Fiscal Reform and Fiscal Sustainability:
Evidence from Australia and Sweden∗
Tomomi Miyazaki†
February, 2014

This paper examines how the adoption of a ﬁscal rule aﬀects thesustainability of ﬁscal policy in two OECD countries; Australia andSweden. While recent ﬁscal reforms undertaken in both these countries areuseful for ensuring the sustainability of government budgets, there are a few
diﬀerences. In Australia, we show that government revenues are notnecessarily growing at a faster rate than government expenditures, at leastfrom the viewpoint of a statistical long-run relationship. In contrast, in
Sweden, we show the reform is more beneﬁcial for the attainment of abudget surplus.
K eywords: Fiscal reform; Sustainability of ﬁscal policy; Expenditureceilings; Dynamic OLS
JEL classification: E62; H61; H62∗

I would particularly like to thank William Keech for his kind instruction. I would
also like to thank two anonymous referees, Kazumi Asako, Jacobs De Haan, Takao Fujii,
Toshiya Hatano, Kazuki Hiraga, Kazuyasu Kawasaki, Eric Leeper, Jenny Lighart, Yasutomo Murasawa, Masakatsu Okubo, Cameron Shelton, Hideaki Tanaka, and Richard
Wagner for their insightful comments and suggestions. The participants of the Yugawara
Conference on the Research Group of Business Cycles, the 66th Congress of the International Institute of Public Finance, the Japan Economic Association 2010 Autumn Meeting,and the 2011 Annual Meeting of the Public Choice Society for several useful comments.
This work has been ﬁnancially supported by Grants-in-Aid for Scientiﬁc Research (Young
Scientist). The usual disclaimer applies.
†
E-mail: miyazaki@econ.kobe-u.ac.jp1

1

Introduction

In the 1990s, many OECD countries undertook various approaches toﬁscal reform. Three types of reforms were typically undertaken: (i) the introduction of numerical ﬁscal rules; (ii) the reform of budget procedures; and(iii) the introduction of independent ﬁscal institutions.
Of these three approaches, the introduction of numerical ﬁscal rules has
been necessary and eﬀective for consolidating government budgets. In termsof the outcomes of these approaches in the European Union (EU), Hallerberg et al. (2007) argued that the severity of ﬁscal rules appeared to restrain
public debt regardless of the ideological distance between the ruling parties.
Moreover, as Franco and Zotteri (2010) argued, even some countries whereassigned strong powers to the ﬁnance minister or prime minister introduced
expenditure rules in the 1990s. There is also the expectation that independent ﬁscal institutions provide policy advice through forecasts and ﬁscal
analysis. However, as argued by Fatás (2010), independent ﬁscal institutions
may be the appropriate authorities to complement numerical rules.
Incidentally, to meet balanced budget targets, governments may often
prefer spending cuts to tax increases because of the relatively small macroeconomic trade-oﬀ. Furthermore, for the purpose of ﬁscal adjustment, Konishi(2006) and Alesina (2010) showed that governments tend to focus on expenditure restraint rather than on increasing taxes. Following this, we focus on
the eﬀect of budget expenditure rules (or ceilings). In this context, we deﬁne
the introduction of an expenditure rule as a type of “ﬁscal reform”.
Many countries introduced budget expenditure rules in the 1990s. For
example, since 1996, Sweden has been implementing ceilings that set expenditure limits over a three-year period. Under the Swedish rule, expenditures
are not allowed to increase over a three-year period. Australia has been
setting limits on annual expenditures since 1996. The ceilings are legally
enforced by the Charter of Budget Honesty Act, established in 1998. The
Australian rule is linked to forward estimates of future economic growth andexpected budget balances. However, although the level of expenditure is ﬂex-2

ible, the rule functions as a limit on expenditures in certain years.1 Australia
and Sweden introduced ﬁscal rules following ﬁscal aggravation in the earlyand mid-1990s owing to the economic slowdown. With regard to other countries, as a condition for forming a coalition government in the Netherlands
in 1994, the coalition parties also agreed on expenditure rules. Although
Japan committed to decrease government expenditures through the Fiscal
Structural Reform Act in 1997, the act was suspended in December 1998.2
Until the mid-1990s, both Australia and Sweden were typically categorized as countries without expenditure ceilings, and as countries in which
there was little evidence that the prime minister or ﬁnance minister dominated budget negotiations, as shown by von Hargen and Harden (1995), De
Haan et al. (1999), and Perotti and Kontopoulos (2002). By contrast, theNetherlands was seen as countries that did implement expenditure ceilings
and in which the prime minister or ﬁnance minister dominated budget negotiations.3 Thus, the recent ﬁscal reforms undertaken in these countries areevidence of the reinforcement of “existing” ﬁscal rules and of the reinforcement of the power of the ﬁscal authorities. Moreover, unlike the JapaneseFiscal Structural Reform Act, the reforms undertaken in both Australia andSweden remain eﬀective.
When compared with the reforms conducted in countries with only primitive expenditure ceilings or strong ﬁscal authorities, and compared with theJapanese experience, the reforms conducted in Australia and Sweden clearly
altered their “fragile” public ﬁnance systems and should essentially be con-1

For details of the reforms conducted in Australia and Sweden, see Kennedy and Robbins (2003), Simes (2003), and Wehner (2007).
2
For an appraisal of Japanese ﬁscal institutions and ﬁscal reform, see Tanaka (2003)
and von Hagen (2006).
3
von Hagen and Harden (1995) argued that the budget process in the Netherlands
was governed by a ﬁrm commitment to the setting of a numerical target, even before the1990s. On the basis of this and the agreements subsequently introduced in 1994, Perotti
and Kontopoulos (2002) characterized the Netherlands as having expenditure ceilings from1970 to 1995. The budget process in the UK relies heavily on the authority of the Treasury.
Hallerberg et al. (2007) pointed out that the degree of commitment improved in Denmark
between 1991 and 2004. However, Denmark can be classiﬁed as a country with primitiveﬁscal rules, at least according to surveys conducted from the 1980s to the mid-1990s.

