Deterrence versus brutalization: Capital punishment's differing impacts among states

Michigan Law Review

Volumn 104, Issue 2, 2005, Pages 203-255

  Shepherd, Joanna M ^a  

^a EMORY UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 27844587358     PISSN: 00262234     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (113)

1. In the presidential debate with Al Gore on October 17, 2000, Bush was asked, "Do both of you believe that the death penalty actually deters crime?" He responded, "I do, it's the only reason to be for it.... I don't think you should support the death penalty to seek revenge. I don't think that's right. I think the reason to support the death penalty is because it saves other people's lives." Election 2000 Presidential Debate with Republican Candidate Governor George W. Bush and Democratic Candidate Vice President Al Gore (Oct. 17, 2000) (transcript available at http://www.debates. orf/pages/trans2000c.html). In the debate, Gore also agreed that capital punishment deterred crime. Id.
2. In Gregg v. Georgia, the Court provided as a main reason for upholding capital punishment: We may nevertheless assume safely that there are murderers, such as those who act in passion, for whom the threat of death has little or no deterrent effect. But for many others, the death penalty undoubtedly is a significant deterrent. There are carefully contemplated murders, such as murder for hire, where the possible penalty of death may well enter into the cold calculus that precedes the decision to act. And there are some categories of murder, such as murder by a life prisoner, where other sanctions may not be adequate.
3. The Court noted: The value of capital punishment as a deterrent of crime is a complex factual issue the resolution of which properly rests with the legislatures, which can evaluate the results of statistical studies in terms of their own local conditions and with a flexibility of approach that is not available to the courts. Indeed, many of the post-Furman statutes reflect just such a responsible effort to define those crimes and those criminals for which capital punishment is most probably an effective deterrent. Id. at 186 (citations omitted).
4. For example, when Congress was considering whether to extend the federal death penalty to terrorist acts, I was asked to testify before the House Judiciary Committee about deterrence, See Terrorist Penalties Enhancement Act of 2003: Hearing on H.R. 2934 Before the Subcomm. on Crime, Terrorism, and Homeland Sec. of the House Comm. on the Judiciary, 108th Cong. (Apr. 21, 2004) (written testimony of Joanna M. Shepard), available at http://judiciary.house. gov/media/pdfs/shepherd042104.pdf.
5. For a detailed discussion of the data, see infra text accompanying notes 31-33.
6. See DEATH PENALTY INFORMATION CENTER, HISTORY OF THE DEATH PENALTY, PART I (2005), http://www.deathpenaltyinfo.org/article.php?scid=15&did= 410#EarlyandMid-TwentiethCentury.
7. RAYMOND PATERNOSTER, CAPITAL PUNISHMENT IN AMERICA 20 (1991).
8. THE DEATH PENALTY IN AMERICA 25 (Hugo Adam Bedau ed., 3d ed. 1982).
9. PATERNOSTER, supra note 7, at 19.
10. 408 U.S. 238 (1972).
11. 428 U.S. 153 (1976).
12. 428 U.S. 262 (1976).
13. 428 U.S. 242 (1976).
14. See supra note 2.
15. Jeffrey M. Jones, Support for the Death Penalty Remains High at 74%; Slight Majority Prefers Death Penalty to Life Imprisonment as Punishment for Murder, GALLUP NEWS SERVICE, May 19, 2003, available at http://www. deathpenaltyinfo.org/article.php?scid=23&did=592.
