Some U.S. states have enacted laws that allow citizens to carry concealed weapons. These laws are...

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Some U.S. states have enacted laws that allow citizens to carry concealed weapons. These laws are known as "shall-issue" laws because they instruct local authorities to issue a concealed weapons permit to all applicants who are citizens, are mentally competent, and have not been convicted of a felony. (Some states have som additional restrictions.) Proponents argue that if more people carry concealed weapons, crime will decline because criminals will be deterred from attacking other people. Opponents argue that crime will increase because of accidental or spontaneous use of the weapons. In this exercise, you will analyze the effect of concealed weapons laws on violent crimes. On the textbook, website, "http://www.pearsonhighered.com/stock_watson", you will find the data file Guns.dta (here: "https://wps.pearsoned.com/aw_stock_ie_3/178/45691/11696965. cw/index.html"), which contains a balanced panel of data from the 50 U.S. states plus the District of Columbia for the years 1977 through 1999. A detailed description is given in Guns_Description, available on the website. a. Estimate (1) a regression of In(vio) against shall and (2) a regression of In(vio) against shall, incarc_rate. density, avginc, pop, pb1064, pw1064, and pm1029. b. Interpret the coefficient on shall in regression (2). Is this estimate large or small in a "real-world" sense? ii. Does adding the control variables in regression (2) change the estimated effect of shall-carry law in regression (1) as measured by statistical significance? As measured by the "real-world" significance of the estimate coefficient? iii. Suggest a variable that varies across states but plausibly varies little or not at all over time and that could cause omitted variable bias in regression (2). b. Do the results change when you add fixed state effects? If so, which set of regression results is more credible, and why? c. Do the results change when you add fixed time effects? If so, which set of regression results is more credible, and why? d. Repeat the analysis using In(rob) and In(mur) in place of In(vio). e. In your view, what are the most important remaining theats to the internal validity on this regression analysis? f. Based on your analysis, what conclusions would you draw about the effects of concealed weapons laws on these crime rates? Note: The answer should be a single html file written in R. Markdown. I will be looking at our discussion forum if you have questions and I expect people to help each other too. Please try to start this homework as soon as possible, so we can handle issues together on time. Some U.S. states have enacted laws that allow citizens to carry concealed weapons. These laws are known as "shall-issue" laws because they instruct local authorities to issue a concealed weapons permit to all applicants who are citizens, are mentally competent, and have not been convicted of a felony. (Some states have som additional restrictions.) Proponents argue that if more people carry concealed weapons, crime will decline because criminals will be deterred from attacking other people. Opponents argue that crime will increase because of accidental or spontaneous use of the weapons. In this exercise, you will analyze the effect of concealed weapons laws on violent crimes. On the textbook, website, "http://www.pearsonhighered.com/stock_watson", you will find the data file Guns.dta (here: "https://wps.pearsoned.com/aw_stock_ie_3/178/45691/11696965. cw/index.html"), which contains a balanced panel of data from the 50 U.S. states plus the District of Columbia for the years 1977 through 1999. A detailed description is given in Guns_Description, available on the website. a. Estimate (1) a regression of In(vio) against shall and (2) a regression of In(vio) against shall, incarc_rate. density, avginc, pop, pb1064, pw1064, and pm1029. b. Interpret the coefficient on shall in regression (2). Is this estimate large or small in a "real-world" sense? ii. Does adding the control variables in regression (2) change the estimated effect of shall-carry law in regression (1) as measured by statistical significance? As measured by the "real-world" significance of the estimate coefficient? iii. Suggest a variable that varies across states but plausibly varies little or not at all over time and that could cause omitted variable bias in regression (2). b. Do the results change when you add fixed state effects? If so, which set of regression results is more credible, and why? c. Do the results change when you add fixed time effects? If so, which set of regression results is more credible, and why? d. Repeat the analysis using In(rob) and In(mur) in place of In(vio). e. In your view, what are the most important remaining theats to the internal validity on this regression analysis? f. Based on your analysis, what conclusions would you draw about the effects of concealed weapons laws on these crime rates? Note: The answer should be a single html file written in R. Markdown. I will be looking at our discussion forum if you have questions and I expect people to help each other too. Please try to start this homework as soon as possible, so we can handle issues together on time.

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a To estimate the regr... View the full answer