3

sidered examples of the “permanent” introduction of ﬁscal rules.4
The Australian and Swedish reforms may contribute to ﬁscal policy sustainability when the government budget deﬁcit has not been on a hitherto
sustainable path. The mechanisms are as follows. First, the government
may attempt to restrict spending based on the ceiling. Second, the rules
may lead to a reduction in the government’s deﬁcit. Third, the government
may achieve ﬁscal soundness through deﬁcit reduction and, consequently,
maintain the sustainability of ﬁscal policy.
Conversely, countries that do not adopt or apply the rules may not contribute to ﬁscal policy sustainability. For example, as mentioned earlier, theJapanese government abolished the Fiscal Structural Reform Act in 1998.
The Japanese government has not since adopted a legal rule to consolidate
the government budget. This may explain the continuous increase in theJapanese government’s debt, and some existing researches show that ﬁscal
policy in Japan may not be sustainable.5 The fact that reforms in Australia
and Sweden have been eﬀective suggests that they, unlike Japan’s reforms,
may contribute to ﬁscal policy sustainability.
Some recent studies, such as those of Fatás (2010), Franco and Zotteri(2010), and Rose (2010), have identiﬁed a relationship between ﬁscal rules
and the sustainability of ﬁscal policy. Franco and Zotteri (2010), in particular, discussed the relationship between ﬁscal reform and ﬁscal sustainability
and emphasized that ﬁscal rules play an important role in ensuring the sustainability of ﬁscal policy, as we assume. However, these researchers did
4

New Zealand also introduced a ﬁscal rule in the form of the Fiscal Responsibility
Act in 1994. However, to our knowledge, unlike in the cases of Australia and Sweden,
there has been no examination of the eﬀect of ﬁscal rules or of the strength of the ﬁscal
authorities in New Zealand’s public ﬁnancial system before the introduction of the Act.
Therefore, we cannot investigate this case with conﬁdence because it may be diﬃcult
to judge evenhandedly whether New Zealand’s ﬁscal reform represents evidence of theintroduction of “brand-new” ﬁscal rules or simply of the reinforcement of existing rules
and procedures.
5
For example, by using a dynamic stochastic general equilibrium model, Hosono and
Sakuragawa (2010) and Hosono and Sakuragawa (2011) found that the debt–GDP ratio has
gradually increased, thereby causing Japanese government debt to become unsustainable.
A recent paper by Doi et al. (2011) demonstrated the unsustainability of Japanese ﬁscal
policy by estimating a ﬁscal policy function with Markov switching.

4not examine the issue by using econometric techniques. On the other hand,
there exist many previous studies that consider the sustainability of ﬁscal
policy in general, including those of Hakkio and Rush (1991), Trehan andWalsh (1991), Ahmed and Rogers (1995), Quintos (1995), Bohn (1998), Martin (2000), Bravo and Silvestre (2002), Afonso (2006), Uctum et al. (2006),
Bohn (2007), Bohn (2008), Afonso and Rault (2009), and Legrenzi and Milas(2011). Olekalns (2000), Hatemi-J (2002a), and Hatemi-J (2002b) examinedsustainability in the Australian and Swedish cases. However, to our knowledge, no empirical work has been undertaken on the sustainability of ﬁscal
policy that incorporates ﬁscal reform.6
Hence, our objective is to analyze how the adoption of a “permanent”
ﬁscal rule aﬀects the sustainability of ﬁscal policy in Australia and Sweden.
We focus on a numerical ﬁscal target and, in particular, we deﬁne the introduction of expenditure rules as a ﬁscal reform, which is assumed to be a
structural change. This is why such an introduction changes the intertemporal relationship between revenues and expenditures and puts ﬁscal policy
on a sustainable path. Furthermore, we use our empirical results to discuss
the implications for EU countries that have experienced recent ﬁscal crises.
The process is as follows. First, we test for cointegration between government revenues and expenditures. Second, we estimate the cointegration
vector in order to determine whether the introduction of a ﬁscal rule would
contribute to making the government’s budget sustainable. Third, to examine the inﬂuence of ﬁscal reform, we use the dynamic ordinary least squares(DOLS) approach developed by Stock and Watson (1993) to estimate thecoeﬃcient of the ﬁscal variable that exhibits a structural break.
The remainder of the paper is structured as follows. In Section 2, we
describe the theoretical and empirical framework. In Section 3, we report
our estimation results. Section 4 concludes the paper.

