Jaesang Sung

Bio: Jaesang Sung is an academic researcher from Georgia State University. The author has contributed to research in topics: Metropolitan statistical area & Behavioral Risk Factor Surveillance System. The author has an hindex of 4, co-authored 11 publications receiving 40 citations.

Does More Public Health Spending Buy Better Health

Abstract: 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 d...

The Impact of Housing Prices on Health in the United States Before, During, and After the Great Recession

Abstract: 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.

New Evidence on the Relationship between Inequality and Health

Abstract: The relative income hypothesis suggests that an individual’s health is impacted by the income of others. However, prior studies suffer from mixed empirical findings that could be due to a lack of annual individual income data with sufficient sample size. We apply a new methodology to calculate a variety of income inequality measures based on aggregate income and household size data from various Federal data sources. Our proposed methodology provides a way to express various income inequality measures as a function of the ratio of mean to median household income under the assumption that individual income is log-Normally distributed. This approach produces a variety of precise annual income inequality measures at different levels of geography, thus solving the sample size problem by incorporating externally calculated inequality measures. Combining the 2001-2012 editions of the U.S. Behavioral Risk Factor Surveillance System with annual regional income inequality measures derived from our methodology enables us to estimate both the contemporaneous and the lagged effect of income inequality on individual health outcomes. In general, we find statistically significant evidence supporting the income inequality hypothesis and the relative deprivation hypothesis, which suggests that greater income inequality adversely affects health status in the United States.

Relative deprivation: a new derivation and application

Abstract: We derive and express the Yitzhaki index of relative deprivation as a function of mean and median household income within a reference group and one’s income, under the assumption that individual in

対数正規分布(Lognormal Distribution)のあてはめについて

Abstract: Ecological data are often lognormally distributed. Nutrient concentrations, population densities and biomasses, rates of production and other flows are always positive, and generally have standard deviations that increase as the mean increases. Lognormally distributed variables have these characteristics, whereas normally distributed variables can be negative and have a standard deviation that does not change as the mean changes. Lognormal errors arise when sources of variation accumulate multiplicatively, whereas normal errors arise when sources of variation are additive. Given a normally distributed random variable Y, one can calculate a lognormally distributed random variable Z = exp(Y) where exp stands for the exponential function (exp(x) = e x). The mean Z and the standard deviation s Z of the lognormal variable are related to the mean Y and standard deviation s Y of the normal variable by) 2 / exp() exp(2 Y s Y Z = [1] 5. 0 2 ] 1) [exp(− = Y Z s Z s [2] Equation 1 can be used to correct for transformation bias in logarithmic regression. Suppose that lognormally-distributed observations Z have been log transformed as Y = log(Z) to fit a regression model such as ε + =) , (ˆ b X f Y [3] where Y is the log-transformed response variable which is predicted to be Y ˆ computed from the function f, X is a matrix of predictors, b is a vector of parameters, and the errors ε are normally distributed with mean zero and standard deviation s ε. Predictions Z ˆ in the original units are calculated using equation 1 as ] 2) ˆ exp[(ˆ 2 ε s Y Z + = [4] Note that estimates the median prediction of Z, which will be smaller than the mean for a lognormally distributed variate. Thus it makes sense to adjust the median upward, as in equation 4.) ˆ exp(Y Equation 1 is also used in drawing random numbers from a lognormal distribution. Generators for normally-distributed random variables Y are common. Suppose we draw many values of Y with mean zero and standard deviation s Y. Then from equation 1, the mean of exp(Y) will not be 1 = e 0 ; instead the mean of exp(Y) will be. Generally, however, one would prefer to have the mean of a set of lognormally distributed random numbers be 1. This can be accomplished by shifting the random numbers to Y) 2 / exp(2 Y …

Spatial modeling of regional variables

Abstract: In this article, accumulated sudden infant death syndrome (SIDS) data, from 1974–1978 and 1979–1984 for the counties of North Carolina, are analyzed. After a spatial exploratory data analysis, Markov random-field models are fit to the data. The (spatial) trend is meant to capture the large-scale variation in the data, and the variance and spatial dependence are meant to capture the small-scale variation. The trend could be a function of other explanatory variables or could simply be modeled as a function of spatial location. Both models are fit and compared. The results give an excellent illustration of a phenomenon already well-known in time series, that autocorrelation in data can be due to an undiscovered explanatory variable. Indeed, for 1974–1978 we confirm a dependence of SIDS rate on proportion of nonwhite babies born, along with insignificant spatial correlation. Without this regressor variable, however, the spatial correlation is significant. In 1979–1984, perhaps due to reporting bias o...

Inequality and crime

Abstract: This paper considers the relationship between inequality and crime using data from urban counties. The behavior of property and violent crime are quite different. Inequality has no effect on property crime but a strong and robust impact on violent crime, with an elasticity above 0.5. By contrast, poverty and police activity have significant effects on property crime, but little on violent crime. Property crime is well explained by the economic theory of crime, while violent crime is better explained by strain and social disorganization theories.