Dynamic Model of Racial Comepetiton, Racial Inequality, and Interracial Violence

Patricia L. McCall, North Carolina State University

Karen F. Parker, University of Florida

Race relations and stratification literatures offer explicit expectations concerning

Interracial conflict. Causal arguments derived from these perspectives are examined in this study to explore their ability to explain interracial violence above and beyond criminological perspectives of economic deprivation and racial inequality. The vast majority of previous aggregate-level studies on violence are cross-sectional, ignoring the importance of a dynamic model that incorporates the influence of changing structural conditions in urban areas on interracial violence. We explore theories that incorporate dynamic explanations for the influence of structural factors related to crime as well as racial conflict and employ a methodological approach that models the change in structural conditions for rare events such as interracial homicide. We find that changes between 1980 and 1990 in urban Black and Hispanic population composition, racial competition and racial inequality differentially explain the variation in White and Black interracial homicide offending.

Introduction

Since researchers began disaggregating homicides in order to examine race specific offending, unique contributions have been made to understanding the influence of social and economic forces on homicide behavior of subgroups in the population (Harer and Steffensmeier 1992; Krivo and Peterson 2000; Messner and Golden 1992; Parker and McCall 1997, 1999; Peterson and Krivo 1993; Shihadeh and Flynn 1996; Shihadeh and Maume 1997; Williams and Flewelling 1988). The field also has benefitted in recent decades from the wedding of race relations and stratification literature with criminological theories in pursuing explanations for the widely disparate violent offending rates between Whites and Blacks (Krivo and Peterson 2000; Messner and Golden 1992; Messner and South 1986; Parker and McCall 1997; Peterson and Krivo 1993; Sampson 1987). Others have linked racial conflict to interracial homicide (Jacobs and Wood 1999; Parker and McCall 1999) and provide evidence that local opportunity structure, racial inequality, and political competition differentially influence White on- Black versus Black-on-White homicide offending.

Central to these race relations and stratification literatures is the notion that changes in minority population composition (Blalock 1967) and economic forces (Massey and Eggers 1990; Wilson 1987, 1991) are catalysts for interracial conflict and concentrated disadvantage. An examination of race relations theories, such as Blalock’s minority-threat thesis, requires a dynamic model or analysis over time. Yet most aggregate-level studies that incorporate this literature into studies of race-specific violence are cross-sectional (Krivo and Peterson 2000; Parker and McCall 1999; Peterson and Krivo 1993). Our current study provides an examination of the influence that change in racial competition and racial inequality has on interracial violence. We propose that incorporating change into the research model is needed in exploring the dynamics that are central to some of these theories and in evaluating their application to the study of interracial violence. Thus, the present study examines the dynamic nature of the relationships between racial competition, racial inequality and interracial homicide between 1980 and 1990 in large U.S. cities using Poisson regression estimation techniques. These theories should find support during these periods which witnessed change in the social and economic forces underlying these causal arguments. We begin by briefly outlining the key concepts from the race-relations and stratification literatures that inform our study of interracial violence.

Threat and Conflict between Racial Groups

Traditionally economic deprivation and racial inequality arguments have been used to explain race-specific violence (Harer and Steffensmeier 1992; Messner and Golden 1992; Messner and South 1992; Parker and McCall 1997).  More recently race relations arguments have been introduced along with criminological perspectives in addressing race-specific criminal offending (Jacobs and Wood 1999, Parker and McCall 1999). Our research builds on the efforts of others by emphasizing the importance of shifts or change in economic resources and urban conditions identified in these literatures.

Blalock’s (1967) theory of minority-group relations—also referred to as his “minority threat” thesis—claims that as the relative size of the minority group increases, members of the majority group perceive a growing threat to their positions and will take steps to reduce the competition. Blalock argues that the competition between interracial groups assumes two forms—competition over economic resources, and power threats. Most extant research examining Blalock’s thesis relies on the concentration of Blacks in the population to measure minority threat.

Racial Composition

Blalock (1967) postulates that racial threat is positively related to the amount of minority concentration in a population with a decreasing slope. That is, as the concentration of minorities increases in a location, so too will the threat Blacks pose to Whites; and as this concentration of Blacks reaches some threshold, the threat of competition will diminish. Importantly, Blalock’s theory emphasizes the dynamic forces associated with an increase or change in the minority group (Black) population.

