Problem Set: The Impact of Adverse Weather on the Vote Choice
Research by political scientists, Larry Bartels and Chris Achen, suggests that the act of voting can be more irrational than previously thought. They examine whether voters tend to punish incumbents for acts that could not in any way be the fault of an incumbent or an incumbent party. For instance, they looked at the effects of shark attacks in New Jersey in 1916 on the vote for Wilson in the presidential election. Findings suggested that the shark attacks had a negative impact on the vote for Wilson. The counties that experienced the attacks failed to support the incumbent in that election. It seemed voters blamed the incumbent for something the incumbent could not control. Bartels and Achen examined this hypothesis on other contexts, noting that voters blame incumbents for droughts and other natural events outside of the government's influence.
Do voters blame incumbents for situations the government has no control over? In this problem set you will test this hypothesis using the dataset florida2004. This dataset is part of an ongoing study on this question. In this work, I have looked at the impact of back to back hurricanes that hit FL just weeks before the 2004 election. The dependent variable is the Bush vote (of the 2-party vote) in the 2004 presidential election in Florida.
Below is a description of the variables included in the data set. Each observation is a county.
|
Variable
|
Description
|
|
county
|
County name.
|
|
county_id
|
County id.
|
|
bush04
|
Bush 2004. This is the proportion of the 2-party vote that Bush won in each county.
|
|
bush00
|
Bush 2000. This is the proportion of the 2-party vote that Bush won in each county.
|
|
max_wind
|
Maximum wind gusts. This is the highest speed at which a hurricane hit a county in miles per hour (mph). For example, a max_wind value of 145 indicates the county sustained winds of at most 145 mph.
|
Part 1: Descriptive Statistics
1. Complete the following table of descriptive statistics. Note: You do not need to run a regression for this question.
Table 1: Descriptive Statistics
|
Minimum |
Maximum |
Mean |
Standard Deviation |
Number of Observations |
| Bush 2004 |
|
|
|
|
|
| Maximum wind gusts |
|
|
|
|
|
Part 2: Regression Analysis
2. Using Stata, run a regression of the vote for Bush in 2004 (dependent variable) on the Maximum wind gusts(independent variable) with robust standard errors. Copy and paste the Stata output into your write-up.
3. Report the sample regression function (with robust standard errors in parentheses beneath the coefficients).
4. Interpret the coefficient on Maximum Wind Gusts(β ^Maxwindgusts).
5. Is β ^Maxwindgusts statistically significant? Explain how you arrived at your conclusion.
6. Report the 95% confidence interval forβ ^Maxwindgusts.
7. What is the estimated effect on the vote for Bush in '04 in a county where hurricane strength increases from category 1 (minimum 75 mph) to category 5 (maximum 55 mph), an increase of 80 mph?
8. Does this analysis provide sufficient evidence in support of the Bartels and Achen hypothesis? Can you confidently conclude that voters blame the incumbent for events outside the incumbent's control? Explain.
Part 3: Regression Analysis Continued, Bush '04 and the past vote
9. It is well established that one of the key predictors of the vote choice is past voting history. Generate a scatter plot in which the Bush 2004 vote is in the Y-axis and the Bush 2000 vote is in the X-axis. Include the regression line on the plot. Add a title to the plot and ensure that the axes have meaningful labels. Include this graph in your write-up.
10. Do you notice an outlier on this graph? Report the values of Bush04 and Bush00 for this outlier.
11. Run a regression of Bush04 on Bush00 and report the sample regression function with robust standard errors in parentheses below the coefficients.
12. Re-run the regression without the outlier. Report the new sample regression function with standard errors in parentheses below the coefficients.
13. Interpret the new coefficient on the Bush00. Is the coefficient statistically significant?
14. How does the slope coefficient from the regression without the outlier compare to the slope coefficient from the regression with the outlier? In your answer, address both statistical significance and magnitude.
15. Generate a new scatter plot that includes the data points (minus the outlier) and include the new sample regression line on the graph.
Part 4: Conclusion
16. Does this analysis provide satisfactory evidence in support of the idea that voters can be irrational by blaming the incumbent for natural disasters? Explain. In your explanation, describe what other variables in the dataset might improve the analysis.
Attachment:- Assignment.rar