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Viewing archives for Dr. Caspar Kaiser

Improving wellbeing scales

Dr Caspar Kaiser and Dr Michael Plant sparked lively discussion at the latest of the Wellbeing Research Centre’s Seminar Series after sharing results of their latest pilot study assessing how subjective wellbeing measurement might be improved.

Their work, supported by the Happier Lives Institute, examines the neutrality, comparability and linearity of individual wellbeing scales: three issues that need to be met in successfully implementing wellbeing policy.

Watch the full presentation on the Centre’s YouTube channel.

Assessing the neutrality, comparability, and linearity of subjective wellbeing measurements: a pilot study

Using memories to assess the intrapersonal comparability of wellbeing reports

Using memories to test the interpersonal compatibility of wellbeing reports

Dr. Caspar Kaiser

Caspar is an Assistant Professor with the Behavioural Science Group at Warwick Business School.  He is also a research fellow at Oxford University’s Wellbeing Research Centre, a research associate at the Institute for New Economic Thinking, an associate member of Nuffield College, and a trustee of the Happier Lives Institute. 

His research focuses on the measurement and determinants of wellbeing.

Regarding measurement, he works on improving the comparability of survey data on people’s feelings and analyses whether such data can measure welfare cardinally. Concerning determinants, he investigates how social comparisons and inequalities, particularly with respect to people’s incomes, shape wellbeing.

Beyond these foci, he is interested in the wider normative implications of using subjective data, questions of welfare measurement more generally, the determinants and consequences of social mobility, as well as developments in causal inference and machine learning.  

Caspar holds a DPhil in Social Policy from Nuffield College and the Department of Social Policy & Intervention. Brian Nolan (INET, Oxford) and Maarten Vendrik (SBE, Maastricht) supervised his doctorate. He was previously an Assistant Professor at the Department of Methodology and Statistics at Tilburg University.

Inequality, well-being, and the problem of the unknown reporting function

Caspar Kaiser and Andrew J. Oswald

Every politician, in every nation and in every era of history, eventually has to face a complex and emotive question. Should I try to redistribute money from my richer citizens to my poorer citizens? If so, by how much? This is a timeless issue.

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Feelings integers are highly predictive of future human behaviour, research shows

New research shows that a person’s own rating of their feelings – even on a seemingly arbitrary scale – is of greater predictive power than a collection of socioeconomic measures.

The findings, published today (Monday) in Proceedings of the National Academy of Sciences, were made by researchers at the Wellbeing Research Centre, University of Oxford, using data from approximately 700,000 people across multiple countries.

Professor Andrew Oswald (Warwick) and Dr Caspar Kaiser (Oxford) examined the relationship by comparing self-reported feelings integers – for example, where individuals were asked to rate their satisfaction on a scale of 0 to 10 – to later ‘get-me-out-of-here’ actions.

These actions, where individuals choose to leave their current setting, are an unambiguous signal of human dissatisfaction with the status quo. For the purposes of this study, the authors looked at four types of get-me-out-of-here action: moving dwellings, changing intimate partners, leaving jobs, and hospital visits.

Across 34 years of data in Germany, 25 years in the UK and 20 years in Australia, their research shows that feelings integers are generally of greater predictive power than combined socioeconomic variables including household income, marital status, education and number of children, among others.

The researchers describe a stable and almost linear relationship between a single feelings integer and these self-driven life changes, in all three of the countries examined in the study.

Dr Caspar Kaiser, Research Fellow at the Wellbeing Research Centre and a Research Officer at Oxford’s Institute for New Economic Thinking (INET), and corresponding author for the study, added: “It is unknown whether our results will replicate more globally, especially in low- and middle-income countries. Another interesting next step would be to examine whether the observed action-satisfaction associations systematically differ across population groups, e.g. between men and women or across age.”

‘The scientific value of numerical measures of human feelings’ is published in Proceedings of the National Academy of Sciences of the United States of America.

The scientific value of numerical measures of human feelings

Caspar Kaiser and Andrew J. Oswald

Significance

Human feelings cannot be expressed on a numerical scale. There are no units of measurement for feelings. However, such data are extensively collected in the modern world—by governments, corporations, and international organizations. Why? Our study finds that a feelings integer (like my happiness is X out of 10) has more predictive power than a collection of socioeconomic influences. Moreover, there is a clear link between those feelings numbers and later get-me-out-of-here actions. Finally, the feelings-to-actions relationship appears replicable and not too far from linear. Remarkably, therefore, humans somehow manage to choose their numerical answers in a systematic way as though they sense within themselves—and can communicate—a reliable numerical scale for their feelings. How remains an unsolved puzzle.

