Explaining Spatial Variation in Support for Capital ... - CiteSeerX

Explaining Spatial Variation in Support for Capital Punishment: A Multilevel Analysis1 Eric P. Baumer University of Missouri—St. Louis Steven F. Messner University at Albany, SUNY Richard Rosenfeld University of Missouri—St. Louis

This research examines the effects of social context on support for the death penalty using individual-level data from the 1974–98 General Social Survey (GSS), which have been linked with aggregatelevel data on homicide rates and sociodemographic, political, and economic characteristics. Consistent with instrumental, social threat, and constructionist perspectives, this study finds that residents of areas with higher homicide rates, a larger proportion of blacks, and a more conservative political climate are significantly more likely to support the death penalty, net of compositional differences. These results warrant further attention to contextual and individual sources of public support for the death penalty. The United States is one of the few developed societies in the world that retains the death penalty. Various explanations for this aspect of American exceptionalism have been proposed, including distinctive features of American federalism and the populist nature of American politics (Zimring and Hawkins 1986; Hood 1998; Radelet and Borg 2000). Whatever 1 We thank Tom Smith, National Opinion Research Center (NORC), for valuable technical assistance, and Scott South, Janet Lauritsen, and the AJS reviewers for helpful comments on an earlier version of this article. The research reported in this article was supported in part by a grant from the National Consortium on Violence Research (NCOVR), which is supported by the National Science Foundation (grant SBR 9513040). Direct correspondence to Eric Baumer, Department of Criminology and Criminal Justice, University of Missouri, St. Louis, Missouri 63121. E-mail: [email protected]

䉷 2003 by The University of Chicago. All rights reserved. 0002-9602/2003/10804-0003$10.00

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Support for Capital Punishment the merits of these accounts, capital punishment receives substantial public support in the United States. Recent national surveys indicate that about two-thirds of American adults support the death penalty for persons convicted of murder.2 However, the national figure conceals the substantial variation in death penalty support that exists across space within the United States. Some studies have demonstrated significant regional variation in levels of support (e.g., Bohm 1991; Fox, Radelet, and Bonsteel 1991), and independently conducted state-level surveys indicate that the often-quoted national figures do not adequately describe public sentiment in all U.S. states. For example, in 1999, support for the death penalty was much lower in Kentucky (59%) than in Missouri (78%) (Brinker 1999; Death Penalty Information Center 1999). But with the exception of this type of descriptive evidence for large geographic units, very little is known about spatial variation in support for the death penalty in the United States, including how much variation exists at relatively localized areas and what social conditions might account for that variation. We begin to fill some of these gaps in the literature by examining the sources of variation in death penalty support across a representative sample of U.S. metropolitan areas and nonmetropolitan counties. Our analyses address two interrelated questions: Is there meaningful variation in support for capital punishment across these localized areas, and if so, how can this variation be explained? With respect to the latter question, we are particularly interested in exploring contextual effects. Support for the death penalty might vary across areas simply as a function of the nonrandom distribution of the population. Specifically, areas with strong death penalty support might be those with relatively large numbers of persons with the individual attributes that have been linked with pro–death penalty attitudes. Prior research and theory, however, suggest that attitudes about punishment and social control are likely to be affected by features of the social environment, especially the level of lethal violence, political conservatism, and racial and economic composition. Drawing on this literature, we formulate hypotheses about the contextual determinants of death penalty support and test these hypotheses with a unique data set that permits multilevel modeling. 2

Gallup poll results for 2001 reveal that 67% of American adults support the death penalty for persons convicted of murder, while a recent Harris poll (August 2000) indicates that 64% of American adults support the death penalty (the most recent Gallup and Harris death penalty poll results are available at www.gallup.com and www.harrisinteractive.com, respectively). Nearly identical results were obtained from a 2000 ABC News poll (see Daniel Merkle, “Death Penalty Remains in Favor,” http: //abcnews.go.com/sections/politics/dailynews/poll000619.html [last accessed February 3, 2003]).

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American Journal of Sociology BACKGROUND AND HYPOTHESES

An extensive body of research has accumulated on the relationship between individual attributes and attitudes toward criminal justice policies, including the death penalty. For example, past studies have identified a broad set of personality characteristics and deeply held beliefs, such as authoritarianism, dogmatism, religious fundamentalism, and a belief in retribution, that give rise to a worldview supportive of harsh punishment (Neapolitan 1983; Bohm 1987; Finckenauer 1988; Grasmick et al. 1992; Smith and Wright 1992). However, although the literature acknowledges that public opinion about social issues is responsive to macrolevel events and conditions (e.g., Page and Shapiro 1992; Zaller 1992; Steiner 1999), very little attention has been directed to features of the social context that may influence punitive attitudes toward punishment in general and support for capital punishment more specifically. Perhaps the most obvious contextual factor that might be related to death penalty support is the level of homicide (Stinchcombe et al. 1980; Gross 1998; Garland 2000; Jacobs and Carmichael 2002). Previous researchers have noted that national trends in support for the death penalty over the past 30 years correspond fairly closely to trends in homicide rates (Ellsworth and Gross 1994). Less than half of American adults—between 40% and 45%—polled in the mid-1960s reported that they supported the death penalty for persons convicted of murder (e.g., Zeisel and Gallup 1989; Bohm 1991; Ellsworth and Gross 1994). But support grew steadily during the 1970s and 1980s and, by the mid-1990s, more than threequarters of the adult population were in favor of capital punishment (Warr 1995; Longmire 1996; Shaw, Shapiro, and Lock 1998; Radelet and Borg 2000). This 30-year period of increasing support was followed by an appreciable decline in support during the latter half of the 1990s. As noted above, recent population surveys indicate that about two-thirds of Americans polled support capital punishment. In a roughly similar manner, homicide rates rose from the mid-1960s through the 1980s and then fell rapidly beginning in the early 1990s and continued to decline through the decade (Blumstein and Rosenfeld 1998; LaFree 1998).3 Although far from perfect (Fox et al. 1991; Smith and Wright 1992), this correspondence has led to the widespread speculation that public opinion about the death penalty may be sensitive to levels of homicide (e.g., Gelles and Strauss 1975; Thomas and Foster 1975; Smith 1976; Stinchcombe et al. 1980;

3 Between 1993 and 1999, the national homicide rate fell by 40%, from 9.5 to 5.7 homicides per 100,000 persons. The homicide data were compiled by the Bureau of Justice Statistics and are available at www.ojp.usdoj.gov/bjs/homicide/nomtrnd.htm (last accessed February 3, 2003).