6

Hatemi-J (2002b) examined the sustainability of Swedish ﬁscal policy incorporating
structural breaks to conﬁrm that it satisﬁed EMU criteria. However, Hatemi-J (2002b) did
not examine the eﬀects of the introduction in 1996 of the rules considered in this paper.

5

2

The Sustainability of Public Debt

2.1

Theoretical Framework

We assume that the government’s one-period budget constraint is:∆Dt = Gt + rt Dt−1 − Rt ,(1)

where Gt denotes government expenditure (excluding interest payments), rt
is the real interest rate, Rt is government revenues, and Dt is government
debt. In our speciﬁcation, following Hakkio and Rush (1991), Quintos (1995),
Martin (2000), and Afonso (2006), we assume that rt is stationary around its
mean r .7 Given this assumption, we deﬁne the following: G∗ t = Gt + (rt −r)Dt−1 . This expression is used to rewrite equation (1) as follows:G∗ t + (1 + r)Dt−1 = Rt + Dt .

(2)

Rewriting equation (2) for subsequent periods and expressing debt yields:
Dt =

1
1
St+j+1 +Dt+j+1 ,(1 + r)(1 + r)(3)

where St+j+1 = Rt+j+1 − G∗ t+j+1 . Then, solving the resulting equations
recursively yields the following intertemporal budget constraint:
Dt =

∞
∑

1
1
St+j+1 + lim
Dt+j+1 .
j+1j→∞ (1 + r)j+1(1 + r)j=0(4)

By taking conditional expectations, we can write equation (4) as:
Dt =

∞
∑

1
1
Et [St+j+1 ] + lim
Et [Dt+j+1 ].
j+1j→∞ (1 + r)j+1(1 + r)j=0(5)

From equation (5), the intertemporal budget balances if and only if thecurrent value of outstanding government debt is equal to the expected present
value of future budget balances. That is:
Dt =

∞
∑

1
E [S
]
j+1 t t+j+1
(1
+
r)
j=0

7

(6)

Treating the real interest rate as endogenous dramatically changes the restrictions
that need to be tested. However, following existing studies on the sustainability of ﬁscal
policy that incorporate structural change, we assume that rt is stationary around its mean.
Treating the real interest rate as endogenous awaits further research.

6

must be equivalent to the following:1
Et [Dt+j+1 ] = 0.
j→∞ (1 + r)j+1
lim

(7)

Equation (7) is commonly known as the no-Ponzi game condition (hereafter,
NPG condition).

2.2

The Econometric Model

To proceed from theory to empirical testing, we ﬁrst diﬀerence equation(4) to obtain:
∆Dt =

∞
∑

1
1
[∆St+j+1 ] + lim[∆Dt+j+1 ].
j+1j→∞ (1 + r)j+1
(1
+
r)
j=0

(8)

Given ∆Dt = Dt −Dt−1 , and using equation (1), equation (8) can be rewrittenas:
Gt + rt Dt−1 − Rt =∞∑1[∆St+j+1 ]
j+1
(1
+
r)
j=0
1
[∆Dt+j+1 ].
j→∞ (1 + r)j+1+ lim

Given the NPG condition, this equation can be written as:
Gt + rt Dt−1 − Rt =∞∑1[∆St+j+1 ].
j+1
(1
+
r)
j=0

(9)

To test the NPG condition (that is, whether equation (9) holds), we follow
standard procedure of testing for the stationarity of Gt + rt Dt−1 − Rt after
imposing the cointegration vector (1,1,–1).8 By deﬁning GGt = Gt + rt Dt−1 ,8

As an alternative, Bohn (2007) proposes the policy reaction function approach, developed by Bohn (1998). However, Bohn’s (1998) approach may not be applicable to thelong-run relationship between government revenues and expenditures that is the focus of
our research. Moreover, as pointed out by Li (2009), Bohn’s (1998) reaction function cannot be identiﬁed because the equilibrium condition that the market value of debt equals
the expected present value of primary surpluses may induce positive correlation between
the value of debt today and future surpluses.

7we can use the following equivalent procedure to test for cointegration in the
regression equation:
Rt = α + βGGt + ut .
(10)
To determine the sustainability of the budget deﬁcit, we estimate equations for, and conduct tests on, public expenditures including net interest
payments, as in the cointegration approach.9 In this respect, some researchers, such as Quintos (1995) and Martin (2000), test the necessary andsuﬃcient conditions for deﬁcit sustainability by testing whether GGt and Rt
are cointegrated, with β = 1 in equation (10).
However, in the context of this approach, Bohn (2007) argued that if
the revenue and with-interest spending series are stationary after any ﬁnite
number of diﬀerencing operations, the intertemporal budget constraint issatisﬁed. Moreover, for the sustainability test developed by Quintos (1995),
Bohn (2007) showed that it is misleading to determine whether the necessary or suﬃcient condition holds based on the coeﬃcient of the cointegration
vector. Bohn (2007) argued that all cointegrating conditions are merely “sufﬁcient” for transversality. Given Bohn’s (2007) argument, the cointegration
approach for judging the sustainability of public debt allows us to examine
only the suﬃcient condition. In addition, Bohn (2007) suggested that all of
the sustainability conditions, be they strong, weak, or absurdly weak, imply
that the transversality condition and the intertemporal government budget
constraints are satisﬁed.10 Therefore, we modify the scenarios for deﬁcit
sustainability suggested by Quintos (1995) and Martin (2000) as follows:(1) The deﬁcit is “sustainable” if there is a cointegration relationship between GGt and Rt , with 0 < β≤1.
9

