Wooldridge Source: From the Statistical Abstract of the United States, 1995 (Tables 310 and 357), 1992 (Table 289). The execution data originally come from the U.S. Bureau of Justice Statistics, Capital Punishment Annual. Data loads lazily.
data('murder')A data.frame with 153 observations on 13 variables:
id: state identifier
state: postal code
year: 87, 90, or 93
mrdrte: murders per 100,000 people
exec: total executions, past 3 years
unem: annual unem. rate
d90: =1 if year == 90
d93: =1 if year == 93
cmrdrte: mrdrte - mrdrte[_n-1]
cexec: exec - exec[_n-1]
cunem: unem - unem[_n-1]
cexec_1: cexec[_n-1]
cunem_1: cunem[_n-1]
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Prosecutors in different counties might pursue the death penalty with different intensities, so it makes sense to collect murder and execution data at the county level. This could be combined with better demographic information at the county level, along with better economic data (say, on wages for various kinds of employment).
Used in Text: pages 480, 505, 548
str(murder)
#> 'data.frame': 153 obs. of 13 variables:
#> $ id : int 1 1 1 2 2 2 3 3 3 4 ...
#> $ state : chr "AL" "AL" "AL" "AK" ...
#> $ year : int 87 90 93 87 90 93 87 90 93 87 ...
#> $ mrdrte : num 9.3 11.6 11.6 10.1 7.5 ...
#> $ exec : int 2 5 2 0 0 0 0 0 3 0 ...
#> $ unem : num 7.8 6.8 7.5 10.8 6.9 ...
#> $ d90 : int 0 1 0 0 1 0 0 1 0 0 ...
#> $ d93 : int 0 0 1 0 0 1 0 0 1 0 ...
#> $ cmrdrte: num NA 2.3 0 NA -2.6 ...
#> $ cexec : int NA 3 -3 NA 0 0 NA 0 3 NA ...
#> $ cunem : num NA -1 0.7 NA -3.9 ...
#> $ cexec_1: int NA NA 3 NA NA 0 NA NA 0 NA ...
#> $ cunem_1: num NA NA -1 NA NA ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"