Diener, E., Oishi, S. & Tay, L. Advances in subjective well-being research. Nat. Human Behav. 2(4), 253–260 (2018).
Google Scholar
OECD. (2020a). How’s Life? 2020: Measuring Well-being.
ONS. (2021). Well-being – Office for National Statistics.
Cheung, F. & Lucas, R. E. Assessing the validity of single-item life satisfaction measures: Results from three large samples. Qual. Life Res. 23(10), 2809–2818 (2014).
Google Scholar
Tov, W., Keh, J.S., Tan, Y.Q., Tan, Q.Y.J., & Aziz, I.A.S.B. (2022). Assessing subjective well-being: A review of common measures. in Handbook of Positive Psychology Assessment.
OECD. (2013a). Methodological considerations in the measurement of subjective well-being (tech. rep.). OECD. Paris.
Diener, E., Inglehart, R. & Tay, L. Theory and validity of life satisfaction scales. Social Indicators Res. 112(3), 497–527 (2013).
Google Scholar
Benjamin, D. J., Heffetz, O., Kimball, M. S. & Rees-Jones, A. Can marginal rates of substitution be inferred from happiness data? Evidence from residency choices. Am. Econ. Rev. 104(11), 3498–3528 (2014).
Google Scholar
Charpentier, C. J., De Neve, J.-E., Li, X., Roiser, J. P. & Sharot, T. Models of affective decision making: How do feelings predict choice?. Psychol. Sci. 27(6), 763–775 (2016).
Google Scholar
Kaiser, C. & Oswald, A. J. The scientific value of numerical measures of human feelings. PNAS 119(42), e2210412119 (2022).
Google Scholar
Layard, R., Clark, A. E., Cornaglia, F., Powdthavee, N. & Vernoit, J. What predicts a successful life? A life-course model of well-being. Econ. J. 124(580), F720–F738 (2014).
Google Scholar
Lucas, R. E. Long-term disability is associated with lasting changes in subjective well-being: Evidence from two nationally representative longitudinal studies. J. Personality Social Psychol. 92, 717–730 (2007).
Google Scholar
Oswald, A. J. & Powdthavee, N. Does happiness adapt? A longitudinal study of disability with implications for economists and judges. J. Public Econ. 92(5), 1061–1077 (2008).
Google Scholar
Lucas, R. E., Clark, A. E., Georgellis, Y. & Diener, E. Unemployment alters the set point for life satisfaction. Psychol. Sci. 15(1), 8–13 (2004).
Google Scholar
Kassenboehmer, S. C. & Haisken-DeNew, J. P. You’re fired! the causal negative effect of entry unemployment on life satisfaction. Econ. J. 119(536), 448–462 (2009).
Google Scholar
Blanchflower, D. G. & Oswald, A. J. Well-being over time in Britain and the USA. J. Public Econ. 88(7–8), 1359–1386 (2004).
Google Scholar
Rohrer, J. M., Richter, D., Brümmer, M., Wagner, G. G. & Schmukle, S. C. Successfully striving for happiness: Socially engaged pursuits predict increases in life satisfaction. Psychol. Sci. 29(8), 1291–1298 (2018).
Google Scholar
Boyce, C. J. Understanding fixed effects in human well-being. J. Econ. Psychol. 31(1), 1–16 (2010).
Google Scholar
Boyce, C. J. & Wood, A. M. Personality prior to disability determines adaptation: Agreeable individuals recover lost life satisfaction faster and more completely. Psychol. Sci. 22(11), 1397–1402 (2011).
Google Scholar
Anglim, J., Horwood, S., Smillie, L. D., Marrero, R. J. & Wood, J. K. Predicting psychological and subjective well-being from personality: A meta-analysis. Psychol. Bull. 146, 279–323 (2020).
Google Scholar
Ryan, E. & Deci, R. M. On happiness and human potentials: A review of research on Hedonic and Eudaimonic well-being. Annu. Rev. Psychol. 52, 141–166 (2001).
