they also poten- tially have simple solutions: sharing hotlist or LPR data among law-enforcement juris- dictions often requires new, formal agreements, and developing such agreements has created a considerable barrier. Instead, agencies should forgo


strategically about integrating the systems into the agency’s day-to-day operations. Departments should expect and be prepared for significant initial effort to set up their systems, as well as a long learning curve to see their full use. The proces


rns and trends, LPR proponents clearly must build and spread needed information and tools. This would empower agencies to deploy the technology so that it brings the most value in these times of tight budgets, allowing law enforcement to target its r


other agency/department. Server data can be accessed by investigators and civil- ian analysts.
National and state criminal hotlist data (from the FBI’s NCIC) are updated to LPR units twice daily. State motor vehicle department data (registration info


 hit about a sex offender but might want it if the match is near a school. This capability is unavailable now.
• Because security concerns generally do not allow law-enforcement agencies to provide remote access to their computers, system troubleshoo


ng scofflaws (vehicles with more than two out- standing parking tickets), and to identify out-of-state vehicles that need to be registered locally. The public works units alert police about hits on vehicles associated with police matters (e.g., stole


s and alerts) are stored for two years, and there are no time limits on keeping information that is part of an active investigation and that may be saved as part of a case file.
Interoperability/Information-Sharing Cost Issues
This agency does not sh


 population it serves, and below the median in crime rates.
Use Cases
This agency primarily uses its system to recover stolen cars, although it also applies it in other ways, too, including to locate wanted persons, such as suspects in an active case


s. All system alerts must be verified with dispatch to ensure they are still valid before an offi- cer may act on them.
Interoperability/Information-Sharing Cost Issues
Data sharing is a challenge for this agency, which now shares with only a few oth


facing camera for each unit. These rear-facing cameras were added because this agency is near a border with a state that does not require front license plates. The agency hopes to acquire some covert, portable units for surveillance use.
Its system i


department, headquarters for a large plate-reader regional cooperative with 84 agencies in it, itself has four fixed and 109 mobile plate-reader units. The regional cooperative (including the department) has a total of 134 mobile and 14 fixed units.


 many agencies experienced problems with the vendor’s customer service.
This agency considers it important to train on how to use the system data, as well as on how officers on patrol should approach a motorist whose vehicle comes up on a system hit;


for border security. The agency bought one plate reader unit with the grant, found it beneficial, and then sought more: units for 12 cars, eight trailers, and three portable systems. The purchase price included a vendor system warranty for three year


to get more senior officers to use the plate reader system whose units often are mounted in the newest cars, which are assigned to them. The department hopes to buy portable systems that can be checked out by more- junior, IT-savvy officers who want


uy fixed units, which may create greater privacy concerns.
Other Issues/Findings
• This department makes no secret of its system but does not publicize its use, con- sidering it “just another tool” and not wanting “criminals” to learn how officers go


or, who is a part of the traffic division and is the main point of access to the data. The typical analytic use by this department is to help identify suspects, as opposed to locating or building cases against suspects identified by some other means.


rmation separately. That is because its plate reader cameras incorrectly identified certain out-of-state/ country plates. The cameras cannot differentiate plates from different states and coun- tries. Because its area has many tourists, the departmen


urgh, PA and I’ll be facilitating the interview today.
We are conducting these interviews so that we will have a better understanding of how your agency uses License Plate Reader technology for analytical activities, such as mining previously obtaine


mory jogger protocol first; if interviewee answers “many times,” start with a general question and come back to case studies at the end.]
Case Studies
• Can you give me an account of the last time LPR was used for an analytical activity? Can you prov


the capital investment?
• Are there maintenance, training, data storage, and/or upgrade costs?
• Are there any differences in cost between LPR administration at the local law-
enforcement and LPR administration at the interagency level?
Interview Pro


• Number of times LPR is used? By individual investigations?
• Is it possible to rank LPR as an investigative tool compared to other investigative
methods and tools?
Challenges
Tell me about any other challenges that you have not mentioned in regard


anning Services Research and Development, Columbus, OH, 2005.
“Automatic License Plate Reader Helps Jersey Police Fight Crime,” Homeland Security News Wire, 22 March 2011. As of 6 May 2014:
http://www.homelandsecuritynewswire.com/ automatic-license-p


ite/license-plate-recognition-databases%E2%80%94good-for-cops- or-invasion-of-privacy/
“High-Tech License Plate Readers Aid Police but Raise Ethical Issues,” The Tennessean, 6 May 2012. As of 6 May 2014:
http://w w w.wbir.com/news/local/story.aspx?st


hter in Death of Roommate’s Friend,” Press Release, 4 November 2012. As of 6 May 2014:
http://w w w.queensda.org/newpressreleases/2011/november/zeledon,%20luis_11_03_2011_ple.pdf
Ozer, Murat M., Assessing the Effectiveness of the Cincinnati Police De


2008. As of 6 May 2014: http://www.washingtonpost.com/wp-dyn/content/article/2008/08/01/AR2008080103570_pf.html
Law enforcement agencies across the country have quickly been adopting a new technol- ogy to combat auto theft and other crimes: automated


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D is evaluating predictive policing strategies conducted by the SPD and the Chicago Police Department (contract #2009-IJ-CX-K114). This report is one product from Phase II.
The RAND Safety and Justice Program
The research reported here was conducted


. . . . . . . . . . . . 46
4.4. Summary of Cost Savings Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
A.1. Pairwise Correlation of Group-Level Officer Man-Hours and PropertyCrime.....


activities and expected effects of PILOT, as well as the assumptions describing how the activities lead to the impacts in the intervention context.
ix
x Evaluation of the Shreveport Predictive Policing Experiment
 Program Prediction Prevention Interm


he maps to help inform daily decisions of which of the predicted hot spots to target with available resources. Plotted data included locations of recent crimes, 911 calls, field interviews, and recently targeted hot spots.
Prevention Model
As propose


s largely on their own. The resulting interventions all did assign overtime resources to predicted hot spots, as required. However, the strat- egies and levels of effort employed varied widely by district and over time. One com- mand devoted many mor


olation (and suspicious activity) perceived as preceding property crime by individuals with serious and/or multiple priors; individuals with warrants.
Two patrol cars, each with two officers; one dedicated to driving and one to looking for suspicious


(of persons with prior criminal histories, who were not in good standing with their community supervisor, etc.).
Selective recruitment of officers expressing interest and demonstrating specific skills; continued compliance with the elements above was


, if the three districts of the control group used fewer man-hours on average to achieve the same level of public safety as the three districts of the treatment group). There does not appear to be a reason to presume that such differences exist. Furt


ers at the Shreveport Police Department for the strong encouragement and assistance that they provided to support this evaluation. We are grateful to Rob Davis and Greg Ridgeway for contributions and advice in the early stages of this research. We wo


1
2 Evaluation of the Shreveport Predictive Policing Experiment
The underlying theory of PILOT is that signs of community disorder (and other indi- cators) are precursors of more-serious criminal activity, in this case property crime. By focusing res


cted
3 Squares of this size were chosen since they were approximately block sized.
  Program Prediction Prevention Intermediate Final
Logic Model for the Shreveport Predictive Policing Experiment
Figure 1.1
Theory Models Models Outcomes Outcomes
NOTE


ich clusters of property crimes had already occurred—that is, hot spots derived from conventional crime-mapping techniques rather than from a predictive model. In addition, the only permitted special operations targeting property crime in the experim


elated special operations in the control group. While the treatment group could not have spent more than the NIJ grant amount allocated to overtime operations, it could have spent less during the experimental period. This could occur because the gran


 implementers, or partici- pants necessary to attain project goal.
9. Implementation—the extent to which the program is implemented as designed.
10. Initial use—the extent to which a participant conducts activities specified in the
materials.
11. Con


s described by the SPD (Reno and Reilly, 2013), forecasted monthly changes in crime at the district and beat levels. The models were evaluated on their accuracy in predicting “excep- tional increases”—cases in which “the monthly seasonal percentage c


cted the logistic regression model, which iden- tified cells at high risk of crime in the coming month using the following seven input variables:
• Presence of residents on probation or parole
• Previous six months of tactical crime (the six types of


he PILOT treatment varied across commands over time. There were three police commands involved in the trial by virtue of managing the control areas, treatment areas, or both:
• Command 1: District A and B (both treatment groups)
• Command 2: District


d/or serious prior convictions.
Officers reported that PILOT changed the amount of recent information pro- vided per case. This occurred because PILOT officers at a crime scene asked more ques- tions of victims, their neighbors, and individuals in th


targeted Staffing—directed patrol
Staffing—support
Directed patrol—field interviews
Directed patrol—responses to property crimes in PILOT action areas
Collection and analysis Situational awareness
Decisionmaking
Key criteria
Human resources policies


d money (e.g., fuel) to drive to another hot spot and ask questions. The overtime incentives to participate in PILOT operations competed with
other, more preferable overtime funds available to officers. Another practical problem arose: for the office


istricts not included in the trial, the crime analyst continued to provide typical crime statistics and bulletins of interest, and the information technology (IT) specialist continued to provide technical support. The usual crime statistics include b


port, as funded by the NIJ.
Summary
The intervention carried out in two of the treatment districts (both under one com- mand, Command 1) may be considered more comprehensive and intensive. Officers were instructed to stop persons violating ordinances


w officers actually used the situational awareness details to develop strategies for locations. We just have anec- dotal reports from command and field personnel that the daily maps were useful in selecting focus areas for the day.
Examples of the tw


trict A submitting up to 163 cards, District B submitting up to 15 cards, and District C submitting a maximum of 103 cards.
Program Dosage
In terms of the rate at which the predictive model was provided to officers (dosage), initially, the predictive


and B typically were officers from those districts, whereas in Dis- trict C, PILOT operations were typically conducted by officers recruited from outside the district. The predictive analytics team pointed out that this resulted in officers operatin


l and the crime analysts stated that having fewer available officers at both command and tactical levels degraded the command’s ability to conduct effective PILOT operations. In District C, based on interviews, the drop-off appears to be due to insuf


revention model activities
Monthly deployment meetings
Daily roll call maps and FIs
Intelligence-led activities (deployed according to maps)
* Activities differed.
Treatment Compliance
Predictive Analytics
Team Command 1
Yes N/A
N/A No
Yes Yes N/A Ye


with officers with the same frequency.
Control groups were not exposed to the prevention model and did not receive maps or the same collected intelligence. However, they may have been exposed to the prediction models, since the SPD has COMPSTAT-style


