Download Chandika Gunasinghe

Fiscal Policy, Economic Growth and Income Inequality: A Case Study on Australia
Chandika Gunasinghe1
PhD Scholar, Department of Accounting, Finance and Economics, Griffith University
Abstract
This paper investigates the impact of fiscal policy on economic growth and income inequality
in Australia under a structural vector autoregressive (SVAR) framework using annual data
from 1962 to 2012. Our empirical results reveal that: (1) both tax- and debt-financed fiscal
policies have trade-offs between economic growth and net income equality; (2) direct tax
system is progressive whereas indirect tax system plays a neutral role in the determination of
income redistribution; (3) the negative effects of deficit financing on economic growth
outweigh its positive effects; and (4) financing government expenditure through indirect taxes
does not create a trade-off between equity and efficiency.
Key words: Fiscal policy, economic growth, income inequality, debt- and tax financing fiscal
policies, SVAR, Australia.
JEL Classifications: E62, F43, D63, C32, C54, O23, O43
1
This paper is based on one Chapter of my PhD thesis. I would like to thank my academic supervisors
for their guidance of my thesis and their invaluable comments and suggestions in earlier drafts of
this paper.
1
FREFACE
Thesis title: Measuring the effects of fiscal policy on economic growth and income inequality
Supervisors: Prof. E.A Selvanathan, Dr. Athula Naranpanawa, Assoc. Prof. John Forster and
Dr. Alex Robson
The increasing income disparity across and within countries around the world has given rise to
broader attention to the role of fiscal policy in mitigating this complex problem. However, the
conventional wisdom of economic theory postulates that this objective can only be achieved at
a cost of economic efficiency, and hence using fiscal policy to meet such a target has an
unavoidable trade-off between equity and efficiency. On the other hand, according to politicoeconomy growth models, the determination of fiscal policy itself is influenced by income
inequality, which in turn is affected by its outcomes. Therefore, it is essential to measure the
impact of fiscal policy on economic growth and income inequality in a context where these
three macroeconomic variables are hypothesised to evolve interdependently as discussed in
different economic models.
Based on eight economic and politico-economy growth models, the present study develops a
triangular framework in which economic growth, income inequality, and fiscal policy are
determined interdependently. Based on this theoretical framework and on published empirical
research, the study constructs a simultaneous equations system (SES) in which each variable
mentioned above is formulated as a function of other two endogenous variables and a set of
controlled variables that are widely used in the growth literature. The SES is then used to derive
twelve testable hypotheses that underlie answers to the study’s research questions. The validity
of these hypotheses are tested under SES using panel data consisting of time period 1980–2011
for 28 developed and 45 developing countries. A structural vector autoregressive (SVAR)
model is employed to test two hypotheses at individual country level based on the annual time
series data from 1962 to 2012. This paper is based on one individual country study.
The thesis will take the following structure:
Chapter 1: Introduction
Chapter 2: Literature Review
Chapter 3: Development of Econometric Model and Hypothesis
Chapter 4: Research Methodology
Chapter 5: A. Results and Analysis of Panel Data Model
B. Results and Analysis of Time Series Model
Chapter 6: Conclusions
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1. Introduction
We investigate the impact of fiscal policy on economic growth and income inequality in
Australia using a structural vector autoregressive (SVAR) model. Income inequality has
increased at a considerable level in Australia irrespective of the rising average standard of
living and the sustained economic growth over the last several decades (Herault and Azpitarte
2015; Wilkins 2014). The most important fact is that income inequality has increased in a
context where a number of fiscal policy reforms such as changes to income tax rates and
welfare payments implemented during the last three decades (Herault and Azpitarte 2015). The
increased income disparity has therefore given rise to a wider attention to reconsider the role
of tax and expenditure components of fiscal policy in achieving two countervailing objectives
simultaneously: equity and economic growth. However, there are no consensus among
economists on the signs and the magnitude of the effects of fiscal policy on income inequality
and on economic growth. Therefore, the conventional wisdom of economic theory postulates
that equity objectives can only be achieved at a cost of economic efficiency and hence the use
of fiscal policy to meet such a target has an unavoidable trade-off between equity and
efficiency.
Furthermore, politico-economy growth theories claim that fiscal policy itself is influenced by
income inequality, which in turn is affected by its outcomes. Therefore, based on different
theoretical models that claim for different causal relationships between economic growth, fiscal
policy and income inequality, we construct a tri-angular framework where these three
macroeconomic variables are determined interdependently. We use a SVAR framework to
model the joint evolution of these three macroeconomic variables, their dynamic interactions
and feedback effects on each other in an economically meaningful manner. After reviewing
current literature on this topic, we found that not much research has been done on this topic in
relation to Australia in particular. Hence, the unique contribution of this paper is to fill this
vacuum in the existing empirical literature.
The specific contributions of this paper take three forms. First, we incorporate government
fiscal policy into the relationship between economic growth and income inequality in the
SVAR frame work. Following the strategy used in Miller and Russek (1997), Bleaney et al.
(2001), Kneller et al. (1999), and Muinelo-Gallo and Roca-Sagales (2011; 2013), we exclude
one fiscal policy component to eliminate possible misspecifications errors that could arise in
the fiscal policy components. The excluded fiscal policy is considered as the implicit financing
element. To the best of our knowledge, the use of this strategy in a SVAR approach with
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Australian data is novel. Second, this study helps to identify the potential effects and policy
implications of different fiscal policy strategies in a context where economic growth, fiscal
policy and income inequality are evolved interdependently. Third, in this study we test
endogenously determined structural breaks in those variables that are used in the SVAR model
and find evidences supporting to such breaks. Our analysis is done after removing the break
effects of relevant series and hence the reliability of findings is expected to be at a higher level.
Hence, this can be considered as another contribution to the Australian literature.
The remainder of the paper is organised as follows. Section 2 presents the literature review in
which we study both theoretical and empirical studies that are directly related to the topic of
the study. Section 3 presents the research methodology to be used and discuss of the data
requirement to investigate our research question. Section 4 presents the results. Finally,
concluding remarks are given in Section 5.
2. Literature review
Review of theoretical studies
There are several theoretical approaches which claim different channels through which fiscal
policy, income inequality and economic growth are interrelated. Ricardian equivalence
hypothesis states that fiscal policy does not have significant positive effects on aggregate
demand and hence on economic growth. This occurs due to the neutral effects of debt- or tax
financing strategies to stimulate aggregate demand as consumers tend to internalize
government fiscal strategy in making their decisions to save money for future tax payments. In
contrast to Ricardian hypothesis, Keynesian economic model claims that government
investment and consumption expenditure which are financed by debts stimulate aggregate
demand, which in turn promote economic growth. These two traditional models provide an
empirically testable hypothesis on the effect of fiscal policy on economic growth. Kuznets
hypothesis postulates that economic growth leads to increase in income inequality at the early
stages of development and to decrease it at the later stages of development (Kuznets 1955,
1960). Neo-classical-growth models introduced by Kaldor (1956, 1957), Stiglitz (1969),
Bourguignon (1981), and Mirrlees (1971) imply a positive relationship with unidirectional
causality from income inequality to economic growth. These models also suggest a negative
relationship with unidirectional causality from fiscal policy to economic growth. The main
drivers of the former are the presence of marginal propensity to savings and the investment
indivisibilities (Kaldor 1956, 1957; Stiglitz 1969; Bourguignon 1981) whereas the negative
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effects of taxation to work, save and invest (incentive considerations) are the main drivers of
the latter (Mirrlees 1971).
Public-finance endogenous growth model introduced by Barro (1990), and Barro and Sala-iMartin (1992, 1995) claim that economic growth and fiscal policy have a bi-directional
causality. According to this model, the causality running from fiscal policy to economic growth
has both negative and positive effects. The signs of these effects on economic growth depend
on the types of government expenditures (i.e. productive vs. non-productive), the way they are
financed and the size of the government relative to GDP. Under this model, tax income is also
affected by the rate of economic growth through its effects on the tax base. Therefore, a
causality is also expected to run from economic growth to fiscal policy with having positive
effects.
First-generation politico-economy growth models introduced by Alesina and Rodrik (1994),
Persson and Tabellini (1994, 2000), and Bertola (1993) suggest a unidirectional causality
running from income inequality to economic growth through fiscal policy. These models
emphasize that the extent of income inequality, through a political mechanism, generates
positive effects on the rate of redistribution that is associated with higher taxation. These higher
taxes, through an economic process, are expected to have negative effects on economic growth.
Furthermore, some theorists such as Bertola (1993) in the group of the first-generation politicoeconomy growth models suggest that economic growth reduces income inequality when
investment enhancing policies are implemented as those policies have positive effects on
optimal savings and investment decisions. Under the second-generation politico-economy
growth models, Benabou (1996, 2000, and 2004) emphasizes two types of causalities: (1) a bidirectional causality between income inequality and fiscal policy, and (2) a unidirectional
causality from fiscal policy to economic growth. This two-way causality is mainly driven by
accumulation and political mechanisms.
New endogenous growth models with imperfect capital markets introduced by Galor and Zeira
(1993), Aghion et al. (1999), Saint-Paul and Verdier (1993), Galor and Moav (2004) and
Aghion et al. (1999) postulate that income inequality negatively affects economic growth. The
main drivers of this causality are the presence of diminishing returns to individual investments
and inter- and intra-generational transmission of wealth. Aghion et al. (1999) suggest also that
the skills-biased technological development and non-inclusive growth policies lead to increase
in income inequality. Saint-Paul and Verdier (1993) and Galor and Moav (2004) claim that
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redistribution in the form of the provision of public education improves the potentials for
economic growth and reduces income inequality through improved stock of human capital.
Based on the theoretical claims of above studies, they can be classified into four groups: (1)
models on the relationship between fiscal policy and economic growth, (2) models on the
relationship between fiscal policy and income inequality, (3) models on the relationship
between income inequality and economic growth and (4) models on the relationship between
fiscal policy, economic growth and income inequality. The published empirical studies are
reviewed according to the above classification in the next section.
Review of empirical studies
Having incorporated government budget constraint into the growth regression, Miller and
Russek (1997) estimate a fixed and random effect model with data from 16 developing
countries and 23 developed countries over the period from 1975 to 1984. Specifically, they
examine the effects of fiscal policy on economic growth. Miller and Russek (1997) find that
an increase in debt-financed government expenditure retards economic growth in developing
countries whereas an increase in tax-financed government expenditure stimulates it. In contrast,
the economic growth in developed countries is not affected by the former mode of financing