Economic and Labor Force Competition

Racial competition perspectives suggest that conflicts between racial groups are a result of minorities being positioned disproportionately among the lower classes and typically relegated to unskilled or semiskilled occupations. As Whites attempt to maintain their economic positions in the labor market, minorities are disproportionately found among lower paid positions with little hope for advancement (Bonacich 1976). Racial conflict ensues because Blacks pose an economic threat to low-skilled White workers as they compete for lower-level jobs (Blalock 1967; Blauner 1982; Lieberson 1980). Attempts to exclude Blacks from participating in the labor market and competing against Whites for jobs can take the form of violence by Whites against Blacks (Huff-Corzine, Corzine, and Moore 1991; Olzak 1990; Parker and McCall 1999; Tolnay, Beck, and Massey 1992). Therefore, Whites’ hostilities toward Blacks are a function of the (real or perceived) threat of competition Blacks pose as their numbers increase. Moreover, these theories describe a dynamic process, in that the change in economic opportunities is the key to linking racial competition to White interracial violence.

Labor force competition is posited to be related to Black interracial violence because labor force competition between Whites and Blacks is likely to be manifested in racial inequality—the aggravations and hostilities engendered by their history of subordination and discrimination by Whites (Bobo and Gilliam 1990; Bobo and Hutchings 1996). Because minorities are disproportionately represented among the lower class and the unemployed, discrimination experienced within these economic spheres engenders strain, frustration, and animosity. Relatively few studies have incorporated these arguments to explain interracial homicide (for exceptions, see Jacobs and Wood 1999; Messner and Golden 1992; Parker and McCall 1997, 1999).

Political Competition

In early U.S. history, Blacks had no power or status in the political arena. More recently, Blacks’ relative powerlessness has improved as growing numbers of Blacks win elections for municipal and national political offices. As Jacobs and Wood (1999) argue, these political victories for Blacks alter racial tensions and decrease interracial violence. Not only does Black political presence diminish racial strife and decrease incidents of Black racial violence against Whites, Blacks’ growing political strength may be perceived by Whites as another form of threat that increases resentment on the part of Whites, and, thereby, increases the chances for White-on-Black interracial violence (Jacobs and Wood 1999). Thus, it can be argued that political competition will threaten Whites’ dominant position and engender hostility among Whites as Blacks improve their economic and political status. Because Whites have long dominated the political domain, changes in political competition are not likely to influence Black-on- White violence.

Hypotheses

While the structural conditions reviewed above have been incorporated in previous studies of interracial homicide, by and large, the change in these structural conditions has not. Based on the theoretical arguments described above, we test the following relationships. First, according to Blalock, an increase in the percentage of the Black population elevates rates of White violence. This measure has been used widely in the race relations literature for the purpose of testing the power threat thesis (Olzak, Shanahan, and McEneaney 1996; Tolnay and Beck 1992; Tolnay, Beck, and Massey 1989). Consistent with this literature, we pose that growing Black population composition (or an increase in the Black population from 1980 to 1990) will increase the likelihood of White interracial homicide.

Second, racial competition theorists posit that as Whites lose out to, or perceive themselves as threatened by, Blacks in the labor force, Whites will become (more) hostile toward Blacks, thereby increasing the likelihood for conflict between members of these two groups. Therefore, with regard to our examination of the changing forces occurring between 1980 and 1990, we hypothesize that cities with increasing labor force competition (declining labor market security for Whites) will experience increasing White interracial homicides.

Labor force competition faced by Blacks and the injustice Blacks endure through discriminatory hiring practices breeds resentment and animosity toward Whites. While researchers suggest improved conditions for Blacks may decrease Black interracial competition and interracial conflict (Jacobs and Wood 1999), we propose that as competition between these two groups increases between 1980 and 1990, Black interracial homicide should increase. In summary, the parameter coefficients for the variables measuring this concept are posited to be negative in the White and positive in the Black interracial homicide models. The data and strategy we employ to analyze these proposed relationships are described in the following sections.

 

Data and Methods

U.S. cities with populations of 100,000 and over in 1980 constitute our sample of cases included in the analyses. The selection of cities during this time period allows for greater comparability with existing research. Wilson (1987, 1991) as well as Massey and Eggers (1990) focus on the changing structure of central cities in recent decades and demonstrate how poor and minority populations have been stranded in urban areas where local opportunities for employment have diminished along with deindustrialization—the exodus of major industries into suburban and rural areas. The resulting social isolation and poverty concentration have dealt a harsh hand—one that is accompanied with dwindling hope and even fewer opportunities for escape from poverty. Inclusion of this time period allows us to estimate the impact of various structural indicators on interracial homicide events during a decade of significant change (Shihadeh and Ousey 1996, 1998). Because our conceptual model emphasizes change between 1980 and 1990, measures for key concepts were collected for the two decennial points. The full data set based on the 1980 largest cities is restricted to 168 cases. To minimize the impact of year-to-year fluctuations for the rare events of interracial homicide, interracial offending counts are based on a five-year average of homicide data for the years, 1978 to 1982 and 1987 to 1991—years circa 1980 and 1990 for which data were available at the time of data collection. Missing data on some variables further reduced our sample to 146 cases for the two time points.1

The Comparative Homicide Files (CHF), which are derived from the FBI’s Supplemental Homicide Reports, provide data for our measure of interracial homicide.2 These data are widely employed in race-specific analyses of homicide (Allen and Buckner 1997; Harer and Steffensmeier 1992; Jacobs and Wood 1999; Krivo and Peterson 1996, 2000; Messner and Golden 1992; Ousey 1999; Parker and McCall 1997, 1999; Peterson and Krivo 1993; Sampson 1987; Shihadeh and Flynn 1996; Shihadeh and Maume 1997; Shihadeh and Steffensmeier 1994; Williams and Flewelling 1988). U.S. Bureau of the Census population statistics are sources for our social and economic indicators and represent those widely employed in studies of homicide offending.

