In order to provide more detailed documentation about the construction of flow variables in the LIS and LWS Databases, and assets and liabilities variables in the LWS Database, LIS has published detailed content tables for each dataset on our website, available in two Excel documents for LIS and three for LWS. In all documents, the information is organised by country and within each country by year, giving a comprehensive overview to the users. These documents will be updated every time LIS releases new datasets with the new countries added, additional years for existing countries, and any revisions to previous data that might occur.

How to read the tables:

These tables show the mapping of content at the level of each LIS/LWS flow variable and LWS asset and liability variable. If a field is empty, no data was mapped at the level of that variable for that specific country in that year. However, due to the nesting system in LIS/LWS databases (see the variable list), an empty field for a lower-level variable means that either the information was not collected at all or that it was not collected at that level of detail. For example, if all pension data is collected in one variable, only pipension variable will be filled, and not the lower-level variables (universal pensions, assistance pensions, public contributory pensions, occupational and individual pensions). Consequently, the total pension income is available, but detailed information on the types of pensions is not.
If an upper-level flow variable (e.g., pilabour) field is empty, no data was mapped directly at that level. Nevertheless, if the lower-level variables that constitute the components of this upper-level variable (in this example, wage income (pi11), self-employment income (pi12), and fringe benefits (pi13)) are filled, the upper-level variable contains all the contents of these lower-level variables. If, in addition to the lower-level variables, there is content in the field of an upper-level variable (e.g., in pilabour, the content ‘income from secondary labor market activities’), this content describes the differential amounts in the microdata to which is added the content of the lower level variables that enter in its composition.

In the contents_combined documents that exist both for LIS (contents_combined_LIS) and LWS (contents_combined_LWS), the users can find the original content that was mapped in each LIS/LWS flow variable (at the detail level provided by the data provider) from the main flow variables blocks: Current Income (with details on labour income, capital income, pensions, other public social benefits, and private transfers), Extraordinary Income, Income Deductions Transfers Paid and Loans Repayments (with details on income taxes and contributions, other direct taxes, inter-household transfers paid, mortgage and other loans instalments paid), Consumption Expenditure (by 12 categories), and Imputed Rent. The details are particularly useful for the content of public social benefits in which are listed the original benefits included in the original variables at the level provided by the original data source. The document details what is mapped at the individual level for the variables provided at the individual level and at the household level for all variables.

The contents_iua_combined documents, which exist both for LIS (contents_iua_combined_LIS) and LWS (contents_iua_combined_LWS), provide the contents of the alternative set of variables that splits the benefits by type in insurance-based (contributory) – p/hpub_i, universal benefits (non-contributory and non-means tested) – hpub_u, and assistance benefits (means and/or assets tested) – hpub_a. Please note that this breakdown might not be available for all datasets or all benefits (e.g., some of the original variables could contain more benefits of different types). In the cases where the original variable contains a mix of benefits of different types, or if for other reasons the benefit type cannot be determined, these variables are mapped at the upper level in the hpublic variable. Additionally, a benefit type may change over time; for example, policymakers can restrict eligibility for an initial universal benefit to only those in need, effectively transforming it into a means-tested benefit.

In the contents_balance_combined_LWS document, users can find the contents of LWS assets and liabilities variables. For example, under Investment Funds and Alternative Investments (hafii), users can find detailed information about country-specific financial assets categorised there (e.g., the market value of managed accounts, value of unit and investment trusts, value of tax-free bond mutual funds, etc.). The same approach applies to other assets and liabilities variables. The contents for all assets and liabilities variables refer to household‐level information, except for Pension Assets and Other Long‐Term Savings variables, available at both household and individual levels.

Note:These documents will be updated every time LIS releases new datasets with the new countries added, additional years for existing countries, and any revisions to previous data that might occur.

by Francisco Ceron and Andrea Canales, (Pontificia Universidad Católica de Chile / The Millennium Nucleus for the Study of Labour Market Mismatch: Causes and Consequences (LM2C2))

This study examines how the expansion of higher education has influenced labour market returns in the liberal welfare regimes across the Americas. Using the LIS Database, the analysis explores whether increased educational attainment has maintained its value in the labour market or if its relative returns have diminished over time. The findings highlight the complex interplay between education, labour market deregulation, and inequality trends.

