What individual and contextual factorsexplain what happens to offenders whohave been sentenced to death? An execution

clearly is the most severe legal punishment,

but no systematic research on the combined

political and individual factors that determine

which death row inmates will be executed

apparently exists. A few studies describe what

happens to these offenders after sentencing

(Aarons 1998; Liebman, Fagan, and West

2000), yet there are almost no systematic

investigations on the most important deter-

minants of executions. Studies by Spurr (2002)

and Blume and Eisenberg (1999) are partial

exceptions, but these studies focus only on

offender characteristics and ignore environ-

mental conditions. Although studies repeatedly

show that victim race is the most important

determinant of death sentences, we do not

know if this factor influences the fate of

offenders on death row because no investiga-

tions have determined whether this account

explains executions.

The factors that influence execution prob-

abilities are of interest partly because there are

such substantial disparities in this outcome.

Largely as a result of the appeals process that

occurs after a death sentence, less than 10

percent of all offenders on death row ulti-

Who Survives on Death Row? An Individual and Contextual Analysis

David Jacobs Zhenchao Qian

The Ohio State University The Ohio State University

Jason T. Carmichael Stephanie L. Kent

McGill University Cleveland State University

What are the relationships between death row offender attributes, social arrangements,

and executions? Partly because public officials control executions, theorists view this

sanction as intrinsically political. Although the literature has focused on offender

attributes that lead to death sentences, the post-sentencing stage is at least as important.

States differ sharply in their willingness to execute and less than 10 percent of those

given a death sentence are executed. To correct the resulting problems with censored

data, this study uses a discrete-time event history analysis to detect the individual and

state-level contextual factors that shape execution probabilities. The findings show that

minority death row inmates convicted of killing whites face higher execution

probabilities than other capital offenders. Theoretically relevant contextual factors with

explanatory power include minority presence in nonlinear form, political ideology, and

votes for Republican presidential candidates. Inasmuch as there is little or no systematic

research on the individual and contextual factors that influence execution probabilities,

these findings fill important gaps in the literature.

AMERICAN SOCIOLOGICAL REVIEW, 2007, VOL. 72 (August:610–632)

#3154-ASR 72:4 filename:72406-jacobs page 610

Direct cor respondence to David Jacobs,

Department of Sociology, 300 Bricker Hall, 190

Nor th Oval Mall, The Ohio State University,

Columbus, OH 43210 ([email protected]). We

thank Douglas Berman for his valuable advice on

criminal procedure law applied to the death penalty

and Ruth Peterson for her comments. We are indebt-

ed to Dan Tope for his research assistance. We also

thank Ohio State colleagues for their comments in

presentations at the law school, the political science

department, the Criminal Justice Research Center,

colleagues at the University of Cincinnati School of

Criminal Justice, the editors, and the referees. All data

used in this study and in the analyses discussed in the

text but not shown are available on request. This

research was supported by NSF grant #0417736.

mately are executed (Liebman et al. 2000).1

There is great variation in the duration of this

process as well, apparently because death penal-

ty states differ sharply in their willingness to

execute. In many reluctant jurisdictions capital

offenders can spend well over two decades on

death row, but other states execute in far less

time. To assess the determinants of these legal

decisions about who will live and who will die,

we test theoretically based explanations using

an event history approach to discover the fac-

tors that influence post-death sentence execution

likelihoods.

Trial court studies on the offender attributes

that lead to death sentences show that offend-

ers who kill whites are far more likely to receive

this sentence (Baldus and Woodworth 2003;

Dodge et al. 1990; Paternoster 1991). Yet

whether victim race continues to explain the

fate of condemned prisoners after they have

been sentenced remains a complete mystery.

There are good reasons to think that this factor

will continue to matter. Yet it is equally plausi-

ble that the appellate court decisions that large-

ly determine death row outcomes are unaffected

by this consideration that, of course, should not

be relevant. In any event, both theoretical con-

siderations and concerns about equity make

this relationship between victim race and exe-

cution probabilities a critical issue.

This article will provide important evidence

about whether the death penalty is adminis-

tered impartially. But our primary goal is to

refine theories of punishment by using a com-

prehensive approach to gauge the explanatory

power of both individual and contextual effects.

It is unlikely that judges and the political offi-

cials who decide which death row inmates will

be executed are unaffected by their political

environment, especially because this punish-

ment is such an intensely moral issue. In part

because some death row offenders face far lower

execution probabilities than those in less lenient

jurisdictions (Liebman et al. 2000), we gauge the

effects of the sociopolitical environment as well

as offender and victim attributes.

There are strong reasons for such a com-

bined approach. Two isolated traditions have

coexisted in the literature. Many studies use

individual data to explain trial court sentencing,

but others rely on aggregate data to study addi-

tional criminal justice outcomes. State or nation-

al attributes have been used to explain shifts in

incarceration rates (Jacobs and Carmichael

2001; Jacobs and Helms 1996; Stucky, Heimer,

and Lang 2005; Sutton 2000; Western 2006).

The urban conditions that explain police depart-

ment size (Jacobs 1979; Kent and Jacobs 2005),

arrest rates (Brown and Warner 1992), or the use

of deadly force by the police (Jacobs and

O’Brien 1998) have been researched as well. Yet

except for a few studies that assess how com-

munity and individual determinants combine

to affect the sentencing of non-capital offend-

ers (Helms and Jacobs 2002; Myers and Talarico

1987), there is little research on the combined

effects of individual and contextual determi-

nants. But the many results based on aggregate

data showing that context is a strong determi-

nant of multiple criminal justice outcomes make

it difficult to believe that such environmental

factors do not influence post-sentencing deci-

sions about executions.

We therefore use an integrated theoretical

approach that emphasizes political explanations

and the racial accounts in earlier conflict stud-

ies (Turk 1969). An execution is an intrinsical-

ly political act. Foucault (1977) views executions

as rituals designed to enhance political power by

reminding potential miscreants of the state’s

vast coercive resources. But there are more con-

crete reasons for studying political effects. Most

researchers who first tested conflict explanations

hypothesized that larger and therefore more

threatening minority populations would increase

support for repressive law and order measures.

Citizens threatened by expansions in a minori-

ty presence often react by demanding harsh

criminal justice policies. Because criminal jus-

tice agencies are operated by the state, this pres-

sure has to be directed at political officials. By

WHO SURVIVES ON DEATH ROW?—–611

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1 Local trial courts sentence, but appeals are han-

dled by higher state and federal courts. The first two

capital appeals typically are decided by state appel-

late courts. Condemned offenders then can petition

the federal courts. About 41 percent of all death sen-

tences are reversed on first state appeal and about 9.5

percent are reversed in the second. About 40 percent

of those who then seek federal relief are successful

(Liebman et al. 2000). Most of the remainder are exe-

cuted. But a few receive executive clemency, some

die before execution, and a few are removed from

death row for miscellaneous reasons. Almost all of

the condemned who obtain appellate relief are resen-

tenced to long prison terms.

assessing the political factors that result from

added demands for this severe punishment, we

seek to broaden the conflict approach to pun-

ishment.

This article therefore offers the promise of

filling many important gaps in the sparse liter-

ature on post-death sentence execution proba-

bilities by using a survival analysis that adjusts

for censoring and assesses both offender and

political characteristics. The multiple advan-

tages that result from the inclusion of both indi-

vidual and contextual factors suggest that this

analysis will provide an accurate picture of the

post-sentencing death penalty process. Results

based on models that assess many explanations

are most accurate (Johnston 1984, see note 11),

but such an inclusive approach means that the

theoretical section cannot focus on only a few

explanations.

THEORY

Inasmuch as scholars claim that race continues

to have powerful effects on U.S. politics

(Goldfield 1997; Jacobs and Tope 2007; Key

1949), and because the administration of the

death penalty is such an intense political issue,

racial politics provide the primary conceptual

basis for this analysis. Most research on the

death sentence focuses on the race of individ-

ual offenders and their victims. We assess such

micro minority accounts by gauging the

explanatory power of various offender-victim

minority-majority combinations, but we fill

contextual gaps in the literature by analyzing the

influence of minority threat in the political envi-

ronments in which these decisions are made.

Ideology also should matter as this penalty is

such an intensely moral issue and since we

study it in the most direct of all large democra-

cies. In contrast to other nations, U.S. politicians

routinely encourage citizens to vote on the basis

of their views about capital punishment. Another

closely related account suggests that the

parochial interests of politicians influence exe-

cutions. Tactical rhetoric that stresses the deprav-

ity of a minority underclass and the need for

harsh measures should affect support for this

penalty (Beckett 1997) and its probability.

We present the theoretical foundation for

four interrelated proposition sets. First, we dis-

cuss the conceptual basis for micro-level racial

hypotheses derived from the literature on sen-

tencing. Second, we present theoretically based

hypotheses about the contextual effects of

minority threat, political ideology, and parti-

san politics. At the end of this section, we pre-

sent justifications for additional explanations for

execution probabilities.

