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Public Choice (2007) 131:157–175

DOI 10.1007/s11127-006-9111-3

O R I G I N A L A R T I C L E

The growth effects of fiscal policy in Greece 1960–2000

Konstantinos Angelopoulos · Apostolis Philippopoulos

Received: 17 August 2005 / Accepted: 25 September 2006C Springer Science+Business Media B.V. 2006

Abstract This empirical paper uses annual data for Greece 1960–2000 to study the link

between fiscal policy and economic growth. Our regression analysis implies that, although

a smaller public sector can be good for growth, it is necessary to look beyond size; the

composition and quality/efficiency of the public sector are equally important. The policy

lesson is that a smaller government share in GDP, a reallocation of funds away from the wage

bill to public investment, and an improvement in government quality/efficiency can become

engines of long-term growth.

Keywords Fiscal policy . Economic growth

JEL Classification: E6, O5

1 Introduction

The role of government, and in particular its fiscal policy, as determinant of economic growth

has received a lot of attention in the empirical literature. The evidence of the growth effectsof government expenditure and taxation has so far been mixed.1 This is consistent with the

predictions of the endogenous growth literature: government intervention may require higher

taxes, distort incentives, etc, but it can also protect property rights, address externalities, etc.

Thus, the relation between growth and fiscal variables need not be monotonic.2

1See e.g. Levine and Renelt (1992), Tanzi and Zee (1997), Folster and Henrekson (2001), Gemmel and

Kneller (2001), Mueller (2003, Chapter 22) and Barro and Sala-i-Martin (2004, Chapter 12).

2A popular conceptual framework has been provided by Barro (1990).

K. Angelopoulos

Athens University of Economics and Business, and University of Stirling



158 Public Choice (2007) 131:157–175

This empirical paper uses annual data for Greece to study the link between fiscal policy

and economic growth for the years 1960–2000 (our results remain practically the same if we

omit the 1997–2000 observations that have been recently revised by Eurostat). Following

most EU policy reports, we focus on the growth effects of the size, composition and quality

of the public sector.3

To do so, and since this is a time-series study, we use a small selectivenumber of time-varying growth determinants (fiscal policy measures and standard regime

dummies only).

The main results of our regression analysis are as follows. First, when we follow common

practice by using the government consumption share in GDP as a measure of the overall

size of the public sector, there is evidence that larger sizes hurt growth. Specifically, a rise in

the rate of change of government consumption share in GDP by one standard deviation (or

7.655%) decreases GDP growth by 1.38%. On the other hand, alternative measures of the

overall size, like total government expenditure or total tax revenue, both as a percentage of

GDP, are not found to be significant.

Second, when we use disaggregated fiscal policy data, a general message is that wagesand salaries in the public sector as a share of GDP are bad for growth. For instance, over

1972–1998, a rise in the growth rate of expenditure on wages and salaries by one standard

deviation (or 9.7%) reduces GDP growth by 1.72%. There is also evidence (although less

robust) that public investment as a share of GDP is good for growth, and transfers as a share

of GDP are bad for growth. On the other hand, constructed effective tax rates on various

sources of income do not seem to matter to growth.

Third, we construct a simple proxy for the quality of public infrastructure by using an

intuitive index with a sufficiently long time-series dimension, also used by Tanzi and Davoodi

(1998). Our regressions show that only in those years in which our measure of government

quality deteriorates relative to the previous year, a larger government size is bad for growth.

By contrast, when our measure of quality improves relative to the previous year, the growth

effect of government size is insignificant. It is the significantly negative effects that dominate

over time; this is why on average larger sizes are found to hurt growth (see the first result

above). Thus, what really matters to growth is the size-efficiency nexus, not size per se.

It is worth pointing out that the introduction of the above quality index improves the fit of

our regressions impressively. The growth in the share of government in GDP, when allowing

for a non-linear Laffer curve-type effect, can explain – along with some simple political

dummies – around 80% of the variation of the growth rate in the Greek economy over the

last forty years.These findings imply some policy lessons. Other things equal, a reduction in government

consumption can become an engine of growth. But, it is necessary to look beyond size:

the composition and efficiency of the public sector are also important. In particular, a fiscal

consolidation can become especially expansionary if it takes the form of cuts in expenditure

on wages and salaries in the public sector. A reallocation of funds into public investment

could further contribute to growth. Concerning efficiency or quality, it is striking that in years

of improved government quality or efficiency, increases in an already over-sized public sector

have not hurt growth.

Our paper provides a relatively rich study of the Greek case. It also studies not only the

growth effects of the size and composition of the public sector, but also its quality. As far as



Public Choice (2007) 131:157–175 159

we know, there are no papers that study how public sector quality shapes the growth effect

of size.

The rest of the paper is as follows. Section 2 reviews the literature. Section 3 describes the

data and their time-series properties. In section 4, we test how the government size affects

growth. In Section 5, we analyze the effects of disaggregated fiscal-tax policies. Section 6examines how the growth effects of size depend on the quality of government. Conclusions

and extensions are in Section 7.

2 Related literature and econometric problems

Empirical studies of growth have primarily focused on cross-country differences in long-time

period averages. In these cross-section and panel studies, there is some general indication

that the overall size of the public sector (see below how this size is measured) is negatively

associated with economic growth (see e.g. Barro & Sala-i-Martin, 2004; Folster & Henrekson,2001).

But this negative association is statistically fragile and sensitive to model specification (see

e.g. Levine & Renelt, 1992; Tanzi & Zee, 1997). In their widely cited critique, Levine and

Renelt (1992, p. 951) have emphasized two reasons for non-robustness: (i) The overall size

of government cannot capture the different implications of different government activities.

Hence, several authors use disaggregated data to investigate the growth effects of specific

categories of public expenditure and taxation (see e.g. Kneller, Bleaney, & Gemmel, 1999).

(ii) Ignoring government efficiency may yield inaccurate measures of the actual delivery of

public services. Hence, Angelopoulos, Philippopoulos, and Tsionas (2006) have constructedan indicator of public sector efficiency and have provided evidence that there is a non-linear

effect of government size, in the sense that “efficient” and “inefficient” governments are

situated on different sides of a Laffer curve in economic growth.

