AER (version 1.2-2)

GSS7402: US General Social Survey 1974--2002

Description

Cross-section data for 9120 women taken from every fourth year of the US General Social Survey between 1974 and 2002 to investigate the determinants of fertility.

Usage

data("GSS7402")

source

Online complements to Winkelmann and Boes (2009).

http://www.sts.uzh.ch/research/publications/microdata/datasets/kids.zip

Details

This subset of the US General Social Survey (GSS) for every fourth year between 1974 and 2002 has been selected by Winkelmann and Boes (2009) to investigate the determinants of fertility. To do so they typically restrict their empirical analysis to the women for which the completed fertility is (assumed to be) known, employing the common cutoff of 40 years. Both, the average number of children borne to a woman and the probability of being childless, are of interest.

References

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

See Also

WinkelmannBoes2009

Examples

## completed fertility subset
data("GSS7402", package = "AER")
gss40 <- subset(GSS7402, age >= 40)

## Chapter 1
## exploratory statistics
gss_kids <- prop.table(table(gss40$kids))
names(gss_kids)[9] <- "8+"

gss_zoo <- as.matrix(with(gss40, cbind(
  tapply(kids, year, mean),
  tapply(kids, year, function(x) mean(x <= 0)),
  tapply(education, year, mean))))
colnames(gss_zoo) <- c("Number of children",
  "Proportion childless", "Years of schooling")
gss_zoo <- zoo(gss_zoo, sort(unique(gss40$year)))

## visualizations instead of tables
barplot(gss_kids,
  xlab = "Number of children ever borne to women (age 40+)",
  ylab = "Relative frequencies")

library("lattice")
trellis.par.set(theme = canonical.theme(color = FALSE))
print(xyplot(gss_zoo[,3:1], type = "b", xlab = "Year"))


## Chapter 3, Example 3.14
## Table 3.1
gss40$nokids <- factor(gss40$kids <= 0, levels = c(FALSE, TRUE), labels = c("no", "yes"))
gss40$trend <- gss40$year - 1974
nokids_p1 <- glm(nokids ~ 1, data = gss40, family = binomial(link = "probit"))
nokids_p2 <- glm(nokids ~ trend, data = gss40, family = binomial(link = "probit"))
nokids_p3 <- glm(nokids ~ trend + education + ethnicity + siblings,
  data = gss40, family = binomial(link = "probit"))
lrtest(nokids_p1, nokids_p2, nokids_p3)


## Chapter 4, Figure 4.4
library("effects")
nokids_p3_ef <- effect("education", nokids_p3, xlevels = list(education = 0:20))
plot(nokids_p3_ef, rescale.axis = FALSE, ylim = c(0, 0.3))


## Chapter 8, Example 8.11
kids_pois <- glm(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16,
  data = gss40, family = poisson)
library("MASS")
kids_nb <- glm.nb(kids ~ education + trend + ethnicity + immigrant + lowincome16 + city16,
  data = gss40)
lrtest(kids_pois, kids_nb)


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