Viewing archives for Dr. Caspar Kaiser

Investigating the analytical robustness of the social and behavioural sciences

Balazs Aczel, Barnabas Szaszi […], Caspar Kaiser et al.

Abstract

The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts’ choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, in which, for one claim per study, at least five reanalysts independently reanalysed the original data. The statistical appropriateness of the reanalyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent reanalyses yielded the same result (within a tolerance region of ±0.05 Cohen’s d) as the original report; with a four times broader tolerance region, this indicator increased to 57%. Of the reanalyses conducted, 74% reached the same conclusion as the original investigation, 24% yielded no effects or inconclusive results and 2% reported the opposite effect. This exploratory study indicates that the common single-path analyses in social and behavioural research should not be simply assumed to be robust to alternative analyses. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.

Is AI sentient?

Dr Caspar Kaiser (Warwick) shared findings on the capacity for AI models to experience wellbeing at the latest of the Wellbeing Research Centre’s Seminar Series.

His research uses multiple practical experiments to test large language models (LLMs)’ sentience, and their capacity for wellbeing and decision-making.

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

AI sentience and wellbeing

Equality of opportunity and mortality around the world: implications for global public health

Alexi Gugushvili, Elias Nosrati and Caspar Kaiser

Abstract

Background Educational mobility is considered a key driver of population health. While prior studies suggest that intergenerational equality of opportunity may be linked to mortality, most evidence comes from high-income countries. Little is known about whether these associations apply globally.

Objective This study assesses the relationship between intergenerational educational mobility and all-cause mortality across a global sample of countries.

Methods We combine country-level data from the WHO Mortality Database and the World Bank’s Global Database on Intergenerational Mobility, covering five birth cohorts across 148 countries. Using multilevel random effects models, we estimate associations between four dimensions of educational mobility (upward, downward, stagnant, and correlation-based) and age- and sex-adjusted all-cause mortality, controlling for national indicators of education, income, inequality, health spending, unemployment, and political freedoms.

Results Higher upward educational mobility and lower stagnant mobility are significantly associated with reduced all-cause mortality. In fully adjusted models, a one standard deviation greater measure of upward mobility is associated with a reduction of 29.1 deaths per 100,000 population, while a one standard deviation lower stagnation measure is associated with a reduction of 27.3 deaths per 100,000 population. These patterns are consistent across high- and low-income countries.

Conclusions Our findings suggest that promoting educational equality of opportunity may reduce mortality and improve public health worldwide. Strengthening social mobility, especially in settings with persistent educational inequality, can be an effective policy lever for reducing health disparities and supporting healthier populations.

The paradox of women’s well-being: Why they report higher happiness despite worse mental health

El País

The bottom line: global evidence points to a decline in women’s well-being, particularly in terms of emotional distress.

The research focused on subjective well-being, which refers to how a person perceives and describes their quality of life. “Our study analyzes and explores gender disparities in well-being. There are two disconcerting contradictions that we aimed to investigate: why they exist, whether they persist in different countries, and what really drives them,” says Caspar Kaiser, a researcher at the University of Oxford and the study’s author, in an email response.

Two paradoxes in women’s well-being

Caspar Kaiser, Naomi Muggleton, Edika Quispe-Torreblanca, and Jan-Emmanuel De Neve

Abstract

We review the literature on the gender gap in well-being, identifying two key paradoxes. First, although women today report higher levels of life satisfaction and overall happiness than men, they experience worse outcomes in mental health and negative affect. Second, despite substantial advances in women’s social and economic status over the past 50 years, their well-being relative to men has declined. We explore the evidence supporting these paradoxes, considering potential explanations related to differential expectations, biology, and scale use. Using global data from 2006 to 2023 and long-term data from Europe and the US since the 1970s, we provide empirical illustrations. These findings reveal a diverse and seemingly inconsistent pattern of gender well-being gaps between countries, suggesting that the first paradox is not universally applicable. However, there is clear global evidence of a relative decline in women’s well-being, particularly in terms of negative affect.

Mind the Gap: Research identifies worrying wellbeing disparities between men and women on a global scale

Relative to men, women’s wellbeing has been declining globally.
Researchers reviewed two decades of global data, long-run trends since the 1970s, and existing analyses.
Study further reveals vast differences in the size of this gender wellbeing gap between countries.

Women’s wellbeing relative to men has declined in the past two decades despite substantial social and economic progress, newly-published research shows.

Researchers from the Wellbeing Research Centre at the University of Oxford, Warwick Business School, and Leeds University Business School studied trends of wellbeing and health outcomes using global data for the period 2006 to 2023, and reviewed existing research on the so-called ‘gender wellbeing gap’.

The findings are published today (Wednesday) in a special women’s health edition of the journal Science Advances, and challenge long-held assumptions about women’s wellbeing on a global scale.

While women, on average, report higher levels of life satisfaction and overall happiness than men, women experience worse outcomes in both mental health and reports of negative ‘affect’, or negative emotions.

This trend, which has been described as a paradox, has widened over the same period – meaning women’s wellbeing, particularly in the affective domain, has dropped relative to men.

But while there is clear evidence of a relative decline in women’s wellbeing on a global scale, the findings show vast differences in the size of the gender wellbeing gap across different countries.

In addition to two decades of global data, the research team also looked at long-run trends in Europe and the USA since the 1970s, and reviewed existing literature in search of explanations for the relative decline in women’s wellbeing.

Their findings underscore the need for broader-reaching gender-sensitive policies in order to reduce and subsequently eliminate the observed gender wellbeing gaps, and further research into gender disparities between reported life satisfaction, and both positive and negative emotions.

“This seems puzzling and paradoxical.”

