| fertil2 | R Documentation |
Wooldridge Source: These data were obtained by James Heakins, a former MSU undergraduate, for a term project. They come from Botswana’s 1988 Demographic and Health Survey. Data loads lazily.
data('fertil2')
A data.frame with 4361 observations on 27 variables:
mnthborn: month woman born
yearborn: year woman born
age: age in years
electric: =1 if has electricity
radio: =1 if has radio
tv: =1 if has tv
bicycle: =1 if has bicycle
educ: years of education
ceb: children ever born
agefbrth: age at first birth
children: number of living children
knowmeth: =1 if know about birth control
usemeth: =1 if ever use birth control
monthfm: month of first marriage
yearfm: year of first marriage
agefm: age at first marriage
idlnchld: 'ideal' number of children
heduc: husband's years of education
agesq: age^2
urban: =1 if live in urban area
urb_educ: urban*educ
spirit: =1 if religion == spirit
protest: =1 if religion == protestant
catholic: =1 if religion == catholic
frsthalf: =1 if mnthborn <= 6
educ0: =1 if educ == 0
evermarr: =1 if ever married
Currently, this data set is used only in one computer exercise. Since the dependent variable of interest – number of living children or number of children every born – is a count variable, the Poisson regression model discussed in Chapter 17 can be used. However, some care is required to combine Poisson regression with an endogenous explanatory variable (educ). I refer you to Chapter 19 of my book Econometric Analysis of Cross Section and Panel Data. Even in the context of linear models, much can be done beyond Computer Exercise C15.2. At a minimum, the binary indicators for various religions can be added as controls. One might also interact the schooling variable, educ, with some of the exogenous explanatory variables.
Used in Text: page 547
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
str(fertil2)