Hayashi Source: Blackburn, M., and D. Neumark, 1992, "Unobserved Ability, Efficiency Wages, and Interindustry Wage Differentials," Quarterly Journal of Economics, 107, 142 1-1436.
data('griliches')
A data.frame with 758 observations on 20 variables:
southern_residence: = 1 if individual lives in southern state when first interviewed
southern_residence_80: = 1 individual lives in south in 1980
married: = 1 if individual is married when first interviewed
married_80: = 1 if individual is married when interviewed in 1980
lives_metro: = 1 if individual lives in metropolitan area when first interviewed
lives_metro_80: = 1 if individual lives in metropolitan area in 1980
mothers_educ: mother's education measured in years
iq_score: score on the IQ test
kww_score: score on "Knowledge of the World of Work" test
year: year was first interviewed
age: age of individual when first interviewed
age_80: age of individual in 1980
education: years of completed schooling when first interviewed
education_80: years of completed schooling in 1980
experience: years of work experience when first interviewed
experience_80: years of work experience in 1980
tenure: years of tenure when first interviewed
tenure_80: years of tenure in 1980
log_wage: log wage when first interviewed
log_wage_80: log wage in 1980
https://sites.google.com/site/fumiohayashi/hayashi-econometrics/data-for-empirical
A panel data extract of the Young Men's Cohort of the National Longitudinal Survey (NLS-Y).
Used in Chapter 3.9 and the Empirical Exercise of Chapter 3. Data is the Blackburn - Neumark sample after eliminating data with missing mother's education.
str(griliches)#> Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 758 obs. of 20 variables: #> $ southern_residence : num 0 0 0 0 0 0 0 0 0 0 ... #> $ southern_residence_80: num 0 0 0 0 0 0 0 0 0 0 ... #> $ married : num 0 0 0 0 1 0 1 1 1 1 ... #> $ married_80 : num 1 1 1 1 1 0 1 1 1 1 ... #> $ lives_metro : num 1 1 1 1 1 1 1 0 1 0 ... #> $ lives_metro_80 : num 1 1 1 1 1 1 1 0 1 0 ... #> $ mothers_educ : num 8 14 14 12 6 8 8 14 12 13 ... #> $ iq_score : num 93 119 108 96 74 91 114 111 95 132 ... #> $ kww_score : num 35 41 46 32 27 24 50 37 44 44 ... #> $ year : num 68 66 67 66 73 66 73 67 66 73 ... #> $ age : num 19 23 20 18 26 16 30 23 22 30 ... #> $ age_80 : num 31 37 33 32 34 30 38 36 36 38 ... #> $ education : num 12 16 14 12 9 9 18 15 12 18 ... #> $ education_80 : num 12 18 14 12 11 10 18 15 12 18 ... #> $ experience : num 0.462 0 0.423 0.333 9.013 ... #> $ experience_80 : num 10.6 11.4 11 13.1 14.4 ... #> $ tenure : num 0 2 1 1 3 1 6 1 2 5 ... #> $ tenure_80 : num 2 16 9 7 5 0 14 1 16 13 ... #> $ log_wage : num 5.9 5.44 5.71 5.48 5.93 ... #> $ log_wage_80 : num 6.64 6.69 6.71 6.48 6.33 ...