To capture the historical and socio-political characteristics of when and where the defendants were sentenced to death, men and women were “matched” by the county and year of sentencing. This was accomplished by first identifying the counties and years of the women defendants sentenced to death. Then men that were sentenced in the same year and county were located and included in the sample dataset. Federal cases were excluded altogether. The analytical sample (N= 461) contains 151 women and 310 men who, as of early 2021, had active death sentences, were resentenced to life or less, or were executed. Ninety-eight women were sentenced in the same year and county with at least one male defendant. For thirty women, there was only a year’s difference in sentencing between them and their male counterparts. Twenty-one women’s sentences differed from men’s by three to six years. There were, three different counties in which no men were executed in more than twenty years. For these cases, neighboring counties were found for men sentenced in the same or a couple years apart. Fortunately, men already in the dataset, due to their matches with other women defendants, were already included. For example, Shirley R. Tyler, a woman sentenced to death in 1979 in Pike County, Georgia, was sentenced in a county in which a man, Leeland Braley, was sentenced to death twenty years later. Given that the dataset already contained two men sentenced to death in a nearby county in 1975, Braley’s exclusion and Tyler’s inclusion is justifiable.

Current case outcomes—the dependent variables.

The Death Penalty Census (DPIC, 2021) collects the “current case status,” a twenty-category variable, defined as the “final outcome of the court proceedings related to those charges” (p. 7). The full definitions for each of the twenty types of case statuses are available in the Death Penalty Census codebook (DPIC, 2021). For purposes of analyses here, the twenty categories were collapsed into seven of the existing categories: “active death sentences,” those who “died on death row,” were “executed,” “exonerated,” received a “grant of relief,” were “resentenced to life or less,” or had their “sentence commuted.” Three categories, along with all the cases contained within, were omitted altogether because no woman was “found not guilty by reason of insanity,” had their “retrial barred,” or were “no longer on death row” for unknown reasons.

The DPI distinguishes between whether or not defendants were executed in the same state, a different state, or for a different crime. For purposes of this study, all three were consolidated under one category, “executed.” The categories “died pending retrial or resentencing,” “grant of relief (retrial/resentencing pending),” and “grant of relief (relief not final)” were also combined given that the definition of “died pending retrial or resentencing is; “after receiving a final grant of guilt or penalty relief, the defendant died prior to a retrial or resentencing” (DPI, 2021, p. 8). Nila J. Wacaser, for example, had “died pending retrial or resentencing” upon receiving a “final grant of guilt or penalty relief.” Therefore she, and the twenty-seven men who also died after receiving a grant of penalty relief were categorized as having received a “grant of relief.” While the categories are comparable for statistical convenience, the various circumstances are certainly qualitatively distinctive.1

“Sentence commuted” is an amalgamation of those who were commuted through executive action or through “administrative” action (referring to commutations resulting from a court decision). As with the category “resentenced to life or less,” commuted sentences can range from a reduced sentence to life without parole. Lastly, there are the categories “active death sentence” (referring to defendants still on death row as of January 1, 2021) and the “exonerated” (overturned convictions that later resulted in all charges being dropped or receiving a governor’s pardon). To visualize the distribution of case outcomes, Table 1 is organized around the seven case outcomes and by a defendant’s race and sex. 

The racial distribution of the full dataset  and the analytical sample are similar except for percentages of men in two racial groups. In the full dataset, 47% (n = 4022) of the men are white whereas in the analytical dataset the percentage of white men drops to 41.9% (n = 166). Women who are white comprise 64.4% of women sentenced to death. Nine percent (n = 771) of men were Latinx in the full dataset versus 12.1% (n = 48) in the analytical sample (Latinx women comprise 6.2% of women sentenced to death). Given that the most death sentences have come from California and Texas, which sentenced 247 and 237, respectively, (729, 66.3%, of all Latinx men), is not all too surprising in states where approximately 40% of the population identify as Hispanic or Latino (U.S. Census, 2024). When considering the total number of men sentenced in these two states, black men are overrepresented in both California (34.7% 365 out of 1051 men) and Texas (37.9%, 416 out of 1095 men) where the black populations are about 6.5% and 13.4% (U.S. Census, 2024).

The more pronounced gender and race differences are visible when the categories are examined. Beginning with the largest group, defendants who were resentenced to life or less, the percentage of men drops somewhat from the full dataset to the sample dataset in Table 1 (39.7%, n = 3395 vs. 32.8%, n = 86). By far this is the largest percentage for women with 48.6% (n = 86). Taking a closer look at race, the percentage of white men resentenced to life drops (45.2%, n = 1534 vs. 38%, n = 63). This compared to the 69.8% (n = 60) of women that are white. Black women make up 20.9% (n = 18) of women resentenced compared to black men who are 34.5% (n = 60) of men who were resentenced. Black men’s percentages also dropped from the full dataset (47.5%, n = 1612) whereas Latinx men experienced an increase (5.6%, n = 191 vs. 12.5%, n = 6).

Percentages among men increased for active death sentences (25.3% in the full dataset vs. 31.3% in the sample) and sentences commuted (4.1% vs. 7.8%). The percentage of women with active death sentences are 28.2% (n = 50) and those with commutations is 6.8% (n = 12). The percentage of white men with active death sentences dropped from 42.1% (n = 911) to 24.7% (n = 41) whereas 60% of women are white. The percentage of black men also dropped from 40.6% (n = 879) to 28.2% (n = 49) which is closer to the percentage of black women with active death sentences (22%, n = 11). The percentage of Latinx men experienced the most dramatic increase (14.3% to 60.4%) especially compared to the only 12% (n = 6) of Latinx women with active death sentences.

Death Penalty Information Center. (2021). Death penalty census codebook. Death Penalty Information Center. https://dpic-cdn.org/production/documents/Death-Penalty-Census-Codebook.pdf 

Hypotheses 1a and 1b predicting that southern regions would be more likely to result in active death sentences and less likely to result in resentences were initially questioned by the chi-square tests as well as the univariate logistic regressions. With that in mind, the following states in the analytical dataset abolished the death penalty: Delaware in 2016, Illinois in 2011, Maryland in 2013, New Jersey in 2007, and Virginia in 2021. These were also the states with the smallest number of death sentences since 1974.2 The data that might have been expected to affect the data would have been California (n = 100) if it were not for the fact that death row was not dismantled until after 2021. Also, the majority of Californians (68 men and 21 women) were coded as having “active death sentences” (DPIC, 2021). Having an active death sentence may not be the worst outcome. Other outcomes were not examined in the logistic analyses, exonerations, grants of relief, and sentence commutations, all require an active death sentence first. This combined with one was more likely to be executed in the South than anywhere else speaks to the need for additional variables that address, for example, abolitionist efforts and the conditions under which someone has an “active death sentence.”  As Kleinstuber and Coldsmith’s (2020) analysis of states using life without parole is similar in findings to studies of the death penalty in that geographic location matters a lot. The over-representation of death sentences in the South are partially due to states with clear LWOP laws. And, as scholars like Villaume (2005) have pointed out, “life without parole” is not so different from the death penalty. There are racial disparities, human suffering, and financial costs.

With that, hypotheses 2 and 3, pertaining to the percentage of women in state legislatures, were also significant but in unexpected directions both in the univariate logistic analyses and the logistic regressions . In other words, if there is a correlation between these variables and outcomes, one or more variables is likely needed to account for the political sphere in which legislators work; like, for example, the interaction between state legislatures and governors. Republican governors have been found to be most supportive of executions when supported by a Republican majority legislature (Kim, Ki, & Kim, 2024). On the other hand, Wozniak (2012) found that Democratic governors are more likely to pass legislation that abolishes the death penalty, regardless of a unified government. Though Ricknell (2023) warns of exaggerating findings based on small numbers, women governors might be less likely to support the death penalty than their male counterparts.

         Separating the men from the women into subsamples was important in that the socio-political context, captured through the “matching” process, remained but the effect of the variables for men and women were examined rather than looking at the effect of. Unfortunately, separating the analytical sample into groups by race and gender (and race alone) did not yield significant models. The race variables were clearly significant in the binary statistical tests, but not in the models that included the controls. Still, evidence of the importance of race, especially for men, was found. The control variables, the percentage of a county in poverty and white, were not significant in Models 1 and 2 but, once men and women were separated into subsamples, they were significant for men (Model 3) in predicting active death sentences.

         Turning to the primary hypotheses 4a-c and 5a-c, evidence for Hypothesis 4a, male defendants sentenced to death are more likely to remain on death row than women defendants. Taken together with all the analyses conducted, however, there are a number of potentialities for this finding beyond what would appear to be gender favoritism (i.e., the case characteristics themselves). As Table 1 demonstrates, eliminating counties in which women have not been sentenced to death demonstrates that men are actually less likely to be exonerated (1.0% of men and 1.1% of women) but more likely to have their sentences commuted (7.8% of men versus 6.8% of women) than the full dataset that includes all men. Since 1974, as shown in Appendix A, 2.1% (n = 178) of men have been exonerated and 4.1% (n = 347) of men have had their sentences commuted. Sentence commutations also result in resentencing to life or less. Still, combining the two categories’ results in 40.2% (n = 161) of men (44.1%, n = 3742 in the full dataset) having their sentences reduced versus 55.3% of women (n = 98).

