An empirical test of Wagner’s Law with disaggregated data for Spain

Manuel Jaén- García, Department of Economics and Business

University of Almería (Spain), Cañada de S/Urbano s/n 04120 Almería (Spain)

e-mail: mjaen@ual.es

Abstract

Although Wagner’s Law has been empirically tested many times, very few

studies have utilized disaggregated data, and none, to our knowledge, have considered

public spending according to functional classification. Our hypothesis is that the

expenditures attributable to the welfare state (education, healthcare and social

expenditures) increase at a higher rate than gross domestic product (GDP) and public

spending in its totality. That is, these expenditures behave like luxury goods and,

consequently, fulfill the law in question. At the same time, we analyze the influence of

public spending on the growth of GDP following the Keynesian hypothesis.

Keywords: public expenditure, gross domestic product, unit

root, cointegration, disaggregated data.

JEL Classification: H11, H50, E62.

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An empirical test of Wagner’s Law with disaggregated data for Spain

1. Introduction

Wagner’s Law (Wagner, 1890) is widely used in the research on the relationship

between public spending and economic growth. In general terms, this law states that

public spending growth is absolute and relative within the national economy, in

particular for government services destined for public purposes, at the expense of the

private sector. The growth path may be different for the various branches of

government, but it would always include the traditional services such as defense, law

and order, and the development of new functions associated with expansion of

education, healthcare services and structural changes in the economy (Peacock and

Scott, 2000). Our goal is to show how the expenditures mentioned above, particularly

those that constitute welfare services, behave with respect to GDP. The hypothesis

which we propose is that these expenditures (education, healthcare and social aid) grow

more rapidly than GDP does, thus, fulfilling Wagner’s Law. This, in our view, is a

result of social pressure during the 1950s, at which time, thanks to Keynes and

Beveridge, the welfare state was instituted in Great Britain, subsequently spreading to

the rest of Europe.

Very few works exist which analyze the fulfillment of Wagner’s Law in

disaggregated terms. Those which we are aware of consider economic classification of

public spending as disaggregated expenditures spent on consumption of goods and

services, wages and salaries, and transfers and expenditures of capital. In the case of the

present work, we have chosen to consider public spending by functional classification,

which includes, according to official information, the following items: General public

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services, defense, education, healthcare, public order and safety, economic affairs,

environmental protection, housing and community services, recreation, culture and

religion, public order and safety and social protection. With this functional classification

we consider social expenditures which include environmental protection, healthcare,

housing and community services, and education, in addition to general services, defense

and economic affairs. To carry out a more in-depth analysis, we divide social

expenditures into healthcare and all other social expenditures which include items such

as pensions for disabilities, age, unemployment and social housing.

With this approach, we consider the expenditures corresponding to the welfare

state in Spain: education, healthcare and social protection. In our opinion, said

expenditures are indissolubly linked to economic growth and, unlike other expenditures

such as general public services or defense, grow at a faster rate than the economy itself.

The fundamental reason for this is that as GDP increases, so do the needs and

requirements of the population for these social expenditures. Consequently, our

hypothesis is that these expenditures fulfill Wagner’s Law in the sense that they

constitute luxury goods, meaning their income elasticity is greater than one. However,

the expenditures on defense, general services and economic affairs, which are normal

goods with an income elasticity greater than zero, do not fulfill Wagner’s Law, meaning

public spending in general grows at a slower rate. As a result, the law can be rejected

for public spending in its totality or accepted, but the income elasticity for public

expenditure is lower than for social expenditures. In parallel, we maintain our alternate

hypothesis, namely the Keynesian hypothesis, which considers that this type of

spending, fundamentally on education and healthcare, contributes to economic growth.

So it follows that we have bi-directional growth of public spending as expenditure on

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education and healthcare increases due to growth in the economy, but, at the same time,

the economy grows due to the increase of said expenditures.

According to Chletsos and Kollias (1997), the use of disaggregated data better

explains the role of each component in economic development. As for Kukuckkale and

Yamak (2012), they state, in keeping with Granger (1987), that the process of

generating aggregated variables is strongly determined by the common factors in the

mechanism of disaggregated variables that is generated, and that a component of an

aggregated variable does not necessarily have to have the same mechanism as those of

the other components. Moreover, Granger (1988) asserts that if any component of an

aggregated variable contains a unit root, then the aggregated variable must contain a

unit root, meaning that the combination of I (0) variables with I (1) variables produces

an I (1) variable.