An obvious shortcoming of cross-section and panel studies is that they rely on the assump-

tion of common coefficients across countries although different countries may have different

structures. This naturally leads to time-series studies of single countries.4 But time-series

studies are still liable to the criticisms of Levine and Renelt (1992) discussed above. In ad-

dition, they may suffer from two well-known problems: (a) If the series are non-stationary,

the regressions might be spurious. (b) The effect of government size might be biased due to

endogeneity if higher government spending is triggered by negative income shocks.Our paper uses annual data for the period 1960–2000 to study the growth effects of fiscal

policy in Greece.5 In doing so, we will try to deal with all the above problems. Namely, we will

first pay attention to the time-series properties of the data to avoid the spurious regression

problem and to investigate for possible breaks or regime switches; second, we will test

for exogeneity of government size; third, we will examine whether different categories of

government expenditures and taxes have different implications for growth; fourth, we will

4 Time-series studies of single countries include Grossman (1988) who has estimated a non-linear effect of

the size of government on economic growth by using annual data for the US over 1929–1982; Peden and

Bradley (1989) who have used annual US data for the post-war period and report that increases in the scale of

l d d i h h R (1986) h i i i f



160 Public Choice (2007) 131:157–175

examine whether the data reveal a non-monotonic growth effect of fiscal policy depending

on the mix between government size and government quality.

3 Data, integrability and breaks

This section introduces the raw data and looks at their statistical properties. We start with

data sources.

3.1 Data sources and graphs

There are annual data for various types of government expenditures and taxes for Greece,

which are available for different time intervals over 1960–2000. Our main data sources are the

Penn World Tables (PWT), version 6.1 (see Heston, Summers, & Aten, 2002), and the World

Development Indicators (WDI) dataset. To construct the economy’s growth rate (denoted asgrowth in the regressions), we use real GDP per capita in constant prices (we use the PWT

measure of GDP per capita; there are no differences if we use the WDI measure instead).

The size of government is usually measured by government spending, tax revenues and

the budget balance, all expressed as shares of GDP (see e.g. Tanzi & Zee, 1997; Persson &

Tabellini, 2003). In our study, again following usual practice, by government spending we

mean the share of government consumption in GDP (denoted as govshare and being available

from PWT)6 and the ratio of total government expenditures over GDP (denoted as govexp

and being available from WDI).7

The evolution of these two measures of government spending is shown in Figure 1. As can

be seen, govexp is always larger than govshare, despite the fact that the former refers to the

central government and the latter to the general government. Their difference is primarily due

to interest payments and transfer payments, which are not calculated in GDP and thus are not

included in govshare. Also, notice that the govexp series is more volatile than the govshare

series. One can detect two breaks in govshare, a rise around 1974 and a fall around 1994.

The govexp series, on the other hand, reveals two big breaks, a sharp rise around 1980 and

an abrupt fall around 1991 (this fall is due not only to an actual decrease in some categories

of government spending, like transfers, but also to changes in accounting definitions as the

Greek governments started transferring central government deficits to public enterprises).

Concerning disaggregated government expenditure,and following most of the related liter-ature,8 we use data from WDI coming from the Government Financial Statistics (GFS) Year-

book, for four types of central government expenditure as a share of GDP over 1972–1998:

government capital expenditure as a share of GDP (denoted as gcap), government goods and

services expenditure as a share of GDP (denoted as ggs), government wages and salaries ex-

penditure as a share of GDP (denoted as gws), and government subsidies and other transfers

6 This variable (govshare) refers to the general government and it comprises government spending on goods

and services that are included in GDP, except for fixed public investment (the latter is included in the PWT in

the investment share in GDP).

7 This variable (govexp) includes all expenditures of the central government (thus, it includes capital expendi-

ture, transfers, and interest payments on public debt on the part of the central government). Note that measures

f l d l diff b i ll ( l b l )



Public Choice (2007) 131:157–175 161

Fig. 1 Government spending

as a share of GDP (denoted as gst ).9 These four components are plotted in Figure 2A. The

evolution of gst is particularly striking: a tremendous rise in subsidies and other transfers

occurred in the late 70s that continued during the whole decade of the 80s and was reversed

in the early 1990s. At the same time (namely, early 1990s), there is also a clear fall in ggs

and gws.

In our regressions, we will also use the above four components as shares of total govern-

ment expenditure: namely, capital expenditure as a share of govexp (denoted as gcapexp),

goods and services expenditure as a share of govexp (denoted as ggsexp), wages and salaries

expenditure as a share of govexp (denoted as gwsexp), and subsidies and other transfers as a

share of govexp (denoted as gstexp).10

Data on some categories of government expenditure can be also obtained from the OECD

Economic Outlook Database. The latter provides the share of government investment in

GDP (we denote it as ginv), the share of government wage consumption in GDP (we denote

it as gwag) and the share of government transfer payments in GDP (we denote it as gtran

and is obtained by summing the Economic Outlook variables “social benefits paid by thegovernment”, “capital transfers” and “transactions and subsidies”). Although this dataset

provides a less explicit categorization of government expenditure than the GFS (this is why it

is less popular), it has the advantage of being available over a longer time-period, 1960–2000.

As can be seen in Figure 2B, the motion of ginv, gwag and gtran is similar to that of gcap,

gws and gst in Figure 2A, at least for the time interval that both series are available. 11

The WDI dataset also provides GFS data on total tax revenues of the central government

over GDP, denoted as trev in the regressions, and the overall budget balance as a share of GDP,

denoted as budget , both over 1972–1998. In addition, we will use effective tax rates on labor

9 Wages and salaries are a subcomponent of goods and services.10 WDI l d i H i d il bl f ll i i d



162 Public Choice (2007) 131:157–175

Fig. 2 (A) Expenditure by type. (B) Components of expenditure

income (denoted as teflab), capital income (denoted as tefcap) and consumption (denoted

as tefcon), over 1970–2000, obtained from Martinez-Mongay (2000).12 It is recognized thatseries that have to do with tax revenues are problematic measures of government size mainly



Public Choice (2007) 131:157–175 163

Fig. 3 Tax revenue and effective tax rates

because of tax evasion (see e.g. Tanzi & Zee, 1997; Folster & Henrekson, 2001). The same

applies to the government budget since it includes tax revenues. Keeping these reservations

in mind, Figure 3 plots the tax revenue-to-GDP ratio and the three effective tax rates. Up to

1990, trev was higher than the effective tax rates, but after 1990 the situation has clearly been

reversed.

Our reading of Figure 3 is as follows. The recent increase of effective tax rates, combined

with the concurrent decrease in tax revenues as a share of GDP, implies smaller effective tax

bases in the 1990s (i.e. the denominator in the calculation of effective tax rates is smaller).

At the same time, there is international evidence that “statutory” tax bases have not been

narrowed during the nineties (see Devereux, Griffith, & Klemm, 2002). Therefore, in Greece,

the 1990s is probably associated with increased tax avoidance and/or tax evasion.

