ASPIRE: Harmonized data on Social Protection and Labor Maddalena Honorati and Ruslan Yemtsov (Social Protection, World Bank) Joint World Bank-LIS workshop on database creation.

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Transcript ASPIRE: Harmonized data on Social Protection and Labor Maddalena Honorati and Ruslan Yemtsov (Social Protection, World Bank) Joint World Bank-LIS workshop on database creation.

ASPIRE:
Harmonized data on Social Protection and Labor
Maddalena Honorati and Ruslan Yemtsov
(Social Protection, World Bank)
Joint World Bank-LIS workshop on database creation and survey
harmonization
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Washington, June 6, 2013
Motivation and background
• Lack of comprehensive and systematic data
collection tools on SP programs and systems in
developing world (especially LICs)
• Optimize and capitalize on existing data collection
efforts by the WB regional teams
– data collected/compiled in a decentralized way and in
different format making comparisons difficult
• Increasing demand from policymakers, civil society,
World Bank staff and other international
stakeholders to access SPL data.
2
Objectives
1. Create a comprehensive, standardized and up-to-date
database of SPL indicators on program design,
performance and “environment” across countries and
time
2. Need to build empirical evidence for SP systems and
their components
– Focus also on program complementarities in addressing
risks, overlaps…
– Develop common matrix for assessing performance of SP
and monitor over time
3. Contribute to improve the quality/availability of survey
data on SP
4. Monitor the implementation of the new WB 2012-22
SPL Strategy
3
ASPIRE components
1. Data collection, harmonization and validation
– “Environment” indicators
– Program/system indicators of design and performance
2.
3.
4.
5.
6.
Tools for data analysis (ADePT SP)
Training in tools (Bank and non-Bank clients)
Analytical notes and reports
External partnership and internal collaboration
Dissemination
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ASPIRE classification of programs
SA
• Social assistance (Social
Safety Nets)
LM
• Labor Market Programs
(active and passive)
SI
• Social Insurance
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ASPIRE framework
Indicator type
SA
LM
SI
ENVIRONMENT
HC and poverty gap,
% of children
economically active
Primary activity rates,
employment status,
share of employment
in main sectors
Life expectancy, old age
dependency ratio, coresidence rate, poverty
rates of old population
DESIGN
Benefit modality (in
kind, cash), eligibility
criteria, targeting
method, benefit
formula
Ratio of front-line
counselors to total
PES staff, number of
registered vacancies
Modalities of pension
schemes, contribution
rates, qualifying
condition, defined
benefit parameters
PERFORMANCE
Coverage, benefit
and beneficiary
incidence, adequacy,
simulated impacts on
poverty and
inequality, spending
Coverage, benefit and
beneficiary incidence,
adequacy, simulated
impacts on poverty
and inequality,
spending, activation
Coverage (recipients
and contributors),
benefit and beneficiary
incidence, adequacy,
simulated impacts ,
pension spending, 6
ASPIRE data sources
Primary sources: national
1. Administrative: published or regional/other databases
2. Nationally representative household surveys data (AdePT SP)




