www.lisdatacenter.org joint world bank-lis workshop on database creation and survey harmonization...
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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• 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
• 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 missionTo 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
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 DatabasesLuxembourg 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
Pathways to the data
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
Web-based tabulator (“the WebTab”)
LIS Key Figures (no registration needed)
Current coverage: 62% of world population
84% of world GDP
Current axis of growth: middle-income countries (now 17 out of 47 countries)
Australia Denmark India Paraguay * Spain
Austria Dominican Republic *
Ireland Poland Sweden
Belgium Egypt * Israel Peru Switzerland
Brazil Estonia Italy Romania Taiwan
Canada Finland Japan Russia United Kingdom
Chile * France Luxembourg Serbia * United States
China Germany Mexico Slovak Republic
Uruguay
Colombia Greece Netherlands Slovenia
Cyprus Guatemala Norway South Africa
Czech Republic
Hungary Panama * South Korea
Our leadership
Janet GornickDirector of LIS | Director of LIS Center (CUNY)Professor of Political Science and Sociology Graduate Center, City University of New York.
Markus JänttiResearch Director of LISProfessor of Economics, Stockholm University
Tony Atkinson President of LIS BoardEconomist at Nuffield College, Oxford University
Serge AllegrezzaPresident of LIS Local Advisory BoardDirector 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
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)EUROMODHarvard Population Center
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!)
Research using the LIS and LWS data:
some highlights
LIS provides evidence forcomparative research on socio-economic outcomes
• assessing income inequality• measuring poverty• comparing employment outcomes• analyzing assets and debt• researching policy impacts
Assessing Income Inequality Inequality Across Households
Income inequality in the US is the highest among 25 high-income countries included in the LIS Database.
Denmark
Slove
nia
Swed
en
Slova
k Rep
ublic
Finlan
d
Norway
Czech Rep
ublic
Netherl
ands
Switz
erlan
d
Luxe
mbourg
Beligiu
mFra
nce
Hungary
German
y
Taiw
anKorea
Austria
Irelan
d
Poland
Spain
Canad
a
Greece Ita
ly
United Kingd
om
United St
ates
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Inequalit
y In
dic
ato
r: G
ini I
ndex
Source: Luxembourg Income Study Key Figures (publicly available online – www.lisdatacenter.org).
Measuring Poverty - IHousehold Poverty Rates
The poverty rate in the US is the highest among 25 high-income countries included in the LIS Database.
Denmark
Swed
en
Czech Rep
ublic
Netherl
ands
Finlan
d
Norway
Slove
nia
Hungary
Slova
k Rep
ublic
Switz
erlan
d
Beligiu
mFra
nce
Luxe
mbourg
German
y
Taiw
an
Poland
United Kingd
omGree
ce Italy
Austria
Canad
a
Irelan
dKorea
Spain
United St
ates
0
2
4
6
8
10
12
14
16
18
Pove
rty
Rate
(50%
of
media
n d
isposa
ble
house
hold
in
com
e)
Source: Luxembourg Income Study Key Figures (publicly available online – www.lisdatacenter.org).
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.
United Kingdom
United States
Australia
Germany
Netherlands
Belgium
Canada
France
Finland
Denmark
Sweden
Switzerland
Norway
0 20 40 60 80 100 120 140 160 180
89
100
103
114
120
126
126
126
131
137
137
146
157
As Percent of Low US Child Income
Sweden
Netherlands
Denmark
Germany
Australia
Norway
United Kingdom
Belgium
Finland
France
Canada
Switzerland
United States
0 20 40 60 80 100 120
54
61
63
68
69
70
71
71
76
77
87
92
100
As Percent of High US Child Income
Comparing Employment Outcomes Earnings Equality between Women and Men
Earnings 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).
Switz
erlan
dFra
nceSp
ain
Hungary
Taiw
an
German
y
Swed
en
Finlan
d
Austria
Irelan
d
Slove
nia
United Kingd
om
Luxe
mbourg
Denmark
Netherl
ands
Beligiu
m
Canad
a
Slova
k Rep
ublic
United St
ates
Korea
Czech Rep
ublic
Poland
Greece
Norway Ita
ly0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Ratio o
f W
om
en’s
Earn
ings
to M
en’s
Earn
ings
Analyzing Assets and DebtOlder 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.
United States Finland Germany Italy Sweden United Kingdom0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
18
52
4237 34 38
27
12
1315
5
1812
4
54
10
8
43
3141 43
50
36Neither Income nor Asset Poor
Income Poor, NOT Asset Poor
Income Poor AND Asset Poor
Asset Poor, NOT Income Poor
45% As-set Poor
39% In-come Poor
16% In-come Poor 18% In-
come Poor
19% In-come Poor
20% In-come Poor
26% In-come Poor
64% Asset Poor 55%
Asset Poor
52% Asset Poor
39% Asset Poor
56% Asset Poor
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.
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.
Denmark 47%
Finland 36%
Netherlands 36%
Norway 39%
Sweden 45%
Czech Rep. 41%
Germany 43%
Romania 27%
Switzerland 22%
Poland 41%
Taiwan 9%
Canada 28%
Australia 34%
United Kingdom 33%
Israel 33%
United States 23%
23
25
25
25
25
26
28
28
28
29
30
30
32
34
35
37
42
38
39
41
46
44
48
38
36
50
33
42
48
51
52
48
Gini Indices: income before taxes and transfers (upper bars) and after taxes and transfers (lower bars)
Gini index of market income Gini index of disposable income
Reduction in Gini Index
through taxes and transfers
Linking LIS Data with Other DataIncome 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)
Harmonisation
Data harmonisation at LIS: an overview
Harmonisation
Data harmonisation at LIS: an overview
Harmonisation
The origins of the LIS data
Data harmonisation at LIS: an overview
Harmonisation
The origins of the LIS data
The harmonisation process
Data harmonisation at LIS: an overview
Harmonisation
The origins of the LIS data
The harmonisation process
The final output: LIS data
Harmonisation process in 5 steps
: Data acquisition
Get the original data and documentation
Opening of the original dataUnderstand 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 dataCheck final LIS files for consistency
Creation of LIS metadataCreate harmonised user documentation of the LIS files
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
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
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
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
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 rerunOverall guiding principle: COMPARABILITY
Remote Execution System
Primary PathwayOutput
AccessibilityPublicly available
Registration required
Researchers only
Any advanced statistics
Cross-national descriptive tables
Ready-made indicators
Key Figures
Web Tabulator
LISSY System
Programming
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 downloaded 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)
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 implementation
Researchers’ needs
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
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
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!
Thank YouJanet Gornick, Teresa Munzi, Thierry Kruten
www.lisdatacenter.org