Monday, February 5, 2018

ONS releases - Early indicator estimates from the wealth and assets survey: attitudes towards saving for retirement, automatic enrolment into workplace pensions, credit commitments and debt burden, July 2016 to June 2017, Population statistics research update: February 2018

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5 February 2018


People, population and community

Early indicator estimates from the wealth and assets survey: attitudes towards saving for retirement, automatic enrolment into workplace pensions, credit commitments and debt burden, July 2016 to June 2017

In the period July 2016 to June 2017, of all adults questioned, 40% believed employer pension schemes were the safest way to save for retirement; this is unchanged from the period July 2014 to June 2016.

In July 2016 to June 2017, 49% considered that property would make the most of their money, continuing the rising popularity for this option since July 2012.

Of eligible employees asked, 85% said they had heard of automatic enrolment prior to the interview, whereby all employers must now automatically enrol eligible employees into a workplace pension, introduced as part of the 2012 pension reforms.

Population statistics research update: February 2018

This article describes the research and development conducted and planned for our standard population statistics outputs as at February 2018. To help users quickly find the information they need, the article includes a description of each project, followed by an update paragraph on progress for ongoing projects included in the October 2017 research update.

A separate programme of work looking at alternative methods of producing statistics following the 2021 Census is available.

We welcome your comments on our proposed work and any suggestions for other research and development that would increase the value of our statistics to you.


Visit the National Statistical blog to see the many ways ONS is working to give the UK better statistics for better decisions.

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Find out how our Data Science Campus is exploring how sources such as administrative data & social data, and techniques such as machine learning and natural language processing, can improve our understanding of the UK's economy, communities & people.

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