Pretrial FTA and Assessing Risk 99 by Administrator

CJA

NEW YORK CITY CRIMINAL JUSTICE AGENCY

Jerome E. McElroy Executive Director

ASSESSING RISK OF PRETRIAL FAILURE TO APPEAR IN NEW YORK CITY A RESEARCH SUMMARY AND IMPLICATIONS FOR DEVELOPING RELEASE-RECOMMENDATION SCHEMES

Qudsia Siddiqi, Ph.D. Project Director

FINAL REPORT November 1999

52 Duane Street, New York, NY 10007

(646) 213-2500

ASSESSING RISK OF PRETRIAL FAILURE TO APPEAR IN NEW YORK CITY A RESEARCH SUMMARY AND IMPLICATIONS FOR DEVELOPING RELEASE-RECOMMENDATION SCHEMES

Qudsia Siddiqi, Ph.D. Project Director Research Assistance: Pauline Brennan Junior Research Analyst Lauren Springer Senior Research Assistant Raymond Caligiure Senior Research Assistant Alexandra Gorelik Senior Research Assistant Joseph DeAngelis Research Assistant Elyse Revere Senior Research Assistant Systems Programming: Barbara Diaz Associate Director, Information Systems Robert Shea Senior Programmer/Analyst Administrative Support: Bernice Linen-Reed Administrative Assistant November 1999 This report can be downloaded from www.nycja.org\research\research.htm

ÂŠ 1999 NYC Criminal Justice Agency

ACKNOWLEDGMENTS I am in great debt to Dr. Mary Eckert, Associate Director for Research, for providing invaluable suggestions and indispensable feedback throughout the various phases of the project. I am also grateful to Barbara Geller Diaz, Director for Systems, for her expertise and patience. A special debt of gratitude is owed to Dr. Colleen Cosgrove, former Senior Research Associate, for her input to the project. She played a major role in developing the â&#x20AC;&#x2DC;89 Dataset and was extremely helpful in resolving complex data-cleaning issues A number of current and former Research Department staff contributed to this project. I wish to extend my sincere thanks to all of them. In particular, I would like to thank Dr. Pauline Brennan, former Junior Research Analyst, who played a key role in performing statistical analysis and writing sections of the report. I am also grateful to Lauren Springer, former Senior Research Assistant, who participated in data collection and analysis, and constructed tables for the report. I also appreciate the work done by Robert Kaminski, former Research Analyst, in the early stages of the project. I would like to thank Dr. Steven Belenko, former Senior Research Fellow, Dr. Richard Peterson, and Dr. Mary T. Phillips, Senior Research Analysts, for their technical assistance. In addition, I would like to thank Dr. Freda Solomon, Deputy Director for Research, for her valuable suggestions. And finally, I would like to offer special thanks to former Coders who contributed to the seemingly endless tasks of data entry.

Table of Contents LIST OF TABLES ......................................................................................................................... ii INTRODUCTION.......................................................................................................................... 1 CURRENT RECOMMENDATION SCHEME............................................................................... 3 METHODOLOGY ......................................................................................................................... 6 A. Sampling and Data Sources ...................................................................................................... 6 B. Dependent Variable ................................................................................................................. 7 C. Independent Variables ............................................................................................................. 8 D. Statistical Methods ................................................................................................................. 9 RESULTS ..................................................................................................................................... 17 I. Criminal Court Analysis .............................................................................................................. 17 A. Defendant Characteristics ........................................................................................................ 17 B. Current CJA ROR Recommendation Scheme and FTA ............................................................ 18 C. Current CJA ROR Recommendation Scheme: A Logistic Regression Model............................. 18 D. Logistic Regression Analysis of FTA ....................................................................................... 20 E. Construction of a New Point Scale .......................................................................................... 22 F. Alternative Risk-Classification Schemes ................................................................................... 26 II. Supreme Court Analysis ............................................................................................................ 31 A. Defendant Characteristics ........................................................................................................ 31 B. Logistic Regression Analysis of FTA ....................................................................................... 32 III. Analysis of FTA Regardless of the Court of Disposition ........................................................... 34 A. Defendant Characteristics ....................................................................................................... 34 B. Logistic Regression Analysis of FTA ....................................................................................... 35 C. Construction of the Point Scale ............................................................................................... 37 D. Alternative Risk-Classification Schemes .................................................................................. 40 E. Alternative Risk-Classification Schemes: A Comparison with the Current CJA ROR Recommendation Scheme .............................................................................................. 44 F. A New Risk-Classification Scheme and Its Implications ........................................................... 45 BIBLIOGRAPHY ......................................................................................................................... 48 TABLES APPENDIX A Relative Improvement Over Chance (RIOC)........................................................... A1 APPENDIX B Mean Cost Rating................................................................................................... B1 APPENDIX C Sample Selection Bias: A Further Assessment of the Combined-Court FTA Analysis.................................. C1

LIST OF TABLES

Table 1:

Characteristics of Defendants Released Pretrial in Criminal Court

Table 2:

Pretrial FTA by Current CJA ROR Recommendation Scheme

Table 3:

Multiple Logistic Regression Model Predicting Pretrial Failure to Appear in Criminal Court: Variables included in the Current CJA ROR Recommendation Scheme

Table 4:

Model 1: Multiple Logistic Regression Analysis Predicting Pretrial Failure to Appear: The Most Predictive Criminal Court Model

Table 5:

Model 2: Multiple Logistic Regression Analysis Predicting Pretrial Failure to Appear: The Re-Estimated Criminal Court Model Used To Create The New Point Scale

Table 6:

Points Derived from Model 2

Table 7:

Criminal Court Alternative Risk-Classification Schemes

Table 8:

A Comparison of the Current CJA ROR Recommendation Scheme with the Alternative Risk-Classification Schemes Suggested for Defendants in Criminal Court: The Difference in the Number and FTA Rate of Low-, Moderate-,and High-Risk Defendants

Table 9:

Characteristics of Defendants Released Pretrial in Supreme Court

Table 10:

Model 3: Multiple Logistic Regression Analysis Predicting Pretrial FTA among Supreme Court Defendants

Table 11:

Characteristics of Defendants Released Pretrial Regardless of Court of Disposition

LIST OF TABLES (continued)

Table 12:

Model 4: Multiple Logistic Regression Analysis Predicting Pretrial FTA Regardless of Court of Disposition: The Most Predictive Model

Table 13:

Model 5: Multiple Logistic Regression Analysis Predicting Pretrial FTA Regardless of Court of Disposition: The Re-Estimated Model Used to Create the New Point Scale

Table 14:

Points Derived from Model 5

Table 15:

Pretrial FTA by Current CJA ROR Recommendation Scheme

Table 16:

Alternative Risk-Classification Schemes for Defendants Regardless of Court of Disposition

Table 17:

A Comparison of the Current CJA ROR Recommendation Scheme with the Alternative Risk-Classification Schemes Suggested for Defendants Regardless of Court of Disposition: The Difference in the Number and FTA Rate of Low-, Moderate-, and High-Risk Defendants

Table 18:

A Comparison of the Current CJA ROR Recommendation Scheme with the Suggested Alternative Risk-Classification Schemes

Table B-1:

Distribution of FTA by New Point Scale Scores

Table B-2:

Mean Costing Rating of FTA by New Point Scale Scores

Table C:

Estimating Sample Selection Bias

ASSESSING RISK OF PRETRIAL FAILURE TO APPEAR IN NEW YORK CITY: A RESEARCH SUMMARY AND IMPLICATIONS FOR DEVELOPING RELEASE-RECOMMENDATION SCHEMES INTRODUCTION The New York City Criminal Justice Agency, Inc. (CJA) uses an objective “point scale” to make pretrial release-on-recognizance (ROR) recommendations for defendants arrested in New York City and held for arraignment in the lower court (Criminal Court).

The

recommendation is submitted to the arraigning judge who makes the first release decision, as well as to the prosecutor and defense attorney. Section 510.30 of the New York State Criminal Procedure Law permits judges to consider many factors in their pretrial release (and detention) decisions. Judges are to set conditions of release consistent with their belief primarily on what is necessary to insure the defendant’s attendance at subsequent court appearances, if released. Accordingly, release decisions are to take into consideration the following factors and criteria (Matthew Bender and Company, 1988): •

The principal’s character, reputation, habits, and mental condition;

•

His employment and financial resources; and

•

His family ties and length of his residence, if any, in the community; and

•

His criminal record if any; and

•

His record of previous adjudication as a juvenile delinquent, as retained pursuant to section 354.2 of the family court act, or, of pending cases where fingerprints are retained pursuant to section 306.1 of such act, or a youthful offender, if any; and

•

His previous record if any in responding to court appearances when required or with respect to flight to avoid criminal prosecution; and

•

The weight of the evidence against him in the pending criminal action and any other factor probability or improbability of conviction; or, in the case of an application for bail or recognizance pending appeal, the merit or lack of merit of the appeal; and

•

The sentence which may be or has been imposed upon conviction.

CJA’s current release recommendation scheme is based, however, solely upon a defendant's ties to the community, leaving to the arraigning judge consideration of all other factors from other sources. Defendants having strong community ties are considered “good risks” to return for scheduled court appearances.

Through interviews with arrestees and verification of the

information they provide with a third party, CJA determines a defendant’s risk by assigning “points” for specific community ties found in previous research to distinguish defendants who are more likely to appear at subsequent court dates from those less likely to appear. The points are then summed to arrive at the recommendation category on a “scale” assigned to the defendant. The current point scale has not been statistically validated since 1974 (Lazarsfeld, 1974). This validation was based solely on Brooklyn data and looked only at risk in the Criminal Court. In the years since this research, CJA’s standard reports continue to show that those defendants recommended for release and actually released at arraignment on their own recognizance were less likely to fail to appear than those not recommended, even for defendants prosecuted in the other counties comprising the city.1 However, the current point scale may not be valid for determining flight risk in Supreme Court (upper court) defendants and, given the length of time that has elapsed since the last validation, the point scale may no longer be the best statistical scheme even for the lower court. Furthermore, in considering only the defendant's community ties, the relationship between other factors (such as a defendant's criminal history or the information on the arrest charge) and pretrial failure to appear (FTA) is unknown.

1 CJA has a Semi-Annual Report series beginning in 1980 which supports this statement.

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In light of this, CJA began a research project in November 1989 to address these issues. The research had three major objectives: 1) to assess the predictive ability of the current point scale; 2) to identify other predictors of FTA; and 3) to formulate other alternative riskclassification schemes based on potentially new predictors. The study was conducted in several phases.2 The first analytical phase focused on FTA in Criminal Court. The second phase of the analysis concentrated on FTA among Supreme Court defendants. In the final phase of the study, FTA was examined regardless of the court of disposition (combined-court analysis). The major findings from each phase of the study are summarized below, following a brief description of the methodology employed in each phase. First, the categories of the current recommendation scheme are detailed. CURRENT RECOMMENDATION SCHEME Under the current recommendation scheme the items used to assess a defendant's ties to the community include: 1. whether there is a working telephone in the defendant's residence; 2. whether the defendant has resided at his or her current address for one and one-half years or longer; 3. whether the defendant expects someone (other than the complainant or defense attorney) at Criminal-Court arraignment; 4. whether the defendant lives with parent(s), spouse, or common-law spouse of sixmonths, grandparent, or legal guardian; 5. whether the defendant is employed, in school, or in a job-training program (or some combination of these) full time; 2 At the completion of the FTA analysis, the point-scale research focused on another form of pretrial misconduct â&#x20AC;&#x201C; pretrial rearrest. The objective was to determine whether the same factors that predict pretrial FTA also predict pretrial rearrest. The findings from that analysis will be presented in a separate report.

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6. whether the defendant's address is in the New York City area (the five boroughs of the City, and Nassau, Suffolk, and Westchester counties). With the exception of item 3, there are five possible outcomes for the aforementioned items: "yes", "yes verified", "no", "no verified", or "unresolved conflict." The "yes" and "no" outcomes indicate that the defendant's response has not been verified. The "yes verified" and "no verified" outcomes are used when the information provided by the defendant has been verified through a third-party contact.

"Unresolved conflict" means that the information

provided by the defendant in the interview does not match with the information given by the verifier and attempts to resolve the conflict were unsuccessful. Item 3 has only "yes" and "no" responses. The responses to the items are used to classify defendants into the various categories that are included in the CJA ROR recommendation scheme. Based on the classification category, a release recommendation is made to a Criminal Court arraignment judge.

The CJA ROR

recommendation scheme in 1989 consisted of four main categories, two of which have subcategories, as follows: 1. "Recommended": Verified Community Ties (defendant must have a verified New York City area address, have items 2, 4, or 5 verified, and have at least two other true items); 2. "Qualified": Unverified Community Ties (defendant has an unverified New York City area address and has three other items assessed in the affirmative); 3. No Recommendation due to: A. Insufficient community ties (less than three items were answered affirmatively) B. Residence outside the New York City area C. Conflicting residence information (defendant and verifier did not agree) D. Incomplete interview; 4. No Recommendation due to: A. Open bench warrant attached to the New York State criminal history sheet ("rap sheet") B. Criminal history not available C. Bail jumping charge D. For information Only: murder charge E. For information Only: juvenile offender. -4-

The first three categories summarize the strength of the defendantâ&#x20AC;&#x2122;s community ties. The fourth major category of the risk-assessment scheme consists primarily of excluding from the ROR recommendation those defendants who have demonstrated that they will not show up for scheduled court appearances by failing to appear on a pending case or those for whom the absence of a rap sheet precludes ascertaining that information. Defendants are also excluded if arrested on a bail jumping offense, which may be charged in New York State after a defendant does not return to court for thirty days or more after failing to appear while on bail or ROR. Previous failures to appear, for which the defendant returned to court and the warrant was vacated, do not preclude ROR recommendation on a new arrest. Two other categories for which no recommendation is given concern policy-developed categories that were added after the last empirical validation work in 1974.

Murder and

manslaughter charges were excluded from recommendation in 1977. In 1978, the State adopted legislation permitting youths between the ages of 13 and 15 years who were charged with certain violent crimes to be treated as adults in the criminal courts. Because it was believed that the adult criteria would not be appropriate for a younger population, the extra exclusion category was added to the adult scheme and plans were made to develop a separate recommendation scheme for juvenile offenders (Metchik, 1987).3 Even defendants who are given no recommendation (classification "4") are, nonetheless, interviewed and attempts are made to verify their community ties. The information is provided to the court at arraignment in the same manner that it is for those classified according to their

3 Juvenile offenders, the official designation for those under the age of 16 years who are treated as adults, comprise less than one percent of all interviews conducted by CJA. In April 1996, based on new research, a separate new recommendation scheme was introduced for juveniles (Phillips, 1999).

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community ties, in addition to the description of the recommendation category into which they fall.

METHODOLOGY A. Sampling and Data Sources Data for this study were drawn from a random sample of 15,359 arrests made in 1989 in which defendants were held by the police until Criminal Court arraignment. Because the aim of this research was to examine defendant behavior, the arrest-based sample was converted into a defendant-based sample (n=14,380), where only the defendantâ&#x20AC;&#x2122;s first arrest during the sampling period was included in the study sample (even if the defendant had multiple arrests). In order to examine pretrial FTA, the study focused on defendants whose cases were not completed at Criminal Court arraignment and who were released pretrial: i.e., ROR'd or made bail prior to the disposition of all charges. It should be noted, however, that the three phases of the study examined distinct, but not mutually exclusive, samples of defendants. More specifically, the Criminal Court analysis focused on the subsample of 7,105 defendants whose cases were adjourned at Criminal Court arraignment and who were released on their own recognizance or made bail prior to the disposition of their case in that court. In contrast, the Supreme Court analysis focused on 1,674 defendants whose cases were transferred to Supreme Court and who were at risk for pretrial FTA in that court. For these defendants, the determination of release began with the release status at the final disposition in Criminal Court and included all who were released on their own recognizance or made bail prior to the disposition of their case in Supreme Court. In the third phase of the study, FTA was examined among 7,595 defendants whose cases were adjourned at Criminal Court arraignment and who

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were at risk of FTA in either of the previous two analyses (Criminal Court or Supreme Court). Defendants classified as juvenile offenders by CJA were excluded from the study sample. The primary data source was the CJA database.4

Database information was

supplemented with data from the New York City Police Department (NYPD), the New York State Office of Court Administration (OCA, for supplementary case outcome data), the New York City Department of Correction (DOC, for detention and release data), and the New York State Division of Criminal Justice Services (DCJS, for detailed criminal history data).5

B. Dependent Variable The dependent variable, a measure of whether or not the defendant failed to appear, was defined as the issuance of a bench warrant at any appearance prior to the disposition of a defendant's case in the court under study.6 About one-third of the defendants in each of three sub-samples failed to appear for at least one court appearance prior to the disposition of the case. More specifically, for the Criminal Court analysis, the dependent variable was whether or not a 4 CJA maintains a computerized database containing arrest and case-processing information about most New York City arrestees. Data are collected during a pre-arraignment interview, which is used to ascertain community-ties information and make a recommendation for release on recognizance at the defendant's first court appearance. In 1989, court information on all interviewed defendants was gathered from the Criminal and Supreme Court calendars. Defendants were not interviewed if they were arrested solely on bench warrants, given summonses, or charged solely with prostitution offenses. Although arrestees issued Desk Appearance Tickets (DATs) were not interviewed by CJA, police arrest and Criminal Court information for them was included in the CJA database. However, those issued DATs were excluded from the arrest-based sample and thus from the analysis. 5 DCJS, NYPD, OCA, and DOC bear no responsibility for the methods of analysis used in this report or its conclusions. It should also be noted that the criminal history information provided by DCJS excluded sealed cases. 6 A defendant-based measure of FTA was used in the analysis, where only the first non-stayed bench warrant was counted. As a result, the dependent variable was dichotomized. Defendants who failed to appear were coded as "1" and defendants who did not fail to appear were coded as "0." It is important to note that the defendant-based measure of FTA differs from that which is used in CJA's standard reports. In these reports, an appearance-based FTA measure for Criminal Court is used, where the total number of appearances missed by the defendant are counted with the FTA rate calculated against the total number of appearances scheduled. The analyses summarized in this report did not use this measure because the objective of the research was to identify a group of defendants who failed at any point prior to disposition, rather than to determine which factors increased or decreased the number of failed appearances. In addition, this measure was consistent with the FTA measure used to develop the scale being validated (Lazarsfeld, 1974) and has been used in other studies (Schaffer, 1970; Thomas, 1976; Goldkamp, 1981; Center for Governmental Research, 1983; Cuvelier and Potts, 1993).

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bench warrant was issued prior to the Criminal Court disposition. The FTA rate for defendants at risk in Criminal Court was 30.5 percent. With regard to the Supreme Court analysis, the dependent variable indicated the presence of a pretrial bench warrant in Supreme Court. The Supreme Court FTA rate was 33.1 percent. For the third phase of the study, the pretrial FTA rate was measured regardless of the court of disposition. Here, the rate was 35.2 percent.

