ubduration: Austrian unemployment duration data

In causalweight: Estimation Methods for Causal Inference Based on Inverse Probability Weighting

Austrian unemployment duration data

Description

A dataset containing unemployed females between 46 and 53 years old living in an Austrian region where an extension of the maximum duration of unemployment benefits (from 30 to 209 weeks under particular conditions) for job seekers aged 50 or older was introduced.

Usage

ubduration

Format

A data frame with 5659 rows and 10 variables:

y

Outcome variable: unemployment duration of the jobseeker in weeks (registered at the unemployment office). Variable is numeric.

z

Running variable: distance to the age threshold of 50 (implying an extended duration of unemployment benefits), measured in months divided by 12. Variable is numeric.

marrstatus

Marital status: 0=other, 1=married, 2=single. Variable is a factor.

education

Eductation: 0=low education, 1=medium education, 2=high education. Variable is ordered.

foreign

Migrant status: 1=foreigner, 0=Austrian. Variable is a factor.

rr

Replacement rate (of previous earnings by unemployment benefits). Variable is numeric.

lwageljob

Log wage in last job. Variable is numeric.

experience

Ratio of actual to potential work experience. Variable is numeric.

whitecollar

1=white collar worker, 0=blue collar worker. Variable is a factor.

industry

Industry: 0=other, 1=agriculture, 2=utilities, 3=food, 4=textiles, 5=wood, 6=machines, 7=other manufacturing, 8=construction, 9=tourism, 10=traffic, 11=services. Variable is a factor.

References

Lalive, R. (2008): "How Do Extended Benefits Affect Unemployment Duration? A Regression Discontinuity Approach", Journal of Econometrics, 142, 785–806.

Frölich, M. and Huber, M. (2019): "Including covariates in the regression discontinuity design", Journal of Business & Economic Statistics, 37, 736-748.

Examples

## Not run: 
# load unemployment duration data
data(ubduration)
# run sharp RDD conditional on covariates with user-defined bandwidths
output=RDDcovar(y=ubduration[,1],z=ubduration[,2],x=ubduration[,c(-1,-2)],
 bw0=c(0.17, 1, 0.01, 0.05, 0.54, 70000, 0.12, 0.91, 100000),
 bw1=c(0.59, 0.65, 0.30, 0.06, 0.81, 0.04, 0.12, 0.76, 1.03),bwz=0.2,boot=19)
cat("RDD effect estimate: ",round(c(output$effect),3),", standard error: ",
 round(c(output$se),3), ", p-value: ", round(c(output$pvalue),3))
## End(Not run)

causalweight documentation built on May 4, 2023, 5:10 p.m.