Pamela Oliver Presentation to Governor’s Commission May 22 2007
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Pamela Oliver Presentation to Governor’s Commission May 22 2007 The Scope of the Problem & How to Measure it Pamela Oliver Outline National overview Compare Wisconsin to US Scatterplots Timetrends Wisconsin Trends by Admission type, race & offense County Imprisonment Patterns County Arrest Patterns Addressing the disparities Steps in the process Evidence at steps Where we lack evidence Pamela Oliver National Trends: The Magnitude of the Problem Pamela Oliver Comparing International Incarceration Rates (Source: Sentencing Project) Pamela Oliver World Incarceration Rates in 1995: Adding US Race Patterns US Blacks prison 1995 US whites prison 1995 US blacks prison & jail 1995 US whites prison & jail 1995 Russia Romania South Africa Ukraine England & Wales Scotland Switzerland Sweden Netherlands Japan Italy Germany France Denmark China Canada Belgium Austria 0 1000 Pamela Oliver 2000 3000 4000 Nationally, The Black Population is Being Imprisoned at Alarming Rates Nearly 40% of the Black male population is under the supervision of the correctional system (prison, jail, parole, probation) Estimated “lifetime expectancy” of spending some time in prison is about 32% for young Black men. About 12% of Black men in their 20s are incarcerated (prison + jail), about 20% of all Black men have been in prison 7% of Black children, 2.6% of Hispanic children, .8% of White children had a parent in prison in 1997 – lifetime expectancy much higher Pamela Oliver About Rates & Disparity Ratios [Relative Rate Ratios] Imprisonment and arrest rates are expressed as the rate per 100,000 of the appropriate population Example: In 1999 Wisconsin new prison sentences 1021 Whites imprisoned, White population of Wisconsin was 4,701,123. 1021 ÷ 4701123 = .000217. Multiply .00021 by 100,000 = 22, the imprisonment rate per 100,000 population. 1,266 Blacks imprisoned, Black population of Wisconsin was 285,308. 1266 ÷ 285308 = .004437. Multiply by 100,000 = 444 Calculate Disparity Ratios by dividing rates: 444/22 = 20.4 the Black/White ratio in new prison Pamela Oliver sentence rates Black and White prison admissions, historical Black & White Prison Admits per 100,000 1200 10 9 1000 8 7 6 600 5 4 400 3 2 200 1 0 1925 0 1930 1935 1940 1945 1950 1955 1960 1965 Black Pamela White Oliver 1970 1975 Disparity 1980 1985 1990 1995 2000 Disparity Ratio Prison Admissions 800 Imprisonment Has Increased While Crime Has Declined Imprisonment rates are a function of responses to crime, not a function of crime itself Property crimes declined steadily between 1970s and 2000 Violent crime declined modestly overall, with smaller ups and downs in the period Pamela Oliver Crime Trends Based on Bureau of Justice Statistics data from National Crime Victimization Survey. Pamela Oliver Property Crime Property Crime Rates Adjusted victimization rate per 100,000 age 12 and over 600 400 200 0 1973 1978 1983 1988 1993 Source: Bureau of Justice Statistics - National Crime Victimization Survey Pamela Oliver 1998 2003 Violent Crime Violent Crime Rates Adjusted victimization rate per 100,000 age 12 and over 60 40 20 0 1973 1978 1983 1988 1993 Source: Bureau of Justice Statistics - National Crime Victimization Survey Pamela Oliver 1998 2003 Violent Crime by Sex of Victim Violent Crime Rates by Gender of Victim Adjusted victimization rate per 1,000 persons age 12 and over 75 Males 50 25 Females 0 