Activation of Safety Nets Beneficiaries and Active Inclusion in Western Balkans

Activation of Safety Nets Beneficiaries and Active Inclusion in Western Balkans

The Challenge Employment and active inclusion are among the most critical challenges for countries across the Western Balkans

Framework for the analysis Target Groups? Inactive Unemployed SSN Beneficiaries Barriers to Work? Employability barriers (skills, experience, etc.) Participation constraints Activation for Who? PROFILING (Dis)Incentives in Benefit Design Incentives in the tax and benefit systems Benefit formula/ generosity Mutual obligations Earned income disregards Coordination between welfare and employment services Specific activation policies and ALMPs Implementation capacity (financing, staffing, etc.) Institutional Readiness for Activation Policies

Analytical Framework: constraints to employment for safety nets beneficiaries Labor demand Employability barriers Education / credentials Basic skills (literacy, etc) Job specific skills Behavioral skills Tax and Benefit Disincentives Participation constraints Disincentives to formal work from the interaction of taxation and benefit rules Care-taking duties Lack of empowerment Distance from markets Information deficit 4

Objective of Profiling of Social Safety Net beneficiaries: provide tailored activation strategies for diverse clients Client segments What are the employability barriers (skills, experience, etc)? Are there constraints to labor force participation (caretaking duties, disincentives, mobility)? Who can be activated among SSN beneficiaries? Who can be activated in the population?? Activation Strategies 5

Basic Profiling SERBIA Summary Findings About half of SSN beneficiaries in Serbia are work-able (potentially activable ) Worse labor market outcomes for activable SSN beneficiaries (based on HBS data) Lower employment rate (57% vs 63%) Higher unemployment rate (21% vs 16%) Due to multiple barriers Employability barriers (more than half has basic or no education) Participation constraints (higher caretaking duties: 30% with young children; 15% with disabled)

Who can be activated? Of working age (15-64) Able bodied Activables: Individuals who can be presumed to be able to work Not in education or training Who can be activated among the population? Who can be activated among the SSN beneficiaries? Are these groups coinciding? 7

More than half of population in Serbia are work-able and more than ¾ participate in the labor force Age Composition of SSN Beneficiaries Relative to General Population in Serbia, 2010 FSA beneficiary 26.4 11.0 4.7 54.8 Out of labor force, 20.58% Labor Market Status of Work-Able Population in Serbia, 2010 Employed, 63.01% FSA+CA beneficiaries 11.3 32.1 6.1 50.0 Salaried employee, 75.52% SSN all 27.1 9.6 8.6 52.9 Employer, 2.72% Whole population 12.6 22.1 8.0 56.8 0 20 40 60 80 100 Percent Unemployed 16.42% Self-employed, 15.31% Zero Income 6.46% Child Working age (disabled) Working age (work-able) Old Working age (in education) Source: Serbia HBS data 2010. Note: Work-able includes all individuals of working age (15 64) who are neither disabled nor in education or training. 8

SSN beneficiaries represent a small fraction of the workable population Safety Net Coverage of the Work-Able Population in Serbia, 2010 Out of labor force 72.8 13.5 9 5. Unemployed 83.3 8.5 6 Employed 79.0 13.0 6 0 20 40 60 80 100 Percent Nonbeneficiaries in Q2-Q5 Nonbeneficiaries in Q1 Beneficiaries of other SSN FSA beneficiaries Source: Serbia HBS data 2010. Note: Work-able includes all individuals of working age (15 64) who are neither disabled nor in education or training. 9

However, they are more likely to be unemployed or inactive or have low-quality jobs Employment and Unemployment rates among the work-able population in Serbia, 2010 Sector of Employment for work-able Population in Serbia, 2010 FSA beneficiaries 56.5 20.7 22.8 FSA beneficiaries 56.1 FSA + CA beneficiaries 58.1 21.9 20.1 SSN beneficiaries 13 30.3 12.7 44.1 SSN beneficiaries 56.8 20.7 22.4 Nonbeneficiaries, poor 17.2 27.8 14.7 40.3 Nonbeneficiaries, poor 55.2 25.9 18.8 Whole population 34.4 28.6 11.2 25.7 Whole population 63.0 16.4 20.6 0 20 40 60 80 100 Percent Employed Unemployed Out of labor force 0 20 40 60 80 100 Percent Public and professions Constr., industry, transport Not identified* Source: Serbia HBS data 2010. Note: Work-able includes all individuals of working age (15 64) who are neither disabled nor in education or training * Because of the sample size, conclusions cannot be drawn about the sectors other than Agriculture and manual jobs.. Retail, trade, crafts Agriculture and manual jobs 10

Which could be largely explained by lower educational attainment Nonbeneficiarie FSA SA + CA beneficiaries Nonbeneficiaries, poor Education Distribution of SSN Beneficiaries in Serbia, 2010 FSA beneficiaries SSN beneficiaries Activable population 9.0 5.4 12.1 11.0 17.3 19.0 29.2 28.5 28.0 33.5 59.3 52.5 56.4 53.0 40.8 0 20 40 60 80 100 Percent s, poor Activable population beneficiaries Targeted SSN Employment Status of SSN Beneficiaries in Serbia, by Education Level, 2010 All Sec + Prim < Prim Sec + Prim < Prim Sec + Prim < Prim Sec + Prim < Prim 53.0 54.7 55.2 49.5 57.8 60.4 58.6 58.4 60.0 58.2 61.2 65.7 54.0 12.7 8.1 8.1 17.7 18.1 15.0 18.5 14.6 15.5 18.0 25.8 32.5 23.4 Never attended Elementary school Higher education (college or higher) No education completed Secondary/Vocational Source: Serbia HBS data 2010. Note: Work-able includes all individuals of working age (15 64) who are neither disabled nor in education or training. 0 20 40 60 80 100 Percent Employed Unemployed 11

