tury Together with Some Origi- nal Matter, London: J. Hatchard and Son, 1844, p. 268) quotes the passage as “Corpora- tions have neither bodies to be punished, nor souls to be condemned; they therefore do as they like.” Another version of this quotat


4–35) and Frederick Pollock (“Has the Common Law Received the Fiction Theory of Corporations?” Law Quarterly Review, Vol. 27, No. 2, 1911, pp. 219–235).
4 For example, a recent headline in the New York Times reads, “E-Mails Imply JPMorgan Knew Some M


ent grew in part out of perceptions that large corporations face little scrutiny for actions that harmed millions.11
Generally, consternation about the role of criminal law in con- trolling behavior in the corporate context is not a new set of concer


orporate Criminal Liability
This report addresses the criminal liability of both corporations and individuals acting within these corporations. Corporations may be criminally liable for the crimes of their employees and agents. The agents and employe


e criteria used to prosecute and the sen- tencing consequences. Typically, these include the strength of the case, the extent and history of misconduct, whether there is a compliance program, the corporation’s cooperation with the investigation, the


s issue, we first con- ducted legal research on the origins and development of criminal lia- bility for corporations, vicarious liability for employers for the acts of their employees, and the evolution of the requirement of criminal intent. As noted


identify whether a defendant is an individual or a corporation.
Civil-Case Data
The civil-case data contain similar information to that in the criminal cases, such as the district in which the dispute was disposed, the con- trolling circuit, and dock


ately, the data col- lected by the Sentencing Commission are incomplete and thus not a full representation of all federal criminal cases involving organizations. Several studies have noted important omissions of large companies that were not included


e to provide suitable information that can be used to identify individual cases.27
TRACFED
This data set is compiled by the Transactional Records Access Clearing- house (TRAC), a research center at Syracuse University. TRACFED is an online data wareh


e supplying government agency. If a dis- crepancy cannot be resolved, the data are flagged to alert the user. A federal judge ordered DOJ to disclose information containing the name of the defendant corporation to TRAC (TRAC, “Department of Justice O


riminal Justice, Vol. 28, No. 1, Spring 2013, pp. 4–9.
2 Roman law addressed the problem of criminal behavior on the part of corporations by simply discouraging corporations. Noting that Trajan refused to incorporate even a small company of firefight


id Criminal Law Come to Be Applied to Corporate Behavior? 17
for these inactions.8 But the question of whether a corporation could be held liable for actions that were directly inconsistent with its charter remained unresolved until Regina v. Great N


if they do an act within the scope of their corporate powers it is legal, and they are not answerable for the consequences. If the act be not within the range of their corporate powers, they had no right to do it; it was not one of the objects for wh


s interests, savors strongly of the injustice of condemning them unheard.19
The idea that the corporation itself might have an independent stake and standing in the outcome of proceedings against an individ- ual is not an argument often made today.
A


68 (1892); Southern Express Co. v. State, 1 Ga. App. 700 (1907).
22 Joel Prentiss Bishop, Commentaries on the Criminal Law, 3rd ed., 1865, § 506.
 How Did Criminal Law Come to Be Applied to Corporate Behavior? 21
words, of crime—as an individual man


 the statute deprive the corporation of the presumption of innocence—a presumption which is part of due process in crimi- nal prosecutions.29
The Court also considered the arguments that any criminal action committed by the corporation would be ultra


 true enough, but neither the doctrine of ultra vires, nor the difficulty of imputing intent or motive, should be regarded any longer to determine the result.33
On Hand’s reasoning, because corporations had long been found to have committed intention


n Restraint of Trade, Section 7, “Person” or “Persons” Defined, 1976; U.S. Code, Title 15, Commerce and Trade, Chapter 2A, Securities and Trust Indentures, Subchapter I, Domes- tic Securities, Section 77b, Definitions—Promotion of Efficiency, Competi


uction of Bridges, Piers, etc.—Removal of Structures, 1976; U.S. Code, Title 33, Naviga- tion and Navigable Waters, Chapter 9, Protection of Navigable Waters and of Harbor and River Improvements Generally, Subchapter I, In General, Section 411, Penal