Officers in all districts reported that the key challenge of PILOT was to recruit officers. The PILOT strategy required gathering more information than usual; typically, offi- cers have to be available to respond to calls, so they do not have the ti


rs in District A and B commented that the most positive outcome of PILOT was an improvement in relationships with residents. Prior to PILOT, officers felt that residents of areas with crime spikes had negative percep- tions of the police and thus pro


ts of PILOT officers, such as identifying patterns and working in teams, were instrumental in gathering relevant and timely informa- tion. Officers also reported that the maps made it easier to link a strategy with a loca- tion and with specific info


 the control group. The second randomization selected Dis- trict B for treatment and District E for the control group. Within groups, experimental
1 For this study, the districts have been relabeled with letters rather than their actual numbers for t


on of the Shreveport Predictive Policing Experiment
Given that the SPD implemented an RCT, we performed a series of statistical tests that compared control and treatment groups. For robustness, we also tested the difference between the crime change i


meaning that PILOT did not reduce property crime. Since these statistics were derived from a RCT, it can be con- cluded that there is no statistical evidence that PILOT as implemented reduced crime or any of its subtypes.
Exploratory Analyses
There w


in terms of changes in crime from the previous year. The results in the last two columns of the table confirm previous findings that PILOT had no overall effect on crime. The table provides results from an analysis of the short run (in the last colum


n as the first four months because of a lagged effect of police force labor on crime; this is not incompatible with the theory-driven approach, since one would expect a delay in the decrease of crime for increases in manpower from previous research (


g from Figure 3.4 is that in Command 1, year-over-year crime drops occurred in September and October, despite the fact that the labor hours devoted to the intervention fell from close to 800 to just over 300 hours per month. One pos- sibility for the


ontrol group) and −0.041 (treatment group). Test 2 and 3: α error probability = .05. Test 1: β/α ratio = 6. α is the level of significance used in the test, and β/α is a measure of sensitivity to specificity.
ficult to detect anything other than a ve


conduct special operations for the expected increase in criminal activity rather than recent past crime surges.
One aspect of predictive policing that receives less attention, however, is the costs associated with implementing a prediction and preven


ncurred if not for implementing PILOT.
For this assumption to be valid, one key condition must hold: control districts operate the same as the treatment districts in the absence of the intervention. There- fore, using their resource utilization for t


 large changes annually in overtime man-hours. On the other hand, it is the case that there would have been an upper bound on the labor resources over the full period.
Table 4.1 presents an overview of the key assumptions and data used for the CSA, b


• Same as treatment group
• Literature estimates for police vehicle cost per mile
• Hours worked based on SPD data, with assumed range of hours traveled by job class
• Same as control group
• Literature estimates for police miles traveled per
 In ad


imates suggest that the total labor expenses in the control group are $195,500 to $204,500. Regarding analyst and administrative labor, no additional labor was used for PILOT, since the control group did not receive the treatment and as was described


that compares the minimum, maximum, and best estimates of labor and equipment expenses for the treatment and control groups. Results show that PILOT cost approximately $13,000 to $20,000 less than the status quo. In other words, the treatment group s


esents evidence from a process, outcome, and cost evaluation of a seven-month field trial implemented in Shreveport, Louisiana, in 2012. The policing strategy—PILOT—included a prediction and a prevention model aimed to reduce residential, auto-relate


 in where and how interventions were conducted.
A final possibility is that the PILOT preventative interventions are insufficient to generate additional crime reductions. As noted, the lack of monthly meetings meant that preventive measures within pr


 spots come from predictive algo- rithms or crime mapping. Below are some potential elements to include in future pre- dictive policing experiments:
• Test the differences between predictive maps and hot spots maps. What are the mathematical differen


tial predictions, but adding additional information that would be more useful than traditional hot spots. The results from Shreveport do provide one intriguing possibility. As noted, declines in crime rates in Command 1 continued for two months after


ions and thus low power to detect an association, yet there may be a pattern in which up to a point (e.g., nonlinear effect) increasing man-hours is associated with reductions in crime in the following month.
Testing for simple correlation and consid


a whole. Table A.2 presents the number of hours in total and by job class, where available. As seen in the table, the control and treatment group used approximately 6,000 hours and 5,230 hours, respectively, for property crime– related special opera


532
$62,221 $1,076 $14,712 $9,849 $22,495 $33,764 $167,649
 By job class
Probationary police 948 officer
Officer 2,148
Corporal I
Corporal II
Corporal III
Corporal IV
Sergeant 776
Total 5,233
NOTE: Any differences are due to rounding. a Overtime rate


ted average.
opportunity cost. The next assumption was that only ranks between probationary offi- cer and corporal were in the field. Given reports of altering numbers of person-vehicle teams of one person, one car and two people, one car, a rate of


isk:
• The analyst uses the tool to identify correlations between crime risk and the pres- ence of a number of hypothesized geospatial risk factors.
• For the most-correlated factors, the tool then counts the number of factors pres- ent in each cell,


o Estimate the Effects of Police on Crime: Reply.” American Economic Review, Vol. 92, pp. 1244–1250.
Linnan, L., and A. Steckler. 2002. “Process Evaluation for Public Health Interventions and Research: An Overview.” In Process Evaluation for Public H


 null effect. In addition, it is likely that the predictive policing program did not cost any more than the status quo.
   NSaAfeTtyIOanNdAJuLstSicEeCPrUoRgrIaTmY RESEARCH DIVISION
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tribute.html.
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Design and cover image: Bernhard Moosbauer, exsample.org
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ion in understanding the differences in mobility levels between countries.
2. Applying these nine factors for predicting mobility levels in the BRIC countries provides a solid ground for taking a future-oriented perspective on the mobility of people


in the RAND Transportation, Space, and Technology Program, which addresses topics relating to transportation systems, space exploration, information and telecom- munication technologies, nano- and biotechnologies, and other aspects of science and tec


ia, India, and China 57 Workshop Results 58
 Chapter Seven Conclusions 73 Relationship Between Factors and Automobility 74 Policy Implications 76 Relevance with Regard to Existing Work 77 Suggestions for Future Research 78
 Appendix A Data Sources 83


in the “evolutionary paths” of mobi- lity in different countries—that is, why do countries at similar levels of economic development vary so much in the amount and mode of travel? And more importantly, what can this understanding tell us about the p


has the highest, so those formed the outer limits of VKT among developed countries. We also selected Germany as a typical European country, as well as Australia, which is similar to the United States in many respects but has markedly lower VKT per c


r the OECD countries over time and how much they might change in the future for the BRIC countries. At a workshop, experts representing
the eight countries rated the situation at the beginning and end of the motorization period for each of the nine f


igure S.2. Automobility Scores and Forecasts of Long-Term Travel Saturation Levels
SOURCE: Project workshop (see Chapter Six). NOTE: Rounded to nearest 100.
             Implications and Observations
We draw three main policy implications from this w


del results can be more accurate. We could consider different factors and other countries.
› We could look at vehicle ownership or other forms of mobility. The qualitative-quantitative methodology developed for this report could be useful in addressi


 with three questions:
› First, what factors besides economic development affect automobility? We see very different paths of mobility in countries with similar levels of income, so this question seeks to understand what other factors are in play.
›


ns seven chapters. Chapter Two explores the idea of evolutionary paths of mobility and whether and how this evolution in developed countries may prefigure a similar evolution in large developing countries. That chapter also defines our key analytical


s in which people travel or in the amount of travel using different modes. For example, one country might move quickly into personal vehicle ownership and thus higher rates of driving when rising income levels make vehicles affordable to a large numb


ountries was limited, for reasons discussed in Chapter Three. Therefore, we focused our research on explaining the use of personal vehicles.
A key contributor to high levels of automobility is economic development (Dargay and Gately, 1999). It is dif


nt concepts in thinking about evolution of automobility: the speed of growth and the saturation level. The speed of growth is defined as the rate of change in the level of annual VKT. This is indicated by the slope of a line; the steeper the slope, t


velopment since the 1960s but has low automobility. In our view, the influence of rapid GDP growth on saturation levels has been mitigated by other factors, such as land use. Therefore, GDP growth has only a contributing, not a direct, influence on l


s to fit the model to the data. Conducting such work for Brazil, Russia, India, and China—the BRIC countries—would require developing detailed estimates of future GDP per capita growth and experimenting with various curves to determine the most reaso


ied eight countries to study. Four are developed countries that represent different evolutionary paths of mobility: Australia, Germany, Japan, and the United States. The other four are large developing countries, the BRIC countries.
› Third, we selec


p comparisons. No indicator is perfect or captures every important aspect.
Mobility is generally measured in two ways: distances traveled and number of trips taken. Although there are good reasons to use each depending on the issue to be studied, for


cle. So, although helpful as a measure of vehicle travel, it cannot easily be used to measure personal travel.
Vehicle ownership information also tends to be readily available. In most countries, vehicle owners are required to register their vehicles


For the developed-country comparison, we used data compiled by BITRE (2012b). This was published shortly after we began our work. Because it covers several countries of interest, we used it for VKT and vehicle ownership, as shown in Figure 2.1 in Cha


all others. For GDP per capita, Bolt and van Zanden, 2013.
  Turkey
India China
Brazil
Russia
Italy
Spain
Germany
Japan
UK
France Belgium Netherlands
Australia
Canada Switzerland
Finland
Sweden Norway
Denmark
United States
         Personal vehicles


countries, using GDP per capita and all motorized travel. They found a clear pattern of four clusters, with very similar patterns in the four European countries that they studied (France, Germany, Sweden, and the United Kingdom). Canada and Australi


ch for the selection of developing countries. The BRIC countries are all large developing countries with the potential to contribute significantly to global carbon emissions and markets for auto- mobile ownership (China is currently the world’s large


dies form the basis of the discussions in Chapter Four.
21
 3 The TRID database integrates the content of two major databases: OECD’s Joint Transport Research Centre’s International Transport Research Documentation (ITRD) database and the Transportat


ous factors are those that transportation policymakers cannot change or over which they have very limited control. For example, whether a country has a strong domestic car industry depends on variables ranging from historical precedent to a skilled w


