The optimum share of government consumption expenditures in
low and low-middle income countries: A threshold panel
approach
Mehdi Hajamini1, Mohammad Ali Falahi2
Department of Economics, Ferdowsi University of Mashhad, Mashhad, Iran
1
Corresponding author.
Tel.: +98 511 8813090; fax: +98 511 8829584.
E-mail address: [email protected]
2
Tel.: +98 511 8811240; fax: +98 511 8811243.
E-mail address: [email protected]
Abstract
This paper investigates the impact of government consumption spending as a share
of GDP on economic growth in low and low-middle income countries. The impact of size
of government on economic growth is similar to a hump-shaped curve which can be used
to determine the optimum government size (see Barro, 1990; and Armey, 1995). In this
study, 32 countries with low and low-middle income levels (according to the World Bank
ranking in 2008) were selected during 1981-2007. Using threshold panel approach, the
optimum share of government consumption expenditures for low and low-middle income
countries was estimated to be 16.2% and 16.9%, respectively.
JEL classification: C33; H50; O40
Keywords: Economic growth; Government consumption expenditures; Optimum size of
government; Threshold panel approach; Low and low-middle income countries
1. Introduction
Government size has negative and positive impacts on economic growth. On
one hand, enlargement of government results in a boost in economic growth rate
through establishing protections of property rights, standardization, ruling law and
providing infrastructures and public goods. On the other hand, it leads to
deceleration of economic growth through disincentive effects of taxation and
borrowing and increased inefficiencies. Thus, the final impact of government size
on economic growth depends on the weight of negative and positive effects.
Suppose that there is an economy without government. In this situation,
anarchy reigns and there would be no motivation for saving and investment;
finally, economic growth would be low and, even in cases, negative. As Thomas
Hobbes writes in 1651, life without any government is “nasty and brutish, and
short” (Gwartney et al., 1998). The case becomes better at first with government
intervention. Through legislation and property rights, the government decreases
transaction costs and creates an environment conducive to investment. It also
provides infrastructures necessary to public services such as healthcare and
education, as a result of which economic growth raises significantly. Thus, it is
expected that in initial stages, with enlarging government size, economic growth
also increases. With more and more enlarging the government size, public sector
gradually trespasses to domains where private sector would act successfully and
could provide services at higher quality and lower costs. Therefore, the negative
impact of the government size on economic growth would escalate in intensity.
Eventually, negative impact will dominate positive impact and their sum will be
negative and with it, economic growth will decrease. Therefore, based on a
certain government size, the economic growth will be in its maximum rate. These
explanations are demonstrated using a hump-shaped curve or an inverted Ushaped curve as in Fig. 1.
By the late 1970s and for the first time, Arthur Laffer introduced a curve
similar to the curve in Fig. 1 for expressing the relationship between tax revenues
and tax rate. That curve was named as Laffer curve (Laffer, 2004). After that,
Robert Barro (1990) reached such a curve regarding government size and
economic growth using an endogenous growth model. The curve was known in
the growth literature as “Barro curve”. In 1995, Richard Armey introduced this
curve and its use for determining the optimal government size. The curve was
named “Armey curve” consequently (Vedder and Gallaway, 1998).
In Section 2, theoretical and empirical studies of the impact of government size on
economic growth are discussed briefly. Section 3 describes Hansen (1999)
method that the government size has a threshold exceeding which would invert its
effect. Section 4 presents empirical findings. Policy implications are discussed in
section 5 and finally conclusion is presented in Section 6.
2. Theory and empirical studies
In the growth literature, for the first time, Barro (1990) entered government
sector in a simple endogenous growth model with constant returns and infinite
horizons. He assumed that government revenues from proportional tax are spent
for public services, in a way that all producers are benefited equally and there is
not any cumulative effect. Thus, government spending is entered as a production
factor in the production function. In this framework, Barro (1988) concluded that
“The economy's growth rate and saving rate initially rise with the ratio of
productive government expenditures to GNP, g/y, but each rate eventually
reaches a peak and subsequently declines”. The curve corresponding to this
relationship is known as “Barro curve” in growth literature and considered as a
base for determining the optimal government spending.
Mourmouras and Lee (1999) extended Barro’s work by combining Barro’s
production function and consumer with finite horizons (Blanchard, 1985) and
reached the Barro curve. Considering the Barro’s endogenous growth model in a
two-country world, with the presumption of perfect capital mobility and finite
horizons, Ghosh and Mourmouras (2002) deduced that the effect of government
expenditures share on economic growth and trade balance improvement is similar
to the Barro curve.
Kosempel (2004) extended the Mourmouras and Lee’s model (1999). He
assumed a situation in which the government spends its expenditures in two ways:
first, Government spends a portion of its tax incomes for providing free services
to consumers (e.g. parks, museums, art galleries and healthcare). These services
are directly entered in consumer’s utility function. Second, Government spends a
portion of its revenue to provide free services to producers. Services provided via
constructing roads, airports, railroads, research and development institutes and
programs for improving the skills of labor force are examples of this kind of
services. Therefore, as in Barro (1988) and Mourmouras and Lee (1999), these
expenditures are entered in the production function. Based on the results, Barro
curve is approved for the second-type expenditures, but not for the first-type
expenditures. Although increasing the share of first-type expenditures leads to
increased utility of households, it always causes decline in economic growth.
In empirical studies, most of researches on the effect of government size on
economic growth used linear methods. Ram (1986) has carried out a
comprehensive study using cross-sectional data and separate time series for 115
developing (those with market economy) and developed countries. He concluded
that the results of time series are consistent with cross-sectional estimations and
the overall impact of government size on economic growth is positive almost in
all cases. Guseh (1997) has examined the impact of government size on economic
growth using the data on 59 developing middle income countries (based on 1984
classification by World Bank). Drawing on the fixed effects method, he
concluded that the impact of government size on economic growth is negative.