To determine whether the NPG condition is satisﬁed by using a unit-root test or
cointegration test, we also test for the stationarity of the ﬁrst diﬀerence of public debt.
However, it is diﬃcult to test for the “direction” of ﬁscal reform by examining the stationarity of public debt. Moreover, as suggested by Ahmed and Rogers (1995) and Bohn
(1998), it seems appropriate to deal with sustainability in a stochastic environment. However, since our motivation is to examine not only the sustainability problem but also the“direction” of ﬁscal reform for the government budget, it is not necessarily important toassume a stochastic environment in deriving the NPG condition. For these reasons, weemploy a simple cointegration approach.
10
For details, see the arguments on pp. 1839–1843 of Bohn (2007).

8

(2) β > 1 is not consistent with a deﬁcit because revenues are growing at
a faster rate than expenditures, including interest payments.
Scenario (1) implies that only the suﬃcient condition (not the necessary and suﬃcient conditions) for deﬁcit sustainability is satisﬁed.11 Even
if β < 0, a conﬁrmed cointegration relationship between GGt and Rt does
not necessarily imply that the budget deﬁcit is unsustainable, according to
the arguments of Bohn (2007). Moreover, despite a conﬁrmed cointegration
relationship between revenues and expenditures, the estimated β could bepositive but insigniﬁcant. In these cases, we simply report the existence of a
conﬁrmed cointegration relationship between GGt and Rt .
To estimate β, we use the DOLS method developed by Stock and Watson(1993). Using DOLS gives a more eﬃcient estimate of the coeﬃcient of thecointegration vector than does straight OLS.
An estimated coeﬃcient of between zero and unity on the variable GGt
multiplied by the dummy indicating ﬁscal reform implies that reform makes
the government budget sustainable. Given the above arguments, an estimated coeﬃcient exceeding unity implies that the reform enables the government to run budget surpluses.

11

In a recent paper, Legrenzi and Milas (2011) assumed that the government’s intertemporal budget constraint is satisﬁed if government revenues and expenditures are cointegrated and if the estimated β = 1, whereas ﬁscal policy might not be sustainable if β < 1,even if there is a cointegration relationship between government revenues and expenditures.
However, we do not apply the scenarios suggested by these authors because, according toBohn (2007), they could be misleading.

9

3

Empirical Results

3.1

Datasets

All datasets are from the OECD Economic Outlook database (No.82).12
We use this database because, for comparing the inﬂuence of ﬁscal reformin two diﬀerent countries, it seems appropriate to use country data from thesame database, rather than using data from individual countries’ governments. For both Australia and Sweden, our quarterly data cover the period
1980:Q1–2007:Q4.13 GGt is current general government disbursements (including interest payments) and Rt is current general government receipts.14
All variables are measured in real terms, based on GDP deﬂators, and areseasonally adjusted, based on the X-12-ARIMA method.

3.2

Unit-root Tests for rt , GGt , and Rt

We report the results of the unit-root tests for each variable. To do this,
we ﬁrst check whether both GGt and Rt are I(1) before we test for cointegration between GGt and Rt .
In Section 2.1, we assumed that the interest rate rt is stationary around
its mean r. Thus, following Hatano (1999), before we perform the unit-root
tests for the ﬁscal variables, we check whether this assumption holds.
12

The ﬁscal rules in two countries concern only the federal government budget, whereas
the data from the OECD cover general government, including local government. However,the local government budget is hardly aﬀected because the federal government exerts
strong control over the local public ﬁnancial system through the system of intergovernmental transfers.
13
We choose 1980 as the start of the sample period to avoid incorporating the eﬀects ofthe two oil crises of the 1970s.
14Property income received and paid by the government is excluded from both current
disbursement and receipts because these items have a rather incomplete coverage in theOECD Economic outlook database and are typically very small, as indicated in Perotti
and Kontopoulos (2002). The averages of the share of property income received by thegovernment per current receipts in the sample periods are 6.184% in Australia and 7.628%
in Sweden. The averages of the share of property income paid by the government per
current disbursements are 9.621% in Australia and 8.243% in Sweden.

10

To check whether each variable is stationary, we use the Augmented
Dickey–Fuller (1979) test (hereafter, ADF test). We also use the GLS-based
Dickey–Fuller unit-root test (hereafter, DF-GLS test) developed by Elliott
et al. (1996), and the Zivot and Andrews (1992) unit-root test. The reason underlying the use of the former is that the DF-GLS test is suﬃciently
powerful to detect a unit root in small samples. The Zivot and Andrews(1992) test is useful because it can detect a unit root in a time series withunknown structural change.15 More speciﬁcally, we perform both the ADF
and DF-GLS tests as benchmarks, and if we cannot conﬁrm that the variableis I(1), we repeat the test by including a structural break. To be compatible
with our theoretical arguments, and following standard practice, all our tests
include only a constant.
Table 1 reports the results for the unit-root test for rt (the real long-term
interest rate). For both countries, both the ADF and DF-GLS tests cannot
reject the null hypothesis that rt is nonstationary. By contrast, the Zivot
and Andrews (1992) unit-root test, including an unknown single structural
break, rejects the null of nonstationarity. These results validate an important
assumption underlying our model when a structural break is incorporated.
Table 2 shows the results for the ﬁscal variables. The results reveal thatnearly all the level variables are nonstationary. The exceptions include Rt
level, which is stationary for Australia according to the DF-GLS test, and for
Sweden according to the Zivot and Andrews (1992) test. However, there are
no rejections for the ADF test. Hence, we need to test the ﬁrst diﬀerencesof the series.
All tests strongly reject the null hypothesis of nonstationarity for the ﬁrst
diﬀerences. Therefore, all variables may be treated as single (I(1)) unit-root
process.

