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).

https://global.oup.com/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
library("mlogit")
fm_mlogit <- mlogit(job ~ 1 | education + minority, data = BankWages,
  subset = gender == "male", shape = "wide", reflevel = "custodial")
summary(fm_mlogit)