1973 1978 1983 1988 Pamela Oliver 1993 1998 2003 So what has been going on? Pamela Oliver The 1970’s Policy Shift Shift to determinate sentencing, higher penalties LEAA, increased funding for police departments Crime becomes a political issue (Social turmoil & crime were high) Drug war funding gives incentives to police to generate drug arrests & convictions: this escalates in the 1980s Post-civil rights post-riots competitive race relations, race-coded political rhetoric.? Pamela Oliver Black/White RRI by type of prison admission B lack/ Whit e D isparit y R at io s in Impriso nment R at es ( St at e P riso ns, T o t als) 12 Revocations 11 All Admits 10 New Sentences 9 8 In Prison 7 19 81 19 82 19 83 19 84 19 85 19 86 19 87 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 6 1982 Revocation Prob/Parole NewSentence Pamela Oliver InPrison AllAdmits 1999 RRI by offense: new sentences) only B/W Disparity Ratios in Prison Admits, by Of f ense. All States in NCRP 25.0 Drug 20.0 15.0 Violent Rob & Burg 10.0 5.0 Theft Other Violent Rob/Burg Thef t Drug Other 19 99 19 98 19 97 19 96 19 95 19 94 Pamela Oliver 19 93 19 92 19 91 19 90 19 89 19 88 19 87 19 86 19 85 19 84 19 83 0.0 Rates: Black & White, drug vs other sentences Black & White Prison Sentence Rates (NCRP) per 100,000, by Of f ense Type 450 400 350 300 250 200 150 100 50 0 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Drug White Pamela Oliver Non-drug White Drug Black Non-drug Black National White Prison Sentence Rates by Offense White Ne w Se nte nce s pe r 100,000 pop, by offe ns e . All State s in NCRP 18 18 16 14 12 10 8 6 4 Drug 2 Rob/burg Other Theft Violent 0 0 1983 19 8 3 19 8 4 19 8 5 19 8 6 19 8 7 19 8 8 V io lent 19 8 9 19 9 0 19 9 1 19 9 2 Pamela Oliver Thef t Ro b / B ur 19 9 3 19 9 4 Drug 19 9 5 Ot her 19 9 6 19 9 7 19 9 8 19 9 9 1999 National Black Prison Sentences by Offense Black Ne w Se nte nce s pe r 100,000 pop, by offe ns e . All State s in NCRP 300 300 Drug Rob/burg Violent Theft Other 250 200 150 100 50 0 0 1983 1984 1983 1985 1986 1987 1988 V iolent 1989 1990 1991 1992 Pamela Oliver Thef t Rob/ B ur 1993 1994 Drug 1995 Ot her 1996 1997 1998 1999 1999 Drug Disparities Nationally, Black juveniles & young adults (those under 26) use illegal drugs at LOWER RATES than White juveniles Only among those over 25 are illegal drug use rates higher for Blacks than Whites, but the disparities are much lower than the imprisonment disparities Pamela Oliver Black/White disparity in self-reported illegal drug use within the past year Compare to prison sentence disparity of 15 at end of 1990s 5 4.5 4 3.5 3 Marijuana Cocaine All Cocaine Crack 2.5 2 1.5 1 Disparity < 1, Whites use more than Blacks 0.5 0 Age 26+ Age 18-25 Calculated from 2003 National Survey on Drug Use & Health, Department Pamela Oliver of Health & Human Services Comparing Wisconsin to Other States Sources are from the Bureau of Justice Statistics Pamela Oliver Prisons and Jails in Midyear 2005 This is “total incarceration” rate per 100,000 population Pamela Oliver 5000 In Prison or Jail in 2005 SD WI 4000 IA VT UT MT CO 3000 AZ 2000 CT NJ IL MN 1000 RI NY MA PA NDNH NE KS CA OR . OH WA DE VAMI WV ME MD NC NV KY IN MO LA OK ID FL AK TN AL AR MS SC TX GA DC HI 0 r= .33 200 400 WhiteNH Pamela Oliver 600 800 20 In Prison or Jail in 2005 15 DC IA VT 10 NJ CT NY RIMN IL MA ME ND NHPA NE WI SD UT MT 5 KS CO . CA WA OH DE VA OR WV AZ MDNC MI IN MO KY LA FL NV TXOK SC TN AK ID AL GA MSAR 0 HI 1000 r= .22 2000 3000 BlackNH 4000 Pamela Oliver Black/White Disparity is not the same as the Black rate 5000 20 In Prison or Jail in 2005 15 DC 10 NJCT NYRI MN IL MA IA VT WI ND NH PA NE ME MT . OH MD NC 5 SD UT WA DE VAMI WV SC KS CO CA OR AZ IN MO KY FL NV TX AKLA ID TN ARMS AL GA OK 0 HI 0 r= -.74 200 400 WhiteNH 600 Pamela Oliver Black/White Disparity is negatively related to the White rate 800 In State Prisons, 1998 (This is the most recent year for which I have been able to find these data) Pamela Oliver 3000 In Prison in 1998 WI IA TX CT 2500 OK AZ DE CA 2000 RI UT OH KS OR KY MO MI LA FL IN WA NJ 1500 PA IL 1000 MN MD NC MA 0 r= .4 NE VA CO NV NM AR NY SC AL MS GA WV 200 400 Whites in Prison per 100000 Pamela Oliver Note: Rates include Hispanics, who are almost all counted as White 600 In Prison in 1998 20 MN 15 CT IA WI PA NJ 10 IL MD NE VA 5 NC WV AR AL NY MS SC GA RI UT KS OR OH MA IN WA MI CA KY FL LA MO CO r= .28 1500 TX OK NV NM 1000 DE 2000 Blacks in Prison per 100000 AZ 2500 Pamela Oliver Note: Rates include Hispanics, who are almost all counted as White 3000 In Prison in 1998 20 MN 15 WI CT IA PA 10 IL 5 MA NJ RI UT KS NE OH OR MD IN WA MI KY CA VA NC LA AR FLMO WV AL SC NY MS GA CO TX DE OK NV AZ NM 0 r= -.63 200 400 Whites in Prison per 100000 Pamela Oliver Note: Rates include Hispanics, who are almost all counted as White 600 0 In Prison 1978 1980 1982 1984 1986 1988 Year Black Wisconsin White Wisconsin Hispanics Included in W hite & Black Rates Rate per 100000 population Pamela Oliver 1990 1992 1994 Black Other US White Other US 1996 1998 6 8 10 12 14 16 Disparity in Rate of Being in Prison 1978 1980 1982 1984 1986 1988 Year Wisconsin Hispanics Included in W hite & Black Rates Black/White Disparity Ratio Pamela Oliver 1990 1992 1994 Other US 1996 1998 Prison Admissions: National Corrections Reporting Program 1983-1999 (Hispanics not included in Black & White rates) Pamela Oliver 0 500 All Prison Admissions 1983 1985 1987 1989 1991 Year Black Wisconsin Hispanic Wisconsin White Wisconsin Hispanics Not Included in White & Black Rates Rate per 100,000 population Pamela Oliver 1993 1995 1997 Black Other US Hispanic Other US White Other US 1999 0 5 10 15 20 Disparity All Prison Admissions 1983 1985 1987 1989 1991 Year Black Wisconsin Hispanic Wisconsin Hispanics Not Included in White & Black Rates Minority/White Disparity Ratios Pamela Oliver 1993 1995 1997 Black Other US Hispanic Other US 1999 2000 Prison Admits in 1999 1500 CA WI 1000 IA MN NJ IL CO OR WA NEVA PANY MI FL SC MD AL MS OH WV NC TX GA 0 500 NV KY 50 100 150 White NH 200 National Corrections Reporting Program Rates per 100,000 population correlation = .58 Pamela Oliver 250 0 100 200 300 Non-Drug Sentences 1983 1985 1987 1989 1991 Year Black Wisconsin Hispanic Wisconsin White Wisconsin Hispanics Not Included in White & Black Rates Rate per 100,000 population Pamela Oliver 1993 1995 1997 Black Other US Hispanic Other US White Other US 1999 0 5 10 15 20 Disparity Non-Drug Sentences 1983 1985 1987 1989 1991 Year Black Wisconsin Hispanic Wisconsin Hispanics Not Included in White & Black Rates Minority/White Disparity Ratios Pamela Oliver 1993 1995 1997 Black Other US Hispanic Other US 1999 Non-Drug Sentences in 1999 500 IA 400 MN VA 300 NE NV IL WACA FL KY 200 WI NJ PA OH CO OR NC MD MI AL SC TX MS WV GA 100 NY 20 40 60 80 White NH National Corrections Reporting Program Rates per 100,000 population correlation = .1 Pamela Oliver Note: MN counts probation revocations as new sentences while WI