Description Usage Format Details Source References Examples
1985 North American population survey containing information on union membership and various worker's attributes.
1 | data("wage_data")
|
A data frame with 534 observations on the following 11 variables.
education
a numeric vector.
south
a factor with levels no
yes
.
gender
a factor with levels female
male
.
experience
a numeric vector.
union
a factor with levels member
not_member
.
wage
a numeric vector.
age
a numeric vector.
race
a factor with levels Hispanic
Other
White
.
occupation
a factor with levels Clerical
Management
Other
Professional
Sales
Service
.
section
a factor with levels Construction
Manufacturing
Other
.
marital
a factor with levels Married
Unmarried
.
The data consist of n=534 observations, with the response being a Bernoulli variable of whether they were a member of union (1 = yes; 0 = no), and six covariates: gender (1 = female, 0 = male), race (1 = white; 0 = other), an indicator variable for whether the worker lives in the south (1 = yes; 0 = no), age in years, hourly wage, and number of years in education.
One of the aims of the survey is to uncover associations between workers' characteristics and their probability of union membership. The dataset is used in Ruppert et al., (2003) and Hui et al. (2018), among others, to illustrate the application of Semiparametric regression, as it is believed that union membership may vary non-linearly with the three continuous variables (age, wage, education).
http://mldata.org/repository/data/viewslug/statlib-20050214-cps_85_wages/
Berndt, E. (1991). The Practice of Econometrics: Classic and Contemporary. Addison-Wesley Publishing Company, Reading, Massachusetts.
Hui, F. K. C., You, C., Shang, H. L., and Mueller, S. (2018). Semiparametric regression using variational approximations, Journal of the American Statistical Association, forthcoming.
Ruppert, D., Wand, M. P., and Carroll, R. (2003). Semiparametric Regression. Cambridge University Press, New York.
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