16. FRANKLIN E. ZIMRING & GORDON HAWKINS, CAPITAL PUNISHMENT AND THE AMERICAN AGENDA 30 (1986).
17. See, e.g., Dov Cohen & Richard E. Nisbett, Self-Protection and the Culture of Honor: Explaining Southern Violence, 20 PERSONALITY & SOC. PSYCHOL. BULL. 551 (1994);
18. Richard E. Nisbett, Violence and U.S. Regional Culture, 48 AM. PSYCHOLOGIST 441 (1993).
19. See, e.g., H.J. EYSENCK, CRIME AND PERSONALITY (Paladin 1970) (1964);
20. J. THORSTEN SELLIN, THE DEATH PENALTY (1959).
21. Isaac Ehrlich, Capital Punishment and Deterrence: Some Further Thoughts and Additional Evidence, 85 J. POL. ECON. 741 (1977);
22. Isaac Ehrlich, The Deterrent Effect of Capital Punishment: A Question of Life and Death, 65 AM. ECON. REV. 397 (1975).
23. See, e.g., Gary S. Becker, Crime and Punishment: An Economic Approach, 76 J. POL. ECON. 169 (1968).
24. Dale O. Cloninger, Deterrence and the Death Penalty: A Cross-Sectional Analysis, 6 J. BEHAV. ECON. 87, 98 (1977);
25. Isaac Ehrlich & Joel Gibbons, On the Measurement of the Deterrent Effect of Capital Punishment and the Theory of Deterrence, 6 J. LEGAL STUD. 35 (1977);
26. James A. Yunker, Is the Death Penalty a Deterrent to Homicide? Some Time Series Evidence, 5 J. BEHAV. ECON. 45 (1976).
27. William J. Bowers & Glenn L. Pierce, The Illusion of Deterrence in Isaac Ehrlich's Research on Capital Punishment, 85 YALE L.J. 187 (1975);
28. Stephen A. Hoenack & William C. Weiler, A Structural Model of Murder Behavior and the Criminal Justice System, 70 AM. ECON. REV. 327 (1980);
29. Peter Passell & John B. Taylor, The Deterrent Effect of Capital Punishment: Another View, 67 AM. ECON. REV. 445 (1977).
30. Edward E. Learner, Let's Take the Con out of Econometrics, 73 AM. ECON. REV. 31 (1983);
31. Michael McAleer & Michael R. Veall, How Fragile are Fragile Inferences? A Re-Evaluation of the Deterrent Effect of Capital Punishment, 71 REV. ECON. & STAT. 99 (1989);
32. Walter S. McManus, Estimates of the Deterrent Effect of Capital Punishment: The Importance of the Researcher's Prior Beliefs, 93 J. POL. ECON. 417 (1985).
33. Theodore Black & Thomas Orsagh, New Evidence on the Efficacy of Sanctions as a Deterrent to Homicide, 58 SOC. SCI. Q. 616 (1978).
34. James Peery Cover & Paul D. Thistle, Time Series, Homicide, and the Deterrent Effect of Capital Punishment, 54 S. ECON. J. 615 (1988);
35. Stephen K. Layson, Homicide and Deterrence: A Reexamination of the United States Time-Series Evidence, 52 S. ECON. J. 68 (1985).
36. George A. Chressanthis, Capital Punishment and the Deterrent Effect Revisited: Recent Time-Series Econometric Evidence, 18 J. BEHAV. ECON. 81 (1989).
37. Jeffrey Grogger, The Deterrent Effect of Capital Punishment: An Analysis of Daily Homicide Counts, 85 J. AM. STAT. ASS'N 295 (1990).
38. Technically, cross-sectional studies are affected by unobserved heterogeneity that cannot be controlled for in the absence of time variation. The heterogeneity is caused by jurisdiction-specific characteristics that may correlate with other variables of the model, resulting in biased, incorrect estimates.
39. See, e.g., K.L. Avio, Capital Punishment, in 1 THE NEW PALGRAVE DICTIONARY OF ECONOMICS AND THE LAW 205 (Peter Newman ed., 1998);
40. Samuel Cameron, A Review of the Econometric Evidence on the Effects of Capital Punishment, 23 J. SOCIO-ECON. 197 (1994).
