County Sentencing Project · The Commission is an agency of the General Assembly affiliated with The Pennsylvania State University. Pennsylvania Commission on Sentencing County Sentencing

Pennsylvania Commission on Sentencing

State College Office: 009 Brumbaugh Hall Penn State University Park Campus

Mail: PO Box 1200 State College, PA 16804-1200

Phone: 814.863.2797

Fax: 814.863.2129

URL: http://pasentencing.us

County Sentencing Project

Report 2

Predictors of Recidivism

2013

The Commission is an agency of the General Assembly affiliated with The Pennsylvania State University.


County Sentencing Project Report 2

Predictors of Recidivism [July 2013]

Highlights

This report presents findings from a recidivism study of less serious offenders sentenced at the ‘county’ level. The study is part of a larger project that was undertaken to collect information on county sentencing practices. The purpose of the recidivism study was twofold: 1) to see whether the same factors we found predicting recidivism in our risk assessment study were found with this sample, and 2) to determine whether additional factors not available in the risk study contributed further to the prediction of arrest. The major findings from the study were: • Overall, about 35% of the offenders were re-arrested for a new crime during a two year tracking

period.

• Six of the eight factors found to predict recidivism in our risk assessment study were found to predict re-arrest in this study:

o Current offense o Offense Gravity Score o Number of prior arrests o Prior drug arrest o Prior property arrest o Race o Age

• Gender and county were not found to be significant predictors or re-arrest.1

• Information on seven additional factors was obtained that was unavailable in the risk assessment study. Three of the factors were found to predict recidivism:

o Employment o Education o Living Status

• The overall conclusion was that the factors considered in our risk assessment scale provided the best

predictors of recidivism, though there may be some benefit to the inclusion of additional demographic information.

• Additional analyses included offenses committed out of state [see Addendum]. We found that:

o The recidivism rate was 38% after two years, rather than 35%. o The prediction model did not perform as well with the inclusion of out of state offenses. o The major factors that predicted recidivism were consistent with and without out of state

offenses.

1 It was noted that, with respect to county, the 18 counties in this study may not be representative of the 67 counties statewide. With respect to gender, there was a higher percentage of females in this study than in the risk assessment study. Further, females had a higher rate of theft offenses than males, and theft offenders had the highest rate of recidivism, which may explain why males were not found to recidivate more than females in this study.

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University Co-PI CountyPenn State University: University Park Kempinen, Ruback CentrePenn State University: Harrisburg Sims Dauphin

CumberlandPenn State University: Blair Cox BlairVillanova University Arvanites Delaware

MontgomeryIndiana University of PA Gido IndianaSusquehanna University Smyth Northumberland

[received funding from the Arlin M. Adams SnyderCenter for Law and Society at UnionSusquehanna University ] Juniata

LehighYork College Verrecchia York Waynsburg University Baer; Jellison GreeneUniversity of PA Hyatt PhiladelphiaMillersville University Glazier LancasterGannon University Garase ErieLebanon Valley College Goodfellow Lebanon

Partners for the County Sentencing Project

Backgound The correctional reform legislation enacted in 2008 [Acts 81, 82, 83 and 84 of 2008] required the Commission to develop and adopt new guidelines for parole (county and state) and reparole, as well as for re-sentencing following revocation of probation, County Intermediate Punishment [CIP], and State Intermediate Punishment [SIP]. To help inform this process, the Commission undertook a project that involved a partnership with eleven universities. The faculty at these universities, along with their students, collected data on county sentencing practices in 18 counties. The Commission received the approval of the President Judge, Chief Probation Officer, and Jail Wardens in each of the counties to collect data from their respective agencies. Without the assistance from our research partners, and the cooperation of the county agencies, this project would not have been possible. We are extremely appreciative of the time and effort that all of those involved devoted to the project. [See Appendix A for listing of research partners and Appendix B for listing of county contacts at the time of the project.] Partnership Meetings. A statewide meeting of research partners was held on May 20, 2010 to introduce the partners, provide an overview to the project, and discuss the field research protocol. Follow-up meetings were held via teleconference to discuss status updates and questions from the field on July 8, 2010, October 7, 2010, November 13, 2010, January 13, 2011, and March 10, 2011. A wrap-up meeting was held on June 16, 2011.

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Project Goals The major purpose of the project was to obtain information on county sentencing practices that would assist the Commission in the implementation of their new responsibilities of developing guidelines for parole, reparole, and resentencing. Specifically, the project had four primary objectives: What is the amount of time served on county jail sentences? The Commission on Sentencing collects information of all convictions for felonies and misdemeanors. While the Commission has the type and length of sentence imposed, it does not have information on the actual amount of time served on these sentences. In order for the Commission to develop parole guidelines at the county level, we first needed to obtain some information on current paroling practices. As judges have the paroling authority for county jail sentences, and counties can determine their own ‘good time’ policies, a defendant can be released prior to the expiration of the minimum sentence.2 Further, since Pennsylvania statute requires the imposition of a minimum and maximum sentence, a defendant can also be held in jail up to the maximum sentence imposed. What factors result in the revocation of probation and county intermediate punishment, and what sentence is imposed as a result of revocation? In addition to the development of parole guidelines, the Commission has also been mandated to develop guidelines for resentencing following the revocation of probation and county intermediate punishment. Since the Commission did not collect information on violations or revocations at the time of this project, the second objective of this project was to collect this information.3 What factors result in the revocation of county parole, and how much time is served as a result of the revocation? Act 81 of 2008 mandated the Commission to develop guidelines not only for county parole, but for reparole as well. Thus, in addition to collecting information on the amount of time served on the original sentence imposed, we also asked for information on revocation of county parole. Upon revocation the judge imposes the balance of the original sentence to be served. The court may, on cause shown by the probation officer that the inmate has violated his parole, recommit and reparole the inmate in the same manner and by the same procedure as in the case of the original parole if, in the judgment of the court, there is a reasonable probability that the inmate will benefit from being paroled. The court may also recommit for violation of that parole. (42 Pa.C.S.A. §9776(e)). The judge then has the authority of re-paroling the offenders. What factors predict future recidivism? Act 81 of 2008 also mandated that, in the development of guidelines for parole, the guidelines consider a validated risk assessment tool. The Commission has underway another project that is working on the development of a risk assessment instrument. That project primarily includes criminal justice factors, along with a limited number of demographic factors. Other information, such as substance abuse, education, and employment is not readily available. Thus, a fourth objective of this project was to obtain information beyond criminal justice factors to see their contribution in the prediction of recidivism.

2 Act 81 of 2008 revised the judge’s paroling authority for county jail sentences. Judges may only parole an offender prior to the expiration of their minimum sentence if the offender was made eligible to participate in a reentry plan at the time of sentencing. However, this policy was not yet in effect for our sample. 3 The Commission started to collect information on revocations in November 2011.

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Data Collection Process For this project, the Commission entered into partnerships with faculty at seven universities who arranged for data collection in eighteen counties [Figure 1]. Data collection in these counties began in the summer of 2010, and was closed out in the spring of 2012.

Figure 1.

After the counties collected the data, the data collection forms were sent to PCS. PCS staff entered, cleaned, and prepared a data set for each research partner containing a copy of the data they collected, along with the PCS data for the year the sample was collected. Additionally, the Commission obtained criminal history records from the state police for the recidivism portion of the project. Sample Selection. We selected the non-DUI sample from Levels One, Two, and Three of the sentencing guidelines, which provide for recommendations to county jail, county intermediate punishment, and probation. To ensure that we had enough cases to obtain information on sentence type [jail, CIP, probation], offense type, and prior record [yes/no] we based our sample selection along those dimensions. If an offense constituted at least 5% of the sentences imposed for county jail, CIP, or probation, we included that offense. This resulted in seven offense categories being included in the sample selection: felony theft, misdemeanor theft, felony retail theft, misdemeanor retail theft, felony drug, misdemeanor drug, and simple assault. These seven offenses also allow the opportunity to conduct analysis with respect to offense type: property, personal, and drug. For the DUI sample, we randomly selected ten cases from each sentence type for each county to obtain a sample consisting of about 300 DUI cases, which were needed to conduct the analyses. Table 1 shows the final sample by county and offense type. Since the Commission had not collected data on revocation cases, we received a list of revocation

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offenders from the Administrative Office of Pennsylvania Courts so we could identify the offenders in our sample who had revocations [and select additional revocation cases if need be].

Report 1: County Time Served and Revocations. Report 1 for this project addressed the first three goals. Major findings from that report included: • Most offenders were released on bail or ROR [Release on Recognizance] prior to trial.

• About 38% of the sample received a jail sentence, with an average minimum sentence of 3.6 months. Felony drug offenders had the longest average minimum [6.5 months].

• About 27% of the offenders sentenced to jail were released at the expiration of their minimum sentence; 34% were released before, and 38% were released after the expiration of their minimum.

