The future of capital punishment: determinants of the time from death sentence to execution

International Review of Law and Economics 22 (2002) 1–23

The future of capital punishment: determinants of the time from death sentence to execution Stephen J. Spurr∗ Department of Economics, 2115 F.A.B., Wayne State University, Detroit, MI 48202, USA Accepted 19 February 2002

Abstract This paper analyzes data on all inmates under a death sentence in the U.S. since 1972, to determine the effect of various factors on the time from sentence to execution, and the probability of execution. We find that over time the probability of execution is increasing, and the time to execution is declining. The probability of execution is greater for males and inmates with a high level of education, and may also be greater for those who were previously convicted of murder. There is no evidence of any effect of race. The fact that there are sharp differences between States within the same Federal Circuit with respect to both the likelihood of execution and waiting time, suggests that the lower federal courts play a relatively minor role in death penalty litigation. © 2002 Elsevier Science Inc. All rights reserved.

1. Introduction A New Jersey judge who recently imposed a death sentence on a 21-year-old for his role in the ambush and murder of two pizza deliverymen, added a most unusual proviso to his order. The judge ordered that unless the execution was carried out within 5 years, the sentence would automatically be changed to life in prison. The judge then proceeded to read the following statement to those gathered in the courtroom: The process has become unacceptably cruel to defendants, who spend long years under sentence of death while the judicial system conducts seemingly interminable proceedings which remind many observers of a cruelly whimsical cat toying with a mouse.1

∗

Tel.: +1-313-577-3232; fax: +1-313-577-9564. E-mail address: [email protected] (S.J. Spurr).

0144-8188/02/$ – see front matter © 2002 Elsevier Science Inc. All rights reserved. PII: S 0 1 4 4 - 8 1 8 8 ( 0 2 ) 0 0 0 6 5 - 0

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The judge noted that he fully expected that his order would be overturned by a State appellate court. He stated that he felt compelled to speak out on this issue nonetheless because, in his view, delays in executions amounted to cruel and unusual punishment for those sentenced to death.2 The principal objective of this paper is to analyze the time to execution, for all persons in the U.S. who were under a death sentence after 1972. We use duration analysis to determine the effect of various factors on the time from sentence to execution. However, there is some ambiguity in the results of any duration model when there are possible outcomes other than the primary one of interest, in this case execution. An inmate’s death sentence may be overturned by the courts, or he might die before his scheduled date of execution. Suppose, e.g., that being female has a positive effect on time to execution. This may mean that it simply takes longer for a woman to be executed, or, alternatively, that a woman is more likely to avoid execution altogether. To differentiate between these two scenarios, we estimate ordered probability models—an ordered probit and logit—which are designed to determine how the variables affect the probability that an inmate will ultimately be executed. There are many controversies concerning the length of time between a death sentence and its execution. While some argue that a long delay is “cruel and unusual punishment,” others, like the American Bar Association, have called for a moratorium on executions. In one State, Illinois, the Governor imposed a moratorium after expressing concern about the possibility that an innocent person could be executed.3 Advocates of the death penalty have argued that the federal courts are primarily responsible for delays. Some have argued that the costs of the death penalty exceed the benefits, and it seems clear that at least some of these costs increase with delay. There is an extensive literature of empirical studies on different facets of the death penalty in law and the social sciences. A random sample of topics in this literature (with references that are both very incomplete and arbitrary) would include the deterrent effect of the death penalty,4 the factors that determine who receives a death sentence (with special attention to race),5 the potential bias of juries in death penalty cases,6 whether sentencing jurors take responsibility for the sentence they impose,7 and the characteristics of those who are ultimately executed. However, we have not found any research that provides a multivariate analysis of the length of time from death sentence to execution. The study that seems closest to ours is Blume and Eisenberg (1999), who used a duration model to analyze the time to removal of a death sentence; moreover, their dataset was a then-current version of our dataset.8 They found that the time to removal of the sentence increased with the inmate’s level of education, and was also increased by a prior murder conviction. They also found that the inmate’s age, and especially being female, reduced the time to relief or increased the probability of relief. Finally, Liebman, Fagan, and West (2000) analyze a dataset that provides information on the separate decisions made by State and federal courts, and many other aspects of capital litigation, including the different types of error found in the review process. We will compare a number of our findings to those of their study.

2. The time from death sentence to execution One important issue is whether delay is increasing over time. It is clear that this has been true over a long period. Table 1 shows the median elapsed time from death sentence to execution,

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Table 1 Years from death sentence to execution Year

N

1960 1961 1962 1963 1964 1965

56 42 47 21 15 7

1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 (through 2/8/2001)

0 (1) 0 1 (2) 0 1 1 (2) 5 21 14 (18) 17 (18) 23 (25) 10 (11) 14 (16) 14 (23) 14 30 (31) 32 (38) 27 (31) 48 (56) 35 (45) 67 (74) 68 98 85 14

Mean – – – – – – (0.25) – 5.42 (3.54) – 1.08 5 (4.42) 5.88 6.15 6.98 (5.94) 7.44 (7.25) 7.42 (7.21) 6.89 (6.65) 8.61 (7.90) 9.21 (7.94) 9.63 9.84 (9.53) 10.38 (9.39) 10.86 (10.19) 11.96 (11.13) 11.22 (10.42) 11.42 (11.05) N.A. N.A. N.A. N.A.

Median 1.42 1.35 1.71 1.33 1.71 3.71 (0.25) – 5.42 (3.54) – 1.08 5 (4.42) 5.42 6.00 6.88 (6.58) 7.92 (7.92) 6.75 (6.33) 6.79 (6.67) 8.67 (8.17) 8.42 (8.00) 8.79 9.42 (9.42) 11.25 (9.96) 12.58 (11.42) 13.08 (11.67) 11.58 (11.25) 11 (10.75) N.A. N.A. N.A. N.A.

Standard Deviation – – – – – – – – – – – – 3.78 2.61 2.16 (2.78) 2.43 (2.50) 3.35 (3.32) 3.09 (3.04) 2.12 (2.82) 2.79 (3.71) 3.60 2.90 (3.31) 3.06 (3.83) 3.80 (4.16) 4.03 (4.50) 3.97 (4.39) 3.56 (3.79) N.A. N.A. N.A. N.A.