DEVELOPING COUNTRIES_STUDy mETHODOLOGy
Two factors we discussed at length but ultimately decided not to use are technology and geographic size. New technology can potentially encourage mobility by making travel faster or easier. For example, the adve


ure 3.4, we concluded that the period of intermediate economic development is key to the evolution of automobility in the study countries. At very low levels of GDP per capita (generally, below $5,000), personal vehicles are affordable only to small,


scores describe historical conditions at the start and end points of the motorization period. For the BRIC study countries, the factor scores describe forecasted future conditions during the next decades, when the countries are expected to reach the


beginning and end of the motorization period. We began by asking the experts from each BRIC country to comment on their own countries, but the entire group then discussed the factor scores. Factor weights were established in a general discussion. Aft


ds were in poor condition when railroads were introduced in the 1850s, and a lack of funding meant that many roads declined in quality. From about 1880 to 1930, rail was the dominant mode, with roads and tram services often built to serve the rail ne


ilroad network but a fairly poor road system. Urban rail systems were introduced in the early 1900s. Although much early work on automotive technology was carried out in Germany, the German auto industry was one of the smallest in Europe before World


 mObILITy DEVELOPmENT IN FOUR OECD COUNTRIES
 Japan
Japan represents a path of high personal income but relatively low vehicle travel per person. In the mid-1800s, when Japan was a largely feudal state, there was a very limited road network and no ra


of its oil from the Middle East. One response was to begin manufacturing more fuel-efficient vehicles, which paved the way into the lucrative U.S. market. Although the Japanese auto industry remains important, driving and vehicle ownership have leve


 the widespread entry of women into the workforce.
Although vehicle ownership and vehicular travel remain the highest in the world, some of these trends have been tempered recently. Both have not only leveled off but also declined since the mid-2000s


ing World War II, some roads were improved for military purposes, while others deteriorated; after the war, the national government assisted the states with rebuilding (Australian Bureau of Statistics, 1974). However, a system of national highways, n


ple.
 United States
In the United States, the demand for paved roads dates back to the 1880s. Many rural roads were unpaved and nearly impassible, and bicyclists rallied behind the “good roads” movement to advocate for paved roads. The U.S. governmen


alent market price, differing by up to $0.15 or more per liter (in 1989–1990 Australian dollars), but, subsequently, they have tracked very closely (BITRE, 2012a, Figure 1.2). As of 2010, fuel taxes make up about 40 percent of the cost of gasoline (B


8 percent of the pump price of gasoline was tax, and 1 liter cost only $0.73 (in 2010 U.S. dollars) (BITRE, 2012b, Appendix A, Per Cent Tax Table).
 3 Although inflation was generally low during this time, this is an unusually low rate of increase (4


tax benefits for home-to-work trips longer than 6 km and low interest rates for auto loans resulted in increased ownership of vehicles. Motorcycles and motor scooters were initially popular, but, eventually, car ownership rose with incomes (Baron, 1


OUR OECD COUNTRIES
Lack of Alternatives to Driving
This factor considers the availability of alternative transportation modes, with a focus on public transportation and, in particular, rail. (Data on bus systems and nonmotorized modes are less readil


ty via rail. Even as car ownership expanded enormously in the 1960s and 1970s, rail systems continued to grow; six new subway systems were built from 1950 through the 1980s (Aoki, 1993b). Only 4 percent of Japanese say they have no access to transit,


(1) the share of the population in the age range 15 to 64, beginning in 1950 (OECD, undated; see Figure 5.3); (2) the percentage of that age cohort who actively participate in the labor force, beginning in 1980 (World Bank, 2013); and (3) the overall


tively low, decreasing from 63 to 60 percent from 1980 to 2010. Women’s labor-force participation remained essentially stable, increasing from 48 to 50 percent during this period.
 United States
The share of population between 15 and 64 years of age


icant amount of oil, and, at the time of the 1973 oil shock, Japan imported 80 percent of its oil from the Middle East (JAMA, undated). Japan produces only 136,000 barrels of oil per day, 3 percent of the 4.5 million barrels it consumes. Consumption


ore than 2 million in 1960 (Pratten and Silberston, 1967). Currently, Germany produces more than 6 million vehicles per year, making it the world’s fourth-largest manufacturer (BTS, undated, Table 1-23). The auto industry is the country’s largest emp


Spatial Dispersion
This factor includes both the degree of urbanization and densities within urban areas. Data on the density of urban areas in 2000 are taken from Angel et al.’s Atlas of Urban Expansion (2010, Table 3); see Figure 5.6. Australia has


ended streetcar lines out to suburban areas to create transportation for new housing developments. Suburbanization was also abetted in the 1950s and 1960s with the creation of a national interstate system that brought not only residents but also empl


only 45 percent of survey respondents were concerned about climate change. On the car-culture scoring, Australia was second-highest, with a 1.2. Experts rated Australia as the highest of the four countries on the car as personal living space and for


s of mobility, as well as long-term travel saturation rates, as measured in VKT per capita.
  58 THE FUTURE OF DRIVING IN DEVELOPING COUNTRIES_FUTURE mObILITy PaTHS OF bRazIL, RUSSIa, INDIa, aND CHINa
Workshop Results
At the workshop, each of the eig


onal stakeholders in the field of mobility products and services. These study countries are Germany, the United States, and the BRIC countries; each country is represented by a group of up to three experts. The program includes expert workshops and o


ic car industry suggest about how prominent the industry might be when the country enters the motorization period at $5,000 per capita? This information was summari- zed for side-by-side comparison between countries.
We developed a system for assigni


troduced factor weights on a three-point scale (on which 3 represents the strongest influence on automobility and 1 the lowest) to capture the variance of influence across factors. We developed the factor weights based on expert judgment at the works


ING IN DEVELOPING COUNTRIES_FUTURE mObILITy PaTHS OF bRazIL, RUSSIa, INDIa, aND CHINa
Table 6.1. Factor Scores, Factor Weights, and automobility Scores for Eight Countries
           Country
Good Car Infrastructure (3)
Inexpensive Fuel (2) Pro-Car Po


     Exogenous factors -1.5 are disadvantagous
for automobility
China
2000s - 2030s
1.5 Exogenous factors are advantagous
for automobility
     India
2010s - 2040s
Japan
1960s - 1990s
Germany
1950s - 2000s
-1.5
Transport policy factors are disadvanta


the increasing affordability of fuel and the extension of the car infrastructure were kept in check by tougher regulation and expansion of non-car alternatives. This was not the case in Australia and the United States, where policies contributed to i


s of GDP and is generic across countries (α = –5.495). The resulting country-specific saturation levels γ are shown at the right of Figure 6.5. Further details of the models are presented in Appendix D.
67
 68 THE FUTURE OF DRIVING IN DEVELOPING COUN


y scores derived from expert elicitation for each OECD country explained the forecasted saturation levels. On a simple scatter plot, there appeared to be a linear relationship; that is, as the automobility score rose, so did a country’s estimated sat


saturation level = 6,400 km automobility score = –0.51
brazil
saturation level = 11,300 km automobility score = 0.23
United States
saturation level = 16,300 km automobility score = 0.87
y = 6,000*AS + 9,959
R2 = 0.86
(based on OECD countries)


ies that discourage car travel and infrastructure that supports alternatives to driving. Although the exogenous factors are far less responsive to policy interventions, the link between VKT and GDP can be manipulated by levers associated with the pol


recent studies, see Committee for the Study on the Relationships Among Development Patterns, Vehicle Miles Traveled, and Energy Consumption, 2009).
How Can We Assess the Influence of These Factors?
We developed automobility scores to summarize the in


e in the near-term future will have a major impact. Using our methodology, we forecast long-term saturation levels in China and India well below those in the United States (7,000 and 7,800 VKT per capita versus 16,300 VKT per capita). Russia and Braz


for fuel more expensive than the market price, discourage excessive driving, and encourage alternatives to driving. This suggests that policymakers can steer some elements of travel demand if desired. Although rising incomes tend to encourage driving


er, when we used a single generic growth term for the four countries, we found the resulting saturation levels to be much less reasonable. Given what we know about the growth rates in the OECD countries, we could examine how different growth assumpti


given the difficulty of collecting data, we decided to focus exclusively on VKT as the only measure of mobility. However, with appropriate data, we think that the methodology developed here would be helpful for addressing those other mobility topics


company, compiles data on vehicle ownership and VKT. Data are available only from 1995 forward. ProgTrans was selected for the stage of the project in which we were identifying study countries and thus needed a data source with multiple countries. I


tained from the Historical Statistics of Japan, Table 12-10 (Japan Statistics Bureau, 2012b). Later years were obtained from the 2010 and 2012 Japan Statistical Yearbook (Japan Statistics Bureau, 2010, 2012a), Table 12-5 in each. Vehicle ownership in


ureau, 2000); for 2000 through 2008, we used “Table 1, Annual Estimates of the Resident Population for the United States, Regions, States, and Puerto Rico: April 1, 2000 to July 1, 2009” (U.S. Census Bureau, 2009).
 Fuel Prices
We used both internati


t German Institute for Economic Research, Berlin
   Japan Toshiyuki Yamamoto Nagoya University
   India Ashish Verma Indian Institute of Science, Bangalore
   Senathipathi Velmurugan Central Road Research Institute, Delhi
   Subhamay Gangopadhyay Cen


y (and in the midterm future), there has been substantial investment in road infrastructure.
                                                                          Inexpensive Fuel
This factor describes whether fuel is inexpensive relative to inco


 fleet turnover. • Taxes on car ownership are high.
United States:
• There was little regulation in the past.
• Funding allocation historically favored roads over transit. • Today, there are stricter CAFE standards.
 bRIC countries
brazil:
• Governme


nd the development.
                                                                           98 9 8
    Active Population
This factor describes the share of population in a life situation with high mobility rates. This includes both demographic eff


l).
• Brazil: Since 1980, consumption has doubled and production
has increased tenfold; the projected production increase is 40%.
• China: Since 1980, consumption has increased fivefold and production doubled; the projected production increase is 20%


ct Sheet and Expert Observations
Population density in urban areas (people per hectare), 2000
 18 Australia
OECD countries
australia:
32 Germany
56
Japan
20 United States
35
Brazil
97
China
94
India
41
Russia
Figure C.8. Fact Sheet, Expert Observatio


n, we relied exclusively on expert judgment. Favorability of car culture was assessed in a separate flag scoring exercise in which experts scored the factors 1 through 7 separately. The fact sheet at hand reports the most-significant findings from th


