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

• Incentives in the tax

and benefit systems

• Benefit formula/

generosity

• Mutual obligations

• Earned income

disregards

(Dis)Incentives in

Benefit Design

• 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

Employability barriers

Participation constraints

Tax and Benefit

Disincentives

4

Labor demand

• Education / credentials

• Basic skills (literacy, etc)

• Job specific skills

• Behavioral skills

• Care-taking duties

• Lack of empowerment

• Distance from markets

• Information deficit

• Disincentives to formal work from the interaction of

taxation and benefit rules

Objective of “Profiling” of Social Safety Net beneficiaries: provide

tailored activation strategies for diverse clients

5

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

Client

segments What are their

employability

barriers? Are there

constraints to LF

participation? Who can be

activated among

SSN beneficiaries? Who can be

activated in the

population?

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

Not in education or training

7

Activables: Individuals who can be presumed

to be able to work

• Who can be activated among the population?

• Who can be activated among the SSN beneficiaries?

• Are these groups coinciding?

More than half of population in Serbia are “work-able”

and more than ¾ participate in the labor force

8 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.

12.6

27.1

11.3

26.4

22.1

9.6

32.1

11.0

8.0

8.6

6.1

4.7

56.8

52.9

50.0

54.8

0 20 40 60 80 100

Whole population

SSN all

FSA+CA beneficiaries

FSA beneficiary

Percent

Age Composition of SSN Beneficiaries Relative to General Population in Serbia, 2010

Child Old

Working age (disabled) Working age (in education)

Working age (work-able)

Unemployed 16.42%

Out of labor force,

20.58%

Salaried employee, 75.52%

Employer, 2.72%

Zero Income 6.46%

Employed, 63.01%

Labor Market Status of Work-Able Population in Serbia, 2010

Self-employed,

15.31%

79.0

83.3

72.8

13.0

8.5

13.5

6

6

9 5.

0 20 40 60 80 100

Employed

Unemployed

Outof labor force

Percent

Safety Net Coverage of the Work-Able Population in Serbia, 2010

Nonbeneficiaries in Q2-Q5 Nonbeneficiaries in Q1 Beneficiaries of other SSN FSA beneficiaries

SSN beneficiaries represent a small fraction of the work-

able population

9 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.

63.0

55.2

56.8

58.1

56.5

16.4

25.9

20.7

21.9

20.7

20.6

18.8

22.4

20.1

22.8

0 20 40 60 80 100

Whole population

Non-beneficiaries, poor

SSN beneficiaries

FSA + CAbeneficiaries

FSA beneficiaries

Percent

Employment and Unemployment rates among the work-able population in Serbia, 2010

Employed Unemployed Out of labor force

However, they are more likely to be unemployed or

inactive or have low-quality jobs

10

34.4

17.2

13

28.6

27.8

30.3

11.2

14.7

12.7

25.7

40.3

44.1

56.1

0 20 40 60 80 100

Wholepopulation

Nonbeneficiaries, poor

SSN beneficiaries

FSA beneficiaries

Percent

Sector of Employment for work-able Population in Serbia, 2010

Public and professions Retail, trade, crafts

Constr., industry, transport Agriculture and manual jobs

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.” .

5.4

11.0

9.0

12.1

17.3

19.0

28.0

29.2

28.5

33.5

59.3

53.0

56.4

52.5

40.8

0 20 40 60 80 100

Activablepopulation

SSN beneficiaries

Nonbeneficiaries, poor

SA + CA beneficiaries

FSA beneficiaries

Percent

Education Distribution of SSN Beneficiaries in Serbia, 2010

Never attended No education completed

Elementary school Secondary/Vocational

Higher education (college or higher)

Which could be largely explained by lower educational

attainment

11 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.

58.2

49.5

60.0

58.4

55.2

54.7

58.6

53.0

60.4

57.8

54.0

65.7

61.2

14.6

18.1

23.4

8.1

18.5

32.5

15.0

17.7

25.8

8.1

12.7

18.0

15.5

0 20 40 60 80 100

< Prim

Prim

Sec +

< Prim

Prim

Sec +

< Prim

Prim

Sec +

< Prim

Prim

Sec +

All

FSA

ben

efic

iari

esT

arge

ted S

SN

No

nb

enef

icia

rie

s, p

oo

rA

ctiv

able

po

pula

tion

Percent

Employment Status of SSN Beneficiaries in Serbia, by Education Level, 2010

Employed Unemployed

Work-able SSN beneficiaries display greater caretaking

needs than the work-ready population as a whole

12

5.3

20.4

4.9

23.7

9.4

31.5

8.2

29.8

2.6

9.5

2.4

15.4

0 10 20 30 40

General population

SSN all

FSA+CA beneficiaries

FSA beneficiaries

Percent

Share of work-able population living with at least one person in need of care in Serbia, 2010

% 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.

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%

Inexperienced unemployed women,, 12% Chronic

unemployed, 8%

Educated unemployed

youth, 8%

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%

Ind

ica

tors

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%

Act

ive

cov

ari

ate

s

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%

Sta

tist

ics

Married 62% 65% 64% 48% 55% 9%

Discouraged inactive

(% of total)

20% 56% 78% 8% 0% 14%

Willing inactive

(% of total)

2% 44% 22% 8% 0% 5%

Mean age 46 32 47 36 31 23

Source: Calculations from MOP/FSA Beneficiaries Survey 2011.

Matching Beneficiary Profiles and Activation

Services in Serbia, by Client Group

Elder experienced unemployed

Inactive uneducated

women Inexperienced unemployed

women

Experienced inactive elder

Chronic unemployed

Educated unemployed

youth

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.5 1 1.5 2 2.5 3 3.5

Em

plo

yab

ilit

y o

bst

acle

s

Other barriers to participation

Market Ready

(job info, matching, search

assistance)

Intensified Activation

(TVET, Skills)

Hard-to-serve

(skills, special support)

Special Support

(care for dependents,

transport, social, health)

(Dis)Incentives in Benefit Design—

KOSOVO

Activation for Who?

PROFILING

• Benefit formula/

generosity

• Mutual obligations

• Incentives in the tax

and benefit systems

• Earned income

disregards

(Dis)Incentives in

Benefit Design

Institutional Readiness

for Activation Policies

Main characteristics of the Asistenca

Sociale

Asistenca Sociale‘s (AS) main features - type of

program

• AS combines elements of (i) last-resort social assistance; (ii) non-contributory 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

Design, financing and implementation

• 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

Basic administrative and survey data

• 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 able-bodied (Category II) among AS recipient families

18

Asistenca Sociale’s design implies more

disincentives than incentives to be active

19

Disincentives Incentives

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

21

• AS contributes a significant share to consumption of the poor (over 40%), due to low consumption level but also relatively high nominal transfers

Core AS benefit is generous

• AS beneficiary status provides automatic eligibility for electricity subsidy and some other financial benefits

‘Packaging’ of AS with other benefits

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, non-state providers etc.

Improved cost-effectiveness of the ALMPs– e.g. increased competition, advanced (statistical) profiling etc.

22