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

6

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

7

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.

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 HUM INSTAB 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 1 INSTAB -0.03124 -0.06289 -0.18592 0.144221 -0.20454 0.041455 -0.16462 1 Mean 0.060558 0.384484 5334.544 0.016806 16.04713 18.46128 5.759288 0.28677 Median 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

13

TABLE 2 The effect of Social Fractionalization on the Growth Rate (World dataset)

Estimation: B1 B2 B3 B4 Dependent Variables

Independent variables

GR GR GR GR

SOCF -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)

RP

2P 0.06 0.11 0.07 0.07

No. of obs. 348 336 336 284

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)

14

TABLE 2(Con’t) The effect of Social Fractionalization on the Growth Rate (World dataset)

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

Estimation: B5 B6 B7 B8 Dependent Variables

Independent variables

GR GR GR GR

SOCF -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)

INSTAB -0.007112 (-2.05)

-0.003741 (-0.97)

-0.002418 (-0.63)

CONSTANT 0.075378 (10.34)

0.053584 (4.80)

0.064841 (5.57)

0.056568 (4.68)

RP

2P 0.07 0.11 0.08 0.10

No. of obs. 323 275 257 257

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)

15

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.

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

17

Table 3 The effect of Social Fractionalization on Growth Rate (Developing countries dataset)

Estimation: B1 B2 B3 B4 Dependent Variables

Independent variables

GR GR GR GR

SOCF -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)

RP

2P 0.21 0.21 0.23 0.18

No. of obs. 258 246 246 197

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)

18

Table 3 (Con’t) The effect of Social Fractionalization on Growth Rate (Developing countries dataset)

Estimation: B5 B6 B7 B8

Dependent Variables Independent variables

GR GR GR GR

SOCF -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)

INSTAB -0.006316 (-1.75)

-0.003185 (-0.76)

-0.002703 (-0.64)

CONSTANT 0.082728 (10.49)

0.069226 (5.24)

0.076029 (5.50)

0.071278 (4.97)

RP

2P 0.19 0.20 0.23 0.22

No. of obs. 245 188 182 182

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)

19

Table 4 The effect of Social Fractionalization on Growth Rate (Developed countries dataset)

Estimation: B1 B2 B3 B4 Dependent Variables

Independent variables

GR GR GR GR

SOCF -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)

RP

2P 0.44 0.46 0.44 0.46

No. of obs. 90 90 90 87

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)

20

Table 4 (Con’t) The effect of Social Fractionalization on Growth Rate (Developed countries dataset)

Estimation: B5 B6 B7 B8 Dependent Variables

Independent variables

GR GR GR GR

SOCF -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)

INSTAB -0.014724 (-2.55)

-0.015490 (-2.77)

-0.011622 (-2.05)

CONSTANT 0.083654 (7.55)

0.052907 (3.06)

0.088927 (6.14)

0.059460 (3.17)

RP

2P 0.48 0.50 0.51 0.54

No. of obs. 78 87 85 75

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)

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.

Table 5

Direct impact (a) Total effect (b) (a) / (b) -0.029533 -0.042638 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

23

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) )(SOCF

IYIYGR

SOCFGR

dSOCFdGR

∂∂

∂∂

+∂∂

=

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 )

RP

2P = 0.542 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

24

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.

Table 6

Direct effect Investment channel (a) Total effect (b) (a) / (b) -0.039369

0.000609 *(-7.722503) =-0.004702

-0.042638

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) )(SOCF

INSTABINSTAB

GRSOCF

GRdSOCF

dGR∂∂

∂∂

+∂∂

=

We estimate the effect of ethnicity on political instability by following regression:

25

(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)

RP

2P=0.18 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

Table 7

Direct effect Political instability channel (a)

Total effect (b) (a) / (b)

-0.040701

-0.006316 *0.341912 =-0.002160

-0.042638

0.051

<-0.040701+(a)>= -0.042860

U

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

Alberto Alesina, Arnaud Devleeschauwer, William Easterly, Sergio Kurlat and Romain Wacziarg, (2002), “Fractionalization”, Harvard Institute of Economic Research, Discussion Paper Number 1959

