uniond: Union Membership
In DPpackage: Bayesian Nonparametric Modeling in R

Description Usage Format Details Examples

This data set consider growth information on wages and union membership for 534 workers. The datafile contains observations on 11 variables sampled from the Current Population Survey of 1985. This data set demonstrates multiple regression, confounding, transformations, multicollinearity, categorical variables, ANOVA, pooled tests of significance, interactions and model building strategies.

1	data(uniond)

A data frame with 534 observations on the following 11 variables.

education: a numeric vector giving the number of years of education.
south: a numeric vector gving an indicator variable for Southern Region (1=Person lives in South, 0=Person lives elsewhere).
sex: a numeric vector giving an indicator variable for sex (1=Female, 0=Male).
experience: a numeric vector giving the number of years of work experience.
unionv: a numeric vector giving an indicator variable for union membership (1=Union member, 0=Not union member).
wage: a numeric vector giving the Wage (dollars per hour).
age: a numeric vector giving the Age in years.
race: a numeric vector giving the race (1=Other, 2=Hispanic, 3=White).
occupation: a numeric vector giving the occupational category (1=Management, 2=Sales, 3=Clerical, 4=Service, 5=Professional, 6=Other).
sector: a numeric vector giving the Sector (0=Other, 1=Manufacturing, 2=Construction).
marr: a numeric vector giving the Marital Status (0=Unmarried, 1=Married).

The Current Population Survey (CPS) is used to supplement census information between census years. These data consist of a random sample of 534 persons from the CPS, with information on wages and other characteristics of the workers, including sex, number of years of education, years of work experience, occupational status, region of residence and union membership. We wish to determine (i) whether wages are related to these characteristics and (ii) whether there is a gender gap in wages. Based on residual plots, wages were log-transformed to stabilize the variance. Age and work experience were almost perfectly correlated (r=.98). Multiple regression of log wages against sex, age, years of education, work experience, union membership, southern residence, and occupational status showed that these covariates were related to wages (pooled F test, p < .0001). The effect of age was not significant after controlling for experience. Standardized residual plots showed no patterns, except for one large outlier with lower wages than expected. This was a male, with 22 years of experience and 12 years of education, in a management position, who lived in the north and was not a union member. Removing this person from the analysis did not substantially change the results, so that the final model included the entire sample. Adjusting for all other variables in the model, females earned 81 the wages of males (p < .0001). Wages increased 41 additional years of education (p < .0001). They increased by 11 for every additional 10 years of experience (p < .0001). Union members were paid 23 paid 11 positions were paid most, and service and clerical positions were paid least (pooled F-test, p < .0001). Overall variance explained was R2 = .35. In summary, many factors describe the variations in wages: occupational status, years of experience, years of education, sex, union membership and region of residence. However, despite adjustment for all factors that were available, there still appeared to be a gender gap in wages. There is no readily available explanation for this gender gap.