Pardons, Executions and Homicide

This paper uses a data set that consists of the entire history of 6,143 death sentences between 1977 and 1997 in the United States to investigate the impact of capital punishment on homicide. This data set is merged with state panels that include crime and deterrence measures as well as state characteristics to analyze the impact of executions and governors' pardons on criminal activity. Because the exact month and year of each execution and pardon can be identified, they are matched with criminal activity in the relevant time frame. Controlling for a variety of state characteristics, the paper investigates the impact of the execution rate, pardon rate, homicide arrest rate, the imprisonment rate and the prison death rate on the rate of homicide. The models are estimated in a number of different forms, controlling for state fixed effects, common time trends, and state-specific time trends. Each additional execution decreases homicides by 5 to 6, while three additional pardons generate one to 1.5 additional homicides. These results are robust to model specifications and measurement of the variables.

The Impact of Incentives on Human Behavior: Can We Make It Disappear? The Case of the Death Penalty

Although decades of empirical research has demonstrated that criminal behavior responds to incentives, non-economists frequently express the belief that human beings are not rational enough to make calculated decisions about the costs and benefits of engaging in crime and therefore, a priori drawing the conclusion that criminal activity cannot be altered by incentives. However, scientific research should not be driven by personal beliefs. Whether or not economic conditions matter or deterrence measures such police, arrests, prison deaths, executions, and commutations provide signals to people is an empirical question, which should be guided by a solid theoretical framework. In this paper we extend the analysis of Mocan and Gittings (2003). We alter the original model in a number of directions to make the relationship between homicide rates and death penalty related outcomes (executions, commutations and removals) disappear. We deliberately deviate from the theoretically consistent measurement of the risk variables originally employed by Mocan and Gittings (2003) in a variety of ways. We also investigate the sensitivity of the results to changes in the estimation sample (removing high executing states for example) and weighting. The basic results are insensitive to these and a variety of other specification tests performed in the paper. The results are often strong enough to even hold up under theoretically meaningless measurements of the risk variables. In summary, the original findings of Mocan and Gittings (2003) are robust, providing evidence that people indeed react to incentives induced by capital punishment. Research findings about the deterrent effect of the death penalty evoke strong feelings, which could be due to political, ideological, religious, or other personal beliefs. Yet, such findings do not mean that capital punishment is good or bad, nor does it provide any judgment about whether capital punishment should be implemented or abolished. It is simply a scientific finding which demonstrates that people react to incentives. Therefore, there is no need to be afraid of this result.

Getting off Death Row: Commuted Sentences and the Deterrent Effect of Capital Punishment

University of Denver Law School, and the 2002 Law and Society Association Meetings for helpful suggestions, and Michael Grossman and Sara Markowitz for providing us with drinking age data. 1“I have inquired for most of my adult life about studies that might show that the death penalty is a deterrent, and I have not seen any research that would substantiate that point.” Former U. S. Attorney General Janet Reno at a Justice Department Press Briefing; January 20, 2000

Dynamically consistent noise infusion and partially synthetic data as confidentiality protection measures for related time-series

Presented at FCSM.The Census Bureau's Quarterly Workforce Indicators (QWI) provide detailed quarterly statistics on employment measures such as worker and job ows, tabulated by detailed worker characteristics in various combinations. The data are released for detailed NAICS industries and for several levels of geography, the lowest aggregation of which are counties. OnTheMap, another Census Bureau product, provides a subset of these tabulations at the tract level. Disclosure avoidance methods are required to protect the information about individuals and businesses that contribute to the underlying data. The QWI disclosure avoidance mechanism we describe here relies heavily on the use of noise infusion through a permanent multiplicative noise distortion factor, used for magnitudes, counts, differences and ratios. There is minimal suppression and no complementary suppressions. To our knowledge, the release in 2003 of the QWI was the first large-scale use of noise infusion in any official statistical product. We show that the released statistics are analytically valid along several critical dimensions -- measures are unbiased and time series properties are preserved. We provide an analysis of the degree to which con dentiality is protected. Furthermore, we show how the judicious use of synthetic data, injected into the tabulation process, can completely eliminate suppressions, maintain analytical validity, and increase the protection of the underlying con dential data.fcsm2012_noise_synthetic.pdf: 398 downloads, before Oct. 1, 2020