Who’s miserable now? Identifying clusters of people with the lowest subjective wellbeing in the UK

Who’s miserable now? Identifying clusters of people with the lowest subjective wellbeing in the UK. Paul Dolan, Kate Laffan & Alina Velias. Social Choice and Welfare, Nov 1 2021. https://link.springer.com/article/10.1007/s00355-021-01365-4

Abstract: Policymakers are generally most concerned about improving the lives of the worst-off members of society. Identifying these people can be challenging. We take various measures of subjective wellbeing (SWB) as indicators of the how well people are doing in life and employ Latent Class Analysis to identify those with greatest propensity to be among the worst-off in a nationally representative sample of over 215,000 people in the United Kingdom. Our results have important implications for how best to analyse data on SWB and who to target when looking to improve the lives of those with the lowest SWB.

Discussion

In this paper, we define misery using the four measures of SWB used by the ONS. We consider someone to be in the most miserable group in society if they report low wellbeing on all four measures. In this way, we partly circumvent the debate about which of the four questions best reflects SWB and address concerns surrounding fuzzy preferences and simply mistaken subjective reports. According to this definition, 1.1% of the total sample are miserable. We examine who is among the worst-off in society by using LCA to identify groups of people united by specific observable characteristics and highlighting those characteristics that differentiate groups more vulnerable to misery from those at lower-than-average risk of being miserable.

The LCA highlights two groups that are at higher-than-average risk of being miserable. By far the most vulnerable are those belonging to class 1. Of the miserable people included in our analysis, class 1 account for 77%. Members of this group tend to be aged 30 + , economically inactive, face disability and health problems, live in rented accommodation, have compulsory or lower levels of education and tend not to be in a partnership. Those in class 2 are also vulnerable to misery, making up 19% of the miserable people in our sample. People in this class share some but not all of the characteristics which define class 1. Members of class 2 also report some health issues and have a higher-than-average risk of disability. They also tend not to be in a partnership. Unlike class 1, this group tends to be employed, is younger, more educated and is just as likely to have a mortgage as to be renting.

Together the members of these two classes make up just over 15% of the sample but they account for 96% of the most miserable members of society. These people, therefore, answer the question of who is miserable now. Their shared characteristics are perhaps unsurprising given some of the existing SWB literature. Many of the same characteristics that matter on average appear to be linked to misery too. Health, marital status and job security, for example, are long-established factors associated with SWB (Dush and Amato 2005; Steptoe et al. 2015; Dawson et al. 2017). The current work builds on existing studies by highlighting the substantive risk of misery facing those who concurrently lack a number of these different protective factors. Health literature is known to use clustering approaches to identify high and low health risk groups by looking at a combination of self-assessed, lifestyle and socio-demographic characteristics and propose tailored interventions (see e.g. Dodd et al. 2010)—and SWB literature can benefit from identifying misery-risk groups too.

Much of the existing literature has examined the determinants of LS. An analysis of the most miserable 5% of the population on LS yields similar results, with classes 1 and 2 remaining the classes which are the most vulnerable to misery. The major difference in the response to our overarching question of who is miserable when we look across the two definitions of misery, therefore, is one of scale rather than composition: Many more people are miserable when we define misery as low life satisfaction, compared to reporting low SWB across all four measures, but class 1 and 2 still account for the vast majority of the miserable in both cases.

We are aware that our approach is not without its limitations. In terms of identifying who are the most miserable, we must rely on the APS survey questions on people’s life circumstances and we must rely on those surveyed in the sample. The APS includes a broad range of questions, but it does not cover all of the dimensions of wellbeing of potential interest, nor all of the determinants of SWB that have been identified in the literature. For example, the APS is lacking indicators on people’s evaluation of the meaning of their lives and how people spend their time, which existing work identifies as an important dimension and predictor of SWB respectively (Stone and Mackie 2013; Laffan 2018). Furthermore, those interested in SWB and misery must do more to get at populations who do not participate in population surveys, such as the homeless and those in institutions such as care homes and prisons, many of whom we might expect to be among the worst-off in society. For example, homeless people, which, depending on the definition, constitute about 0.5% (320,000) of the UK population (Shelter 2018) and we do not capture them in our analysis.Footnote8

In terms of establishing the factors associated with who is the worst-off, LCA helps us to identify groups of individuals at the highest risks of misery but like most data science tools it requires large volumes of complete observations. This means that once a person fails to answer one of the survey questions (e.g., housing tenure) their entire entry is dropped from the clustering analysis, which can be a problem for the cases where the non-response to certain questions is group-specific (Heffetz and Rabin 2013). This can be particularly challenging if the non-response behaviour is correlated with the variable of interest, i.e., if the miserable tend to avoid answering certain questions about themselves.

We also cannot make causal claims based on our analysis. Like other correlational SWB research, the associations we present are vulnerable to reverse causality and omitted variable bias. As a result, insights from the current work do not suggest how to address people’s misery but rather identify those groups of people that policymakers should pay particular attention to. In particular, our results emphasise the importance of considering how and why individual factors may interplay to make people more or less vulnerable to misery. For example, the misery of those in poor health whilst active in the workforce may be driven by daily concern about job security. In contrast, the misery of those individuals whose poor health prevents them from participating in the workforce may be, in part, caused by the resulting loneliness they experience. Optimal policies to address misery should be informed by evidence on the way combinations of factors influence people’s SWB.

Even if the combination of characteristics that the analysis identifies as being predictive of misery do represent causal impacts on wellbeing, some characteristics will be more susceptible to policy intervention than others: job security compared to marital and disability status, for example. Several of the shared characteristics in both groups with a higher-than-average percentage of miserable—including a relatively high risk of being in poor health and having a disability—suggest that members of this group may be inelastic suppliers of wellbeing and the potential for policy intervention to improve their wellbeing may be limited.

Notwithstanding these limitations, the current work makes significant contributions to our understanding of who’s miserable now. One of the most important yardsticks for judging a society is how well it treats its worst-off. By looking across the four ONS wellbeing questions, we classify just over 1% of the APS sample as being in the most miserable group. By identifying which clusters of people are most vulnerable, we hope to have provided researchers and policymakers with insights which can assist them in more accurately identifying who to target when trying to improve the lives of the worst-off.