UK is under-saving – based on our previous research, 55% of defined contribution (DC) savers are either not on track or not expecting to meet the Pensions and Lifetime Savings Associations (PLSA) minimum retirement living standard. That begs the questions of both whether they could save more, and if the benchmark is a suitable target for them. Before someone can work out how much money to invest for tomorrow, they need to understand what their contributions really cost them today. Currently it is difficult for most people to answer this question.
What’s needed is a clear definition of ‘pensions adequacy’ – the optimal level of retirement income that any given individual should aim for. Alongside the PLSA standards, a number of helpful models have been proposed and there is active debate in the retirement industry and among policymakers about which is the most suitable. But retirement income adequacy benchmarks on their own cannot give definitive answers to the question that is actually facing individual savers: ‘How much should I save for retirement?’
AE helps, but not enough
There are good reasons to say that many DC savers need to contribute more than they are currently doing. This was already true in the UK before the introduction of automatic enrolment (AE), but the more pressing challenge then was that so many workers were not saving for retirement at all. In this sense, AE has been a resounding success, greatly increasing the number of people contributing to a pension through their work. But there remain significant concerns about whether people are saving enough, whether or not they were auto enrolled.
In recently years it has become something of a truism to say that the minimum AE contribution rate of 8% is not enough to deliver an adequate retirement income for most people. However, for some, saving more is simply not an option because they are struggling to meet basic living standards while at work. And even those saving at higher rates may not have chosen this rate for any specific reason. They are often contributing at the default rate set by their employer, or paying the exact amount needed to take full advantage of an employer match. Neither amount will have been set with their specific needs in mind.
Given this, it’s reasonable to assume that many DC members aren’t saving at an optimal rate. Still, given the diversity of people’s financial lives, it’s hard to say exactly who is saving too little – or too much.
Developing a meaningful adequacy model to all
For an adequacy model to be meaningful to all, it needs to allow for the enormous differences between the financial lives of different people in different circumstances at different times of life – and for the different strategies they might have. Some will want to maximise their chances of being able to buy a property before they retire. Others will need to tackle the short-term costs of servicing debt. For certain people, the ‘right’ level of retirement savings will be lower than the amount recommended by traditional models of adequacy at certain times in their lives. In some cases, their target savings rate may be zero, at least in the short term. An understanding these moments will allow for better targeting of nudges and other interventions that can help people make the right choices at the right times.
In this collaboration between Phoenix Insights and Nest Insight, we are exploring the different household and financial circumstances experienced by different groups of people, to develop a more nuanced definition of ‘retirement savings adequacy’. This will mean looking beyond the average person and the average retirement outcome. By working out which specific factors make the biggest difference to a worker’s optimal savings rate, we can improve our understanding of whether adequacy targets, and the broader retirement savings system, can be better tailored to the diverse needs of real people living in real households.
Key findings
The first report of this project will talk about initial findings from the exploratory phase. In summary, we will see that:
These findings will shape our modelling work in phase 2 of the project, which will seek to understand: