During the course of their work, researchers at IFS create large volumes of research data on a variety of topics. Sometimes these data are collected by IFS researchers, but we also work on creating extended time-series of cleaned secondary datasets. Both types of data are valuable resources to the wider research community and we endeavour to make these available wherever possible.
Data that we have made available to date are:
Family Expenditure Survey and Living Costs and Food Survey Derived Variables
A time series of data on expenditure demographics and labour supply based on the Family Expenditure Survey (and its successors the Expenditure and Food Survey and the Living Costs and Food Survey) coving the period since the 1970s.
Property Value Uplift Tool data (PVU data)
A team of researchers at the IFS and UCL were commissioned by the NIC to produce a publicly-available software tool that estimates how land values respond to changes in land purpose or infrastructure improvements. These data and programs underlie this tool and link together Property Price data, Energy Performance Certificate Data (downloadable externally) with local area covariates.
English Longitudinal Study of Ageing
IFS researchers are part of an interdisciplinary team of investigators who collect data from people aged over 50 in order to understand all aspects of ageing in England. The data are available to download (subject to safeguarding) at the UKDS.
English Longitudinal Study of Ageing Pension Wealth Variables
The ELSA core dataset contains a huge amount of information on the pensions that people hold now and pensions that they have saved in over their lifetimes. It is a complex task to use this information to create meaningful variables that can be used for analysis. IFS researchers have created a dataset that give the discounted present value of the stream of income that an individual will receive from their pensions between starting to draw these pensions and death, under various alternative scenarios.
English Longitudinal Study of Ageing Derived Variables (financial and non-financial)
The ELSA financial and non-financial derived variables are designed and created by IFS researchers in order to make economic and demographic information quickly and easily available to ELSA users.
Institute for Fiscal Studies Households Below Average Income Dataset (1961-1993)
This dataset contains detailed information on income of households based on the Family Expenditure Surveys between 1961 and 1993. The definitions used follow those used by the Department of Work and Pensions for its Households Below Average Income series.
The MaiMwana - IFS Economic Survey is a longitudinal dataset of approx. 3200 women aged 17-43 years and their households collected in Mchinji District, Malawi in 2008-09 and 2009-10. The data were collected to investigate the causal effect of 2 randomised health interventions - a volunteer counselling intervention and a women's group intervention - on poverty outcomes (such as consumption, labour supply and investment in health and education of children).
These data were collected in collaboration with our partners in Malawi - The MaiMwana Project. The dataset includes information on household demographics, individual schooling, labour supply and health, infant feeding, household consumption, assets, adverse events, health knowledge, family planning and information networks of the woman and anthropometric measurements of young children and their mothers. They are available for use from the UK Data Archive.
Low and Middle Income Longitudinal Population Study Directory
The Low and Middle Income Longitudinal Population Study Directory (LMIC LPS Directory) has been developed by the Institute for Fiscal Studies (IFS) on behalf of the Economic and Social Research Council (ESRC), the Medical Research Council (MRC) and the Wellcome Trust.
It aims to provide a valuable resource for researchers, funders and those interested in understanding changing socio-economic and health circumstances, and to enhance opportunities for international and interdisciplinary research collaboration. Its development has been supported by the Grand Challenges Research Fund, which funds cutting-edge research addressing challenges faced by LMICs.