3.2 Integrability tests and structural breaks

Before proceeding to estimation, it is necessary to test for the order of integration of time-

series (when we do not have variables of the same order of integration, regressions are

unbalanced). As a first step, we use the standard augmented Dickey-Fuller (ADF) test for

unit roots. The test statistics from ADF tests (when a constant, a time trend and one lag

are included) are reported in Table 1 together with the critical values at 1%, 5% and 10%

significance levels. The ADF statistic for growth rejects the null of a unit root at the 10%

level, but the null cannot be rejected for the fiscal variables. When we use the growth rates

of fiscal variables (the growth rate of a variable is denoted by adding the symbol ∆before it),



164 Public Choice (2007) 131:157–175

Table 1 Unit root tests (augmented Dickey-Fuller and Zivot-Andrews)

Period for ADF 1% 5% 10% Zivot-Andrews

which test critical critical critical (minimum DF

Variable available statistic value value value statistic)

GDP 1960–2000 −1.730 −4.251 −3.544 −3.206 −2.941 (1969)

growth 1961–2000 −3.363 −4.260 −3.548 −3.209 −7.709 (1974)

govshare 1960–2000 −0.915 −4.251 −3.544 −3.206 −3.297 (1989)

∆govshare 1961–2000 −4.453 −4.260 −3.548 −3.209 −6.911 (1994)

exp 1972–1998 −1.713 −4.380 −3.600 −3.240 −6.111 (1991)

∆exp 1973–1998 −4.337 −4.380 −3.600 −3.240 −6.183 (1991)

trev 1972–1998 −1.738 −4.380 −3.600 −3.240 −4.616 (1991)

∆trev 1973–1998 −3.376 −4.380 −3.600 −3.240 −7.340 (1991)

budget 1972–1998 −2.016 −4.380 −3.600 −3.240 −5.048 (1989)

∆budget 1973–1998 −4.624 −4.380 −3.600 −3.240 −6.650 (1980)

gcapexp 1972–1998 −1.327 −4.380 −3.600 −3.240 −4.832 (1992)∆gcapexp 1973–1998 −4.240 −4.380 −3.600 −3.240 −6.469 (1991)

gcap 1972–1998 −2.935 −4.380 −3.600 −3.240 −4.650 (1991)

∆gcap 1973–1998 −5.304 −4.380 −3.600 −3.240 −5.511 (1987)

ggsexp 1972–1998 −1.814 −4.380 −3.600 −3.240 −6.967 (1980)

∆ggsexp 1973–1998 −3.021 −4.380 −3.600 −3.240 −4.903 (1983)

ggs 1972–1998 −2.762 −4.380 −3.600 −3.240 −3.974 (1991)

∆ggs 1973–1998 −3.662 −4.380 −3.600 −3.240 −5.289 (1991)

gstexp 1972–1998 −1.449 −4.380 −3.600 −3.240 −5.671 (1980)

∆gstexp 1973–1998 −2.959 −4.380 −3.600 −3.240 −6.010 (1982)

gst 1972–1998 −1.436 −4.380 −3.600 −3.240 −3.837 (1991)

∆gst 1973–1998 −2.756 −4.380 −3.600 −3.240 −6.382 (1983)

gwsexp 1972–1998 −0.644 −4.380 −3.600 −3.240 −4.402 (1978)

∆gwsexp 1973–1998 −4.340 −4.380 −3.600 −3.240 −5.004 (1983)

gws 1972–1998 −2.537 −4.380 −3.600 −3.240 −3.536 (1991)

∆gws 1973–1998 −3.516 −4.380 −3.600 −3.240 −6.305 (1991)

teflab 1971–2000 −1.995 −4.343 −3.584 −3.230 −4.830 (1988)

∆teflab 1970–2000 −4.192 −4.352 −3.588 −3.233 −9.189 (1985)

tefcap 1970–2000 −1.639 −4.343 −3.584 −3.230 −5.025 (1993)

∆tefcap 1971–2000 −4.604 −4.352 −3.588 −3.233 −5.884 (1988)

tefcon 1970–2000 −3.332 −4.343 −3.584 −3.230 −4.570 (1984)

∆tefcon 1971–2000 −4.504 −4.352 −3.588 −3.233 −5.243 (1987)ginv 1960–2000 −2.418 −4.251 −3.544 −3.206 −3.275 (1978)

∆ginv 1961–2000 −4.479 −4.260 −3.548 −3.209 −6.404 (1980)

gwag 1960–2000 −2.065 −4.251 −3.544 −3.206 −3.545 (1981)

∆gwagp 1961–2000 −4.956 −4.260 −3.548 −3.209 −8.963 (1973)

gtran 1960–2000 −1.892 −4.251 −3.544 −3.206 −3.726 (1981)

∆gtran 1961–2000 −4.142 −4.260 −3.548 −3.209 −6.735 (1982)

govqual 1960–2000 −1.767 −4.251 −3.544 −3.206 −4.648 (1972)

∆govqual 1961–2000 −4.983 −4.260 −3.548 −3.209 −8.047 (1967)

Notes. 1. ADF statistics for the variable X t are obtained from OLS estimation of the fol-

lowing autoregression ∆ X t = δ0 + δ1t + δ2 X t −1 + δ3∆ X t −1 + ut . 2. The ZA test statisticreported is the minimum Dickey-Fuller statistic calculated across all possible breaks in the

data, when both a break in the intercept and the time trend is allowed for. The year in paren-



Public Choice (2007) 131:157–175 165

that allows for an endogenous break in both the intercept and the time trend.13 Apart from

statistical reasons, an advantage of the ZA test is that it can endogenously detect regime

switches. As can be seen in Table 1, the most important difference from the ADF tests is in

the series for total government expenditures and its components. Big breaks for these series

are evident as in the graphs discussed above. In particular, govexp and ∆

gst , turn out to beI(0) once a break is allowed in 1991 and 1983 respectively, while ggsexp and gstexp turn out

to be I(0) once a break is allowed in 1980.14 The rest of the variables follow, more or less,

the pattern indicated by the ADF tests: namely, the levels of fiscal variables cannot reject the

null of a unit root, whereas their growth rates clearly reject the null at the 5% or even at the

1% level.

These statistical properties of the data will be taken into account in the regression analysis

below by, firstly, using the rates of change in the level of fiscal variables (rather than the level

itself)15 and, secondly, introducing appropriate dummies to capture the regime switches

suggested by the plots and confirmed by the ZA tests.16

Specifically, we will allow for four regimes: First, the early period 1960–1973, whichwas an autocratic regime both politically and economically, especially the period of the

military junta 1967–1973. Second, the period 1974–1979, which is the beginning of the

democratic era without serious public finance problems. Third, the period 1980–1993, which

is a period of high spending and heavy public borrowing (in addition, all the breaks in

the public finance measures are taking place during this period). Finally, the “Maastricht

Treaty” period, 1994-onwards, during which the Greek governments started taking measures

to stabilize the macro-economy. Therefore, in addition to an intercept for the whole period,

we will also use a dummy for 1960–1973 (denoted as d (1960–73)), a dummy for 1980–1993

(denoted as d (1980–93)), and a dummy for 1994–2000 (denoted d (1994–2000)). Moreover,

in addition to a time trend for the whole period, we will use a time trend for 1960–1973 (this

is because the ZA tests in Table 1 indicated the presence of a structural break in the growth

rate of GDP at around 1973, in both the intercept and the trend).

