Racial disproportionality in state prison admissions: Can regional variation be explained by differential arrest rates?

Journal of Criminal Justice 31 (2003) 73 – 84

Racial disproportionality in state prison admissions: Can regional variation be explained by differential arrest rates? Jon Sorensen*, Robert Hope, Don Stemen Vera Institute of Justice, New York, NY 10279, USA

Abstract Recent studies have found that states in the Midwest incarcerate Blacks at a higher rate than those in the South. This article examines the extent to which this regional pattern of racial disproportionality in incarceration rates could be explained by controlling for race-specific arrest rates. The findings showed that the level of racial disproportionality in imprisonment decreased among all of the regions after controlling for race-specific arrest rates, but previously observed regional differences remained. These findings indicate that differences in the level of racial disproportionality in incarceration rates among regions were due to differential involvement in serious crimes by race resulting from a higher concentration of Blacks in urban areas relative to Whites in the Midwest. D 2002 Elsevier Science Ltd. All rights reserved.

Introduction Historically, studies of racial disparity in the United States focused on southern jurisdictions (Mangum, 1940; Myrdal, 1962). This is not surprising given the rather obvious nexus between the penal system during the reconstruction era and the former system of chattel slavery. Southern prisons in the latter half of the nineteenth century consisted almost entirely of former slaves who were either leased out to work the fields of former plantation owners or put to work on road gangs and other government projects, thus serving to maintain a caste system while rebuilding the infrastructure of the former confederate states (McKelvey, 1936; Oshinsky, 1996). Northern prisons, in comparison, served mainly to correct the behaviors of the ever-increasing number of newly

* Corresponding author. Behavioral Sciences Department, Fitchburg State College, Fitchburg, MA 01420, USA. Tel.: +1-978-665-3241; fax: +1-978-665-3608. E-mail address: [email protected] (J. Sorensen).

arrived European immigrants. Though the incarceration rates for Blacks were much higher than expected given their representation in the population (Beaumont & Tocqueville, 1833, p. 93; Litwack, 1961), the raw number of Whites dwarfed that of Blacks incarcerated in northern industrial prisons. In accordance with these facts, relatively little attention was paid to racial disparities in incarceration rates in northern jurisdictions. Recently, a new and seemingly anomalous pattern of racial disproportionality in imprisonment across regions has been uncovered. States in the Northeast and Midwest have been shown to incarcerate Blacks at a higher rate relative to Whites than those in the South (Blumstein, 1993; Christianson, 1981; Tonry, 1991). To date, however, few studies have examined these seemingly anomalous findings. As a result, the reason for the existence of such a regional pattern of disparity is uncertain. In this article, a refined measure, one that controls for race-specific arrest rates, is employed to gauge the extent of unexplained racial disproportionality in incarceration rates across regions. Results from this study provide evidence

0047-2352/02/$ – see front matter D 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 0 4 7 - 2 3 5 2 ( 0 2 ) 0 0 2 0 0 - 3

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for an explanation of the seemingly anomalous regional pattern of racial disproportionality in incarceration rates described in previous studies.

Regional variation in Black-to-White imprisonment ratios The first to bring attention to the anomalous pattern of regional disparities in Black versus White incarceration rates, Christianson (1981, p. 366) found that while southern states typically had the highest incarceration rates for Whites during the 1970s, ‘‘contrary to expectations, this pattern did not apply to the imprisonment of Blacks.’’ Although he did not calculate the actual ratios of Black-to-White incarceration rates, his figures showed that relative to White incarceration rates, Black incarceration rates were two to three times higher in the Northeast and Midwest than in the South. Alfred Blumstein (1993) found a similar regional pattern of discrepancies. Using data on prison populations for 1991, Blumstein calculated the ratio of Black-to-White incarceration rates across states. While he found that the average ratio of Black-toWhite incarceration rates was 7.1:1, it varied from 1.5:1 in Hawaii to 20.4:1 in Minnesota, with northeastern and midwestern states having the highest and southern states the lowest ratios of Black-to-White incarceration rates. He hypothesized that the observed regional variation likely resulted from differences in overall rates of incarceration. He predicted that states with lower incarceration rates, located primarily in the North, would reserve incarceration for serious and violent offenses in which Blacks were disproportionately involved, while states with higher incarceration rates, located primarily in the South, would incarcerate for lower-level offenses in which Whites were more likely involved. Blumstein offered an inverse correlation between Black-to-White incarceration ratios and overall incarceration rates among states as initial support for his hypothesis. Other researchers provided a more direct test of Blumstein’s hypothesis, examining the extent to which variations in arrest rates for serious crimes explained differences in imprisonment rates between Blacks and Whites. Crutchfield, Bridges, and Pitchford (1994, p. 174) calculated the ratio of disproportionality in incarceration rates in 1982 explained by arrests for index crimes in 1981. They found that the amount of racial disproportionality in incarceration rates explained by arrest ranged from 115 percent in the Midwest to 70 percent in the Northeast, with differential arrest rates accounting for about 90 percent of racial disproportionality in the South and West. Crutchfield et al. (p. 178) also calculated the