41. See NAT'L. ACAD. Sci., Report of the Panel on Research on Deterrent and Incapacitative Effects, in DETERRENCE AND INCAPACITATION: ESTIMATING THE EFFECTS OF CRIMINAL SANCTIONS ON CRIME RATES 1 (Alfred Blumstein et al. eds., 1978).
42. Hashem Dezhbakhsh et al., Does Capital Punishment Have a Deterrent Effect? New Evidence from Postmoratorium Panel Data, 5 AM. LAW & ECON. REV. 344 (2003).
43. Technically, it extends the analysis' degrees of freedom, increases variability, and reduces colinearity among variables.
44. The deterrent effect remains with different choices of functional form (double-log, semilog, or linear), state-level vs. county-level analysis, sampling period, endogenous vs. exogenous probabilities, and level vs. ratio specification of the main variables.
45. Joanna M. Shepherd, Murders of Passion, Execution Delays, and the Deterrence of Capital Punishment, 33 J. LEGAL STUD. 283 (2004).
46. They claim that murders by intimates or crimes of passion are products of uncontrollable rage, and they are therefore nondeterrable. Others even argue executions could even increase the number of murders by strangers, as the brutality of executions incites criminals.
47. Intimates are defined as spouses, common-law spouses, parents, children, siblings, inlaws, step-relations, and other family. Crime-of-passion murders include lovers' triangles, murders by babysitters, brawls under alcohol, brawls under drugs, arguments over money, other arguments, and abortion-murders (abortions performed during the murder of the mother).
48. Hashem Dezhbakhsh & Joanna M. Shepherd, The Deterrent Effect of Capital Punishment: Evidence from a 'Judicial Experiment,' (Emory Univ., Law & Econ. Research Paper Series, Working Paper No. 04-04, 2004).
49. We also confirm that our results hold up to changes in our choice of regressors, estimation method, and functional form. The deterrent variables' coefficients are remarkably consistent in sign and significance across eighty-four different regression models. In addition, we verify that the negative relationship between the death penalty and murder is not a spurious finding. Before-and-after moratorium comparisons and regressions reveal that the death penalty does not cause a decrease in property crimes, suggesting that the deterrent effect is not reflecting general trends in crime.
50. John R. Lott, Jr. & William M. Landes, Multiple Victim Public Shootings, Bombings, and Right-to-Carry Concealed Handgun Laws: Contrasting Private and Public Law Enforcement (Univ. of Chicago, John M. Olin Law & Econ., Working Paper No. 73, 2000).
51. Paul R. Zimmerman, Estimates of the Deterrent Effect of Alternative Execution Methods in the United States: 1978-2000, AM. J. ECON. & SOC. (forthcoming) [hereinafter Zimmerman, Alternative Execution Methods];
52. Paul R. Zimmerman, State Executions, Deterrence, and the Incidence of Murder, Mar. 3, 2003, available at http://ssrn.com/abstract=354680.
53. H. Naci Mocan & R. Kaj Gittings, Getting Off Death Row: Commuted Sentences and the Deterrent Effect of Capital Punishment, 46 J.L. & ECON. 453 (2003).
54. Lawrence Katz et al., Prison Conditions, Capital Punishment, and Deterrence, 5 AM. L. & ECON. REV. 318 (2003).
55. Dale O. Cloninger & Roberto Marchesini, Execution and Deterrence: A Quasi-Controlled Group Experiment, 33 APPLIED ECON. 569 (2001).
56. Harold J. Brumm & Dale O. Cloninger, Perceived Risk of Punishment and the Commission of Homicides: A Covariance Structure Analysis, 31 J. ECON. BEHAV. & ORG. 1 (1996).
57. Isaac Ehrlich & Zhiqiang Liu, Sensitivity Analyses of the Deterrence Hypothesis: Lets Keep the Econ in Econometrics, 42 J.L. & ECON. 455 (1999);
58. Zhiqiang Liu, Capital Punishment and the Deterrence Hypothesis: Some New Insights and Empirical Evidence, 30 E. ECON. J. 237 (2004). The study by Ehrlich and Liu offers a theory-based sensitivity analysis of estimated deterrent effects. Liu's study uses switching regression techniques in estimations that take into account the endogenous nature of the status of the death penalty.
