The impact of institutional quality on economic growth: panel evidence.
Nawaz, Saima ; Iqbal, Nasir ; Khan, Muhammad Arshad 等
The aim of the present study is twofold. First, we develop a
theoretical model which incorporates the role of institutions in
promoting economic growth. The theoretical model predicts that rent
seeking activities decrease as institutional quality improves, and hence
income increases and vice versa. Second, we conduct an empirical
analysis to quantify the impact of institutions on economic growth in
selected Asian economies over the period 1996-2012 by employing both
static and dynamic panel system Generalised Method of Moments (GMM)
technique with fixed effects. The empirical results reveal that
institutions indeed are important in determining the long run economic
growth in Asian economies. However, the impact of institutions on
economic growth differs across Asian economies and depends on the level
of economic development. The results reveal that institutions are more
effective in developed Asia than developing Asia. This evidence implies
that different countries require different set of institutions to
promote long term economic growth.
Keywords: Institutions, Economic Growth, Panel Evidence, Asia
1. INTRODUCTION
The path breaking studies by North (1981), Jones (1987) and Olson
(1982) inspired the researchers as well as policy-makers to investigate
the impact of institutions on economic growth. Earlier empirical
studies, inter alia by Knack and Keefer (1995), Mauro (1995) and Barro
(1997) reveal that institutions are important for investment and long
term sustainable growth. Hall and Jones (1999) demonstrate that
differences in the institutions across the globe cause huge variations
in capital accumulation, education attainment, and productivity growth,
hence account for income disparities. More recently, Rodrik,
Subramanian, and Trebbi (2004) find that rule of law has a positive
impact on economic growth. Similarly, Acemoglu, Cutler, Finkelstein, and
Linn (2006) concluded that private property right institutions are the
main drivers of long run economic growth, investment and financial
development. These studies suggest that institutions are the fundamental
determinants of the long run economic growth across countries.
The existing literature primarily indicates a positive association
between institutions and economic growth. However, institutions do not
exert similar impact on economic growth across different set of
countries. The positive contribution of institutions is shaped by
various factors like the perception of the individual about the
institutions and the social norms and community rules of a particular
group of individuals. Sometimes institutions with similar
characteristics produced extremely different outcomes across different
groups, regions and societies. For example, in Latin American countries
similar laws and solutions were adopted to achieve different levels of
economic growth and development [Yifu Lin and Nugent (1995)]. In this
context, Alonso and Garcimartin (2013) signify the role of stages of
economic development in determining the growth effects of institutions
and found that level of development determines the quality of
institutions which, in turn, enhances higher economic growth.
Few studies have empirically investigated the growth effects of
institutions at various stages of development [Nawaz (2014); Valeriani
and Peluso (2011)]. These studies have shown that the impact of
institutions on economic growth is different across countries. These
studies conclude that institutions perform better in developed countries
as compared to developing ones. A study on transitional economies shows
that control over corruption is growth enhancing if complemented by
strong democratic institutions not necessarily otherwise. Institutional
measures promote economic growth in strongly democratic economies and
fail to boost growth in weakly democratic countries [Iqbal and Daly
(2014)]. However, these studies lack a theoretical foundation to capture
the linkages between institutions and economic growth, and also suffered
from possible endogeneity problem. It can be argued that theoretical
foundation is essential to understand the mechanism though which
institutions are linked with economic growth. Furthermore, controlling
endogeneity is important for reliable and robust empirical findings.
Nawaz (2014) has investigated the impact of different institutions on
economic growth assuming different stages of development using the
SYS-GMM estimation technique. However, this study fails to control the
possibility of heterogeneity by combining different countries into one
group. The present study fills that gap in literature after taking care
of above mentioned shortcomings.
The main objective of this study is to develop theoretical model
that incorporates the role of institutions with respect to economic
growth. Furthermore, the present study empirically estimates the impact
of various institutions on economic growth at the cross-country level.
Particularly, we examine the impact of institutions on economic growth
by classifying developed and developing Asian economies over the period
1996-2012. This study contributes to the existing literature in various
ways. First, this study develops a theoretical model by incorporating
the role of institutions on economic growth following Gradstein (2007)
and Chong and Gradstein (2007). Second, this study addresses the issues
of heterogeneity and endogeneity using the SYS-GMM estimation technique.
Third, this study develops institutional quality index to capture
different dimensions of the institutions. Fourth, this study quantifies
the impact of institutions at different stages of development.
The rest of the paper is structured as follows: Section 2 provides
theoretical framework based on extended version of the endogenous growth
theory to incorporate the impact of institutions on economic growth.
Section 3 explains the data sources, and outlines estimation
methodology. The empirical results and discussion are presented in
section, while conclusion and policy implications are given in Section
5.