• Among offenders who were released before the expiration of their minimum sentence, 18% were released within two days of the minimum, 53% were within one month, and 93% within two months. Among offenders who were released after the expiration of their minimum sentence, 13% were within two days, 48% were within one month; and 85% were within two months.

• The average amount of time served was 4.2 months, with the average time served prior to trial being 1.8 months and the average amount of time post sentencing being 2.4 months.

• Felony offenders were more likely to be released before the expiration of their minimum sentence, and misdemeanor offenders were more likely to be released beyond their minimum sentence.

• There was quite a bit of variation among counties in the amount of time served by offenders. The percentage of offenders released at the expiration of their minimum sentence ranged from 4% to 59%; The range was broad for both the percentage released below and above the minimum sentence [15% to 55% and 5 to 61%, respectively].

• About 38% of the offenders on probation or CIP and 40% of the offenders on parole had a violation for their current offense. For all offenders the most common reason for a violation was non-payment of fines. Most offenders with a violation had their sentence revoked.

NUMBER PERCENT

CountyAssault-Simple

Drug-Felony

Drug-Misd DUI-M

DUI-M1

Theft-Felony

Theft-Misd

Theft-Retail Fel

Theft-Retail Misd Total County

Assault-Simple

Drug-Felony

Drug-Misd DUI-M

DUI-M1

Theft-Felony

Theft-Misd

Theft-Retail Fel

Theft-Retail Misd Total

Blair 29 47 46 25 5 11 58 15 30 266 Blair 11% 18% 17% 9% 2% 4% 22% 6% 11% 100%Centre 78 51 70 18 8 10 59 6 17 317 Centre 25% 16% 22% 6% 3% 3% 19% 2% 5% 100%Cumberland 53 50 24 19 11 32 70 24 27 310 Cumberland 17% 16% 8% 6% 4% 10% 23% 8% 9% 100%Dauphin 41 63 65 22 8 29 48 20 25 321 Dauphin 13% 20% 20% 7% 2% 9% 15% 6% 8% 100%Delaware 30 56 80 25 5 14 43 15 25 293 Delaware 10% 19% 27% 9% 2% 5% 15% 5% 9% 100%Erie 74 26 74 14 10 22 92 5 23 340 Erie 22% 8% 22% 4% 3% 6% 27% 1% 7% 100%Greene 14 12 21 9 5 12 25 0 1 99 Greene 14% 12% 21% 9% 5% 12% 25% 1% 100%Indiana 22 17 48 24 5 10 66 5 22 219 Indiana 10% 8% 22% 11% 2% 5% 30% 2% 10% 100%Juniata 14 9 22 8 3 6 29 2 4 97 Juniata 14% 9% 23% 8% 3% 6% 30% 2% 4% 100%Lancaster 31 32 28 22 8 30 46 32 32 261 Lancaster 12% 12% 11% 8% 3% 11% 18% 12% 12% 100%Lebanon 57 21 76 20 10 21 71 17 31 324 Lebanon 18% 6% 23% 6% 3% 6% 22% 5% 10% 100%Lehigh 49 47 55 18 12 32 48 18 30 309 Lehigh 16% 15% 18% 6% 4% 10% 16% 6% 10% 100%Montgomery 28 47 62 20 10 35 71 16 39 328 Montgomery 9% 14% 19% 6% 3% 11% 22% 5% 12% 100%Northumberlan 38 55 60 15 8 14 52 8 8 258 Northumberla 15% 21% 23% 6% 3% 5% 20% 3% 3% 100%Philadelphia 37 226 21 7 5 36 26 18 2 378 Philadelphia 10% 60% 6% 2% 1% 10% 7% 5% 1% 100%Snyder 6 3 26 19 2 7 29 6 9 107 Snyder 6% 3% 24% 18% 2% 7% 27% 6% 8% 100%Union 13 5 12 12 12 4 23 0 5 86 Union 15% 6% 14% 14% 14% 5% 27% 6% 100%York 31 52 65 21 9 42 61 21 51 353 York 9% 15% 18% 6% 3% 12% 17% 6% 14% 100%Total 645 819 855 318 136 367 917 228 381 4666 Total 14% 18% 18% 7% 3% 8% 20% 5% 8% 100%

Table 1. County Sentencing Project Sample by County and Offense [n=4,666]

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• About 54% of the offenders who had a probation or CIP revocation received a jail sentence as a result of their revocation, with an average minimum sentence of 5 months. An additional 6% were sentenced to prison, with an average minimum sentence of 13 months.

• The violation rate was highest among retail theft offenders [both felony and misdemeanor] and lowest among DUI offenders.

• The violation rate among the counties ranged from 18% to 74%. Current Report: Predictors of Recidivism As indicated earlier, the Commission was mandated to consider a validated risk assessment tool for parole [Act 81 of 2008]. Additionally, the Commission has been mandated to develop a risk assessment tool to be considered at sentencing [Act 95 of 2010]. The Commission has underway several projects to assist in the development of a risk assessment tool that primarily utilize criminal justice factors, with a limited number of demographic factors [age, county, gender]. While the Commission is interested in knowing the impact that factors such education, employment, and substance abuse has on recidivism, this information is not systematically collected at sentencing, and not readily available. Thus, in addition to collecting information on time served and county parole practices, the Commission also collected information on additional criminal justice and demographic factors to help determine the contribution these factors make to our prediction of offender recidivism. We used arrest for a new crime as our measure of recidivism, and obtained the criminal history records from the Pennsylvania State Police. We used a two year tracking period for the study, as we were able to track 94% of the offenders for that length of time. Sample Description. The sample for the recidivism study consisted of 4,408 offenders [258 offenders who were not tracked for two years were removed]. Table 2 provides a description of the sample. Demographics. The majority of the sample was white [66%], male [75%], and had a mean age of 30. Most of the offenders had a high school diploma, with 18% having some post high school training or college. About 54% were employed, 41% employed full-time. The majority had never been married [67%], though about 27% were living with a spouse or partner, and most of those had minor children living with them. About 29% were living with one or both parents. It should be noted, however, that much of the demographic information on offenders was missing [ranging from 14% to 33% missing]. Many offenders do not have a pre-sentence investigation conducted, which is the most likely source for this type of information. Criminal history and substance abuse. As the primary purpose of the project was to collect information on offenders sentenced to county jail or probation, the offenders were selected among Levels 1, 2, and 3 of the guidelines. Further, to ensure enough cases for analyses by crime, the sample had certain selection criteria [see sample selection above]. Thus, the sample consisted of the most frequently committed offenses for those guidelines levels: felony theft, misdemeanor theft, felony retail theft, misdemeanor retail theft, felony drug, misdemeanor drug, and simple assault. About 40% of the sample were convicted of theft offenses, 36% of drug offenses; 14% of simple assault, 10% of DUI. The mean Offense Gravity Score was 3 [on a scale of 1 to 8]. Most offenders [80%] had a prior arrest, with the mean number of arrests being 3.5. Among those with a prior arrest, [57% had an arrest for a property offense, with a large percentage also having a previous arrest for a personal misdemeanor offense [34%] and/or drug misdemeanor offense [40%]. About 10%

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had a previous institutional placement as a juvenile and about 46% had been previously incarcerated as an adult. Over half of the sample had a probation sentence [53%], with 36% receiving jail, and 11% receiving County Intermediate Punishment. Most offenders used drugs in the past [83%], though less [32%] currently used drugs. Marijuana [68%] was the most prevalent drug used, followed by cocaine [23%]. About 41% had alcohol abuse problems. About 20% were taking psychiatric medicine, and about 23% were referred to a Base Service Unit. However, again it must be noted that for a high percentage of offenders, information on substance abuse and mental health was unavailable. [The missing information rate ranged from 35% to 51%].