The remainder of the article is organized as follows. Section two contains a brief

analysis of the literature related to Wagner’s Law and several issues of methodology are

addressed. Section three presents the empirical test of different versions of the law

utilizing the six variables cited and public spending in general. Section four explains our

conclusions.

2. Review of the empirical literature.

Wagner's analysis, as he himself argued, is based on the observation of reality.

The law of increasing expansion of public and, particularly, state activities, becomes for

the fiscal economy the law of increasing expansion of fiscal requirements. Interpreted

from an economic-political point of view, this law expresses the absolute, and also

relative, extension of the public organization structure along with, and replacing, the

economic-private structure within the public economy. It maintains that there is an

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absolute and relative expansion of the public sector, in particular government services

for public purposes, at the expense of growth of the private sector. The growth path may

be different for the various branches of government, but it would always include the

traditional services such as defense, law and order, and also the development of new

functions associated with expansion of education, healthcare services and structural

changes in the economy. From this perspective, Wagner presents three reasons for this

increasing State participation (Bird, 1971): 1) Increased administration and security

functions of the State due to the substitution of private activity for public; 2)

Considerable relative expansion of "cultural and welfare" expenditures; 3) Inevitable

changes in technology, and increasing investment volume required in many activities.

All of this would create an ever-increasing number of private monopolies which the

State would have to suppress, or at least neutralize their effects, for reasons of economic

efficiency.

The first two reasons are what led us to investigate public spending in Spain in

disaggregated terms, specifically in its functional classification.

It is interesting to note that Wagner’s reasoning has been questioned by

numerous researchers in the past (Tim, 1961; Gupta, 1967; Peacock and Wiseman,

1967; Andic and Veverka, 1964). Despite these criticisms and the ambiguity in the

law’s formulation, empirical tests have repeatedly been carried out following the

modifications made to the empirical methodology. They range from cross-section tests

to highly sophisticated time-series econometrics.

Two interpretations of the law have been used: one based on the absolute

expansion of public spending in relation to income, and another based on relative

expansion. These have been utilized to formulate six different versions of the law

(Mann, 1980): the traditional version by Peacock and Wiseman (1967) PE=f(GDP), where

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PE is public expenditure and GDP is gross domestic product; Pryor’s version (1968),

C=f(GDP) where C is consumption expenditure; the version by Goffman (1968),

PE=(GDPcap) where GDPcap is the gross domestic product per capita; the version by

Musgrave (1970), PE/GDP=f(GDPcap); the version by Gupta (1967) and Michas (1975),

PEcap=f(GDPcap) and, finally, a modified version of Peacock and Wiseman’s formulated

by Mann (1980), PE/GDP=f(GDP).

An important issue is the measure of public spending which is employed. In

accordance with Wagner, all branches of government should be taken into consideration

(central and local) as well as all of their possible expenditures. From the point of view

of Peacock and Scott (2000), the interpretations made by various authors are erroneous

in that they believe Wagner clearly states that public companies, specifically public

utilities, must be considered as part of the public sector.

Published studies related to this subject have carried out empirical tests of the

law in two different ways: for only one country over time, and second, for various

countries at a certain point in time, although there is a chronological order utilized to do

so. Seminal studies on the subject (Martin and Lewis, 1956; Williamson, 1961; Thorn,

1967; Gupta, 1967; Musgrave, 1970; Gandhi, 1971, Goffman and Mahar, 1971, Bird,

1971; Gray, 1976) and other more recent ones (Lowery and Berry, 1983; Abizadeh and

Gray, 1985; Ram, 1987) utilize transversal or cross-section data to compare different

countries with different degrees of growth. Some of the first studies to use time series

(Tim, 1961; Andic and Veverka, 1964; Musgrave, 1970; Bird, 1971) analyze different

statistics for public expenditure and income per capita by making comparisons among

them or measurements of the elasticity of public expenditures in relation to GDP. Gupta

(1967), Henning and Tussing (1974), and numerous authors, use various functional

forms that are almost always bivariate, normally taking into consideration logarithms

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for both variables, thus carrying out the test with ordinary least squares or a related

variant.