2. THEORETICAL FRAMEWORK
Traditional economic growth theories postulate that level of output
per capita is determined by the amount of physical and human capital and
level of technology in a country. In the production process, economic
growth is linked with the ability of the nation to enhance its physical
and human capital along with the technological developments. Acemoglu
and Robinson (2010) however, state that:
"[The] differences in human capital, physical capital, and
technology are only proximate causes in the sense that they pose the
next question of why some countries have less human capital, physical
capital, and technology and make worse use of their factors and
opportunities. To develop more satisfactory answer to question of why
some countries are much richer than others and why some countries grow
much faster than others, we need to look for potential fundamental
causes, which may be underlying these proximate differences across
countries" [p.2]
Acemoglu and Robinson (2010) argue that institutions are the
fundamental determinant of economic growth and cause development
differences across countries. North (1981) defines institutions as the
rule of the game in a society or, more formally the humanly devised
constraints that shape human interaction. This means that institutions
shape the incentive structure in the society that may increase or hamper
the economic activities. Poor quality institutions may slow down the
economic activities by providing room to economic agents to remain busy
in redistributive politics with lower economic returns rather than
growth promoting economic activities [Murphy, Shleifer, and Vishny
(1993)]. On the other hand, good quality institutions may promote
incentive structure that leads to higher economic growth through
reducing uncertainty and promoting efficiency [North (1990)]. Hall and
Jones (1999) argued that overall productivity of factors of production
in a country is driven by the quality of its institutions. Efficient,
well developed and uncorrupt institutions guarantee that labour can only
be used for productive purposes and not wasted in rent seeking
activities, which leads to higher economic growth [North (1990)]. Good
quality institutions enhance the ability of a country to adopt new
technologies invented elsewhere which may play an important role in
upgrading the development process of a country [Bernard and Jones
(1996)].
Iqbal and Daly (2014) argue that weak institutions divert resources
from productive sector to unproductive sector hence promote rent seeking
activities. While, strong institutions reduce the chances of rent
seeking activities and accelerate economic growth process and
productivity of the reproducible factors. This study argues that weak
institutional framework creates an opportunity for rent seeking
behaviour that may divert resources to unproductive sectors. (1) The
consequences of these activities for growth can be negative: resources
may not be efficiently allocated, externalities may be ignored,
transaction costs may be increased. North (1990) argues that
institutional weaknesses lead to rent seeking activities hence low
development. The incomplete rule of law, non-enforcement of property
rights, inadequate policies and the lack of reliable infrastructure
constitute a weak institutional framework that may promote rent seeking
activities [Iqbal and Daly (2014)].
To put the above discussion in a framework, we use the endogenous
growth model. Following the Gradstein (2007) and Chong and Gradstein
(2007), we specify Cobb Douglas type production function of the
following form:
[y.sub.it] = [Ak.sup.[alpha].sub.it] ... ... ... (1)
where y is output per worker, k is the stock of physical capital
per worker which includes both private and public capitals, A>0
represents total factor productivity. Countries are indexed by i and t
represents time period, a is the elasticity of output per worker with
respect to physical capital per worker and 0 < [alpha] < 1. To
incorporate the role of institutions in promoting economic growth, we
modify the basic endogenous growth model. The above discussion reveals
that weak (strong) institutions divert resources to unproductive
(productive) sectors hence cause low (high) development. Gradstein
(2007) and Chong and Gradstein (2007) also argued that weak institutions
divert resources from productive sectors to unproductive sectors and
promote rent seeking activities. However, strong institutions reduce the
chances of rent seeking activities and accelerate economic growth and
productivity of the reproducible factors. To capture this notion, we
redefine production function specified in Equation (1) by including
rent-seeking activities that act as a distortion in the production
process. Now the production function takes the following form:
[y.sub.it] = (1 - [r.sub.it]) [Ak.sup.[alpha].sub.it] ... ... ...
(2)
where [r.sub.it] [member of] [0, [??]], [??] [much less than] 1
indicate rent seeking activities, [??] is appoint at which institutional
quality is degraded to such an extent that the modeling framework ceases
to apply. Assume that appropriate share of rent-seeking by each firm
depends on the amount of rent seeking and quality of institutions. With
strong institutions, the value of rent seeking [r.sub.it] is close to 0,
whereas with weak institutions the value of [r.sub.it] is close to 1 and
the marginal utility of rent-seeking is maximal. Higher marginal utility
of [r.sub.it] implies weak institutions and hence low productivity of
factors of production and vice versa. This augmentation provides
meaningful explanation about the cross country differences in long run
growth rates. (2) Thus [r.sub.it] reduces the marginal product of
reproducible factors due to economic distortions resulting from low
quality institutions. To determine the long run growth patterns across
countries, we need to examine the consumption and investment decisions
made by the individuals. Consider one representative agent facing an
infinite planning horizon and maximising intertemporal utility subject
to dynamic budget constraint. The representative agent's
preferences have the following form:
[U.sub.it] = [[integral].sup.[infinity].sub.0]
[c.sup.1-[sigma].sub.it]/ 1 -[sigma] [e.sup.-[rho]t] dt ... ... ... (3)
where [c.sub.it] represents private consumption in per capita form
and [sigma] > 0 and [sigma] [not equal to] 1 which shows that the
elasticity of marginal utility equals the constant -[sigma]. The other
multiplier, [e.sup.-[rho]t], involves the rate of time preference, [rho]
> 0. Positive time preferences rate [rho] means so that utils are
valued less the later they are received. The dynamic budget constraint
in per capita terms is given by the following equation:
[[??].sup.*.sub.it] = dk/dt = (1 -
[r.sub.it])[Ak.sup.[alpha].sub.it] - [c.sub.it] ... ... ... (4)
It is assumed that the initial capital stock at time 0 is 1 i.e.