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Support for Capital Punishment Finckenauer 1988; Niemi, Mueller, and Smith 1989; Page and Shapiro 1992; Ellsworth and Gross 1994; Schneider 2000; Shapiro 2000). Two main theoretical rationales have been cited for the apparent empirical association between homicide rates and death penalty support. One emphasizes instrumental/pragmatic concerns (Thomas and Foster 1975; Taylor, Scheppele, and Stinchcombe 1979; Stinchcombe et al. 1980; Tyler and Weber 1982; Tyler and Boeckmann 1997; Garland 2000). This perspective posits that persons exposed to high or rising rates of violent crime are likely to support extreme forms of social control, such as the death penalty, for the practical reason that such measures may deter violence. As Ellsworth and Gross note, “This is a commonsensical explanation: when crime goes up, people look for harsher punishments to bring it back down” (1994, p. 40). A second perspective links violent crime rates and support for capital punishment via direct and indirect socialization experiences. Gelles and Straus (1975, p. 609) suggest that persons exposed to higher levels of violence are more likely than others to support the death penalty because they are socialized to accept the normalcy of violence and to regard violence as an acceptable and effective form of punishment and social control (see also Borg 1998). Despite speculation to the contrary (e.g., Stinchcombe et al. 1980; Ellsworth and Gross 1994; Garland 2000; Jacobs and Carmichael 2002), other than the widely observed correspondence in temporal patterns of homicide rates and death penalty support at the national level, systematic evidence to substantiate an empirical association between these factors is scant (Beckett 1997). An important exception is the work by Rankin (1979). Using data from the 1972–76 General Social Surveys, Rankin estimated the effects of national crime rates on support for the death penalty. Controlling for race, region of residence, and year of interview, he observed a significant positive association between death penalty support and a three-year lagged measure of violent crime rates.4 Rankin’s findings provide suggestive empirical support for a positive effect of homicide rates on support for the death penalty. However, the limited number of control variables included in this study raises the concern that the effects reported for national homicide rates may be spurious. On the other hand, Rankin’s (1979) focus on national-level homicide rates may have resulted in a deflated estimate of the impact of homicide on 4 The magnitude and functional form of this association varied across regions, with a relatively strong linear effect observed in the South and a weaker nonlinear effect found for all other regions. For respondents who reside in nonsouthern regions, Rankin (1979) observed significant effects for both a linear and quadratic form of the national homicide rate. The nature of these effects suggests that although higher homicide rates are generally associated with increasing support for capital punishment, this association dampens as homicide rates reach very high levels.

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American Journal of Sociology death penalty attitudes. Individual attitudes about capital punishment may be more responsive to the level of homicide in the local environment than to the national homicide rate. Despite Rankin’s suggestive evidence consistent with the instrumental/ pragmatic perspective and its intuitive plausibility, the sociological literature on social problems provides grounds for skepticism about the extent to which objective levels of criminal violence will adequately account for variation in public opinion on crime, including support for the death penalty (Blumer 1971; Spector and Kitsuse 1977). In particular, studies informed by the constructionist approach have demonstrated the complexity of the processes through which social meanings are attached to objective conditions (e.g., Schneider 1985; Miller and Holstein 1993; Beckett 1994). As Beckett (1994) observes, conditions become social problems to the extent that they have been defined as such, and this is ultimately the result of “claims-making activities.” Beckett further maintains that “efforts to signify social problems are typically components of larger political battles” (1997, p. 6). Political elites try to frame issues in ways that serve their interests, often by directing attention away from “inconvenient social conditions” that might challenge the status quo (p. 6). Beckett and Sasson’s (2000) assessment of the growth in popular support for punitive crime-control policies in the United States since the 1960s is a good example of research in the constructionist tradition. Although levels of serious criminal violence in the United States are high by comparative standards, Beckett and Sasson argue that they have not risen enough over the last 30 years to account for the growing harshness of public attitudes toward crime and criminals. Rather, changes in popular opinion on crime and punishment “reflect the ascendance of a particular way of framing the crime problem” (Beckett and Sasson 2000, p. 73). Conservative politicians, from Barry Goldwater to Richard Nixon and Ronald Reagan, exploited public concerns about crime and racial divisions with a “law and order” rhetoric that framed criminal behavior as a matter of individual moral choice encouraged by the permissive crime and welfare policies promoted by the liberals. The goal was to effect an electoral realignment, especially in the South, that would favor the Republicans by driving a wedge between working-class whites and blacks who had composed the traditional Democratic base. The law and order rhetoric, say Beckett and Sasson, was from the beginning a thinly veiled appeal to racial prejudice. There was never any question regarding the presumed racial identification of most of the violent criminals or welfare cheats the conservatives had in mind. The strategy worked. Public concern with crime and support for punitive crime-control policies grew within an 848

Support for Capital Punishment increasingly conservative political climate that affected the views of liberals and conservatives alike (see also Garland 1990).5 The constructionist perspective on public opinion, as explicated by Beckett and others, suggests that death penalty support will not be a simple function of objective levels of criminal violence. Rather, it should vary along with the political climate and the corresponding claims-making activities of political officials. Our data do not permit the direct analysis of claims-making activity, but we can measure the degree of conservatism of the political climate. Drawing on the constructionist perspective, we anticipate a positive effect of political conservatism on death penalty support, net of official homicide rates. Respondents residing in areas with a highly conservative climate should be more likely to support the death penalty than those in less conservative environments. Note that we are hypothesizing a genuine contextual effect of conservatism. The degree of political conservatism characteristic of the area should increase the likelihood of death penalty support controlling for individual ideological position. Our final hypotheses about contextual effects are also predicated on the basic premise of constructionism that social problems must be actively problematized and that elites play a prominent role in the process (e.g., Schneider 1985). These hypotheses elaborate the traditional constructionist approach by incorporating insights from conflict theory concerning the structural conditions that are likely to be conducive to the mobilization of public opinion on crime (Scheingold 1991; Garland 2000). A longstanding tradition of research informed by conflict theory has identified racial and economic divisions as conditions that threaten the rule of dominant groups and lead to more extensive and repressive forms of social control (e.g., Hawkins 1987; Liska 1992). The basic logic of the threat hypotheses is that whites and economic elites perceive nonwhites and poor people as threatening (Blumer 1958; Chambliss 1964; Blalock 1967; Chambliss and Seidman 1980; Quillian 1995, 1996). When these latter groups increase in relative size, the level of threat increases, and dominant groups exert pressure for greater crime control to protect the status quo. A number of studies have assessed the threat hypotheses by examining the effects of racial composition (typically %black) and economic inequality on police size. The results of this research are generally supportive of the hypothesis of racial threat, whereas support for the economic threat

5 Beckett and Sasson argue that public opinion on crime and punishment has always been more complex than the law and order discourse of conservative politicians. For example, as support increased for harsher criminal sanctions, substantial fractions of the electorate also remained committed to rehabilitative prison programs, drug treatment, and crime prevention initiatives (2000, pp. 136–42).