Ghali (1998), using data on 10 OECD member states and VECM concluded that
the government size exerts influence directly or indirectly (via investment and
trade), on economic growth. In this study, the ratio of government expenditure to
GDP has been used as indicator of government size. Gwartney et al. (1998), by
investigating the United States, 23 OECD countries, and 60 less developed and
high-income countries concluded that in all the three cases, the effect of
government size on economic growth is negative. They used government
consumption spending as a share of GDP as a measure of the government size.
Fölster and Henrekson (2001) considered two criteria for government size: the
total taxes as a share of GDP and the government consumption spending as a
share of GDP. By investigating two groups of countries (rich and non-OECD
countries), they concluded that the negative effect of government size on
economic growth is confirmed by both criteria in the case of non-OECD
countries, while in the case of rich countries, it is confirmed by only the second
criterion. Using data from 19 OECD countries, Dar and AmirKhalkhali (2002)
deduced that the effect of government size on economic growth is negative. They
used government consumption spending as a share of GDP as a criterion of
government size. Loizides and Vamvoukas (2005), developing the framework by
Ghali (1998), has examined the relationship between government size and real per
capita income (based on GNP) in Greece, UK and Ireland. They concluded that in
all three countries, the government size is the Granger cause of national income
growth in short term (all three countries) and in long term (Ireland and UK). They
used the ratio of government expenditures to GNP as an indicator of the
government size. By investigating the trend of government expenditures and
economic growth in 2-3 past decades of the United States and 15 selected
countries within European Union, Mitchell (2005) concluded that extension of
government expenditures would not necessarily tend to improve economic
activities. Romero-Ávila and Strauch (2008) showed that for 15 European
countries: if the government size is measured by share of total expenditures,
consumption expenditures, government revenues, or direct taxes, its effect on
economic growth would be negative; but, if the government size is measured by
total public investment as a share of GDP, it would bring about positive effects.
Wu et al. (2010) investigate the causal relationship between government size and
economic growth in 182 countries. Using panel Granger causality and considering
eight sample countries (all countries, OECD countries, non-OECD countries,
high-income countries, middle-income countries, low-income countries, highcorruption countries and low-corruption countries), they concluded that between
government size and economic growth there is bi-directional causality (except
low-income countries).
Some researches, based on economic growth and other criteria, determined
the optimal size of government. Using Armey curve, Vedder and Gallaway (1998)
reached the optimal amount of government expenditure share for America,
Canada, and four European countries. According to their results, the optimal
government size for America, Canada, United Kingdom, Italy, Sweden, and
Denmark are 17.45%, 21.37%, 20.97%, 22.23%, 19.43%, and 26.14%,
respectively. Witte and Moesen (2009) stated that the Armey curve does not
consider causal relations and thus determining the optimal government size using
this curve would be far from reality. Using Data Envelopment Analysis (DEA),
they estimated optimal government size as 40%. Davies (2009) used Human
Development Index (HDI) instead of economic growth rate for determining the
optimal government size. He considered two criteria for government size:
government consumption expenditures as a share of GDP; and government
investment expenditures as a share of GDP. Considering 154 countries for seven
years (1975, 1980 1985, 1990, 1995, 2000, and 2002), and categorizing the
countries into two groups (all countries and low income countries), he reached the
following results: first, the optimal amount of consumption and investment in all
countries are 17% and 13%, respectively. So the optimal government size in these
countries is 30%. Second, in low income countries, the share of consumption
expenditures always has positive effect on HDI, while the share of investment
expenditure may have up to 40% negative effect on this indicator. Therefore, a
certain size cannot be declared for these countries. Third, for all countries, the
optimal government size determined 30%, which is more than the optimal
government size with respect to the indicator of economic growth rate (for
instance 17.5%-26.5% of Vedder and Gallaway’s study). Chiou-Wei et al. (2010)
investigated the nonlinear impact of government size on economic growth in five
Asian countries (South Korea, Singapore, Taiwan, Thailand and Malaysia)
drawing on the Solow growth model and using a dynamic STAR model. They
used the ratio of government expenditures to GDP as an indicator of government
size. They verified the nonlinear relation LSTAR for four countries including
South Korea, Singapore, Taiwan and Thailand and estimated the optimum share
of government expenditures as 10.8%, 11%, 15.9% and 10.8%, respectively (of
course, for Singapore the nonlinear relation was estimated as U-shape).
3. Estimation method
According to the Barro curve or the Armey curve, the effect of government
size on economic growth looks like a hump-shaped curve (it is not linear).
Therefore, this curve should also be examined while investigating the effect of
government size on economic growth. From the statistical point of view, one
method would be to consider that the government size has a threshold exceeding
it would invert its effect. Hansen (1999) has introduced estimation, testing, and
construction of the threshold confidence intervals in the non-dynamic panel with
individual-specific effects, in order to consider differences between countries.
3.1. Regression with one threshold
Assume that a group of observations exists in the form of {𝑦𝑖𝑡 , 𝑥𝑖𝑡 , 𝑖 =
1, … , 𝑁 , 𝑡 = 1, … , 𝑇 }. Here i represents individual, t represents time. y is the
dependent variable and x is the column vector of explanatory variables none of
which is supposed to be time invariant. For this group of observations, a
regression function with one threshold and individual-specific fixed effects is
considered:
𝑦𝑖𝑡 = 𝜇𝑖 + 𝛽 ′ 𝑥𝑖𝑡 (𝛾) + 𝑢𝑖𝑡 ,
(1)
′
In which 𝛽 ′ = (𝛽1 , 𝛽2 ) and 𝑥𝑖𝑡 (𝛾) = (𝑥𝑖𝑡 𝐼(𝑞𝑖𝑡 ≤ 𝛾) , 𝑥𝑖𝑡 𝐼(𝑞𝑖𝑡 > 𝛾)) . Here q is
the threshold variable and I(∙) is the indicator function. The threshold variable can
be an explanatory variable or any other variable. It is supposed that the threshold
variable is not time invariant. Being there is a threshold, the observations can be
divided into two regimes. One regime when the threshold variable is lower than γ,
and the other regime when the threshold variable is higher than γ. To estimate the
Eq. (1), fixed-effects transformations are used, 𝑦̃𝑖𝑡 = 𝛽 ′ 𝑥̃𝑖𝑡 (𝛾) + 𝑢̃𝑖𝑡 . Its matrix
form is 𝑌̃ = 𝑋̃(𝛾)𝛽 + 𝑢̃. In which 𝑌̃ = [𝑦̃1 , … , 𝑦̃𝑁 ]′ , 𝑋̃ = [𝑋̃1 (𝛾), … , 𝑋̃𝑁 (𝛾)]′ and
𝑢̃ = [𝑢̃1 , … , 𝑢̃𝑁 ]′ . Coefficients are estimated using the LS method that 𝛽̂ (𝛾) =
−1
(𝑋̃(𝛾)′ 𝑋̃(𝛾))
𝑋̃(𝛾)′ 𝑌̃, and the sum of squared residuals is:
−1
𝑆1 (𝛾) = 𝑒̂ (𝛾)′ 𝑒̂ (𝛾) = 𝑌̃ ′ (𝐼 − 𝑋̃(𝛾)′ (𝑋̃(𝛾)′ 𝑋̃(𝛾))
𝑋̃(𝛾)′ ) 𝑌̃.
