{"id":15964,"date":"2011-12-14T10:14:56","date_gmt":"2011-12-14T15:14:56","guid":{"rendered":"http:\/\/law.marquette.edu\/facultyblog\/?p=15964"},"modified":"2011-12-14T10:17:38","modified_gmt":"2011-12-14T15:17:38","slug":"a-tale-of-three-states-part-4-the-racial-threat-hypothesis","status":"publish","type":"post","link":"https:\/\/law.marquette.edu\/facultyblog\/2011\/12\/a-tale-of-three-states-part-4-the-racial-threat-hypothesis\/","title":{"rendered":"A Tale of Three States, Part 4: The Racial Threat Hypothesis"},"content":{"rendered":"

In the\u00a0previous post in this series<\/a>, I highlighted\u00a0a\u00a0wide gap in the incarceration rates of Indiana and Minnesota, with Wisconsin in the middle.\u00a0 The ordering of the three states from highest incarceration rate to lowest corresponds\u00a0with the ordering from highest rate of violent crime to lowest.\u00a0 However, for reasons I explained in the previous post, I don\u2019t think \u00a0we\u00a0ought to end our analysis with the simple assertion that\u00a0high crime drives high incarceration.\u00a0 For one thing, there is Minnesota: with a crime rate only a little lower than Wisconsin\u2019s, Minnesota has an incarceration rate that is\u00a0much\u00a0<\/em>lower.\u00a0 There must be other factors\u00a0at play besides\u00a0just the\u00a0crime rate to account for Minnesota\u2019s incarceration rate.\u00a0 For another, to\u00a0focus on\u00a0the crime-incarceration connection begs the question of what drives the very different crime rates of the three states.<\/p>\n

In this post, I\u2019ll explore another possible way of accounting for differences in the three states\u2019 incarceration rates, the racial threat hypothesis. \u00a0The basic idea is this: a larger racial minority population causes the majority to feel more threatened by the minority and consequently to prefer to stronger social control measures.<\/p>\n

Here are the relevant numbers from Indiana, Wisconsin, and Minnesota:<\/p>\n\n\n\n\n\n\n
<\/td>\n\n

IN<\/p>\n<\/td>\n

\n

\u00a0 WI<\/p>\n<\/td>\n

\n

\u00a0 MN<\/p>\n<\/td>\n<\/tr>\n

\n

Black Population (2010)<\/p>\n<\/td>\n

\n

591,397<\/p>\n<\/td>\n

\n

359,148<\/p>\n<\/td>\n

\n

274,412<\/p>\n<\/td>\n<\/tr>\n

\n

Blacks as Percentage of Total Population (2010)<\/p>\n<\/td>\n

\n

9.1%<\/p>\n<\/td>\n

\n

6.3%<\/p>\n<\/td>\n

\n

5.2%<\/p>\n<\/td>\n<\/tr>\n

\n

Imprisonment Rate (2010, per 100,000)<\/p>\n<\/td>\n

\n

459.9<\/p>\n<\/td>\n

\n

387.2<\/p>\n<\/td>\n

\n

177.8<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

As\u00a0you can see, the incarceration-rate order tracks the order based on the size of the\u00a0each state\u2019s black population.<\/p>\n

<\/p>\n

Interestingly, the pattern does not hold if you focus on the size of the white population.\u00a0 The three states are almost indistinguishable in how white they are, and the order of \u201cwhiteness\u201d does not follow the incarceration-rate order: Wisconsin is number one (86.2% white), followed by Minnesota (85.3%) and Indiana (84.3%).\u00a0 We might hypothesize, then, that there is something about having a relatively large percentage of a particular minority group that tends to push incarceration rates higher.<\/p>\n

A similar pattern is evident nationally.\u00a0 Consider the top ten states by imprisonment rate (from highest to lowest):<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n\n

