by Denys Orlov, (National Bank of Slovakia, Bratislava University of Economics and Business)

Understanding the determinants of voluntary savings is essential for ensuring both individual financial security and broader economic stability. Savings act as a crucial buffer against unexpected financial shocks and play a central role in sustaining living standards throughout retirement. In this article, Denys Orlov explores how the propensity to save for old age in the United Kingdom may be associated with financial literacy and a range of socio-demographic characteristics.

Full article is available here.

by Chiara Mussida, (Università Cattolica del Sacro Cuore), Dario Sciulli, (University of Chieti-Pescara)

In-work poverty (IWP) has become a persistent challenge in Europe, reflecting both the expansion of non-standard and low-paid employment, as well as the varying ability of welfare states to mitigate income inequalities. This contribution by Chiara Mussida and Dario Sciulli examines how social protection expenditure and labor market institutions affect the incidence of IWP across 22 European countries over the period 2009 to 2023.

Full article is available here.

by Jörg Neugschwender, (LIS)

Luxembourg’s unique demographic and economic profile, marked by a large foreign population and daily inflows of cross-border workers, raises important questions about inequality and social protection. Using LIS data for Luxembourg, France, Germany, and Belgium, this contribution by Jörg Neugschwender compares median incomes across household types, focusing on differences shaped by labour markets, pensions, and family benefits.

Full article is available here.

LIS is happy to announce the following data updates:

• France (2 new LIS datasets and 23 revised) – Addition of FR21 and FR22 to the LIS Database.
  Read more »
  Two new datasets from France (FR21 and FR22) have been added to the LIS Database. The datasets are based on the Tax and Social Incomes Survey (ERFS), conducted by the National Institute of Statistics and Economic Studies (INSEE). In addition, many datasets of the existing series have been revised to incorporate several improvements. FR98-FR20: The General Social Contribution on capital income, previously recorded under private transfers (hiprivate), is now recorded together with the capital incomes they relate to hi21 (interest and dividends) and hicapital (capital income). This reallocation has no effect on hitotal (total income) or dhi (disposable household income). FR19 and FR20: The 100€ “indemnité inflation” (a one-time payment to most working-age people earning below a certain income threshold) has been moved from pi11 (wage income) to hipubsoc (public social benefits (excl. pensions)). Minor revisions were also made to the calculation of social security contributions for the self-employed workers, resulting in small adjustments to pxitsc (income taxes and contributions), pi12 (self-employment income), and the corresponding upper level variables until dhi (disposable household income).
• Germany (2 new LIS datasets and 37 revised; 4 revised LWS datasets) – Update of the LIS series for DE21 and DE22 and revision based on SOEP-Core v40.1eu Edition.
  Read more »
  Update of the LIS series
  Two new datasets from Germany have been added to the LIS Database (DE21 and DE22). The data are harmonised from the German Socio-Economic Panel (GSOEP) and the German series DE84 to DE22 in LIS now aligns with the SOEP-Core v40.1eu edition provided by the German Institute for Economic Research (DIW), superseding the previous release that relied on v38.1eu edition. This new version implements besides the improved weighting and income imputation for non-respondents revised variables for edmom_c / eddad_c (mother’s / father’s highest education level). Information on parents country origin (immigr_c) is now available for the entire GSOEP series.
  
  
  Revisions of the LWS series
  The previous released datasets from the GSOEP series DE02-DE17 have been updated to reflect the improvements in the latest version v40.1eu edition by DIW, mostly concerning the update of the imputation of incomes for non-respondents. The balance sheet amounts are not concerned by this update.
• India (4 new LWS datasets) – NEW country! Addition of IN91, IN02, IN12, IN18 to the LWS Database.
  Read more »
  One new country has been added to the LWS Database: India. Four LWS datasets from have been harmonised and added (IN91, IN02, IN12, IN18) — these are based on the All India Debt and Investment Survey (AIDIS) and the related National Sample Survey (NSS) rounds carried out by the National Sample Survey Office (NSSO), Ministry of Statistics and Programme Implementation (MoSPI).
• Luxembourg (2 new LIS datasets and 6 revised) – Addition of LU22 and LU23 to the LIS Database.
  Read more »
  Two new datasets from Luxembourg have been added to the LIS Database (LU22 and LU23). The data are based on the Socio-economic Panel “Living in Luxembourg” / Panel socio-économique “Liewen zu Letzebuerg“ (PSELL III), from which is also created the Survey on Income and Living Conditions (EU-SILC). The data are provided by the National Institute for Statistics and Economic Studies of the Grand Duchy of Luxembourg (STATEC).
  The datasets LU15-LU18, and LU21 and LU22 have been revised for consistency. In LU15-LU18 public contributory pensions (pi32) were revised for a small number of individuals which had an impact on the poverty rate of elderly in LU15-LU17, decreasing it slightly. Minor consistency revisions were carried out in LU21 and LU22 which do not affect the LIS Key Figures.
• Switzerland (3 new LIS datasets and 14 revised) – Annualisation to CH22 in the LIS Database.
  Read more »
  Three new datasets from Switzerland have been added to the LIS Database (CH20, CH21, and CH22). The datasets are based on the Swiss Statistics on Income and Living Conditions (SILC) from the Swiss Federal Statistical Office (FSO). Revisions to the earlier datasets CH06-CH19 concern the variable wexptl (total work experience), where additional ‘0 years work experience’ values were placed.
• United Kingdom (1 new LWS dataset and 7 revised) – Addition of UK21 to the LWS Database.
  Read more »
  One new LWS dataset from the UK has been added (UK21 ). The dataset is harmonised from the Wealth and Assets Survey (WAS) produced by the Office for National Statistics (ONS).
  The previous datasets of the UK wealth series (UK07, UK09, UK11, UK13, UK15, UK17, and UK19) were revised following changes in the methodology introduced by the UK Office for National Statistics (ONS) to estimate defined benefit (DB) pension wealth. Because revised estimates for earlier waves have not yet been produced using the new approach, the variable hasodb (defined benefit occupational pensions) was emptied in all these datasets. As a result, the variables inw (integrated net worth) and haso (occupational pensions) differ from their values in previous releases of the LWS UK data. For more information see Compare.It.
  In addition, the UK19 dataset was revised to ensure consistency with other UK waves in the treatment of public transfers. Specifically, the variables hpub_i (insurance transfers) and hpublic (public transfers) were adjusted.

