1. www.lisdatacenter.org Joint World Bank-LIS Workshop on database creation and survey harmonization Thursday, June 6, 2013

2. LIS: an overview LIS: Cross-National Data Center parent organization located in Luxembourg independent, chartered non-profit organization cross-national, participatory governance acquires, harmonizes, and disseminates data for research venue for research, conferences, and user training staff: approximately 10 persons LIS Center @ CUNY satellite office located at the Graduate Center of the City University of New York administrative, managerial, development support to parent office venue for research, teaching, and graduate student supervision staff: approximately 10 persons (mostly part-time PhD students)

3. History LIS was founded in 1983 by two US academics (Tim Smeeding and Lee Rainwater) and a team of multi-disciplinary researchers in Europe. It began as a “study”, which later grew and was institutionalized as “LIS”. For nearly 20 years, LIS was part of a local research institute, CEPS (Centre d'Etudes de Populations, de Pauvreté et de Politiques Socio- Economiques). In 2002, LIS became an independent non-profit institution. LIS is supported by the Luxembourg government, by the national science foundations and other funders in many of the participating countries, and by several supranational organizations We are building a growing partnership with the new University of Luxembourg.

4. Our mission To enable, facilitate, promote, and conduct cross-national comparative research on socio-economic outcomes and on the institutional factors that shape those outcomes.

5. What we do Step 1. We identify appropriate datasets. Data must be neutral, reliable, and high-quality. Step 2. We negotiate with each data provider. Step 3. We collect, harmonize and document the data. LIS’ data experts harmonize the data into a common, cross-national template, and create comprehensive documentation. Teresa will discuss Step 4. We double-check the harmonized data. Step 5. We make the data available to researchers via remote execution, and other user-friendly pathways. Thierry will discuss

6. LIS and LWS Databases Luxembourg Income Study Database (LIS) First and largest available database of harmonized income data, available at the household and person levels In existence since 1983 Data mostly start in 1980, some go back to the 1960s (recollected every 3-5 years) 45 countries 205 datasets Used to study: poverty; income inequality; labor market outcomes; policy effects Luxembourg Wealth Study Database (LWS) First available database of harmonized wealth data, available at the household level In existence since 2007 Data going back to 1994 12 countries 20 datasets (planned expansion underway) Used to study: household assets, debt, and expenditures; wealth portfolios; policy effects

7. Pathways to the data

8. Remote-execution system (“LISSY”) This is the primary means of access; it uses a software system that was designed specifically for LIS. Researchers write programs (in SPSS, SAS, or Stata) and send them to the LIS server; results are returned to the researcher, with an average processing time of under two minutes.

9. Two other pathways to the LIS data Web-based tabulator (“the WebTab”) LIS Key Figures (no registration needed)

10. Current coverage: 62% of world population 84% of world GDP Current axis of growth: middle-income countries (now 17 out of 47 countries) AustraliaDenmarkIndiaParaguay *Spain AustriaDominican Republic * IrelandPolandSweden BelgiumEgypt *IsraelPeruSwitzerland BrazilEstoniaItalyRomaniaTaiwan CanadaFinlandJapanRussiaUnited Kingdom Chile *FranceLuxembourgSerbia *United States ChinaGermanyMexicoSlovak RepublicUruguay ColombiaGreeceNetherlandsSlovenia CyprusGuatemalaNorwaySouth Africa Czech RepublicHungaryPanama *South Korea

11. Our leadership Janet Gornick Director of LIS | Director of LIS Center (CUNY) Professor of Political Science and Sociology Graduate Center, City University of New York. Markus Jäntti Research Director of LIS Professor of Economics, Stockholm University Tony Atkinson President of LIS Board Economist at Nuffield College, Oxford University Serge Allegrezza President of LIS Local Advisory Board Director of Luxembourg National Statistical Office We are governed by an elected Executive Committee and an international Board, comprising representatives from our funders and data providers.

