SOCIAL FRACTIONALIZATION
AND ECONOMIC GROWTH (1985 -2000)
BY
Shim Yuen Chi
03005518
Applied Economics Option
An Honours Degree Project Submitted to the
School of Business in Partial Fulfillment
of the Graduation Requirement for the Degree of
Bachelor of Business Administration (Honours)
Hong Kong Baptist University
Hong Kong
APRIL 2006
Acknowledgement
I would like to express my sincere thanks to my supervisor Dr. Mo Pak Hung for his
helpful comments and suggestions. Dr Mo has given me great flexibility on choosing
topic and research methods. His valuable advices guided me to overcome many obstacles
during the learning process. Furthermore, I wish to thank all parties that give me supports
in completing this paper.
i
Abstract
This paper employs the framework developed by Dr. Mo to estimate the role of social
fractionalization in economic growth. From the empirical result, with 1% increase in the
social fractionalization index (i.e. change from complete homogeneity to complete
heterogeneity), the real GDP growth rate reduces by 2.11 %.The impacts of social
fractionalization on developing and developed countries are investigated. Social
fractionalization may not be harmful to developed countries while the effect on
developing countries is more adverse. With 1 % increase in the social fractionalization
index, the real GDP growth rate drops by 2.95 %. And the direct and indirect effect of it
on developing countries is reported. The direct effect contributes 69.3 % to the overall
effect. Among the transmission channels, the investment channel, which accounts for
11.0 %, and political instability channel accounts for 5.1 %.
ii
Table of Contents
Acknowledgement.........................................................................................................i
Abstract…………………………………………………………………………………ii
Table of Contents……………………………………………………………………...iii
1. Introduction.................................................................................................................1
2. Literature Review......................................................................................................3
3. Methodology...............................................................................................................6
4. Data Description........................................................................................................8
5. Empirical Analysis...................................................................................................12
6. The Effect on Developing and Developed countries….............................. …16
7. The Direct and Indirect effect on Developing countries................................22
8. Limitations.................................................................................................................26
9. Conclusion..................................................................................................................27.
References
Appendix
iii
1. Introduction
In the field of economic development, economic growth is one of the most important
indicators of economic development. From years of researches made by the economists,
there are many factors affecting economic growth. These factors include investment,
government consumption, political instability, human capital, technological change and
openness.
There are numbers of papers find out that government size (Mo 2004) and political
instability (Anthony Annett 2001) are both negatively related to the economic growth.
While investment and technology improvement has long been regarded as an important
factor contributing to economic growth.
What are the causes leading to higher political instability and lower investment? Social
fractionalization (or social diversity) may be the answer. In 1960s, a team of Soviet
ethnographers carried out a research and published as Atlas Narodov Mira. Their list of
“ethnolingistic” groups and population figures has been employed by political scientists,
socicologists, and economists to produce the cross-national estimates of ethnic
fractionalization, such as Easterly and Levine’s work on economic growth have
employed this measure. James D. Fearon has done the similar effort of the Soviet
ethnographers to provide the more updated statistics of ethnic and cultural
fractionalization as the average of 1980s and 1990s.
1
The basic idea of how social fractionalization hinders economic growth is that when there
is higher number of groups with different racial, languages, value and belief, it is more
difficult in coordination and communication, these cost could be a high transaction cost.
This would also rise the uncertainty and instability, thus lower investment. In the game
theory, when the number of competitors rise, the harder for the society to achieve the
cooperative Nash equilibrium. Therefore, social fractionalization is harmful to economic
growth.
The objective of this paper is to employ the economic growth framework developed by
Dr. Mo and the data of James D. Fearon to estimate the comprehensive effect of social
fractionalization on economic growth. The result on developed and developing countries
are reported separately. Finally, decomposition is made to investigate the direct and
indirect effect through transmission channels.
The paper is organized as follows. Literature review about social fractionalization is
shown in the next section. In section 3, we introduce the analytical framework of this
paper. The data and descriptive statistics are shown in section 4. Section 5 is the report of
the regression results. In section 6, the different results on developing and developed
countries are reported. Section 7 shows the effect of social fractionalization on different
transmission variables .Section 8 shows the limitations of this paper. The last section is
the conclusion.