whereas the latter retards it. Kneller et al. (1999) examine the impact of fiscal policy on
economic growth in a panel of 22 OECD countries during 1970-1995 based on five year
averages of data. The main objective of their study is to test the validity of public policy
endogenous growth theory by avoiding the biases to the parameter estimates attached to the
implicit financial variables. They find strong evidence supporting to the public finance
endogenous growth model when the government budget constraint is correctly specified.
Specifically, they find that distortionary taxation is harmful to economic growth whereas nondistortionary taxation makes no impact on the growth in a panel of 22 OECD countries.
Baldacci, et al. (2004) examine the relative effectiveness of investment and factor productivity
as two economic channels through which fiscal policy transmits its effects on economic growth
of selected 39 low-income countries over the period from 1999 to 2001. They find that, in lowincome countries, the factor productivity is the main channel through which fiscal policy
transmits its effects on economic growth. Private investment, which is the main fiscal policy
transmission channel in high-income countries, is four times less effective than the factor
productivity as a channel for enhancing economic growth.
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Perotti (2004) examines the effects of fiscal policy on economic growth, inflation and interest
rates using a SVAR model for 5 OECD countries for the quarterly data from 1960:1 to 2001:4.
The countries considered include Australia, Canada, West Germany, United Kingdom and
United States. Perotti finds that a shock to net taxes and government spending generate
respectively positive and mixed results for the per capita income in most countries studied.
Kamps (2005) estimates a SVAR model with annual time series data from 1960 to 2001 for 22
OECD countries to investigate the impact of public capital on economic growth and
employment. Kamps (2005) finds that the response of private capital to a shock in public capital
is positive for all the OECD countries concerned. The author also concludes that the public
capital is productive but it is insignificant in terms of employment generations. Based on a
SVAR model with data from 1970 to 2005, Ramos and Roca-Sagales (2008) examine the longterm effects of fiscal policy on economic growth and income inequality in the UK. They find
that an increase in public spending and direct tax income decrease both economic growth and
income inequality whereas an increase in indirect tax income increases income inequality. They
also find evidence for the presence of the standard efficiency-equity trade-off. Using a
multivariate co-integration method and a long-run Granger non-causality test, Gaston and
Rajaguru (2009) investigate the determinants of income inequality in Australia during the
1970-2001 period. Authors find that improved social globalization and de-unionisation (union
memberships divided by labour force) increase income inequality whereas increased terms of
trade and higher minimum wages reduce inequality in Australia. They also find no evidence in
support of the presence of Kuznets curve relationship between economic growth and income
inequality in Australia.
Using a SVAR approach for the quarterly data covering 1990:1-2008:4 period, Kofi (2011)
investigates the impact of fiscal policy on real economic growth and interest rate in South
Africa. The estimated impulse response functions reveal that the response of economic growth
to shocks in fiscal variables seems modest and persistent whereas that the response of interest
rate to above shocks appears to be substantial but it seems to be temporary. Muinelo- Using
an unbalanced panel of 43 upper-middle and high income countries for the period from 1972
to 2006, Gallo and Roca-Sagales (2011) estimate a simultaneous equation model to examine
the impact of different fiscal policy components on economic growth and income inequality.
They find evidence for the presence of a trade-off between efficiency and equity. This is due
to the fact that an increase of current expenditure (the size of the government) reduces income
inequality at the cost of economic growth (efficiency). This impact is severe when the current
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expenditure is financed by increasing direct taxes. Muineolo-Gallo and Roca-Sagales (2011)
conclude that the only fiscal policy that helps policy makers to resolve this confrontation is the
public investment. The reason is that, irrespective how it is financed (by increasing either direct
or indirect taxes) an increase of public investment leads to decline income inequality without
harming economic growth.
Using an unbalanced panel data of 21 high-income OECD countries for the period from 19722006, Muineolo-Gallo and Roca-Sagales (2013) estimate a simultaneous equation model to
examine the evolution of economic growth, income inequality and fiscal policy
interdependently. They find evidences on the support for the presence of a trade-off between
equity and efficiency as distributive expenditure and direct taxes (regardless of how they are
financed) reduce both economic growth and net income inequality. Davtyan (2014) examines
the interrelationship between economic growth, income inequality and fiscal performance
(government net lending/borrowing) for the UK, the USA and Canada over the period from
1960 to 2010 based on annual time series data. Davtyan (2014) finds that (1) government
spending reduces growth through crowding out, and worsens fiscal performance, and (2) the
dominant impact of indirect taxation leads to raise income inequality. Using of 21 developed
and 41 developing countries over the period from 1980 to 2000, Alcantar-Toledo and Venieris
(2014) examine the effects of fiscal policy on economic growth in the presence of sociopolitical instability (SPI). Specifically, the aim of their study is to investigate how and to what
degree SPI, fiscal instruments (government spending and taxes) and income distribution affect
economic growth. They argue that the uncertainty generated by SPI makes adverse effects on
investment and saving decisions, which in turn retards economic growth. They also find that
socio-political instability (SPI), fiscal policy and income distribution affect the rate of
economic growth.
Based on sign-restricted SVAR models, Jha, et al. (2014) examine the countercyclical impact
of government tax and spending on economic growth in a selected 10 emerging Asian countries
during 1977-2009 period. Specifically, the main objective of the study is to test what type of
fiscal policy, whether it is tax cuts or higher spending, stimulates future economic growth
especially in the presence of business cycles. They find that fiscal policies with tax cuts have
greater countercyclical impacts on output than those policies with increased government
spending. Based on a SVAR model with quarterly data from 1978Q1 to 2014Q1, Carmignani
(2014) finds that increase in government consumption expenditure increases GDP and full time
employment, but not necessarily total employment in Australia from. The author further finds
8
that the effects of government consumption on output and employment are higher when the
SVAR model is re-estimated for a sample that excludes global financial (GFC) crisis period
and subsequent years. Based on a regression model with annual time series data from 1980 to
2011, Bal and Rath (2014) examine the effects of public debt (domestic and external debt) on
economic growth in India. They find that higher public debt, irrespective its sources, retards
economic growth in the long run. Panizza and Presbitero (2014) investigate whether public
debt has a causal effect on economic growth in 17 OECD countries. They find that public debt
and economic growth is negatively correlated but there is no evidence that public debt has a
causal effect on economic growth.
Liu and Martinez-Vazquez (2015) examine to what extent the design of tax structures of both
developed and developing countries determines the potential trade-off between economic
growth and income inequality. Their study covers 150 countries during 1970 -2009 period with
five-year-averages of data to take into account for business cycle fluctuations and
measurements errors. They find that there is a trade-off between growth and inequality in terms
of both direct and indirect taxes. Liu and Martinez‐Vazquez (2015) examine the potential tradeoff between economic growth and income inequality in the design of tax systems in selected
150 countries from 1970 to 2009. Liu and Martinez‐Vazquez (2015) find evidence in support
of the presence of a trade-off between economic growth and income inequality. They find that
direct (personal income tax and corporate income tax) taxes are harmful for economic growth
although they make positive impact on income distribution. On the other hand, indirect taxes
which make positive effects on growth tend to increase expenditure inequality.
Application of SVAR models to investigate the effects of monetary policy and international
shocks on economic growth in Australia include research of Brischetto and Voss (1999),
Berkelmans (2005), Dungey (2002), Dungey and Pagan (2000), and Dungey and Fry (2003).
However, none of these studies do not consider the simultaneous effects of fiscal policy on
economic growth and income inequality in Australia. Therefore, one of the aims of this study
is to bridge this gap in the existing empirical literature.
The theoretical and empirical models reviewed in this Section suggest that different forms of
mutual interdependencies between fiscal policy, economic growth and income inequality.
9
Based on this review, we construct a tri-angular framework to show how the above three
macroeconomic variables are interrelated. This will be explained in the next section.
3. Methodology and Data
Methodology:
Figure 1 represents the theoretical determination of economic growth, income inequality and
fiscal policy in a tri-angular framework. The models in Figure 1 are: M1-Ricardian equivalence
hypothesis; M2-Keynesian model; M3-Kuznets model; M4-Neo-classical economic growth
models; M5-Public-finance endogenous growth model; M6- First-generation politico-economy
growth models; M7-Second-generation politico-economy growth models; and M8- Newendogenous growth models. While the capital letter “M” with a number in Figure 1 represents
a particular theoretical model, added simple letters such as a, b, c and d show having more than
one relationships attached to the model. Arrows show the direction of causality. The positive
(+) and negative (-) signs given in brackets ((+) and (-)) show the sign of the impact/influence.
Economic growth
M6c (-)
M8b (+)
M3 (+/-)
M4b (-)
M1
M4a (+)
M6b (-)
M7b (+/-)
M8a (-)
M2 (+/-)
M5 (+/-)
M8c (+)
Income inequality
M7a (+/-)
M6a (+)
Fiscal policy
M8d (-)
Source: Author
Figure 1. Theoretical determination of economic growth, income inequality and fiscal policy
in a tri-angular framework
As can be seen, the determination of the three variables (economic growth, income inequality
and fiscal policy) is a joint evolution process. Therefore, any economic modelling involving
these 3 variables should be able to capture these joint effects in order to minimize any
misspecification errors and to obtain structural estimates that represent relatively autonomous
relations. When parameters do not represent autonomous relations they are more likely to be
unintelligible, unstable and limited use for policy analysis (Hoyle and Ebooks, 2012, p. 22).
10
One of the most suitable ways to model these mutual interdependencies and their joint effects
is to specify these interrelationships in a structural vector autoregressive (SVAR) framework.
Specification of the relationship between economic growth, fiscal policy and income inequality
in a SVAR framework
Specification of growth equation:
Growth-fiscal policy nexus derived from public financed endogenous growth model (Barro
1990, Barro and Sala-i-Martin 1992, 1995) is augmented by including all (m) contemporaneous
components of government fiscal policy ( FPCt ), one period lagged values of fiscal policy
components ( FPCt 1 ),one period lagged values of economic growth ( EGt 1 ), one period lagged
values of net income inequality ( NetGinit1 ) and other exogenous variables ( Exogt ) that affect
economic growth. As suggested by a number of studies (i.e. Kneller et al. 1999; Bleaney et al.
2001; Miller and Russek 1997; Muinelo-Gallo and Roca-Sagales 2011, 2013), one element is
excluded from the budget constraint in order to avoid perfect-collinearity among
contemporaneous fiscal variables. To the best of our knowledge, incorporation of government
budget constraint in this way in a SVAR framework is new. The inclusion of other m 1
coefficients in the model helps to “control for the sources and uses of funds” (Miller and Russek
1997, p. 607). The vector ( Exogt ) includes controlled variables which are common to growth,
inequality and fiscal policy equations. These variables, which are commonly used in growth
literature, are population growth (POG), trade openness (TROPEN), inflation (INF), one period
lagged values of index of human capital (LHCI), and globalization index (GLOBI). We
consider only the limited number of exogenous variables in the model in order to control the
degrees of freedom problem.
Therefore, economic growth equation is formulated as follows:
t