INDIVIDUAL EXPLANATIONS: EXTRA-LEGAL

MICRO RACIAL ACCOUNTS AND LEGAL

FACTORS

If penal measures are best explained by their

interrelationships with other social arrange-

ments rather than by their alleged legal pur-

poses (Garland 1990:91), it would be surprising

if the most important U.S. social division did not

influence the administration of the most severe

legal punishment. A conceptual grounding in

racial politics therefore should provide the best

theoretical foundation for a study that analyzes

the administration of the death penalty. In the-

oretical essays Wacquant (2000, 2001) uses

arguments about social impurity and taboo to

show how the Jim Crow racial caste system

persists in socially altered but less conspicuous

forms in the contemporary U.S. criminal justice

system. To discover if such racial considera-

tions still affect decisions about legal punish-

ments, the question that motivates almost all of

the many sentencing studies is whether the trial

courts treat minorities the same as nonminori-

ties (Chiricos and Crawford 1995; Walker,

Spohn, and DeLone 1996; Zatz 1987).

Findings about non-capital sentences, and

those from the less ample literature on the fac-

tors that account for death sentences, should

provide the best available empirical basis for

selecting the individual factors that explain

which condemned prisoners will be executed.

Despite their number, only a bare majority of the

non-capital sentencing studies show that the

trial courts give harsher sentences to minorities,

but almost as many careful investigations do not

find such biases (Chiricos and Crawford 1995;

Walker et al. 1996). Studies on the links between

offender race and death sentences are even less

likely to suggest that white offenders are treat-

ed with greater leniency than minorities (Baldus

and Woodworth 2003; Dodge et al. 1990;

Paternoster 1991). In light of the systematic

discrimination faced by African Americans,

however, we follow precedent and hypothesize

that: The likelihood of executions should be

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greater for African Americans on death row. In

some jurisdictions Hispanics are the most threat-

ening minority that faces severe discrimina-

tion. It is equally plausible that: Hispanics on

death row should face higher execution proba-

bilities than whites.

Offenders convicted of killing whites are par-

ticularly likely to be sentenced to death (Baldus

and Woodworth 2003; Dodge et al. 1990;

Paternoster 1991), especially if the offender is

black (Baldus and Woodworth 2003). Wacquant

(2000) provides one theoretical foundation for

this persistent association when he claims that

harsh legal punishments continue to be used to

maintain the “symbolic distance needed to pre-

vent the odium of ‘amalgamation’ with [minori-

ties] considered inferior, rootless, and vile” (p.

380). The ultimate symbolic assault on such a

caste system occurs when an underclass minor-

ity kills a white. In this research we discover if

this account that explains death sentences so

well also explains execution probabilities.

Two causal paths seem most plausible. In

contrast to the great majority of homicides,

media outlets focus on interracial murders, par-

ticularly if the victim is white (Bandes 2004,

Lipschultz and Hilt 2002). This increased cov-

erage is critical as victories in well-publicized

capital trials often help politically ambitious

prosecutors reach higher office. But such tri-

umphs must be protected. Prosecutors there-

fore have strong reasons to vigorously oppose

capital appeals that may jeopardize legal victo-

ries that are likely to further their political

careers.2 Even if other facilitative conditions

are absent, the media’s focus on those murders

in which a white was killed by a minority puts

added pressure on state appellate court justices

to rule against these death row offenders when

they appeal (Liebman, Fagan, and West 2002).

And disregarding these pressures can be cost-

ly. State judges who ignored intense public sup-

port for particular executions and granted

appellate relief to such petitioners have lost

their seats in retention elections, even though

only the incumbent appears on retention elec-

tion ballots (Brace and Hall 1997; Bright and

Keenan 1995). Hence: African American or

Hispanic offenders convicted of murdering a

white should be less likely than other offenders

to avoid the death chamber.

Most studies of the determinants of non-cap-

ital sentencing that assess the effects of gender

find that in comparison to females, trial courts

give less lenient sentences to males (Bickle and

Peterson 1991). This finding may be partially

based on a failure to control for the ways offend-

ers participated in their crimes. Women con-

victed of robbery, for instance, often do not

engage in the violence associated with this

felony. Inasmuch as their involvement is less

pernicious, such offenders receive lighter sen-

tences than their male associates. It nevertheless

is reasonable to expect that chivalrous inclina-

tions should reduce female execution probabil-

ities, so: Women on death row should be less

likely to be executed than males. Finally, find-

ings show that offenders with prior convictions

are sentenced more severely by trial courts. This

practice is reasonable as repeat offenders are

likely to pose a greater threat to the communi-

ty after their release or to guards and other

inmates while they are imprisoned. Hence:

Execution probabilities will be greater for those

condemned prisoners with previous convictions.

CONTEXTUAL EXPLANATIONS: RACIAL

THREAT, POLITICAL IDEOLOGY, AND

PARTISANSHIP

The multiple decisions that ultimately produce

an execution do not occur in a social vacuum.

A few studies of sentencing decisions for non-

capital crimes productively treat the trial courts

as complex organizations. Yet analyses of con-

textual forces external to organizations have

sharply increased our understanding of organi-

zational behavior (Perrow 1986; Scott 1987).

The multiple findings based on aggregate data

that provide such robust explanations for crim-

inal justice outcomes and the strong organiza-

tion-environment relationships so often

uncovered in the organizational literature point

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2 After a death sentence, local prosecutors can

have important effects on the subsequent appeals. For

example, their delays in filing for an execution date

slow appeals. In fact, any failure to act promptly

favors condemned offenders as delayed appeals are

not as likely to be vigorously contested. For this and

other reasons, the local prosecutor who won an ini-

tial death sentence verdict often plays an important

role in resisting subsequent appeals, although this role

may be informal. Hence, prosecutor commitment to

efforts to resist death row appeals should influence

execution probabilities.

in the same direction. Both research streams

suggest that legal decision makers who are

embedded in political environments do not

ignore such conditions when they decide if an

execution will occur.

RACIAL MIX. The fierce U.S. disputes about

race in the past probably make this fissure the

most resilient and influential division in con-

temporary U.S. politics (Goldfield 1997; Jacobs

and Tope 2007; Key 1949). A majority’s eth-

nocentric views and that group’s inclination to

view minorities as trespassers enhance such a

group’s presumption that they should retain

exclusive claims over important rights and priv-

ileges (Blalock 1967; Blumer 1958; Bobo and

Hutchings 1996). Hostility and entrenched

beliefs about a majority’s “rightful” position

are solidified by the political struggles that

occur when minority groups seek to alter these

arrangements (Blumer 1958). According to

threat theorists, when large minority popula-

tions endanger their dominance, whites often

react by supporting law and order measures that

at least indirectly target these minorities.

Findings are supportive. Racist views are

more widespread in cities with more black res-

idents (Fosset and Kiecolt 1989; Quillian 1996;

Taylor 1998). An enhanced minority presence

produces added votes for anti-minority candi-

dates (Giles and Buckner 1993; Giles and Hertz

1994; Heer 1959) who are likely to endorse

harsh criminal punishments. With crime rates

held constant, Liska, Lawrence, and Sanchirico

(1982) and Quillian and Pager (2001) find that

fear of crime is greater in cities or neighbor-

hoods with more black residents. Larger minor-

ity populations lead to additional police officers

(Jacobs 1979; Kent and Jacobs 2005). Other

findings show that the death penalty is likely to

be legal in states with the highest percentages

of African American residents (Jacobs and

Carmichael 2002), while the number of death

sentences is greater in states with the largest

African American populations (Jacobs,

Carmichael, and Kent 2005).

These results suggest that severe punishments

will increase in areas after a growth in minori-

ty presence. Yet if minority proportions expand

and their political influence becomes sufficient,

the positive relationship between this threat and

punitiveness may reverse. All governors, almost

all local prosecutors, and most state appellate

justices are elected. Expansions in African

American or Hispanic proportions past a thresh-

old should give these minorities enough votes

to influence decisions about executions. Hence:

The relationship between the percentage of

blacks and execution probabilities should be

positive if minority presence is modest, but after

this percentage reaches a threshold, this asso-

ciation should become negative and executions

should diminish. For this sign reversal to occur,

a minority need not outnumber other groups.

Minority size need only reach the point where

their votes may help decide elections, and this

proportion can be modest if other voting blocs

are evenly matched. This logic and nonlinear

findings about death sentence frequency (Jacobs

et al. 2005) suggest that an inverted U-shaped

relationship between African American pres-

ence and executions will be present.

The relationship between Hispanic presence

and executions may take a different nonlinear

form. In many non-southwestern states, the pro-

portion of Hispanic residents is minute. The

median percentage of Hispanics was 2.4 percent

in the sampled states, yet the same statistic for

blacks was 6.8 percent or 2.9 times greater. As

the Hispanic population was so modest in so

many states, it is plausible that this population

must reach a threshold size before whites see

Hispanics as sufficiently threatening. Hence:

The nonlinear relationship between the per-

centage of Hispanic residents and execution

probabilities should become increasingly pos-

itive only after the comparative size of this eth-

nic minority reaches a level sufficient to threaten

majority Anglos.

