What drives the quality of institutions in Asian economies?
Directions for economic reforms.
Key Theme: Economic Reforms, Competitiveness
Muhammad Nadeem Javaid1, Muhammad Naveed Iftikhar2
1
Suleman Dawood School of Business, Lahore University of Management Sciences, DHA, Lahore, Cantt. 54792,
Pakistan. E-mail: [email protected] (Corresponding author)
2
Ministry of Finance, Government of Pakistan, Islamabad, Email: [email protected]
Ist Draft – As on 27-October 2011
Not to be quoted without authors‘ permission
What drives the quality of institutions in Asian economies?
Directions for economic reforms.
Key Theme: Economic Reforms, Competitiveness
Abstract
This study attempts to construct a comprehensive indicator of institutional quality for eleven Asian
countries and then identifies it‘s potential determinants. Our penal data regression results show that, a
raise in the efficiency of the tax system, per capita income, international openness, level of education of
the population and a decrease in the level of indebtedness have the potential to improve the institutions
of the respective countries in short run. In addition to this a decrease in military spending and an
increase in the number of checks over the political elite have further prospects to improve the quality of
the institutions of the countries of the sample in the longer run. However, there is no straight jacket to fit
to all; as quantile analysis shows that an increase in GDP per capita, tax collection and a decrease in
military spending have better potential to enhance the institutional quality in Bangladesh, India, Sri
Lanka, Pakistan, Philippines and Thailand, whereas a decrease in indebtedness and an increase in the
level of education, international openness and tax collection have better potential to enhance the
institutional quality in Singapore, South Korea, Malaysia, Indonesia and China. A notable fact is that
adult literacy rate, a proxy of education system, does not have any potential to influence the institutional
quality of the above mentioned first set of countries. It necessitates that these countries should reexamine and refurbish their education system to make it deliverable.
1)
Introduction
We are still lacking sufficient understanding about ever increasing gaps in productivity and income per
capita across the globe. One line of the argument emphasizes the strategic role of institutions to explain
these differences in the wealth nations. Institutions are defined as rules of game in society or the
humanly devised constraints which shape human interaction (North 1991). Institutions can be both
formal (laws and regulations) and informal (Tabellini 2005). Good institutions play a pivotal role in
lowering transaction costs, ensuring contract enforcement, protecting property rights and enhancing
productivity while providing a level playing field to economic agents. There is considerable empirical
evidence that the institutions of the most developed economic systems have played a crucial role in their
voyage to higher levels of growth [see e.g. relationship between economic freedom, democracy and
economic growth (Barro 1996 ; Minier 1998) property rights and economic growth
(North and
Weingast1989; North, 1990) the impact of inequality and political instability on growth (Alesina and
Rodrik 1994; Lee and Roemer 1998; Barro 2000) social capability, trust and economic growth (Knack
and Keefer 1995, 1997a, 1997b; Hall and Jones, 1997, 1999; Zak and Knack, 2001)]. These studies
show that sound, credible and efficient institutional framework is an imperative precondition for
economic activity and growth. According to Acemoglu (2008), there is now a growing understanding
that economic, political, legal, and social institutions are essential to the economic success and failure of
nations. He further asserts that the institutions of a country create incentives for investment and
technology adoption, for its businesses to invest, and the opportunity to accumulate human capital for its
workers, thus propelling economic growth. Opposite scenario could be that country‘s institutions may
discourage such activities and as a result leading the economy to stagnation. Conversely, Chang (2006)
is of the view that historical study of evolution of institutions in developed world reveals that much of
the institutions being considered necessary for economic growth emerged after not before development
process in the developed economies. But this argument does not imply that any effort for improving the
institutional quality a priory to attain a certain level of development is worthless. Gerschenkron (1962)
has pointed out that there is an advantage of backwardness; to us its interpretation is that backward
countries can learn from the experiences of the developed world and could develop certain appropriate
institutions beforehand rather than the development induces them. Therefore, there is a logical quest;
how to develop good institutions?
Above mentioned studies provide clear evidence for an important regularity in economic development
but with certain limitations. First of all indicators of institutional quality being used in these studies are
not the comprehensive proxies. Secondly, these results barely identify the drivers of institutional quality,
particularly in Asian context. Asian countries have diverse scenarios in terms of current state of
economic growth, institutional architecture, socio-cultural norms, military‘s role in politics and colonial
legacy. Following this pursuit, there are two premier focus of this study: i) to construct a comprehensive
indicator of institutional quality for different Asian countries, ii) to identify an important factor that
could have serious implications on the institutional quality of this region. Literature review suggests that
income per capita, international openness, educational level of the population, the efficiency of the tax
system, foreign aid/debt, checks and balances and colonial legacy are the instrumental determinants of
the quality of institutions (for details see Siba 2008; Alonso et al 2009). To this end, we are going to add
another dimension i.e. military‘s influence on the institutions of the respective countries as most of the
countries in our sample have strong military presence. Rest of the paper is structured as; second section
presents literature review, third section deals with the data, methodology and empirical results, fourth
section comprises the discussion and the last section concludes and presents some policy insights.
