Wooldridge Source: From C. Cornwell and W. Trumball (1994), “Estimating the Economic Model of Crime with Panel Data,” Review of Economics and Statistics 76, 360-366. Professor Cornwell kindly provided the data. Data loads lazily.
data('crime4')A data.frame with 630 observations on 59 variables:
county: county identifier
year: 81 to 87
crmrte: crimes committed per person
prbarr: 'probability' of arrest
prbconv: 'probability' of conviction
prbpris: 'probability' of prison sentenc
avgsen: avg. sentence, days
polpc: police per capita
density: people per sq. mile
taxpc: tax revenue per capita
west: =1 if in western N.C.
central: =1 if in central N.C.
urban: =1 if in SMSA
pctmin80: perc. minority, 1980
wcon: weekly wage, construction
wtuc: wkly wge, trns, util, commun
wtrd: wkly wge, whlesle, retail trade
wfir: wkly wge, fin, ins, real est
wser: wkly wge, service industry
wmfg: wkly wge, manufacturing
wfed: wkly wge, fed employees
wsta: wkly wge, state employees
wloc: wkly wge, local gov emps
mix: offense mix: face-to-face/other
pctymle: percent young male
d82: =1 if year == 82
d83: =1 if year == 83
d84: =1 if year == 84
d85: =1 if year == 85
d86: =1 if year == 86
d87: =1 if year == 87
lcrmrte: log(crmrte)
lprbarr: log(prbarr)
lprbconv: log(prbconv)
lprbpris: log(prbpris)
lavgsen: log(avgsen)
lpolpc: log(polpc)
ldensity: log(density)
ltaxpc: log(taxpc)
lwcon: log(wcon)
lwtuc: log(wtuc)
lwtrd: log(wtrd)
lwfir: log(wfir)
lwser: log(wser)
lwmfg: log(wmfg)
lwfed: log(wfed)
lwsta: log(wsta)
lwloc: log(wloc)
lmix: log(mix)
lpctymle: log(pctymle)
lpctmin: log(pctmin)
clcrmrte: lcrmrte - lcrmrte[_n-1]
clprbarr: lprbarr - lprbarr[_n-1]
clprbcon: lprbconv - lprbconv[_n-1]
clprbpri: lprbpri - lprbpri[t-1]
clavgsen: lavgsen - lavgsen[t-1]
clpolpc: lpolpc - lpolpc[t-1]
cltaxpc: ltaxpc - ltaxpc[t-1]
clmix: lmix - lmix[t-1]
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
Computer Exercise C16.7 shows that variables that might seem to be good instrumental variable candidates are not always so good, especially after applying a transformation such as differencing across time. You could have the students do an IV analysis for just, say, 1987.
Used in Text: pages 471-472, 479, 504, 580
str(crime4)
#> 'data.frame': 630 obs. of 59 variables:
#> $ county : int 1 1 1 1 1 1 1 3 3 3 ...
#> $ year : int 81 82 83 84 85 86 87 81 82 83 ...
#> $ crmrte : num 0.0399 0.0383 0.0303 0.0347 0.0366 ...
#> $ prbarr : num 0.29 0.338 0.33 0.363 0.325 ...
#> $ prbconv : num 0.402 0.433 0.526 0.605 0.579 ...
#> $ prbpris : num 0.472 0.507 0.48 0.52 0.497 ...
#> $ avgsen : num 5.61 5.59 5.8 6.89 6.55 ...
#> $ polpc : num 0.00179 0.00177 0.00184 0.00189 0.00192 ...
#> $ density : num 2.31 2.33 2.34 2.35 2.36 ...
#> $ taxpc : num 25.7 24.9 26.5 26.8 28.1 ...
#> $ west : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ central : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ urban : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ pctmin80: num 20.2 20.2 20.2 20.2 20.2 ...
#> $ wcon : num 206 213 220 223 244 ...
#> $ wtuc : num 334 369 1395 399 359 ...
#> $ wtrd : num 182 190 197 201 207 ...
#> $ wfir : num 272 301 310 350 383 ...
#> $ wser : num 216 232 240 252 261 ...
#> $ wmfg : num 229 240 270 282 299 ...
#> $ wfed : num 409 420 439 459 490 ...
#> $ wsta : num 236 254 250 262 281 ...
#> $ wloc : num 231 237 249 264 289 ...
#> $ mix : num 0.0999 0.103 0.0807 0.0785 0.0932 ...
#> $ pctymle : num 0.0877 0.0864 0.0851 0.0838 0.0823 ...
#> $ d82 : int 0 1 0 0 0 0 0 0 1 0 ...
#> $ d83 : int 0 0 1 0 0 0 0 0 0 1 ...
#> $ d84 : int 0 0 0 1 0 0 0 0 0 0 ...
#> $ d85 : int 0 0 0 0 1 0 0 0 0 0 ...
#> $ d86 : int 0 0 0 0 0 1 0 0 0 0 ...
#> $ d87 : int 0 0 0 0 0 0 1 0 0 0 ...
#> $ lcrmrte : num -3.22 -3.26 -3.5 -3.36 -3.31 ...
#> $ lprbarr : num -1.24 -1.08 -1.11 -1.01 -1.12 ...
#> $ lprbconv: num -0.911 -0.837 -0.643 -0.503 -0.547 ...
#> $ lprbpris: num -0.75 -0.679 -0.735 -0.654 -0.699 ...
#> $ lavgsen : num 1.72 1.72 1.76 1.93 1.88 ...
#> $ lpolpc : num -6.33 -6.34 -6.3 -6.27 -6.25 ...
#> $ ldensity: num 0.836 0.846 0.851 0.853 0.861 ...
#> $ ltaxpc : num 3.25 3.21 3.28 3.29 3.34 ...
#> $ lwcon : num 5.33 5.36 5.39 5.41 5.5 ...
#> $ lwtuc : num 5.81 5.91 7.24 5.99 5.88 ...
#> $ lwtrd : num 5.21 5.24 5.28 5.3 5.33 ...
#> $ lwfir : num 5.61 5.71 5.74 5.86 5.95 ...
#> $ lwser : num 5.37 5.44 5.48 5.53 5.56 ...
#> $ lwmfg : num 5.43 5.48 5.6 5.64 5.7 ...
#> $ lwfed : num 6.01 6.04 6.08 6.13 6.2 ...
#> $ lwsta : num 5.46 5.54 5.52 5.57 5.64 ...
#> $ lwloc : num 5.44 5.47 5.52 5.58 5.66 ...
#> $ lmix : num -2.3 -2.27 -2.52 -2.54 -2.37 ...
#> $ lpctymle: num -2.43 -2.45 -2.46 -2.48 -2.5 ...
#> $ lpctmin : num 3.01 3.01 3.01 3.01 3.01 ...
#> $ clcrmrte: num NA -0.0394 -0.2353 0.1362 0.0518 ...
#> $ clprbarr: num NA 0.1545 -0.0229 0.0926 -0.1081 ...
#> $ clprbcon: num NA 0.0741 0.194 0.14 -0.0439 ...
#> $ clprbpri: num NA 0.071 -0.0553 0.0809 -0.0453 ...
#> $ clavgsen: num NA -0.00357 0.03688 0.17221 -0.05061 ...
#> $ clpolpc : num NA -0.0114 0.0384 0.0269 0.0202 ...
#> $ cltaxpc : num NA -0.0326 0.0615 0.0147 0.0472 ...
#> $ clmix : num NA 0.0309 -0.2447 -0.0273 0.1721 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"