does not 0 50 100 150 200 Drug Sentences 1983 1985 1987 1989 1991 Year Black Wisconsin Hispanic Wisconsin White Wisconsin Hispanics Not Included in White & Black Rates Rate per 100,000 population Pamela Oliver 1993 1995 1997 Black Other US Hispanic Other US White Other US 1999 0 20 40 60 Disparity Drug Sentences 1983 1985 1987 1989 1991 Year Black Wisconsin Hispanic Wisconsin Hispanics Not Included in White & Black Rates Minority/White Disparity Ratios Pamela Oliver 1993 1995 1997 Black Other US Hispanic Other US 1999 300 Drug Sentences in 1999 NJ 250 IL 200 WA MD WI KY FLCO CA 150 MN 100 NY SC AL WV PA NV NE GA MI NCOH 50 TX MS IA VA OR 0 10 20 30 White NH National Corrections Reporting Program Rates per 100,000 population correlation = .33 Pamela Oliver Note: MN counts probation revocations as new sentences while WI does not 0 200 400 600 Revocations 1983 1985 1987 1989 1991 Year Black Wisconsin Hispanic Wisconsin White Wisconsin Hispanics Not Included in White & Black Rates Rate per 100,000 population Pamela Oliver 1993 1995 1997 Black Other US Hispanic Other US White Other US 1999 0 5 10 15 20 Disparity Revocations 1983 1985 1987 1989 1991 Year Black Wisconsin Hispanic Wisconsin Hispanics Not Included in White & Black Rates Minority/White Disparity Ratios Pamela Oliver 1993 1995 1997 Black Other US Hispanic Other US 1999 Revocations in 1999 600 WI CA 400 IA NV GA CO MNNJ KY 200 IL NY PA NE TX MI SC 0 VAOH AL WV FL MS WA 0 20 40 White NH 60 National Corrections Reporting Program Rates per 100,000 population correlation = .71 Pamela Oliver Note: MN counts probation revocations as new sentences 80 Revocations in 1995-99 800 1000 UT 600 OR WI 400 IA CA NJ KY 200 MN 0 FL WA 0 IL CO NY PA MI NE SC NC MS VA OH MD AL WV 20 GA TX 40 60 White NH National Corrections Reporting Program Rates per 100,000 population correlation = .72 MO NV Pamela Oliver 80 100 30 Revocations in 1999 25 MN 20 NJ PA WI NY 15 FL NE CO IL IA 10 VA WA KY MI CA OH SC TX 5 MS GANV WVAL 0 200 400 Black NH National Corrections Reporting Program Rates per 100,000 population correlation = .31 Pamela Oliver Disparity is different from Black rate 600 Revocations in 1995-99 25 30 MN 20 NJ 15 NE PA NY IL FL 10 WA IA OR MI OH NC MD WV SC 5 UT CO VA KY CA TX GA AL MS 0 WI 200 NV MO 400 600 Black NH 800 National Corrections Reporting Program Rates per 100,000 population correlation = .25 Pamela Oliver 1000 Wisconsin vs. US Trends Summary Steep rise in Black imprisonment rates of all types after 1988 Revocations far above average in Wisconsin. Some due to data coding differences. Much is “real.” Drug sentences in Wisconsin are even more disparate than the nation as a whole: high Black & low White rates Black non-drug sentences in Wisconsin are a little above average while the White sentence rate is far below average, thus yielding a high disparity. Pamela Oliver Graphs from my analysis of Wisconsin Department of Corrections Data Wisconsin Pamela Oliver Wisconsin Total Prison Admits: Includes Parole/Probation Violators 1400 Black Rate per 100,000 population 1200 1000 800 AmerInd 600 Hispanic 400 200 Asian White 0 1990 1991 1992 1993 White, NH total 1994 1995 1996 1997 Pamela Oliver Black, NH total Hispanic total 1998 1999 2000 2001 American Indian Total 2002 2003 Asian Total Proportion of Admissions Involving New Sentences (1991-9) 60% 43% 39% 40% 18% 20% 0% New Only New + Viol Pamela Oliver Viol Only White Admissions StatusTotal Whites Wisconsin 35 Violation Only 30 New Sentence Only 