Assessing Elderly Welfare and Pension Performance with Survey Data
April 3, 2013 Pensions Core Course This presentation builds on the work of colleagues in HDN and PREM
Outline
• (1) Overview of survey data & social protection – Role of social protection – Application to elderly and pensions – Pensions framework applied to survey data – Understanding survey data strengths and weaknesses
• (2) Application: Survey data for elderly welfare and pensions analysis – – Environment
• Living arrangements • Poverty
– Performance
• (3) Future work
2
(1) SP Context & Overview of Survey Data
Classification of programs
4
• Social assistance (Social Safety Nets) SA
• Labor Market Programs (active and passive) LM
• Social Insurance SI
Objectives of pensions
• Elderly poverty protection
• Consumption smoothing
• Encourage savings
• Insurance against shocks
5
Understanding poverty
• No common definition for poverty exists – General agreement: insufficient commodities
leading to constrained choices (Harold Watts)
– More narrow definition: lack of specific consumptions (e.g. too little food energy intake)
– Less narrow definition: Poverty as lack of “welfare” e.g., lack of “capabilitiy”: inability to achieve certain “functionings” (“beings and doings”) (Amartya Sen)
6 Based on DEC presentation
How poverty is commonly measured
• Individuals or households are ranked by income or consumption
• The measure of income or consumption is referred to as the ‘welfare aggregate’
• Poverty lines are then set either on a relative or absolute basis, often based on an extreme and basic standard of living
• Those with income or consumption (welfare aggregate) below a given poverty line are considered poor
7 Based on DEC presentation
Poverty measures • Poverty headcount (FGT0) - % of individuals or households with welfare
below the poverty line
• Poverty gap (FGT1) - mean shortfall of poor from the poverty line, expressed as a percentage of the poverty line
• Poverty severity (FGT2) – average of squared poverty gap ratio
8
Distance squared
% Below line Avg distance below line
Social Protection Objectives
9
Why Social Protection and Labor Systems Are Important
10
SPL over the life cycle
11
How is social protection applied to the elderly?
• Equity - Elderly poverty protection
• Opportunity
– Consumption smoothing in retirement
– Encourage savings
• Resilience - Insurance against shocks
12
Pension Diagnostic Assessment – Evaluation Process & Criteria
Enabling environment (Motivating reform,
framing & constraining reform options)
Existing design Demographic
profile Macro-economic
environment Institutional
Capacity Financial market
status Political economy
Initial Conditions &
Inherited System
Demand -
consumption smoothing & elderly poverty protection
Supply - mandatory & voluntary pension & social security schemes
Family & community support
Reform objectives
Primary:
improving coverage, adequacy, & sustain-ability for the long-term
Secondary: improving labor markets, macro/fiscal position, & contributing to financial market development.
Reform Design & Implementation
Options
Design reforms -introduce new schemes, parametric & structural reforms
Governance, Institutional and regulatory reforms
Strengthening institutions & implementation
Diagnostic Assessment – Data, Indicators and Tools
Information/ Data
Elderly
incomes,
vulnerability &
poverty
Initial conditions
Mandatory &
voluntary
pension
systems &
social security
schemes
Tools
Country HH survey data ADEPT-SP x/country data
Environment – UN Population Projections Country admin data Financial market data Macro & fiscal data (country/IMF) System design – Admin data/country laws WB database comparators Performance – Admin data/country laws WB database comparators HH survey data
Administrative data from social welfare schemes, housing, health provision.
HH survey data.
Additional
state support
Indicators
Environment Demographic Economic Financial Informal Support ADEPT-SP
Apex PROST ASPIRE & ext.
x-country data
Design Structure of pension system Qualifying conditions Parameters
Performance Coverage Adequacy Financial sustainability
Diagnostic Assessment – Tools
Tools
APEX Evaluation of individual level benefits across instruments + for different income groups. Individual replacement rates Replacement of average wage Pension wealth
ADEPT-SP Elderly welfare Elderly poverty
Co-residence Elderly income
generation
Comparisons of welfare, poverty across elderly, non-elderly & household types.
PROST Baseline. Long-term projections
of financing gap for existing schemes + replacement rates for current and future retirees
Reform scenarios. Long-term
projections financing gap + replacement rates for parametric and/or structural reforms
Outputs to simulate other
instruments (social pensions, voluntary savings)
WB Database & External X-Country Data
Cross-country comparisons Demographics Coverage Adequacy Affordability Sustainability
What is survey data?