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Offender CharacteristicsN % N %

Race Employment White 2905 66% Working full-time 1438 41% Black 1084 25% Working part-time 470 13% Hispanic 374 8% In school 52 1% Other 45 1% Working and in school 18 1%

Looking for work 123 4%Gender Unemployed, not looking 1209 34% Male 3309 75% Other 45 1% Female 1099 25% SSI/Disability 158 4%

Missing 895 21%Age (Date of offense) < 21 888 20% Marital Status 21 - 24 898 20% Married 545 14% 25-29 784 18% Widowed 28 <1% 30-39 458 10% Divorced 334 9% 40-49 829 19% Married, but separated 177 5% > 50 551 13% Never Married 2,547 67% Mean 30.1 Unmarried, living with partner 197 5%

Missing 580 13%County Blair 253 6% Living Status Centre 306 7% Alone 636 19% Cumberland 294 7% With spouse/partner 922 27% Dauphin 307 7% With both mother and father 356 11% Delaware 277 6% With either mother or father 609 18% Erie 324 7% With other family member 363 11% Greene 95 2% With non-family member 299 9% Indiana 210 5% Homeless 40 1% Juniata 95 2% Other 37 1% Lancaster 249 6% Family Not Specified 109 3% Lebanon 312 7% Missing 1037 24% Lehigh 286 6% Montgomery 309 7% Living with Children Northumberland 233 5% Yes 888 30% Philadelphia 344 8% No 1,179 40% Snyder 103 2% No children 906 30% Union 82 2% Missing 1435 33% York 329 7%

Education Less than HS degree 1,161 30% High school graduate/GED 1,969 52% Trade School/some college 565 15% College degree 119 3% Missing 594 13%

* 258 offenders removed who were not tracked for 2 years

Table 2. Descriptive Statistics for County Recidivism Study (N = 4408)

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Criminal History Information N % N % Offense type Prior Incarceration as Adult

Assault-Simple 613 14% Yes 1166 46%Drug-Felony 734 17% No 1394 54%Drug-Misd 829 19% Missing 1848 42%DUI-M 309 7%DUI-M1 127 3% Sentence TypeTheft-Felony 335 8% CIP 489 11%Theft-Misd 879 20% Jail 1598 36%Theft-Retail Fel 209 5% Prob 2321 53%Theft-Retail Misd 373 8%

Drug Use InformationOffense Gravity Score

1 956 22% Past Drug Use2 612 14% Yes 1845 83%3 1849 42% No 391 17%4 15 0% Missing 2172 49%5 498 11%6 314 7% Type of Past Drug Use [N=705; not mutually exclusive}7 110 2%8 54 1% Marijuana 1402 76%

Mean 3.0 Cocaine 681 37% Heroin 333 18%

Prior Total Arrests Opiates 257 14%0 896 20% Barbiturates 108 6%1 836 19% Hallucinogens 108 6%2 613 14% Other 100 5%3 513 12%4 350 8% Current Drug Use5 268 6% Yes 705 32%over 5 932 21% No 1,531 69%mean 3.5 Missing 2172 49%

Prior Offenses [n=3512 with prior arrest] Type of Current Drug Use [N=705; not mutually exclusive}Property 2508 57% Marijuana 481 68%Personal felony 796 18% Cocaine 159 23%Personal Misd. 1502 34% Heroin 122 17%Drug felony 935 21% Opiates 110 16%Drug misd. 1778 40% Barbiturates 47 7%Firearms 263 6% Hallucinogens 19 3%DUI 867 20% Other 40 6%Other 2100 48%

Alcohol AbusePRS Yes 1171 41%

0 2518 57% No 1681 59%1 623 14% Missing 1556 35%2 472 11%3 276 6% Taking Psychiatric Medication4 201 5% Yes 532 80%5 283 6% No 2177 20%

RFEL 35 1% Missing 1699 39%mean 1.00

Referred to Base Service UnitPrior Incarceration as Juvenile Yes 497 23% Yes 196 11% No 1648 77% No 1656 89% Missing 2263 51% Missing 2556 1%

* 258 offenders removed who were not tracked for 2 years

Table 2 [cont.] Descriptive Statistics for County Recidivism Study (N = 4408)

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Factors related to recidivism: bivariate analyses. We used re-arrest for a new crime as our measure of recidivism, and used a two year follow-up as 94% of the original sample was able to be tracked for this time period. Table 3 shows the bivariate relationship between recidivism and the factors we had available for our study. Overall, the two year recidivism rate was 35%. The bivariate relationships indicate that offenders in our sample were more likely to recidivate if they had the following characteristics:

• Non –white • Male • Young • From an urban county • Less educated • Unemployed • Never married • Lived with their parents or other family member • Had prior arrests [exception was prior DUI, which was not related to recidivism] • Prior juvenile placement • Incarcerated as adult • Had lower Offense Gravity Scores • Had rior convictions • Convicted of a theft offense [compared to drug, DUI , or simple assault] • Received a jail sentence • Took psychiatric medication

We found the following factors not to be related to recidivism:

• Living with children • Current drug use • Past drug use • Referral to Base Service Unit [drug use indicator] • Alcohol abuse

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No Arrest

ArrestNo

ArrestArrest

No Arrest

ArrestNo

ArrestArrest

% % N N % % N N

Overall 65% 35% 2861 1547 Criminal History Information

Offender Characteristics Prior Arrests ***0 83% 17% 743 153

Race *** 1 72% 28% 600 236 White 68% 32% 1973 932 2 63% 37% 384 229 Black 57% 43% 620 464 3 63% 37% 321 192 Hispanic/Other 64% 36% 268 151 4 57% 43% 199 151

5 51% 49% 614 586Gender* mean *** 2.9 4.7 2118 1394

Male 64% 36% 2118 1191Female 68% 32% 743 356 Type of Prior Arrest

Property ***Age (Date of offense) *** Yes 56% 44% 1407 1101

< 21 55% 45% 485 403 No 77% 23% 1454 44622 to 44 65% 35% 1941 1028 Personal felony ***Over 44 79% 21% 435 116 Yes 54% 46% 429 367Mean No 67% 33% 2432 1180

Personal misd. ***County *** Yes 56% 44% 837 665

Urban 65% 35% 1738 949 No 70% 30% 2024 882Rural 68% 32% 933 444 Drug felony ***Philadelphia 55% 45% 190 154 Yes 54% 46% 506 429

No 68% 32% 2355 1118Education *** Drug misd. ***

Less than HS degree 61% 39% 707 454 Yes 65% 36% 559 308 High school grad/GED 64% 36% 1266 703 No 65% 35% 2302 1239 Trade School/college 76% 24% 427 138 Firearms *** College degree 82% 18% 98 21 Yes 49% 51% 130 133

No 66% 34% 2731 1414Employment *** DUI n.s.

Employed/school 70% 30% 1382 596 Yes 64% 36% 559 308Unemployed 59% 41% 781 551 No 65% 35% 2302 1239SSI/disability 75% 25% 153 50 Prior Incarceration as Juvenile ***

Yes 56% 44% 109 87Marital Status *** No 71% 29% 1169 487

Married 72% 28% 392 153 Prior Incarceration as Adult ***Never Married 64% 36% 1620 927 Yes 61% 39% 709 457Currently unmarried 67% 33% 496 240 No 72% 28% 1005 389

Living Status ***Alone 72% 28% 458 178Spouse 67% 33% 618 304Parents, other family 60% 40% 856 581Other 67% 33% 252 124

Living with children n.s.Yes 64% 36% 565 323No 66% 34% 782 397No children 66% 34% 599 307

*** sig. at .001 level ** sig. .01 level * sig. at .05 level # approaching sig. n.s. not statistically significant

Table 3. Bivariate Recidivism Rates: two year tracking (N = 4408)

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No Arrest

ArrestNo

ArrestArrest

No Arrest

ArrestNo

ArrestArrest

% % N N % % N N

OGS *** Drug Use current n.s.#1 71% 29% 676 280 No 62% 38% 951 5802 55% 45% 337 275 Yes 58% 42% 412 2933 63% 37% 1172 677 4 67% 33% 10 5 Drug Use past n.s.5 70% 30% 351 147 No 59% 41% 231 1606 61% 39% 193 121 Yes 61% 39% 1132 7137 70% 30% 77 338 83% 17% 45 9 Base Service Unit n.s.

No 68% 32% 1122 526PRS*** Yes 68% 32% 339 158

0 70% 30% 1768 7501 60% 40% 375 248 Alcohol Abuse n.s.2 57% 43% 269 203 No 65% 35% 1094 5873 60% 40% 165 111 Yes 65% 35% 764 4074 55% 45% 111 905 55% 45% 157 126 Taking Psychiatric Medication ** RFEL 46% 54% 16 19 No 67% 33% 1465 712

Yes 61% 39% 326 206Offense type ***

Assault-Simple 68% 32% 419 194Drug-Felony 67% 33% 494 240Drug-Misd 66% 34% 545 284DUI-M 81% 19% 249 60DUI-M1 91% 9% 115 12Theft-Felony 64% 36% 215 120Theft-Misd 57% 43% 501 378Theft-Retail Fel 51% 49% 107 102Theft-Retail Misd 58% 42% 216 157

Type of sentence **CIP 72% 28% 350 139Jail 62% 38% 990 608Probation 66% 34% 1521 800

*** sig. at .001 level ** sig. .01 level * sig. at .05 level # approaching sig. n.s. not statistically significant

Table 3 [cont.] Bivariate Recidivism Rates: two year tracking (N = 4408)