number of Blacks expected to be imprisoned based on the proportion of arrests that were Black, and then computed the percentage of observed Black imprisonment explained by the expected imprisonment estimates. Regional differences essentially disappeared, with explained percentages of Black imprisonment ranging from 62 percent in the Northeast to 67 percent in the South. Hawkins and Hardy (1989) compared Black and White rates of serious arrests in 1979 to their proportions in the prisoner census data for 1980 for thirty-nine continental states with a Black population of at least 1 percent. They found that differences in arrest rates explained 22 – 96 percent of the variation in ratios of Black-to-White incarceration rates across states, with no clear-cut regional patterns. Hawkins and Hardy found that the rankings of states’ ratios of Black-to-White incarceration rates controlling for differential arrest rates differed markedly from incarceration ratios based on the number of Blacks and Whites in the general population, with a Spearman’s rank order correlation of
.52 between the two measures. Some states showed marked decreases in their levels of racial disproportionality after controlling for arrest rates. Minnesota, for instance, had the highest ratio of Black-to-White incarceration rates based on their proportions in the general population, but was average in terms of explained racial disproportionality in imprisonment given Blacks’ rate of arrest for serious crimes. Only three of the ten states with the highest rankings in the ratios of Black-toWhite imprisonment rates scored in the highest ten in terms of variance unexplained by differential arrest. Hawkins and Hardy, however, did find some consistency between the two measures for states with the smallest initial differences in their Black-to-White incarceration rates. Hawkins and Hardy also tested some possible explanations for the observed levels of unexplained racial disproportionality in incarceration rates across states. They found a correlation between the level of explained disproportionality and the percent of a state’s Black population residing in urban areas. They hypothesized that percent Black urban served as a proxy for offense seriousness, states with higher proportions of Blacks residing in urban areas having a greater mixture of more serious offenses involving Blacks than states with largely rural Black populations. These studies (Crutchfield et al., 1994; Hawkins & Hardy, 1989) offered a better measure of disproportionality by controlling for rates of arrest, yet they failed to control for the potential influence of the mixture of offenses on the level of explained racial disproportionality in incarceration across jurisdictions. That is, arrest rates for a group of offenders

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were compared to all incarcerated inmates without controlling for the distribution of offense types expected. The authors compared the Black arrest rate for serious offenses to the overall rate of Black incarceration, instead of first calculating the incarceration rates by race for each particular type of offense. Because the authors did not control for imprisonment rates by offense, they were unable to test assertions that the offense mixtures may have influenced the level of explained racial disproportionality in incarceration across states. A recent study clearly illuminated the problems associated with using an aggregate measure of arrest rates in calculating the level of racial disproportionality in incarceration (Austin & Allen, 2000). Austin and Allen (2000) charged that the failure of previous researchers to control for the mixture of offenses led to vastly different estimates of racial disproportionality explained by arrest rates. Specifically, Hawkins and Hardy (1989) reported 52 percent of explained variance in the racial disproportionality in incarceration in Pennsylvania prisons in 1980, compared to Crutchfield et al.’s (1994) estimate of 107 percent in 1982. Austin and Allen’s procedure involved calculating the expected ratio of Black-to-White incarceration rates separately for each index offense, as well as manslaughter, other assaults, and drug offenses. They found the five-year average in racial disproportionality in incarceration rates explained by arrest during the early 1990s to be lower than previous researchers had estimated at 42.4 percent. The researchers also found a negative relationship between crime seriousness and the level of explained variance in disproportionality, especially for drug crimes. Without drug offenses, the level of explained racial disproportionality in incarceration rates increased to 70.4 percent. For murder and robbery only, the level of explained disproportionality in incarceration rates increased to 79 percent.

Disaggregation by offense type In the pioneering study in this field of research, Blumstein (1982) attempted to account for the national level of disproportionality in Black-to-White incarceration rates, a ratio of nearly 7:1. Given their rate of arrest for serious offenses reported in the Uniform Crime Reports in 1974, Blumstein calculated the percentage of inmates expected to be Black at 42.7 percent, while the Survey of Inmates in State Correctional Facilities showed that Blacks actually made up 48.3 percent of the prison population during the same year. Figures for 1979 showed the expected percentage of Black prisoners to be 43.5, compared to the actual percentage of 49.1. To analyze the discrep-