• Uruguay (2 new LIS datasets and 15 revised) – Addition of UY23 and UY24 to the LIS Database.
  Read more »
  Two new datasets from Uruguay have been added to the LIS Database (UY23 and UY24). The datasets are based on the Continuous Household Survey (ECH) from the Uruguayan National Institute of Statistics (INE).
  The datasets UY06 to UY22 have been slightly revised. The variable nrooms (number of rooms available in the household) is now included in UY22. In addition, the variable disabled (indication of disability) was constructed for all datasets from UY06 to UY18, based on an approximation using the type of pensions received. The income section has been marginally revised for UY13-UY22, with little to no effect on the LIS Key Figures.








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Marc Fleurbaey, Chaired Professor at Paris School of Economics, presented the LIS Summer Lecture: Social contagion, inequality and mobility.

Marc Fleurbaey is a CNRS Senior Researcher, Chaired Professor at the Paris School of Economics, and Associate Professor at ENS-Ulm, where he co-directs the Center on the Environment and Society (CERES). Formerly Robert E. Kuenne Professor at Princeton University, he has authored several books including Beyond GDP and A Theory of Fairness and Social Welfare, and published over 200 academic articles. A coordinating lead author for the IPCC and a founding member of the International Panel on Social Progress, he has served on the UN Committee for Development Policy and received the CNRS Silver Medal in 2024. He is a Fellow of the Society for the Advancement of Economic Theory.

by Supriya Lakhtakia, Deepak Malghan (Indian Institute of Management Bangalore), and Hema Swaminathan, (Asian Development Bank & Indian Institute of Management Bangalore)

The authors explore occupational assortative mating and its implications for gender inequality in earnings. Using LIS data across countries and time, they investigate how patterns of occupational similarity between partners influence both inter- and intra-household inequality, offering new insights into the global dynamics of household-level gender disparities.

Full article is available here.

by Jonathan Bradshaw, (University of York), Gianluca Munalli, (The Learning for Well-being Institute), and Dominic Richardson, (The Learning for Well-being Institute)

Using recent LIS data, the authors conduct a comparative analysis of child poverty across countries. They analyse a set of poverty rates by household composition and offer evidence-based policy recommendations to address child poverty and its long-term consequences.

Full article is available here.

by Vladimir Hlasny , (UN Economic and Social Commission for Western Asia (ESCWA))

This article addresses the issue of earnings underreporting and tax overreporting in global household surveys. Drawing on earlier literature and comparative LIS-based analysis, the author examines the risks of measurement error in survey-based income data, highlighting its impact on assessments of inequality and poverty.

Full article is available here.

We are pleased to announce the release of version 0.2.0 of the lissyrtools R package. This version introduces several improvements aimed at making the use of LIS and LWS microdata more efficient, clear and better structured for researchers—whether working locally or via the LISSY remote execution system.

One of the most significant updates is the ability to develop and test your full code workflow locally using built-in sample datasets. This facilitates debugging and testing by taking full advantage of user-friendly IDE features, without needing to submit jobs to LISSY during the exploratory phase. This feature is intended to streamline the process from development to execution.

Version 0.2.0 also introduces the new lissyuse() function, which allows users to load harmonized datasets from over 50 countries with a single line of code. It automatically merges household-level and person-level data based on selected variables, offering a straightforward entry point to the dataset.

