Wooldridge Source: Professor Henry Farber, now at Princeton University, compiled these data from the 1978 and 1985 Current Population Surveys. Professor Farber kindly provided these data when we were colleagues at MIT. Data loads lazily.
data('cps78_85')A data.frame with 1084 observations on 15 variables:
educ: years of schooling
south: =1 if live in south
nonwhite: =1 if nonwhite
female: =1 if female
married: =1 if married
exper: age - educ - 6
expersq: exper^2
union: =1 if belong to union
lwage: log hourly wage
age: in years
year: 78 or 85
y85: =1 if year == 85
y85fem: y85*female
y85educ: y85*educ
y85union: y85*union
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
Obtaining more recent data from the CPS allows one to track, over a long period of time, the changes in the return to education, the gender gap, black-white wage differentials, and the union wage premium.
Used in Text: pages 451, 476
str(cps78_85)
#> 'data.frame': 1084 obs. of 15 variables:
#> $ educ : int 12 12 6 12 12 8 11 15 16 15 ...
#> $ south : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ nonwhite: int 0 0 0 0 0 0 0 0 0 0 ...
#> $ female : int 0 1 0 0 0 0 0 1 1 0 ...
#> $ married : int 0 1 1 1 1 1 0 0 0 1 ...
#> $ exper : int 8 30 38 19 11 43 2 9 17 23 ...
#> $ expersq : int 64 900 1444 361 121 1849 4 81 289 529 ...
#> $ union : int 0 1 1 1 0 0 0 0 0 1 ...
#> $ lwage : num 1.22 1.61 2.14 2.07 1.65 ...
#> $ age : int 25 47 49 36 28 56 18 29 38 43 ...
#> $ year : int 78 78 78 78 78 78 78 78 78 78 ...
#> $ y85 : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ y85fem : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ y85educ : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ y85union: int 0 0 0 0 0 0 0 0 0 0 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"