Hansen (1999) recommended that a desirable estimation of the real
threshold be reached by minimizing the sum of squared residuals with respect to
γ. Thus:
𝛾̂ = 𝑎𝑟𝑔𝑚𝑖𝑛 𝑆1 (𝛾).
(2)
After estimating γ, 𝛾̂, coefficients vector and variance, will be 𝛽̂ = 𝛽̂ (𝛾̂) and 𝜎̂ 2 =
1
1
𝑒̂ ′ 𝑒̂ = 𝑁(𝑇−1) 𝑆1 (𝛾̂).
𝑁(𝑇−1)
After estimating the threshold, investigating the statistical significance of the
threshold should be examined. Considering Eq. (1), the null hypothesis is
𝐻0 : 𝛽1 = 𝛽2. In the case where this hypothesis is rejected, the threshold will be
statistically significant. Based on the likelihood ratio test, the 𝐹1 statistic for
testing the null hypothesis is provided:
𝐹1 =
𝑆0 − 𝑆1 (𝛾̂)
.
𝜎̂ 2
(3)
In which S0 is the sum of squared residuals of linear regression. The distribution
of 𝐹1 is non-standard and depends on the moments of the sample. As a result, it is
not possible to calculate its critical values in general form. Hansen (1999)
suggests using the below bootstrap procedure for examining the significance of
𝐹1 : (i) By minimizing the sum of squared residuals, Eq. (2), the threshold value,
the corresponding coefficients are estimated. (ii) With the first stage of residuals,
a new sample are generated under supposition of the null hypothesis (explanatory
variables are supposed to be non-stochastic so they do not change). With this
sample, coefficients and residuals are estimated under the null and alternative
hypothesis. Then, the simulated 𝐹1 statistic is calculated. (iii) The above
calculations are repeated so many times. Using the simulated 𝐹1 , critical values of
F and the bootstrap p-value will be calculated. Finally, P-value is the percentage
that the simulated 𝐹1 exceeds the actual value. In fact, this will be the estimation
of asymptotic p-value under 𝐻0 . Now, if this percentage is lower than the
considered significance level (i.e. 5%), the null hypothesis will be rejected.
While there is a threshold, rejecting the null hypothesis 𝐻0 : 𝛽1 = 𝛽2 , Hansen
(1999) indicated that 𝛾̂ would be a consistent estimation of the true threshold (𝛾̂),
but its asymptotic distribution will be non-standard. He suggested using the
likelihood ratio for constructing the confidence interval. The null hypothesis for
the true threshold test will be 𝐻0 : 𝛾 = 𝛾0 , So the likelihood ratio would be in the
form below:
𝐿𝑅1 (𝛾) =
(𝑆1 (𝛾) − 𝑆1 (𝛾̂))
.
𝜎̂ 2
(4)
It is clear that the probability of rejecting the null hypothesis increases with
the value of this statistic. Hansen (1999) showed that under some assumptions,
this statistic converges in distribution to the random variable 𝜉 which have the
2
probability distribution of 𝑃(𝜉 ≤ 𝑥) = (1 − 𝑒𝑥𝑝(−𝑥/2)) , and its reverse 𝜉
distribution is 𝑐(𝛼) = −2 𝑙𝑜𝑔(1 − √1 − 𝛼). This function can be used to
estimate the critical values. Provided that 𝐿𝑅1 ≤ 𝑐(𝛼), the confidence interval
(1 − 𝛼)% will be made for sum of squared residuals and consequently the
threshold.
It should be noted that the hypothesis 𝐻0 : 𝛽1 = 𝛽2 is different from the
hypothesis 𝐻0 : 𝛾 = 𝛾0 . The 𝐹1 statistic is for testing the presence of the threshold,
while the 𝐿𝑅1 statistic is used for constructing the confidence interval of the
present threshold.
3.2. Regression with more than one threshold
If the first threshold is statistically significant, regression should be
estimated using two thresholds and significance of the second threshold should
also be examined. The regression function with two thresholds is defined in this
form of 𝑦𝑖𝑡 = 𝜇𝑖 + 𝛽 ′ 𝑥𝑖𝑡 (𝛾) + 𝑢𝑖𝑡 , in which 𝛽 ′ = (𝛽1 , 𝛽2 , 𝛽3 ) and 𝑥𝑖𝑡 (𝛾) =
′
(𝑥𝑖𝑡 𝐼(𝑞𝑖𝑡 ≤ 𝛾1 ) , 𝑥𝑖𝑡 𝐼(𝛾1 < 𝑞𝑖𝑡 ≤ 𝛾2 ), 𝑥𝑖𝑡 𝐼(𝑞𝑖𝑡 > 𝛾2 )) . Suppose that the smaller
threshold is estimated in the previous step. Thus, it is enough to estimate the
second (larger) threshold by minimizing the sum of squared residuals:
𝛾̂2 = 𝑎𝑟𝑔𝑚𝑖𝑛 𝑆2 (𝛾2 |𝛾̂1 ).
