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Randomized experiments provide unbiased estimates of treatment effects, but are costly and time consuming. We demonstrate how a randomized experiment can be leveraged to measure selection bias by conducting a subsequent observational study that is identical in every way except that subjects choose their treatment--a quasi-doubly randomized preference trial (quasi-DRPT). Researchers first strive to think of and measure all possible confounders and then determine how well these confounders as controls can reduce or eliminate selection bias. We use a quasi-DRPT to study the effect of class time on student performance in an undergraduate introductory microeconomics course at a large public university, illustrating its required design elements: experimental and choice arms conducted in the same setting with identical interventions and measurements, and all confounders measured prospectively to treatment assignment or choice. Quasi-DRPTs augment randomized experiments in real-world settings where participants choose their treatments.

Cigarette smoking is concentrated among low-income groups. Consequently, cigarette taxes are considered regressive. However, if poorer individuals are much more price sensitive than richer individuals, then tax increases would reduce smoking much more among the poor and their cigarette tax expenditures as a share of income would rise by much less than for the rich. Warner (2000) said this phenomenon would make cigarette tax increases progressive. We test this empirically. Among low-, middle-, and high-income groups, we estimate total price elasticities of -0.37, -0.35, and -0.20, respectively. We find that cigarette tax increases are not close to progressive, using both tax expenditure-based and traditional welfare measures. This finding is robust to cross-border purchasing, generic cigarettes, and substantial external effects. However, we find that taxes can be progressive under some behavioral economic models (Gruber & Koszegi, 2004) but that these may only apply to a small share of smokers. (Contains 1 figure, 4 tables and 5 footnotes.)

Estimates of the costs and consequences of many types of public policy proposals play an important role in the development and adoption of particular policy programs. Estimates of the same, or similar, policies that employ different modeling approaches can yield widely divergent results. Such divergence often undermines effective policymaking. These problems are particularly prominent for health insurance expansion programs. Concern focuses on predictions of the numbers of individuals who will be insured and the costs of the proposals. Several different simulation-modeling approaches are used to predict these effects, making the predictions difficult to compare. This paper categorizes and describes the different approaches used; explains the conceptual and theoretical relationships between the methods; demonstrates empirically an example of the (quite restrictive) conditions under which all approaches can yield quantitatively identical predictions; and empirically demonstrates conditions under which the approaches diverge and the quantitative extent of that divergence. All modeling approaches implicitly make assumptions about functional form that impose restrictions on unobservable heterogeneity. Those assumptions can dramatically affect the quantitative predictions made.

This article reviews the origins and use of the terms quasi-experiment and natural experiment. It demonstrates how the terms conflate whether variation in the independent variable of interest falls short of random with whether researchers find, rather than intervene to create, that variation. Using the lens of assignment—the process driving variation in the independent variable—we distinguish three dimensions: control of assignment by the researcher, rather than by a policy process or naturally occurring event; exogeneity of the assignment process; and directness of the link between assignment and the independent variable. These dimensions generate a typology of causal study designs that we illustrate with examples from the social and health sciences. Our framework can assist researchers and practitioners from diverse disciplines to better understand and communicate with one another and, importantly, to recognize opportunities in real-world settings to find or create variation that can be leveraged for valid impact estimates. [ABSTRACT FROM AUTHOR]

The traditional view that excise taxes are regressive has been challenged. I document the history of the term regressive tax, show that traditional definitions have always found cigarette taxes to be regressive, and illustrate the implications of the greater price responsiveness observed among the poor. I explain the different definitions of tax burden: accounting, welfare-based willingness to pay, and welfare-based time inconsistent. Progressivity (equity across income groups) is sensitive to the way in which tax burden is assessed. Analysis of horizontal equity (fairness within a given income group) shows that cigarette taxes heavily burden poor smokers who do not quit, no matter how tax burden is assessed.

Many uninsured Americans are already eligible for free or low-cost public coverage through Medicaid or Children's Health Insurance Program (CHIP) but do not 'take up' that coverage. However, several other public programs, such as food stamps and unemployment insurance, also have less-than-complete take-up rates, and take-up rates vary considerably among programs. This article examines the take-up literature across a variety of programs to learn what effects nonfinancial features, such as administrative complexity, have on take-up. We find that making benefit receipt automatic is the most effective means of ensuring high take-up, while there is little evidence that stigma is important.

The issue of explosive growth in medical care costs has been forgotten in the debates over health care reform. Medical care costs are high because there is no link between how much individuals want to spend for medical care and the individual decisions that they make regarding the resources for medical care. This is because of the very nature of illness and the medical profession themselves. Five strategies for treatment decisions are presented as means cost-containment in health care.