2.
Literature Review
The notion that institutions profoundly influence the wealth of nations was primarily expressed by Adam
Smith (e.g. Snowdon et al. 2005). World Bank (2002) has also proclaimed that there is now widespread
acceptance of the idea that good institutions and incentive structures are important preconditions for
successful economic growth and development. North (1991) argues that the central issue of economic
history and of economic development is to account for the evolution of political and economic
institutions that create an economic environment that induces efficiency and boosts productivity. An
appropriate approach would be to find that how existing literature defines the institutions and their
quality. According to Hodgeson (2006), institutions are systems of established and prevalent social rules
that structure social interactions. He considers the organizations as a special kind of institutions, with
additional features; ―those involve (a) criteria to establish their boundaries and to distinguish their
members from nonmembers, (b) principles of sovereignty concerning who is in charge and (c) chains of
command delineating responsibilities within the organization‖ (Hodgeson 2006: 8) Institutions can be in
both forms i.e. formal and informal (Tabellini 2005). Country's endowments of physical and human
capital, labour, natural resources and the overall quality of its institutions are the main determinants of
relative costs and the patterns of production (Rodrik et al. 2005). Institutional development is a complex
and long term process which requires concerted efforts because it is very difficult to alter the behavior of
masses and organizational routines due to socio-cultural inertia. However, existing literature lists a
number of factors which could be the potential determinants of the institutional quality of the economic
systems, which are as follows;
Alonso (2008) has classified certain variables which contribute towards supply and/or demand sides of
institutional quality. For example, international openness of an economy creates a demand for more
refined institutions and boosts competitive environment. It also facilitates learning and imitation of best
practices around the world. Acording to Rajan and Zingales (2003), the openness of a country to
external trade and finance has the potential to erode the resistance of the local political elite to
modernization. Bonagalia et al (2001) have also found positive relationship between globalization and
governance based on cross country analysis. Educational level and tax collection contributes towards
demand and as well as supply sides. Because educated citizens are well aware of their rights and hence
exert pressures for the development of better institutions. Similarly, tax payers also call for better
institutions. On the contrary, foreign aid dependence is found to erode quality of governance as
measured by rule of law. Ararel (2008) finds that foreign aid plays an important role in developing
countries, but little is empirically known how it affects incentives of recipient bureaucracies. He has
developed a model and analytic case study to understand the strategic games that donors and bureaucrats
play. His findings are broadly consistent with the theoretical expectations of institutional rational choice:
bureaucrats attempt to ensure bureaucratic survival, whereas donors ensure growth of loan portfolio.
Brautigam et al (2004) have analyzed the impact of foreign aid on governance in Sub-sahran Africa.
They have reported very important findings (i) there is a robust statistical relationship between high aid
levels in Africa and deteriorations in governance, (ii) similarly there is a strong relationship between
higher aid levels and a lower tax share of GDP; and (iii) foreign aid continues to negatively impact the
governance even when they have introduced the control for per capita GDP and violence. This implies
that easy income flowing through foreign aid or debt may cause less pressure on the government to
generate more income domestically for this reason governments may have less incentive to improve the
institutional quality3.
Colonial legacy is also considered one of the fundamental determinants of institutional quality.
According to Acemoglu et al (2001), economic institutions of a society depend on the nature of political
institutions and the distribution of political power in society. They have recognized that ―in a large
number of colonies, especially those in Africa, Central America, the Caribbean, and South Asia,
European powers set up extractive states. These institutions (again broadly construed) did not introduce
much protection for private property, nor did they provide checks and balances against the government.
The explicit aim of the European in these colonies was ―extraction of resources‘, in one form or another.
This colonization strategy and the associated institutions contrast with the institutions Europeans set up
in other colonies, especially in colonies where they settled in large numbers, for example, the United
States, Canada, Australia, and New Zealand‖ (Acemoglu et al., 2001). Picard (2006) has carried out a
study to explore the patterns of variations of local governance in different regions. Regions are
categorized in, (i) Sub-Saharan Africa (ii) Latin American and the Caribbean (iii) Europe and Eurasia
(iv) Asia and the Near East. He has shown four kinds of influences which affect local governance
mechanisms which are as follows; 1) historical external influences e.g. colonial experience/historic
3
A widely accepted explanation is that a windfall coming from a terms-of-trade improvement or a discovery of
natural resource deposits can lead to a ‗feeding frenzy‘ in which competing factions fight for natural resource
rents and end up inefficiently exhausting the public good (Lane and Tornell, 1996). Similarly, foreign aid or
concessional debt could have negative impact on the efficiency of institutions of the state.
imperialism, 2) internal influences e.g. culture, political institutions and decentralization policy, 3)
contemporary external influences e.g. foreign assistance and 4) the role of mass-based political and
nongovernmental organizations. Similarly, Naritomi et al (2009) have reported that characteristics of
Brazilian municipalities related to institutional quality and distribution of political power are partly
inherited from the colonial histories experienced by different areas of the country.