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American Journal of Sociology hypothesis is more mixed (Jacobs 1979; Jackson and Carroll 1981; Liska, Lawrence, and Benson 1981; Chamlin 1989; Jackson 1989). Research on perhaps the most repressive form of legal control—killings by police—has also offered some support for the hypothesis of racial threat. Jacobs and O’Brien (1998) examined the structural correlates of total police killings and police killings of blacks for a sample of 170 cities. They found that, although the percentage of the population that is black has no significant association with total police killings, it is positively related to police killings of blacks.6 The threat hypotheses have generally been applied to the explanation of the actual exercise of social control (e.g., police size, arrests, police exercise of lethal violence). We extend the basic argument to public attitudes about crime and punishment. Consistent with the work of Beckett and Sasson reviewed above, we propose that elites mobilize public opinion in the direction of more punitive attitudes to further their interests, and they are particularly likely to do so under threatening conditions. Accordingly, the relative size of the minority population and economic inequality should be positively related to death penalty support, net of the officially recorded homicide rate. A recent study by Jacobs and Carmichael (2002) that examined the presence of the death penalty in American states provides evidence consistent with these hypotheses. They found that states with relatively large minority populations and high levels of economic inequality were more likely than others to have legalized the death penalty. Also, in line with the constructionist hypothesis on the effects of political climate, their analyses revealed that measures of Republican legislative strength and conservative political climate are associated with death penalty legalization. Interestingly, they found no significant effects on the presence of capital punishment for violent crime rates or murder rates. To summarize, the prior literature suggests four hypotheses about contextual determinants of death penalty support. Persons residing in areas with high homicide rates, a strongly conservative political climate, a relatively large minority population, and high income inequality should be more likely to express support for the death penalty. These effects should emerge net of individual attributes that have been linked with attitudes toward capital punishment, indicating that spatial variation in support for the death penalty is not simply a function of population distribution. They also should persist after holding constant other contextual variables

6 Jacobs and O’Brien also detected significant positive effects of racial inequality on both total police killings and killings of blacks. Their measure of income inequality was not significant for either dependent variable. They speculated that the use of a city sample might attenuate the impact of economic inequality because the affluent suburbs are excluded from the analysis (1998, p. 857).

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Support for Capital Punishment that may be associated with these conditions and with support for the death penalty.

DATA AND METHODS

We estimate spatial variation in support for the death penalty with multilevel models applied to data from the General Social Survey (GSS) that have been linked with homicide data from the National Center for Health Statistics (NCHS) and socioeconomic data from the U.S. Census Bureau. Data The GSS is a cross-sectional probability sample survey of adults in the United States conducted annually or biannually since 1972.7 The GSS has been a valuable source of data for individual-level analyses of public opinion (e.g., Firebaugh and Davis 1988; Ellison and Musick 1993; DiMaggio, Evans, and Bryson 1996; Quillian 1996; Taylor 1998), and it has been used extensively to examine the effects of individual attributes on death penalty attitudes (e.g., Bohm 1991; Fox et al. 1991; Barkan and Cohn 1994; Longmire 1996; Borg 1998). A distinctive feature of the GSS data used for our research is that they contain geographic codes that enable us to append data that describe the geographic areas in which respondents reside (see also Taylor 1998; Rosenfeld, Messner, and Baumer 2001). These geographic areas are the primary sampling units (PSUs) used to select the GSS national samples. Roughly two-thirds of the PSUs are single- or multiple-county metropolitan areas, and the remaining third are nonmetropolitan counties. Samples selected within these PSUs are self-representing in the sense that aggregated individual responses are representative of the PSU from which they are drawn, and within any given survey year, the combined samples across PSUs compose a sample of households that is representative of the continental United States (Davis and Smith 1998). The sample upon which our analyses are based includes 32,632 respondents sampled from 268 PSUs between 1974 and 1998.8 7 The full samples for 1972–74, and half-samples for 1975 and 1976, were selected using a modified probability design. A stratified, multistage area probability sample design was implemented for half-samples in 1975–76 and has been used for the selection of all respondents interviewed between 1977 and 1998 (for a detailed discussion of GSS sampling methods, see Davis and Smith [1998, app. 1]). 8 We exclude the 1972 GSS data from our analysis because PSU identifiers are not available for these respondents. Data for 1973 are excluded because of a difference in the wording of the death penalty question compared to the one used in all subsequent years. Overall, 34,136 persons were interviewed in the GSS between 1974 and 1998; our sample excludes 1,434 of these cases due to missing data on the dependent and independent variables.

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American Journal of Sociology To assess contextual determinants of death penalty support, we merged data from the NCHS and the U.S. Census Bureau with the individuallevel GSS data files using the geographic identifiers described above. The homicide data used in the analysis are from the U.S. vital statistics compiled from death certificates at the county level for 1973–98 (NCHS 1998). We attached contemporaneous and lagged homicide counts to each GSS respondent record using the census codes that correspond to the county or counties that compose the PSU in which the respondent resided. This process was a straightforward one-to-one merge in the case of singlecounty PSUs. For multiple-county PSUs, we aggregated homicides across all counties within PSUs and linked the aggregated homicide counts to the individual GSS records by matching on PSU codes. The counts were later converted to homicide rates, as described below. We use data from the U.S. Census Bureau to construct measures of two of our key explanatory variables—minority population size and economic inequality—as well as some other contextual variables that we include as control variables (discussed below). PSU-level indicators of these socioeconomic conditions were derived using county-level census data for 1970, 1980, and 1990 from the County and City Data Book Consolidated File, 1947–77, and the USA Counties 1996 CD-ROM. Because county-level census data are not available on an annual basis, we used the decennial data to estimate values for the intercensal years. We then constructed PSU-level measures of these variables and merged them with the GSS using the procedures described above. Measures The dependent variable for our analysis is a binary measure of respondents’ attitudes toward the death penalty, coded “1” for those who favor the death penalty for persons convicted of murder and coded “0” for those who oppose the death penalty or who report that they do not know (favor death penalty).9 The key independent variables in our research are the homicide rate, the degree of conservatism in the local political climate, the relative size of the minority population, and the level of economic inequality for the PSUs in which GSS respondents reside. The homicide rate was created using annual data on homicide from the NCHS and population estimates from the U.S. Census Bureau, and it represents the number of homicides

9 We reestimated all regression models shown below after excluding respondents who responded “don’t know” to the death penalty question (N p 1,990). The results of these analyses were substantively identical to those reported.

