Wooldridge Source: Hamermesh, D.S. and J.E. Biddle (1994), “Beauty and the Labor Market,” American Economic Review 84, 1174-1194. Professor Hamermesh kindly provided me with the data. For manageability, I have included only a subset of the variables, which results in somewhat larger sample sizes than reported for the regressions in the Hamermesh and Biddle paper. Data loads lazily.
data('beauty')A data.frame with 1260 observations on 17 variables:
wage: hourly wage
lwage: log(wage)
belavg: =1 if looks <= 2
abvavg: =1 if looks >=4
exper: years of workforce experience
looks: from 1 to 5
union: =1 if union member
goodhlth: =1 if good health
black: =1 if black
female: =1 if female
married: =1 if married
south: =1 if live in south
bigcity: =1 if live in big city
smllcity: =1 if live in small city
service: =1 if service industry
expersq: exper^2
educ: years of schooling
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
pages 238-239, 265-266
str(beauty)
#> 'data.frame': 1260 obs. of 17 variables:
#> $ wage : num 5.73 4.28 7.96 11.57 11.42 ...
#> $ lwage : num 1.75 1.45 2.07 2.45 2.44 ...
#> $ belavg : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ abvavg : int 1 0 1 0 0 0 0 1 0 0 ...
#> $ exper : int 30 28 35 38 27 20 12 5 5 12 ...
#> $ looks : int 4 3 4 3 3 3 3 4 3 3 ...
#> $ union : int 0 0 0 0 0 0 0 1 0 0 ...
#> $ goodhlth: int 1 1 1 1 1 0 1 1 1 1 ...
#> $ black : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ female : int 1 1 1 0 0 1 0 0 1 1 ...
#> $ married : int 1 1 0 1 1 1 1 0 0 0 ...
#> $ south : int 0 1 0 0 0 0 0 0 0 0 ...
#> $ bigcity : int 0 0 0 1 0 1 1 0 0 0 ...
#> $ smllcity: int 1 1 1 0 1 0 0 1 0 1 ...
#> $ service : int 1 0 0 1 0 0 0 0 0 0 ...
#> $ expersq : int 900 784 1225 1444 729 400 144 25 25 144 ...
#> $ educ : int 14 12 10 16 16 12 16 16 16 12 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"