15

Economic circumstances within each country aﬀect public expenditures, revenues, andthe long-term interest rate. Moreover, changes in the deﬁcit may result more from thegovernment’s own eﬀorts than from the binding rules set by foreign countries or international organizations. Hence, the structural breaks evident in the variables we use mayarise because of endogenous factors. Therefore, we use the unit-root test of Zivot andAndrews (1992) with an endogenous structural break.

11

3.3

On the Existence of Cointegration Relationships
Between Gt , rt Dt−1 , and Rt

The results reported in Tables 1 and 2 validate the assumptions necessary
for testing the sustainability of the budget deﬁcit. In this subsection, we test
for cointegration between Rt and GGt .
We ﬁrst perform Johansen’s (1995) cointegration test for Rt and GGt
by employing the speciﬁcation that there are no linear time trends in thelevels of the data. The result in Table 3 shows that the null hypothesis of no
cointegration vector (r = 0) is rejected in favor of at least one cointegration
vector at the 1% level of signiﬁcance for the two countries.
Second, we employ the unit-root test for the budget deﬁcit GGt − Rt .
The unit-root test for GGt − Rt is equivalent to the test of the cointegration
relationship among Gt , rt Dt−1 , and Rt . Especially, we implement the Zivot
and Andrews (1992) test with a single unknown break. The results arereported in Table 4. Table 4 shows that the stationarity of GGt − Rt is
conﬁrmed when we assume a structural break.16

3.4

The DOLS Results and the Influence of Fiscal Reform

Based on the results of testing for cointegration between Rt and GGt , wecan conﬁrm a cointegration relationship between Gt , rt Dt−1 , and Rt . In thissubsection, we use the DOLS approach to estimate the cointegration vector
and check whether the ﬁscal reform aﬀects the sustainability of ﬁscal policy.
16

A method developed by Gregory and Hansen (1996) is useful for performing a cointegration test when there is a single break. However, when using this test, we could only
ﬁnd evidence of a cointegration relationship among Gt , rt Dt−1 , and Rt by limiting thesample period. This suggests that there are multiple breaks during the sample period.
Dealing with this problem would require testing for a unit root (cointegration) when there
are multiple breaks, as did Martin (2000), Bai and Perron (2003), Arghyrou and Luintel (2007), and Kejriwal and Perron (2010). Although a variety of tests are proposed inthese papers, there seems to be no established method for testing for cointegration when
there are multiple breaks. Thus, we are limited to detecting single structural breaks andobserving the data. Addressing this limitation awaits further research.

12

According to the results based on incorporating a break shown in Table
3, there may have been a structural change in the relationship between Rt
and GGt around 1984. In 1984, the ﬁscal condition in both two countries
were gradually improving as shown in Figures 1 and 2. This perhaps reﬂects the economic recovery observed in the developed countries this year.
Moreover, as discussed in Section 1, since 1996, Australia has set an expenditure ceiling by balancing the government budget across the business cycle.
Likewise, the Swedish government introduced an expenditure ceiling in 1996.
In this context, Figures 1 and 2 depict the trends in the budget balance(Rt − GGt ) in the two countries. According to Figure 1, although the Australian government ran deﬁcits from 1991 to 1995, its budget balance has
been positive since 1996. Further, as shown in Figure 2, the budget deﬁcit in
Sweden decreased between 1995 and 1997, but was positive between 1997:Q4
and 2002:Q1. This suggests that there may have been a structural change
between Rt and GGt before and after the introduction of the ﬁscal rules.17
Alternatively, we observe a downward trend in the government budget balance around the early 2000s in both countries (2000–2002 in Australia and
2002–2003 in Sweden).18 Based on these arguments, which are supportedby the data as well as the statistical results, we can legitimately argue thata dummy variable representing a structural break should be included whenestimating the DOLS equation.
For this purpose, we incorporate dummy variables identifying the periods
in which the ﬁscal rules were introduced, which are represented as structural
17

Furthermore, to examine how governments strive to restrict government expendituresand successfully reduce the budget deﬁcit, we purify the “outcome” gained from the eﬀort
made to restrain expenditure. We do this to exploit the cyclically adjusted data or trend
component in the original time-series data. These movements illustrate clearly that bothgovernments have succeeded in restricting expenditures to be less than revenues in mostperiods since the introduction of the ﬁscal rule. These results also support the notion that
there may have been a structural change before and after the introduction of the ﬁscal rules.
Detailed arguments can be obtained from the former version of this paper, which is available from the author’s website: https://sites.google.com/site/tomomisite/research/dp.
18
The global economy entered a recessionary phase in 2000-2001 owing to the slowdownof the U.S. economy and bursting of so-called “information-technology bubble.” Owing to
the recession, the budget balance temporarlily worsend around the year 2000 in both thecountries.