Dependent Variables

This study focuses on 1980 and 1990 murders and no negligent manslaughters with a single offender and single victim, which is consistent with previous research investigating race-specific offending (Krivo and Peterson, 2000; Messner and Golden 1992; Parker and McCall 1997, 1999; Williams and Flewelling 1988). Although instances of multiple-offender homicides are omitted from this analysis, this method avoids ambiguous classifications of incidents with multiple victims and offenders of different racial groups. Until further information is available on the nature of those homicides omitted by this data, it is difficult to assess the implications of these omissions on the findings of the present study.3 The interracial (White-on-Black and Black-on-White) homicide counts for each race-specific offending group are computed as the average number of homicides involving an offender and victim of opposite races around the two decennial time points, 1980 and 1990. For example, the White interracial homicide count is the total number of homicides involving a White offender with a Black victim. Rather than interracial homicide rates, we use interracial homicide counts which are more appropriate for Poisson-based regression models, which are preferable in analyzing data with the distributional properties of such rare events (Osgood 2000).

Explanatory and Control Variables

The concepts comprising our causal models include racial composition, racial competition (political, labor market, and economic), racial inequality, economic disadvantage, and racial segregation. The reader should bear in mind the theories driving this analysis imply that changes in the relative well-being of Whites vis-à-vis Blacks are the catalyst for conflict between members of these two groups. Therefore, it is the contextual dynamics of urban centers about which we are concerned and our discussion of the relationships of these social and economic factors on interracial homicide is an examination of how the changes in U.S. cities during this period influenced interracial homicide offending.

We begin by operationalizing racial composition which is measured by calculating the percent of the total urban population that is Black. This measure of Black population composition is posited to be positively related to White interracial homicide.

There are separate concepts derived from competition theory—in particular, those pertaining to racial competition. Political competition is measured as whether or not the city has a Black mayor (Bobo and Gilliam 1990; Bobo and Hutchings 1996; Jacobs and Wood 1999). The rationale for employing this measure is that Whites are likely to feel that their political power is diminished and that their majority position is threatened when a Black mayor is elected (Bobo and Gilliam 1990; Bobo and Hutchings 1996).

We operationalize labor force competition as the ratio of the percentage of Blacks not employed in the labor force to the percentage of Whites not employed in the labor force. This ratio reflects the need or competition for jobs by race specific populations. Thus, lower values of this measure capture greater labor force competition faced by Whites relative to Blacks while higher values represent greater labor force competition for Blacks. Following Krivo, Peterson, Rizzo, and Reynolds (1998) and Parker and McCall (1999), the percent of persons not employed is used because it includes those persons who are not actively seeking employment relative to the official definition of unemployment which excludes these individuals. The percent not employed for each racial group is computed by dividing the number of employed by the number of persons 16 years of age and over, multiplying by 100, and then subtracting the result from 100.

Economic competition is operationalized as the ratio of White to Black median family income—one that has been traditionally used to measure the economic aspect of racial inequality. Larger values of the ratio represent a better economic situation for Whites relative to Blacks.

Previous research exploring race-specific homicide offending and racial inequality have found economic disadvantage (Krivo and Peterson 1996, 2000) and racial segregation (Massey and Eggers 1990; Messner and Golden 1992) to be contributing factors. These concepts are employed as explanatory variables and are measured, respectively, as the percentage of the population living below the poverty level for each race-specific population and the index of dissimilarity which is based on the racial composition of urban census tracts and depends on the relative size of the two groups.4

Population size and Hispanic population composition also are employed as control measures in this study. Population size is measured by the race-specific resident population residing in these central cities and is included as an exposure measure (elaborated below). Another aspect of population composition, the percentage of the population which is Hispanic, is introduced as a control for police error when identifying and recording victims and offenders as Whites or Blacks rather than correctly identifying them as Hispanics in police reports (Parker and McCall 1999). Regional differences are captured with three dummy measures for the South, West, and Midwest regions—the Northeast region omitted as the reference category.

Finally, because the model investigates the influence of changing social and economic conditions on interracial homicide offending, each of these measures (except the regional indicators) also are entered as the change between 1980 (t – 1) and 1990 (t)—with the change (delta) calculated as: D = (t – (t -1)).

