<< Back to Faculty John Pepper Professor of Economics and Public Policy Education & Training Ph.D. (economics), University of Wisconsin-Madison, 1996 B.A. (quantitative economics), Tufts University, 1987 434-924-3402 jvp3m@virginia.edu 252 Monroe Hall Curriculum Vitae (130.29 KB) Areas of focus Economics UVA partners Department of Economics John V. Pepper is a Professor of Economics at the University of Virginia. He received his Ph.D. from the University of Wisconsin in 1996, and his B.A. in Quantitative Economics from Tufts University in 1987. 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. He has served as the study director of the National Research Council Committee for Improving Research Information and Data on Firearms, and as member of Committee on Improving Evaluation of Anti-Crime Programs. He is an author of numerous published papers, conference presentations and edited books including several National Research Council reports—Measurement Problems in Criminal Justice Research (2003, with Carol Petrie), Informing America’s Policy on Illegal Drugs: What We Don’t Know Keeps Hurting Us (2001, with Charles Manski and Carol Petrie), Assessment of Two Cost-Effectiveness Studies on Cocaine Control Policy (1999, with Charles Manski and Yonette Thomas), and Firearms and Violence: A Critical Review (2005, with Charles Wellford and Carol Petrie). Related Content 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. The Economics of Food Insecurity in the United States Research Food insecurity is experienced by millions of Americans and has increased dramatically in recent years. Due to its prevalence and many demonstrated negative health consequences, food insecurity is one of the most important nutrition-related public health issues in the U.S. The Impact of the National School Lunch Program on Child Health: A Nonparametric Bounds Analysis Research Children in households reporting the receipt of free or reduced-price school meals through the National School Lunch Program (NSLP) are more likely to have negative health outcomes than observationally similar nonparticipants. Assessing causal effects of the program is made difficult, however, by missing counterfactuals and systematic underreporting of program participation. Identification of Expected Outcomes in a Data Error Mixing Model with Multiplicative Mean Research 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. View All
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.
The Economics of Food Insecurity in the United States Research Food insecurity is experienced by millions of Americans and has increased dramatically in recent years. Due to its prevalence and many demonstrated negative health consequences, food insecurity is one of the most important nutrition-related public health issues in the U.S.
The Impact of the National School Lunch Program on Child Health: A Nonparametric Bounds Analysis Research Children in households reporting the receipt of free or reduced-price school meals through the National School Lunch Program (NSLP) are more likely to have negative health outcomes than observationally similar nonparticipants. Assessing causal effects of the program is made difficult, however, by missing counterfactuals and systematic underreporting of program participation.
Identification of Expected Outcomes in a Data Error Mixing Model with Multiplicative Mean Research 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.