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Missing information. The purpose of the recidivism study was twofold: 1) to see whether the same factors we found predicting recidivism in our risk assessment study were found with this sample, and 2) to determine whether additional factors not available in the risk study contributed further to the prediction of arrest. Based upon the bivariate analysis, there were seven additional factors to be included: prior incarceration, use of psychiatric medication, drug use, employment status, education level, marital status, and living situation. As indicated earlier, however, we were missing a large percentage of data for some of the variables [ranging from 14%-42%]. To address the issue of missing information, we conducted three additional analyses. We ran bivariate analyses to determine whether there was a significant difference in the recidivism of offenders missing the information compared to those not missing the information. For six of the seven additional factors to be included in the multivariate analysis, there was a significant difference [there was no difference for living status]. For five factors, [previous incarceration, psychiatric medicine, employment, education, and marital status] those missing information were significantly more likely to recidivate than those not missing the information. For one factor, drug use, those missing information were significantly less likely to recidivate than those not missing the information. Thus, not including offenders with missing information into the analyses could significantly impact the findings. We conducted two additional analyses that utilized two different methods of including offenders with missing information. The first method involved creating a ‘missing’ category for each variable that had missing information and running the multivariate models again. For three of the seven factors with missing information, we found offenders with missing information to be significantly different: prior incarceration, psychiatric medication, and drug use. Upon controlling for all factors in the model, we found that offenders who had been previously incarcerated or were missing incarceration information were more likely to recidivate than those who had not been incarcerated, and those who took psychiatric medicine or were missing this information were more likely to recidivate than those who did not take this medication. For drug use, however, we found that those offenders who used drugs were more likely to recidivate than those who did not, but those missing the information were less likely to recidivate than those who did not. The final analyses involved a multiple imputation method that replaces missing values with plausible values based upon factors included in the analyses. This method generally is thought to be the preferred method of dealing with missing values and having better statistical validity. Since none of the cases from Philadelphia had previous incarceration information, this resulted in all Philadelphia cases being dropped from the original logistic regression analyses. As offenders missing the information on previous incarcerations were more likely to recidivate, we conducted the imputation analyses both with and without Philadelphia. Overall, there was a great deal of consistency in the factors found to predict recidivism among all methods, and since the imputation model is seen as having the best validity, we decided to present the findings in relation to that model. [See Appendix C for the logistic regression models with missing information; Appendix D for logistic regression models with missing categories; and Appendix E for logistic regression models with imputation method.] Predictors of recidivism: multivariate analyses. As indicated above, the purpose of the recidivism study was to see whether the same factors predicted recidivism in this study as in our risk assessment study, and to determine whether additional factors had any additional benefit in predicting recidivism. Thus, we did the analyses in stages: 1) using the original factors included in the analysis conducted for our risk assessment project; 2) adding additional criminal justice related factors; 3) adding additional demographic factors; and 4) including all of these factors together.

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Table 4 shows the four models included in the analyses. Model 1 includes those factors that we used in our risk assessment study. We found similar results for both the risk assessment sample and this county sentencing sample. As with our risk assessment analysis, we found that offenders who had a current conviction for a property offense, lower Offense Gravity Scores, a prior property offense, a prior drug offense, a greater number of prior arrests, were Black, and were younger were more likely to be re-arrested for another offense. However, unlike our analyses, we did not find county or gender to be related to recidivism. Since the county sentencing sample includes only 18 of the 67 counties, it is possible that they are not representative of the state, and thus not found to be a significant factor for recidivism. This sample also has a higher percentage of women [25% compared to 14% of the risk assessment sample] who are usually found to have lower recidivism rates than men. However, our sample also has a high rate of theft offenses, which women are more likely to commit than other types of offenses [57% of the female offenders committed a theft offense compared to 39% of the male offenders.] Theft offenders also have the highest rate of recidivism, and thus, and gender differences may be ‘washed out’ for this study. Model 2 added three additional factors: drug use, previous incarceration, and psychiatric medicine. None of these factors were found to be significantly related to recidivism. Further, having these factors included in the model, did not contribute to prediction of recidivism beyond that found in Model 1 [R2= .144 vs. R2

= .146]. Model 3 added four demographic factors: education, employment, marital status, and living status. Education, employment, and living status were found to be significantly related to recidivism; marital status was not. Offenders who attended community college or trade school were less likely to recidivate than those with less than a high school degree. Being a high school graduate or having a college degree, however, was not found to be related to recidivism. Offenders who were employed, going to school, or receiving disability were less likely to recidivate than those who were unemployed. Offenders living with family members [other than spouse] were more likely to recidivate than those living alone. One explanation for this is that younger offenders, who were much more likely to recidivate, were more likely to live with their family than alone or with a spouse. [About 57% of the offenders who lived with their family were under age 25, while 25% of offenders who lived with a spouse or were single were under age 25.] Overall, when these additional demographic factors were included in the analyses, they did provide some additional predictive power for recidivism [R2 =.158 vs. R2 =.144]. Model 4 used all of the factors together, and the factors found to be significant in the earlier models continued to be significant. In this model, offenders who used psychiatric medication were more likely to recidivate [p=.05] though this finding did not hold when Philadelphia was removed from the model.

14


Table 4. Summary of findings for predictors of recidivism from the four logistic regression models.

Model 1 Model 2 Model 3 Model 4 Model 4 [no Phil.]

Original risk factors

Add other CJ factors

Add other demographic

factors

All factors All factors

R2=.144 R2=.146 R2=.155 R2=.158 R2=.157

Original Risk FactorsCurrent Offense [compared to theft]

DUI *** *** *** *** ***

Drug *** *** *** *** ***

Simple Assault * * * * #

Offense Gravity Score [OGS] * * * * *

Number of prior arrests *** *** *** *** ***

Prior drug arrest *** *** *** *** ***

Prior Property arrest *** *** *** *** ***

Gender No No No No No

Race [compared to White]Black *** *** *** *** ***

Other No No No No No

Age [at date of current offense] *** *** *** *** ***

CountyUrban No No No No No

Philadelphia No No No No not included

Additional Criminal Justice FactorsDrug use [compared to no] -- No No No

Psch medication [compared to no] No * No

Adult inc. [compared to to] No # No

Additional Demographic FactorsEducation [compared to less than HS]

High school grad No No No

Trade/community college ** * *

College degree No No No

Employment [compared to unemployed]Employed/school * * *

SSI/disability * * *

Marital status [compared to married]Never married No No No

Currently unmarried # No #

Living status [compared to alone]Spouse No No No

Family * * *

Other No No No

* p < .05 ** p < .01 *** p < .001 # approaches significance

Shaded area indicates where variable is significant.

15


Table 5 shows more specific findings from the final model:

• The odds of offenders with a current conviction for a theft offense recidivating were two times greater than for offenders convicted of a DUI offense; 41% greater than for offenders convicted of a drug offense, and 26% greater than for offenders convicted of simple assault.

• For each increase in the OGS the odds of offenders recidivating were decreased by 5%.

• For each additional prior arrest, the odds of offenders recidivating were increased by 6%.

• The odds of offenders with a prior arrest for a drug offense recidivating were 36% greater than

for offenders without a prior arrest for a drug offense. • The odds of offenders with a prior arrest for a property offense recidivating were 66% greater

than for offenders without a prior arrest for a property offense.

• The odds of Black offenders recidivating were about 37% greater than for White offenders

• For each additional year in age, the odd of offenders recidivating were decreased by 4%.

• The odds of offenders who took psychiatric medication recidivating were 24% greater than for offenders who did not take psychiatric medication.

• The odds of offenders who had less than a high school education recidivating were 32% greater

than for offenders who went to trade school or community college.

• The odds of offenders who were unemployed recidivating were 19% greater than for offenders who were employed or in school and 52% greater than for offenders on disability.

• The odds of offenders living with a non-spousal family member recidivating were 30% greater

than for offenders who were living alone.

16


VariablesAll

counties

All counties but

Philadelphia Findings [% in parentheses is finding without Philadelphia.]

[N=4408] [N=4064]

ORIGINAL RISK VARIABLESCurrent Offense [compared to theft offense]

DUI Yes *** Yes ***The odds of offenders with a current conviction for a theft offense recidivating were two times greater than for offenders convicted of a DUI offense.

Drug Yes *** Yes ***The odds of offenders with a current conviction for a theft offense recidivating were 41% [43%] greater than for offenders convicted of a drug offense.

Simple Assault Yes * No #The odds of offenders with a current conviction for a theft offense recidivating were 26% greater than for offenders convicted of a simple assault.

Offense Gravity Score Yes *** Yes ***For each increase in the OGS the odds of offenders recidivating were decreased by 5% [6%].

Number of prior arrests Yes *** Yes ***For each additional prior arrest, tdds of offenders recidivating were increased by 6% [6%].

Prior drug arrest Yes *** Yes ***The odds of offenders with a prior arrest for a drug offense recidivating were 36% [39%] greater than for offenders without a prior arrest for a drug offense offense.

Prior property arrest Yes *** Yes ***The odds of offenders with a prior arrest for a property offense recidivating were 66% [67%] greater than for offenders without a prior arrest for a property offense.

Gender No No

Race [compared to white]

Black Yes *** Yes ***The odds of Black offenders recidivating were about 37% [39%] greater than for white offenders

Other race No No

Age Yes *** Yes ***For each additional year in age, the odds of offenders recidivating were decreased by 4% [4%].