[k.sub.(0)] = 1, The terminal condition is defined as [lim.sub.t[right
arrow][infinity]]k[lambda][e.sup.-[rho]t] = 0 which indicates that the
capital stock left over the end of the planning horizon, when discounted
at the time discount rate is zero. This restriction rules out the type
of chain-letter finance. Equation (4) suggests that increase in the
capital stock equals the total saving, which in turn, equals to the
difference between output and consumption. The individual chooses
optimal consumption {[c.sub.it]: t [greater than or equal to] 0} and
investment path to determine the level of capital stock {[k.sub.it]: t
[greater than or equal to] 0}. To find this optimal allocation of
resources by the individual, we can write the Hamiltonian as:
H = [c.sup.1-[sigma].sub.it] - 1/1 - [sigma] [e.sup.-[rho]t] +
[lambda][(1 - [r.sub.it]] [Ak.sup.[alpha].sub.it] - [c.sub.it]] ... ...
... (5)
The expression within bracket is equal to [??] and [lambda] is
Lagrange multiplier representing the present value of shadow price of
income. Differentiation of Lagrange function with respect to [c.sub.it]
and [k.sub.it] and the first order conditions give us Equations (6) and
(7).
[partial derivative]H/[partial derivative][c.sub.it] = 0 [??]
[c.sup.1-[sigma].sub.it] [e.sup.-[rho]t] - [lambda] = ... ... ... (6)
[partial derivative]H/[partial derivative][k.sub.it] + [??] = 0
[??] [lambda](1 - [r.sub.it]) A[alpha][k.sup.[alpha]-1.sub.it] = -[??]
... ... ... (7)
Using first-order conditions; fixing the initial capital stock
[k.sub.(0)] = 1; applying transversality condition [lim.sub.t[right
arrow][infinity]] [k.sub.t][lambda][e.sup.-[rho]t] = 0; the budget
constraint is given in Equation (4), we find the growth rate of per
capita consumption which is the same as the capital and the output
growth rate. The growth rate of the economy is given as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (8)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9)
Equation (8) shows that as institutional quality improves, the rent
seeking activities decrease and hence consumption (or income) increases.
Now differentiating with respect to [r.sub.it] = [partial
derivative]([[??].sub.it]/[y.sub.it])/ [partial derivative][r.sub.it] =
- A[alpha][k.sup.[alpha]-1.sub.it]/[[sigma].sup.2] > 0. This shows
that as the value of [r.sub.it] increases, the output decreases as
[sigma] > 0.
Propositions. The larger the [r.sub.it], the lower will be the
growth rate of the economy and vice versa. As institutional quality
improves, the rent seeking activities decrease and hence
consumption/income increases and vice versa.
We consider two cases for example for validation, these include:
(i) When [r.sub.it] = 0 (strong institutions): Under strong
institutions regime, economic growth is with 1/[sigma]
[A[alpha][k.sup.[alpha]-1.sub.it] - [rho]]
(ii) When 0 < [r.sub.it] < [??] (weak institutions): Under
weak institutions regime, economic growth is with 1/[sigma] [(1 -
[r.sub.ir])A[alpha][k.sup.[alpha]-1.sub.it] - [rho]]
In essence, the theoretical model highlights that long run growth
rate of per capita output is a function of physical capital and rent
seeking--a proxy for institutions. After logarithmic transformation,
Equation (9) can be rewritten as:
[[??].sub.it] = [[alpha].sub.0] + [phi][I.sub.it] +
[theta][k.sub.it] ... ... ... (9)
where [[??].sub.it] represents GDP growth rate across cross-section
i at time period t. [I.sub.it] represents institutional quality index
and [k.sub.it] indicates physical capital. We use Equation (10) to
examine the impact of institutions on economic growth.
3. DATA AND METHODOLOGY
To determine the impact of institutions on growth, we employ a
panel data set of 35 Asian countries over the period 1996-2012. (3) The
selected countries are divided into developed Asia and developing Asia
on the basis of income levels following the World Bank classification.
(4) The data on the institutional variables are collected from the
Worldwide Governance Indicators (WGI) published by the World Bank. The
database provides six different measures capturing different dimensions
of the institutional framework. These indicators include: (i) control of
corruption, (ii) government effectiveness, (iii) political stability and
absence of violence/terrorism, (iv) regulatory quality, (v) rule of law,
and (vi) voice and accountability. The indicator ranges from -2.5 to
+2.5. The low value indicates bad quality institutions and vice versa.