POLITICAL IDEOLOGY. Claims that ideology

helps shape legal penalties are especially com-

pelling when U.S. sanctions are at issue, as this

nation is such an exceptionally direct democracy

(Savelsberg 1994; Whitman 2003). The result-

ing close voter control over criminal punish-

ments, which are decided by bureaucratic

experts in the less direct European democracies,

ought to make mass ideologies crucial when

such intensely moral decisions about who will

die must be made. Images of evil and the result-

ing beliefs about the most appropriate punish-

ments that stem from these assumptions about

human nature are a foundational component of

political ideologies. Conservatives often see

crime as resulting from freely made but amoral

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choices (Lacey 1988). If such presumptions are

correct, increases in expected costs should be

effective. Many conservatives therefore stress

the irreversibility and the deterrent effects of

executions. Such views about the efficacy of

incapacitation and deterrence provide the basis

for empirically dubious conservative claims

that a few executions will protect many innocent

victims from criminal brutality.3

Liberals instead see criminal acts as imposed

by circumstances (Garland 2001; Thorne 1990).

These acts result from noxious environmental

conditions such as poverty or discrimination.

Liberals view policies that alleviate these inju-

rious conditions (Taylor, Walton, and Young

1973) and therapeutic efforts to resocialize

offenders (Garland 2001) as the best remedies.

In contrast to conservatives, liberal survey

respondents are far less likely to support the

death penalty (Lakoff 1996; Langworthy and

Whitehead 1986). Findings corroborate these

claims as they show that the most liberal states

are unlikely to legalize this punishment (Jacobs

and Car michael 2002). It follows that:

Executions should not be as probable where

liberal views predominate, as prosecutors and

appellate justices should be less likely to

endorse this penalty in these jurisdictions.

POLITICAL PARTISANSHIP. Because they seek

outcomes that help the prosperous, political

parties closer to the right face election obstacles.

These parties, for example, usually choose tax

policies that benefit their affluent core sup-

porters at the expense of the less affluent (Allen

and Campbell 1994). Yet prosperous voters are

in the minority. Because such parties have a

smaller voter base than their rivals, conservatives

often use law and order claims to appeal to less

affluent voters who are more likely to be crime

victims and who often live where such risks

are greater. Officials in the Nixon and first

Bush campaigns for the presidency admit that

they emphasized this issue to attract anti-minor-

ity voters.4 By focusing on street crime and

other social problems readily blamed on under-

class minorities, Republicans won elections by

using this “wedge” issue to gain sufficient votes

from less prosperous citizens. Multiple findings

show that Republican (Jacobs and Carmichael

2001; Stucky et al. 2005; Western 2006) or con-

servative political strength (Sutton 2000) led

to severe criminal justice outcomes. Because

capital punishment has been an important issue

in many state political campaigns (Constanzo

1997) and because Jacobs and Carmichael

(2002) find that this punishment is likely to be

legal in states with the strongest Republican

parties, we expect that greater Republican polit-

ical strength in a state should increase execution

probabilities.

Republican campaigns for the presidency

have relied on and probably accentuated

(Beckett 1997) mass perceptions about the links

between purportedly venal underclass life styles

and lawlessness. Findings show that votes for the

f irst of these presidents who successfully

exploited public views about the linkages

between race and crime (Nixon in 1968) help

explain how quickly states relegalized capital

punishment after the 1976 court decisions that

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3 Careful reviews of the multiple empirical stud-

ies on this issue conducted by legal scholars (Zimring

and Hawkins 1986), criminologists (Hood 1998;

Paternoster 1991), sociologists (Bailey and Peterson

1999), and economists (Donohue and Wolfers 2005;

Levitt 2002) conclude that the death penalty has no

discernable general deterrent effects beyond those

imposed by long prison terms. This list of skeptics

about the deterrent effects of executions includes a

scholar (Levitt 2002) who has published multiple

findings showing that imprisonment and other poli-

cies designed to control crime are effective deterrents.

4 A participant described Nixon’s 1968 campaign:

“We’ll go after the racists. That subliminal appeal to

the anti-black voter was always present in Nixon’s

statements and speeches” (Ehrlichman 1982:233).

Other vivid examples occurred in the 1988 Bush

campaign against Dukakis. Republicans ran an adver-

tisement declaring, “‘Dukakis not only opposed the

death penalty, he allowed first-degree murderers to

have weekend passes from prison.’.|.|. [as the] clear-

ly black [offender]—Willie Horton stared dully into

the camera.” They next released an advertisement fea-

turing a victim. “‘Mike Dukakis and Willie Horton

changed our lives forever .|.|. Horton broke into our

home. For twelve hours, I was beaten, slashed, and

terrorized. My wife Angie was brutally raped’”

(Carter 1996:76–77). This emphasis did not abate. In

a House debate in 1994 “29 Republican[s] .|.|. spoke

derisively about midnight basketball .|.|. character-

izing the program as ‘hugs for thugs’” (Hurwitz and

Peffley 2005:99–100).

forced changes in these statutes (Jacobs and

Carmichael 2002). Incarceration rates also were

higher after more voters supported a Republican

law and order presidential candidate (Weidner

and Frase 2003). Hence: Death row inmates

should be less likely to avoid the death cham-

ber in states in which more voters support

Republican presidential candidates because

prosecutors, appellate justices, and governors

will face stronger pressures to allow executions

in these jurisdictions.

ADDITIONAL CONTROLS

Murder is the most threatening crime. Execution

probabilities therefore should be greater in states

with higher murder rates. Following Durkheim’s

emphasis on the determinants of restitutive law,

states with a larger and more diverse population

and an enhanced division of labor should not be

as likely to use this most severe punishment.

This perspective also suggests that solidarity

should matter. Migration interferes with social

cohesion. Outsiders inspire hostility and fear, but

their absence strengthens bonds and intragroup

empathetic feelings (Hale 1996). Citizens in

states with few outsiders therefore should not be

as willing to support executions. This factor

accounts for the legality of the death penalty

(Jacobs and Carmichael 2002), so executions

should be less likely when most residents were

born in the states where they now live.

Research on imprisonment has focused on the

Marxist view that punishment is used to control

the supply of labor (Rusche and Kirchheimer

1939). Many researchers have assessed the link

between unemployment and incarceration, but the

results are mixed. A review (Chiricos and Delone

1992) shows that about 60 percent of the 147

associations between unemployment and impris-

onments are significant. Despite such mixed

results, we expect that high joblessness will

enhance execution probabilities because the pros-

perous may view the unemployed as a threat or

because high unemployment enhances resent-

ments against criminals and accentuates demands

for harsh sanctions. Yet the unemployment rate

may have to reach a threshold before it matters,

so we test a nonlinear relationship between this

variable and execution probabilities. Primarily as

a result of the multiple state and federal appeals,

expensive expert testimony, and the other costs

required for due process in decisions that may end

a life, executions are far more expensive than

alternatives such as life imprisonment. States

with a superior tax base should be more likely to

use this expensive punishment. Finally, to see if

the unique arrangements in the South influence

death row outcomes, we also control for this

region.

METHODS

ESTIMATION

Our aim is to discover how offender attributes

and the political and social characteristics of the

states affect post-sentencing execution likeli-

hoods. To test these hypotheses, we use an event

history approach. This procedure provides a

remedy for the censoring that occurs because

offenders face different execution risks. Some

offenders remained on death row after 2001, so

this right-censored group was in danger of being

executed after the end of the observation peri-

od. The other right-censoring occurs when

offenders were removed from death row large-

ly as a result of successful appeals. Event his-

tory analysis uses the information from cases

with incomplete duration who were not exe-

cuted to avoid the bias that would occur if these

censored cases were not included.5 We employ

discrete-time logit models to predict execution

probabilities. Our first model will assess the

influence of individual factors. This model takes

the form:

Pijtlog ( 1 – Pijt) = atd +

M

Sm=1

bmXmi (1)

where Pijt is the conditional probability of exe-

cution for death row offender i in state j at time

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5 Because event history analysis takes advantage

of all available information, we include death row

offenders near the end of the analysis period although

such offenders are not as likely to be executed.

Offenders still on death row are right-censored regard-

less of how long they have been on death row, but our

estimation approach will not be biased by this or

other forms of censoring. Note that selection biases

should not be problematic because we only explain

what happens to offenders sentenced to death. We

make no claims about whether those who received a

less severe punishment than death would have dif-

ferent likelihoods of being executed if they had been

sentenced to death.

t given that the execution has not already

occurred to that individual prior to time t. t is

years from death sentence. The log odds of exe-

cution at time t is a function of a set of time-con-

stant covariates as well as a set of temporal

dummies (td) to account for time dependence

(atd).6 The individual level covariates entered in

this model include offender race, ethnicity, gen-

der, prior convictions, and victim race. The

models also include interactions between

offender and victim race. We assess the explana-

tory power of state-level contextual effects by

using variables such as the percentage of a

state’s vote for Republican candidates in time-

varying form. The subsequent and more exhaus-

tive models therefore take the following form:

Pijtlog ( 1 – Pijt) =

atd +

M

Sm=1

bmXmi +

N

Sn=1

bnXnj(t–1) (2)

where the conditional probability of execution

at time t is a function of a set of individual con-

demned prisoner factors as well as state char-

acteristics at time t-1. Death row offenders may

be treated similarly within a state with the same

death penalty provisions and the same officials

deciding appeals. To adjust for this possible

departure from statistical independence, we

report z-values corrected for within-state cor-

related errors with a cluster procedure (Rogers

1993).

