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, ryemstov@worldbank.org

Mr. Robert Palacions rpalacios@worldbank.org or Mr. Brooks Evans bevans2@worldbank.org