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ancy between the expected and observed Black incarceration rates and the resultant racial disproportionality in incarceration, Blumstein calculated expected Black and White incarceration rates based on race-specific arrest data for particular offenses and compared them to observed Black and White incarceration rates for those same offenses. Blumstein’s measure of explained racial disproportionality, referred to as the X value, showed that 80 percent of the racial disproportionality in imprisonment during both years was accounted for by differential arrest.1 According to Blumstein, the remaining unexplained 20 percent of the Black-to-White disproportionality in incarceration rates could have resulted from a number of factors, such as Blacks being of lower educational and socioeconomic status, having longer criminal records, or committing more serious crimes within each of the offenses types. Even after accounting for all of these possibilities, Blumstein reflected that a residual amount of disproportionality could likely be attributable only to discrimination. The importance of controlling for offense type can be seen in the analyses performed by Blumstein. Upon calculating crime-specific levels of disproportionality, Blumstein (1982) found that for less serious offenses, more racial disproportionality in incarceration rates was left unexplained by differential arrest. For example, he found that the percentage of unexplained racial disproportionality in incarceration rates remaining after controlling for arrests ranged from 2.8 percent for homicide to 48.9 percent for drug offenses. Generally, there was more explained racial disproportionality in incarceration rates for violent offenses than for property offenses. In his later study, Blumstein (1993) found that 76 percent of the racial disproportionality in incarceration rates in 1991 was explained by differences in arrest rates, similar to the 80 percent he had found in his earlier study. Again, the crime-specific analyses showed that as the seriousness of the crime decreased, the percent of racial disproportionality in incarceration rates explained by arrest also decreased, mirroring the pattern found in the 1970s data. He found that incarceration for drug offenses had the most significant influence on racial disproportionality in incarceration rates in 1991. Although the proportion of racial disproportionality in drug incarcerations explained by arrest remained at about 50 percent, the tremendous growth in incarcerated drug offenders (from 5.7 percent of the prison population in 1979 to 21.5 percent in 1991) had a noticeable impact on the overall level of explained racial disproportionality in incarceration rates. The level of explained racial disproportionality increased from 76 to 93.8 percent when drug offenses were removed from the analysis of 1991 data.

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Blumstein’s (1982) calculations offer a more refined measure than other studies for analyzing the regional pattern of racial disproportionality in incarceration rates. In this study, Blumstein’s formula, with some modifications, was used to more accurately estimate the expected level of racial disproportionality in incarceration rates given differential rates of arrest for specific crimes and to determine the extent to which the observed regional pattern of racial disproportionality was explained by differential arrest. Having used this formula, it was also possible to determine how each offense type contributed to the overall level of explained racial disproportionality within states across time and jurisdictions.

Methodology Data sources The National Corrections Reporting Program (NCRP) supplied the necessary data on prison admissions.2 Race of incoming prisoners was reported on admissions for participating states. The race of incoming inmates was disaggregated to supply the actual percentage of incoming prisoners that were Black for each offense, one of the major components needed to calculate the measure of explained racial disproportionality (Blumstein’s X value) in incarceration rates for each state. The offenses used in calculating the X values were murder (including nonnegligent manslaughter), rape (including sexual assault), robbery, aggravated assault, burglary, theft (including auto theft), forgery/fraud/embezzlement, buying and selling of stolen property, weapons violations, and drug offenses. Together, these ten offenses made up 87 percent of all admissions to state prisons in 1997, the latest year for which information was available.3 From these data, counts were made of the number of Black and White inmates sentenced to one year or more in prison by their primary offense (the ones resulting in the longest prison sentences).4 Data on the race of adult offenders arrested by offense type for each state were collected directly from the Federal Bureau of Investigation. These data supplied the racial characteristics of arrestees for each type of offense used in calculating the proportion of Blacks and Whites expected among prison admissions, the other component needed to calculate Blumstein’s X value. The arrest data from one year previous to the prison admissions data were used in calculations to allow for a one-year lag period from arrest to prison admission. For example, arrest data from 1996 were used to project the expected racial characteristics of prisoners admitted in 1997.5

The analysis was limited to jurisdictions for which adequate data were available. Admissions data were limited to the thirty-six states that reported data to the NCRP in 1997. Data limitations among the arrest data also restricted the choice of states to be included in the analyses. States with poor reporting coverage were dropped from the analyses.6 Those states having a population that was less than 1 percent Black were also dropped from the analyses as the calculations of racial disparity could be easily influenced by a small number of cases.7 Upon restricting the states to those that met the criteria listed above, twenty-three states were retained for the initial analysis of data from 1997. Sixteen states meeting these criteria for the years 1985 – 1997 were included in the longitudinal analysis.8 Measures Blumstein’s (1982) original formula was used to derive the fraction of racial disproportionality in imprisonment accounted for by arrests, the composite measure he referred to as X. The formula used to derive X was as follows: X ¼

ratio of expected Black
to
White incarceration rates based on arrests ratio of Black
to
White incarceration rates actually observed

Or, expressed as a percentage, X ¼

expected ðBlack incarceration rate=White incarceration rateÞ 100: actualðBlack incarceration rate=White incarceration rateÞ

Blumstein’s formula was further simplified using the following notation, X ¼ 100ðRð100
QÞ=ð100
RÞQÞ;

ð1Þ

where Q = the actual percentage of the prison (admission) population that was Black and R = the expected percentage of the prison (admission) population that was Black based on arrests. To calculate the expected percentage of the prison (admission) population that was Black based on arrests, the following formula was used: X R¼ Rj ; ð2Þ j

where Rj = BjFj = the expected percentage of prisoners (admitted) for crime type j that was Black based on arrests; Bj = the percentage of persons arrested for crime type j that was Black; and Fj = the percentage of prisoners (admitted) for crime type j. Crime-specific measures were also calculated using the formula: Xj ¼ 100 ðRj ð100
Qj Þ=ð100
Rj ÞQj Þ;

ð3Þ

where Qj = the actual percentage of the prison (admission) population for crime type j that was Black.