   106 THE FUTURE OF DRIVING IN DEVELOPING COUNTRIES_ESTImaTING THE PaRamETERS FOR a GOmPERTz mODEL OF VEHICLE-KILOmETERS TRaVELED PER CaPITa
In this study, we used nonlinear regression to estimate a Gompertz function to explain the growth of VKT pe


_reports/RR443-1.html
angel, Shlomo, Jason Parent, Daniel L. Civco, and alejandro m. blei, Atlas of Urban Expansion, Cambridge, Mass.: Lincoln Institute of Land Policy, 2010. As of March 26, 2013: http://www.lincolninst.edu/subcenters/atlas-urban-exp


ect Working Paper 4, January 2013. As of March 25, 2014: http://www.ggdc.net/maddison/maddison-project/ abstract.htm?id=4
brinkhoff, Thomas, “Major Agglomerations of the World,” City Population, January 11, 2013; referenced July 17, 2012. As of March


org/Publications/Blurbs/162093.aspx
Dargay, Joyce, and Dermot Gately, “Income’s Effect on Car and Vehicle Ownership, Worldwide: 1960–2015,” Transportation Research Part A: Policy and Practice, Vol. 33, No. 2, February 1999, pp. 101–138.
Dargay, Joyce


FHWa—See Federal Highway Administration.
Flath, David, Regulation, Distribution Efficiency, and Retail Density, Cambridge, Mass.: National Bureau of Economic Research, Working Paper 9450, January 2003. As of March 25, 2014: http://www.nber.org/paper


apan automobile manufacturers association, “Japan’s Auto
Industry,” undated; referenced March 25, 2013.
———, Motor Vehicle Statistics of Japan 2011, Tokyo, c. 2011. As of March 26, 2014: http://jama.org/motor-vehicle-statistics-of-japan-2011/
Japan m


, 2011.
Kojima, Katsumi, Takao Goto, and Kazusei Kato, “Changes in Traffic Safety Policies and Regulations in Japan (1950–2010),” in International Exchange Department Planning Committee for FY2011, Changes in Traffic Safety Policies and Regulations i


Wage or Population of Japan Then?” MeasuringWorth, 2012. As of June 3, 2013: http://www.measuringworth.com/japandata/
Organisation for Economic Co-Operation and Development, “OECD.StatExtracts: Short-Term Labour Market Statistics,” undated; reference


77/tyndallfinalreport.pdf
Statistics Canada, “Table 051-0001, Estimates of Population, by Age Group and Sex for July 1, Canada, Provinces and Territories,” modified November 25, 2013; referenced January 7, 2014. As of March 26, 2014: http://www5.stat


tical Report 2, 2011.
As of June 13, 2013: http://www.internationaltransportforum.org/statistics/ StatPapers/SP201102.pdf
Williams, Barbara T., These Old Houses: 2001, U.S. Census Bureau, U.S. Department of Housing and Urban Development Office of Pol


apita for 22 Countries, 1963–2008 8
Diagram of Study Methodology 14 GDP Per Capita and Personal Vehicle Ownership per 1,000 People, 2008 18 GDP Per Capita and Vehicle-Kilometers Traveled Per Capita, 2008 19 Motorization Rates and GDP Per Capita Growt


cumentation
TRIS Transportation Research Information Services
Viz Verkehr in Zahlen
VKT vehicle-kilometers traveled
VmT vehicle-miles traveled
   116
   117
 www.rand.org
RR-636-ifmo 9 780833 086044
$59.95
ISBN-10 0-8330-8604-9 ISBN-13 978-0-8330-860


  CORPORATION
Los Angeles County
Juvenile Justice Crime
Prevention Act
Fiscal Year 2012–2013 Report
Terry Fain, Susan Turner, Sarah Michal Greathouse
 ELECTRONIC COPIES OF RAND RESEARCH ARE PROVIDED FOR PERSONAL USE; POSTING TO A NONRAND WEB SITE IS


es be measured for locally identified service needs. JJCPA programs were first implemented in the summer and fall of 2001 and are now in their 13th year of funding.
The RAND Corporation received funding from the Los Angeles County Probation Departmen


alif.: RAND Corporation, WR-218-LACPD, February 2005
• Susan Turner, Terry Fain, and Amber Sehgal, Validation of the Risk and Resiliency Assess- ment Tool for Juveniles in the Los Angeles County Probation System, Santa Monica, Calif.: RAND Corporatio


ion. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6. Juvenile Justice Costs for the Gender-Specific Community Program . . . . . . . . . . . . . . . . . . . . . .
3.7. Juvenile Justice Costs for the High-Risk/High-Need Program . . . . . .


e responsibility of the BSCC, for- merly called the Corrections Standards Authority (CSA), the successor to BOC. The BSCC is required to submit annual reports to the California state legislature measuring the success of JJCPA. The legislation identif


youth receiving Probation services. If no appropriate comparison group could be identified, a pre-post measurement design was used. Generally, outcomes for program participants are measured for a six-month period after starting the program (for comm


given youth is counted as a participant within each program from which he or she receives services and may therefore be counted more than once.
Table S.1 lists the JJCPA programs in each initiative in FY 2012–2013 and the number of participants who r


n program is determined by who received services during the fiscal year, which goes from July 1, 2012, to June 30, 2013. To allow a six-month eligibility period for recidivism, however, the number for whom outcomes are reported uses a reference perio


t, and camp.
• Several programs had smaller samples for supplemental outcomes than for big six out-
comes.
• This report is based on officially recorded outcome data only and makes no attempt to
evaluate the quality of program implementation.
In the


he six months following program entry than at the time of program entry. HB housing-project crime rates were lower in FY 2012–2013 than in FY 2011–2012, but significance testing between the two rates is not possible because they are the universe of e


es of Simple Comparisons Between Cohorts
The BSCC mandates that, for seven Los Angeles County JJCPA programs (MH, YSA, GSCOMM/YWAR, HRHN, SBHS-AR, SBMS-AR, and IOW), outcomes are to be evalu- ated by comparing the current cohort’s results with those


the results of difference-in-differences analyses for the seven JJCPA programs that used the previous year’s cohort as a comparison group.
Table S.4
Results of Difference-in-Differences Analyses for Programs That Used the Previous Year’s Cohort as a


e total budget for each program, the number of youth served in FY 2012–2013, and the cost per program participant. Overall, the cost per youth in the Enhanced Mental Health Services initiative in FY 2012–2013 was $527, whereas the Enhanced Services t


ch program and to allow comparisons for program participants in the six months after entering JJCPA programs versus the prior six months.
Total Cost of Programs and Initiatives
Table S.6 shows the mean baseline and follow-up costs per participant in


et the relatively high initial investment made in program costs. We are not able to extend the time frame to measure changes, however, because not enough time has elapsed to allow us to obtain data beyond a six-month period. With a longer follow-up p


limitations in our assessment of the JJCPA pro- gram in Los Angeles County. As discussed in Chapter One, the current evaluation uses quasi- experimental designs to test the effectiveness of JJCPA programs. Quasi-experimental designs construct compari


nnual basis and to report outcomes to the BSCC annually.
Summary xxvii
Acknowledgments
 We are grateful to Laura J. Hickman of Portland State University and RAND, Jodi Lane of the University of Florida, and Brian A. Jackson and Tom LaTourrette of RAN


an Association YSA Youth Substance Abuse Intervention YWAR Young Women at Risk
Abbreviations xxxiii
CHAPTER ONE
Background and Methodology
The Juvenile Justice Crime Prevention Act
In 2000, the California state legislature passed the Schiff-Cardenas


, (2) arrests, (3) probation violations, (4) incarcerations, (5) successful completion of restitution, and (6) successful completion of community service. Each county can also request that supplemental outcomes be measured for locally identified serv


t include various school- and community-based resources.
This coordinated strategy allows JJCPA school-based and other JJCPA DPOs to closely supervise and support youth in the context of the school environment and the community, providing a continuum


to use a historical comparison group. If neither a contemporaneous nor a historical comparison group can be identified, program youth them- selves can constitute the comparison group, and their behavior after intervention can be com- pared with that


ation in identifying an appro- priate initial comparison group for the High-Risk/High-Need (HRHN) program, for which outcomes were reported for the first time in FY 2005–2006. These comparison groups were selected by Probation, matching comparison-gr


nce-of-means test) to assess whether the differences in outcomes between JJCPA youth and comparison-group youth are statistically significant, i.e., whether we can assert with a reasonable degree of certainty that the difference in outcomes between t


ypothesis.
2 For programs based in juvenile halls, the big six outcomes are measured for the six months after the youth returns to the community, rather than from program start.
Background and Methodology 5
 6 Los Angeles County Juvenile Justice Crim


lity of a binary outcome, the odds ratio for that outcome is defined as p (1− p). Logistic regres- sion analysis predicts the logarithm of the odds ratio as a linear combination of exogenous variables. The difference-in- differences analysis involves


for the previous year’s cohort using the revised definition.
Organization of This Report
The remainder of this report focuses specifically on JJCPA programs in Los Angeles County in FY 2012–2013. Chapter Two details JJCPA programs and presents brief


vement in fights). Overall, in FY 2012–2013, 27,546 youth received JJCPA services in Los Angeles County. Of these, 10,973 (39.8 percent) were at risk and 16,573 (60.2 percent) were on probation. Youth in one or more JJCPA programs receive services, o


onth eligibility period for recidivism, however, the number for whom outcomes are reported uses a reference period of January 1, 2012, through December 31, 2013. The youth whose outcomes can be reported during the fiscal year must enter the program i


ee pro- grams within the Enhanced Mental Health Services initiative: MH, SNC, and MST.
Youth in the Enhanced Mental Health Services initiative were evaluated based on com- parison with an appropriate group for each program. Detailed statistics for FY


tic environment with inten- sive mental health and other ancillary services for juvenile hall minors.
On entry into juvenile hall, detained minors are screened by professional staff from DMH. The staff employs the Massachusetts Youth Screening Instru


ce system. For juveniles who do not pose a danger to public safety, community-based treatment is likely to be a better option than detention.
Comparison Group and Reference Period
Although everyone who enters a juvenile hall is tested, only a subset—


 cluster and gender were not available for MH participants for FY 2012–2013.
Difference-in-Differences Analyses
As noted in Chapter One, we include difference-in-differences analyses for all JJCPA programs that use the previous year’s cohort as a com


d with mental health problems and increase the number of juveniles who receive appropriate mental health treatment.
This program initiates a comprehensive, judicially monitored program of individualized mental health treatment and rehabilitation serv


rcent for two high-quality randomized studies (Wilson, Mitchell, and Mackenzie, 2006).
Although initially founded to treat adults, the drug court model quickly expanded to include juvenile drug courts. Between 1995 and 2001, more than 140 juvenile dr


 their cases were not deemed sufficiently serious. SNC and comparison-group youth showed comparable demographic distributions, as indicated in Table 2.5. None of the differences between the two groups was statistically significant.
For SNC participan


youth are embedded. MST strives to promote behavior change in a youth’s natural environment, using the strengths of each system (e.g., family, peers, school, neighborhood, indigenous support network) to facilitate change. Within a context of support


her rates of completion of probation, than Hispanic com- parison-group youth. MST youth of other ethnicities, which made up about 25 percent of the sample, showed no comparable improvements in these outcomes versus comparison-group youth of the same


gram represent 91 percent of all youth in the Enhanced Mental Health Services initiative for whom big six outcomes were reported, the results for the initia- tive as a whole will necessarily be primarily influenced by those for the MH program. Echo-


p Community Transition Program (CCTP), Intensive Gang Supervision, and school-based DPOs to a community-based provider for a comprehensive assessment. A central focus of this programming is to ensure that high-risk
Table 2.6
Programs in the Enhanced


nstration of the desired behavior and consistent reward of incre- mental steps toward achieving it. Therapeutic activities include fulfilling specific assignments, rehearsing desired behaviors, and recording and reviewing progress, with praise and pr


7.4 percent who tested positive in the six months before program entry. This difference is not statistically significant.
Cluster and gender data were not available for YSA participants from FY 2012–2013. For details on big six and supplemental outco


998):
• Prevention services aim to eliminate or minimize behaviors or environmental factors that increase girls’ risk of delinquency (Center for Substance Abuse Prevention, 1993). Pri- mary prevention focuses on helping girls to develop the knowledge


ment in school, neighborhood of residence, and lack of availability of community-based programs, increased the risk of delinquency for both sexes.
Current JJCPA Programs and FY 2012–2013 Outcome Measures 27
Some additional factors had more effect on


r this program because it uses the previous year’s program participants as a comparison group. For each of the big six outcomes in the GSCOMM program (including YWAR), Table 2.8 shows the baseline and follow-up means, the odds ratio of the interactio


ss domains—family, peer, school, and neighborhood) and incor- porates the phase model of Functional Family Therapy (FFT). Additionally, such programs as MST and multidimensional-treatment foster care (MTFC) place a strong emphasis on skill training f


1998).
• FFT: FFT is a family-based prevention and intervention program that has been applied
successfully in a variety of contexts to treat a range of these high-risk youth and their families. It was developed to serve adolescents and families who l


tensive supervision is likely to uncover numerous technical violations and program infractions. The IAP model indi- cates the need for a range of graduated sanctions tied directly and proportionately to the seriousness of the violation instead of rel


 components are based on many of the design elements in JOB- START and the recommended practices listed above.
Not all HRHN participants receive all of the above-listed services. DPOs who supervise HRHN probationers and CBOs that provide services for


lyses for the HRHN program. For each of the big six outcomes in the HRHN program, Table 2.9 shows the baseline and follow-up means, the odds ratio of the interaction term year × post in the logistic regression, and 95-percent CI for the odds ratio.
D


h the six months before
change of the current year compared with
the previous year. A negative
34 Los Angeles County Juvenile Justice Crime Prevention Act: Fiscal Year 2012–2013 Report
entering. One of the two supplemental outcome measures for YSA, t


y noted, all of the outcome differences listed were statistically significant (p < 0.05), meaning that the performance of JJCPA youth was significantly different from that of comparison-
Current JJCPA Programs and FY 2012–2013 Outcome Measures 35
Tab


es) did not meet this requirement, so Fisher’s exact test was used for those with very small cell sizes. For programs that used a pre-post evaluation, McNemar’s test was used to determine significance for arrests and incarcerations. For pre-post com-


anagement interventions.
Also consistent with the what-works research is the school-based probation supervision program’s call for case-management interventions that
• assess the probationer’s strengths and risk factors
• employ strength-based case-m


in the group. i i i
 38 Los Angeles County Juvenile Justice Crime Prevention Act: Fiscal Year 2012–2013 Report
PROB youth, by definition, are not. This could potentially put comparison-group youth at higher risk for negative outcomes.
The big six ref


ble F.2 in Appendix F. More detail on big six outcomes by cluster are in Table G.1 in Appendix G. In this program, youth from cluster 2 had higher arrest, incarceration, and probation-violation rates than youth in other clusters. Youth in clus- ter 2


re compared at program entry and at six months thereafter.
Outcomes for School-Based Probation Supervision for Middle School Probationers
For outcome analyses, we examined 62 school-based middle school probationers and 308 com- parison-group youth. C


son Group and Reference Period for School-Based Probation Supervision for At- Risk High School Youth
The comparison group for SBHS-AR consists of 694 participants in the program whose out- comes were calculated during the previous year (FY 2011–2012)


oup members (%)
35 30 25 20 15 10
5 0
      Arrest
Incarceration
Completion of probation
NOTE: A missing bar for a cluster indicates that no one in the cluster had the indicated outcome.
entering (7.3 percent versus 25.9 percent). Mean strength score


and comparison-group youth, big six outcomes were measured during the six months following entry into the program. For supplemental school outcomes— attendance, suspensions, and expulsions—program participants were compared in the term before program


ut one at-risk participant in the school-based middle school program. As Figure 2.16 indicates, cluster 2 had the highest rate of both arrests and incarcerations, while cluster 1 had no arrests or incarcerations. More-complete details are in Table G.


uld not be computed because the baseline for the previous year’s cohort was 0. Diff – Diff gives the percentage change of the current year compared with the previous year. A negative value in that column indicates a reduction, while a positive value


-
Current JJCPA Programs and FY 2012–2013 Outcome Measures 49
Figure 2.15
Outcomes for School-Based Probation Supervision for At-Risk Middle School Youth, FY 2012–2013
 100 90 80 70 60 Group members (%) 50 40 30 20 10 0
Arrest
Incarceration
School at


eline for the current year was 0. Diff – Diff gives the percentage change of the current year compared with the previous year. A negative value in that column indicates a reduction, while a positive value shows an increase.
truancy.14 Because those s


on and City of Los Angeles Department of Recre- ation and Parks agencies collaborate with Probation Department DPOs in providing super- vision and individualized treatment services for at-risk and probationer youth. The program strives to reduce juve


search.
Comparison Group and Reference Period
A pre-post design was used to evaluate the PARKS program. Because all PARKS participants were at-risk youth, the pre-post design is less problematic here than with other programs that include probationers


probationers and at-risk youth and their families. HB DPOs employ strength-based case-management interventions based on the MST and FFT models. The HB program and case-management interventions are designed to empower parents with the skills, resourc


lemental outcomes were school attendance and housing- project crime rate. Attendance was measured in the last academic period before program entry and in the first complete academic period after program entry. Housing-project crime rates were measure


   Baseline Program youth
             56 Los Angeles County Juvenile Justice Crime Prevention Act: Fiscal Year 2012–2013 Report
the participating youth and provide activities consistent with resiliency research. The program activities involve
• clea


 outcome, juvenile hall behavior violations, was measured by the number of special incident reports (SIRs) in the first 30 days of the program and in the last 30 days of the program or during month 6 of the program, whichever came first.
Outcomes
For


Angeles County Juvenile Justice Crime Prevention Act: Fiscal Year 2012–2013 Report
Table 2.17
Means, Odds Ratios, and Confidence Intervals for Outcomes for Inside-Out Writers
   Mean: Current Year (%)
Outcome Baseline Follow-Up
Arrest 49.28 32.82
Inc


9
0.823–1.425
    43.95 25.58 17.81 18.48 1.97 13.03
7.88 13.88 1.38 8.05
21.10 13.27
  change of the current year compared with the previous year. A negative
CHAPTER THREE
Juvenile Justice Costs for JJCPA Participants
In this chapter, we present ana


xit for the hall stay during which program services were received.
  59
60 Los Angeles County Juvenile Justice Crime Prevention Act: Fiscal Year 2012–2013 Report
savings could result if at-risk youth are deterred from future offending, but data to ma


t estimates in this chapter include arrests during the six-month eligibility period mandated for big six outcomes, the number of program youth will match the number used to report outcomes to the BSCC, not the total number served during the fiscal ye


 fee of $35. To adjust for inflation, we have converted the $2,181.33 cost of an LAPD arrest in 2014 to $2,133.14 in 2012 dollars.
Estimates of arrest costs were provided by the Los Angeles County Sheriff’s Department (LASD) in response to a request


ment, not a cost-increase adjustment, so our court cost is still likely an underestimate.
 As of the date of this writing, the most-recent available data for computing court costs come from 2009. The Judicial Council of California (2010) reports that


 Health Services Initiative
Our cost comparisons involve estimates of program and other juvenile justice costs during the six months after starting the program (follow-up) versus those in the six months before start- ing (baseline). In the case of pr


834 0.67 1,401 9.72 5,358
Camp 402.88 Day
Court 3,796.31 Appear.
Total 13,695
NOTE: A positive number in a difference column
beginning the program than in the six months before beginning. A negative number indicates that the cost was higher after ent


uding Young Women at Risk)
Juvenile Justice Costs for JJCPA Participants 67
    Baseline
Median Units Mean ($) ($)
0.00 0 0 17.67 127 0 0.07 144 0 1.30 716 0
Camp 402.88 Day
Court 3,796.31 Appear.
Total 1,767 0
Follow-Up
Mean ($)
1,254 160 79 275
58


 IOW participants is therefore defined as the six months after release from the hall, and the baseline period is the six months before entering the hall.
We also included school attendance as a contributor of total cost for the four school-based prog


des $1,469 in program costs. As Table 3.13 indicates, supervision juvenile hall, camp, and court costs were also slightly higher in the follow-up period than in the baseline. With overall total costs averaging only $345 per youth in the baseline peri


s
Table 3.16 shows the mean baseline and follow-up costs per participant in each JJCPA program in FY 2012–2013. Weighted averages are also shown for each initiative. It should be noted that the costs for each initiative are largely driven by the cost


e are not able to extend the time frame to measure changes, however, because not enough time has elapsed to allow us to obtain data beyond a six-month period. With a longer follow-up period, the initial program costs may be offset by reductions in su