 Title 7, Agriculture, Chapter 19, Cotton Statistics and Estimates, Section 473c-3, Liability of Principal for Act of Agent, 1976.
41 For example, Boson v. Sandford was a case in which the plaintiffs urged that the ship owners were liable for the neg


ible for the acts of another, even though such other be his agent or servant, unless something more than the mere relation of master and servant is shown, but there are many exceptions to the rule.50
Similarly, in 1912, vicarious criminal liability w


f canon law brought a religious cast to the evaluation of the defendant’s mental state:54 “Under the pervasive influence of the Church, the teaching of the penitential books that punishment should be dependent upon moral guilt gave powerful impetus t


ntly punishing those who have been guilty of no moral wrong.”58 He summarized criminal liability as follows:
In the characteristic type of substantive crime acts are rendered criminal because they are done under circumstances in which they will proba


the passen- gers and civil remedies were ineffective (because the carriers were too poor to sue) (Holmes, 1881, p. 40); see also Holmes, 1881, p. 50 (“[T]he tests of [criminal] liability are external, and independent of the degree of evil in the par


us penalizes the transaction though con- sciousness of wrongdoing be totally wanting,” it found that Congress balanced this hardship against that of the “innocent public who are wholly helpless.”67
The dissent, in contrast, noted that “it is a fundam


olumes and varieties unheard of came to subject the wayfarer to intol- erable casualty risks if owners and drivers were not to observe new cares and uniformities of conduct. Congestion of cities and crowding of quarters called for health and welfare


te Behavior
announced a “frontal attack” on strict criminal liability for serious offenses.77 Building on Jackson’s distinction, the MPC also adopted a division between “public welfare offenses,” which did not require mens rea, and traditional crimes


Penalties, 1976; U.S. Code, Title 15, Commerce and Trade, Chapter 2B, Secu- rities Exchanges, Section 78dd-1, Prohibited Foreign Trade Practices by Issuers, 1976; U.S.
 How Did Criminal Law Come to Be Applied to Corporate Behavior? 37
These new laws


rict criminal liability, and our resistance often takes the form of reinterpreting the applicable doctrine to introduce a limited measure of culpability”); see also Kathleen F. Brickey, “Criminal Liability of Corporate Officers for Strict Liabil- ity


ate context must require a showing of criminal intent is inconsistent with this history.
Third, the underlying motivation for the development of both vicarious individual liability and corporate liability was pragmatic. As Professor Kathleen F. Brick


tional requirements of proving crimi- nal intent that made this possible. We noted that, “whether wisely or not” (in Justice Jackson’s phrase), Congress has passed many laws that included criminal penalties without requiring criminal intent. We now t


ective tools to reform errant corporations, their profusion calls for increased scrutiny and an appreciation of their disadvantages.
The Traditional Approach: Prosecuting the Corporation, Not Individuals
Prior to the 1990s, DOJ focused relatively mor


ishment: An Update on Sentencing Practice in the Federal Courts, 1988–1990,” Boston University Law Review, Vol. 71, No. 2, March 1991, pp. 247–280, p. 268.
3 Cohen, 1991, pp. 254–256.
4 Graham v. Allis-Chalmers Mfg. Co., 188 A.2d 125, 130 (Del. 1963)


manifested by the enactment of the Federal Sentencing Guidelines for Organizations from 1991 that put a new emphasis on (1) impos- ing structural changes on convicted organizations, (2) increasing fines, (3) prosecuting individuals, and (4) prosecut


sing the same sanctions that were set out in the statute as applicable to individ- ual defendants. The deterrent effect, however, of a $10,000 fine is likely to be substantially greater for an individual defendant than for a large or even midsized co


ficiently. See generally Stith and Cabranes, 1998, pp. 38–48.
 48 The Changing Role of Criminal Law in Controlling Corporate Behavior
white-collar criminals were much less likely to go to prison than non–white-collar criminals who stole similar amoun