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points of the actual Black incarceration rate were to be expected for every point of the actual White incarceration rate. Calculating an adjusted ratio which set the White incarceration rate to 1 would simply have involved adding the expected Black incarceration rate controlling for arrests (0.8) to the portion of the unadjusted ratio unexplained (1.4), resulting in an adjusted ratio of 2.2. This means that, after controlling for arrests, Blacks were incarcerated at a rate 2.2 times that of Whites. The level of racial disproportionality in Black-to-White incarceration rates would have decreased from 6.9:1 (unadjusted ratio) to 2.2:1 (adjusted ratio).

The initial interest in disproportionality between Black and White incarceration rates lay in determining the percentage of this disproportionality explained by arrests and the percentage unexplained by arrests, X and 1
X, respectively. An example of the interpretation of X was provided in Blumstein’s (1982) study. Using the composite formula for 1974 data, he found that R = 42.7 percent, Q = 48.3 percent, and X = 80 percent. His interpretation was that 80 percent (X) of the racial disproportionality in Blackto-White incarceration rates was accounted for by differential arrests. Thus, 20 percent (1
X) of the disproportionality in Black-to-White incarceration rates was not accounted for or was unexplainable by differential arrests. The interpretation of racial disproportionality, however, would be incomplete without applying X and 1
X to the actual ratio of Black-to-White incarceration rates. For example, explaining 80 percent (X = 0.8) of the racial disproportionality would be quite different if the unadjusted ratio of Black-toWhite incarceration rates was 2:1 as compared to 10:1. In his original article, Blumstein multiplied the ratio of actual Black-to-White incarceration rates by (1
X). This provided a measure of the ratio of Black-to-White incarceration rates unexplained by arrests. In his analysis, the unadjusted ratio of Black-to-White incarceration rates was 6.9:1, so a ratio of Black-to-White incarceration rates of 1.4:1 remained unexplained by arrests (6.9*0.2 = 1.4). An adjusted ratio, however, should necessarily incorporate that portion of the Black incarceration rate that was expected given arrest rates. The X value provides the portion of the Black incarceration rate relative to the White incarceration rate expected, controlling for their respective arrest rates. In Blumstein’s original analysis, the X value indicated that 0.8

Findings Arrest and prison admission data used in calculating the overall measure of racial disproportionality for the entire sample were included in Table 1. The expected Black percentage of prison admission for each offense (Rj) was the product of percent Black arrests (Bj) and percent of the offense distribution of admissions for each offense (Bj). For example, since Blacks made up 54.2 percent of murder arrests and murder made up 2.6 percent of the total prison admissions, it was expected that 1.4 percent of the total prison admissions would consist of Black murderers (54.2 percent*2.6 percent) = 1.4 percent. As specified in Eq. (2), the expected Black percentage of prison admissions based on arrest (R) is simply the sum of expected percentages for each offense (Rj). In this case, the expected Black percentage among total prison admissions for these ten offenses was 39.3 percent. If no post-arrest discrimination had been present in processing, then the same percentage of Blacks should be found among actual prison admis-

Table 1 Estimation of the Black percentage among admissions to prison, assuming no post-arrest discrimination Crime type

Arrests Total White + Black arrests

Violent Murder Rape Robbery Assault Property Burglary Theft Monetary Stolprop Weapons Drugs Total

State prison admissions Black arrests

Percent Black (Bj)

Percent offense distribution ( Fj)

9,828 61,273 67,732 260,771

5,330 16,289 39,949 98,081

54.2 26.6 59.0 37.6

2.6 5.6 11.1 7.1

132,323 580,454 331,527 67,177 94,688 766,026 2,371,804

44,744 210,075 117,658 28,580 38,849 289,306 888,866

33.8 36.2 35.5 42.5 41.0 37.7

14.1 11.7 4.2 2.5 3.6 37.5

Expected percent Black (Rj) 1.4 1.5 6.5 2.7 4.8 4.2 1.5 1.1 1.5 14.2 R = 39.3

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prison admissions for assaults and weapons violations. Drug offenses were the crime type with the least amount of racial disproportionality in admissions, only about half, accounted for by arrests. Since drug offenses were the largest single category of arrests and admissions, the level of explained disproportionality was heavily influenced by them. A recalculation of the X value excluding drug offenses found that 79 percent of prison admissions were explained by arrest, compared to the overall X value of 67 percent including drug offenses. To further examine the influence of drug offenses, the level of explained disproportionality by crime categories from the longitudinal sample has been displayed in Fig. 1. To create this figure, Xj was calculated for each category of crime.10 Fig. 1 shows that the level of explained disproportionality in imprisonment decreased from the beginning of the series in the mid-1980s through the early 1990s, followed by a leveling off and slight ascent in the mid-1990s. Xj levels for violent and property crimes remained nearly stable throughout the series. The Xj levels for drug offenses, on the other hand, started out on par with the other categories, but made a sharp descent in the late 1980s, a trend which coincides with the punitive response against primarily Black crack distributors during this era. Despite a slight ascent in the mid-1990s, the level of explained disproportionality in drug offenses has remained around one-half. That the overall trend in explained disproportionality closely tracked the trend for drug offenses suggests that drug offenses had a considerable influence on the overall X values in this time series. Disaggregating data by jurisdictions provided a more complex view of the level of racial disproportionality than data aggregated at the national level. In

