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).
A data frame with 2782 observations on the following 17 variables.
Matched pair, 1,2,...,1391.
Treatment indicator, 1=after benefits increase, 0=before benefits increase
a factor with levels
PBD and RR
Duration of unemployment in weeks.
Duration of unemployment benefits in weeks
1 if worked for at least 3 of the past 5 years, 0 otherwise.
1 if apprenticeship, 0 otherwise
1 if married, 0 otherwise
1 if divorced, 0 otherwise
1 if lost a blue colar job, 0 otherwise
1 if lost a seasonal job, 0 otherwise
1 if lost a manufacturing job, 0 otherwise
Age in years
Wage in the prior job in Austrian schillings
0 if primary education, 1 if secondary education, 2 if tertiary education
An estimated propensity score
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).
Lalive, R., Van Ours, J., & Zweimüller, J. (2006).
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.
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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)
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