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Volume :4 Issue : 14 1987      Add To Cart                                                                    Download

The Effect of Econoomic Variables on Enrollment in Higher Education in Different Stage of Development

Auther : Mustafa M. Motwalli

Higher education has become one of the major determinants of economic and social development. This is not only because of its vital role in creating new knowledge, but also because it trains potential employees for highly specialized occupations. Therefore, most, if not all, countries give serious attention to higher education.

However, the enrollment in higher education depends on many factors. This study attempts to examine the effect of some relative economic variables on the enrollment of the students in higher education within each stage of development and between different stages of development.

The study applies simple regression analysis 53 countries representing different stages of development according to the World Bank classification. Five relative economic variables were tested. These are per capita income, expenditure on education per head, percentage of labor force in industry, share of manufacturing in the Gross National Product and the degree of urbanisation.

The study finds out that enrollment in higher education is determined by economic and non-economic factors. The economic factors would seem to exerit their influence as countries move up from one stage of development to another. While non-economic factors seem to play an important role each stage of development.

Further analysis with foregoing formula is presented, Which shows that it is consistent with the findings of previous theoretical and empirical research on inflation. This analysis, and the empirical validation results give support to the theoretically derived approximation as a comprehensive and integrated, yet simple and realistic, formulation of the relationship between the amount of inflation and its determining factors (si, st, and ri).

A formula approximating ri, is also derived. Given rit (the item-test correlation coeffient), si, st, and n (the number of items in the test), then r* as an estimate of ri, is obtained by:

ri* = rit st - si

       n  si - si

The foregoing expression tends to slightly overestimate the average correlations as revealed by an application of the formula to a set of 40 items. The differences between the actual averages obtained from the inter-item correlation matrix, and their estimated values ranged from - 0.007 to 0 (ri = .09, ri* = .093). The correlation coeffient between the actual and the estimated values was .998. The formula can, therefore, be useful for practical purposes.<

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