Note: Cases are assigned to the year the inmate was executed. For 1977 and later years, the statistics on time to execution that are not in parentheses are based on all nonconsensual executions, while the statistics that are in parentheses are based on all executions, including those of volunteers. Source: Information for 1960–1965 is from NPS Bulletin (1966), at 7. Information for 1977 and later years was obtained from Capital Punishment in the United States 1973–1997, ICPSR 2737.

for inmates executed during the years 1960 through 1965, before a decline in executions resulting from constitutional challenges, and then from 1977 to date. In 1960, e.g., there were 56 executions with a median elapsed time of 1.42 years. Between 1930 and 1970, the mean period from death sentence to execution was 36.7 months.9 For prisoners executed between 1977 and 1995, the mean time from imposition of their most recent sentence to execution was more than 8 years.10 Some prisoners abandon their right to contest their capital judgment at some stage of litigation, which reduces their time to execution.11 In Table 1, we report statistics on time to execution for (A) nonconsensual executions only (these are not in parentheses), and for (B) all executions, including those of volunteers (these are in parentheses). Columns 3 and 4

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of Table 1 show that for the relatively few persons who are executed (few, that is, in relation to the total number under a death sentence), the mean and median time to execution seem to have increased slightly over the period from 1983 through 1989, but then to have increased substantially over the next 6 years. This pattern holds true whether one examines only nonconsensual executions or all executions. To determine rigorously whether there is a time trend, we must use a duration model with covariates, which exploits the information available on those who have not been executed.

3. The effects of Supreme Court decisions Table 2 indicates that the number of executions began to decline in 1960 to a veritable trickle by 1966; there were no executions from 1968 through 1976, and very few thereafter until 1984. Much if not all of this pattern can be explained by the time required for litigation of constitutional issues that culminated in two Supreme Court decisions, followed by the time required for State legislatures and the courts to absorb and respond to these decisions. In Furman v. Georgia, 408 U.S. 238 (1972), the Court held that the death penalty, as applied in Georgia and Texas, violated the prohibition against cruel and unusual punishment in the Eighth Amendment, which applied to the states through the Fourteenth Amendment’s requirement that the states provide “due process of law.” As a result of the Furman decision, the moratorium on executions continued until the Supreme Court again considered the death penalty in Gregg v. Georgia, 428 U.S. 153 (1976). There the Court held that death penalty statutes of three States, Georgia, Florida, and Texas, met the standards of the Constitution, but that those of two others, North Carolina and Louisiana, did not. After the Gregg decision many States adopted statutes similar to those upheld in that case. While some observers predicted a wholesale resumption of executions following the Gregg decision, there were in fact no more than two in any year until 1983. In cases after Gregg, the Supreme Court has given the states more leeway in imposing capital punishment. A recent article notes that “by 1983, in cases such as Zant v. Stephens, 462 U.S. 862 (1983) and Barclay v. Florida, 463 U.S. 939 (1983), the Court had dismantled most of the procedural restrictions that were imposed on capital sentencing by Gregg and the other 1976 death penalty cases.”12 The effect of the Furman decision (and the enormous power of the U.S. Supreme Court) is reflected in Table 3, which shows the number of executions and removals of the death sentence, for each year in which the sentences were entered. A “removal” occurs when the conviction or sentence is overturned on legal grounds, or the sentence is commuted; it does not include cases in which the inmate died while under sentence for a reason other than execution, e.g., natural causes, suicide, or murder by another inmate.13 It is striking that none of the 326 death sentences entered before 1973 have led to executions, and that all but 8 of them have been officially removed. Column 3 shows the ratio of executions to removals, and column 4 shows the ratio of total resolutions (executions plus removals) to the total cases for each sentencing year. It is noteworthy that the ratio of executions to removals generally increased between the sentencing years 1973 and 1983. The entries for years after 1983, although reported in the table, are likely to be incomplete. The conclusion to be drawn from Table 3 is that for cases in which the sentences were entered after the Furman decision, there was a trend for the share

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Table 2 Annual number of executions, 1930–1976 Year

Number of Executions

1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976

155 153 140 160 168 199 195 147 190 160 124 123 147 131 120 117 131 153 119 119 82 105 83 62 81 76 65 65 49 49 56 42 47 21 15 7 1 2 0 0 0 0 0 0 0 0 0

Note: The value shows all prisoners executed under civil authority in the United States during the years indicated. Source: Table 7.26, U.S. Department of Justice, Correctional Populations (1997).

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Table 3 Outcome by year of sentence Year of Sentence

Total (2)

Executions (3)

Removals (4)

(3)/(4)

((3) + (4))/(2)

Prior to 1957 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

3 1 1 2 6 12 10 6 14 13 21 13 34 33 57 57 43 44 161 318 249 159 211 173 204 251 287 266 302 295 321 305 316 275 265 280 296 295 319 325 299

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 9 6 11 17 31 20 29 40 43 37 28 15 15 13 13 5 5 2 5 6 2 3 1

3 1 1 2 6 12 8 6 14 13 21 13 32 33 55 56 42 42 144 303 227 127 148 113 114 137 120 90 116 130 111 108 104 91 72 59 52 26 15 2 0

– – – – – – – – – – – – – – – – – 0.048 0.063 0.020 0.048 0.13 0.21 0.18 0.25 0.29 0.36 0.41 0.24 0.12 0.14 0.12 0.13 0.055 0.069 0.034 0.096 0.23 0.13 1.50

– – – – – – – – – – – – – – – – – 1.00 0.95 0.97 0.96 0.91 0.85 0.77 0.70 0.71 0.57 0.48 0.48 0.49 0.39 0.40 0.37 0.35 0.29 0.22 0.19 0.11 0.053 0.015 0.0033

Note: The numbers provided are different from those in Appendix Table 1 of Snell (1998), since the latter table includes only the most recent death sentence for persons who were sentenced to death more than once. A case is classified as a “removal” if the death sentence was removed for any of the following reasons set forth in the codebook of ICPSR 2736 for Variable 31: “(3): capital sentence declared unconstitutional by State or U.S. Supreme Court; (4) sentence commuted; (5) conviction affirmed, sentence overturned by appellate court; (6) conviction and sentence overturned by appellate court; or (7) other.” Source: Capital Punishment in the United States 1973–1996, ICPSR 2736.

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of executions to increase for about 11 years. After then the data are too incomplete to support any inferences. Another issue concerns the effect of technological change. In May 1999, it was reported that more than 60 criminal convictions had been overturned as a result of DNA testing; at least nine of these were inmates on death row.14 However, the effects of such advances on the time to execution and the probability of removal of a death sentence are ambiguous. While DNA results may exonerate an inmate who has previously been sentenced to death, they may also discredit a claim of innocence. Moreover, in the long run technical advances are likely to both increase the probability that a criminal is apprehended, and reduce the probability that someone is convicted of a crime that he did not commit. It is quite conceivable that one of the consequences of technical change will be a decline in the probability of relief from a death sentence.

4. The role of the federal courts Another issue involves the apportionment of responsibility between State and federal courts, for both delay and the overturning of death sentences. There are essentially three successive avenues of litigation available to an inmate who has been sentenced to death in a State court: firstly, direct review of the conviction and sentence; secondly, State collateral (post-conviction) proceedings; and finally federal collateral (habeas corpus) proceedings. State post-conviction review takes different names and forms across different States.15 However, in most States, it is restricted to claims based on State or federal constitutional law that the defendant could not have discovered or adequately litigated at trial or on direct appeal. For example, if the defendant’s trial or appellate lawyer was incompetent, the defendant would be unable to recognize or fairly litigate his claim. Finally, an inmate may challenge the constitutionality of his conviction or sentence by filing a petition for a writ of habeas corpus in a federal District Court in the State in which he was convicted. There are suggestions throughout the literature that the different phases of litigation are substitutes for each other. For example, a thorough exploration of issues at trial leaves less scope for collateral proceedings.16 Or if State collateral review is perfunctory, federal collateral review will tend to be more extensive than otherwise. Liebman et al. (2000) found evidence suggesting that when federal courts are unusually lax in finding errors in capital cases, State courts compensate by increasing their efforts to find such errors, and conversely, that federal courts are more vigilant in reviewing cases when the State courts have unusually low error-detection rates.17 One might, therefore, expect a negative correlation between the duration of one phase and another, e.g., between State and federal collateral proceedings, once other factors affecting duration of litigation have been taken into account. Concerns about delay in death penalty litigation prompted the Chief Justice of the United States to appoint a committee in 1988 to evaluate this problem and, if appropriate, propose legislation “directed toward avoiding delay and the lack of finality in capital cases.” In an effort to determine the share of delay attributable to different types of litigation, the committee analyzed 50 death penalty cases from five states that culminated in executions. The total time

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from sentence to execution was allocated among direct appeals, State collateral review, federal collateral review and “down time”—time when no proceedings were pending in any court. The committee found that collateral review of both kinds accounted for about half the time from sentence to execution, and that federal collateral review accounted for 80% of all collateral review. The Committee made recommendations which led to the enactment by Congress of the Antiterrorism and Effective Death Penalty Act of 1996.18 Several key provisions of this statute are motivated by an intent to reduce the time consumed by federal habeas corpus proceedings. For example, the Act requires that a habeas corpus petition be filed within 1 year of the conclusion of direct review,19 and severely restricts the filing of additional petitions after the first one is filed. It also requires the federal District Courts and Circuit Courts of Appeal to decide petitions within prescribed time limits, and to give capital cases priority over all other cases.20 Our empirical work is motivated by what appears to be a widely held belief that the lower federal courts—the District Courts and the Circuit Courts of Appeal—are responsible for a substantial portion of the delay in death penalty litigation (readers who are nonlawyers should be aware that the 50 states are divided into 11 regional Federal Circuits; there is a separate circuit for the District of Columbia). Newspaper reports have, e.g., cited the Fourth Circuit, which includes North and South Carolina, Maryland, and Virginia, as being especially inhospitable to appeals in death penalty cases.21 We would like to distinguish between the State and lower federal courts, in terms of their separate impact on both the time spent in litigation and removals of death sentences. Unfortunately our dataset does not identify the actions of State and federal courts. The time from sentence to execution that we have represents the sum of all the time spent in federal and State courts, and, if an inmate’s sentence has been removed, we do not know which type of court removed it. We can, however, gain some insight by comparing the effect of being in a particular State with the effect of being in the Federal Circuit that includes that State. We make this comparison in the empirical work reported here. By such indirect means we test the hypothesis that the Federal Circuit of the courts that hear the inmate’s case is important in determining both the outcome and the time from sentence to execution.

5. The data Our dataset was constructed for public use by the Bureau of Justice Statistics of the U.S. Department of Justice, and is made available to researchers by the Inter-university Consortium for Political and Social Research (ICPSR).22 The data universe is described as “all inmates on death row since 1972 in the United States.” The dataset was collected from official prison records under the National Prisoner Statistics Program. It provides information on all prisoners in the U.S. who were under a sentence of death between 1973 and 1997, and reports whether each prisoner is still on death row, has been executed, whether his sentence was commuted or vacated, or if he died through some means other than execution. The data include demographic characteristics such as age, sex, race, level of education, state of incarceration and prior felony convictions.23