It is expected that these indicators are likely to be correlated,
therefore, we construct institutional quality index using the Principle
Component Method (PCM) methodology. The PCM indicates how much variance
of a variable is explained by a specific principal component. The
principal component is derived by computing the eigenvalues of the
sample covariance matrix. These eigenvalues are the variances of the
variables (institutional indicators in this case). Therefore, the number
of principal components is equal to the number of variables. Typically
most of the variance is explained by the first principal component and
therefore its value is used for computation of the index. The main
advantage of PCM is that the weights to be assigned to the variables are
determined by the data itself. The Figure 1 depicts the average quality
of institutions across full sample, developing Asia and developed Asia.
The average value of institutional quality index across the full sample
is 4.5, while this value is 5.8, 3.0 for developed Asia and developing
Asian countries respectively during the period 1996-2012. The individual
indicators also show similar behaviour.
The data on all economic variables are taken from the World
Development Indicators (WD1) published by the World Bank. These
variables include GDP per capita growth, investment, trade openness,
inflation and the government size. Investment is measured as the Gross
Fixed Capital Formation as a percent of GDP. Openness is the sum of
exports and imports divided by the GDP. Inflation is measured by the log
difference of consumer price index (CPI). We use general government
final consumption expenditure relative to GDP as a proxy for the
government size. The descriptive statistics (Table 1A appendix) show
that the annual average GDP per capita growth rate is 3.7 over the
period 1996-2012. The annual average investment as percent of GDP is 24
over the same period. The annual average inflation across the full
sample is 7.15, while annual average inflation is relatively high in
developed countries (5.35) as compared to developing countries (9.29)
over the period 1996-2012. The average government size as percent of GDP
is 13 over the same period.
The model described in previous section emphasises the role of
institutions as determinants of output per capita. Based on the
theoretical framework an empirical model can be written as:
[[??].sub.it] = [[alpha].sub.0] + [phi][I.sub.it] +
[theta][k.sub.it] + [beta]X + [[epsilon].sub.it] ... ... ... (11)
where [[??].sub.it] represent GDP growth rate of country t at time
period t. [I.sub.it] represents institutional quality index (INS Index)
and [k.sub.it] indicates physical capital, and X is the set of control
variables, while [[epsilon].sub.it] is the disturbance term which is
assumed to be serially uncorrelated and orthogonal to the explanatory
variables. The vector of control variables X includes: investment (INV),
government size (EXP), inflation (INF) and trade openness (OPEN). These
variables have been frequently used in growth literature and have been
identified by Mankiw, Romer, and Weil (1992), Levine and Renelt (1992)
and Barro and Lee (1996).
The choice of appropriate estimation technique is important for
obtaining robust estimates. To measure the impact of institutions on
economic growth we, employ panel data estimation technique. The panel
data estimation technique is considered as an efficient analytical
method, since it allows combining different cross sections and time
periods, and provides more reliable and robust inference. We use the
Fixed Effects Model (FEM) based on the Hausman test. Before proceeding
further, it is important to highlight the possibility of endogeneity
between institutions and economic growth. Acemoglu, Johnson, Robinson,
and Yared (2009) conclude that traditional empirical literature
generally carries problems like endogeneity, measurement errors and
omitted variables bias.
A popular method to tackle the endogeneity is the Generalised
Method of Moments (GMM). The GMM estimator is as an extension of
Instrumental Variable (IV) methodology. The main advantage of GMM
estimation is that the model need not be homoscedastic and serially
independent. Another advantage of the GMM estimation is that it finds
the parameters estimates by maximising an objective function which
includes the moment restriction that the correlation between errof term
and lagged regressor is zero. In essence, the GMM takes into account the
time series dimension of the data, non-observable country specific
effects, inclusion of lagged dependent variables among the explanatory
variables and the possibility that all explanatory variables are
endogenous [Bond, Bowsher, and Windmeijer (2001); Caselli, Esquivel, and
Lefort (1996)]. In particular, the system GMM, developed by Arellano and
Bover (1995) and Blundell and Bond (1998) and applied by Bond, et al.
(2001) to the growth equation, was found to reduce a small sample bias
that characterises the first differenced GMM used by Caselli, et al.
(1996).
Anderson and Hsiao (1982) propose a strategy to choose instruments
to solve the endogeneity. This study suggests transforming to first
differences to eliminate the time-invariant fixed effects and applying
IV with lagged difference or level as instruments. Anderson and Hsiao
(1982) estimator is an example of simple IV estimation, in which there
is one instrument for each endogenous variable. A simple generalisation
of this estimator is the GMM in which the number of instruments is
permitted to exceed the number of endogenous variables. Arellano and
Bond (1991) suggest using all valid lags of all the regressors as
instruments. The efficiency of GMM estimation generally increases in the
number of valid and effective moment conditions. Therefore, Arellano and
Bond (1991) estimator should be superior to Anderson and Hsiao (1982)
estimator. However, this superiority might be minimal if the panel has a
shorter time span. Given that our data span over 30 years, there is
limited opportunity for applying the Arellano and Bond (1991)
instrumentation method. To solve this problem, Arellano and Bover (1995)
and Blundell and Bond (1998), assuming stationarity justify additional
zeromoment restrictions that can be applied to a model in levels,
instrumented with lagged differences. These additional moment
restrictions can be combined with those in Arellano and Bond (1991) to
provide a "system-GMM" estimator in which GMM is applied to a
system of two equations: an equation in difference form instrumented by
lagged levels, and an equation in levels instrumented by lagged
difference.