 

Analytic Procedures

Poisson regression is employed because the dependent variables are based on discrete counts of rare events (i.e., the number of interracial homicides), have skewed distributions and include cases (cities) with zero counts. Poisson gives unbiased, consistent, and efficient estimates for these types of dependent variables and is preferred over ordinary least squares (OLS) regression when one is not able to meet the assumptions for OLS—such as, the assumptions of homogeneity of error variance and normal error distributions (see Osgood 2000:22–3).5 The statistical software we employ provides the capacity to correct for the city’s race-specific population (of offenders) as an exposure variable by constraining its coefficient to equal one (STATA, version 7). This method converts the counts of interracial homicide into the equivalent of a rate for each city (Maddala 1983; Osgood 2000).

There is no clear consensus regarding the best way to model change in sociological research (Firebaugh and Beck 1994; Hausman, Hall, and Griliches 1984; Kessler and Greenberg 1981). To test the element of change underlying the theoretical approaches outlined above, we follow the model specification employed by Greenberg and West (2001:635) which includes difference measures for the independent variables.6 The model specification takes the following form: yt = a + b1yt-1 + b2xt-1 + b3(xt – xt-1) + . . . + et, where t represents 1990 and t –1 represents 1980, xt–1 represents the 1980 independent variables, and xt – xt–1 represents the change between 1980 and 1990 for the independent variables.

Table 1 provides descriptive information for our city-level variables measured in 1980 and 1990. Changes between 1980 and 1990 are presented in the third column which reveals increasing mean numbers of Black and White interracial homicides committed during this period for these U.S. cities and increasing (positive) means for all explanatory measures except for racial segregation—likely declining as a result of gentrification of segments of these urban centers.

The bi-variate correlations reveal evidence of collinearity or problems associated with the partialing fallacy. Techniques for identifying the extent of this problem are undertaken in estimating the regression models to determine whether these high correlations have a substantive influence on the findings.7 The overall support for the theories examined in this analysis are largely unchanged when estimating alternate models.8 Bi-variate scatter plots revealed no curvilinear relationships between the dependent and independent variables; therefore, there is no need for variable transformations.

Findings

Table 2 presents the parameter coefficients estimated from the Poisson regression analyses. Models 1 and 3 represent the results for Black and White interracial homicide offending, respectively, that omit the change measures and are provided only as a baseline for comparison with the Models of interest, 2 and 4, that include the change measures indicated with “D” (symbolizing “delta”) preceding the variable name that represents the change between 1980 and 1990.

We focus our review of the results on the change measures in Models 2 and 4 that are statistically significant. The implications of these results will be elaborated in the discussion section.

The primary support for the influence of change on interracial violence is found for Blalock’s minority threat thesis—the coefficient for the change in percent Black population is positive and statistically significant in the White interracial homicide model. In addition, one of the three measures for racial competition, labor force competition, is statistically significant in the White interracial model although the relationship is positive and, therefore, not consistent

 with theoretical prediction. Competition theory states that increasing racial competition would be associated with interracial conflict. The labor force competition measure (ratio of not-employed Blacks to not-employed Whites) indicates that White-on-Black homicides were higher in 1990 in cities where Blacks faired worse relative to Whites in the labor force between 1980 and 1990. This finding is contrary to the theoretical prediction because Black’s declining labor force participation relative to Whites’ would not pose any threat of job security for Whites. On the other hand, in the Black interracial homicide model, the coefficient for this labor force competition variable is statistically significant and positively related to Black-on-White homicides. This supports the hypothesis that increasing competition between the races in the labor force between 1980 and 1990 and resulting frustrations engendered among Blacks were associated with higher Black-on-White homicides with our sample of cities for 1990.1

Table 1

Means, Standard Deviations (in parentheses), and Percent Change for

Characteristics of Cities in 1980 and 1990

1980 1990 Percent change (%)

Black interracial 6.63 6.71 1.21

homicide (counts) (18.00) (20.91)

White interracial 2.98 3.66 22.82

homicide (counts) (9.30) (15.69)

Percent Black population 20.26 22.26 9.87

(16.85) (18.01)

Proportion of cities .07 .14 100.00

with Black mayor (.25) (.34)

Ratio of not employed 1.11 1.16 4.50

Blacks to Whites (.14) (.18)

Ratio of median family 1.58 1.73 9.49

Income of Whites to Blacks (.26) (.37)

Percent Blacks in poverty 26.68 28.12 5.40

(6.94) (8.18)

Percent Whites in poverty 10.16 10.83 6.59

(3.35) (3.86)

Racial segregation 71.89 53.77 −25.20

(10.95) (16.91)

Population size 363.95 386.59 6.22

(in thousandths) (690.18) (719.23)