C. Independent Variables A number of variables were examined for their statistical effects on pretrial FTA. Included among these variables were community-ties items, criminal history indicators, severity and type of the top arrest charge, a defendant's socio-demographic attributes, and caseprocessing characteristics. The community-ties items contained information on whether the defendants had a working telephone in the residence, the length of time at their current address, whether they had a New York City area address, family ties within the residence, whether they expected someone at their Criminal Court arraignment, and whether they were either employed, in school, or in a training program full time at the time of their arrest.7 The criminal history variables provided data on the defendantsâ&#x20AC;&#x2122; prior arrests, prior convictions, open warrants, pending cases, and prior FTA.8 The top arrest charge "type" was based on its Uniform Crime Reports' (UCR) category;

7 These are the same variables that are currently used in CJAâ&#x20AC;&#x2122;s ROR recommendation. Analysis with other variables and

cutpoints for them proved not to be fruitful. The current research did not have available verification for any additional items. 8 Information on prior arrests, classification of prior arrests, prior convictions, open warrants, pending cases, and prior FTA is not currently collected by CJA on a routine basis. Thus, the analysis broadened the criminal history elements considered for their effect on FTA.

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the "severity" of the top arrest charge was derived from its New York State Penal Law offense class. Included among the socio-demographic variables were sex, ethnicity, age, and marital status. The case-processing variables included information on time at risk, type of first release, court of disposition, and the borough of arrest. Time at risk was defined as the number of days a defendant was in the community on ROR or bail prior to the issuance of the first non-stayed bench warrant or the disposition of the case. The type of first release variable indicated whether a defendant was initially released on own recognizance or by the posting of bail. The court type variable accounted for whether a case was disposed in Criminal Court or was transferred to Supreme Court. Included in the borough of arrest were the five boroughs comprising the City of New York: Brooklyn, Manhattan, Queens, the Bronx, and Staten Island.

D. Statistical Methods The primary task in each analytical phase was to identify significant predictors of pretrial FTA. To achieve that objective, a number of models were constructed where items were added or excluded, depending upon their contribution to predicting pretrial FTA.

Due to the

dichotomous nature of the dependent variable (pretrial FTA or no FTA), multiple logistic regression was utilized. Multiple logistic regression is a statistical technique that is used to test the individual effect of a number of independent variables on a categorical dependent variable, while controlling for the other variables in the model. A logistic regression procedure predicts the log-odds (the logit coefficient) of an observation being in one category of the dependent variable versus another (in this case, FTA versus no FTA). When reporting the results from a logistic regression model, one may also wish

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to transform the log-odds into odds ratios. This is accomplished by taking the antilog of the logit coefficient. The result is then interpreted as how much the odds of an outcome change, given a specific category of an independent variable. In other words, the obtained logistic coefficient, transformed into an odds ratio, tells one how much the odds of an outcome change given a one unit change in an independent variable, controlling for the effects of the other variables. An odds ratio greater than one indicates an increase in the likelihood of an event occurring, and an odds ratio of less than one indicates a decrease in the likelihood of an event occurring. An odds ratio of one indicates the odds remain unchanged (no association between the independent and dependent variable). As an example, assume that a dichotomized independent variable is coded "1" if a defendant has a history of failure to appear, and "0" otherwise (prior FTA). Also assume that the dependent variable, indicating current FTA, is coded "1" if a defendant fails to appear for a court appearance on the present arrest, and "0" if they appear for all appearances. Estimating a univariate logistic regression with prior FTA as the only independent variable produces a logit coefficient (log-odds) of .706. This suggests that when the variable prior FTA changes from 0 to 1, there is an associated increase of .706 in the log-odds of failure to appear. Taking the antilog of the logit coefficient gives an odds ratio of 2.03.

This indicates the odds of FTA for

defendants with prior FTA are about two times greater than that for defendants who do not have a history of failure to appear. In most of the analyses presented in this report, if the independent variable is categorical, the results are interpreted with reference to a specified excluded category (simple contrast technique). In the present analysis, a .05 level (or less) was used to ascertain whether an observation had a statistically significant effect on the dependent variable. A .05 level of significance means

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that the observation could have occurred by chance alone five times in one hundred. In this report, the tables presenting the logistic regression results display the logit coefficient, odds ratio, and the affiliated level of statistical significance for each independent variable.

However,

throughout this report, the interpretation of the effect for each independent variable is based on logit coefficient and the affiliated level of statistical significance. In addition to statistically significant predictors, the models were examined in terms of their ability to correctly classify defendants. For comparison purposes, a .5 cutpoint was selected.9 The cutpoint is a value which determines how defendants will be classified by a prediction model. Defendants scoring higher than the cutpoint are classified as predicted to be “yes” on the dependent variable (pretrial FTA for this research), while those scoring lower than the cutpoint are predicted as “no.” The predicted values are compared with the actual scores on the dependent variable and rates of correct and false predictions are computed. The proportion of the sample predicted to fail is known as the selection ratio and the proportion of the sample that actually failed is defined as the base rate (Gottfredson and Gottfredson, 1986). For the present analysis, these statistics were generated by the SPSS (Statistical Package for Social Sciences) in the course of running logistic regression analysis (Norusis, 1990). To be more specific, when running a logistic regression procedure, the SPSS generates a two-by-two table of observed versus predicted behavior, reflecting the number of correct and false predictions. By default, the table is run at a .5 cutpoint. The following table illustrates each of the cells.

9 When applying a .5 cutpoint, it is assumed that 50 percent of the sample would fail and 50 percent would succeed.

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PREDICTED BEHAVIOR OBSERVED BEHAVIOR Non-Failure

Failure

Non-Failure

Failure

True Negative False positive (predicted non-failure, (predicted failure, observed non-failure) observed non-failure) True Positive False Negative (predicted failure, (predicted non-failure, observed failure) observed failure) Percent Correctly Classified= True Negative + True Positive

The correct predictions include true positives and true negatives. In this research, a true positive results when a defendant is predicted to fail and is observed as failing. In comparison, a true negative is ensued when it is predicted that a defendant would not fail to appear and is observed as not failing. The errors in prediction can be false positives or false negatives. A false positive (also known as Type I or alpha error) results when failure is predicted but non-failure is observed. In contrast, a false negative (Type II or beta error) denotes cases that are predicted as not failing but indeed fail. Both types of errors have their costs. The costs associated with false positives (Type 1 error) can be ethical and financial. The ethical costs include detaining a defendant who could be safely released. The financial costs include the costs associated with the detention of such defendants. The false positives can also result in jail overcrowding. The costs associated with Type II error (false negatives) can include the financial, physical, and emotional burdens on the victims and the community (Clear, 1988).

These costs should be considered carefully before

translating a prediction model into a policy decision. The predictive accuracy of the two-by-two classification table was assessed in terms of its relative improvement over chance (RIOC) (Loeber and Dishion, 1983; Copas and Tarling,

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1986; Gottfredson and Gottfredson, 1986). The RIOC statistic indicates how well a model performs relative to its expected performance and its best possible performance, allowed by the selection ratio and base rate. The RIOC statistic can range from 0 (or 0%), indicating no relative improvement, to 1 (or 100%), indicating total improvement. (See Appendix A for a more detailed discussion of RIOC.) As an example, the final logistic regression model for Criminal Court defendants produced the following table of predicted versus observed behavior at a .5 cutpoint.

PREDICTED BEHAVIOR OBSERVED BEHAVIOR Non-Failure (No Warrant)

Failure (Warrant)

Total

Non-Failure (No Warrant) True Negative N=4,259 63.2% False Negative N=1,270 18.8% 5,529 82%

Failure (Warrant) False Positive N=455 6.8% True Positive N = 755 11.2% 1,210 18% Percent Correctly Classified= 74.4 RIOC=.46

Total 4,714 70% 2,025 30% 6,739

Based on the above table, 74.4 percent of the defendants were correctly classified. Of this number, 11.2 percent were correctly classified as failing to appear (true positives) and 63.2 percent were correctly classified as not failing to appear (true negatives). The overall error rate was 25.6 percent. Most of the errors in prediction were false negatives (18.8%) and fewer were false positives (6.8%). The RIOC statistic indicated an improvement of 46 percent over chance alone.

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It should be noted that although the two-by-two classification table provides useful information about a model’s predictive accuracy, it has limitations. The information provided is representative of only one particular cutpoint for a given model (Cuvelier and Pottts, 1993). If a prediction model, for example, classifies defendants into three groups, low, moderate, and high risks, the proportions of predicted failures and non-failures will be determined either on a cutpoint classifying low and moderate risks into predicted non-failures and high risks as predicted failures, or categorizing low risks as predicted non-failures and moderate and high risks as predicted failures. The predictive accuracy of a model where low and moderate risks are treated as successes will be different from a model where only low risks are treated as successes. There are a number of other methods which are less sensitive to the base rate and the selection ratio. One of them is the mean cost rating which is used in this report to assess the predictive accuracy of the new point scales. With the exception of the Supreme Court analysis, the best model10 for each sample of the at-risk defendants was used to guide the construction of a new point scale.11 “Points” were assigned to each of the independent variables based on the logit coefficients and significance levels. For the purpose of standardization, the statistically significant logit coefficients were divided by .15 and were then rounded to the nearest whole number. The decision to divide by .15 was arbitrary, although consistent with that used in several studies (Goldkamp et al., 1981; Goodman, 1992). If the coefficient was negative and statistically significant, a negative value was given, indicating that a defendant was less likely to FTA. Likewise, positive values were given for positive significant coefficients, meaning that the likelihood of FTA increased. A value of zero was given to categories that did not produce a statistically significant effect on FTA. The total score for each defendant was obtained by summing these points. The predictive accuracy of each new point scale was assessed by using the mean cost rating (MCR). MCR, introduced by Duncan, Ohlin, Reiss, and Stanton (1953) measures the predictive efficiency of an instrument over its base rate. The values for this statistic range from 0

10 Here “best” is defined as the model that had the greatest predictive ability with the smallest number of variables. 11 The Supreme Court analysis ended with the identification of the significant predictors of pretrial FTA. If the agency decides to make separate release recommendations at Supreme Court arraignment, a new scale will be constructed for Supreme Court defendants.

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to 1, with 0 indicating no improvement in prediction and 1 suggesting perfect prediction. (See Appendix B for MCR computation.) A number of risk-classification schemes were developed by dividing the at-risk sample of defendants at various points on the scales. The objective was to create subgroups of defendants which would have different average FTA rates and as such represent different risk levels. Comparisons were made to determine which scheme was able to identify the largest proportion of low-risk defendants. The alternative schemes were compared with the current CJA recommendation scheme with respect to the distribution of defendants in various risk categories and their corresponding FTA rates. The objective was to determine whether the alternative riskclassification schemes would: 1) improve prediction by recommending more defendants for ROR, 2) decrease the current FTA rate, or 3) do both. Finally, the alternative risk-classification schemes suggested for defendants at risk in Criminal Court only were compared with those suggested for defendants at risk in Criminal or Supreme Court.

These comparisons were

undertaken with the purpose of ascertaining which scheme was better at identifying low risk defendants and whether one point scale could be used to aid in identifying at-risk defendants in both Criminal and Supreme Court, or whether a separate scale was needed for each court. It is important to note that the logistic regression analyses used in this report are based on a sample of defendants who were released (either on bail or ROR) prior to the disposition of their case. Because defendants who are perceived as being most likely to FTA are more apt to be held in detention and not released pretrial, they may differ in some ways from those who are released prior to the disposition of their case. This constitutes a nonrandom selection process, where the differences between those held and released may be related to factors associated with the probability of pretrial failure. In addition, in New York City where many cases are disposed

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at arraignment, even the likelihood of having a continued case is nonrandom. Therefore, estimates from a model using only released defendants may confound the effects of variables used to predict pretrial FTA with those that determine whether a defendant is ever released pretrial, or whether a defendant has a case continued at arraignment. Consequently, using the logistic regression results to recommend defendants who should be released from jail pending trial may not be valid. To control for the potential problem of sample selection bias, sequential logit models were estimated following the methodologies articulated by Rhodes (1985) and Smith et al. (1989). (See Appendix C for a detailed discussion of sample selection bias.)

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RESULTS I. Criminal Court Analysis A. Defendant Characteristics The majority of the defendants in the Criminal Court sample were male, single, and unemployed (Table 1).12 Half the defendants were black and half had less than a high school education.

The median age was 27 years.

One third of the defendants were arrested in

Manhattan. Most of the defendants (89.3%) were released on recognizance and almost 70 percent were at risk for 80 days or less, prior to the issuance of the first non-stayed warrant or the disposition of the case in Criminal Court. The majority of the defendants were arrested on felony charges, with the greatest proportion having been charged with B or D felonies. Although most of the defendants had no convictions prior to the sample arrest, half had been arrested previously. Slightly more than onethird had one or more cases at the time of the sample arrest, and 13.4 percent had a bench warrant attached to their rap sheet. With regard to a defendantâ&#x20AC;&#x2122;s ties to the community, the majority of the defendants who reported living in the New York City were living at their current address for at least 18 months and were living with someone.

Slightly less than half expected a friend or a relative at

arraignment, had a working telephone in their residence, and were employed, in school, or in a training program full time.

12 All tables can be found following the Bibliography.

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B. Current CJA ROR Recommendation Scheme and FTA Half of the defendants released pretrial (i.e., at risk of failing to appear) in Criminal Court were given a positive recommendation for release on their own recognizance, based on the current ROR recommendation scheme (32.3% “recommended” and 18.1% “qualified”). Of the 2,292 defendants “recommended” for release, 17.2 percent failed to appear for at least one scheduled court appearance in Criminal Court (Table 2). For the 1,287 defendants receiving a “qualified” recommendation for release, the Criminal Court FTA rate was 26.1 percent. In contrast, among the other half of defendants who were “not recommended” for release, the FTA rate was 40.8 percent. Thus, as noted earlier, the current recommendation scheme continues, despite the time elapsed since the research which developed it, to distinguish defendants by their relative risk of FTA.

Low-risk defendants were recommended for ROR, moderate-risk

defendants had a qualified rating, and high-risk defendants were not recommended for ROR.

C. Current CJA ROR Recommendation Scheme: A Logistic Regression Model Before exploring new variables in relationship to FTA, a logistic regression model was constructed to ascertain the effectiveness of the individual items currently used to make pretrial release recommendation in predicting pretrial failure to appear in Criminal Court.13 This will serve as a base to compare subsequent models. The results suggested that having a telephone, living at one's current address for at least 18 months, expecting someone at arraignment, being 13 These items included information on whether defendants had a working telephone, whether they had a New York City area address, the length of time at their current address, family ties within the residence, whether they expected someone at their arraignment, whether they were employed, in school, or in a training program full time, whether they had an open bench warrant, and the composite item. The composite item was based on one or more verified responses to the community-ties variables (having a working telephone, length of time at current address, expecting someone at arraignment, being either employed, in school, or in a training program full time, and living arrangements), in addition to having a verified New York City area address. It should be noted that the actual responses to questions that made up the composite item other than the address could be either affirmative or negative, as long as these responses were verified.

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employed, in school, or in a training program full time, and having an open bench warrant were significantly related to pretrial FTA (Table 3). All other factors being equal, defendants who were verified as not having a telephone in the residence were more likely to fail to appear than those with a “yes verified” response to that item. The odds of failing to appear were also higher for those who were recorded as "no verified" or "unresolved conflict" on the length of current address item, when compared with defendants whose affirmative responses were verified. With regard to being engaged in a full time activity, defendants recorded as “yes,” “no,” “no verified,” or “unresolved conflict” were more likely to FTA than defendants with a “yes verified” response.

The results also showed that the likelihood of failure was higher among

defendants who did not expect anyone at arraignment. Finally, defendants with an open bench warrant were more likely to FTA than defendants with no such warrant, holding all the other variables constant. The measures of family ties within the residence, having a New York City area residence, and composite item did not attain statistical significance.14 When using a .5 cutpoint, 69.8 percent of the defendants were correctly classified by the model that controlled for the items currently used to make pretrial release recommendation. Almost all the correct predictions (66.3%) applied to those who were correctly classified as not failing to appear (true negatives). The overall error rate was 30.2 percent. Almost all the errors in prediction were false negatives; 26.7 percent of the defendants were predicted as not failing to

14 With regard to the composite item and having an address in the New York City area, the results were most likely an artifact of the perfect correlation between the composite item and having an address in the New York City area (the correlation for the composite item and a verified affirmative response to New York City area address was .98).

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appear when in fact they did. The modelâ&#x20AC;&#x2122;s RIOC statistic indicated a 29 percent improvement over chance alone. In sum, the above findings suggested that most of the variables comprising the current CJA ROR scheme were significantly related to pretrial FTA. However, to examine the effect of other variables (such as a defendantâ&#x20AC;&#x2122;s criminal history or the information on the arrest charge) on pretrial FTA, a number of logistic regression models were estimated. The models were compared with respect to their predictive accuracy at a .5 cutpoint. The section that follows describes the best model from the new analysis of Criminal Court pretrial behavior.

D. Logistic Regression Analysis of FTA Table 4 presents the best model from the analyses conducted on a sample of defendants who were at risk for FTA in Criminal Court. This model included community-ties items,15 criminal-history variables,16 measures of both the type and the severity of the top arrest charge, socio-demographic attributes,17 and case-processing characteristics.18 All of these domains had statistically significant relationships to FTA. With regard to the community-ties variables, defendants who were verified as not having a telephone were more likely to fail to appear than those with a verified telephone. The odds of

15 The community-ties variables contained information on whether defendants had a working telephone, whether they had a New York City area address, the length of time at their current address, whether they expected someone at their arraignment, and whether they were employed, in school, or in a training program full time. 16 The criminal history variables included information on prior violent felony convictions, prior FTA, and open cases. 17 The socio-demographic attributes were comprised of defendantsâ&#x20AC;&#x2122; sex, ethnicity, age, and marital status. 18 Included among the case-processing characteristics were the type of first release prior to case disposition, time at risk, and the borough of arrest.

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FTA were also higher for those who were recorded as “no verified” or “unresolved conflict” on the length of the current address item, when compared with defendants whose affirmative responses were verified. Defendants who were verified as being New York City area residents were less likely to fail to appear than those who did not reside in the New York City area, holding all other factors constant. The FTA rate was also lower among defendants who expected someone at Criminal Court arraignment. Finally, all else being equal, the odds of failing were lowest for defendants who were verified as being employed, in school, or in a training program full time. The findings also indicated that defendants with prior FTA and defendants with open cases were more likely to FTA than defendants without a history of failure and defendants with no open cases. Defendants who had previously been convicted of a violent felony offense were more likely to FTA than those who had no such conviction. Both top arrest charge type and severity were found to be significant predictors of FTA. Defendants arrested for a D felony, an E felony, or an A misdemeanor were more likely to FTA than those arrested for an A or B felony, controlling for the effects of the other variables. The FTA rate was higher among defendants who were arrested for property offenses than for those arrested for drug offenses. Furthermore, when compared with those arrested for drug offenses, defendants arrested for weapon or gambling offenses were less likely to FTA. An examination of the socio-demographic variables indicated that female, black, single, and younger defendants were more likely to FTA than male, white, married, and older defendants, when holding all other factors constant.