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Support for Capital Punishment per 100,000 residents in the year preceding the interview (homicide rate).10 A lagged measure of homicide is preferable to a contemporaneous measure because the latter might include homicides that occurred after the date of the survey, and because it is likely to take some time for the public to become aware of the crime rate.11 We measure the degree of political conservatism in respondents’ PSUs by aggregating responses to a GSS question about political ideology. Respondents are asked to report where they would place their political views on a scale from one (extremely liberal) to seven (extremely conservative). Our measure of conservative political climate reflects the mean level of conservatism in respondents’ PSUs during the period in which the GSS interview was conducted (conservative climate). Because single-year sample sizes for some PSUs are relatively small, we combine responses to the conservatism question across three surveys, then aggregate responses within PSUs and compute moving averages for each PSU centered on the survey year.12 This strategy seems reasonable given that the political climate for counties and metropolitan areas is likely to be stable in the short run. The relative size of the minority population in the respondents’ PSU is measured with census data on the percentage of residents who identify themselves as black (%black). To reduce skewness, we use the natural log of this variable in the regression models. The level of economic inequality for the PSUs in our analysis is measured with the Gini index for the relative distribution of family incomes within these areas (income inequality). Both measures are derived from census data as described above and characterize the demographic and economic conditions of the respondent’s PSU in the year preceding the interview. To isolate the effects of these contextual predictors, we include wellestablished correlates of death penalty attitudes as control variables in our analysis. Prior research on death penalty attitudes reveals higher levels of support among whites, older persons, males, wealthier individuals, conservatives, religious fundamentalists, married persons, and those who 10 The annual county population estimates were obtained from the Inter-University Consortium for Political and Social Research (ICPSR) data archive (see studies #8384, #6031, and #2372). 11 Although a one-year lagged measure of homicide rate seems justified on theoretical grounds, we also estimated all models shown below using two- and three-year lagged measures of homicide. The results obtained from these analyses (not shown) were very similar to those reported. 12 For years in which the GSS sampling frame changes (i.e., 1982 and 1992), we centered the three-year moving average on the year prior to the year in which the respondent was surveyed. For the later years of the GSS data used in our study (1994, 1996, and 1998), which contain much larger annual sample sizes than earlier periods, we combine data for two survey years.

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American Journal of Sociology reside in less populated areas (for reviews, see Zeisel and Gallup [1989], Bohm [1991], Fox et al. [1991], and Longmire 1996). Although more ambiguous, there is some evidence that church attendance reduces support for the death penalty (Harvey 1986) and that the effect of educational attainment on support for the death penalty is nonlinear, with low levels of support observed among persons who did not finish high school and those who graduated from college, and higher levels of support for those whose educational attainment falls between these points (Fox et al. 1991). Some research has uncovered regional differences in support for the death penalty. Persons who reside in the U.S. West and, to a lesser extent, the U.S. South exhibit higher levels of support than persons from other regions (Bohm 1991; Fox et al. 1991; Barkan and Cohn 1994; Borg 1997).13 We include each of these variables in our analysis as controls, and our measurement of them conforms closely to strategies used in prior research (see app. table A1 for a more detailed description of the control variables).14 In addition, we capture time trends in support for the death penalty by including a variable that distinguishes the year in which respondents were interviewed. To facilitate a more intuitive interpretation of estimated coefficients than would be the case if the actual year of interview (e.g., 1974, 1975) were used, we constructed a rescaled measure by subtracting 73 from the year of the interview (i.e., 1974 respondents received a code of “1,” 1975 respondents received a code of “2,” and 1998 respondents received a code of “25”). To model the well-documented nonlinear trend in support for capital punishment during this period, we also include a

13 There is some evidence in the literature that fear of crime also has a significant, albeit weak, positive effect on support for the death penalty (Rankin 1979; Tyler and Weber 1982; Seltzer and McCormick 1987; Keil and Vito 1991; Longmire 1996). The GSS includes an indicator of fear of crime (e.g., Warr 1995), but it is unavailable for about one-third of the cases in our sample. To avoid dropping data for years in which this indicator is unavailable, we do not include it in the analysis presented below. However, we replicated our analysis on the sample of cases for which the fear item is available. These analyses reveal that fear exerts a significant, positive effect on death penalty support and that the effects of the other variables are virtually identical to those shown below. 14 We used mean replacement to minimize the loss of cases due to missing data for some of the control variables. For church attendance and education, we mean-replaced less than 1% of all responses. About 7% of the cases were mean-replaced because of missing data on the conservatism scale (N p 2,282). In all regression models reported below in which these variables appear, we include dummy variables that identify the cases in which mean replacement was used. To assess the sensitivity of our results to this strategy, we also reestimated all regression models after excluding respondents for whom information on these variables was missing; the results obtained from these supplementary analyses were substantively identical to those reported.

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Support for Capital Punishment squared version of this variable.15 Finally, to minimize the possibility of drawing misleading conclusions about the hypothesized contextual effects, we conduct sensitivity analyses that incorporate three additional contextual variables: resource deprivation,16 the unemployment rate, and the male divorce rate. Although these variables have not been discussed in the death penalty literature, they are often included in studies of crime and punishment (e.g., Myers and Talarico 1986; Land et al. 1990; Myers and Massey 1991; Greenberg and West 2001; Jacobs and Carmichael 2001). Moreover, they are significantly correlated with one or more of the contextual predictors that form the focus of our analysis (see app. table A2), and they may influence death penalty support (Garland 2000; Jacobs and Carmichael 2002). Holding these factors constant increases confidence in our assessment of the hypotheses evaluated in the study. Analytic Strategy We employ a multilevel modeling strategy to examine the effects of homicide rates on support for the death penalty. Multilevel regression models have become the standard method for estimating the effects of community characteristics on individual attitudes and behaviors, especially when the data used for such studies contain a substantial amount of respondent clustering within communities (e.g., Raudenbush and Bryk 2002). Conceptually, multilevel regression models provide a direct and efficient means of describing the degree to which a given individual-level outcome, such as support for the death penalty, varies across geographic areas. In addition, an important methodological benefit is that multilevel models formally adjust for nonindependence of sample members who reside in the same community. Failure to model this type of nonindependence can result in estimated standard errors that are biased downward, which may in turn produce misleading conclusions about the statistical and substantive importance of community variables (DiPrete and Forristal 1994; Snijders and Bosker 1999). The two most general types of multilevel models used to estimate the effects of community variables on a given individual-level outcome variable are random-slope and random-intercept models. Random-slope models are particularly well-suited for examining cross-level interactions, such

15 We also conducted the analysis by modeling the time trend with dummy variables for each interview year using 1974 as the reference year (analyses not shown). The substantive conclusions drawn from these models were identical to those we describe below. 16 Resource deprivation is a principal components index composed of the poverty rate, median family income, and female-headed families (see app. table A1).

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American Journal of Sociology as whether the effect (i.e., the slope) of a specified individual-level explanatory variable varies across communities. In contrast, the main purpose of random-intercept models is to evaluate the degree to which the mean value of a given dependent variable (i.e., the intercept) varies across communities and to examine whether a specified set of explanatory variables helps to account for that variation. Because our theoretical focus in the present research is on the extent to which our explanatory variables help to explain variation in death penalty support across geographic areas, we estimate random-intercept models in which all slope parameters are treated as fixed across the geographic areas represented in our data. Specifically, given the binary coding of our dependent variable, we estimate a series of two-level hierarchical logistic random-intercept regression models (for detailed descriptions of these models, see Wong and Mason [1985], Patterson [1991], Guo and Zhao [2000], and Raudenbush and Bryk [2002]).17 Our analysis proceeds in the following manner. We begin by estimating multilevel regression models that describe the extent of variation in support for the death penalty across GSS PSUs and evaluate the degree to which that variation is due to compositional differences. We then assess whether our contextual predictors affect support for the death penalty, net of other factors, and whether they account for spatial variation in levels of support.

