Wooldridge Source: D. Card (1995), Using Geographic Variation in College Proximity to Estimate the Return to Schooling, in Aspects of Labour Market Behavior: Essays in Honour of John Vanderkamp. Ed. L.N. Christophides, E.K. Grant, and R. Swidinsky, 201-222. Toronto: University of Toronto Press. Professor Card kindly provided these data. Data loads lazily.
data('card')A data.frame with 3010 observations on 34 variables:
id: person identifier
nearc2: =1 if near 2 yr college, 1966
nearc4: =1 if near 4 yr college, 1966
educ: years of schooling, 1976
age: in years
fatheduc: father's schooling
motheduc: mother's schooling
weight: NLS sampling weight, 1976
momdad14: =1 if live with mom, dad at 14
sinmom14: =1 if with single mom at 14
step14: =1 if with step parent at 14
reg661: =1 for region 1, 1966
reg662: =1 for region 2, 1966
reg663: =1 for region 3, 1966
reg664: =1 for region 4, 1966
reg665: =1 for region 5, 1966
reg666: =1 for region 6, 1966
reg667: =1 for region 7, 1966
reg668: =1 for region 8, 1966
reg669: =1 for region 9, 1966
south66: =1 if in south in 1966
black: =1 if black
smsa: =1 in in SMSA, 1976
south: =1 if in south, 1976
smsa66: =1 if in SMSA, 1966
wage: hourly wage in cents, 1976
enroll: =1 if enrolled in school, 1976
KWW: knowledge world of work score
IQ: IQ score
married: =1 if married, 1976
libcrd14: =1 if lib. card in home at 14
exper: age - educ - 6
lwage: log(wage)
expersq: exper^2
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
Computer Exercise C15.3 is important for analyzing these data. There, it is shown that the instrumental variable, `nearc4`, is actually correlated with `IQ`, at least for the subset of men for which an IQ score is reported. However, the correlation between `nearc4“ and `IQ`, once the other explanatory variables are netted out, is arguably zero. At least, it is not statistically different from zero. In other words, `nearc4` fails the exogeneity requirement in a simple regression model but it passes, at least using the crude test described above, if controls are added to the wage equation. For a more advanced course, a nice extension of Card's analysis is to allow the return to education to differ by race. A relatively simple extension is to include black education (blackeduc) as an additional explanatory variable; its natural instrument is blacknearc4.
Used in Text: pages 526-527, 547
str(card)
#> 'data.frame': 3010 obs. of 34 variables:
#> $ id : int 2 3 4 5 6 7 8 9 10 11 ...
#> $ nearc2 : int 0 0 0 1 1 1 1 1 1 1 ...
#> $ nearc4 : int 0 0 0 1 1 1 1 1 1 1 ...
#> $ educ : int 7 12 12 11 12 12 18 14 12 12 ...
#> $ age : int 29 27 34 27 34 26 33 29 28 29 ...
#> $ fatheduc: int NA 8 14 11 8 9 14 14 12 12 ...
#> $ motheduc: int NA 8 12 12 7 12 14 14 12 12 ...
#> $ weight : num 158413 380166 367470 380166 367470 ...
#> $ momdad14: int 1 1 1 1 1 1 1 1 1 1 ...
#> $ sinmom14: int 0 0 0 0 0 0 0 0 0 0 ...
#> $ step14 : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ reg661 : int 1 1 1 0 0 0 0 0 0 0 ...
#> $ reg662 : int 0 0 0 1 1 1 1 1 1 1 ...
#> $ reg663 : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ reg664 : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ reg665 : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ reg666 : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ reg667 : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ reg668 : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ reg669 : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ south66 : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ black : int 1 0 0 0 0 0 0 0 0 0 ...
#> $ smsa : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ south : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ smsa66 : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ wage : int 548 481 721 250 729 500 565 608 425 515 ...
#> $ enroll : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ KWW : int 15 35 42 25 34 38 41 46 32 34 ...
#> $ IQ : int NA 93 103 88 108 85 119 108 96 97 ...
#> $ married : int 1 1 1 1 1 1 1 1 4 1 ...
#> $ libcrd14: int 0 1 1 1 0 1 1 1 0 1 ...
#> $ exper : int 16 9 16 10 16 8 9 9 10 11 ...
#> $ lwage : num 6.31 6.18 6.58 5.52 6.59 ...
#> $ expersq : int 256 81 256 100 256 64 81 81 100 121 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"