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6. Ordered probability models Table 4 sets forth the results of an ordered probit and logit. The dependent variable has the highest value if the inmate has been executed, an intermediate value if the inmate is still under a death sentence, and the lowest value if the inmate’s sentence has been overturned or commuted.24 One advantage of this formulation is that it exploits the fact that there are, after all, pretty clear qualitative differences between the states of being executed, being under a death sentence, and having the death sentence removed. The explanatory variables include the number of years of schooling, dummy variables for race (White, Black and Hispanic), for sex, and for whether the inmate was married at the time he was imprisoned for the capital offense. We include the inmate’s age at the time of his arrest for the capital offense, as a proxy for his age at the time of the crime, which is not provided in the dataset.25 There are dummy variables for all the States for which there were at least four executions in this dataset; thus, the omitted reference category is all the States that had fewer than four executions.26 Another dummy variable indicates whether the inmate was convicted of murder (specifically murder, not just criminal homicide) before the capital offense for which he is now under sentence. It is clear that, other things equal, the probability of State (2), being under a death sentence, will be higher relative to either State (1) execution, or (3) removal of the death sentence, the more recent was the date of sentencing. Hence, we include variables for the date of the inmate’s sentence, and its square and cube. The effect of calendar time is fully captured by these variables. Since, as explained in the discussion of Table 3 and the Furman decision, none of the death sentences entered before 1973 led to executions, we have omitted observations with a sentence prior to 1973. We should consider the fact that the closer the sentencing date is to the present, the less likely it is, ceteris paribus, that the death sentence has either been carried out or overturned, simply because there has been less time for the courts to evaluate the case. If the data indicate that the probability of execution declines as the year of sentence increases, we will not know whether this apparent decline is just an illusion resulting from the premature end of the period of observation. Similarly, if we find that the probability that the sentence is overturned declines with time, we must disentangle two reinforcing effects—one, that the courts have had less time to consider the case, and the other, that courts have become less inclined to overturn death sentences over time. To differentiate between these effects, and to provide a benchmark for the ordered probit and logit reported in Table 4, we estimated two additional binary probits: one on whether there was an execution, and the other on whether the inmate’s death sentence was removed, i.e., by being overturned or commuted. 6.1. Results when States are included as variables The results of the ordered probit and logit reported in Table 4 indicate that persons with a high level of education, and males, are more likely to have an adverse outcome—either execution or remaining under death sentence. The probability of execution appears to be greater when there is a prior murder conviction, although this coefficient is only significant at the 11% level. The States with the most severe outcomes are, in declining order of severity, Virginia, Delaware, Texas, Missouri, Utah, California, Louisiana, and Nevada, while the most lenient are North

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Table 4 Effects of variables on probabilities of (1) execution, (2) remaining under death sentence, and (3) removal of death sentence Variable

Ordered Probit Coefficient

Including States One (intercept) Years of schooling Age at time of arrest Married Male Black Hispanic White Prior conviction for murder Date of death sentence (D) D2 D3 Texas Florida Louisiana Georgia Virginia South Carolina North Carolina Alabama Arkansas Missouri Illinois Oklahoma Arizona Delaware Nevada Indiana Utah California Mississippi µ Log-likelihood Including Federal Circuits One (intercept) Years of schooling Age at time of arrest Married Male Black Hispanic White Prior conviction for murder Date of death sentence (D) D2 D3 Third Circuit Fourth Circuit

−3.47 0.028 −0.0023 0.00057 0.54 −0.042 −0.042 −0.013 0.12 5.35 −3.28 0.68 0.85 0.035 0.32 0.18 1.32 0.019 −0.26 0.16 0.21 0.75 0.099 −0.024 0.078 0.87 0.30 0.075 0.72 0.55 −0.25 1.96 −4050.7 −3.53 0.027 −0.0037 0.025 0.58 0.0042 0.044 0.036 0.16 5.44 −3.32 0.68 0.076 0.0056

Ordered Logit Probability 0.0000 0.00077 0.30 0.99 0.00013 0.80 0.59 0.94 0.11 0.0000 0.0000 0.0000 0.0000 0.60 0.00062 0.029 0.0000 0.86 0.0027 0.098 0.048 0.0000 0.30 0.81 0.45 0.00001 0.034 0.58 0.00004 0.00008 0.046 0.00000 – 0.0000 0.00094 0.089 0.49 0.00003 0.98 0.56 0.82 0.037 0.0000 0.0000 0.0000 0.44 0.94

Coefficient −6.63 0.046 −0.0049 0.014 0.91 −0.092 −0.067 −0.067 0.20 10.2 −6.04 1.22 1.46 −0.13 0.36 0.15 2.43 −0.16 −0.52 0.17 0.11 1.23 0.078 −0.11 0.12 1.61 0.49 0.037 0.90 0.94 −0.52 3.59 −3937.9 −6.53 0.046 −0.0071 0.049 0.96 −0.029 0.078 0.0078 0.24 9.98 −5.83 1.17 0.084 −0.16

Probability 0.0000 0.0028 0.22 0.84 0.00028 0.74 0.63 0.81 0.13 0.0000 0.0000 0.0000 0.0000 0.28 0.041 0.34 0.0000 0.44 0.00077 0.32 0.59 0.0000 0.64 0.53 0.52 0.0000 0.050 0.88 0.026 0.00005 0.023 0.00000 – 0.0000 0.0030 0.074 0.48 0.00007 0.92 0.57 0.98 0.072 0.0000 0.0000 0.0000 0.62 0.19

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Table 4 (Continued ) Variable

Ordered Probit Coefficient

Including Federal Circuits Fifth Circuit Eighth Circuit Ninth Circuit Tenth Circuit Eleventh Circuit µ Log-likelihood

0.54 0.43 0.24 −0.085 0.025 1.87 −4205.7

Ordered Logit Probability

Coefficient

0.0000 0.0000 0.0022 0.34 0.69

0.84 0.60 0.39 −0.24 −0.11

0.0000 –

3.39 −4089.3

Probability 0.0000 0.00006 0.0031 0.15 0.32 0.0000 –

Note: There are 5351 observations. The dependent variable has the highest value if the inmate has been executed (406 cases), an intermediate value if the inmate is still under a death sentence (2832), and the lowest value if the inmate’s sentence has been overturned or commuted (2113). One hundred and fifty-one cases in which the inmate died for a reason other than execution, e.g., natural causes, suicide, murder by another inmate, etc., are omitted from the dataset. Because of the effect of the Furman decision, cases in which the inmate received his sentence before 1973 are also omitted. For sentences in subsequent years the scaling of D may be illustrated by an example: for a sentence entered halfway through 1984, D would have the value (1984.5–1972)/10 = 1.25.