25 20 15 10 5 Violation + New 0 1990 1991 1992 1993 1994 1995 prison admits per 100,000 White viol only Pamela Oliver White new only 1996 White viol+new 1997 1998 1999 Blacks Admission Status Total Blacks Wisconsin 700 600 Violation Only New Sentence Only 500 400 300 200 100 Violation + New 0 1990 1991 1992 1993 1994 1995 1996 prison admits per 100,000 black viol only Pamela BlackOliver new only Black viol+new 1997 1998 1999 Wisconsin Prison Admissions (Violations Only) 600 Black Rate per 100,000 population 500 400 White AmerInd 300 200 Hispanic 100 Asian 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Pamela Oliver White, NH total Black, NH total Hispanic total American Indian Total Asian Total Wisconsin Prison Admissions (New Sentences Only) 600 Black Rate per 100,000 population 500 400 300 AmerInd Hispanic 200 100 Asian White 0 1990 1991 1992 1993 White, NH total 1994 1995 1996 1997 Pamela Oliver Black, NH total Hispanic total 1998 1999 2000 2001 American Indian Total 2002 2003 Asian Total Wisconsin Prison Admissions (All New Sentences) New only plus (new + violation) 900 Black 800 Rate per 100,000 population 700 600 500 400 AmerInd Hispanic 300 200 Asian White 100 0 1990 1991 1992 1993 White, NH total 1994 1995 1996 1997 Pamela Oliver Black, NH total Hispanic total 1998 1999 2000 2001 American Indian Total 2002 2003 Asian Total Offense trends in new prison sentences by race. Pamela Oliver 14 14 Wisconsin Imprisonment Rates (All New Sentences), White Non-Hispanics (3-Year Averages) Whites Violent Imprisonment Rate (per 100,000) 12 10 Rob/burg Other 8 Theft 6 4 Drug 2 0 1990 1991 VIOLENT OFFENSES 1992 1993 1994 ROBBERY/BURGLARY 1995 1996 1997 DRUG OFFENSES Pamela Oliver 1998 1999 LARCENY/THEFT 2000 2001 2002 OTHER OFFENSES 2003 UNKNOWN 300 Wisconsin Imprisonment Rates (All New Sentences), Black Non-Hispanics (3-Year Averages) 300 Blacks Imprisonment Rate (per 100,000) 250 Drug Violent 200 150 Rob/burg 100 50 Theft Other 0 1990 1991 VIOLENT OFFENSES 1992 1993 1994 ROBBERY/BURGLARY 1995 1996 1997 DRUG OFFENSES Pamela Oliver 1998 1999 LARCENY/THEFT 2000 2001 2002 OTHER OFFENSES 2003 UNKNOWN 100 100 Wisconsin Imprisonment Rates (All New Sentences), Hispanics (Any Race) (3-Year Averages) Hispanics 90 Imprisonment Rate (per 100,000) 80 Drug 70 Violent 60 50 Rob/burg 40 Other 30 20 10 Theft 0 1990 1991 VIOLENT OFFENSES 1992 1993 1994 ROBBERY/BURGLARY 1995 1996 1997 Pamela Oliver DRUG OFFENSES 1998 1999 LARCENY/THEFT 2000 2001 2002 OTHER OFFENSES 2003 UNKNOWN 120 Wisconsin Imprisonment Rates (All New Sentences), American Indians (NonHispanic) (3-Year Averages) Amer Inds Imprisonment Rate (per 100,000) 120 100 Violent 80 60 Rob/burg Other Theft 40 20 Drug 0 1990 1991 VIOLENT OFFENSES 1992 1993 1994 ROBBERY/BURGLARY 1995 1996 1997 DRUG OFFENSES Pamela Oliver 1998 1999 LARCENY/THEFT 2000 2001 2002 OTHER OFFENSES 2003 UNKNOWN 20 20 Wisconsin Imprisonment Rates (All New Sentences), Asian/PIs (Non-Hisp) (3-Year Averages) Asians 18 Violent Imprisonment Rate (per 100,000) 16 14 12 10 Rob/burg Drug 8 6 Theft 4 2 Other 0 1990 1991 VIOLENT OFFENSES 1992 1993 1994 ROBBERY/BURGLARY 1995 1996 1997 DRUG OFFENSES Pamela Oliver 1998 1999 LARCENY/THEFT 2000 2001 2002 OTHER OFFENSES 2003 UNKNOWN Age Patterns for Imprisonment Pamela Oliver Wisconsin Total New Prison Sentence Rates (No Prior Felony) 1998-9 (annualized) By Age Rate per 100,000 population 1600 1200 800 400 0 <18 18-19 20-21 22-24 25-29 30-34 Age WhiteOliver Pamela Black 35-39 40-44 45+ Whites: Prison