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
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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.
$$ {\mathrm{FRAC}}_{\mathrm{j}}=1-\sum \limits_{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{S}}_{\mathrm{i}\mathrm{j}}^2 $$
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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.
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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.
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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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