SAMPLE

Most offender information was taken from

(ICPSR study 3958) “Capital Punishment in

the United States” (CPUS) from 1973 to 2002

(this source is limited to those years). This

source gives state, date of removal from death

row, and reasons for removal, but it does not

contain data on victim race. For all offenders

who were executed, race of the victim, offend-

er race, state, and execution date are available

from the Death Penalty Information Center.7

To obtain victim race we merged this data into

the CPUS file by matching execution date and

race of executed offenders.8

To obtain race of victim for former death

row offenders removed from death row before

execution or for offenders still on death row, we

had to use another source. The Supplemental

Homicide Reports (SHR) contain information

on offense date, victim race, offender race, and

age. We obtain victim race by matching SHR

data with offender data from state correction

department records by f irst using date of

offense, and then offender race and age, but

only 16 states could or would provide offense

date. We merge the offender data with the CPUS

data using date of sentence, offender race, and

age. This matching process yields victim race

for 1,012 current and former death row offend-

ers who were not executed in 16 states. To cap-

ture the effects of this explanatory variable, the

analysis must be restricted to 16 states, but these

states are selected by a presumably random

process based on whether a state provides date

of offense—an administrative decision inde-

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6 Because execution risk is small in the first 12

years after a death sentence, we combine these years

into one period and use it as the reference group. We

then create three additional dummy variables: the

first is coded 1 for periods since death sentence

between 13 and 20 years, the second between 21 and

23 years, and the third from year 24 on death row and

later. This categorization captures the distribution of

execution likelihoods as this distribution has a long

flat tail to the left resulting from low initial risks and

an inverted-U shaped distribuion thereafter. To dis-

cover if this categorization is unrepresentative, we cre-

ated alternative codes for death row years beyond 12.

The significance test results and the theoretical impli-

cations persist when we reestimate the models with

these alternatives.

7 This Web site is www.deathpenaltyinfo.org.

Although the Supreme Court’s 1972 Furman decision

made executions unconstitutional, Furman left open

the possibility of a constitutional death penalty. Many

death penalty states therefore maintained their death

row populations and altered their statutes. In 1976 in

Gregg and other cases the Court declared some mod-

ified death penalty statutes constitutional (Paternoster

1991; Zimring and Hawkins 1986). The data for a

plausible study of death row outcomes before 1973

apparently do not exist.8 Since 1973, 768 of the 820 offenders executed

were matched. Data inconsistencies in these two

sources explain the modest unmatched remainder.

States analyzed are: Arizona, California, Delaware,

Florida, Georgia, Illinois, Kansas, Kentucky,

Maryland, Missouri, New Jersey, Ohio, Tennessee,

Texas, Virginia, and Washington.

Z

A single

line was

permitted

here to

avoid

excessive

vertical

justifica-

tion; other-

wise,

everything

from the A-

head on

would have

been

carried

over.

pendent of the state or offender characteristics

at issue.9

We therefore have information on victim race

for all executed offenders and for about 28 per-

cent of current or former death row offenders

in these 16 states who were not executed.

Although the execution rate from our sample is

much higher than the equivalent rate in the

CPUS data, such disparate rates should not bias

our multivariate results. Our goal is to discov-

er which explanatory variables affect execution

rates. We are interested in how relative execu-

tion risks shift based on, for example, the mur-

der of a white instead of a nonwhite. Because

we include all executed offenders in these states,

but only a fraction of death row offenders who

were not executed, our sample design is equiv-

alent to a response-based sample design. Such

a design uses all events that occur in a specified

time period, but samples individuals who are at

risk of experiencing the event but did not

(Prentice and Pyke 1979). To analyze a

response-based sample, Xie and Manski (1989)

propose a weighted maximum likelihood esti-

mator for a logit analysis. Xie and Manski

demonstrate that estimates from a response-

based sample behave asymptotically with no

bias and with little loss in efficiency. We apply

this weighted maximum likelihood estimator

in our logit model. Following Xie and Manski

(1989), we define a weight variable that equals

qpwt =

fp(3)

where if p = 1, qp is the proportion executed

from the CPUS sample and fp is the propor-

tion executed from our sample; if p = 0, qp is

the proportion not executed in the CPUS sam-

ple and fp is the proportion not executed in our

sample.10

The selection procedures we use are like-

ly to produce a random sample because our

selection method depends on between data

source matches. This assumption that our esti-

mates of explanatory effects will be unbiased

is corroborated by the results in Table 1 show-

ing between sample contrasts. Most between

sample offender attributes are extremely sim-

ilar. Although Hispanics are underrepresent-

ed and whites are overrepresented in our

sample, this bias is eliminated by including

offender race and ethnicity in all analyses.

Table 1 shows a total of 4,145 (3,597 + 548)

death row offenders between 1973 and 2002

in 16 states from the CPUS data, and 13.2 per-

cent were executed (548/4,145). In compari-

son to their proportion in the population,

disproportionately more African Americans

and Hispanics were on death row. Of those

executed, slightly more were white. An over-

whelming majority of death row offenders

were male (98 percent); and even higher pro-

portions of males were executed (99.3 per-

cent). Absent controls, death row offenders

with prior conviction records faced a higher

chance of execution than those with no prior

conviction. Among current or former death

row offenders, two-thirds killed a white, but

among the executed offenders, four-f ifths

killed a white. The multivariate analyses will

show if victim race interacts with race of

offender to alter execution likelihoods with

other relevant factors held constant.

Finally, because the proportion of Asian

and Native American death row offenders is

too modest to produce statistically signif i-

cant results, these offenders are excluded

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9 Included states exhibit considerable variation in

execution probabilities. Most but not all of the miss-

ing death penalty states are small with few at risk of

execution. Two exceptions are North Carolina and

Pennsylvania, but the corrections departments in

these and the other missing states with death row pop-

ulations would or could not provide offense dates, so

the effects of victim race are not analyzed in these

states. The funds from a generous grant were exhaust-

ed by the matching process we had to use to obtain

data on victim race. We therefore cannot employ

costly supplemental data sources such as newspaper

accounts, local prosecutor records (if any exist), or

make detailed appraisal of this process in particular

death penalty states.

10 Weighted maximum likelihood estimates of a

logit model make response-based samples behave

asymptotically as if they were random (Manski and

Lerman 1977). This method does not require that

researchers select the best estimator. Even if the best

estimator is probit rather than logit, Xie and Manski

(1989) use Monte Carlo simulations to show that

the weighted maximum likelihood logit estimator

gives good results as long as response-based samples

are larger than 1,000 as ours is.

from our analyses. We also exclude offenders

who died on death row for reasons other than

an execution, but these decisions have no dis-

cernable effects on the results.11 We therefore

analyze outcomes experienced by 1,560 post-

Furman death row offenders in 16 states from

1973 to 2002. The corrections we use—that

are grounded in the econometric literature on

how such samples can be analyzed to pro-

vide accurate f indings—should give unbi-

a s e d a n d c o n s i s t e n t e s t i m a t e s o f t h e

determinants of executions. This is so even

though our sample includes all offenders who

were executed, but only about 28 percent of

those who were not executed in these 16

states.

MODEL SPECIFICATION AND EXPLANATORY

VARIABLE MEASUREMENT

One general specification of the discrete time

logit model that predicts the log of execution

odds for death row offender i in state j at time

t is:

Log EXECUTION ODDSij(t) =

(td DURATION DUMMIES) +

b1BLACK ij + b2HISPANICij +

b3VWHITEij + b4(BLACK ij 3

VWHITE ij) + b5(HISPANICij 3

VWHITE ij) + b6MALEij +

b7PRIORij + b8%BLKj(t–1) +

b8%BLK2j(t–1) + (4)

b10%HISP3j(t–1) + b11POPj(t–1) +

b12MURDRTj(t–1) + b13BORNINSTj(t–1) +

b14IDEOLOGYj(t–1) + b15%PPRSVOTEj(t–1) +

b16 RLMEDINCj(t–1) + b17 %UNEMPj(t–1) +

b18 %UNEMP2j(t–1) + b19YEAR92PLUSj +

b20STHj

where all explanatory variables are def ined

below.12

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Table 1. Percentage Distributions of Offender Characteristics in 16 States by Samplea

Sample in Which RaceTotal CPUS Sample of Victim is Available

Not Executed Executed Not Executed Executed

3,597 548 1,012 548

Race (Percent)

—White 48.1 55.5 53.6 55.5

—Black 39.8 34.9 40.1 34.9

—Hispanic 12.1 09.7 06.2 09.7

Sex

—Percent Maleb 98.3 99.3 98.0 99.3

Marital Status at Offense

—Percent Marriedb 24.0 30.8 21.2 30.8

College Education at Offense

—Percent Yesb 07.6 07.7 10.0 07.7

Prior Convictions

—Percent Yesb 59.4 68.2 59.5 68.2

Race of the Victim (Percent)

—White 66.5 80.3

—Nonwhite 33.5 19.7

a Marital status and college education at offense cannot be used in the logit regressions because too many missing

values are present; we provide their distributions to better compare samples that do not include and include race

of victim.b These percentages plus the omitted category for each variable sum to 100 percent.