Percent Hispanic population 8.41 11.21 33.29

(10.93) (13.44)

South .34

(.47)

West .32

(.47)

Northeast .16

(.36)

Midwest .19

(.40)

Table 2

Poisson Regression Coefficients (and Z-Scores) for Change in 1990 Black and White Interracial Homicides, N 146

Black Interracial White Interracial

(1) (2) (3) (4)

Interracial homicides 1980 0.003** 0.004** 0.012** 0.012**

(3.16) (2.58) (5.73) (4.06)

Percent Black population −.008* −.0060.037** 0.034**

(Racial composition) (−2.23) (−1.28) (6.99) (5.43)

Black mayor 0.125 0.061 0.212 −.021

(Political competition) (1.00) (0.43) (1.13) (−.10)

Ratio of not employed B/W −.707 .004 −.618 0.536

(Labor force competition) (−1.22) (0.01) (−.87) (0.57)

Ratio of income of W/B .995** 0.924** −.030 −.144

(Racial income inequality) (3.68) (2.74) (−.07) (−.29)

Race-specific poverty −.030** −.022−.029 −.001

(Poverty concentration) (−3.15) (−1.76) (−1.14) (−.05)

Racial residential segregation −.009* −.015* 0.0100.007

(Interracial contact) (−1.76) (−2.24) (1.51) (0.76)

Percent Hispanic population 0.022** 0.018** 0.023** 0.011

(4.83) (2.71) (3.41) (1.11)

A DYNAMIC MODEL OF RACIAL COMPETITION 283

South −.021 −.102 −.437* −.087

(−0.14) (−.53) (−2.15) (−.34)

West 0.436** 0.335* −.058 0.078

(2.89) (2.08) (−.28) (0.34)

Midwest .220 .085 0.521* −.432

(1.41) (0.44) (−2.28) (−1.55)

D-Percent Black population — 0.022 — 0.100**

(1.01) (3.17)

D-Black mayor — 0.100 — −.007

(0.61) (−.03)

D-Ratio of not employed — 1.006* — 1.846**

Blacks to Whites (1.66) (2.42)

D-Ratio of income of — −.020 — −.230

Whites to Blacks (−.07) (−.63)

D-Race-specific poverty — 0.007 — −.035

(0.46) (−1.03)

D-Racial residential segregation — −.008 — 0.013

(−.91) (1.19)

D-Percent Hispanic population — 0.033* — 0.088**

(2.02) (3.74)

Constant −9.18** −9.90** −12.15** −13.58**

Log-likelihood −263.99** −258.90** −209.59** −196.35**

Pseudo R-square .354 .367 .466 .500

Notes: **p .01, *p .05, p .10 (one-tailed). “D” change measure (1980–1990).

Turning to our control measure for Hispanic population composition, change in the percent Hispanic population is statistically significant and positive in both of the interracial homicide models. This implies that cities with growing proportions of Hispanics in the population between 1980 and 1990 had larger numbers of interracial homicides involving Black as well as White offenders in 1990. Recall that this control measure is included primarily to account for police misrecording the race of the offender in police reports. Among the regional effects, the West has a significant, positive influence only in the Black interracial homicide model—that is, cities in the west have higher numbers of Black interracial homicides than cities in the Northeastern part of the United States (the reference category for region). We now elaborate these findings in relation to their theoretical underpinnings.

Discussion and Conclusions

The purpose of this research is to model and test the dynamic nature of the arguments underlying major race relations theories to determine the extent to which these theories account for interracial violent behavior. Based on race relations literature, we hypothesized that mounting racial economic, labor force, and political competition would spur White interracial homicide offending whereas Black interracial homicide would be associated with increasing labor force competition

and racial inequality. By and large, our findings show mixed support for the importance of change implicit in these competition arguments in the White interracial homicide model. We begin our discussion of our findings with the classic indicator of racial competition—growing Black population.

We find support for Blalock’s minority threat thesis that members of the majority group perceive a growing minority population as a threat to their dominant social position. Consistent with Jacobs and Wood’s (1999) cross-sectional analysis of interracial homicide and with many extant race relations analyses that employ percentage Black population as a measure of minority threat (Olzak and Shanahan 1996; Olzak, Shanahan and McEneaney 1996; Tolnay and Beck 1992; Tolnay, Beck, and Massey 1989), our analysis provides evidence that the increasing Black population between 1980 and 1990 is related to White interracial homicide. Blau’s (1977) macro structural perspective provides another explanation for these findings. As Blau argues, in cities where there are higher proportions of the urban population comprised of Blacks, there will be an increase in the likelihood of interracial interaction and, in turn, a greater likelihood of interracial violence. As Blau noted in his macro structural theory, the opportunity for interracial contact is required for meaningful interracial association. Therefore, the population composition of a community sets the stage for the likelihood of these contacts. More importantly, the nature, as well as the extent of interracial contact, are sculpted by the social and economic conditions in which interracial contact occurs (Messner and Golden 1992; Messner and South 1992; Sampson 1987; South and Messner 1986). Therefore, the statistical significance of the percent Black population may be explained by Blalock’s minority threat thesis, Blau’s macro structural (opportunity) perspective, or both.