Acemoglu et al (2009) have presented the theory of military dictatorship. They explain the phenomenon
that how military poses a threat to the nascent democratization process in developing economies.
Hernadez et al (2010) has documented the evidences of veto influence of military in political and
economic decisions in Philippines. They have concluded that military will continue to play a significant
role in the civil affairs of Philippines. Military has played a role in shaping the institutions and their
quality in a number of Asian economies particularly Pakistan, Bangladesh, Sri Lanka and Philippines
among others. Siddiqa (2007) has documented the different forms of civil military relationship around
the world and also mentioned that military is playing a role more than a policy instrument in number of
countries. She further mentions that military has institutionalized its role in a few countries including
Pakistan, Turkey and Indonesia. Therefore we have strong reasons to believe that military might have
connotations for the institutional quality of our sample of nations.
In this section, we have highlighted the conceptual and theoretical links between institutional quality and
international openness, literacy level, tax collection, foreign aid/debt, income per capita, colonial legacy
and checks and balances. According to the review of literature, a basic claim is that an increase in the
level of international openness, education level of the population, revenue collection, checks and
balances over the political elite while a decrease in foreign aid/debt and military‘s encroachment into
civil matters should have the potential to improve the institutional quality of the different economic
systems whereas controlling for the colonial legacy. In the next section we make an attempt to
empirically examine this basic claim and nuance the relative importance of these factors.
3) Data and Methodology
3.1) Dataset
The data used in this paper are from three different sources. We have used the seven indicators; i.e.
government stability, socioeconomic conditions, internal conflict, corruption, law and order, ethnic
tension and quality of bureaucracy from International Country Risk Guide (ICRG). Government
stability, an assessment of the government‘s ability to carry out its declared program(s) and its ability to
stay in office, is based on government unity, legislative strength and popular support. Socioeconomic
condition, an assessment of the socioeconomic pressures at work in society that could constrain
government action or fuel social dissatisfaction, is based on unemployment, consumer confidence and
poverty. Internal conflict, an assessment of political violence in the country and its actual or potential
impact on governance, is based on civil war/coup threat, terrorism/political violence and civil disorder.
These three indicators government stability, socioeconomic conditions, internal conflict are being rated
at the scale of 1—12; lower rating (closer to 1) indicating lower level of risks and vice versa.
Corruption, an assessment of dishonesty within the political system, is based on demands for special
payments and bribes connected with import and export licenses, exchange controls, tax assessments,
police protection or loans; besides excessive patronage, nepotism, job reservations, favor-for-favors,
secret party funding, and suspiciously close ties between politics and business. Law and order, is based
on the assessment of two components; i) strength and impartiality of the legal system, ii) popular
observance of the law. Ethnic tension is based on the assessment of the degree of tension within a
country attributable to racial, nationality, or language divisions. These three indicators corruption, law
& order and Ethnic tension are being rated at the scale of 1—6; lower rating (closer to 1) indicating
lower level of risks and vice versa. Bureaucracy quality is an assessment of the institutional strength and
quality of the civil service as a shock absorber that tends to minimize revisions of policy when
governments change. It is being rated at the scale of 1—4; high scores are assigned to countries where
the bureaucracy has the strength and expertise to govern without drastic changes in policy or
interruptions in government services and vice versa. Real GDP per capita growth rate, Tax revenue as %
of GDP, National debt as % of gross national income, Exports as % of GDP, Imports as % of GDP,
Inward foreign direct investment as % of GDP, Outward foreign direct investment as % of GDP and
Military expenditure as % of GDP have been taken from World Development Indicators (WDI). Adult
literacy rate has been taken from the United Nations Publications (UNP). Data on Checks & Balances,
an assessment of constraints on political elite, has been taken from World Bank‘s Database of Political
Institutions (DPI 2010). All above mentioned indicators are for the sample of eleven Asian countries
i.e. Bangladesh, China, Indonesia, India, South Korea, Sri Lanka, Malaysia, Pakistan, Philippines,
Singapore and Thailand covering the period of observation from 1984 to 2007. Missing values have
been completed by the various issues of the National Income Accounts of the respective countries e.g.
Debt to GNI ratio for Singapore and South Korea, Tax to GDP ratio for Thailand. Additionally, we have
generated three dummies to evaluate the impact of the colonial legacy in the sample of our countries i.e.
United States4 (for Philippines), Dutch (for Indonesia), and British (for Bangladesh, India, Sri Lanka,
Singapore, Malaysia, Pakistan and Thailand)
3.2) Methodology
Following the Adelman and Morris (1965, 1967), we use exploratory factor analysis to developed a latent
factor ―institutional quality‖ for eleven Asian countries using the data on seven indicators from ICRG.