 Initiative
the mean cost was lower in the six months after A negative number indicates that the mean cost
For each of the three FY 2012–2013 initiatives, Table 3.17 shows the mean net cost for each juvenile justice cost—i.e., the mean difference bet


ntegral part of many JJCPA programs and can reasonably be expected to be somewhat higher in the follow-up period than in the baseline—in fact, we define program costs as zero in the baseline, guaranteeing that program costs will be greater in the fol


l.
77
78 Los Angeles County Juvenile Justice Crime Prevention Act: Fiscal Year 2012–2013 Report
Outcomes
Because youth in the MH program represent 91 percent of all youth in the Enhanced Mental Health Services initiative for whom big six outcomes wer


 statistical samples.
Historical and Contemporaneous Comparison Groups and Pre-Post Comparisons
Three of the four programs that used contemporaneous comparison groups (SNC, MST, and SBMS-PROB) were quite small. SNC and MST participants were not signi


erforming at least as well in the current year as in the prior year. As Table 4.1 indicates, the FY 2012–2013 cohort equaled or surpassed the performance of the FY 2011–2012 cohort in 31 of 34 outcomes. In two outcomes, the current year’s cohort perf


 cohorts. NA = not applicable.
difference-in-differences analyses for the seven JJCPA programs that used the previous year’s cohort as a comparison group.
Among the programs that used the previous year’s cohort as a comparison group, a suc- cessful o


cent) SBHS participants reported school attendance, and 1,635 (65.0 percent) were tested for strengths and risks. In the MH program, by contrast, only 92 of 1,324 (7.0 percent) who received mental health treatment reported BSI scores. These program-t


and then compare the performance of the treatment population with that of the comparison group. Such com- parison groups are always vulnerable to the criticism that they are somehow not comparable to the program group such that observed differences


 to evaluate “what works” in the treatment process. Because we do not have the data, we are unable to report on implemen- tation measures or what was delivered.
This is the 12th year of RAND’s JJCPA evaluation findings. Over the years, the strength a


ld, and Carmenita (ABC) Unified School District (USD) Services
Asian American Drug Abuse Program (AADAP)
Description
Individual and family counseling, tutoring, and after-school services
Individual and family counseling, supportive services, and subs


s; anger-management counseling; tutoring; and community services
No-cost and low-cost legal representation for a wide array of issues, including landlord/tenant disputes, substandard housing, veteran benefits, kinship care, elder abuse, patient right


ohol counseling
Faith-based youth after-school literacy and tutoring, family support services, and meeting space
Substance abuse treatment program, school-based services, mentoring, tutoring, and community services
Anger management and drug and alcoh


lth services
Community-service hours
Individual, group, and family counseling and parenting classes
Substance abuse treatment, workforce readiness, health and mental health care, tutoring, study skills, and educational support
Individual, family, and


roup home, foster care, family reunification, mental health, and family support services
Crisis intervention and mental health services
Counseling
Substance abuse services and individual, family, and group counseling
Family preservation in-home outre


onal youth basketball initiative, launched by the National Basketball Association and the Women’s National Basketball Association, to connect players, parents, and coaches
Family-preservation services, in-home case management to help families mobiliz


l services and representation for low-income clients
Drug and alcohol counseling and treatment, drug testing
Individual and family counseling
Individual and family counseling, anger management, and parenting classes
Parenting classes and individual c


ing, including testing, individual and group counseling, and community services
Treatment, prevention, and disciplinary components to alter negative behavior, specifically for at-risk youth, overseen by Baldwin Park Police personnel
Parenting classes


ndent bookstore in an effort to promote arts enrichment and literacy in the culturally neglected northeast San Fernando Valley and beyond
Counseling and parenting classes
Community Providers of JJCPA Program Services 93
    94 Los Angeles County Juve


A
Youth Opportunity Movement Youth Speak Collective
Description
Employment and workforce readiness
Substance abuse counseling; individual, group, and family counseling; parenting classes; anger-management counseling and tutoring; and mental health co


Weights for treatment-group youth are defined to be 1. These weights are then used to compare the mean values of the two groups on each of the independent variables. If the treatment and comparison groups show similar mean values when weights are ap


4. incarceration: Similar to arrest, incarceration is a valid indicator of delinquency and recidivism. However, incarceration can also be used as a sanction for case-management purposes, and courts often impose incarceration as a sanction to get the


Home-based HRHN, male
HRHN employment Home-based HRHN, male HRHN employment Substance abuse treatment
Gender specific Home-based HRHN, female
FFT and MST FFT
Home-based HRHN, male
Cluster
3 1,5 5
4 3 1 4 2, 4
1,5
1,5 2
2 2, 5 2
4 3 2
4 4
2, 4 2
3


tatistical testing is not possible if one of the measures is 0.
BSCC-Mandated and Supplemental Outcomes for Individual JJCPA Programs, FY 2012–2013 105
Table E.3
Outcomes for Multisystemic Therapy, FY 2012–2013
     BSCC-Mandated Outcome Number
Arres


 Outcome Number Percentage
Arrest 19 2.97 Incarceration 3 0.47
Sample Size
   639
   639
    83
    63
    67
83
Number
25 3 27 30 21
6
Comparison
Percentage Sample Size
3.34 748
0.40 748 23.08 117 34.09 88 22.58 93
5.17 116
Follow-Up
Mean Sample Siz


,671
2,047
Sample Size
1,618 1,347 1,245 1,635 1,635
   Completion of probation
Completion of restitution
Completion of community service
Probation violation
BSCC Supplemental Outcome
School attendance School suspensions School expulsions Strength sc


d Outcome
Arrest
Incarceration
Completion of probation
Completion of restitution
Completion of community service
Probation violation
BSCC Supplemental Outcome
School attendance School suspensions School expulsions Strength score Barrier score
Number


Completion of community service
Probation violation
BSCC Supplemental Outcome
School absences
Number
3 0
Baseline
Percentage Sample Size
0.10 3,144 0.00 3,144
Follow-Up
Number Percentage
8 0.25 3 0.10 NA NA NA
NA
Sample Size
3,144 3,144
   NA NA NA
N


plicable.
a Difference is statistically significant (p < 0.05).
BSCC-Mandated and Supplemental Outcomes for Individual JJCPA Programs, FY 2012–2013 115
Table E.14
Outcomes for Inside-Out Writers, FY 2012–2013
   BSCC-Mandated Outcome
Arrest
Incarcera


community service 2 Probation violation 0
Females
Percentage
20.00 6.67 22.22 50.00 28.57 0.00
Sample Size
15 15 9 4 7 9
Males
Number Percentage Sample Size
8 17.02 47 4 8.51 47 2 5.00 40 8 25.81 31 2 6.45 31 2 5.00 40
     Table F.4
Outcomes for Sch


ssembly, 2000. BJS—See Bureau of Justice Statistics.
Board of State and Community Corrections, Juvenile Justice Crime Prevention Act: March 2014 Annual Report, Sacramento, Calif., March 2014. As of July 2, 2014: http://www.bscc.ca.gov/downloads/JJCPA


nd Emerging Responses,” OJJDP Juvenile Justice Bulletin, Vol. 7, No. 1, April 2000, pp. 3–13. As of July 2, 2014: https://www.ncjrs.gov/html/ojjdp/jjjnl_2000_4/youth.html
Cocozza, Joseph J., and Kristin A. Stainbrook, The Ohio Linkages Project: Final


90.
Fain, Terry, Sarah Michal Greathouse, Susan F. Turner, and H. Dawn Weinberg, “Effectiveness of Multisystemic Therapy for Minority Youth: Outcomes over 8 Years in Los Angeles County,” OJJDP Journal of Juvenile Justice, Vol. 3, No. 2, Spring 2014,


o, Thomas, and Richard Barnum, Massachusetts Youth Screening Instrument, Version 2: MAYSI-2—User’s
Manual and Technical Report, Sarasota, Fla.: Professional Resource Press, 2006.
Hammond, Sarah, Mental Health Needs of Juvenile Offenders, Washington,


T. Cullen, and Paul Gendreau, “Beyond Correctional Quackery: Professionalism and the Possibility of Effective Treatment,” Federal Probation, Vol. 66, No. 2, September 2002, pp. 43–49. As of April 9, 2014: http://www.uscourts.gov/viewer.aspx?doc=/usco


. 2012:4, 2012. As of July 3, 2014:
http://www.campbellcollaboration.org/lib/download/2356/
Mulvey, Edward P., and Anne E. Brodsky, “Secondary Prevention of Delinquency: More Than Promises from the Past?” paper presented at the 98th Annual Convention


ity of Colorado, Boulder, July 1994.
Turner, Susan, and Terry Fain, “Validation of the Risk and Resiliency Assessment Tool for Juveniles in the Los Angeles County Probation System,” Federal Probation, Vol. 70, No. 2, September 2006, pp. 49–55. As of


Peggy Giordano, Denise C. Gottfredson, Allison A. Payne, Barry C. Feld, and Meda Chesney-Lind, Causes and Correlates of Girls’ Delinquency, Washington, D.C.: Office of Juvenile Justice and Delinquency Prevention Bulletin NCJ 226358, April 2010. As of


  CORPORATION
Shining a Light on State Campaign Finance
An Evaluation of the Impact of the National Institute on Money in State Politics
Geoffrey McGovern, Michael D. Greenberg
 ELECTRONIC COPIES OF RAND RESEARCH ARE PROVIDED FOR PERSONAL USE; POSTIN