With respect to the case-processing

characteristics, defendants arrested in Brooklyn were more likely to FTA than those arrested in Queens. Defendants who were released on ROR and defendants who were at risk for 80 days or

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less had higher FTA rates than those who were released on bail or who were at risk for more than 80 days. It should be noted, however, that only 11 percent of the defendants were released on bail and it is most likely that a majority of them posted cash bail. The model was also examined in terms of its predictive adequacy. Overall, when applying a .5 cutpoint, the model accurately classified three-fourths of the defendants. Approximately 11 percent of the correct predictions were true positives, while 63.2 percent were true negatives.

Most of the errors in prediction were false negatives; 18.8 percent of the

defendants were predicted as not having a warrant when in fact they did. In contrast, 6.8 percent were predicted as having a warrant when in fact they did not. The RIOC score indicated a 46 improvement over chance alone. A comparison of the predicted versus observed behavior for Model 1 with the Model that regressed the items from the current point scale revealed that the former did better in terms of improving prediction over chance alone: when applying a .5 cutpoint, the RIOC statistic indicated a 46 percent improvement over chance alone. The comparable figure for the other model was 29 percent. The two models also differed with respect to the distribution of true and false predictions. More specifically, Model 1 correctly classified more defendants (74.4% versus 69.8%), decreased the percentage of false negatives (18.8% versus 26.7%), and identified more defendants as true positives (11.2% versus 3.5). In short, FTA was better predicted by including additional factors.

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E. Construction of a New Point Scale Because of its strength in predicting FTA in Criminal Court, Model 1 was selected to guide the construction of a new point scale.19 The first step in formulating a point scale was to assign "points" to each of the predictors included in the model. This was not a problem for the dichotomous variables. However, the assignment of points to the categorical variables was not straightforward. This complexity of point designation was primarily because Model 1 was initially estimated by using the simple-contrast technique of logistic regression for the categorical variables. While easing interpretation, this technique makes it difficult to derive points for the categorical variables. More specifically, for each of these variables, the effect of each category is compared with a specified reference category (the excluded category).

Although the

coefficients and significance levels for the included categories are provided, these statistics are not known for the reference category. Changing the reference category alters the effects of the other categories. Therefore, by using that technique, it was not possible to assign points to all the categories for a categorical independent variable. To address this issue, Model 1 was re-estimated using a different coding method (deviation contrast technique) for categorical independent variables. When using this method, the effect of each category for a categorical independent variable is compared with the average effect of all the categories for that variable. The effect for the excluded category is obtained by choosing an alternative reference category and rerunning the model. Because the effect for each 19 The findings from a series of sequential logit models indicated that the sample selection (both the arraignment outcome and the pretrial release selection processes) did not appear to bias the estimates of FTA for Model 1, and therefore, this model could be used as a guide to constructing a new point scale.

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category is compared with the mean effect for that variable, changing the reference category does not alter the effects of the other categories. In addition to changing the reference category, adjustments to the model were made to address policy and practical issues. More specifically, socio-demographic variables and caseprocessing characteristics were excluded from the re-estimated model. For example, it would be unreasonable to suggest that, based on the findings from Model 1, defendants who are female, black, younger, single, and arrested in Brooklyn should not be recommended for release on recognizance. Neither type of first release nor time at risk were included because both occur after the release decision concerning the defendant at Criminal Court arraignment has been made. Table 5 presents the findings from the re-estimated model (Model 2). All the variables that were significant in Model 1 continued to be significant and could be interpreted as affecting FTA in the same fashion, with the exception of the following differences in the interpretation of the strength of the individual effects. 1) For the telephone variable, the “yes” category became significantly associated with a lower FTA. 2) For the variable indicating whether a defendant was employed, in school, or in a training program full time, the “yes” and “no verified” categories were no longer significantly related to FTA. 3) For the length of time at current residence variable, the “no verified” response ceased to be significantly related to FTA. 4) For the severity of the top arrest charge variable, D felonies and E felonies no longer significantly predicted FTA. 5) For the top arrest charge type indicator, the “violent” and “other” categories became significantly associated with increased likelihood of FTA. significance levels for the dichotomous variables remained unchanged.

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The coefficients and

In addition to the changes in the interpretation of the categorical independent variables, minor differences were observed in the distribution of true and false predictions at a .5 cutpoint. More specifically, in comparison to Model 1, Model 2 had slightly lower percentage of defendants who were correctly classified (74.4% versus 72.2%) at a .5 cutpoint. Furthermore, when applying Model 2, the rate of false negatives increased and the rate of true positives decreased by three percentage points. However, the difference between the two models was greater when examining the RIOC statistics. In comparison to Model 1â&#x20AC;&#x2122;s RIOC statistic of 46 percent, the relative improvement over chance alone for Model 2 was 40 percent. In sum, in comparison to Model 1, the re-estimated model (Model 2) was slightly weaker in terms of its predictive accuracy. It is important to note, however, that all the variables, in general, which were significant in Model 1 remained significant in Model 2. In addition, the coefficients and significance levels for the dichotomous variables were precisely the same. Thus, because of its practical relevance, Model 2 was used to develop a new point scale for defendants at risk in Criminal Court. Points were assigned to each of the independent variables based on their estimated coefficients and significance levels in Model 2 (Table 6). Depending on the direction and magnitude of the coefficients, positive and negative values were assigned to each significant category of a variable. The insignificant coefficients were assigned a value of zero. The prior FTA variable was among the strongest contributors to a defendant's point-scale score. Defendants with a history of failure had five points added to their overall scores, whereas those without a history of failure had five points subtracted. The top-arrest-charge-type variable also contributed strongly to the total point-scale score. Defendants charged with property crime had

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five points added to their overall scores. In contrast, defendants charged with gambling offenses were less likely to FTA. They had five points subtracted from their overall scores. All possible scores were calculated; the resulting overall scores ranged from -21 to 21 points, although theoretically, scores of -26 to 26 points were possible. Generally speaking, the likelihood of FTA increased with an increase in the number of points scored. Few defendants had very high or very low scores. As an illustration, no defendants with a score of -21 points failed to appear. The adequacy of the scale developed herein was assessed in terms of its mean cost rating. The MCR was calculated as being .452, indicating that the new point scale differentiated well between groups of defendants on their likelihood of pretrial FTA.

F. Alternative Risk-Classification Schemes Four risk-classification schemes were developed by dividing the sample of defendants at various points on the new scale. The range of points scored between those "cut-off points" and their corresponding FTA rates were examined. These varying FTA rates were then used to categorize defendants according to which represented the lowest risks for FTA and, thus, for ROR recommendation.

The schemes were compared with the current CJA ROR

recommendation scheme, in terms of the proportion of defendants in various risk categories and their corresponding FTA rates. Alternative Risk-Classification Scheme 1 The first alternative risk-classification scheme divided the sample of defendants into two approximately equal parts, at about the 50th percentile (Table 7). The defendants in the first half

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(51.1%) scored -3 points or less and had an FTA rate of 16.8 percent. The FTA rate for the second half was 44.2 percent, with point-scale scores ranging from -2 to 21 points. Because of their lower FTA rate, the defendants in the first group of Scheme 1 would be considered good risks for ROR recommendation. When compared with defendants classified as low risks for FTA under the current ROR recommendation scheme (recommended), this group would contain 18.8 percentage points more defendants (Table 8). In addition, the FTA rate for these defendants would remain the same. Group 1-II defendants had substantially higher FTA rate than Group 1-I defendants and were thus categorized as high risks. If using alternative Scheme 1 rather than the current CJA ROR recommendation scheme, the proportion of defendants considered high risks would remain the same. However, their FTA rate would increase by 3.4 percentage points. Scheme 1 did not contain a moderate-risk category. Alternative Risk-Classification Scheme 2 The second alternative risk-classification scheme divided the at-risk sample into three groups. The defendants in the first group (32.5% of the sample) had scores of -7 points or less on the point scale and had an FTA rate of 11.9 percent. Group 2-II (34.8%) had scores ranging from 1 to -6 points, and an FTA rate of 30.6 percent. Group 2-III contained 32.7 percent of the sample. Scores for this final group ranged from 2 to 21 points and the FTA rate was 47.9 percent. As can be seen from Table 7, defendants in Group 2-I had the lowest FTA rate and thus would be considered good risks for ROR recommendation. When compared with defendants classified as recommended under the current CJA ROR recommendation scheme, this group would have the same proportion of defendants. However, their FTA rate would be better as it would decrease by 5.3 percentage points (Table 8).

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The FTA rate for Group 2-II defendants, 30.6 percent, falls approximately midway between the FTA rates reported for Groups 2-I and 2-III. As such, these defendants would be considered to present a moderate risk for FTA. When compared with the proportion in the "qualified" category of the current CJA ROR recommendation scheme, alternative Scheme 2 would classify 16.7 percentage points more defendants as the second best group, but their FTA rate would increase by 4.5 percentage points. Group 2-III defendants had substantially higher rates of failure than either Group 2-I or 2-II defendants and thus would not be considered good risks for an ROR recommendation. In comparison to the "not recommended" category of the current scheme, the proportion of defendants in this high-risk category would decrease by 17 percentage points. Their FTA rate, however, would increase by seven percentage points. In short, if one were to use alternative classification Scheme 2 instead of the current CJA ROR recommendation scheme, one would: 1) classify the same proportion of defendants as low risks, but actually decrease their FTA rate; 2) increase the proportion of defendants who would be classified as moderate risks, and slightly increase the FTA rate for these defendants; and 3) produce a smaller proportion of high-risk defendants, but increase their FTA rate. Alternative Risk-Classification Scheme 3 The third alternative risk-classification scheme classified the at-risk sample into four groups (Table 7). Group 3-I was composed of 22.2 percent of the defendants in the sample. These defendants scored -9 points or less and had an FTA rate of 9.4 percent. The FTA rate for the second group of defendants (28.9% of the sample) was 22.4 percent. The scores for Group 3-II ranged from -8 to -3 points. There were 1,671 defendants (24.5%) in Group 3-III, with scores ranging from -2 to 4 points. Approximately 37.3 percent of these defendants failed to

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appear for at least one scheduled court appearance. The remaining 24.4 percent of the sample made up Group 3-IV. The defendants in this group scored 5 to 21 points and had an FTA rate of 51.0 percent. Because Group 3-I had the lowest FTA rate, these defendants would be considered best risks for an ROR recommendation.

When compared with the proportion of defendants

recommended under the current CJA ROR recommendation scheme, this group would be ten percentage points lower. However, the FTA rate among the defendants in the group would be substantially less, decreasing by 7.8 percentage points. The second lowest FTA rate was found among defendants classified in Group 3-II. In comparison to the group of defendants who received a "qualified" recommendation under the current CJA scheme, this group would not only increase the proportion of defendants by 10.8 percentage points, but their FTA rate would also decrease by 3.7 percentage points. Defendants found in Groups 3-III and 3-IV had substantially higher rates of failure than defendants in either Group 3-I or Group 3-II and would be regarded as high or very high risks. A total of 48.9 percent of the defendants were found in these groups, which did not differ much from the percentage not recommended with the current CJA ROR recommendation scheme (49.6%). Despite the relatively high rate of failure for the last two groups, it is important to note that the FTA rate among Group 3-III defendants was considerably lower than the FTA rate for Group 3-IV defendants (37.3% versus 51.0%).

Therefore, as an alternative to not

recommending both groups, it may be suggested that Group 3-III defendants be considered for release, with conditions aimed at reducing FTA. In sum, when compared with the current CJA ROR recommendation scheme, alternative classification Scheme 3 would: 1) classify a smaller proportion of defendants as low risks, but

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decrease the FTA rate; 2) produce a higher proportion of moderate-risk defendants, while decreasing their FTA rate; and, 3) place about the same percentage of defendants in the high-risk category (high and very high) while differentiating among high-risk defendants. Alternative Risk-Classification Scheme 4 Although Scheme 1 identified the largest proportion of defendants as low risks, it did not take into account defendants with a moderate risk of failure. As a result, the proportion of highrisk defendants would also be greatest when using that scheme. Schemes 2 and 3 classified a smaller proportion of defendants as high risks, primarily because they both contained moderaterisk categories. However, the proportion of low-risk defendants in each was substantially lower than the proportion found for Scheme 1. Thus, another scheme (Scheme 4) was created that combined Scheme 1 and Scheme 3. As shown by Table 7, Group 4-I was precisely the same as the Group 1-I, containing 51.1 percent of the defendants with an FTA rate of 16.8 percent. Group 4-II was the same as the third group from Scheme 3 (24.5% of the defendants and an FTA rate of 37.3%). The remaining 24.4 percent of the defendants comprised Group 4-III, which were the same as Group 3-IV defendants. The FTA rate for these defendants was 51 percent, with scores ranging from 5 to 21 points. A comparison of the three groups forming Scheme 4 indicated that the FTA rate was lowest among Group 4-I defendants. Therefore, these defendants would be considered to be the best for an ROR-recommendation consideration.

Relative to the current CJA ROR

recommendation scheme (Table 8), this alternative risk-classification scheme would increase the proportion of defendants in the low-risk category by 18.8 percentage points as well as maintain the FTA rate at about the current level. Defendants in Group 4-II formed a group with a

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moderate risk of FTA. Compared to the "qualified" category of the current scheme, more defendants would fall into this middle-level-risk category, if one were to use Scheme 4. The FTA rate for these defendants would be 11.2 percentage points higher than the FTA rate found under the current CJA ROR recommendation scheme. Defendants in Group 4-III had the highest FTA rate and would not be considered for ROR recommendation. In comparison to those currently not recommended, the proportion of defendants falling into a high-risk category would decrease by 25.2 percentage points, with a corresponding 10.2 percentage point increase in the FTA rate. In sum, if applying Scheme 4, rather than the current CJA ROR recommendation scheme, one would: 1) classify a higher proportion of defendants as the best risks for ROR recommendation, without simultaneously increasing the FTA rate; 2) increase both the proportion of, and FTA rate for, moderate-risk defendants; and 3) decrease the proportion of defendants considered high risks for release, while increasing their FTA rate. A comparison of the four alternative risk-classification schemes suggested that Scheme 4 produced the most desirable effects. That scheme classified the largest proportion of low-risk defendants and, at the same time, had the smallest proportion of defendants who would be considered high risks. Scheme 4 also accounted for moderate-risk defendants.

II. Supreme Court Analysis A. Defendant Characteristics Table 9 displays the characteristics of defendants who were at risk for FTA in Supreme Court. The majority of defendants in the Supreme Court sample were male and single. Half the defendants were black. The median age was 25 years. About 60 percent had less than a high-

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school education and were unemployed. The median weekly take home pay for the employed defendants was 203 dollars. Among those at risk for FTA in Supreme Court, two-thirds were released on their own recognizance. The median time from arrest to Supreme Court arraignment was 51 days. With regard to the severity of the top indictment charge at Supreme Court arraignment, half the defendants were charged with a B felony. More than 50 percent of the defendants were charged with a drug offense. There were few misdemeanor cases in Supreme Court, the majority of which were charges of driving while under the influence of drugs or alcohol. Although most of the defendants had no convictions prior to the sample arrest, sixty percent had been arrested previously. More than two-fifths had one or more open cases at the time of their arrest. Approximately fifteen percent had a bench warrant attached to their rap sheet and less than ten percent had a warrant issued in Criminal Court on the sample arrest. Slightly less than two-thirds of the defendants did not have a history of FTA prior to the sample arrest. With regard to a defendant's ties to the community, the majority of the defendants reported living in the New York City area. Approximately two-thirds reported living at their current residence for at least 18 months, as well as living with someone. Half the defendants expected someone at arraignment. Less than half the defendants had a working telephone in their residence or were employed, in school, or in a training program full time. Half the defendants were either “recommended” or “qualified” for an ROR recommendation.

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B. Logistic Regression Analysis of FTA A number of models assessing pretrial FTA in Supreme Court were examined. Table 10 presents the most predictive model (Model 3) from that analysis. In comparison to the most predictive Criminal Court model, fewer variables attained statistical significance when Supreme Court defendants were examined separately. These variables included: 1) having a telephone within the residence, 2) being employed, in school, or in a training program full time, 3) prior FTA, 4) the type of the top amended charge at Criminal Court final disposition, 5) borough of arrest,20 and 6) the time from arrest to Supreme Court arraignment.21 The results indicated that defendants verified as being employed, in school, or in a training program full time were less likely to fail than those who were not engaged in such activity on a full-time basis, regardless of verification ("no" and "no verified"). In addition, defendants verified as not having a telephone were more likely to FTA than those with a verified affirmative response. Having a history of FTA increased the odds of failure in Supreme Court. With regard to the top amended charge type at Criminal Court final disposition, defendants charged with drug offenses were more likely to miss a scheduled appearance in Supreme Court than those charged with violent, weapon, or "other" offenses (all offenses which did not fall under the violent, property, weapon, gambling, or driving while under the influence of drugs or alcohol categories).

20 Due to an insufficient number of defendants in the sample who were arrested in Staten Island and were released in Supreme Court, this borough was excluded from the analysis. This model was re-estimated using an oversample of Staten Island defendants. The re-estimated model generated the same results. 21 The time from arrest to Supreme Court arraignment focused on the length of time during that period regardless of whether a defendant was at risk to FTA.

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According to the results, defendants arraigned in Supreme Court within the first two weeks following their arrest were less likely to FTA than those arraigned after this two-week period.22 With regard to the borough of arrest, the likelihood of FTA was higher for defendants arrested in Brooklyn than for those arrested in the Bronx, in Queens, or in Manhattan. When applying a .5 cutpoint, Model 3 correctly classified 72.4 percent of all the defendants at risk for Supreme Court FTA. While 10.8 percent of the defendants were correctly predicted as failing to appear (true positives), 61.6 percent were correctly predicted as not failing to appear (true negatives). Most of the errors in prediction were false negatives (21.7%) and fewer were false positives (5.9%), for a total error rate of 27.6 percent. The RIOC statistic indicated a 48 percent improvement over chance alone. III. Analysis of FTA Regardless of the Court of Disposition A. Defendant Characteristics In the third phase of the analysis, FTA was examined among defendants who were released in either Criminal or Supreme Court. Table 11 presents defendant characteristics from that combined-sample of Criminal and Supreme Courts. The majority of the defendants were male and single. Half the defendants were black and slightly more than half had less than a high-school education. The median age was 27 years. Less than half the defendants reported being employed, with a median weekly self-reported take-home pay of $250.