11 We eliminated 179 offenders from the sample

because they died on death row before execution; 118

Native Americans and 34 Asians or Pacific Islanders

also were eliminated leaving a total of 1,560 offend-

ers. In analyses (not shown) we find that these exclu-

sions do not affect the reported results. Deficiencies

in the data made efforts to code race of victim in the

few instances when there were multiple victims too

speculative, so most of these cases were omitted.

12 We use exhaustive models. According to

Johnston (1984), “It is more serious to omit relevant

INDIVIDUAL EFFECTS. We create two dummy

variables for minority offenders: the first equals

1 if an offender is African American (BLACK),

the second equals 1 if an offender is Hispanic

(HISPANIC), and both dummies are set equal

to 0 if the condition in question is not true.

Another dummy variable is coded 1 only if the

victim is white (VWHITE). We next create two

interaction ter ms: the f irst (BLACK 3

VWHITE) is equal to the dummy variable coded

1 for an African American offender times the

dummy variable coded 1 for a white victim; the

second (HISPANIC 3 VWHITE) is equal to the

dummy variable coded 1 for an Hispanic offend-

er times the dummy variable coded 1 for a white

victim. Additional individual effects are held

constant with a dummy variable coded 1 if a

death row inmate is male (MALE) and anoth-

er coded 1 if a death row inmate has prior con-

victions (PRIOR).13

CONTEXTUAL EFFECTS. We measure racial

threat effects with the percentage of African

Americans (%BLK) and the percentage of

Hispanic residents (%HISP) in each state, but

in contrast to all other contextual variables, the

percentage of Hispanics in the states is unavail-

able in the non-census years before 1990. We

therefore use decennial census figures in the five

years after a census and figures from the next

census in the next five years before 1990; after

1990 this variable is time varying by year (the

results persist if we use yearly linear interpola-

tions to estimate these missing values).14 To

capture nonlinear quadratic relationships, both

minority threat variables are entered in untrans-

formed and squared form. All state-level vari-

ables, save the percentage of Republican votes

for president and Hispanics (which time vary by

four or five years), are time-varying by year.

Following convention in studies of public pol-

icy, the yearly time-varying explanatory vari-

ables are lagged by a year because their effects

should not be instantaneous.

Theory, however, suggests that Hispanic pres-

ence should have a nonlinear relationship with

execution probabilities that becomes increas-

ingly stronger as this indicator reaches extreme

values, yet there is no reason to think this asso-

ciation should shift direction. Higher percent-

ages of Hispanics, for example, may well

produce increasingly greater execution proba-

bilities, but it is difficult to see how growth

from a low to a somewhat higher percentage of

Hispanics could lead to reductions in execu-

tion probabilities. When a priori considerations

suggest that such nonlinear relationships that

should not change direction are present, power

transformations are most appropriate (Cohen et

al. 2003:225–54). Threat theory thus suggests

that a single exponential transformation will be

best. To avoid iterative searches for the best

exponent and overfitting, we cube the percent-

age of Hispanics. This power transformation is

somewhat unconventional in sociology (but not

in psychology). To provide comparisons and

reassure the reader, we present otherwise iden-

tical models with and without this transforma-

tion.

We assess the influence of the division of

labor with population (POP). Following Jacobs

and Carmichael (2002), who find that this meas-

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variables than to include irrelevant variables since in

the former case the coefficients will be biased, the dis-

turbance variance overestimated, and conventional

inference procedures rendered invalid, while in the

latter case the coefficients will be unbiased, the dis-

turbance variance properly estimated, and the infer-

ence procedures properly estimated. This constitutes

a fairly strong case for including rather than exclud-

ing relevant variables in equations. There is, howev-

er, a qualification. Adding extra variables, be they

relevant or irrelevant, will lower the precision of esti-

mation of the relevant coefficients” (p. 262). Hence

our comprehensive models should produce more

accurate point estimates but the significance tests will

be relatively conservative.13 Although we cannot introduce a control for

offense severity because information is not avail-

able, a legal requirement probably makes such a con-

trol unnecessary. In the Gregg case that relegalized

capital punishment, the Court held that jurors can sen-

tence offenders to death only if they deem the crime

to be sufficiently horrific. Aggravating criteria stip-

ulated by legislatures that jurors must consider when

making this judgment include the killing of multiple

victims, murdering a child, or homicides that involve

deliberate torture (Paternoster 1991). Such prereq-

uisites probably create a more precise control for

offense severity than the alternatives used in studies

that assess sentencing for non-capital crimes.

14 The great majority of the death row outcomes

we study occurred after 1990 when the percentage of

Hispanics in the states is calculated with yearly data.

ure of outsider presence explains whether a

state has a legal death penalty, outsiders are

measured with a dummy coded 1 if over 75

percent of a state’s population was born in-state

(BORNINST). We assess partisanship with the

percentage of a state’s vote for the Republican

candidate in the last presidential election

(PPRSVOTE). The threat from higher murder

rates is gauged by murder rates provided by the

Uniform Crime Reports.

Berry and colleagues (1998) view mass ide-

ologies (IDEOLOGY) as the mean on a liber-

al-conservative continuum. They identify the

ideological position of each member of

Cong ress using interest-g roup ratings

(Americans for Democratic Action, Committee

on Political Education) of a representative’s vot-

ing record and then estimate mass ideology in

each congressional district with this score for the

district’s incumbent and with an estimated score

for the incumbent’s challenger in the last elec-

tion. Incumbent ideology scores are combined

with estimated challenger ideology scores

weighted by district election results to capture

district ideologies. Berry and colleagues cal-

culate state-level scores on liberalism-conser-

vatism with the mean of these within-state

congressional district scores. We analyze the

most recent version of this index, which gauges

congressional votes until 2003 and adds a few

corrections to the values first published in 1998.

The most liberal states receive the highest

scores, so the coefficients on this variable should

be negative.15

We measure the tax base and a state’s abili-

ty to support this costly punishment with real

median household income (RLMEDINC). We

capture joblessness with the percentage of

unemployed (%UNEMP). Because this factor

may have a nonlinear relationship, we include

its square. We also include a dummy variable

coded 1 (YEAR92PLUS) for years after 1992

when execution frequencies increased and a

dummy variable coded 1 for southern states

(STH). We use one-tailed tests because theo-

retically based predictions about sign have been

stipulated.16

ANALYSES

DESCRIPTIVE STATISTICS AND

MULTIVARIATE MODELS OF EXECUTION

PROBABILITIES

INITIAL ANALYSES. Figure 1 shows the distribu-

tion of death row outcomes during the years in

question and Table 2 shows the predicted signs,

means, and standard deviations. In Table 3 we

begin the multivariate analyses by restricting the

first model to individual explanatory factors to

show contrasts with subsequent models that

include contextual effects. Recall that two

dummy variables are set equal to 1 if offenders

are African American or Hispanic. A third is set

equal to 1 if the victim is white, and this victim

variable is interacted with the two minority

offender variables to create two interaction

terms. The coefficients on these two product

terms capture what happens to either African

American or Hispanic offenders who killed

whites (all necessary main effects are included).

A sixth dummy variable is set equal to 1 if a con-

demned offender is male, while the last is equal

to 1 if an offender has prior convictions. In

Model 2 we begin to add contextual variables

by entering the percentage of African American

and Hispanic residents in quadratic form. To

assess division of labor effects, population is

included as well. We add the percentage born in-

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15 This index has face validity. From 1972 to 2002

Rhode Island and Massachusetts tied for second as

the most liberal states. But the sole Vermont repre-

sentative—who probably was the only member of

Congress who claimed to be a socialist in this peri-

od—earned the highest liberalism score. Mississippi

in 1972 had the most conservative score followed by

Virginia in 1974. Specialists have accepted the use

of roll-call, vote-based indexes to identify ideology

(Fowler 1982; Poole and Rosenthal 2000). Rankings

by Americans for Democratic Action (ADA) and the

AFL-CIO’s Committee on Political Education

(COPE) are the most widely used and have withstood

considerable scrutiny (Herrera, Epperlein, and Smith

1995; Shaffer 1989).