(5)
Bia (1997) showed that estimating the second threshold is efficient, but
estimating the first threshold is not. Therefore, it is necessary to repeat the method
used for estimating the second threshold, after it is estimated, for getting a better
estimation of the first threshold (Hansen, 1999). Bia’s correction will be 𝛾̂̂1 =
𝑎𝑟𝑔𝑚𝑖𝑛 𝑆2 (𝛾1 |𝛾̂2 ). This is to ensure that both estimations are efficient. After
estimating the two thresholds, significance of the second threshold should be
examined. The null hypothesis is defined 𝐻0 : 𝛽2 = 𝛽3. The F2 statistic related to
the null hypothesis is:
𝐹2 =
𝑆1 (𝛾̂1 ) − 𝑆2 (𝛾̂2 |𝛾̂1 )
,
𝜎̂̂ 2
(6)
1
1
in which 𝜎̂̂ 2 = 𝑁(𝑇−1) 𝑒̂̂ (𝛾̂2 |𝛾̂1 )′ 𝑒̂̂ (𝛾̂2 |𝛾̂1 ) = 𝑁(𝑇−1) 𝑆2 (𝛾̂2 |𝛾̂1 ). However, if the
larger threshold is estimated in the first step, the next step will be in this form:
𝛾̂1 = 𝑎𝑟𝑔𝑚𝑖𝑛 𝑆2 (𝛾1 |𝛾̂2 ).
(7)
Bia’s correction will be 𝛾̂̂2 = 𝑎𝑟𝑔𝑚𝑖𝑛 𝑆2 (𝛾2 |𝛾̂1 ). So the null hypothesis will be
𝐻0 : 𝛽1 = 𝛽2 and the 𝐹2 statistic related to the null hypothesis is:
𝐹2 =
𝑆2 (𝛾̂2 ) − 𝑆2 (𝛾̂1 |𝛾̂2 )
,
𝜎̂̂ 2
(8)
1
1
in which 𝜎̂̂ 2 = 𝑁(𝑇−1) 𝑒̂̂ (𝛾̂1 |𝛾̂2 )′ 𝑒̂̂ (𝛾̂1 |𝛾̂2 ) = 𝑁(𝑇−1) 𝑆2 (𝛾̂1 |𝛾̂2 ).
In practice both minimizations i.e. Eqs. (5) and (7) should be carried out and
the smallest one should be chosen. Here 𝐹2 distribution is again non-standard, and
the Hansen’s bootstrap method (1999) should be used. The three bootstrap steps
described in section 3.1 can also be performed for the 𝐹2 statistic. However, in
this case, residuals of the double threshold model are used to generate the new
sample, and then the sample is extracted under the new null hypothesis, 𝐻0 : 𝛽2 =
𝛽3 or 𝐻0 : 𝛽1 = 𝛽2 . If the null hypothesis is not rejected, there will be only one
threshold. But if the second threshold is confirmed, then the presence of the third
threshold should be examined. Likewise these rules can be extended for larger
numbers of thresholds.
4. Estimation results
The main aim of this study is to investigate the effect of government size on
economic growth in low and low-middle income countries. According to the
World Bank ranking (2008), low and low-middle income countries were
considered. The method provided in the previous section is for balanced panel
data and its results for unbalanced panel data are not known (Hansen, 1999). For
this reason, a large number of these countries were omitted and finally, 32
countries remained in the study. These countries are mainly from Africa and
Asia, and their names are given in the Appendix A (Table A.1). Since the effect of
government size on economic growth may be different in low income from lowmiddle income countries, three groups were considered: (i) group of countries
with low income; (ii) group of countries with low-middle income; and (iii) all
countries from the first and second groups. A summary of information about each
group can be found in Table 1.
Table 1
Information of different groups of the study.
Group
GNI per capita (2006)
Low income (LIC)
$905 or less
Low-middle income (LMC)
$906 - $3595
LIC&LMC
$3595 or less
Coutry
21
11
32
Period
1981-2007
1981-2007
1981-2007
Three specifications were considered for estimation;
(i) linear model:
(9)
𝐺𝐺𝐷𝑃𝑖𝑡 = 𝛽0 + 𝛽1 𝐺𝐿𝑖𝑡 + 𝛽2 𝐼𝑌𝑖𝑡 + 𝛽3 𝐺𝑆𝑖𝑡 + 𝛽4 𝑂𝑃𝑁𝑖𝑡 + 𝑢𝑖𝑡 ,
(ii) nonlinear model with one threshold:
𝐺𝐺𝐷𝑃𝑖𝑡 = (𝛽10 + 𝛽11 𝐺𝐿𝑖𝑡 + 𝛽12 𝐼𝑌𝑖𝑡 + 𝛽13 𝐺𝑆𝑖𝑡 + 𝛽14 𝑂𝑃𝑁𝑖𝑡 ) 𝐼(𝐺𝑆𝑖𝑡 ≤ 𝑂𝐺𝑆)
+ (𝛽20 + 𝛽21 𝐺𝐿𝑖𝑡 + 𝛽22 𝐼𝑌𝑖𝑡 + 𝛽23 𝐺𝑆𝑖𝑡 + 𝛽24 𝑂𝑃𝑁𝑖𝑡 ) 𝐼(𝐺𝑆𝑖𝑡 > 𝑂𝐺𝑆) + 𝑢𝑖𝑡 ,
(10)
(iii) nonlinear model with two thresholds:
𝐺𝐺𝐷𝑃𝑖𝑡 = (𝛽10 + 𝛽11 𝐺𝐿𝑖𝑡 + 𝛽12 𝐼𝑌𝑖𝑡 + 𝛽13 𝐺𝑆𝑖𝑡 + 𝛽14 𝑂𝑃𝑁𝑖𝑡 )𝐼(𝐺𝑆𝑖𝑡 ≤ 𝑂𝐺𝑆1 )
+(𝛽20 + 𝛽21 𝐺𝐿𝑖𝑡 + 𝛽22 𝐼𝑌𝑖𝑡 + 𝛽23 𝐺𝑆𝑖𝑡 + 𝛽24 𝑂𝑃𝑁𝑖𝑡 )𝐼(𝑂𝐺𝑆1 < 𝐺𝑆𝑖𝑡 ≤ 𝑂𝐺𝑆2 )
+ (𝛽30 + 𝛽31 𝐺𝐿𝑖𝑡 + 𝛽32 𝐼𝑌𝑖𝑡 + 𝛽33 𝐺𝑆𝑖𝑡 + 𝛽34 𝑂𝑃𝑁𝑖𝑡 )𝐼(𝐺𝑆𝑖𝑡 > 𝑂𝐺𝑆2 ) + 𝑢𝑖𝑡 .