RESULTS

Table 1 presents descriptive statistics for all of the variables included in the analysis. Although support for the death penalty has fluctuated substantially between 1974 and 1998, overall, 70% of GSS respondents interviewed during this period reported that they favor the death penalty for persons convicted of murder. On average, GSS respondents reside in geographic areas (i.e., metropolitan areas and counties) in which there are about 9 homicides annually per 100,000 residents and in which there is a moderately conservative political climate, a population composition of slightly more than 11% black (before logging), and a distribution of family incomes that is moderately unequal. Table 1 also demonstrates that there is variation in the social contexts to which respondents are exposed; our analysis explores whether this translates into geographic differences in support for the death penalty.

17 All models presented are estimated with HLM for Windows 4.01 (Bryk, Raudenbush, and Congdon 1996). The results shown are from unit-specific models (see Raudenbush and Bryk 2002).

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Support for Capital Punishment TABLE 1 Descriptive Statistics for Variables Included in Analysis of Contextual Effects on Support for the Death Penalty Mean

Dependent variable: Favor death penalty . . . . . . . . . . . . Contextual variables: Homicide rate . . . . . . . . . . . . . . . . . . . Conservative climate . . . . . . . . . . . . %black (logged) . . . . . . . . . . . . . . . . . . Income inequality . . . . . . . . . . . . . . . Control variables: Individual-level: Time . . . . . . . . . . . . . . . . . . . . . . . . . . . Time2 . . . . . . . . . . . . . . . . . . . . . . . . . . . White . . . . . . . . . . . . . . . . . . . . . . . . . . Male . . . . . . . . . . . . . . . . . . . . . . . . . . . Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bachelor’s degree or more . . . Junior college degree . . . . . . . . . High school degree . . . . . . . . . . . No high school degree . . . . . . . Family income . . . . . . . . . . . . . . . . Married . . . . . . . . . . . . . . . . . . . . . . . . Conservatism . . . . . . . . . . . . . . . . . . Religious fundamentalism . . . Church attendance . . . . . . . . . . . Place size (logged) . . . . . . . . . . . . South . . . . . . . . . . . . . . . . . . . . . . . . . . . West . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSU-level: Resource deprivation . . . . . . . . . Unemployment rate . . . . . . . . . . Male divorce rate . . . . . . . . . . . . .

SD

.70

.45

8.73 4.13 1.66 .37

6.96 .28 1.61 .03

13.47 237.03 .85 .44 45.30 .18 .05 .52 .25 9.73 .56 4.11 .32 2.63 3.44 .35 .18

7.46 201.84 .35 .50 17.60 .39 .21 .50 .43 2.85 .50 1.28 .47 1.17 2.11 .48 .38

-.01 5.97 5.93

.99 2.02 2.35

Note.—Descriptive statistics for the dependent and individual-level control variables are based on 32,632 cases, and descriptives for the contextual variables and PSU-level control variables are based on the 268 PSUs within which these respondents reside.

Geographic Variation in Death Penalty Support We begin by evaluating the degree to which support for the death penalty actually varies across geographic areas sampled in the GSS. We do so formally in model 1 of table 2, which presents results from a regression equation that includes an intercept parameter that describes the mean log odds of death penalty support for GSS respondents and a variance component that describes whether there is significant variation in support across the geographic communities represented by the GSS. The estimated 857

TABLE 2 Hierarchical Logistic Regressions of Death Penalty Support

858 1 Fixed effects: Intercept, g00 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Time2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Male . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . No high school degree . . . . . . . . . . . . . . . . . . Bachelor’s degree or more . . . . . . . . . . . . . . Family income . . . . . . . . . . . . . . . . . . . . . . . . . . . Married . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conservatism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Religious fundamentalism . . . . . . . . . . . . . .

.901*

2 (.026)

⫺1.590* .090* ⫺.003* .975* .497* .0003 ⫺.235* ⫺.516* .044* .157* .215* .034

3 (.103) (.009) (.0003) (.038) (.027) (.001) (.034) (.035) (.005) (.028) (.010) (.031)

⫺2.220* .090* ⫺.003* .989* .498* .0004 ⫺.236* ⫺.520* .042* .158* .214* .026

4 (.465) (.009) (.0003) (.039) (.027) (.001) (.034) (.035) (.005) (.028) (.010) (.031)

⫺3.010* .088* ⫺.003* .987* .498* .0004 ⫺.234* ⫺.521* .042* .158* .214* .029

(.643) (.009) (.0003) (.039) (.027) (.001) (.034) (.035) (.005) (.029) (.010) (.031)

859

Church attendance . . . . . . . . . . . . . . . . . . . . . . Place size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . South . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . West . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Homicide rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conservative climate . . . . . . . . . . . . . . . . . . . . %black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Income inequality . . . . . . . . . . . . . . . . . . . . . . . . Resource deprivation . . . . . . . . . . . . . . . . . . . . Unemployment rate . . . . . . . . . . . . . . . . . . . . . Male divorce rate . . . . . . . . . . . . . . . . . . . . . . . . Random effects: Intercept, t00: j ........................................ x2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Note.—N p 32,632. SEs are given in parentheses. * P ! .05, two-tailed test. † P ! .05, one-tailed test.

⫺.105* ⫺.027* .107* .241*

.129 1,117*

.094 837*

(.012) (.008) (.051) (.067)

⫺.104* ⫺.035* ⫺.012 .288* .012* .246* .054* ⫺1.37

(.012) (.008) (.061) (.067) (.004) (.087) (.019) (1.03)

.078 731*

⫺.103* ⫺.038* .005 .242* .012* .244* .042* .958 ⫺.076† ⫺.005 .010

(.012) (.008) (.068) (.074) (.004) (.090) (.019) (1.57) (.046) (.014) (.016)

.077 717*

American Journal of Sociology intercept corresponds (within rounding) to the mean level of support for the death penalty across all geographic areas from which GSS respondents were sampled (.71 p exp (.901)/1 ⫹ exp (.901)). More important for the purposes of the present research, the random effects variance parameter and test statistic shown in model 1 (t00 p .129; x 2 p 1117) indicate that there is significant variation in support for the death penalty between these geographic areas. Figure 1 displays a histogram that summarizes the degree of variation in death penalty support across geographic areas represented in the GSS. This figure demonstrates that levels of support for the death penalty vary from less than 50% in some areas to greater than 90% in others. Thus, although the United States is often described as a nation that exhibits very high levels of support for capital punishment (e.g., Hood 1998), that characterization ignores the substantial internal heterogeneity in attitudes toward the death penalty that exists in the United States. Indeed, public sentiment about the death penalty is decidedly mixed in many of the geographic areas sampled in the GSS, and in some areas, a majority of persons disapprove of the death penalty for persons convicted of murder. Figure 1 prompts the question as to what factors might account for differences in levels of support for capital punishment across space. One possibility is that these differences merely reflect compositional differences in populations. In other words, it is possible that geographic areas in which levels of support for the death penalty are higher simply contain more individuals with attributes that are associated with support for the death penalty. Alternatively, rates of support for the death penalty may be higher in some geographic areas because residents of those areas are exposed to higher rates of homicide or because of a more conservative political climate, a larger percentage of black residents, or higher levels of economic inequality. Models 2–4 of table 2 assess the validity of these possible explanations. Compositional Effects Model 2 includes individual-level attributes shown in prior research to be related to death penalty support. The fixed-effects portion of model 3 shows that whites, males, married persons, individuals with more conservative political views, and those with higher family incomes are significantly more likely to favor the death penalty. Individuals with relatively high or low levels of education, those who attend church more regularly, and those who reside in places with larger populations are significantly less likely to favor the death penalty. Net of these factors, respondents who reside in the southern or western region of the United States are more likely than those from other regions to favor capital 860

