Swiss Labor Market Participation Data

Description

Cross-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)?

Source

Journal of Applied Econometrics Data Archive.

http://qed.econ.queensu.ca/jae/1996-v11.3/gerfin/

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)

### alternatively
fm_logit <- glm(participation ~ . + I(age^2), data = SwissLabor,
  family = binomial)
summary(fm_logit)

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/SwissLabor.Rd_%03d_medium.png", width=480, height=480)
> ### Name: SwissLabor
> ### Title: Swiss Labor Market Participation Data
> ### Aliases: SwissLabor
> ### Keywords: datasets
> 
> ### ** 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)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9191  -0.9695  -0.4792   1.0209   2.4803  

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)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9061  -0.9627  -0.4924   1.0171   2.3915  

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

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>