Ratio of Blacks to Whites<\/p>\n<\/td>\n

\n

Rank Among States Based on Black:White Ratio<\/p>\n<\/td>\n

\n

Ratio of Hispanics to Non-Hispanic Whites<\/p>\n<\/td>\n

\n

Rank Based on Hispanic Ratio<\/p>\n<\/td>\n<\/tr>\n

\n

LA<\/p>\n<\/td>\n

\n

0.5<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

40<\/p>\n<\/td>\n<\/tr>\n

\n

MS<\/p>\n<\/td>\n

\n

0.6<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

38<\/p>\n<\/td>\n<\/tr>\n

\n

OK<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

25<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

20<\/p>\n<\/td>\n<\/tr>\n

\n

AL<\/p>\n<\/td>\n

\n

0.4<\/p>\n<\/td>\n

\n

6<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

34<\/p>\n<\/td>\n<\/tr>\n

\n

TX<\/p>\n<\/td>\n

\n

0.2<\/p>\n<\/td>\n

\n

17<\/p>\n<\/td>\n

\n

0.5<\/p>\n<\/td>\n

\n

4<\/p>\n<\/td>\n<\/tr>\n

\n

AZ<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

33<\/p>\n<\/td>\n

\n

0.7<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

FL<\/p>\n<\/td>\n

\n

0.2<\/p>\n<\/td>\n

\n

11<\/p>\n<\/td>\n

\n

0.3<\/p>\n<\/td>\n

\n

7<\/p>\n<\/td>\n<\/tr>\n

\n

GA<\/p>\n<\/td>\n

\n

0.5<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

15<\/p>\n<\/td>\n<\/tr>\n

\n

AR<\/p>\n<\/td>\n

\n

0.2<\/p>\n<\/td>\n

\n

14<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

27<\/p>\n<\/td>\n<\/tr>\n

\n

SC<\/p>\n<\/td>\n

\n

0.4<\/p>\n<\/td>\n

\n

5<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

29<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

 <\/p>\n

Thus, among the top ten imprisoning states, eight are also among the top ten in the\u00a0number of blacks or Hispanics relative to whites.\u00a0 A ninth, Arkansas, is only a little outside the top ten in proportion of blacks.\u00a0 The tenth, Oklahoma, seems to deviate from the pattern, but is still in the top half of both the black and Hispanic scales.<\/p>\n

Now consider the bottom ten imprisoning states (from lowest to highest imprisonment rate):<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n\n

Ratio of Blacks to Whites<\/p>\n<\/td>\n

\n

Rank Among States Based on Black:White Ratio<\/p>\n<\/td>\n

\n

Ratio of Hispanics to Non-Hispanic Whites<\/p>\n<\/td>\n

\n

Rank Based on Hispanic Ratio<\/p>\n<\/td>\n<\/tr>\n

\n

ME<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

44<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

48<\/p>\n<\/td>\n<\/tr>\n

\n

MN<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

32<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

36<\/p>\n<\/td>\n<\/tr>\n

\n

NH<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

46<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

45<\/p>\n<\/td>\n<\/tr>\n

\n

RI<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

29<\/p>\n<\/td>\n

\n

0.2<\/p>\n<\/td>\n

\n

13<\/p>\n<\/td>\n<\/tr>\n

\n

MA<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

27<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

22<\/p>\n<\/td>\n<\/tr>\n

\n

ND<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

43<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

46<\/p>\n<\/td>\n<\/tr>\n

\n

UT<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

45<\/p>\n<\/td>\n

\n

0.2<\/p>\n<\/td>\n

\n

13<\/p>\n<\/td>\n<\/tr>\n

\n

NB<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

34<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

25<\/p>\n<\/td>\n<\/tr>\n

\n

WA<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

37<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

16<\/p>\n<\/td>\n<\/tr>\n

\n

VT<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

47<\/p>\n<\/td>\n

\n

<0.1<\/p>\n<\/td>\n

\n

47<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

 <\/p>\n

Note that\u00a0not one\u00a0<\/em>of the bottom ten for imprisonment\u00a0is among the top ten based on the proportion of either blacks or Hispanics.<\/p>\n

A possible explanation for these patterns comes from scholars who write about the \u201cracial threat\u201d phenomenon. \u00a0I\u2019ll crib a little bit from a fascinating new article by Christian Breunig and Rose Ernst, \u201cRace, Inequality, and the Prioritization of Corrections Spending in the American States,\u201d 1 Race & Justice 233 (2011)<\/a>:<\/p>\n

\u201cRacial threat,\u201d in the most simplified terms, describes a group of theories positing a relationship between the sizes of the Black population in one area to the extent of social control measures aimed at that population. \u00a0Broadly speaking, this theory posits that the presence of a racialized \u201cother\u201d in a population increases fear and\/or hostility among White Americans toward this other group which, in turn, provokes support for social control policies. \u00a0Social control policies include but are not limited to social service policies such as \u201cwelfare,\u201d as well as a host of criminal justice policies. \u00a0For example, Pamela Irving Jackson\u2019s work in the area of policing has found a connection \u201cbetween minority group size, competition for sociopolitical dominance, and the level of policing resources.\u201d \u00a0(235)<\/p><\/blockquote>\n