(11)
Variables are as follows: GGDP denotes the rate of economic growth which
is calculated based on gross domestic product (at constant 1990 prices in USD).
GL denotes labor force growth rate. GK denotes the ratio of gross capital
formation (at constant 1990 prices in USD) to gross domestic product (at constant
1990 prices in USD). Since the data related to physical capital of some countries
was not available, this variable was used instead of the physical capital growth
rate. GS denotes measure of the government size, which is defined in the form of
the ratio of general government final consumption expenditures (at constant 1990
prices in USD) to gross domestic product (at 1990 constant prices in USD). OPN
denotes degree of openness, which is calculated using the ratio of sum of exports
and imports of goods and services (at current prices in USD) to gross domestic
product (at current prices in USD). OGS denotes the optimal value of GS.
First, unit root test was carried out for all variables and the results show that
all variables are stationary (Table A.2). The Hausman test was performed based
on Eq. (9) for the three groups. The results of Hausman test are shown in the last
column of Table 3. The null hypothesis is rejected in the three groups, so the
model with fixed effects should be used. The linear model with fixed effects was
estimated and the results are given in Table 2. Interestingly, based on the linear
model, the government size has no significant effect on economic growth in the
first and third groups. However, this effect is significant and negative in the
second group.
Table 2
Linear regression model.
Group Constant
GL
LIC
-0.031
1.272
(-2.102)b
(4.919)a
LMC
-0.023
0.421
(-1.189)
(2.545)b
LIC&
-0.023
0.739
LMC
(-2.02)b
(5.074)a
IY
0.026
(0.703)
0.089
(3.052)a
0.053
(2.259)b
Note: Numbers in parentheses are t-statistics.
respectively.
a
and
GS
-0.0008
(-0.012)
-0.202
(-2.208)b
-0.042
(-0.79)
b
OPN
0.05
(2.477)b
0.069
(3.934)a
0.056
(4.148)a
̅2
R2 /R
0.132
0.094
0.263
0.226
0.162
0.127
Hausman
10.64a
15.52a
14.25a
denote significance at the 1% and 5% levels,
The presence of threshold was tested based on the three-step method
described in sections 3.1 and 3.2 and the results are presented in Table 3.
According to these results, presence of a threshold is confirmed in all groups at
the 1% significance level, but the presence of second threshold was not confirmed
in any of them. For each threshold, 10000 samplings and estimations of the
simulated 𝐹 statistic was repeated, 10000 bootstrap replications. In the Table 3,
the results are presented, under 𝐹 critical values and “bootstrap p-value” columns
(for more details on “bootstrap p-value” refer to the Appendix B Fig. B.1).
For the estimations to be reliable, availability of enough observations in
each regime is necessary (Hansen, 1999). So in each group a range was
determined for the government size and minimization was performed in this
range. The government size was arranged from small to large in each group and
then a fraction of the smallest and largest ones was omitted. However, these
omitted observations were used for estimation, but the threshold variable was not
selected in their range. These ranges were considered in a way that data
availability for each regime are enough while not belonging to a certain country.
In this way, the estimations would be more reliable. These ranges are: [9.5%
23.2%] for low income countries, [9.9%
20.5%] for low-middle income
countries, and [9.5% 31.4%] for the whole two groups.
Table 3
Tests for thresholds.
Group
LIC
LMC
LIC&LMC
Group
LIC
LMC
LIC&LMC
OGS
0.162
0.169
0.177
OGS1
0.162
0.169
0.103
OGS2
0.225
0.199
0.177
Single threshold
F1
Critical values(10%,5%,1%)
20.158
11.33, 13.61, 18.62
20.036
11.23, 13.67, 18.64
25.53
10.93, 13.49, 18.79
Double threshold
F2
Critical values(10%,5%,1%)
6.386
8.88, 10.64, 14.73
13.646
11.10, 13.79, 20.01
9.298
9.36, 11.32, 15.81
Bootstrap p-value
0.007a
0.006a
0.001a
Bootstrap p-value
0.24
0.051
0.102
Note: a denotes significance at the 1% level.
The optimal government size was determined to be 16.2% for low income
and 16.9% for low-middle income countries, both of which are significant at the
1% level. Then 95% confidence intervals are made, which were [13.7% 17.3%]
for the first group and [16.5% 16.9%] for the second group. The results are
presented in Table 4. For constructing confidence interval, likelihood ratio was
estimated for different threshold values under the null hypothesis 𝐻0 : 𝑂𝐺𝑆 =
𝑂𝐺𝑆0. Considering that 𝐿𝑅1 around the estimated threshold should not be higher
than c(0.05)=7.3523. The confidence interval was estimated for each threshold
(these descriptions are shown in Appendix B Fig. B.2). These confidence intervals
may not be symmetric. Whenever the likelihood ratio declines more rapidly in
one side of the threshold, the confidence interval in that side would be more
extended. For example, in the first group, the confidence interval is more
extended in the underside. Likewise, in the second group, the confidence interval
reaches the threshold in its climax. In the third group, the confidence interval is
more extended in its upper side.
