12 Computing and plotting inequality

 

This chapter covers

  • Computing and understanding Gini coefficients
  • Creating and interpreting Lorenz curves
  • Performing significance testing
  • Conducting effect size testing

Social scientists, economists, philosophers, and others have claimed for many years that income inequality exacerbates crime and other social ills. This is why, they say, taxation, income redistribution, and other state-level corrective actions aren’t zero-sum, but critical for the common good. An equal society is a prosperous society, and an unequal society is a declining society.

How might this idea translate to the NBA? The NBA is a remarkably unequal “society” in that most of the money paid out in salaries is distributed to just a few players. In fact, you’ll discover soon enough that salary inequality across the league is most recently much higher than it used to be. But at the same time, salary inequality varies significantly from one team or “community” to the next.

Might it be true that teams with relatively equal salary distributions are more prosperous than other teams? That is, do such teams win more regular season games and more league championships than teams with relatively unequal salary distributions? In any event, that’s our going-in hypothesis.

Here’s what you can expect to get out of this chapter:

12.1 Gini coefficients and Lorenz curves

12.2 Loading packages

12.3 Importing and viewing data

12.4 Wrangling data

12.5 Gini coefficients

12.6 Lorenz curves

12.7 Salary inequality and championships

12.7.1 Wrangling data

12.7.2 T-test