card
card.RdThis data, which originates from D. Card (1995) was released in the Wooldridge R-Package. Sadly the wooldridge package (Shea 2023) was archived on CRAN on the 3rd of December 2024. As we use it, e.g., in our examples to show how our package works, we also added it to our package, so we can further use it. Further we cite the original description of the wooldrigde package. 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.
Usage
data('card')Format
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
Source
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
Notes
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
References
Shea J (2023). wooldridge: 115 Data Sets from "Introductory Econometrics: A Modern Approach, 7e" by Jeffrey M. Wooldridge. R package version 1.4-3, https://CRAN.R-project.org/package=wooldridge.
Examples
data("card")
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"