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The Relationship Between Ethnic Diversity and Stock Market Development: A Global Perspective

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Contemporary Trends and Challenges in Finance

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Abstract

Diversity plays a crucial role in investor’s decision-making behaviour. If the culture of heterogeneity escalates, consumer choices also create catastrophic shifts in the financial markets. This study aims to investigate the relationship between diversity and stock market development by using the data of 187-countries of the world. The results indicate that diversity either ethnic or religious, or both have a significant positive association with stock market development (including market capitalization, market liquidity, turnover ratio, listed domestic companies, S&P/global equity index). This study suggests that diversity is a natural phenomenon; however, productive results of diversity can be obtained by providing an equal and peaceful society through cohesiveness.

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Appendices

Appendix 1: Construction of the Fractionalization Index

  1. 1.

    Approach. This paper used ethnic fractionalization index, developed by the Alesina et al. (2003) for diversity calculation (on the basis of ethnic and religious) using the following formula.

    $$ {\mathrm{FRAC}}_{\mathrm{j}}=1-\sum \limits_{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{S}}_{\mathrm{i}\mathrm{j}}^2 $$
  • Whereas, Sij is the share of group i, (i = 1,…,N) in the country j. The range of the fractionalization index is between 0 and 1. Zero “0” means homogenous country and “1” shows total heterogeneous country.

  1. 2.

    Data. The data of diversity (ethnic and religious) has been taken from database of Cline Centre for Democracy, University of Illinois, USA. The dataset contain annual data from 1990 to 2010 for 187-countries. Data at the Cline Centre based on various projects that document the changing varieties of social identity around the world (composition of religious and ethnic groups, CREG). In addition, they also identified the causes of conflict between religious and ethnic groups. The Composition of Religious and Ethnic Groups Project (CREG) has started to create a set of time-varying measures that gauge the nature and depth of country-specific socio-cultural cleavages. It focused on the largest countries in the world (all countries with a population above 500,000 (in 2014) during the post-WWII era to create country-specific projections on the relative sizes of the different groups during the post-war era.

  1. 3.

    Final index. The data at CREG project shows various types of groups each and every country at annual basis on the ethnic and religious identity. We have found the number of ethnic and religious groups of each country out of total populations. Therefore, it easy to apply the Alesina’s fractionalization index formula to calculate the diversity for ethnic and religious basis. We constructed the index values for the year 1990, 1995, 2000, 2005 and 2010 because diversity is not change over time. We have also compiled the index for 187-countries (out of 195 totals) due to limitation of data. The result of index can be used for relative ranking of countries on basis of ethnic and religious identity in current scenarios. (Please see results at Appendix 3).

Appendix 2: Summary Statistics

Descriptive statistics

 

VSTG

VSTM

MC

DC

GEI

FRACED

FRACRD

IC

IST

GE

FDI

GDPPC

CI

Mean

34.02

48.77

65.49

498.3

5.159

0.457

0.400

0.599

0.499

0.698

5.151

1285

42.82

Median

8.713

26.22

36.39

158.0

3.789

0.455

0.423

0.606

0.521

0.706

2.687

6631

34.50

Maximum

668.5

1721

1185

7487

158.0

0.989

0.925

0.788

0.773

1.021

341.0

1277

98.14

Minimum

0.000

0.015

0.010

2.000

−61.04

0.013

0.003

−0.031

0.231

0.211

−16.58

257.7

8.400

Std. Dev.

71.31

113.0

104.5

1049

28.97

0.258

0.218

0.100

0.100

0.100

14.76

1632

22.17

Skewness

5.117

11.21

6.942

3.839

1.153

−0.037

−0.130

−0.921

−0.414

−0.171

15.41

2.547

0.934

Kurtosis

38.72

160.4

66.62

19.16

6.617

1.696

1.746

5.936

2.874

4.537

325.5

11.66

2.696

Jarque-Bera

1985

3204

5654

5004

252.2

51.53

51.83

291.9

15.44

87.89

3714

3678

93.79

Probability

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Sum

1173

1482

2095

1868

1697

331.9

304.0

349.7

263.6

593.5

4373

1123

2689

Sum sq. dev.