Full article is available here.

Following the success of the Inaugural III-LIS Comparative Economic Inequality Conference in 2023 (London, UK), the 2nd III-LIS Conference was held on February 27-28, 2025, in Luxembourg. The conference featured 79 research papers, two keynote lectures, and a special event dedicated to the discussion of Professor Branko Milanovic’s latest book, “Visions of Inequality: From the French Revolution to the End of the Cold War.”

Full article is available here.

On 27 February — as part of the 2nd III/LIS Comparative Economic Inequality Conference — a special evening event was held, focused on Branko Milanovic’s book Visions of Inequality: From the French Revolution to the End of the Cold War (Harvard University Press, 2023). Francisco Ferreira and Janet Gornick engaged in an insightful discussion with Milanovic, exploring the book’s key themes. Co-sponsored by POST Luxembourg, the event attracted an audience of approximately 150 people.

Full article is available here.

LIS is happy to announce the following data updates:

• Austria (1 new LIS dataset and 10 revised) – Addition of AT22 to the LIS Database.
  Read more »
  LIS has added one new data point to the Austrian data series in the LIS Database. The dataset is based on the Austrian Survey on Income and Living Conditions (EU-SILC), conducted by Statistics Austria. Minor consistency revisions have been applied to ten previous datasets AT12-AT21, additional information for variables education background of parents (edmom_c / eddad_c) has been retrieved from the panel structure across the years. Note, however, that the information is not available for each person in all the years. Information in Compare.It gives more detail. AT21 contains now newly added information on mother’s and father’s country of birth.
• Australia (1 new LIS dataset and 1 new LWS dataset) – Addition of AU20 to both the LIS and LWS Database.
  Read more »
  LIS has added one new data point to the Australian data series in the LIS and LWS databases. The data are drawn from the Survey of Income and Housing (SIH) conducted by the Australian Bureau of Statistics (ABS).
• Brazil (9 new LIS datasets and 29 revised) – Annualisation from BR81 to BR89 in the LIS Database.
  Read more »
  Nine new datasets (BR81 to BR89) have been added to the Brazilian series, extending the annual data coverage to over 40 years. The data are based on the National Household Sample Survey (PNAD) by the Brazilian Geographical and Statistical Institute (IBGE). Additionally, the previously available datasets have undergone minor consistency revisions in the income taxes and social contributions.
• Canada (1 new LIS dataset) – Addition of CA21 to the LIS Database.
  Read more »
  One more datapoint has been added to the Canadian data in the LIS Database. The new dataset CA21 is derived from the Canadian Income Survey (CIS) conducted by Statistics Canada (StatCan).
• France (4 new LWS datasets) – France has been added to the LWS Database.
  Read more »
  France is now available in the LWS Database! The new data series includes the datapoints of FR20, FR17, FR14, and FR09. The data originate from the Household Wealth Survey (Patrimoine / HVP) conducted by the National Institute of Statistics and Economic Studies (INSEE) in collaboration with the Banque de France.
• Greece (15 new LIS datasets and 7 revised) – Annualisation from GR02 to GR21 in the LIS Database.
  Read more »
  The Greek data have been annualized from GR02 to GR21 in the LIS Database. The data are sourced from the Greek Survey of Income and Living Conditions (SILC), conducted by the Hellenic Statistical Authority (ELSTAT). All previous datasets have been revised for improved consistency. GR04, GR07, GR10, GR13, and GR16 have been adjusted to the latest harmonisation practices; note since LIS could rely for these datapoints on more detailed information on the Greek source data received before, some variables contain more detailed information for these data points. Additionally, GR95 and GR00 were reviewed for consistency; the revision impacted for some households the variable disposable household income (dhi).
• Norway (1 new LIS dataset and 9 revised) – Addition of NO22 to the LIS Database.
  Read more »
  LIS added one more Norwegian dataset to the LIS Database. The new dataset NO22 is based on the fully register-based Household Income Statistics maintained by Statistics Norway (SBB). Further clarification by Statistics Norway allowed LIS to better split pensions from other social benefits in NO07 to NO22 .
• Norway (1 new LWS dataset and 6 revised) – Addition of NO22 to the LWS Database.
  Read more »
  The dataset is based on the fully register-based Household Income and Wealth Statistics maintained by Statistics Norway (SBB). The six previous datasets have been revised for consistency. Further clarification by Statistics Norway allowed LIS to better split pensions from other social benefits, and to reallocate some items in the balance sheet (notably, financial assets and consumer goods). These revisions also slightly affect the upper-level aggregates haf, han, and dnw.
• Slovenia (1 new LWS dataset and 2 revised) – Addition of SI21 to the LWS Database.
  Read more »
  The data point SI21 has been added to the LWS Database. The data come from the Household Finance and Consumption Survey (HFCS), conducted by the Central Bank of Slovenia. Two previous datasets have been revised in the section assets acquired in the past.
• Finland – Variable region_c is now available for all Finnish LWS datasets.