Google Scholar
Dolan, P., Peasgood, T. & White, M. Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being. J. Econ. Psychol. 29(1), 94–122 (2008).
Google Scholar
Clark, A. E. Four decades of the economics of happiness: Where next?. Rev. Income Wealth 64(2), 245–269 (2018).
Google Scholar
Kong, F. et al. Examining gray matter structures associated with individual differences in global life satisfaction in a large sample of young adults. Social Cognit. Affect. Neurosci. 10, 952–960 (2019).
Google Scholar
Nikolova, M. & Graham, C. The economics of happiness. In Handbook of Labor, Human Resources and Population Economics (ed. Zimmermann, K. F.) 1–33 (Springer International Publishing, 2022).
Google Scholar
Frijters, P. & Beatton, T. The mystery of the U-shaped relationship between happiness and age. J. Econ. Behav. Organ. 82(2–3), 525–542 (2012).
Google Scholar
Cheng, T. C., Powdthavee, N. & Oswald, A. J. Longitudinal evidence for a midlife nadir in human well-being: Results from four data sets. Econ. J. 127(599), 126–142 (2017).
Google Scholar
Wunder, C., Wiencierz, A., Schwarze, J. & Küchenhoff, H. Well-being over the life span: Semiparametric evidence from British and German longitudinal data. Rev. Econ. Stat. 95(1), 154–167 (2013).
Google Scholar
Kahneman, D. & Deaton, A. High income improves evaluation of life but not emotional well-being. PNAS 107(38), 16489–93 (2010).
Google Scholar
Jebb, A. T., Tay, L., Diener, E. & Oishi, S. Happiness, income satiation and turning points around the world. Nat. Human Behav. 2(1), 33–38 (2018).
Google Scholar
Stevenson, B. & Wolfers, J. Subjective well-being and income: Is there any evidence of satiation?. Am. Econ. Rev. 103(3), 598–604 (2013).
Google Scholar
Killingsworth, M. A. Experienced well-being rises with income, even above §75,000 per year. PNAS 118(4), e2016976118 (2021).
Google Scholar
Kaiser, M., Otterbach, S. & Sousa-Poza, A. Using machine learning to uncover the relation between age and life satisfaction. Sci. Rep. 12(1), 5263 (2022).
Google Scholar
Margolis, S., Elder, J., Hughes, B., & Lyubomirsky, S. (2021). What are the most important predictors of subjective well-being? Insights from machine learning and linear regression approaches on the MIDUS datasets (tech. rep.). PsyArXiv.
Prati, G. Correlates of quality of life, happiness and life satisfaction among European adults older than 50 years: A machine-learning approach. Arch. Gerontol. Geriatr. 103, 104791 (2022).
Google Scholar
Dukart, J., Weis, S., Genon, S. & Eickhoff, S. B. Towards increasing the clinical applicability of machine learning biomarkers in psychiatry. Nat. Human Behav. 5(4), 431–432 (2021).
Google Scholar
Breiman, L. Random forests. Machine Learn. 45(1), 5–32 (2001).
Google Scholar
Hastie, T., Tibshirani, R., Friedman, J.H., & Friedman, J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Vol. 2. (Springer, 2009).
Friedman, J.H. (2001). Greedy function approximation: A gradient boosting machine. Ann. Stat. 1189–1232.
Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Front. Neurorobot. 7, Article 21.
Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Series B (Methodol.) 58(1), 267–288 (1996).
Google Scholar
Shwartz-Ziv, R. & Armon, A. Tabular data: Deep learning is not all you need. Inform. Fusion 81, 84–90 (2022).
Google Scholar
Borisov, V., Leemann, T., Seßler, K., Haug, J., Pawelczyk, M., & Kasneci, G. (2022). Deep Neural Networks and Tabular Data: A Survey. arXiv:2110.01889 [cs].
OECD. (2013b). OECD Guidelines on Measuring Subjective Well-being.
Fudenberg, D., Kleinberg, J., Liang, A. & Mullainathan, S. Measuring the completeness of economic models. J. Political Econ. 130(4), 956–990 (2022).