ent, some felt that the guidelines did not go far enough in reducing penal- ties for firms with compliance programs. Arlen, for example, argued that criminal liability still resulted in “enormous negative consequences . . . as a result of collateral


te prosecu- tions. These new directions in policy both help explain some of the variation that we observe in corporate prosecutions and, at times, prove controversial as a matter of procedural law.
Holder Memorandum
The first steps toward the increas


pretrial diversion programs, as well as an effective way to reform corporations from within.
Although neither the potentially expansive scope of corporate liability based on the actions of employees of the organization nor the use of pretrial diversi


ber 2001, Enron was having serious difficulties. Its CEO, Jeffrey Skilling, abruptly resigned on August 14, and Michael C.
30 A joint defense agreement allows information to be shared between two parties (e.g., a defendant corporation and an individu


ials felt that such an admission would be just as destructive as an indictment or guilty plea, and they believed that they had broken no law.36 Instead, they decided to take the case to trial.
In March 2002, Andersen was indicted in the Southern Dist


Andersen as a result of flaws in the jury instruction that removed a vital element of mens rea, whether the defendant realized that the document destruc- tion was wrongful. The Court noted that it was not illegal to per- suade a person to withhold d


he glass tower, and all of the people inside. They—the mahogany and all of them—are responsible for the oil spill, the price-fixing, and the illegal campaign contributions. To superstitious people, villains need not breath; they may include Exxon and


rating.47 It also affirmatively stated that it was not DOJ policy to encourage
45 Paul J. McNulty, deputy attorney general, “Principles of Federal Prosecution of Busi- ness Organizations,” memorandum to heads of department components and U.S. Attorne


tes Attorneys’ Manual, Washington, D.C.: U.S. Department of Jus- tice, 1997, § 9-28.710.
49 Filip,2008,§9-2200.
 Recent History: A Shift to Reforming Corporations from Within 59
possibly substantial consequences to a corporation’s employ- ees, invest


y from doing business with governmen- tal entities if it is indicted. This may also lead to enormous pressure on the corporation to avoid indictment.
Richard A. Epstein has decried what he calls the “grand inversion.”54 He argues that the effective p


ch he or she has no control. Unleashing hard-to-predict reputa- tional and other collateral consequences that may or may not match the prosecutor’s estimates can be problematic, with the Arthur Andersen LLP v. United States prosecution being an excel


to retain independent research firms to advise their customers. In another settlement, he required mutual funds to lower their fees. Similarly, while serving as U.S. Attorney for the District of New Jersey, Chris Christie negotiated a DPA agreement w


ysis of the relative institutional expertise and structures of prosecutors and regu- latory agencies reveals that federally mandated structural reforms are most likely to be effective if the task of designing and imposing structural reforms is left e


irectly manage or direct SEP funds” (John Peter Suarez, assistant administrator of the U.S. Environmental Protection Agency, “Guidance Concerning the Use of Third Parties in the Performance of Supplemental Environmental
 Recent History: A Shift to Re


criticism because they can sometimes require the dismissal of particular unindicted employees. Critics have argued that, in this instance, prosecutors are usurping the role of the board of directors, particularly for those employees who have not bee


nly after a corporation was con- victed. Over time, however, the importance of compliance programs and restructuring moved up in the criminal process and became a cri- terion on which a decision to prosecute was made.
In the wake of the Arthur Anders


clining since the mid-1990s.
Overall Trend: Declining Prosecutions
First, the number of corporations convicted in federal courts appears to have declined in the past decade, while use of DPAs and NPAs has increased. Second, there has been an overall


 to bring charges against a corporation or an individual defendant.
Data from the Sentencing Commission show an overall increase in the number of convictions involving both corporations and individ- uals guilty of the same offense. As Figure 4.3 show


12-4.4
2006 2007
2008 2009 2010
Year
Trends in Prosecutions of Corporations and Individuals 75
Figure 4.5
Deferred-Prosecution and Nonprosecution Agreements from 1992 to 2010
     Agreements obtained
40 30 20 10
0
1992 1994
    SOURCE: Garrett and As


