Caleb Hia ’18 wrote the following article on health economics from his research for his undergraduate dissertation at the University of Edinburgh.
From 2006 to 2007, almost half of the UK’s National Health Service’s (NHS) costs were attributed to behavioural risk factors: diet-related sickness, sedentary lifestyles, smoking, alcohol and obesity cost more than £15 billion (Scarborough et al., 2011). This mammoth sum, deemed an economic burden on public resources, attracted the government’s attention. In the recent Budget, the Chancellor introduced a tax on the sugar content of soft drinks from 2018 to tackle childhood obesity aimed at compelling individuals to consider external costs associated with its consumption which they do not bear such as the publicly-funded health costs of treating diet-related diseases. The effectiveness of this or any further government intervention in an attempt to correct this “externality” will influence the way the NHS allocates its limited resources in healthcare provision.
Beyond this political issue runs an underlying discussion of the social determinants of health which have long been studied (Wilkinson and Marmot, 2003; Adams et al., 2003). In particular, the effects of education on health has been of interest since the inception of Grossman’s (1972) health model. Grossman’s model suggests health can be maintained by health investments, depending on goods and activity consumption, which affect health although health depreciates as individuals age. As better health gives an individual more time to work and enjoy consumption, more educated individuals are expected to demand more health and invest more in their health. This implies more educated individuals are also more efficient health producers.
A possible causal link between education and health exists possibly because higher productivity from more education directly translates to a higher level of health production through allocative efficiency (Kenkel, 1991; Rosenzweig, 1995) and productive efficiency (Grossman, 1972). For example, low literacy is associated with a poor understanding of hospitals’ discharge instructions (Spandorfer et al., 1995) while higher educated individuals are more likely to follow medical treatments (Goldman and Smith, 2002). Relatedly, higher educated people spend more time on health-related activities because they are better at allocating inputs (Grossman, 1972). Additionally, higher educated individuals use their higher earnings to purchase healthier lifestyles (Glied and Lleras-Muney, 2003) which entail more expensive medical treatments, healthier food consumption and living in healthier areas.
I use a natural experiment in England, the increase in compulsory schooling laws from fifteen to sixteen years old following the Raising of School Leaving Age Order in 1972, and an instrumental variable (IV) regression model to examine the relationship between education and health in greater detail. My sample incorporates additional years of data from Health Survey England between 1991 and 1993 which were not analysed before. I measure various health-related measures and behaviours including Body Mass Index (BMI) which has not been considered before. I run Ordinary Least Squares (OLS) and two-stage least squares (2SLS) regressions in a sample containing all individuals and a discontinuity sample comprising individuals born only in January and February using February-born individuals as my instrument. I show education has no causal effect on various health-related measures and behaviours.
A possible explanation for this lies in time inconsistent preferences supported by behavioural economics. Quasi-hyperbolic discounting (Phelps and Pollak, 1968; Laibson, 1997) induces dynamically inconsistent preferences contrary to geometric discounting. The following payoff matrices models a hypothetical situation where an individual fails to quit smoking due to quasi-hyperbolic discounting:
Under geometric discounting where ∝ ≈ 1 and β ≈ 0.8,
he makes time consistent choices regardless of when benefits to those choices are delayed. Since he gets more utility from quitting in both periods, he quits immediately.
However, under Quasi-hyperbolic discounting where ∝ ≈ 1 and β ≈ 0.8,
he changes his choices based on his distance in the future. Unlike geometric discounting, he gets more utility from quitting only in future and not at present and hence do not quit.
The empirical evidence from Gruber and Köszegi’s (2001) addictive behaviour model which incorporates time-inconsistent preferences to the standard “rational addiction” model (Becker et al., 1994) suggests smokers exhibit forward-looking behaviour with time inconsistent preferences concerning smoking. Thus, individuals start smoking often as adolescents when they are most present biased (Hammond, 2005) and do not anticipate the difficulty of quitting.
Therefore, lifestyle habits may not be correlated with education. In the case of smoking, individuals who quit smoking successfully may have used commitment devices (Ashraf et al., 2006; Kaur et al., 2010; Beshears et al., 2011) like quitting with friends to constrain their own future choices by deciding ahead of time to make future deviations costly. Increasing the education budget may be a sound way to promote public health but understanding behaviours and exploring policies to incentivise individuals to adopt healthy habits may be more effective in the long-run.
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