Admits by Age, Offense (New Sentences Only, No Prior Felony)Wisconsin Total, 1998-9 summed 30 Rate per 100,000 population 25 20 15 10 5 0 <17 18-19 20-21 violent 22-24 25-29 Pamela Oliver rob/bur drug 30-34 theft 35-39 other unk 40-44 45+ Black Prison Admits by Age & Offense (New Sentences, No Prior Felony) Wisconsin Total, 1998-9 annualized 800 700 Rate per 100,000 population 600 500 400 300 200 100 0 <17 18-19 20-21 violent 22-24 25-29 rob/bur drug Pamela Oliver 30-34 theft 35-39 other unk 40-44 45+ Black/White Disparity Ratios in Prision Admissions by Age, Offense (New Sentences, No Prior Felony) Wisconsin Total Ratio of Per Capita Imprisonment Rates 100 80 60 40 20 0 <17 18-19 20-21 22-24 25-29 30-34 35-39 Age violent Pamela Oliver rob/burg drug theft other 40-44 45+ White kids are more likely to use and sell illegal drugs than Black kids, but Black kids are MUCH more likely to be arrested and prosecuted for drug offenses Pamela Oliver Incarceration Exacerbates the Effects of Racial Discrimination Next few slides are from research by Devah Pager, earned PhD from University of Wisconsin Sociology, now professor at Princeton University This was a controlled experiment in which matched pairs of applicants applied for entry-level jobs advertised in Milwaukee newspapers Pamela Oliver Figure 4. The Effect of a Criminal Record on Employment Opportunities for Whites Percent Called Back 40 34 35 30 25 20 17 15 10 5 0 Criminal Record Pamela Oliver No Record Figure 5. The Effect of a Criminal Record for Black and White Job Applicants Percent Called Back 40 34 35 30 Criminal Record 25 20 17 14 15 10 5 5 0 Black White Pamela Oliver No Record Optional: Compare County Imprisonment Patterns See “County Comparisons” Presentation Pamela Oliver Tracking disparities through the system Pamela Oliver Rates vs. Disparities (RRI) High RATES of incarceration are the major social problems Disparities are higher when White rates are lower Costs of incarceration are tied to rates, not disparities You can lower disparities by raising White rates Disparities are most appropriate for tracking fairness and justice within the system Rates are most appropriate for assessing impacts on budgets and communities Both are important, but they are not the same Policies to reduce disparities can increase rates, and vice versa Pamela Oliver OJA’s map of the flow through the system Pamela Oliver My Map of the System Pamela Oliver Decision Points 2 1 3 7 6 4 5 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 Report Focuses on sentence after adjudicated guilty of a particular offense Pamela Oliver Sentencing Commission Study Staff: Kristi Waits, Executive Director; Andrew Wiseman, Deputy Director; Brenda R. Mayrack, Analyst CCAP + DOC data Offenses committed after January 31, 2003 and sentenced before October 1, 2006 5 common offenses: sexual assault of child, sexual assault, robbery + armed robbery, burglary, drug trafficking Sentencing for worst offense, in cases of multiple offenses Pamela Oliver Sample sizes Notes: “Other” includes Asians + American Indians + any others; White, Black & Other exclude Hispanics. Pamela Oliver Main Findings 1. 2. 