Finally, Eq. (10) was estimated based on the optimal government size and
the results are presented in Table 4. The government size coefficient in the second
regime and also the difference between two regimes are significant in all three
groups, while they were not significant in the linear model in two groups. In LIC
(LMC), before reaching the optimal size (16.2% for LIC and 16.9% for LMC) a
10% increase in the government size causes a 2% (1%) decline in economic
growth, provided that the government size does not exceed the optimum. After
passing the optimal size, this increase causes a 2% (3.7%) decline in economic
growth. Except intercepts and the government size coefficients, almost all other
coefficients are not different in the two regimes, so the effect of these variables is
not dependent to the government size.
Table 4
The optimal government size, 95% confidence interval and Single threshold regression.
Group
LIC
LMC
LIC & LMC
Optimal government size
16.2%
16.9%
17.7%
Confidence interval 95%
[13.7%
17.3%]
[16.5%
16.9%]
[17.2%
19.9%]
Threshold
GS <16.2
GS >16.2
GS < 16.9
GS > 16.9
GS <17.7
GS >17.7
↓
↓
↓
↓
↓
↓
Constant
-0.051
0.045
-0.032
-0.005
-0.05
0.04
(-2.501)b
(1.289)
(-1.202)
(-0.191)
(-3.316)a
(1.788)c
GL
1.095
1.745
-0.543
0.769
0.943
0.374
(4.027)a
(2.399)b
(-1.768)c
(3.996)a
(5.973)a
(1.153)
IY
-0.029
0.128
0.11
0.115
0.037
0.095
(-0.568)
(2.365)b
(3.169)a
(2.235)b
(1.326)
(2.424)b
GS
0.22
-0.196
0.092
-0.365
0.116
-0.139
(1.525)
(-2.018)b
(0.531)
(-2.853)a
(1.189)
(-1.665)c
(ß13-ß23)
0.416
0.458
0.255
(2.374)b
(2.094)b
(1.971)b
OPN
0.068
-0.044
0.061
0.068
0.066
0.01
(3.139)a
(-1.417)
(3.134)a
(3.516)a
(4.589)a
(0.605)
̅2
0.163 , 0.1178
0.3108 , 0.2635
0.187 , 0.148
R2 , R
Note: Numbers in parentheses are t-statistics. a, b,
respectively.
c
denote significance at the 1%, 5% and 10% levels,
The mean government consumption spending as a share of GDP, for three
periods (20-year, 5-year, and 2-year), is presented in Table 5. In 18 countries,
government consumption spending as a share of GDP, at least for the last periods,
has been lower than the optimal size. In 7 countries, government consumption
spending as a share of GDP is almost or completely in the optimal level.
Government consumption spending as a share of GDP has been higher than the
optimal size in 7 countries (except Nigeria) in three periods.
Table 5
Mean government consumption spending as a share of GDP.
1981
2001
2006
Country
Country
2000
2005
2007
Consumption Spending as a Share of GDP < Optimal Value
1981
2000
2001
2005
2006
2007
Albania
12.75
14.4
12.47
Iran
14.94
10.81
10.37
Bangladesh
4.161
4.707
5.111
Mozambique
11.97
13.17
13.75
Benin
10.59
9.807
9.54
Niger
14.28
12.41
12.61
Cameroon
10.76
10.66
11.28
Pakistan
9.948
9.054
11.67
Comoros
23.37
14.24
12.46
Senegal
15.52
12.39
11.49
Egypt
9.212
9.013
8.284
Somalia
10.44
9.96
9.96
Gambia
14.04
9.424
8.903
Sudan
9.748
14.51
14.33
Guinea
7.793
5.568
5.698
Togo
13.4
10.38
11.3
Indonesia
7.295
6.355
6.761
Uganda
9.848
9.151
9.223
Morocco
15.76
17.8
18.06
Sierra leone
8.126
17.39
18.79
Tunisia
16.1
15.56
15.07
Consumption Spending as a Share of GDP ≈ Optimal Value
Burkina faso
19.72
19.13
18.84
Guinea-bissau
13.79
17.12
17
Guyana
10.78
16.87
17.65
Mali
16.61
17.31
18.94
Consumption Spending as a Share of GDP > Optimal Value
Algeria
19.34
20.77
22.18
Maldives
17.58
24.37
34.15
Chad
43.92
29.81
29.18
Mauritania
20.45
27.38
19.8
Cote d’ivoire
21.96
22.74
22.47
Nigeria
5.1
8.197
22.74
Djibouti
35.59
28.51
28.26
Based on the results above, it is obvious that negative and positive effects of
the government size on economic growth are not independent from government
size. Verifying their relationship as a U-shaped curve, implementation of the
following policies for low and low-middle income countries becomes worthy of
attention:
(1) Fixing the share of government consumption expenditures in countries
with the optimum government size (the policy of raising consumption expenditure
proportionate to economic growth): this policy can guarantee high and stable
economic growth. Tunisia has experienced an average economic growth rate of
4.4% present with relatively stable and optimum share of government
consumption expenditures (average of 15.9 per cent, except for 1982 and 2007
with low percentage of 14.9). Maldives has also experienced the same conditions
in two decades of 1980s and 1990s with higher economic growth. The share of
government consumption expenditures in Maldives has been near the optimum
(averaging 17.3%) for period of 1981-1998, bringing about a 10 per cent
economic growth rate for the country, while from 1999 to 2007 the share of
government expenditures has exceeded the optimum level (averaging 25.6 per
cent) and has consequent decrease in economic growth up to 7.6 per cent.
It is surprising that among the countries under study only Tunisia
(4.4%±2.3%) and Maldives (except for 1982 and 2005 due to global economic
recession, 10.3%±5.5%) and some other countries (Bangladesh: 4.9%±1.3%,
Indonesia; 5.9%±2.2% except for 1998 due to the east Asian recession of 1997
and Uganda: 6.3%±1.9%, except for 1984 and 1985) have experienced
sustainable and high economic growth.
