Cross-section data from the 1980 US Census on married women
aged 21–35 with two or more children.
Usage
data("Fertility")
data("Fertility2")
Format
A data frame containing 254,654 (and 30,000, respectively) observations on 8 variables.
morekids
factor. Does the mother have more than 2 children?
gender1
factor indicating gender of first child.
gender2
factor indicating gender of second child.
age
age of mother at census.
afam
factor. Is the mother African-American?
hispanic
factor. Is the mother Hispanic?
other
factor. Is the mother's ethnicity neither African-American nor
Hispanic, nor Caucasian? (see below)
work
number of weeks in which the mother worked in 1979.
Details
Fertility2 is a random subset of Fertility with 30,000 observations.
There are conflicts in the ethnicity coding (see also examples). Hence, it was
not possible to create a single factor and the original three indicator
variables have been retained.
Not all variables from Angrist and Evans (1998) have been included.
Angrist, J.D., and Evans, W.N. (1998). Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size
American Economic Review, 88, 450–477.
Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.
See Also
StockWatson2007
Examples
data("Fertility2")
## conflicts in ethnicity coding
ftable(xtabs(~ afam + hispanic + other, data = Fertility2))
## create convenience variables
Fertility2$mkids <- with(Fertility2, as.numeric(morekids) - 1)
Fertility2$samegender <- with(Fertility2, factor(gender1 == gender2))
Fertility2$twoboys <- with(Fertility2, factor(gender1 == "male" & gender2 == "male"))
Fertility2$twogirls <- with(Fertility2, factor(gender1 == "female" & gender2 == "female"))
## similar to Angrist and Evans, p. 462
fm1 <- lm(mkids ~ samegender, data = Fertility2)
summary(fm1)
fm2 <- lm(mkids ~ gender1 + gender2 + samegender + age + afam + hispanic + other, data = Fertility2)
summary(fm2)
fm3 <- lm(mkids ~ gender1 + twoboys + twogirls + age + afam + hispanic + other, data = Fertility2)
summary(fm3)
Results
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> library(AER)
Loading required package: car
Loading required package: lmtest
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Loading required package: survival
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AER/Fertility.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Fertility
> ### Title: Fertility and Women's Labor Supply
> ### Aliases: Fertility Fertility2
> ### Keywords: datasets
>
> ### ** Examples
>
> data("Fertility2")
>
> ## conflicts in ethnicity coding
> ftable(xtabs(~ afam + hispanic + other, data = Fertility2))
other no yes
afam hispanic
no no 25389 811
yes 1308 894
yes no 1568 0
yes 30 0
>
> ## create convenience variables
> Fertility2$mkids <- with(Fertility2, as.numeric(morekids) - 1)
> Fertility2$samegender <- with(Fertility2, factor(gender1 == gender2))
> Fertility2$twoboys <- with(Fertility2, factor(gender1 == "male" & gender2 == "male"))
> Fertility2$twogirls <- with(Fertility2, factor(gender1 == "female" & gender2 == "female"))
>
> ## similar to Angrist and Evans, p. 462
> fm1 <- lm(mkids ~ samegender, data = Fertility2)
> summary(fm1)
Call:
lm(formula = mkids ~ samegender, data = Fertility2)
Residuals:
Min 1Q Median 3Q Max
-0.4108 -0.4108 -0.3440 0.5892 0.6560
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.343979 0.003962 86.83 <2e-16 ***
samegenderTRUE 0.066820 0.005585 11.96 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4837 on 29998 degrees of freedom
Multiple R-squared: 0.004749, Adjusted R-squared: 0.004716
F-statistic: 143.1 on 1 and 29998 DF, p-value: < 2.2e-16
>
> fm2 <- lm(mkids ~ gender1 + gender2 + samegender + age + afam + hispanic + other, data = Fertility2)
> summary(fm2)
Call:
lm(formula = mkids ~ gender1 + gender2 + samegender + age + afam +
hispanic + other, data = Fertility2)
Residuals:
Min 1Q Median 3Q Max
-0.7078 -0.3823 -0.3000 0.5715 0.8317
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1645094 0.0255081 -6.449 1.14e-10 ***
gender1male -0.0056508 0.0055293 -1.022 0.3068
gender2male -0.0128023 0.0055300 -2.315 0.0206 *
samegenderTRUE 0.0683138 0.0055294 12.355 < 2e-16 ***
age 0.0164593 0.0008182 20.116 < 2e-16 ***
afamyes 0.0962535 0.0123360 7.803 6.26e-15 ***
hispanicyes 0.1481327 0.0116248 12.743 < 2e-16 ***
otheryes 0.0240816 0.0131694 1.829 0.0675 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4786 on 29992 degrees of freedom
Multiple R-squared: 0.02573, Adjusted R-squared: 0.0255
F-statistic: 113.1 on 7 and 29992 DF, p-value: < 2.2e-16
>
> fm3 <- lm(mkids ~ gender1 + twoboys + twogirls + age + afam + hispanic + other, data = Fertility2)
> summary(fm3)
Call:
lm(formula = mkids ~ gender1 + twoboys + twogirls + age + afam +
hispanic + other, data = Fertility2)
Residuals:
Min 1Q Median 3Q Max
-0.7078 -0.3823 -0.3000 0.5715 0.8317
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1773117 0.0255629 -6.936 4.11e-12 ***
gender1male 0.0071515 0.0078416 0.912 0.3618
twoboysTRUE 0.0555115 0.0077023 7.207 5.85e-13 ***
twogirlsTRUE 0.0811161 0.0079363 10.221 < 2e-16 ***
age 0.0164593 0.0008182 20.116 < 2e-16 ***
afamyes 0.0962535 0.0123360 7.803 6.26e-15 ***
hispanicyes 0.1481327 0.0116248 12.743 < 2e-16 ***
otheryes 0.0240816 0.0131694 1.829 0.0675 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4786 on 29992 degrees of freedom
Multiple R-squared: 0.02573, Adjusted R-squared: 0.0255
F-statistic: 113.1 on 7 and 29992 DF, p-value: < 2.2e-16
>
>
>
>
>
> dev.off()
null device
1
>