Does Gen Z really not want to work? What statistics can (and cannot) tell us
‘Generation Z doesn’t want to work’ is a claim that frequently appears across social media and in newspaper headlines. Recent headlines have fuelled the debate, pointing to rising economic inactivity among younger people and raising concerns about the potential emergence of a ‘lost generation’. Some from older generations argue that those now entering the workforce are less willing, and less committed, to their jobs than they were at the same age. But ask Gen Z and often a very different picture starts to emerge. Many argue that they are entering adulthood under significant economic pressures that differ from those faced by previous generations. And as the picture becomes clearer, it is becoming increasingly apparent that no single factor can explain these trends, let alone provide an easy and straightforward solution. This blog is not about providing solutions, but as a self-confessed Gen Zer, who is currently on placement at the Office for Statistics Regulation (OSR), I’ve seen how official statistics can help move debates like this beyond headlines and influence our own opinions. In this blog, I will explain the lessons I have learnt about how a regulator at OSR would approach such a claim.
What should we consider before we draw conclusions?
Part of the challenge is that no single statistic can answer such a broad statement. Young people’s relationship with work is shaped by a multitude of factors, so looking at just one statistic in isolation risks oversimplifying a far more complex landscape. OSR’s role is not to tell people what conclusions they should reach from statistics, but to promote their appropriate use so that public debate is informed by robust evidence. To support informed debate, readers can begin by asking themselves the following questions:
1. What do the statistics actually measure?
When using mental health statistics to explore Gen Z’s relationship with work, it is important to understand what those statistics are measuring. Headlines may suggest that younger people are experiencing poorer mental health than previous generations. But different datasets capture different aspects of mental health and wellbeing which can lead users to quite different conclusions about Gen Z. The Adult Psychiatric Morbidity Survey (APMS) may suggest that younger adults in England are experiencing a genuine rise in treated and untreated common mental health conditions, potentially affecting how they enter and experience employment, or whether they enter employment at all. The Mental Health of Children and Young People in England (MHCYP) shows that many mental health difficulties are already present among teenagers and young adults before they fully enter the workforce. Lastly, the UK measures of national well-being published by the Office for National Statistics (ONS), offers a third and much broader perspective. The data captures life satisfaction, happiness, anxiety and whether people feel their lives are worthwhile. And although it is not specific to Gen Z, these insights provide a broader picture of national sentiment, which may help explain changes in motivations and attitudes towards work among younger generations and the population more broadly. Collectively these statistics demonstrate that different measures can support different explanations for Gen Z’s experiences of, and attitudes towards work. This is something users should keep in mind when interpreting statistics and coming to their own conclusions.
2. Are the statistics comparable over time?
Even where a statistic appears to be directly comparable over time, users need to understand how the data was produced. For example, this is important when using long-term housing statistics to assess the economic circumstances facing Gen Z and how these may shape attitudes towards work. The UK House Price Index has experienced changes to the treatment of new-build transactions and improvements to its imputation methodology. As a result, when comparing house price trends over time, users should be aware that changes in methods, data availability and revisions affect how confidently different periods can be compared. This matters because housing affordability is often used as evidence in debates about the rewards of work.
3. What do statistics producers say the figures can, and cannot, tell us?
When assessing claims about Gen Z’s relationship with work, users should not overlook the user guidance that producers publish alongside their statistics. This guidance helps users understand limitations, methodological changes, uncertainty and how the statistics should be interpreted. For example, the ONS Labour Force Survey (LFS), a key source for understanding employment trends among younger people, contains important notes on reweighting changes and comparability issues. The ONS also advises users not to rely on the LFS in isolation when assessing labour market conditions, recommending that it be considered alongside statistics on workforce jobs, the Claimant Count and Pay As You Earn Real Time Information (PAYE RTI) data. This is particularly important when using labour market statistics to draw conclusions about Gen Z’s attitudes towards work, as no single dataset provides a complete picture of their experiences in the labour market.
In conclusion
Therefore, claims such as “Generation Z doesn’t want to work” cannot be assessed using labour market statistics alone. Housing, mental health and other statistics can provide important context, and using the questions set out here are a good starting point to ensure that statistics are used well. Bringing together evidence from multiple sources can provide a more complete and balanced understanding of the challenges facing Gen Z and their relationship with work.
Even after considering all this evidence, we may never fully agree on why young people’s relationship with work appears to be changing. Different people will continue to interpret the evidence in different ways. But during my time at OSR, what I have learnt is that official statistics do not tell us what to think. That is not their purpose. Their purpose is to help us understand the evidence, question our assumptions and have a more informed conversation. And hopefully, to support better public debate and decision making.