly away from zero. Moreover, the relatively insignificant effect of government expenditure on national income in the short-run is negative. The t-statistic values imply absence of any short-run causality but presence of a long-run causal impact from government expenditure to national income. Therefore, E Granger causes G unidirectionally in the long-run alone.

Table: 5 Granger Causality Test - Wald’s F Test

Per Capita Government Expenditure and Per Capita Income

Time Period: 1970-71 to 2016-17Lag = 1

Null Hypothesis

Observation

F-Statistic

Probability

∆[G/P] does not Granger cause ∆[E/P]

∆[E/P] does not Granger cause ∆[G/P]

2.91248

1.52615

0.09528

0.22356

Inference:

The significance of the F-statistic values for the Wald’s test for Granger causality is studied to accept or reject the null hypothesis. The F-statistic for both the cases is found to be insignificant at 5% level, thereby accepting the null hypotheses. The results indicate absence of any causal relationship between per capita government expenditure and per capita national income in either direction. G/P does not Granger cause E/P and also E/P does not Granger cause G/P.

Final Summary:

The Granger causality outcomes for both the Peacock-Wiseman and Gupta/Michas models negate the validity of Wagner’s hypothesis for the Indian economy for the chosen variables and for the time period under study. Rather long-run unidirectional causality is witnessed from government expenditure to GDP lending support to the prevalence of Keynesian hypothesis in the Indian subcontinent in the long-run. However, there is no causal impact from public expenditure to national income in the short-run and any limited influence of government expenditure is also detrimental to the growth of the economy. The Gupta/Michas model refutes the role of any causal association between the public expenditure and GDP in per capita terms for India.

5. CONCLUSION:

The Wagner’s law states that as the economic activity grows, there is a tendency for government activities to increase faster. It suggests a cause and effect relationship between public spending and economic growth. The present study investigates the nature and direction of causal relationship between public expenditure and economic progress for India for the period 1970-2016. Two models of Wagner’s law have been examined for the purpose. Peacock-Wiseman traditional version and Gupta/Michas version of Wagner’s hypothesis establish association between government expenditure and gross domestic product in gross terms and per capita terms respectively. The two interpretations of Wagner’s law have been empirically tested for Granger causality following the cointegration technique. The empirical analysis has been carried out in three steps. First, the time series variables are tested for unit roots; second, the long-run relationship is examined between variables for the two models expressing Wagner’s law; in the third step, Granger causality is established between bivariate pairs of public spending and national income. Wagner’s law does not get support from either model in the Indian case. However, Keynesian hypothesis is found to persist in the long-run; with a unidirectional causality running from public spending to national income. It is expected of government activities to play an important role in influencing economic development in the country in the long-run.

6. REFERENCES:

1. Abizadeh S, Gray J. Wagner’s Law: A pooled Time Series, Cross-Sectional Comparison. National Tax Journal. 1985; 38: 209-218.

2. Adedokun A, Olaniyi CO. Nigeria Economic Recess Versus Wagner’s Law and Keynesian Proposition. International Journal of Economics & Management Sciences. 2017; 6(3). DOI: 10.4172/2162-6359.1000424

3. Afxentiou PC, Serletis A. Government Expenditures in the European Union: Do They Converge or Follow Wagner’s Law? International Economic Journal. 1996; 10: 33-47.

4. Albert W, Ton G. Wagner’s Law and Social Welfare: The Case of the Kingdom of Saudi Arabia. Applied Econometrics and International Development. 2009; 9(2).

5. Ansari MI, Gordon DV, Akuamoah C. Keynes versus Wagner: Public Expenditure and National Income for Three African Countries. Applied Economics. 1997; 29: 543-550.

6. Aregbeyen O. Cointegration, Causality and Wagner’s Law: A Test for Nigeria. Economic and Financial Review. 2006;44(2): 1-18.

7. Arghyrou MG. Public Expenditure and National Income: Time Series Evidence from Greece. Paper presented at the International Economics and finance Society Conference. City University. London. April. & The 3rd Conference on Macroeconomics analysis and International Finance. University of Crete, Rethymno. May. 1999.