59. John K. Cochran et al., Deterrence or Brutalization? An Impact Assessment of Oklahoma's Return to Capital Punishment, 32 CRIMINOLOGY 107 (1994).
60. William C. Bailey, Deterrence, Brutalization, and the Death Penalty: Another Examination of Oklahoma's Return to Capital Punishment, 36 CRIMINOLOGY 711 (1998).
61. Jon Sorenson et al., Capital Punishment and Deterrence: Examining the Effect of Executions on Murder in Texas, 45 CRIME & DELINQ. 481 (1999).
62. The authors restricted their analysis to an ordinary least squares regression that assumed that the causality between murder and law enforcement variables ran in only one direction: conviction rates, incarceration rates, and executions affected crime rates, but crime rates did not affect conviction rates, incarceration rates, or executions. In contrast, almost all other capital punishment papers assumed that causality runs in both directions; for example, increasing murders may lead officials to direct more resources to fighting crime, increasing convictions, incarcerations, and executions. Ignoring the reverse causality could lead to biased results that underestimate, overestimate, or reverse the impact of law enforcement variables on crime.
63. James A. Yunker, A New Statistical Analysis of Capital Punishment Incorporating U.S. Postmoratorium Data, 82 SOC. SCI. Q. 297 (2001).
64. Richard Berk, New Claims about Executions and General Deterrence: Déjà Vu All Over Again?, Mar. 11, 2005, available at http://preprints.stat.ucla.edu/396/JELS.pap.pdf.
65. Craig J. Albert, Challenging Deterrence: New Insights on Capital Punishment Derived from Panel Data, 60 U. PITT. L. REV. 321 (1999).
66. They use "fully recursive vector ARMA [regressions] . . . ." Lisa Stolzenberg & Stewart J. D'Alessio, Capital Punishment, Execution Publicity and Murder in Houston, Texas, 94 J. CRIM. L. & CRIMINOLOGY 351, 352 (2004).
67. For this table, I limit my sample to 1977-1996 because my empirical estimations cover this period.
68. Colorado and Kentucky performed their first executions in 1997, New Mexico executed its first prisoner in 2001, Ohio performed its first execution in 1999, and Tennessee executed its first prisoner in 2000. New York's death penalty law was declared unconstitutional in 2004. People v. LaValle, 817 N.E.2d 341, 344 (N. Y. 2004).
69. Nebraska's last execution was in 1997, by electrocution.
70. Two of Utah's six executions since 1977 have been by firing squad. Four more executions by firing squad are scheduled for upcoming years.
71. I used LexisNexis to search for the name of each executed person in the month before the execution, the month of the execution, and the month after the execution. I searched both newspapers in the state where the execution took place and all news transcripts. Although the numbers are good approximations of the amount of publicity each execution receives, they are not perfect because LexisNexis does not cover all newspapers and started covering some newspapers in the mid to late 1990s. I searched for executions only after 1997 to minimize the problem of lack of or uneven coverage.