22 Model 3 was re-estimated replacing the time from arrest to Supreme Court arraignment with the time-at-risk variable (the number of days a defendant was released on ROR or bail from Criminal Court arraignment to the first FTA or to the final disposition in Supreme Court). In comparison to Model 3, the re-estimated model was found to be weaker on various measures of predictive accuracy. Thus, it was decided to retain the time from arrest to Supreme Court arraignment variable in the Supreme Court analysis.

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One-third of the defendants were arrested in Manhattan and almost one-fourth had their cases transferred to Supreme Court. Most of the defendants were released on their own recognizance. Slightly less than two-thirds were at risk for FTA for eighty days or less. Three-fourths of the defendants were arrested on felony charges, with the greatest proportion having been charged with B or D felonies. One-third of the defendants were charged with a drug offense. More than half the defendants in the sample had been arrested previously. One-fourth of the defendants had prior felony convictions and almost one-third had prior misdemeanor convictions. Slightly less than two-fifths had one or more open cases at the time of their arrest and 14 percent had a bench warrant attached to their rap sheet. One-third had a history of FTA prior to the sample arrest. An overwhelming majority of the defendants reported living in the New York City area. Two-thirds of the defendants reported living at their current residence for 18 months or longer. Three-fifths reported living with someone at the time of their arrest. More than two-fifths expected someone to appear at their Criminal Court arraignment, had a working telephone in their residence, and were employed, in school, or in a training program full time. Half the defendants were recommended for release on their own recognizance (Table 11).

B. Logistic Regression Analysis of FTA Table 12 presents the most predictive logistic regression model (Model 4) for the combined sample of Criminal and Supreme Court defendants. As the table shows, Model 4 controlled for the same variables as Model 1 from the Criminal Court analysis, with the exception of prior violent felony convictions and a defendant's marital status.

These two

variables significantly predicted FTA only in the Criminal Court analysis, and were thus

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excluded from Model 4. In addition, Model 4 controlled for court of disposition. This variable was not included in the Criminal Court model, as it was not applicable. All the variables in Model 4 could be interpreted in the same fashion as for Model 1, with the exception of minor differences in the top arrest charge type, severity of the top arrest charge, and borough of arrest. Regarding the charge type variable in Model 4, with the exception of defendants arrested for property and "other" offenses, defendants arrested for drug offenses were more likely to FTA than defendants arrested for any other charge. No statistically significant differences were found between defendants with a drug arrest charge and those arrested for a property offense or an "other" offense. Recall that in the Criminal Court analysis, the likelihood of FTA was higher for defendants who were arrested for property offenses than for those who were arrested for drug offenses. Furthermore, in the Criminal Court analysis, defendants charged with violent offenses did not differ significantly from those arrested for drug offenses. It was not possible to compare the effect of the charge severity variable as the two models differed with respect to the reference category chosen. However, the combined-court model indicated that the odds of failure were higher for defendants who were arrested for an A misdemeanor than for those who were arrested for a D felony. The borough of arrest variable seemed to exert a stronger effect in the combined-sample of Criminal and Supreme Court defendants than in the sample that focused on Criminal Court defendants only. In the combined-sample, defendants arrested in the Bronx, in Queens, and in Manhattan were less likely to FTA than those arrested in Brooklyn, controlling for the other variables in the model. In contrast, in the Criminal Court analysis, only defendants arrested in Queens differed significantly from those arrested in Brooklyn. Finally, the probability of FTA was higher in Supreme Court than in Criminal Court.

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When applying a .5 cutpoint, the model presented in Table 12 correctly classified 71 percent of all the defendants. Of this number, 15 percent were correctly classified as failing to appear (true positives) and 56 percent were correctly classified as not failing to appear (true negatives). With regard to errors in prediction, 20.2 percent were false negatives and 9.2 percent were false positives (9.2%). The RIOC statistic indicated a 41 percent improvement over chance alone.

C. Construction of the Point Scale Model 4 was used as a guide to construct a point scale for the combined-sample of Criminal and Supreme Court defendants.

In order to assign points to all the categories of the

categorical independent variables, Model 4 was re-estimated by using the deviation contrast technique of logistic regression (Table 13, Model 5). As with the Criminal Court analysis, the socio-demographic variables and case-processing characteristics were excluded from the revised model to address certain policy and practical issues. More specifically, it is not possible to include the type of first release, the time at risk, or the court of disposition in a scale that would be used to make a release recommendation at Criminal Court arraignment; information regarding these variables does not exist at, or prior to, Criminal Court arraignment. Additionally, it would be unreasonable to suggest that, based on the findings from Model 4, defendants who are female, black, younger, and arrested in Brooklyn should not be recommended for release.

23 The findings from a series of sequential logit models indicated that the sample selection (both the arraignment outcome and the pretrial release selection processes) did not appear to bias the estimates of FTA for Model 4, and therefore, this model could be used as a guide to constructing a new point scale.

-37-

A comparison of the re-estimated model with Model 4 revealed that the two models did not differ in terms of the significance of the variables, with the exception of the following changes in the interpretation of the some of the individual effects. 1) For the telephone variable, the “yes” category became statistically significant; when compared with the mean effect of that variable, defendants having a “yes” response were less likely to FTA. 2) For the variable indicating whether a defendant was employed, in school, or in a training program full time, the “yes” and “no verified” categories lost their statistical significance. 3) With regard to the length of time at current residence, the “unresolved conflict” response ceased to predict FTA. 4) For the top arrest charge type indicator, the “property” and “other” crimes became significantly associated with increased likelihood of FTA and “weapon” charges no longer significantly predicted FTA. Furthermore, the direction of the relationship for “violent” crimes changed. More specifically, defendants arrested for violent offenses were more likely to FTA, when compared with the overall effect of the variable in Model 5. In Model 4, when compared with those arrested for drug offenses, such defendants were less likely to FTA. The two models differed slightly with respect to the distribution of true and false predictions at a .5 cutpoint. When applying the re-estimated model, the proportion of defendants correctly classified decreased slightly. Furthermore, the rate of true positives decreased by three percentage points. In contrast, an increase of three percentage points was observed in the rate of false negatives. The two models also differed with respect to their predictive accuracy. The RIOC statistic for the re-estimated model suggested a 34 percent improvement over chance alone. The comparable figure for Model 4 was 41 percent. To summarize, the predictive accuracy of Model 4 declined somewhat when the sociodemographic characteristics and case processing variables were excluded and a different

-38-

statistical technique was employed to measure the effect of categorical variables (deviation contrast technique). It should be noted, however, that all the variables, in general, which were significant in Model 4 remained significant in the re-estimated model.

In addition, the

coefficients and significance levels for the dichotomous variables were precisely the same. Thus, in order to address practical and policy issues and to assign points to all the categories of the independent variables, Model 5 was selected to construct a new point scale for defendants at risk both in Criminal and Supreme Courts. Table 14 presents the points assigned to each of the independent variables in Model 5. As shown by the table, top arrest charge type was the strongest contributor to a defendantâ&#x20AC;&#x2122;s point scale score, with the gambling category showing the largest effect; defendants arrested for gambling had six points subtracted from their overall scores. In contrast, defendants arrested for driving while under the influence of drugs or alcohol had five points subtracted from their overall scores. Continuing with that variable, defendants arrested for property or drug offenses had four points added, those charged with â&#x20AC;&#x153;otherâ&#x20AC;? offenses had two points added, and finally, defendants charged with a violent offense had one point added to their point-scale scores. The table also shows the two variables that contributed least to the overall score because they had the smallest effect on FTA--the severity of the top arrest charge and the length of time at current address. Beginning with the former, a defendant arrested for an A misdemeanor had one point added to his or her total score. With regard to the latter, one point was also added when a defendant was not verified as living at his or her current address for 18 months or longer. For both of these variables, none of the other categories was found to significantly related with FTA.

-39-

A comparison of the point scale constructed for both courts with the Criminal Court point scale revealed slight differences in the strength of the variables. For example, the type of the top arrest charge was the strongest contributor to a defendant's score on a point scale that would predict FTA regardless of the court of disposition.

Prior FTA was the second strongest

contributor to a defendant's score. In contrast, both the prior FTA and charge type variables were the strongest contributors to a defendant's score in Criminal Court. All possible scores were calculated, ranging from -18 to 17 points.24

In general,

defendants scoring lower points had lower FTA rates, whereas higher FTA rates were found for those scoring at the high end of the scale. The MCR statistic was .40, suggesting significant improvement over the base rate. D. Alternative Risk -Classification Schemes As with the Criminal Court analysis, four alternative risk-classification schemes were developed by dividing the sample of defendants at various points on the new scale. The alternative schemes were compared with the current CJA ROR recommendation scheme, in terms of the proportion of defendants in various risk categories and their corresponding FTA rates. To facilitate these comparisons, the current recommendation scheme was applied to the combined-sample of at-risk defendants. Current CJA ROR Recommendation Scheme As shown by Table 15, when using the current scheme, 31.5 percent of the defendants at risk in either Criminal Court or Supreme Court had verified community ties, and were

24 Theoretically, scores of -20 to 20 points were possible.

-40-

recommended for ROR release. Of this number, 21.5 percent failed to appear for at least one scheduled court appearance. Eighteen percent of the defendants had strong, but unverified community ties, thus receiving a â&#x20AC;&#x153;qualifiedâ&#x20AC;? recommendation. Their FTA rate was 31.3 percent. Half of the defendants were not recommended for ROR. These defendants had the highest FTA rate (45.3%). Alternative Risk-Classification Scheme 1 As shown by Table 16, the first alternative risk-classification scheme divided the sample of defendants into low and high risks, each containing approximately one-half of the sample. The first group scored zero points or less on the point scale and the second group had scores ranging from 1 to 17 points. The FTA rates for the two groups of defendants were 21.7 percent and 49.4 percent, respectively. When comparing this scheme with the current CJA ROR recommendation scheme (Table 17), the proportion of defendants classified as low risks for FTA and thus good risks for a release-on-recognizance recommendation

would

increase by 20.9 percentage points.

Furthermore, the FTA rate for these defendants would remain the same.

In contrast, the

proportion of defendants in the high-risk release category would decrease by three percentage points.

However, their FTA rate would increase by four percentage points. Thus, when

compared with the current CJA ROR recommendation scheme, alternative risk-classification Scheme 1 would: 1) categorize more defendants as good risks for ROR recommendation, without increasing the FTA rate; 2) eliminate a moderate-risk group of defendants; and 3) classify a slightly smaller proportion of defendants as high FTA risks.

-41-

Alternative Risk-Classification Scheme 2 The second risk-classification scheme divided the at-risk sample into three groups. The defendants included in the first group had the lowest FTA rate (17.5%) and would be considered the best risks for an ROR recommendation consideration. These defendants scored -3 points or less on the point scale. Defendants in Group 2-II constituted a moderate-risk group, with an FTA rate falling approximately midway between that reported for Group 2-I and Group 2-III defendants. These defendants had scores ranging from -2 to 3 points. Defendants comprising the third group had the highest FTA rate; slightly more than half the defendants did not appear for their scheduled appearances in Criminal or Supreme Court. These defendants had scores ranging from 4 to 17 points. When compared with the current CJA ROR recommendation scheme, under Scheme 2, more defendants (3.8 percentage points) would be classified as good risks, and their FTA rate would decrease by four percentage points. The number of moderate-risk defendants would increase by twelve percentage points and the FTA rate would increase by four percentage points. Finally, the high-risk group of this alternative scheme would have sixteen percentage points fewer defendants with an increase of 6.7 percentage points in their FTA rate. Therefore, use of alternative risk-classification Scheme 2 would: 1) increase the number of good-risk defendants while decreasing their FTA rate, 2) increase the number of moderate-risk defendants and increase the percentage which fail to appear, and 3) slightly decrease the proportion of high-risk defendants while increasing the percentage which fail to appear. Alternative Risk-Classification Scheme 3 The third alternative risk-classification scheme divided the combined sample of at-risk defendants into four groups--low, medium, high, and very high risks. Each group contained

-42-

approximately one-fourth of the sample. Defendants in the first group scored -5 points or less, and had an FTA rate of 14.4 percent. The second group of defendants had scores ranging from 4 to zero points, and an FTA rate of 27.8 percent. The FTA rate for the defendants in Group III was 43.7 percent, with scores ranging from 1 to 6 points. Of the fourth group, more than half the defendants did not appear for at least one scheduled appearance in Criminal or Supreme Court. Defendants comprising this group scored 7 to 17 points on the new point scale. A comparison of the third risk-classification scheme with the current CJA ROR recommendation scheme revealed that the former would: 1) classify fewer defendants (7.5 percentage points) as good risk for ROR recommendation, but decrease the FTA rate among these defendants by 7.1 percentage points; 2) produce more defendants (10.5 percentage points) as moderate risks, while decreasing their FTA rate by 3.5 percentage points; and, 3) place about the same percentage of defendants in the combined high- and very high- risk category, while decreasing their FTA rate by four percentage points. Taken together, Scheme 1 identified the largest proportion of defendants as low risks. However, the proportion of high-risk defendants was also greatest when using that scheme. Schemes 2 and 3 classified a smaller proportion of defendants as high risks, primarily because they both contained moderate-risk categories. However, the proportion of low-risk defendants in each was substantially lower than the proportion found for Scheme 1. Because that was the case, Schemes 2 and 3 may be too restrictive. Therefore, another scheme was created that combined Schemes 1 and 3 (Scheme 4). Alternative Risk-Classification Scheme 4 Scheme 4 consisted of three groups of defendants. As shown by Table 15, Group 4-I is precisely the same as Group 1-I. Group 4-II is the same as the third group from Scheme 3. The

-43-

defendants in Group 4-III are the same as Group 3-IV defendants. If using alternative riskclassification Scheme 4, rather than the current CJA ROR recommendation scheme (Table 16), the proportion of good-risk defendants likely to be recommended for ROR would increase by 20.9 percentage points. Furthermore, the FTA rate for these defendants would not be altered. Relative to those currently receiving a "qualified" recommendation, Scheme 4 would increase the proportion of defendants by 6.2 percentage points. However, the FTA rate among these defendants would also increase by 12.4 percentage points. Defendants in Group 4-III had the highest FTA rate. In comparison to the current scheme, this alternative scheme would decrease the proportion of defendants categorized as high risks by 27.1 percentage points, accompanied by a ten-percentage-point increase in the FTA rate. A comparison of the four alternative risk-classification schemes suggested that Scheme 4 had several advantages over the other three schemes. Comparatively, that scheme showed a greater proportion of good-risk defendants (without increasing the FTA above its current level), identified moderate-risk defendants, and decreased the proportion of defendants who would be considered high risks.

E. Alternative Risk-Classification Schemes: A Comparison with the Current CJA ROR Recommendation Scheme The predictive attributes of the Criminal Court alternative risk-classification Scheme 4 and the alternative risk-classification Scheme 4 devised for a combined-sample of Criminal and Supreme-Court defendants were compared with that of the current CJA ROR recommendation scheme.

The objective was to determine which scheme was better at identifying low-risk

defendants and whether one point scale could be used to aid in identifying at-risk defendants in

-44-

both Criminal and Supreme Courts or whether two separate point scales were needed--one for Criminal Court defendants and another for Supreme Court defendants. The two alternative risk-classification schemes produced very similar results when they were compared with the current CJA ROR recommendation scheme (Table 17). Both were able to classify a larger proportion of defendants as good risks for ROR release (Groups 4-I), while keeping the FTA rate at the same level. With regard to moderate risks (Group 4-II), both schemes increased the number and FTA rate of defendants classified as such. And, finally, both schemes decreased the number of high-risk defendants, but increased their FTA rate. It should also be noted, from a numerical perspective, that the previously mentioned increases and decreases were very similar between the two schemes. In sum, in comparison with the current scheme, the alternative risk-classification schemes suggested for defendants at risk in Criminal Court only or at risk in either court differed minimally with respect to the percent of defendants classified into the various risk categories and their FTA rates.

F. A New Risk-Classification Scheme and Its Implications Both the Criminal Court alternative risk-classification Scheme 4 and the fourth alternative scheme suggested for the combined sample of at-risk defendants have several advantages over the current recommendation scheme. First, they take into consideration three of the several factors mentioned in Section 510.30 of the New York State Criminal Procedure Law: employment, previous record of FTA, and open cases. The current recommendation scheme, on the other hand, is based solely upon a defendantâ&#x20AC;&#x2122;s ties to the community. Second, the alternative schemes are based on a citywide analysis of defendants. The current scheme, on the other hand,

-45-

was validated on a sample of Brooklyn defendants only. Finally, each alternative scheme improves upon the current CJA ROR recommendation scheme by increasing the number of lowrisk defendants while keeping the FTA rate at its current level. In other words, both schemes have the potential of reducing the jail population while awaiting trial, if followed by judges at arraignment. If all the defendants classified as good risks under Schemes 4 were released on ROR, the population of defendants currently held in the city's jails could possibly be reduced, thereby reducing the likelihood of jail overcrowding and the costs associated with detaining defendants awaiting trial. Furthermore, the identification of defendants at moderate risk for FTA would offer an opportunity to consider other release options, such as supervised- or conditionalrelease, aimed at reducing their risk of FTA. Similar suggestions could be made for defendants categorized as high risks--such defendants should be recommended for release under conditions that would improve the likelihood of their appearance for court. One advantage of Scheme 4 suggested for both courts is its ability to identify at-risk defendants regardless of the court of disposition.

The fourth alternative risk-classification

scheme for only Criminal Court defendants, in contrast, is limited in its application; it identifies the at-risk population exclusively in Criminal Court. If one were to apply that scheme, a separate point scale might need to be constructed for Supreme Court defendants, if recommendations were wanted there. This raises two issues. Firstly, it may not be cost-effective to have two separate scales.

The second issue involves determining when to make Supreme Court

recommendations. More specifically, unlike Criminal Court, a defendant may not always be arraigned at his or her first appearance in Supreme Court. As such, it would be difficult to make recommendations at the same time for all the defendants. The implementation of an alternative risk-classification scheme, based on an analysis of both Criminal Court and Supreme Court

-46-

defendants, however, would eliminate the need to make separate recommendations for Criminal and Supreme Court defendants.