Factor analysis is a statistical method used to identify a small set of unobserved dimensions that
represent relationships among a large set of correlated observed variables. Our analysis proceeds in three
steps; first we have identified the variables from ICRG which could have the potential to display the
quality of the institutions prevailing in the respective countries. Following are the variables; government
stability, socioeconomic conditions, internal conflict, corruption, law and order, ethnic tension and
quality of bureaucracy. Summary statistics and correlation matrix are given in the Appendix in Table 5
& 6 respectively. Correlation matrix shows that out of 28 Pearson correlation coefficients, 25 are greater
than 0.4. This implies that most of our observed variables are significantly correlated with each other,
resultantly; there must be some unobserved common/latent factor(s). In the second step, we deal with
factor extraction using the method of principal component analysis. According to the practice, only
those factors are retained in the analysis whose eigenvalues are above an arbitrary threshold i.e. unity.
Following this imperative in our analysis, we were able to derive one factor with 4.06 eigenvalue. This
factor describes 58% of the variance among the variables.
4
Each former colony is attributed to its most recent occupier; therefore, the Philippines is associated to US.
Government Unity
Government
stability
Legislative Strength
Popular Support
Unemployment
Socioeconomic
conditions
"Institutional Quality"
(captures 58% Variance
Corruption
Consumer Confidence
Poverty
among these variables)
Civil war/Coup Threat
Internal conflict
Terrorism/Political Violence
Ethnic tension
Law & Order
Bureaucracy
Quality
Civil Disorder
Strength & Impartiality of courts
Observance of Law
Third step deals with to generate the clearer patterns of interaction among variables & factors through
maximizing the high correlations and minimizing the low ones. We applied the most commonly used
―Varimax‖ rotation procedure to arrive at the solution. Table 6, in the Appendix, presents the rotated
factor loading. The given values in this matrix can be interpreted as correlation coefficient. All the
values are well above than 0.5, indicating that all the variables are significantly and positively
represented by this single factor. We labeled this factor as ―institutional quality‖.
Following the similar procedure; we have also developed a latent factor to characterize the openness of
the countries. We use the data on Exports as % of GDP, Imports as % of GDP, Inward foreign direct
investment as % of GDP, Outward foreign direct investment as % of GDP for our sample of countries.
Summary statistics and correlation matrix are given in the Appendix in Table 5 and 6 respectively.
Correlation matrix shows that all the 10 Pearson correlation coefficients are above 0.65; signifying that
there must be a latent unobserved factor. Factor has been extracted through principal component
analysis method. We were able to derive one factor with 3.39 eigenvalue. This factor describes 85% of
the variance among the variables. Again ―Varimax‖ rotation procedure was used to arrive at the final
solution. Table 7, in the Appendix, presents the rotated factor loading matrix. The given values in this
matrix can be interpreted as correlation coefficient. All the values are well above than 0.84, indicating
that all the variables are significantly and positively represented by this single factor. We labeled this
factor as ―Openness‖. To investigate the determinants of Institutional quality we model the relationship
as follows;
+
(1)
where i is the country; t is the time period; T is a time lag i.e. taken to be one year or five years in the
different regressions; IQ is the indicator for institutional quality
Equation (1) estimates the random effects models with one year and five year lags respectively. Here,
variation across entities is assumed to be random and uncorrelated with the independent variables
included in the model. Choice between random effect and fixed effect model is made by the Hausman
specification test. This test compares the fixed versus random effects model under the null hypothesis
that the individual effects are uncorrelated with the other regressors in the model (Hausman 1978). In
our case this test suggests that random effects model is appropriate. Furthermore we applied BreuschPagan Lagrange multiplier (LM) test which also confirms the appropriateness of the random effects
model for our panel data. However Wooldridge test for autocorrelation in panel data (Lagram-Multiplier
test for serial correlation) confirms the presence of first order autocorrelation (AR1) in our penal.
Therefore equation (3) estimates the Feasible Generalized Least Squares (FGLS) Estimator: An
estimator that accounts for a known structure of the error variance (heteroskedasticity), serial correlation
pattern in the errors, or both, via a transformation of the original model. Here also, all the independent
and control variables predict the variation in institutional quality with five year lag. An advantage of
random effects is that one can include time invariant variables as regressors 5 , therefore, we have
introduced the dummies to control the affect of colonial legacy on institutional quality of our sample.
Random effects assume that the entity‘s error term is not correlated with the predictors which allows for timeinvariant variables to play a role as explanatory variables.
5
(2)
The possibility of an endogeneity bias in the estimates, due to possible feedback from institutional
quality on GDP per capita growth or other dimensions represented by our independent or control
variables, was investigated by the Hausman procedure (Durbin-Wu-Hausman augmented regression test;
for details see Davidson and MacKinnon 1993 and Wooldridge 2002:118-122). The test failed to detect
evidence of endogeneity bias. In the next section we present the empirical results obtained by estimating
the equations 1 and 2.
4) Results and Discussion
Descriptive statistics and the correlation matrix for the latent factors for Institutional Quality and
Openness, described so for, and the other variables included in our model are presented in Table 1 & 2
respectively. Institutional quality of sample of countries falls within the range of -2.3 to 2.1. Correlation
matrix shows that out of 66 correlations only four are greater than 0.50 implying that there is very less
degree of co-linearity among all the variables, whereas Debt % of GNI, checks & Balances and Military
expenditure % of GDP are negatively correlated with most of the variables.
