R: Bank Wages

BankWages {AER} | R Documentation

## Bank Wages

### Description

Wages of employees of a US bank.

### Usage

    data("BankWages")

### Format

A data frame containing 474 observations on 4 variables.

job

  Ordered factor indicating job category, with levels `"custodial"`, `"admin"` and `"manage"`.

education

  Education in years.

gender

  Factor indicating gender.

minority

  Factor. Is the employee member of a minority?

### Source

Online complements to Heij, de Boer, Franses, Kloek, and van Dijk (2004).

http://www.oup.com/uk/booksites/content/0199268010/datasets/ch6/xr614bwa.asc

### References

Heij, C., de Boer, P.M.C., Franses, P.H., Kloek, T. and van Dijk, H.K. (2004). Econometric Methods with Applications in Business and Economics. Oxford: Oxford University Press.

### Examples

    data("BankWages")

    ## exploratory analysis of job ~ education
    ## (tables and spine plots, some education levels merged)
    xtabs(~ education + job, data = BankWages)
    edcat <- factor(BankWages$education)
    levels(edcat)[3:10] <- rep(c("14-15", "16-18", "19-21"), c(2, 3, 3))
    tab <- xtabs(~ edcat + job, data = BankWages)
    prop.table(tab, 1)
    spineplot(tab, off = 0)
    plot(job ~ edcat, data = BankWages, off = 0)

    ## fit multinomial model for male employees
    library("nnet")
    fm_mnl <- multinom(job ~ education + minority, data = BankWages,
      subset = gender == "male", trace = FALSE)
    summary(fm_mnl)
    confint(fm_mnl)

    ## same with mlogit package
    if(require("mlogit")) {
    fm_mlogit <- mlogit(job ~ 1 | education + minority, data = BankWages,
      subset = gender == "male", shape = "wide", reflevel = "custodial")
    summary(fm_mlogit)
    }

[Package AER version 1.2-5 Index]