sions; however, the actual percentage of prison admissions that were Black ( Q) was 49.05 percent. In calculating Eq. (1), it was found that 67 percent (X = 0.67) of the ratio of Black-to-White imprisonment was explained by arrests while 33 percent (1
X = 0.33) remained unexplained. These findings can be expressed in terms of the unadjusted versus the adjusted ratios described earlier. As shown in the bottom row of Table 3, the rate of prison admissions was 625 Black admissions per 100,000 Black population compared to 102 White admissions per 100,000 White population, thus, making the unadjusted ratio of Black-to-White incarceration rates 6.1:1. The adjusted ratio was computed by first setting the White prison admission rate to 1, which made the expected Black prison admission rate 0.67. Next, the portion of the unadjusted ratio that was unexplained was calculated (6.1*0.33 = 2.01). The unexplained ratio was then added to the expected rate of Black prison admissions (2.01 + 0.67 = 2.68), making the final adjusted ratio nearly 2.7:1, which means that, after controlling for arrests, the level of racial disproportionality in Black-to-White admission rates was 2.7:1. Crime-specific measures of disproportionality in prison admissions explained by arrest were calculated using Eq. (3). As shown in Table 2, a distinct pattern emerged. The highest percentage of racial disproportionality explained by arrests was for the crime of murder, with 110.6 percent (Xj) explained.9 With the exception of aggravated assault, violent crimes generally had the highest percentages of disproportionality explained by arrest, with over four-fifths explained. Property crimes closely followed violent crimes with about three-fourths of racial disproportionality explained by arrests. Arrests accounted for about two-thirds of the racial disproportionality in

Table 2 Comparison of crime-type-specific percentages of Blacks among prison admissions and arrests Crime type

Violent Murder Rape Robbery Assault Property Burglary Theft Monetary Stolprop Weapons Drugs Total

Arrests expected percent Black (Rj)

Percent explained (Xj)

51.7 30.1 62.0 47.1

54.2 26.6 59.0 37.6

110.6 84.3 88.1 67.7

40.3 43.2 42.0 44.5 52.2 54.2 Q = 49.05

33.8 36.2 35.5 42.6 41.0 37.8 R = 39.30

75.8 74.7 75.9 92.3 63.7 51.3 X = 67.3

State prison admissions Total White + Black admissions

Black admissions

7,206 15,468 30,423 19,447

3,728 4,649 18,865 9,163

38,917 32,243 11,476 6,891 9,782 103,327 275,180

15,665 13,918 4,823 3,067 5,108 55,984 134,970

Actual percent Black ( Qj)

J. Sorensen et al. / Journal of Criminal Justice 31 (2003) 73–84

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Fig. 1.

Table 3, states have been listed in order of their unadjusted Black-to-White prison admission ratios. As noted previously, racial disproportionality was highest for Minnesota, with an unadjusted ratio of 24:1. Racial disproportionality was lowest for Arkansas, with an unadjusted ratio of 3.7:1. The level of explained racial disproportionality (X) ranged from 45.0 percent in Wisconsin to 95.5 percent in Arkansas. In other words, nearly all of the racial disproportionality in prison admissions was explained by arrest rates in Arkansas, while in Wisconsin less than half the racial disproportionality in prison admissions was explained by arrest. The adjusted ratio provides additional information concerning the level of racial disproportionality. For example, New Jersey and Colorado had identical X values of 49.9 percent, yet they began with very different unadjusted ratios of Black-to-White imprisonment, 11.2:1 and 7.9:1, respectively. While just under half of the disproportionality in prison admissions was explained by arrest rates for each state, New Jersey retained a higher ratio of racial disproportionality (6.1:1) than Colorado (2.7:1) after controlling for arrests. While these were significant reductions, some states experienced even greater decreases in their ratios of disproportionality after controlling for arrests. In Minnesota, for example, the ratio of disproportionality decreased from 24:1 to 6.5:1 after controlling for arrests. In Arkansas, the ratio decreased

from 3.7:1 to nearly even at 1.1:1. In many instances, these changes within states also changed their position relative to other states in terms of levels of racial disproportionality in prison admissions. Regional variations were also apparent in Table 3; the six states with the highest unadjusted ratios of Black-to-White prison admissions were located in the Midwest or Northeast. Averaged by region in Table 4, these figures show that midwestern states had unadjusted ratios about twice that of the other regions.11 The X values for midwestern states indicated that they actually had a similar portion of their unadjusted ratios explained by arrest when compared to the other regions. Differences in case processing, then, was not the main reason for the higher levels of observed racial disproportionality in prison admissions in the Midwest. This is not to say that discrimination did not enter into case processing in the Midwest. Some portion of the 33 percent (1
X) of disproportionality not accounted for by arrest could have resulted from discriminatory case processing, although much of it was likely due to Blacks’ having lengthier prior records or more aggravated cases within crime types. While the gap between regions closed slightly after controlling for arrests, the adjusted ratios of racial disproportionality in prison admissions remained highest in the Midwest. As evidenced by their similar X values, differences in the ratios of disproportionality, both the

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Table 3 Racial disproportionality in state prison admission rates State

Prison admission rates Percent Adjusted Black White Ratio explained ratio (X )

