Wooldridge Source: These data were collected by David Dicicco, a former MSU undergraduate, for a final project. They came from various issues of the County and City Data Book, and are for the years 1982 and 1985. Unfortunately, I do not have the list of cities. Data loads lazily.
data('crime2')A data.frame with 92 observations on 34 variables:
pop: population
crimes: total number index crimes
unem: unemployment rate
officers: number police officers
pcinc: per capita income
west: =1 if city in west
nrtheast: =1 if city in NE
south: =1 if city in south
year: 82 or 87
area: land area, square miles
d87: =1 if year = 87
popden: people per sq mile
crmrte: crimes per 1000 people
offarea: officers per sq mile
lawexpc: law enforce. expend. pc, $
polpc: police per 1000 people
lpop: log(pop)
loffic: log(officers)
lpcinc: log(pcinc)
llawexpc: log(lawexpc)
lpopden: log(popden)
lcrimes: log(crimes)
larea: log(area)
lcrmrte: log(crmrte)
clcrimes: change in lcrimes
clpop: change in lpop
clcrmrte: change in lcrmrte
lpolpc: log(polpc)
clpolpc: change in lpolpc
cllawexp: change in llawexp
cunem: change in unem
clpopden: change in lpopden
lcrmrt_1: lcrmrte lagged
ccrmrte: change in crmrte
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
Very rich crime data sets, at the county, or even city, level, can be collected using the FBI’s Uniform Crime Reports. These data can be matched up with demographic and economic data, at least for census years. The County and City Data Book contains a variety of statistics, but the years do not always match up. These data sets can be used investigate issues such as the effects of casinos on city or county crime rates.
Used in Text: pages 313-314, 459-460
str(crime2)
#> 'data.frame': 92 obs. of 34 variables:
#> $ pop : num 229528 246815 814054 933177 374974 ...
#> $ crimes : num 17136 17306 75654 83960 31352 ...
#> $ unem : num 8.2 3.7 8.1 5.4 9 ...
#> $ officers: int 326 321 1621 1803 633 685 245 259 504 563 ...
#> $ pcinc : int 8532 12155 7551 11363 8343 11729 7592 10802 7558 10627 ...
#> $ west : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ nrtheast: int 0 0 0 0 0 0 0 0 0 0 ...
#> $ south : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ year : int 82 87 82 87 82 87 82 87 82 87 ...
#> $ area : num 44.6 44.6 375 375 49.8 ...
#> $ d87 : int 0 1 0 1 0 1 0 1 0 1 ...
#> $ popden : num 5146 5534 2171 2488 7530 ...
#> $ crmrte : num 74.7 70.1 92.9 90 83.6 ...
#> $ offarea : num 7.31 7.2 4.32 4.81 12.71 ...
#> $ lawexpc : num 851 2262 875 1070 1122 ...
#> $ polpc : num 1.42 1.3 1.99 1.93 1.69 ...
#> $ lpop : num 12.3 12.4 13.6 13.7 12.8 ...
#> $ loffic : num 5.79 5.77 7.39 7.5 6.45 ...
#> $ lpcinc : num 9.05 9.41 8.93 9.34 9.03 ...
#> $ llawexpc: num 6.75 7.72 6.77 6.98 7.02 ...
#> $ lpopden : num 8.55 8.62 7.68 7.82 8.93 ...
#> $ lcrimes : num 9.75 9.76 11.23 11.34 10.35 ...
#> $ larea : num 3.8 3.8 5.93 5.93 3.91 ...
#> $ lcrmrte : num 4.31 4.25 4.53 4.5 4.43 ...
#> $ clcrimes: num NA 0.00987 NA 0.10417 NA ...
#> $ clpop : num NA 0.0726 NA 0.1366 NA ...
#> $ clcrmrte: num NA -0.0627 NA -0.0324 NA ...
#> $ lpolpc : num 0.351 0.263 0.689 0.659 0.524 ...
#> $ clpolpc : num NA -0.0881 NA -0.0302 NA ...
#> $ cllawexp: num NA 0.978 NA 0.201 NA ...
#> $ cunem : num NA -4.5 NA -2.7 NA ...
#> $ clpopden: num NA 0.0726 NA 0.1366 NA ...
#> $ lcrmrt_1: num NA 4.31 NA 4.53 NA ...
#> $ ccrmrte : num NA -4.54 NA -2.96 NA ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"