m1 
g.k
*
EG 
k 1


m1 
n
g.k
t
k
t 1
k
t 1
g
t 1
g
 FPC   FPC  EG  NetGini 
k 1
where :  ( 
11
g.k
t
t
 Exog j g
j 1
), k 1, 2...m 1, j 1, 2...n
*
g.k
g. j
g.m
(1)
Where: ‘m’ shows number of fiscal policy components and ‘n’ shows number of exogenous
variables included in the growth equation; ‘k’ and ‘j’ stand for a particular fiscal variable and
an exogenous variable respectively; ‘g’ attached to parameters stands for the growth equation;
*
g.k (g.k g.m ) measures the contemporaneous effect of a change in the k
th
fiscal policy
component (FPC) on economic growth when the mth element of fiscal policy is excluded;
g
other variables are as defined before; and t is the error term of the growth equation.
Based on the theoretical predictions in Figure 1 and empirical findings reviewed, we specify
the determination of fiscal policy equation as follows. Equations for fiscal policy variables are
specified as functions of contemporaneous fiscal policy variables (FPCt ), their lagged values
(FPCt 1), lagged of economic growth (EGt 1) , lagged of net income inequality (NetGinit1) and
other exogenous variables (Exogt ) mentioned above.
t

m1

k ,k *
*
k 1
*
n
k ,k *
t 1
t 1
k
k
 FPC  EG
 FPC 
FPC 
k

m1
t
k
k 1
k
  NetGini
k. j
t
t
t 1
 Exog j k

(2)
j 1
*
where : * (  ), k, k * 1, 2...m 1, j
k
k
1, 2...n
m
*
Where:   measures the contemporaneous effect of a change in the kth fiscal policy
k
k
m
component (FPC) on fiscal policy components (FPC) themselves when the
mth element of
k
fiscal policy is excluded; the term FPCit in equation (2) denotes the k th fiscal policy outcome.
It means that equation (2) is estimated for each k component in fiscal policy; that is,
k 1, 2, 3,...m 1equations are estimated from Model (2); other variables are as defined before
and  t is the error term of the kth fiscal policy equation.
k
Net income inequality equation is specified as a function of contemporaneous fiscal policy
variables (FPCt ), contemporaneous economic growth (EGt ), one period lagged values of
fiscal policy components (FPCt 1 ), economic growth (EGt 1), net income inequality
(NetGinit 1), and exogenous variables vector (Exogt ) that affects net income inequality.
12

NetGini 
m1 
ng.k
*
k 1


m1 
t
t
k
ng
t
n
ng.k
t 1
k
k 1

ng.k
t 1
t 1
ng
 FPC  EG   FPC  EG NetGini 
where :* ( 
ng.k
Where; *
ng

ng.k
ng.k
ng. j
t
t
 Exog j ng
(3)
j1
), k 1,2...m 1, j 1,2...n
ng.m
measures the contemporaneous effect of a change in the kth fiscal
ng.m
th
policy component (FPC) on net income inequality when the m element of fiscal policy is
excluded; other variables are as defined before; and tng is the error term of the net income
inequality equation.
For the simplicity, without the effects of exogenous variables (including constant term),
equations 1-3 is specified in a general form of SVAR (1) as follows. The specification of SVAR
model is based on the following assumptions: (1) all fiscal policy variables are not
contemporaneously interrelated; (2) fiscal policy variables are not contemporaneously affected
by both economic growth and net income inequality; (3) economic growth and net income
inequality are contemporaneously affected by fiscal policy components; (4) fiscal policy
components and economic growth have contemporaneous effects on net income inequality;
and (5) fiscal policy components, economic growth and net income inequality are affected by
their lagged values. This SVAR (1) model is re-specified to test the following two hypotheses
of the study:
Hypothesis 1: Based on economic and politico-economy growth models, we hypothesize that
the use of fiscal policy to reduce income disparity has a trade-off between economic growth
(economic efficiency) and equality.
Hypothesis 2: According to the conventional public policy debate, we hypothesize that a taxfinanced fiscal policy has a larger negative impact on economic growth than a debt-financed
fiscal policy.
SVAR Model:
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1
 *
0
 FPC1
  t
  
1,1 1,2 1,3
 
.
 
0  FPCt
 2,1 2,2 2,3
.
.
.
.
.
0
. .
. .
. 1
*
*
, 3 . m1,m 1
m1
* . *
. *
.
.
.
.
0
.
0
.
.
.
.
0
. 0
.
.
.
.
0
. 0
.
.
.
.
.
.
*
*
* k,2 . k 1,m1 1
.
 1 0
*
2,1

3,2
1

.
 3,1
.
 *
k,1
.

.
 *
m1,1
 *


 g ,1

*
ng ,1
.
.
*
m1,2
*
g,2

*
ng ,2
.
.
g,3
*
ng ,3
.
.
.
.
.
g ,k
. *
0 0
1 0
g ,m1
. *
ng ,k
ng ,m1
 1
ng
2
k
m

.
. . 
1
1 FPC1
. 2,k
. . 2,m1  2
2 FPC
1,k
1,m1

t1
2
 3    . 
. . 

3,k
3,m1
3
 FPCt   3,1 3,2 3,3
  .
. .
.
. .
. . .
.



  FPC k    
. . . 


k ,k
k ,m1
k
.  t  k,1 k,2 k,3
.
.
.
. . . . .
.
 .

 

.
.
. . . . .
.
 .
 .




F m1

PC 
EG
m1,1 m1,2 m1,3 ..m1,k
 .. .. m1,m1 m1
t

g ,1
t
g

 ng,1 ngg,2,2 gng,3,3 . gng,k,k . . ngg ,m1
 
,m1  ng
 

NetGini

t


ng.k

* 

ng.k
m
ng.m
1
et 
  2 
e 
t1
*
*
Where:   and 
k


t

 e3
  t 
  .
. 
  . 
  (4)
  FPC k 
 etk 
k  
 t1
 
 . 
. .  .