County No No

ADDITIONAL CRIMINAL JUSTICE VARIABLES

Drug use No No

Psychiatric medicaiton Yes * NoThe odds of offenders who took psychiatric medicaton recidivating were 24% greater than for offenders who did not take psychiatric medication.

Prior incarceration [adult] No # No

OTHER DEMOGRAPHIC VARIABLESEducation [compared to less than high school]

High school grad No No

Trade school/community college Yes * Yes *The odds of offenders who had less than a high school education recidivating were 32% [40%] greater than for offenders who went to trade school or community college.

College degree No NoEmployment [compared to unemployed]

Employed/school Yes * Yes *The odds of offenders who were unemployed recidivating were 19% [23%] greater than for offenders who were employed or in school.

SSI/disability Yes * Yes *The odds of offenders who were unemployed recidivating were 52% [67%] greater than for offenders who were on SSI or disability.

Marital status No NoLiving status [compared to living alone]

Spouse No No

Famly member Yes * Yes *The odds of offenders living with a non-spousal family member recidivating were 30% [28%] greater than for offenders who were living alone.

Other No No


Table 5. Predictors of recidivism for entire sample [N=4408] and sample without Philadelphia [N=4064]

17


Summary and Conclusion The County Sentencing Project was conducted in response to a legislative mandate directing the Sentencing Commission to establish guidelines for county parole and resentencing following revocations. The primary purpose of the project was to collect information on county sentencing practices, particularly in relation to the amount of time served on county jail sentences and sanctions imposed for revocations. Since we were collecting information in the field for this project, we also decided to collect some additional information that could be used to conduct a recidivism study with this sample of offenders. The two major objectives of the recidivism study were: 1) to see whether the factors found to predict recidivism in our risk assessment study were also found to predict recidivism with this sample, and 2) to determine whether additional factors not available in the risk study contributed further to the prediction of arrest. It should be noted that our sample for this project is different than the sample in the risk assessment project. While the risk assessment project included offenders from Levels 3 and 4 of the Sentencing Guidelines [mid-level seriousness], this sample consisted of mostly of offenders from Level 1 [15%] and Level 2 [52%] of the Sentencing Guidelines. Further, by design, the sample for this project consisted of offenders who committed nine specific types of offenses, while the offenders in the risk assessment study consisted of all offenders at Levels 3 and 4. Despite the sample differences, however, our findings concerning factors predicting recidivism were consistent with both this sample and our risk assessment study sample. Overall, our findings indicated that six of the eight factors found to predict recidivism in our risk assessment project were also found to predict recidivism in this sample of offenders:

• Current offense is a property offense • Had a prior arrest for a property offense • Had a prior arrest for a drug offense • Had a larger number of prior arrests • Had an offense with a lower Offense Gravity Score • Was a Black offender • Was a younger offender

Unlike the findings from the risk assessment study, two of the factors, gender and county, were not found to predict arrest in this sample. However, since this sample included only 18 of the 67 counties, it is possible that they were not representative of the state, and thus, not found to be a significant factor for recidivism. This sample also has a higher percentage of women [25% compared to 14% of the risk assessment sample] who are usually found to have lower recidivism rates than men. However, the sample also has a high rate of theft offenders, which women are more likely to commit. Theft offenders also have the highest rate of recidivism, and thus, and gender differences may be ‘washed out’ for this study. The second objective of this study was to see whether adding additional information into our recidivism model added any additional predictive power. When we added additional three criminal justice factors that were available for this study, but not our risk assessment study [previous incarceration, drug use, and use of psychiatric medication], we found that the use of psychiatric medication was a predictor of recidivism only when Philadelphia was in the model. [There was a broad range among the counties with respect to psychiatric medication use, ranging from 1% to 86%, with Philadelphia being 30%],

18


When we added four additional demographic factors available for this study, but not our risk assessment study [education, employment, marital status, and living status], we found three factors to be predictors of recidivism:

• Education • Employment • Living Status

With respect to education, the finding was specific to offenders who were going to trade school or community college. While these offenders were less likely to recidivate than those with less than a high school degree, we did not find this to be the case among offenders with a high school or college degree. Offenders who were employed or receiving SSI or disability were less likely to recidivate than offenders who were unemployed. Offenders living with family [other than spouse] were more likely to recidivate than those who lived alone. However, younger offenders, who were more likely to recidivate, were also more likely to be living with their family [mostly parents] than by themselves. When we looked at the predictive power of the various models, we found that the original risk factors included in the model [eight factors used in the risk assessment study] provided the most predictive ability. However, we also found some additional benefit in the prediction of arrest with the inclusion of additional demographic factors in the model. There are some caveats to our findings. First, there was a large amount of missing information for the additional factors that we included in this study. We did extensive analyses to determine the best method to deal with the missing information, and it is encouraging that our findings were consistent with all of the methods we used. Nevertheless, we cannot be certain that the findings would be the same if all of the information were available. Since this information was collected in the field from official court documents, it also raises the concern that this type of information is not readily available to the court, and thus, not practical at this point in time to be utilized in any type of risk assessment instrument. A second caveat is that our sample consisted of specific types of offenders representing 18 of Pennsylvania’s 67 counties. These offenders were the less serious offenders who offenders who committed one of nine offenses at Levels 1 through 4 of the Sentencing Guidelines. This, we cannot say that the findings in this study are representative of all offenders statewide, tough we did find a great deal of consistency in the findings with this sample and our risk assessment study sample. With these caveats in mind, our overall conclusion is that the factors considered in our risk assessment scale continue to provide the best predictors of recidivism, though there may be some benefit to the inclusion of additional demographic information.

19


Appendix A: County Sentencing Project Research Partners *

Thomas M. Arvanites, Ph.D. (Delaware & Montgomery) Chair, Dpt. of Sociology and Criminal Justice Villanova University Amanda K. Cox, Ph.D. (Blair) Assistant Professor of Criminal Justice Penn State Altoona Maria L. Garase, Ph.D. (Erie) Associate Professor Criminal Justice Department Mercyhurst University Rosemary L. Gido, Ph.D. (Indiana) Professor of Criminology Department of Criminology Indiana University of Pennsylvania Mary H. Glazier, Ph.D. (Lancaster) Professor of Sociology Department of Sociology/Anthropology Millersville University Marianne Goodfellow, Ph.D. (Lebanon) Associate Professor of Sociology Department of Sociology Lebanon Valley College Jordon Hyatt, J.D. (Philadelphia) Senior Research Coordinator Jerry Lee Center of Criminology University of Pennsylvania *Positions at the time of the project.

Jenny L. Jellison, Ph.D. (Greene) Assistant Professor of Psychology Human Studies Waynesburg University R. Barry Ruback, Ph.D., J.D. (Centre) Professor of Crime, Law, & Justice and Sociology The Pennsylvania State University, University Park

Barbara A. Sims, Ph.D. (Dauphin & Cumberland) Professor of Criminal Justice School of Public Affairs Penn State Harrisburg

Michael A. Smyth, Ph.D. (Juniata, Lehigh, Northumberland, Union, Snyder) Director, Arlin M. Adams Center for Law and Society Assistant Professor of Sociology Department of Sociology and Anthropology School of Natural and Social Sciences Susquehanna University Philip J. Verrecchia, Ph.D. (York) Assistant Professor of Behavioral Sciences Criminal Justice Program Coordinator York College of Pennsylvania

20


Appendix B: County Sentencing Project County Contacts*

Blair The Honorable Jolene Grubb-Kopriva President Judge Janice Meadows District Court Administrator Thomas G. Shea Chief Probation Officer Michael Johnston Warden, Blair County Jail Centre The Honorable David E. Grine President Judge Maxine O. Ishler District Court Administrator Thomas J. Young Director, Probation and Parole Department Edward P. DeSabato Warden, Centre County Correctional Facility Cumberland The Honorable Kevin A. Hess President Judge Melissa H. Calvanelli District Court Administrator Lyle M. Herr Chief Probation Officer Earl F. Reitz, Jr. Warden, Cumberland County Jail Rebekah Finkey Coordinator, Criminal Justice Planning Dauphin The Honorable Todd A. Hoover President Judge Carolyn C. Thompson, Esquire District Court Administrator Michael C. Potteiger Director, Dauphin County Adult Probation Dept Dominick L. DeRose Warden, Dauphin County Prison * Positions at the time of the project.