16 Despite repeated attempts we have not located

information on the partisanship of state appellate

court justices. In almost all states, one appellate court

handles each level of state death row appeals, so

variation in offender outcomes probably cannot be

attributed to within state appellate court differences.

Data on factors such as offender demeanor, their

behavior in prison, offense characteristics other than

those measured, and additional information on

offender appeals unfortunately are not available.

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Figure 1. Three Death Row Outcomes in 16 States by Years After Death Sentence

Table 2. Predicted Signs, Means, and Standard Deviations

Variable Predicted Sign Mean SD

1 if Executed .010 .099

1 if Black Offender + .390 .488

1 if Hispanic Offender + .068 .251

1 if White Victim + .710 .454

1 if Black 3 1 if White Victim + .161 .367

1 if Hispanic 3 1 if White Victim + .036 .1871 if Male + .986 .116

1 if Prior Conviction + .650 .477

Percent Black + 12.772 5.331

Percent Black2 – 191.548 171.170

Percent Hispanic 0 15.212 11.189

Percent Hispanic3/10,000 + .943 1.169

Population/1,000,000 – 13.751 8.021

1 if Percent Born in State > 75 Percent – .225 .417

Murder Rate per 100,000 + 9.332 2.906

Citizen Ideology – 43.608 9.149

Percent Republican Vote for President + 48.695 9.330

Real Median Household Income/100 + 366.480 47.638

Percent Unemployed + 6.051 1.532

1 if Southern State + .528 .499

Note: Weighted by 16,361 offender-years from 16 sampled states.

state and a quadratic test of the effects of unem-

ployment in Model 3.

The results in Model 1 show that several indi-

vidual offender attributes contribute to execu-

tion probabilities. The coefficient on the first

main effect shows that African American offend-

ers whose victims are not white are less likely

to be executed. But the coefficients on the two

interaction terms suggest that African Americans

or Hispanics found guilty of killing members of

the majority race face a greater likelihood of this

punishment (we defer discussing significance

tests on contrasts between the coefficients on

individual variables until discussion of the best

model). If they persist, these last findings are

particularly important as they suggest that the

extra-legal attribute with the most powerful

effects on death sentences accounts for post-

death sentence execution probabilities as well.

In Model 2 we add five contextual variables

to the individual determinants in the first model.

Although these additions increase model

explanatory power as the BIC statistic falls

sharply, the coefficients on the interaction terms

that assess execution probabilities for minorities

who kill whites remain significant. We again

find that African Americans convicted of mur-

dering nonwhites are less likely to be executed,

but execution probabilities remain higher for

African Americans who kill a white. The con-

textual results show that execution probabilities

are g reater in states with more African

Americans until a threshold in this proportion

is reached (we also defer reporting this inflec-

tion point until the presentation of the best

model). Although the percentage of Hispanic

residents has no effect, this ethnic threat vari-

able in squared form enhances execution prob-

abilities. Additional findings show that states

with larger populations and a greater division of

labor are not as likely to execute. After we add

the unemployment rate and its square together

with the percentage born in-state, these initial

results suggest that unemployment rates influ-

ence execution probabilities, but this penalty is

less likely in states with few outsiders.

COMPREHENSIVE MODELS. Yet other accounts

may matter. In Model 4 of Table 4, we add state

murder rates, citizen ideology, votes for

Republican presidential candidates, real medi-

an income, and a dummy variable for years

after 1992 when the number of executions

expanded. Model 5 is identical to Model 4, but

we replace the quadratic Hispanic threat spec-

ification with the cubic power transformation.

Model 6 only differs from Model 5 because a

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Table 3. Post-Capital Sentencing Determinants of Executions Estimated with Discrete-Time Logit

Model 1 Model 2 Model 3

Explanatory Variables Coef. SE Coef. SE Coef. SE

Individual Effects

—1 if Black Offender –1.015*** .349 –.761*** .220 –.758*** .215

—1 if Hispanic Offender –.375 .465 .131 .228 .102 .238

—1 if White Victim –.348 .258 –.067 .230 –.157 .219

—1 if Black Off. 3 1 if White Victim 1.394*** .371 .898** .322 1.042*** .337

—1 if Hispanic Off. 3 1 if White Victim 1.130** .422 .332 .255 .449* .257—1 if Male .458 .499 .907* .545 .870 .539

—1 if Prior Conviction .082 .130 .134 .103 .123 .086

Contextual Effects

—Percent Black .— .— .443*** .106 .423*** .077

—Percent Black2 .— .— –.011*** .003 –.012*** .002

—Percent Hispanic .— .— –.052 .058 –.036 .054

—Percent Hispanic2 .— .— –.005*** .001 .003** .001

—Population/1,000,000 .— .— –.149*** .040 –.100*** .029

—1 if Percent Born in State > 75 Percent .— .— .— .— –1.813*** .322

—Percent Unemployed .— .— .— .— –1.144** .392

—Percent Unemployed2 .— .— .— .— .069** .027

Constant –5.101*** .633 –8.556*** 1.118 4.093*** 1.258

Log Likelihood –873.3*** –816.6*** –793.7***

BIC Statistic 1853.4 1778.8 1732.8

Notes: N = 1,560 offenders and 16,361 offender-years from 16 states; state-level cluster corrected standard

errors; coefficients on offender-year dummies not shown.

* p ≤ .05; ** p ≤ .01; *** p ≤ .001 (one-tailed tests except for intercept).

control for state location in the South is added

to the explanatory variables entered in Model 5.

The results in Model 4 show that the addition

of political variables and the tax base measure

increase model explanatory power as the BIC

statistic again declines from its value in Model

3. The findings confirm other contextual expec-

tations: liberal states are less likely to execute,

but capital punishment likelihoods are higher in

states where Republican presidential candidates

received the most votes and where the tax base

is substantial. These findings show that the mur-

der rates have the expected positive relationship

with executions, but the unemployment rates are

no longer significant after the addition of con-

textual variables that assess state political envi-

ronments. When we replace the quadratic

Hispanic specification with the cubic power

alternative in Model 5, all results persist, but the

coefficient on Hispanic threat becomes signif-

icant.

A test of the significance of the difference

between the absolute value of the coefficient on

the black offender main effect and the coeffi-

cient on the interaction term that gauges exe-

cution likelihoods for blacks who kill whites

shows that the interaction term coefficient is

greater than its counterpart on the main effect

(two-tailed p = .038). Other tests that gauge the

statistical significance of contrasts reveal that

the coefficient on the blacks who kill whites

term is most substantial. For example, when

we replace the condemned blacks main effect

with the same variable for whites, we find iden-

tical significance test results again indicating

that the coeff icient on the black killings of

whites interaction term is more substantial.

Similar tests show that the coefficient on the

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Table 4. Post-Capital Sentencing Determinants of Executions Estimated with Discrete-Time Logit

Model 4 Model 5 Model 6

Explanatory Variables Coef. SE Coef. SE Coef. SE

Individual Effects

—1 if Black Offender –.737*** .233 –.744*** .234 –.764*** .231

—1 if Hispanic Offender .108 .248 .107 .248 .099 .253

—1 if White Victim –.141 .202 –.144 .202 –.158 .200

—1 if Black Off. 3 1 if White Victim 1.007** .339 1.011** .342 1.033*** .334

—1 if Hispanic Off. 3 1 if White Victim .429* .256 .437* .263 .454* .269—1 if Male .845 .527 .852 .531 .848 .530

—1 if Prior Conviction .118* .069 .119* .073 .121 .075

Contextual Effects

—Percent Black .450*** .066 .439*** .065 .479*** .113

—Percent Black2 –.014*** .002 –.014*** .002 –.014*** .003

—Percent Hispanic –.019 .068 .— .— .— .—

—Percent Hispanic2 .002 .002 .— .— .— .—

—Percent Hispanic3/10,000 .— .— .533*** .109 .569*** .134

—Population/1,000,000 –.099*** .025 –.099*** .027 –.099*** .026

—1 if Percent Born in State > 75 Percent –2.000*** .265 –1.998*** .284 –2.089*** .397

—Murder Rate .088* .045 .102** .043 .102* .045

—Citizen Ideology –.030** .011 –.029** .011 –.033*** .009

—Percent Republican Vote for President .048*** .011 .048*** .012 .052*** .010

—Real Median Household Income /100 .010*** .003 .010*** .003 .009** .003

—Percent Unemployed –.749 .463 –.778 .493 –.783 .500

—Percent Unemployed2 .050 .033 .052 .035 .052 .036

—1 if after 1992 1.597*** .307 1.602*** .316 1.635*** .302

—1 if Southern State .— .— .— .— –.291 .611

Constant –12.123*** 2.268 –12.016*** 1.951 –12.004*** 1.966

Log Likelihood –776.1*** –775.6*** –775.5***

BIC Statistic 1697.7 1696.8 1696.5

Notes: N = 1,560 offenders and 16,361 offender-years from 16 states; state-level cluster corrected standard

errors; coefficients on offender-year dummies not shown.

