GSOEP9402 {AER} | R Documentation

## German Socio-Economic Panel 1994–2002

### Description

Cross-section data for 675 14-year old children born between 1980 and 1988. The sample is taken from the German Socio-Economic Panel (GSOEP) for the years 1994 to 2002 to investigate the determinants of secondary school choice.

### Usage

    data("GSOEP9402")

### Format

A data frame containing 675 observations on 12 variables.

school

  factor. Child's secondary school level.

birthyear

  Year of child's birth.

gender

  factor indicating child's gender.

kids

  Total number of kids living in household.

parity

  Birth order.

income

  Household income.

size

  Household size

state

  factor indicating German federal state.

marital

  factor indicating mother's marital status.

meducation

  Mother's educational level in years.

memployment

  factor indicating mother's employment level: full-time, part-time, or not working.

year

  Year of GSOEP wave.

### Details

This sample from the German Socio-Economic Panel (GSOEP) for the years between 1994 and 2002 has been selected by Winkelmann and Boes (2009) to investigate the determinants of secondary school choice.

In the German schooling system, students are separated relatively early into different school types, depending on their ability as perceived by the teachers after four years of primary school. After that, around the age of ten, students are placed into one of three types of secondary school: `"Hauptschule"` (lower secondary school), `"Realschule"` (middle secondary school), or `"Gymnasium"` (upper secondary school). Only a degree from the latter type of school (called Abitur) provides direct access to universities.

A frequent criticism of this system is that the tracking takes place too early, and that it cements inequalities in education across generations. Although the secondary school choice is based on the teachers' recommendations, it is typically also influenced by the parents; both indirectly through their own educational level and directly through influence on the teachers.

### Source

Online complements to Winkelmann and Boes (2009).

http://www.econ.uzh.ch/faculty/groupwinkelmann/research/publications/microdata/datasets/school.zip

### References

Winkelmann, R., and Boes, S. (2009). Analysis of Microdata, 2nd ed. Berlin and Heidelberg: Springer-Verlag.

### See Also

`WinkelmannBoes2009`

### Examples

    ## data
    data("GSOEP9402", package = "AER")

    ## some convenience data transformations
    gsoep <- GSOEP9402
    gsoep$year2 <- factor(gsoep$year)

    ## visualization
    plot(school ~ meducation, data = gsoep, breaks = c(7, 9, 10.5, 11.5, 12.5, 15, 18))

    ## Chapter 5, Table 5.1
    library("nnet")
    gsoep_mnl <- multinom(
      school ~ meducation + memployment + log(income) + log(size) + parity + year2,
      data = gsoep)
    coeftest(gsoep_mnl)[c(1:6, 1:6 + 14),]

    ## alternatively
    if(require("mlogit")) {
    gsoep_mnl2 <- mlogit(
      school ~ 0 | meducation + memployment + log(income) + log(size) + parity + year2,
      data = gsoep, shape = "wide", reflevel = "Hauptschule")
    coeftest(gsoep_mnl2)[1:12,]
    }

    ## Table 5.2
    library("effects")
    gsoep_eff <- effect("meducation", gsoep_mnl,
      xlevels = list(meducation = sort(unique(gsoep$meducation))))
    gsoep_eff$prob
    plot(gsoep_eff, confint = FALSE)

    ## omit year
    gsoep_mnl1 <- multinom(
      school ~ meducation + memployment + log(income) + log(size) + parity,
      data = gsoep)
    lrtest(gsoep_mnl, gsoep_mnl1)

    ## Chapter 6
    ## Table 6.1
    library("MASS")
    gsoep_pop <- polr(
      school ~ meducation + I(memployment != "none") + log(income) + log(size) + parity + year2,
      data = gsoep, method = "probit", Hess = TRUE)
    gsoep_pol <- polr(
      school ~ meducation + I(memployment != "none") + log(income) + log(size) + parity + year2,
      data = gsoep, Hess = TRUE)

    ## compare polr and multinom via AIC
    gsoep_pol1 <- polr(
      school ~ meducation + memployment + log(income) + log(size) + parity,
      data = gsoep, Hess = TRUE)
    AIC(gsoep_pol1, gsoep_mnl)

    ## effects
    eff_pol1 <- allEffects(gsoep_pol1)
    plot(eff_pol1, ask = FALSE, confint = FALSE)

    ## More examples can be found in:
    ## help("WinkelmannBoes2009")

[Package AER version 1.2-4 Index]