In order to test the racial threat hypothesis, Breunig and Ernst have studied data from all fifty states over a fifteen-year time period and attempted to control for many different variables. \u00a0Their focus was on corrections spending, not imprisonment rates per se (my focus), but one would expect a correlation between the two. \u00a0More specifically, their dependent variable was what they call the \u201ccorrections priority index\u201d (CPI), which is simply the percentage of state spending that goes to corrections.<\/p>\n

Surprisingly, Breunig and Ernst found that CPI does not seem to be determined by any of the obvious political factors, such as which party is in power or whether the population is more liberal or conservative:<\/p>\n

An intriguing aspect of our analysis is that we did not discover any evidence that institutional and political factors, including partisanship, divided government, referendum, and citizen ideology, influence the prioritization of corrections spending. \u00a0(243)<\/p><\/blockquote>\n

If not those factors, then what?<\/p>\n

On the whole, our analysis suggests that structural factors, specifically racial threat and inequality, are the dominant forces in determining the prioritization of corrections spending. . . . We also find that the number of people incarcerated in state prisons as well as murder rates are statistically significant but have only small effect. \u00a0(243)<\/p><\/blockquote>\n

Breunig and Ernst more precisely quantified the racial effect as follows: \u201c[A] 1% increase in the percentage of the African American population in a state leads to at least a 0.2% increase in the CPI.\u201d \u00a0(244)<\/p>\n

One potential response to this research is that race per se may not matter since we know that race is closely correlated in this country with poverty. \u00a0In other words, one might wonder if the \u201cracial threat\u201d theory should be recast as a \u201cpoverty threat\u201d theory.<\/p>\n

However, Breunig and Ernst also considered the effect of economic inequality on the CPI, and found that the racial variable had a distinct effect. \u00a0At all levels of inequality, increasing the black percentage of the population also increased the CPI. \u00a0Breunig and Ernst did find that inquality mattered, but only in states with relatively low black populations. \u00a0They suggest that \u201cracial cleavages\u201d are the primary social division that politicians exploit, but that class cleavages become salient in their own right in states in which there is little racial threat (238).<\/p>\n

Putting all of this together, we might hypothesize that one explanation for Indiana\u2019s high imprisonment rate (relative to Wisconsin\u2019s and Minnesota\u2019s) is that Indiana has a much larger black population, which triggers racial threat dynamics and a more powerful demand in the political system for social control.<\/p>\n

But, if that\u2019s right, how do we account for the fact that Indiana has much lower racial disparities in its prison population than its two neighbors to the north? \u00a0If Indiana\u2019s whites feel more threatened by blacks, shouldn\u2019t that translate into more vicious racial disparities?<\/p>\n

Not necessarily. \u00a0This is all quite speculative, but let me suggest three possible reasons why strong racial threat dynamics might not necessarily produce high racial disparities. \u00a0First, the political demand for more aggressive social control must be mediated through a legal system that may be more committed to racial equality norms than the population at large. \u00a0Second, a relatively large black population probably means not only stronger racial threat effects, but also a greater voice for blacks in a state\u2019s political and legal systems. \u00a0Blacks may not be able to (and indeed may not wish to) blunt the state\u2019s demand for penal severity, but may be able to exercise some influence in minimizing the extent to which the demand is met through racially discriminatory policies and practices. \u00a0Finally, racial threat dynamics may serve to undermine social trust generally across a state\u2019s population, leading to relatively higher white crime rates.<\/p>\n

To be clear, I don\u2019t think anything in my analysis here demonstrates that racial threat dynamics play an important role in explaining the relative imprisonment rates of Indiana, Wisconsin, and Minnesota. \u00a0But, to my mind, the racial threat hypothesis remains an intriguing possibility that may warrant further research.<\/p>\n

The next post in the series will examine how imprisonment rates in the three states have changed over the past twenty years.<\/p>\n

Cross posted at Life Sentences and Prawfs.<\/p>\n","protected":false},"excerpt":{"rendered":"

In the\u00a0previous post in this series, I highlighted\u00a0a\u00a0wide gap in the incarceration rates of Indiana and Minnesota, with Wisconsin in the middle.\u00a0 The ordering of the three states from highest incarceration rate to lowest corresponds\u00a0with the ordering from highest rate of violent crime to lowest.\u00a0 However, for reasons I explained in the previous post, I 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