* p ≤ .05; ** p ≤ .01; *** p ≤ .001 (one-tailed tests except for intercept).

black killings of whites interaction term is

greater than the coefficient on the Hispanic-

white victim interaction term (two-tailed p =

.023). Finally, when we add the South in the last

model, we find no noteworthy changes in the

results.

We now can calculate the inflection point

where the relationship between the percentage

of African Americans and execution probabili-

ties shifts from positive to negative. Again using

coefficients from the best model (Model 5), we

find that this change occurs after the percent-

age of African American residents reaches 16.2

or about the 86th percentile in the percentage of

African American residents in these 16 states in

this period. This result suggests that execution

probabilities start to fall only after the potential

African American vote reaches a rather sub-

stantial threshold. And odds ratios suggest that

these effects are not weak. The exponentiated

coefficient from Model 5 that gauges execution

probabilities for African Americans convicted

of killing a white is 2.75. The odds ratio for

African Americans who kill nonwhites is .476.

The odds ratio for Hispanics convicted of killing

a white is 1.55. The size of some contextual

effects are substantial as well: for example, the

odds ratio for the percentage of blacks in these

states is 1.55 and its counterpart on the cube of

Hispanic presence is 1.70.

ADDITIONAL CONSIDERATIONS. Some death

row inmates accept their sentences and try to

stop all appeals. These “volunteers” are dis-

proportionately white (Lofquist 2002). In states

that rarely execute, a greater proportion of the

few executed offenders are volunteers (Lofquist

2002), so a failure to control for this effect may

bias the estimates. The data at hand do not let

us identify such offenders, but we can discov-

er if this effect matters because volunteers are

executed with less delay. If those executed early

differ sharply from those executed later, analy-

ses limited to inmates executed later—or mod-

els restricted to offenders executed from 5 to 10

years after sentencing—should produce findings

that differ, but they do not. The same variables

are significant in each of these five restricted

analyses (not shown but available on request).

Such theoretically identical findings suggest

that our inability to directly control for volun-

teers has not distorted the findings.

We find no evidence for additional interac-

tion effects and these negative results persist

when we assess cross-level interactions between

individual and jurisdictional characteristics.

This result is important for methodological rea-

sons. Cross-level interaction significance tests

are too optimistic if such tests are not estimat-

ed with an Hierarchical Linear Model (HLM)

approach. Yet none of these interactions are sig-

nificant when they are assessed with the non-

HLM method we employ. Such cross-level

effects therefore will not be uncovered if they

are estimated with HLM because this estimator

will produce larger estimates of the standard

errors. The cluster adjustment we use to correct

the standard errors for within state interdepen-

dencies therefore should suffice, as HLM will

produce the same nonsignificant and redun-

dant findings about cross-level interactions.17

We entered religious fundamentalism along

with various measures of Republican gover-

nors and this party’s strength in state legislatures,

but these determinants had no effects (models

not shown). We find no evidence that federal

judge partisanship influences executions.18 To

isolate the effects of otherwise omitted cross-

state national political, social, or macroeco-

nomic changes, we included dummies coded for

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17 For readers who prefer two-tailed significance

tests even if theoretical justification for direction is

provided, we list coefficients that reached the one- but

not the two-tailed .05 level: in Table 3, the male

offender variable in Model 2 is significant at the .05

one- but not the two-tailed threshold and the same is

true for the Hispanics who killed whites interaction

term in Model 3. In Table 4, each of the coefficients

on the Hispanics convicted of killing a white inter-

action term are significant at the one- but not the two-

tailed level and the same generality holds for the

coeff icient on the prior conviction variable in

Model 4.18 We gauged the degree to which the estimates are

robust by dropping each of the 16 sampled states in

analyses not shown. The implications persist but this

dependent variable already was skewed. To avoid a

logit model that could not be estimated after we

removed a state with many executions and increased

dependent variable skewness, we had to remove a few

explanatory variables from some of these 16 equa-

tions. Only one of the 16 sampled death penalty

states (Kansas, which had a tiny death row popula-

tion) had no executions. The elimination of states with

few executions had only tiny effects on the estimates.

each year (models not shown). All explanatory

variables that matter in Table 4 were significant

in these models. Such results suggest that shifts

in the national political or macroeconomic cli-

mate cannot account for these results. The mod-

els in Table 4 pass the link test for

misspecification. These considerations provide

added reasons to think that the most compre-

hensive models capture the primary determi-

nants of death row outcomes.19

We nevertheless must acknowledge that we

were forced to use advanced statistical tech-

niques to overcome data limitations. Although

there are good reasons to believe that the report-

ed estimates are unbiased and consistent, supe-

rior data always are preferable to such statistical

alternatives (for a forceful illustration, see

Donohue and Wolfors 2005). In particular, we

hope that subsequent researchers can obtain

more exhaustive information on victim race

and other considerations from additional death

penalty states. Perhaps researchers with the

resources and the time to achieve a better rap-

port with state correction agency officials who

would not cooperate with our requests for data

can produce superior findings about this issue

by analyzing additional states.

DISCUSSION

THE FINDINGS

Possibly because offender prior convictions

contribute to prosecutors’ decisions to seek the

death sentence and lead trial courts to support

this request, we find weak and inconsistent evi-

dence that this factor affects post-death sen-

tence execution probabilities. The findings show

that gender is nonsignificant, but the proportion

of condemned women in our sample is tiny. Yet

victim race clearly is the most important indi-

vidual finding. The coefficients and the signif-

icance tests that gauge contrasts in the size of

these point estimates show that blacks convict-

ed of killing whites are more likely to be exe-

cuted than other death row offenders. Such

results do not contradict hypotheses that local

prosecutors with an interest in protecting well-

publicized legal victories that should further

their political careers make successful efforts to

resist death row appeals. These findings also

support a hypothesis that state justices do not

ignore pressures to deny appellate relief to

minority death row offenders who kill whites.

But the findings suggest that African Americans

on death row for killing nonwhites are less like-

ly to be executed than other condemned pris-

oners.

The evidence corroborates hypotheses that

Hispanics also face higher execution probabil-

ities if their victims are white. Yet the odds

ratios that gauge the strength of this Hispanic

effect and the untransformed coefficients are

significantly weaker than their counterparts that

assess the strength of the black offender, white

victim effect. Such contrasting results about

the explanatory power of ethnic rather than

racial effects should not be surprising in light

of the fiercely divisive and violent conflicts

about race throughout U.S. history (Myrdal

1944; Tocqueville 1948). Only a few states had

substantial Hispanic populations before the

1990s. This ethnic group may have been too

small in most death penalty states to be suffi-

ciently threatening. Subsequent studies, how-

ever, may provide greater support for this ethnic

threat account because the Hispanic population

recently has expanded so rapidly.

The contextual results always show that

minority proportions matter. Larger percent-

ages of African American residents produce

higher execution probabilities, but this effect

becomes negative only after the proportion of

African Americans reaches a relatively sub-

stantial threshold. The positive sign on the

unsquared coefficient provides evidence for a

racial threat account, but the negative sign on

the squared percentage of African American

residents suggests that execution probabilities

respond to election pressures. The form of the

nonlinear relationship between Hispanic pro-

portions in a state and execution probabilities

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19 Attempts to use other diagnostic tests failed

because these estimates are weighted. Tests con-

ducted without the weights suggest that estimation

problems are not present. No observation has a high

leverage score, and there are only a few modest out-

liers. Estimate stability and a VIF analysis conduct-

ed on the state variables (without the squared terms)

yields a maximum score 4.33. Both considerations

suggest that collinearity is not present. Although we

analyze 16 states, there are 480 state-years as all but

two of our state-level variables are time-varying by

year. Fewer case-years are common in pooled time-

series analysis.

differs from the nonlinear association between

African American presence and these proba-

bilities. Probably because the proportion of

Hispanics in all but a few states was so modest,

the findings indicate that a growth in the per-

centage of Hispanics yields increasingly

stronger execution probabilities without a rever-

sal in the sign of this relationship.

Consistent with prior findings on the factors

that produce a legal death penalty in the states

(Jacobs and Carmichael 2002), the findings in

this article suggest that jurisdictions with the

most residents born in-state are less likely to use

this ultimate punishment. This association prob-

ably is based on latent hostility to strangers

(Hale 1996) and to a corresponding reluctance

to invoke the ultimate penalty against people

who are regarded as one’s neighbors. A differ-

ent aggregate finding supports another threat

account, as the results show that execution prob-

abilities are greater in states that have the high-

est murder rates.