Our penal data regression results (Model-1 to 7 in Table: 3) show that tax revenue as % of GDP,
national debt as % of gross national income, openness , real GDP per capita, adult literacy rate are
significantly determining institutional quality in short run, while military expenditure as % of GDP and
checks & balances have no impact on institutional quality. However, in the long run i.e. 5 year lagged
model (Model-8 in Table: 3), military expenditure as % of GDP is significantly but negatively affecting
the institutions of the country and checks & balances also becomes significant implying that an increase
in the number of constitutional constraints over the political elite of the state helps to improve the
institutional quality of the country.
Table 1: Summary Statistics
Variable
Dependent variable
Institutional quality
Independent variable
Tax Revenue % of GDP
Debt % of Gross National Income
Openess
Control variables
GDP per capita Growth(log)
Adult litracy rate
checks & Balances
Military expenditure % of GDP
Spain
Dutch
British
Mean
Std. Dev.
Min
Max
Obs
1.31e-09
1
-2.324652 2.058424
264
13.08533
39.43598
-1.63e-09
3.751471
24.46542
1
5.607861 20.17229
3.321
168.1971
-.7453398 6.459409
264
264
247
1.430859
76.95026
3.375
2.822727
.0909091
.0909091
.5454545
.6704255
21.73038
2.713069
1.537333
.2880258
.2880258
.4988753
-2.163128 2.617277
30.66753
99
1
18
.8238053 7.549919
0
1
0
1
0
1
239
264
264
264
264
264
264
Table 2: Correlation Matrix (Figures in parenthesis are P values)
1) Institutional Quality
1
1.00
2
3
4
5
6
7
8
9
0.17 1.00
(0.01)
3) Debt % of GNI
-0.52 0.38 1.00
(0.00) (0.00)
4) Openness
0.53 0.29 -0.15 1.00
(0.00) (0.00) (0.02)
5) GDP pc growth(log)
0.46 0.01 -0.43 0.19
1.00
(0.00) (0.92) (0.00) (0.00)
6) Adult Litracy Rate
0.46 0.57 0.06
0.37
0.37
1.00
(0.00) (0.00) (0.31) (0.00) (0.00)
7) Checks and Balances
0.05 0.01 -0.02 -0.06
0.01
-0.10 1.00
(0.39) (0.88) (0.80) (0.38) (0.90) (0.12)
8) Military Exp. % of GDP 0.20 0.11 -0.31 0.08
-0.00 -0.23 0.05 1.00
(0.00) (0.08) (0.00) (0.22) (1.00) (0.00) (0.42)
9) Spain
-0.16 0.11 0.38
-0.01
-0.22 0.24 -0.05 -0.33 1.00
(0.01) (0.08) (0.00) (0.83) (0.00) (0.00) (0.39) (0.00)
10) Dutch
-0.22 0.16 0.37
-0.11
0.02
0.09 -0.21 -0.27 -0.10
(0.00) (0.01) (0.00) (0.09) (0.75) (0.15) (0.00) (0.00) (0.10)
11) British
-0.10 -0.01 -0.13 0.12
-0.27 -0.56 0.21 0.46
-0.35
(0.11) (0.83) (0.04) (0.07) (0.00) (0.00) (0.00) (0.00) (0.00)
* Given numbers in the columns match with the corresponding name of the variables in the rows.
10
11
2) Tax Revenue % of GDP
1.00
-0.35 1.00
(0.00)
a
Table 3 : Dependent Variable : Institutional Quality (Random Effects & Feasible Generalized Least Squares Regressions )
(1)
(2)
-0.0331* -0.0133
[0.0170] [0.0161]
-0.0101***
[0.00202]
(3)
-0.00958
[0.0167]
-0.00955***
[0.00207]
0.148**
[0.0676]
(4)
0.0615***
[0.0123]
-0.0222***
[0.00242]
0.300***
[0.0490]
0.286***
[0.0623]
(5)
0.0253*
[0.0131]
-0.0221***
[0.00225]
0.267***
[0.0459]
0.144**
[0.0631]
0.0115***
[0.00207]
(6)
0.0215
[0.0134]
-0.0217***
[0.00226]
0.276***
[0.0463]
0.142**
[0.0630]
0.0119***
[0.00209]
0.0169
[0.0128]
(7)
0.0260*
[0.0139]
-0.0227***
[0.00241]
0.290***
[0.0475]
0.131**
[0.0634]
0.0125***
[0.00212]
0.0221
[0.0135]
-0.0337
[0.0267]
(8)
0.00666
[0.0149]
-0.0216***
[0.00249]
0.492***
[0.0921]
0.0113
[0.0639]
0.0139***
[0.00220]
0.0395***
[0.0151]
-0.0798***
[0.0268]
(9)
0.0215**
[0.0108]
-0.0217***
[0.00172]
0.384***
[0.0732]
0.0142
[0.0512]
0.0141***
[0.00168]
0.0179
[0.0112]
-0.0517**
[0.0215]
Constant
-0.306
[0.367]
-0.186
[0.283]
0.916***
[0.276]
0.0184
[0.260]
-0.438*
[0.241]
-0.497**
[0.244]
-0.444*
[0.248]
-0.0942
[0.258]
-0.260
[0.196]
(10)
0.0253*
[0.0131]
-0.0179***
[0.00216]
0.543***
[0.0802]
0.00336
[0.0481]
0.000752
[0.00318]
0.0297***
[0.0113]
-0.0729***
[0.0228]
-0.0733
[0.146]
-0.252
[0.182]
-0.724***
[0.141]
0.567**
[0.254]
Observations
Number of country
R-squared within