For lagged endogenous variables and weakly exogenous variables to
be valid as instruments, it is necessary that the transient disturbances
are free of autocorrelation in the basic model [Blundell and Bond
(1998)]. This implies that disturbances in the differenced model have
significant first-order correlation and insignificant second-order
autocorrelation. For this purpose, the Arellano-Bond tests for
first-order and second-order serial correlation in the first-differenced
residuals are used [Arellano and Bond (1991)]. As the first difference
of independently and identically distributed idiosyncratic error will be
serially correlated, rejecting the null hypothesis of no serial
correlation in the first-differenced error at order one does not imply
that model is misspecified. Rejecting the null hypothesis at higher
orders, however, implies that the moment conditions are not valid.
Therefore, to establish the robustness of the estimates, we employ
SYS-GMM.
4. EMPIRICAL RESULTS AND DISCUSSION
We have estimated equation (10) to examine the impact of
institutions on economic growth for a panel of 35 Asian countries over
the period 1996-2012 using the Fixed Effects Model. The estimation
results are presented in Table 2. The estimation has been carried out
separately for the whole panel of countries as well as for the developed
and developing Asian economies. We have used various diagnostics to
ensure the adequacy of the estimated models. The results of diagnostics
are reported below in Table 2. These results confirm that the estimated
models are well specified.
As shown in Table 2 that institutions have a positive impact on
economic growth in Asian countries which implies that institutions are
growth enhancing. The value of estimated coefficient of institutions is
0.7 and significant at the 5 percent level of significance. This implies
that an increase in institutional quality by 1 percentage points
increases the long term economic growth rate by 0.7 percentage points.
This result is consistent with the hypothesis that institutions play a
critical role in the growth process. For example, North (1990) argues
that institutions increase the productivity of factor inputs by
improving the incentive structure. Similarly, Acemoglu, Johnson,
Robinson, and Yared (2008) showed that good quality institutions enhance
a country's ability to utilise modern technologies which, in turn
causes economic growth. Many other empirical studies provide evidence
that institutions promote economic growth [Acemoglu, et al. (2006);
Acemoglu, Johnson, and Robinson (2001); Barro (1997); Hall and Jones
(1999); Iqbal and Daly (2014); Knack and Keefer (1995)].
To examine the role of institutions on economic growth at various
stages of economic development, we have disaggregated our sample into
developed Asia and developing Asia. We find that the impact of
institutions on economic growth is positive for both developed as well
as developing Asia. However, the contribution of institutions to
economic growth is relatively high in developed Asian countries than in
developing Asian countries. The value of estimated coefficient of
institution index is 0.4 for developing Asian countries, while it is
1.17 for developed Asian economies. This shows that a one percentage
point improvement in the quality of institution leads to 0.4 percentage
point increase in GDP per capita in the developing Asian economies and
1.17 percentage point increase in GDP per capita in the developed Asian
countries. The low contribution of institutions to economic growth in
developing Asian nations could be attributed to several reasons. One
reason could be that the political system in these countries is weak.
The politicians and public officials have fewer checks on their power,
making it easier for them to engage in rent seeking. This inefficiency
may act as binding constraint in making institutions growth enhancing.
Various studies have shown that under weak democracy, institutions may
not work effectively [Aidt, Dutta, and Sena (2008); Drury, Krieckhaus,
and Lusztig (2006); Iqbal and Daly (2014); Mendez and Sepulveda (2006)].
Iqbal and Daly (2014) find that corruption has an insignificant impact
on economic growth under weak democracy. Other reason could be that the
institutional framework in developing countries is still underdeveloped
and in the transition stage. This transition process undermines the
effectiveness of institutions. For example, frequent changes in the
design of institutional framework are not effective to promote economic
growth. Another reason could be that the quality of institutions could
be below the certain minimum threshold level. Zhuang, De Dios, and
Lagman-Martin (2010) argue that institutions are only effective when
they are above the world average values. Economies with strong
institutions show higher growth than those with institutions below
threshold level. Finally, causality between institutional quality and
economic growth also explains different impacts on institutions in
developed and developing countries [Fukuyama and McFaul (2008)].