Competition theorists argue that it is the threat of competition for jobs, power, and positions (whether such threats are real or imagined), whereby Blacks pose a political or economic threat to Whites that leads to an inherent conflict between them (Blauner 1982; Lieberson 1980). Change in labor force competition (the ratio of the percent not-employed Blacks to not-employed Whites) affects White interracial homicide, but the effect has a positive coefficient which is contrary to the hypothesis derived from racial competition theory. Although racial competition theory is not supported with our indicators of economic, political competition, or labor force competition as predicted in the White interracial homicide model, one could argue that growing proportions of minorities in the population would pose a sense of threat to the majority population regardless of whether there was an actual economic or political threat as reflected in the empirical indicators employed in this study. A study of labor market influences during a period of more severe economic downturn and job losses such as that between 1970 and 1980 may reveal evidence of such effects, but supplemental homicide data were not available until the mid-1970s.

Whereas the influence of changes in labor force competition is opposite to theoretical prediction in the White interracial homicide model, this competition measure has a positive, statistically significant influence on Black interracial homicide and provides support for this argument. These results indicate that cities with diminishing labor force opportunities for Blacks relative to Whites’ opportunities between 1980 and 1990, experienced higher numbers of Black interracial homicides in 1990. The influence of this aspect of racial competition is an indicator of structural discrimination, and evidence that Whites’ labor force gains at the expense of Blacks’ continue to disrupt to race relations. As noted earlier, when this model was estimated using a three-year average for Black-on- White homicides for the period circa 1990 (1989–1991 three-year average versus 1987–1991 five-year average), these findings were not substantiated. Therefore, these findings are not robust across the two models.

The other statistically significant coefficient among the change measures in the White interracial and Black interracial homicide models is that for the percent of Hispanics in the population. One could argue that the significant influence of the growing Hispanic population may represent a logical extension of Blalock’s threat thesis. Growing Hispanic populations could pose the same type of threats to Whites and Blacks as Hispanics move into an area and compete with them in the labor force and in the political arena. Because the Hispanic population composition was not introduced as an indicator for our theoretical arguments and hypotheses, we will leave this finding to simply reflect that for which it was introduced—a correction for police recording practices. Other researchers interested in the study of minority population composition and the influence of minority population dynamics on interracial interaction may want to consider this potential source of interracial conflict in future research.

In conclusion, Blalock’s minority threat thesis is supported in the White on- Black homicide model and support emerges for the importance of labor force competition in the Black-on-White homicide model. We find evidence that racial competition theory vis-à-vis a growing Black population best explains higher numbers of White interracial homicide offending in large U.S. cities in 1990, whereas changes in labor market opportunities account for higher Black on- White homicides in 1990.

As we take stock of our findings and examine the broader implications of these results, we warn the reader against committing the ecological fallacy. These findings do not necessarily demonstrate that certain social and economic factors or changes in these factors are forces that influence a member of one racial group to commit homicide against persons of other races. We argue, though, that certain social and economic forces create contexts which engender stress, frustration, and hostility among some societal members more so than others. In turn, these interracial hostilities have the potential to result in interpersonal conflict that may have a lethal outcome. The race of the homicide “victim” may not necessarily represent the source of the frustration or hostility, and it is not possible to distinguish the aggressor from the assaulted in these interracial homicide statistics. The fact that the instigator of the conflict may become the “victim” of homicide may account for the anomalous support for the positive effect of the change in labor force competition in the White interracial homicide model. Nevertheless, these analyses shed light on and further refine the theoretical arguments which address race relations in the United States.

The results of these analyses of change do not diminish the importance of the enduring influence of power differentials between Whites and Blacks on interracial conflict. Prior research has demonstrated how economic and political rivalries are related to interracial conflict (Myers 1990; Olzak 1990; Tolnay, Beck, and Massey 1989) and interracial homicide (Jacobs and Wood 1999). Nevertheless, the extent to which change in various aspects of racial competition over time is related to interracial violence is not well established. Our study of the dynamics between 1980 and 1990 provide additional support for this association. Future research that examines periods with more dramatic rates of economic downturn and political power shifts may provide evidence substantiating the dynamic nature of these associations. Our research demonstrates the importance of measuring change and examining the influence that change in population composition and racial competition has on interracial conflict. These findings emphasize how social and economic dynamics contribute to our understanding of interracial violence over and above extant efforts that examine relationships between racial threat, competition, and interracial violence in the cross-sectional literature. Thus, while theoretical arguments have been developed in the past 25 years to account for racial disparities by investigating racially disaggregated homicide events, there is still much work to be done in this area.

