Descriptive Statistics, Regression Analysis, and Tests of Hypotheses of Interdependencies of Health, Education, and Economic Outcomes

Descriptive Statistics, Regression Analysis, and Tests of Hypotheses of Interdependencies of Health, Education, and Economic Outcomes

DOI: 10.4018/978-1-4666-3643-9.ch004
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Abstract

This chapter is composed of two major parts. The first one measures interactions and interconnections between health and education using aggregate data on South Mediterranean countries. It focuses on Principal Components Analysis (PCA), descriptive statistics, and regression analysis. This latter is based on different clusters concerning the likely potential links between education, health, and income. The results attained show how different series of results are obtained. The inter-relations identified do account for health, education, and income variables, and are sensitive to the type of data mobilized. This illustrates how ICTs can be used to respond to the analysis required in this type of situation. The second part addresses the directions of links between health, education, and income, and introduces causality tests. This is established in the context of the regional data on South Mediterranean countries. The analysis is consequently conclusive about the role of education based on the data used. Coordination of actions can then target education as the main source of causal relationships. This type of analysis has the merit of facilitating the use of ICTs in the coordination process.
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A Descriptive Assessment Of Wealth Components

The main objective of this part1 is to check the interdependencies between different sources of wealth (education, health and economic wealth). The analysis is based on datasets made of World Bank and United Nations.

The first section uses descriptive statistics, and simple regressions to make a comparative analysis between South Mediterranean countries and countries of the European Union about the levels of education, health and economic wealth, taking also into consideration the evolutions during the last 10 years, the classification of countries (developed, developing, underdeveloped countries), or membership in different organizations (OPEC or oil exporters). The second section uses factorial methods to detect the interdependencies between the selected indicators of education, health, and economic development (GDP per capita, literacy rate, school life expectancy, life expectancy, and infant mortality rate).

Using these variables, three wealth patterns are observed and are analyzed for SMC and EU countries.

The last section is an econometric analysis of the relation between education, health and economic development. To obtain better results, a dummy variable is introduced to account for country wealth (if a country is rich or with medium wealth dummy=1 or if it is poor dummy=0).

The main conclusion is that all the components of human welfare are strongly interdependent. For SMC countries, the impact of any change in the level of education or health on the level of economic development depends on the wealth patterns. There are some differences between SMC and EU countries. No direct relationship between the level of education and economic development could be seen for EU countries, and the relationship between health and economic development is different from South Mediterranean countries.

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