Numerous control variables have been used in the empirical
analysis. For example, our results show that the impact of government
size measured by government consumption is negative on economic growth
for the whole Asian countries, developing Asia and developed Asia. Our
results are consistent with earlier studies that government size has a
negative impact on economic growth [Agell, Lindh, and Ohlsson (1997);
Barro (1991); Bergh and Karlsson (2010); Cameron (1982); Grier and
Tullock (1989); Landau (1983); Marlow (1986); Romero-Avila and Strauch
(2008); Saunders (1986)]. The results show that the impact of investment
on economic growth is positive. This finding is in line with existing
literature [Barro (1991); Rebelo (1991)]. Inflation has a negative
association with growth in GDP per capita, implying that inflation hurts
the growth process. Many empirical studies have found similar results
[Fischer (1993); Sirimaneetham and Temple (2009)]. Higher inflation
produces detrimental impact on the economic growth. This result could be
justified in many ways. It causes reduction in investment and
productivity by generating uncertainty in the economy [Fischer (1993)]
and produces adverse effects on the productivity of inputs through
distorting the price mechanism [Smyth (1995)]. High inflation also
increases the risk premium and hinders the smooth functioning of
financial markets through the reduction of saving and investment. Trade
openness has a positive and significant impact on the economic growth,
implying that trade is beneficial for economic growth. The positive
association of trade openness and economic growth is due to the benefits
emerging from specialisation, competition and economies of scale. This
result is consistent with the earlier studies [Balassa (1978); Din,
Ghani, and Siddique (2003); Edwards (1998); Sachs, Warner, Aslund, and
Fischer (1995); Tyler (1981)].
4.1. Institutions and Growth: A Disaggregated Analysis
In the previous analysis we used a composite index of institutional
quality to quantify the impact of institutions on economic growth. We
concluded that institutions perform better in developed Asian economies
as compared to developing economies. However, this provides a limited
picture in explaining the influence of institutions on growth assuming
different stages of development. The findings based on composite
institutional quality index do not identify the effect of individual
components of institutional quality. Zhuang, et al. (2010) have pointed
out that various components of institutional quality have differential
effects on growth, depending on a country's history, stages of
development, and the length of time horizon being investigated.
Following Zhuang, et al. (2010) we have investigated the impact of
various components of institutional quality on economic growth. Table 3
reports the results. (5)
The disaggregated analysis has shown that control over corruption
(CC), government effectiveness (GE) and rule of law (RL) are more
important as compared to political stability (PS), regulatory quality
(RQ) and voice and accountability (VA) in the full sample of Asian
countries. Further, we have found that different institutions perform
differently for developed and developing Asia. For Asian developing
economies, the government effectiveness and rule of law play significant
role in promoting economic growth. On the other hand, all most all
measures of institutional quality contribute significantly to economic
growth. These findings support the Zhuang, et al. (2010) view that
different institutions perform differently at different stages of
development.
4.2. Sensitivity Analysis
To examine the issue of reverse causality between institutions
quality and economic growth, we re-estimate the model after controlling
the possibility of reverse causality and endogeneity using dynamic
system GMM (SYS-GMM). The SYS-GMM uses lag of dependent variables to
introduce dynamics in the model. The inclusion of lagged dependent
variable allows for path dependency in the model and works as a partial
adjustment mechanism. Lagged level of per capita GDP is taken to test
the neo-classical hypothesis of convergence to a long run steady state.
The results are presented in the Table 4. A battery of diagnostic tests
have been applied to check the accuracy of the specification and to
ensure that the models are adequately specified. Chi-square statistic
confirms the adequacy of the estimated models. Diagnostic statistics
based on AR1 and AR2 are consistent with the validity of instruments
used in SYS-GMM.
The results show that institutions have a positive impact on
economic growth in a sample of 35 Asian countries as well as for
developed and developing Asian countries. We found that institutions
perform relatively better in developed Asian countries as compared to
developing Asian countries as indicated by the size of the coefficient.
The estimated impact of institutions is high in developed Asian
countries than developing Asia. The impact of control variables remains
the same as we found in case of fixed effects estimation.
As shown in Table 4 the negative coefficient of the lagged level of
GDP per capita (GDPPC(-1)) together with positive coefficient of the
lagged growth rates (GDPPCG(-1)), support the neoclassical hypothesis of
convergence to a long run steady state in the case of full sample. The
impact of individual indicators of institutions on economic growth is
also estimated using the SYS-GMM method (Table 5). The results suggest
that different institutions perform differently at different stages of
development. The results are similar to those found in case of fixed
effects estimation. The results suggest that control of corruption,
government effectiveness and regulatory quality have relatively greater
effect on economic growth in developed Asia as compared to developing
Asia. On the other hand, rule of law and voice and accountability
perform better in developing Asia than in developed Asian nations.
