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Pamela Oliver
Pamela OliverPamela OliverPresentation to Governor’s Commission Presentation to Governor’s Commission May 22 2007May 22 2007
The Scope of the Problem & How The Scope of the Problem & How to Measure itto Measure it
Pamela Oliver
OutlineOutline
National overviewNational overview Compare Wisconsin to USCompare Wisconsin to US
ScatterplotsScatterplots TimetrendsTimetrends
Wisconsin Trends by Admission type, race & Wisconsin Trends by Admission type, race & offenseoffense
County Imprisonment PatternsCounty Imprisonment Patterns County Arrest PatternsCounty Arrest Patterns Addressing the disparitiesAddressing the disparities
Steps in the processSteps in the process Evidence at stepsEvidence at steps Where we lack evidenceWhere we lack evidence
Pamela Oliver
National Trends: The Magnitude of National Trends: The Magnitude of the Problemthe Problem
Pamela Oliver
Comparing International Incarceration Rates (Source: Sentencing Project)Comparing International Incarceration Rates (Source: Sentencing Project)
Pamela Oliver
World Incarceration Rates in 1995: Adding US Race World Incarceration Rates in 1995: Adding US Race PatternsPatterns
0 1000 2000 3000 4000
Austria
Belgium
Canada
China
Denmark
France
Germany
Italy
Japan
Netherlands
SwedenSwitzerland
Scotland
England & Wales
Ukraine
South Africa
Romania
Russia
US whites prison & jail 1995
US blacks prison & jail 1995
US whites prison 1995
US Blacks prison 1995
Pamela Oliver
Nationally, The Black Population is Being Nationally, The Black Population is Being Imprisoned at Alarming RatesImprisoned at Alarming Rates Nearly 40% of the Black male population is under Nearly 40% of the Black male population is under
the supervision of the correctional system (prison, the supervision of the correctional system (prison, jail, parole, probation)jail, parole, probation)
Estimated “lifetime expectancy” of spending some Estimated “lifetime expectancy” of spending some time in prison is about 32% for young Black men.time in prison is about 32% for young Black men.
About 12% of Black men in their 20s are About 12% of Black men in their 20s are incarcerated (prison + jail), about 20% of all Black incarcerated (prison + jail), about 20% of all Black men have been in prisonmen have been in prison
7% of Black children, 2.6% of Hispanic 7% of Black children, 2.6% of Hispanic children, .8% of White children had a parent in children, .8% of White children had a parent in prison in 1997 – lifetime expectancy much higherprison in 1997 – lifetime expectancy much higher
Pamela Oliver
About Rates & Disparity Ratios [Relative About Rates & Disparity Ratios [Relative Rate Ratios]Rate Ratios] Imprisonment and arrest rates are expressed as the rate per Imprisonment and arrest rates are expressed as the rate per
100,000 of the appropriate population100,000 of the appropriate population Example: In 1999 Wisconsin new prison sentencesExample: In 1999 Wisconsin new prison sentences
1021 Whites imprisoned, White population of Wisconsin 1021 Whites imprisoned, White population of Wisconsin was 4,701,123. was 4,701,123. 1021 ÷ 4701123 = .000217. 1021 ÷ 4701123 = .000217. Multiply .00021 by 100,000 = 22, the imprisonment rate per 100,000 Multiply .00021 by 100,000 = 22, the imprisonment rate per 100,000
population.population.1,266 Blacks imprisoned, Black population of Wisconsin was 1,266 Blacks imprisoned, Black population of Wisconsin was
285,308. 285,308. 1266 ÷ 285308 = .004437. 1266 ÷ 285308 = .004437. Multiply by 100,000 = 444Multiply by 100,000 = 444
Calculate Disparity Ratios by dividing rates: Calculate Disparity Ratios by dividing rates: 444/22 = 20.4 the Black/White ratio in new prison sentence 444/22 = 20.4 the Black/White ratio in new prison sentence
ratesrates
Pamela Oliver
Black and White prison admissions, Black and White prison admissions, historicalhistorical
Black & White Prison Admits per 100,000
0
200
400
600
800
1000
1200
1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Pri
son
Adm
issi
ons
0
1
2
3
4
5
6
7
8
9
10
Dis
pari
ty R
atio
Black White Disparity
Pamela Oliver
Imprisonment Has Increased While Imprisonment Has Increased While Crime Has DeclinedCrime Has Declined Imprisonment rates are a function of Imprisonment rates are a function of
responses to crime, not a function of responses to crime, not a function of crime itselfcrime itself
Property crimes declined steadily Property crimes declined steadily between 1970s and 2000between 1970s and 2000
Violent crime declined modestly overall, Violent crime declined modestly overall, with smaller ups and downs in the periodwith smaller ups and downs in the period
Pamela Oliver
Crime TrendsCrime Trends
Based on Bureau of Justice Based on Bureau of Justice Statistics data from National Statistics data from National Crime Victimization Survey. Crime Victimization Survey.
