wage_data {vagam}

Union membership data set

Description

1985 North American population survey containing information on union membership and various worker's attributes.

Usage

data("wage_data")

Format

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.

Details

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).

Source

http://mldata.org/repository/data/viewslug/statlib-20050214-cps_85_wages/

References

• 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.

Examples

data(wage_data)

## Please see examples in the help file for the vagam function.