Carolina and Mississippi.27 It is not clear why there is substantial variation across the States that have the death penalty. Blume and Eisenberg (1999) consider hypotheses based on differences in methods of selecting judges and differences in how selective a State is in imposing the death penalty. Judges who are relatively independent from the electorate (such as federal judges, who have life tenure) may be more apt to incur the risk of popular disfavor by reversing erroneous convictions in capital cases. Also, some States have a higher “death-obtaining rate” than others, i.e., the death penalty is imposed in a relatively high proportion of the cases that are eligible for the death penalty. If States with a high death-obtaining rate are imposing the death penalty in cases that are less “death-deserving” than States with a low death-obtaining rate, one might expect a higher rate of reversals in the States with a high rate. Blume and Eisenberg find that a state’s death-obtaining rate has some value in predicting case outcomes, but the method of selecting judges does not. It is interesting to note that Virginia’s death-obtaining rate is substantially lower than that of most death penalty states.28 Virginia officials also point out that the death penalty is restricted to defendants who both intended to kill and personally caused death, while some other States have broader statutes (Masters, 2000). However, another noteworthy feature of Virginia law is the period within which a defendant is allowed to present new evidence after a capital conviction. While 36 states impose a deadline on evidence offered after such a conviction, Virginia’s statute of limitations is unusually short—only 21 days.29 Moreover, during the period covered by our data Virginia seems to have provided little or no compensation to lawyers for indigent inmates in capital cases.30 As of 1989 Virginia had no system, such as a Federal Death Penalty Resource Center, to monitor capital cases and ensure that inmates would be provided counsel before and during post-conviction proceedings. Liebman et al. (2000) note the foregoing factors and suggest some others: (1) Virginia courts allow even egregious errors to be ignored on appeal unless they were objected to at trial; and (2) Virginia has applied an extremely strict rule for reversing capital judgments based on incompetence of counsel. Indeed Masters (2000)

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states that the Virginia Supreme Court has never overturned a death sentence on account of inadequate legal representation. Returning to our estimates in Table 4, the coefficients of the sentence date, its cube and square indicate that the probability of an adverse outcome is increasing with calendar time. Figure 1 shows the total effect of the sentence date (including its squared and cubed terms) on (1) the probability of execution, estimated from the binary probit for the event of execution; (2) the probability that the death sentence will be removed, estimated from the binary probit for the event of removal; and finally (3) the probability of an adverse outcome (either execution or non-removal of the death sentence) estimated from the ordered probit. The graph of (3), from the ordered probit, is the most reliable of these, since there the downward bias in the probability of execution is offset by the downward bias in the probability of relief from the death sentence. It is clear from Figure 1 that the probability of an adverse outcome is increasing with the sentencing date. The omitted reference category includes three States that had large death row populations (Ohio, Pennsylvania and Tennessee).31 These States are not represented by a variable since they had fewer than four executions as of the end of 1997. In order to learn how our results were affected by the inclusion of these States, we estimated the ordered probit and logit models reported in Table 4 without these observations. The idea was that these States might be fundamentally different from the rest of the sample; their death row populations continually increased, since they had many death sentences, and evidently were not inclined either to remove these sentences or to carry out executions. Accordingly these States made a disproportionately large contribution to the intermediate category, in which the inmate is still under a death sentence. It turns out that when these three States are omitted, the qualitative results are virtually the same as before. However, the estimated coefficient of every State variable, which as noted represents a State with at least four executions (Texas, Virginia, etc.), increases: the positive effects become larger, and the negative coefficients of three States become positive.32 This happens because the average outcome in the reference category, which represents a coefficient of 0, is less adverse to inmates than before; in effect the origin has shifted to the left. This shift reflects the fact that in the States remaining in the reference category, there is a greater probability of removal of the sentence than there is in the three omitted States. 6.2. Results when Federal Circuits are included as variables To assess the marginal impact of the federal courts on the outcome, we estimated the ordered probit and logit again, replacing the state variables with variables for the Federal Circuit that has jurisdiction over the case (Table 4). The First, Second, Sixth and Seventh Circuits and the District of Columbia Circuit were omitted. The Seventh Circuit provides some executions for the reference category, which is helpful to the estimation, given that there have been no executions since 1977 in the First, Second or District of Columbia Circuits, and only one in the Sixth Circuit, in Kentucky.33 The variables that were significant in the specification with state variables—years of schooling, male, and the variables based on the sentencing date—remain highly significant with the same signs. The probability of execution is greater when there is a prior murder conviction.

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As for the circuit court variables, those for the Fifth, Eighth and Ninth Circuits are positive and highly significant. Other sources34 indicate that the federal courts of the Fourth Circuit are least likely to reverse capital judgments of any in the federal court system. Here, however, the variable for the Fourth Circuit, combining as it does decisions of State and federal courts, does not even approach statistical significance. The Fifth Circuit, which emerges as the most severe, contains Louisiana, Mississippi and Texas. Since 1977, Texas has had 144 executions, the most in any state during that period and almost three times as many as the state with the next highest number (Virginia with 46).35 It is striking that the circuit which is the most severe, the Fifth, includes one State, Mississippi, which is more lenient than those in the omitted reference category, and the Second most lenient of the 12 that are estimated to have significant coefficients. It is also noteworthy that the same Circuit, the Fourth, includes both the State which is the most severe, Virginia, and the one that is most lenient, North Carolina.36 The findings of Liebman et al. (2000) are quite relevant here. They find that the overall State “error rates” (i.e., the estimated total rate of reversals by State courts, including both direct review and State post-conviction proceedings) for Virginia and North Carolina are 13% and 65%, respectively. After the reversals by the federal courts are factored in, the overall error rates become 18% and 71%, respectively. Collectively, the above results suggest that the State has a far greater role in determining the status of those who have been sentenced to death, than the lower federal courts that have jurisdiction over its criminal cases.