Pamela Oliver
Property CrimeProperty Crime
Property Crime Rates
0
200
400
600
1973 1978 1983 1988 1993 1998 2003
Adjusted victimization rate per 100,000 age 12 and over
Source: Bureau of Justice Statistics - National Crime Victimization Survey
Pamela Oliver
Violent Crime Rates
0
20
40
60
1973 1978 1983 1988 1993 1998 2003
Adjusted victimization rate per 100,000 age 12 and over
Source: Bureau of Justice Statistics - National Crime Victimization Survey
Violent CrimeViolent Crime
Pamela Oliver
Violent Crime by Sex of VictimViolent Crime by Sex of Victim
Violent Crime Rates by Gender of Victim
0
25
50
75
1973 1978 1983 1988 1993 1998 2003
Adjusted victimization rate per1,000 persons age 12 and over
Males
Females
Pamela Oliver
So what has been going on?So what has been going on?
Pamela Oliver
The 1970’s Policy ShiftThe 1970’s Policy Shift
Shift to determinate sentencing, higher penaltiesShift to determinate sentencing, higher penalties LEAA, increased funding for police departmentsLEAA, increased funding for police departments Crime becomes a political issue (Social turmoil & Crime becomes a political issue (Social turmoil &
crime were high)crime were high) Drug war funding gives incentives to police to Drug war funding gives incentives to police to
generate drug arrests & convictions: this generate drug arrests & convictions: this escalates in the 1980sescalates in the 1980s
Post-civil rights post-riots competitive race Post-civil rights post-riots competitive race relations, race-coded political rhetoric.?relations, race-coded political rhetoric.?
Pamela Oliver
Black/White Disparity Ratios in Imprisonment Rates (State Prisons, Totals)
6
7
8
9
10
11
12
Revocation Prob/Parole NewSentence InPrison AllAdmits
In Prison
Revocations
New Sentences
All Admits
Black/White RRI by type of prison admissionBlack/White RRI by type of prison admission
19821999
Pamela Oliver
B/W Disparity Ratios in Prison Admits, by Offense. All States in NCRP
0.0
5.0
10.0
15.0
20.0
25.0
Violent Rob/Burg Theft Drug Other
RRI by offense: new sentences) onlyRRI by offense: new sentences) only
Drug
Rob & BurgViolent
Theft Other
Pamela Oliver
Black & White Prison Sentence Rates (NCRP) per 100,000, by Offense Type
0
50
100
150
200
250
300
350
400
450
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Drug White Non-drug White Drug Black Non-drug Black
Rates: Black & White, drug vs other sentencesRates: Black & White, drug vs other sentences
Pamela Oliver
National White Prison Sentence Rates by OffenseNational White Prison Sentence Rates by Offense
White New Sentences per 100,000 pop, by offense. All States in NCRP
0
2
4
6
8
10
12
14
16
18
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Violent Rob/Bur Theft Drug Other
DrugDrug Rob/burgRob/burg ViolentViolentTheftTheft OtherOther
1983 1999
0
18
Pamela Oliver
National Black Prison Sentences by OffenseNational Black Prison Sentences by Offense
Black New Sentences per 100,000 pop, by offense. All States in NCRP
0
50
100
150
200
250
300
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Violent Rob/Bur Theft Drug Other1983 1999
DrugDrug
0
300
Rob/burgRob/burg
ViolentViolent
TheftTheft
OtherOther
Pamela Oliver
Drug DisparitiesDrug Disparities
Nationally, Black juveniles & young adults Nationally, Black juveniles & young adults (those under 26) use illegal drugs at (those under 26) use illegal drugs at LOWER RATES than White juvenilesLOWER RATES than White juveniles
Only among those over 25 are illegal drug Only among those over 25 are illegal drug use rates higher for Blacks than Whites, use rates higher for Blacks than Whites, but the disparities are much lower than but the disparities are much lower than the imprisonment disparitiesthe imprisonment disparities
Pamela Oliver
Black/White disparity in self-reported illegal drug use within Black/White disparity in self-reported illegal drug use within the past yearthe past year
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Age 26+ Age 18-25
Marijuana
Cocaine All
Cocaine Crack
Calculated from 2003 National Survey on Drug Use & Health, Department Calculated from 2003 National Survey on Drug Use & Health, Department of Health & Human Servicesof Health & Human Services
Disparity < 1, Whites use more than Blacks
Compare to prison sentence disparity of 15 at end of 1990s
Pamela Oliver
Comparing Wisconsin to Other Comparing Wisconsin to Other StatesStates
Sources are from the Bureau of Sources are from the Bureau of Justice StatisticsJustice Statistics
Pamela Oliver
Prisons and Jails in Midyear 2005Prisons and Jails in Midyear 2005
This is “total incarceration” rate This is “total incarceration” rate per 100,000 populationper 100,000 population
Pamela Oliver
CT
ME
MA
NH
NJ
NY
PA
RI
VT
.
IL
IN
IA
KS
MI
MN
MONE
ND
OH
SD
WI
ALAR
DE
DC
FL
GA
KY
LA
MDMSNC
OK
SCTN
TX
VAWV AK
AZ
CA
CO
HI
ID
MT
NVOR
UT
WA
1000
2000
3000
4000
5000
Bla
ckN
H
0 200 400 600 800WhiteNH
r= .33
In Prison or Jail in 2005
Pamela Oliver
CT
MEMA
NH
NJ
NY PARI
VT
.
