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www.lisdatacenter.org. Joint World Bank-LIS Workshop on database creation and survey harmonization Thursday, June 6, 2013. LIS: an overview. LIS: Cross-National Data Center • parent organization • located in Luxembourg • independent, chartered non-profit organization

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www lisdatacenter org
www.lisdatacenter.org

Joint World Bank-LIS Workshop

on database creation and survey harmonization

Thursday, June 6, 2013

lis an overview
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)

history
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.
our mission
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.

what we do
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

lis and lws databases
LIS and LWS Databases

Luxembourg Income Study Database (LIS)

  • Firstand 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
remote execution system lissy
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.

two other pathways to the lis data
Two other pathways to the LIS data

Web-based tabulator (“the WebTab”)

LIS Key Figures (no registration needed)

slide10

Current coverage: 62% of world population84% of world GDPCurrent axis of growth: middle-income countries (now 17 out of 47 countries)

our leadership
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.

lis partners
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

users products services
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!)
slide14

Research

using the LIS and LWS data:

some highlights

lis provides evidence for comparative research on socio economic outcomes
LIS provides evidence forcomparative research on socio-economic outcomes

• assessing income inequality

• measuring poverty

• comparing employment outcomes

• analyzing assets and debt

• researching policy impacts

slide16

Assessing Income Inequality Inequality Across HouseholdsIncome 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).

slide17

Measuring Poverty - IHousehold 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).

slide18
Measuring Poverty - II “Real Income Levels” of ChildrenUS 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.

slide19

Comparing Employment Outcomes Earnings Equality between Women and MenEarnings equality between working men and women ranks 18th among 25 high-income countries in the LIS Database.

Source: Luxembourg Income Study Key Figures (publicly available online – www.lisdatacenter.org).

slide20

Analyzing Assets and DebtOlder Women’s Income and Asset PovertyIn 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.

slide21

Researching Policy Impacts Income Inequality and RedistributionThe 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.

slide22

Linking LIS Data with Other DataIncome Inequality and Earnings MobilityCountries with higher levels of income inequality have lower levels of intergenerational economic mobility.

Income inequality (from LIS)

Source: OECD 2008. Growing Unequal: Income Distribution and Poverty in OECD Countries. Paris: OECD.

data harmonisation at lis an overview1
Data harmonisation at LIS: an overview

The origins of the LIS data

Harmonisation

data harmonisation at lis an overview2
Data harmonisation at LIS: an overview

The origins of the LIS data

The harmonisation process

Harmonisation

data harmonisation at lis an overview3
Data harmonisation at LIS: an overview

The origins of the LIS data

The harmonisation process

Harmonisation

The final output: LIS data

harmonisation process in 5 steps
Harmonisation process in 5 steps

:

  • Data acquisition
    • Get the original data and documentation
  • Opening of the original data
    • Understand the original data and concepts
  • Dataharmonisation
    • - 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
  • Creationof LIS metadata
    • Createharmonised user documentation of the LIS files
the challenges of harmonisation
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
the challenges of ex post harmonisation
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
the challenges of harmonising income data
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
the challenges of harmonising data from middle income countries
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, highestlevel versus highest qualification)
  • Enlargement of income concept to in-kindincomes (consumptionfromown production, in-kindindividual 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
lis golden rules for harmonisation
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 changingenvironment (over time and space)
      • The 2011 template
      • Backwards rerun

Overall guiding principle: COMPARABILITY

primary pathway
Primary Pathway

Output

Programming

Any advanced statistics

LISSY System

Cross-national descriptive tables

Web Tabulator

Ready-made indicators

Key Figures

Accessibility

Publicly available

Researchers only

Registration required

the lissy system
The LISSY system

Remote Execution System (Version 8)

  • Fully automated, running 24 hours/day and 7 days/week
  • Researchers analysemicrodata 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 downloaded and no direct accessto 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)

security and confidentiality
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

Researchers’ needs

Technical implementation

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

Ancillary support services
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: constantlyadjusting the microdata access services to fulfill researchers’ needs while maintaining the same level of security and communication

Challenges still to face
ideas for afternoon discussion
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 harmonisationopportunities?
  • Joint research opportunities?
  • Joint fundraising opportunities?
  • Any other possibilities that arise!
thank you
Thank You

Janet Gornick, Teresa Munzi, Thierry Kruten

www.lisdatacenter.org