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 some 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 weapons. In this exercise, you will analyze the effect of concealed weapons laws on violent crimes. The dataset Guns from the package AER contains a balanced panel of data from the 50 U.S. states plus the District of Columbia for the years 1977 through 1999. Use ? Guns for a detailed description of the variables.

(a) Estimate 1) regression of log(violent) against the law and 2) regression of log(violent) against the law, prisoners, density, income, population, afam, cauc, and male.

i. Interpret the coefficient on the law 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 a shall-carry law in regression (1) as measured by statistical significance? As measured by the "real-world" significance of the estimated 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 log(robbery) and log(murder) in place of log(violent).

(e) In your view, what are the most important remaining threats to the internal validity of this regression analysis?

(f) Based on your analysis, what conclusions would you draw about the effects of concealed weapons laws on these crime rates?

Traffic crashes are the leading cause of death for Americans between the ages of 5 and 32. Through various spending policies, the federal government has encouraged states to institute mandatory seat belt laws to reduce the number of fatalities and serious injuries. In this exercise, you will investigate how effective these laws are in increasing seat belt use and reducing fatalities. USSeatBelts in the AER package is an unbalanced panel from 50 U.S. States, plus the District of Columbia, for the years 1983-1997. These data were provided by Professor Liran Einav of Stanford University and were used in his paper with Alma Cohen "The Effects of Mandatory Seat Belt Laws on Driving Behavior and Traffic Fatalities," The Review of Economics and Statistics, 2003, Vol. 85, No. 4, pp 828-843. Use?USSeatBelts for a detailed description of the variables. (1) Estimate the effect of seat belt use on fatalities by regressing fatalities on a seatbelt, speed65, speed70, alcohol, drinkage, log(income), and age. Does the estimated regression suggest that increased seat belt use reduces fatalities?

(2) Do the results change when you add state fixed effects? Provide an intuitive explanation for why the results changed.

(3) Do the results change when you add time fixed effects plus state fixed effects?

(4) Which regression specification(1), (2), or (3)is most reliable? Explain why.

(5) Using the results in (3), discuss the size of the coefficient on a seatbelt. Is it large? Small? How many lives would be saved if seat belt use increased from 52% to 90%?

I need a code for these questions in R.