IL
IN
IA
KS
MI
MN
MO
NE
ND
OH
SDWI
ALAR
DE
DC
FLGA
KYLAMD
MS
NCOKSCTN
TXVAWV
AK
AZCA CO
HI
ID
MT
NV
OR
UT
WA
05
1015
20B
lack
/Whi
te D
ispa
rity
1000 2000 3000 4000 5000BlackNH
r= .22
In Prison or Jail in 2005
Black/White Disparity is not the same as the Black rate
Pamela Oliver
CT
MEMA
NH
NJ
NY PARI
VT
.
IL
IN
IA
KS
MI
MN
MO
NE
ND
OH
SDWI
ALAR
DE
DC
FLGA
KYLAMD
MS
NCOKSC TN
TXVAWV
AK
AZCA CO
HI
ID
MT
NV
OR
UT
WA
05
1015
20B
lack
/Whi
te D
ispa
rity
0 200 400 600 800WhiteNH
r= -.74
In Prison or Jail in 2005
Black/White Disparity is negatively related to the White rate
Pamela Oliver
In State Prisons, 1998In State Prisons, 1998
(This is the most recent year for (This is the most recent year for which I have been able to find which I have been able to find
these data)these data)
Pamela Oliver
AL
AZ
AR
CA
CO
CT
DE
FL
GA
ILIN
IA
KSKY
LA
MD
MA
MI
MNMS
MO
NE
NV
NJ
NM
NY
NC
OH
OK
OR
PA
RI
SC
TX
UT
VA
WA
WV
WI10
0015
0020
0025
0030
00B
lack
s in
Pri
son
per
1000
00
0 200 400 600Whites in Prison per 100000
r= .4
In Prison in 1998
Note: Rates include Hispanics, who are almost all counted as White
Pamela Oliver
AL
AZ
AR
CA
CO
CT
DEFL
GA
IL
IN
IA
KS
KYLA
MD
MA
MI
MN
MS
MO
NE
NV
NJ
NM
NY
NC
OH
OK
OR
PA
RI
SC
TX
UT
VA WA
WV
WI
510
1520
Bla
ck/W
hite
Dis
pari
ty in
Impr
ison
men
t
1000 1500 2000 2500 3000Blacks in Prison per 100000
r= .28
In Prison in 1998
Note: Rates include Hispanics, who are almost all counted as White
Pamela Oliver
AL
AZ
AR
CA
CO
CT
DEFL
GA
IL
IN
IA
KS
KYLA
MD
MA
MI
MN
MS
MO
NE
NV
NJ
NM
NY
NC
OH
OK
OR
PA
RI
SC
TX
UT
VAWA
WV
WI
510
1520
Bla
ck/W
hite
Dis
pari
ty in
Impr
ison
men
t
0 200 400 600Whites in Prison per 100000
r= -.63
In Prison in 1998
Note: Rates include Hispanics, who are almost all counted as White
Pamela Oliver
010
0020
0030
00
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998Year
Black Wisconsin Black Other US
White Wisconsin White Other US
Hispanics Included in White & Black Rates
Rate per 100000 population
In Prison
Pamela Oliver
68
1012
1416
(mea
n) b
disc
puso
_W
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998Year
Wisconsin Other US
Hispanics Included in White & Black Rates
Black/White Disparity Ratio
Disparity in Rate of Being in Prison
Pamela Oliver
Prison Admissions: National Prison Admissions: National Corrections Reporting ProgramCorrections Reporting Program
1983-19991983-1999
(Hispanics not included in Black & (Hispanics not included in Black & White rates)White rates)
Pamela Oliver
050
010
0015
00
1983 1985 1987 1989 1991 1993 1995 1997 1999Year
Black Wisconsin Black Other USHispanic Wisconsin Hispanic Other US
White Wisconsin White Other US
Hispanics Not Included in White & Black Rates
Rate per 100,000 population
All Prison Admissions
Pamela Oliver
05
1015
20
1983 1985 1987 1989 1991 1993 1995 1997 1999Year
Black Wisconsin Black Other US
Hispanic Wisconsin Hispanic Other US
Hispanics Not Included in White & Black Rates
Minority/White Disparity Ratios
DisparityAll Prison Admissions
Pamela Oliver
AL
CA
CO
FLGA
IL
IA
KY
MDMI
MN
MS
NE
NVNJ
NY
NCOH
OR
PA SC
TXVA
WA
WV
WI
050
010
0015
0020
00B
lack
NH
50 100 150 200 250White NH
National Corrections Reporting Program Rates per 100,000 population
correlation = .58
Prison Admits in 1999
Pamela Oliver
010
020
030
0
1983 1985 1987 1989 1991 1993 1995 1997 1999Year
Black Wisconsin Black Other USHispanic Wisconsin Hispanic Other US
White Wisconsin White Other US
Hispanics Not Included in White & Black Rates
Rate per 100,000 population
Non-Drug Sentences
Pamela Oliver
05
1015
20
1983 1985 1987 1989 1991 1993 1995 1997 1999Year
Black Wisconsin Black Other US
Hispanic Wisconsin Hispanic Other US
Hispanics Not Included in White & Black Rates
Minority/White Disparity Ratios
