Mortality, lifestyle and socio-economic status
Section snippets
The economic framework
The purpose of this paper is to investigate whether or not lifestyle choices have a direct and significant influence on the risk of mortality and to assess their contribution to the observed socio-economic gradient of mortality. It is well documented that individual socio-economic status is correlated with health and that health inequalities in the population are associated with socio-economic differentials (see e.g., van Doorslaer and Koolman, 2004). However, lifestyles might capture part of
A model of health and lifestyles
We use a dynamic programming approach to define the economic problem.4
Data and empirical model
This paper uses data from the first wave of the Health and Lifestyle Survey. The HALS was carried out between Autumn 1984 and Summer 1985, in two home visits (the second one by a nurse). The questionnaire was designed and piloted by a study team at the University of Cambridge School of Clinical Medicine and funded by the Health Promotion Research Trust. The sample design permits inference about the British population, aged 18 and over in 1984–1985.
In May 2003, 19 years later, the data has been
Variables and sample
The lifestyle variables indicate whether the individual is a non-smoker, a prudent consumer of alcohol, eats breakfast, sleeps the “optimal” numbers of hours, is not obese, and did sufficient physical activity in the last fortnight12
A multivariate probit model for mortality
Our model consists of a recursive system of equations for lifestyles, morbidity and mortality. Its most important feature is that the random components of the lifestyle equations are allowed to be freely correlated with the random component of the mortality equation. If there are unobservable individual characteristics, influencing both individual's healthy behaviours and their probability of death, the model is able to take them into account.
Endogeneity can arise with the inclusion of
Results for the multivariate probit models
First, following Wilde's (2000) result on identification of multiple equation probit models, we tried to estimate a multivariate model where each equation has the same regressor matrix. However, the MSL of the multivariate probit does not converge to a global maximum. Therefore, following Schmidt (1981) and Maddala's (1983) approach, we changed the specification of the model by setting some exclusion restrictions.
We compared four different sets of exclusion restrictions and used information
Results from a decomposition analysis of total health Inequality
In this section we compute the Gini coefficient to give a robust measure of health inequality. Since our mortality indicator is a binary variable, we use predicted mortality, that is the linear index for death predicted from probit models of the mortality equation, to analyze total health inequality, van Doorslaer and Jones (2003), dealing with an ordered categorical dependent variable for SAH from the Canadian National Population Health Survey, used predicted health from ordered probit or
Conclusion
We propose a simple behavioural model where the economic agent maximizes lifetime utility. A value function is used to relate future utility to survival probability. Health investments decisions are assumed to influence longevity. For the empirical model we use the British HALS (1984–1985) data and the longitudinal follow-up of May 2003 to investigate the determinants of premature mortality risk in Great Britain.
We relate the risk of mortality to a set of observable and unobservable factors.
Acknowledgements
The authors wish to thank Paul Contoyannis, Fabrice Etilé, Martin Forster, Angel López, Chiara Monfardini and Nigel Rice for their suggestions and comments. Data from the Health and Lifestyle Survey (HALS) were supplied by the ESRC Data Archive.
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2022, Journal of Health EconomicsCitation Excerpt :She finds that an increase of 250 grams in birth weight leads to roughly 0.1 years of additional schooling. Higher education and income can be translated into higher access to material resources, better quality health insurance, better access to health-related knowledge, and better health behavior, all of which could be pathways through which welfare spending in early life may be linked to higher longevity and better health in later life (Balia and Jones, 2008; Demakakos et al., 2015; Gong et al., 2019; Halpern-Manners et al., 2020; Koch, 2011; Manzoli et al., 2007; Marmot, 2002; Stringhini et al., 2017). The primary source of data is Numident death records of the Social Security Administration linked with 1940 census records from the CENSOC project (Goldstein et al. 2021).