Graph 6 Of Appendix A

For the analysis of the differences of annual income dependent on the individual variable of gender (male or female), we created three graphs to tell the tale of the data we gathered. Graph 5 of Appendix A is a statistical data graph that incorporates the average male, female, and state annual incomes over a 10-year span (2005-2014). Annual Florida income was included to show a balance between the opposite genders. Over the years, the graph shows that the wages for males and females are steadily increasing. Aside from the economic crisis around the 2007-2009 period, wages increased due to the increase in minimum wage and other economic development. Even though male and female both show a trend in increasing annual incomes, there was a large gap in wages, to begin with back in 2005. This paper dives deeper into the data.
In Graph 6 of Appendix A, we performed a two-way kernel density estimation that compared the average salary of males with the average salary of females. When searching online for other graphs to compare the wage gap between men and women, we come across kernel density graphs. Kernel density is helpful when there is finite data in the

For generating the dummy variable, we used the treatment method. Instead of assigning treat=1 randomly, we substituted treat with gender and picked which variables would and would not be treated. We chose females as the ones to be “treated” and the males as “control”. This allowed us to create Graph 7 of Appendix A, which includes a regression line. Graph 3 shows the samples of male income at “point 0” (because we assigned dummy variables) and the female samples align at “point 1” aka female. The regression line shows a downward trend because the annual income samples for the male category (0) are much higher than the samples for the female category (1). This regression has a high R-squared which means that the regression line is a good