Abstract

Human feelings measured in integers (my happiness is an 8 out of 10, my pain 2 out of 6) have no objective scientific basis. They are “made-up” numbers on a scale that does not exist. Yet such data are extensively collected—despite criticism from, especially, economists—by governments and international organizations. We examine this paradox. We draw upon longitudinal information on the feelings and decisions of tens of thousands of randomly sampled citizens followed through time over four decades in three countries (n = 700,000 approximately). First, we show that a single feelings integer has greater predictive power than does a combined set of economic and social variables. Second, there is a clear inverse relationship between feelings integers and subsequent get-me-out-of-here actions (in the domain of neighborhoods, partners, jobs, and hospital visits). Third, this feelings-to-actions relationship takes a generic form, is consistently replicable, and is fairly close to linear in structure. Therefore, it seems that human beings can successfully operationalize an integer scale for feelings even though there is no true scale. How individuals are able to achieve this is not currently known. The implied scientific puzzle—an inherently cross-disciplinary one—demands attention.

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How People Rate Pizza, Jobs and Relationships Is Surprisingly Predictive of Their Behavior

Scientific American

Researchers are perplexed as to why inner feelings about life and love predict our actions better than the best social science…

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International recognition for Wellbeing Research Centre Fellows

Researchers from the Wellbeing Research Centre at the University of Oxford have been recognised by an international organisation at the forefront of wellbeing science.

Dr Caspar Kaiser and Dr Alberto Prati were awarded first prize and joint-second place, respectively, by the International Society for Quality of Life Studies (ISQOLS) in the category of ‘Best Dissertation on Quality-of-Life, Well-being and Happiness’.

The two Research Fellows follow in the footsteps of former Wellbeing Research Centre Research Fellow Dr Lucía Macchia, who won the award in 2021.

Dr Kaiser, recognised for his ‘Four essays on applied and methodological issues in the study of subjective life satisfaction’, also scooped a prestigious Young Scholar Award for his “substantial contribution” to wellbeing research.

As well as his position at the Wellbeing Research Centre, Dr Kaiser is also a Research Officer at Oxford’s Institute for New Economic Thinking (INET) and a trustee and advisor to the Happier Lives Institute.

In response to the recognition, Dr Kaiser said: “I am extremely grateful and honoured to be receiving both of these prizes. ISQOLS has, for a long time, been a kind of academic home for me. This makes this recognition especially valuable to me.

“I especially thank my two DPhil supervisors – Maarten Vendrik and Brian Nolan – who have gone out of their way to guide me during the DPhil. I’m also particularly grateful to the people at INET and the Wellbeing Research Centre, without whom my current research would not be possible.”

Dr Prati, also Assistant Professor at University College London, took second place for his essay ‘Memory and Subjective Well-Being: Empirical Analysis of Workers’ and Consumers’ Endogenous Recall Behaviours’. He added: “I was honoured by this encouraging recognition and I sincerely hope that my doctoral research will help push the boundaries of well-being science.”

Established in 1995, the ISQOLS was one of the first international organisations set up to promote and encourage research in the field of quality-of-life and wellbeing science. In the last three decades it has become a globally recognised organisation with its own publications, journals and an ever-growing membership of some of the brightest minds in the field.

Professor Jan-Emmanuel De Neve, Director of the Wellbeing Research Centre, based at Harris Manchester College, Oxford, said: “Both Caspar and Alberto are fantastically talented members of our research group, and it is hugely rewarding to see their hard work and dedication acknowledged at an international level.

“All of us at the Wellbeing Research Centre wish to congratulate them on their awards.”

Human Wellbeing and Machine Learning

Ekaterina Oparina, Caspar Kaiser, Niccolò Gentile, Alexandre Tkatchenko, Andrew E. Clark, Jan-Emmanuel De Neve, Conchita D’Ambrosio

There is a vast literature on the determinants of subjective wellbeing. International organisations and statistical offices are now collecting such survey data at scale. However, standard regression models explain surprisingly little of the variation in wellbeing, limiting our ability to predict it. In response, we here assess the potential of Machine Learning (ML) to help us better understand wellbeing. We analyse wellbeing data on over a million respondents from Germany, the UK, and the United States. In terms of predictive power, our ML approaches do perform better than traditional models. Although the size of the improvement is small in absolute terms, it turns out to be substantial when compared to that of key variables like health. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms – i.e. material conditions, health, and meaningful social relations – are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches.

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