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 (PFc)

Independent Gini coefficient (Gc) Nominal cost of healthcare

(CHc) Dummy Variable: Location

in E.U. (d) Squared value of Gc

(SQGc) Squared value of CHc

(SQCHc)

Description of Variables

Econometric Model

β1+ β

2G

c + β

3CH

c + β

4d + εcPF

c = + β5SQGc + β6SQCHc b

1+ b

2G

c + b

3CH

c + b

4d PF

c =

+ b5SQGc + b6SQCHc

Equation

Estimationεc = 0Ho: bi = βi

HA: bi =/= βi

with Quadric Explanatory Terms

So far so good?

OLS Results

Regression Equation

PFc = 104.4503 - 105.3245G

c - .0007541CH

c + 4.82531d

Quadric Explanatory Model

PFc = 95.75652 - 82.15835G

c + .0020604CH

c + 4.85018d -

19.84226 SQGc – (0)SQCHc

Test Statistics

•Initial Equation

•Source | SS df MS Number of obs = 30•____________________________________________________________ F( 3, 26) = 6.09•Model | 1821.3834 3 607.127801 Prob > F = 0.0028•Residual | 2593.83967 26 99.7630641 R-squared = 0.4125•____________________________________________________________ Adj R-squared = 0.3447•Total | 4415.22307 29 152.249071 Root MSE = 9.9881

•_______________________________________________________________________________________•PF

c | Coef. Std. Err. t P>|t| [95% Conf. Interval]

•Gc | -105.3245 35.63121 -2.96 0.007 -178.5655 -32.08351

•CHc | -.0007541 .0014905 -0.51 0.617 -.0038178 .0023095

•d | 4.82531 5.180484 0.93 0.360 -5.823326 15.47395•_cons | 104.4503 15.12107 6.91 0.000 73.36854 135.5321•_______________________________________________________________________________________

By t-value tests, it is clear Gc is statistically significant, while CHc and d are statistically insignificant.

•With Quadric Variables

•Source | SS df MS Number of obs = 30•________________________________________________________F( 5, 24) = 3.47•Model | 1851.93737 5 370.387475 Prob > F = 0.0169•Residual | 2563.2857 24 106.803571 R-squared = 0.4194•____________________________________________________________ Adj R-squared = 0.2985•Total | 4415.22307 29 152.249071 Root MSE = 10.335

•_______________________________________________________________________________________•PFc | Coef. Std. Err. t P>|t| [95% Conf. Interval]•Gc | -82.15835 248.1568 -0.33 0.743 -594.3289 430.0122•CHc | .0020604 .0061347 0.34 0.740 -.010601 .0147217•SQGc | -19.84226 354.0629 -0.06 0.956 -750.5922 710.9077•SQCHc | -4.69e-07 9.86e-07 -0.48 0.638 -2.50e-06 1.57e-06•d | 4.85018 5.368222 0.90 0.375 -6.229286 15.92965•_cons | 95.75652 42.66378 2.24 0.034 7.702808 183.8102•_______________________________________________________________________________________

Since SQGc and SQCHc are insignificant by t-value test, the relationship between PFc and Gc / CHc 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 PFc and Gc are linear, along with PFc and CHc.

From this conclusion, it appears that the hypothesis was correct.

References

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Questions