Delaware The Honorable Joseph P. Cronin, Jr. President Judge Gerald C. Montella, Esquire District Court Administrator Michael W. Raith, Esquire Director, Delaware County Court Services Frank C. Green Superintendent, Delaware County Prison Erie The Honorable Ernest J. DiSantis, Jr. President Judge Thomas C. Aaron District Court Administrator Jeffry Shaw Director, Adult Probation James Veshecco Warden, Erie County Prison Greene The Honorable William R. Nalitz President Judge Audrey Szoyka District Court Administrator R. Craig Wise Chief Probation Officer Harry D. Gillispie Warden, Greene County Prison Indiana The Honorable William J. Martin President Judge Michael J. Kuhar District Court Administrator Denise M. Delancey Chief Probation/Parole Officer Carol Hummel Warden, Indiana County Jail

21


Juniata The Honorable Kathy A. Morrow President Judge Christina Zook Court Administrator Michelle A. Beaver Chief Probation Officer H. Thomas Lyter Warden, Juniata County Prison Lancaster The Honorable Joseph C. Madenspacher President Judge Mark M. Dalton Court Administrator Mark Wilson Director, Adult Probation Department Vincent A. Guarini Warden, Lancaster County Jail Lebanon The Honorable John C. Tylwalk President Judge David P. Wingert, Esquire Court Administrator Sally McQuinn-Barry, Chief Probation Officer Robert J. Karnes Warden. Lebanon County Correctional Facility Catharine Kilgore CJAB Planner/Grant Coordinator Lehigh

The Honorable Carol K. McGinley President Judge Susan T. Schellenberg Court Administrator John J. Sikora Chief Adult Probation Officer Dale Meisel Warden, Lehigh County Department of Corrections Montgomery The Honorable Richard J. Hodgson President Judge Michael R. Kehs, Esquire District Court Administrator Michael Gordon Chief Adult Probation Officer Julio Algarin Warden, Montgomery Correctional Facility

Northumberland The Honorable Robert B. Sacavage President Judge Brandy L. Yasenchak, Esquire District Court Administrator Mike Yasenchak Deputy Chief, Adult Probation Dpt. Roy Johnson Warden, Northumberland County Prison Philadelphia The Honorable Pamela Pryor-Dembe President Judge David C. Lawrence District Court Administrator Robert J. Malvestuto Chief Probation Officer Louis Giorla Commissioner, Philadelphia Prison System Snyder The Honorable Michael H. Sholley President Judge Charlotte N. Kratzer District Court Administrator Craig M. Fasold Chief Probation Officer Ruth Rush, Warden, Synder County Prison Union The Honorable Michael H. Sholley President Judge Charlotte N. Kratzer District Court Administrator Scott A. Lizardi Chief Probation Officer Douglas E. Shaffer Warden, Union County Prison York The Honorable Richard Renn President Judge J. Robert Chuk District Court Administrator Albert J. Sabol Chief Adult Probation Officer Mary Sabol Warden, York County Prison

22


Include all factors

B S.E. Sig. Exp(B) B S.E. Sig. Exp(B) B S.E. Sig. Exp(B) B S.E. Sig. Exp(B)

ORIGINAL RISK FACTORSCurrent Offense [compared to theft offense]

DUI -.805 .148 .000 .447 -.904 .363 .013 .405 -.675 .189 .000 .509 -1.154 .422 .006 .315Drug -.368 .080 .000 .692 -.570 .147 .000 .565 -.340 .103 .001 .712 -.493 .166 .003 .611Simple Assault -.235 .105 .025 .790 -.329 .216 .127 .720 -.117 .129 .364 .889 -.213 .236 .367 .808

Offense Gravity Score -.057 .023 .012 .945 -.072 .044 .105 .931 -.064 .029 .030 .938 -.088 .050 .077 .916Number of prior arrests .063 .010 .000 1.065 .052 .021 .011 1.053 .074 .014 .000 1.076 .069 .024 .005 1.071Prior drug arrest .340 .076 .000 1.405 .114 .138 .410 1.120 .249 .096 .009 1.283 .067 .154 .664 1.069Prior property arrest .538 .081 .000 1.713 .496 .155 .001 1.642 .480 .102 .000 1.616 .363 .174 .037 1.438Gender .092 .080 .254 1.096 .106 .153 .487 1.112 .041 .099 .676 1.042 .078 .168 .643 1.081Race [compared to white]

Black .292 .085 .001 1.340 .378 .174 .030 1.459 .457 .110 .000 1.580 .412 .190 .030 1.511Other race .124 .120 .300 1.132 .026 .230 .911 1.026 .083 .159 .602 1.086 -.124 .261 .635 .884

Age -.040 .004 .000 .961 -.045 .008 .000 .956 -.043 .005 .000 .958 -.045 .009 .000 .956County

Urban -.026 .079 .738 .974 -.321 .152 .035 .726 -.155 .100 .119 .856 -.447 .177 .012 .640Philadelphia .094 .152 .539 1.098 -.073 .216 .737 .930

ADDITIONAL CRIMINAL JUSTICE FACTORSDrug use [compared to no] .200 .153 .190 1.222 0.14 0.175 .424 1.150Psch medication [compared to no] .113 .166 .497 1.120 0.129 0.19 .496 1.138Adult inc. [compared to to] .341 .141 .016 1.406 .442 .159 .006 1.556

ADDITIONAL DEMOGRAPHIC FACTORSEducation [compared to less than HS]

High school grad .001 .096 .995 1.001 -.097 .157 .535 .907Trade/community college -.388 .145 .008 .678 -.597 .259 .021 .551College degree -.599 .313 .056 .549 -1.425 .766 .063 .240

Employment [compared to unemployed]Employed/school -.279 .089 .002 .756 -.010 .149 .949 .991SSI/disability -.711 .215 .001 .491 -.246 .383 .521 .782

Marital status [compared to married]Never married -.068 .167 .686 .935 -.081 .292 .783 .923Currently unmarried .324 .159 .042 1.383 .431 .278 .122 1.539

Living status [compared to alone]Spouse .163 .145 .261 1.177 .175 .238 .461 1.192Family .236 .125 .059 1.267 .365 .215 .090 1.441Other -.039 .169 .818 .962 .400 .287 .163 1.492

Add other demographis factors

R2=.144 R2=.144 R2=.167 R2=.174

Note: Shaded area indicates statisitcal significance.

Appendix C. Original Logistic Regression Results [excludes cases with missing data]

Model 1 Model 2 Model 3 Model 4

N=4408 N=1213 [73% missing] N=2855 [35% missing] N=1016 [77% missing]

[Philadelphia no cases] [Philadelphia no cases]

Risk factors in Risk Assessment Study

Add other criminal justice factors

23


Include all factors

N=4408 N=4408 N=4408 N=4408R2=.143 R2=.155 R2=.160 R2=.170

B S.E. Sig. Exp(B) B S.E. Sig. Exp(B) B S.E. Sig. Exp(B) B S.E. Sig. Exp(B)


DUI -.805 .148 .000 .447 -.788 .149 .000 .455 -.758 .149 .000 .469 -.739 .150 .000 .477

Drug -.368 .080 .000 .692 -.424 .082 .000 .655 -.349 .081 .000 .705 -.403 .083 .000 .669

Simple Assault -.235 .105 .025 .790 -.238 .106 .025 .788 -.200 .106 .059 .819 -.206 .107 .054 .814

Offense Gravity Score -.057 .023 .012 .945 -.057 .023 .013 .945 -.057 .023 .013 .945 -.057 .023 .013 .944

Number of prior arrests .063 .010 .000 1.065 .058 .010 .000 1.060 .062 .010 .000 1.064 .058 .011 .000 1.060

Prior drug arrest .340 .076 .000 1.405 .282 .077 .000 1.326 .324 .077 .000 1.383 .269 .078 .001 1.309

Prior property arrest .538 .081 .000 1.713 .539 .081 .000 1.714 .526 .081 .000 1.692 .524 .082 .000 1.689

Gender .092 .080 .254 1.096 .096 .082 .243 1.101 .096 .081 .239 1.101 .107 .083 .200 1.112

Race [compared to white] .003 .003 .004 .004

Black .292 .085 .001 1.340 .292 .086 .001 1.339 .290 .087 .001 1.337 .292 .088 .001 1.339

Other race .124 .120 .300 1.132 .141 .121 .243 1.151 .117 .122 .341 1.124 .139 .123 .259 1.149

Age -.040 .004 .000 .961 -.040 .004 .000 .961 -.039 .004 .000 .962 -.039 .004 .000 .961

County .654 .846 .866 .505

Urban -.026 .079 .738 .974 -.026 .084 .760 .975 -.028 .080 .727 .972 -.032 .085 .707 .969

Philadelphia .094 .152 .539 1.098 -.096 .166 .564 .908 .035 .164 .831 1.036 -.208 .179 .247 .813

ADDITIONAL CRIMINAL JUSTICE Drug use [compared to no]

Yes .163 .102 .110 1.178 0.14 0.104 .175 1.151Missing -.213 .080 .008 .808 -0.226 0.083 .006 .797

Psch medication [compared to no] .021 .015Yes .259 .110 .019 1.296 0.288 0.113 .011 1.334Missing .166 .080 .039 1.180 0.17 0.085 .046 1.185

Adult inc. [compared to to] .003 .002Yes .221 .095 .020 1.248 0.192 0.096 .046 1.211Missing .315 .093 .001 1.370 0.331 0.096 .001 1.392