These breaks are consistent with previous studies (see e.g. Alogoskoufis, 1995;

Makrydakis, Tzavalis, & Balfoussias, 1999; Lockwood, Philippopoulos, & Tzavalis, 2001;

Hondroyiannis & Papapetrou, 2001). In particular, Alogoskoufis (1995) describes a regime

switch in economic growth that took place at about 1974, the year of the restoration of democ-

racy. This has been confirmed by our ZA test. Regarding the fiscal expenditure variables,

a structural break is usually argued to have occurred at around 1980, when the outgoing

conservatives and mainly the incoming socialists increased spending and deficits. This break

13 We use the routine written by Baum (2004). The ZA test can endogenously determine possible breaks in

the data mimicking unit roots. In particular, the ZA test rejects the null of non-stationarity when the minimum

value of the Dickey-Fuller statistic, calculated across all possible breaks in the data, is less than the critical

value of the test.14 The ZA test for ∆ggsexp cannot reject the null of a unit root at the 5% level. However, since the ZA statistic

is very close to the critical value, we will treat ∆ggsexp as an I(0) variable. We report that including ∆ggsexp

in our regressions (see Table 3) does not change our results.15 Using the rates of change of fiscal-tax variables to assess the impact of the size of government on the GDP

growth rate is common in the empirical literature (see e.g. Tanzi & Zee, 1997, p. 188; see also the studies for

the USA mentioned above). Hatzinikolaou (1997a, b) is also using the growth rate of government expenditures

GDP d h i li i f h i f f h i i h G k



166 Public Choice (2007) 131:157–175

has been again picked up by the ZA test. Another structural break is believed to have taken

place around 1993, after the Maastricht Treaty. Again the ZA test has picked up a break in

the growth of the share of government in GDP at this time.

4 The growth effect of the size of public sector

This section runs least square regressions to examine the effects of aggregate measures of

government size on economic growth. Results are presented in Table 2.

4.1 The share of government as a measure of government size

We start by using the growth rate of the share of government in GDP (denoted as ∆govshare)

as a measure of government size. Results are presented in columns 1, 2 and 3 of Table 2.

We start in column 1 by just including a dummy and a time trend, both for the early period1960–1973 (in addition to an intercept and a time trend covering the full period). Recall

that these breaks have been picked up by the ZA test in the growth series. As can be seen,

∆govshare exerts a significantly negatively effect on growth, indicating that fiscal spending

in Greece has been growing too much, at least with respect to macroeconomic growth. The

quantitative effect is substantial too. An increase in the growth rate of ∆govshare by one

standard deviation, or 7.655%, decreases growth by 1.38%.

The d (1960–1973) dummy is positive and significant, implying higher growth in the early

post-war period. This can simply be the result of convergence effects, since output in 1974

was much higher than in 1960. But it can also mean that certain features of the democratic

regime in the post-1974 era have not been favorable to macroeconomic performance (see also

the next section and the discussion in Alogoskoufis, 1995). Also, note that the time trends

are both insignificant. Finally, notice that this simple regression has a R2 of about 62%. Thus,

the growth rate of the share of government in GDP, together with a simple regime dummy,

can explain around 62% of the variation of the growth rate in Greece over the time period

1960–2000. Fiscal policy obviously matters to growth.

In column 2 of Table 2, we introduce the dummies for the high spending period, 1980–

1993, and the Maastricht Treaty period, 1994–2000. Thus, now the effect of the regime

dummies has to be interpreted with respect to the omitted time period 1974–1979, which

is effectively picked up by the constant term. The effect of ∆govshare is as before, and thesignificant dummies are now d (1960–1973) and d (1980–1993). In other words, the autocratic

period is still associated with higher growth, while the period 1980–1993 is associated with

lower growth. The Maastricht Treaty dummy is not significant, as is also the case with the two

time trends. Since the time trends are not significant, we choose to drop them. The new results

are reported in column 3. This regression (column 3, Table 2) will serve as a benchmark. In

what follows, we will build on this.

4.2 Misspecification tests

A number of specification tests is undertaken.17 Results are presented in Table 2 below the

estimates. First, we test for serial correlation. The Durbin-Watson test for first-order serial



Public Choice (2007) 131:157–175 167

e s i z e o

f p u b l i c s e c t o r a n d e c o n o m i c g r o w

t h

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

− 0 . 1 7 8 ∗ ∗ ∗

( − 2 . 9 5 )

− 0 . 1 8 0 ∗ ∗ ∗

( − 3 . 1 8 )

− 0 . 1 8 6 ∗

∗ ∗

( − 3 . 3 1 )

–

–

–

–

–

− 0 . 0 5 6 ( − 1 . 1

3 )

–

–

–

–

–

− 0 . 0 8 5 ( − 1 . 2 7 )

)

0 . 0 5 2 ∗

( 1 . 9 4 )

0 . 0 6 9 ∗ ∗

( 2 . 1 3 )

0 . 0 4 1 ∗

∗ ∗

( 3 . 3 1 )

0 . 0 5 3 ∗

( 1 . 6 9 )

0 . 0 4 9 ( 1 . 5 6 )

)

–

− 0 . 0 4 6 ∗ ∗

( − 2 . 4 3 )

− 0 . 0 2 7 ∗

∗

( − 2 . 2 6 )

− 0 . 0 1 9 ( − 1 – 4 0 )

− 0 . 0 2 2 ( − 1 . 5 8 )

)

–

− 0 . 0 5 0 ( − 1 . 4 9 )

− 0 . 0 1 1 ( − 0 . 7 9 )

0 . 0 0 3 ( 0 . 2 0 )

0 . 0 0 6 ( 0 . 3 5 )

− 0 . 0 0 0 0 2 ( − 0 . 0 4 )

0 . 0 0 1 ( 1 – 2 8 )

–

–

–

9 7 3 )

0 . 0 0 0 7 ( 0 . 3 8 )

− 0 . 0 0 1 ( − 0 . 4 9 )

–

–

–

0 . 0 1 2 ( 0 . 6 2 )

− 0 . 0 0 4 ( − 0 . 1 6 )