R-squared between
R-squared overall
253
11
0.64
0.06
0.15
253
11
0.66
0.30
0.38
236
11
0.68
0.66
0.52
213
11
0.60
0.87
0.83
213
11
0.64
0.89
0.77
213
11
0.65
0.89
0.77
213
11
0.64
0.90
0.77
169
11
0.56
0.92
0.76
169
11
169
11
Tax Revenue % of GDP
Debt % of GNI
Openness
GDP pc growth(log)
Adult Litracy Rate
Checks and Balances
Military Exp. % of GDP
Spain
Dutch
British
a
All Regressions include a full vector of year dummies. Standard errors in brackets * Significant at 10% ; ** Significant at 5%; *** Significant at 1% 1 to 7) Random-Effects Models with
one year lag 8) Random-Effects Model with 5 year lag, 9 & 10) Generalized Least Square Model i.e. to control the autocorrelation (AR1) and heteroskedesticity in the penals with 5 year lag.
On the contrary GDP per capita becomes insignificant in the same model. Feasible Generalized Least
Squares Estimator (Model-9 in Table: 3) shows that all the explanatory variables are significantly
determining the institutional quality except checks and balances and GDP per capita whereas negative
impact of military expenditure on national institutional quality is still significant. Rather this impact
further enhances from 5% significance level to 1% along with an increase in its coefficient value when
we introduce the controls for colonial legacy ( Model-10 in Table: 3). Our penal data results show that
one unit increase in military expenditure reduces the institutional quality by 0.07 unit. Among colonial
heritage, all the three Spain, Dutch and British are negatively impacting the institutional quality for our
sample of countries but only British colonial legacy is significant at the 1% level. It is pertinent to
mention that explanatory power of the model (R-squared) has increased as we have moved from one to
last model.
OLS & Quantile Regression Estimates for Institutional Quality
Fig.B
0.40
0.20
0.00
-0.20
-0.40
.1.2.3.4.5.6.7.8.9
Quantile
0.05
0.00
-0.05
0.20
0.10
0.02
0.01
0.01
0.01
0.05
0.00
.1.2.3.4.5.6.7.8.9
Quantile
1
0
-1
-0.05
-2
-0.10
0.00
0.00
.1.2.3.4.5.6.7.8.9
Quantile
0.10
Military Exp. % GDP
0.30
Audalt Litracy Rate
Openness
0.40
India
Pakistan
S.Korea
2
Fig.F
0.03
China
Malaysia
Singapore
Thiland
-0.03
.1.2.3.4.5.6.7.8.9
Quantile
Fig.E
0.50
-0.02
-0.04
.1.2.3.4.5.6.7.8.9
Quantile
Fig.D
Bangladesh
Indonesia
Philippines
Sri Lanka
-0.01
0.10
Institutional Quality
Tax Revenue % of GDP
GDP per capita Growth
0.60
Dynamics of Institutional Quality in Asia
Fig.C
Debt % of GNI
Fig.A
-0.15
.1.2.3.4.5.6.7.8.9
Quantile
1985
1990
1995
Year
2000
2005
A more comprehensive picture of the effect of the predictors on the response variable can be obtained by
using quantile regression plots. Quantile regression models the relation between a set of predictor
variables and specific percentiles (or quantiles) of the response variable. In fact it specifies changes in
the quantiles of the response. In linear regression, the regression coefficient represents the change in the
response variable produced by a one unit change in the predictor variable associated with that
coefficient. However, the quantile regression parameter estimates the change in a specified quantile of
the response variable produced by a one unit change in the predictor variable. This allows comparing
how some quantiles (or percentiles) of the institutional quality may be more affected by certain
predictors than other quantiles. In each plot, the regression coefficient at a given quantile indicates the
effect on institutional quality of a unit change in that variable, assuming that the other variables are
fixed, with 95% confidence interval bands. This analysis implies that an increase in GDP per capita, tax
collection and a decrease in military expenditure (as shown in Fig A, B and F respectively) have better
potential to enhance the institutional quality in the lower quantiles of our sample of Asian countries e.g.