The package includes a broader set of functions for computing weighted aggregates—such as means, counts, percentiles, poverty rates, and inequality indices. Many of these functions now include support for subgroup disaggregation via a by argument. All major functions handle sampling weights internally, which can help reduce errors and simplify analysis for users less familiar with survey weighting procedures in R.

Weighted percentile calculations have been standardized across functions. Users can choose between two consistent methods, with documentation available to guide usage and selection. This ensures that poverty thresholds, inequality indicators, and other key statistics follow a coherent logic throughout the package.

The layout of the displayed results has also been improved: printed outputs are now grouped by country and sorted by year, with clear formatting that supports comparison across time and countries. For those wishing to visualize results, the new structure_to_plot() function converts results into tidy data frames that integrate seamlessly with ggplot2.

For users working with multiple datasets or reviewing metadata, this release introduces a set of single-line functions executable directly in the local environment. These functions provide quick access to country coverage, available years, survey names, and detailed variable-level metadata—including labels, country-specific categories, notes, and variable availability over time. The tools are intended to support analysis planning, variable auditing, and cross-dataset comparability, serving as a complement to METIS and Compare.It.

The aim of this release is to address common challenges in working with LIS and LWS data—whether in data access, preparation, analysis, or visualization. It is intended to facilitate efficient and organized analysis, whether focused on a single country or cross-national comparisons.

For full documentation, example code, and installation details, please visit the lissyrtools website. The site now includes a searchable reference section and a changelog to track updates. If you encounter issues or bugs, we welcome your feedback and encourage you to get in touch from here.

LIS is happy to announce the following data updates:

• Bulgaria (16 new LIS datasets) – Addition of BG07 to BG22 to the LIS Database.
  Read more »
  With 16 datasets spanning the period BG07 to BG22, Bulgaria, has been added as a NEW country to the LIS Database. This annual series is based on the EU Statistics on Income and Living Conditions (EU-SILC) conducted by the National Statistical Institute of Bulgaria.
• Iceland (12 new LIS datasets and 3 revised) – Annualisation from IS03 to IS17 in the LIS Database.
  Read more »
  Twelve new datasets from Iceland have been added (IS03 through IS17). The annual data series is based on the EU Statistics on Income and Living Conditions (EU-SILC) Iceland (including some national variables), administered by Statistics Iceland.
  LIS received new data from the data provider also for the datasets IS04, IS07 & IS10 which were reharmonized in the new template using the newly received data (including national variables on pension and children benefits) and imputations for children benefits. The maternity/paternity and parental leave wage replacement are available now at the individual level. The split between the three pillars of pension is available now, as well as a more accurate split for the additional set between insurance, assistance and universal benefits.
• Mexico (1 new LWS dataset) – Addition of MX19 to the LWS Database.
  Read more »
  Mexico was added to the LWS Database. The new dataset MX19 is based on the National Survey on Household Finances (ENFIH) carried out by the National Statistical Institute (INEGI) in collaboration with the Bank of Mexico.
• Palestine (1 new LIS dataset and 1 revised) – Addition of PS23 to the LIS Database.
  Read more »
  One new dataset from Palestine has been added to the LIS Database (PS23). The dataset is based on the Palestine Expenditure and Consumption Survey (PECS) carried out by the Palestinian Central Bureau of Statistics (PCBS).
  Minor consistency revisions have been carried out for dataset PS17.
• Poland (3 new LIS datasets and 19 revised) – Addition of PL21, PL22, and PL23 to the LIS Database.
  Read more »
  Three new datasets from Poland have been added to the LIS Database (PL21, PL22, and PL23 ). These datasets are based on the Polish Household Budget Survey (HBS) conducted by Statistics Poland (GUS).
  The previously harmonized datasets PL95-PL20 have been revised for consistency. Among various minor revisions, the 2016 introduced family benfit (500+) can be now analysed on its own in variable hi412 (child allowance) for the datasets PL16-PL23. Various minor children and family allowances are placed in the more aggregated variable hi41 (family benefits).
• Spain (1 new LWS dataset) – Addition of ES22 to the LWS Database.
  Read more »
  One new dataset from Spain has been added to the LWS Database (ES22 ). The dataset is based on the Household Finance and Consumption Survey (HFCS) conducted by the Bank of Spain.
• France (4 revised LWS datasets) – Revisions to FR09/FR14/FR17/FR20, and additional content provided.
  Read more »
  The LWS datasets FR09 to FR20 were significantly revised in the variable business equity (hannb), and other minor adjustments to assets were carried out, including the residual values of non-financial assets (hanno) and money owed to the household (hafom). On the liabilities side, minor revisions were made to loans for real estate other than principal residence (hlro) and adjustments to investment loans (hlni). A significant number of variables were added, including liabilities by security, wealth-related variables (inheritance/gifts), and behavioral variables. For dataset FR14, replicate weights are now provided.








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