Abstract
An Analysis of Preventive Healthcare through the Perspective of Behavioral Economics
The United States health sector is marked by high costs, and low health outcomes. Over the last several years both state and national policies have endeavored to change individual health habits through law. Moreover, preventive healthcare has become a primary focus as a means to achieve a healthier tomorrow. Public health agendas pertaining to preventative health frequently use the method of paternalism through government subsidy of the price of preventive health packages. In order for these prevention initiatives to be effective, compliance to prevention from the overall population must take place. A vital question pertaining to this issue is: does the landscape that the institutions the Affordable Care Act and other preventive health policies provide, encourage adequate incentive for individuals to partake in healthy behavior? Through theoretical analysis I found that there is likely a tradeoff between the price of healthcare, the level of consumption of healthcare, and consequently the level of prevention inputted by an individual in their everyday life; I conducted an experiment to analyze this dynamic further. I built a regression model, which expressed the dependent variable of prevention as a function of price of healthcare, age, income, education, previous health status, and genetics. Preventive health policy implicitly assumes that the variables of prevention and price of healthcare are inversely related; the results of my experiment showed the opposite. The regression ran on the data set expressed that for every 0.26 unit increase in the price of healthcare experienced by a consumer, that that individual puts in 1 unit more of prevention in their daily life. Contrary to common belief, as the price of healthcare increases, the amount of prevention inputted by an individual increases as well. Therefore, it can be argued that the decrease of the price of healthcare actually has a negative effect on the amount of prevention yielded by individuals. In the future, I plan to develop my model further by gaining more observations, testing additional variables, and using more advanced statistical methods to attain more robust results.