ADDITIONAL DEMOGRAPHIC FACTORSEducation [compared to less than HS] .013 .008

High school grad .041 .083 .620 1.042 .053 .083 .521 1.055

Trade/community college -.319 .123 .010 .727 -.337 .124 .007 .714

College degree -.446 .266 .093 .640 -.428 .266 .108 .652

Missing -.095 .143 .508 .910 -.045 .145 .756 .956

Employment [compared to unemployed] .000 .000

Employed/school -.261 .081 .001 .770 -.248 .082 .003 .780

SSI/disability -.548 .186 .003 .578 -.572 .188 .002 .565

Missing .050 .116 .668 1.051 .014 .119 .906 1.014

Marital status [compared to married] .025 .025

Never married -.058 .135 .669 .944 -.045 .136 .739 .956

Currently unmarried .246 .138 .074 1.279 .253 .139 .068 1.288

Missing .127 .184 .491 1.135 .161 .186 .386 1.175

Living status [compared to alone] .036 .019

Spouse .241 .132 .067 1.273 .248 .133 .062 1.282

Family .327 .113 .004 1.387 .306 .115 .008 1.358

Other .105 .151 .484 1.111 .053 .152 .725 1.055

Missing .134 .134 .318 1.144 .037 .139 .790 1.038


Appendix D. Logistic Regression Results with Missing CategoriesModel 1 Model 2 Model 3 Model 4


Add other criminal justice factors

Add other demographis factors

24


N=4408 N=4408 N=4408 N=4408 N=4408

B S.E. Sig. Exp(B) B S.E. Sig. Exp(B) B S.E. Sig. Exp(B) B S.E. Sig. Exp(B) B S.E. Sig. Exp(B)


DUI -.805 .148 .000 .447 -.801 .148 .000 .449 -.753 .149 .000 .471 -.760 .150 .000 .468 -.802 .155 .000 .448Drug -.368 .080 .000 .692 -.364 .081 .000 .695 -.348 .081 .000 .706 -.344 .082 .000 .709 -.358 .086 .000 .699Simple Assault -.235 .105 .025 .790 -.252 .106 .018 .777 -.212 .106 .045 .809 -.235 .106 .027 .791 -.211 .112 .058 .809

Offense Gravity Score -.057 .023 .012 .945 -.056 .023 .015 .946 -.054 .023 .019 .947 -.054 .023 .019 .947 -.061 .024 .012 .940Number of prior arrests .063 .010 .000 1.065 .059 .011 .000 1.060 .062 .010 .000 1.064 .058 .011 .000 1.060 .055 .012 .000 1.056Prior drug arrest .340 .076 .000 1.405 .326 .077 .000 1.386 .324 .077 .000 1.383 .307 .078 .000 1.359 .330 .081 .000 1.391Prior property arrest .538 .081 .000 1.713 .527 .081 .000 1.693 .516 .081 .000 1.675 .505 .082 .000 1.657 .512 .086 .000 1.668Gender .092 .080 .254 1.096 .109 .084 .197 1.115 .101 .081 .214 1.107 .119 .084 .158 1.126 .084 .086 .329 1.088Race [compared to white]

Black .292 .085 .001 1.340 .302 .086 .000 1.353 .300 .087 .001 1.350 .314 .089 .000 1.369 .331 .092 .000 1.392Other race .124 .120 .300 1.132 .139 .120 .249 1.149 .117 .122 .339 1.124 .147 .123 .232 1.158 .182 .131 .164 1.200

Age -.040 .004 .000 .961 -.041 .004 .000 .960 -.039 .004 .000 .962 -.040 .004 .000 .961 -.039 .005 0.000 .961County

Urban -.026 .079 .738 .974 -.015 .084 .857 .985 .034 .157 .827 1.035 -.017 .086 .841 .983 -.049 .086 .568 .952Philadelphia .094 .152 .539 1.098 -.067 .155 .663 1.070 -.024 .080 .761 .976 .011 .084 .739 .972

ADDITIONAL CRIMINAL JUSTICE Drug use [compared to no] .047 .092 .608 1.049 .065 .101 .526 1.067 -.016 .096 .866 .984Psch medication [compared to no]

.187 .122 .145 1.206 .213 .105 .046 1.237 .194 .119 .111 1.214

Adult inc. [compared to to] .120 .101 .252 1.127 .140 .084 .098 1.151 .139 .088 .117 1.149

ADDITIONAL DEMOGRAPHIC Education [compared to less than HS]

High school grad .014 .084 .867 1.014 .026 .082 .752 1.026 .018 .082 .830 1.018Trade/community college -.322 .118 .007 .725 -.280 .125 .027 .756 -.339 .135 .014 .712College degree -.334 .290 .257 .716 -.336 .264 .205 .715 -.407 .273 .137 .666

Employment [compared to unemployed]Employed/school -.198 .085 .023 .821 -.170 .080 .035 .844 -.204 .084 .016 .815SSI/disability -.441 .175 .013 .644 -.418 .186 .028 .659 -.510 .198 .012 .600

Marital status [compared to married]Never married .011 .144 .941 1.011 -.017 .139 .905 .984 -.038 .146 .794 .963Currently unmarried .251 .134 .061 1.286 .202 .137 .140 1.224 .244 .141 .085 1.276

Living status [compared to alone]Spouse .167 .128 .192 1.182 .182 .122 .135 1.200 .161 .131 .221 1.175Family .217 .104 .038 1.242 .260 .103 .012 1.297 .245 .116 .037 1.277Other .069 .167 .680 1.072 .079 .138 .565 1.082 .092 .160 .568 1.096

* R 2 is average of all pooled versions.

Model 4


Include all factors nclude all factors without Philadelphia

Appendix E. Logistic Regression Results with Imputed Data

R2=.157 *


Add other criminal justice

factors

Add other demographis

factors

Model 5

R2=.144 R2=.146 * R2=.155 * R2=.158 *

Model 1 Model 2 Model 3

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ADDENDUM – Addition of out of state offenses

Highlights

This addendum provides findings based upon the inclusion of out of state offenses in our recidivism analyses for the county sentencing project. The major findings from the analyses were:

• About 23% of the offenders had an out of state offense.

• About 4% of the offenders who had not recidivated in Pennsylvania had recidivated in another state. This would raise the 2 year recidivism rate from 35% to 38%.

• The model utilizing out of state offenses did not perform as well in predicting recidivism as the model using only Pennsylvania offenses. This could be due to the lack of standardization in the reporting of offenses among the states or due to differences between offenders who commit their offenses in Pennsylvania compared to those who cross state lines.

• The major factors that predicted recidivism were consistent regardless of whether using only Pennsylvania offenses or including out of state offenses.

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Introduction

One issue that arises when examining recidivism is the extent to which offenders commit out of state offenses that are not included in the study. If offenders commit a recidivist offense [offense committed during the tracking period for recidivism rates], outside of Pennsylvania, but have not committed an offense in Pennsylvania, then these offenders would be erroneously classified as ‘clean’ for the purposes of committing a follow-up offense.

Until recently, the PA Commission on Sentencing has not been able to access the criminal history records from the FBI that would contain arrests nationwide.4 Once the Commission was granted permission to receive these records, we used the county sentencing sample as our ‘test sample’ to examine the impact that out of state records would have on our recidivism findings. There were 4,666 offenders is this sample, and we received a match on 4,595 offenders [98%]. 5

In our recidivism analysis we use arrest information for two purposes: 1) to examine the impact of prior arrests on future recidivism, and 2) to use future arrests as our measure of recidivism. Table 1 shows that, overall, 23% of the sample had an out of state arrest. About 4% of the offenders had a prior out of state arrest and no prior Pennsylvania offense. About 3% of the offenders who were considered ‘clean’ in our study had committed an out of state offense during the 2 year tracking period. Thus, the two-year recidivism rate, which was 35% when using only Pennsylvania offenses, was 38% when including out of state offenses.

4 Mark Bergstrom, Executive Director of the Pennsylvania Commission on Sentencing, and Rick Kern, past Executive Director of the Virginia Sentencing Commission, worked for several years to have Congress amend statute that would allow state sentencing commission access to the nationwide criminal history records. This legislation passed in December 2010. 5 All of the FBI criminal history records were sent as paper copies or on pdf as a hard copy of the records rather than in data files. Thus, staff manually extracted relevant information from about 38,000 pages of records to enter into a database for analyses purposes.

Number Percent

a. Total number of offenders with out of state arrests * 993 23%

b. Number of offenders who had a 'prior out of state arrest' and no PA prior arrest * 167 4%

c. Number of offenders who had a 'recidivist out of state arrest' and no PA recidivist arrest during 2 years tracking period. * 142 3%

d. Two year recidivism rate with PA arrests only 1547 35%

e. Two year recidivism rate with out of state arrests included 1689 38%

Table 1. Out of state arrests for county sentencing sample [n=4408]

* There were 4,408 offenders tracked for two years; 49 of these offenders did not have out of state arrest information.

