1. Evaluation of the Employment Program Opportunity for All
of the Federation of Bosnia and Herzegovina
Merima Balavac and Josefina Posadas
Social Protection and Jobs team
Vienna, March 20, 2018

2. Outline Objective of the paper Review of the literature
Data description
Impact evaluation methodology
Results
Next steps for this paper

3. Objective of the paper This paper
aims to contribute to the debate on effectiveness of ALMPs in the Federation Bosnia and Herzegovina (FBH)
carries out a quasi-impact evaluation of the program Opportunity for All (OfA) of 2014 in the FBH, based on administrative data
OfA is a wage subsidy program given to employers when they hired registered unemployed jobseekers.
OfA lasts 6 months and covers social contributions of new hires, which are about 51 percent of the labor cost in FB&H

4. Literature review
Wage subsidy

5. Wage subsidies-theoretical background
Wage subsidies are controversial regarding its effectiveness
Pros
Cons
Reduction of the information asymmetry between the employer and the jobseeker
Adjustment (prevention) of human capital (loss)
Increasing skills (and experience) through learning on the job
Bridge to regular employment/ retirement
Costly
Negative signal to potential employers on skills of the participant
Reduce one‘s own initiative
Substitution effect
Windfall effects
Presentation Title

6. Wage subsidies- empirical evidence
Empirical evidence from robust impact evaluation have mixed results on employment and earnings
Empirical evidence on the effectiveness of wage subsidies is mixed (Card et al. 2015)
increase employment in the short term
little or no impact in the long term (36 months)
considerable substitution and windfall effects
Presentation Title

7. Data description
FBH Opportunity for All Program participants and control group

8. Opportunity for all (OfA)
Objective: Facilitate of labor market integration and prevention of long-term unemployment
Target population
jobseekers registered as unemployed with FEI, regardless of time unemployed, age, gender and education attainment
Employers: tax and contribution compliant firms
Length of subsidy: 6 months
Amount of subsidy:
Covering SSC (about 51% of labor cost)
Average support per person about 1,985 KM
Additional support of 10% to special groups (women, long-term unemployed)
Call for application for employers between May 5, 2014-May 5, 2015

9. OfA is one of the many ALMPs in FB&H
Some of the programs in 2014
Number of participants
Budget (KM)
Opportunity for all
5,299
10,431,867
The first working experience
1,890
7,128,942
Training
698
1,206,036
Voucher for employment
439
1,300,564
Co-financing of seasonal employment
333
280,820
Employment of Roma population 73
680,000
OfA is the largest ALMP program in FBH
Programs are designed and financed at the federal level, but implemented at the local cantonal level
Cantons have additional programs
Source:

10. Administrative data Data from Employment Office and Tax Administration
Data from the Employment Office
Persons Data
-Socio-demographic data (gender, age, educational attainment, residence)
- Unemployment length
Employers Data
- Type of ownership
- Type of activity
Program Data
- Program participation
Tax Administration Data*
-Type of health insurance (paid by employer or by FEI)
- Employment and unemployment spells (start and end date)
- Socio-demographic data (age, gender, educational attainment)
- Registration date (age of firm)
- Type of activity (NACE Rev2)
- Number of employees (at specific date in time)
- Employment office contribution
*Data extracted on the 13the of April 2016

11. Challenges of working with administrative data
Lack of integration
FEI data is not integrated in one platform with TAD data
FEI is not integrated with cantonal offices
Limitation of administrative data:
does not allow to identify informal workers
no information which with transition to inactivity
less rich information on firms and jobseekers characteristics compared to survey data
possibilities of entry errors
Presentation Title

12. OfA Participants & non-participants characteristics
Participants are younger and less educated
Participants (%)
Non-participants
Age (years)
35.3
36.0
Age group (years)
14-25
13.3
15.8
25-35
43.3
43.1
35-45
25.1
19.6
>45
18.3
21.5
Sex
Male
59.8
57.2
Females
40.2
42.8
Education
Non-qualified worker
9.2
5.9
Qualified worker
30.2
18.9
Secondary school
48.6
54.7
University (0-2 years)
1.2
1.9
University (>2 years)
10.8
18.6