1749

3871

3486

4.124

2753

48.35

36.24

5.839

5.320

8.592

1847

2.334

3081

The above Appendix 2 shows the descriptive statistics of dependent and independents variables. VSTG, VSTM, MC, Dc and GEI have a maximum values such as 668.5, 1721, 1185, 7487 and 158 and minimum values 0.00, 0.015, 0.010, 2.00 and −61.04 respectively. Maximum values of FRACED and FRACRD are 0.98 and 0.92 and minima are 0.01 and 0.03 respectively. In the line of empirical analysis, Table 1 shows that data is well organized respective to dependent and independent variables.

Appendix 3: List of Countries

Andorra

Ireland

Qatar

Samoa

Eritrea

Antigua and Barbuda

Israel

San Marino

Sao and Principe

Ethiopia

Australia

Italy

Saudi Arabia

Serbia and Monten.

Gambia, The

Austria

Japan

Seychelles

Solomon Islands

Guinea

Bahamas, The

Kuwait

Singapore

South Africa

Guinea-Bissau

Bahrain

Latvia

Slovak Rep.

Sri Lanka

Haiti

Barbados

Liechtenstein

Slovenia

St. Lucia

Zimbabwe

Belgium

Lithuania

Spain

Sudan

Senegal

Brunei Darussalam

Luxembourg

Sweden

Afghanistan

Sierra Leone

Canada

Malta

HK, China

Benin

Somalia

Chile

Monaco

Hungary

Burkina Faso

Tanzania

Cyprus

Netherlands

Iceland

Burundi

Togo

Czech Republic

New Caledonia

Switzerland

Central African Rep.

Uganda

Denmark

New Zealand

Trinidad & Tob.

Chad

Rwanda

Estonia

Norway

UAE

Comoros

Liberia

Finland

Oman

UK

Congo, Dem. Rep.

Madagascar

France

Palau

United States

Nepal

Malawi

Germany

Poland

Uruguay

Niger

Mali

Greece

Portugal

Fiji

Kiribati

Mozambique

Albania

Cameroon

Gabon

Kyrgyz Republic

Pakistan

Algeria

Cape Verde

Georgia

Lao PDR

Panama

Angola

China

Ghana

Lebanon

Pap. New Guinea

Argentina

Colombia

Grenada

Lesotho

 

Armenia

Congo, Rep.

Guatemala

Libya

 

Azerbaijan

Costa Rica

Guyana

Macedonia, FYR

 

Bangladesh

Cote d’Ivoire

Honduras

Malaysia

 

Belarus

Croatia

India

Maldives

 

Belize

Cuba

Indonesia

Marshall Islands

 

Bhutan

Djibouti

Iran, Rep.

Mauritania

 

Bolivia

Dominica

Iraq

Mauritius

 

Bosnia and Herz

Dominican Rep.

Jamaica

Mexico

 

Botswana

Ecuador

Jordan

Micronesia, Fed. Sts.

 

Brazil

Egypt, Arab Rep.

Kazakhstan

Moldova

 

Bulgaria

El Salvador

Kenya

Mongolia

 

Cambodia

Equatorial Guinea

Timor-Leste

Turkey

 

Suriname

Taiwan, China

Tonga

Turkmenistan

 

Swaziland

Tajikistan

Tunisia

Ukraine

 

Syrian Arab Republic

Thailand

Vanuatu

Uzbekistan

 

Venezuela, RB

Yemen, Rep.

Morocco

Paraguay

 

Vietnam

Zambia

Namibia

Peru

 

Nicaragua

Philippines

Russia

  

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Amin, S. (2021). The Relationship Between Ethnic Diversity and Stock Market Development: A Global Perspective. In: Jajuga, K., Locarek-Junge, H., Orlowski, L.T., Staehr, K. (eds) Contemporary Trends and Challenges in Finance . Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-73667-5_1

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