12. LIS’ partners Our partners include data providers, data users, and funders, in more than 40 countries … and in major supranational organizations, including: Financial contributors: The World Bank (WB) The Organization for Economic Cooperation and Development (OECD) The International Monetary Fund (IMF) The United Nations Development Program (UNDP) Dataset exchange; joint research projects; joint fundraising: The European Central Bank (ECB) The United Nations Children’s Fund (UNICEF) EUROMOD Harvard Population Center

13. Users, products, services Thousands of data users - and growing remote execution enables use around the world free access for students in all countries free access for data providers and their staffs Pedagogical activities annual training workshops in Luxembourg local workshops self-teaching lessons online Research activities and support visiting scholar program working paper series (600+) research conferences edited books (new one coming in July!)

14. Research using the LIS and LWS data: some highlights

15. LIS provides evidence for comparative research on socio-economic outcomes assessing income inequality measuring poverty comparing employment outcomes analyzing assets and debt researching policy impacts

16. Assessing Income Inequality Inequality Across Households Income inequality in the US is the highest among 25 high-income countries included in the LIS Database. Source: Luxembourg Income Study Key Figures (publicly available online – www.lisdatacenter.org).

17. Measuring Poverty - I Household Poverty Rates The poverty rate in the US is the highest among 25 high-income countries included in the LIS Database. Source: Luxembourg Income Study Key Figures (publicly available online – www.lisdatacenter.org).

18. Measuring Poverty - II “Real Income Levels” of Children US children: the rich are richer, and the poor are poorer. Source: Timothy Smeeding and Lee Rainwater. 2002. Comparing Living Standards Across Nations: Real Incomes at the Top, the Bottom and the Middle, LIS Working Paper 266.

19. Comparing Employment Outcomes Earnings Equality between Women and Men Earnings equality between working men and women ranks 18 th among 25 high-income countries in the LIS Database. Source: Luxembourg Income Study Key Figures (publicly available online – www.lisdatacenter.org).

20. Analyzing Assets and Debt Older Women’s Income and Asset Poverty In the US, 27% of older women are both income poor and asset poor – a higher share than among older women in several other countries. Source: Gornick, Janet C., et al. 2009. “The Income and Wealth Packages of Older Women in Cross-National Perspective.” Journal of Gerontology: Social Sciences 64B(3): 402-414.

21. Researching Policy Impacts Income Inequality and Redistribution The US government does less than other rich countries to reduce income inequality. Source: Andrea Brandolini et al, 2007, Inequality in Western Democracies: Cross-Country Differences and Time Changes, LIS Working Paper 458.

22. Linking LIS Data with Other Data Income Inequality and Earnings Mobility Countries with higher levels of income inequality have lower levels of intergenerational economic mobility. Source: OECD 2008. Growing Unequal: Income Distribution and Poverty in OECD Countries. Paris: OECD. Income inequality (from LIS)

23. Harmonisation

24. Data harmonisation at LIS: an overview Harmonisation

25. Data harmonisation at LIS: an overview Harmonisation The origins of the LIS data

26. Data harmonisation at LIS: an overview Harmonisation The origins of the LIS data The harmonisation process

27. Data harmonisation at LIS: an overview Harmonisation The origins of the LIS data The harmonisation process The final output: LIS data

28. Harmonisation process in 5 steps :  Data acquisition Get the original data and documentation  Opening of the original data Understand the original data and concepts  Data harmonisation - Conceptual: map original variables into LIS variables - Technical: create uniform file structure and variables  Checking of the LIS data Check final LIS files for consistency  Creation of LIS metadata Create harmonised user documentation of the LIS files

29. The challenges of harmonisation Make comparable original data that are:  from various countries  different institutional / societal setups  over time  changes in institutions and original surveys  household / individual level data  confidentiality issues  from various existing datasets  output (or ex-post) harmonisation

30. The challenges of ex-post harmonisation  Different types/purposes of original collection instrument  Survey versus administrative data (coverage and contents)  Cross-sections versus panels (sample selection)  The concepts used in the original data collection are different  Different definitions (employment definition)  Different universes and reference periods  Country-specific classifications (education, occupation, industry, social security benefits)  The level of detail of information collected differs  Labor market (e.g., LFS type of survey)  Incomes /wealth (detailed breakdown vs. overall questions)  Different statistical techniques  Different sampling procedures (e.g., oversampling of the rich)  Weighting procedures (self-weighted, sampling weights, etc.)  Treatment of missing values, imputation methods

