Occupational Mobility in Three Countries

Description

Yamaguchi (1987) presented this three-way frequency table, cross-classifying occupational categories of sons and fathers in the United States, United Kingdom and Japan. This data set has become a classic for models comparing two-way mobility tables across layers corresponding to countries, groups or time (e.g., Goodman and Hout, 1998; Xie, 1992).

The US data were derived from the 1973 OCG-II survey; those for the UK from the 1972 Oxford Social Mobility Survey; those for Japan came from the 1975 Social Stratification and Mobility survey. They pertain to men aged 20-64.

Usage

data(Yamaguchi87)

Format

A frequency data frame with 75 observations on the following 4 variables. The total sample size is 28887.

Son

a factor with levels UpNM LoNM UpM LoM Farm

Father

a factor with levels UpNM LoNM UpM LoM Farm

Country

a factor with levels US UK Japan

Freq

a numeric vector

Details

Five status categories – upper and lower nonmanuals (UpNM, LoNM), upper and lower manuals (UpM, LoM), and Farm) are used for both fathers' occupations and sons' occupations.

Upper nonmanuals are professionals, managers, and officials; lower nonmanuals are proprietors, sales workers, and clerical workers; upper manuals are skilled workers; lower manuals are semi-skilled and unskilled nonfarm workers; and farm workers are farmers and farm laborers.

Some of the models from Xie (1992), Table 1, are fit in demo(yamaguchi-xie).

Source

Yamaguchi, K. (1987). Models for comparing mobility tables: toward parsimony and substance, American Sociological Review, vol. 52 (Aug.), 482-494, Table 1

References

Goodman, L. A. and Hout, M. (1998). Statistical Methods and Graphical Displays for Analyzing How the Association Between Two Qualitative Variables Differs Among Countries, Among Groups, Or Over Time: A Modified Regression-Type Approach. Sociological Methodology, 28 (1), 175-230.

Xie, Yu (1992). The log-multiplicative layer effect model for comparing mobility tables. American Sociological Review, 57 (June), 380-395.

Examples

data(Yamaguchi87)
# reproduce Table 1
structable(~ Father + Son + Country, Yamaguchi87)
# create table form
Yama.tab <- xtabs(Freq ~ Son + Father + Country, data=Yamaguchi87)

# define mosaic labeling_args for convenient reuse in 3-way displays
largs <- list(rot_labels=c(right=0), offset_varnames = c(right = 0.6), 
              offset_labels = c(right = 0.2),
              set_varnames = c(Son="Son's status", Father="Father's status") 
             )

###################################
# Fit some models & display mosaics
  
# Mutual independence
yama.indep <- glm(Freq ~ Son + Father + Country, 
  data=Yamaguchi87, 
  family=poisson)
anova(yama.indep)

mosaic(yama.indep, ~Son+Father, main="[S][F] ignoring country")

mosaic(yama.indep, ~Country + Son + Father, condvars="Country",
       labeling_args=largs, 
       main='[S][F][C] Mutual independence') 

# no association between S and F given country ('perfect mobility')
# asserts same associations for all countries
yama.noRC <- glm(Freq ~ (Son + Father) * Country, 
  data=Yamaguchi87, 
  family=poisson)
anova(yama.noRC)

mosaic(yama.noRC, ~~Country + Son + Father, condvars="Country", 
       labeling_args=largs, 
       main="[SC][FC] No [SF] (perfect mobility)")

# ignore diagonal cells
yama.quasi <- update(yama.noRC, ~ . + Diag(Son,Father):Country)
anova(yama.quasi)

mosaic(yama.quasi, ~Son + Father, main="Quasi [S][F]")

## see also:
# demo(yamaguchi-xie)
##