We estimate the effects of U.S. Metropolitan Statistical Area housing prices on a variety of health outcomes and health‐related behaviors separately for homeowners and tenants. The constructed data set consists of information on individuals from the 2002–2012 Behavioral Risk Factor Surveillance System combined with homeownership data from the March Current Population Survey and housing prices from Freddie Mac. We estimate positive effects on homeowners' mental health when housing prices increase. We also find negative effects on tenants' health and health‐related behaviors with increases in housing prices. These estimated contemporaneous effects are concentrated among low‐income homeowners and tenants, and the effects for tenants are not persistent in the long run. However, the cumulative effects of an increase in housing prices on obesity become more pronounced for homeowners in the long run, resulting in worse self‐reported health.

Background:In this article, we attempt to address a persistent question in the health policy literature: Does more public health spending buy better health? This is a difficult question to answer due to unobserved differences in public health across regions as well as the potential for an endogenous relationship between public health spending and public health outcomes.Methods:We take advantage of the unique way in which public health is funded in Georgia to avoid this endogeneity problem, using a twelve year panel dataset of Georgia county public health expenditures and outcomes in order to address the “unobservables” problem.Results:We find that increases in public health spending lead to increases in mortality by several different causes, including early deaths and heart disease deaths. We also find that increases in such spending leads to increases in morbidity from heart disease.Conclusions:Our results suggest that more public health funding may not always lead to improvements in health outcomes at the county level.

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In this paper, we propose a Bayesian factor analysis model with the purpose of serving as an alternate approach to calculating the UNDP's Human Development Index, as well as providing a general methodology which can be used to augment existing indices or build new ones. In addition to addressing several potential issues of the official HDI, we also estimate an alternative "green HDI" index by adding a new environmental variable, and build a novel MDG index as an example of constructing a new index with a more complex variable structure. Under our methodology, we find the "living standard" dimension provides a greater proportional contribution to human development than it is assigned by the official HDI while the "longevity" dimension provides a lower proportional contribution. The results also show considerable levels of general disagreement when compared to the ranks of the official HDI. We show that incorporating an environmental variable increases the amount of disagreement between model based ranks and the official HDI, but decreases the amount of uncertainty associated with model ranks. In addition, we report the sensitivity of our methods to the choice of functional form and data imputation procedures.

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