Discussion of Hujer, R. and S. Thomsen (2006). “How Do Employment Effects of Job Creation Schemes Differ with Respect to the Foregoing Unemployment Duration?” Wang-Sheng Lee

Overview Analyses the effect of JCS on employment effects (typical duration is 12 months but can be as long as 36 months). Follow-up period of 30 months. – Focus on creating subgroups based on quarters of unemployment duration (i.e., time spent in unemployment). – Policy implication is that the effect of offering the program at different times in the unemployment spell can lead to differing impacts. – Previous studies (using different data and setup) have focused on various subgroups. For example, 5 economic sectors, age ( 50), unemployment duration in weeks ( 52), and education subgroups.

Treatment group are participants in JCS in July 2000, Sep 2000, Nov 2000, Jan 2001, Mar 2001 and May – n = 32,641 Comparison group consists of those still unemployed up until that time and who have not started a program, but can participate in a program later (i.e., “waiters” who postpone participation). – Samples of job-seekers from the months preceding the 6 treatment start dates (i.e., June 2000, Aug 2000, Oct 2000, Dec 2000, Feb 2001 and Apr 2001). – n = 1,104,664

Overall, stratify the sample by Men and Women, East and West Germany, and for u = 1, …, 12 quarters of unemployment preceding treatment (i.e., a total of 2 x 2 x 12 = 48 strata). Aggregate the 6 T/C cohorts into one sample with 48 strata. – Estimate 48 propensity score models. – Have a rich set of covariates which help to make the CIA more plausible. – Program start date dummies are used as regressors in the propensity score model.

In the paper, calendar date of unemployment entry is not aligned. – Assumes it to be of minor importance for the evaluation of impacts (p. 14). Hypothesize that JCS more useful for larger u due to higher relevance of program content and smaller locking-in effect. As u increases, the number of potential comparison group members decreases as non-participants have joined other programs or found employment. – Possible decrease in match quality.

Results for West Germany – For most groups, estimated treatment effects are insignificant at the end of 30 months. The two exceptions are u = 5 and u = 9 who show a significant increase in employment. Results for East Germany – Majority of groups show negative employment effects at the end of 30 months.

Some Thoughts How to explain the effects for u = 5 and u = 9 in West Germany? – Why are effects not clustered in some more meaningful way, like the hypothesis that impacts should be larger for those who have been unemployed longer? – Does not appear that the samples for u = 5, 9 look very different from the samples for u ≠ 5, 9 in terms of characteristics. – No particular imbalance between Ts and Cs for u = 5 and u = 9. But is some kind of a “end of year effect” going on? Note that average program duration for u = 1 is generally larger than it is for u = 2, 3, 4. Similar for u = 5 and u = 9.

Why do u = 5 and u = 9 have spikes in program durations? (More pronounced for West Germany) Notes: From Table 1, Means of Selected Variables. u = 5 u = 9

The rate of inflow into participation decreases from Sep 2000 to Jan 2001 before increasing again. – What’s going on here? Some economy wide macro effects? Cohort effects? Budget/program changes?

Why is there variation over time in entry to participation? Notes: From Figure B.1, Available Data for Analysis.

Aggregating 6 cohorts over a 1 year window into one is perhaps not ideal if cohorts involve different types of people. Is it possible to have the same person in both the T group and C group? – For example, unemployed in June 2000 (so C for 1 st cohort) and participate in Sep 2000 (so T for 2 nd cohort). – Aggregating cohorts allows perfect match to the same person? Or impossible because stratify on u?

Meaning and alignment of follow-up time for Ts and Cs in the aggregated data set. – By creating 6 fictitious program start dates for the non- participants and using ‘Month of Treatment Start’ as a covariate in the propensity score model: Is it possible, for example, to have T with u = 5 and start date of May 2001 matched with C with u = 5 and ‘start date’ of June 2000 as long as their propensity scores are close? Follow-up month 1 is June 2001 for T and July 2000 for C? Differing effect of macro events in June 2001 and July 2000?

Instead, maybe run separate estimations for each of the 6 cohorts with unemployment duration in three groups ( 4 quarters) rather than from 1 to 12 quarters (so won’t have too small sample sizes). – July 2000 Ts with June 2000 Cs for 2 x 2 x 3 = 12 subgroups. – … – May 2001 Ts with Apr 2001 Cs for 2 x 2 x 3 = 12 subgroups. Maybe this is going backwards as it is more similar in spirit to the approach taken in earlier papers based on the Feb 2000 cohort (essentially replicating the previous approach 6 times). – Are subgroup effects the same for all 6 cohorts?

Comparison group contamination. – How many non-participants (or “waiters”) eventually become participants? Differing rates of participation for non-participants could be driving the results for u = 5 and u = 9. Maybe examine time trends of employment for T means and C means, not just time trends for the impacts. – To make a cleaner comparison, maybe impose condition that non-participants do not participate within a few months of the treatment start date? (Will not be conditioning on future employment outcomes by doing so).

The End