7. Duration analysis Next, we do a duration analysis with four alternative models of the hazard function—the Weibull, log-normal, log-logistic, and Gamma specifications. Duration analysis is especially useful when some of the data is censored. Broadly speaking, an observation is censored if it does not provide complete information about the variable of interest—in this case, the total time from sentence to execution. Such information would not be available if, e.g., the inmate’s death sentence was vacated by a court, or the inmate did not live long enough to be executed. Here observations are treated as censored from the right if (1) the individual remains under a death sentence; or (2) the individual’s death sentence has been removed for a reason other than his execution, through (A) death from another cause (e.g., from natural causes, suicide, or murder by another inmate); or (B) because his death sentence was overturned or commuted. The effect of calendar time is fully captured by using sentence date, its square and cube as variables, without using time-dependent covariates. Another variable, Volunteered, indicates whether the inmate abandoned his right to contest the capital judgment at some stage of litigation. In these estimates, there are variables for the individual states. Differences among states in the time to execution reflect, among other factors, differences in both State and federal post-conviction proceedings. There is substantial variation across states in both the availability of State collateral proceedings, and time limits on such proceedings; in addition, the Federal Circuit court of appeals that has jurisdiction over the state may be inclined to decide federal habeas corpus claims summarily, or only after lengthy consideration. If we accept the finding of the Powell Commission that federal collateral review accounts for 80% of all collateral

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review, then the differences among Federal Circuits should be roughly four times as important as variation in State collateral proceedings.37 7.1. Results when States are included as variables Results of the duration analysis are set forth in Table 5. Time to execution is reduced for inmates who are older at the time of their arrest, presumably because the courts are more inclined to defer the execution of one who was young when he committed the crime. More educated inmates, and those who abandon their right of appeal, have a shorter time to execution. The inmate’s sex was highly significant in the ordered probit, but is not significant here, indicating that being male affects the likelihood of execution (positively) but not time to execution. In, e.g., the Weibull specification, the states with the shortest time to execution are, in declining order of severity, Virginia, Louisiana, Utah, Missouri, Arkansas, Texas and Delaware (this is similar to the order of severity found in the ordered probit). Recall that the States in the omitted reference category are those with fewer than four executions as of December 31, 1997. California has the longest time to execution of any State. Finally, time to execution declines over time, as represented by the sentence date. Figure 2 shows the total effect of the sentence date on time to execution during the period from 1973 to 1997.38 While the magnitude of the negative effect generally increases over time, there appears to be a mild reversal of the trend between 1981 and 1990, followed by a precipitous return to the long-run trend thereafter. The figure suggests that time to execution will decline drastically for those who receive a death sentence after 1990. Blume and Eisenberg (1999) found that time to removal of a death sentence increases with the year of sentencing, a result that is consistent with ours. Again we wished to determine the effect on our results of the three States with large death row populations (Ohio, Pennsylvania and Tennessee) that were included in the reference category. Accordingly, we re-estimated the duration models reported in Table 5 without these observations. When these three States are omitted, the effects of the State variables increase across the board: the negative effects become smaller in absolute value or even positive, while the positive effect of one State, California, becomes larger.39 On reflection this is not surprising, since we have eliminated from the reference category many observations of long duration, some of which are right-censored. Thus, the average duration of observations in the reference category, which represents a coefficient of 0, becomes smaller; in effect the origin has moved to the left. 7.2. Results when Federal Circuits are included as variables To determine the impact of the federal courts on time to execution, we estimated the duration models, again replacing the state variables with variables for the Federal Circuits that had jurisdiction over each case. As before the First, Second, Sixth, Seventh and District of Columbia Circuits are omitted to provide a reference category. We present only the results for the Weibull model, but they are qualitatively robust across the other three models (log-logistic, log-normal and Gamma). Table 5 shows that the effects of the sentence date remain highly significant. It turns out that all the variables for the Federal Circuits are negative and significant except40 for

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that of the Ninth Circuit. An inmate in the Fourth Circuit faces a shorter time to execution than those in the reference category, but the reduction of expected lifetime is greater in the Eighth and Fifth Circuits. Compared to the Fourth Circuit, the Eighth and Fifth Circuits are significantly worse for inmates both in terms of the probability of execution, and in terms of the time remaining to an inmate who will ultimately be executed. It appears that the articles in newspapers that depict the Fourth Circuit as the worst possible venue for a prisoner under a death sentence are somewhat overwrought—unless, i.e., they mean a prisoner who has already exhausted his State remedies, in which case they may well be correct. Given the evidence (Liebman et al., 2000) that the Fourth Circuit is most severe with respect to treatment of cases in the federal courts, it must be true that the states that comprise the Eighth and Fifth Circuits are on average far more severe in death penalty cases than those in the Fourth Circuit. It turns out that one State has a dominant position in each circuit: Texas has 34% of executions since 1977 in the Fifth Circuit, while Missouri has 62% of those in the Eighth Circuit. Liebman et al. (2000) compiled the overall “error rate” for various States (i.e., the estimated total rate of reversals by State courts, including both direct review and State post-conviction proceedings). They found that the error rates for courts of Texas and Missouri were 35% and 20%, respectively, compared to a national composite rate of 47%.41 There are also substantial disparities in time to execution between States within the same circuit. For example, the Fourth Circuit includes both Virginia, with the shortest time to execution, and North Carolina, for which there is no significant effect.

8. Conclusion We find that over time the probability of execution is increasing, and the time to execution is declining; the evidence of these effects is strong if not overwhelming. It is clear from the coefficients of the terms for waiting time squared and cubed, and the simulations based on them, that neither of these trends can be dismissed as a statistical artifact arising from the transition from the Furman decision to Gregg and subsequent cases that allow States to apply the death penalty. Since the period covered by the data precedes the enactment of the Antiterrorism and Effective Death Penalty Act of 1996, one would expect that time to execution will continue to decline in the near future, unless that statute is amended or held unconstitutional. Parenthetically, it should be noted that it was necessary to use duration analysis with covariates to find the decline in time to execution; an observer who examined only mean and median times to execution each year, set forth in Table 1, would not be able to detect this trend. The data show that the probability of execution is greater for males and inmates with high levels of education. A prior murder conviction may also increase the probability of execution. There is no evidence whatever of any effect of race. While the inmate’s sex is highly significant in the ordered probit, it is not significant in the duration analysis, indicating that it affects the likelihood of execution but not time to execution. There is wide variation across the States that have the death penalty, with respect to both the likelihood of execution and waiting time. The state that is unquestionably the most severe in both dimensions is Virginia.