The analyses which use time series can be divided into two types: those that

consider changes in the ratio of government spending to national income or that

compare said ratio with changes in per capita income over time and those that make a

regression from a measure of national income (total or per capita) to a measure of public

spending (normally total, per capita or as a ratio of income). Among the second type we

can find those that utilize ordinary least squares, or some variant, for the test, and those

that consider the inherent characteristics of time series and utilize unit root and

cointegration analysis. Regarding said breakthrough, it was necessary to elaborate the

corresponding econometric theory, and it was not until the 1990s (Henrekson, 1990,

1993; Murthy, 1993) that this method was used to test Wagner's Law for the first time.

This type of analysis improved the reliability of the most recent works, allowing a

distinction between long-term relationships and the short-term dynamic relationship

(Henrekson (1990, 1993), Gemmell (1993), Hondroyiannis and Papatreou (1995),

Biswal et al. (1999), Burney and Musallam (1999), Petry et al. (2000), Legrenzi (2000),

Karagianni et al. (2002), Burney (2002), Chang (2002), Chang et al. (2004), Wahab

(2004), Akitoby et al. (2006)).

Although Peacock and Scott (2000) consider that Wagner would have been

satisfied by merely using the cointegration among the variables, for Oxley (1994) the

existence of a unidirectional Granger causality relationship is necessary; more

specifically one running from “measure of income to measure of public expenditure,” in

addition to cointegration between variables and income elasticity greater than one.

The most recent works consider, along with Wagner’s Law, the Keynesian

hypothesis, which states that when government expenditures increase so does national

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income. Therefore, studies published as of 1995 almost all implement the direction of

the causality in order to verify if Wagner’s Law or its antithesis, the Keynesian

hypothesis, hold true in one country or specific group of countries.

The development of the cointegration techniques utilizing panel data has

allowed tests of the law to be carried out for groups of countries (Lamartina and

Zaghini, 2008) and for individual countries (Narayan et al., 2008). Furthermore, it is

notable that numerous works have also considered the possibility of structural breaks

and regime shifts in the data (Priesmeier and Koester, 2012; Richter and Paparas, 2013;

Kuckuck, 2014).

In spite of the numerous empirical tests of the law, very few have been

conducted utilizing the disaggregation of the public sector (Chletsos and Kollias, 1997

Asseery, Law and Perdikis, 1999: Kucukkale and Yamak, 2012; Magazzino, 2012,

among others), yet all of them use the economic classification of public spending. In

this article, we consider disaggregated public spending in its functional classification.

As previously mentioned in the introduction, in our opinion, and bearing in mind

Wagner’s reasoning, the expenditures which constitute the welfare state (education,

healthcare, and social expenditures) must grow at a greater rate in relation to GDP than

all other expenditures. The reason for this stems from the demand of a population with

greater wealth, one interested in receiving special or social goods from the government,

deeming that said goods should be financed by the public sector so they may be

obtainable to the entire population.

3. Empirical test of the model

For a better understanding, we present a brief summary of the recent evolution of

the Spanish economy. After the 1950s, a period of rapid growth took place, driven by

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the Stabilization Plan of 1959. In 1975, Spain’s dictatorship ended and was substituted

by a democratic system. This transition process culminated in 1986 with the entry of

Spain into the European Economic Community (EEC), which would later become the

European Union (EU). At that time, massive demand began for welfare state services

which caused an increase in public spending, while, at the same time, adjustments to the

economy were also being made in order to comply with the conditions of entry into the

CEE. In the years prior to 1992, a period of economic crisis took place due to a break in

the construction bubble owing to the Expo ‘92 in Seville and the Olympic Games in

Barcelona. The beginning of the 21st century was a period of strong expansion, so much

that Spain’s GDP per capita reached the European average. This period came to an

abrupt end in 2008 due to the recession which occurred in the EU and the United States;

a situation that was made even worse in Spain as a result of a sudden and new real estate

bubble. This situation caused a drop in GDP and a decrease in public spending. Also,

the EU demanded restrictions on public expenditures.

In terms of considering social expenditures, there are several dates in recent Spanish

politics that might have influenced said expenditures, thereby causing a structural break.