Minnesota 894 Wisconsin 949 Iowa 1,224 Pennsylvania 380 New Jersey 832 Nebraska 630 Maryland 385 Oregon 648 Michigan 507 North Carolina 403 Virginia 494 Colorado 660 Washington 587 Oklahoma 226 West Virginia 207 Louisiana 664 South Carolina 487 California 1,555 New York 486 Alabama 335 Texas 538 Nevada 724 Arkansas 230 Total 625

37 46 73 30 74 60 37 70 58 49 59 83 75 32 29 96 77 252 85 64 101 150 62 102

24.0 20.8 16.7 12.5 11.2 10.6 10.3 9.3 8.7 8.3 8.3 7.9 7.9 7.1 7.0 6.9 6.3 6.2 5.7 5.3 5.3 4.8 3.7 6.1

76.0 45.0 46.6 64.9 49.9 78.7 45.8 67.5 90.5 55.5 49.2 49.9 49.4 47.2 82.8 50.1 47.3 53.4 78.2 78.2 58.1 80.7 95.5 67.3

6.5 11.9 9.4 5.1 6.1 3.0 6.1 3.7 1.7 4.2 4.7 4.5 4.5 4.2 2.0 3.9 3.8 3.4 2.0 1.9 2.8 1.7 1.1 2.7

unadjusted and adjusted, between the Midwest and other regions could only have resulted from factors occurring prior to case processing, either differential involvement in crime or differential enforcement of the law by race. An analysis of the types of crimes resulting in arrest and confinement across regions provides insight into the question of whether the observed regional pattern resulted from differential involvement in crime or differential enforcement of the law. The least serious crimes, particularly drug offenses, allow authorities the greatest discretion and therefore the levels of racial disproportionality in incarceration rates in these cases are highly dependent on enforcement practices and less driven by involvement. As shown in the bottom panel of Table 4, the X values increased in all of the regions after drug offenses were excluded from the analysis, suggesting that the enforcement of drug laws and criminal justice processing of drug cases substantially contributed to the disproportionate incarceration of Blacks across regions. The pattern of regional variation in the levels of racial disproportionality, however, did not change considerably when drug offenses were removed from the analysis; the ratio of disproportionality remaining two times higher in the Midwest compared to other regions. While the War on Drugs contributed to racial disproportionality in

prison admissions throughout the United States, it did not substantially contribute to higher levels of disproportionality in the Midwest vis-a`-vis other regions. Serious crimes, particularly crimes of violence, allow authorities less discretion, and hence are more indicative of criminal involvement than enforcement patterns. Essentially, these are the crimes remaining in the bottom panel of Table 4 after the removal of drug offenses. That Black prison admissions for these serious crimes remain two to four times higher than those of Whites after controlling for arrests suggests that the majority of the disproportionality in prison admissions resulted from differential involvement. That the Midwest retains twice the level of racial disproportionality in prison admissions for these serious crimes shows that differential involvement similarly varies by region. This was confirmed by data, not presented in the tables, on the rates of arrest for violent crimes by race across regions. The Blackto-White arrest rates for violent crimes were nearly three times higher in the Midwest than other regions, 15.5:1 versus 5.5:1, respectively. The question remaining was why differences in criminal involvement by race would be more likely to occur in the Midwest than elsewhere. There was one obvious and striking difference between the demography of Blacks relative to Whites in the Midwest when compared to other regions. As shown in Table 5, the Midwest region had the largest differences between the proportion of its Black and White populations residing in urban areas.12 While over 93 percent of the Black population in the Midwest resided in urban areas, similar to the Northeast and West, only 46 percent of its White population lived in urban areas.13 Only in the South did a lower proportion of Whites reside in urban areas; however, the proportion of Blacks living in urban areas was also lowest in the Table 4 Regional differences in racial disproportionality in state prison admissions Region

Prison admission rates White

Ratio

Percent Adjusted explained ratio (X )

55 63 126 61

16.2 9.8 7.2 6.9

67.4 64.3 60.2 61.0

6.5 4.4 3.6 3.5

drug offenses 577 43 293 36 506 83 235 46

14.0 8.7 6.3 5.3

81.5 72.9 68.7 77.3

4.0 3.1 2.7 2.1

Black All offenses Midwest 841 Northeast 567 West 835 South 397 Excluding Midwest Northeast West South

J. Sorensen et al. / Journal of Criminal Justice 31 (2003) 73–84 Table 5 Regional differences in the proportion of Blacks and Whites residing in urban areas Region

State population residing in urban areas Percent Black

Midwest 93.2 Northeast 95.0 West 93.4 South 58.2

Percent White 46.4 70.7 68.2 44.3

South. Since the current examination concerns racial disproportionality, the key issue is the difference between the two groups in terms of their place of residence. Relative to Whites, 46 percentage points more of the Black population lived in urbanized areas in the Midwest. In the Northeast and West, the figure was about 25 percentage points, while in the South the difference between Blacks and Whites living in urbanized areas was only 14 percentage points. The relationship between urbanization and violent crime is one of the best documented in criminology.14 Among the states sampled herein, there was a strong relationship between the proportion of Blacks living in urbanized areas relative to Whites and the Black-to-White arrest ratios (Pearson’s R = 0.753, P < .001). From this analysis, it appears that the regional pattern of racial disproportionality in imprisonment was due to differences in demography, and, ultimately, the criminal involvement of Blacks versus Whites in the Midwest in comparison to other regions.