• Examples: HBS, LSMS, DHS
• Organization: Household or individual level
• Timing: Generally collected ever 2-3 years, more frequent than census (~ 10 years)
• Information: Core demographics (eg age and gender), expenditure/ income, employment status, public and private transfers, etc
16
Individual Input File
17
Household
Identification
Individual
IdentificationSTRATA PSU
Urban location =1;
Rural location=2
Household
expansion
factor
Household
Size
Adult
equivalent
scale
Head of the
household
Age of the
household
member
Total
household
income
Poverty
line
Amount
received
from old
age
pensions
Participation in
scholarship
programs
Amount received
by the household
from
Oportunidades
Amount
received by the
household from
Pro-Campo
id_hh id_ind strata psu urban hhweight hhsize adul_eq head age hh_income pob_ing apos becas_ toport tprocam
20060150282 1 1 2 2 305 3 2 1 18 2459.34 938.61 0 180.49
20060150282 2 1 2 2 305 3 2 0 18 2459.34 938.61 0 180.49
20060150282 3 1 2 2 305 3 2 0 1 2459.34 938.61 0 180.49
20060150280 1 1 2 2 305 7 6 1 56 9094.69 938.61 0 334.24
20060150280 2 1 2 2 305 7 6 0 53 9094.69 938.61 0 334.24
20060150280 3 1 2 2 305 7 6 0 29 9094.69 938.61 0 334.24
20060150280 4 1 2 2 305 7 6 0 26 9094.69 938.61 0 334.24
20060150280 5 1 2 2 305 7 6 0 15 9094.69 938.61 0 334.24
20060150280 6 1 2 2 305 7 6 0 13 9094.69 938.61 0 334.24
20060150280 7 1 2 2 305 7 6 0 7 9094.69 938.61 1 334.24
20060150030 1 1 1 1 777 4 3 1 77 18183.37 938.61 1403.81 0
20060150030 2 1 1 1 777 4 3 0 51 18183.37 938.61 0
20060150030 3 1 1 1 777 4 3 0 43 18183.37 938.61 0
20060150030 4 1 1 1 777 4 3 0 9 18183.37 938.61 0
20060150040 1 1 1 1 777 1 1 1 92 4458.78 938.61 1604.35 0
20060150050 1 1 1 1 777 2 2 1 83 6397.05 938.61 1640.45 0
20060150050 2 1 1 1 777 2 2 0 39 6397.05 938.61 0
20060150060 1 1 1 1 859 5 2 1 41 12988.27 938.61 0
20060150060 2 1 1 1 859 5 2 0 32 12988.27 938.61 0
20060150060 3 1 1 1 859 5 2 0 11 12988.27 938.61 0
20060140410 1 1 7 1 638 10 6 1 56 10730.62 938.61 0 514.18
20060140410 2 1 7 1 638 10 6 0 58 10730.62 938.61 0 514.18
20060140410 3 1 7 1 638 10 6 0 86 10730.62 938.61 1411.48 0 514.18
20060140410 4 1 7 1 638 10 6 0 30 10730.62 938.61 0 514.18
20060140410 5 1 7 1 638 10 6 0 29 10730.62 938.61 0 514.18
20060140410 6 1 7 1 638 10 6 0 10 10730.62 938.61 0 514.18
20060140410 7 1 7 1 638 10 6 0 9 10730.62 938.61 0 514.18
20060140410 8 1 7 1 638 10 6 0 4 10730.62 938.61 0 514.18
Household Input File
18
Household
Identification
Individual
IdentificationSTRATA PSU
Urban location
=1; Rural
location=2
Household
expansion
factor
Household
Size
Adult
equivalent
scale
Head of the
household
Age of the
household
member
Total
household
income
Poverty
line
Amount
received
from old
age
pensions
Participation in
scholarship
programs
Amount received
by the household
from
Oportunidades
Amount
received by the
household from
Pro-Campo
id_hh id_ind strata psu urban hhweight hhsize adul_eq head age hh_income pob_ing apos becas_ toport tprocam
20060150282 1 1 2 2 305 3 2 1 18 2459.34 938.61 0 180.49
20060150280 1 1 2 2 305 7 6 1 56 9094.69 938.61 1 334.24
20060150030 1 1 1 1 777 4 3 1 77 18183.37 938.61 1403.81 0
20060150040 1 1 1 1 777 1 1 1 92 4458.78 938.61 1604.35 0
20060150050 1 1 1 1 777 2 2 1 83 6397.05 938.61 1640.45 0
20060150060 1 1 1 1 859 5 2 1 41 12988.27 938.61 0
20060140410 1 1 7 1 638 10 6 1 56 10730.62 938.61 1411.48 0 514.18
Administrative vs Household Data
Administrative data
• - Limited population coverage - only ‘covered’ included
• + Comprehensive data on contributors, beneficiaries
• + Cumulative (over life cycle)
• - Narrow variables (eg age, gender, contribution)
Household data • + Entire population
represented
• -/+ Generally lack data on contributors, though extensive info on recipients (and non-recipients)
• - Static (singe year, usually not
panel) • + Much more comprehensive