Bangladesh, India, Sri Lanka, Pakistan, Philippines and Thailand. Whereas, a decrease in indebtedness
and an increase in the level of education, international openness and tax collection ( as shown in Fig C,
D, E and B respectively) have better potential to enhance the institutional quality in the upper quantiles
of our sample of Asian countries e.g. Singapore, South Korea, Malaysia, Indonesia and China. A
notable fact is that adult literacy rate, a proxy of education system, does not have any potential to
influence the institutional quality of the respective countries in the lower quantiles. This implies that
syllabus and methodologies being used to impart education are not yielding results in these countries.
Therefore, such countries should re-examine and refurbish their education system to make them
deliverable. Likewise, international openness has the greatest value of coefficient along with 1% level of
significance in model-10, showing that one unit increase in openness could enhance the institutional
quality by 0.5 unit. While, quantile plot for openness shows that it has no impact on the institutions of
the lower quantile whereas it has the great potential to uplift the quality of the institutions in upper
quantile. This implies that countries in the lower quantiles are required to enhance the national capacity
and capability to fully harness the benefits of openness otherwise it would be unwise to further open up
their borders.
Conclusion
The main objective of this paper is to construct a comprehensive indicator of institutional quality and to
explore its determinants, particularly in Asian context. Literature shows that good institutions play a
pivotal role in lowering transaction costs, ensuring contract enforcement, protecting property rights and
enhancing productivity while providing a level playing field to all economic agents. Following this
functional and objective definition of institutions, we have developed an index of institutional quality for
the sample of eleven Asian countries using the data about government stability, socioeconomic
conditions, internal conflict, corruption, law and order, ethnic tension and quality of bureaucracy from
International Country Risk Guide (ICRG) while employing the exploratory factor analysis. According to
the review of literature, a basic claim is that an increase in the level of international openness, education
and income level of the population, revenue collection, checks and balances over the political elite while
a decrease in foreign aid/debt and military‘s encroachment into civil matters should have the potential to
improve the institutional quality of the different economic systems whereas controlling for the colonial
legacy.
Our penal data regression results show that tax revenue as % of GDP, national debt as % of gross
national income, international openness , real GDP per capita, adult literacy rate are significantly
determining institutional quality in short run, while military expenditure as % of GDP and checks &
balances over the political elite have no impact on institutional quality in our sample. However, in the
long run i.e. 5 year lagged model, military expenditure as % of GDP is significantly and negatively
affecting the institutions of the country and checks & balances also becomes significant implying that an
increase in the number of constitutional constraints over the political elite of the state helps to improve
the institutions of the country. On the contrary GDP per capita becomes insignificant in the same model.
Feasible Generalized Least Squares Estimator shows that all the explanatory variables are significantly
determining the institutional quality except checks and balances and GDP per capita and negative impact
of military expenditure on national institutional quality is still significant. Rather this impact further
enhances from 5% significance level to 1% along with an increase in its coefficient value when we
introduce the control for colonial legacy. Among colonial heritage, all the three Spain, Dutch and British
are negatively impacting the institutional quality of our sample of countries but only British colonial
legacy is significant at the 1% level.
Overall our results could be interpreted that level of institutional development itself is an important
precondition to evaluate the impact of different influencing variables upon institutional quality. Our
results clearly show that it is not necessary that all the determinants impact the institutional quality
across the board rather a fine distinction is required viz à viz the level of institutional development of the
respective countries. Except tax collection all other determinants, e.g. indebtedness, openness, checks &
balances on political elite and military expenditure, in spite of being significant have very dissimilar
effect on institutional quality across the countries in our sample. Quantile analysis implies that an
increase in GDP per capita, tax collection and a decrease in military expenditure have better potential to
enhance the institutional quality in Bangladesh, India, Sri Lanka, Pakistan, Philippines and Thailand i.e.
the lower quantiles of our sample. Whereas, a decrease in indebtedness and an increase in the level of
education, international openness and tax collection have better potential to enhance the institutional
quality in Singapore, South Korea, Malaysia, Indonesia and China i.e. the upper quantiles of our sample
of Asian countries. A notable fact is that adult literacy rate, a proxy of education system, does not have
any potential to influence the institutional quality of the respective countries in the lower quantiles. This
implies that syllabus and methodologies being used to impart education are not yielding results in these
countries. Therefore, such countries should re-examine and refurbish their education system to make
them deliverable. Likewise, international openness has no impact on the institutions of the lower
quantile whereas it has the great potential to uplift the quality of the institutions in upper quantile. This
implies that countries in the lower quantiles are required to enhance the national capacity and capability
to fully harness the benefits of openness otherwise it would be unwise to further open up their borders.
As for as policy implications of our analysis are concerned; a substantial care needs to be taken while
devising the reforms agenda to uplift the quality of the institutions because our results confirm that there
is no straight jacket to fit to all. Countries having lower institutional quality should enhance GDP per
capita in the short run while increasing the tax collection and decreasing the military expenditure in the
longer-run. Furthermore, such countries should revamp their education system so that it could better
deliver the awareness and sense to understand one‘s rights and duties viz à viz state. International
openness has profound implications for institutional development but an absorptive capacity is an
important pre requisite to fully harness its benefits which could only be ensured through education and
training. However this last claim calls for further empirical validation.
