1. 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 Washington, June 6, 2013 1

2. 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

3. 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

4. ASPIRE components 1.Data collection, harmonization and validation – “Environment” indicators – Program/system indicators of design and performance 2.Tools for data analysis (ADePT SP) 3.Training in tools (Bank and non-Bank clients) 4.Analytical notes and reports 5.External partnership and internal collaboration 6.Dissemination 4

5. ASPIRE classification of programs 5 Social assistance (Social Safety Nets) SA Labor Market Programs (active and passive) LM Social Insurance SI

6. ASPIRE framework 6 Indicator type SALMSI 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, co- residence rate, poverty rates of old population DESIGNBenefit 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 PERFORMANCECoverage, 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 of registered unemployed (placement in jobs) Coverage (recipients and contributors), benefit and beneficiary incidence, adequacy, simulated impacts, pension spending, administrative efficiency

7. 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) Regulations and laws (for design indicators) 7

8. ASPIRE data coverage SURVEY BASED INDICATORS ADMIN. BASED INDICATORS N. of countries Aggregation Level N. of countriesAggregation Level Environment150Country160Country DesignPrivate transfers/NG Os- run SSN (60 developing countries) ProgramLM indicators only for ECA countries, SI for ~ 165 countries SA for 65: Africa (20), ECA (23), LAC(10), MENA(12). Country, program Performance60 developing countries Program, Country SI for ~ 165 countries SA for 65: Africa (20), ECA (21), LAC((8), MENA(12). Country, Program 8

9. Administrative data on SA and LM programs 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 9 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:

10. www.worldbank.org/aspire 10

11. ASPIRE: HH surveys pillar Questionnaires SP program classifications ADePT SP ini files Database Country-specific outcomes Metadata Cross-country figures and tables 11 P ERCENT COVERED BY M AIN T YPES S OCIAL P ROTECTION P ROGRAMS BY R EGIONS (Percent of population receiving transfers from social protection program)

12. 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 following program rules (with ADePT SP): PLANNED 12

13. 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 13  Argentina 2006  Bolivia 2006, 7  Brazil 2006, 9  Chile 2006,9  Colombia 2003  Costa Rica 2008,9  Rep Dominicana 2007,9  Ecuador 2008,9  El Salvador 2007,10  Guatemala 2006  Honduras 2007  Jamaica 2006  Mexico 2008, 10  Nicaragua 2005  Panama 2008  Paraguay 2007, 9  Peru 2008, 9  Suriname 1999  Uruguay 2008  Venezuela 2006  ….More:  Belize+updates

14. 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 14  Armenia 2008  Azerbaijan 2007  Belarus 2008  Bosnia 2007  Bulgaria 2007  Georgia 2007  Estonia 2004 x  Hungary 2004  Kasakhstan 2007  Kosovo 2006  Kyrgyzstan 2006  Latvia 2008  Lithuania 2004  Macedonia 2005  Montenegro 2008 x  Poland 2005  Romania 2008  Russia 2008  Serbia 2007  Turkey 2008 x  Ukraine 2006  More to come….  Tajikistan  Albania  Moldova  Croatia  +updates

15. 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 15  AFR  Cote d’Ivoire 2002x  Ghana 2005  Kenya 2005  Malawi 2005  Mauritius 2005  Mozambique 2002x  More: Nigeria, Rwanda, Uganda, Zambia, Tanzania, Mali+ updates  MENA  Egypt 2008(sub)  Jordan 2003  Morocco 2001  Yemen 2005 x  West Bank 2007  More: Iraq, Djibouti, updates  EAP  Cambodia, 2008  Lao, 2008  Malaysia,2008  Mongolia, 2007  Phillippines 2006  Thailand, 2009  Timor Leste 2007  Vietnam 2002  ……+Updates  SAS  Afghanistan, 2007  Bangladesh 2002,6  India 2005  Pakistan 2005, 8  Sri Lanka 2008…

16. Structure 16

17. Challenges Expanding (this year +10 countries, and 25 updates, next year?) – 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 17

18. THANK YOU! 18