Parallel Computing in the Computer Science Curriculum > Workshops > SIGCSE 2012 > SIGCSE 2012 > Exploring Economic Inequality with Data

Exploring Economic Inequality with Data

This page authored by Nathan D. Grawe, Carleton College.
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This material was originally developed as part of the Carleton College Teaching Activity Collection
through its collaboration with the SERC Pedagogic Service.


Gini Coefficient Map Gini Coefficient Map Legend

This set of assignments exposes students to data pertaining to economic inequality in international and historical context. The first assignment asks students to use Lorenz Curves and Gini Coefficients to summarize inequality in income and taxes reported by the IRS.

The second sends them to the United Nations Human Development Indicators page to find cross-country data on Gini Coefficients and some other development indicator which they hypothesize to be correlated with inequality.

In the third assignment, students estimate wealth inequality from 1774 probate records and compare the result with estimates of 20th-century wealth inequality.

These early assignments serve as "scaffolding" for the ultimate assignment-a thesis-driven argument supported by data drawn from one or more of the sources used here.

Learning Goals

Context for Use

This assignment was designed for a class of 25 students who have taken principles of economics. They have had a lecture defining the Lorenz Curve and the Gini Coefficient. In addition, students need basic spreadsheet skills to complete the assignments.

Description and Teaching Materials

Assignment handout (Microsoft Word 33kB Mar15 06)
Designed to be distributed in class, this handout guides students through all four parts of the assignment including citations to web pages with the required data.

Ultimately, students will write a thesis-driven paper using data to support their argument. To give them the skills to succeed (and to teach them some facts about inequality) I provide three small data assignments as "scaffolding" for the final task. Each of the small assignments draws on data from different sources:
Grading Criteria (Microsoft Word 21kB Mar15 06)

Teaching Notes and Tips

(1)I often assign work with real data. A number of students find this contact with the "real" world to be inspiring. But another group of students find the frustrations of working with real data-in all its tediousness-to be "busy work". One student, for instance, wondered why he could not simply rely on someone else's analysis. I have found two strategies which address this concern.
(2)Many students place too much trust in data labels. For instance, if two sources contain data labeled "income", then surely they both measure identical concepts of income and are equally reliable. Of course, one of the most important critical thinking skills we can teach students about data is to be sure they know precisely what a data point means and how it was measured. Throughout the assignment I have included prompts which provoke questions about this issue. The first assignment, for instance, asks them to examine the 1040 tax form and reflect on how well the IRS definition of income matches our ideal. Similarly, the third assignment requires students to examine several probate records to see what types of personal property are listed. They are then asked to explain how the nature of the property listed creates difficulty when comparing inequality over time. (Among other things, the property list includes slaves-a fact that raises questions concerning how we decide whom to count as an "observation".)


I provide students with a list of grading criteria (and explanation) on the final page of the assignment. Criteria include: I use this rubric for grading. (Microsoft Word 36kB Aug8 13)

References and Resources