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County Total

yes no yes no

Blair 3% 6% 5 248 253Centre 9% 7% 15 291 306Cumberland 10% 7% 18 276 294Dauphin 3% 7% 6 301 307Delaware 7% 6% 12 265 277Erie 11% 7% 20 304 324Greene 2% 2% 4 91 95Indiana 3% 5% 6 204 210Juniata 1% 2% 2 93 95Lancaster 7% 6% 12 237 249Lebanon 2% 7% 3 309 312Lehigh 9% 6% 15 271 286Montgomery 5% 7% 9 300 309Northumberland 3% 5% 6 227 233Philadelphia 3% 8% 6 338 344Snyder 2% 2% 4 99 103Union 1% 2% 1 81 82York 18% 7% 32 297 329TOTAL 100% 100% 176 4232 4408

Percent Number

Table 2. Out of state arrests by county.

Table 2 shows that, of the 18 counties included in our study, the counties with the highest percentage of offenders committing out of state offenses were York [18%], Erie [11%], and Cumberland [10%] counties.

Table 3 shows that offenders had committed recidivist offenses in 28 states. Most of the out of the state offenses were committed in the neighboring states of Maryland [22%], New York [19%], and New Jersey [18%].

State Percent Number

Alabama 2% 3Arizona 1% 2California 1% 2Colorado 1% 1Connecticut 1% 1Delaware 4% 7Florida 7% 13Georgia 3% 5Iowa 1% 1Idaho 1% 1Indiana 1% 1Massachusetts 2% 3Maryland 22% 38Michigan 1% 2Minnesota 1% 1Missouri 1% 2North Carolina 1% 1New Jersey 18% 32New Mexico 1% 1New York 19% 34Ohio 6% 10South Carolina 1% 1Tennessee 1% 1Texas 1% 2Utah 1% 1Virginia 2% 4Vermont 1% 1West Virginia 3% 5TOTAL 100.0% 176

Table 3. States where out of state arrests occurred [n=176].

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Recidivism analysis. In our analysis of recidivism for the county sentencing project, we had conducted a series of analysis to examine the impact of various factors on recidivism. Since a large percentage of cases were missing some of the information, we also conducted various analyses to account for the missing information, including using a method involving the imputation of the missing data based upon known information.

Table 4 shows the predictors of recidivism from our original analyses using Pennsylvania only offenses, compared to the predictors of recidivism when adding out of state offenses [findings from the final model utilizing all of the factors]. Overall, the findings are similar. In both analyses, the following factors predicted recidivism:

• Current offense • Number of prior arrests • Having a prior drug arrest • Having a prior property offense • Race • Age • Employment status

In both analyses, the following factors were not found to predict recidivism:

• County • Drug use • Previous incarceration • Marital status

However, there were some differences in the findings. When out of state offenses were added to the analyses, gender became a significant predictor of recidivism, while Offense Gravity Score, use of psychiatric medication, education, and living status were no longer significant predictors of recidivism. [see Addendum Appendix A for results of logistic models.]

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PA Offenses only

PA and out of state

offenses

R2=.158 R2=.140

Original Risk FactorsCurrent Offense [compared to theft offense]

DUI *** ***

Drug *** ***

Simple Assault * *

Offense Gravity Score * No

Number of prior arrests *** ***

Prior drug arrest *** *

Prior property arrest *** ***

Gender No *

Race [compared to white]Black *** ***

Other race No No

Age *** ***

County Urban No No

Philadelphia No No

Additional Criminal Justice FactorsDrug use [compared to no] No No

Psych medication [compared to no] * No

Adult inc. [compared to to] # No

Additional Demographic FactorsEducation [compared to less than HS]

High school grad No

Trade/community college * No

College degree No

Employment [compared to unemployed]Employed/school * *

SSI/disability * *

Marital status [compared to married]Never married No

Currently unmarried No

Living status [compared to alone]Spouse No

Family * #

Other No


Note: These reflect the findings for the final model including imputations.

Table 4. Summary of findings for predictors of recidivism with and without out of state offenses. [N=4408]

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Conclusion. Overall, it appears that for this sample, the study did not benefit in its recidivism analyses with the addition of the out of state offenses. While the recidivism rate increased by 3% [from 35% to 38%] with the addition of out of state offenses, the predictive capability of the recidivism model decreased with the addition of the out of state offenses [from R 2 = .158 to R2=140]. This may be due to the variability among the states in their reporting of crime statistics [as there is not standardization among the states].

It is also possible that the type of offenders who commit offenses outside of Pennsylvania [and perhaps permanently re-located] are qualitatively different than those offenders who have not committed offenses out of state. We do that there were some significant differences in these two groups. The major differences between the groups were the type of criminal activity. Offenders who recidivated with an out of state offense were significantly more likely to have been convicted of simple assault or a DUI offense, and less likely to have a prior drug or property offenses, than offenders who recidivated with a Pennsylvania offense.6 These are three of the factors associated with reduced rates of recidivism in our study. It is possible that there are factors beyond those considered in our model that would better explain why offenders who commit offenses beyond Pennsylvania recidivate. It is encouraging that the factors that were consistent predictors of recidivism [in this study and other studies] remained the major predictors. Those factors that fluctuated, depending upon whether using Pennsylvania offenses or nationwide offenses were included, were mostly those factors that are generally ‘more volatile’ during analyses and less consistent predictors of recidivism. It should be noted that this study involved a small sample comprised of less serious types of offenders who came from 18 of Pennsylvania’s 67 counties. Thus, it is possible that other studies using other samples would find that the inclusion of out of state offenses would provide more meaningful contributions to the findings of recidivism.

6 Among offenders with only an out of state recidivist offense, 10% had a current conviction for DUI and 16% had a current conviction for simple assault, while among offenders with only a Pennsylvania recidivist offense, 5% had a current conviction for DUI and 13% had a current conviction for simple assault. Among offenders with only an out of state recidivist offense, 20% had a prior arrest for a drug offense and 35% had a prior arrest for a property offense, while among offenders with only a Pennsylvania recidivist offense, 53% had a prior arrest for a drug offense and 71% had a prior arrest for a drug offense.

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B S.E. Sig. Exp(B) B S.E. Sig. Exp(B)


DUI -.760 .150 .000 .468 -.729 .141 .000 .482Drug -.344 .082 .000 .709 -.392 .080 .000 .676Simple Assault -.235 .106 .027 .791 -.222 .103 .031 .801

Offense Gravity Score -.054 .023 .019 .947 -.030 .022 .182 .971Number of prior arrests .058 .011 .000 1.060 .063 .010 .000 1.065Prior drug arrest .307 .078 .000 1.359 .186 .075 .013 1.205Prior property arrest .505 .082 .000 1.657 .377 .077 .000 1.457Gender .119 .084 .158 1.126 .186 .082 .023 1.205Race [compared to white]

Black .314 .089 .000 1.369 .340 .086 .000 1.405Other race .147 .123 .232 1.158 .158 .119 .183 1.172

Age -.040 .004 .000 .961 -.041 .004 0.000 .960County

Urban -.017 .086 .841 .983 .006 .082 .939 1.006Philadelphia .011 .084 .739 .972 -.065 .157 .678 .937

ADDITIONAL CRIMINAL JUSTICE Drug use [compared to no] .065 .101 .526 1.067 -.025 .092 .785 .975

Psych medication [compared to no].213 .105 .046 1.237 .132 .092 .151 1.141

Adult inc. [compared to to] .140 .084 .098 1.151 .096 .089 .291 1.101

ADDITIONAL DEMOGRAPHIC Education [compared to less than HS]

High school grad .026 .082 .752 1.026 .081 .085 .343 1.084Trade/community college -.280 .125 .027 .756 -.194 .121 .109 .824College degree -.336 .264 .205 .715 -.252 .239 .292 .777

Employment [compared to unemployed]Employed/school -.170 .080 .035 .844 -.212 .083 .013 .809SSI/disability -.418 .186 .028 .659 -.453 .190 .021 .636

Marital status [compared to married]Never married -.017 .139 .905 .984 -.151 .137 .271 .860Currently unmarried .202 .137 .140 1.224 .128 .133 .335 1.137

Living status [compared to alone]Spouse .182 .122 .135 1.200 .079 .131 .548 1.082Family .260 .103 .012 1.297 .215 .121 .085 1.240Other .079 .138 .565 1.082 .077 .140 .582 1.080

* R 2 is average of all pooled versions.Note: Shaded area indicates statisitcal significance.

Addendum Appendix A. Logistic Regression Results for Final Model with and without Out- of-State Offenses

PA and out of state offenses

R2=.140 *N=4408

Final Model Original - PA offenses only

Final Model

R2=.158 *N=4408

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Documents

County Sentencing Project · The Commission is an agency of the General Assembly affiliated with The Pennsylvania State University. Pennsylvania Commission on Sentencing County Sentencing