2
2. Literature Review
The idea of how social fractionalization (along ethnolinguistic and religious dimensions)
is related to political instability and government consumption was explored by Anthony
Annett (2001). The empirical evidence showed that greater fractionalization, proxying for
the degree of conflict in society, leads to political instability, which in turn leads to higher
government consumption aimed at placating the opposition.
But our main concern is how the above relations affect economic growth. Easterly and
Levine (1997) concluded the direct effect of social fractionalization on economic growth.
In a cross-country setting, the results have shown that per capita GDP growth is inversely
related to ethnolinguistic fractionalization in a large sample of countries. In particular,
they argued that much of Africa’s growth failure is due to ethnic conflict, partly as a
result of absurd borders left by former colonizers.
Marta Reynal-Querol and José G. Montalvo (2005) examined the harmfulness of the
existence of different religious groups on the process of economic growth of a country.
The study on the relation between social conflicts and growth one of the most important
determinants is religious diversity. The most important religious tensions in the world can
be found in Lebanon and Israel, where there are conflicts among religious communities;
in Malaysia with tension between Christians and Muslims; in India, between Hindus and
Muslims.
3
The indirect effects of social fractionalization on economic growth are also examined by
different economists. Higher social fractionalization leads to higher political instability,
the negative effect of political instability on economic outcomes were shown by sizable
literatures. Barro (1991, 1996) finds that political violence leads to lower growth in a
cross section of countries. Alesina and Perotti (1996) take this approach, arguing that the
disorder created by this form of political instability adversely affects productivity and the
return to investment
The impact of social fractionalization on government activities and quality of institutions
was explained by Canning and Fay (1993) and Mauro(1995). La Porta et al. (1999), in a
broad empirical study of the determinants of the quality of government, suggest that
ethnic fractionalization matters, even though variables related to legal origins may be
more important. A large literature on US localities show that in more ethnically
fragmented communities, public goods provision is less efficient, participation in social
activities and trust is lower, and economic success, measured by growth of city sizes, is
inferior.
Mauro (1995) further examine the relation between social fractionalization and long-run
growth. He argues that ethnic conflict may lead to political instability and, in extreme
cases to civil war. Moreover, he also argues that ethnolinguistic fragmentation may
reduce investment not only by increasing corruption and political instability, but also via
a direct channel. For example, it might slow down the discussion of ideas and
technological innovations within the country.
4
However Many authors have found, social fractionalization is not significant in the
explanation of civil wars and other kinds of conflicts. These results have led many
authors to disregard ethnicity as a source of conflict and civil wars. Fearon and David D.
Laitin (2003) and Collier and Hoeffler (1998) find that neither ethnic fractionalization nor
religious fractionalization have any statistically significant effect on the probability of
civil wars.
5
3. Analytical Framework
In this project, I use a framework which is developed in Mo (2000) to investigate the
effect of inflation on the economic growth. This framework starts with the input and
output relationship. It is characterized by the general production function.
1)
Y= T f (K, L)
Where Y is the total output level, T is total factor productivity, K is the capital stock and
L is the labor stock.
Then, total differentiate Y:
Divide (2) by Y, it becomes:
Simplify the equation:
Where GR represents the real GDP growth rate, γis the growth rate of real GDP and
total factor productivity respectively, IY is the investment output ratio and dLL is the
growth rate of labor.
Base on prior works in the literature, Levine and Renelt (1992), the share of investment
in GDP, the rate of population growth, the initial level of real GDP, and a proxy for
6
human capital are the variables that affect the growth. The share of investment in GDP
and the rate of population growth are growth component, which is related to the factor
availability. The initial level of real GDP and the proxy for human capital are
development component, which is related to the effect of social and technological
changes. Include all these variables in the growth model, and further define the rate of
productivity growth as:
(5)
γ=γ( SOCF, y0, HUMAN)
Therefore, the growth model becomes,
(6)
GR = F(γ(SOCF, y0, HUMAN,), IY(γ), dLL)
Where SOCF is the index for the level of social fractionalization, y0 is the initial GDP
per capita, HUMAN is a proxy of human capital stock.
