We Helped With This R Programming Assignment: Have A Similar One?

Introduction to Social Data (SOC1004 / POL1008) Take-home exam - Refer/Defer

August 2020

Please answer all the questions for both exercises in the exam. Your answer to each question must include: a) R code, b) R output with the results (and graphs where necessary), c) a short (one or two paragraphs) interpretation of the results. Each question is worth 10, 15 or 20 points.

Please submit your assignments on eBart. The submission deadline is 13 August, 2pm.

You can use any sources to work on the assignment (the Internet, your notes, textbook, etc.), but you cannot consult with other students or use other students’ work. Your exam must be the result of your individual work.

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1          The Mark of a Criminal Record (50 points)^[1]

To isolate the causal e↵ect of a criminal record for black and white applicants, Pager ran an audit experiment. In this type of experiment, researchers present two similar people that di↵er only according to one trait thought to be the source of discrimination. This approach was used in the resume experiment described in Quantitative Social Science, where researchers randomly assigned stereotypically African-American-sounding names and stereotypically white-sounding names to otherwise identical resumes to measure discrimination in the labour market.

To examine the role of a criminal record, Pager hired a pair of white men and a pair of black men and instructed them to apply for existing entrylevel jobs in the city of Milwaukee. The men in each pair were matched on a number of dimensions, including physical appearance and self-presentation. As much as possible, the only di↵erence between the two was that Pager randomly varied which individual in the pair would indicate to potential employers that he had a criminal record. Further, each week, the pair alternated which applicant would present himself as an ex-felon. To determine how incarceration and race influence employment chances, she compared callback rates among applicants with and without a criminal background and calculated how those callback rates varied by race.

The names and descriptions of variables in the dataset criminalrecord.csv are:

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Table 1: Criminal Record Data

1.1. Begin by loading the data into R and explore the data. How many cases are there in the data? Show a summary of the data. In how many cases is the tester black? In how many cases is the tester white? What proportion of applicants are black and white? (10 points)

1.2. Now we examine the central question of the study. Calculate the proportion of callbacks for white applicants with and without a criminal record, and calculate this proportion for black applicants with and without a criminal record. (15 points)

1.3. What is the di↵erence in callback rates between individuals with and without a criminal record within each race (i.e. for black and white testers separately). What do these specific results tell us? Consider both the di↵erence in callback rates for records with and without a criminal record and the ratio of callback rates for these two types of records. (15 points)

1.4. Compare the callback rates of whites with a criminal record versus blacks without a criminal record. What do we learn from this comparison? (10 points)

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2      Sources of Empathy in the Circuit Courts (50 points)²

In this exercise, you will analyze the relationship between various demographic traits and pro-feminist voting behavior among circuit court judges. In a recent paper, Adam N. Glynn and Maya Sen argue that having a female child causes circuit court judges to make more pro-feminist decisions.

The dataset dbj.csv contains the following variables about individual judges:

Table 2: Judges Data

     Name                               Description

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²This exercise is in part based on: Glynn, Adam N., and Maya Sen. (2015). “Identifying Judicial Empathy: Does Having Daughters Cause Judges to Rule for Women’s

Issues?.”           American Journal of Political Science Vol.          59, No.      1, pp.     37–54.    doi:

10.1111/ajps.12118

2.1. Load the dbj.csv file. Find how many judges there are in the dataset, as well as the gender and party composition of our dataset. Is the party composition di↵erent for male and female judges? Additionally, note that our outcome in this exercise will be the proportion of pro-feminist rulings. What is the range of this variable progressive.vote?

(10 points)

2.2. Next, consider the di↵erences between some groups. For each of the four groups (Republican men/women, Democratic men/women) defined by gender and partisanship, create a boxplot (using a single command) that illustrates the di↵erences in progressive.vote. Briefly interpret the results of the analysis. For example, do any of the results surprise you? Does it appear that partisanship, gender, or both contribute to progressive voting patterns? Should we interpret any of these e↵ects causally? Why or why not? (10 points)

2.3. Create a new binary variable which takes a value of 1 if a judge has at least one child (that is, any children at all), 0 otherwise. Then, use this variable to answer the following questions. Are Republicans and Democrats equally likely to be parents (that is, have at least one child)? Do judges with children vote di↵erently than judges without? If so, how are they di↵erent? Do republican and democratic parents vote di↵erently on feminist issues?(10 points)

2.4. The final question explores di↵erences in voting among judges born earlier and judges born later. Create a new binary variable which takes a value of ‘before 1935’ if a judge has is born before 1935, and ‘from 1935’ otherwise. Compare the progressive voting of Republican and Democrat judges separately (a) for those born before 1935 and (b) for those born 1935 or after. Produce two bar plots to show this: one for Republican-appointed judges and one for Democrat-appointed judges. Comment on the similarities and di↵erences between the two charts, in terms of the changes to the progressive voting for judges born earlier and judges born later. (20 points)