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

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

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

Literature review Wage subsidy

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

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

Data description FBH Opportunity for All Program participants and control group

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

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: http://www.fzzz.ba/doc/Izvjestaj_o_radu_2015.pdf

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

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

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

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

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

Impact evaluation methodology

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

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 2015 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)

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

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

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

Results

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

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