For decades, economists eschewed the study of subjective wellbeing (SWB) and were dismissive of endeavours to understand its correlates or use wellbeing metrics in economic analysis. That changed when, in 1978, Richard Freeman showed that job satisfaction was a strong predictor of quits (Freeman 1978). For the first time, economists became interested in the potential that SWB might have in predicting economic behaviour. SWB has since been treated as a means of measuring individuals’ utility (Frey and Stutzer 2002) and has even been identified as a major goal of public policy (Layard 2005). But the value of SWB data has been questioned recently by economists who argue that key empirical regularities in the wellbeing literature cannot be replicated using non-parametric
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For decades, economists eschewed the study of subjective wellbeing (SWB) and were dismissive of endeavours to understand its correlates or use wellbeing metrics in economic analysis. That changed when, in 1978, Richard Freeman showed that job satisfaction was a strong predictor of quits (Freeman 1978). For the first time, economists became interested in the potential that SWB might have in predicting economic behaviour. SWB has since been treated as a means of measuring individuals’ utility (Frey and Stutzer 2002) and has even been identified as a major goal of public policy (Layard 2005).
But the value of SWB data has been questioned recently by economists who argue that key empirical regularities in the wellbeing literature cannot be replicated using non-parametric identification techniques due to assumptions regarding the underlying functional form of the ordered responses which are usually elicited in survey questions about SWB (Bond and Lang 2019). This position has in turn been challenged by Chen et al. (2021), who argue the Bond and Lang critique does not hold if one focuses on ranking median happiness as opposed to mean happiness.
Yet, SWB metrics have been causally linked to longevity (Diener and Chan 2011), wound healing (Christian et al. 2006), and improved cardiovascular health (see De Neve et al. 2013 for a review), providing some validation of SWB as a metric capturing individual wellbeing. In addition, the way unhappiness rises then falls in age tracks the same hump shape in anti-depressant medication, providing some further validation of SWB (Blanchflower and Oswald 2016, Blanchflower and Graham 2021, Blanchflower and Bryson 2021a).
Whilst there is clearly some value in SWB metrics, Bond and Lang’s (2019) critique means there is value in identifying other easy-to-measure ways of establishing people’s wellbeing. Another reason for doing so is that self-reports of SWB can be unreliable. For example, Johnston et al. (2009) find no evidence of an income/health gradient using self-reported hypertension as a wellbeing metric, but a sizeable gradient when using objectively measured hypertension. The authors conclude that self-reported health measures may underestimate the true income-related inequalities in health.
In a new study (Blanchflower and Bryson 2021b), we introduce pulse rate as a biometric indicator of wellbeing that has been largely overlooked in the literature. Like SWB, pulse is relatively easy to measure, but it has the advantage of being a biomarker with a cardinal scale.
Using the Health Surveys for England and Scotland and the National Child Development Study (NCDS), which tracks all those born in Britain in a single week in 1958, we first consider whether pulse rate equations look similar to SWB equations. It turns out that they have similar correlates: pulse rates are higher among women, single people, the widowed, the unemployed and disabled, the least educated, those with a higher body mass index (BMI), smokers and drinkers, and those with low income. Pulse rates also vary by area, being lowest in prosperous areas and higher in deprived areas. The fact that a pulse equation looks much like a General Health Questionnaire (GHQ) equation helps validate SWB measures. However, unlike mental and general health, pulse rate falls monotonically with age.
We then go on to show that pulse is highly correlated with SWB in cross-section having controlled for personal characteristics. It is positively correlated with GHQ36 unhappiness scores with an r-squared of 0.66 (see Figure 1). It is negatively correlated with self-assessed general health in the English and Scottish Health Surveys. It is also negatively associated with life satisfaction and the WEMWBS wellbeing scale in the Scottish Health Survey.
Figure 1 GHQ36 and pulse rates from the Health Survey for England, 1998–2018
Notes: n=109,000. The mean for the GHQ36 unhappiness scale is 10.7. The mean pulse rate is 70.22
In the longitudinal NCDS data we find pulse rate collected from 9,000 NCDS cohort members in mid-life (age 42) is predictive of SWB, general health, employment and optimism about the future a decade or more later, even when controlling for lagged dependent variables, health-related behaviours, and other biomarkers such as BMI.
Today, advances in smart device technology mean it is cheap and easy to measure one’s pulse (Gyrard and Sheth 2020). It is common practice in some settings – for example, golfers on the PGA tour have been doing it for some time. It seems sensible, therefore, for health professionals and academics alike to pay more attention to these data, and perhaps for individuals to have greater regard to their pulse rates, alongside other biomarkers such as blood pressure and BMI. Pulse rates might also be used by academics as a plausible instrument for SWB in efforts to draw causal inferences about SWB on economic and social outcomes. Pulse rates appear to be an objective way of measuring wellbeing.
Blanchflower, D G and A Bryson (2021a), “Biden, Covid, and mental health in America”, NBER Working Paper No. 29040.
Blanchflower, D G and A Bryson (2021b), “Taking the pulse of nations: a biometric measure of well-being”, NBER Working Paper No. 29587
Blanchflower, D G and C Graham (2021), “The mid-life dip in well-being: a critique”, Social Indicators Research.
Blanchflower, D G and A J Oswald (2016), “Antidepressants and age: a new form of evidence for U-shaped well-being through life”, Journal of Economic Behavior and Organization 127: 46-58.
Bond, T N and K Lang (2019), “The sad truth about happiness scales”, Journal of Political Economy 127(4): 1629-1640.
Chen, L-Y, E Oparina, N Powdthavee and S Srisuma (2021), “Robust ranking of happiness outcomes: a median regression perspective”.
Christian, L M, J E Graham, D A Padgett, R Glaser and J K Kiecolt-Glaser (2006), “Stress and wound healing”, Neuroimmunomodulation 13: 337-346.
De Neve, J-E, E Diener, L Tay and C Xuereb (2013), “ The objective benefits of subjective well-being”, in J Helliwell, R Layard and J Sachs (eds), World Happiness Report 2013, UN Sustainable Development Solutions Network.
Diener, E and M Chan (2011), “Happy people live longer: Subjective well-being contributes to health and longevity”, Applied Psychology: Health and Wellbeing 3: 1-43.
Freeman, R B (1978), “Job satisfaction as an economic variable”, American Economic Review 68(2): 135-41
Frey, B S and A Stutzer (2002), “What can economists learn from happiness research?”, Journal of Economic Literature 40(2): 402–35.
Johnston, D W, C Propper and M A Shields (2009), “Comparing subjective and objective measures of health: evidence from hypertension for the income/health gradient”, Journal of Health Economics 28: 540-552.
Gyrard, A and A Sheth (2020), “IAMHAPPY: Towards an IoT knowledge-based cross-domain wellbeing recommendation system for everyday happiness”, Smart Health 15: 100083.
Layard, R (2005), Happiness: Lessons from a New Science, Penguin.