31. The challenges of harmonising income data  Income sources included in total household disposable income (irregular payments, non-cash incomes, imputed rents, non-taxable incomes, “informal” incomes )  Current versus annual  Net versus gross (or in between...)  Top- and bottom-coding  Level of detail (e.g., total pensions) and different aggregation (e.g. pensions by type of system versus by function)  Classification of incomes:  Public versus private  Social insurance versus universal versus social assistance systems

32. The challenges of harmonising data from middle income countries  Urban versus rural (sample composition, population coverage)  Household membership and treatment of incomes (live-in domestic servants, family members temporarily absent)  Complex households (multigenerational households, definition of head, polygamy)  Employment definition and labour market characteristics (informal employment, child labour, multiple jobs, status in employment)  Education (attended versus completed, highest level versus highest qualification)  Enlargement of income concept to in-kind incomes (consumption from own production, in-kind individual public goods, subsidies)  Classification of income:  Employer-provided pensions and benefits (labour income, social security)  Social insurance versus assistance versus universal benefits)  Treatment of taxes

33. LIS golden rules for harmonisation  Set clear definitions for LIS variables  Maximise comparability by setting clear definitions for each variable (and trying to stick to them as much as possible)  Document very well any deviation from the general definition  Complement ease of use with flexibility of use  Enhance user-friendliness by providing fully standardised variables (standard variables, recodes, dummies, aggregate variables)  Allow users the flexibility to create other concepts by leaving a large amount of detailed information  Adapt the LIS template to the changing environment (over time and space)  The 2011 template  Backwards rerun Overall guiding principle: COMPARABILITY

34. Remote Execution System

35. Primary Pathway Output Accessibility Publicly available Registration required Researchers only Any advanced statistics Cross-national descriptive tables Ready-made indicators Key Figures Web Tabulator LISSY System Programming

36. The LISSY system Remote Execution System (Version 8) Fully automated, running 24 hours/day and 7 days/week Researchers analyse microdata at their own place of work Statistical programs (e.g., Stata, R ) automatically processed. Outcomes automatically sent back Restricted to social science research purposes only Micro-databases cannot be download ed and no direct access to the data is permitted Users must register with LIS. LIS grants access to databases for a limited time period (1 year) renewable annually Over 4,500 users from 55 countries ever registered In 2012, 1015 applications (new and renewed)

37. Security and confidentiality Working with LISSY Write, submit and view requests Track status of job requests Access and manage history of all jobs you ever submitted 55,000 jobs per year to monitor Security settings defined for an automatic scan each incoming request Suspicious jobs are sent to a review queue for a manual review All incoming jobs and outputs stored allowing to trace back researchers’ job history Data providers’ legal constraints Technical implementationResearchers’ needs

38. Ancillary support services Extensive documentation is available on LIS website Detailed information on original surveys, LIS variables’ content and availability, etc… allowing users to understand the context in which LIS outcomes should be analysed Information on how to access to and work with micro-data: –Data accreditation (access, confidentiality rules…) –Data access system (how-to and FAQ sections) –Learning materials (self-teaching packages …) Support Support facilities as a mean to improve researchers’ ability to work with LISSY and to reduce risks of breaching confidentiality rules User support ( 500 emails per year ) and training sessions through workshops

39. Challenges still to face Challenges to face include revising the LIS databases’ documentation system by supplying a new metadata system that will allow LIS users to create tailored documentation extracts fitted to their individual needs The key objective to work on: constantly adjusting the microdata access services to fulfill researchers’ needs while maintaining the same level of security and communication

40. Ideas for afternoon discussion Possible collaborative activities: Exchange of information and expertise regarding dataset selection/acquisition; harmonisation; micro-simulation/imputation; design and construction of metadata (etc.) Joint data harmonisation opportunities? Joint research opportunities? Joint fundraising opportunities? Any other possibilities that arise!

41. Thank You Janet Gornick, Teresa Munzi, Thierry Kruten www.lisdatacenter.org