Google Scholar
Krueger, A. B. & Schkade, D. A. The reliability of subjective well-being measures. J. Public Econ. 92(8–9), 1833–1845 (2008).
Google Scholar
Clark, A., Flèche, S., Layard, R., Powdthavee, N. & Ward, G. The Origins of Happiness (Princeton University Press, 2018).
Google Scholar
Reis, I., Baron, D. & Shahaf, S. Probabilistic random forest: A machine learning algorithm for noisy data sets. Astron. J. 157(1), 16 (2018).
Google Scholar
Ferrer-i-Carbonell, A. & Frijters, P. How important is methodology for the estimates of the determinants of happiness?. Econ. J. 114(497), 641–659 (2004).
Google Scholar
Proto, E. & Zhang, A. COVID-19 and mental health of individuals with different personalities. PNAS 118(37), e2109282118 (2021).
Google Scholar
OECD. (2020b). Education at a Glance 2020.
Gorry, A., Gorry, D. & Slavov, S. N. Does retirement improve health and life satisfaction?. Health Econ. 27(12), 2067–2086 (2018).
Google Scholar
Wetzel, M., Huxhold, O. & Tesch-Römer, C. Transition into retirement affects life satisfaction: Short- and long-term development depends on last labor market status and education. Social Indicators Res. 125(3), 991–1009 (2016).
Google Scholar
Wolpert, D.H., & Macready, W.G. (1995). No Free Lunch Theorems for Search (tech. rep.). Technical Report SFI-TR-95-02-010, Santa Fe Institute.
Mehta, P. et al. A high-bias, low-variance introduction to machine learning for physicists. Phys. Rep. 810, 1–124 (2019).
Google Scholar
Wager, S. & Athey, S. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113(523), 1228–1242 (2018).
Google Scholar
van Agteren, J. et al. A systematic review and meta-analysis of psychological interventions to improve mental wellbeing. Nat. Human Behav. 5(5), 631–652 (2021).
Google Scholar
Helliwell, J.F., Wang, S., Huang, H., & Norton, M. (2022). in Happiness, Benevolence, and Trust During COVID-19 and Beyond (World Happiness Report), 15–52.
McGuire, J., Kaiser, C. & Bach-Mortensen, A. M. A systematic review and meta-analysis of the impact of cash transfers on subjective well-being and mental health in low- and middle-income countries. Nat. Human Behav. 6(3), 359–370 (2022).
Google Scholar
Breiman, L. Classification and Regression Trees (Routledge, 1984).
Google Scholar
Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Machine Learn. Res. 12, 2825–2830 (2011).
Google Scholar
Cantril, H. The Pattern of Human Concerns (Rutgers University Press, 1965).
SOEP. (2021). SOEP-Core v36 (tech. rep.). SOEP Survey Papers.
UKHLS. (2021). United Kingdom Household Longitudinal Study Understanding Society: Waves 1-10, 2009-2019 and Harmonised BHPS: Waves 1-18, 1991-2009.
Ahrens, A., Hansen, C. B. & Schaffer, M. E. Lassopack: Model selection and prediction with regularized regression in Stata. Stata J. 20(1), 176–235 (2020).
Google Scholar
Bertrand, M. & Mullainathan, S. Do people mean what they say? Implications for subjective survey data. Am. Econ. Rev. 91(2), 67–72 (2001).
Google Scholar
Oparina, E. & Srisuma, S. Analyzing subjective well-being data with misclassification. J. Business Econ. Stat. 40(2), 730–743 (2022).
Google Scholar
Silk, A. J. Test-retest correlations and the reliability of copy testing. J. Marketing Res. 14(4), 476 (1977).
Google Scholar
Kammann, R. & Flett, R. Affectometer 2: A scale to measure current level of general happiness. Austr. J. Psychol. 35(2), 259–265 (1983).
Google Scholar
Molnar, C. (2022). Interpretable Machine Learning. https://christophm.github.io/interpretable-ml-book/
