Swiss Labor Market Participation Data
SwissLabor.RdCross-section data originating from the health survey SOMIPOPS for Switzerland in 1981.
Usage
data("SwissLabor")Format
A data frame containing 872 observations on 7 variables.
- participation
Factor. Did the individual participate in the labor force?
- income
Logarithm of nonlabor income.
- age
Age in decades (years divided by 10).
- education
Years of formal education.
- youngkids
Number of young children (under 7 years of age).
- oldkids
Number of older children (over 7 years of age).
- foreign
Factor. Is the individual a foreigner (i.e., not Swiss)?
References
Gerfin, M. (1996). Parametric and Semi-Parametric Estimation of the Binary Response Model of Labour Market Participation. Journal of Applied Econometrics, 11, 321–339.
Examples
data("SwissLabor")
### Gerfin (1996), Table I.
fm_probit <- glm(participation ~ . + I(age^2), data = SwissLabor,
family = binomial(link = "probit"))
summary(fm_probit)
#>
#> Call:
#> glm(formula = participation ~ . + I(age^2), family = binomial(link = "probit"),
#> data = SwissLabor)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 3.74909 1.40695 2.665 0.00771 **
#> income -0.66694 0.13196 -5.054 4.33e-07 ***
#> age 2.07530 0.40544 5.119 3.08e-07 ***
#> education 0.01920 0.01793 1.071 0.28428
#> youngkids -0.71449 0.10039 -7.117 1.10e-12 ***
#> oldkids -0.14698 0.05089 -2.888 0.00387 **
#> foreignyes 0.71437 0.12133 5.888 3.92e-09 ***
#> I(age^2) -0.29434 0.04995 -5.893 3.79e-09 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 1203.2 on 871 degrees of freedom
#> Residual deviance: 1017.2 on 864 degrees of freedom
#> AIC: 1033.2
#>
#> Number of Fisher Scoring iterations: 4
#>
### alternatively
fm_logit <- glm(participation ~ . + I(age^2), data = SwissLabor,
family = binomial)
summary(fm_logit)
#>
#> Call:
#> glm(formula = participation ~ . + I(age^2), family = binomial,
#> data = SwissLabor)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 6.19639 2.38309 2.600 0.00932 **
#> income -1.10409 0.22571 -4.892 1.00e-06 ***
#> age 3.43661 0.68789 4.996 5.86e-07 ***
#> education 0.03266 0.02999 1.089 0.27611
#> youngkids -1.18575 0.17202 -6.893 5.46e-12 ***
#> oldkids -0.24094 0.08446 -2.853 0.00433 **
#> foreignyes 1.16834 0.20384 5.732 9.94e-09 ***
#> I(age^2) -0.48764 0.08519 -5.724 1.04e-08 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 1203.2 on 871 degrees of freedom
#> Residual deviance: 1017.6 on 864 degrees of freedom
#> AIC: 1033.6
#>
#> Number of Fisher Scoring iterations: 4
#>