How Partisan Is Local Law Enforcement & American Political Science Review - Statistics Assignment Help

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Question 1 – How Partisan is Local Law Enforcement? 

In this section, you will replicate and extend some of the work in: 

Daniel M. Thompson (2019), “How Partisan is Local Law Enforcement? Evidence from Sheri Cooperation with Immigration Authorities”, American Political Science Review, 114(1), 222-236. 

In the United States, the vast majority of executives who wield police power are directly elected. Sheris – directly-elected law-enforcement figures in each county – have a wide range of powers available to them, and are particularly influential with respect to the enforcement of immigration policy. While federal immigration authorities such as Immigration and Customs Enforcement (ICE) are able to order the detention of unauthorised immigrants, it is local sheris that have the power to enforce or refuse to enforce those requests. Given the authority that sheris have regarding enforcement of these detainer requests, many have argued that sheris are responsible for immigration enforcement levels in their counties. 

In addition, the vast majority of sheris in the US are directly elected in partisan elections, where the candidates for oce run either as Democrats or Republicans. As a consequence, many have speculated that there may be a causal eect of the partisanship of the sheri on immigration outcomes. Specifically, a common view is that Democratic sheris are less likely to enforce ICE detention requests than their Republican counterparts. You will use the data below to assess the truth of this assertion. 

Begin by loading the replication data from the paper. You should also load the rdd package. 

library(rdd) 

sheriff <- read.csv("sheriff.csv") 

The data includes the following variables: 

Variable Description 

treat 1 if the Democratic candidate was elected, 0 otherwise 

dem_vote_share The Democratic candidate’s share of the 2-party vote (i.e. DemV otes 

DemV otes+RepV otes ) 

share_detained_sheriffThe sheri enforcement rate with ICE detention requests (proportion, between 0 and 1). 

year The year of observation 

county_id The unique ID of each county 

1. Calculate the mean difference in the enforcement of ICE detention requests between Democratic and Republican Chris. Report this difference and explain whether we can interpret that quantity as the causal effect of partisanship on enforcement. 

2. Another approach to analysing this data is to use a regression discontinuity design (RDD). Explain how an RDD might help us to estimate the causal eect of the election of a Democrat sheri on enforcement with ICE detention requests. 

3. Create the running variable for the RDD analysis. Hint: remember that the running variable needs to be coded as X˜ = X ≠ c, where X is the variable that determines the treatment and c is the threshold at which the treatment occurs. 

4. Discuss some potential violations of the RDD assumptions that could aect the inferences we make. Provide some empirical evidence to evaluate at least one of these issues in the context of this example. 

5. Estimate the Local Average Treatment Effect (LATE) of the election of a Democratic Sheri on enforcement of detainer requests using an appropriate regression model (i.e. use the lm() function). Do not include any polynomial terms, but you may use an interaction if you wish. Interpret the LATE. 

6. Estimate the Local Average Treatment Effect of the election of a Democratic Sheri on enforcement 2

again, this time using the RDestimate function using the “optimal” bandwidth. In addition to reporting your estimate for the LATE, provide a plot of the relationship between the running variable and the outcome. Are the estimates you get from this approach the same as those calculated using the regression in question 5? Why, or why not? 

7. Re-estimate the eects in question 6 using all whole-number bandwidths from 2 to 10. Illustrate how sensitive your results are to these changes. Interpret your results. 

8. An alternative way to evaluate the hypothesis that the election of a Democratic Sheri has a causal effect on the enforcement of ICE detention requests is to use a difference-in-differences design. Implement an appropriate model to estimate the difference-in-dierences and present your results in a table. Describe how the assumptions and estimates from this model dier from the assumptions and estimates from the RDD analysis above. Which do you find more convincing? 

9. Thinking about the research question – how partisan is local law enforcement? – what do you conclude from these analyses? Comment on one strength and one weakness of the analysis you have conducted. 

Question 2 The Long-Term Impact of the Slave Trade 

In this section you will use data from the following paper: 

To help answer this question, first read the paper. Part of your task is to replicate and extend some of Nunn’s results, which he produces using instrumental variables. If you are unable to exactly reproduce Nunn’s results, report your best eort to do so. Whether or not you can exactly replicate the paper’s findings, ensure that both your write-up and your R script clearly indicate how you obtained your results. The dataset for this question is slave_trade.Rdata. It contains these variables: 

Variable name Variable description 

country Country name 

ln_realgdp2000 Log real per capita GDP in 2000, also called “ln y” in the paper ln_export_area Log total number of slaves exported, divided by land area atlantic_dist Sailing distance to nearest destination of Atlantic slave trade indian_dist Sailing distance to nearest destination of Indian slave trade saharan_dist Overland distance to the nearest port of export for Saharan slave trade redsea_dist Overland distance to the nearest port of export for Red Sea slave trade colonial_power Name of the colonizer, if any, prior to independence 

• equator_dist Distance from equator 
• longitude Longitude 
• rain_min Minimum monthly rainfall 
• humid_max Average maximum humidity 
• low_temp Average minimum temperature 
• ln_coastline_area Log coastline divided by land area 
• low_distance 1 if situated at a low distance from a major slave destination, 0 otherwise high_slavery 1 if the country had a high level of slave exports, 0 otherwise

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