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Cross-sectional data from the National Longitudinal Survey of Young Men (1966–1981) used by Card (1995) to estimate the return to schooling using college proximity as an instrument for education.

Usage

card

Format

A data frame with 3,010 observations and 33 variables:

nearc2

Grew up near a 2-year college (binary).

nearc4

Grew up near a 4-year college (binary).

educ

Years of education.

age

Age in years (1976).

fatheduc

Father's years of education.

motheduc

Mother's years of education.

weight

NLS sampling weight.

momdad14

Lived with both parents at age 14 (binary).

sinmom14

Lived with single mother at age 14 (binary).

step14

Lived with stepparent at age 14 (binary).

reg661

Region dummy: New England (1966).

reg662

Region dummy: Middle Atlantic (1966).

reg663

Region dummy: East North Central (1966).

reg664

Region dummy: West North Central (1966).

reg665

Region dummy: South Atlantic (1966).

reg666

Region dummy: East South Central (1966).

reg667

Region dummy: West South Central (1966).

reg668

Region dummy: Mountain (1966).

reg669

Region dummy: Pacific (1966).

south66

Lived in the South in 1966 (binary).

black

Black (binary).

smsa

Lives in SMSA (binary, 1976).

south

Lives in the South (binary, 1976).

smsa66

Lived in SMSA in 1966 (binary).

wage

Hourly wage (cents, 1976).

enroll

Enrolled in school in 1976 (binary).

KWW

Knowledge of the World of Work test score.

IQ

IQ score.

married

Marital status (1976): coded 1–6 in the source data (1 = married; the remaining codes distinguish other marital statuses). Not a binary indicator; recode before use as a regressor.

libcrd14

Had a library card at age 14 (binary).

exper

Years of labor market experience (age - educ - 6).

lwage

Log hourly wage.

expersq

Experience squared (exper^2).

Source

Card, D. (1995). "Using Geographic Variation in College Proximity to Estimate the Return to Schooling." In L.N. Christofides, E.K. Grant, and R. Swidinsky (Eds.), Aspects of Labour Market Behaviour: Essays in Honour of John Vanderkamp. University of Toronto Press.

Obtained from Stata's bcuse archive (Boston College), bcuse card.

Redistribution basis: system.file("COPYRIGHTS", package = "ivreg2r").

See also

Examples

data(card)
# IV regression: instrument education with college proximity.
# Control set from Card (1995, Table 2) / Wooldridge (2020), Example 15.4.
fit <- ivreg2(
  lwage ~ exper + expersq + black + smsa + south + smsa66 +
    reg662 + reg663 + reg664 + reg665 + reg666 + reg667 + reg668 + reg669 |
    educ | nearc4,
  data = card
)
summary(fit)
#> 
#> 2SLS Estimation
#> 
#> Call:
#> ivreg2(formula = lwage ~ exper + expersq + black + smsa + south + 
#>     smsa66 + reg662 + reg663 + reg664 + reg665 + reg666 + reg667 + 
#>     reg668 + reg669 | educ | nearc4, data = card)
#> 
#> Observations: 3,010 
#> VCV type:     Classical (iid) 
#> 
#> Coefficients:
#>               Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)  3.6661519  0.9223682   3.975 7.05e-05 ***
#> educ         0.1315038  0.0548174   2.399 0.016442 *  
#> exper        0.1082711  0.0235956   4.589 4.46e-06 ***
#> expersq     -0.0023349  0.0003326  -7.020 2.22e-12 ***
#> black       -0.1467758  0.0537564  -2.730 0.006326 ** 
#> smsa         0.1118084  0.0315777   3.541 0.000399 ***
#> south       -0.1446715  0.0272120  -5.316 1.06e-07 ***
#> smsa66       0.0185311  0.0215511   0.860 0.389862    
#> reg662       0.1007677  0.0375854   2.681 0.007340 ** 
#> reg663       0.1482588  0.0367162   4.038 5.39e-05 ***
#> reg664       0.0498971  0.0436234   1.144 0.252701    
#> reg665       0.1462719  0.0469387   3.116 0.001832 ** 
#> reg666       0.1629029  0.0517714   3.147 0.001652 ** 
#> reg667       0.1345722  0.0492708   2.731 0.006309 ** 
#> reg668      -0.0830770  0.0591734  -1.404 0.160332    
#> reg669       0.1078142  0.0417024   2.585 0.009729 ** 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> ---
#> R-squared:      0.2382 
#> Adj. R-squared: 0.2343 
#> Wald chi2(15):  51.0 (p < 2.2e-16)
#> Root MSE:       0.3873 
#> 
#> Underidentification test (Anderson canon. corr. LM statistic):
#>   Chi-sq(1) = 13.27 (p = 0.0003)
#> 
#> Weak identification test:
#>   Cragg-Donald Wald F:           13.26 
#>   Stock-Yogo critical values (IV size):
#>      10%  maximal IV size       16.38 
#>      15%  maximal IV size       8.96 
#>      20%  maximal IV size       6.66 
#>      25%  maximal IV size       5.53 
#> 
#> Overidentification test (Sargan):  (equation exactly identified)
#> 
#> Weak-instrument-robust inference:
#>   H0: B1=0 and orthogonality conditions are valid
#>   Anderson-Rubin Wald F(1,2994) = 5.42 (p = 0.0200)
#>   Anderson-Rubin Wald Chi-sq(1) = 5.44 (p = 0.0196)
#>   Stock-Wright LM S Chi-sq(1) = 5.43 (p = 0.0197)
#> 
#> Endogeneity test:
#>   Chi-sq(1) = 1.17 (p = 0.2786)
#>   Tested: educ
#> 
#> First-stage diagnostics:
#>   Endogenous        F-stat   p-value  Partial R2  Shea PR2      SW F      AP F
#>   educ               13.26    0.0003      0.0044    0.0044     13.26     13.26
#> 
#> Instrumented:          educ 
#> Included instruments:  exper, expersq, black, smsa, south, smsa66, reg662, reg663, reg664, reg665, reg666, reg667, reg668, reg669 
#> Excluded instruments:  nearc4 
#>