Dr Caspar Kaiser, Assistant Professor in the Behavioural Science Group at Warwick Business School, Research Fellow at the Wellbeing Research Centre, and co-lead author of the paper, said: “In my view, the most striking finding – both in the literature and in our own analyses – is that despite global improvements in women’s economic standing, gender gaps in wellbeing have not trended in the same direction. To the contrary: for several indicators, and on a global scale, women now fare worse relative to men than twenty years ago. This seems puzzling and paradoxical.”

Dr Naomi Muggleton, Assistant Professor in the Behavioural Science Group at Warwick Business School, and co-lead author of the paper, said: “Purely biological accounts would predict consistent patterns globally. But our findings suggest that cultural norms, societal expectations, and economic conditions play key roles in shaping the paradoxes we observe in women’s wellbeing. These factors vary widely across regions, influencing both the pressures women face and the resources available to them, which helps explain why the patterns differ so much between countries.”

“Understanding these factors is essential for designing effective, gender-responsive policies.”

Dr Edika Quispe-Torreblanca, Associate Professor of Behavioral Decision Making in the Centre for Decision Research at the University of Leeds Business School, and co-lead author of the paper, said: “Wellbeing is a complex concept, and men and women may prioritize different aspects when assessing their subjective wellbeing, making direct comparisons challenging. Our review highlights that, while some research has begun to explore these differences, much remains to be done.

“Recognising these distinctions is crucial for policymakers. For instance, if men and women differ in how they balance present and future wellbeing, policies should reflect this. Measures that address immediate concerns like childcare and healthcare may impact wellbeing differently than those focused on long-term stability, such as financial security and career growth.

“Likewise, if men and women experience wellbeing differently in relation to work, family, or community engagement, policies should take these differences into account when designing workplace flexibility, caregiving support, and community-based programs. Understanding these factors is essential for designing effective, gender-responsive policies.”

‘Two Paradoxes in Women’s Wellbeing’ is published in Science Advances.

Machine learning a better predictor of human wellbeing than existing models

Machine learning algorithms can predict human wellbeing better than traditional econometric models, according to new research.

The significant findings, published in the journal Scientific Reports, are the first of their kind and could dramatically change the way we measure, study, and consider human wellbeing.

Researchers from the Wellbeing Research Centre at the University of Oxford formed part of an interdisciplinary team which pitted two different ‘tree-based’ machine learning algorithms against a variety of standard econometric models, using nationally representative samples from Germany, the UK, and the United States.

Traditionally, economists and other researchers have relied on conventional linear models to attempt to model the variables – or ‘drivers’ – which positively or negatively impact individuals’ self-reported subjective wellbeing. These drivers include measures such as age, income, and household size.

Such traditional techniques only allow a limited number of variables to be tested together at one time and, since the variables to be tested must be selected by a human researcher, may be subject to unintentional bias like any other human-run experiment.

Instead, each machine learning algorithm was fed data across hundreds of different variables, and tasked with assessing the relative importance of each variable to self-reported wellbeing scores, at a population level.

This novel approach, when compared to conventional human-run linear models, allowed the researchers to better identify trends across time and – in particular – explore the interactions between different variables in their impact upon wellbeing.

Dr Ekaterina Oparina, a research economist at the London School of Economics and Political Science and joint first author of the study, said: “It is exciting to leverage machine learning in this context: it helps us better understand what makes people happy and allows us to test earlier knowledge on the subject. We can now confirm that factors identified as important in earlier works, like interpersonal relationships and health, continue to matter in this more nuanced setting.”

“What is this wellbeing ‘dark matter’?”

Dr Caspar Kaiser, Assistant Professor in the Behavioural Science Group at Warwick Business School, Research Fellow at the Wellbeing Research Centre, and joint first author for the study, said: “Perhaps counterintuitively, the thing I am most excited about is our finding that when using all available data in surveys, and when using the most flexible algorithms available, we can explain about 30% of people’s wellbeing. This means that a large share of people’s wellbeing remains unexplored. What is this wellbeing ‘dark matter’? Presumably, only moving beyond traditional surveys will allow us to uncover this – and that’s something I really look forward to.”

Dr Niccolò Gentile, a research data scientist at the University of Luxembourg and joint first author of the study, said: “It’s safe to say that Natural Language Processing – and Large Language Modelling (LLM) in particular – are increasingly capturing the scientific community’s interest. Most of the best known LLMs rely on the Transformer architecture, first published in 2017, well before the start of our research. GPT-1 and BERT, similarly, were first presented in 2018. Progress is instead found in innovative ways of how to combine and optimize them.

“While some literature applying LLMs to tabular data is slowly emerging, at the time of writing, no LLM-related technique has been found to consistently outperform the types of ‘traditional’ machine learning techniques we used in this study. Considering that tabular data still represents a large chunk of what researchers and companies work with, it remains crucial not to underestimate any solution just because it’s ‘old’.”

‘Machine learning in the prediction of human wellbeing’ is published in Scientific Reports.

Machine learning in the prediction of human wellbeing

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

Abstract

Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents’ self-reported wellbeing. We analyse representative samples of more than one million respondents from Germany, the UK, and the United States, using data from 2010 to 2018. We make three contributions. First, we show that ML algorithms can indeed yield better predictive performance than standard approaches, and establish an upper bound on the predictability of wellbeing scores with survey data. Second, we use ML to identify the key drivers of evaluative wellbeing. We show that the variables emphasised in the earlier intuition- and theory-based literature also appear in ML analyses. Third, we illustrate how ML can be used to make a judgement about functional forms, including the existence of satiation points in the effects of income and the U-shaped relationship between age and wellbeing.

Why you should measure subjective changes