Wooldridge Source: Data from the National Highway Traffic Safety Administration: “A Digest of State Alcohol-Highway Safety Related Legislation,” U.S. Department of Transportation, NHTSA. I used the third (1985), eighth (1990), and 13th (1995) editions. Data loads lazily.
data('admnrev')A data.frame with 153 observations on 5 variables:
state: state postal code
year: 85, 90, or 95
admnrev: =1 if admin. revoc. law
daysfrst: days suspended, first offense
daysscnd: days suspended, second offense
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
This is not so much a data set as a summary of so-called “administrative per se” laws atthe state level, for three different years. It could be supplemented with drunk-driving fatalities for a nice econometric analysis. In addition, the data for 2000 or later years can be added, forming the basis for a term project. Many other explanatory variables could be included. Unemployment rates, state-level tax rates on alcohol, and membership in MADD are just a few possibilities.
Used in Text: not used
str(admnrev)
#> 'data.frame': 153 obs. of 5 variables:
#> $ state : chr "AL" "AL" "AL" "AK" ...
#> $ year : int 85 90 95 85 90 95 85 90 95 85 ...
#> $ admnrev : int 0 0 0 1 1 1 0 1 1 0 ...
#> $ daysfrst: int 0 0 0 30 30 30 0 30 30 0 ...
#> $ daysscnd: int 0 0 0 365 365 365 0 90 90 0 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"