13breaks, namely, Df for Australia and D̃f for Sweden.19 Initial testing detects
a breakpoint in 1984:Q3 in Australia and one in 1984:Q2 in Sweden. To deal
with these breaks, the dummy variable D84 for Australia takes a value ofunity from 1984:Q3 to 1996:Q2, and the dummy variable D˜84 for Sweden
takes a value of unity from 1984:Q2 to 1995:Q4 (and both are zero otherwise). For the other dummy variables, Df in Australia takes a value of unity
for the period 1996:Q3–2000:Q4, and D̃f in Sweden takes a value of unity
for the period 1996:Q1–2001:Q4 (zero otherwise). Based on the movements
in Rt and GGt shown in Figures 1 and 2, we propose to deal with possible
breaks around 2000. Thus, for Australia, we construct a dummy variable D01
that takes a value of unity from 2001:Q1 (zero otherwise). For Sweden, thedummy variable D02 takes a value of unity from 2002:Q1 (zero otherwise).
Clearly, we can determine the eﬀects of the ﬁscal reforms based not only on
the coeﬃcients of Df and D̃f but also on those of D01 and D02 . This isbecause the ﬁscal rules adopted in Australia and Sweden have been eﬀective
since being introduced.
We estimate equation (11) for Australia and equation (12) for Sweden as
shown below:
Rt = α + Df + D84 + D01 + β1 GGt + β2 (Df ∗ GGt )p∑+β3 (D84 ∗ GGt ) + β4 (D01 ∗ GGt ) +γj GGt−j
j=−p

∑
p

+

δj (Df ∗ GGt−j ) +

j=−p
p

+

∑

∑
p

ϕj (D84 ∗ GGt−j )j=−pSψj (D01 ∗ GGt−j ) + uAU
,
t

(11)

j=−p

Rt = α̃ + D̃f + D˜84 + D02 + β˜1 GGt + β˜2 (D̃f ∗ GGt )
p
∑
˜
˜
˜
+β3 (D84 ∗ GGt ) + β4 (D02 ∗ GGt ) +γ˜j GGt−jj=−p19

In Australia, the ﬁscal year is from July to the following June. In Sweden, the ﬁscal
year corresponds to the calendar year. Our dummy variables for identifying the introduction of ﬁscal reform are consistent with these deﬁnitions.

14

+

p
∑

δ˜j (D̃f ∗ GGt−j ) +

j=−p
p

+

∑

p
∑

ϕ˜j (D˜84 ∗ GGt−j )j=−pEψ̃j (D02 ∗ GGt−j ) + uSW
,
t

(12)

j=−p

where j is the number of leads and lags in the DOLS equation. We assume
lag and lead lengths of unity.
Tables 5 and 6 report the results of the DOLS estimation. Table 5 reports the estimation results based on excluding the dummy variables. These
results show that in the simpliﬁed model, the estimated β1 is not signiﬁcant.
However, as shown in Table 6, the estimated coeﬃcients for Df ∗ GGt (β2and β˜2 ) are positive and signiﬁcant, particularly in Sweden, where the estimate is greater than unity. This also applies to the estimates of β4 and β˜4 .
Moreover, we test the following null hypotheses: β2 = β4 = Df = D01 = 0(for Australia) and β˜2 = β˜4 = D̃f = D02 = 0 (for Sweden). To test thesehypotheses, we use a Wald test to check for structural change following the
introduction of the expenditure rules. Because we reject the null hypothesis
for both countries, we conﬁrm that structural changes took place, both in
the intercept and the slope parameters, following the introduction of ﬁscal
reform.
To check the robustness of our DOLS estimates, we reestimate the modelby increasing the lag length to three.20 Table 7 reports the results. The estimates for both β˜2 and β˜4 are greater than unity and signiﬁcant in all cases
for Sweden. By contrast, in the Australian case, β2 and β4 in Table 7 range
from 0.686 to 1.027. When two-period leads and lags are used, the estimateof β2 exceeds unity, but β4 is 0.812.
As shown in Tables 6 and 7, the estimates of β1 (β˜1 ) and β3 (β˜3 ) areneither positive nor signiﬁcant in any case. These results suggest that ﬁscal
policy may not have been sustainable in either Sweden or Australia before
these countries introduced their respective ﬁscal rules. However, based on
20

We set a maximum lag length of three because Stock and Watson (1993) showed that,
unlike increasing the number of observations, increasing the number of lags and the kernel
length does not appreciably improve the coverage rate. Nevertheless, because we wereunable to draw on any established method for determining the lag and lead lengths for
DOLS estimation, we could not identify speciﬁc values for lag and lead lengths.

15these results and our discussion of the estimated β coeﬃcient in Section 2.2,
we conclude that the expenditure ceiling introduced in 1996 by the Swedish
government allowed it to run a budget surplus, even after accounting for the
structural break that followed the introduction of the ﬁscal rule. Conversely,
although the Australian reform was also useful in ensuring the sustainabilityof the Australian government’s budget, we cannot necessarily conclude thatit helped yield a budget surplus, at least from the viewpoint of a statisticallong-run relationship, according to the arguments presented in Section 2.2.