(demographic, poverty, public & private transfers)
19
Uses of survey data (continued)
• Quality of data from design, implementation and processing of surveys is critical
• Surveys do have limitations, such as comparability across countries (eg income vs consumption), recall, survey non-response, comparability over time
• Nonetheless, survey data can provide a wealth of information for welfare and program assessment
20
ASPIRE Survey Initiative
• One of the largest datasets on social protection in the world: the combined dataset contains information on almost 5 million individuals in 1.3 mln. households.
• Reveals large information gaps in standard household surveys, many of which do not include questions about participation in social protection programs.
• Advocates for greater use of surveys for assessing social protection policies, and provides tools for enhanced data collection (survey modules, links to data analysis software).
21
ASPIRE household survey components
1. Data collection, harmonization and validation
2. Tools for data analysis (ADePT and Simulators)
3. Training in tools (Bank and non-Bank clients)
4. Analytical notes and reports
5. External partnership and internal collaboration
6. Dissemination
22
Types of Social Protection Programs Category I Category II Type of program
Old age pension
Old age civil servant pension
Veteran's old age pension
Early retirement pension
Survivors pension
Survivors civil servant pension
Occupational injury benefits/ pension
Paid sick leave
Disability Disability pension
Unemployment compensation
Severance pay
Early retirement for labor market reasons
Labor market training
Youth measures
Subsided employment
Employment measures for disabled
Employment service and administration
Low income/ Last-resort program
Non-contributory/ Social pension
Family allowances*
Disability benefits
Housing allowances
Food stamps/ Vouchers
Conditional cash transfers Conditional cash transfers
Food rations
Supplementary feeding
School feeding
Energency food distribution
Fee waivers, education
Scholarships
Fee waivers, health
Food price subsidies
Public distribution systems
Energy and utility subsidiesPublic works Public works
In-kind food transfers
Fee waivers and scholarships
General subsidies
Social safety net programs
Labor market programs Unemployment
Active labor market programs
Cash or near-cash transfers
Pensions and other social
insurance
Old age
Survivors
Occupational injury/sickness
benefits
23
ASPIRE includes the following 12 indicators for measuring social protection:
• Coverage • Beneficiary Incidence • Benefit Incidence • Generosity • Leakage • Targeting differential • Impact on Poverty • Impact on Inequality • Coady-Grosh-Hoddinott indicator • Program duplication • Social protection overlap • Cost benefit ratio
24
(2) Surveys specifically for Elderly Welfare and Pension Analysis
Motivations for work
• “Looking forward, this review suggests several concrete areas for further work. First, investing in strategic knowledge products particularly in areas related to coverage where there are significant gaps including, but not limited to: – Old age poverty, informal support and pension systems, especially in
countries with large agricultural and informal sectors such as in South Asia and Sub-Saharan Africa. This requires efforts in several areas including exploiting existing survey data, collecting administrative data from country sources and micro-simulations with the APEX model.”
• “Measurement of performance must come from systematic evidence provided by household survey data. The surveys provide information on the incidence of benefits and the impact of programs on household poverty, among other things.”
Source: Dorfman and Palacios. 2012. “World Bank Support for Pensions and Social Security”
26
Why use survey data for elderly poverty and pensions?