 

ENDNOTES

*Authors’ names are listed alphabetically. Direct correspondence to Patricia L. McCall, North Carolina State University, Raleigh, NC 27695-8107. Telephone: 919-515-9010 or 919-5150-2610. Email: patty_mccall@ncsu.edu.

The authors thank John MacDonald, David Jacobs, Rodney Engen, William R. Smith, David Greenberg, and anonymous reviewers for their assistance and comments.

1. Our use of different years circa the decennial time point (that is, 1978–1982 versus 1987–1991) simply had to do with the years for which data were available when we initially constructed our data set. At that time, the data for 1992 were not available and because we were collecting data for estimates of the decennial time point (using five rather than three years to take into account the rare nature of interracial homicides). We contend that the choice of years provides only an approximation for these homicide events. The models were estimated using more standardized four-year average homicide measures (1978–1981 and 1988–1991) and we found that there was one substantive difference in the findings regarding the influence of change on interracial homicide: change in labor force competition measure in the Black interracial homicide model was no longer statistically significant.

In addition, the fact that interracial homicide is a rare event raises the question of the reliability of aggregate-level estimates—especially in cities with small populations of Blacks. Other studies of race-specific offending have restricted the cases included in analyses to those with at least five percent of Blacks in the city (Krivo and Peterson, 2000; Messner and Golden 1992; Parker and McCall 1997, 1999; Peterson and Krivo 1993; Sampson 1987). Because we employ one of the Poisson families of regression techniques (which were developed for estimating rare events) and because our focus is on the dynamics related to the impact of varying sizes of minority populations, we do not restrict our sample to those with a minimum percentage of Blacks. In addition to missing data, the sample was reduced also because homicide data are not available for cities in Florida circa 1990. Although the data circa 1990 do not represent the standard five years—from 1988 through 1992—these are the years for which data were available at the time of our data collection. Nevertheless, these five-year averages should provide sufficient estimates for this decennial time period.

2. The Comparative Homicide File (CHF) was created by Williams and Flewelling who compiled this information from the Homicide Supplemental Reports. We acknowledge alternative procedures are also available to deal with missing data (see Messner, Deane, and Beaulieu 2002). However, our read of that literature which analyzes and compares alternative methods (i.e., Messner et al. 2002; Pampel and Williams 2000) leads us to the conclusion that there is no evidence supporting one method over the other. See also Messner and Golden (1992) and Williams and Flewelling (1988) for further detailed descriptions of these data.

3. Another important data issue in this study is the problem of missing data on offenders’ race. Approximately one-fourth of the recorded homicides in the CHF report the race of the offender as unknown. Furthermore, the potential for measurement bias is created as the racial patterning of homicide events in which information is missing may differ from the patterning of nonmissing events (Messner and South 1992). The imputation algorithm developed by Williams and Flewelling (1988) was employed to address this problem. This algorithm is used to “extrapolate the characteristics of the known cases to those with missing information. Essentially the procedure involves the estimation of the race of the offender (where unknown) on the basis of the type of incident under investigation and the observed racial patterning of that type of incident when the offender’s race is known for a given city” (1988: 426).

4. The index of dissimilarity is one of many measures of segregation being proposed for use in criminological research (see Shihadeh and Flynn 1996; Shihadeh and Maume 1997). We employ the index of dissimilarity to capture the distribution of population subgroups across census tracts and the relative size of the two groups (Massey and Denton 1988)—the relative size being one of the key factors underlying competition theses.

5. The basic Poisson regression equation is comparable to the practice of using the logarithmic transformation of the aggregate crime rate dependent variable in Ordinary Least Squares (OLS) regression which is used in most extant homicide research. Employing Poisson regression avoids many estimation problems (e.g., heterogeneity) associated with OLS analyses of crime rates. Chisquare goodness of fit test statistics indicate Poisson rather than negative binomial is the appropriate estimation technique for these models (STATA, version 7). See Osgood (2000:24) for an excellent discussion of related methodological issues.

6. We depart from Greenberg and West’s model specification by regressing the interracial homicides in 1990 rather than the difference between the numbers of homicides in 1990 and 1980 that was central to Greenberg and West’s hypotheses, on the explanatory variables.

7. Other models we estimated included measures of percent of the Black population squared in attempts to model Blalock’s thesis of a positive influence of percent Black population with a decreasing slope—that is, we anticipated a reduction in White interracial homicide as the Black population reaches a point of concentration in these cities. This measure was correlated above .9 with the 1980 interracial homicide measure and could not be estimated without producing collinearity problems. Variance Inflation Factors (VIF) were not available in the software program for Poisson; however, VIFs derived from OLS regressions estimated for these models (while using the log transformed dependent variables) revealed VIF values between 11 and 13 for the percent Black squared measure. Nevertheless, we found no major substantive difference in the findings among the change measures in our models when the quadratic term was included. In addition, the murder rate was initially introduced as a control as did Jacobs and Wood (1999) but VIFs above 5 and 6 also indicated estimation problems.