7
4. Data Description
There are 138 countries in the sample in order to have the cross nation’s analysis and the
country list follows panel data set assembled by Robert Barro and Jong-Wha Lee. The
fifteen-year period chosen is from 1985 to 2000 as to make the data as up-to-date as
possible. Another reason for choosing this period is because the social fractionalization
index is the average for the 1980s to 1990s, therefore the estimation is more accurate and
meaningful.
Most of the data are extracted from the Penn World Table, including the real gross
domestic product per capita and total population from 1985 to 2000. (Average growth
rate is derived) The ratio of investment and government consumption over real gross
domestic product are also obtained from this source. The data are divided as an averaged
five years’ sub-period, i.e. there are 3 five-year-averaged observations for a variable in
the period 1985-2000 so as to smooth over some cyclical features of the data.
Political instability is also one of the variables that shows the indirect impact of social
fractionalization on economic growth. Political instability is defined as average of the
number of assassinations per million population per year and the number of revolutions
per year over the period. This data is obtained from Databanks International.
The growth rate of population is used as a proxy for the growth rate of labor. Although
8
the growth rate of population is different from the growth rate of labor and might have
different effects to the GDP growth, the quality of the data on population growth is better.
It is because different countries have different definitions of labor and the measurements
of labor growth are also not identical. It makes the labor growth rates become
incomparable. Therefore, it is common for researchers to use population growth as a
proxy.
The average schooling years in the total population over age 25 is used as a proxy for
human capital stock. This data is obtained from Barro and Lee dataset.
The variable which this paper is interested in is social fractionalization. The definition of
the index followed Annett Anthony to measure the social fractionalization along ethnical
(racial), cultural (linguistic) and religious diversity. The indices developed in this paper are
defined as follows. Formally, it can be calculated from the following formula:
(7)
N is the total population and ni is the number of people belonging to the i-th group.
Therefore, the index of social fractionalization is defined as the average of ethnic,
cultural and religious fractionalization:
9
(8)
Social Fractionalization = (Ethnic Fractionalization + Cultural Fractionalization
+ Religious Fractionalization) / 3
It measures the probability that two randomly selected individuals in a country will
belong to different ethnolinguistic and religious groups. This variable ranges from 0 to 1.
The larger number of ethnolinguistic and religious groups and the closer the sizes of the
groups are, the larger the index is. For instance, when there is only one ethnic group, the
index equals to 0. The index equals to 1 when there are infinite number of ethnic groups.
When there are only two ethnic groups with the same size, the index equals to 0.5. The
ethnic and cultural fractionalization index are obtained James D. Fearon’s (2003) Ethnic
and Cultural Diversity by Country, which Ethnic Fractionalization present a list of 822
ethnic groups in 160 countries that made up at least 1 percent of the country population,
while Cultural Fractionalization used structural relationships between languages as a
proxy for cultural similarity. Religious Fractionalization used here are constructed by
Alesina et al.(2002), basing on data from the Encyclopedia Britannica, 2001. The index
covered 294 different religions in 215 countries.
The correlation coefficients and descriptive statistics for the major variables are
summarized in Table 1.The variables are defined in the note to the table and the original
acronyms and sources of data are summarized in Appendix.
10
Table 1
Correlation coefficients and descriptive statistics
GR
SOCF
y0
POPG
IY
GOV
GR
1
SOCF
-0.07963
1
y0
-0.09791 -0.16951
1
POPG
0.111441 0.271577 -0.60408
1
IY
0.100991 -0.32096 0.585027 -0.44623
1
GOV
-0.03543 0.264972
-0.5386 0.354201 -0.30386
1
HUM
-0.02458 -0.21679 0.824258 -0.64865 0.616308 -0.43229
INSTAB -0.03124 -0.06289 -0.18592 0.144221 -0.20454 0.041455
Mean
Median
HUM
INSTAB
1
-0.16462
1
0.060558 0.384484 5334.544 0.016806 16.04713 18.46128 5.759288 0.28677
0.059103
0.3852 3604.682 0.018129 15.5419 16.80648
5.521 0.083333
Note. GR = growth rate of real GDP, SOCF = index for social fractionalization, y0 = real
GDP per capita at 1985, POPG = population growth rate, IY = ratio of private investment
to real GDP, GOV = share of government consumption in real GDP, HUM = average
schooling years in the total population over age 25, INSTAB = index for political
instability.