  
.  .
 . 
 m1 


m1
 e g 

 FPC
m1
EG t1 ett 
t 1
gng
  ng 
NetGini


 
t 1   e
 FPC3
3
 t1

t
are as defined before; 
ng
measures the contemporaneous effects of economic growth on net income inequality; and
,  and measure the lagged effects on fiscal policy, economic growth and net income
inequality of changes of these variables respectively.
System of equations (4) can be represented in a compact form as follows.
/
2
0Yt 1Yt1 et where :E (et et ) e I  e
(5)
Where; 0 is a (m+1) x (m+1) matrix of contemporaneous structural coefficients whose
diagonal has values equal to one for the respective dependent variable; Yt is a (m+1) x 1
column vector of the observed endogenous variables in which FPC 1...FPC m1 are fiscal
t
t
policy components and, EG and NetGinit are economic growth and net income inequality
t
respectively; 1 is a (m+1) x (m+1) matrix of coefficients attached to lagged endogenous (or
predetermined) variables; Yt1 is a (m+1) x 1 column vector of observed lagged endogenous
(predetermined) variables; et a (m+1) x 1 column vector of structural innovations; and  e is
a (m+1) x (m+1) variance-covariance matrix of structural innovations in which the elements
on the diagonal denote the variances of respective structural innovations whereas the offdiagonal elements are assumed to be zero. It means that correlations of innovations across
equations in the SVAR model are assumed to be zero (orthogonal). According to Enders (2003,
p. 295), the reason for the covariance terms in a model like (5) setting to zeros is the fact that
innovations are deemed to be “pure structural shocks”.
14
Taking the reduced form of equation (5) yields;
15
*
1
/
/
1 /
1
1 /
Yt Yt1 ut where : E (utut ) 0 E(etet )(0 ) 0 e (0 )
(6)
Where: *  1 and u 1e . The variance-covariance matrix of error terms in the
0
1
t
0
t
reduced form VAR Model (6) is 1(1). The one crucial difference between structural
u
0
e
0
VAR Model (5) and the reduced form VAR Model (6) is that the former has an economic
reasoning whereas the latter has little economic content (Enders, 2003, p. 291). The other
crucial difference is the fact that former has a larger number of unknown parameters than the
known parameters in the latter. Because of this reason the structural VAR model cannot be
identified without imposing additional zero restrictions on the SVAR system. That is, SVAR
2
has (m 1) unknown distinct elements
(m 1)
unknown
[(m 1)2 (m 1)
unknown elements of  0 plus
elements of  e ] whereas reduced
var (et )
form VAR
((m 1) (m 1)) / 2 known distinct elements [known variance-covariance elements of
2
Therefore, in order to identify
known ((m 1)
2
has
u
].
(m 1)2 unknown parameters in the SVAR Model (5) from
 (m 1)) / 2 independent parameters of  u in the reduced form VAR Model
(6), it is essential to impose
[(m 1)2  (m 1)]/ 2 additional zero restrictions on the structural
VAR model. Our objective is to obtain the estimated values for the contemporaneous
coefficients matrix 0 by mapping variance covariance matrix of structural innovations to that
of the reduced form model in which the instantaneous relationships among endogenous
variables are naturally hidden (Giannini and Amisano 1997, p. 15; Hamilton 1994, p. 330;
Enders 2003, p. 291-296; Moneta et al. 2011, p. 98).Therefore, this process requires to impose
[(m 1)2 (m 1)] / 2 zero restrictions on the matrix0  of the structural VAR model (5). This
objective is achieved by employing the Cholesky factorization in such a way that the ordering
of variables in Yt are arranged according to the prior theoretical and empirical claims on the
relationship between fiscal policy, economic growth, and income inequality.
Scenario 1: Baseline SVAR model
The order of variables in the endogenous vector Yt of the baseline model is considered as
following:
Y (TOTExp ,OREV ,TOTAX , BUDS , EG , NetGini )/ .
t
t
t
t
t
t
Four fiscal policy
t
components are considered in this model; total government expenditure as a proportion of GDP
16
(TOTEXP), other revenue as a proportion of GDP (OREV), total tax income as a proportion of
GDP (TOTAX) and budget deficit/surplus (BUDS) as a proportion of GDP (BUDS). We
separately estimate this baseline model by excluding budget deficit (BUDS) and total tax
income (TOTAX) variables respectively. This omitted
mth fiscal policy component is
considered as the implicit financing variable. Any increase in an included fiscal policy variable
is offset by this excluded component. Hence, when excluded BUDS, any increase in total
expenditure (TOTExp) is offset by an equal increase in BUDS. This strategy is defined as debtfinanced fiscal policy (Miller and Russek 1997). Similarly, when total tax income variable
(TOTAX) is excluded from the model, it is identified as tax-financed fiscal policy. Our
objective is to simultaneously measure how these fiscal policy strategies affect economic
growth and income inequality according to different theoretical arguments. The ordering of
variables in vector Yt has following economic reasons: first, government non-tax income and
tax income are conditioned on its expenditure. That is, as governments first decide their
expenditure, the higher the expenditure the larger the possibility is to collect non-tax income
(i.e. through privatization) and tax income; second, budget deficit (or surplus) depends on both
expenditure and total income; third, economic growth is contemporaneously affected by
government spending and taxes through various channels according to different theatrical
models in Figure 1; and fourth, income inequality is also contemporaneously affected by
government spending, taxes and economic growth respectively through various channels
according to different theatrical models in Figure 1. Ramos and Roca-Sagales (2008) allow tax
income to be affected by growth and income inequality but not the other way round. We assume
that tax income is mainly determined by the expansion of tax base which is associated with
economic growth. However, this expansion of tax base is expected to occur gradually rather
than one-to-one increase with economic growth. Hence, we assume that economic growth has
a lagged effect on tax income rather than a contemporaneous impact. Similarly, as policy
makers have information on the previous period’s income inequality, government expenditure
and tax income are assumed to be affected by lagged effects of income inequality. Furthermore,
we assume that all the variables in the model are affected by their values in the previous period.
That is, for example, current period government expenditure is assumed to be affected by its
previous period government expenditure, previous period tax income, economic growth and
previous period income inequality. Figure 1 shows these effects according to different
theatrical models.
17
Scenario 2: Disaggregation of tax variables
The objective of this scenario is to test the validity of two hypotheses by clearly identifying the
effects of direct tax (DTAX) and indirect (ITAX) taxes on economic growth and income
inequality. In this case, the SVAR model is estimated separately when direct and indirect tax
variables are excluded. The order of variables in Yt in this model is considered as following:
Y (TOTExp ,OREV , DTAX ,ITAX , BUDS , EG , NetGini )/
t
t
t
t
t
t
t
In this model, indirect tax income is assumed to be conditioned on direct tax income as direct
taxes affect disposable income which in turn determines the level of consumption and hence
the indirect tax income (Ramos and Roca-Sagales 2008). Breakdowns of these variables are
given in Table A1 in the Appendix.
Data
For estimation, we use annual data for each variable for the period 1962 to 2012. The reason
for the use of annual data is that data for income inequality are not available on quarterly or
monthly basis. Each data series that follows a unit root process is transformed into stationary
by applying Hodrick–Prescott high-pass filter method (Hodrick and Prescott 1997). The
objective of this transformation is to eliminate both unit roots and cyclical effects (Baxter and
King 1999). Stationary property of the variables under consideration are tested using three
endogenously determining unit root tests, namely, (1) Lumsdaine and Papell (1997) two