-47-

BIBLIOGRAPHY

Bender, Matthew and Company, Inc. (1988). Criminal Law 1988-89 Graybook. New York. Clear, Todd (1988). “Statistical Prediction in Corrections,” Research in Corrections, 1 (1): 1-39. Copas, John B. and Roger Tarling (1986). “Some Methodological Issues in Making Predictions,” in Criminal Careers and Career Criminals. edited by Alfred Blumstein, Jacqueline Cohen, Jeffrey A. Roth, and Christy A. Visher. National Academy Press: Washington, D.C. Cuvelier, Steven Jay and Dennis W. Potts (1993). Bail Classification Project, Harris County, Texas. Final Report. State Justice Institute, Virginia. Duncan, O., L. Ohlin, A. Jr. Reiss, and H. Stanton (1953). “Formal Devices for Making Selection Decisions,” American Journal of Sociology. 58(6): 574-584. Fischer, D. (1985). Prediction and Incapacitation: Issues and answers. Statistical Analysis Center, Iowa Office for Planning and Programming: Iowa. Goldkamp, John S., Michael R Gottfredson, and Susan Mitchell Herzfeld (1981). Bail Decisionmaking: A Study of Policy Guidelines. Washington, D.C.: U.S. Department of Justice, National Institute of Corrections. Goodman, Rebecca (1992). Hennepin County Bureau of Community Corrections Pretrial Release Study. Minneapolis, Minnesota: Planning and Evaluation Unit. Gottfredson, D.M., C. A. Cosgrove, L.T. Wilkins, J. Wallerstein, and C. Rauh (1978). Classification for Parole Decision Policy. Washington, D.C.: U.S. Government Printing Office. Gottfredson, Stephen D. and Don M. Gottfredson (1986). “Accuracy of Prediction Models,” in Criminal Careers and Career Criminals. edited by Alfred Blumstein, Jacqueline Cohen, Jeffrey A. Roth, and Christy A. Visher. National Academy Press: Washington, D.C. Lazarsfeld, Paul F. (1974). An Evaluation of the Pretrial Service Agency of the Vera Institute of Justice. New York. Loeber, R. and T. Dishion (1983). “Early Predictors of Male Delinquency: Psychological Bulletin, 94 (1): 68-98.

A Review,”

Metchik, Eric (1987). Recommending Juvenile Offenders for Pretrial Release. New York: New York City Criminal Justice Agency.

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New York City Criminal Justice Agency. Semi Annual Report Series. New York. Norusis, Marija J. (1990). SPSS Advanced Statistics User’s Guide. Chicago, Illinois. Phillips, Mary T. (1999). Release-on-Recognizance Recommendation System for Juvenile Offenders Arraigned in New York City Adult Courts. New York City Criminal Justice Agency, New York. Rhodes, William M. (1985). “The Adequacy of Statistically Derived Prediction Instruments in the Face of Sample Selectivity: Criminal Justice as an Example,” Evaluation Review, 9(3): 369-382. Schaffer, Stephen A. (1970). Bail and Parole Jumping in Manhattan in 1967. New York: Vera Institute of Justice. Smith, Douglas A., Eric D. Wish, and G. Roger Jarjoura (1989). “Drug Use and Pretrial Misconduct in New York City,” Journal of Quantitative Criminology. 5 (2): 101-126. Thomas, Wayne (1976). Bail Reform in America. Berkeley, CA: University of California Press.

-49-

TABLES

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Criminal Court Analysis Table 1: Characteristics of Defendants Released Pretrial in Criminal Court 1 N=7,105 Defendant Characteristics SOCIO-DEMOGRAPHIC ATTRIBUTES Sex Male Female Total

6196 889 7085

87.5 12.5 100.0

Ethnicity Black Hispanic White

3532 2464 896

49.7 34.7 12.6

Other2 Total

213 7105

3.0 100.0

Age at Arrest 18 and under 19 - 20 years 21 - 24 years 25 - 29 years 30 - 34 years 35 - 39 years 40 - 78 years Total

903 672 1276 1479 1203 677 889 7099

12.7 9.5 18.0 20.8 16.9 9.5 12.5 100.0

Marital Status Single Married/Living Together Divorced/Separated/Widowed Total

5085 1615 386 7086

71.8 22.8 5.4 100.0

Education Less than High School High School Graduate GED Some college 4 or more years of college Total

3564 2156 177 683 227 6807

52.4 31.7 2.6 10.0 3.3 100.0

Employment Full-time Part-time Odd Jobs Unemployed Total

2540 398 148 3696 6782

37.5 5.9 2.2 54.5 100.0

Median: 27.0 years

Page 1 of 5

Table 1 (contd.) Defendant Characteristics Average Take-home Pay Unemployed $1 - $165 $166 - $240 $241 - $340 $341 or more Total

3696 598 681 719 699 6393

57.8 9.4 10.7 11.2 10.9 100.0

CASE-PROCESSING CHARACTERISTICS Borough of Arrest Brooklyn Manhattan Queens Staten Island Bronx Total

1906 2398 1144 175 1482 7105

26.8 33.8 16.1 2.5 20.9 100.0

Type of First Release ROR Bail Total

6306 755 7061

89.3 10.7 100.0

Time at Risk 20 days or less 21 - 40 days 41 - 80 days 81 - 140 days 141 - 639 days Total

1493 1751 1745 1260 844 7093

21.0 24.7 24.6 17.8 11.9 100.0

FTA in Criminal Court Yes No Total

2169 4935 7104

30.5 69.5 100.0

Returned on First Non-Stayed Warrant in Criminal Court Yes No Total

1580 589 2169

72.8 27.2 100.0

COMMUNITY TIES ITEMS NYC Area Address Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

3458 2765 394 83 270 6970

49.6 39.7 5.7 1.2 3.9 100.0

Median: $250, if employed

Page 2 of 5

Table 1 (contd.) Defendant Characteristics Verified Length of Residence of at least 18 Months Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

2437 2135 1493 510 423 6998

34.8 30.5 21.3 7.3 6.0 100.0

Verified Family Ties Within the Residence Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

1969 2303 1915 515 296 6998

28.1 32.9 27.4 7.4 4.2 100.0

Expects Someone at Arraignment Yes No Total

3123 3859 6982

44.7 55.3 100.0

Verified Telephone Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

1128 2293 2661 505 416 7003

16.1 32.7 38.0 7.2 5.9 100.0

Verified Full Time Employment/ School/Training Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

1692 1508 2443 1041 310 6994

24.2 21.6 34.9 14.9 4.4 100.0

Composite Item Yes No Total

2745 4220 6965

39.4 60.6 100.0

CRIMINAL HISTORY First Arrest Yes No Total

3415 3631 7046

48.5 51.5 100.0

Page 3 of 5

Table 1 (contd.) Defendant Characteristics Prior Non-Violent Felony Convictions Yes No Total

1130 5916 7046

16.0 84.0 100.0

614 6432 7046

8.7 91.3 100.0

2169 4877 7046

30.8 69.2 100.0

Open Cases Yes No Total

2619 4394 7013

37.3 62.7 100.0

Type of Warrant Attached to Rap Sheet Bench Warrant No Bench Warrant Total

930 6018 6948

13.4 86.6 100.0

Prior FTA Yes No Total

2248 4798 7046

31.9 68.1 100.0

TOP ARREST CHARGE SEVERITY A Felony B Felony C Felony D Felony E Felony A Misdemeanor B Misdemeanor

100 1653 742 2042 613 1501 84

1.4 23.3 10.4 28.7 8.6 21.1 1.2

370 7105

5.2 100.0

2057 1481 2137 388 107 292 643 7105

29.0 20.8 30.1 5.5 1.5 4.1 9.0 100.0

Prior Violent Felony Convictions Yes No Total Prior Misdemeanor Convictions Yes No Total

Other Total TOP ARREST CHARGE TYPE Violent Property Drug Weapon Gambling DWI (alcohol or drugs) Other Total

Page 4 of 5

Table 1 (contd.) 1

Due to missing data, the N for some variables is less than 7,105. OTHER includes Asian, American Indian, and others. 3 OTHER includes arrest, family, and other types of warrants. 4 OTHER includes Unclassified Misdemeanors, Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 5 VIOLENT CRIMES include: negligent murder, non-negligent murder, forcible rape, robbery, aggravated assault, kidnapping. PROPERTY CRIMES include: burglary, larceny-theft, forgery & counterfeiting, stolen property, possession of burglary tools. DRUG OFFENSES include: A) Controlled Substances Sale/Manufacture; opium, cocaine, or derivatives, marijuana, synthetic narcotics, other dangerous drugs, and B) Use/Possession; opium, cocaine, or derivatives, marijuana, synthetic narcotics, other dangerous drugs. Dangerous weapons comprise the WEAPON category. The GAMBLING category consists of bookmaking, numbers, lottery, and other activities. The DWI category refers to driving while under the influence of alcohol or drugs. The OTHER category consists of all other offenses not included in the aforementioned categories but which are included in the FBI Uniform Crime Report Codes. 2

Page 5 of 5

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Criminal Court Analysis Table 2: Pretrial FTA by Current CJA ROR Recommendation Scheme N=7,104 1 Current CJA ROR Recommendation RECOMMENDED (Low Risk)

N 2292

% 32.3

N 394

% 17.2

QUALIFIED (Moderate Risk)

1287

18.1

336

26.1

NOT RECOMMENDED (High Risk)

3525

49.6

1439

40.8

TOTAL _____________________________

7104

100.0

2169

30.5

Defendants

Due to missing data, the N for this table is less than 7,105

FTA

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Table 3: Multiple Logistic Regression Model Predicting Pretrial Failure to Appear in Criminal Court: Variables included in the Current CJA ROR Recommendation Scheme 1 N= 6,748 Logit Significance Odds Variable Coefficient Level Ratio TELEPHONE Reference Category: Yes Verified Yes -0.097 0.651 0.907 No 0.374 0.072 1.454 No Verified 0.607 0.000 1.835 Unresolved Conflict 0.165 0.452 1.179 EXPECTS AT ARRAIGNMENT

-0.326

0.000

0.722

FAMILY TIES WITHIN THE RESIDENCE Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

-0.258 -0.157 -0.059 0.291

0.245 0.481 0.655 0.259

0.773 0.855 0.943 1.337

EMPL/SCHOOL/TRAINING Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

0.631 0.996 0.816 0.952

0.000 0.000 0.000 0.000

1.879 2.707 2.261 2.591

LENGTH OF RESIDENCE Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

0.199 0.242 0.380 0.522

0.299 0.218 0.003 0.004

1.220 1.273 1.462 1.686

NYC AREA ADDRESS Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

-0.076 0.233 -0.015 -0.646

0.799 0.464 0.970 0.102

0.927 1.262 0.986 0.524

OPEN BENCH WARRANT

0.495

0.000

1.640

COMPOSITE ITEM

-0.357

0.247

0.700

CONSTANT

-0.550

Page 1 of 2

Table 3 (contd.) cutpoint=.5 PREDICTED Failure Non-Failure (Warrant) (No Warrant)

OBSERVED Non-Failure (No Warrant)

True Negative N = 4473 66.3%

False Positive N = 237 3.5%

Failure (Warrant)

False Negative N = 1803 26.7%

True Positive N = 235 3.5%

Total Correctly Classified = 69.8% RIOC= .29 ______________________________________ 1 2

Due to missing data, the N for this table is less than 7,105. COMPOSITE ITEM refers to whether the defendant had one or more verified point scale items in addition to having a verified New York City area address.

Page 2 of 2

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Criminal Court Analysis Table 4 (Model 1): Multiple Logistic Regression Analysis Predicting Pretrial Failure to Appear: The Most Predictive Criminal Court Model N=6,7391 Logit

Significance

Odds

Variable TELEPHONE Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

Coefficient

Level

Ratio

-0.191 0.183 0.560 0.123

0.376 0.381 0.000 0.582

0.826 1.201 1.751 1.136

EXPECTS AT ARRAIGNMENT

-0.262

0.000

0.770

EMPL/SCHOOL/TRAINING Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

0.489 0.710 0.636 0.838

0.002 0.000 0.000 0.000

1.631 2.033 1.888 2.311

LENGTH OF RESIDENCE Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

0.104 0.259 0.399 0.563

0.592 0.192 0.002 0.003

1.109 1.295 1.491 1.757

NYC AREA ADDRESS Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

0.316 0.604 0.372 -0.125

0.096 0.006 0.212 0.647

1.372 1.829 1.450 0.883

SEX

-0.311

0.000

0.733

AGE

-0.011

0.005

0.989

MARITAL STATUS Reference Category: Married/ Living Together Divorced/Separated/Widowed Single

0.071 0.240

0.627 0.003

1.074 1.271

ETHNICITY Reference Category: White Black Hispanic 3 Other

0.232 0.086 -0.049

0.022 0.423 0.816

1.261 1.090 0.955

Page 1 of 3

Table 4 (contd.) Logit Coefficient

Significance Level

Odds Ratio

-0.116 -0.283 -0.092 -0.093

0.133 0.004 0.650 0.291

0.890 0.753 0.912 0.911

TYPE OF FIRST RELEASE

0.988

0.000

2.685

TIME AT RISK

-0.853

0.000

0.426

PRIOR FTA

0.709

0.000

2.033

PRIOR VIOLENT FELONY CONVICTIONS

0.224

0.026

1.251

OPEN CASES

0.247

0.000

1.280

TOP ARREST CHARGE SEVERITY Reference Category: A or B Felony C Felony D Felony E Felony A Misdemeanor

0.159 0.264 0.457 0.656

0.193 0.006 0.001 0.000

1.172 1.303 1.579 1.927

0.278

0.215

1.320

0.067 0.440 -0.619 -0.857 -0.383

0.440 0.000 0.000 0.006 0.201

1.069 1.553 0.539 0.425 0.682

0.119

0.343

1.126

CONSTANT

-1.45

Variable BOROUGH OF ARREST Reference Category: Brooklyn Manhattan Queens Staten Island Bronx

B or U Misdemeanor/Other

TOP ARREST CHARGE TYPE Reference Category: Drug Violent Property Weapon Gambling DWI (alcohol or drug) Other

Cutpoint=.5 PREDICTED Non-Failure Failure (Warrant) (No Warrant)

OBSERVED Non-Failure (No Warrant)

True Negative N = 4259 63.2%

False Positive N = 455 6.8%

Failure (Warrant)

False Negative N = 1270 18.8%

True Positive N = 755 11.2%

Total Correctly Classified = 74.4% RIOC=.46 Page 2 of 3

Table 4 (contd.) 1

Due to missing data, the N for this table is less than 7,105.

The dichotomous variables for this and other multivariate models were coded as follows: Sex: Female=0, Male=1; Time at Risk: 80 days or less=0, 81 days or more=1; Type of First Release: Bail=0, ROR=1; Open Cases: No=0, Yes=1; Prior Violent

Felony Convictions: No=0, Yes=1; and Prior FTA: No=0, Yes=1. OTHER includes Asian, American Indian, and others. 4 OTHER includes Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 5 The OTHER category consists of all offenses not included in the categories of DRUG OFFENSES, VIOLENT CRIMES, PROPERTY CRIMES, GAMBLING, DWI, or WEAPON but which are included in the FBI Uniform Crime Report Codes. 3

Page 3 of 3

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Criminal Court Analysis Table 5 (Model 2): Multiple Logistic Regression Analysis Predicting Pretrial Failure to Appear: The Re-Estimated Criminal Court Model Used To Create The New Point Scale 1 N=6,826 Logit Significance Variable Coefficient Level

Odds Ratio

TELEPHONE Yes Yes Verified No No Verified Unresolved Conflict

-0.385 -0.146 0.076 0.426 0.028

0.000 0.223 0.453 0.001 0.838

0.680 0.865 1.079 1.532 1.029

EXPECTS AT ARRAIGNMENT

-0.285

0.000

0.752

EMPL/SCHOOL/TRAINING Yes Yes Verified No No Verified Unresolved Conflict

-0.072 -0.597 0.245 0.119 0.305

0.417 0.000 0.001 0.151 0.008

0.931 0.551 1.277 1.126 1.357

LENGTH OF RESIDENCE Yes Yes Verified No No Verified Unresolved Conflict

-0.140 -0.249 -0.005 0.119 0.275

0.204 0.012 0.969 0.303 0.052

0.869 0.780 1.000 1.127 1.316

NYC AREA ADDRESS Yes Yes Verified No No Verified Unresolved Conflict

0.045 -0.236 0.355 0.210 -0.375

0.697 0.062 0.012 0.365 0.060

1.046 0.791 1.426 1.234 0.687

PRIOR FTA

0.685

0.000

1.983

PRIOR VIOLENT FELONY CONVICTIONS

0.204

0.035

1.226

OPEN CASES

0.247

0.000

1.280

TOP ARREST CHARGE SEVERITY A or B Felony C Felony D Felony E Felony A Misdemeanor B or U Misdemeanor/Other2

-0.280 -0.127 -0.061 0.076 0.333 0.059

0.000 0.158 0.349 0.420 0.000 0.730

0.756 0.881 0.941 1.079 1.395 1.060 Page 1 of 2

Table 5 (contd.) Variable TOP ARREST CHARGE TYPE Violent Property Drug Weapon Gambling DWI (alcohol or drug) Other3 CONSTANT

Logit Coefficient

Significance Level

Odds Ratio

0.292 0.742 0.314 -0.428 -0.796 -0.471 0.347

0.001 0.000 0.000 0.003 0.002 0.052 0.000

1.339 2.100 1.369 0.652 0.451 0.625 1.415

-1.229

Cutpoint=.5 PREDICTED Failure Non-Failure (Warrant) (No Warrant)

OBSERVED Non-Failure (No Warrant)

True Negative N = 4353 63.8%

False Positive N = 414 6.1%

Failure (Warrant)

False Negative N = 1487 21.8%

True Positive N = 572 8.4%

Total Correctly Classified = 72.2% RIOC=.40 ______________________________________ 1

Due to missing data, the N for this table is less than 7,105. OTHER includes Violations, Infractions, ad charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 3 The OTHER category consists of all offenses not included in the categories of DRUG OFFENSES, VIOLENT CRIMES, PROPERTY CRIMES, GAMBLING, DWI or WEAPON but which are included in the FBI Uniform Crime Report Codes. 2

Page 2 of 2

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Criminal Court Analysis 1

Table 6: Points Derived from Model 2 Variable Logit Coefficient COMMUNITY-TIE ITEMS TELEPHONE Yes -0.3853 Yes Verified -0.1455 No 0.0762 No Verified 0.4264 Unresolved Conflict 0.0281

Points

-3 0 0 3 0

EXPECTS AT ARRAIGNMENT Yes No

-0.2849 0.2849

-2 2

EMPL/SCHOOL/TRAINING Yes Yes Verified No No Verified Unresolved Conflict

-0.0715 -0.5970 0.2446 0.1187 0.3052

0 -4 2 0 2

LENGTH OF RESIDENCE Yes Yes Verified No No Verified Unresolved Conflict

-0.1404 -0.2491 -0.0045 0.1193 0.2747

0 -2 0 0 2

NYC AREA ADDRESS Yes Yes Verified No No Verified Unresolved Conflict

0.0445 -0.235 0.3550 0.2104 -0.3748

0 0 2 0 0

CRIMINAL HISTORY PRIOR FTA Yes No

0.6846 -0.6846

5 -5

PRIOR VIOLENT FELONY CONVICTIONS Yes No

0.2035 -0.2035

1 -1

OPEN CASES Yes No

0.2470 -0.2470

2 -2

Page 1 of 2

Table 6 (contd.) Variable TOP ARREST CHARGE SEVERITY A or B Felony C Felony D Felony E Felony A Misdemeanor B,U Misdemeanors/Other