0 . 0 2 9 ∗

∗ ∗

( 2 . 8 4 )

0 . 0 2 2 ∗

( 1 . 8 6 )

0 . 0 2 3 ∗

( 1 . 9 4 )

6 2 . 1 9 %

6 9 . 1 2 %

6 7 . 4 2 %

3 1 . 7 %

3 2 . 7 5 %

d ( 5 , 4 0 ) =

1 . 7 6 d u ≈

1 . 7 9

d ( 7 , 4 0 ) =

2 . 1 9 d u ≈

1 . 7 9

d ( 5 , 4 0 ) =

2 . 0 8 d u ≈

1 . 7 9

d ( 5 , 2 6 ) =

2 . 1 8 8 d u ≈

1 . 8 3

d ( 6 , 2 6 ) =

2 . 2 0 8 d u ≈

1 . 8 9

c

X 2 ( 1 ) =

0 . 3 1 0 ( 0 . 5 7 7 )

X 2 ( 1 ) =

0 . 6 0 6 ( 0 . 4 3 6 )

X 2 ( 1 ) = 0 . 1 9 6 ( 0 . 6 5 8 )

X 2 ( 1 ) =

0 . 2 3 7 ( 0 . 6 2 6 )

X 2 ( 1 ) =

0 . 3 0 2 ( 0 . 5 8 2 )

c

X 2 ( 2 ) =

0 . 4 8 9 ( 0 . 7 8 3 )

X 2 ( 2 ) =

2 . 2 1 4 ( 0 . 3 3 0 )

X 2 ( 2 ) = 1 . 8 3 1 ( 0 . 4 0 0 )

X 2 ( 2 ) =

1 . 4 7 5 ( 0 . 4 7 8 )

X 2 ( 2 ) =

1 . 4 0 2 ( 0

. 4 9 6 2 )

i s t i c

t =

− 0 . 0 7 ( 0 . 9 4 6 )

t =

0 . 0 3 ( 0 . 9 7 6 )

t =

− 0 . 0

4 ( 0 . 9 6 7 )

t =

1 . 4 2 ( 0 . 1 7

2 )

t =

− 0 . 8 3 ( 0 . 4 1 9 )

E T

F ( 3 , 3 2 ) =

3 . 2 3 ( 0 . 0 3 5 )

F ( 3

, 3 0 ) =

3 . 0 7 ( 0 . 0 4 3 )

F ( 3 , 3 2 )

=

4 . 0 2 ( 0 . 0 1 5 )

F ( 3 , 1 8 ) =

0 . 4

6 ( 0 . 7 1 5 )

F ( 3 , 1 7 ) =

0 . 4 4 ( 0 . 7 3 0 )

4 0

4 0

4 0

2 6

2 6

t i o s a r e

s h o w n i n p a r e n t h e s e s n e x t t o t h e

e s t i m a t e d c o e f fi c i e n t s .

∗

d e n o t e s

s i g n i fi c a n c e a t t h e 1 0 % l e v e l ,

∗ ∗

a t t h e 5 % a n d ∗ ∗ ∗

a t t h e 1 % 2 . D

W

i s t h e

o n d - s t a t i s t i c t o t e s t f o r fi r s t o r d e r s e r i a l c o

r r e l a t i o n i n t h e r e s i d u a l s . T h e u p p e r l e v e l f o r t h e s i g n i fi c a n c e l e v e l o

f d i s s h o w n i n p a r e n t h e s e s . 3 . B G

( 1 ) ( r e s p .

B r e u s c h

- G o d f r e y L M s t a t i s t i c t o t e s t f o r fi r s t ( r e s p . s e c o n d ) o r d e r s e r i a l c o r r e

l a t i o n i n t h e r e s i d u a l s . N o s e r i a l c o

r r e l a t i o n i s t h e n u l l h y p o t h e s i s . T h e p - v a l u e

h o w n i n p a r e n t h e s e s . 4 . T h e H a u s m a n s t a t i s t i c i s a t s t a t i s t i c t o t e s t t h e n u l l o

f e x o g e n e i t y o f t h e r e l e v a n t fi s c a l v a r i a b l e . T h e p - v a l u e o f t h e t e s t i s s h o w n i n

5 . R E S E

T i s t h e R a m s e y r e g r e s s i o n s p e c i fi

c a t i o n e r r o r t e s t f o r o m i t t e d v a r i a b l e s a n d n o n - l i n e a r i t y . A c o r r e c t f u

n c t i o n a l f o r m s p e c i fi c a t i o n i s t h e n u l l . T h e

t e s t i s s h o w n i n p a r e n t h e s e s



168 Public Choice (2007) 131:157–175

correlation cannot reject the null of no autocorrelation, as the d -test statistic is either greater

than, or very close to, d u , which is the upper level of significance. In addition, we perform

the Breusch-Godfrey LM test for higher-order serial correlation. Results of testing for first-

and second-order autocorrelation are again reported in Table 2, and do not reject the null of

no autocorrelation. Thus, the residuals are not serially correlated.The usual concern in growth regressions with annual data is the possibility that the effects

of fiscal policy may be estimated with significant biases due to possible endogeneity of

fiscal variables. We therefore use valid instruments for ∆govshare to test the assumption

that any potential simultaneity does not introduce significant biases in estimating the effect

of ∆govshare on growth. In particular, we use a Hausman-type test to test for endogeneity

of ∆govshare, as suggested by Wooldridge (2002). As instruments for ∆govshare, we use

the regime dummies, the lagged value of ∆govshare to capture policy persistence, and a

pre-election dummy that takes the value of 1 in an election year and zero otherwise (see

Lockwood et al., 2001). These instruments are exogenous and so valid for our purposes.

The Hausman test, also reported in Table 2, does not reject the null that ∆govshare is notcorrelated with the error term of the growth equation.

There is, however, one catch. The Ramsey RESET test for functional form misspecification

rejects the null of a correct functional form specification. Recall that RESET is a general

specification test against possible omitted variables or nonlinearity in the data. We interpret

this rejection as an indication that something is missing from our story so far (we will come

back to this in Section 6 below).

4.3 Using other measures of government size

We now examine the growth implications of two other measures of the overall size of gov-

ernment. In column 4 of Table 2, we report results when we use the growth rate of total

government expenditure as a share of GDP, ∆govexp, as a measure of size, while in column

5 we use the growth rate of total tax revenues as a share of GDP, ∆trev, as a measure of

government size. Both variables are not significant and the associated R2 drop by almost

50% (both regressions pass the serial correlation, the Hausman and the RESET tests). As

argued above, tax revenue is a problematic variable. In the case of total expenditure, this is

a very general measure consisting of many components, each of which may have a different

effect on the growth rate. This explains the popularity of ∆govshare in this type of studies.

