Cross-Sectional Data on Wages

Cross-section wage data consisting of a random sample taken from the U.S. Current Population Survey for the year 1976. There are 526 observations in total.

Usage

data("wage1")

Format

A data frame with 24 columns, and 526 rows.

wage: column 1, of type numeric, average hourly earnings
educ: column 2, of type numeric, years of education
exper: column 3, of type numeric, years potential experience
tenure: column 4, of type numeric, years with current employer
nonwhite: column 5, of type factor, =“Nonwhite” if nonwhite, “White” otherwise
female: column 6, of type factor, =“Female” if female, “Male” otherwise
married: column 7, of type factor, =“Married” if Married, “Nonmarried” otherwise
numdep: column 8, of type numeric, number of dependents
smsa: column 9, of type numeric, =1 if live in SMSA
northcen: column 10, of type numeric, =1 if live in north central U.S
south: column 11, of type numeric, =1 if live in southern region
west: column 12, of type numeric, =1 if live in western region
construc: column 13, of type numeric, =1 if work in construc. indus.
ndurman: column 14, of type numeric, =1 if in nondur. manuf. indus.
trcommpu: column 15, of type numeric, =1 if in trans, commun, pub ut
trade: column 16, of type numeric, =1 if in wholesale or retail
services: column 17, of type numeric, =1 if in services indus.
profserv: column 18, of type numeric, =1 if in prof. serv. indus.
profocc: column 19, of type numeric, =1 if in profess. occupation
clerocc: column 20, of type numeric, =1 if in clerical occupation
servocc: column 21, of type numeric, =1 if in service occupation
lwage: column 22, of type numeric, log(wage)
expersq: column 23, of type numeric, exper\(^2\)
tenursq: column 24, of type numeric, tenure\(^2\)

Source

Jeffrey M. Wooldridge

References

Wooldridge, J.M. (2000), Introductory Econometrics: A Modern Approach, South-Western College Publishing.

Examples

if (FALSE) { # \dontrun{
data(wage1)

## Cross-validated model selection for spline degree and bandwidths Note
## - we override the default degree.max and segments.max here given the 
## large number of predictors and small sample size (the tensor spline basis 
## will become singular hampering search)

model <- crs(lwage~married+
             female+
             nonwhite+                
             educ+
             exper+
             tenure,
             degree.max=5, 
             segments.max=5,              
             data=wage1)
             
summary(model)

## Partial mean plots (control for non axis predictors)
plot(model)
## Partial first derivative plots (control for non axis predictors)
plot(model,gradients = TRUE)
## Partial second derivative plots (control for non axis predictors)
plot(model,gradients = TRUE, gradient_order = 2)

} # }