lalive {dstat}

Unemployment Duration Following an Increase in Unemployment Benefits

Description

Data from a study by Lalive, van Ours and Zweimüller (2006) concerning the duration of unemployment before and after an increase in unemployment benefits, both the benefit amount and the duration of benefits. The original study takes account of many relevant considerations not included in the current subset of the data The data were used as methodological example in Rosenbaum (2019).

Usage

data("lalive")

Format

A data frame with 2782 observations on the following 17 variables.

id

ID number

mset

Matched pair, 1,2,...,1391.

after

Treatment indicator, 1=after benefits increase, 0=before benefits increase

type

a factor with levels PBD and RR

dur

Duration of unemployment in weeks.

bdur

Duration of unemployment benefits in weeks

e3_5

1 if worked for at least 3 of the past 5 years, 0 otherwise.

lehre

1 if apprenticeship, 0 otherwise

married

1 if married, 0 otherwise

divorced

1 if divorced, 0 otherwise

bc

1 if lost a blue colar job, 0 otherwise

seasonal

1 if lost a seasonal job, 0 otherwise

manuf

1 if lost a manufacturing job, 0 otherwise

age

Age in years

nwage_pj

Wage in the prior job in Austrian schillings

educ

0 if primary education, 1 if secondary education, 2 if tertiary education

propensity

An estimated propensity score

Details

The data are from Lalive, van Ours and Zweimüller (2006), by way of the web-page for the textbook Cahuc, P., Carcillo, S. and Zylberberg, A. (2014).

In August 1989, Austria increased its unemployment benefits for certain categories of workers. The category considered here, type=PBD and RR, had an increase in the duration of unemployment benefits and an increase in unemployment compensation. There are two groups, those unemployed in the two years before the benefit increase, after=0, and those unemployed in the two years after the increase, after=1.

The data are 1391 matched pairs, matached for e3_5, lehre, married, divorced, bc, seasonal, manuf, age, nwage_pj, and educ, with fine balance for quintiles of the propensity score. All are men, and none were temporarily laid off. The matching used a simplified version of the method in Rosenbaum (2017).

The original study by Lalive et al. (2006) sensibly takes account of many relevant considerations not included in the current subset of the data. The limited data available here were used to illustrate certain methodological issues in Rosenbaum (2019).

Source

Lalive, R., Van Ours, J., & Zweimüller, J. (2006).

References

Cahuc, P., Carcillo, S. and Zylberberg, A. (2014). Labor Economics, Second Edition. Cambridge, MA: MIT Press. https://mitpress.mit.edu/books/labor-economics-second-edition

Lalive, R., Van Ours, J., & Zweimüller, J. (2006). How changes in financial incentives affect the duration of unemployment. The Review of Economic Studies, 73, 1009-1038. <doi:10.1111/j.1467-937X.2006.00406.x>

Rosenbaum, P. R. (2017). Imposing minimax and quantile constraints on optimal matching in observational studies. Journal of Computational and Graphical Statistics, 26, 66-78.

Rosenbaum, P. R. (2019). A highly adaptive test for matched observational studies. Manuscript.

Examples

data(lalive)
attach(lalive)
# covariate balance
boxplot(propensity~after,names=c("Before","After"),ylab="Propensity Score")
boxplot(age~after,names=c("Before","After"),ylab="Age")
boxplot(nwage_pj~after,names=c("Before","After"),ylab="Prior Wage")
table(after,seasonal)
# outcome
y<-log2((1+dur[after==1])/52)-log2((1+dur[after==0])/52)
boxplot(y,ylab="Pair Difference in base 2 logs",
    main="Unemployment Duration")
abline(h=c(-1,0,1),lty=2)
rm(y)
detach(lalive)