  Click on `Read more’ to access more details on the newly added and revised datasets

LIS is excited to announce that the application to its Summer introductory workshop is now open. This year’s workshop marks the 33rd edition after the first workshop took place in 1988. As in the last 5 editions, LIS, the University of Luxembourg and LISER will jointly organize and teach the workshop on “Comparative Inequality Measurement using the LIS & LWS Databases”. This workshop is a one-week intensive course designed to introduce researchers in the social sciences to comparative research on income and wealth distribution, employment and social policy, using the harmonised Luxembourg Income Study (LIS) and Luxembourg Wealth Study (LWS) Databases.

The Workshop will be held at the University of Luxembourg, Belval Campus, Esch-sur-Alzette, Luxembourg from 30 June-04 of July 2025.

For more details about the workshop programme and practical information, please visit the workshop page.

Applications should be submitted online through this application form by April 11, 2025.

For questions and inquiries, please write to workshop@lisdatacenter.org.

Renewal of registration to access the LIS and LWS Databases is required each year. The annual renewal period begins on January 01, 2025 (Luxembourg Time).

To renew your registration for access to the LIS databases, please go here and complete the LIS Microdata User Renewal Form.

The calls are now closed!

This call for proposals is in the context of the (LIS)²ER initiative – an institutional collaboration between the LIS Data Center in Luxembourg (LIS) and the Luxembourg Institute for Socio-Economic Research (LISER). Both institutions are located in the Maison des Sciences Humaines at Belval Campus in Luxembourg.

The collaboration aims to foster collaborative research on Policies to Fight Inequality. Grants for research visits is one of the instruments in place to this end. Research proposals can be submitted by individual researchers or by small teams of up to three researchers. Applicants from any level of seniority will be considered and we hope to strike a balance between junior and senior visitors.

Visitors will be hosted on LIS or LISER premises and will have privileged access to LIS and LWS microdata on-site in a secure data access lab for the duration of their visit.

We expect visitors to engage with local researchers at the LISER, LIS and the University of Luxembourg – all based on campus. (Potential or foreseen collaboration with local researchers will be a key criterion for the selection of proposals.)

Two calls are currently open:

1. Senior Fellowship at LIS and LISER: all the information related to this call is available here.
2. Research Stays at LIS and LISER: all the information related to this call is available here.

More information about the (LIS)²ER Initiative past visitors is available here.

by Sylwia Radomska, (Institute of Economics, Polish Academy of Sciences; FAME|GRAPE) and Eva Sierminska, (LISER; Institute of Economics, Polish Academy of Sciences)

The share of single-parent households has grown globally since 1980, with these households facing greater poverty and holding substantially less wealth as compared to two-parent households. Using data from 13 countries in the LWS Database, this article by Sylwia Radomska and Eva Sierminska highlights disparities in wealth across household types and education systems, emphasizing how private education financing exacerbates inequalities, particularly for single parents.

Full article is available here.

by Pablo Arriagada and Joe Hassell (Our World in Data)

Our World in Data (OWID) has developed three interactive Data Explorers—Poverty, Inequality, and Incomes Across the Distribution—now accessible on the Luxembourg Income Study (LIS) website. Designed for a general audience, these tools provide intuitive access to key income indicators enhancing our understanding of global income trends. The Explorers prioritize simplicity, offering concrete metrics like average income by decile and avoiding technical jargon.

Full article is available here.