Finally, we report that we have also tried ∆budget as a measure of the size of government in

our growth regression. This variable is again insignificant.

4.4 Summary of the section

When we follow common practice by using the government share in GDP as a measure of

the overall size of government, there is robust evidence that larger sizes hurt macroeconomic

growth. Specifically, a rise in the growth rate of government share in GDP by one standard

deviation (or 7.655%) decreases GDP growth by 1.38%. On the other hand, alternative

measures of the overall size, like total government expenditures or tax revenues, both asshares of GDP, are insignificant. Actually, the significance of the government consumption



Public Choice (2007) 131:157–175 169

5 The growth effect of the composition of the public sector

Useful insights can be gained by considering the effects of sub-categories of expenditure and

taxes (see e.g. Kneller et al., 1999). To do so, we use the disaggregated data described in

Section 3 above.

5.1 Results from the WDI dataset

We start with decomposition by type, over 1972–1998, as obtained from WDI and illustrated

in Figure 2A. Column 1 of Table 3 augments the regression of column 4 in Table 2 by adding

some key components of government expenditure expressed as shares of total government

expenditure (see Baldacci, Hillman, & Kojo, 2004, for a similar specification). Specifically we

add ∆gcapexp, ∆gstexp and ∆wsexp (which have been defined in Section 3) in the regression

of column 4 in Table 2.18 Although the effect of total expenditure remains insignificant, two of

its components are significant. In particular, capital expenditures are good for growth, whilewages and salaries are bad for growth. Hence, the composition of public finances matters to

economic performance. Also, notice that the R2 of this regression rises to 55.88% (compare

this to that in column 4 of Table 2) and that this specification passes the serial correlation

and the RESET tests.

It makes also sense to use the above fiscal components expressed as shares of GDP

(rather than as shares of total government expenditure). The results of regressing growth

on ∆gcap, ∆ws and ∆gst (as defined in Section 3), as well as the usual set of dummies,

are presented in column 2 of Table 3. Capital expenditures (∆gcap) accelerate growth, while

wages and salaries (∆

ws) reduce growth. Specifically, an increase in the growth rate of capitalexpenditures by one standard deviation (or 10.7%) increases GDP growth by 1.21%, while

an increase in the growth rate of wages and salaries by one standard deviation (or 9.7%)

reduces GDP growth by 1.72%. Notice that the role of the regime dummies remains as in

Table 2, while the R2 is higher compared to that in e.g. column 4 of Table 2. Also, note that

our specification passes the serial correlation and the RESET tests.

We also include the effective tax rates ∆tefcon, ∆tefcap and ∆teflab (as defined in

Section 3). The results, reported in column 3 of Table 3, show that the effective tax rates are

not significant, although the signs are intuitive. In this specification, tne effect of ∆gcap is

positive but not significant, while the effect of ∆ws remains as before.

These are intuitive results. Namely, public investment is usually associated with highergrowth (see e.g. Barro, 1990). On the other hand, the unprecedented increase in public sector

employment that took place in Greece after 1974, and especially during 1980–1993, has

imposed a significant burden on the economy. This can happen via various channels, e.g.

crowding out effects, distortion of private incentives, the creation of powerful interest groups

mainly in the form of public sector unions, etc.

In column 4 of Table 3, we use ∆ggsexp (as defined in Section 3) instead of ∆wsexp. As

can be seen, the former is negative but not statistically significant. In this specification, no

fiscal variable is significantly related to growth and the fit of the regression is reduced. This

is the reason we prefer the specification in column 1. The same results are obtained if we

replace ∆wsexp with ∆ggsexp in the previous regressions of Table 3.



170 Public Choice (2007) 131:157–175

e c o m p o s i t i o n o f p u b l i c s e c t o r a n d e c o n o m i c g r o w t h

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

− 0

. 0 2 5 ( − 0 . 3 4 )

–

–

− 0 . 0 2 2 ( − 0 . 2 5 )

–

0

. 1 2 5 ∗ ∗

( 2 . 1 5 )

–

–

0 . 0 6 2 ( 1 . 0 0 )

–

–

–

–

− 0 . 1 5 1 ( − 1 . 3 3 )

–

− 0

. 2 3 4 ∗ ∗

( − 3 . 0 3 )

–

–

–

–

− 0

. 0 0 1 ( − 0 . 1 )

–

–

− 0 . 0 2 1 ( − 0 . 9 4 )

–

–

0 . 1 1 3 ∗ ∗

( 2 . 1 4 )

0 . 0 7 5 ( 1 . 2 1 )

–

–

–

− 0 . 1 7 7 ∗ ∗ ∗

( − 2 . 9 9 )

− 0 . 1 5 8 ∗ ∗

( − 2 . 5 6

)

–

–

–

0 . 0 0 6 ( 0 . 5 2 )

0 . 0 0 7 ( 0 . 6 1 )

–

–

–

–

0 . 1 0 5 ( 1 . 1 6 )

–

–

–

–

− 0 . 1 5 6 ( − 1 . 1 0 )

–

–

–

–

− 0 . 0 1 9 ( - 0 . 0 5 1 )

–

–

–

–

–

–

0 . 0 2 8 ( 0 . 9 8 )

–

–

–

–

− 0 . 2 2 6 ∗ ∗

( − 2 . 6 1 )

–

–

–

–

− 0 . 0 8 1 ∗

( − 1 . 7 8 )

)

0

. 0 4 7 ( 1 . 6 8 )

0 . 0 4 8 ∗ ( 1 . 7 6 )

0 . 0 3 7 ( 1 . 1 7 )

0 . 0 5 4 ( 1 . 6 8 )

0 . 0 3 3 ∗ ∗

( 2 . 7 0 )

)

− 0

. 0 2 1 ( − 1 . 6 4 )

− 0 . 0 2 1 ∗ ( − 1 . 7 5 )

− 0 . 0 2 4 ∗

( − 1 . 9 3 )

− 0 . 0 3 1 ∗

( − 1 . 9 2 )

− 0 . 0 3 3 ∗ ∗ ∗

( − 2 . 8 5 )

)

0

. 0 1 0 ( 0 . 6 7 )

0 . 0 0 6 ( 0 . 4 3 )

0 . 0 0 8 ( 0 . 0 5 3 )

− 0 . 0 1 0 ( − 0 . 0 7 )

− 0 . 0 0 7 ( − 0 . 5 7 )

0

. 0 1 8 ∗

( 1 . 7 6 )

0 . 0 2 1 ∗ ∗

( 2 . 0 1 )

0 . 0 2 6 ∗ ∗

( 2 . 3 8 )