13. OfA Participants and non-participants by cantons
Important variation in program participation across cantons:
Most beneficiaries are from Tuzla and Srednjo-Bosanski cantons
Distribution of beneficiaries across cantons
Cantons
Participant (%)
Non-participant (%)
Unsko-Sanski
14.1
9.2
Posavski
4.1
1.2
Tuzla
30.0
24.6
Zenicko-Dobojski
14.3
20.7
Bosansko-Podrinjski
1.7
0.7
Srednjo-Bosanski
14.7
6.3
Hercegovacko-Neretvanski
4.9
8.7
Zapadno-Hercegovacki
3.2
2.4
Kanton Sarajevo
10.2
24.1
K10
2.8
2.1

14. OfA Participants & non-participants by length of unemployment
Non-participants tend to be unemployed for longer
Length of unemployment (in years)

15. Impact evaluation methodology

16. Methodology Propensity score matching, using exact matching
Given design of a program, the IE can only be carried out using propensity score matching:
It creates a control group based on observable characteristics
Preferred matching algorithm: nearest neighbor with bias adjustment
Various matching algorithms included to test for robustness

17. Methodology Control group
We need to find control group of individuals who are as similar as possible to treatment group, to act as a counterfactual of what would have happened to the beneficiaries had not received the treatment.
We look for registered jobseekers who have similar characteristics to program beneficiaries in the TAD
Treatment starts at different months over the May 2014-May period which creates a challenge to identify a comparable relevant period of analysis for jobseekers in the control group
We work under two assumption
the control group entered the program in the midpoint of the program period (November 5th, 2014)
entry point at the beginning of the program (May 2014)

18. Assessing program success
Various outcome variables are evaluated
The probability of being employed and the number of days employed at different moments in time after the starting date of the program:
at the program completion
3, 6, 9 and 12 months after the program
The attachment to the job
whether person stay with the same employer
Employment dynamics after employment contract with subsidized firm ends
number of jobs held
the average duration of unemployment spells
Presentation Title

19. Propensity Score Matching
PSM performs well
Propensity score is estimated with variables that capture:
skills (age, age squared, education  and time the registered jobseekers has been unemployed)
local labor market context (cantonal dummies)
gender
number of unemployment spells
the length of unemployment spell interacted with canton
Assessment of matching quality show that matching performs well, i.e. our sample is well balanced between treated and non-treated individuals for all observed characteristics

20. Treatment and control after matching
Matching effectively corrects for differences in observable characteristics
Treatment
Control
Female (percentage)
41.672
41.286
Age (years)
35.424
  35.234
Education
Non-qualified
9.408
9.269
Primary school
29.735
Secondary school
48.211
48.404
Higher diploma
1.388
1.357
Higher-education
11.258
11.235
Length of last unemployment spell (months)
34.336
34.13
Number of unemployment spells since 2009
1.28
1.27
Treatment
Control
Cantons
Unsko-Sanski
11.72
Posavski kanton
4.75
Tuzla kanton
30.54
Zenicko-Dobojski
14.1
Bosansko-Podrinjski
2.19
Srednjo-Bosanski kanton
15.58
Hercegovacko-Neretvanski
4.72
 4.72
Zapadno-Hercegovacki
3.11
   3.11
Kanton Sarajevo
10.33
K10
2.96

21. Results

22. Results Results are preliminary and need to be further analyzed
Overall, our results are aligned with the most common findings in the literature, with impacts losing effectiveness with time
However, the results will be shared after have been discussed with a government
Heterogeneous analysis is carried out for the following subgroups
Age (youth from 15 to 24 and from 25 to 30 years; close to the retirement from 55 to 65 years old; late-mid career of 40 and older)
gender
long-term unemployed (1 year and longer)
Level of education
No previous experience
Cantons
And combinations of the above characteristics

23. What’s next for this paper
Two main activities ongoing
Double checking data and results
Explore firm dimension