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Appendix – I
Table 5: Summary Statistics for Factor Analysis
Variable
Mean
Std. Dev.
Min
Max
Government stability
7.491162 2.439174
1
12
Socioeconomic conditions 6.244634 2.083038
1
10.91667
Internal confilict
8.057201 2.943317
0
12
Corruption
2.645991 1.223526
0
6
Law and order
3.27992
1.405636
0
6
Ethinic tension
3.375947 1.756254
0
6
Bureaucracy’s quality
2.308171 .9826613
0
4
Inward FDI as % of GDP
2.551412 3.865486 -2.757439 20.23999
Outward FDI as % of GDP .805884
2.360532 -.196586 23.3083
Exports as % of GDP
38.43588 38.89028 3.279997 233.5448
Imports % of GDP
38.28546 33.26433 7.058327 204.5468
Obs
264
264
264
264
264
264
264
264
264
247
247
Table 6: Correlation Matrix for Factor Analysis (Figures in parenthesis are P values)
Variables*
1)Government stability
1
2) Socioeco. conditions
0.25
2
3
4
5
6
7
8
9
10
11
1.00
1.00
(0.00)
3) Internal confilict
4) Corruption
5) Law and order
6) Ethinic tension
7) Bureaucracy quality
8) Inward FDI as % of GDP
9) Outward FDI as % of GDP
10) Exports as % of GDP
11) Imports % of GDP
0.52
0.54
1.00
(0.00)
(0.00)
0.19
0.46
(0.00)
(0.00) (0.00)
0.56
0.54
(0.00)
(0.00) (0.00)
(0.00)
0.51
0.48
0.29
0.63
(0.00)
(0.00) (0.00)
(0.00)
(0.00)
0.43
0.43
0.62
0.60
(0.00)
(0.00) (0.00)
(0.00)
(0.00) (0.00)
0.39
0.47
0.48
0.57
(0.00)
(0.00) (0.00)
(0.00)
(0.00) (0.00) (0.00)
0.29
0.39
0.31
0.41
(0.00)
(0.00) (0.00)
(0.00)
(0.00) (0.00) (0.00)
(0.00)
0.41
0.46
0.38
0.40
0.82
(0.00)
(0.00) (0.00)
(0.00)
(0.00) (0.00) (0.00)
(0.00) (0.00)
0.38
(0.00)
0.43
0.31
(0.00) (0.00)
0.41
(0.00)
0.37
0.32
0.40
(0.00) (0.00) (0.00)
0.83 0.69 0.99 1.00
(0.00) (0.00) (0.00)
0.45
0.80
0.71
0.44
0.48
0.32
0.37
1.00
0.59
1.00
1.00
0.49
0.42
0.38
0.36
1.00
0.43
0.41
0.42
1.00
0.77
1.00
0.70
* Given numbers in the columns match with the corresponding name of the variables in the rows
1.00
Factor analysis/correlation
Number of obs =
Method: principal-component factors Retained factors =
Rotation: (unrotated)
Number of params =
264
1
7
Factor Eigenvalue Difference Proportion Cumulative
Factor1
4.05958
3.08335
0.5799
0.5799
Factor2
0.97623
0.27386
0.1395
0.7194
Factor3
0.70236
0.24513
0.1003
0.8197
Factor4
0.45723
0.03734
0.0653
0.8851
Factor5
0.41989
0.18802
0.0600
0.9450
Factor6
0.23188
0.07905
0.0331
0.9782
Factor7
0.15283
.
0.0218
1.0000
LR test: independent vs. saturated: chi2 (21) = 1034.62 Prob>chi2 = 0.0000
Table 6: Pattern Matrix - Rotated Factor Loadings
Variable
I-Quality* Uniqueness
Government stability
0.6434
0.5860
Socioeconomic conditions
0.6901
0.5238
Internal confilict
0.8566
0.2663
Corruption
0.6729
0.5472
Law and order
0.9008
0.1885
Ethinic tension
0.7809
0.3901
Bureaucracy Quality
0.7493
0.4385
(Rotation method: Oblimin) * We call it Institutional Quality
Factor analysis/correlation
Number of obs
=
Method: principal-component factors Retained factors =
Rotation: (unrotated)
Number of params =
247
1
4
Factor
Eigenvalue
Difference Proportion Cumulative
Factor1
3.39268
2.99143
0.8482
0.8482
Factor2
0.40124
0.20804
0.1003
0.9485
Factor3
0.19320
0.18032
0.0483
0.9968
Factor4
0.01288
.
0.0032
1.0000
LR test: independent vs. saturated: chi2 (6) = 1392.55 Prob>chi2 = 0.0000
Table 6: Pattern Matrix - Rotated Factor Loadings
Variable
Openness* Uniqueness
Inward FDI as % of GDP
0.9238
0.1465
Outward FDI as % of GDP
0.8423
0.2905
Exports as % of GDP
0.9574
0.0835
Imports % of GDP
0.9556
0.0868
(Rotation method: Oblimin)* We call it Openness