With regard to education, two laws must be considered: The General Education Law

from 1970 (LGE, in Spanish), which instated compulsory education until 14 years of

age, and the General Organization of the Education System Act (LOGSE, in Spanish)

from 1989, which extended the mandatory period of education to 16 years of age. As for

education and social expenditure, after the Spanish Civil War, social spending grew but

remained quite stable until 1966. It was not until 1967 that it began to grow rapidly,

coinciding with a rapid convergence process with Europe.

In our analysis we utilize data from Spanish Public Administrations (PP.AA.) for

the period 1924-2015. These data were taken from various sources. For data

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corresponding to social expenditures, we used information from the publication by

Espuelas (2013) until 2005, and from 2006 to 2015 these data come from the General

State Comptroller (IGAE, in Spanish) and the National Statistics Institute (INE, in

Spanish). All other expenditure data were taken from Comín and Diaz (2005) until 2001

and from IGAE and INE from 2002 to 2015. Data on GDP were obtained from Prados

and Rosés (2005) until 2000 and from INE from 2001 to 2015. All data are expressed in

millions of current pesetas1. Since there are both arguments in favor of and against using

current and constant values, and bearing in mind the difficulty of finding suitable

deflators for such a long period of time among all the variables, we opted for current

values, which means the analysis also considers the possible price effect. As for the

empirical test, we follow the standard process of considering data logarithmically,

allowing us to directly obtain the income elasticity as the GDP coefficient.

Table 1 specifies the reference variable that was used.

Table 1. Variables of the different models

Log GDP Gross Domestic Product

LogEXP Public Expenditure

LogEDU Education

LogHE Health

Log SE Social Expenditures

LogDEF Defense

LogGS General Public Services

Log ES Economic Services

Graph 1 displays the variables that were considered.

Graph 1. Variables of the different models

1 National currency until implementation of the Euro. The conversion of the data from euros to pesetas was done on the basis that one euro equals 166.386 pesetas.

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Out of the six different formulations of the law, we choose that which relates

public spending to GDP (Peacock and Wiseman, 1967). We expect that the elasticity of

public spending with regard to GDP is greater than one. The equations are written both

logarithmically and generally as follows

log X=α+ βLogY (1)

Where X represents the adopted form of spending and Y is GDP.

This study conducts the empirical test using the customary unit roots and

cointegration methodology, although in this case the existence of structural breaks in the

data series should be taken into account due to frequent political and economic changes.

The empirical analysis must be carried out carefully to verify the nature of the

series because, if they are not stationary, problems may arise in the estimation of the

regression equation coefficients. Valid estimations for the seven models require that

data be stationary (integrated zero-order), or, if they are not stationary (integrated first-

order), they must be cointegrated. More specifically, the first step is to verify whether

the variables are stationary or whether they have one or more unit roots. If they are

integrated, an analysis must be made to verify the possible existence of cointegration

between the two. If they are cointegrated, the relationships or cointegration equations

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have to be estimated. These cointegration equations specify the long-run relationships

between the variables. According to Engle and Granger (1987), if two variables are

integrated of order one I (1) and are cointegrated, there must be a uni or bi-directional

Granger causality relationship between the I (0) variables.

Firstly we apply the standard unit root tests: Augmented Dickey-Fuller,

ADFGLS, Philip-Perron test for those in which the null hypothesis is the existence of a

unit root, and that of Kwiatkowski, Phillips, Schmidt and Shin (KPSS) where the null

hypothesis is that the series is stationary.

Table 2. Unit root of variables in level2

Variables ADF p-value ADFGLS C.V. 5% PP p-value KPSS C.V. 5%

LogEXP 0.036 0.96 -0.25 -1.94 -0.09 0.94 1.24 0.46

LogGDP -0.59 0.56 -0.57 -1.94 -0.42 0.90 1.23 0.46

LogEDU -1.43 0.52 0.26 -1.94 -0.77 0.81 1.23 0.46

LogHE -1.40 0.57 0.36 -1.94 -1.38 0.58 1.25 0.46

LogSE -1.26 0.64 1.40 -1.94 -1.07 0.72 1.29 0.46

LogDEF 0.43 0.98 1.55 -1.94 0.43 0.98 1.15 0.46

LogGS -0.95 0.76 0.02 -1.94 -0.85 0.80 1.21 0.46

LogES -0.52 0.88 2.09 -1.94 -0.51 0.88 1.21 0.46

Table 3.Unit roots of variable in first differences

Variables ADF p-value ADFGLS C.V.5% PP p-value KPSS C.V. 5%

2 In cases where we have the p-value, we display it in the table; otherwise, we use the critical value. Critical Value ADF -2.915, DFGLS -1.94, PP -2.915 for constant KPSS 0.463(LOG EXP, LOGGDP LOGEDU, LOGHE, LOGSE, LOGDEF, LOGSEG). In all cases, a constant was used in the process of generating data since the corresponding t-statistic accepts the null that the trend coefficient is equal to zero. The number of lags was selected using AIC and SIC criteria.