DisparityNon-Drug Sentences
Pamela Oliver
AL
CA
CO
FL
GA
IL
IA
KY
MD
MI
MN
MS
NE NV
NJ
NY
NCOH
ORPA
SCTX
VA
WA
WV
WI
100
200
300
400
500
Bla
ck N
H
20 40 60 80White NH
National Corrections Reporting Program Rates per 100,000 population
correlation = .1
Non-Drug Sentences in 1999
Note: MN counts probation revocations as new sentences while WI does not
Pamela Oliver
050
100
150
200
1983 1985 1987 1989 1991 1993 1995 1997 1999Year
Black Wisconsin Black Other USHispanic Wisconsin Hispanic Other US
White Wisconsin White Other US
Hispanics Not Included in White & Black Rates
Rate per 100,000 population
Drug Sentences
Pamela Oliver
020
4060
1983 1985 1987 1989 1991 1993 1995 1997 1999Year
Black Wisconsin Black Other US
Hispanic Wisconsin Hispanic Other US
Hispanics Not Included in White & Black Rates
Minority/White Disparity Ratios
DisparityDrug Sentences
Pamela Oliver
AL
CACOFL
GA
IL
IA
KY
MD
MI
MN
MS
NE
NV
NJ
NY
NCOH
OR
PA
SC
TXVA
WA
WV
WI
5010
015
020
025
030
0B
lack
NH
0 10 20 30White NH
National Corrections Reporting Program Rates per 100,000 population
correlation = .33
Drug Sentences in 1999
Note: MN counts probation revocations as new sentences while WI does not
Pamela Oliver
020
040
060
0
1983 1985 1987 1989 1991 1993 1995 1997 1999Year
Black Wisconsin Black Other USHispanic Wisconsin Hispanic Other US
White Wisconsin White Other US
Hispanics Not Included in White & Black Rates
Rate per 100,000 population
Revocations
Pamela Oliver
05
1015
20
1983 1985 1987 1989 1991 1993 1995 1997 1999Year
Black Wisconsin Black Other US
Hispanic Wisconsin Hispanic Other US
Hispanics Not Included in White & Black Rates
Minority/White Disparity Ratios
DisparityRevocations
Pamela Oliver
AL
CA
CO
FL
GA
IL
IA
KY
MI
MN
MS
NE
NV
NJ
NY
OH
PA
SC
TX
VA
WA WV
WI0
200
400
600
Bla
ck N
H
0 20 40 60 80White NH
National Corrections Reporting Program Rates per 100,000 population
correlation = .71
Revocations in 1999
Note: MN counts probation revocations as new sentences
Pamela Oliver
AL
CA
CO
FL
GA
IA
ILKY
MD
MI
MN
MO
MSNC
NE
NJNV
NY
OH
OR
PA
SC
TX
UT
VA
WA
WI
WV
020
040
060
080
010
00B
lack
NH
0 20 40 60 80 100White NH
National Corrections Reporting Program Rates per 100,000 population
correlation = .72
Revocations in 1995-99
Pamela Oliver
AL
CA
COFL
GA
ILIA
KYMI
MN
MS
NE
NV
NJ
NY
OH
PA
SC TX
VA
WA
WV
WI
510
1520
2530
Bla
ck /
Whi
te D
ispa
rity
0 200 400 600Black NH
National Corrections Reporting Program Rates per 100,000 population
correlation = .31
Revocations in 1999
Disparity is different from Black rate
Pamela Oliver
AL
CA
COFL
GA
IA
IL
KY
MD
MI
MN
MOMS
NC
NE
NJ
NV
NY
OH
OR
PA
SC TX
UT
VAWA
WI
WV
510
1520
2530
Bla
ck /
Whi
te D
ispa
rity
0 200 400 600 800 1000Black NH
National Corrections Reporting Program Rates per 100,000 population
correlation = .25
Revocations in 1995-99
Pamela Oliver
Wisconsin vs. US Trends SummaryWisconsin vs. US Trends Summary
Steep rise in Black imprisonment rates of all Steep rise in Black imprisonment rates of all types after 1988types after 1988
Revocations far above average in Wisconsin. Revocations far above average in Wisconsin. Some due to data coding differences. Much is Some due to data coding differences. Much is “real.”“real.”
Drug sentences in Wisconsin are even more Drug sentences in Wisconsin are even more disparate than the nation as a whole: high Black disparate than the nation as a whole: high Black & low White rates& low White rates
Black non-drug sentences in Wisconsin are a Black non-drug sentences in Wisconsin are a little above average while the White sentence little above average while the White sentence rate is far below average, thus yielding a high rate is far below average, thus yielding a high disparity.disparity.
Pamela Oliver
Graphs from my analysis of Graphs from my analysis of Wisconsin Department of Corrections Wisconsin Department of Corrections DataData
WisconsinWisconsin
Pamela Oliver
Wisconsin Total Prison Admits: Includes Parole/Probation Violators
0
200
400
600
800
1000
1200
1400
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Rat
e p
er 1
00,0
00 p
op
ula
tion
White, NH total Black, NH total Hispanic total American Indian Total Asian Total
Black
AmerIndAmerInd
HispanicHispanic
AsianAsianWhiteWhite
Pamela Oliver
Proportion of Admissions Involving Proportion of Admissions Involving New Sentences (1991-9)New Sentences (1991-9)
39%
18%
43%
0%
20%
40%
60%
New Only New + Viol Viol Only
Pamela Oliver
White Admissions StatusWhite Admissions StatusWhites Wisconsin Total
0
5
10
15
20
25
30
35
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
prison admits per 100,000
White viol only White new only White viol+new
New Sentence Only
Violation Only
Violation + New
Pamela Oliver
Blacks Admission StatusBlacks Admission StatusBlacks Wisconsin Total
0
100
200
300
400
500
600
700
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
prison admits per 100,000
black viol only Black new only Black viol+new
New Sentence OnlyViolation Only
Violation + New
Pamela Oliver
Wisconsin Prison Admissions (Violations Only)
0
100
200
300
400
500
600
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Rat
e p
er 1
00,0
00 p
op
ula
tion
White, NH total Black, NH total Hispanic total American Indian Total Asian Total
Black
AmerIndAmerInd
HispanicHispanic
AsianAsian
WhiteWhite
Pamela Oliver
Wisconsin Prison Admissions (New Sentences Only)
0
100
200
300
400
500
600
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Rat
e p
er 1
00,0
00 p
op
ula
tion
White, NH total Black, NH total Hispanic total American Indian Total Asian Total
Black
AmerIndAmerIndHispanicHispanic
AsianAsianWhiteWhite
Pamela Oliver
Wisconsin Prison Admissions (All New Sentences)
0
100
200
300
400
500
600
700
800
900
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Rat
e p
er 1
00,0
00 p
op
ula
tion
White, NH total Black, NH total Hispanic total American Indian Total Asian Total
New only plus (new + violation)
Black
AmerIndAmerIndHispanicHispanic
AsianAsianWhiteWhite
Pamela Oliver
Offense trends in new prison Offense trends in new prison sentences by race.sentences by race.
Pamela Oliver
Wisconsin Imprisonment Rates (All New Sentences), White Non-Hispanics (3-Year Averages)
0
2
4
6
8
10
12
14
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Imp
ris
on
me
nt
Ra
te (
pe
r 1
00
,00
0)
VIOLENT OFFENSES ROBBERY/BURGLARY DRUG OFFENSES LARCENY/THEFT OTHER OFFENSES UNKNOWN
ViolentViolent
Rob/burgRob/burg
DrugDrug
TheftTheft
OtherOther
WhitesWhites14
Pamela Oliver
Wisconsin Imprisonment Rates (All New Sentences), Black Non-Hispanics (3-Year Averages)
0
50
100
150
200
250
300
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Imp
riso
nm
ent R
ate
(per
100
,000
)
VIOLENT OFFENSES ROBBERY/BURGLARY DRUG OFFENSES LARCENY/THEFT OTHER OFFENSES UNKNOWN
BlacksBlacks300
ViolentViolent
Rob/burgRob/burg
DrugDrug
TheftTheft OtherOther
Pamela Oliver
Wisconsin Imprisonment Rates (All New Sentences), Hispanics (Any Race) (3-Year Averages)
0
10
20
30
40
50
60
70
80
90
100
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Imp
riso
nm
ent R
ate
(per
100
,000
)
VIOLENT OFFENSES ROBBERY/BURGLARY DRUG OFFENSES LARCENY/THEFT OTHER OFFENSES UNKNOWN
HispanicsHispanics100
ViolentViolent
Rob/burgRob/burg
DrugDrug
TheftTheft
OtherOther
Pamela Oliver
Wisconsin Imprisonment Rates (All New Sentences), American Indians (Non-Hispanic) (3-Year Averages)
0
20
40
60
80
100
120
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Imp
riso
nm
ent R
ate
(per
100
,000
)
VIOLENT OFFENSES ROBBERY/BURGLARY DRUG OFFENSES LARCENY/THEFT OTHER OFFENSES UNKNOWN
Amer IndsAmer Inds120
ViolentViolent
Rob/burgRob/burg
DrugDrug
TheftTheftOtherOther
Pamela Oliver
Wisconsin Imprisonment Rates (All New Sentences), Asian/PIs (Non-Hisp) (3-Year Averages)
0
2
4
6
8
10
12
14
16
18
20
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Imp
riso
nm
ent R
ate
(per
100
,000
)
VIOLENT OFFENSES ROBBERY/BURGLARY DRUG OFFENSES LARCENY/THEFT OTHER OFFENSES UNKNOWN
AsiansAsians20
ViolentViolent
Rob/burgRob/burg DrugDrug
TheftTheft
OtherOther
Pamela Oliver
Age Patterns for ImprisonmentAge Patterns for Imprisonment
Pamela Oliver
Wisconsin Total New Prison Sentence Rates (No Prior Felony) 1998-9 (annualized) By Age
0
400
800
1200
1600
<18 18-19 20-21 22-24 25-29 30-34 35-39 40-44 45+Age
Rate
per
100
,000
pop
ulat
ion
White Black
Pamela Oliver
Whites: Prison Admits by Age, Offense (New Sentences Only, No Prior Felony)Wisconsin Total, 1998-9 summed
0
5
10
15
20
25
30
<17 18-19 20-21 22-24 25-29 30-34 35-39 40-44 45+
Rate
per
100
,000
pop
ulat
ion
violent rob/bur drug theft other unk
Pamela Oliver
Black Prison Admits by Age & Offense (New Sentences, No Prior Felony) Wisconsin Total, 1998-9 annualized
0
100
200
300
400
500
600
700
800
<17 18-19 20-21 22-24 25-29 30-34 35-39 40-44 45+
Rate
per
100
,000
pop
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Pamela Oliver
Black/White Disparity Ratios in Prision Admissions by Age, Offense (New Sentences, No Prior Felony) Wisconsin Total
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Pamela Oliver
