Does Higher Education Increase Income Level of a Country?

USAID Education, Development, And Foreign Aid Grant

Of late it has become progressively more apparent that education is a key factor and aspect in the development of a country. Foreign aid in particular is an aspect that has come to be long-standing and existing since the Second World War and in the present day is a normal part and parcel of social as well as political associations and affiliations amongst different nations. There has been plenty of consideration and debate surrounding the effectiveness of the foreign aid and also its need. There has also been much debate and consideration regarding the terms laid out, the conditions that the foreign aid comes along with and also the purposes (Heyneman, 2005). Much of the foreign aid handed out in modern-days has been for both primary and secondary educational programs with the main purpose and aim of increasing economic growth and also the income levels of a nation.

This research paper will be centered on the hypothesis that more education levels which are measured in both primary education and also secondary education implies that there will be higher income levels for a country. The income levels will be measured in the GNP per capita data for different nations. The data came from the World Bank's World Development Indicators 2000. The World Bank was selected as a source for data for the reason that it is an international organization that has a proper repute and status.

So as to assess and evaluate the impact of increased levels of education on the income levels for a nation, I picked out data for both country and consolidated data for GNP per capita in terms of billions of dollars for the year 1998. I also selected data for education for the year 1997. This was with regards to Net Primary enrollment in terms of the percentage of the relevant age group and also the Net Secondary enrollment in terms of the percentage of the relevant age group. The data collected with regards to GNP per capita and also the rates of educational enrollment for both secondary and primary levels were done in four different income brackets. These are Low Income brackets excluding India and China due to the high population numbers as this would interfere with the data, Lower Middle Income brackets, Upper Middle Income brackets and also the High Income brackets for both the years 1997 and 1998.

This paper makes considerable effort to make comparisons of four relationships. These are the per capita income levels using the primary enrollment figures, the per capita income levels using the secondary enrollment figures, the per capita GNP and the primary enrollment figures by country and lastly the per capita GNP and the secondary enrollment figures by country. In order to examine and evaluate these four relationships, the corresponding data for each of the relationships was plotted into four different graphs.

Data Presentation

Figure One

Source: World Development Indicators 2000.

Figure Two

Source: World Development Indicators 2000.

Figure Three

Source: World Development Indicators 2000.

Figure Four

Source: World Development Indicators 2000.

Analysis

In figure one, the steepest gradient in the line for the GNP per capita takes place between the Upper Middle Income bracket and the High Income bracket at the point when the GNP per capita shifts from $4,870 billion to $25,480 billion. On the other hand, the % Primary Enrollment line increases gradually and all the progressively more with the sharpest spike of the line taking place between the Low Income bracket and the Lower Middle Income bracket rising from seventy eight percent to ninety three percent. The rate also increases gradually but at a lower rate from 93% to a rate of 105% from the Lower Middle Income bracket to the Upper Middle income. However, as the line moves on to the High Income bracket, there is a decrease in the rate as it declines from 105% to 101%. Irrespective of the Income Level, the % of the Primary Enrollment appears to be quite high in the Low Income, Lower Middle Income and Upper Middle Income brackets with the rate only declining in the last phase of the line but at the same time maintaining the rate past the one hundred percent mark at the High Income bracket. In as much as both the GNP per capita and % primary enrollment trends go upwards, the rate of change in each of the trend lines does not give any indication of a high correlation and the association that does exist indicates diminishing marginal returns. This basically implies that as the level of income rises, the rate of change for the % of primary enrollment appears to decline. The points between the levels of income do not appear to take place at equivalent intervals and there does not appear to be a substantial or considerable variation or dissimilarity between the % Primary Enrollment in the Low Income, Lower Middle Income, and Upper Middle Income brackets.

In figure two, the sharpest inclination in the trend line for the GNP per capita takes place between the Upper Middle Income bracket and the High Income bracket at the point when the GNP per capita shifts from $4,870 billion to $25,480 billion. On the other hand, the % Secondary Enrollment line rises steadily and all the increasingly more with the steepest gradient of the trend line taking place between the Low Income bracket and the Lower Middle Income bracket rising from 79% to 93%. The rate also increases gradually but at a lower rate from 93% to a rate of 106% from the Lower Middle Income bracket to the Upper Middle income. However, as the line moves on to the High Income bracket, there is a decrease in the rate as it declines from 106% to about 102%. Regardless of the level of income, the percentage of the enrollment in secondary programs seems to be considerably high in the income brackets of ranging between the low income bracket and the upper middle income bracket with the rate only going down once in the last income bracket but still being higher than the one hundred percent mark settling for 102% at the high income bracket.

