In the contemporary society, the per capita GDP of a country is the most common measure of the economic well-being of a country. Per capita GDP may be defined as the measure of a country’s gross domestic production divided by the total population in the country (Becker, 2017). Notably, there are various factors that impact the per capita GDP of a country include the main ones being, inflation level, banking interest rates, literacy levels, levels of investments, level of unemployment and life expectancy (Asteriou, & Hall, 2015). This proposal will outline how an econometric analysis may be conducted to investigate the relationship between the dependent variable, per capita GDP and the causative variables inflation rate, interest rates, literacy level, investment levels, and life expectancy. This will be done by collecting economic data on the variables in countries in Europe in the year 2018.
The research question in the econometric analysis is finding out the main factors that influence the per capita GDP od a country given the numerous causative factors that are thought to affect the total GDP of a country. Namely, the factors being investigated are; inflation level, banking interest rates, literacy levels, levels of investments, level of unemployment and life expectancy
Inflation may be defined as the general increase in the prices of commodities and services in the consumer market. According to Bernanke, Laubach, Mishkin, and Posen, (2018) with increase with inflation, there is a decline in consumer purchasing power and this consequently lowers the per capita GDP of a a country. Equally, high banking interest rates undermine the purchasing power of consumers affecting the GDP of a country. However, in some cases, high-interest rates and levels of inflation do not significantly affect the GDP of a country especially when inflation is caused by increased demand resulting to increased production which increases the overall production in a country (Bernanke et al., 2018).
Equally, according to Klapper, Lusardi, & Van Oudheusden, (2015) countries with high literacy levels have higher per capita GDP when compared with countries with low levels of literacy. This is because literacy foster innovation and efficiency in production and this overall contribute towards the overall growth of a countries GDP. Similar, high levels of investment increase the available capital in the country and this grows the economy (Leamer, & Stern, 2017). The age expectancy of a country is also important since countries with long life expectations are more productive than countries with low life expectations. However, there lacks any solid proof to link long life expectancy with improved GDP (Leamer, & Stern, 2017). Lastly, the level of employment in a country significantly affects the GDP of the country. This is because countries with low levels of unemployment have higher GDP per capita due to more production as compared to countries with high levels of unemployment (Wooldridge, 2015).
Data for the analysis will be collected from world bank statistics which provide economic data on the variables that are being investigated in the project. Notably, data will be mainly collected from countries in Europe and Asia where economic data on the variables in the study are available. To guarantee the authenticity and accuracy of the data, data will be compared from different sources such as Statista, World Bank, and other scholarly websites. Data collected will be recorded in a spreadsheet and later imported to R studio for analysis.
The descriptive section will involve plotting graphs to visually inspect the variables in the countries. The main plots in the study will include scatter plots, line graphs, and bar graphs which will be generated using R studio.
The method that will be used in the analysis is the use of ordinary least squares with per capita GDP being the independent variables while the independent variables are inflation level, banking interest rates, literacy levels, levels of investments, level of unemployment and life expectancy. Accordingly, the multiple linear regression model is as shown below.
yi = 0 + 1×1 + 2×2 + 3×3+ 4×4 + 5×5 +
Where yi = dependent variables
xi = independent variables
The analysis will also involve performing a residual analysis of the regression to investigate the presence of outliers in the mode. Consequently, any observation with outliers will be eliminated and a subsequent regression model will be modeled to get more precise outcomes. Lastly, the analysis will sleety only the significant variables in the model and perform a regression analysis with only the significant variables.
The analysis will also use a Ramsey reset test of the specification to test for functional specification. A heteroskedasticity test will also be used to find out whether the variance in the error term is constant or it varies with the independent variables.
In conclusion, finding out the variables that profoundly affect the per capita GDP of a country is vital in coming up with policies to help grow the economy of countries. Equally, with Per capita GDP being the most common way of evaluating the well being of people in a country, it is paramount that more research is conducted to investigate the significance of the various factors that are thought to affect the GDP of a country.
Becker, G. S. (2017). Economic theory. London UK: Routledge.
Asteriou, D., & Hall, S. G. (2015). Applied econometrics. Macmillan International Higher Education.
Bernanke, B. S., Laubach, T., Mishkin, F. S., & Posen, A. S. (2018). Inflation targeting: lessons from the international experience. Princeton University Press.
Klapper, L., Lusardi, A., & Van Oudheusden, P. (2015). Financial literacy around the world. Standard & Poor’s Ratings Services Global Financial Literacy Survey., Access mode: http://media. mhfi. com/documents/2015-Finlit_paper_17_F3_SINGLES. pdf.
Leamer, E. E., & Stern, R. M. (2017). Quantitative international economics. Routledge.
Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Nelson Education.