structural breaks unit root test; (2) Vogelsang and Perron (1998) one structural break unit root
test; and (3) Lee and Strazicich (2003) two structural breaks unit root test for breaks in the
intercept and in the slope (Model CC). Lee and Strazicich (2003, P. 1083) mention that most
macroeconomic time series can be adequately described by two specifications suggested by
them. The main difference between tests (1) and (2) above and the 3rd one is the assumption
used in these tests about the null hypothesis in which critical values are generated. The tests
(1) and (2) assume no breaks under the null hypothesis whereas breaks are allowed to occur
under the null hypothesis in the test (3). Lee and Strazicich (2003) criticise the assumption used
in the first two tests mentioning that the rejection of the null hypothesis can lead someone to
wrongly conclude that the series considered is trend-stationary with breaks when in fact it is
difference-stationary with breaks. Lee and Strazicich (2003) further mention that the higher
divergence of the unit root test statistic, which is resulted from the presence of a break under
the null hypothesis, can lead to an over-rejection of a unit root process. Lee and Strazicich
18
(2003) test helps unambiguously to decide whether the series considered is differencestationary with (or without) breaks or trend-stationary with (or without) breaks. Therefore, we
mainly relay on Lee and Strazicich (2003) test to verify the stationarity of variables in this
study as it outperforms all the other tests available so far. A summary of the codes, definition
of variables and their sources is given in Table A2 in the Appendix.
4. Results and analysis
Results of unit root tests
Table 1 presents results of two break unit root tests proposed by Lee and Strazicich (2003).
Following the procedure proposed by Lumsdaine and Papell (1997, p. 215), lag length for both
Lee and Strazicich (2003) and Lumsdaine and Papell (1997) tests is selected using the generalto-specific (GTS) method by setting the maximum lags ( kmax ) as 4 and using 10% value of the
asymptotic normal distribution to determine the significance of the last lag. The GTS method
selects the lags for the model based on the last significant lag (Hall 1994). As can be seen in
Table 1, seven variables are stationary with breaks whereas other seven variables of the study
are non-stationary with breaks under any of the conventional significant levels (1%, 5% or
10%). Critical values under Lee and Strazicich (2003) test depends on the relative location of
breaks (i.e. 1  BD1 / T , 2  BD2 / T where T is the sample size).
19
Table 1: Lee and Strazicich (2003) two-break minimum LM unit root test (Model C: two breaks
in the intercept and the trend)
Variables
BD1
BD2
St-1
k
IBt1
IBt2
SBt1
SBt2
I(d)
in levels
DTAX
1974
2007
1
-5.47***
2.92*
2.52*
-0.39
3.95*
I(0)
ITAX
1978
2000
1
-6.27*
2.5**
7.22*
1.7***
1.60
I(0)
TOTTax
1975
1999
4
-4.33
-0.51
-0.97
1.43
2.6**
I(1)
OREV
1974
1988
3
-4.87
-0.51
-1.7***
-2.5**
-1.39
I(1)
TOTRev
1990
2000
1
-5.14
1.06
-1.31
-3.78*
2.1**
I(1)
TOTExp
1974
1987
3
-7.23*
3.35*
0.13
5.6*
-6.5*
I(0)
BUDS
1974
2001
3
-5.27***
-0.10
-2.1*
-3.64*
4.09*
I(0)
NetGini
1977
1991
2
-6.21*
-3.28*
6.06*
-0.49
-4.55*
I(0)
PCRGDP
1971
1998
0
-5.16
1.44
1.03
-4.89*
3.5*
I(1)
TROPEN
1974
1994
2
-5.03
2.5**
0.41
-0.34
2.3**
I(1)
LHCI
1973
1985
2
-5.94**
-0.07
2.88*
1.60
-7.87*
I(0)
INF
1972
1998
3
-5.59**
2.96*
-1.59
0.99
4.62*
I(0)
GLOBI
1970
1984
4
-5.02
-2.74*
0.37
4.12*
-4.87*
I(1)
POG
1973
2004
4
-5.11
1.15
0.12
-2.4**
1.12
I(1)
Critical values of Lee and Strazicich (2003, Table 2) two-break minimum LM unit root test
2
1
0.4
1%
5%
0.6
10%
1%
5%
0.8
10%
1%
5%
10%
0.2
-6.16
-5.50
-5.27
-6.41
-5.74
-5.32
-6.33
-5.71
-5.33
0.4
-
-
-
-6.45
-5.67
-5.31
-6.42
-5.65
-5.32
0.8
-
-
-
-
-
-
-6.32
-5.73
-5.32
Note: BD1 and BD2 are first and second break dates (break years in present study). k is the lag length, which is
selected using the general-to-specific method by setting the maximum lags (kmax) up to 4. St-1 is the minimum LM
statistic of the coefficient on the unit root parameter. We determine the significance of coefficients attached to
dummy variables using the standard ‘t’ values (Canarella et al. 2012, p. 768). IBt1 and IBt2 are the standard ‘t’
values of coefficients attached to the first and second dummy variables to capture breaks in the intercept. SBt1 and
SBt2 are the standard ‘t’ values of the coefficients attached to the first and second dummy variables to capture
breaks in the slope. I(d) denotes the order of integration of relevant variable. is
a vector containing the location
i
of breaks. *,**, and *** symbols show statistical significance of relevant coefficients at 1%, 05% or 10% levels
respectively. All the calculations were obtained using RATS 9.1 version.
20
As can be seen, the null of unit root of direct tax income (DTAX) is rejected at the 10%
significant level under the location parameters of = 0.2 and  = 0.4, 0.6, and 0.8. Under the
1
2
null hypothesis of DTAX, two breaks in the intercept (IBt1 and IBt2) occurred in 1973 and 2004
are significant at 5% and 10% levels respectively whereas one break in the slope occurred in
2007 is significant at 1% level. Indirect tax income (ITAX) is also trend-stationary at 1% level
with breaks in the intercept occurred in 1978 and 2000, and in the slope occurred in 1978. Total
tax income (TOTAX) has a unit root with a break in the slope occurred in 1999. Other revenue
(OREV) has a unit root with a break in the intercept (1974) and a break in the slope (1988).
Total revenue (TOTRev) variable is also following a unit root process with two breaks in the
slope which have occurred in 1990 and 2000 years respectively. Total government expenditure
(TOTExp), and budget deficit/surplus (BUDS) variables are trend-stationary with breaks.
TOTExp has a one break in the intercept (1974) and two breaks in the slope (1974 and 1987).
Similarly, BUDS has a one break in the intercept (2001) and two breaks in the slope (1974 and
2001). Three out of seven non-fiscal policy variables, namely, net Gini index (NetGini), lagged
human capital index (LHCI), and inflation (INF) in Table 1 are also trend-stationary with
breaks either in the intercept, slope or in both. NetGini has two breaks in the intercept (1977
and 1991).
LHCI has a one break in the intercept (1985) and a one break in the slope (1985). INF has a
one break in the intercept (1972) and a one break in the slope (1998). Four non-fiscal policy
variables, which have unit roots with breaks are per capita real GDP (PCRGDP), trade
openness (TROPEN), globalization index (GLOBI), and population growth (POG). PCRGDP
has two breaks in the slope (1971 and 1998). TROPEN has a one break in the intercept (1974)
and one in the slope (1994). GLOBI has a one break in the intercept (1970) and two breaks in
the slope (1970 and 1984). POG has a one break in the slope (1973).
Table 2 provides results of Lumsdaine and Papell (1997) two-break unit root test. As can be
seen in the last column in the Table 2, only three variables out of fourteen are trend-stationary
with breaks. These variables are; ITAX, INF and GLOBI. Except GLOBI, the other two
variables were also trend-stationary under Lee and Strazicich (2003) test. The non-rejection of
the null of unit root in favour of other eleven variables can mainly be due to non-capturing of
breaks under the null hypothesis of Lumsdaine and Papell (1997) test. As a result, critical
values generated under the null hypothesis of no breaks seem to be biased towards accepting
the null of unit root without breaks when in fact variables are trend-stationary with breaks.
20
Otherwise, all break years in fourteen variables are approximately the same in both Lumsdaine
and Papell (1997) and Lee and Strazicich (2003) tests.
Table 2 Lumsdaine and Papell (1997) two-break unit root test (Model CC: two breaks in the
intercept and the trend)
Variables
BD1
BD2
yt-1
k
IBt1
IBt2
SBt1
SBt2
I(d)
in levels
DTAX
1973
2004
0
-5.18
2.7*
1.64
-3.1*
-3.1*
I(1)
ITAX
1975
2000
0
-8.32*
3.7*
0.005
8.38*
-5.18*
I(0)
TOTTax
1974
2000
0
-4.79
2.5**
3.18*
-2.57*
-3.64*
I(1)
OREV
1972
1988
0