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As previously noted, we do not have data that distinguishes between State and federal courts in terms of either the time spent in litigation or removals of the death sentence. This shortcoming of the data clearly hinders any effort to compare Federal Circuits. However, the fact that there are such sharp differences among states within the same circuit with respect to both the likelihood of execution and waiting time, strongly suggests that the lower federal courts play a secondary and relatively minor role in death penalty litigation. Notes 1. Statement of Judge Reginald Stanton of New Jersey Superior Court, quoted in Hanley (1999). 2 Id. 2. Id. 3. Governor George Ryan imposed the moratorium on January 31, 2000 by staying all proposed executions indefinitely (Johnson, 2000). A 2-year moratorium on executions was enacted by the unicameral legislature of Nebraska, but was then vetoed by the Governor (Johnson, 1999a, 1999b). 4. See, e.g., Ehrlich (1975), Cover and Thistle (1988), and Chressanthis (1989). 5. See, e.g., Baldus, Woodworth, and Pulaski (1998), Gross and Mauro (1989). 6. Kadane (1983, 1984). 7. Eisenberg, Garvey, and Wells (1996). 8. Our dataset includes supplemental data for 1996–1997 which was not included in their dataset. 9. U.S. Department of Justice, Sourcebook of Criminal Justice Statistics 1973, table 6.145, at 467. 10. Capital Punishment in the United States (1997), at p. xvi. 11. The source of information on those who cease their legal resistance to execution is various issues of Death Row U.S.A., the quarterly death row census published by the NAACP Legal Defense and Educational Fund, Inc. This information is now available online at http://www.deathpenaltyinfo.org/dpicexec 12. Blume and Eisenberg (1999), 480 at no. 53, quoting Gross and Mauro (1989), pp. 13–15 and no. 30. 13. In this dataset, 151 inmates who were under a death sentence died for reasons other than execution: 89 by natural death, 47 by suicide, 10 from murder by another inmate, and 5 from other causes. We classify the case as a removal, here and in the other empirical work described below, if the death sentence was removed for any of the following reasons set forth in the codebook for Variable 31 of ICPSR 2736: “(3) capital sentence declared unconstitutional by State or U.S. Supreme Court; (4) sentence commuted; (5) conviction affirmed, sentence overturned by appellate court; (6) conviction and sentence overturned by appellate court; or (7) other.”

The last category could apply if, e.g., the inmate’s death sentence was removed by executive action of the governor, which was not regarded by the state as a commutation since it was not done by the parole board, or the sentence was removed in State proceedings for post-conviction relief that were not deemed to fall under any of the

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14. 15. 16.

17. 18. 19.

20. 21. 22. 23.

24. 25.

26.

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categories (3) through (6). Telephone conversation with Tracy L. Snell of the Bureau of Justice Statistics, August 3, 1999. Johnson (1999a, 1999b). Lovinger (1999) summarizes the facts concerning nine inmates on death row exonerated by DNA evidence. For example, habeas corpus, coram nobis, extraordinary motion for new trial, and State post-conviction procedures acts. Liebman et al. (2000) at 21. See, e.g., Robbins (1990), at 65: “A justice of the Georgia Supreme Court, the Attorney General of Georgia, and others have attributed the length of post-conviction review in that state to the inadequacy of counsel for indigent defendants at trial.” Liebman et al. (2000), at pp. 71–72. Pub. L. No. 104–132, 110 Stat. 1214 (1996), codified as amended at 28 U.S.C.A. Sec. 2261–2266 (West Suppl. 1997). 28 U.S.C.A. Sec. 2244(d)(1) (West Suppl. 1997). Moreover, a state may reduce the filing period to 180 days if it satisfies certain requirements, including measures to ensure that inmates will be represented by competent counsel in state post-conviction proceedings. 28 U.S.C.A. Sec. 2261(a)–(b). 28 U.S.C.A. Sec. 2266. See generally Ogletree (1998). Lewis (1999). West Virginia is also in the Fourth Circuit, but does not currently have the death penalty. ICPSR 2737, which may be downloaded from the world-wide web at http://www.icpsr. umich.edulcgiiarchive.prl It should, however, be noted that the unit of observation is not the inmate, but rather the death sentence. Thus, if an inmate receives a death sentence which is subsequently vacated by a court, and after retrial receives a second death sentence, there are two observations, one for each sentence. That is, the dependent variable has the lowest value if there has been a “removal” of a death sentence as defined in the preceding discussion and Table 3. For 2633 observations, the dataset provides the inmate’s sentencing date but not his arrest date. For these individuals, we impute the date of arrest to be 1.7049 years prior to the sentencing date. This figure is the median of the distribution of time from arrest to sentence, from all our observations which have both arrest and sentence date. More precisely, the omitted reference category includes all death sentences entered after 1973 in States that had fewer than four executions of those sentences as of December 31, 1997 and in the federal judicial system. These States were Colorado, Connecticut, Kentucky, Idaho, Maryland, Massachusetts, Montana, Nebraska, New Jersey, New Mexico, New York, Ohio, Oregon, Pennsylvania, Rhode Island, South Dakota, Tennessee, Washington and Wyoming. Maryland, Nebraska and Washington have each had three executions since 1977. Although a few inmates receive a death sentence under federal law rather than state law, it is not feasible to use a variable for federal convictions in the empirical work described below. As of December 31, 1997 only 15 out of the 3335 inmates who were under a death sentence had been convicted under federal law, and there has been no federal execution since 1963, when Victor Fegeur was hanged in Iowa after being convicted of kidnapping. Appendix Table 2, Capital Punishment, 1997 at 14.