 Civil Justice
The RAND Institute for Civil Justice (ICJ) is dedicated to improving the civil justice system by supplying policymakers and the public with rigorous and nonpartisan research. Its studies identify trends in litigation and inform policy


 established legal framework for campaign finance disclosure in the United States. In principle, the Institute’s data can help to encourage greater awareness, transparency, and accountability in the political system. The
 ix
Institute has been collec


as an advocate for campaign finance reform or other direct policy changes—we adopt the “ecological view” as our preferred approach for guiding an impact evaluation. This focuses the attention of the report on the uptake and use of Institute data and


oth at the state level and nationally. The users we interviewed see the Institute as the gold standard resource for data on money in state politics. Indeed, the Institute has been a driving force behind informed public discussion of state campaign fi


nowledgments
This report would not have been possible without the contributions of several professionals at the National Institute on Money in State Politics; input from the academic, journalistic, and advocacy sectors; and support and assistance fro


et of controversial campaign finance reforms established under the McCain-Feingold law, voiced concern over “the actual corruption threatened by large financial contributions and the eroding of public confidence in the electoral process through the a


al speech; and disclosure permits citizens and shareholders to react to the speech of corporate entities in a proper way. This transparency enables the electorate to make informed decisions and give proper weight to different speakers and messages.4


udents and the public at large.”9
In 2013, the Institute hired RAND to conduct an evaluation of the impact of the Institute’s data, research, and outreach activities. As described in Section 2, RAND previously conducted a study on the Institute’s dat


flow of political money at the state level would have had to contact each states’ election official, to obtain copies of the relevant [hardcopy] disclosure forms. As a practical matter, the distributed and analog nature of state campaign finance reco


a staff of 26, are financed mainly through foundation generosity. Major foundation support in 2013 came from the Bauman Foundation, the California Endowment, the Energy Foundation, the Ford Foundation, the Hewlett Foundation, the MacArthur Foundation


stry Influence allows users to look at 130 industry sectors and to evaluate giving patterns within and across states.
• The Legislative Committee Action Tool (L-CAT) attempts to link campaign contributions with legislative outcomes.
• Other tools and


the Institute is limited to the extent that the states are limited or inconsistent in the amount of information that must be disclosed. Likewise, if states fail to enforce their own laws to ensure compliance with disclosure rules, the Institute is li


 the public campaigns toward greater electoral transparency, as evinced by the creation and operations of the FEC, as discussed in Section 1 and earlier in this section.
The potential and actual ability to contribute to an informed electorate, an emp


Center for Responsive Politics, Brief on Citizens United v FEC, 2009 WL 2365228, July 31, 2009.
 9
If the Institute had spent the last decade compiling data that did not have the potential to unlock new insight, understanding, and clarity about the s


-authored analyses, drawing on the data. The Institute’s “customers” include a variety of stakeholders that seek access to campaign finance data, with journalists, academics, and public interest groups being some of the most obvious users. A correspo


d search of social science publications on the academic journal database JSTOR revealed about two dozen scholarly articles, mostly in the field of political science, that cite the Institute or use its data as part of their quantitative analyses (this


ercount the degree to which Institute reports and data have been used. Journalists are using the Institute for information, context, and background that do not show up in their copy but is nonetheless essential to the content-generating process.
Trac


g and facilitating access to state campaign finance information, the Institute aims to make that information more useful and, ultimately, to encourage others in sustained examination of the role and effect of money in politics. The degree to which th


13.
26 For example, some staff we spoke with suggested that the Institute’s impact closely corresponds with its data repository function. Consequently, they suggested improving impact by expanding the data that the Institute collects, by improving th


more like an advocacy group that pursued an agenda in a specific public policy area (for example, like the Sierra Club or the National Rifle Association), we could more conventionally assess the degree to which lobbying and public awareness raising


late to impact), we provide more detailed recommendations in Section 5.
Some preliminary comments are due regarding our process for identifying and selecting candidates for interviews. By design, we focused only on users of the Institute’s data and r


28 We think that the ways the Institute’s data could be transmitted to the voting public might include intermediaries, such as the media (discussed below), and voter education efforts, such as Project Vote Smart or the League of Women Voters.29 These


luation. That is beyond the scope of our effort. We focused on users of the Institute’s data to understand why they are using the data and how they viewed the experience. Overall, each of the journalists with whom we spoke was very appreciative of th


 Cooper noted that the “new guys all link to Nate Silver and the like, so they can better spend their resources elsewhere; any expectation that these news organizations are going to have the capability to
20
analyze the data in a timely way is unreal


t to say that using other states’ data as a point of reference is unlikely. Knowing whether a neighboring or similar state experiences similar patters of giving to political campaigns can add to a richer and more informative story. We mean only to su


o use when reporting on issues of campaign finance in the states. Suggestions for greater impact in the community were distinctly in the direction of new and social media strategies, coupled with digested data.34
The Academic Communities
The academic


wn argued that the likelihood of winning is what attracts money, not the other way around. Campaign donors, he argued, are strategic in their use of donations, directing their support to candidates that are already of high quality and likely to win t


lation pending before committees.40 Others have targeted search results, such as the amount of money above $50 given in the state of Texas for electoral contests for the Supreme Court of Texas.41 And still others come from advocacy organizations that


dangered Balance Between Impartial Courts and Judicial Election Campaigns,” Georgetown Journal of Legal Ethics, Vol. 21, No. 4, Fall 2008, pp. 1229–1258.
 24
Beyond the political science and legal literature, fewer articles appear to cite the Institu


 and then performed statistical analysis, the Institute’s data are already coded in many ways.
43 Deniz Igan, Prachi Mishra, and Thierry Tressel, “A Fistful of Dollars: Lobbying and the Financial Crisis,” NBER Macroeconomics Annual, Vol. 26, No. 1, 2


n peer-reviewed journals. However, not all relevant academic fields are publishing analyses informed by the Institute’s data; the main users are the political and legal communities.
The Advocacy and Research Groups
The last category of Institute data


nstitute when there is a very specific need for linking campaign finance with state policy.53 Rather than the partnership model (Sunlight), the Justice Policy Institute is using the Institute as a resource that informs their specific policy needs.54


n collecting data from every state election office). Freed and his staff use the database itself, looking into corporate spending in the states. The result of their research and advocacy is the adoption of political donation disclosure and corporate


e that provides secondary and tertiary users with a wealth of information that they can then use to bolster their own policy- motivated and knowledge-generating activities.
Taking this perspective from ecology, we focused on identifying and interview


ons that loosely track “impact.” We have assumed, from the beginning of this research, that this broader community of interest was an important intended audience for this work.
As we reported earlier, we believe that impact assessment for a data reso


the advent of more information and more powerful computing comes a set of challenges that need to be overcome before users are aware of, interested in, and able to use the data for their purposes. These three challenges to big data presently face the


treme end, some users who are interested in untangling the systemic role money plays in state politics would need to be able to perform very sophisticated statistical analyses to wrest the insights that are potentially locked within the Institute’s d


communities has a natural affinity for the type of information the Institute has gathered, yet their needs when using the data and the paths they take to the Institute itself can vary dramatically. Keeping the multiple customer base in mind will be


data sources, suggesting that there is demand for this type of information from state agencies. For example, see Idaho Secretary of State Election Division, website, undated; Montana Commissioner of Political Practices, “Political Practices—General


may be more productive to partner more formally with the communication and journalism schools for annual symposia and training courses.
The Academic Community Is a Permanent Audience for the Database
The group with the highest level of awareness, int


e financial supporters are attracted to the blossoms, but not to the root, there is a risk that the data resource will not survive. Data gathering is not a flashy business; but it is essential to the informed debate that the advocacy groups and their


irkland, Virginia Gray, David Lowery, Jennifer Sykes, and Mary Deason, “Mediated Density: The Indirect Relationship Between U.S. State Public Policy and PACs,” State Politics & Policy Quarterly, Vol. 11, No. 4, December 2011, pp. 440–459.
Brandeis, L


lection Commission, website, undated. As of July 21, 2014:
http://www.fec.gov
FiveThirtyEight, website, undated. As of July 24, 2014: http://fivethirtyeight.com/
Greenfield, Victoria, Valerie L. Williams, and Elisa Eiseman, Using Logic Models for Str


mber 2014,” 2014. As of July 29, 2014:
http://www.marinlwv.org/election_nov2014/
Lowery, David, and Virginia Gray, “The Population Ecology of Gucci Gulch, or the Natural Regulation of Interest Group Numbers in the American States,” American Journal o


4:
http://www.nytimes.com/2014/04/03/opinion/how-not-to-enforce-campaign-laws.html?_r=0
Senior, Jennifer S., “Expanding the Court’s First Amendment Accessibility Framework for Analyzing Ballot Initiative Circulator Regulations,” University of Chicago


  CORPORATION
Police Department Investments in Information Technology Systems
Challenges Assessing Their Payoff
Brian A. Jackson, Victoria A. Greenfield, Andrew R. Morral, and John S. Hollywood
In the wake of the economic downturn that began in 2007


s on
law enforcement performance can benefit from the results of the logic modeling and exploratory analysis. Specifically, it is important to collect data not just on department acquisition of IT systems, but also on how the systems are used and the


t closed-circuit televisions (CCTVs) in stores, meaning that it took hours or longer to disseminate images of crimes and criminals. Now, officers use their phones to record the video displays and can relay those images immediately.
Such novel applica


paths forward and to inform law enforcement executives in making IT decisions.
PoliCe Use oF inFormATion TeChnology
To do their jobs effectively, law enforcement professionals at
all levels depend on information. According to Brown (2001,
p. 352), “b


ith the benefits one might expect from IT. The size and complexity of the area that a law enforce- ment organization protects will presumably shape the value of computerized hotspot identification—which could be a more difficult analytic task for a l


p. 27). Video conferencing was also highlighted as a form of information and communication technology that reduced the time required for officers to appear at hearings and even substituted for physically transporting inmates to judicial proceedings (


tool kit as to mismanagement by developers and users of IT.” Many of the studies that followed his review and assessment have, more or less effectively, attempted to address these deficiencies.
Broadly speaking, the literature tells a story about the


rtment investments in IT and improvements in clearance rates, crime rates, or other productivity measures. However, when organizational characteristics and management practices are included in the analysis, they did find expected productivity effects


 as a component of policing requires both identifying the range of ways it can contribute to different police goals and determin- ing effective ways to measure those varied contributions.
6 These complications affect the assessment of IT in other sec


t al. (2009).8 They employ a framework that takes the mission of a program or organization as a foundation and then charts the path from inputs to activities and outputs and, eventually, to outcomes. In effect, the mission statement serves as a guide


s dis- patch, personnel management, surveillance, and in-service training. Specific types of IT investments hypothesized to support these functions include:
- Administrative systems, including records management
- Communications systems, including co


ow they are intended to be used. We think of planning inputs as those drawn into decisions about allocating resources to activities. As a practical matter, it may be difficult to draw clear distinctions in either a box or real life. For example, some


h and wellness services
Resource assignments, employee appraisals, determinations, records (e.g., case, employee, complaint, and audit), skills and competencies, and health and well-being
 Improvements in safety, security, and quality of life
  Proac


we pro- ceed to the right of the model, the department can be expected to have less and less control over how events unfold; neverthe- less, we suggest the possibility of monitoring and evaluation, even in this region, as means of strengthening the