8. Evidence of potential partialing problems was explored by estimating separate equations by omitting variables with bi-variate correlations above .5. This revealed problems with the percentage Black population measure in the White interracial homicide model. Omitting this variable resulted in the following additional statistically significant variables in the indicated direction: Black mayor 1980 (), racial labor market competition (), south (), change 1980–1990 Black mayor (, p .09, one-tailed), and change 1980–1990 race segregation ()—all differences, by and large, supporting the hypotheses. Results from the alternative model showed no major substantive changes in statistical significance of the coefficients. The correlation matrix is provided in the Appendix.

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Appendix

Correlation Matrices for White-on-Black (upper diagonal) and Black-on-White (lower diagonal) Interracial Homicide

Offending, 1980 Independent Variables and 1990–1980 Change Measures (“D”), N = 146

Y1 X1 X2 X3 X4 X5 X6 X7 X8 X9

Y1: Interracial hom. 1990 1.00 .975 .097 .080 .123 .045 .096 .150 .150 −.060

X1: Interracial hom. 1980 .977 1.00 .134 −.049

X2: Percent Black .170 .220 1.00 .170

X3: Black mayor .211 .244 .489 1.00 .273

X4: Ratio B/W not employed .160 .162 .140 .044 1.00 .011

X5: Ratio W/B med. income .089 .089 .376 .100 .529 1.00 −.077

X6: Race-specific poverty .062 .105 .473 .067 .409 .590 1.00 .097 .306 −.176

X7: Racial segregation .150 .211 .565 .090 .261 .418 .584 1.00

X8: Percent Hispanic .150 .142 −.278 −.014 −.127 −.247 −.221 −.340 1.00

X9: South −.049 −.058 .392 −.020 .146 .437 .220 .379 −.106 1.00

X10: West −.026 −.034 −.396 .050 −.209 −.310 −.441 −.467 .269 −.482

X11: Northeast .126 .134 .038 −.043 −.142 .021 .291 .011 .059 −.307

X12: Midwest −.027 −.015 −.038 .006 .202 −.178 −.014 .087 −.245 −.346

X13: D-Percent Black −.029 −.014 .391 .009 −.100 −.016 .254 .148 −.327 .117

X14: D-Black mayor .257 .294 .243 −.164 −.064 −.026 .114 .171 −.055 .034

X15: D-B/W not employed −.042 −.035 −.083 .133 −.177 −.144 −.086 .043 −.005 −.152

X16: D-W/B med. income −.067 −.067 .105 .136 .036 .042 .130 .253 −.307 .081

X17: D-Race-specific poverty −.091 −.091 −.137 −.025 −.016 −.218 −.161 .114 −.202 −.137

X18: D-Racial segregation .304 .343 .516 .305 .288 .272 .412 .307 −.091 −.039

X19: D-Percent Hispanic .136 .126 −.265 −.017 −.263 −.175 −.225 −.329 .613 −.207

A DYNAMIC MODEL OF RACIAL COMPETITION 293

Y1: Interracial hom. 1990 −.044 .183 −.045 .005 .297 −.076 −.082 −.035 .252 .099

X1: Interracial hom. 1980 −.058 .159 −.020 .012 .276 −.050 −.060 −.009 .317 .100

X2: Percent Black −.103

X3: Black mayor −.084

X4: Ratio B/W not employed −.073

X5: Ratio W/B med. income −.272

X6: Race-specific poverty −.061 .384 −.073 .005 .050 .026 −.042 −.085 .352 .102

X7: Racial segregation .092

X8: Percent Hispanic .146

X9: South −.079

X10: West 1.00 .086

X11: Northeast −.293 1.00 −.267

X12: Midwest −.330 −.211 1.00 .240

X13: D-Percent Black −.375 .169 .146 1.00 .187

X14: D-Black mayor −.114 .164 −.057 .186 1.00 −.046

X15: D-B/W not employed .173 −.111 .081 −.273 −.060 1.00 .028

X16: D-W/B med. income −.116 −.222 .246 .106 −.082 .404 1.00 .121

X17: D-Race-specific poverty .037 −.328 .423 .147 −.056 .299 .592 1.00 −.135 .044

X18: D-Racial segregation −.205 .225 .081 .153 .197 .100 .170 −.031 1.00

X19: D-Percent Hispanic .216 .293 −.277 −.271 −.046 −.019 −.308 −.285 −.092 1.00

Note: Correlations among independent variables provided in the White on Black (upper diagonal) only when the variables differ for the interracial models.