72. See PETER KENNEDY, A GUIDE TO ECONOMETRICS 91 (3d ed. 1992).
73. Dezhbakhsh et al., supra note 31.
74. Publicity surrounding the original study included television interviews on CNN Sunday; National Fox News; The O'Reilly Factor on the National Fox News Network; and CBS, ABC, and FOX local affiliates. Print interviews included the Chronicle of Higher Education and The Atlanta Business Chronicle. Radio interviews included BBC; Five Alive; WJR in Detroit, MI; KRLD in Arlington, TX; WLW in Cincinnati, OH; KTSA in San Antonio, TX; CHED in Edmonton, Canada; WRVA in Richmond, VA; CJME in Saskatoon, Canada; NTR in Saskatoon, Canada; WMVZ in Detroit, MI; KXNT in Las Vegas, NV; and KRLA in Los Angeles, CA. The paper was also cited in the National Center for Policy Analysis; Executive Alert; The Weekly Standard; and The National Journal. The paper was also requested for use by the Senate Judiciary Committee; U.S. Naval Academy; House of Representatives (Rep. Bob Goodlatt); Attorney General of Alabama; New York State Assembly (Stephen Kaufman); and the Chief of Criminal Appeals Division of Chicago (Renee Goldfarb).
75. Dezhbakhsh et al., supra note 31;
76. see also EARL L. GRINOLS ET AL., CASINOS, CRIME AND COMMUNITY COSTS (Univ. of Ill. & Univ. of Ga., Working Paper, 2000);
77. LOTT, JR. & LANDES, supra note 39;
78. Eric D. Gould et al., Crime Rates and Local Labor Market Opportunities in the United States: 1979-1997, 84 REV. ECON. & STAT. 45 (2002);
79. John R. Lott, Jr. & David B. Mustard, Crime, Deterrence, and Right-to-Carry Concealed Handguns, 26 J. LEGAL STUD. 1, 39-48 (1997);
80. David B. Mustard, Reexamining Criminal Behavior: The Importance of Omitted Variable Bias, 85 REV. ECON. & STAT. 205 (2003);
81. Joanna M. Shepherd, Fear of the First Strike: The Full Deterrent Effect of California's Two- and Three-Strikes Legislation, 31 J. LEGAL STUD. 159 (2002);
82. Joanna M. Shepherd, Police, Prosecutors, Criminals, and Determinate Sentencing: The Truth about Truth-in-Sentencing Laws, 45 J.L. & ECON. 509 (2002).
83. Moreover, panel data allow me to overcome the unobservable heterogeneity problem that affects cross-sectional studies. Neglecting heterogeneity can lead to biased estimates. I use the time dimension of the data to estimate county-fixed effects and condition my two-stage estimation on these effects. This is equivalent to using county dummies to control for unobservable variables that differ among counties. This way I control for the unobservable heterogeneity that arises from county specific attributes such as attitudes towards crime, or crime reporting practices. These attributes may be correlated with the justice-system variables (or other exogenous variables of the model) giving rise to endogeneity and biased estimation. An advantage of the data set is its resilience to common panel problems such as self-selectivity, nonresponse, attrition, or sampling design shortfalls.
84. See all of the modern economics papers listed supra Section III.B.1.
85. Dezhbakhsh et al., supra note 31, at 352.
86. Lott, Jr. & Mustard, supra note 62.
87. This does not include returns of parole violators, escapees, failed appeals, or transfers.
88. 2/n) are proportional to county population. Tests for heteroskedasticity indicate that the error term in the unweighted regression is indeed heteroskedastic. Tests indicate that the heteroskedasticity has been corrected after weighting by the square root of the county population. In addition, tests for overidentification indicate that the model is correctly specified and employs valid instruments.
89. I chose a single-equation method, two-stage least squares, over a systems method because in a systems method any specification error in one equation is propagated throughout the system, which can lead to inconsistency. WILLIAM H. GREENE, ECONOMETRIC ANALYSIS 616 (2d ed. 1993). Single-equation methods, such as two-stage least squares, confine the error to the particular equation in which it appears.
90. Dezhbakhsh, et al., supra note 31, at 361.
91. See Ehrlich, supra note 19.
92. THE DEATH PENALTY IN AMERICA, supra note 8.
93. Raaj K. Sah, Social Osmosis and Patterns of Crime, 99 J. POL. ECON. 1272 (1991).
94. See id. For the states that have never had an execution, the conditional probability of execution takes a value of zero. For the states that have never sentenced anyone to death row, the conditional probability of a death row sentence takes a value of zero.