White kids are more likely to use and sell White kids are more likely to use and sell illegal drugs than Black kids, but Black illegal drugs than Black kids, but Black kids are MUCH more likely to be arrested kids are MUCH more likely to be arrested and prosecuted for drug offensesand prosecuted for drug offenses
Pamela Oliver
Incarceration Exacerbates the Effects of Incarceration Exacerbates the Effects of Racial DiscriminationRacial Discrimination
Next few slides are from research by Devah Next few slides are from research by Devah Pager, earned PhD from University of Pager, earned PhD from University of Wisconsin Sociology, now professor at Wisconsin Sociology, now professor at Princeton UniversityPrinceton University
This was a controlled experiment in which This was a controlled experiment in which matched pairs of applicants applied for matched pairs of applicants applied for entry-level jobs advertised in Milwaukee entry-level jobs advertised in Milwaukee newspapersnewspapers
Pamela Oliver
Figure 4. The Effect of a Criminal Record on Figure 4. The Effect of a Criminal Record on Employment Opportunities for Whites Employment Opportunities for Whites
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Pamela Oliver
Figure 5. The Effect of a Criminal Record for Figure 5. The Effect of a Criminal Record for Black and White Job Applicants Black and White Job Applicants
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Pamela Oliver
Optional: Compare County Optional: Compare County Imprisonment PatternsImprisonment Patterns
See “County Comparisons” See “County Comparisons” PresentationPresentation
Pamela Oliver
Tracking disparities through the Tracking disparities through the systemsystem
Pamela Oliver
Rates vs. Disparities (RRI)Rates vs. Disparities (RRI)
High RATES of incarceration are the major social High RATES of incarceration are the major social problemsproblems
Costs of incarceration are tied to rates, not disparitiesCosts of incarceration are tied to rates, not disparities Disparities are higher when White rates are lowerDisparities are higher when White rates are lower
You can lower disparities by raising White ratesYou can lower disparities by raising White rates Disparities are most appropriate for tracking fairness Disparities are most appropriate for tracking fairness
and justice within the systemand justice within the system Rates are most appropriate for assessing impacts on Rates are most appropriate for assessing impacts on
budgets and communitiesbudgets and communities Both are important, but they are not the sameBoth are important, but they are not the same
Policies to reduce disparities can increase rates, and vice versaPolicies to reduce disparities can increase rates, and vice versa
Pamela Oliver
OJA’s map of the flow through the systemOJA’s map of the flow through the system
Pamela Oliver
My Map of the SystemMy Map of the System
Pamela Oliver
Decision PointsDecision Points
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Numbers indicate data sources. Green are readily available in UCR, CCAP or DOC data; light blue would be in local sources
Pamela Oliver
Sentencing Commission Draft ReportSentencing Commission Draft Report
Focuses on sentence after Focuses on sentence after adjudicated guilty of a particular adjudicated guilty of a particular
offenseoffense
Pamela Oliver
Sentencing Commission StudySentencing Commission Study
Staff: Kristi Waits, Executive Director; Andrew Staff: Kristi Waits, Executive Director; Andrew Wiseman, Deputy Director; Brenda R. Mayrack, Wiseman, Deputy Director; Brenda R. Mayrack, Analyst Analyst
CCAP + DOC dataCCAP + DOC data Offenses committed after January 31, 2003 and Offenses committed after January 31, 2003 and
sentenced before October 1, 2006sentenced before October 1, 2006 5 common offenses: sexual assault of child, 5 common offenses: sexual assault of child,
sexual assault, robbery + armed robbery, sexual assault, robbery + armed robbery, burglary, drug traffickingburglary, drug trafficking
Sentencing for worst offense, in cases of Sentencing for worst offense, in cases of multiple offensesmultiple offenses
Pamela Oliver
Sample sizesSample sizes
Notes: “Other” includes Asians + American Indians + any others; White, Black & Other exclude Hispanics.
Pamela Oliver
Main FindingsMain Findings
1.1. ““Legal” factors of offense severity and prior convictions Legal” factors of offense severity and prior convictions have the largest effect on sentences. (As we would have the largest effect on sentences. (As we would hope!)hope!)
2.2. Men are more likely than women to be sentenced to Men are more likely than women to be sentenced to prison, controlling for all other factors. prison, controlling for all other factors.
3.3. Blacks & Hispanics are more likely to be sentenced to Blacks & Hispanics are more likely to be sentenced to prison rather than put on probation after controls for prison rather than put on probation after controls for offense type, felony class, prior convictions, number of offense type, felony class, prior convictions, number of other charges, sex, and county of sentencing.other charges, sex, and county of sentencing.
a)a) Race difference is larger for less serious offenses Race difference is larger for less serious offenses b)b) Race difference even comparing people with no prior Race difference even comparing people with no prior
convictions.convictions.
