GDP since its development in 1934 has become one of if not the preeminent standard for measuring international wealth as well as wealth in general.  One of the most basic functions of Economics is to study GDP and its effects.  Often times, Economists are also called upon to determine how to increase GDP in their respective nations.  Those countries that have high GDP’s are universally accepted as “rich”.  Succinctly however, there would be no study of GDP, or for that matter even the development of the concept of GDP, without highly educated individuals to develop and study this concept.  Thus, the logical question then becomes what effect does education have on levels of Gross Domestic Product?  It is almost evidently clear that higher levels of GDP correspond to higher levels of education at the international level. 


            In order to test this concept, I ran three different tests using three different variables.  Table one provides a summary of the variables used in any subsequent test mentioned.  Variable avgsy90 is a variable which was gained from a study done by Robert J. Barro, and Lee Jong-Wha of the Center for International Development at Harvard University.  The study contained data on educational attainment by country for various years and also by gender and age group.  For the purposes of this study, I focused on the general average years in school as of the year 1990 in citizens twenty five years of age and older.  The variable GDP is a simple measure of GDP in each country from the year 1990 from a study conducted on Trade Openness by Jeffrey D. Sachs and Andrew M. Warner also from the Center for International Development at Harvard University.  Finally, the variable tradeopen, also comes from the previously mentioned study, however, it labels countries as either a 1 or a 0 for whether or not they have restricted trade or free trade.


Table 1: Summary of Data




Standard Deviation
























            In order to adequately test the previously stated hypothesis, I first ran a scatter test which plotted countries GDP on the x-axis against their average years of schooling per person on the y-axis.  The results of this test are depicted in the first two scatter plot graphs at the end of the paper.  I then ran a Pearson’s r correlation test in order to test for the statistical significance of the previously mentioned scatter plot test.  In order to control for trade restrictions, I reran the previous scatter plot, but this time running it against the tradeopen variable.  The results of this test are presented in the third graph at the end of the paper.  In order to further test this theory, I ran a bivariate regression test between average years of schooling.  The results are presented in Table 3 in conjunction with a significance test that establishes the statistical significance of the results.  Finally, a multivariate regression was run this time accounting for trade restrictions with the tradeopen variable.  The results of this test are presented in Table 4 along with statistical significance of this analysis.


            The results of the previously mentioned tests are very striking and conclusive.  The graph of the scatter plot at first glance seems to show a very strong positive relation between GDP and average years of schooling.  In addition, once a Pearson’s r correlation test was run, the correlation coefficient was a striking 0.83.  This result shows that the numbers do in fact support the graphical image presented by the scatter plot.  Thus, as a result of the picture and the Pearson’s r test, there does seem to be a strong correlation between GDP and the average number of years of schooling.

            As for the bivariate regression test, the results of that do agree with the previously run test.  However, the coefficient for GDP does not imply the significantly strong relationship presented by the Pearson’s r correlation.  However, the adjusted R2 for the regression test is a rather high 0.69.  This means that knowing the GDP of a country can account for 69% of the variance between the two variables.  Thus, while the results of the bivariate regression do not agree on the strength of the relationship between GDP and average number of years of schooling, the knowledge of the two does have a significantly high R2 value and thus the two variables are very highly related.

            When the multivariate regression was run, the results were not as strong as the previous tests.  For one, the P value associated with the tradeopen coefficient was such that it was not able to be deemed statistically significant.  In addition to this, the change in the coefficients between the multivariate and the bivariate regression were hardly noticeable.  Despite this, the R2 did increase slightly to just over 70%.  Thus, the lack of a significant change between the bivariate and the multivariate regression proves that trade restrictions have little or no effect on GDP and average years of schooling in any form.

Table 2: Pearson's r Correlation


Pearson’s r

P Value

GDP & Average Years of Schooling




Table 3 Bivariate Regression between GDP and Average Years of Schooling


Average Years of Schooling


0.000465** (0.0000324)


2.984** (0.241)

Number of Observations


Adjusted R2


Standard errors in parentheses + p<.1, * p<.05, ** p<.01


Table 4 Multivariate Regression between GDP and Average Years of Schooling Controlling for Free Trade


Average Years of Schooling


0.000464** (0.0000396)

Free Trade

0.131 (0.408)


2.881** (0.268)

Number of Observations


Adjusted R2


Standard errors in parentheses + p<.1, * p<.05, ** p<.01







           In conclusion, this result has interesting impact on what countries general policy should be towards education and the promotion of it.  It has shown that as a country becomes “richer”, there is a good possibility that the nation’s citizens will become more educated as a result.  This would logically then mean that governments should promote higher education for its citizens as its GDP and economy grow so as to continue this pattern of growth.  It will be interesting to take these results and apply them to emerging economies such as Brazil, Russia, India, China, and South Africa.  In these cases, governments would be apt to invest in the building of schools as well as developing some sort of mass education system.  With global economies desperately trying to regain momentum after the US credit crunch, it is likely that education as well as GDP with more relevant now than ever before.