8. Ashworth J. Spurious in Mexico: A Comment on Wagner’s Law. Public Finance / Finances Publiques. 49: 282-286.

9. Babatunde MA. A Bound Testing Analysis of Wagner's Law in Nigeria: 1970-2006. Applied Economics. 2011; 43(21):2843-2850. DOI: 10.1080/00036840903425012

10. Bird RM. Wagner’s ‘Law’ of Expanding State Activity. Public Finance/Finances Publiques. 1971; 26(1): 1-26.

11. Bojanic AN. Testing the Validity of Wagner’s Law in Bolivia: A Cointegration and Causality Analysis with Disaggregated Data. Revista de Analisis Economico. 2013; 28(1): 25-45.

12. Burney NA. Wagner’s Hypothesis: Evidence from Kuwait Using Cointegration Tests. Applied Economics. 2002; 34(1): 49-57. DOI: 10.1080/00036840010027540

13. Chang T. An Econometric Test of Wagner’s Law for Six Countries based on Cointegration and Error-Correction Modelling Techniques. Applied Economics. 2002; 34(9): 1157-1169.

14. Chang T, Liu W, Caudill SB. A Re-Examination of Wagner’s Law for Ten Countries based on Cointegration and Error-Correction Modelling Techniques. Applied Financial Economics. 2004; 14(8): 577-589. DOI: 10.1080/0960310042000233872

15. Charemza WW, Deadman DF. New Directions in Econometric Practice. Aldershot. Edward Elgar. 1992.

16. Courakis AS, Moura-roque F, Tridimas G. Public Expenditure Growth in Greece and Potugal: Wagner’s Law and Beyond. Applied Economics. 1993; 25: 125-134.

17. Demirbas S. Cointegration Analysis-Causality Testing and Wagner’s Law: The Case of Turkey, 1950-1990. Discussion Papers in Economics Series – 3. Department of Economics. University of Leicester. UK. 1999.

18. Dogan E, Tang TC. Government Expenditure and National Income: Causality Tests for Five South East Asian Countries. International Business & Economics Research Journal. 2006; 5(10): 49-58.

19. Ganger CWJ. Causality, Cointegration and Control. Journal of Economic Dynamics and Control. 1988b; 12(2/3): 551-559.

20. Ganger CWJ. Developments in the Study of Cointegrated Economic Variables. Oxford Bulletin of Economics and Statistics. 1986; 48(3): 213-228.

21. Ganger CWJ. Some Recent Developments in a Concept of Causality. Journal of Econometrics. 1988a; 39(1/2): 199-211.

22. Goffman IJ. On the Empirical Testing of Wagner’s Law: A Technical Note. Public Finance / Finances Publiques. 1968; 23(3): 359-364.

23. Gupta SP. Public Expenditure and Economic Growth: A Time-Series Analysis. Public Finance / Finances Publiques. 1967; 22(4): 423-461.

24. Hallim H. Testing of Wagner’s Law for Industria (G7) Countries. İstanbul Medeniyet University. Department of Economy. İstanbul. January. 2018. https://www.academia.edu/36017965/TESTING_OF_WAGNERS_LAW_FOR_INDUSTRIA_G7_COUNTRIES

25. Homles JM, Hutton PA. On the Causal Relationship between Government Expenditures and National Income. Review of Economics and Statistics. 1990; 72(1): 87-95.

26. Hondroyiannis G, Papapetrou E. An Examination of Wagner’s Law for Greece: A Cointegration Analysis. Public Finance / Finances Publiques. 1995; 50(1): 67-79.

27. Huang CH. Government Expenditures in China and Taiwan: Do They Follow Wagner’s Law. Journal of Economic Development. 2006; 31(2): 139-148.

28. Ighodaro CAU, Oriakhi DE. Does the Relationship Between Government Expenditure and Economic Growth Follow Wagner’s Law in Nigeria? Annals of the University of Petroşani. Economics. 2010; 10(2): 185-198.

29. Islam A. Wagner’s Law Revisited: Cointegration and Exogeneity Tests for USA. Applied Economics Letters. 2001; 8(8): 509-515.

30. Iyare SO, Lorde T. Cointegration, Causality and Wagner’s Law: Tests for Selected Caribbean Countries. Applied Economics Letters. 2004; 11: 815-825.

31. Jackson PM, Fethi MD, Fethi S. Cointegration, Causality and Wagner’s Law: A Test for North Cyprus, 1977-1996. Discussion Papers in Public Sector Economics. No. 99/2. University of Leicester. Leicester. 1999.

32. Keho Y. Testing Wagner’s Law in the Presence of Structural Changes: New Evidence from Six African Countries (1960-2013). International Journal of Economics and Financial Issues. 2016; 6(1): 1-6.

