Wooldridge Source: Collected by Christopher Torrente, a former MSU undergraduate, for a term project. He obtained the salary data and the career statistics from The Complete Handbook of Pro Basketball, 1995, edited by Zander Hollander. New York: Signet. The demographic information (marital status, number of children, and so on) was obtained from the teams’ 1994-1995 media guides. Data loads lazily.
data('nbasal')A data.frame with 269 observations on 22 variables:
marr: =1 if married
wage: annual salary, thousands $
exper: years as professional player
age: age in years
coll: years played in college
games: average games per year
minutes: average minutes per year
guard: =1 if guard
forward: =1 if forward
center: =1 if center
points: points per game
rebounds: rebounds per game
assists: assists per game
draft: draft number
allstar: =1 if ever all star
avgmin: minutes per game
lwage: log(wage)
black: =1 if black
children: =1 if has children
expersq: exper^2
agesq: age^2
marrblck: marr*black
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
A panel version of this data set could be useful for further isolating productivity effects of marital status. One would need to obtain information on enough different players in at least two years, where some players who were not married in the initial year are married in later years. Fixed effects (or first differencing, for two years) is the natural estimation method.
Used in Text: pages 222-223, 264-265
str(nbasal)
#> 'data.frame': 269 obs. of 22 variables:
#> $ marr : int 1 1 0 0 0 0 1 0 1 1 ...
#> $ wage : num 1002 2030 650 2030 755 ...
#> $ exper : int 4 5 1 5 3 9 1 3 1 12 ...
#> $ age : int 27 28 25 28 24 31 28 27 25 35 ...
#> $ coll : int 4 4 4 4 4 4 0 3 4 3 ...
#> $ games : int 77 78 74 47 82 82 80 67 60 74 ...
#> $ minutes : int 2867 2789 1149 1178 2096 1971 2303 1131 542 2700 ...
#> $ guard : int 1 1 0 0 1 0 0 0 1 0 ...
#> $ forward : int 0 0 0 1 0 1 1 1 0 1 ...
#> $ center : int 0 0 1 0 0 0 0 0 0 0 ...
#> $ points : num 15.5 13.3 5.5 7.3 10.8 ...
#> $ rebounds: num 3.9 2.5 3.3 5.1 4.3 ...
#> $ assists : num 4.5 8.8 0.2 1.5 2.6 ...
#> $ draft : int 19 28 19 1 24 4 40 47 NA 3 ...
#> $ allstar : int 0 0 0 0 0 0 0 0 0 1 ...
#> $ avgmin : num 37.2 35.8 15.5 25.1 25.6 ...
#> $ lwage : num 6.91 7.62 6.48 7.62 6.63 ...
#> $ black : int 1 1 1 1 1 1 0 1 1 1 ...
#> $ children: int 0 1 0 0 0 0 0 0 0 1 ...
#> $ expersq : int 16 25 1 25 9 81 1 9 1 144 ...
#> $ agesq : int 729 784 625 784 576 961 784 729 625 1225 ...
#> $ marrblck: int 1 1 0 0 0 0 0 0 1 1 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"