How Does Savings Rate Affect Economic Growth?
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The following paper focuses on determining how the savings rate affects the economic growth in terms of income by analyzing a cross section data of 100 countries in 2010. In order to infer the relationship between these two variables, two types of studies are conducted: simple and multiple regressions. For the simple regression analysis between savings rate and income, it is shown that these two variables are positively correlated with a coefficient of correlation of about 25% indicating that there are other explanatory variables that should be taken into account when evaluating the disparities in income across countries. The first multiple regression analysis conducted replicates the studies found in “A Contribution to the Empirics of Economic Growth" (Mankiw, Gregory, Romer, Weil, 1992). For this analysis, population growth rate and education are introduced as explanatory variables, leading to a coefficient of correlation of 71%. Furthermore, our study introduces one more explanatory variable of number of science journals published as an indicative of technology growth in each country. For this analysis the correlation coefficient obtained is 72%, showing that the introduction of the new variable did not significantly contribute to the explanation of income disparities. Finally, the countries are divided into high and low-middle income in order to determine if the relationship between savings rate and income is altered by this factor. It is observed that the relationship still holds true for both cases and it is proved that technology growth is statistically significant for middle-low income countries whereas for high-income countries it is insignificant. It can be concluded that savings rate, education index and technology are positively correlated to income whereas population growth is negatively correlated to the dependent variable. In order to better understand and explain these relationships it is recommended to look into other indicators of technology that have a higher statistical significance in the model.