Understanding Race and Gender Inequality in Income

1. Module Example: Influence of Race and Gender on Income1 Used in Social Problems class, 100-level course • 20 students in class (all have laptops) • Takes 4 class days • Could be modified to be shorter or longer Substantive GOALS: • Learn about race and gender inequality in income • Make national and state comparisons in terms of earnings using American Community Survey (08) 1module available online at: http://serc.carleton.edu/sp/ssdan/examples/31584.html

2. Quantitative Skills Acquired: Students will: • Create and read frequency tables • Learn logic of independent and dependent variables • Create and interpret bivariate tables/cross-tabs • Learn to make data-based comparisons across states • Read and write a “story” about income inequality using data as evidence

3. Day 1: How to Read Frequencies in a Handout [First, define and examine sample and variables] Reading Frequencies: Example 1: ACS sample of US full-time, year-round workers in 2008. What is the sex composition of the fulltime workforce? Points to make to students about a frequency table: • Have both percentages and numbers • To make comparisons, we will usually focus on the percentages • Percentages should add up to 100% • Must understand base (all full-time year-round workers in 2008)

4. Day 1: Start by Learning How to Read Frequencies in a Handout Test for common mistakes: Sex Composition of Full-Time, Year-Round Workers, 2008 Which of the following is true? • 58.7% of the workforce is male. • 58.7% of men are in the workforce. Answer: A is correct.

5. Day 1: Reading Frequencies Example 2: examine earnings of full-time workers Start by asking students to guess: What percent of full-time workers earn over $100,000? What percent earn less than $15,000? Table 2: Earnings for Full-Time Year-Round Workers, US, 2008

6. After frequencies, examine bivariate tables • Now ask students to guess: Who makes more, men or women? • How might we determine that? • Show a bivariate table of sex and income, and ask them to interpret:

7. Day 1: Reading a Bivariate Table Earnings by Sex, ACS 2008 • Must determine how to read this table – where to focus? • Teach students to focus on top and bottom portions for comparisons

8. Day 1: Learn How to Read Bivariate Table Earnings by Sex, ACS 2008 • Test for common mistakes: True or False? • 14% of those who make between $15,000 and $24,000 are men. • False • 14% of men make between $15,000 and $24,000. • True • 25.1% of men earn more than $70,000 • True • 17.2% of men and women earn more than $100,000 • False

9. Day 1: Learn How to Read Bivariate Table Earnings by Sex, ACS 2008 • Give Rules for reading table (included in module materials) • Start with general statement; use percentages as evidence; end with summary • Teach students useful phrases: • e.g. “A disproportionately high percentage of women fall into the low-income categories. For example, ….” • Most important take-home message: Emphasize “telling a story” with numbers

10. Homework that night: describe effect of race on income

11. Day 2: Students Run Module in Class (or could do as homework) • Module will walk students through an exercise, step by step, for a state of their own choosing to examine • sex  earnings • race  earnings • Learn independent and dependent variables • Make hypotheses about relationship between variables • Learn how to run frequencies and set up simple bivariate tables • Learn how to create properly labeled tables from the data generated • Write a story about income and sex differences in income

12. Handout with Module • http://ssdan.net/webchip/webchip4/ • Examine individual state: KY • racial and sex composition of workforce (frequencies) • Differences in earnings by sex and race (cross-tabs)

13. Handout with Module • http://ssdan.net/webchip/webchip4/ • Choose state and examine: • racial and sex composition of workforce (frequencies) • Differences in earnings by sex and race (cross-tabs)

14. Day 3: Learn How to Present Data • Students work in pairs on state of own choosing • 5-minute presentation of findings to class: • Give hypothesis (and let others guess) • Show table of results • Describe findings with proper language

15. Day 4: Peer Review of Paper • Students come to class with completed draft of data analysis paper • In pairs, review and edit one another’s papers, following guided prompts • Main goal: students learn to write “story” using data as evidence

16. Assessment A) Used 2 forms of assessment a) pre/post-test b) paper, graded by rubric B) Tried to assess both skills and confidence levels

17. Comparison of Pre-test to Post-test (past four years) Overall score on pre-test : 55 - 60% Overall score on post-test: 80 - 94% Assessment of Pre and Post-test: • Great improvement in basic skills at reading and interpreting exactly this kind of table • Improved confidence in working with data and numbers

18. Assessment of Paper: • Demands higher-order skills: difficult paper • Skills vary quite a bit • Peer review helpful • Allow re-writes for students with most trouble • Students report that paper is difficult, but worth it

19. Comments on Student Evals • “I worked a lot in this class, and was always taken to the brink of overwhelmed but not crossing over. I think this is a sign of an excellent class. The data analysis we did was a particular challenge. I came away from the exercise knowing I learned something completely out of my comfort zone.” • “Keep on trying with the Data Analysis.... we (students) need it... no matter how badly we do not like it at first.”

20. Overview of Module • Have been using for several years, recently updated with 2008 American Community Survey data • Cheerleading helps – keep telling them they’re learning useful skills • Fun to teach– hands-on activity; improves own engagement in teaching these content areas • Students generally enjoy (positive evals) • Pre/post test shows students learn skills • Exams and papers show modules reinforces content [truly see race and gender inequality] • See evidence of skills in later courses

21. Benefits of Including Data Analysis in Classes • Piques student interest in content • Promotes content retention through active learning • Engages students in the discipline: • - shows how social scientists work • - prevents “disconnect” between substantive and technical courses • Meets larger institutional goals • Subsequent courses continue building on these skills

22. Final overview: what this module includes: • Two hand-outs : • Handout 1: explanations of variables and descriptions for how to read frequencies and bivariate tables (used first day) • Handout 2: step by step instructions for students to go online and examine data for individual states; includes assignment for final paper (used second day) • Ancillary materials: • T/F test to catch common mistakes • List of “rules” for reading and writing about tables (good to create own rules as well) • Peer review guidelines • Rubric for evaluating paper • Pre- and post-test for assessing student learning