5. CONCLUDING REMARKS
This study develops a theoretical model and assesses the role of
institutions on economic growth for a panel of 35 Asian countries over
the period 1996-2012. We have used the fixed effects and SYS-GMM
estimation techniques to examine the impact of different institutions
including: control over corruption, government effectiveness, political
stability, rule of law, regulatory quality and voice and accountability
on economic growth. We have constructed institutional quality index
using six component institutions by employing principle component
method. The theoretical model reveals that as institutional quality
improves, the rent seeking activities decrease and hence income
increases and vice versa. The empirical results support the hypothesis
that institutions exert positive impact on economic growth. Our findings
suggest that control of corruption and maintenance of rule of law are
the key determinants of long term economic growth for sampled Asian
countries. Furthermore, results reveal that the impact of institutions
on economic growth varies across Asian countries depending on the stages
of economic development. The estimated impact of institutions on
economic growth is relatively higher in the developed Asia than in the
developing Asian countries. This result highlights the role of
institutions and level of economic development in determining the long
run economic growth. Therefore, different countries require different
set of institutions and policies to promote long run economic growth.
Appendix Table 1A
Descriptive Statistics
Variable Observations Mean Std. Dev.
Full Sample
GDP Per Capita Growth Rate 595 3.70 4.84
Investment (INV) 595 24.14 8.33
Government size (EXP) 595 13.59 5.62
Inflation (INF) 595 7.15 11.33
Openness (OPN) 595 101.95 75.89
Institutions (INS Index) 595 -0.14 0.70
Control of Corruption (CC) 595 -0.10 0.86
Government Effectiveness (GE) 595 0.09 0.80
Political Stability (PS) 595 -0.35 0.92
Rule of Law (RL) 595 -0.05 0.79
Regulatory Quality (RQ) 595 0.06 0.82
Voice and Accountability (VA) 595 -0.44 0.72
Low Income/Developing
Countries
GDP Per Capita Growth Rate 272 4.38 4.26
Investment (INV) 272 24.59 9.18
Government Size (EXP) 272 11.21 4.68
Inflation (INF) 272 9.29 11.43
Openness (OPN) 272 76.66 31.42
Institutions (INS Index) 272 -0.57 0.38
Control of Corruption (CC) 272 -0.62 0.47
Government Effectiveness (GE) 272 -0.42 0.40
Political Stability (PS) 272 -0.79 0.87
Rule of Law (RL) 272 -0.57 0.48
Regulatory Quality (RQ) 272 -0.44 0.40
Voice and Accountability (VA) 272 -0.59 0.56
High Income/Developed
Countries
GDP Per Capita Growth Rate 323 3.12 5.21
Investment (INV) 323 23.76 7.54
Government Size (EXP) 323 15.59 5.57
Inflation (INF) 323 5.35 10.93
Openness (OPN) 323 123.25 93.80
Institutions (INS Index) 323 0.22 0.71
Control of Corruption (CC) 323 0.33 0.88
Government Effectiveness (GE) 323 0.53 0.80
Political Stability (PS) 323 0.03 0.78
Rule of Law (RL) 323 0.38 0.73
Regulatory Quality (RQ) 323 0.48 0.85
Voice and Accountability (VA) 323 -0.31 0.82
Variable Min. Max.
Full Sample
GDP Per Capita Growth Rate -14.39 38.06
Investment (INV) 8.01 64.43
Government size (EXP) 3.46 30.50
Inflation (INF) -8.53 128.42
Openness (OPN) 18.76 448.31
Institutions (INS Index) -1.45 1.44
Control of Corruption (CC) -1.49 2.42
Government Effectiveness (GE) -1.28 2.43
Political Stability (PS) -2.50 1.40
Rule of Law (RL) -1.52 1.77
Regulatory Quality (RQ) -1.73 2.25
Voice and Accountability (VA) -1.86 1.14
Low Income/Developing
Countries
GDP Per Capita Growth Rate -14.39 38 06
Investment (INV) 8.01 64.43
Government Size (EXP) 3.46 25.88
Inflation (INF) -1.71 128.42
Openness (OPN) 21.55 162.91
Institutions (INS Index) -1.45 0.29
Control of Corruption (CC) --1.49 0.82
Government Effectiveness (GE) -1.28 0.78
Political Stability (PS) -2.50 1.31
Rule of Law (RL) -1.52 0.37
Regulatory Quality (RQ) -1.50 0.68
Voice and Accountability (VA) -1.82 0.50
High Income/Developed
Countries
GDP Per Capita Growth Rate -11.53 33.03
Investment (INV) 9.66 57.71
Government Size (EXP) 6.77 30.50
Inflation (INF) -8.53 85.73
Openness (OPN) 18.76 448.31
Institutions (INS Index) -1.24 1.44
Control of Corruption (CC) -1.25 2.42
Government Effectiveness (GE) -1.07 2.43
Political Stability (PS) -1.62 1.40
Rule of Law (RL) -1.19 1.77
Regulatory Quality (RQ) -1.73 2.25
Voice and Accountability (VA) -1.86 1.14
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Bank.
(1) Rent seeking activity is defined as an activity through which
public power is exercised for private gain; this may involve misuse of
public resources or, more generally, any attempted capture and
commodification of state, social or commercial authority by politicians,
public officials, elites and private interests [Iqbal and Daly (2014)].