• Ability to answer new and different policy questions – Environment – poverty, distribution of
income/consumption, living arrangements, key demographics
– Design – N/A – Performance – coverage (receipt), poverty impact,
adequacy, targeting, etc • Cross-tabulate by key characteristics, eg geography, gender,
age, income
• More breadth of information on individuals and households
27
Some practical uses of survey data
• Understand characteristics of elderly and non-elderly population (e.g demographics, living arrangements and welfare)
• Produce evidence- based findings on pension and other programs, such as coverage, adequacy, poverty impact, etc
• Provide snapshot of income by sources (public, private) for different age groups
28
Pensions Survey Work
• East Asia and the Pacific (EAP) – 7 countries, Eastern Europe and Central Asia (ECA)– 20 countries, Latin America and Caribbean (LAC) – 20 countries, Middle East and Northern Africa (MNA) – 5 countries, South Asia (SAR) – 7 countries. The surveys vary in size from approximately 15,000 in Albania to 600,000 individuals in India.
29
Applications of Survey Data
• (1) Environment – Living arrangements – Poverty (elderly and non-elderly)
• Design – N/A • (2) Performance
– Coverage – Adequacy – Poverty impact – Program overlap – Cost-benefit – Targeting
30
Characteristics of elderly sample
31
Variable Obs Mean Std. Dev. Min Max
Age 481,629 69.21 8.09 59.00 119.00
Sex (1=male) 480,278 0.45 0.50 0.00 1.00
Household Size 481,629 4.03 2.79 1.00 48.00
Urban 469,719 0.57 0.49 0.00 1.00
Pension received 450,279 0.44 0.50 0.00 1.00
Remittances received 344,945 0.16 0.36 0.00 1.00
Co-resident 464,384 0.70 0.46 0.00 1.00
Employed 213,625 0.34 0.47 0.00 1.00
Widower 249,264 0.33 0.47 0.00 1.00
Age60_69 481,629 0.52 0.50 0.00 1.00
Age70_79 481,629 0.30 0.46 0.00 1.00
Age80_plus 481,629 0.13 0.33 0.00 1.00
Quintile welfare 473,053 3.88 1.40 1.00 5.00
Decile welfare 473,053 7.35 2.90 1.00 10.00
Living Arrangements
• What – the structure of households by age, gender, size
• Main indicator – Co-residence rate (elderly living with non-elderly)
• Why – proxy for informal support by non-elderly, ‘voluntary pillar’ of family support can complement formal pension systems
32
Co-residence
• Correlates of co-residence in existing studies - lower welfare, living in urban areas, widowers, higher country level GDP per capita)
• Initial findings
– Co-residence more likely for: urban, not receiving a pension, not receiving remittances, lower welfare deciles, lower income countries
– Wide variation between, and within some regions (eg ECA)
33
Co-residence rate, by region
34
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00%
AFR-MUS
AFR-NGA
AFR-RWA
EAP-MNG
EAP-VNM
EAP-TMP
EAP-LAO
ECA-HUN
ECA-BLR
ECA-HRV
ECA-POL
ECA-BGR
ECA-MNE
ECA-KRZ
ECA-GEO
ECA-ARM
ECA-TJK
LAC-URY
LAC-BRA
LAC-CRI
LAC-PAN
LAC-MEX
LAC-PER
LAC-COL
LAC-SLV
LAC-HND
LAC-NIC
MNA-WBG
MNA-MAR
MNA-DJI
SAR-IND
SAR-NPL
SAR-MDV
SAR-AFG
Average
GNI per capita and co-residence rate
35
R² = 0.509
-
2,000.00
4,000.00
6,000.00
8,000.00
10,000.00
12,000.00
14,000.00
16,000.00
18,000.00
0.4 0.5 0.6 0.7 0.8 0.9 1
Co-residence by pension receipt
36
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
AFR
-MW
I
AFR
-MU
S
EAP
-KH
M
EAP
-LA
O
EAP
-PH
L
EAP
-TH
A
EAP
-VN
M
ECA
-ALB
ECA
-AR
M
ECA
-BIH
ECA
-BG
R
ECA
-HR
V
ECA
-HU
N
ECA
-KSV
ECA
-KR
Z
ECA
-LTU
ECA
-MD
A
ECA
-MN
E
ECA
-PO
L
ECA
-SR
B
ECA
-SV
K
ECA
-TJK
ECA
-TU
R
ECA
-UK
R
LAC
-AR
G
LAC
-BO
L
LAC
-BR
A
LAC
-CH
L
LAC
-CO
L
LAC
-CR
I
LAC
-DO
M
LAC
-EC
U
LAC
-SLV
LAC
-GTM
LAC
-HN
D
LAC
-JA
M
LAC
-MEX
LAC
-NIC
LAC
-PA
N
LAC
-PR
Y
LAC
-UR
Y
LAC
-VEN
MN
A-M
AR
MN
A-I
RQ
MN
A-J
OR
SAR
-AFG
SAR
-BTN
SAR
-IN
D
SAR
-MD
V
SAR
-NP
L
SAR
-PA
K
SAR
-LK
A
Tota
l
No pension Yes pension
Coresidence rate by key characteristics- country level
37
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
Total
Urban
Rural
Male
Female
60-74
75+
Bottom Quintile
Top Quintile
Pension Receipt
No Pension Receipt
Remittance Receipt
No Remittance Receipt
Employed
Not Employed
Poverty of elderly
• Are elderly household more poor then non-elderly households?