Figure three to a great extent signifies a disaggregated form of Figure one demonstrated previously. From the figure, it can be seen that both the percentage of Primary enrollment and the GNP per Capita have upward trends. Nevertheless, Figure one has an adjusted R2 of 0.51594 which implies that 51.55% of the variance in GNP per Capita is explained by the % of the Primary enrollment. This in turn gives the suggestion of a fairly strong correlation between the GNP per capita and the levels of primary enrollment. In addition, as we analyze the data demonstrated in the figure, it can be seen that the data appears to fluctuate greatly on the lower income end of the spectrum and goes on to be steadier towards the high income bracket of the nations ranked. The standard deviation of the data is 23.60 though majority of the deviations in the line seem to be restricted in the bottom half of the graph. One other aspect to note is that most of the nations that are ranked to be of low income have reached and exceeded the 100% enrollment rates in terms of their primary level education. This can be linked to the conception that majority of these low income nations barely have any technological infrastructure and therefore these individuals in primary are not included in the skilled labor workforce of the nation which also reduces the level of output of the nation.

In figure four, similar to the previous figure three, this is a disaggregated version of Figure two. It can be seen that the trend for the data of both the % for secondary enrollment as well as that of GDP per capita is upwards. The data in figure four has R2 of 0.51415 and this implies that 51.41% of the variance in GNP per Capita is explained by the % of the Secondary enrollment. This in turn gives the suggestion of a fairly strong correlation between the GNP per capita and the levels of secondary enrollment. This in turn also gives the suggestion that there are a number of other factors which are involved in the forecast and projection of the GNP per capita. Nevertheless, it is imperative to take note that % of secondary enrollment offers a better indication of the GNP per capita in comparison to the % of the Secondary enrollment. This also brings out the consideration as to whether the increase in the enrollment in secondary school level of education increases the GNP per capita level or whether the higher demand for skilled laborers at this level compared to the primary level is what pushes individuals to seek further education. This indicates that there is need for more research to be undertaken and this can be done by taking into account data from the wide range number of years to examine the trend and the impact they have on the GNP per capita.

In overall, the four figures representing the four graphs all indicated or showed a positive correlation to be in existence between the level of education and the output of a nation. This basically goes hand in hand with the hypothesis of the memo which is that more education levels imply higher income levels for a country. However, it is imperative to take note that the correlation between the enrollment in primary educational programs and the GNP per capita was considerably strong while on the other hand the enrollment in secondary educational programs appeared to have a weaker correlation to GNP per capita. This as a result highly shows that there are various other factors that need to be taken into consideration. More so, this particular approach or methodology used for the analysis was not able to provide proof regarding causation. This is for the reason that it was not fully apparent to show that the rates of education levels were the main cause for the increase in the GNP per capita or whether these nations that had a high level of GNP per capita offered proper initiatives and programs that increased the level of education.

It is also important to take note that in the analysis of the data obtained from the World Bank Indicators, majority of the nations at the lower middle income bracket and low income bracket were not included in the data analysis for the reason that they did not have complete or comprehensive information regarding the percentage of primary enrollment or the percentage of secondary enrollment. It is deemed that at this time most of the nations did not have stability which could give an explanation to the lack of the percentage of primary enrollment or the percentage of secondary enrollment data.

In order to have a more accurate and conclusive analysis and evaluation, samples of data ought to be retrieved for an expanded period of time or a longer time interval so as to assess and evaluate the impact of higher and increased rates or levels of education over the time period. In addition, notes of the income levels of the nation as well as the growth level of the output of the nation over a period of time also needs to be taken. This would also give a clear and more comprehensive insight or understanding as to whether the higher education levels bring about higher income levels for a country or whether the higher income levels of the nation in fact increase the education levels of a nation. So as to be in response of the initial RFP for the grant, there would have to be the undertaking of research linking or showing the association of foreign aid to the changes in the percentage of primary enrollment programs and also changes in the percentage of the secondary enrollment programs.

As a final remark, it is important for other variables to be taken into consideration when making an evaluation of the magnitude and degree of the level of education, both primary and secondary, on the level of income of a country. To begin with, the data used in this analysis does not give any indication or description regarding the quality level of the education both in primary and secondary levels. Regardless of the low income nations hitting and surpassing the 100% enrollment mark, the quality of education could in fact end up being of poor quality.