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')

Format

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

Source

https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041

Notes

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

Examples

str(fertil2)
#> 'data.frame': 4361 obs. of 27 variables: #> $ mnthborn: int 5 1 7 11 5 8 7 9 12 9 ... #> $ yearborn: int 64 56 58 45 45 52 51 70 53 39 ... #> $ age : int 24 32 30 42 43 36 37 18 34 49 ... #> $ electric: int 1 1 1 1 1 1 1 1 0 1 ... #> $ radio : int 1 1 0 0 1 0 1 1 1 1 ... #> $ tv : int 1 1 0 1 1 0 1 1 0 0 ... #> $ bicycle : int 1 1 0 0 1 0 1 1 0 0 ... #> $ educ : int 12 13 5 4 11 7 16 10 5 4 ... #> $ ceb : int 0 3 1 3 2 1 4 0 1 0 ... #> $ agefbrth: int NA 25 27 17 24 26 20 NA 19 NA ... #> $ children: int 0 3 1 2 2 1 4 0 1 0 ... #> $ knowmeth: int 1 1 1 1 1 1 1 1 1 1 ... #> $ usemeth : int 0 1 0 0 1 1 1 1 1 0 ... #> $ monthfm : int NA 11 6 1 3 11 5 NA 7 11 ... #> $ yearfm : int NA 80 83 61 66 76 78 NA 72 61 ... #> $ agefm : int NA 24 24 15 20 24 26 NA 18 22 ... #> $ idlnchld: int 2 3 5 3 2 4 4 4 4 4 ... #> $ heduc : int NA 12 7 11 14 9 17 NA 3 1 ... #> $ agesq : int 576 1024 900 1764 1849 1296 1369 324 1156 2401 ... #> $ urban : int 1 1 1 1 1 1 1 1 1 1 ... #> $ urb_educ: int 12 13 5 4 11 7 16 10 5 4 ... #> $ spirit : int 0 0 1 0 0 0 0 0 0 1 ... #> $ protest : int 0 0 0 0 1 0 0 0 1 0 ... #> $ catholic: int 0 0 0 0 0 0 1 1 0 0 ... #> $ frsthalf: int 1 1 0 0 1 0 0 0 0 0 ... #> $ educ0 : int 0 0 0 0 0 0 0 0 0 0 ... #> $ evermarr: int 0 1 1 1 1 1 1 0 1 1 ... #> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"