data-wage1: Cross-Sectional Data on Wages

In np: Nonparametric Kernel Smoothing Methods for Mixed Data Types

Cross-Sectional Data on Wages

Description

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. data("wage1") makes available the dataset "wage" plus additional objects "bw.all" and "bw.subset".

Usage

data("wage1")

Format

A data frame with 24 columns, and 526 rows.

Two local-linear rbandwidth objects (bw.all and bw.subset) have been computed for the user's convenience which can be used to visualize this dataset using plot(bw.all)

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 dependants

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 construction industry

ndurman

column 14, of type numeric, =1 if in non-durable manufacturing industry

trcommpu

column 15, of type numeric, =1 if in transportation, communications, public utility

trade

column 16, of type numeric, =1 if in wholesale or retail

services

column 17, of type numeric, =1 if in services industry

profserv

column 18, of type numeric, =1 if in professional services industry

profocc

column 19, of type numeric, =1 if in professional 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

data("wage1")
attach(wage1)
summary(wage1)
detach(wage1)

np documentation built on March 31, 2023, 9:41 p.m.