• Are elderly individuals more poor then non-elderly?
• Preliminary findings (point estimates robust, though less for s.e.)
– By household type ( any elderly, only elderly, some elderly, no elderly)
– By individual – youth, working age, elderly
38
Welfare distribution by age group – individual level
39
0.2
.4.6
.8
Den
sity
8 10 12 14Log of percapita welfare
Youth Working Age
Elderly
Poverty line
Global comparison – elderly poverty %
40
0.000 0.050 0.100 0.150 0.200 0.250
AFR-GHA
AFR-MWI
AFR-MUS
AFR-NGA
AFR-RWA
EAP-KHM
EAP-LAO
EAP-MNG
EAP-PHL
EAP-TMP
EAP-VNM
ECA-ALB
ECA-ARM
ECA-BLR
ECA-BIH
ECA-BGR
ECA-HRV
ECA-GEO
ECA-HUN
ECA-KSV
ECA-KRZ
ECA-LTU
ECA-MDA
ECA-MNE
ECA-POL
ECA-SRB
ECA-SVK
ECA-TJK
ECA-TUR
ECA-UKR
LAC-ARG
LAC-BOL
LAC-BRA
LAC-CHL
LAC-COL
LAC-CRI
LAC-DOM
LAC-ECU
LAC-SLV
LAC-GTM
LAC-HND
LAC-MEX
LAC-NIC
LAC-PAN
LAC-PRY
LAC-PER
LAC-URY
LAC-VEN
MNA-MAR
MNA-DJI
MNA-JOR
MNA-WBG
SAR-AFG
SAR-BTN
SAR-IND
SAR-MDV
SAR-NPL
SAR-PAK
SAR-LKA
Regional comparison – poverty % by age group
41
0.000
0.050
0.100
0.150
0.200
0.250
Chart Title
Overall Youth Working Age Elderly
Country level - Poverty Headcount by Household Type
42
0%
5%
10%
15%
20%
25%
30%
Average 1) Elderly:lone
2) Elderly:2+
5) Elderlywith
WorkingAge
7) Elderlywith
WorkingAge and
Youth
6) Elderlywith Youth
3) Workingage only
8) Workingage and/or
Youth
HH OnlyElderly
HH SomeElderly
HH NoElderly
Poverty rate by age and gender
43
Select performance indicators
– Coverage - receipt
– Adequacy – transfer amount/ poverty line or /welfare
– Poverty impact – reduction in poverty due to transfer
– Program overlap – receipt of 2+ programs
– Cost-benefit – reduction in poverty gap for each $ spent
– Targeting - % intended beneficiaries receiving transfers
44
Simulated poverty impact
45
Poverty Rate (FGT0)* Poverty Gap (FGT1)* Poverty Severity (FGT2)*
All Income No pension transfer All Income No pension transfer All Income No pension transfer
Individual level
Population 0.5077 0.5878 0.2312 0.3265 0.1487 0.2435
Youth (0-14) 0.6196 0.6651 0.2873 0.3617 0.1860 0.2642
Working Age (15-60) 0.4718 0.5248 0.2157 0.2789 0.1405 0.2024
Elderly (60+) 0.3502 0.7611 0.1330 0.5455 0.0687 0.4685
Elderly (60-74) 0.3562 0.7380 0.1279 0.5224 0.0656 0.4485
Elderly (75+) 0.3331 0.8279 0.1476 0.6124 0.0777 0.5262
Non-Elderly (0-59) 0.5218 0.5723 0.2400 0.3069 0.1559 0.2233
Household level
Elderly-only households 0.0410 0.8512 0.0177 0.7742 0.0126 0.7337
Some elderly households 0.4817 0.7283 0.1817 0.4697 0.0917 0.3767
Non-Elderly Households 0.4325 0.4550 0.2088 0.2335 0.1452 0.1690
Total 0.4197 0.5321 0.1924 0.3116 0.1269 0.2429
Notes
The poverty line is set at total median income (deflated) – Note that for illustration purposes as typical poverty line is 50% median
The simulated welfare subtracts pension income (deflated)
Weights are applied for statistical accuracy
(3) Pipeline Work
Pipeline work
• Using survey data for country work, particularly environment and performance diagnostics – poverty profile, living arrangements, coverage, adequacy, etc
• Regional and cross-regional comparison