0 . 0 2 8 ∗ ∗

( 2 . 2 0 )

0 . 0 3 8 ∗ ∗ ∗

( 3 . 7 6 )

5 5 . 8 8 %

5 4 . 0 3 %

6 1 . 2 9 %

3 9 . 2 9 %

7 3 . 0 6 %

D ( 8 , 2 6 ) =

1 . 7 6 d u ≈

2 . 2 0

d ( 7 , 2 6 ) =

1 . 8 3 d u ≈

2 . 1

d ( 1 0 , 2 6 ) =

1 . 9 1 d u ≈

2 . 4 0

d ( 8 , 2 6 ) =

1 . 9 7 d u ≈ 2 . 2 0

d ( 7 , 4 0 ) =

2 . 1 3 d u ≈

1 . 7 9

c

X 2 ( 1 ) =

0 . 3 6 8 ( 0 . 5 4 4 3 )

X 2 ( 1 ) =

0 . 1 8 0 ( 0 . 6 7 1 )

X 2 ( 1 ) =

0 . 0 6 4 ( 0

. 8 )

X 2 ( 1 ) =

0 . 0 0 2 ( 0 . 9 6 0 )

X 2 =

0 . 2 2 8 ( 0 . 6 3 3 )

c

X 2 ( 2 ) =

1 . 5 0 2 ( 0 . 4 7 2 )

X 2 ( 2 ) =

0 . 7 6 3 ( 0 . 6 8 2 )

X 2 ( 2 ) =

3 . 0 6 2 ( 0

. 2 1 6 )

X 2 ( 2 ) =

0 . 3 0 5 ( 0 . 8 5 8 )

X 2 ( 2 ) =

2 . 6 9 1 ( 0 . 2 6 0 )

E T

F ( 3 , 1 5 ) =

1 . 9 2 ( 0 . 1 6 9 )

F ( 3 , 1 6 ) =

0 . 2 5 ( 0 . 8 6 2 )

F ( 3 , 1 4 ) =

1 . 6 6 ( 0 . 2 2 )

F ( 3 , 1 5 ) =

1 . 1 8 ( 0 . 3 4 8 )

F ( 3 , 3 0 ) =

3 . 3 9 ( 0 . 0 3 )

2 6

2 6

2 6

2 6

4 0

b l e 2



Public Choice (2007) 131:157–175 171

5.2 Results from the OECD Economic Outlook dataset

As a robustness test, we also use the variables provided by the OECD Economic Outlook

dataset that cover the whole 1960–2000 period and have been illustrated in Figure 2B. Results

are reported in column 5 of Table 3. The new measure of wages and salaries expenditure(∆gwag) again exerts a significantly negative effect. The positive effect of government in-

vestment (∆ginv), on the other hand, is now not significant, while the new measure of transfer

payments (∆gtran) exerts a significantly negative effect at 10% level (compare this with the

insignificant effect of ∆gst in the previous subsection). Therefore, the main result (namely,

that the composition matters) does not change when we use the OECD Economic Outlook

data instead of the WDI data (it should be noted however that the variables in the two datasets

do not measure exactly the same things). Finally, the RESET test rejects the null of a correct

functional form specification. In other words, all regressions for the whole period, 1960–

2000, do not pass this test. Hence, as noted above, it seems that something is missing (see

below in Section 6).


When we use disaggregated fiscal policy data, a general message is that wages and salaries

in the public sector are bad for growth. Specifically, when we use the GFS decomposition

over 1972–1998, a rise in the growth rate of wages and salaries by one standard deviation

(or 9.7%) reduces GDP growth by 1.72%. There is also evidence (although less robust) that

public investment as a share of GDP is good for growth, and transfers as a share of GDP are

bad for growth. On the other hand, as it was also the case with total tax revenue as a shareof GDP, various effective tax rates do not seem to matter; this is consistent with the related

literature where the link between taxes and growth is mixed (see e.g. Stokey & Rebelo, 1995;

Park, Philippopoulos, & Vassilatos, 2005).

6 Does the growth effect of the size depend on government quality?

We now explore the well-known prediction (implied e.g. by Barro, 1990) that it is not the size

of government per se that matters to growth, but the mix between size and efficiency/quality.As said in Section 2 above, behind this there are two key issues: first, the effect of fiscal

policy on growth is not monotonic; and second, non-monotonicity is driven by efficiency or

quality in the public sector.

6.1 A measure of public sector quality and econometric specification

We need a proxy for the quality of public sector in Greece, which should have an annual vari-

ation. In Angelopoulos, Philippopoulos, and Tsionas (2006), we have constructed an index of

relative public sector efficiency for a group of 64 countries over four 5-year periods, 1980–

1985, 1985–1990, 1990–1995 and 1995–2000, by following the methodology of Afonso,

Schuknecht, and Tanzi (2005) for the OECD countries. However, most of the data used for



172 Public Choice (2007) 131:157–175

infrastructure.19 Obviously, this index can only provide a crude approximation of the quality

of public sector; however, it is fortunate that there exists such a variable, which also fulfills

our time-series requirements. All related studies, we are aware of, are cross-section studies

(see Angelopoulos, Philippopoulos, & Tsionas 2006, for a review of the literature).

To get a proxy for the quality/efficiency of public sector, we first take the log of the inverseof Electric Power Transmission and Distribution Losses and denote this as govqual. In turn,

by first-differencing, we get the growth rate of govqual, denoted as∆govqual. Table 1 reports

that ∆govqual is an I(0) variable. As a preliminary step, we just include ∆govqual in our

basic regression (the one in column 3 of Table 2). As can be seen in column 1 of Table 4, by

simply adding efficiency into the growth regression does not alter anything.

To test our main idea, we work in two steps. In the first step, we use our proxy for

government efficiency, ∆govqual, to divide years into efficient and inefficient. In the second

step, we examine whether the effect of government size differs depending on whether we are

in an efficient or an inefficient year.

Consider the first step. When ∆govqual is positive, which means an improvement in thequality of public infrastructure relative to the previous year, we classify the government

in that year as being “efficient”. Conversely, when ∆govqual is negative, we classify the

government in that year as being “inefficient”. This simple classification rule implies that the

Greek public sector is classified as “efficient” in 22 years and as “inefficient” in 18 years,

over the period 1960–2000.20

6.2 Estimation and tests

Given this classification, we move on to the second step by regressinggrowth

on our principalmeasure of the overall size of government (namely, the growth rate of the share of govern-

ment in GDP, ∆govshare) by allowing the size effect to differ between the efficient and the

inefficient sub-sample (denoted respectively as ∆govshareeff and ∆govshareineff ). Results

are reported in column 2 of Table 4. As can be seen, ∆govshareineff is significantly negative

at the 1% level, while ∆govshareff (although negative too) is very small and insignificant.