TOP ARREST CHARGE TYPE Violent Property Drug Weapon Gambling DWI (alcohol or drug) 3 Other

Logit Coefficient

Points

-0.2796 -0.1270 -0.0606 0.0759 0.3328

-2 0 0 0 2

0.0585

0.2920 0.7418 0.3137 -0.4278 -0.7962 -0.4706 0.3471

2 5 2 -3 -5 -3 2

__________________________________ For purposes of standardization, the significant coefficients were divided by .15 and were then rounded to the nearest whole number. 2 OTHER includes Unclassified Misdemeanors, Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 3 The OTHER category consists of all offenses not included in the categories of DRUG OFFENSES, VIOLENT CRIMES, PROPERTY CRIMES, GAMBLING, DWI or WEAPON but which are included in the FBI Uniform Crime Report Codes. 1

Page 2 of 2

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Criminal Court Analysis Table 7: Criminal Court Alternative Risk-Classification Schemes 1 N=6,826 Alternative RiskTotal Defendants FTA N % N % Classification Scheme Points Scored Scheme 1 Group I (Low Risk) -3 or less 3489 51.1 585 16.8 Group II (High Risk) -2 or more 3337 48.9 1474 44.2 Scheme 2 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk)

-7 or less -6 to 1 2 or more

2217 2375 2234

32.5 34.8 32.7

264 726 1069

11.9 30.6 47.9

Scheme 3 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk) Group IV (Very High Risk)

-9 or less -8 to -3 -2 to 4 5 or more

1517 1972 1671 1666

22.2 28.9 24.5 24.4

143 442 624 850

9.4 22.4 37.3 51.0

Scheme 4 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk)

-3 or less -2 to 4 5 or more

3489 1671 1666

51.1 24.5 24.4

585 624 850

16.8 37.3 51.0

_____________________________ 1

Due to missing data, the N for this table is less than 7,105

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Criminal Court Analysis Table 8: A Comparison of the Current CJA ROR Recommendation Scheme with the Alternative Risk-Classification Schemes Suggested for Defendants in Criminal Court: The Difference in the Number and FTA Rate of Low-, Moderate-, and High-Risk Defendants N=6,8261

Alternative Risk-Classification Scheme Scheme 1 Group I (Low Risk) Group II (High Risk) Scheme 2 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk) Scheme 3 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk and Very High Risk) Scheme 4 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk)

Difference in Total Number of Defendants Classified Percentage N Point Difference

Difference in Total Number of Defendants with FTA Percentage N Point Difference

+1197 -188

+18.8 -0.7

+191 +35

-0.4 +3.4

-75 +1088 -1291

+0.2 +16.7 -16.9

-130 +390 -370

-5.3 +4.5 +7.1

-775 +685 -188

-10.1 +10.8 -0.7

-251 +106 +35

-7.8 -3.7 +3.4

+1197 +384 -1859

+18.8 +6.4 -25.2

+191 +288 -589

-0.4 +11.2 +10.2

Due to missing data the N for this table is less than 7,105.

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Supreme Court Analysis Table 9: Characteristics of Defendants Released Pretrial in Supreme Court 1 N=1,674 Defendant Characteristics N % SOCIO-DEMOGRAPHIC ATTRIBUTES Sex Male 1509 90.5 9.5 Female 159 Total 1668 100.0 Ethnicity Black Hispanic White 2 Other Total

831 653 142 48 1674

49.6 39.0 8.5 2.9 100.0

Age at Arrest 18 and under 19 - 20 years 21 - 24 years 25 - 29 years 30 - 34 years 35 - 39 years 40 - 78 years Total

265 209 347 312 242 138 159 1672

15.8 12.5 20.8 18.7 14.5 8.3 9.5 100.1

Marital Status Single Married/Living Together Divorced/Separated/Widowed Total

1301 303 70 1674

77.7 18.1 4.2 100.0

Education Less than High School High School Graduate GED Some college 4 or more years of college Total

917 490 41 132 27 1607

57.1 30.5 2.6 8.2 1.7 100.1

Employment Full-time Part-time Odd Jobs Unemployed Total

507 110 45 942 1604

31.6 6.9 2.8 58.7 100.0

Median 25.0 years

Page 1 of 5

Table 9 (contd.) Defendant Characteristics Average Take-home Pay Unemployed $1 - $165 $166 - $240 $241 - $340 $341 or more Total

942 158 152 135 111 1498

62.9 10.6 10.1 9.0 7.4 100.0

CASE-PROCESSING CHARACTERISTICS Type of First Release in Criminal Court ROR Bail Held Total

879 298 497 1674

52.5 17.8 29.7 100.0

Type of Release at Criminal Court Final Disposition ROR Bail Held Total

875 315 469 1659

52.7 19.0 28.3 100.0

Type of First Release in Supreme Court ROR Bail Total

1090 584 1674

65.1 34.9 100.0

FTA in Supreme Court Yes No Total

554 1120 1674

33.1 66.9 100.0

Returned on First Non-Stayed Warrant in Supreme Court Yes No Total

465 89 554

83.9 16.1 100.0

96 1108 250 201 1655

5.8 67.0 15.1 12.1 100.0

Median $203, if employed

Time from Arrest to Supreme Court Arraignment 1-15 days 16-90 days 91-150 days 151-846 days Total Median 51 days

Page 2 of 5

Table 9 (contd.) Defendant Characteristics COMMUNITY TIES ITEMS NYC Area Address Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

842 634 76 17 72 1641

51.3 38.6 4.6 1.0 4.4 100.0

Verified Length of Residence of at least 18 Months Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

575 489 351 117 116 1648

34.9 29.7 21.3 7.1 7.0 100.0

Verified Family Ties Within the Residence Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

511 528 421 114 74 1648

31.0 32.0 25.5 6.9 4.5 100.0

Expects Someone at Arraignment Yes No Total

823 823 1646

50.0 50.0 100.0

Verified Telephone Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

238 525 661 127 100 1651

14.4 31.8 40.0 7.7 6.1 100.0

Verified Full Time Employment/ School/Training Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

378 317 612 262 78 1647

23.0 19.2 37.2 15.9 4.7 100.0

Composite Item3 Yes No Total

632 1012 1644

38.4 61.6 100.0 Page 3 of 5

Table 9 (contd.) Defendant Characteristics CRIMINAL HISTORY First Arrest Yes No Total

677 996 1673

40.5 59.5 100.0

Prior Non-Violent Felony Convictions Yes No Total

355 1318 1673

21.2 78.8 100.0

Prior Violent Felony Convictions Yes No Total

173 1500 1673

10.3 89.7 100.0

Prior Misdemeanor Convictions Yes No Total

530 1143 1673

31.7 68.3 100.0

Open Cases Yes No Total

696 969 1665

41.8 58.2 100.0

Type of Warrant Attached to Rap Sheet Bench Warrant No Bench Warrant Total

246 1400 1646

14.9 85.1 100.0

Prior FTA Yes No Total

599 1074 1673

35.8 64.2 100.0

Criminal Court FTA Yes No Total

120 1554 1674

7.2 92.8 100.0

TOP ARREST CHARGE SEVERITY A Felony B Felony C Felony D Felony E Felony A Misdemeanor B Misdemeanor Other 4 Total

100 859 216 387 54 31 2 25 1674

6.0 51.3 12.9 23.1 3.2 1.9 0.1 1.5 100.0 Page 4 of 5

Table 9 (contd.) Defendant Characteristics 5 TOP ARREST CHARGE TYPE Violent Property Drug Weapon Gambling DWI (alcohol or drugs) Other Total

315 215 902 166 5 21 50 1674

18.8 12.8 53.9 9.9 0.3 1.3 3.0 100.0

TOP SUPREME COURT ARRAIGNMENT CHARGE SEVERITY A Felony B Felony C Felony D Felony E Felony A Misdemeanor B Misdemeanor Other 4 Total

85 761 217 439 63 18 1 24 1608

5.3 47.3 13.5 27.3 3.9 1.1 0.1 1.5 100.0

TOP SUPREME COURT ARRAIGNMENT CHARGE TYPE Violent Property Drug Weapon Gambling DWI (alcohol or drugs) Other Total

307 209 858 160 5 22 41 1602

19.2 13.0 53.6 10.0 0.3 1.4 2.6 99.9

Due to missing data, the N for some variables is less than 1,674. OTHER includes Asian, American Indian, and others. 3 COMPOSITE ITEM refers to whether the defendant had one or more verified point scale items in addition to having a verified New York City address. 4 OTHER includes Unclassified Misdemeanors, Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 5 VIOLENT CRIMES include: negligent murder, non-negligent murder, forcible rape, robbery, aggravated assault, kidnapping. PROPERTY CRIMES include: burglary, larceny-theft, forgery & counterfeiting, stolen property, possession of burglary tools. DRUG OFFENSES include: A) Controlled Substances Sale/Manufacture; opium, cocaine, or derivatives, marijuana, synthetic narcotics, other dangerous drugs, and B) Use/Possession; opium, cocaine, or derivatives, marijuana, synthetic narcotics, other dangerous drugs. Dangerous weapons comprise the WEAPON category. The GAMBLING category consists of bookmaking, numbers, lottery, and other activities. The DWI category refers to driving while under the influence of alcohol or drugs. The OTHER category consists of all other offenses not included in the aforementioned categories but which are included in the FBI Uniform Crime Report Codes. 2

Page 5 of 5

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Supreme Court Analysis Table 10 (Model 3): Multiple Logistic Regression Analysis Predicting Pretrial FTA among Supreme Court Defendants 1 N=1,618 Logit Significance Variable Coefficient Level TELEPHONE Reference Category: Yes Verified Yes -0.016 0.955 No 0.351 0.159 No Verified 0.448 0.052 Unresolved Conflict 0.157 0.572 EMPL/SCHOOL/TRAINING Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

Odds Ratio

0.985 1.421 1.566 1.170

0.312 0.515 0.556 0.515

0.252 0.055 0.008 0.086

1.366 1.673 1.744 1.674

BOROUGH OF ARREST Reference Category: Brooklyn Manhattan Queens Bronx

-0.321 -0.447 -0.623

0.034 0.012 0.001

0.725 0.640 0.536

TIME FROM ARREST TO SUPREME COURT ARR. Reference Category: 1 to 15 days 16 to 90 days 91 to 150 days 151 to 846 days

1.849 2.695 3.322

0.000 0.000 0.000

6.352 14.802 27.704

PRIOR FTA

0.499

0.000

1.647

TOP AMENDED CHARGE TYPE AT CRIMINAL COURT FINAL DISPOSITION Reference Category: Drug Violent Property Weapon

-0.426 -0.337 -0.816

0.008 0.061 0.000

0.653 0.714 0.442

-0.848

0.011

0.428

CONSTANT

-1.369

Other

Page 1 of 2

Table 10 (contd.) Cutpoint=.5 PREDICTED Failure Non-Failure (Warrant) (No Warrant)

OBSERVED Non-Failure (No Warrant)

True Negative N = 997 61.6%

False Positive N = 95 5.9%

Failure (Warrant)

False Negative N = 351 21.7%

True Positive N = 175 10.8%

Total Correctly Classified = 72.4% RIOC=.48 ______________________________________ 1

Due to missing data, the N for this table is less than 1,674. Due to an insufficient number of defendants, the borough of Staten Island was excluded from this equation. 3 Due to an insufficient number of defendants, the GAMBLING category has been included in the OTHER category. The OTHER category consists of all offenses not included in the categories of DRUG OFFENSES, VIOLENT CRIMES, PROPERTY CRIMES, or WEAPON but which are included in the FBI Uniform Crime Report codes. 2

Page 2 of 2

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Combined-Court Analysis Table 11: Characteristics of Defendants Released Pretrial Regardless of Court of Disposition N=7,5951 Defendant Characteristics N SOCIO-DEMOGRAPHIC ATTRIBUTES Sex Male 6641 Female 932 Total 7573

87.7 12.3 100.0

Ethnicity Black Hispanic White 2 Other Total

3792 2649 927 227 7595

49.9 34.9 12.2 3.0 100.0

Age at Arrest 18 and under 19 - 20 years 21 - 24 years 25 - 29 years 30 - 34 years 35 - 39 years 40 - 78 years Total

962 732 1385 1574 1282 724 930 7589

12.7 9.6 18.3 20.7 16.9 9.5 12.3 100.0

Marital Status Single Married/Living Together Divorced/Separated/Widowed Total

5486 1688 402 7576

72.4 22.3 5.3 100.0

Education Less than High School High School Graduate GED Some college 4 or more years of college Total

3826 2299 192 725 233 7275

52.6 31.6 2.6 10.0 3.2 100.0

Employment Full-time Part-time Odd Jobs Unemployed Total

2681 429 164 3978 7252

36.9 5.9 2.3 54.9 100.0

Median: 27.0 years

Page 1 of 4

Table 11 (contd.) Defendant Characteristics Average Take-home Pay Unemployed $1 - $165 $166 - $240 $241 - $340 $341 or more Total

3978 646 727 755 721 6827

58.2 9.5 10.6 11.1 10.6 100.0

CASE-PROCESSING CHARACTERISTICS Borough of Arrest Brooklyn Manhattan Queens Staten Istand Bronx Total

2021 2608 1244 176 1546 7595

26.6 34.3 16.4 2.3 20.4 100.0

Type of Court Criminal Court Supreme Court Total

5820 1771 7591

76.7 23.3 100.0

Type of First Release ROR Bail Total

6536 1055 7591

86.1 13.9 100.0

Time at Risk 80 days or less 81 or more days Total

4779 2799 7578

63.1 36.9 100.0

FTA Regardless of Court of Disposition Yes No Total

2676 4916 7592

35.2 64.8 100.0

COMMUNITY TIES ITEMS Verified NYC Area Address Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

3727 2915 421 88 295 7446

50.1 39.1 5.6 1.2 4.0 100.1

Verified Length of Residence of at least 18 Months Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

2612 2248 1618 533 465 7476

35.0 30.1 21.6 7.1 6.2 100.0

Median: $250, if employed

Page 2 of 4

Table 11 (contd.) Defendant Characteristics Verified Family Ties Within the Residence Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

2122 2429 2063 543 319 7476

28.3 32.5 27.6 7.3 4.3 100.0

Expects Someone at Arraignment Yes No Total

3345 4114 7459

44.8 55.2 100.0

Verified Telephone Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

1180 2415 2897 540 450 7482

15.8 32.3 38.7 7.2 6.0 100.0

Verified Full Time Employment/ School/Training Yes, Unverified Yes Verified No, Unverified No Verified Unresolved Conflict Total

1800 1573 2647 1118 334 7472

24.1 21.0 35.4 15.0 4.5 100.0

Composite Item3 Yes No Total

2894 4549 7443

38.9 61.1 100.0

CRIMINAL HISTORY First Arrest Yes No Total

3268 4268 7536

43.4 56.6 100.0

Prior Violent Felony Convictions Yes No Total

695 6841 7536

9.2 90.8 100.0

Prior Non-Violent Felony Convictions Yes No Total

1268 6268 7536

16.8 83.2 100.0 Page 3 of 4

Table 11 (contd.) Defendant Characteristics Prior Misdemeanor Convictions Yes No Total

2371 5165 7536

31.5 68.5 100.0

Open Cases Yes No Total

2883 4616 7499

38.4 61.6 100.0

Type of Warrant Attached to Rap Sheet Bench Warrant No Bench Warrant Total

1047 6382 7429

14.1 85.9 100.0

Prior FTA Yes No Total

2494 5042 7536

33.1 66.9 100.0

TOP ARREST CHARGE SEVERITY A Felony B Felony C Felony D Felony E Felony A Misdemeanor B Misdemeanor Other 4 Total

159 1890 813 2134 630 1510 85 374 7595

2.1 24.9 10.7 28.1 8.3 19.9 1.1 4.9 100.0

TOP ARREST CHARGE TYPE5 Violent Property Drug Weapon Gambling DWI (alcohol or drugs) Other Total

2185 1555 2372 417 107 294 665 7595

28.8 20.5 31.2 5.5 1.4 3.8 8.8 100.0

Due to missing data, the N for some variables is less than 7,595. OTHER includes Asian, American Indian, and others. 3 COMPOSITE ITEM refers to whether the defendant had one or more verified point scale items in addition to having a verified New York City address. 4 OTHER includes Unclassified Misdemeanors, Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 5 VIOLENT CRIMES include: negligent murder, non-negligent murder, forcible rape, robbery, aggravated assault, kidnapping. PROPERTY CRIMES include: burglary, larceny-theft, forgery & counterfeiting, stolen property, possession of burglary tools. DRUG OFFENSES include: A) Controlled Substances Sale/Manufacture; opium, cocaine, or derivatives, marijuana, synthetic narcotics, other dangerous drugs, and B) Use/Possession; opium, cocaine, or derivatives, marijuana, synthetic narcotics, other dangerous drugs. Dangerous weapons comprise the WEAPON category. The GAMBLING category consists of bookmaking, numbers, lottery, and other activities. The DWI category refers to driving while under the influence of alcohol or drugs. The OTHER category consists of all other offenses not included in the aforementioned categories but which are included in the FBI Uniform Crime Report Codes. 2

Page 4 of 4

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Combined-Court Analysis Table 12 (Model 4): Multiple Logistic Regression Analysis Predicting Pretrial FTA Regardless of Court of Disposition: The Most Predictive Model 1 N=7,256 Logit Coefficient

Significance Level

Odds Ratio

TELEPHONE Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

0.027 0.331 0.566 0.220

0.891 0.077 0.000 0.275

1.027 1.392 1.761 1.245

EXPECTS AT ARRAIGNMENT

-0.224

0.002

0.799

EMPL/SCHOOL/TRAINING Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

0.453 0.687 0.582 0.785

0.001 0.000 0.000 0.000

1.573 1.988 1.789 2.191

LENGTH OF RESIDENCE Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

-0.038 0.135 0.376 0.373

0.830 0.458 0.001 0.027

0.963 1.144 1.456 1.452

NYC AREA ADDRESS Reference Category: Yes Verified Yes No No Verified Unresolved Conflict

0.201 0.399 0.289 -0.159

0.248 0.050 0.288 0.519

1.223 1.491 1.335 0.853

BOROUGH OF ARREST Reference Category: Brooklyn Manhattan Queens Staten Island Bronx

-0.157 -0.346 -0.164 -0.310

0.027 0.000 0.397 0.000

0.854 0.708 0.849 0.733

SEX

-0.190

0.021

0.827

AGE

-0.012

0.000

0.988

ETHNICITY Reference Category: White Black Hispanic Other 2

0.247 0.124 -0.113

0.009 0.215 0.565

1.280 1.131 0.893

Variable

Page 1 of 2

Variable TYPE OF FIRST RELEASE

Table 12 (contd.) Logit Coefficient 0.350

Significance Level 0.000

Odds Ratio 1.419

TIME AT RISK 3

-0.870

0.000

0.419

PRIOR FTA

0.672

0.000

1.957

OPEN CASES

0.231

0.000

1.260

TYPE OF COURT4

0.331

0.000

1.392

TOP ARREST CHARGE TYPE Reference Category: Drug Violent Property Weapon Gambling DWI (alcohol or drug)

-0.255 0.159 -0.547 -1.122 -0.985

0.001 0.084 0.000 0.000 0.005

0.775 1.172 0.579 0.326 0.374

-0.147

0.220

0.863

TOP ARREST CHARGE SEVERITY Reference Category: D Felony A Felony B Felony C Felony E Felony A Misdemeanor B Misdemeanor

-0.067 0.011 0.037 0.129 0.207 -0.345

0.743 0.902 0.712 0.264 0.016 0.211

0.936 1.011 1.038 1.137 1.230 0.708

U Misdemeanor/Other 6

0.217

0.451

1.242

CONSTANT

-0.739

Other

Cutpoint=.5 PREDICTED Non-Failure Failure (No Warrant) (Warrant)

OBSERVED Non-Failure (No Warrant)

True Negative N = 4061 56.0%

False Positive N = 665 9.2%

Failure (Warrant)

False Negative N = 1463 20.2%

True Positive N = 1067 14.7%

Total Correctly Classified = 70.7% RIOC=.41

______________________________________ 1 Due to missing data, the N for this table is less than 7,595. 2 OTHER includes Asian, American Indian, and others. 3 TIME AT RISK measures the number of days a defendant was at risk from Criminal Court Arraignment to the first FTA or to the disposition of the case. 4 The type of court variable was coded as follows: Criminal Court=0, Supreme Court=1. 5 The OTHER category consists of all offenses not included in the categories of DRUG OFFENSES, VIOLENT CRIMES, PROPERTY CRIMES, DWI, or WEAPON but which are included in the FBI Uniform Crime Report Codes. 6 OTHER includes Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes).

