Course Announcement: "Statistics of Inequality and Discrimination" (36-313)

Attention conservation notice: Advertisement for a course you won't take, at a university you don't attend, in which very human and passionately contentious topics deliberately have all the life sucked from them, leaving only the husk of abstractions and the dry bones of methodology.

In the fall I will, again, be teaching my class on inequality

36-313, Statistics of Inequality and Discrimination 9 units Time and place: Tuesdays and Thursdays, 1:25 -- 2:45 pm, in Wean Hall (WEH) 6403 (tentatively) Description: Many social questions about inequality, injustice and unfairness are, in part, questions about evidence, data, and statistics. This class lays out the statistical methods which let us answer questions like Does this employer discriminate against members of that group?, Is this standardized test biased against that group?, Is this decision-making algorithm biased, and what does that even mean? and Did this policy which was supposed to reduce this inequality actually help? We will also look at inequality within groups, and at different ideas about how to explain inequalities between groups. The class will interweave discussion of concrete social issues with the relevant statistical concepts. Prerequisites: 36-202 ("Methods for Statistics and Data Science") (and so also 36-200, "Reasoning with Data"), or similar with permission of the instructor

Last year was the first time I got to teach it, and it was a mixed experience. The students who stuck with it were, gratifyingly, uniformly very happy with it (and I am pretty sure they learned a lot!). But it also had the biggest "melt" of any class I've taught, with fully half of those who initially signed up for it eventually dropping it. The most consistent reason why --- at least, the one they felt comfortable telling me! --- was that they were expecting something with a lot more arguing about politics, and a lot less math and data analysis. I have taken this feedback to heart, and decided to do even more math and data analysis.

Tentative topic schedule

Slightly more than one week per. A more detailed listing, with related readings, can be found on the class homepage.

1. "Recall": Reminders about probability and statistics: populations, distribution within a population, distribution functions, joint and conditional probability; samples and inference from samples.
2. Income and wealth inequality: What does the distribution of income and wealth look like within a population? How do we describe population distributions, especially when there is an extreme range of values (a big difference between the rich and poor)? Where does the idea of "the 1%" wealthy elite come from? How has income inequality changed over recent decades? Statistical tools: measures of central tendency (median, mode, mean), of dispersion, and of skew; measures of dispersion (standard deviation etc.); measures of concentration and inequality (ratios between percentiles, the Lorenz curve, Gini coefficient); the concept of "heavy tails" (the largest values being orders of magnitude larger than typical values); log-normal and power law distributions; fitting distributions to existing data; positive feedback, multiplicative growth and "cumulative advantage" processes.
3. Speed-run through social and economic stratification: Reminders (?) about social concepts: ascriptive and attained social statuses, and qualitative/categorical vs. more-or-less dimensions of differentiation. Important forms of differentiation, including (but not necessarily limited to): sex, gender, income, wealth, consumption, caste, race, ethnicity, citizenship, class, order, education. The legal notion of "protected categories".
4. Income disparities: How does income (and wealth) differ across groups? How do we compare average or typical values? How do we compare entire distributions? How have income inequalities by race and sex changed over recent decades? Statistical tools: permutation tests for differences in mean (and other measures of the average); two-sample tests for differences in distribution; bootstrapping; inverting tests to find the range of differences compatible with the data; the "analysis of variance" method of comparing populations; the "relative distribution" method of comparing populations
5. Explaining, or explaining away, inequality