Skip to contents

Bureau of Labor Statistics March 2000 CPS Individual Data

Source: R/data.R
individuals.Rd

Individual-level data from the March 2000 Annual Demographic Supplement of the Current Population Survey (CPS), conducted by the Bureau of Labor Statistics (BLS). The sample includes 55,899 respondents with information on earnings, education, sex, age, and job sector. This large-scale survey dataset is used to illustrate interactions between dummy variables (sex and job sector) in predicting income, and to demonstrate two-way ANOVA via regression with unbalanced observational data.

Usage

individuals

Format

A tibble with 55,899 rows and 6 columns:

id

Individual identifier. Type: numeric. Range: (1, 55899).

age

Age in years. Type: numeric. Range: (25, 65).

educ

Education level. Type: numeric. Values: 1 = less than high school, 2 = high school diploma, 3 = some college, 4 = bachelor's degree, 5 = master's degree, 6 = professional/doctoral degree.

sex

Sex of respondent. Type: numeric. Values: 1 = male, 2 = female.

earn

Total annual earnings from all sources, in U.S. dollars. Type: numeric. Range: (-24998, 425510). Mean approximately $37,865.

job

Job sector. Type: numeric. Values: 5 = government, 6 = private sector, 7 = self-employed.

Source

U.S. Bureau of Labor Statistics. Current Population Survey, March 2000 Annual Demographic Supplement. U.S. Census Bureau. Original data file: individuals.dta

Details

This dataset is used in Chapter 5 (Interactions) to demonstrate interactions between two dummy variables. The primary analysis focuses on respondents with postgraduate degrees aged 30-40 working in the private sector or government, examining how income depends on sex and job sector. Key concepts illustrated include: interaction between dummy variables, two-way ANOVA via regression, interaction diagrams, unbalanced designs and the distinction between Type II and Type III sums of squares, and interpretation of interaction coefficients as differences in differences.

Examples

data(individuals)
# Subset to postgraduate, ages 30-40, private/government
sub <- individuals[individuals$educ >= 5 &
                   individuals$age >= 30 &
                   individuals$age <= 40 &
                   individuals$job %in% c(5, 6), ]
sub$male <- ifelse(sub$sex == 1, 1, 0)
sub$private <- ifelse(sub$job == 6, 1, 0)
# Interaction model for income
lm(earn ~ male * private, data = sub)
#> 
#> Call:
#> lm(formula = earn ~ male * private, data = sub)
#> 
#> Coefficients:
#>  (Intercept)          male       private  male:private  
#>        42283         23849         -6324        -12922  
#>