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
#> '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"