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27. The order of severity is slightly different in the results of the logit—Virginia, Delaware, Texas, Missouri, California, Utah, Nevada and Louisiana—but Virginia is still the most severe by a substantial margin. Mississippi and North Carolina are again the most lenient States. 28. See Blume and Eisenberg (1999), Table 2 at 486. 29. Lovinger (1999). 30. A study of compensation of counsel appointed in State post-conviction proceedings was prepared in 1988 for the Virginia State Bar and the Virginia legislature (The Spangenberg Group, 1988). The study found that, although Virginia law authorizes the State trial courts to pay “reasonable” fees for attorney services, the lawyers obtained no payment whatsoever in 30 of the 41 cases in the sample. In the 11 cases where some payment was made, the effective hourly rate ranged from a maximum of $27.26 to $.57/h. 31. On December 31, 1997 the death row populations of Ohio, Pennsylvania and Tennessee were 177, 214 and 98, respectively. 32. For two States, North Carolina and Mississippi, while the coefficient estimate becomes positive rather than negative, it is no longer significant (in both the ordered probit and logit). In the case of Oklahoma, a formerly insignificant negative effect becomes positive and significant. 33. Here we are referring to the number of executions in our dataset, which ends with 1997. As of July 26, 2000, Kentucky had had two executions since 1976. 34. Notably Liebman et al. (2000), at pp. 110–111. 35. Here we are referring to the number of executions in our dataset, which ends with 1997. As of July 26, 2000, the actual total number of executions since 1976 was 225 in Texas and 77 in Virginia. 36. In the ordered logit results, however, Mississippi is estimated to be more lenient than North Carolina. 37. The qualification is necessary because (1) the coefficient of variation of the time spent in federal post-conviction proceedings may differ from that of State post-conviction proceedings, and (2) in general there is more than one state in a given Federal Circuit. 38. This figure was generated from the estimates of the log-logistic model of the effect of the sentence date, including its squared and cubed terms. These estimates were used because of the good fit of the log-logistic model, indicated by its log-likelihood. Estimates from the other three duration models yield the same qualitative results. 39. In the Weibull results, e.g., the coefficient estimate for California increases from 0.52 to 0.88. 40. The variable for the Third Circuit is significant at the 9% level. 41. Liebman et al. (2000), at 57.

Acknowledgments I am grateful to Tracy L. Snell of the Bureau of Justice Statistics, and Chris Dunn, the Director of the National Archive of Criminal Justice Data, for helpful advice and

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explanations of the dataset. I also greatly appreciate the suggestions of two anonymous referees. References Baldus, D. C. Woodworth, G. Zuckerman, D. Weiner, N. A. & Broffitt, B. (1998). Racial discrimination and the death penalty in the post-Furman era: An empirical and legal overview, with recent findings from Philadelphia. Cornell Law Review, 83, 1638–1770. Blume, J. & Eisenberg, T. (1999). Judicial politics, death penalty appeals, and case selection: An empirical study. Southern California Law Review, 72, 465–503. Capital Punishment in the United States (1973–1997) [computer file]. U.S. Department of Justice, Bureau of Justice Statistics: ICPSR 2737, Ann Arbor, Michigan, 1998. Chressanthis, G. A. (1989). Capital punishment and the deterrent effect revisited: Recent time-series econometric evidence. Journal of Behavioral Economics, 18(2), 81–97. Cover, J. P. & Thistle, P. D. (1988). Time series, homicide, and the deterrent effect of capital punishment. Southern Economic Journal, 54(3), 615–622. Ehrlich, I. (1975). The deterrent effect of capital punishment. American Economic Review, 65, 397. Eisenberg, T. Garvey, S. P. & Wells, M. T. (1996). Jury responsibility in capital sentencing: An empirical study. Buffalo Law Review, 44, 339–380. Gross, S. R., & Mauro, R. (1989). Death and discrimination: Racial disparities in capital sentencing. Hanley, R. (1999). Judge orders death penalty with a five-year deadline. New York Times, May 8, 1999, p. A17. Johnson, D. (1999a). 12th death row inmate in Illinois is cleared. New York Times, May 19, 1999, p. A14. Johnson, D. (1999b). Legislature of Nebraska votes pause in executions. New York Times, May 21, 1999, p. A14. Johnson, D. (2000). Illinois, citing verdict errors, bars executions. New York Times, February 1, 2000, p. Al. Kadane, J. P. (1983). Juries hearing death penalty cases: Statistical analysis of a legal procedure. JASA, 78, 544. Kadane, J. P. (1984). A note on taking account of the automatic death penalty jurors. Law and Human Behavior, 8, 115. Lewis, N. A. (1999). A court becomes a model of conservative pursuits. New York Times, May 24, 1999, pp. Al and A21. Liebman, J. S., Fagan, J., & West, V. (2000). A broken system: Error rates in capital cases, 1973–1995. Available: www.theiusticeproject.org. Texas Law Review, submitted for publication. Lovinger, C. (1999). Death row’s living alumni. New York Times, August 22, 1999, Sec. 4, pp. 1 and 4. Masters, B. A. (2000). A rush on Va.’s death row. Washington Post, April 28, 2000, p. Al. Ogletree, B. R. (1998). The Antiterrorism and Effective Death Penalty Act of 1996 (Chapter 154): The key to the courthouse door or slaughterhouse justice. Catholic University Law Review, 47, 603. Robbins, IraP. (1990). Background report on death penalty habeas corpus issues, Prepared for the American Bar Association Criminal Justice Section’s Task Force on Death Penalty Habeas Corpus. American University Law Review, 40, 1–193. Snell, T. L. (1998). Capital punishment 1997. Bureau of Justice Statistics Bulletin NCJ 172881. Washington, DC: U.S. Department of Justice. The Spangenberg Group. (1988). Study of representation of capital cases in Virginia, Prepared for the Criminal Law Section of the Virginia State Bar and the Joint Subcommittee Study: Alternative Indigent Defense Systems, Virginia General Assembly. U.S. Department of Justice, Bureau of Justice Statistics (1997). Correctional population in the United States, 1995. NCJ-163916, Washington, DC: U.S. Department of Justice.