A Dual-Phase Health Capital Model and Its Application to Health Co-benefit Modelling of Decarbonisation
This thesis is developed in the context of investigating the health co-benefit of decarbonisation. Health co-benefit refers to the collateral benefit which arises from decarbonisation policies external to the main intended benefit of climate change mitigation via the reduction of Greenhouse Gases (GHG). Health co-benefit of this kind often arises via the corresponding reduction in air pollutants when GHG is reduced. This is because GHG and air pollutants such as particulate matter are often derived from the same source – the combustion of fossil fuels which drive economic activities. Existing literature in the health co-benefit of decarbonisation fail to give consider the effect of socio-economic variables such as income and education on the expected health co-benefits, and this is where the thesis begins. The backdrop of health co-benefit modelling and the need to incorporate socioeconomic considerations provide the impetus to develop a health economics model. However, in many ways this health economic model deviates from the health co-benefit studies methodologically and instead follows the tradition of the Health Capital Model developed by Grossman (1972). This is due to the micro-economic nature of this health economic model which employs standard economic theory and technique of optimisation, which differs from the fundamentally empirically driven approach of health co-benefit studies. The health economic model developed here is an opportunity to address some of the short-comings of the Health Capital Model. The health co-benefit background however provides some concrete context and inspiration for the application of the theoretical insights which can be drawn from this model. The main contribution of the model develop in this thesis from the theoretical point of view lies in the division of the lifecycle analysis of health into two distinct but related phases of childhood and adulthood. The two phases are specified with different assumptions reflecting the differing characteristics of childhood and adulthood. The most important distinction between the two phases is the manner in which investment in health capital (using time and goods resources) enters the modelling framework. In the childhood phase, health investment augments or increases the existing stock of health capital, while during the adulthood phase health investment prevents the decline of health but does not increase its stock. I believe this better reflects the biological behaviour of health over one’s life than the HCM which implicitly assumes that new stock of health and existing stock are perfectly substitutable. In my model, this substitutability is possible only during the childhood corresponding with the body and mental development. On the other hand, during adulthood when them body no longer grows, health investment may only preserve health. After developing the model, I went about to test it empirically. I used the Understanding Society youth questionnaire to test the child model and the British Household Panel Survey (BHPS) to test the adulthood model. Due to the way that optimisation problem was specified, the terminal end time conditional in the optimal control model became another endogenous variable. This variable is treated empirically as the life expectancy at the national level. I find that in general the empirical data strongly supports the theoretical propositions of my model. It should be noted here that since the main contribution of this thesis is in theoretical development, the empirical efforts were designed primarily with the intention of validating the propositions of the model, and not really for direct policy application. This is also reinforced by the use of ordered logit models where the coefficients of the independent variables on the dependent variable generally have no meaning, where we only concentrate on the signs of the relationship. Having successfully developed the model, it is applied in two policy settings. Firstly, through reformulation of the model gives the inclusion of socio-economic variables in the measure of Relative Risk (RR) a theoretical grounding. We utilised the Global Burden of Disease (GBD) data to compute RR across 180 countries in the world and regressed with World Bank data on ambient particulate matter pollution as well as GDP per capita. The former variable represents the exogenous rate of depreciation while the latter socio-economic variables, particularly income. I find that the RR is negatively associated with the GDP per capita at the national level. Using the estimated coefficients with the help of Professor Crawford-Brown we attempted to forecast how GDP per capita will interact with the health co-benefits of decarbonisation under a range of future scenarios. The second application of the model is in its use to predict the inequality implications of decarbonisation policy. This is performed by taking the second order partial derivative of an endogenous variable such as health, as will be described in detail later. This approach is sufficiently flexible to accommodate the prediction of inequality over range of policies and variables. The inequality implications and predictions according to this model are not tested empirically here. However, they are perhaps the most fruitful area for future research.
Health capital model, decarbonisation, health co-benefit
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