Fig. 1.—Distribution of levels of support for the death penalty for 268 GSS primary sampling units, 1974–98

American Journal of Sociology punishment. These results are highly consistent with an extensive body of prior research on death penalty attitudes (for reviews, see Bohm [1991] and Longmire [1996]). The control variables explain a nontrivial portion of the variance in death penalty support across communities. A comparison of the variance components for models 1 and 2 indicates that more than one-quarter of the variation across PSUs in levels of support for the death penalty is accounted for by these individual-level attributes (.271 p .129 ⫺ .094/.129). Further analyses (not shown) revealed that about half of the overall spatial variance in death penalty support explained by the control variables is due to the time trend variables, while the other half is accounted for primarily by respondent race and political conservatism. Thus, part of the explanation for why some geographic areas exhibit higher levels of support for the death penalty is that respondents were sampled from these areas during years in which levels of support were higher overall and that these areas contain more whites and persons who self-identify as being politically conservative. Nonetheless, the variance component statistics in model 2 indicate that a significant amount of between-community variation in support for the death penalty remains even after controlling for these indicators. This suggests that other factors also contribute to differences across communities in levels of support for the death penalty. Contextual Effects on Support for the Death Penalty As documented above, a variety of contextual factors have been identified in the literature as potential predictors of geographic variation in support for the death penalty. Instrumental/pragmatic theories and socialization theories emphasize direct and indirect exposure to high levels of violence; constructionist perspectives on public opinion emphasize, among other things, the degree of conservatism in the local political climate; and threat perspectives highlight the relative size of minority groups and the concentration of economic resources. Models 3 and 4 of table 2 present regression results that evaluate these hypotheses. Model 3 includes all of the individual-level control variables, plus the four contextual explanatory variables that are of primary interest in our research. In model 4, we assess the robustness of our findings by introducing controls for additional contextual variables that may be associated with death penalty attitudes.18 18 Some of the contextual variables examined exhibit relatively strong correlations (see app. table A1), and therefore, multicollinearity is a potential concern. We inspected variance inflation factors (VIFs) for each of the contextual variables, and none was over 4 (the highest VIF was 3.8), indicating that multicollinearity is not a major problem

862

Support for Capital Punishment Model 3 indicates that three of the four contextual variables exhibit the expected effects. The estimated effect of the level of homicide is in the expected direction and is statistically significant. Consistent with instrumental/pragmatic theories, holding other factors constant, persons who live in areas with higher homicide rates are significantly more likely than others to favor the death penalty.19 We also find support for our hypothesis about the contextual effect of political conservatism. Several prior studies have shown that persons who identify with a conservative political orientation are more likely to support the death penalty. Beyond this, our results indicate that regardless of one’s own political views, residing in a more politically conservative area increases the likelihood that individuals support the death penalty. Model 3 reveals mixed evidence for the threat hypotheses examined in our research. The coefficient for %black is in the expected direction and is statistically significant. Note that the contextual effect of %black is opposite of the individual race effect. Although blacks are significantly less likely to support the death penalty, persons who reside in areas with a higher percentage of blacks are significantly more likely to favor the death penalty. In contrast, the coefficient for income inequality is negative and is not statistically significant.20 Although not the main focus of our analysis, two other noteworthy findings emerge in model 3. First, comparing the coefficients for the time trend variables in models 2 and 3 indicates that temporal changes in support for the death penalty are not due to changes over time in the control variables or the contextual predictors. The inability of changes in factors such as marital status, political conservatism, education, and homicide rates to explain temporal trends in support for capital punishment challenges much speculation in the research literature (e.g., Ellsworth and Gross 1994; Niemi et al. 1989; Page and Shapiro 1992) and the popular press (Shapiro 2000). Second, controlling for the contextual predictors in our models. Evaluation of the condition indices, however, reveals some evidence of multicollinearity between the indicators of income inequality and resource deprivation included in model 4. Nevertheless, this does not alter the substantive conclusions drawn about these two variables (e.g., neither is statistically significant when the other is removed) and, more important, the coefficients for the other contextual predictors exhibit stability across specifications that include or exclude these variables. 19 In subsequent analyses, we considered the possibility that short-term change in local homicide rates, rather than the level of homicide, might affect death penalty attitudes. We constructed a variety of change measures (e.g., absolute change, %change) ranging from one to five years. None of these change measures yielded a significant effect on support for the death penalty in our multivariate models (results not shown). 20 We evaluated a variety of different functional forms for these variables (e.g., linear splines defined at various points of their distributions, a quadratic transformation), but none yielded a significant coefficient or an improvement in model fit.

863

American Journal of Sociology explains fully the effect on death penalty support of residence in the South. Further analyses (not shown) reveal that higher levels of support for the death penalty observed in the South are a function of regional differences in racial composition and homicide rates.21 The most important findings shown in model 3 reveal that the homicide rate, a conservative political climate, and the relative size of the black population are significant predictors of death penalty support. In model 4 of table 2, we assess whether these conclusions hold up under a different model specification. In particular, we evaluate whether the effects observed for these variables are confounded with other community characteristics, including levels of resource deprivation, unemployment rates, and divorce rates. Although past studies of public opinion on the death penalty have not considered these contextual variables, macrolevel studies have demonstrated that each is at least moderately correlated with homicide rates, %black, and income inequality (e.g., Land et al. 1990). Moreover, areas characterized by a more conservative political climate tend to have higher levels of resource deprivation and higher male divorce rates (see app. table A2). Thus, it is potentially important to hold these variables constant in evaluating the effects on death penalty support observed for homicide, conservative climate, and %black. Model 4 shows that the unemployment rate and the male divorce rate do not exert significant effects on support for the death penalty. The coefficient for resource deprivation, in contrast, is negative and statistically significant using a one-tailed test (P ≤ .05). Controlling for several individual-level attributes and other contextual conditions, persons who reside in communities with lower income levels and a larger percentage of families headed by females are significantly less likely to favor the death penalty. Most important for the purposes of our analysis, even after controlling for these factors, we find significant effects of homicide, conservative climate, and %black on death penalty support. Overall, the contextual predictors considered account for 18% (.18 p .094 ⫺ .077/.094) of the spatial variance in death penalty support not accounted for by the control variables, and, collectively, the variables included in the analysis

21 We reestimated model 3 of table 2 with each of the contextual predictors entered separately and in various combinations. The coefficient for southern regional location was not attenuated when conservative climate and income inequality were controlled. In contrast, it was reduced substantially and was no longer statistically significant when either %black or the homicide rate was considered.