Conclusion Recent authors have found an unexpected and seemingly anomalous regional pattern of racial disproportionality in incarceration rates, the ratio of Black-to-White incarceration rates being highest in the Midwest and lowest in the South (Blumstein, 1993; Christianson, 1981). The findings from the current study also indicated that the level of racial disproportionality was higher in midwestern states than elsewhere. Initial results (unadjusted ratios) showed that Blacks were admitted to prison at much higher rates than Whites given their representation among states’ populations in midwestern jurisdictions. Findings from the current study also showed the importance, however, of including a measure of the incidence of serious arrests by race when estimating expected Black and White incarceration rates. Like other studies that have included such a measure (Crutchfield et al., 1994; Hawkins & Hardy, 1989), the current study found that the proportion of racial disproportionality in prison admissions explained by arrest (X value) was similar across regions, meaning

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that the proportion of prison admissions resulting from differential case processing by race was also static across regions. In short, racial differences were as likely to result from decisions made in the criminal justice system from the point of arrest through sentencing in the Midwest as elsewhere. That the level of racial disproportionality entering into the processing of cases (X values) was similar across regions, however, does not mean that the regions also had similar levels of racial disporportionality in prison admissions remaining after controlling for arrest rates (adjusted ratios). Since the states in the midwestern region began with higher levels of racial disproportionality (unadjusted ratios), their levels of racial disproportionality in prison admissions after controlling for arrests (adjusted ratios) remained nearly twice as high, on average, as states in the other regions, a point previous authors had failed to mention (Crutchfield et al., 1994; Hawkins & Hardy, 1989). Findings from the current study indicate that these differences among regions were due to differential involvement in serious crimes by race resulting from a higher concentration of Blacks relative to Whites in the urban areas in the Midwest. Viewed in this light, previously observed regional variations in the level of racial disproportionality in prison admissions no longer seem anomalous. Previous studies failed to disaggregate arrests by offense type when estimating expected Black and White incarceration rates. For the current study, a modified version of Blumstein’s (1982) formula, which disaggregated by offense type, was employed for the first time in the estimation of expected Blackto-White prison admission rates across jurisdictions. By calculating the adjusted ratio, this study extended the interpretation to the level of racial disproportionality in prison admissions remaining after controlling for arrests, which showed a considerable decrease in all regions when compared to the unadjusted ratios. Only by attributing the levels of racial disproportionality to particular offenses was it possible to identify the genesis of racial disproportionality in prison admissions among the states. Disaggregation made it possible to gauge the influence of specific offenses on the overall level of racial disproportionality in admissions. Drugs were the offense type with the lowest level of racial disproportionality in prison admissions explained by arrest rates. While the presence of drug offenses did not account for higher levels of racial disproportionality in the Midwest vis-a`-vis other regions, across jurisdictions and over time drug offenses heavily influenced overall levels of racial disproportionality in prison admissions. As noted in other recent studies (Lynch & Sabol, 1997; Mauer, 1997; Schiraldi, Holman, & Beatty, 2000), a direct correlation was found

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between the rising proportion of inmates incarcerated for drug offenses and increased racial disparity in incarceration rates. Many of the insights gained in this study resulted from the disaggregation of data. Studies of racial disproportionality are needed that further disaggregate by offense type and jurisdiction (Arvanites & Asher, 1998; Mosher, 2001). For example, a breakdown of drug offenses may illuminate the specific type of offenses where most of the racial disproportionality has entered the system. Existing differences in the racial composition of urban versus rural areas of states suggests that geographical areas should also be disaggregated in future research. Future studies could focus on the levels of racial disparity occurring across counties, for example, and how these disparities are related to local courtroom culture versus criminal justice policies in effect at the state level. The results of the current study have specific implications for criminal justice policy. It appears that there is no easy solution to the obvious differentials between Black and White incarceration rates. Most of the racial disproportionality in prison admissions results from differential involvement in crimes by Blacks and Whites. As suggested by its correlation with urbanization, racial disproportionality in imprisonment results from forces deeply entrenched in our society and not easily remedied within the criminal justice apparatus. It is possible, however, to identify those offenses that directly influence the level of racial disproportionality in case processing and target them for ameliorative action. The most obvious example is, of course, drug offenses. Alternatives to incarceration for drug offenders, if properly implemented, would significantly decrease levels of racial disproportionality resulting from the incarceration of drug offenders in the short term. If treatment were successful, such alternatives to incarceration would also decrease the levels of racial disproportionality by preventing future drug-related criminal activity among those diverted from incarceration. In particular jurisdictions, researchers should be able to identify the level of racial disproportionality, its source, and potential solutions. With this information, policymakers who are interested in reform can then enact programs and policies directed to reduce the most influential sources of racial disproportionality.

Acknowledgements Support for this project was provided by a grant from the Edna McConnell Clark Foundation. We wish to thank Chris Stone, Eileen Sullivan, Nick Turner, and other colleagues at the Vera Institute of

Justice who reviewed and commented on previous drafts of this article.

