Econometric Predictions From Demographic Factors Affecting Overall Health

Efforts to accurately predict health outcomes with a focus on informing policy makers of where to best spend limited resources have been made in the past. This paper builds on the efforts of those studies in an attempt to build an accurate predictor of health from readily available data. The American Time Use Survey (2010, 2012, and 2013) provides the majority of the data from which this model is built, and it is then tested via several methods. The analysis finds that the existing freely available data is significant in its predictive power, however is missing too many predictors to reduce the confidence interval about each individual prediction to a point of bearing meaningful fruit. That does not eliminate the usefulness of the study however, as by reducing the confidence required and accepting that the data is used for predicting societal means, the model is able to accurately predict average outcomes. This paper further attempts to analyze state level date to provide a geographic target for public funds expenditures, and accomplishes this through the analysis of various risk factors by region. Notable in this analysis is an attempt to correct for self-reporting errors. The literature review did not reveal any previous attempts to do so using a similar methodology (beyond recognizing that such errors exist and using robust methods to account for them), making this attempt possibly unique. The correction did not result in significantly different estimates, however that may be a result of the minimal resources applied to this small aspect of the analysis.