-5.53
-3.38*
-3.29*
0.77
1.7***
I(1)
TOTRev
1991
2004
0
-5.34
-4.32*
1.43
2.1**
-4.59*
I(1)
TOTExp
1974
1987
0
-5.96
-5.01*
-2.91*
-0.37
-1.16
I(1)
BUDS
1974
2002
1
-5.69
-3.09*
2.58*
0.27
-3.04*
I(1)
NetGini
1978
1990
0
-6.10
2.39*
-3.35*
3.39*
-0.12
I(1)
PCRGDP
1972
1992
0
-6.18
-4.52*
3.69*
-3.82*
-1.9***
I(1)
TROPEN
1974
1992
0
-5.31
2.5**
2.87*
2.91*
-0.71
I(1)
LHCI
1981
2006
1
-6.04
-10.5*
4.56**
-6.95*
-1.37
I(1)
INF
1972
1999
4
-6.6***
5.12*
2.53**
-2.23*
3.7*
I(0)
GLOBI
1971
2001
3
-7.73*
6.67*
-0.5
-0.09
-2.9
I(0)
POG
1972
1985
3
-3.62
1.8***
1.99**
0.56
1.95***
I(1)
Note: Critical values of Lumsdaine and Papell (1997) two-break unit root test are: -7.19 (1%), -6.75 (5%) and 6.48 (10%). Note: BD1 and BD2 are first and second break dates (break years in present study). k is the lag length,
which is selected using the general-to-specific method by setting maximum lags up to 4. yt-1is the minimum ‘t’
statistic of the coefficient on the unit root parameter. We determine the significance of coefficients attached to
dummy variables using the standard ‘t’ values (Canarella et al. 2012, p. 766).IBt1 and IBt2 are the standard ‘t’
values of coefficients attached to the first and second dummy variables to capture breaks in the intercept. SBt1 and
SBt2 are the standard ‘t’ values of the coefficients attached to the first and second dummy variables to capture
breaks in the slope. I(d) denotes the order of integration of relevant variable. *,**, and *** symbols show statistical
significance of relevant coefficients at 1%, 05% or 10% levels respectively. All the calculations were obtained
using RATS 9.1 version.
Table 3 presents results of Vogelsang and Perron (1998) one break unit root test. The null
hypothesis of Vogelsang and Perron (1998) test is that the series follows a unit root process
without any kind of breaks whereas the alternative hypothesis states that the series is trendstationary with a break in the intercept, slop or both in the intercept and trend.
21
Variables
in levels
Innovational outlier (IO) method
Additive outlier (AO) method
Intercept Slope
Intercept
Intercept Slope
Intercept
break
break
and slop
break
break
and slop
break
break
Vogelsang (1993) asymptotic one-sided p-values
Order of
integration
Table 3: Vogelsang and Perron (1998) one-break unit root test
DTAX
0.972
0.330
I(0)
0.246
0.585
0.499
0.080**
(1981)
ITAX
0.200
0.586
0.010*
0.707
0.405
0.341
I(0)
(2000)
TOTTAX
0.888
0.811
0.854
0.967
0.202
0.960
I(1)
OREV
0.351
0.249
0.360
0.278
0.157
0.452
I(1)
TOTRev
0.806
0.720
0.810
0.969
0.118
0.919
I(1)
TOTExp
0.128
0.010*
0.136
0.018**
0.053*** 0.017**
(1984)
(1975)
(1982)
0.098*** 0.304
0.112
0.053*** 0.237
BUDS
0.142
(1984)
NetGini
0.348
0.163
I(0)
(1975)
I(0)
(1982)
0.069***
0.147
(1978)
0.061*** 0.047**
(1973)
(1977)
I(0)
PCRGDP
0.277
0.314
0.424
0.269
0.220
0.379
I(1)
TROPEN
0.480
0.490
0.656
0.404
0.101
0.303
I(1)
LHCI
0.971
0.010*
0.078***
0.987
0.188
0.705
I(0)
(1980)
(1977)
0.990
0.226
0.048**
I(0)
INF
-
-
-
(1982)
GLOBI
0.990
0.399
0.963
0.990
0.180
0.902
I(1)
POG
0.990
0.010*
0.882
0.990
0.010*
0.054**
I(0)
(1981)
(1983)
(1987)
Note: *,**, and *** symbols show the rejection of the null hypothesis that the series has a unit root under 1%,
05% or 10% respectively. Lags are selected according to modified Akaike information. Respective break years
are given in brackets.
The results in Table 3 show that the null of non-stationarity is rejected in terms of seven
variables under 1%, 5% or 10% significant levels. These variables are: DTAX, ITAX,
TOTExp, BUDS, NetGini, LHCI and INF. Although Vogelsang and Perron (1998) test is
22
different from Lee and Strazicich (2003) test in terms of both the number of breaks and the null
hypothesis, both tests confirm that the same seven variables are trend-stationary with breaks.
Interestingly, both tests also provide approximately the same break dates in these variables.
Regardless of the differences of these two tests, we assure the robustness of our results as they
coincide with each other. Accordingly, the break effects in each variable are removed
considering the relevant null or alternative hypothesis which was accepted under Lee and
Strazicich (2003) two break LM test. To remove break effects, dummy variables are used in
individual regression for each variable only for breaks (either in the intercept, slope or in both)
which were significant at 1%, 5% or 10% level under Lee and Strazicich (2003) two break LM
test. After removing break effects of non-stationary variables, they are further cleared using
HP filter method to eliminate both unit root and cyclical effects. After removing break effects
of trend-stationary variables, they are directly used to the estimation of SVAR model.
Table 4 gives possible economic/policy shocks that could have led to create breaks in fourteen
variables which are significant at 1%, 5% or 10% level under Lee and Strazicich (2003) two
break LM test. Interestingly, it is evident that identified economic/policy shocks given in Table
6 coincide with endogenously determined breaks of series in respective years. One of the main
advantages of these unit root tests than other root test (such as non-linear unit test proposed by
Kapetanios et al. (2003) and other conventional unit root tests) is that these tests help to identify
whether breaks are associated with a particular government policy, economic crisis or other
factors (Canarella et al 2012). Accordingly, once break dates are identified, the effects of these
breaks on particular variables can be removed from data before they are used for modelling
purposes. To the best of our knowledge, this finding is novel in terms of fiscal policy variables
in the Australian economy.
23
Table 4. Possible economic/policy shock(s) caused to breaks
Variable
Break
Main possible economic/policy shock(s) caused to the break
dates
1974
Economic recession by mid-19741, oil price shock in 19733
2007
Global financial crisis4
1978
Economic recession by early-1980s2, oil price shock in 19793
2000
Introduction of GST in July 20005
TOTTax
1999
The New Tax System introduced in July 20005
OREV
1974
Economic recession by mid-19741, oil price shock in 19733
1988
Economic recession by late-1980s2
1990
Economic recession by late-1980s2
2000
The New Tax System introduced in July 20005
1974
Economic recession by mid-19741, oil price shock in 19733
1987
Economic recession by late-1980s2
1974
Economic recession by mid-19741, oil price shock in 19733
2001
The New Tax System introduced in July 20005
1977
Economic recession by mid-19741, oil price shock in 19733
1991
Economic recession by late-1980s2
1971
Stagflation (higher inflation and unemployment) by early 1970s1
1998
Introduction of significant changes to the item coverage and the population
DTAX
ITAX
TOTRev
TOTExp
BUDS
NetGini
PCRGDP
group of CPI index in 19986
TROPEN
INF
1974
Trade liberalization (first phase), Oil price shock in 19733
1994
Trade liberalization (second phase)
1972
Oil price shock in 19733
1998
Introduction of significant changes to the item coverage and the population
group of CPI index in 19986
GLOBI
1984
Introduction of economic reforms since 1983 including financial
deregulation and the floating of the Australian dollar
POG
1973
The highest number of migrants (185000) settled in 1969–70 under the
Gorton Government7
Sources: Dyster and Meredith (1990, chapter 12)1, Gruen and Sayegh (2005)2, The Treasury Annual Report
(2000-2001)3, Morling and McDonald (2011)4, Australian Treasury (2003)5, McLennan (1998)6, and Phillips et
al. (2010)7.
24
Results of scenario 1: Results of the baseline SVAR model
Table 5 provides contemporaneous effects of debt-and tax-financed fiscal policies on economic
growth and income inequality. The order of the VAR that underlies the SVAR models was
chosen using the Likelihood Ratio (LR) statistic. The LR statistic for lag one against lag zero
was significant at the 1% level (with p-value 0.000) for both debt- and tax-financed VAR
models. The LR statistic for lag two against lag one was insignificant at the 1% level and thus
confirmed the order of VAR as one. Specification of a VAR (1) model is consistent with the
limited sample size (50 observations) of the study.