Page 2 of 2

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Combined-Court Analysis Table 13 (Model 5): Multiple Logistic Regression Analysis Predicting Pretrial FTA Regardless of Court of Disposition: The Re-Estimated Model Used to Create the New Point Scale 1 N=7,294 Logit Coefficient

Significance Level

Odds Ratio

TELEPHONE Yes Yes Verified No No Verified Unresolved Conflict

-0.275 -0.196 0.124 0.364 -0.018

0.006 0.071 0.183 0.001 0.890

0.760 0.822 1.132 1.439 0.982

EXPECTS SOMEONE AT ARRAIGNMENT

-0.248

0.000

0.781

EMPL/SCHOOL/TRAINING Yes Yes Verified No No Verified Unresolved Conflict

-0.061 -0.564 0.241 0.104 0.279

0.453 0.000 0.003 0.167 0.009

0.941 0.569 1.273 1.110 1.322

LENGTH OF RESIDENCE Yes Yes Verified No No Verified Unresolved Conflict

-0.194 -0.165 -0.037 0.207 0.189

0.057 0.070 0.727 0.053 0.148

0.824 0.848 0.964 1.230 1.208

NYC AREA ADDRESS Yes Yes Verified No No Verified Unresolved Conflict

0.042 -0.171 0.262 0.107 -0.240

0.696 0.143 0.049 0.621 0.194

1.042 0.843 1.300 1.113 0.787

PRIOR FTA

0.674

0.000

1.961

OPEN CASES

0.248

0.000

1.282

TOP ARREST CHARGE TYPE Violent Property Drug Weapon Gambling DWI (alcohol or drug) Other 2

0.206 0.676 0.525 -0.128 -0.858 -0.719

0.017 0.000 0.000 0.331 0.001 0.012

1.229 1.965 1.691 0.880 0.424 0.487

0.298

0.002

1.347

Variable

Page 1 of 2

Table 13 (contd.) Logit Coefficient

Significance Level

Odds Ratio

-0.108 0.048 0.020 -0.047 0.038 0.149 -0.300

0.528 0.552 0.832 0.536 0.710 0.047 0.198

0.898 1.049 1.021 0.954 1.039 1.160 0.741

U Misdemeanor/Other 3

0.200

0.408

1.221

CONSTANT

-1.114

Variable TOP ARREST CHARGE SEVERITY A Felony B Felony C Felony D Felony E Felony A Misdemeanor B Misdemeanor

Cutpoint=.5 PREDICTED Non-Failure Failure (Warrant) (No Warrant)

OBSERVED Non-Failure (No Warrant)

True Negative N = 4104 56.3%

False Positive N = 645 8.8%

Failure (Warrant)

False Negative N = 1680 23.0%

True Positive N = 865 11.9%

Total Correctly Classified = 68.1% RIOC=.34 ______________________________________ 1

Due to missing data, the N for this table is less than 7,595. The OTHER category consists of all offenses not included in the categories of DRUG OFFENSES, VIOLENT CRIMES, PROPERTY CRIMES, GAMBLING, DWI, or WEAPON but which are included in the FBI Uniform Crime Report Codes. 3 OTHER includes Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 2

Page 2 of 2

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Combined-Court Analysis 1

Table 14: Points Derived from Model 5 Variable Logit Coefficient

Points

TELEPHONE Yes Yes Verified No No Verified Unresolved Conflict

-0.2749 -0.1956 0.1243 0.3640 -0.0177

-2 0 0 2 0

EXPECTS AT ARRAIGNMENT Yes No

-0.2477 0.2477

-2 2

EMPL/SCHOOL/TRAINING Yes Yes Verified No No Verified Unresolved Conflict

-0.0605 -0.5642 0.2413 0.1042 0.2793

0 -4 2 0 2

LENGTH OF RESIDENCE Yes Yes Verified No No Verified Unresolved Conflict

-0.1941 -0.1646 -0.0370 0.2069 0.1888

0 0 0 1 0

NYC AREA ADDRESS Yes Yes Verified No No Verified Unresolved Conflict

0.0415 -0.1712 0.2624 0.1068 -0.2395

0 0 2 0 0

PRIOR FTA Yes No

0.6737 -0.6737

4 -4

OPEN CASES Yes No

0.2482 -0.2482

2 -2

TOP ARREST CHARGE SEVERITY A Felony B Felony C Felony D Felony E Felony A Misdemeanor B Misdemeanor 2 U Misdemeanor/Other

-0.1079 0.0475 0.0203 -0.0468 0.0384 0.1487 -0.2998 0.1996

0 0 0 0 0 1 0 0 Page 1 of 2

Variable TOP ARREST CHARGE TYPE Violent Property Drug Weapon Gambling DWI (alcohol or drug) 3 Other

Table 14 (contd.) Logit Coefficient 0.2059 0.6755 0.5252 -0.1275 -0.8583 -0.7187 0.2980

Points 1 4 4 0 -6 -5 2

__________________________________ For purposes of standardization, the significant coefficients were divided by .15 and were then rounded to the nearest whole number. 2 OTHER includes Unclassified Misdemeanors, Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 3 The OTHER category consists of all offenses not included in the categories of DRUG OFFENSES, VIOLENT CRIMES, PROPERTY CRIMES, GAMBLING, DWI, or WEAPON but which are included in the FBI Uniform Crime Report Codes. 1

Page 2 of 2

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Combined-Court Analysis Table 15: Pretrial FTA by Current CJA ROR Recommendation Scheme N=7,590 1 Current CJA ROR Recommendation RECOMMENDED (Low Risk)

N 2394

% 31.5

N 515

% 21.5

QUALIFIED (Moderate Risk)

1363

18.0

426

31.3

NOT RECOMMENDED (High Risk)

3833

50.5

1735

45.3

TOTAL _____________________________

7590

100.0

2676

35.2

Defendants

Due to missing data, the N for this table is less than 7,595

FTA

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Combined-Court Analysis Table 16: Alternative Risk-Classification Schemes for Defendants Regardless of Court of Disposition N=7,2941 Alternative Risk-Classification Scheme Scheme 1 Group I (Low Risk) Group II (High Risk)

Points Scored

Total Defendants N %

0 or less 1 to 17

3823 3471

Scheme 2 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk)

-3 or less -2 to 3 4 to 17

Scheme 3 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk) Group IV (Very High Risk) Scheme 4 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk)

52.4 47.6

829 1716

21.7 49.4

2578 2204 2512

35.3 30.2 34.4

452 787 1306

17.5 35.7 52.0

-5 or less -4 to 0 1 to 6 7 to 17

1747 2076 1765 1706

24.0 28.5 24.2 23.4

252 577 772 944

14.4 27.8 43.7 55.3

0 or less 1 to 6 7 to 17

3823 1765 1706

52.4 24.2 23.4

829 772 944

21.7 43.7 55.3

_____________________________ 1

FTA N

Due to missing data, the N for this table is less than 7,595.

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Combined-Court Analysis Table 17: A Comparison of the Current CJA ROR Recommendation Scheme with the Alternative Risk-Classification Schemes Suggested for Defendants Regardless of Court of Disposition: The Difference in the Number and FTA Rate of Low-, Moderate-, and High-Risk Defendants 1 N=7,294

Difference in Total Number of Defendants Classified Percentage N Point Difference +1429 -362

+20.9 -2.9

+314 -19

+0.2 +4.1

+184 +841 -1321

+3.8 +12.2 -16.1

-63 +361 -429

-4.0 +4.4 +6.7

-647 +713 -362

-7.5 +10.5 -2.9

-263 +151 -19

-7.1 -3.5 +4.1

+1429 +402 -2127

+20.9 +6.2 -27.1

+314 +346 -791

+0.2 +12.4 +10.0

_____________________________ 1

Difference in Total Number of Defendants with FTA Percentage N Point Difference

Due to missing data the N for tthis table is less than 7,595.

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Combined-Court Analysis Table 18: A Comparison of the Current CJA ROR Recommendation Scheme with the Suggested Alternative Risk-Classification Schemes Difference in Total Number Difference in Total Number of Defendants Classified of Defendants with FTA Alternative Percentage Percentage Risk-Classification Scheme N Point Difference N Point Difference 1

Criminal Court Scheme 4 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk)

+1197 +384 -1859

+18.8 +6.4 -25.2

+191 +288 -589

-0.4 +11.2 +10.2

+1429 +402 -2127

+20.9 +6.2 -27.1

+314 +346 -791

+0.2 +12.4 +10.0

Criminal and Supreme Court Scheme 4 Group I (Low Risk) Group II (Moderate Risk) Group III (High Risk)

_____________________________ Due to missing data the N for this scheme is less than 7,105.

1 2

Due to missing data the N for this scheme is less than 7,595.

APPENDICIES

Appendix A Relative Improvement Over Chance Relative improvement over chance (ROIC) is a measure of predictive efficiency. Introduced by Loeber and Dishion (1983), this statistic indicates how well a prediction model performs relative to its expected performance and its best possible performance, allowed by the selection ratio and base rate. Observed performance, expected performance, and best possible performance are all a function of the base rate and the selection ratio. The base rate is the number of defendants who are observed as failing to appear in the population. Using the most predictive Criminal Court model as an example (Table 4, Model 1), this number was 2,025 (or 30.0%). The selection ratio is the number of defendants the model predicts as failing to appear. In this case, when applying a .5 cutpoint, the model predicted that 1,210 (or 18.0%) defendants would fail to appear. The value of RIOC can range from 0 (or 0%), indicating no improvement over chance, to 1 (or 100%), indicating total improvement.

Efficiency increases as the of RIOC

approaches 1 (or 100%). The formula used to calculate the RIOC statistic is presented below: IOC RIOC = ---------------------- X 100 %MC - % RC Where RIOC is the Relative Improvement Over Chance, MC is the maximum correct (best possible), RC is the random correct (expected by chance alone), and IOC is the improvement over chance.

A-1

In order to determine how many defendants were expected as true positives and true negatives by chance alone (RC), the following calculation was necessary: Expected TP = ((Base Rate / Total N) X (Selection Ratio / Total N)) X 100 Expected TN = ((Total N observed as not failing / Total N) X (Total N predicted as not failing / Total N)) X 100 RC = Expected TP + Expected TN For Model 1, when applying a .5 cutpoint, the percentage of true positives expected by chance alone was 5.4 percent, derived from the formula: TP = (2025/6739 X 1210/6739) X 100. The percentage of true negatives expected by chance alone was 57.4 percent, derived from the formula: TN = (4714/6739 X 5529/6739) X 100. Therefore, by chance alone, one would expect that 62.8 percent (5.4% + 57.4%) of defendants would be classified correctly (RC thus equals 62.8%). In order to calculate the maximum percentage of defendants who would be properly classified (MC), a new two-by-two table was formulated. As a general rule, when the number of defendants who are observed as failing to appear (base rate) is greater than the number who are predicted as failing (selection ratio), the true negative cell should be used to begin the calculation of MC. However, when the number of defendants who are observed as failing to appear (base rate) is less than the number of defendants who are predicted as failing (selection ratio), the true positive cell should be used to begin the calculation of MC.

A-2

For Model 1, 2,025 defendants were observed as failing, while 1,210 were predicted as failing. Thus, the base rate outnumbered the selection ratio. For this reason, the true negative cell was used to begin the calculation. A look at the 2-by-2 table derived from the logistic regression printout revealed that a total of 4,714 defendants were observed as not failing to appear. Thus, the maximum number of defendants who could be correctly classified as not failing was 4,714. Thus, the format of the table was as follows:

Predicted Behavior Failure (Warrant)

Failure (Warrant) TP ?

Non-Failure (No Warrant)

FN ? N=2,025 30.0%

Observed Behavior Non-Failure (No Warrant) FP ? TN N=4,714 70.0% N=4,714 70.0%

N=1,210 18.0% N=5,529 82.0% N=6,739 100.0%

Notice that the outlying percentages were fixed, as were the total numbers. Thus, the frequencies and the percentages in the remaining cells were also fixed. Hence, the full MC 2-by-2 table was:

Predicted Behavior Failure (Warrant) Non-Failure (No Warrant)

Failure (Warrant) TP N=1,210 18.0% FN N=815 12.0% N=2,025 30.0%

Observed Behavior Non-Failure (No Warrant) FP N=0 0% TN N=4,714 70.0% N=4,714 70.0%

A-3

N=1,210 18.0% N=5,529 82.0% N=6,739 100.0%

Therefore, the maximum number of cases that could be classified correctly was 88.0 percent (18.0% TP + 70.0% TN). The IOC statistic is simply the difference between the observed percentage of defendants classified correctly and the number of defendants who could be expected to be correctly classified by chance alone (RC). For Model 1, the IOC is 11.6 percentage points (74.4% - 62.8%). Using all these numbers, the RIOC formula yields the following: 11.6 RIOC = --------------- X 100 88.0 - 62.8

RIOC = .46 or 46%

The modelâ&#x20AC;&#x2122;s RIOC at a .5 cutpoint is .46, indicating that the actual improvement over chance alone is slightly less than half of the maximum possible improvement.

A-4

APPENDIX B MEAN COST RATING Mean Cost Rating (MCR) is an instrument-assessment method--a measure of classification efficiency (Cuvelier and Potts, 1993).

This statistic indicates the

proportion by which an instrument improves prediction over the base rate. The MCR can range from 0 (or 0%) to 1 (or 1%). A value of 0 indicates that an instrument is totally non-predictive; predictive ability is equal to the base rate. If an instrument perfectly predicts failure/success, it will achieve a value of 1. According to Cuvelier and Potts (1993) and Fischer (1985), an MCR of .25 shows utility for classification, while an MCR of .35 or greater indicates significant improvement over the base rate. The formula used to calculate the MCR is presented below: MCR = â&#x2C6;&#x2018; Ci k Ui - 1 - â&#x2C6;&#x2018; Ui k Ci - 1 i=1

i=1

Where: i = each of the risk levels taken in succession from high to low risk k = the number of risk levels (the total number of point scores) C = the cumulative relative frequency of successes at the ith level U = the cumulative relative frequency of failures at the ith level Table B-1 displays the distribution of FTA by new point scale scores for Criminal Court defendants. This will be used to illustrate the computation of MCR (Table B-2). Table B-2 shows the performance characteristics of the newly developed point scale. In the first column are the individual point scores, followed by the number of cases, the frequency of success and failure, the proportion of cases with a particular score that

B-1

failed and did not fail, the cumulative proportion of success and failure, and the calculated MCR success and failure figures. To obtain the values reported in each cell of the MCR success column, each cell in the cumulative success column was multiplied by the cell diagonally above it in the cumulative failure column. In other words, .000 was multiplied by 0, .001 by .000, .001 by .006, and so on. Likewise, the values reported in the MCR failure column were derived by multiplying each cell in the cumulative failure column by the cell diagonally above it in the cumulative success column. In other words, .000 was multiplied by 0, .006 by .000, .008 by .001, and so on. The sum of the values in the MCR failure column was then subtracted from the sum of the values in the MCR success column to obtain the overall MCR statistic (14.937 - 14.485 = .452). Thus, the MCR was calculated as being .452 (or 45%), demonstrating significant improvement over the base rate and ability to differentiate well between groups of defendants and their likelihood of FTA.

B-2

Appendix C SAMPLE SELECTION BIAS: A FURTHER ASSESSMENT OF THE COMBINED-COURT FTA ANALYSIS The logistic regression analyses reported throughout this report were based on a sample of defendants who had their cases adjourned at Criminal Court arraignment and who were released on their own recognizance or made bail prior to the disposition of their case in either Criminal Court or Supreme Court. This may constitute a nonrandom selection process, where the differences between those released and detained may be related to factors associated with the probability of pretrial failure. In addition, there is also the possibility that nonrandom sample selection may operate to determine which cases are continued past Criminal Court arraignment. Taking these into consideration, the issue of sample selection bias was assessed, through an estimation of sequential logit models. The first model contained a number of variables that predicted whether a case would be continued or disposed at Criminal Court arraignment. Based on this model, a new variable was created that measured the probability of being disposed for each defendant. The second model focused on the predictors of pretrial release in Criminal Court and Supreme Court. Given that cases can only be released if they are continued past Criminal Court arraignment, the released/detained model controlled for the probability of case disposition at arraignment.

When using the second model, the

probability of being detained prior to case disposition was calculated and saved out as a new variable for each defendant. In the third model, the original combined-court FTA model (Table 12, Model 4) was rerun correcting for selection bias, by including the

C-1

probability variable derived from the second model. Because this variable was based upon a model that included the probability of case disposition, it was a measure that simultaneously accounted for both the probability of being disposed at Criminal Court arraignment and the probability of not being released prior to the disposition of the case in either Criminal Court or Supreme Court. To determine whether this new variable had any effect on the statistical significance of the other variables, the results of the third model were compared with the original combined-court model of FTA.

