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: [email protected]
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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