Table 5: Contemporaneous effects ( 0 matrix) of debt- and tax-financed total government
expenditure
Fiscal policy strategy: Debt-financing (Theoretical assumption: increased expenditure is
entirely financed by increasing debts)
TOTExp
1
ORev
TOTTax
EG
NetGini
0.492*** (0.087)
0(con.)
1
-0.117* (0.005)
1
-0.135** (0.012)
0(con.)
0(con.)
1
-0.332* (0.008)
0(con.)
0(con.)
0(con.)
1
Fiscal policy strategy: Tax-financing (Theoretical assumption: increased expenditure is
entirely financed by increasing taxes)
TOTExp
1
ORev
0.506*** (0.069)
BUDS
-0.002*
(0.000)
0(con.)
1
EG
-0.124** (0.002)
0(con.)
0(con.)
1
NetGini
-0.321*
0(con.)
0(con.)
0(con.)
(0.009)
1
1
Note: Probability values are given in brackets. *,**,*** indicate the statistical significance of relevant
estimates at 1, 5 and 10 percent levels. The bracket ‘(con.)’ shows the zero constraint imposed on the
relevant coefficient. Six over identified (zero) constraints imposed on the coefficients in debt-financed
model are not rejected at any conventional significant levels as chi2 (6) = 3.768 (Prob. > chi2 = 0.708).
Six over identified (zero) restrictions imposed on the coefficients in tax-financed model are not rejected at
any conventional significant levels as chi2 (6) = 2.377 (Prob. > chi2 = 0.882).Exogenous variables
included in both models are: population growth (POG), one period lagged values of imports plus exports
as a ratio of GDP (TROPEN), inflation (INF), one period lagged of human capital index (LHCI) and
globalization index (GLOBI).
25
The first panel of Table 5 shows the results related to the debt-financed fiscal policy under the
theoretical assumption that increased total government expenditure is entirely financed by
increasing debts. The estimate attached to the total government expenditure (TOTExp) in the
growth equation (EG) (fourth row and the second column in the first section of Table 5) shows
that one percent increase in total government expenditure leads to a 0.14 percent reduction of
real economic growth (measured as the growth of real per capita GDP). Similarly, the estimate
attached to the same variable in the net income inequality equation (NetGini) (fifth row and
the second column in the first section of Table 5) shows that one percent increase in total
government expenditure leads to a 0.33 percent reduction of net income inequality. Both
estimates are statistically significant at 5% and 1% levels respectively. These results indicate
that there is a trade-off between economic growth and income equality when debts are used to
finance government expenditure.
The use of debts to fiancé government expenditure can deteriorate economic growth in several
ways. First, it can create crowding out effects by directing resources of the private sector to
purchase government bonds (sovereign bonds) and hence declining private sector expenditure
on investment (Checherita and Rother 2010, Islam 2014). According to the standard economic
theory, private expenditure on investment can be further declined with the increased pressure
of fiscal expansion for higher interest rates. Second, the finding that the negative effect of debtfinanced government expenditure on economic growth is consistent with the Ricardian
equivalence hypothesis, which states that increased government expenditure financed by
increasing debts (or equally cutting taxes) does not stimulate aggregate demand and hence
growth. If individuals are sufficiently forward-looking, they will not increase consumption, but
instead they will tend to save the entire tax cut to pay future tax liability (Comley et al. 2002).
Brittle (2010) examines whether saving behaviour of households and organisations in Australia
is consistent with Ricardian equivalence hypothesis. Brittle (2010, p. 266) finds that Australian
households and organisations are more forward-looking and show some “partial Ricardian
behaviour” as the offset coefficient of public savings is approximately near minus one half.
According to them, fiscal policy in Australia has some impact on economic growth.
The presence of a prolonged fiscal deficit can push individuals to oversee that the government
is following a countercyclical fiscal policy (i.e. fiscal stimulus or tax cuts followed by higher
taxes). Makin and Narayan (2011) highlight that Australian governments over the recent
decades have deliberately used fiscal policy to influence economic growth in two contradictory
ways; to run budget deficits at times of recessions and to run budget surpluses at times of
26
booms. The periods in which budget deficits followed are the early 1980s, early 1990s, and
late 2000s whereas budget surpluses have been maintained in the other times in the past three
decades (Makin and Narayan 2011). The former context is consistent with current empirical
inquiry on the implementation of debt-financed fiscal policies. Makin and Narayan (2011) find
that private saving has offset changes in public saving in Australia as the offset coefficient of
public saving is approximately near minus one. Makin and Narayan (2011, p. 384) further find
evidence supporting to the presence of Ricardian equivalence hypothesis and non-Keynesian
views on private saving behaviour, including the lifecycle and permanent income theories of
consumption. Taylor (2009) argues that countercyclical discretionary fiscal policies are not
effective in promoting economic growth as consumers are rational. Taylor (2009, p. 354)
further suggests to focus on longer-term fiscal policy reforms with “automatic stabilizers”
giving more weight on productive government services including infrastructure development.
Martineau and Smith (2015) find that government spending in 20 countries during the 1920s
and 1930s was countercyclical and did not have significant impact on economic growth. Miller
and Russek (1997) find that debt-financed government expenditure retards economic growth
in developing countries but it does not have a significant impact on growth in developed
countries.
Third factor that a debt-financed fiscal strategy can have negative effects on economic growth
comes from its effects on exchange rate. Government debts financed by foreign borrowing and
instantaneous inflows of capital due to higher interest rate can appreciate exchange rate leading
to a reduction of the contribution of net exports to economic growth (Comley et al. 2002). This
is specifically important as Australia is a major exporter of commodities. The presence of a
negative sign of the estimate attached to the total government expenditure in the growth
equation confirm that negative effects (i.e. crowding-out effects and reduction of net-export
growth) seem to outweigh positive effects (i.e. deficit-financing as a tool to promote aggregate
demand) of debt-financed government expenditure on economic growth. Comley et al. (2002)
find that fiscal policy in Australia is less effective as a demand management tool due to the
presence of offset effects of private savings. They further find that budget deficit has a
significant positive effect on the interest rate. This in turn can deteriorate growth potentials by
reducing both investment and foreign competitiveness in the export market.
The negative sign of the estimate attached to the total government expenditure in the net income
inequality equation (NetGini) in Table 5 is in line with theoretical claims of both secondgeneration politico-economy growth theories (Benabou 1996, 2000, 2004) and new27
endogenous growth models (Saint-Paul and Verdier 1993; Galor and Moav 2004). Benabou
(1996, 2000, 2004) claims that government’s (redistributive) expenditure helps households
acquire economic assets (human and physical capital) in lifting credit constraints faced by them
in the presence of capital market imperfections. Saint-Paul and Verdier (1993) and Galor and
Moav (2004) claim that increased expenditure on public education reduces income inequality
through improving stock of human capital.
The estimate attached to the total government expenditure (TOTExp) in the other revenue
(ORev) equation is statistically significant at 10% level with a positive sign. This implies that
when debts are used to finance government expenditure it makes a positive effect (0.492
percent) on the other revenue. This can imply that the Australian governments running in debts
tend to find other sources of income such as incomes from privatization of state own
enterprises. The estimate attached to the other revenue variable in the total tax equation
(TOTAX) is statistically significant at 1% level with a negative sign, which implies that the
use of other revenue to fiancé debts can reduce the total tax burden. Six zero constraints
imposed on the insignificant estimates in the exactly identified debt-financing SVAR model
are not rejected at any conventional significant levels.
The second panel of Table 5 shows the results related to the tax-financed fiscal policy under
the theoretical assumption that increased total government expenditure is entirely financed by
increasing both direct and indirect taxes. The estimate attached to the total government
expenditure (TOTExp) in the growth equation (EG) is statistically significant at 1% level with
a negative sign. This shows that one percent increase in total government expenditure which is
financed by increasing both direct and indirect tax income leads to a 0.124 percent reduction
of real economic growth. Similarly, the estimate attached to the same variable in the net income
inequality equation (NetGini) is statistically significant at 1% percent level with a negative
sign. This implies that one percent increase in total government expenditure which is financed
by increasing both direct and indirect tax income leads to a 0.32 percent reduction of net income
inequality. These results indicate that there is also a trade-off between economic growth and
income equality when tax incomes are used to finance government expenditure.
The estimate attached to the total government expenditure in the budget deficit/surplus (BUDS)
equation is statistically significant at 1% level with a negative sign. This implies that, the higher
expenditure, irrespective how it is financed by increasing taxes, worsens fiscal performance
(i.e. negative effect on budget surplus).The estimate attached to the total government
28
expenditure in the other revenue equation (OREV) is statistically significant at 10% level with
a positive sign. This shows having more pressure on the rise of other revenue when government
expenditure is financed by increasing taxes. Twelve zero constraints imposed on the
insignificant estimates in the exactly identified model are not rejected at any conventional
significant levels.
Results of scenario 2:
We re-estimated the tax-financing baseline SVAR model by disaggregating total taxes into
direct and indirect in order to clearly identify how and to what extent these two different tax
components affect economic growth and income inequality simultaneously. Table 6 presents
the contemporaneous effects on economic growth and income inequality of total government
expenditure which is financed by direct and indirect taxes.
First panel of Table 6 shows that the total government expenditure financed by direct taxes
retards economic growth and improves income redistribution. One percent increase in total
government expenditure which is financed by increasing direct tax income (DTAX) reduces
economic growth by 0.118 percent whereas it also reduces net income inequality by 0.314
percent. These two estimates are statistically significant at 5% and 1% levels respectively. The
estimate attached to the indirect tax income (ITAX) in the inequality equation (NetGini) is
statistically significant at 10% level with a positive sign. This implies that an increase in
indirect tax income which is entirely financed by reducing direct tax income increases income
inequality. The estimate attached to the total government expenditure in the budget
deficit/surplus (BUDS) equation is statistically significant at 1% level with a negative sign.
This implies that the higher expenditure weakens fiscal performance, regardless of how it is
financed. The estimate attached to the total government expenditure in the other revenue
equation is positive and statistically significant at 10% level. This means that an increase in
total government expenditure financed by increasing direct tax income makes more pressure
on the rise of other revenue. Ten zero constraints imposed on the (direct) tax-financing model
are not rejected at any conventional significant levels. On the whole, these results reveal that
the Australian economy faces a trade-off between economic growth and income equality when
direct tax income is used to finance total government expenditure. Furthermore, results reveal
that direct tax income which is used to finance total government expenditure is progressive as
it helps to reduce income inequality. An increase in indirect tax income financed by reducing
direct tax income causes income inequality to go up. The negative effect on economic growth
29
of an increase of total government expenditure financed by direct tax income is in line with the
theoretical claims of neo-classical economic growth models (Mirrlees 1971), politico-economy
growth models, and public-finance endogenous growth model.