11
5. Empirical Analysis
Pooled Least Squares was used for regressions to estimate the effect of social
fractionalization on the economic growth.
The regressions reported in Table 2 reveals the sensitivity of the estimated effects.
12
TABLE 2
The effect of Social Fractionalization on the Growth Rate (World dataset)
Estimation:
B1
Independent
variables
SOCF
GR
B2
B3
Dependent Variables
GR
GR
B4
GR
-0.043117
(-4.41)
-0.033597
(-3.37)
-0.043043
(-4.39)
-0.031099
(-2.82)
y0
-1.597826
(-0.32)
-9.730966
(-1.80)
-2.051331
(-0.38)
-1.616718
(-2.12)
POPG
0.273134
(1.28)
0.416716
(1.96)
0.319025
(1.48)
0.521982
(2.18)
IY
0.001047
(3.55)
GOV
-0.000075
(-0.36)
HUM
0.003167
(2.45)
INSTAB
CONSTANT
0.073763
(10.61)
0.055309
(6.54)
0.074506
(8.73)
0.053495
(5.35)
R2
0.06
0.11
0.07
0.07
348
336
336
284
P
P
No. of obs.
Dummy 1
0.003407
(0.75)
0.004186
(0.93)
0.004691
(1.01)
0.004837
(0.96)
Dummy 2
-0.005819
(-1.28)
-0.006537
(-1.45)
-0.006156
(-1.34)
-0.003624
(-0.72)
13
TABLE 2(Con’t)
The effect of Social Fractionalization on the Growth Rate (World dataset)
Estimation:
B5
Independent
variables
SOCF
GR
B6
B7
Dependent Variables
GR
GR
B8
GR
-0.040256
(-3.92)
-0.019830
(-1.78)
-0.027476
(-2.42)
-0.021051
(-1.81)
y0
-2.699334
(-0.51)
-2.437331
(-3.05)
-2.201242
(-2.51)
-0.000003
(-2.86)
POPG
0.325031
(1.43)
0.594354
(2.53)
0.609207
(2.35)
0.605345
(2.36)
IY
0.000877
(2.69)
0.000794
(2.30)
GOV
-0.000535
(-2.08)
-0.000473
(-1.62)
-0.000498
(-1.72)
HUM
0.002167
(1.63)
0.003130
(2.28)
0.002272
(1.61)
-0.003741
(-0.97)
-0.002418
(-0.63)
INSTAB
-0.007112
(-2.05)
CONSTANT
0.075378
(10.34)
0.053584
(4.80)
0.064841
(5.57)
0.056568
(4.68)
R2
0.07
0.11
0.08
0.10
323
275
257
257
P
P
No. of obs.
Dummy 1
0.000762
(0.16)
0.007133
(1.41)
0.004746
(0.89)
0.004368
(0.83)
Dummy 2
-0.006900
(-1.46)
-0.003862
(-0.78)
-0.005059
(-0.97)
-0.005404
(-1.04)
Note: Dummy 1 is the dummy for period 1985-1989 (i.e. 1985-1989=1, 1990-1994:0,
1994-2000=0) Dummy 2 is the dummy for period 1990-1994
14
In table 2, estimation B1 indicates that social fractionalization has a significant negative
effect on the real GDP growth rate when all the transmission channels are excluded in the
regression. Estimations B2 to B5 show the effects of social fractionalization to the real
GDP growth rate when the possible transmission channels, which are the share of
investment, share of government consumption ,human capital stock and political
instability, are added to the regression independently. As expected, the magnitude and
significant levels of the SOCF coefficient in B2 to B5 are smaller that in B1. However,
the coefficient is still significant at the conventional confidence interval.
The magnitude and the significant level of the SOCF coefficient are -0.021051 and
-1.81 in estimation B8 when all the possible transmission channels are included. It
showed that in the world sample, social fractionalization only has weak significant
impact on economic growth.
In the next section, this paper will investigate in whether the effects of social
fractionalization on economic growth differ in developing and developed countries.
15
6. The Effect on Developing and Developed
countries
Two regressions are run separately for the developing and developed countries. The
definition of developing countries followed the World Bank’s classification system.