3. “Legal” factors of offense severity and prior convictions have the largest effect on sentences. (As we would hope!) Men are more likely than women to be sentenced to prison, controlling for all other factors. Blacks & Hispanics are more likely to be sentenced to prison rather than put on probation after controls for offense type, felony class, prior convictions, number of other charges, sex, and county of sentencing. a) b) 4. Race difference is larger for less serious offenses Race difference even comparing people with no prior convictions. There is no consistent racial difference in the LENGTH of the sentence if a prison sentence is given Pamela Oliver Regression summaries These use multi-variable statistics to assess the impact of each factor while controlling for all other factors in the model They show clear evidence of an overall effect of race on likelihood of being sentenced to prison, given that there is a guilty finding Note there is a sex effect, too! Pamela Oliver Nondrug offenses. Pamela Oliver Drug Trafficking Offenses Pamela Oliver Verbal summary of statistical results Statistically controlling for other factors Blacks 47% & Hispanics 65% more likely to get a prison sentence for non-drug crimes Blacks nearly twice as likely (196%) and Hispanics nearly 2 and a half times as likely (243%) to get a prison sentence for a drug crime Men were 272% more likely than women to get a prison sentence for a non-drug offense and 250% more likely to get a prison sentence for a drug offense. Pamela Oliver See report appendix for bar graphs for percentages for specific offenses http://wsc.wi.gov/ (When the report is final) Pamela Oliver Policy implications of Sentencing Study Focus on WHETHER to give a prison sentence, not just how long a sentence should be given Examine plea bargaining processes which often pre-determines the sentence type as well as the severity of the charged offense Consider impact of social factors (i.e. job, marriage, home) on sentencing Remember that a record of prior arrests & misdemeanors may be due to patterns of policing Pamela Oliver Arrests Pamela Oliver Crime & Arrest MOST crime does not result in arrest! MOST crime is relatively minor: petty theft, disorderly conduct Arrest is a function of Crime Reporting of crime to police Policing patterns & practices: WHERE you police & HOW you police Officer decisions Impossible to assess fairness in arrest without data on crime, which we don’t have! Pamela Oliver Arrest Patterns (1997-99): Adult (I did this analysis in the past; it can be updated) Most arrests are for the least serious offenses & never result in incarceration Patterns of arrests for low-level offenses contribute to prior records at sentencing Race is officer’s perception: most probably default to White “White” arrests include Hispanics because there is no separate Hispanic category in official arrest reports Pamela Oliver of Adult Arrests, Wisc. Total, Average arrests Annual 1997-9 OffenseProportion Proportions, Adult Other, Except Traffic Wrong Place Disorderly Conduct Alcohol-Related Weapons & Misc Other Property Black White Simple Assault Theft/Larceny Other Drug Offenses Marijuana Possession Serious 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 “Serious” offenses include homicide,Pamela sexualOliver assault, aggravated assault, robbery, burglary, motor vehicle theft Adult 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 1997-99 Pamela Oliver Totalarrests Adult Arrest Rate 1997-9, Annual Average Adult, Total 100000 90000 Arrests per 100,000 population 80000 Dane Kenosha Milwaukee Racine Rock Waukesha WIBalance Wisc Total 70000 60000 50000 40000 30000 20000 10000 0 White Black AmerInd Pamela Oliver Asian Adult Arrest Rate 1997-9, Annual Average,Serious Offenses Adult Serious arrests (Homicide, Agg. Assault, Sexual