Table 6: Contemporaneous effects ( 0 matrix) of total government expenditure financed by
direct and indirect taxes
Fiscal policy strategy: Tax-financing (Theoretical assumption: increased expenditure is
entirely financed by increasing direct taxes)
TOTExp
1
ORev
ITAX
0.50*** (0.073)
0 (con.)
1
0 (con.)
1
BUDS
-0.002* (0.000)
0 (con.)
0 (con.)
1
EG
-0.118**(0.026)
0 (con.)
0 (con.)
0 (con.)
1
NetGini
-0.314* (0.009)
0 (con.)
0.17***(0.100)
0 (con.)
0 (con.)
1
Fiscal policy strategy: Tax-financing (Theoretical assumption: increased expenditure is
entirely financed by increasing indirect taxes)
TOTExp
1
ORev
0.52*** (0.061)
1
DTAX
0 (con.)
0(con.)
1
BUDS
-0.002 (0.000)
0 (con.)
0.001 (0.000)
1
EG
0 (con.)
0(con.)
0(con.)
0(con.)
1
NetGini
0 (con.)
0(con.)
0(con.)
0(con.)
0(con.)
1
Note: Probability values are given in brackets. *,**,*** indicate the statistical significance of relevant
estimates at 1, 5 and 10 percent levels. The bracket ‘(con.)’ shows the zero constraint imposed on the
relevant coefficient. Ten over identified (zero) constraints imposed on the coefficients in debtfinanced model are not rejected at any conventional significant levels as chi2 (10) = 4.265 (Prob. >
chi2 = 0.935). Twelve over identified (zero) restrictions imposed on the coefficients in tax-financed
model are not rejected at any conventional significant levels as chi2 (12) = 17.44 (Prob. > chi2 =
0.134). Exogenous variables included in both models are: population growth (POG), one period
lagged values of imports plus exports as a ratio of GDP (TROPEN), inflation (INF), one period lagged
of human capital index (LHCI) and globalization index (GLOBI).
The results in the second panel of Table 6 shows that the total government expenditure financed
by increasing indirect tax income does not affect economic growth or income inequality
significantly. As the total government expenditure financed by increasing indirect tax income
does not increase net income inequality, we find no evidence on the regressivity of the indirect
30
tax system. The estimate attached to the direct tax income (DTAX) in the budget deficit/surplus
(BUDS) equation is statistically significant at 1% level with a positive sign. This implies that
an increase in direct tax income financed by reducing indirect tax income improves fiscal
performance (i.e. negative effect on budget surplus). This is plausible as direct tax income as a
ratio of GDP on average accounts for around 14% compared to 6% of indirect tax income as a
ratio of GDP over the 1962-2012 period in Australia. The estimate attached to the total
government expenditure in the budget deficit/surplus (BUDS) equation is negative and
statistically significant at 1% level. This implies that the higher expenditure, regardless of being
financed by increasing indirect taxes, deteriorates fiscal performance. The positive sign of the
estimate attached to the total government expenditure in the other revenue equation means a
more pressure on increasing other revenue. The pressure on other revenue is higher when total
government expenditure is financed by increasing indirect taxes than direct taxes. Twelve zero
constraints imposed on the (indirect) tax-financing model are not rejected at any conventional
significant levels.
Robustness of results
We tested the robustness of results by changing the order of variables of respective SVAR models.
The re-estimated models strongly confirmed that our results do not change with changes of the ordering
of variables. These results can be provided upon request.
5. Concluding remarks
In this study, we investigated the effects of fiscal policy on economic growth and income
inequality in Australia using annual data from 1962 to 2012. We found evidence on the
presence of a trade-off between economic growth and equality not only in terms of tax-financed
fiscal strategy but also with regard to debt-financed fiscal policy. The negative effect of debtfinanced fiscal policy on economic growth is slightly higher (-0.135 percent) than the negative
effect of tax-financed fiscal policy (-0.124 percent). The reduction of income inequality is also
somewhat higher under debt-financed fiscal policy (-0.332) than under tax-financed fiscal
strategy (-0.321). The main policy message of this result is that it is required to focus on the
reduction of budget deficit as the negative effects of deficit financing on economic growth (i.e.
possible crowding out effects and reduction of net export to GDP) seems to outweigh its
positive effects (i.e. as a tool to promote aggregate demand).
31
The trade-off between economic growth and income inequality in the tax-financed model
originates from direct taxes whereas indirect taxes make no any effects on economic growth or
income inequality. However, reduction of direct taxes increases income inequality. The main
policy message of this finding is that it is required to increase indirect tax income to finance
government expenditure as indirect taxes neither increase income inequality (unless reducing
direct taxes) nor retard economic growth. However, appropriate measures are vital to take into
account in order to control or to reduce income inequality if direct taxes are reduced to promote
economic growth. Results under debt-financed fiscal policy support to the presence of
Ricardian equivalence hypothesis. Reduction of economic growth under debt-financed fiscal
policy shows that the implementation of countercyclical fiscal policies is not effective in
promoting economic growth although such policies help to reduce income inequality. This
could mainly be due to the forward-looking behaviour of Australian individuals. We found no
evidence that economic growth reduces income inequality. This means that the lack of
inclusiveness of the mainstream economic growth process. Hence, appropriate measures are
also required to create opportunities (i.e. employment opportunities, skill enhancement
programmes etc.) available for those who are suffering from inequality. We found that direct
tax system is progressive whereas indirect tax system plays a neutral role in the determination
of income redistribution. We accept the first hypothesis about the presence of a trade-off
between equity and efficiency. However, we reject the second hypothesis.
Regardless of different methods and countries used by other research, our results are consistent
with their results in terms of; (1) the presence of a trade-off between economic growth and
income inequality in direct taxation (i.e. Muinelo-Gallo and Roca-Sagales 2011, 2013; Liu
and Martinez-Vazquez 2015); (2) having positive effects of fiscal policy on income
redistribution (i.e. Ramos and Roca-Sagales 2008; Davtyan 2014; and Muinelo-Gallo and
Roca-Sagales 2013); (3) having negative effects of distortionary (direct) taxation on economic
growth (i.e.Bleaney et al. 2001; Kneller et al. 1999; Miller and Russek 1997); (4) the presence
of insignificant impact of indirect taxation on economic growth (i.e. Kneller et al. 1999); and
(5) inefficiency of countercyclical fiscal policies in promoting economic growth (Brittle 2010;
Comley et al. 2002; Makin and Narayan 2011; Taylor 2009; Martineau and Smith 2015).
32
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Appendix
Table A1: Theoretical and empirical classification of components of fiscal policy (FPC)
Theoretical definition
Government expenses
(1) Non-redistributive expenditure
(NRDExp)
(2) Redistributive expenditure
(RDExp)
(3) Total expenditure (1+2) (TOTExp)
Government revenue
(4) Direct tax income (DTax)
(5) Indirect tax income (ITax)
Empirical classification by Australian Bureau
of Statistics
Commonwealth General Government
Expenses by Purpose
General public services
Defence, Public order and safety
Recreation and culture, Fuel and energy
Transport and communications
Other economic affairs
Nominal interest on superannuation
Public debt transactions
Other purposes
Education, Health
Social security and welfare
Housing and community amenities
Commonwealth General Government
Taxation
Taxes on income, Taxes on payroll and
workforce, Taxes on property
Taxes on profits and capital gains
General taxes (sales taxes), Goods and
services tax (GST), Excises and levies
Taxes on international trade and transactions
(6) Total tax income (4+5) (TOTTax)
(7) Other revenue (ORev)
(8) Total revenue (6+7) (TOTRev)
(9) Budget deficit/surplus
(8-3)(BUDS)
Other taxes (taxes on the use of goods and
performance of activities, taxes received
from public corporations, and from other
levels of government), Social contributions
Grants and Other revenues
Commonwealth Government Total Revenue
GFS Net Operating Balance
Source: ABS (2016)
38
Table A2. Codes, Definitions and Sources of Variables
Endogenous variables
Codes
Definition
DTAX
Direct tax income as a ratio of GDP. The components included in DTAX are given in Table A1. (All fiscal
policy variables are converted to real values using GDP deflator (2005=100) before dividing by real GDP)
ITAX
Indirect tax income as a ratio of GDP. The components included in ITAX are given in Table A1.
TOTTAX Total tax income (DTAX+ITAX) as a ratio of GDP
OREV
Other revenue as a ratio of GDP. The components included in OREV are given in Table A1.
TOTREV Total revenue (TOTTAX + OREV) as a ratio of GDP
TOTExp
Government’s total expenditure (NRDExp + RDExp) as a ratio of GDP.
BUDS
Budget deficit/surplus (TOTREV-TOTExp) as a ratio of GDP
NetGini
Net Gini index (market income-government taxes + government transfers); values of the index range from
0 (least inequality) to 1 (highest inequality).
PCRGDP Real per capita gross domestic product (GDP)
Exogenous variables
TROPEN Lagged values of trade openness: the sum of exports and imports of goods and services measured as a
share of GDP
LHCI
Lagged values of index of human capital per person, based on years of schooling and returns to education
INF
Inflation: measured using GDP deflator (2005 = 100)
POG
Population growth; Annual growth rate of population
GLOBI
Index of Globalization: min: 0 (no globalization), max 100 (maximum globalization); three criteria are
used to measure this index: social, economic and political factors
39
Source(s)
ABS (2016)
-do-do-do-do-do-doSolt (2014)
Feenstra et al.
(2015)
Feenstra et al.
(2015)
-do-do-doDreher et al.
(2008)