A. Logistic Regression Model Predicting Disposition at Criminal Court Arraignment Model A in Table A-1 shows the results of the logistic regression model predicting the likelihood of disposition at Criminal Court arraignment. The dependent variable was dichotomized, with defendants who had their cases disposed at arraignment coded as "1" and "0" otherwise. Theory and correlation with the dependent variable guided the selection of the independent variables. The predictive variables included a defendantâ&#x20AC;&#x2122;s age, ethnicity, borough of arrest, criminal history, top arrest charge type and severity, and community ties. With regard to criminal history, the model contained information on whether the defendant had a history of pretrial failure to appear, open cases, prior non-violent felony convictions, prior violent felony convictions, and prior misdemeanor convictions.

Included among the community-ties items were: having

family ties within the residence, having a New York City area address, and being employed, in school, or in a training program full time.

C-2

The results derived from that model indicated that all the variables were significantly related to the likelihood of having one’s case disposed at Criminal Court arraignment. Turning first to the community-ties items, defendants who were verified as not having family ties within the residence were more likely to have their cases disposed at Criminal Court arraignment than those who were verified as living with someone, controlling for the effects of the other variables. This was also the case for those coded as "unresolved conflict" on this item. Holding all other factors constant, the likelihood of having one’s case continued past Criminal Court arraignment was higher among defendants who were verified as having a New York City area address, when compared with those who did not live in the New York City area (both "no" and "no verified" categories). The odds that a case would be continued were also higher among defendants who were verified as being employed, in school, or in a job training program full time, when contrasted with those in the "no," "no verified," and "unresolved conflict" categories on this item. Older and white defendants were more likely to have their cases disposed than younger and “other” defendants, when the effects of the other variables were controlled. Turning to the influence of the borough of arrest, defendants arrested in Manhattan, Queens, Staten Island, and the Bronx were less likely to have their cases disposed than those arrested in Brooklyn. An examination of the severity of the top arrest charge indicated that defendants charged with a D felony were more likely to be disposed at Criminal Court arraignment than those charged with an A felony, all else being equal. However, the odds of case disposition were lower among defendants with D felony arrest charges than among those charged with B felonies, E felonies, A misdemeanors, B

C-3

misdemeanors, or U misdemeanors and violations, infractions, or charges outside the New York State Penal Law and Vehicle and Traffic Law ("other" offenses). The odds of case disposition were higher for persons charged with a drug offense, when compared with any other offense type. The likelihood of case disposition at Criminal Court arraignment was higher when there were no open cases and when there was no history of pretrial failure to appear. The probability of case disposition was observed as being lower for defendants with either prior violent or non-violent felony convictions.

Defendants with prior

misdemeanor convictions, in contrast, were more likely to have their cases disposed. Because the predisposition-release model and the combined-court FTA model may be conditioned by the Criminal Court arraignment outcome, the predicted probability of being disposed for each defendant was â&#x20AC;&#x153;saved outâ&#x20AC;? as a new variable.

B. Logistic Regression Model of the Release Decision Model B in Table A-1 shows the results of the logistic regression model predicting which defendants were released prior to the disposition of their case in Criminal Court or Supreme Court. The dependent variable was dichotomized, with defendants who were not released in either court coded as "1" and "0" otherwise. The selection of the independent variables was guided by theory as well as by an examination of their correlation with the dependent variable. Model B contained variables pertaining to a defendant's marital status, borough of arrest, type of court, top arrest charge type and severity, criminal history, and community ties. Only two community-ties items proved influential--having a working telephone and

C-4

being employed, in school, or in a job training program full time. With regard to criminal history, the model contained information on whether the defendant had a history of pretrial failure, prior non-violent felony convictions, prior violent felony convictions, prior misdemeanor convictions, open bench warrants, and open cases. In addition to these variables, Model B controlled for the probability of case disposition at Criminal Court arraignment, because defendants can only be released pretrial if their cases are continued beyond this stage. All of the independent variables significantly predicted the likelihood of being released prior to the disposition of their case in Criminal Court or Supreme Court. Controlling for the other variables in the model, defendants who were verified as having a telephone were less likely to be held than those who were coded as "unresolved conflict" on this item. With regard to the full-time activity variable, defendants who were verified as being employed, in school, or in a job training program full time were more likely to be released than those in the "yes," "no," or "no verified" categories on this item. Those who were married/living together were more likely to gain predisposition release than persons who were single, all else being equal. The odds of predisposition release also increased if the defendant was arrested in Brooklyn, rather than in Queens or the Bronx. Holding all other factors constant, the likelihood of gaining release was lower in Supreme Court than in Criminal Court. Defendants who were charged with a D felony had a higher probability of being released in either Criminal Court or Supreme Court than those with an A felony, B felony, or C felony arrest charge. An examination of the top charge type indicator

C-5

showed the odds of gaining release were likewise higher for persons charged with drug offenses, when compared with persons who were charged with property offenses. However, drug offenders were less likely to be released than those charged with gambling offenses, all other factors being equal. Turning to criminal history, defendants with prior FTA, open bench warrants, and open cases were less likely to gain release than defendants without this history. The odds of being released were also lower among those with prior non-violent felony convictions, prior violent felony convictions, and prior misdemeanor convictions. The final variable included in Model B was the probability of case disposition at Criminal Court arraignment, as a control for selection bias. This variable was found to be significantly related to gaining release in either court, suggesting that the selection process involved in determining which cases are continued versus disposed at Criminal Court arraignment may need to be considered when examining predisposition release. Because a defendantâ&#x20AC;&#x2122;s release status may affect the findings of the FTA model, the predicted probability of being detained for each defendant was saved out as a new variable and was added to the original FTA model. The section that follows describes the results from that analysis. C. Logistic Regression Model of FTA Model C in Table A-1 shows the results from the model that corrected for selection bias, by including the probability variable derived from Model B along with all the variables from the most predictive combined-court FTA model. Because the added variable was derived from a model that included the probability of case disposition at

C-6

Criminal Court arraignment, it was a measure that simultaneously accounted for both the probability of being disposed at that stage and the probability of not being released prior to case disposition in either Criminal Court or Supreme Court. For comparison purposes, Model D is included in the table for comparison purposes. It presents the results from the most predictive combined-court model of FTA (Table 12, Model 4), which did not account for sample selection. The findings for the two combined-court FTA models were similar, with two minor exceptions. First, the New York City area of address variable lost statistical significance, when the variable measuring the probability of disposition at Criminal Court arraignment and the probability of gaining release prior to the disposition of the case in either Criminal Court or Supreme Court was added to the model; persons who did not have a New York City area address were no longer more likely to FTA than those verified as having a New York City address. This may have resulted because the original relationship

was

weak

(logit

coefficient=.399,

affiliated

level

statistical

significance=.05). Thus, when the additional variable was included, the initially weak relationship disappeared. Second, and unlike the model that did not account for sample selection, the model with the added probability variable showed that defendants who were charged with property offenses were more likely to FTA than those charged with drug offenses, holding all other factors constant. However, this relationship was weak (logit coefficient=.184, affiliated level of statistical significance=.05).

Putting both

aside, all the other previously significant variables behaved as they did in the original combined-court FTA model, showing only small changes in the magnitude of their coefficients.

C-7

D. Conclusion Sample selection bias does not appear to be a problem in our original estimates of FTA for the combined Criminal Court and Supreme Court sample. This statement is based on findings (reported above) which indicated that there was little or no change in variable significance. Thus, it can be concluded that sample selection bias does not appear to be a problem in the analyses conducted on the combined sample of Criminal Court and Supreme Court defendants.

In other words, both the Criminal Court

arraignment outcome and the predisposition-release selection processes do not appear to cause bias in the estimates of FTA. Therefore, one can be confident that the most predictive combined-court FTA model can be used to guide the construction of a new scale that would be used to make release recommendations for both Criminal Court and Supreme Court defendants.

C-8

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Table B-1: Distribution of FTA by New Point Scale Scores N=6,8261 2

Point Score -21

N 2

% of Sample 0.0

-19

1.2

0.0

-18

0.0

-17

0.5

9.7

-16

1.3

6.6

-15

1.4

3.2

-14

197

2.9

5.1

-13

1.3

12.1

-12

234

3.4

6.8

-11

200

2.9

12.5

-10

292

4.3

11.3

-9

201

2.9

17.9

-8

370

5.4

16.8

-7

330

4.8

17.9

-6

347

5.1

25.6

-5

271

4.0

24.7

-4

458

6.7

109

23.8

-3

196

2.9

28.6

-2

401

5.9

134

33.4

-1

184

2.7

31.0

320

4.7

119

37.2

198

2.9

48.0

226

3.3

39.4

152

2.2

40.8

190

2.8

35.8

139

2.0

36.7

203

3.0

41.9

105

1.5

46.7

199

2.9

46.7

1.4

44.3

229

3.4

101

44.1

106

1.6

64.2

172

2.5

56.4

1.3

64.0

113

1.7

65.5

1.5

60.6

0.6

55.3

0.8

64.9

0.1

66.7

0.2

68.8

0.0 Median: -3.000

100.0 Mode: -4.000

21 Mean: -1.775 _________________________ 1

Due to missing data, the N for this table is less than 7,105. There were no defendants with scores of -20 or 20.

N with FTA 0

% FTA 0.0

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset

Point Score 21 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -13 -14 -15 -16 -17 -18 -19 -21 Total

Total N 1 16 6 57 38 99 113 86 172 106 229 97 199 105 203 139 190 152 226 198 320 184 401 196 458 271 347 330 370 201 292 200 234 91 197 93 91 31 1 84 2 6826

Freq Success 0 5 2 20 17 39 39 31 75 38 128 54 106 56 118 88 122 90 137 103 201 127 267 140 349 204 258 271 308 165 259 175 218 80 187 90 85 28 1 84 2 4767

Table B-2: Mean Cost Rating of FTA by New Point Scale Scores N=6,826 1 Freq Prop Prop Cumulative Cumulative Failure Success Failure Prop Success Prop Failure 1 0.000 0.000 0.000 0.000 11 0.001 0.005 0.001 0.006 4 0.000 0.002 0.001 0.008 37 0.004 0.018 0.006 0.026 21 0.004 0.010 0.009 0.036 60 0.008 0.029 0.017 0.065 74 0.008 0.036 0.026 0.101 55 0.007 0.027 0.032 0.128 97 0.016 0.047 0.048 0.175 68 0.008 0.033 0.056 0.208 101 0.027 0.049 0.083 0.257 43 0.011 0.021 0.094 0.278 93 0.022 0.045 0.116 0.323 49 0.012 0.024 0.128 0.347 85 0.025 0.041 0.153 0.388 51 0.018 0.025 0.171 0.413 68 0.026 0.033 0.197 0.446 62 0.019 0.030 0.216 0.476 89 0.029 0.043 0.244 0.519 95 0.022 0.046 0.266 0.565 119 0.042 0.058 0.308 0.623 57 0.027 0.028 0.335 0.651 134 0.056 0.065 0.391 0.716 56 0.029 0.027 0.420 0.743 109 0.073 0.053 0.493 0.796 67 0.043 0.033 0.536 0.829 89 0.054 0.043 0.590 0.872 59 0.057 0.029 0.647 0.900 62 0.065 0.030 0.712 0.931 36 0.035 0.017 0.746 0.948 33 0.054 0.016 0.801 0.964 25 0.037 0.012 0.837 0.976 16 0.046 0.008 0.883 0.984 11 0.017 0.005 0.900 0.989 10 0.039 0.005 0.939 0.994 3 0.019 0.001 0.958 0.996 6 0.018 0.003 0.976 0.999 3 0.006 0.001 0.982 1.000 0 0.000 0.000 0.982 1.000 0 0.018 0.000 1.000 1.000 0 0.000 0.000 1.000 1.000 2059 MCR:

________________ 1 Due to missing data, the N for this table is less than 7,105.

MCR Success 0.000 0.000 0.000 0.000 0.000 0.001 0.002 0.003 0.006 0.010 0.017 0.024 0.032 0.041 0.053 0.066 0.081 0.096 0.116 0.138 0.174 0.209 0.254 0.301 0.367 0.427 0.489 0.564 0.641 0.695 0.759 0.807 0.862 0.886 0.929 0.952 0.972 0.980 0.982 1.000 1.000 14.937 0.452

MCR Failure 0.000 0.000 0.000 0.000 0.000 0.001 0.002 0.003 0.006 0.010 0.014 0.023 0.030 0.040 0.050 0.063 0.076 0.094 0.112 0.138 0.166 0.201 0.240 0.290 0.334 0.409 0.467 0.532 0.602 0.675 0.720 0.782 0.824 0.874 0.895 0.935 0.957 0.976 0.982 0.982 0.982 14.485

NEW YORK CITY CRIMINAL JUSTICE AGENCY 1989 Dataset Combined-Court Analysis Table C1 Estimating Sample Selection Bias2 A: Arraignment Disposition Variable 3

D: Original FTA Model

N=7,154 6

N=7,256 6

Logit

Yes

----

-0.019

0.044

0.027

----

0.130

0.342

0.331

No Verified

----

0.099

0.557d

0.556d

Unresolved Conflict

----

0.261a

0.258

0.220

EXPECTS AT ARRAIGNMENT

----

-0.160b

-0.254d

-0.224c

Yes

-0.003

----

0.068

----

No Verified

0.292b

----

Unresolved Conflict

0.601b

----

N=9,324

C: FTA

Logit

N=12,932

Selection Models B: Release Status

TELEPHONE Reference Category: Yes Verified

FAMILY TIES WITHIN THE RESIDENCE Reference Category: Yes Verified

EMPL/SCHOOL/TRAINING Reference Category: Yes Verified Yes

0.159

0.375b

0.508c

0.453b

0.446c

0.571d

0.780d

0.687d

No Verified

0.248b

0.299b

0.626d

0.582d

Unresolved Conflict

0.352b

0.162

0.810d

0.785d

LENGTH OF RESIDENCE Reference Category: Yes Verified Yes

----

0.000

-0.038

----

0.165

0.135

No Verified

----

0.367b

0.376c

Unresolved Conflict

----

0.375a

0.373a

Yes

0.069

----

0.164

0.201

0.404a

----

0.381

0.399a

No Verified

0.572b

----

0.268

0.289

Unresolved Conflict

-0.273

----

-0.147

-0.159

----

-0.192a

-0.190a

----

-0.013d

-0.012d

NYC AREA ADDRESS

SEX AGE

0.006a

MARITAL STATUS Divorced/Separated/Widowed

----

0.217

----

Single

----

0.147a

----

ETHNICITY Reference Category: White Black

0.110

----

0.234a

Hispanic

-0.098

----

0.099

0.247b 0.124

Other 7

-0.552b

----

-0.122

-0.113

Manhattan

-0.456d

-0.116

-0.170a

-0.157a

Queens

-1.309d

0.318b

-0.313c

-0.346d

Staten Island

-1.549d

0.157

-0.238

-0.164

Bronx

-0.602d

0.297c

-0.284c

-0.310d

BOROUGH OF ARREST Reference Category: Brooklyn

TYPE OF FIRST RELEASE

----

0.341d

0.350d

TIME AT RISK

----

-0.877d

-0.870d

OPEN BENCH WARRANT

----

0.958d

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Table C (contd.) Selection Models A: Arraignment Disposition

D: Original

B: Release Status

C: FTA

FTA Model

N=12,932 4

N=9,324 5

N=7,154 6

N=7,256 6

Logit

PRIOR FTA

-0.162a

0.626d

0.816d

0.672d

PRIOR VIOLENT FELONY CONVICTIONS

-0.268b

0.465d

----

CONVICTIONS

-0.213b

0.349d

----

PRIOR MISDEMEANOR CONVICTIONS

0.301d

0.394d

----

OPEN CASES

-0.232d

0.497d

0.327d

0.231c

----

0.946d

0.457d

0.331d

A Felony

-2.605d

1.237d

0.047

-0.067

B Felony

1.622d

0.617d

0.073

0.011

C Felony

-0.160

0.268b

0.067

0.037 0.129

Variable 3

PRIOR NON-VIOLENT FELONY

TYPE OF COURT TOP ARREST CHARGE SEVERITY Reference Category: D Felony

E Felony

0.401c

0.041

0.149

A Misdemeanor

2.001d

-0.373

0.202a

B Misdemeanor

2.272d

-0.590

-0.360

-0.345

U Misdemeanor/Other 8

2.771d

-0.463

0.234

0.217

Violent

-1.042d

0.135

-0.249b

-0.255c

Property

-0.323d

0.329c

0.184a

0.159

Weapon

-1.564d

-0.031

-0.568d

-0.547c

Gambling

-0.707c

-1.568b

-1.210c

-1.122c

DWI (alcohol or drug)

-2.599d

-0.755

-1.047b

-0.985b

Other 9

-0.275c

0.139

-0.119

-0.147

----

1.315b

----

0.207a

TOP ARREST CHARGE TYPE Reference Category: Drug

PROB OF CASE DISP AT ARRAIGNMENT PROB OF CASE DISP AT ARRAIGNMENT AND NOT BEING RELEASED PRIOR TO CASE DISP

----

-0.917b

----

CONSTANT

-2.344d

-3.348d

-0.686c

-0.739d

________________________________ The models presented in Table A are used for comparison purposes only. Therefore, the table provides only logit coefficients and their associated level of significance. 2 a=significant at the .05 level; b = .01; c = .001; d = .0001 3 The reference category for the community-ties items is Yes Verified. The individual reference categories for all other variables are as follows: Borough of Arrest--Brooklyn; Top Arrest Charge Severity--D Felony; Top Arrest Charge Type--Drugs; Marital Status-- Married/Living Together; Ethnicity--white. 4 Due to missing data, the N for this equation is less than 13,769. 5 Due to missing data, the N for this equation is less than 9,875. 6 Due to missing data, the N for this equation is less than 7,595. 7 OTHER includes Asian, American Indian, and others. 8 OTHER includes Violations, Infractions, and charges outside the N.Y. State Penal Law and Vehicle and Traffic Law (e.g., Administrative and Public Health Codes). 9 VIOLENT CRIMES include: negligent murder, non-negligent murder, forcible rape, robbery, aggravated assault, kidnapping. PROPERTY CRIMES include: burglary, larceny-theft, forgery & counterfeiting, stolen property, possession of burglary tools. DRUG OFFENSE include: A) Controlled Substances Sale/Manufacture; opium, cocaine, or derivatives, marijuana, synthetic narcotics, other dangerous drugs, and B) Use/Possession; opium, cocaine, or derivatives, marijuana, synthetic narcotics, other dangerous drugs. Dangerous weapons comprise the WEAPON category. The GAMBLING category consists of bookmaking, numbers, lottery, and other activities. TheDWI category refers to driving while under the influence of alcohol or drugs. The OTHER category consists of all other offenses not included in the aforementioned categories but which are included in the FBI UCR Crime Codes. 1

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