Assault, Robbery, Burglary, Auto Theft) 6000 Arrests per 100,000 population 5000 Dane Kenosha Milwaukee Racine Rock Waukesha WIBalance Wisc Total 4000 3000 2000 1000 0 White Black AmerInd Pamela Oliver Asian Adult, OtherAdultExc Traffic arrests Arrest Rate 1997-9, Annual Average Other Except Traffic 50000 45000 Arrests per 100,000 population 40000 Dane Kenosha Milwaukee Racine Rock Waukesha WIBalance Wisc Total 35000 30000 25000 20000 15000 10000 5000 0 White Black AmerInd Pamela Oliver Asian Adult Arrest Rate 1997-9, Annual arrests Average Adult Drug not Marijuana Other Drug Offenses (Excludes Marijuana possession) 4500 4000 Arrests per 100,000 population 3500 Dane Kenosha Milwaukee Racine Rock Waukesha WIBalance Wisc Total 3000 2500 2000 1500 1000 500 0 White Black AmerInd Pamela Oliver Asian Adult Marijuana Adult Arrest Arrests Rate 1997-9, Annual Average Marijuana possession 3000 Arrests per 100,000 population 2500 Dane Kenosha Milwaukee Racine Rock Waukesha WIBalance Wisc Total 2000 1500 1000 500 0 White Black AmerInd Pamela Oliver Asian Disparity in Crime & Arrest Some is doubtless due to real differences in crime, can be addressed only through the underlying causes of crime Some is due to patterns of policing High disparities in arrest for lesser offenses that many commit may indicate policing patterns Police focus on “high crime” areas Different police jurisdictions have different racial compositions & different practices These give young people “prior records” that affect subsequent treatment Drug crimes are different from other crimes: most differences in drug arrests arise from policing practices rather than differences in actual crime Pamela Oliver Comparing Arrest and Imprisonment Group offenses in arrest & prison sentence data so they match up Count number of arrests by offense & race for 19971999 Count number of prison sentences by offense & race for 1997-1999 Ratio prison sentences to arrests is roughly chances of going to prison after arrest (i.e. post-arrest processing) This ratio is lower for lesser offenses, higher for more serious offenses Not matching up particular people, but overall rates Disparity or RRI is the ratio of the ratios: are minorities more likely to end up in prison after arrest? Pamela Oliver Wisconsin Total: Ratio of Prison Sentences to Arrests by Race & Offense Pamela Oliver Wisconsin total: RRI Prison/Arrest Ratio Pamela Oliver The disparity in the prison/arrest ratio is especially high for Black drug possession cases, where it is nearly 9 to 1. This merits strong scrutiny. Other disparities that stand out (>2) include Black ratios for non-aggravated assault, theft & fraud, prostitution and other sex offenses, drug MDI, weapons and public order offenses; Native American homicide, assault, arson, burglary, theft, weapons, family/child, and public order offenses; and Asian aggravated assault, assault, and burglary cases. Pamela Oliver Where else to look? Charging decisions (by police & prosecutors) Prosecution decisions Legal defense options Plea bargains Sentencing Sanctioning within prisons Probation & Parole revocations Custody awaiting revocation Community reintegration: job, housing, driver’s license Pamela Oliver Conclusions: Data, Disparities, & Rates Data does not solve the problem BUT data tells you where to look for problems & solutions Individual cases are complex: data look for patterns across cases where the individual details average out Data make us accountable for our actions Pamela Oliver
