U.S. Participation in the Development of LIS, a Transnational Database: Luxembourg Income Study Database & Luxembourg Wealth Study Database, 2019 to 2023

Cuny Graduate School University Center, New York NY

Investigators

Abstract

This project provides continuing support for LIS, Cross-National Data Center (formerly Luxembourg Income Study). The LIS is a unique, cross-national, data infrastructure that is essential for understanding the social and economic wellbeing of persons and families in the globalized world. Created to foster primary research by providing access to household microdata, LIS enables both theoretical and empirical social research that informs analyses of a broad array of social and economic policies and institutions. LIS maintains two social science data archives for the purposes of enabling and fostering cross-national research on socio-economic outcomes in high- and middle-income countries. The current project will add new data sets from currently included countries as well as datasets from additional countries. Thousands of researchers worldwide use the Luxembourg Income Study (LIS) and Luxembourg Wealth Study (LWS) databases to analyze outcomes including poverty, income inequality, employment status, wage patterns, gender inequality, family formation, immigration, wealth and debt accumulation. A large share of LIS-based studies focus on the ways in which, and the extent to which, economic and social policies shape these outcomes. These studies provide policy input regarding income and wealth distributions in the United States and across the globe. This project harmonizes (renders comparable) micro-datasets from multiple countries that include data on income, wealth, employment and demography. It provides a secure method that: (1) allows investigators access to data with privacy restrictions; 2) maintains a remote-execution system to enable research conducted from off-site locations; and 3) promotes the use of microdata in comparative research on social and economic wellbeing on a global level. The project conducts research onsite, and sponsors and hosts scholars using the LIS data. By providing an infrastructure with datasets from geographically and economically diverse countries, the project will continue to broaden the basis for interdisciplinary researchers pursuing intellectual inquiry using household microdata. The LIS datasets include income, employment, and demographic variables at the person- and household-level. The LIS Database currently includes microdata from 50 countries and contains over 300 datasets spanning the years 1968 to 2016; the newer LWS Database contains 20 wealth datasets from 15 countries and covers the period 1994 to 2015. LIS and LWS datasets together cover approximately 86% of the world GDP and 64% of world population. The project prepares and maintains standardized national indicators on household inequality and poverty that are publicly available on the LIS website, www.lisdatacenter.org. Extensive documentation is available on the underlying surveys, the harmonization process, and the country-specific institutions that shape income and wealth levels and distributions through METIS, the new metadata system. The project provides online instructional materials, user support, and annual training workshops as well as periodic conferences. LIS is a physical and virtual venue for researchers to exchange ideas, results, and methods; these exchanges take place through the Working Paper Series, the Visiting Scholar Program, LIS-based conferences, workshops, and public programs, as well as pre- and post-doctoral scholarships; excellence is promoted through an annual research award. LIS serves thousands of researchers worldwide from various disciplines including economics, sociology, political science, policy studies, and public health. Users come from academia, government agencies, non-governmental agencies, supranational organizations and news agencies; one-third of registered users are from the United States. LIS is supported by substantial funding from the Luxembourg government. The current project will continue to broaden its impact beyond academia by expanding collaborations, communicating through various media, and developing data visualization. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.