33. Kesavarajah M. Wagner's Law in Sri Lanka: An Econometric Analysis. International Scholarly Research Network (ISRN) Economics. 2012; 2012: 1-8. DOI: 10.5402/2012/573826

34. Lamartina S, Zaghini A. Increasing Public Expenditure: Wagner’s Law in OECD Countries. German Economic Review. 2011; 12(2): 149-164.

35. Lin C. More Evidence on Wagner’s Law for Mexico. Public Finance / Finances Publiques. 1995; 50(2): 267-277.

36. Mann AJ. Wagner’s Law: An Econometric Test for Mexico, 1925-1976. National Tax Journal. 1980; 33(2): 189-201.

37. Menyah K, Wolde-Rufael Y. Government Expenditure and Economic Growth: The Ethiopian Experience, 1950-2007. The Journal of Developing Areas. 2013; 47: 263-280. DOI:10.1353/jda.2013.0015

38. Michas NA. Wagner’s Law of Public Expenditures: What is the Appropriate Measurement for a Valid Test?. Public Finance / Finances Publiques. 1975; 30(1): 77-84.

39. Mohammadi H, Rati R. Economic Development and Government Spending: An Exploration of Wagner’s Hypothesis during Fifty Years of Growth in East Asia. Economies. 2015; 3: 150-160. DOI: 10.3390/economies3040150

40. Musgrave RA. Fiscal Systems. New Haven: Yale University Press. 1969.

41. Narayan PK, Nielsen IL, Smyth R. Panel Data, Cointegration, Causality and Wagner’s Law: Empirical Evidence from Chinese Provinces. China Economic Review. 2006; 19: 297-307.

42. Ogbonna BC. Does the Wagner’s Law Hold for Nigeria?: 1950-2008. JORIND. 2012; 10(2): 290-299.

43. Oxley L. Cointegration, Causality and Export-Led Growth in Portugal. Economics Letters. 1993; 43(2): 163-166.

44. Park WK. Wagner’s Law Vs. Keynesian Paradigm: The Korean Experience. Public Finance / Finances Publiques. 1996; 51(1): 71-91.

45. Payne JE, Ewing BT. International Evidence on Wagner’s Hypothesis: A Cointegration Analysis. Public Finance / Finances Publiques. 1996; 51(2): 258-274.

46. Peacock AT, Wiseman J. The Growth of Public Expenditure in the United Kingdom. Princeton, Princeton University Press. 1961.

47. Phiri A. Nonlinearities in Wagner’s law: Further evidence from South Africa. MPRA Paper No. 71702. June. 2016. https://mpra.ub.uni-muenchen.de/71702/

48. Pryor FL. Public Expenditures in Communist and Capitalist Nations. London. George Allen and Unwin. 1968.

49. Ram R. Causality between Income and Government Expenditure: A Broad International Perspective. Public Finance / Finances Publiques. 1986; 41(3): 393-414.

50. Sideris D. Wagner’s Law in 19^th Century Greece: A Cointegration and Causality Analysis. Working Papers No. 64. Bank of Greece. 2007.

51. Singh B, Sahni BS. Causality between Public Expenditure and National Income. Review of Economics and Statistics. 1984; 66(4): 630-644.

52. Singh G. Wagner’s Law-A Time Series Evidence from Indian Economy. The Indian Journal of Economics. 1997; 77(306): 349.

53. Thornton J. Cointegration, Causality and Wagner’s Law in 19^th Century Europe. Applied Economics Letters. 1999; 6(7): 413-416.

54. Wagner RE, Weber WE. Wagner’s Law, Fiscal Institutions, and the Growth of Government. National Tax Journal. 1977; 30(1): 59-68.

55. Wahab M. Economic Growth and Government Expenditure: Evidence from a New Test Specification. Applied Economics. 2004; 36(1): 2125-2135.

Received on 23.11.2018 Modified on 07.12.2018

Res. J. Humanities and Social Sciences. 2018; 9(4): 785-792.

DOI: 10.5958/2321-5828.2018.00132.8

Eqn. No.	Variables		Augmented Engle-Granger Test^#
	Variables		ADF Test Statistic for Residual^$
	Dependent	Independent	Residual	Level	First Difference	Order of Integration	Cointegration
1.	E	G	ECT 01	-1.80^***	---	I[0]	Yes
2.	E/P	G/P	ECT 02	-1.59	-4.96^*	I[1]	No
			Mackinnon Critical values: 1% = -2.61 5% = -1.95 10% = -1.62