Identification of Expected Outcomes in a Data Error Mixing Model with Multiplicative Mean May 2009 By John PepperBrent Kreider Identification of Expected Outcomes in a Data Error Mixing Model with Multiplicative Mean We consider the problem of identifying a mean outcome in corrupt sampling where the observed outcome is drawn from a mixture of the distribution of interest and another distribution. Relaxing the contaminated sampling assumption that the outcome is statistically independent of the mixing process, we assess the identifying power of an assumption that the conditional means of the distributions differ by a factor of proportionality. For binary outcomes, we consider the special case that all draws from the alternative distribution are erroneous. We illustrate how these models can inform researchers about illicit drug use in the presence of reporting errors. Journal of Business & Economic Statistics Journal of Business & Economic Statistics Areas of focus Ethics John Pepper John V. Pepper is a professor of economics and public policy at the Batten School and a professor of economics in the Department of Economics at the University of Virginia. His work examines identification problems that arise when evaluating a wide range of public policy questions including such subjects as health and disability programs, welfare policies (e.g., SNAP), and drug and crime policies. Read full bio Brent Kreider Related Content John Pepper Identifying the Effects of Food Stamps on the Nutritional Health of Children when Program participation is Misreported Research The literature assessing the efficacy of the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp Program, has long puzzled over positive associations between SNAP receipt and various undesirable health outcomes such as food insecurity. Assessing the causal impacts of SNAP, however, is hampered by two key identification problems: endogenous selection into participation and extensive systematic underreporting of participation status.Using data from the National Health and Nutrition Examination Survey (NHANES), we extend partial identification bounding methods to account for these two identification problems in a single unifying framework. Deterrence and the Death Penalty: Partial Identification Analysis Using Repeated Cross Sections Research Objectives Researchers have used repeated cross sectional observations of homicide rates and sanctions to examine the deterrent effect of the adoption and implementation of death penalty statutes. The empirical literature, however, has failed to achieve consensus.