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LogEXP -6.47 0.00 -1.28 -1.90 -6.45 0.00 0.28 0.46

LogGDP -5.13 0.00 -3.26 -1.90 -5.06 0.00 0.29 0.46

LogEDU -3.64 0.00 -2.71 -1.90 -6.94 0.00 0.35 0.46

LogHE -3.15 0.02 -2.67 -1.90 -4.12 0.00 0.35 0.46

LogSE -6.00 0.00 -4.56 -1.90 -5.97 0.00 0.43 0.46

LogDEF -5.90 0.00 -2.52 -1.94 -9.14 0.00 0.29 0.46

LogGS -3.18 0.02 -2.20 -1.94 -8.14 0.00 0.37 0.46

LogES -8.71 0.00 -8.52 -1.94 -8.71 0.00 0.19 0.46

From the results above, it can be deduced that the variables are integrated of order one

I(1) as they have a unit root in levels while they are I(0) in differences.

Consequently, we can analyze whether the series are cointegrated, and we proceed by

applying the standard Johansen-Juselius (JJ) test. Table 4 displays the results obtained.

Table 4. Johansen-Juselius cointegration test

Model3 Eigenvalue Trace

Statistic

Critical Value p-value Max Eigenvalue

Statistics

Critical Value p-value

Model 1 r=0 0.24

r≤1 0.06

29.04

5.53

20.26

9.16

0.00

0.23

23.50

5.54

19.89

9.16

0.00

0.23

Model 2 r=0 0.13

r≤1 0.005

12.47

0.33

12.32

4.13

0.04

0.62

12.14

0.33

11.22

4.13

0.03

0.62

Model 3 r=0 0.23 27.89 20.26 0.003 21.99 15.89 0.00

3 Model 1 corresponds to GP, 2 to EDU, 3 to HE, 4 to SE, 5 to DEF, 6 to GS, 7 to ES. The number of lags was calculated using AIC, SIC and HQ criteria. In all cases VAR was utilized with two lags, meaning the ECM has a lag as well.

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r≤1 0.028 5.99 9.16 0.19 5.99 9.16 0.19

Model 4 r=0 0.17

r≤1 0.10

24.84

8.80

20.26

9.16

0.01

0.06

16.04

8.80

15.89

9.16

0.04

0.06

Model 5 r=0 0.43

r≤1 0.06

44.94

0.08

20.26

9.16

0.00

1.00

44.85

0.08

15.89

9.16

0.00

1.00

Model 6 r=0 0.41

r≤1 0.14

44.73

1.19

15.50

3.84

0.00

0.27

0.00 15.89

9.16

0.00

0.48

Model 7 r=0 0.24

r≤1 0.08

29.61

6.85

20.26

9.16

0.00

0.13

22.75

6.85

15.89

9.16

0.00

0.13

If the variables are cointegrated, then there is a long-term relationship between

them. In order to characterize the long-term balance relationships and short-term

adjustment processes among the various definitions of public spending and GDP, we

construct an error correction model (ECM) for the first version of the law in the

following manner

∆ LnGP t =θ0+∑k

θ1k ∆ LnGPt−k +∑k

θ2k ∆ LnPIBt −k+θ3 ECM 1t−1+μ1t (2)

where ∆ is the first difference of the lag operator, k is the lag length. ECM represents

the error correction term, specifically ECM 1t−1=LnGP t−1−φ LnPIBt−1−cte. The

parameter θ3 is the adjustment speed to the long-term balance and μ1 t is statistical

noise.

In parallel to the previous model, we can also consider the error correction model for

GDP by relating it to the remaining variables.

∆ log PIBt = β0+∑k

β1k ∆ LnGPt−k +∑k

β2 k ∆ LnPIBt−k+β3 k ECM 2t−1+μ2 t (3)

Similarly, we consider all other equations for the different types of spending.

