NBER Working Papers by Willard G. Manning
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Working Papers
June 2008  Use of Propensity Scores in NonLinear Response Models: The Case for Health Care Expenditures
with Anirban Basu, Daniel Polsky: w14086
Under the assumption of no unmeasured confounders, a large literature exists on methods that can be used to estimating average treatment effects (ATE) from observational data and that spans regression models, propensity score adjustments using stratification, weighting or regression and even the combination of both as in doublyrobust estimators. However, comparison of these alternative methods is sparse in the context of data generated via nonlinear models where treatment effects are heterogeneous, such as is in the case of healthcare cost data. In this paper, we compare the performance of alternative regression and propensity scorebased estimators in estimating average treatment effects on outcomes that are generated via nonlinear models. Using simulations, we find that in moderate si... 
September 2006  The Health Effects of Medicare for the NearElderly Uninsured
with Daniel Polsky, Jalpa A. Doshi, Jose Escarce, Susan M. Paddock, Liyi Cen, Jeannette Rogowski: w12511
We study how the trajectory of health for the nearelderly uninsured changes upon enrolling into Medicare at the age of 65. We find that Medicare increases the probability of the previously uninsured having excellent or very good health, decreases their probability of being in good health, and has no discernable effects at lower health levels. Surprisingly, we found Medicare had a similar effect on health for the previously insured. This suggests that Medicare helps the relatively healthy 65 year olds, but does little for those who are already in declining health once they reach the age of 65. The improvement in health between the uninsured and insured were not statistically different from each other. The stability of insurance coverage afforded by Medicare may be the source of the he... 
October 2003  Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data
with Anirban Basu, John Mullahy: t0293
There are two broad classes of models used to address the econometric problems caused by skewness in data commonly encountered in health care applications: (1) transformation to deal with skewness (e.g., OLS on ln(y)); and (2) alternative weighting approaches based on exponential conditional models (ECM) and generalized linear model (GLM) approaches. In this paper, we encompass these two classes of models using the three parameter generalized gamma (GGM) distribution, which includes several of the standard alternatives as special cases OLS with a normal error, OLS for the log normal, the standard gamma and exponential with a log link, and the Weibull. Using simulation methods, we find the tests of identifying distributions to be robust. The GGM also provides a potentially more robust al... Published: Manning, Willard G., Anirban Basu and John Mullahy. "Generalized Modeling Approaches To Risk Adjustment Of Skewed Outcomes Data," Journal of Health Economics, 2005, v24(3,May), 465488.

November 1999  Estimating Log Models: To Transform or Not to Transform?
with John Mullahy: t0246
Data on health care expenditures, length of stay, utilization of health services, consumption of unhealthy commodities, etc. are typically characterized by: (a) nonnegative outcomes; (b) nontrivial fractions of zero outcomes in the population (and sample); and (c) positivelyskewed distributions of the nonzero realizations. Similar data structures are encountered in labor economics as well. This paper provides simulationbased evidence on the finitesample behavior of two sets of estimators designed to look at the effect of a set of covariates x on the expected outcome, E(yx), under a range of data problems encountered in every day practice: generalized linear models (GLM), a subset of which can simply be viewed as differentially weighted nonlinear leastsquares estimators, and those de... Published: Manning, Willard G. and John Mullahy. "Estimating Log Models: To Transform Or Not To Transform?," Journal of Health Economics, 2001, v20(4,Jul), 461494.

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