With an optimum share of government consumption expenditures from 1999
to 2003, Mali has experienced an economic growth rate of 5.2 per cent, although
from 2004 afterwards, with re-increase and excessive amount of the share of
consumption expenditures, this rate has fallen to 4 per cent.
(2) Cuts in the share of government consumption expenditures if the
government size is larger than its optimum size (reducing the government size): in
this condition, reducing the government size has brought about positive outcomes,
such as increasing efficiency, decreasing economic distortions, boosting private
investments and curbing rent-seeking activities.
In Burkina Faso (from 1995), Chad (from 1995) and Djibouti (from 1997) a
higher growth rate (6.5, 8 and 2.6 per cent versus 3, 5 and 1.2 per cent) due to the
policy of reducing government size (18, 32 and 29 per cent versus 20, 47 and 36
per cent, respectively) has been seen than in the years prior to economic reform.
In Comoros, the share of government expenditures has decreased from 28.8% in
1981 to 12.2% in 2007. Up to 1999, in this country, economic growth has
increased with decrease in the share of government expenditures (between 1997
and 1999, the government size has been equal to that of optimum, with
consequent growth rate of 2.6%), but from 2000, with a decrease in the share of
government consumption expenditures growth rate has been down to 1.5 per cent.
Algeria has experienced three periods: (i) 1981 to 1985, with the share of
expenditures 19.5% and the growth rate 3.7%, (ii) 1986 to 1991, the share of
expenditures 17% and the growth rate 5.2%, and (iii) 1992 to 2007, with reincrease in the share of expenditures (roughly 21%), the growth rate has been 4
per cent.
In 1980s and 1990s, Cote d’Ivoire has experienced a growth rate of 3.3 per
cent as the result of a linearly decreasing the share of government consumption
expenditures (from 30% in 1981 to 17% in 1998), but from 1999, with re-raising
the share of expenditures, economic growth rate has been down to 0 per cent. It is
also seen that countries like Togo (from 1988), Iran (from 1989), Senegal (from
1994) and Niger (from 1995) has succeeded in having higher economic growth by
decreasing the share of consumption expenditures. Togo and Iran has seen an
average growth rate of 2 and 5 per cent through reducing the government size to
10 per cent. Senegal and Niger have higher growth rate reducing the government
size (4.5 per cent versus 2.5 per cent for Senegal, 4 per cent versus 0.5 per cent
for Niger).
(3) Increasing the share of government consumption expenditures when the
government size is smaller than that of optimum: a large government size is never
favorable; however, neither small government is necessarily good. For example,
while the share of government consumption expenditures has always been under
the optimum level for Bangladesh and Indonesia, Bangladesh has reached higher
economic growth rate by gradual increase in the share of consumption
expenditures, and Indonesia achieved only lower economic growth by cutting this
share. Government investments (especially in countries with the lack of fully
developed markets of capital, insurance and information) can succeed in
improving the performance of production factors, eliminating market failures and
favoring the private sector by spillover effects of these factors.
From 1996 Mozambique and Sudan have seen favorable economic growth
of 8 and 9.5 per cent (before that, their growth rate has been 0 and 2 per cent,
respectively) through increasing the share of government consumption
expenditures. Also Guyana and Sierra Leone in 2000 and 2001 (after the civil war
of 1991-2001) get a growth rate of 1.5% and 1%, by increasing the share of
government consumption expenditures, respectively (in 1980s and 1990s, the
share of expenditures of these two countries have been 10 and 8 percent,
respectively and their economic growth, -2 and 0 per cent).
The policy of increasing the share of consumption expenditures in
developing countries should be implemented with caution, care and selectiveness.
Wide scale intervention of state, non-democratic political system, inefficient
public sector, high corruption and rent-seeking activities are common in
developing countries. These attributes may contribute to the failure of increasing
the share of government consumption expenditures in improving the economic
condition and higher economic growth. According to the statistics issued by
Heritage Foundation (2010), the level of corruption in the majority of countries
under study (except for Sudan and Somalia, whose data are not accessible) is very
high.
5. Policy implications
In recent century and particularly, in recent few decades, the relationship
between the government size and economic growth has attracted the attention of
economists and policymakers. The reason behind this may be due to the
importance of economic growth and development from the mid- twenties century
to the present and the important role of the state in economic growth and
development. The mechanisms of the impact of government on economic growth
can be traced in two levels:
(1) In the short run: it is a usual phenomenon that policymakers in
developing countries target economic boom through Keynesian policies, but they
should be warned if the share of government consumption expenditures is higher
than the optimum level. In this case, any increase in the share cannot trigger
economic growth and do not have positive and favorable reaction (Chen and Lee,
2005; Loizides and Vamvoukas, 2005).
(2) In the medium and long run: an understanding of the role of government
in economic growth can be favorable in setting strategies for the optimum growth.
Policymakers in developing countries often interested in choosing the option of
government intervention (policy of enlarged government) for a rapid economic
growth. But it should be noted that such strategies may or may not impede
growth. The mechanism of the impact of government on economic growth is
under the influence of both the share of government consumption expenditures
and unique features of any country such as political system, efficiency of public
sector and share of corruption and rent-seeking activities.
The majority of countries under study suffer non-democratic political
systems, and consequent negative effects of the government size. According to
the data provided by Freedom House in 2010, among 32 countries, 11 have closed
(not free) political system, 17 have partly free political system and 4 have free
political systems. Guseh (1997) has shown that the impact of government size on
economic growth is negative and these in non-democratic and socialist societies
can amount to up to 3 times as much that in democratic societies with market
economy. 24 out of 32 countries under study (75 per cent of the sample) belonged
to Africa.
Gupta and Verhoeven (2001) have shown that government in African
countries is more inefficient than government in Asian countries in providing
educational and health services (due to inefficient allocation of resources to
different sectors and higher wages in public education). Then, an increase in the
share of government consumption expenditures will have the consequent rise in
inefficiency. Therefore, improving efficiency in these sectors must be done at the
first.
