Labor Market Income Inequality And Mortality In North American Metropolitan Areas

The research report is about labor market income inequality and mortality in North American metropolitan areas. It uses Pearson correlation to investigate the research question proposed in the article, what are the relations between labor market income inequality and mortality in North American metropolitan areas.

In attempts to research on the appropriate reaction to the research question, the researchers used two labor market income concepts: the labor market income for households with all those with zero and negative incomes inclusive and labor market income for households with non-trivial attachment to the labor market. With respect to this, the conclusion provided suggests that there is a robust nature of the relation between mortality and income inequality. The investigation also provides room for a detailed understanding of the nature of the relation in Canada. The role of unemployment came out clear in generating Canadian metropolitan level of health inequalities.

In the research report, there were multiple comparisons or investigations besides the major one. The results show diverse outcomes. However, according the given data analysis conclusion, there was an evidently high correlation between income inequality measures in North America.  The correlation analysis report outlines values such as 0.80 and 0.99, an indication that the compared variables were highly positively correlated. This suggested a strong association between middle sensitive measures. Correlation (r) is used to statistically measure the degree or extent to which the two variables fluctuate with reference to each other; it measures the relationship of two variables or more. In the research, the variables were the MAs in the US (n=282) with the range in population in Enid, Oklahoma and Newyork City and the MAs in Canada (n=53) with the range in population in Saint-Hyacinthe, Quebec, Toronto and Ontario.  The reported values of correlation for the variables were 0.80 and 0.99 respectively. This describes that the variables were highly positively correlated.

I once used correlation to investigate whether the height of a son relates with the height of the father. I gathered sample heights of fathers X and sample heights respective sons Y and determined the Pearson’s coefficient that was +0.81 leading to my decision and suggestion that tall fathers have tall sons. I felt that correlation is just like causation.

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