Health Care Inequality

The Fraser Institute’s World Index of Economic Freedom (Area 5B) provides a measure of how regulated a country labor market is. It takes into account minimum wage, hiring and firing regulations, existence of centralized collective bargaining, hours regulations, mandated cost of worker dismissal or even conscription.

D.1.7 Health inequality (health_inequality)

Part of income inequality may also be driven by inequality in access to the healthcare system. Inability or difficulty to work may result in lower wages thus increasing income inequalities.

We will use the female death rate per country as a measure of health-care access inequality within a country, as gender discrimination tend to make women more subject to healthcare access difficulties.

When data was lacking, World Bank estimates and national statistical estimates were used. It was checked that all sources provided very close or identical measurement of net income Gini coefficient when both were available. As such, we will also use these data sources for our cross country study (EuroStat, United Nations University - World Institute for Development Economics Research, Australian Bureau of Statistics, Statistics Denmark, World Bank - Povcal).

D.2 Model of inequality

Since all of the different factors seem to have an effect on one another, we introduced a simple logarithmic model where:

ln(net\_gini_{c,t})=\alpha_1*ln(labour\_reg_{c,t})+\alpha_2*ln(finance_{c,t})+\alpha_3*ln(gov\_spending_{c,t})+\alpha_4*ln(global\_trade_{c,t})+\alpha_5*ln(education\_gini_{c,t})+\alpha_6*ln(skill\_premium_{c,t})+\alpha_7*ln(technology_{c,t})+\delta_c+\gamma_t+\varepsilon_{c,t} where corresponds to the country and to the year. (For statistical reasons, we imposed as well as without any loss of generality.

The data on which the model was fitted did not take into account Australia after 2008 as it would have otherwise biased the estimation of the inequality level.

Model Summary

It seems that, after the introduction of both fiscal packages in 2008 and 2009, inequalities in Australia were lowered more than what could have been expected from the classic drivers of inequality. Yet this decrease is not significant and so hard to analyse.

Figure 8 : Australian net income Gini prediction error

In order to have statistically significant results, a more precise model would be needed. However, the current model takes into account all major factors believed to drive net income inequalities in the literature. Yet, time control variables and countries variables represent near two third of the variance explanation.

Furthermore, yearly variables present a particular form that suggests a phenomenon increased inequalities between the 1990 and 2005, and that this phenomenon is not related to any of the previous explanations presented earlier in this