The level of corruption in most of the countries under study is higher than
that in industrial countries (Heritage, 2010). It is surprising that among countries
under study, Tunisia enjoys better conditions, attaining a sustainable and
favorable growth rate of 4.4 per cent through a relatively the fixed and optimum
share of government consumption expenditures. Wu et al. (2010) stated that in
developing countries government expenditures would not succeed in targeting
development and eliminating poverty without improving quality of institutions
and lowering the level of corruption. Although in recent years, the government
size in most of the countries under study has been lower than the optimum level,
for reasons above, increasing the share of government consumption expenditures
could not boost economic growth.
Based on the specific and structural problems which are mentioned above, it
is highly recommended that developing countries (generally) and countries under
study (particularly) avoid any increase in the share of government expenditures
before adopting the reforms following reforms: gradual move to market economy
and the least intervention of the state in economic activities, setting democratic
political system, privatization of public properties, improving efficiency in public
sectors such as education and health, improving the quality of institutions to curb
rent-seeking activities and corruption. According to the results by Ram (1986),
though, the favorable impact of the government size on economic growth is
higher in poor countries, only after the above reforms can one be certain that
increasing the government size and investment by government in infrastructure
and market failures would bring about higher economic growth.
6. Conclusion
In this study, the effect of government size on economic growth was
investigated in 32 low income and low-middle income countries during 19812007. The results show that the effect of government consumption spending as a
share of GDP on economic growth, at least in the studied countries, is not linear.
Initially, economic growth increases with rising government consumption
spending as a share of GDP. Finally, with more increase in government
consumption spending as a share of GDP, economic growth declines. Countries
have different structures such as physical, natural and human capital. Therefore,
using threshold panel approach (Hansen, 1999) the Barro curve (Barro, 1990) and
the Armey curve (Armey, 1995) for the relationship between government
consumption spending as a share of GDP and economic growth are approved.
Based on this relationship, the optimal government consumption spending as a
share of GDP was estimated for these countries. The optimal share for LIC and
LMC countries was estimated to be 16.2% with the confidence interval [13.7%
17.3%] and 16.9% with the confidence interval [16.5% 16.9%], respectively. The
estimated optimum government size in this study is smaller than results Vedder
and Gallaway (1998), Witte and Moesen (2009) and Davies (2009) for high
income countries, and conversely the results show the optimum size is larger than
the findings of Chen and Lee (2005) and Chiou-Wei et al. (2010) for Southeast
Asian countries.
In seven countries, Algeria, Chad, Cote d’ivoire, Djibouti, Maldives,
Mauritania and Nigeria, the mean government consumption spending as a share of
GDP has been higher than the optimal size. These countries can experience higher
economic growth via decreasing government consumption spending as a share of
GDP. In seven countries, Burkina faso, Guinea-bissau, Guyana, Mali, Morocco,
Sierra leone and Tunisia, the government consumption spending as a share of
GDP is almost or completely in the optimal level. These countries should increase
government expenditures proportional to the increase in production. Theses 14
countries would have a permanent maximum economic growth by keeping the
government size in its optimal size with improving efficiency and performance in
public sectors.
In the eighteen remaining countries, the government consumption spending
as a share of GDP, at least for the last periods, has been lower than the optimal
size. In these countries, the government can increase economic growth by
increasing the expenditures (mainly through increasing the production) and
spending them in building infrastructures, healthcare, education, improvement of
labor force, and R&D. This policy, however, should be applied with care and
considering the efficiency issue. Although the share of expenditures is low in
these countries, government interferences and its authorities in the economy may
be much higher. These governments should decrease their interferences and work
in fields such as provision of infrastructures only.
Appendix A
Table A.1
Countries in the sample.
Bangladeshas
Comorosaf
Low income
Guinea-bissauaf
(LIC)
Nigeraf
Sierra leoneaf
Ugandaaf
Albaniae
Low-middle
Egyptaf
income (LMC)
Maldivesas
Note: af Africa
as
Asia
e
Europe
sa
Beninaf
Cote d’ivoireaf
Maliaf
Nigeriaaf
Somaliaaf
Burkina fasoaf
Gambiaaf
Mauritaniaaf
Pakistanas
Sudanaf
Chadaf
Guineaaf
Mozambiqueaf
Senegalas
Togoaf
Algeriaaf
Guyanasa
Moroccoaf
Cameroonaf
Indonesiaas
Tunisiaaf
Djiboutiaf
Iranas
South America.
Source: http://www.sesric.org/databases-index.php
Table A.2
Panel unit root test, Fisher-ADF test.
Group
GGDPF
LIC
259.15a
LMC
122.24a
LIC&LMC
381.4 a
GLT
122.21a
78.2a
200.41a
IYT
72.17a
33.81c
105.97 a
GST
94.79a
37.46b
132.25a
OPNT
77.84a
54.4a
132.24a
Note: H0: unit root. a, b, c denote significance at the 1%, 5% and 10% levels, respectively.
individual fixed effects, T denotes individual fixed effects and individual trends.
F
denotes
Appendix B
In Fig. B.1, it is clearly evident that a small part of the simulated statistics of
𝐹1 are above the actual 𝐹1 line (Eq. [3]). Therefore, bootstrap p-value is calculated
as a low percent and the null hypothesis is rejected. Thus, the presence of one
threshold in each group is confirmed. In contrast, a high part of simulated
statistics of 𝐹2 are above the actual 𝐹2 line, (Eqs. [6] or [8]), so the null hypothesis
is not rejected for the second threshold.
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Mehdi Hajamini is a Ph.D student of Economics at the Ferdowsi University of
Mashhad. His research interests include growth models, international economics,
and econometrics.
E-mail address: [email protected]
Tel.: +98 511 8813090.
Fax: +98 511 8829584.
Mohammad Ali Falahi is an associate professor at the Ferdowsi University of
Mashhad, Department of Economics. His research focuses on macroeconomics
and econometrics with a special emphasis on time series and panel data models.
E-mail address: [email protected]
Tel.: +98 511 8811240.
Fax: +98 511 8811243.