H4b and H4c state that white male defendants and white women sentenced to death are less likely to remain on death row than their nonwhite counterparts. In the chi-square tests in Table 3, this truly seems to be the case as are the remaining hypotheses (H5a-c) stating that male defendants are less likely to be resentenced to life or less than women but white men and women defendants are more likely to be granted resentences to life or less than their nonwhite counterparts. When controlling for other variables in the logistic regressions, these relationships disappeared except for H5a, discussed above, which found that male defendants were significantly less likely than women to be resentenced to life or less (see Model 2 in Table 4). The stability of significant values for women legislators, though directionally different than the hypotheses, suggest that they are important variables to include but that the dependent variables themselves might require a closer look.  

Aside from specific case differences, the different abilities of facilities to accommodate death row inmates may be a factor in higher resentencing probabilities that could range anywhere from case characteristics to death row itself which could result in experiences tantamount to solitary to administrative issues of having death row inmates interacting with the general population. Angela McAnulty, for example, as the only woman sentenced to death in Oregon since 1984, was also the only woman on death row for nine years (2011–2019) meaning that her access to other inmates was severely limited in comparison to the 28 men housed at the Oregon State Penitentiary death row in 2019. Christa Pike, sentenced to death in 1996, is the only woman on death row in Tennessee (TN Department of Correction, 2024). According to the DPIC (2024) there are additional women: Brenda Andrew in Oklahoma, Michelle Sue Tharp in Pennsylvania, Robin Row in Idaho, Virginia Caudill in Kentucky, Antoinette Frank in Louisiana, and Tiffany Moss in Georgia.

         To begin with, quantification of demographic characteristics often results in reductive or diluted categories. Demographic characteristics are politically fraught concepts but, in analyses like this current study, reducing race (and gender) to binaries is not a minor limitation of the study. Take for instance Linda A. Carty, sentenced in 2002, is identified as Black by the DPIC (2021) but identifies as Kittitian-American. Her status as an immigrant with dual citizenship and her ethic group’s history and experiences with slavery are undeniably different from African and black Americans. To develop measures more representative of identity (including gender) is time-consuming but most definitely are worth the effort both statistically and what is gained qualitatively.

Numerical excuses aside, the complexities involved in the institutional and political landscape, notwithstanding gendered and raced processes, are difficult to model but can be included. Take for example gubernatorial politics as highlighted by Ricknell’s (2023) examination of governor vetoes of death penalty bills. Ricknell (2023) found that Democrats were not more likely than Republicans to veto bills that restricted the death penalty. Years in office, found to be negatively related to vetoes, and “lame duck years,” were significantly related but only under certain conditions (e.g., vocal allegations of racism, discussions of execution method, etc.). When actual executions are of interest, studies suggest that governors play a sizeable role on state executions and that time in office is a significant predictor (Gerez & Miller, 2024; Kubik & Moran, 2003). Kubik and Moran (2003) election years increase the likelihood that black defendants will be executed (especially in the South) whereas Gerez and Miller (2024) found that lame duck governors were more likely to issue commutations.

Two county level-variables, percent in poverty and percent white, were significant for men but not women suggesting future work should explore other county-level variables that may also impact men and women differently. These variables emerged mainly from literature predicting outcomes for men. For instance, percent rural and percent with bachelor’s degrees (significant in Schmuhl, et al.’s 2020 analyses), are also commonly used in political science research predicting women’s political representation. The different effects of population and region in the men and women samples further suggest that, in addition to selecting defendants presumably for a social context that is more comparable, other variables are needed that are distinctly “gendered” and are informed by research suggesting their explanatory power. For example, qualitative research has provided invaluable insight into black women on death row (e.g., Philofsky, 2008) and can assist in the creation of variables that might be helpful for quantitative researchers. In reading Philofsky’s (2008) work, a number of possibilities arise such as the percentage of black women incarcerated in a county or the percentage of black women living in poverty.

The analytical approach taken was novel but there are a number of statistical techniques that may yield more robust results and are not as limited to the number of variables appropriate for inclusion. County-level of analyses have employed a wide range of statistical tests, including cross-sectional time-series, Baumgartner and colleagues (2016, 2020) were able to demonstrate the statistical power of place on the likelihood of executions. There have been a number of studies that have used the synthetic control method (Chalfin, Haviland, & Raphael, 2013) in assessing the deterrent effect of the death penalty (Gius, 2020; Oliphant, 2022; Parker, 2021). Propensity score matching, in which a quasi-experimental design is used control for defendant and victim characteristics, has been successful in isolating the effects of race (Jennings, et al., 2014) and gender (Richards, et al, 2014) on death penalty outcomes.

Future research could include the actual case characteristics not only for descriptive purposes but also to control for the seriousness of the crime. Still, Richards et al. (2014) found that when controlling for case characteristics, the gender of the defendant was not a determining factor in receiving the death penalty; ergo, no chivalry to be found.