While ideally one might also like the coefficient on ∆govshareff to be significantly positive

(so as to get a typical Laffer curve result), what is important is that the two coefficients differ

and there are two different regimes. The hypothesis that the coefficients of ∆govshareff and

∆govshareineff are equal is clearly rejected by an F -test; the relative F 1,33 = 18.77 rejects

that null (the p-value of the test is 0.001). Therefore, the growth effect of the public sectordiffers significantly depending on whether the latter has been “efficient” or “inefficient”. Note

that these results are net of any direct effect of ∆govqual on growth since we have controlled

for ∆govqual in the regression (the latter is positive but not significant).

The effects of dummy variables are also affected relative to Section 4 (compare the new

results to those in column 3 of Table 2). That is, the d (1960–1973) dummy is now insignificant

although still positive, while the d (1980–1993) period remains negative and significant. It is

interesting that the d (1994–2000) dummy now turns to be significantly negative. An obvious

explanation is that recent years have been classified as efficient, so that the positive effect on

growth has been already controlled for by the size-efficiency mix.



Public Choice (2007) 131:157–175 173

Table 4 The quality of public sector and economic growth

Dep. variable: growth (1) (2)

∆govshare −0.186∗∗ ∗ (−3.26) –

∆govshareeff – −0.040 (−0.71)

∆govshareineff – −0.486∗∗ ∗ (−5.84)

∆govqual −0.008 (−0.16) 0.03 (0.08)

d (1960–1973) 0.041∗∗ ∗ (3.26) 0.019∗ (1.71)

d (1980–1993) −0.027∗∗ (−2.24) −0.043∗∗ ∗ (−4.06)

d (1994–2000) −0.011 (−0.78) −0.019 (−1.57)

Constant 0.029∗∗ ∗ (2.81) 0.046∗∗ ∗ (4.97)

R2 67.45% 79.25%

DW statistic d (6,40) = 2.093 d u ≈ 1.79 d (7, 40) = 2.097 d u ≈ 1.79

BG(1) statistic X 2(1)= 0.195 (0.658) X 2(1) = 0.166 (0.683)

BG(2) statistic X 2(2) = 1.184 (0.389) X 2(2) = 2.554 (0.278)

Ramsey RESET F (3, 31) = 3.72 (0.025) F (3,30) = 0.59 (0.627)Observations 40 40

Notes. See Table 2

It is also important to note that the R2 in this model jumps to about 80%. This is a rather

impressive fit. It implies that the growth in the share of government in GDP, when allowing for

a non-linear Laffer curve-type effect, can explain – along with some simple political dummies

– around 80% of the variation of the growth rate over the last forty years. This again highlights

the importance of fiscal policy (now both its size and quality) for macroeconomic outcomes.

Finally, notice that the specification in column 2 of Table 4 passes the serial correlationtests. Also, the RESET test for non-linear functional form cannot reject the null of a correct

specification. Since the RESET tests of the regressions in columns 1–3 of Table 2, column

5 of Table 3 and column 1 of Table 4 (namely, all regressions that cover the whole period,

1960–2000, without taking account of the size-quality mix) reject the null, this adds to our

confidence that there is a Laffer curve pattern from fiscal policy to growth, and this pattern

is captured by our model specification in column 2 of Table 4.


Is a larger government size, as measured by the government share in GDP, always bad for

growth? Although this seems to be the case when one ignores efficiency in the public sector,

the results change drastically once we take account of the mix between size and efficiency.

Our regressions show that only when our measure of government efficiency deteriorates

relative to the previous year, a larger government size is bad for growth. It is the significantly

negative effects that dominate over time, this is why on average larger sizes were found to

hurt growth. Therefore, what really matters to growth is the size-efficiency nexus. This is

consistent with the theoretical literature (see e.g. the literature initiated by Barro, 1990) as

well as with empirical evidence for a number of countries (see Angelopoulos, Philippopoulos,

& Tsionas 2006).





Public Choice (2007) 131:157–175 175

Hondroyiannis, G., & Papapetrou, E. (2001). An investigation of the public deficits and government spending

relationship: Evidence for Greece. Public Choice, 107 , 169–182.

Kneller, R., Bleaney, M., & Gemmel, N. (1999). Public policy and the government budget constraint: Evidence

from the OECD. Journal of Public Economics, 74, 171–190.

Levine,R., & Renelt, D. (1992). A sensitivity analysis of cross-countrygrowth regressions. American Economic

Review, 82, 942–63.Lockwood, B., Philippopoulos, A., & Tzavalis, E. (2001). Fiscal policy and politics: Theory and evidence

from Greece 1960–1997. Economic Modelling, 18, 253–268.

Makrydakis, S., Tzavalis, E., & Balfoussias, A. (1999). Policy regime changes and the long-run sustainability

of fiscal policy: An application to Greece. Economic Modelling, 16 , 71–86.

Martinez-Mongay, C. (2000). ECFIN’s effective tax rates: Properties and comparisons with other tax indica-

tors, Economic Paper no. 146 . Brussels: European Commission, Directorate for Economic and Financial

Affairs.

Mueller, D. (2003). Public Choice III . Cambridge: Cambridge University Press.

Park, H., Philippopoulos, A., & Vassilatos, V. (2005). Choosing the size of the public sector under rent seeking

from state coffers. European Journal of Political Economy, 21, 830–850.

Peden, E., & Bradley, M. (1989). Government size, productivity and economic growth: The post-war experi-

ence. Public Choice, 61, 229–245.Persson, T., & Tabellini, G. (2003). The Economic Effects of Constitutions. Cambridge, Mass: The MIT Press.

Ram, R. (1986). Government size and economic growth: A new framework and some evidence from cross-

section and time-series data. American Economic Review, 76 , 191–203.

Stokey, N., & Rebelo, S. (1995). Growth effects of flat-rate taxes. Journal of Political Economy, 103, 519–550.

Tanzi, V., & Davoodi, H. R. (1998). Corruption, public investment and growth. In H. Shibata, & T. Ihori (Eds.),

The Welfare State, Public Investment and Growth, Tokyo: Springer-Verlag. Reprinted in Governance,

Corruption and Economic Performance, edited by G. T. Abed, & S. Gupta, International Monetary Fund,

Washington, D.C.

Tanzi, V., & Schuknecht, L. (2000). Public Spending in the 20th Century. Cambridge: Cambridge University

Press.

Tanzi, V., & Zee, H. (1997). Fiscal policy and long-run growth. IMF Staff Papers, 44, 179–209.

Wooldridge, J.W. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, Mass: The MITPress.



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