Ran Wang
1 Do not forget your do-file
2 Double check your result before sending
3 Correct address: econ107lab@gmail.com
Data: Guns.dta
Setting: Some US states have enacted laws that allow citizens to carry concealed weapons. Proponents of the law argue that carry concealed weapons can deter crime. Opponents argue that accidental or spontaneous use of weapons can increase crime.
Data Analysis: Use the outreg2 command to create a table:
(1) A regression of \( ln(vio) \) on \( shall \).
(2) A regression of \( ln(vio) \) against \( shall \), \( incarc~rate \), \( density \), \( avginc \), \( pop \), \( pb1064 \), \( pw1064 \) and \( pm1029 \).
(3) Add state fixed effect to regression (2).
(4) Add time fixed effect to regression (2).
(5) A regression of \( ln(rob) \) on variables in (2) also includes state and time fixed effect.
(6) A regression of \( ln(mur) \) on variables in (2) also includes state and time fixed effect.
Write-Up:
(a) Interpret the coefficient on shall in regression (2). Is this estimate large or small in a “real-world” sense? Does adding the control variables in regression (2) change the estimated effect of a shall-carry law in regression (1) as measured by statistical signicance?
(b) 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).
( c) Suggest a variable that varies across time, but plausibly varies little - or not at all - over states and that could cause omitted variable bias in regression (2).
Write-Up:
(d) Use your answers to (b) and © and discuss why your results in regressions (3) and (4) are different than (1) and (2).
(e) Are you still concerned about omitted variable bias after including fixed effects?
(f) Briey discuss the relationship between concealed carry laws and robbery and murder rates specically. What conclusions would you draw about the effects of concealed carry laws on crime rates?