Objective. To evaluate the effects of specification choices on the accuracy of estimates in difference-in-differences (DID) models. Data Sources. Process-of-care quality data from Hospital Compare between 2003 and 2009. Study Design. We performed a Monte Carlo simulation experiment to estimate the effect of an imaginary policy on quality. The experiment was performed for three different scenarios in which the probability of treatment was (1) unrelated to pre-intervention performance; (2) positively correlated with pre-intervention levels of performance; and (3) positively correlated with pre-intervention trends in performance. We estimated alternative DID models that varied with respect to the choice of data intervals, the comparison group, and the method of obtaining inference. We assessed estimator bias as the mean absolute deviation between estimated program effects and their true value. We evaluated the accuracy of inferences through statistical power and rates of false rejection of the null hypothesis. Principal Findings. Performance of alternative specifications varied dramatically when the probability of treatment was correlated with pre-intervention levels or trends. In these cases, propensity score matching resulted in much more accurate point estimates. The use of permutation tests resulted in lower false rejection rates for the highly biased estimators, but the use of clustered standard errors resulted in slightly lower false rejection rates for the matching estimators. Conclusions. When treatment and comparison groups differed on pre-intervention levels or trends, our results supported specifications for DID models that include matching for more accurate point estimates and models using clustered standard errors or permutation tests for better inference. Based on our findings, we propose a checklist for DID analysis. Key Words. Hospitals, econometrics, health economics, quality of care, health policy Health care delivery in the United States is changing at a dramatic pace. Millions of uninsured citizens, unsustainable cost growth, and uneven quality of care have prompted numerous policy responses at the state and national level.

The timing of the financial incentives in HVBP was not associated with improved quality of care. It is unclear whether improvement for the clinical process measures prior to the start of HVBP was driven by the expectation of the program or was the result of other factors.

Why is the difference in redistribution preferences between the rich and the poor high in some places and low in others? In this paper we argue that it has a lot to do with the rich and very little to do with the poor. We contend that while there is a general relative income effect on redistribution preferences, the preferences of the rich are highly dependent on the macro-level of inequality. The reason for this effect is not related to immediate tax and transfer considerations but to other-regarding concerns. Altruism is an important omitted variable in much of the Political Economy literature. While material self-interest is the base of most approaches to redistribution (first affecting preferences and then politics and policy), there is a paucity of research on other-regarding concerns. Using data for the US from 1978 to 2010, we show that the rich in more unequal states are more supportive of redistribution than the rich in more equal states. In making these distinctions between the poor and the rich, the arguments in this paper challenge some influential approaches to the politics of inequality.

Despite the increasing popularity of comparative work on other-regarding preferences, the implications of different models of altruism are not always fully understood. This article analyzes different theoretical approaches to altruism and explores what empirical conclusions we should draw from them, paying particular attention to models of redistribution preferences where inequality explicitly triggers other-regarding motives for redistribution. While the main contribution of this article is to clarify the conclusions of these models, we also illustrate the importance of their distinct implications by analyzing Western European data to compare among them. We draw on individual-level data from the European Social Survey fielded between September 2002 and December 2013.