4.4. There is no consistent racial difference in the LENGTH There is no consistent racial difference in the LENGTH of the sentence if a prison sentence is givenof the sentence if a prison sentence is given
Pamela Oliver
Regression summariesRegression summaries
These use multi-variable statistics to These use multi-variable statistics to assess the impact of each factor while assess the impact of each factor while controlling for all other factors in the controlling for all other factors in the modelmodel
They show clear evidence of an overall They show clear evidence of an overall effect of race on likelihood of being effect of race on likelihood of being sentenced to prison, given that there is a sentenced to prison, given that there is a guilty findingguilty finding
Note there is a sex effect, too!Note there is a sex effect, too!
Pamela Oliver
Non-Non-drugdrugoffenses. offenses.
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Drug Drug Trafficking Trafficking OffensesOffenses
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Verbal summary of statistical resultsVerbal summary of statistical results
Statistically controlling for other factorsStatistically controlling for other factors Blacks 47% & Hispanics 65% more likely to get Blacks 47% & Hispanics 65% more likely to get
a prison sentence for non-drug crimesa prison sentence for non-drug crimes Blacks nearly twice as likely (196%) and Blacks nearly twice as likely (196%) and
Hispanics nearly 2 and a half times as likely Hispanics nearly 2 and a half times as likely (243%) to get a prison sentence for a drug (243%) to get a prison sentence for a drug crimecrime
Men were 272% more likely than women to get Men were 272% more likely than women to get a prison sentence for a non-drug offense and a prison sentence for a non-drug offense and 250% more likely to get a prison sentence for a 250% more likely to get a prison sentence for a drug offense. drug offense.
Pamela Oliver
See report appendix for bar graphs See report appendix for bar graphs for percentages for specific offensesfor percentages for specific offenses
http://wsc.wi.gov/http://wsc.wi.gov/
(When the report is final)(When the report is final)
Pamela Oliver
Policy implications of Sentencing StudyPolicy implications of Sentencing Study
Focus on WHETHER to give a prison sentence, Focus on WHETHER to give a prison sentence, not just how long a sentence should be givennot just how long a sentence should be given
Examine plea bargaining processes which often Examine plea bargaining processes which often pre-determines the sentence type as well as the pre-determines the sentence type as well as the severity of the charged offenseseverity of the charged offense
Consider impact of social factors (i.e. job, Consider impact of social factors (i.e. job, marriage, home) on sentencing marriage, home) on sentencing
Remember that a record of prior arrests & Remember that a record of prior arrests & misdemeanors may be due to patterns of misdemeanors may be due to patterns of policingpolicing
Pamela Oliver
ArrestsArrests
Pamela Oliver
Crime & ArrestCrime & Arrest
MOST crime does not result in arrest!MOST crime does not result in arrest! MOST crime is relatively minor: petty theft, MOST crime is relatively minor: petty theft,
disorderly conductdisorderly conduct Arrest is a function ofArrest is a function of
CrimeCrime Reporting of crime to policeReporting of crime to police Policing patterns & practices: WHERE you police & Policing patterns & practices: WHERE you police &
HOW you policeHOW you police Officer decisionsOfficer decisions
Impossible to assess fairness in arrest without Impossible to assess fairness in arrest without data on crime, which we don’t have!data on crime, which we don’t have!
Pamela Oliver
Arrest Patterns (1997-99): Adult Arrest Patterns (1997-99): Adult
(I did this analysis in the past; it can be (I did this analysis in the past; it can be updated)updated)
Most arrests are for the least serious offenses & Most arrests are for the least serious offenses & never result in incarcerationnever result in incarceration
Patterns of arrests for low-level offenses Patterns of arrests for low-level offenses contribute to prior records at sentencingcontribute to prior records at sentencing
Race is officer’s perception: most probably Race is officer’s perception: most probably default to Whitedefault to White
““White” arrests include Hispanics because there White” arrests include Hispanics because there is no separate Hispanic category in official is no separate Hispanic category in official arrest reportsarrest reports
Pamela Oliver
Offense Proportions, Adult arrestsOffense Proportions, Adult arrestsProportion of Adult Arrests, Wisc. Total, Average Annual 1997-9
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40
Serious
Marijuana Possession
Other Drug Offenses
Theft/Larceny
Simple Assault
Other Property
Weapons & Misc
Alcohol-Related
Disorderly Conduct
Wrong Place
Other, Except Traffic
Black
White
“Serious” offenses include homicide, sexual assault, aggravated assault, robbery, burglary, motor vehicle theft
Pamela Oliver
Adult Disparity (RRI) Ratios in ArrestsAdult Disparity (RRI) Ratios in Arrests
Disparity (RRR) in Arrest Rate average 1997-9 Wisconsin Total(Ratio of Minority Arrest Rate to White Arrest Rate) ------------------------------------------------- | White Black Native Asian -----------------+------------------------------- Homicide | 1.0 25.4 2.6 2.2 Sex Assault | 1.0 9.8 6.4 3.5 Agg Assault | 1.0 11.7 7.1 1.1 Other Assault | 1.0 11.6 8.0 0.9 Robbery | 1.0 41.7 4.9 1.0 Arson | 1.0 7.4 3.8 1.0 Burglary | 1.0 6.0 4.2 0.6 Theft Fraud etc. | 1.0 7.9 2.7 0.9 Prostit & Sex | 1.0 10.6 2.5 1.2 Drug MDI | 1.0 18.2 3.0 0.7 Drug Poss | 1.0 6.9 3.0 0.3 Weapons | 1.0 16.7 3.8 1.3 Fam/Child | 1.0 12.3 3.1 1.3 Disord OWI etc | 1.0 3.8 3.7 0.7 Other Arrest | 1.0 7.6 4.0 0.9 -------------------------------------------------