LSMS
HH budget surveys
Other surveys (program participation, etc.)
I2D2
Secondary sources: international organizations
OECD, IMF, ILO , ISSA , Helpage, WHO, UN, UCW
SI coverage indicators are 65% based on primary administrative sources in the country, and 25% on ILO
statistics.
World Bank country-level SP system assessments, targeting assessment
reports, regional databases (SPEED in ECA)
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Regulations and laws (for design indicators)
ASPIRE data coverage
SURVEY BASED INDICATORS
N. of
countries
Aggregation
Level
ADMIN. BASED INDICATORS
N. of countries
Aggregation
Level
Environment
150
Country
160
Country
Design
Private
transfers/NG
Os- run SSN
(60
developing
countries)
Program
LM indicators only for Country,
ECA countries,
program
SI for ~ 165 countries
SA for 65: Africa (20),
ECA (23), LAC(10),
MENA(12).
Performance
60
developing
countries
Program,
Country
SI for ~ 165 countries
SA for 65: Africa (20),
ECA (21), LAC((8),
MENA(12).
Country,
Program
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Administrative data on SA and LM programs
Regional existing efforts to collect program-level comparable data (ECA (23), LAC
(10), Africa (20), MENA (8) + 35 countries in EAP and SAR by ADB SPI) on focus on
the following indicators:
• Program design/description
–
–
–
–
–
–
–
–
–
Program objective
Eligibility criteria
Targeting mechanism
Payment type
Benefit amount/indexation
Frequency of payment
Source and structure of financing
Implementing agency
Year in which the program
started
• Program performance
– Total expenditure/GDP
– Number of
beneficiaries/population
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www.worldbank.org/aspire
10
ASPIRE: HH surveys pillar
• Questionnaires
• SP program
classifications
• ADePT SP ini files
• Database
• Country-specific
outcomes
• Metadata
• Cross-country
figures and tables
PERCENT COVERED BY MAIN TYPES SOCIAL PROTECTION PROGRAMS BY REGIONS
(Percent of population receiving transfers from social protection program)
No transfer
2
16
7
Social Insurance
3
17
Social Safety Nets
4
Combination of programs (incl. labor market prog.)
13
9
18
22
15
22
6
8
21
20
7
9
12
16
29
76
74
67
60
59
53
37
Africa
Middle East & South Asia
North Africa
East Asia and Latin America Europe &
Pacific
& Caribbean Central Asia
World
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ASPIRE SURVEY Database : What else is inside?
• Country-specific standard tables on coverage (direct and indirect), adequacy, and
benefit incidence analysis
– By quintiles of welfare, with counterfactuals – pre-transfer; BWR variations);
 Using household income/consumption per capita as welfare
– By program
– Using relative poverty line (20% percentile)
– Using absolute poverty line ($1.25 in PPP)- NEW
• Impact of SP transfers on FGT (0, 1, 2) and inequality (Gini); cost-benefit ratios
– By program
• Country-specific data base in STATA with harmonized SP programs and welfare
variables; ADePT routines
• Full documentation of main variables/peculiarities of data
• Decomposition of SP impact into budget adequacy/efficiency: PLANNED
• Metadata with administrative statistics; facility to make imputations following12
program rules (with ADePT SP): PLANNED
ASPIRE SURVEY Database: LAC
• Most recent household survey data available at CEDLAS (Centro de
Estudios Distributivos Laborales y Sociales) with information on
household income and social protection programs
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Argentina 2006
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El Salvador 2007,10
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Suriname 1999
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Bolivia 2006, 7
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Guatemala 2006
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Uruguay 2008
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Brazil 2006, 9
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Honduras 2007
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Venezuela 2006
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Chile 2006,9
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Jamaica 2006

….More:
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Colombia 2003
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Mexico 2008, 10
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Belize+updates
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Costa Rica 2008,9
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Nicaragua 2005
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Rep Dominicana
2007,9
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Panama 2008
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Paraguay 2007, 9
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Peru 2008, 9

Ecuador 2008,9
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ASPIRE SURVEY Database: ECA
• Most recent household survey data available at ECA data bases (SPEED
and ECAPOV) with information on household income and social
protection programs
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Armenia 2008
Azerbaijan 2007
Belarus 2008
Bosnia 2007
Bulgaria 2007
Georgia 2007
Estonia 2004x
Hungary 2004
Kasakhstan 2007
Kosovo 2006
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Kyrgyzstan 2006
Latvia 2008
Lithuania 2004
Macedonia 2005
Montenegro 2008x
Poland 2005
Romania 2008
Russia 2008
Serbia 2007
Turkey 2008x
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Ukraine 2006

More to come….
Tajikistan
Albania
Moldova
Croatia
+updates
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ASPIRE SURVEY Database:
AFRICA, SAR, MENA, EAP
• Most recent household survey data available collected from AFR, MENA,
EAP and SAS with information on household income and social protection
programs

AFR
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Cote d’Ivoire 2002x
Ghana 2005
Kenya 2005
Malawi 2005
Mauritius 2005
Mozambique 2002x
More: Nigeria,
Rwanda, Uganda,
Zambia, Tanzania,
Mali+ updates
MENA
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Egypt 2008(sub)
Jordan 2003
Morocco 2001
Yemen 2005x
West Bank 2007
More: Iraq, Djibouti,
updates
EAP
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SAS
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Cambodia, 2008
Lao, 2008
Malaysia,2008
Mongolia, 2007
Phillippines 2006
Thailand, 2009
Timor Leste 2007
Vietnam 2002
……+Updates
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
Afghanistan, 2007
Bangladesh 2002,6
India 2005
Pakistan 2005, 8
Sri Lanka 2008…
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Structure
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Challenges
• Expanding (this year +10 countries, and 25 updates, next
year?)
–
–
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–
•
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•
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New data collection : coordination
I2D2
China?
HIC?
Classification/ Harmonization
Deflation/ scale economies/equivalence
Counterfactual (pre-, post- transfer, in between)
Quality checks on SP/ Imputations
Developing good instrument(s) for SP data collection
User-friendly / target different audiences
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THANK YOU!
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