As a result, better evaluation of IT in law enforcement agencies is not just about matching IT tools to tasks; it must also consider whether the department or departments being evaluated are actually undertaking particular tasks. In the literature,


typal mission statement: “To improve quality of life by protect-
ing life and property; detecting, solving, and reducing crime; reducing fear of crime; and enhancing safety in cooperation with community.”
Below are the mission statements that we sele


licing.” (http://www.police.co.nassau.ny.us/VisionMission.aspx)
• Sacramento Police Department: “The mission of the Sacramento Police Department is to work in partnership with the Com- munity to protect life and property, solve neighborhood problems,


uld even be expected from particular IT investments, given departmental strategies and officer allocation decisions.
effects of multiple changes in IT or practice were mistakenly ascribed to only one system or if offsetting effects in different parts


to reconcile these findings is to run causality effects in reverse—that is, arguing that these technologies were purchased in response to increasing crime or problems clear- ing crimes rather than being associated with crime increasing after they wer


ctivities. A depart- ment that was not doing community policing—or that had a community-policing unit in name only—might buy the same system and would get little or no benefit from it. Our team sought to build measures for different elements of react


on, Erik, “The Productivity Paradox of Information Technology: Review and Assessment,” Communications of the ACM, Vol. 36, No. 12, 1993, pp. 66–77.
Brynjolfsson, Erik, and Lorin Hitt, “Information Technology as a Factor of Production: The Role of Dif


 tance, Office of Justice Programs, U.S. Department of Justice, March 2008.
Greenfield, Victoria A., Valerie Williams, and Elisa Eiseman,
Using Logic Models for Strategic Planning and Evaluation: Application to the National Center for Injury Preventi


hnology and Its Impact on Productivity: Firm-Level Evidence from Gov- ernment and Private Data Sources, 1977–1993,” The Cana- dian Journal of Economics, Vol. 32, No. 2, 1999, pp. 335–362.
Lichtenberg, Frank R., “The Output Contributions of Computer E


06, Philadel- phia, Pa.: Temple University, 2008. As of August 21, 2014: http://www.threatanalysis.com/misc/ CCTV%20-%20Philly.pdf
Reaves, Brian A., “Local Police Departments, 2007,” Bureau of Justice Statistics, NCJ 231174, December 2010.
Santhaman,


his report
With declining revenues over the past five years, cities and states in the United States have sought to limit the growth, and in many cases reduce the budgets, of their police forces. This budget tightening has presented police chiefs and


, see http://www.rand.org/safety-justice or contact the director at sj@rand.org.
Limited Print and Electronic Distribution Rights
This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property


  CORPORATION
Does San Francisco’s Community Justice Center Reduce Criminal Recidivism?
Beau Kilmer and Jesse Sussell
SUMMARY ■ The community court model is a novel approach for processing criminal offenders. The goals of community courts are largely


case processing at the CJC and Hall of Justice (and subsequent service utilization) into future analyses.
 1. INTRODUCTION
The community court model is a novel alternative approach for processing criminal offenders. The first community court opened i


tchment area borders.9 There are guidelines as to which cases are eligible to be heard at the CJC: for example, theft, drug possession, and disorderly conduct are explicitly eli- gible, while felony violence, drunk driving, and cases involving guns a


tive at accomplishing this goal is an open question.13 We are unaware of attempts to evaluate community courts using ran- domized controlled experiments; thus, published evaluations rely on inferior approaches that require various assumptions. Table


es (race, age, etc.) are added to the econometric models, no factors remain which are correlated with both the outcome variable (a proxy for recidivism) and the treatment status (whether or not the individual was processed by the community court). To


occurs after each offender’s first visit to the court. The treatment plan is created by a provider from the San Francisco Department of Public Health (DPH) and is tailored to individual offender needs and criminal behavior. All treatment plans requi


n in the CJC is also optional for defendants. At any point they can leave the program and resume traditional criminal justice processing at the HOJ. This poses a challenge for researchers attempting to estimate the causal effect of the CJC on a varie


data on arrests for new charges, the CMS extract also contained records for “rearrests”—probation revocation or parole violation events related to prior arrests. The CMS extract contained records of out-of-jurisdiction warrant arrests (when individua


within the state. We matched CMS records to CADOJ records using name, date of birth (DOB), and, when available, Social Security number (SSN). In addi- tion, we used a “fuzzy” matching procedure that allowed for close and partial matches, meaning for


rticular sensitivity analysis.
Our DD model is constructed from an indicator variable which identifies whether a charge-eligible arrest occurred inside or outside of the CJC catchment area, an indicator variable which identifies whether the arrest oc


a jointly) is modeled, and γ is the estimate of the treatment effect.
Because eligibility for the CJC is determined not just by geographic eligibility but also by charge eligibility, we subset our analysis to those cases so classified. Developing thi


 within any of the included districts will be more likely to equally affect those inside and outside of the catchment area, consistent with the key assumption of the research design.
The research design we describe here produces estimates of the Inte


individuals who bench warrant following their initial arrest (by failing to appear at a hearing), and individuals who violate probation following case adjudication at the HOJ.
Within our analytic sample (see Table A1 in the online appendix) and in th


t area it is lower. These results are consistent with the hypothesis that the CJC has a reduced rearrest rate.
5.3 Ordinary Least Squares (OLS) Regression Results
Our main model results are displayed in Table 4. The group of results columns on the le


to add charges to the initial arrest, but for administrative reasons inserted those charges into a new arrest. This could occur (for example) if identification following a fingerprinting procedure allowed the DA to associate an offender with a previo


iduals will be cited for loitering but not for felony assault. However, it also conveys information about law enforcement perceptions about the arrestee: the office of the district attorney will occasionally issue “do not cite” orders for chronic re-


th slightly smaller in absolute value (–0.029, t=–2.10). When the entire
Figure 5: Areas for Sensitivity Analysis Control Groups—Concentric Circles Around CJC Catchment Area Centroid (each circle = 2,000 feet)
  RAND RR735-5
city is used as a control


t only offset an increase but actually decreased recidivism might be chal- lenged if there were another intervention in the post-period that affected only the CJC or control areas; however, we are not aware of any other interventions (police or other


Center for Court Innovation summarized that the Midtown Community Court had “mixed recidivism impacts; no clear effect on individual offender recidivism.”36 Table 1 also highlights how the evalu- ation of the Red Hook CJC (RHCJC) exploited a natural


tp://www.courtinnovation.org/research/community-courts-around-world?mode=4&url=research%2F4%2Farticle
3. M. S. Frazer, The Impact of the Community Court Model on Defendant Perceptions of Fairness: A Case Study at the Red Hook Justice Center, New York


t That Has Succeeded,” Connecticut Law Review, Vol. 34, 2001, p. 123; and Frazer, 2006).
14 Westat, East of the River Community Court (ERCC) Evaluation, Rockville, Md., July 2012. 15 Westat, 2012.
16 CJC, 2012.
17 Online appendix to this report may b


ue new rearrests. So the indicator for recidivism (the outcome variable) is equal to 1 if either of the following occur in the year subsequent to the initial arrest: (a) any new arrest or street citation, or (b) any bench war- rant arrest occurring t


ervision (rather than committing a new crime), they can no longer be returned to State prison but must be sanctioned in local (county) jail or community alternatives, including house arrest, drug treatment, or flash incarceration.
34 Model includes d


the data required to conduct the analyses. We are also indebted to the CJC staff, CJC Advisory Board, CMS Committee, SFPD, and others for addressing our numerous inquiries and steering us down the correct path. We thank Layla Parast for reviewing ear


TIONAL AFFAIRS LAW AND BUSINESS NATIONAL SECURITY POPULATION AND AGING PUBLIC SAFETY SCIENCE AND TECHNOLOGY
TERRORISM AND HOMELAND SECURITY
The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research


 The Changing Role of Criminal Law
in Controlling Corporate Behavior James M. Anderson, Ivan Waggoner
 CORPORATION
For more information on this publication, visit www.rand.org/t/rr412
  Library of Congress Cataloging-in-Publication Data is available


 19, 1977.
 iii
iv The Changing Role of Criminal Law in Controlling Corporate Behavior
regulation, risk management, and insurance. The institute builds on a long tradition of RAND Corporation research characterized by an interdisciplinary, empirical


 . . . . . . . . . . . . . . . . Conclusion........................................................................
CHAPTER THREE
Recent History: A Shift to Reforming Corporations from Within. . . . The Traditional Approach: Prosecuting the Corporati


lar Crimes . . . . . . . . . . . .......
4.7. Prosecution Data on Securities Fraud . . . . . . . . . . . . . . . . . . . . . .......
4.8. Federal Prosecutions for Lending Fraud . . . . . . . . . . . . . . . . . . .......
4.9. Federal Prosecutions for


to benefit themselves, e.g., embezzlement.
ix
x The Changing Role of Criminal Law in Controlling Corporate Behavior
To address the question of the proper role of criminal sanctions, we (1) describe the current state of the use of criminal sanctions i


rate monitors as a condition of probation, after the corporation was convicted. Over time, however, compliance programs and acquiescence to restructuring moved up in the criminal process to become criteria on which a decision to prosecute was made. T


ions (Brandon L. Garrett and Jon Ashley, “Federal Organizational Plea Agreements,” Charlottesville, Va.: University of Virginia School of Law, undated).
    Sentencing Commission data
Garrett data
       Corporations
  prosecuting offenders to encour


ral consequences. Federal agencies are increasing debarment and suspension proceedings in an effort to minimize waste and fraud. The result is the huge increase in DPAs and NPAs, which lack some of the key benefits of public law. Typically, these agr


fendant has to be reviewed by an appropriate federal judge, so should these agree- ments be reviewed for fairness and to ensure that they are in the public interest. This practice would provide some assurance that the agree- ments are genuinely in th


of ICJ during this project and Pamela Calig for her able administration of the project. We thank Jennifer H. Arlen for her helpful comments on the project and Joseph W. Doherty and Ingrid V. Eagly for their assistance, as well as the UCLA-RAND Center