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The ECM confirms that GDP causes long-term spending or vice versa as long as

the coefficient of the error correction term is negative and statistically significant, even

if the other coefficients are not. A statistically significant negative value in the

adjustment parameters implies long-term causality as described by Engle and Granger

(1987). To test short-term causality we must consider the joint significance of the

coefficients of the lagged variables. As previously highlighted, the effect of GDP on

public spending would confirm Wagner’s Law while the effect public spending on GDP

would confirm the Keynesian hypothesis.

The following table displays the cointegration equations and the error correction

models of the seven versions of Wagner’s Law considered.

Table 5. Cointegration equations

Dependent variables Independent variables

LogGDP Constant

LogEXP 1.12 (57.28) 3.04 (9.7)

LogEDU 1.20 (35.61) 6.36 (11.46)

LogHE 1.33 (72.59) 9.41 (30.52)

LogSE 1.35 (48.00) -8.47 (17.82)

LogDEF 0.86 (65.59) 2.53 (10.07)

LogGS 0.94 (100) -1.66 (10.39)

LogES 1.08 (39.52) 5.45 (12.10)

Table 6. Error correction model

Dependent

variables

Independent variables

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∆Log GDP-1 ∆LogEXP-1 Cointegration-1 R2 F-statistics

∆LogEXP 0.37 (1.98) 0.08 (0.8) -0.11(-3.58) 0.37 24.04

∆Log GDP-1 ∆LogEDU-1 Cointegration -1

∆LogEDU 0.54 (3.23) 0.09 (0.88) -0.08 (-3.41) 0.28 15.78

∆Log GDP-1 ∆LogHE-1 Cointegration -1

∆LogHE 0.54 (3.36) 0.6 (5.99) -0.05 (-1.46) 0.46 22.93

∆Log GDP-1 ∆LogSE-1 Cointegration -1

∆LogSE 0.62 (2.98) 0.35 (3.17) -0.11 (-0.3) 0.065 2.8

∆Log GDP-1 ∆LogDEF-1 Cointegration -1

∆LogDEF 0.71 (2.35) -0.14 (-1.2) -0.03 (0.08) 0.11 3.28

∆Log GDP-1 ∆LogSEG-1 Cointegration -1

∆LogGS 0.26 (1.08) -0.01 (-0.08) 0.08 (1.77) 0.06 1.82

∆Log GDP-1 ∆LogAsec-1 Cointegration -1

∆LogES 1.20 (4.16) 0.014 (0.13) -0.18 (-2.58) 0.14 4.45

As for the test of the Keynesian hypothesis, we calculate the cointegration

equations and the error correction model considering GDP as a dependent variable and

the different versions of spending as independent variables. In the following table we

show the ECM coefficient, as well as its R2, and the F statistic which enables us to

examine short-term causality.

Table 7. Cointegration between GDP and the rest of variables

Cointegration coefficient-1

Eq2

R2 F

LogEXP 0.17(3.69) 0.39 17.08

LogEDU 0.11 (2.07) 0.31 11.54

LogHE 0.09 (1.60) 0.47 13.32

LogSE 0.017 (0.30) 0.17 8.31

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LogDEF 0.027 (0.72) 0.29 7.03

LogSEG -0.039 (-0.73) 0.06 1.82

LogAsec 0.18 (2.58) 0.26 5.34

The results obtained in the cointegration equations and the ECM fulfill the

hypotheses formulated at the outset of this work. Education, healthcare, and social

expenditures all fulfill the law with elasticities that are greater than one and also rather

high. If we follow the opinion of Peacock and Scott (2000), Wagner’s Law is fulfilled

for total spending, education, healthcare and social services, in addition to economic

affairs. However, this is not the case for defense and general services. Moreover, in

accordance with our initial suppositions, the coefficients of special goods are greater

than those of total spending, yet those of the latter are greater than all other types of

expenditures.

If we consider the causality relationship between the different models, in line

with the suggestion of Oxley (1994), we obtain causality from GDP to public spending

in its totality and to education and healthcare (at a 10% confidence level). There is no

causality between GDP and the remaining variables, nor does it exist in the opposite

direction. Precisely as the values of the short-term F statistic indicate, there is only a bi-

directional causality relationship between GDP and total spending, GDP and education

spending, and GDP and healthcare spending. Causality exists from defense and

economic affairs to GDP but not in the opposite direction.