• Multi-country indicators and research examining factors such as correlates to coresidence & elderly poverty
47
ADePT Training
• There will be a hands-on computer training next week for those interested.
• Please sign-up
48
Select References
• Cameron. 2000. “The Residency Decision of Elderly Indonesians: A Nested Logit Analysis”. Demography. Volume 37-No. 1. • Costa 1998.The Evolution of Retirement: An American Economic History, 1980-1990. University of Chicago Press. Chicago. • Deaton. 2000. The Analysis of Household Surveys: A Microeconmetric Approach to Development. World Bank. • Dorfman et al. 2012. “Malaysia Elderly Protection Study”. World Bank. Washington DC. • Evans, M. 2012. “Profiling Elderly Populations and Elderly Poverty – Practice Note”. • Giles et al. 2010. “Can China’s Rural Elderly Count on Support from Adult Children?”. World Bank. Washington DC. • Holzman, R. and Hinz, R. “Old Age Income Support in the 21st Century: An International Perspective on Pension Systems and Reform”, World Bank,
2005. • OECD. 2011. Pensions at a Glance 2011. Paris. • Rofman, R. et al. 2008. Pension Systems in Latin America: Concepts and Measurements of Coverage. World Bank. • Whitehouse, E. 2007. Pensions Panaroma. World Bank. Washington DC. • World Bank. 2009. Closing the Coverage Gap. Washington DC. • World Bank. “International Patterns of Pension Provision II: A Worldwide Overview of Facts and Figures”. Washington DC. • World Bank. “Pension Indicators: reliable statistics to improve pension policymaking”, World Bank Pension Indicators and Database, Briefing 1,
Washington DC. • World Bank. “Coverage: How much of the labor force is covered by the pension system?”, World Bank Pension Indicators and Database, Briefing 2,
Washington DC. • World Bank. “Adequacy (1): Pension entitlements, replacement rates and pension wealth”, World Bank Pension Indicators and Database, Briefing 3,
Washington DC. • World Bank. “Adequacy (2): Pension entitlements of recent retirees”, World Bank Pension Indicators and Database, Briefing 4, Washington DC. • World Bank. “Financial sustainability: Assessing the finances of pension systems over the long term”, World Bank Pension Indicators and Database,
Briefing 6, Washington DC. • World Bank. “Economic efficiency: Minimizing the pension system’s distortions of individual choices”, World Bank Pension Indicators and Database,
Briefing 7, Washington DC. • World Bank. “Administrative efficiency: assessing the cost of running public pension systems”, World Bank Pension Indicators and Database, Briefing
8, Washington DC. • World Bank. World Bank, “Security: Risk and uncertainty in retirement-income systems” , World Bank Pension Indicators and Database, Briefing 9,
Washington DC. • World Bank. 2012. “Pensions Database”. Washington DC.
49
Thank you!
50
• If your country is interested in survey training on Social Protection and Poverty (1/2 day to 3 day courses): – Please contact Mr. Ruslan Yemtsov, [email protected]
Mr. Robert Palacions [email protected] or Mr. Brooks Evans [email protected]
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