864

Support for Capital Punishment explain 40% (.40 p .129 ⫺ .077/.129) of the total spatial variance in death penalty support.22 In general, the individual-level attributes exert the strongest effects on support for the death penalty, but the magnitude of the effects of homicide, conservative climate, and %black are nontrivial. Table 3 presents predicted probabilities of death penalty support for respondents who reside in communities that differ substantially along these dimensions. In each case, the predicted probabilities were computed using the coefficients from model 4 of table 2 and assuming mean values for all other variables (see, e.g., Hosmer and Lemeshow 2000). The predicted probabilities associated with the estimated homicide effect imply that levels of support for capital punishment range from about 70% in areas with very low homicide rates (the 5th percentile) to about 75% in areas with very high homicide rates (the 95th percentile), assuming mean values for all other variables (including the other contextual predictors). Conservative climate and %black exhibit effects of similar magnitude. Although modest in magnitude, it is important to note that the effects of these contextual variables are additive and, because some of them covary significantly, it is informative to consider their combined effects.23 For instance, among those who reside in predominantly white communities with very low homicide rates, the predicted probability of death penalty support is 0.67, while the comparable figure for persons who reside in predominantly black areas with very high homicide rates is 0.77. This difference represents more than half of the range of observed temporal change in support for the death penalty across the years included in our study. 22 We also considered the effects on death penalty attitudes of the legal status of the death penalty (i.e., legal vs. illegal) and the number of executions carried out in the state in which GSS respondents reside. The state of residence for GSS respondents is not available, but can be inferred from the PSU geographic identifier for most respondents. For persons who live in PSUs that cross state boundaries, we created measures for all possible states in which they could have resided. The various measures yielded similar effects: persons who reside in states in which the death penalty is legal or in which more persons were executed by the state (in the year prior to the GSS interview) are significantly more likely to support the death penalty, net of the individual and other contextual variables. The causal order in these complex relationships is uncertain: the relationships could reflect the influence of state policy and action on public support for the death penalty, or they could reflect the responsiveness of the state to public opinion. A full assessment of this issue is beyond the scope of the present research. Nevertheless, the fact that the effects of homicide, conservative political climate, and racial composition persist after controlling for these variables bolsters our conclusions about their role in shaping public opinion about the death penalty. 23 We tested for the possibility that the effects of the contextual independent variables were multiplicative. Of the numerous two- and three-way interactions considered, none was statistically significant.

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American Journal of Sociology TABLE 3 Predicted Probabilities of Death Penalty Support in Different Social Contexts Percentile on Contextual Variables Contextual variables

Homicide rate . . . . . . . . . . . Conservative climate . . . %black . . . . . . . . . . . . . . . . . . .

5

10

25

50

75

90

95

.702 .697 .695

.706 .706 .703

.711 .714 .717

.720 .723 .727

.732 .734 .734

.745 .741 .737

.750 .745 .739

SUMMARY AND DISCUSSION

The sociological literature acknowledges that public opinion about social issues, including capital punishment, may be shaped by various features of the social context. However, despite a large body of research on the individual-level attributes that contribute to the development of punitive attitudes toward punishment in general and support for capital punishment more specifically, few studies have considered whether community contextual characteristics shape these attitudes. Using individual-level data from the GSS that have been linked with macrolevel data on the social and political contexts in which respondents of the survey reside, our analysis examined contextual effects on support for the death penalty. Drawing on instrumental and socialization theories of punitiveness, as well as insights from social constructionist and conflict perspectives, we hypothesized that individuals who reside in areas with a higher rate of homicide, a more conservative political climate, a relatively larger percentage of blacks, and a higher level of income inequality will be more likely than others to favor the death penalty for persons convicted of murder. We expected these effects to persist net of individual-level attributes and other contextual factors that may be associated with support for the death penalty. The possibility of geographic variation in attitudes about capital punishment had been hinted at in past research, but never evaluated systematically. Our analysis revealed significant community-level variation in support for the death penalty. This challenges conventional wisdom and popular portrayals that support for capital punishment in the United States is universally high (e.g., Singh 2000; Hood 2001). Instead, there is substantial heterogeneity in attitudes about the death penalty within the United States, with some areas exhibiting very high levels of support and others more modest levels of support, including areas in which a majority of residents disapprove of the death penalty. We found that differences in population composition account for a considerable portion of the geographic variation in public opinion on the 866

Support for Capital Punishment death penalty, but we also found support for the hypothesized effects of the homicide rate, political climate, and racial composition. Consistent with expectations, higher levels of homicide in the local area increase the likelihood that individuals will favor capital punishment, net of a wide array of individual-level predictors and other contextual factors. The theoretical literature on attitudes about crime and punishment suggests that exposure to high homicide rates may increase support for the death penalty by motivating pragmatic or instrumental crime-control responses or through socialization processes whereby residents of high violence areas come to consider violence, including violence carried out by the state, as an acceptable and effective form of punishment.24 Although plausible, there are other possible interpretations of the association between homicide rates and support for capital punishment. For instance, Garland (2000) suggests that high crime rates have weakened the effectiveness of some informal social control mechanisms (see also Anderson 1999; Sampson and Raudenbush 1999), which in turn has created an insecure and precarious social environment in which support for punitive crime control policies has flourished. Somewhat similarly, Black (1976) has argued that where informal social controls are weak, populations tend to be more open to formal measures of social control. Thus, it is conceivable that high homicide rates increase support for capital punishment (and presumably other measures of formal social control) by increasing social disorganization and reducing confidence in informal social controls. Whatever the mechanisms are that link homicide to support for capital punishment, our findings are inconsistent with the extreme constructionist view that objective conditions are irrelevant to the formation of public opinion about the death penalty. The framing of the crime issue in local areas is likely to be highly influential in the formation of attitudes about the death penalty, but objective levels of homicide also are important. We suspect that objective levels of crime and the rhetoric and imagery used by elites and the media to frame the crime issue coalesce in shaping public opinion about capital punishment (see also Garland 2000).25 Consistent 24 For a subset of the years included in our study, the GSS contains items that have been used in prior research as indicators of “fear of crime” (see Warr 1995) and “support for violence” (see Dixon and Lizotte 1987; Cao, Adams, and Jensen 1997). To evaluate whether these factors mediate the homicide effect, we reestimated our regression models based on the subset of cases for which these indicators were available (see also n. 12). Although fear of crime and the indicators of support for violence exert significant positive effects on death penalty support, they do not mediate the effects of any of the contextual predictors. 25 For a general discussion of the importance of both objective and subjective features in the construction of social problems, see Best (1993).

