For helpful discussions for this paper, I would like to give thanks to Dr. Jacho-Chavez, Dr. Nair-Reichert, Dr. Zha, and especially to Dr. Klumpp.

ABSTRACT This paper analyzes the factors that affect the level of funding that a country provides for a public health system using an OLS regression. The dependent variable in this study is the percent of healthcare costs that is publicly funded. The independent variables are the income distribution within a country, measured using the Gini coefficient and the average cost of healthcare, measured in absolute dollar value. The dummy variable used in this study is whether or not the country lie in the EU. Results show that there is a statistically significant negative correlation between Gini index and public healthcare funding, while the average cost of healthcare and the dummy variable were shown to be insignificant. Further results also indicate that the relationship between the dependent and independent variables are linear.

THE ARGUMENT Often times, the whole debate on healthcare does not address what is important. Implementing socialized healthcare is not a matter of what is morally correct, it is a matter of having the right economic conditions Both sides want a system that is affordable, effective, and accessible There is no theoretical reason why either a healthcare system that is publicly or privately funding cannot reach this goal So why do certain countries have higher/lower public funding? Hypothesis: Social funding for healthcare will be directly correlated with income equality of a country. That is, a country with a relatively even wealth distribution will have a higher percentage of healthcare publicly funded. Furthermore, due to monetary neutrality there should not be a relationship between nominal healthcare costs and percentage of public funding. Questions?

DATA AND METHODOLOGY Data for level of healthcare funding and nominal cost of healthcare is provided by the OECD. Data for gini coefficients are provided by the World Bank and the Central Intelligence Agency. The study was conducted with an OLS regression using STATA® software, with the first model using the data sets directly and the second model using quadric explanatory terms to test for diminishing marginal effects.

Dependent Percentage of healthcare funding provided publicly (PF c ) Independent Gini coefficient (G c ) Nominal cost of healthcare (CH c ) Dummy Variable: Location in E.U. ( d) Squared value of G c (SQG c ) Squared value of CH c (SQCH c ) DESCRIPTION OF VARIABLES

ECONOMETRIC MODEL β1β1 + β 2 G c + β 3 CH c + β 4 d+ ε c PF c =+ β 5 SQG c + β 6 SQCH c b1b1 + b 2 G c + b 3 CH c + b 4 dPF c = + b 5 SQG c + b 6 SQCH c Equation Estimation ε c = 0 H o : b i = β i H A : b i =/= β i with Quadric Explanatory Terms So far so good?

OLS RESULTS Regression Equation PF c = G c CH c d Quadric Explanatory Model PF c = G c CH c d SQG c – (0)SQCH c

TEST STATISTICS Initial Equation Source | SS df MS Number of obs = 30 ____________________________________________________________ F( 3, 26) = 6.09 Model | Prob > F = Residual | R-squared = ____________________________________________________________Adj R-squared = Total | Root MSE = _______________________________________________________________________________________ PF c | Coef. Std. Err. t P>|t| [95% Conf. Interval] G c | CH c | d | _cons | _______________________________________________________________________________________ By t-value tests, it is clear G c is statistically significant, while CH c and d are statistically insignificant. With Quadric Variables Source | SS df MS Number of obs = 30 ________________________________________________________ F( 5, 24) = 3.47 Model | Prob > F = Residual | R-squared = ____________________________________________________________Adj R-squared = Total | Root MSE = _______________________________________________________________________________________ PF c | Coef. Std. Err. t P>|t| [95% Conf. Interval] G c | CH c | SQG c | SQCH c | -4.69e e e e-06 d | _cons | _______________________________________________________________________________________ Since SQG c and SQCH c are insignificant by t-value test, the relationship between PF c and G c / CH c is linear. Therefore the slope coefficients in this model cannot be sensibly interpreted and we use the previous estimation.

CONCLUSION The first regression indicates that for every.01 unit increase in the Gini coefficient, public funding for healthcare decreases by 1.05%. Nominal cost of healthcare and location of the country do not have a significant effect. The quadric explanatory variables confirm that the relationship between PF c and G c are linear, along with PF c and CH c. From this conclusion, it appears that the hypothesis was correct.

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