ps_6 (1)

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University of Massachusetts, Amherst *

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452

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Economics

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Jan 9, 2024

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pdf

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Problem Set 6 Econ 452 Read me first! 1. Both datasets are available on the Assignment page on Moodle 2. Please knit the RMarkdown document into a PDF file and submit the file on Moodle. 3. Be sure to show your coding work, i.e. don’t set echo = FALSE in the code chunks. 4. 8 points can be earned from the exercises, 1 point from the presentation Exercise 1: Instrumental Variables and Compliance (5 pts) In an effort to better understand the effects of get-out-the-vote (GOTV) messages on voter turnout, Gerber and Green (2005) conducted a randomized field experiment involving approximately 30,000 individuals in New Haven, Connecticut, in 1998. One of the experimental treatments was randomly assigned in-person visits where a volunteer visited the person’s home and encouraged him or her to vote. The following table gives a description of the variables in the gotv.RData dataset: Hint: Think carefully about how the variables in this dataset correspond to the different elements discussed in the instrumental variables lectures (actual treatment, randomized treat- ment assignment, outcome). (a) Estimate a bivariate model of the effect of actual contact on voting. Is the model biased? Why or why not? [1 pts] (b) Estimate compliance by estimating what percentage of treatment-assigned people actually were con- tacted. [1 pts] (c) Use intention-to-treat (ITT) to estimate the effect of being assigned treatment on whether someone turned out to vote. Is this estimate likely to be higher or lower than the actual effect of being contacted? Is it subject to endogeneity? [1 pts] 1

(d) Use Instrumental Variables estimation to get an unbiased estimate of the treatment effect. Here you will take a 3-step approach, the so-called “Wald estimator”: 1. Calculate how much the outcome changes due to the treatment being assigned (the ITT effect, which you calculated in part c.). 2. Calculate the amount by which the treatment changes due to the treatment being assigned (the compliance rate). 3. Divide the first number by the second number. This gives you the treatment effect in the get- out-the-vote experiment. Compare the result to the estimates from (a) and (c). [1 pts] (e) What are the conditions for “Contact Assigned” to be a valid instrument in the Get-out-the-vote experiment? Are they satisfied in this case? This question does not require you to run any regressions. [1 pts] Exercise 2 (3 pts) We are trying to understand what determines students’ overall evaluation of an instructor. Our dataset evals.RData contains observations about the following variables: Hint: Display the content of the dataset to understand its panel structure. Make sure you understand why an ID variable like InstrID or CourseID can be repeated multiple times. 1. Estimate a linear regression model ignoring the panel structure of the data. Use overall evaluation of the instructor as the dependent variable and the class size, required, and grades variables as independent variables. Report and briefly describe the results. [1 pt] 2. Explain what a fixed effect for each of the following would control for, and give examples: instructor, course, and year. [1 pt] 3. Use the equation from part (1) to estimate a model that includes a fixed effect for instructor. Report your results (without displaying the individual fixed effects, which are not relevant to the reader) and explain any differences from part (1). [1 pt] 2

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