Annett Anthony, (2001), “Social Fractionalization, Political Instability, and the Size of Government”, IMF Staff Papers, Vol. 48, No. 3, pp. 561-592 Barro, R. (1991), "Economic Growth in a Cross Section of Countries," Quarterly Journal of Economics, CVI, 407-43. Barro, R. (1996), "Determinants of Economic Growth: A Cross-Country Empirical Study,", NBER Working Paper 5698. Canning, David and Marianne Fay (1993), “The Effects of Transportation Networks on Economic Growth” Columbia UniversityWorking Paper. Collier, Paul and Hoeffler, Anke. “On Economic Causes of Civil War.” Oxford Economic Papers, 1998, 50(4), pp. 563–73. Easterly, William and Ross Levine, (1997), “Africa’s Growth Tragedy: Politics and Ethnic, Divisions,” Quarterly Journal of Economics, Vol. 112, pp. 1203–50. Fearon, James D. and Laitin, David D. “Ethnicity, Insurgency, and Civil War.” American Political Science Review, 2003, 97(1), pp. 75–90. Holger Strulik, (2005), “Social Composition, Social Conflict, and Economic Development” Ines Lindner and Holger Strulik, (2004), “Social Fractionalization, Endogenous Property Rights, and Economic Development” James D. Fearon, (2003), “Ethnic and Cultural Diversity by Country”, Journal of

Economic Growth, 8, 195-222, 2003

Jose´ G. Montalvo and Marta Reynal-Querol, (2005), “Ethnic Polarization, Potential Conflict, and Civil Wars” La Porta R. Lopez de Silanes, F. Shleifer, and R. Vishiny, (1999), “The Quality of Government”, Journal of Law Economics and Organization 15, 1, 222-279 Liu Ka Ho, (2005), “Effect of ethnicity diversity on economic growth Mauro, Paolo, (1995), “Corruption and Growth,” Quarterly Journal of Economics, CX (1995), 681–712 Marta Reynal-Querol and José G. Montalvo, “A theory of religious conflicts and its effect on economic growth”, IVIE working papers, WP-EC 2000-04 Mo, Pak Hung, (2001), “Corruption and Economic Growth”, Journal of Comparative Economics 29, pp66-79 Mo, Pak Hung, (2002), “Human Capital and Economic Growth: Alternative Estimation Methods”, BRC Working Papers, Hong Kong Baptist University Mo, Pak Hung, (2004), “Government Size, Investment and Economic Growth: Supply-side and Demand-side” Mo, Pak Hung, (2005), “Democracy and Economic Growth: Optimal Level and Transmission Channels” Rodrik, Dani, (1999), “Where Did All the Growth Go? External Shocks, Social Conflict, and Growth Collapses”, Journal of Economic Growth 4, 385-412.

Appendix A. Data Source: Variables Involved Source Real GDP per capita Penn World Table Total population Penn World Table Average schooling years in the total population over age 25 Barro and Lee

Measure of political instability Databanks International Ratio of private investment to real GDP 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: GR SOCF y0 POPG IY GOV HUM INSTAB Mean 0.060558 0.384484 5334.544 0.016806 16.04713 18.46128 5.759288 0.28677 Median 0.059103 0.3852 3604.682 0.018129 15.5419 16.80648 5.521 0.083333 Maximum 0.246512 0.786267 17504.8 0.060089 45.96749 56.29625 12.247 3.333333 Minimum -0.06159 0.0049 511.8285 -0.02175 2.460271 4.785823 0.547 0 Std. Dev. 0.034694 0.198054 4717.981 0.011145 8.149807 8.870923 2.932949 0.547497 Observations 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 Rwanda Jamaica India Tonga Comoros Senegal Mexico Indonesia Vanuatu

Congo Seychelles Nicaragua Iran, I.R. of Western Samoa

Egypt Sierra Leone Panama Iraq

Ethiopia Somalia St.Lucia 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 Tunisia Chile Philippines

Kenya Uganda Colombia 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