Developing countries are those with low-, lower-middle, or upper-middle incomes, while
developed countries are those high-income OECD countries and other high-income
countries.
Table 3 shows the impacts of social fractionalization on developing countries.
Table 4 shows the impacts of social fractionalization on developed countries.
16
Table 3
The effect of Social Fractionalization on Growth Rate (Developing countries dataset)
Estimation:
B1
Independent
variables
SOCF
GR
B2
B3
Dependent Variables
GR
GR
B4
GR
-0.042638
(-4.37)
-0.039369
(-4.03)
-0.043551
(-4.56)
-0.034664
(-2.90)
y0
-7.033960
(-0.85)
-9.701227
(-1.21)
-7.289654
(-0.87)
-1.581667
(-1.36)
POPG
0.036042
(0.16)
0.149800
(0.66)
0.065464
(0.29)
0.336886
(1.22)
IY
0.000609
(1.75)
GOV
0.000030
(0.15)
HUM
0.001642
(1.10)
INSTAB
CONSTANT
0.081306
(10.80)
0.070713
(7.18)
0.080580
(8.78)
0.067650
(5.56)
R2
0.21
0.21
0.23
0.18
258
246
246
197
P
P
No. of obs.
Dummy 1
-0.002944
(-0.50)
-0.001807
(-0.30)
-0.001783
(-0.29)
-0.004796
(-0.67)
Dummy 2
-0.006816
(-1.09)
-0.007562
(-1.17)
-0.007442
(-1.14)
-0.004812
(-0.68)
17
Table 3 (Con’t)
The effect of Social Fractionalization on Growth Rate (Developing countries dataset)
Estimation:
B5
Independent
variables
SOCF
GR
B6
B7
Dependent Variables
GR
GR
B8
GR
-0.040701
(-3.90)
-0.028211
(-2.43)
-0.032648
(-2.75)
-0.029533
(-2.46)
y0
-6.433091
(-0.76)
-2.178531
(-1.89)
-2.175321
(-1.84)
-2.160107
(-1.85)
POPG
0.061788
(0.26)
0.405446
(1.50)
0.389754
(1.31)
0.406225
(1.38)
IY
0.000574
(1.36)
0.000484
(1.12 )
GOV
-0.000472
(-1.73)
-0.000395
(-1.27)
-0.000436
(-1.42)
HUM
0.001325
(0.87)
0.002035
(1.35)
0.001479
(0.93)
-0.003185
(-0.76)
-0.002703
(-0.64)
INSTAB
-0.006316
(-1.75)
CONSTANT
0.082728
(10.49)
0.069226
(5.24)
0.076029
(5.50)
0.071278
(4.97)
R2
0.19
0.20
0.23
0.22
245
188
182
182
P
P
No. of obs.
Dummy 1
-0.004125
(-0.68)
-0.001920
(-0.26)
-0.002649
(-0.36)
-0.002613
(-0.35)
Dummy 2
-0.007390
(-1.15)
-0.005071
(-0.70)
-0.005833
(-0.78)
-0.005937
(-0.79)
18
Table 4
The effect of Social Fractionalization on Growth Rate (Developed countries dataset)
Estimation:
B1
Independent
variables
SOCF
GR
B2
B3
Dependent Variables
GR
GR
B4
GR
-0.030965
(-2.01)
-0.021663
(-1.35)
-0.030756
(-1.99)
-0.016269
(-1.02)
y0
-2.355546
(-3.15)
-2.572652
(-3.44)
-2.451587
(-3.22)
-3.361348
(-3.64)
POPG
1.672536
(4.65)
1.339061
(3.35)
1.714516
(4.69)
1.352706
(3.69)
IY
0.000751
(1.82)
GOV
-0.000285
(-0.70)
HUM
0.001470
(0.82)
INSTAB
CONSTANT
0.079750
(7.62)
0.064132
(4.78)
0.083148
(7.20)
0.074269
(5.32)
R2
0.44
0.46
0.44
0.46
90
90
90
87
P
P
No. of obs.
