1. Impact of measures to improve employability
Anna Adamecz
Ágota Scharle
Budapest Institute for Policy Analysis
| Brussels 13 June 2013

2. Outline Selecting programmes for the study Data sources
Raw reemployment rates
Impact analysis
Lessons
| Brussels 13 June 2013

3. Selecting programmes
HU vs EU15 employment gap: largely due to low emp of uneducated
most long term unemployed are uneducated
NLO admin data are accessible
5 programmes targeting the uneducated, administered by NLO, between 2007 and 2010
covering 56 % of total expenditure in SROP
| Brussels 13 June 2013

4. Design of selected programmes
complex: mentoring, training, wage subsidy
srop 111 – disabled jobseekers srop 112 – primary ed, long term unemployed srop 113 – long term unemployed (SA)
targeted wage subsidy
srop 121 – long term unemployed (low ed/older)
training and adult education
srop 211 – jobseekers with primary education
| Brussels 13 June 2013

5. Data sources NLO unemployment register
stock of 20 Jan 2009
inflow bween 20 Jan 2009 – 20 Jan 2010
NLO programme participation records
entering before 31 Dec 2010
Tax registry data on start of work contract
for control and treated, until Oct 2012
 linked together at the level of the individual
| Brussels 13 June 2013

6. Variables in the NLO data
age, sex, education
disability
previous spells of unemployment
spells of benefit receipt (UI or UA)
programme participation (entry, exit)
measures within complex programme
date of entering job (if registered)
| Brussels 13 June 2013

7. Share of uneducated (%)
uneducated= max primary education. grey bar= participant red bar= non participant (but eligible) line: gap in %
| Brussels 13 June 2013

8. Raw reemployment indicators
Official progress reports (OPR)
during/straight after programme or on day 180
excludes public works
only those completing the programme
NLO
within 180 days or any time until Oct 2012
or did not reregister within 180 days
includes public works
all those entering the programme
| Brussels 13 June 2013

9. Raw reemployment rates %
1.1.1
1.1.3
OPR indicators
Exit to employment 16 60
Employed on day 180 25 40
NLO indicators
Worked at least 30 days within 180 days* 44 52
Exit to job any time until Oct 2012 70 88
Did not return to NLO register 87 75
For 211, OPR did not include reemployment indicators. 121 is excluded as reemployment indicators are not comparable due to programme design specificities.
* during programme or within180 days after exit from programme
| Brussels 13 June 2013

10. Impact analysis: the method
Impact of programme participation on probability of reemployment
Compare observed outcome to „What if?”
Compare to counterfactual
Select control group by matching (propensity score)
Control group with same observed characteristics (age, sex, education, employment history, location)
| Brussels 13 June 2013

11. Impact of SROP 113 (men)
About 25 % of treated reregister with the NLO within 2 years, cf about 40 % of control group. This suggests that re-registration is a better measure of reemployment than the tax records, possibly because non-participants are more likely to find employment in the unregistered economy, which is not recorded in our data (by definition, as it is from the tax authority). This induces and upward bias in our estimates based on the tax data.
| Brussels 13 June 2013

12. Impact of SROP and
Reemployment rate much higher for participants
srop 1.1.1: higher by %points
srop 1.1.3: higher by %points
Upper bound: large, positive w upward bias
Much larger than international evidence
Possible selection bias in unobserved characteristics (e.g. motivation, ethnicity)
No data on unregistered employment
Includes deadweight loss and substitution effects
Separate estimates for men / women
| Brussels 13 June 2013

13. SROP1.1.1. w/wout wage subsidy
men
control
treat
treat, no wage subsidy N %
reemployed 1. 11 2%
275
53%
134
26%
reemployed 2. 6 1%
152
30%
101
20%
reemployed 3. 13 3%
356
69%
214
42%
women 17
531
55%
240
25%
280
29%
183
19% 25
694
71%
402
41%

14. SROP1.1.1. for long term unemp
men
control
treat
treat, no wage subsidy N %
reemployed 1. 6 2%
144
46% 68
22%
reemployed 2. 2 1% 88
28% 60
19%
reemployed 3. 8 3%
196
63%
120
38%
women 7
217 99
21%
118
25% 81
17% 10
296
62%
178
37%

15. Lessons re effectiveness
reemployment rates are high and increase in time
but: participants are better educated
srop 111 and 113: large positive impact
training and mentoring improves reemployment even without wage subsidy
impact of short term wage subsidy fades out fast
srop 111: significant impact for long term unemployed
| Brussels 13 June 2013

16. Lessons re evaluation methods
NLO register suitable for analysis if linked to tax data
NLO data: relatively cheap and available soon after
Quality of analysis could be improved by
recording all characteristics that determine eligibility
additional variables (e.g. level of disability, duration of employment spell)
qualitative surveys on selection process
recording costs at the level of the prog. participant
| Brussels 13 June 2013

17. Thank you for your attention

18. Access and targeting srop111 srop112 srop113 srop121 srop211
relevant target group
primary ed long term unemp
SA + Roma, primary ed
primary ed long term unemp + SA
primary ed
other target groups
new disab. benefit
+ DA
school leaver <25 ys >50 ys maternity
<35 ys school leaver disabled lone parent
School leaver >50 ys maternity
vocation
participants (thnds)
11.0
50.3
5.8
(13.0)
18.5
p/t (%) 23 27 2
n.a. 3
srop121: start extra
| Brussels 13 June 2013

19. Recommendations
increase funding for training and mentoring for uneducated jobseekers
adjust programme design based on impacts
improve targeting: target group, sub-indicators, profiling
unify indicators across programmes
add indicators on long term (1-2 years) impact
accompany new programmes with detailed longitudinal survey of participants and control
| Brussels 13 June 2013