Notes 1. Although the term ‘‘differential involvement’’ has often been employed instead of ‘‘differential arrest’’, researchers typically have not accounted for differences in the actual level of criminal involvement. Instead, they have used arrest rates as a proxy for differential involvement. Researchers using victimization data have generally shown that minorities were arrested in similar proportion to their involvement in serious crimes (Hindelang, 1978), although the relationship was weaker for offenses in which authorities had a broad degree of discretion in enforcement decisions, such as those related to drug use and distribution (Blumstein, 1993). Findings from the only study that estimated the expected level of racial disproportionality in imprisonment using actual criminal involvement as measured by victimization surveys supported the overall assumption that arrest was an adequate proxy for criminal involvement for serious crimes (Langan, 1985). 2. Using prison admission provided a measure of racial disproportionality that resulted mainly from pretrial and sentencing decisions. It was preferable to using prison stock because postsentencing decisions that influence the length of time served could affect the racial mixture of the prison stock. Further, predicting characteristics of prison stock that accrue over several years using arrest ratios from selected previous years would have presented analytic difficulties. As one reviewer noted, the drawback to using admissions as opposed to stock population was that disproportionality effects associated with time served were not captured. 3. Some felony offense categories were excluded because they made up only a minute proportion of the total admissions. Other offenses were excluded because they were generally considered misdemeanors and as such did not typically result in prison sentences. In fact, only 2.4 million of the nearly seven million arrests, or 34 percent, in the sampled states were for one of the ten offenses included in this study, indicating that the other offenses had very low probabilities of resulting in prison sentences. Inclusion of these categories of offenses would have biased the estimates far more than their exclusion. 4. Several states contained a sizable proportion of cases missing data on the race variables. Closer examination of the data revealed that some states simply failed to code the race of Hispanic inmates. Where race was not indicated for Hispanic inmates, they were recoded as White to be consistent with the arrest data, which did not include ethnicity. While it is possible that some of these Hispanics were non-White, the evidence strongly suggests that they were White in the vast majority of cases. First, some of the states coded racial categories other than White for Hispanics, but failed to code White for any Hispanics, suggesting that the residual were White. In other states, the reporting agency did not code race on any of the Hispanic inmates, the inference also being that race would take precedence over ethnicity if the inmate were non-White.

J. Sorensen et al. / Journal of Criminal Justice 31 (2003) 73–84 Finally, census data showed that in most states with large Hispanic populations (e.g., Texas; California), only a minute proportion of Hispanics were non-White. 5. Since cases were generally processed over a period of time, the one-year lag was more appropriate than a simple cross-sectional comparison. Previous researchers showed that racial proportions in arrest and imprisonment rates remained fairly constant in consecutive years (Blumstein, 1982), suggesting that violations of this assumption could be tolerated. The analyses were computed without the one-year lag, with little effect on the intermediate figures and no change in the overall conclusions. 6. Existing estimation procedures using these data were questionable, especially when arrest rates played a prominent role in the analysis, as herein where they were being used in the numerator. Rather than attempt to estimate these arrest data at the state level, states with more than onethird of their population not covered by arrest statistics were dropped from consideration. Among the remaining states, the median coverage level for reporting arrests was 93 percent. Some corrections were made to two of the remaining states. While three-fourths of their populations were covered by arrest statistics, arrests for Minnesota in 1990, 1991, and 1992 and for Nebraska in 1992 and 1997 were markedly different from adjacent years. Arrests for specific crimes in these states for these years were estimated assuming a stable relationship between crime rates and arrests rates from previous years. 7. Although Hawaii met all of the above criteria, it was also dropped from the analysis because of its rather unique racial make-up. This was reflected in the high level of missing data in its arrest statistics, wherein the race of more than half of Hawaii’s arrested adults were listed as unknown. The average level of missing race data across sampled states was 2 percent, with only one state missing more than 7 percent in 1997. 8. Although NCRP data collection began in 1983, many states which later reported consistently failed to report during the program’s first year or two after implementation. 9. The most likely explanation for this finding was that more Blacks had been arrested per murder in comparison to Whites, with proportionately fewer Blacks arrested per murder being sentenced to prison. 10. Violent crimes included murder, rape, and robbery, while property crimes included burglary, theft, and monetary crimes. Assaults and stolen property were unrepresentative of their respective categories and therefore dropped from the longitudinal analysis. 11. It is important to note that the exclusion of New England states from the sample, due to data limitations, likely resulted in an underestimate of the ratios of racial disproportionality in the Northeast. 12. Figures on urbanization were culled from the 1990 census. Urbanized areas were defined as those with a population over 50,000 and a density of more than 1,000 persons per square mile. 13. The percentages reported in Table 5 and the associated text included only the sampled states and represented an average of these states’ populations as opposed to summary measures for entire regions.

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14. One of the reviewers raised the issue of how traditionally lenient sentencing practices in urban areas might confound the findings by region. If anything, leniency in case processing in urban areas would act to lower the ratio of Black-to-White disproportionality found in prison admissions because Blacks are more likely to reside in urban areas. Further, the X values in Table 4 indicate that the percent of disproportionality in the Midwest explained by arrest is similar to that of other regions, ruling out differential case processing as a potential cause of the higher than average level of racial disproportionality in prison admissions in that region.

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