Dummy 1
0.024098
(4.31)
0.023129
(4.17)
0.025460
(4.29)
0.024277
(4.29)
Dummy 2
-0.004834
(-0.86)
-0.004378
(-0.79)
-0.004466
(-0.79)
-0.003661
(-0.66)
19
Table 4 (Con’t)
The effect of Social Fractionalization on Growth Rate (Developed countries dataset)
Estimation:
B5
Independent
variables
SOCF
GR
B6
B7
Dependent Variables
GR
GR
B8
GR
-0.028515
(-1.91)
0.000234
(0.01)
-0.011192
(-0.72)
0.005007
(0.30)
y0
-2.429079
(-3.00)
-3.730502
(-4.09)
-3.219158
(-2.74)
-4.702251
(-3.62)
POPG
1.790260
(5.04)
0.820692
(1.93)
1.473270
(3.92)
0.678439
(1.37)
IY
0.001070
(2.54)
0.001175
(2.36)
GOV
0.000025
(0.06)
-0.000097
(-0.24)
0.000128
(0.32)
HUM
0.001329
(0.76)
0.000043
(0.02)
0.001807
(0.86)
-0.015490
(-2.77)
-0.011622
(-2.05)
INSTAB
-0.014724
(-2.55)
CONSTANT
0.083654
(7.55)
0.052907
(3.06)
0.088927
(6.14)
0.059460
(3.17)
R2
0.48
0.50
0.51
0.54
78
87
85
75
P
P
No. of obs.
Dummy 1
0.018176
(3.23)
0.022617
(3.85)
0.017255
(2.85)
0.016032
(2.72)
Dummy 2
-0.008231
(-1.45)
-0.002945
(-0.54)
-0.007260
(-1.29)
-0.005612
(-1.02)
20
There are two interesting points to notice. First, comparing with equation B8 in Table 2
and Table 3, the coefficient and significant level of SOCF in developing countries are
both higher. It showed that the impact of social fractionalization is more harmful in
affecting the developing countries than the overall world. The result may confirm to Ines
Lindner and Holger Strulik (2004) ‘s model that predicts the countries with low social
capability through low initial endowment of means of mass communication and insecure
property rights, that is the developing countries suffer more from sufficiently polarized
society. Therefore, further investigation in the transmission channels in developing
countries will be examined in the next section.
Second, looking at the result of equation B8 in Table 4, the coefficient of SOCF in
developed countries dataset is positive rather than negative shown in Table 2 and Table 3.
Although the significant level is low, it showed that social fractionalization may has
positive impact on economic growth in developed countries. The reasons behind are
complicated and further research is needed. One of the reasons is that the developed
countries could be a proxy for better government quality, legal system, higher and more
equal education level and income, higher freedom and more secured property rights…etc,
these factors may diminish the adverse effect of social fractionalization on economic
growth. While better coordination make people who belong to different groups are able to
cooperate, and make the domestic competition become productive, so social
fractionalization may be not harmful in these countries.
21
7. The Direct and Indirect Effect
7.1 Direct impact
Refer to the estimation B1 and B8 in Table 5, the direct effect of social fractionalization
accounts for about 69.3% of the total effect.
Direct impact (a)
-0.029533
Table 5
Total effect (b)
-0.042638
(a) / (b)
0.693
7.2 Indirect effect
Two transmission channels are chosen here to investigate how social fractionalization
affects economic growth through these channels. These channels are investment channel
and political instability channel.
7.2a Investment channel
When social fractionalization is high, implies an increase in uncertainty and instability ,
therefore, could lead to a reduction of the investment rate The potential ethnic conflicts
has a negative impact on investment and, indirectly, on growth. Second, ethnic diversity
may generate a high level of corruption which, in turn, could deter investment.