95. The effects of many other variables on murder are also consistent across models. As expected, the murder rate has a statistically significant, positive relationship with both the aggravated assault rate and the robbery rate in all six models. Many murders are committed during another crime. The arrest rate and probability of receiving a death row sentence are negatively related to the murder rate in most models, indicating a deterrent effect of these variables. However, the probability of a death row sentence is not statistically significant in some of the models. Many of the demographic variables also have the expected relationships with murder rates. The percent of the county population that is African American has a statistically significant, positive relationship with murder in most of the models. Some minority groups have fewer legitimate earning opportunities, and thus a lower opportunity cost of criminal activities relative to their white counterparts. The percentage of the population that is male has a statistically significant, positive relationship with murder in all models; most murders are committed by men. The percentage of the population that is ten to nineteen years of age has a negative and significant relationship with murder in most of the models. The murder rate is positively related to both per capita real income and per capita real welfare payments in all models. This suggests that overall income measurements for a county represent the amount of wealth available to steal in the county; as the amount of wealth available to steal increases, crime increases. The real per capita unemployment insurance payments have a statistically significant, negative relationship with murder rates: more aid to unemployed people lowers their need for criminal activity. Population density has a statistically significant, negative relationship in all models. Although murder rates are higher in more densely populated cities, they are not higher in more densely populated counties-my unit of measurement. The majority of the most densely populated counties are suburban counties that tend to have lower crime rates than either urban or rural counties. The coefficients for three variables are statistically insignificant in most models: the percentage of the population that is twenty to twenty-nine, the percentage of the population that belongs to a minority group other than African American, and the NRA membership rate.
96. 1987] for models 3 and 6, where S is the number of individuals sentenced to death. I perform these transformations for every coefficient in Table 1 that is significant at the 10% level. Then, I find the median result of the transformations for each state to obtain the median increase or decrease in number of murders after one execution. I use medians instead of means so that the numbers will not be influenced by extreme outliers. Results using the mean value of the transformations are similar.
97. The special characteristics of the executions in Utah and Oregon may cause the large increase in murders. Utah has executed people by firing squad, and Oregon executed only one person during the sample period.
98. My results are consistent with a recent study that shows that, in analyses that estimate capital punishment's average effect on murders across all states, dropping certain states from the analyses makes the overall deterrent effect disappear. Berk, supra note 51.
99. See id.
100. Regression results from another project I am working on suggest that the more publicity each execution receives, the greater the deterrent effect.
101. See supra note 58 and accompanying text for a description of the publicity measure.
102. See Joanna M. Shepherd, Executions, Deterrence, and the Characteristics of the Person Executed (2005) (unpublished regressions) (on file with author). The full theoretical explanation of these results is beyond the scope of this paper. The varying deterrent effects for executions of different types of criminals is probably caused by both the publicity surrounding the different types of criminals and by how similar potential criminals think they are to the executed criminal.
103. Zimmerman, Alternative Execution Methods, supra note 40.
104. Unreported regressions exploring the relationship between murder and the number of executions per prisoner yield similar results to those reported below.
105. See GREENE, supra note 69, at 237.
106. CESARE BECCARIA, OF CRIMES AND PUNISHMENTS 50 (H. Paolucci trans., 1964) (1764).
107. William J. Bowers & Glenn Pierce, Deterrence or Brutalization: What is the Effect of Executions?, 26 CRIME & DELINQ. 453, 456 (1980).
108. Cochran et al., supra note 46, at 110.
109. The brutalization curve may also be flat in some jurisdictions. The marginal brutalization effect would still be eventually outweighed by the marginal deterrent effect.
110. The weight is the square root of the state population to correct the heteroskedasticity of the error term.
111. These states were significant at the 90% confidence level.
112. Dezhbakhsh & Shepherd, supra note 37, at 13-20.
113. These states were significant at the 90% confidence level.