Votes for Republican presidential candidates,

who often run on law and order platforms, help

explain execution likelihoods as well. Beckett’s

(1997) findings suggest that law and order cam-

paign rhetoric magnifies the public salience of

the crime issue. Successful law and order polit-

ical campaigns therefore should produce greater

support for the harshest punishment. The sen-

sitizing effects of this rhetoric and the relative

success of Republican law and order candidates

in some jurisdictions—which almost certainly

indicates considerable preexisting support for

capital punishment—help explain why votes

for Republican candidates provide such a robust

explanation for executions. It is interesting that

the presence of a Republican governor does not

increase execution probabilities (results not

shown), but this finding should not be surpris-

ing as this factor does not explain the legaliza-

tion of capital punishment (Jacobs and

Carmichael 2002). Governors must decide the

final appeal before an execution. This awesome

moral responsibility gives these political offi-

cials good reason to be ambivalent about this

punishment (Jacobs and Carmichael 2002;

Zimring and Hawkins 1986).

As theorists such as Garland (1990, 2001) and

Savelsberg (1994) would expect, the explanatory

power of political ideology suggests that polit-

ical values contribute to the propensity to exe-

cute. When or where there is greater political

support for liberal values, execution probabili-

ties diminish. This result supports claims

(Lakoff 1996) that support for the death penal-

ty is one of the most important differences

between liberals and conservatives. Finally, the

results suggest that the affluent states that can

better afford this costly punishment use it more

often.

WIDER IMPLICATIONS

This analysis fills critical gaps in the sparse lit-

erature on the application of capital punish-

ment. First, we gauged the effects of

offender-victim racial contrasts and found that

victim race is a strong determinant of execu-

tions. Until this study, and despite the empiri-

cal strength of victim race in trial-court studies

about the death sentence process, apparently

no research has gauged the explanatory power

of this factor. In addition to the findings about

theoretically important individual and legal fac-

tors, our data set lets us test environmental

determinants that have been ignored in the lit-

erature. The findings suggest that these con-

textual omissions are unfor tunate, as

environmental accounts have substantial

explanatory power in this and in other analyses

that seek to explain criminal justice outcomes.

Compared to the studies of the determinants of

death sentences that are restricted to individual

data from one or a few jurisdictions, this study’s

coverage is far greater as we assessed the effects

of both individual and contextual accounts over

a 30-year period in a diverse set of multiple

states.

Reviews of the related research on race and

felony sentencing (Chiricos and Crawford 1995;

Walker et al. 1996) suggest that substantial dis-

agreement exists in this literature. These reviews

both base their conclusions on results that appear

in only a bare majority of the many available

investigations. Almost all of the many studies

that analyze trial court sentencing are based on

offender samples from just one or only a few

trial courts in a specific region. Yet since trial

courts exist in diverse environments, inconsis-

tent results can be expected. If each court study

investigates death penalty sentences in just one

or a few localities, and their political climates

differ, it will be difficult to uncover general

relationships, particularly because court envi-

ronments have so much explanatory power

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(Helms and Jacobs 2002). The same reasoning

holds for death penalty investigations. It follows

that a study of executions in many states that

assesses both contextual and individual factors

should provide more accurate results than would

analyses restricted to individual explanations

in just one or only a few jurisdictions.

Another plausible explanation for these dis-

agreements concerns the neglect of political

effects in almost all of the studies of criminal

justice system outcomes. As one might expect

in light of the recent theoretical emphasis on the

political nature of legal punishments (Chambliss

1994; Foucault 1977; Garland 1990, 2001) and

the Republican party’s tactical use of law and

order appeals to covertly enhance voters’ racial

fears (see the quotes in note 4), these results

show that executions increased after states

awarded additional votes to Republican candi-

dates. Yet increased electoral support for law and

order Republicans who enthusiastically embrace

the death penalty may be based on preexisting

conservative values. But this alternative hypoth-

esis is unlikely as votes for Republican presi-

dential candidates account for executions after

citizen ideology has been held constant. Such

findings, of course, show that these supposed-

ly objective and purely legal decisions about

who will die are shaped by factors that should

not be relevant.

These findings also confirm the racial poli-

tics perspective that provided the primary con-

ceptual impetus for this research. In the

homogeneous European democracies, predom-

inant penal emphasis is on the reintegration of

offenders into a solidaristic society adminis-

tered by experts who are only distantly account-

able to the voters (Savelsberg 1994; Whitman

2003). In the United States, however, the pub-

lic’s Manichean image of human nature, com-

bined with a history of bitter racial conflict,

has produced an exclusionary penal system.

According to Wacquant (2000, 2001), the

resilient divisions produced by slavery and by

the later virulent measures used to maintain

white supremacy created a segregative penal

solution consistent with the dominant public

image of criminals as members of a vile racial

underclass. This racial history and the resulting

cultural premises helped to produce a criminal

justice system that primarily uses incapacitation

to control street crime. The most reliable way to

incapacitate incorrigible offenders is to kill

them. This incapacitative solution fits with the

prevailing public view—often forcefully

expressed politically in this most direct of all

large democracies—that the primary goal of

the U.S. criminal justice system should be

vengeance.

Executions therefore should be most likely in

jurisdictions in which covertly racist law and

order political appeals have been most suc-

cessful and where the presence of a large African

American population—that has not become

quite large enough to enforce its political val-

ues—threatens dominant whites. Prior research

supports such political and racial results.

Findings show that the same aggregate politi-

cal and racial factors account for the likelihood

that a state will legalize the death penalty

(Jacobs and Carmichael 2002). In another study,

Jacobs and colleagues (2005) report that simi-

lar political and racial factors explain the num-

ber of death sentences. But compared to these

two studies and other aggregate-level research,

this investigation is unique as it analyzes both

state and individual characteristics. Probably

the most important finding that emerges from

this multilevel approach concerns victim race.

African Americans who breach the racial caste

system by killing whites can more often expect

the harshest of all penalties, and this finding

holds after many contextual level political and

racial effects have been held constant. Such

results do not contradict Wacquant’s (2000,

2001) theoretical claims that a primary goal in

the current U.S. criminal justice system is to sus-

tain the prior racial caste system even though the

current methods are less transparent than the

lawless brutality used in the past.20

The most important legal implication con-

cerns racial equity. Many claims have been

made that the U.S. criminal justice system is not

colorblind, yet after multiple studies, definitive

evidence showing that the trial courts sentence

African Americans less leniently than whites has

remained elusive. This study instead analyzed

racial effects on the post-sentencing execution

process. The findings show that despite efforts

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20 For additional findings on how past racial, eth-

nic, or religiously based brutality continues to affect

current practices, see Archer and Gartner (1984),

Messner, Baller, and Zevenbergen (2005), Savelsberg

and King (2005), and Jacobs and colleagues (2005).

to transcend an unfortunate racial past, residues

of this fierce discrimination evidently still linger,

at least when the most morally critical decision

about punishment is decided. Findings indicat-

ing that African Americans who kill nonwhites

are less likely to be executed than their coun-

terparts who kill whites show that the post-sen-

tencing capital punishment process continues to

place greater value on white lives. Evidently,

inclinations to devalue African Americans in

constitutional compromises in the distant past

remain today, although they are expressed in less

conspicuous ways. Associate Justice William

Brennan helps us understand the importance

of such discriminatory findings:

Those whom we would banish from society or

from the human community itself often speak in

too faint a voice to be heard above society’s

demand for punishment. It is the particular role of

the courts to hear these voices, for the Constitution

declares that the majoritarian chorus may not alone

dictate the conditions of social life. (Brennan 1987)

This evidence shows that the state and federal

appellate courts—who should pay special atten-

tion to those accused of the most horrif ic

crimes—evidently continue to listen to some

voices more than others.

David Jacobs is Professor of Sociology and (by cour-

tesy) Political Science at The Ohio State University.

He uses a political economy approach to study out-

comes in the criminal justice system and other issues

in political sociology. A study of racial politics recent-

ly appeared in the American Journal of Sociology

while another publication on the determinants of

yearly execution frequencies will soon appear in

Social Problems. In addition to these interests, he con-

tinues to investigate the politics of labor relations.

Zhenchao Qian is Professor in the Department of

Sociology and research associate in the Initiative in

Population Research at The Ohio State University. His

research focuses on family demography, race and

ethnicity, and immigration. He studies changes in

mate selection by taking into account marriage mar-

ket conditions. His work with Daniel T. Lichter, recent-

ly published in American Sociological Review,

centers on changes in racial/ethnic intermarriage. His

other research examines changing racial identifica-

tion among children born to interracial couples.

Jason T. Carmichael is an Assistant Professor in the

Department of Sociology at McGill University. His

interests include criminology, criminal justice, and,

more broadly, political sociology. Current projects

include, among other things: an analysis of the polit-

ical and social determinants of the number of juve-

nile delinquents who are adjudicated in the adult

criminal justice system, and an examination of the

ability of foundation funding to shape the size and/or

success of social movement organizations.

Stephanie L. Kent is an Assistant Professor of

Sociology at Cleveland State University. Her research

focuses on the politics of crime control and uses

macrosocial explanations to predict social control

outcomes. Recent publications include a pooled-

time-series analysis of police strength in U.S. cities

published in Criminology. Additional work on capi-

tal punishment has or will be published in the

American Sociological Review and Social Problems.

She is currently exploring the social and political

determinants of homicide and the use of lethal force

by and against the police in U.S. cities.

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