Furthermore, in rent-seeking models, the resources spent by each groups in order to
22
obtain political influence (e.g. time, labor, etc.) can be considered as a social cost that has
a negative effect on economic growth because it implies a non-productive usage of these
inputs. This would clearly reduce the investment in the productive sector (Montalvo and
Reynal-Querol, 2003). If the growth rate of real GDP depends on the share of investment
and the share of investment is affected by social fractionalization, the effect of social
fractionalization on the real GDP growth rate can be decomposed as:
(9)
∂GR
∂GR ∂IY
dGR
)
+(
=
∂IY ∂SOCF
dSOCF ∂SOCF
To find out the effect of ethnicity to share of investment, we estimate the effect of
ethnicity on share of investment together with the non-transmission variables by:
(10)
IY = -7.722503 SOCF + 0.000740 y0 - 24.814523 POPG -0.190483 DUMMY1
(-3.28)
(3.82)
(-0.86)
(-0.32)
+ 0.086210 DUMMY2 + 14.419003
(0.20 )
(9.84 )
R2 = 0.542
P
P
No. of observations = 82
Equation (10) indicates that SOCF has a negative effect on the share of investment. The
direct effect of social fractionalization on the real GDP growth rate is shown in the
coefficient of SOCF in estimation B2, while that in B1 incorporates the direct effect and
the indirect effect from the share of investment.
Base on equation (10), regression B1 and B2, we can calculate the role of the share of
23
investment by using equation (9). The results are reported in Table 6. We can see that
about 11% of the growth rate reduction is due to the investment channel.
Direct effect
-0.039369
Table 6
Investment channel (a)
0.000609 *(-7.722503)
=-0.004702
Total effect (b)
-0.042638
(a) / (b)
0.110
<-0.039369+(a)>=
-0.044072
7.2b Political instability channel
Some researchers show that religious and ethnic polarization have a negative effect on
growth through the increase in the probability of civil wars (Montalvo and Reynal-Querol,
2000). Although we do not include the effect of civil wars in our definition of political
instability, it is reasonable that social fractionalization make the overall political
environment more unstable. While higher political instability and social disruption that
will lead the government and society to trade off private rents in order to devote
resources toward the alleviation of this instability. (Anothy Anetty). If the real GDP
growth rate is affected by political instability, we can decompose the effect of ethnicity to
political instability by equation (13):
(11)
dGR
∂GR
∂GR ∂INSTAB
=
)
+(
dSOCF ∂SOCF
∂INSTAB ∂SOCF
We estimate the effect of ethnicity on political instability by following regression:
24
(12)
INSTAB = 0.341912 SOCF + 0.000024 y0 + 5.129235 POPG
(3.15)
(2.69)
(2.42)
+ 0.028780 DUMMY1+ 0.102362 DUMMY2 - 0.097381
(0.66)
(2.38)
(-1.36)
R2=0.18
P
P
No. of observations = 82
Equation (12) indicates that ethnicity has a positive effect on the political instability. The
direct effect of ethnicity on the real GDP growth rate is shown in the coefficient of SOCF
in estimation B5, while that in B1 incorporates the direct effect and the indirect effect
from the political instability channel.
Base on equation (12), regression B1 and B5, we can calculate the role of the share of
government consumption by using equation (11). The results are reported in Table 7. It
indicates that about 5.1 % of the growth rate reduction is due to the political instability
channel
Direct effect
-0.040701
Table 7
Political instability channel
(a)
-0.006316 *0.341912
=-0.002160
Total effect (b)
(a) / (b)
-0.042638
0.051
<-0.040701+(a)>=
-0.042860
U
25
8. Limitations
In this paper, the index for social fractionalization is obtained from 2 different sources.
Due to the different in research methods and definition of variables, the accuracy of the
index is affected.
The definition of social fractionalization followed James D Fearon’s one. It is assumed
that ethnic, languages and religious are the only 3 aspects of fractionalization and they
share the equal weights and importance. The index may not able to reflect the full degree
of fractionalization in a society. One example is in Hong Kong, most people are in same
ethnic and have the same mother language, but the society still can be fractionalization by
people holding different value or opinion towards difficult issues, e.g. the voting system.
These difference could create political instability and harm to economic growth. The
social fractionalization may be higher than the index mentioned.
Another critique is about the index of political instability. The index takes the average of
revolution and assassination per year as a proxy. Although it is widely used, it still faces
the problem as the index of fractionalization mentioned above. Especially in this topic,
the social fractionalization may not directly lead to revolution or assassination, but raise
the probability of civil war, riots and other domestic unproductive competitions. The
result in the political instability transmission channels is therefore affected and
underestimated.
