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

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

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 www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

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 www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

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 www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

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) www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

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

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 www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

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 www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

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) www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

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. www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

Impact of SROP 1.1.1. and 1.1.3. Reemployment rate much higher for participants srop 1.1.1: higher by 53-51 %points srop 1.1.3: higher by 57-50 %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 www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

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%

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%

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 www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

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 www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

Thank you for your attention

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 www.ujszechenyiterv.gov.hu | Brussels 13 June 2013

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 www.ujszechenyiterv.gov.hu | Brussels 13 June 2013