4

Conclusion

In this paper, we examined the inﬂuence of ﬁscal reform on the sustainability of ﬁscal policy. We analyzed Australia and Sweden because these twocountries have been adopting ﬁscal rules since the mid-1990s. Based on the
scenarios used to determine the sustainability of ﬁscal policy discussed in
Section 2.2, although the Australian reform has been useful for ensuring thesustainability of ﬁscal policy, revenue has not necessarily grown at a fasterrate than expenditure from the viewpoint of a statistical long-run relationship between the two variables. By contrast, for Sweden, we found that,in terms of the long-run relationship between revenue and expenditure, reform has been beneﬁcial for running a budget surplus because, for the period
following the reform, the estimated coeﬃcients of GGt , namely β˜2 and β˜4 ,exceed unity.
Although many existing empirical studies on the sustainability of ﬁscal
policy have merely investigated whether the intertemporal government budget constraint holds in the long run, our results suggest that the introductionof a ﬁscal rule contributes to restoring the sustainability of ﬁscal policy.
Incidentally, the ﬁscal rules in both countries only cover the federal (central) government. However, if the ﬁscal rule also covers the local government
budget, it may exert a stronger inﬂuence to suppress government expenditures within a country. The inclusion of local public sector in the coverageof the rule may be desired.
Putting this aside, government of most EU countries have adopted the16

ﬁscal rule in accordance with the Maastricht Treaty and the Stability and
Growth Pact. Since we have focused on examining the reforms initiated in
the wake of ﬁscal aggrevation in countries, we do not examine the post-EMU
ﬁscal reforms. However, binding rules in the wake of pressure from foreigncountries or international organizations may also aﬀect the sustainability ofﬁscal policy. In that sense, post EMU ﬁscal reforms should be strongly addressed in future research. Further, the ﬁscal rules in some states within one
country might be changed. In such case, the intranational comparison may
be also worthy of investigation.

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21

Figure 1: The budget balance (R–GG) in Australia

Source: OECD Economic Outlook (No.82)
Note: Quarterly data used.

Figure 2: The budget balance (R–GG) in Sweden

Source: OECD Economic Outlook (No.82)
Note: Quarterly data used.

22

Table 1
Unit-root test for long-term interest rate (rt ). Sample period = 1980:Q1–2007:Q4 forAustralia (number of observations = 112) and 1980:Q1–2007:Q4 for Sweden (number ofobservations = 112)

ADF
Lags
DF-GLS
Lags
Z-A
Lags
Break point

Australia–1.1063–1.0134–5.015∗∗
3
1991:Q1

Sweden–1.22910.000
4–6.334∗∗∗
1
1996:Q3

Note: ADF indicates the results of the Augmented Dickey–Fuller stationarity test, DF-GLS is the
GLS detrended Dickey-Fuller stationarity test suggested by Elliott et al. (1996), and Z-A is the unit-root
test including an unknown structural break suggested by Zivot and Andrews (1992). The lag length ischosen using the Akaike Information Criterion (AIC) by setting the maximum length as eight. Asterisks
indicate level of signiﬁcance: ∗ = 10%, ∗∗ = 5%, and ∗ ∗ ∗ = 1%.

23

Table 2
Unit-root test for the government revenues (Rt ) and the government expenditures with
net interest payment (GGt ). Sample period = 1980:Q1–2007:Q4 for Australia (number ofobservations = 112) and 1980:Q1–2007:Q4 for Sweden (number of observations = 112)
Country
Australia

SwedenADF
Lags
DF-GLS
Lags
Z-A
Lags
Breakpoint
ADF
Lags
DF-GLS
Lags
Z-A
Lags
Breakpoint

Rt
1.417
2
–1.639∗7–3.372
3
1990:Q3
0.024
8
1.253
8
–4.609∗2
1991:Q1

GGt
1.201
3
1.001
7
–3.016
3
1992:Q3
0.102
8
1.188
7
–3.563
2
1990:Q3∆Rt–11.680∗∗∗1–2.323∗∗
6–12.381∗∗∗1
1993:Q2–3.331∗∗7–2.517∗∗
6–5.916∗∗∗
1
1989:Q4∆GGt–5.810∗∗∗2–2.241∗∗7–6.736∗∗∗
2
1989:Q2–4.248∗∗∗7–2.671∗∗∗6–6.855∗∗∗
3
1994:Q4

Note: All tests include an intercept. ADF indicates the results of the Augmented Dickey–Fuller
stationarity test, DF-GLS is the GLS detrended Dickey–Fuller stationarity test suggested by Elliott etal. (1996), and Z-A is the unit-root test including an unknown structural break suggested by Zivot andAndrews (1992). The lag length is chosen using the AIC by setting the maximum length as eight. Asterisks
indicate level of signiﬁcance: ∗ = 10%, ∗∗ = 5%, and ∗ ∗ ∗ = 1%.

24

Table 3
Trace statistics for Johansen’s (1995) cointegration tests for the government revenues (Rt )and the government expenditures with net interest payment GGt ). Sample period =1980:Q1–2007:Q4 for Australia (number of observations = 112) and 1980:Q1–2007:Q4 forSweden (number of observations = 112)
Country
Australia
Swedentest statistics
60.533∗∗∗36.729∗∗∗Note: Note: Our model assumes no linear time trends in levels. The lag length is three in Australia
and eight in Sweden after setting the maximum length as eight (using the AIC in both cases). Critical values are from MacKinnon et al. (1999) (critical value = 25.08 (1%)). Asterisks indicate level ofsigniﬁcance: ∗ = 10%, ∗∗ = 5%, and ∗ ∗ ∗ = 1%.