26
9. Conclusion
This paper employs the framework developed by Dr. Mo to estimate the role of social
fractionalization in economic growth. The impacts of social fractionalization on
developing and developed countries are investigated. And the direct and indirect effect of
it on developing countries is reported.
In the world dataset, with 1% increase in the social fractionalization index (i.e. change
from complete homogeneity to complete heterogeneity), the real GDP growth rate
reduces by 2.1 %. And this paper discovers that social fractionalization may has different
result on developing and developed countries. Social fractionalization may not be
harmful to developed countries while the effect on developing countries is more adverse.
With 1 % increase in the social fractionalization index, the real GDP growth rate drops by
2.95 %. The direct effect contributes 69.3 % to the overall effect. Among the transmission
channels, the investment channel, which accounts for 11 %, and political instability
channel accounts for 5.1 %. These two channels are what we are interested in our model.
However, it does not mean that social fractionalization is solely related to these two
channels. For other possible channels such as openness, communication, technology,
level of democracy that other papers employed, are not included in this paper.
27
References
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Appendix
A. Data Source:
Variables Involved
Real GDP per capita
Total population
Average schooling years in the total population over age 25
Source
Penn World Table
Penn World Table
Barro and Lee
Measure of political instability
Ratio of private investment to real GDP
Databanks International
Penn World Table
Ratio of government consumption expenditure to real GDP
Penn World Table
Index for social fractionalization:
Index for ethnic and cultural fractionalization
James D. Fearon (2003)
Index for religious fractionalization
Alesina et al. (2002)
B. Dataset:
Mean
Median
Maximum
Minimum
Std. Dev.
Observations
GR
SOCF
y0
POPG
IY
GOV
HUM
INSTAB
0.060558 0.384484 5334.544 0.016806 16.04713 18.46128 5.759288 0.28677
0.059103
0.3852 3604.682 0.018129 15.5419 16.80648
5.521 0.083333
0.246512 0.786267 17504.8 0.060089 45.96749 56.29625
12.247 3.333333
-0.06159
0.0049 511.8285 -0.02175 2.460271 4.785823
0.547
0
0.034694 0.198054 4717.981 0.011145 8.149807 8.870923 2.932949 0.547497
257
257
257
257
257
257
257
257
C. Country samples
1. Developing countries
Algeria
Madagascar
Zimbabwe
Paraguay
Thailand
Angola
Malawi
Costa Rica
Peru
Yemen, N.Arab
Benin
Mali
Dominica
Suriname
Hungary
Botswana
Mauritania
Dominican Rep.
Uruguay
Poland
Burkina Faso
Mauritius
El Salvador
Venezuela
Yugoslavia
Burundi
Morocco
Grenada
Afghanistan
Fiji
Cameroon
Mozambique
Guatemala
Bangladesh
New Zealand
Cape verde
Niger
Haiti
Myanmar (Burma) Papua New Guin.
Central Afr. R.
Nigeria
Honduras
China
Solomon Islands
Chad
Comoros
Rwanda
Senegal
Jamaica
Mexico
India
Indonesia
Tonga
Vanuatu
Congo
Seychelles
Nicaragua
Iran, I.R. of
Western Samoa
Egypt
Ethiopia
Sierra Leone
Somalia
Panama
St.Lucia
Iraq
Jordan
Gabon
South Africa
St.Vincent & G.
Kuwait
Gambia
Sudan
Trinidad & Tob.
Malaysia
Ghana
Swaziland
Argentina
Nepal
Guinea
Tanzania
Bolivia
Oman
Guinea-Bissau
Togo
Brazil
Pakistan
Cote d'Ivoire
Kenya
Tunisia
Uganda
Chile
Colombia
Philippines
Saudi Arabia
Lesotho
Zaire
Ecuador
Sri Lanka
Liberia
Zambia
Guyana
Syria
2. Developed countries
Bahamas
Singapore
Germany, West
Portugal
Barbados
Taiwan
Greece
Spain
Canada
United Arab Em.
Iceland
Sweden
United States
Austria
Ireland
Switzerland
Bahrain
Belgium
Italy
Turkey
Hong Kong
Cyprus
Luxembourg
United Kingdom
Israel
Denmark
Malta
Australia
Japan
Finland
Netherlands
Korea
France
Norway