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2010 - Southern Political Science Association Pages: unavailable || Words: unavailable || 
1. Kim, Hae. "The Differences in the Effect of Economic Globalization between Developing and Developed Countries." Paper presented at the annual meeting of the Southern Political Science Association, Crowne Plaza Hotel Ravinia, Atlanta, Georgia, Jan 06, 2010 Online <APPLICATION/OCTET-STREAM>. 2019-11-14 <>
Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: 1. This paper aims to analyze the differences in the effect of economic globalization on the economic development and quality of life between developing and developed countries. 215 countries will be classified into developing and developed countries. The time spans the 2005-9 period.

2. Economic globalization (independent variable) will be defined and measured by the following indicators:

(1) International economic interdependence (export +import/GDP)
(2) Openness of market to foreign businesses and investors (GDP minus GNI)
(3) Foreign direct investment (FDI)

3. Economic development (dependent variable) is based on the following indicators :

(1) GDP, GNI , and PPP (purchasing power parity)
(2) Quality of life: Human Development Index (HDI)
(3) Income equality (Gini Coefficient)

4. The following variables are selected as they each are theoretically assumed to affect economic development and quality of life in both developing and developed countries. Those variables will be treated as ‘control variables’ in order to identify a ‘pure’ effect of economic globalization on economic development and quality of life:

(1) Defense spending
(2) External debt sustainability/burden
(3) Terms of trade
(5) Ethnic-racial heterogeneity/composition
(6) Population growth,
(7) Internal political fragmentation
(8) Internal/civil strife
(9) Types of political system
(10) Urbanization
(11) industrialization

V. The statistical methods will be based on SPSS (Statistical Packages for Social Sciences), in which factor analysis, discriminant analysis, multiple regression, crosstab, and logistic regression, among others, will be extensively utilized.

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