With the aim of reinforcing the previous results, we consider the impulse

response function and variance decomposition. The former measures the effect of a

variation in the error term on the current and future values of the variable itself and the

future value of another variable. The latter measures the percentage of variability of

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each variable which is explained by the disturbance of each equation and can be

interpreted as the relative dependence that each variable has over the rest of the

components.

By observing this group of graphs, it can be seen how the response to the

impulse of EXP in its totality decreases for each period with respect to EXP itself, yet

there is an increase in relation to GDP. The response of GDP to EXP is almost null at

first and becomes negative as of the fourth period. In terms of variance, it can be seen

how the relative dependence of EXP increases with respect to GDP but not vice versa4.

Consequently, if we take causality into consideration, Wagner’s Law is fulfilled

for public spending, healthcare and education, but in no case does this occur for the

Keynesian hypothesis. This result has important implications for economic policy as

fulfillment of WL means that an increase in GDP produces an even greater percentage

increase in EXP, since income elasticity is greater than one. If there is no increase in

revenue using standard tax mechanisms, we would then find a situation of fiscal deficit

which would simultaneously provoke an increase in public debt. In contrast, if the

Keynesian hypothesis is fulfilled, public spending can act as a type of stabilization

mechanism for the economy, particularly during economic crises like the current one,

since increased spending would produce an increase in GDP with the possibility of

creating jobs and increasing private consumption.

4. Summary and conclusions4 The results are similar for the rest of the variables.

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This paper conducts an empirical test of Wagner’s Law using a functional

classification of disaggregated expenditures for Spain for the period 1924-2015. We

utilize a methodology based on unit roots, cointegration and error correction models.

The results obtained reveal the importance of social expenditures on public spending in

Spain but also the importance of said expenditures on economic growth itself. The

increase in social expenditures in recent years follows a trend in Europe in which the

public sector invests in special goods with the belief that all individuals, simply for

being a member of society, must have the right to education and healthcare, and, in the

cases of those most in need, to housing and other social benefits, such as non-

contributive pensions for those that have not contributed to social security. Furthermore,

the result reveals the influence of education and healthcare on social progress and

economic growth. Greater education of the public contributes to better quality of work,

greater productivity and, in some cases, greater efficiency. A healthy population, free of

diseases with a sound prevention system, is also more efficient and productive than one

with health problems.

Since the 2010 budget, public spending in Spain has fallen, so much so that past

growth rates greater than 7% dropped to 1% in 2015 and even lower in years prior.

Naturally, this had a considerable effect on the various expenditures, above all on social

expenditures which saw very low and even negative growth. As for education, an 8%

decrease was registered in 2012 with respect to the previous year, albeit there was a

very modest 3% increase in 2015 with respect to 2014. This effect on spending is also

observed for healthcare and to a much lower extent on social aid, which remains

stagnant. However, expenditures on defense, general services and economic affairs have

evolved independently of all other public spending. Similarly, GDP saw negative

growth from 2010 onward, until 2015 when it registered slightly positive growth.

19

Nevertheless, the EU enacted formidable impediments which hindered the growth of

Spain’s GDP. This was done to ensure Spain conformed to the Stability and Growth

Pact, effectively obliging Spain to maintain fiscal deficits below 4.6%, although it is

currently at 5.1% with a public deficit that surpasses 100% of its GDP.

This scenario raises serious doubt about the possibilities of growth of public

spending on education and healthcare. Moreover, as the aforementioned expenditures

have a feedback effect, this situation will prevent adequate growth of the Spanish

economic from taking place.

On the other hand, as of 2008, the year of the Great Recession, the public sector was

obliged to rescue most of the Spanish bank system (mainly savings banks). The cost to

the public treasury was over 60 billion euros, which were diverted from other funds

that, most likely, would have contributed to positive growth of the Spanish economy.

Finally, it is necessary to mention the intervention of the European Central Bank,

which bought a great deal of public debt from indebted European countries, among

them Spain. This action was a great relief in the service of debt and allowed new

emissions, substituting previous ones with lower interest rates.

Thus, the Spanish economy and public sector are now surrounded by uncertainty,

and there is no clear idea about what policies, be they restrictive or expansionary,

should be instated in the near future.

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