pamela oliver pamela oliver presentation to governor’s commission may 22 2007 the scope of the...

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


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


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


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


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


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


Hispanic Wisconsin Hispanic Other US


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





Non-Drug Sentences

Pamela Oliver

05

1015

20

1983 1985 1987 1989 1991 1993 1995 1997 1999Year





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


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





Drug Sentences

Pamela Oliver

020

4060

1983 1985 1987 1989 1991 1993 1995 1997 1999Year





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


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





Revocations

Pamela Oliver

05

1015

20

1983 1985 1987 1989 1991 1993 1995 1997 1999Year





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


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


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


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


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


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


Black

AmerIndAmerIndHispanicHispanic


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


New only plus (new + violation)

Black

AmerIndAmerIndHispanicHispanic


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

)


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

)


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

)


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

)


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

ulat

ion

violent rob/bur drug theft other unk

Pamela Oliver

Black/White Disparity Ratios in Prision Admissions by Age, Offense (New Sentences, No Prior Felony) Wisconsin Total

0

20

40

60

80

100

<17 18-19 20-21 22-24 25-29 30-34 35-39 40-44 45+

Age

Ratio

of P

er C

apita

Impr

ison

men

t Rat

es

violent rob/burg drug theft other

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

17

34

0

5

10

15

20

25

30

35

40

Criminal Record No Record

Per

cent

Cal

led B

ack

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

5

1714

34

0

5

10

15

20

25

30

35

40

Black White

Perc

ent

Called B

ack

CriminalRecord

NoRecord

Pamela Oliver

Optional: Compare County Optional: Compare County Imprisonment PatternsImprisonment Patterns

See “County Comparisons” See “County Comparisons” PresentationPresentation

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Tracking disparities through the Tracking disparities through the systemsystem

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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

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OJA’s map of the flow through the systemOJA’s map of the flow through the system

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My Map of the SystemMy Map of the System

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Decision PointsDecision Points

1

4

3

5

7

2

6

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

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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

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Sample sizesSample sizes

Notes: “Other” includes Asians + American Indians + any others; White, Black & Other exclude Hispanics.

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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!

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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.

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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)

http://wsc.wi.gov/

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

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ArrestsArrests

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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!

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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

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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

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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 -------------------------------------------------

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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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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

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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

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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

pamela oliver pamela oliver presentation to governor’s commission may 22 2007 the scope of the...

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