Work-able SSN beneficiaries display greater caretaking needs than the work-ready population as a whole Share of work-able population living with at least one person in need of care in Serbia, 2010 FSA beneficiaries 15.4 29.8 23.7 FSA+CA beneficiaries 2.4 4.9 8.2 SSN all 9.5 20.4 31.5 General population 2.6 5.3 9.4 0 10 20 30 40 Percent % hh with disabled % hh with child 5 % hh with child 2 Source: Serbia HBS data 2010. Note: Work-able includes all individuals of working age (15 64) who are neither disabled nor in education or training. 12

Putting various traits into a multi-dimensional analysis of vulnerability using Latent Class Analysis Objective: to define sub-groups of SSN clients with similar labor market vulnerability Non parametric method to identify similar latent classes of the population through a number of indicator variables Uses socio/economic/demographic characteristics that we believe are relevant for targeting policies age, gender, family situation, location education, experience, past/present occupation employment status, work restrictions, type of vulnerability -> Statistical method that searches for distinct groups using all these characteristics (minimizes heterogeneity within each group and maximize differences across groups 13

Latent Class Analysis SERBIA Elder experienced unemployed, 35% Inactive uneducated women, 21% Elder experienced inactive, 16% Educated unemployed youth, 8% Chronic unemployed, 8% Inexperienced unemployed women,, 12%

Statistics Active covariates Indicators Latent Class Analysis SERBIA Elder experienced unemployed Inactive uneducated women Elder experienced inactive Inexperienced unemployed women Chronic unemployed Educated unemployed youth Class size 35% 21% 16% 12% 8% 8% Worked before 100% 19% 95% 24% 20% 21% Willing to retrain 54% 23% 5% 45% 66% 73% Inactive 22% 100% 100% 16% 0% 19% Long-term unemployed 66% 0% 0% 63% 99% 6% Short-term unemployed 12% 0% 0% 21% 0% 75% Uneducated 6% 31% 21% 31% 20% 6% Elementary education 34% 54% 36% 38% 37% 21% Secondary+ education 61% 16% 43% 31% 43% 73% Young (15 29) 4% 41% 8% 26% 39% 92% Adult (30 54) 54% 52% 45% 59% 61% 4% Prime age (55 64) 42% 7% 47% 15% 0% 4% Female 41% 82% 34% 92% 28% 26% Caretaker 0% 33% 32% 0% 0% 0% Married 62% 65% 64% 48% 55% 9% Discouraged inactive 20% 56% 78% 8% 0% 14% (% of total) Willing inactive 2% 44% 22% 8% 0% 5% (% of total) Mean age 46 32 47 36 31 23

Employability obstacles Matching Beneficiary Profiles and Activation Services in Serbia, by Client Group 4 Intensified Activation (TVET, Skills) Hard-to-serve (skills, special support) 3.5 3 2.5 2 Elder experienced unemployed Chronic unemployed Inactive uneducated women Inexperienced unemployed women 1.5 1 0.5 Educated unemployed Market Ready youth (job info, matching, search assistance) Experienced inactive elder Special Support (care for dependents, transport, social, health) 0 0 0.5 1 1.5 2 2.5 3 3.5 Other barriers to participation

(Dis)Incentives in Benefit Design KOSOVO (Dis)Incentives in Benefit Design Benefit formula/ generosity Mutual obligations Incentives in the tax and benefit systems Earned income disregards Activation for Who? PROFILING Institutional Readiness for Activation Policies

Main characteristics of the Asistenca Sociale Asistenca Sociale s (AS) main features - type of program Design, financing and implementation Basic administrative and survey data AS combines elements of (i) last-resort social assistance; (ii) noncontributory unemployment benefit and (iii) child allowance AS is granted based on multiple criteria: (i) income and asset test; (ii) workability / dependence; (iii) family demographics; (iv) unemployment status Centrally designed: by the Ministry of Labor and Social Welfare Centrally financed Implementation is at local level: by Centers for Social Work which belong to the municipal administration Average monthly spending in 2012 2.33 million EUR; 28.26 million EUR in 2011 Number of beneficiary families 17,570 (Category I) and 13,541 (Category II) Spending - 0.7% of GDP (2012) Increasing share of ablebodied (Category II) among AS recipient families 18

Asistenca Sociale s design implies more disincentives than incentives to be active Disincentives Incentives 19

Disincentives for work stem from the Asistenca Sociale benefit formula The due benefit is calculated as difference between the AS threshold applicable to a family of that size and its monthly income Each additional euro of income will be 100% taken away from the benefit amount due: Earned income loss of benefit completely Complete loss of benefit only for formal / legal income Income from informal employment, household agriculture, remittances not measured : assessed through assets (either as exclusionary filters or fully overlooked / not considered) bias in both cases 20

(Dis)incentives due to AS generosity Core AS benefit is generous AS contributes a significant share to consumption of the poor (over 40%), due to low consumption level but also relatively high nominal transfers Packaging of AS with other benefits AS beneficiary status provides automatic eligibility for electricity subsidy and some other financial benefits 21

Going Forward: Activation agenda much broader than just focusing on addressing welfare dependency Social assistance beneficiaries are only a fraction of the inactive, and activation measures that only target them will not bring significant impact Room for improvement in the design of LRSA programs e.g. introduction of gradual income disregard, in-work benefits etc. Closer institutional cooperation between EAs and SWCs is needed for effective activation of vulnerable. The capacity and effectiveness of the EA work need to be strengthened for broader activation e.g. staffing realignment, nonstate providers etc. Improved cost-effectiveness of the ALMPs e.g. increased competition, advanced (statistical) profiling etc. 22