Table 4Unit-root test for budget balance (Rt –GGt ). Sample period = 1980:Q1–2007:Q4 for Australia (number of observations = 112) and 1980:Q1–2007:Q4 for Sweden (number of observations = 112)

Z-A
Lags
Breakpoint

Australia–5.008∗∗3
1984:Q3

Sweden–4.942∗∗
2
1984:Q2

Note: Z-A is the unit-root test including an unknown structural break suggested by Zivot and Andrews(1992). The lag length is chosen using AIC by setting the maximum length as eight. Asterisks indicate
level of signiﬁcance: ∗ = 10%, ∗∗ = 5%, and ∗ ∗ ∗ = 1%.

25

Table 5
Results of DOLS (without the dummies for breaks). Dependent variable = the government
revenues (Rt ). Sample period = 1980:Q1–2007:Q4 for Australia (number of observations= 112) and 1980:Q1–2007:Q4 for Sweden (number of observations = 112)
Country
Australia
Swedenβ0.382(0.325)0.552(0.783)

R¯2
0.975
0.815

Note: The coeﬃcients for the leading and lagged values of GGt are not shown for brevity. A constant term is included (results not shown). Standard errors in parentheses. Asterisks indicate level ofsigniﬁcance: ∗ = 10%, ∗∗ = 5%, and ∗ ∗ ∗ = 1%.

Table 6
Results of DOLS. Dependent variable = the government revenues (Rt ). Sample period =1980:Q1–2007:Q4 for Australia (number of observations = 112) and 1980:Q1–2007:Q4 forSweden (number of observations = 112)
Australia

Swedenβ1–0.067(0.407)β˜1
0.210(0.640)β2
0.504∗(0.328)β˜21.617∗∗∗(0.568)β3–0.030(0.274)β˜3–0.457(0.449)β4
0.639∗∗(0.304)β˜41.663∗∗∗(0.436)R̄2
0.984

Wald statistics
15.6∗∗∗

R̄2
0.952

Wald statistics
45.7∗∗∗

Note: The results of the coeﬃcients for the leading and lagged values of GGt , Df ∗ GGt in Australia,D˜f ∗ GGt in Sweden, D84 ∗ GGt in Australia, D˜84 ∗ GGt in Sweden, D01 ∗ GGt in Australia, and D02 ∗ GGt
in Sweden are not shown for brevity. A constant term is added, and Df in Australia, D˜f in Sweden, D84
in Australia, D˜84 in Sweden, D01 in Australia, and D02 in Sweden are included in the regression equation
as dummy variables for the intercept (results not shown for brevity). Standard errors are in parentheses.
Wald statistics are the results of tests of the null hypothesis that β2 = β4 = Df = D01 = 0 (in Australia)or β˜2 = β˜4 = D˜f = D01 = 0 (in Sweden). Asterisks indicate level of signiﬁcance: ∗ = 10%, ∗∗ = 5%, and∗ ∗ ∗ = 1%.

26

Table 7
Results of DOLS (robustness check of changing lag and lead length). Dependent variable =the government revenues (Rt ). Sample period = 1980:Q1–2007:Q4 for Australia (numberof observations = 112) and 1980:Q1–2007:Q4 for Sweden (number of observations = 112)
Australia

Lags & Leads
2
3

SwedenLags & Leads
23

β1–0.399(0.417)–0.618(0.458)β˜1

β21.027∗∗∗(0.363)0.773∗∗(0.434)β˜2

0.191(0.698)0.420(0.788)∗∗∗1.651(0.626)
1.528∗∗(0.716)β3
0.060(0.297)
0.093(0.346)β˜3

β4
0.812∗∗∗(0.326)
0.686∗∗(0.382)β˜4–0.563(0.502)–0.805∗(0.600)∗∗∗1.545(0.499)1.509∗∗∗(0.574)R¯2
0.986

Wald statistics
29.6∗∗∗

0.986

13.8∗∗∗R¯2
0.950

Wald statistics
41.4∗∗∗

0.948

50.8∗∗∗

Note: The coeﬃcients for the leading and lagged values of GGt , Df ∗ GGt in Australia, D˜f ∗ GGt
in Sweden, D84 ∗ GGt in Australia, D˜84 ∗ GGt in Sweden, D01 ∗ GGt in Australia, and D02 ∗ GGt in
Sweden are not shown for brevity. A constant term is added, and Df in Australia, D˜f in Sweden, D84 inAustralia, D˜84 in Sweden, D01 in Australia, and D02 in Sweden are included in the regression equation
as dummy variables for the intercept (results not shown for brevity). Standard errors are in parentheses.
Wald statistics are the results of tests of the null hypothesis that β2 = β4 = Df = D01 = 0 (in Australia)or β˜2 = β˜4 = D˜f = D01 = 0 (in Sweden). Asterisks indicate level of signiﬁcance: ∗ = 10%, ∗∗ = 5%, and∗ ∗ ∗ = 1%.

27