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American Journal of Sociology with this assertion, Beckett suggests that “the increased incidence of crimerelated problems may facilitate their politicization and contribute to growing support for getting-tough” (1997, p. 15). The significant contextual effect observed for our indicator of the political climate provides support for the constructionist argument that, independent of levels of violence, the political context in which social problems are framed has an important influence on public sentiment about capital punishment. Although we do not have direct measures of the claims-making process, the results are consistent with suggestions that conservative politicians exploit concerns about crime in a manner that effectively increases support for punitive policies, even among those who do not hold a conservative ideology themselves. This could occur directly through exposure to political initiatives or media coverage of crime and punishment, or indirectly through collective socialization processes whereby some elements of conservative ideology (e.g., support for capital punishment), but not others, become contagious and spread throughout the community. The results reveal mixed support for the threat hypotheses examined. Consistent with much of the literature on threat effects on the exercise of various forms of social control (for a review, see Liska and Messner [1999]), controlling for levels of homicide and a variety of other factors, we found that %black exhibits a significant positive effect on support for the death penalty, whereas the effect of income inequality is nonsignificant. It is perhaps not surprising that racial composition affects attitudes about capital punishment more than economic composition given the substantial degree to which race and punishment are inextricably intertwined in the United States (Hawkins 1997; Kennedy 1997; Blume, Eisenberg, and Johnson 1998; Bowers, Steiner, and Sandys 2001; Jacobs and Carmichael 2002). We cannot test directly the causal mechanisms that account for the association between racial composition and support for the death penalty. It could reflect differences in perceived threat, but it also could represent some other macro- or microlevel process. For instance, Anderson and others (Massey and Denton 1993; Kennedy 1997) have suggested that in many predominantly black communities in the United States there is a “sense of alienation from mainstream society and its institutions” (Anderson 1999, p. 34), and as noted above, Garland (2000) argues that such conditions may increase public support for punitive measures of social control. Overall, our analyses underscore the general importance of the larger social context for explaining individual attitudes toward capital punishment. The results suggest that a comprehensive understanding of public opinion on capital punishment requires information both about the char868

Support for Capital Punishment acteristics of individuals and the social environment in which they live. Nonetheless, although our results have identified some of the factors that help to account for spatial variation in support for capital punishment, a substantial amount of variation remains unexplained by the variables considered. An important avenue for further inquiry is to explore whether other variables enhance our ability to explain that variation. One possibility is that spatial differences in support for the death penalty are better explained by disaggregated rather than total homicide rates. For example, stranger, felony, and multiple-victim homicides might have a particularly strong effect on attitudes toward the death penalty. It also may be that interracial homicides have a stronger effect than intraracial incidents on death penalty attitudes. These types of murders frame much of the public debate about capital punishment, and they tend to dominate local media coverage of violence. Alternatively, perhaps the incidence of wrongful conviction in capital cases or, more generally, perceptions of bias or a lack of confidence in the criminal justice system are keys to crosssectional variation in public opinion about the death penalty (Westervelt and Humphrey 2001). Further research along these lines might contribute to a fuller understanding of spatial variation in death-penalty support. Finally, future research on death penalty support might also consider the degree to which micro- and macrolevel factors interact in their effects on death penalty attitudes. The objective of our research led us to purposely fix the effects of individual-level covariates to be constant across geographic areas, but it is plausible to speculate that the effects of some individual-level attributes may vary across local contexts. For example, consistent with a large body of research on punitiveness, we found that race and gender were among the two strongest predictors of support for capital punishment, with whites and males significantly more likely than nonwhites and females to report that they favor the death penalty. But are race and gender differences in punitiveness invariant across social contexts, or are there certain structural and cultural conditions that moderate these differences? Pursuing these types of questions would advance considerably our understanding of how individual and aggregate factors affect death penalty attitudes. In light of recent calls for a moratorium on executions and renewed debate over the death penalty, greater attention to the sources of public support for capital punishment in the United States assumes special urgency.

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APPENDIX TABLE A1 Definitions and Metrics for Control Variables Included in Analysis of Contextual Effects on Support for the Death Penalty Variable

Individual-level variables: White . . . . . . . . . . . . . . . . . . . . . . . . . . Male . . . . . . . . . . . . . . . . . . . . . . . . . . . Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Education . . . . . . . . . . . . . . . . . . . . .

Family income . . . . . . . . . . . . . . . . Married . . . . . . . . . . . . . . . . . . . . . . . . Conservatism . . . . . . . . . . . . . . . . . .

Religious fundamentalism . . . Church attendance . . . . . . . . . . .

Place size . . . . . . . . . . . . . . . . . . . . . .

Variable Definition and Metric

Respondent’s race (0 p nonwhite; 1 p white) Respondent’s sex (0 p female; 1 p male) Respondent’s age in years Four dichotomous variables indicating the highest educational degree attained by the respondent (bachelor’s degree or more; junior college degree; high school diploma or GED; no high school degree) Eight-point scale ranging from less than $1,000 to over $25,000 Respondent’s marital status (0 p unmarried; 1 p married) Seven-point scale indicating respondent’s political views (1 p extremely liberal; 2 p liberal; 3 p slightly liberal; 4 p moderate; 5 p slightly conservative; 6 p conservative; 7 p extremely conservative) Fundamentalism/liberalism of respondent’s religion (1 p fundamentalist; 0 p moderate or liberal) Four-point scale indicating how often respondent attends religious services (1 p less than once per year; 2 p several times per year; 3 p 1–3 times per year; 4 p weekly or more) Logged population size of the census place in which respondent lives Respondent resides in the south Respondent resides in the west

South . . . . . . . . . . . . . . . . . . . . . . . . . . . West . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSU-level variables: Resource deprivation . . . . . . . . . Three-item principal components factor that combines percentage of families living in poverty, median family income, and the percentage of families headed by a female Unemployment rate . . . . . . . . . . Percentage of persons age 16 and older in the civilian labor force who are not employed Male divorce rate . . . . . . . . . . . . . Percentage of males age 14 and older who are divorced

Support for Capital Punishment

TABLE A2 Correlations for PSU-Level Variables in Analysis of Contextual Effects on Support for the Death Penalty

1. 2. 3. 4. 5. 6. 7.

Homicide rate . . . . . . . . . . . Conservative climate . . . %black (logged) . . . . . . . . . Income inequality . . . . . . . Resource deprivation . . . Unemployment rate . . . . Male divorce rate . . . . . . .

1

2

3

4

5

6

7

. . .

⫺.042

.591* ⫺.105

.424* .208* .271*

.266* .254* .044 .794*

⫺.056 .093 ⫺.071 .416* .452*

.003 .240* .108 .175* ⫺.081 .172* . . .

Note.—N p 268. * P ! .05.

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