(2) Steger (2000) introduces the similar index (distortion index in
the production function to capture the role of detrimental government
policies on economic growth. Iqbal (2013) incorporates instability index
in the model using similar formulation to capture the impact on
macroeconomic instability due to weak institutions and macroeconomic
policies on output.
(3) The choice of 35 countries is mainly based on the availability
of data on all variables.
(4) The World Bank classifies the countries on the basis of income
per capita. The sub-groups are: (i) Low income countries/Developing
countries and (ii) High income countries/Developed countries, in
developing countries sub-group, we have selected 16 countries, while in
developed countries sub-group we have selected 19 countries.
(5) We have also used other control variables in the estimation,
but for presentation purposes we have omitted these variables from the
Table. The detailed estimation Tables are available upon the request
from authors.
Saima Nawaz <
[email protected]> is Assistant
Professors at COMSATS Institute of Information Technology, Islamabad.
Nasir Iqbal <
[email protected]> is Assistant Professor at Pakistan
Institute of Development Economics, Islamabad Muhammad Arshad Khan
<
[email protected]> is Associate Professor at COMSATS
Institute of Information Technology, Islamabad.
Table 2
Impact of Institutions on Economic Growth (Institutional Quality
Index)
Variables Asia Developing Asia Developed Asia
INS Index 0.702 0.406 1.172
(0.30) ** (0.21) * (0.48) **
EXP -0.369 -0.441 -0.300
(0.08) *** (0.14) *** (0.11) ***
INV 0.087 0.191 0.031
(0.03) *** (0.05) *** (0.05)
OPN 0.034 -0.001 0.041
(0.01) *** (0.02) (0.01) ***
INF -0.037 -0.025 -0.028
(0.02) ** (0.02) (0.03)
Constant 0.259 3.707 -4.591
(1.91) (2.02) * (3.50)
Observations 595 272 323
R-squared 0.083 0.106 0.102
F-values 10.10 5.97 6.78
Hausman test 29.83 (0.00) 25.37 (0.00) 33.62 (0.00)
Number of Countries 35 16 19
Standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1.
Table 3
Impact of Institutions on Economic Growth (Components of
Institutional Quality)
Variable CC GE PS RL RQ
Full Sample 0.762 1.497 0.245 0.832 0.779
(0.41) * (0.49) *** (0.25) (0.48) * (0.43) *
Developing 0.620 1.194 0.292 0.682 -0.699
(0.56) (0.68) * (0.31) (0.41) * (0.57)
Developed 1.095 2.099 0.140 1.428 3.041
(0.63) * (0.78) *** (0.42) (0.74) * (0.72) ***
Variable VA
Full Sample 0.411
(0.44)
Developing 0.920
(0.57)
Developed 0.173
(0.66)
Standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1.
Table 4
SYS-GMM (Results of Institutional and Economic Growth)
Variables Asia Developing Asia Developed Asia
INS Index 1.304 1.568 1.992
(0.36) *** (0.51) *** (0.36) ***
EXP -0.259 -0.210 -0.225
(0.10) ** (0.14) (0.11) **
INV 0.024 0.244 -0.096
(0.04) (0.06) *** (0.06) *
INF -0.109 -0.065 -0.207
(0.03) *** (0.03) ** (0.05) ***
GDPPC(-1) -2.870 -2.748 -5.050
(0.58) *** (1.00) *** (0.89) ***
GDPPCG(-l) 0.096 -0.104 0.226
(0.03) *** (0.05) ** (0.04) ***
Constant 24.047 15.183 49.023
(4.07) *** (5.48) *** (8.10) ***
Observations 560 256 304
Number of Countries 35 16 19
Wald Chi2 Value 60.72 36.16 83.60
AR1 Test 0.0018 0.0396 0.0042
AR2 Test 0.1729 0.1140 0.1645
Standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1.
Table 5
SYS-GMM (Components of Institutional Quality)
Variable CC GE PS RL
Full Sample 1.570 2.728 0.142 2.465
(0.46) *** (0.55) *** (0.38) (0.57) ***
Developing 2.287 2.336 0.446 3.645
(0.68) *** (0.79) *** (0.42) (0.88) ***
Developed 2.932 2.370 -0.134 2.406
(0.52) *** (0.62) *** (0.43) (0.59) ***
Variable RQ VA
Full Sample 2.308 1.239
(0.55) *** (0.51) **
Developing 0.144 2.310
(0.67) (0.74) ***
Developed 2.832 0.875
(0.66) *** (0.51) *
Standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
Fig. 1. Average Quality of Institutions across Full Sample, Developing
Asia and Developed Asia
Full Asia Developing Asia Developed Asia
INS 4.5 3.0 5.8
CC 4.8 3.8 5.7
GE 5.2 4.2 6.1
RL 4.9 3.9 5.8
RQ 5.1 4.1 6.0
VA 4.1 3.8 4.4
PS 4.3 3.4 5.1
Note: Table made from bar graph.