Pamela Oliver
Black/White Disparities in Arrests 1997-99Black/White Disparities in Arrests 1997-99
Pamela Oliver
Adult, Total arrestsAdult, Total arrestsTotal Adult Arrest Rate 1997-9, Annual Average
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Adult Serious arrestsAdult Serious arrestsAdult Arrest Rate 1997-9, Annual Average,Serious Offenses (Homicide, Agg. Assault, Sexual Assault, Robbery, Burglary, Auto Theft)
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Adult, Other Exc Traffic arrestsAdult, Other Exc Traffic arrestsAdult Arrest Rate 1997-9, Annual AverageOther Except Traffic
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Adult Drug not Marijuana arrestsAdult Drug not Marijuana arrestsAdult Arrest Rate 1997-9, Annual AverageOther Drug Offenses (Excludes Marijuana possession)
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Pamela Oliver
Adult Marijuana ArrestsAdult Marijuana ArrestsAdult Arrest Rate 1997-9, Annual AverageMarijuana possession
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Pamela Oliver
Disparity in Crime & ArrestDisparity in Crime & Arrest
Some is doubtless due to real differences in crime, can Some is doubtless due to real differences in crime, can be addressed only through the underlying causes of be addressed only through the underlying causes of crimecrime
Some is due to patterns of policingSome is due to patterns of policing Police focus on “high crime” areasPolice focus on “high crime” areas Different police jurisdictions have different racial compositions & Different police jurisdictions have different racial compositions &
different practicesdifferent practices High disparities in arrest for lesser offenses that many High disparities in arrest for lesser offenses that many
commit may indicate policing patternscommit may indicate policing patterns These give young people “prior records” that affect subsequent These give young people “prior records” that affect subsequent
treatmenttreatment Drug crimes are different from other crimes: most Drug crimes are different from other crimes: most
differences in drug arrests arise from policing practices differences in drug arrests arise from policing practices rather than differences in actual crimerather than differences in actual crime
Pamela Oliver
Comparing Arrest and ImprisonmentComparing Arrest and Imprisonment
Group offenses in arrest & prison sentence data so they Group offenses in arrest & prison sentence data so they match upmatch up
Count number of arrests by offense & race for 1997-Count number of arrests by offense & race for 1997-19991999
Count number of prison sentences by offense & race for Count number of prison sentences by offense & race for 1997-19991997-1999
Ratio prison sentences to arrests is roughly chances of Ratio prison sentences to arrests is roughly chances of going to prison after arrest (i.e. post-arrest processing)going to prison after arrest (i.e. post-arrest processing)
This ratio is lower for lesser offenses, higher for more serious This ratio is lower for lesser offenses, higher for more serious offensesoffenses
Not matching up particular people, but overall ratesNot matching up particular people, but overall rates Disparity or RRI is the ratio of the ratios: are minorities Disparity or RRI is the ratio of the ratios: are minorities
more likely to end up in prison after arrest?more likely to end up in prison after arrest?
Pamela Oliver
Wisconsin Total: Ratio of Prison Sentences to Arrests by Race & Wisconsin Total: Ratio of Prison Sentences to Arrests by Race & OffenseOffense
Pamela Oliver
Wisconsin total: RRI Prison/Arrest RatioWisconsin total: RRI Prison/Arrest Ratio
Pamela Oliver
The disparity in the prison/arrest ratio is The disparity in the prison/arrest ratio is especially high for Black drug possession especially high for Black drug possession cases, where it is nearly 9 to 1. This merits cases, where it is nearly 9 to 1. This merits strong scrutiny. strong scrutiny.
Other disparities that stand out (>2) include Other disparities that stand out (>2) include Black ratios for non-aggravated assault, theft & fraud, Black ratios for non-aggravated assault, theft & fraud,
prostitution and other sex offenses, drug MDI, prostitution and other sex offenses, drug MDI, weapons and public order offenses; weapons and public order offenses;
Native American homicide, assault, arson, burglary, Native American homicide, assault, arson, burglary, theft, weapons, family/child, and public order theft, weapons, family/child, and public order offenses; and offenses; and
Asian aggravated assault, assault, and burglary Asian aggravated assault, assault, and burglary cases. cases.
Pamela Oliver
Where else to look?Where else to look?
Charging decisions (by police & prosecutors)Charging decisions (by police & prosecutors) Prosecution decisionsProsecution decisions Legal defense options Legal defense options Plea bargainsPlea bargains SentencingSentencing Sanctioning within prisonsSanctioning within prisons Probation & Parole revocationsProbation & Parole revocations
Custody awaiting revocationCustody awaiting revocation Community reintegration: job, housing, driver’s Community reintegration: job, housing, driver’s
licenselicense
Pamela Oliver
Conclusions: Data, Disparities, & RatesConclusions: Data, Disparities, & Rates
Data does not solve the problem BUT Data does not solve the problem BUT data tells you where to look for problems data tells you where to look for problems & solutions& solutions
Individual cases are complex: data look Individual cases are complex: data look for patterns across cases where the for patterns across cases where the individual details average outindividual details average out
Data make us accountable for our actionsData make us accountable for our actions