–957.
10 Garrett, 2007, pp. 938–957. Garrett hoped that his data would serve as a foundation for prosecutors, scholars, and others who may be interested in assessing these agreements’ effectiveness (Garrett, 2007, p. 937). To date, however, no study


osecution involves the filing of a charge against an individual or organiza- tion. One of the developments we discuss is an increase in prosecutorial activity that does not result in the filing of a charge (e.g., negotiating a DPA or NPA). This is no


Charges filed
        1998 2000 2002 2004 2006
Year
2008 2010
 Terminated, 2005, Ann Arbor, Mich.: Inter-university Consortium for Political and Social Research, March 8, 2011l; BJS, Federal Justice Statistics Program: Defendants in Federal Crim- ina


Federal Justice Statistics Program: Suspects in Federal Criminal Matters Concluded, 2004, Ann Arbor, Mich.: Inter-university Consortium for Political and Social Research, March 8, 2011z; BJS, Federal Justice Statistics Program: Suspects in Federal Cr


mes, Chapter 47, Fraud and False Statements, Section 1014, Loan and Credit Applications Generally—Renewals and Discounts, Crop Insurance, 1976.
13 A referral for prosecution represents a case investigated by an agency and referred to the U.S. Attorne


minal Law in Controlling Corporate Behavior
tutions.18 Referrals and the number of charges filed by U.S. Attorneys have declined since a mid-1990s spike in prosecution activity, as shown in Figure 4.12. This spike was probably related to the aftermat


.13
    EOUSA
 Referrals Charges filed
      Prosecutorial activity
Prosecutorial activity
1998 2000 2002 2004 2006 2008 2010
Year
    TRACFED
 Referrals Charges filed
          1998 2000 2002 2004 2006 2008 2010
Year
80
60
40
20
0
1994 1996
80
60
40


han in 2004.24
The Garrett data appear to corroborate Gibson Dunn’s findings that FCPA prosecutions are on the rise (see Figure 4.16). His database shows an increase in the number of defendants convicted in federal courts for violating the FCPA. The


t the 26th National Con- ference on the Foreign Corrupt Practices Act, Washington, D.C., November 8, 2011.
 Trends in Prosecutions of Corporations and Individuals 93
Debarments
Government agencies use debarment and suspension to ensure that they cont


ance in the use of civil law over criminal law, or vice
versa, depends on the context.
• Enforcement of SOX and the FCPA have increased.
Debarment actions have dramatically increased. We see an increase in DPAs and NPAs following the prosecution of A


struc- tural reform. At first, this occurred as a condition of probation after a corporation was convicted, but gradually the incentives to having a robust compliance program have increased and are often relevant to the decision as to whether to pro


useful tool to discover and deter criminal activity by individuals within organizations, the kind of moral condemnation that is usually associated with a criminal penalty is not typically useful in thinking about the culpability of a large entity. An


ate corporate governance incentives, we should think carefully about whether the benefits outweigh the costs. The instinct to punish “bad” corporations can prevent us from conducting this analysis.
Similarly, we should carefully scrutinize the tempta


They must also recognize the risk of collateral damage to innocent parties. Although this is always a risk in criminal law—defendants often have innocent families who are deeply dam- aged by the state’s action—the diffusion of harm to innocent partie


he more- political, elected executive branches.
Debarment provisions that automatically require a licensing agency or an entity of government to refuse to do business or refuse to license a corporation upon the corporations’ indictment or conviction


Wall Street Journal Washington Wire, May 14, 2013; see
Conclusions and Policy Implications 103
 104 The Changing Role of Criminal Law in Controlling Corporate Behavior
the new chair of the SEC, Mary Jo White, has recently announced that, at least in


nally criminal enforce- ment already resembles a civil system (albeit one with a very power- ful regulatory authority). Formal criminal proceedings are very rare, almost no one is incarcerated, and the most common outcome is some kind of company-fund


serve the expressive power that criminal sanctions should possess.
References
 Abrams, Norman, “Criminal Liability of Corporate Officers for Strict Liability Offenses—A Comment on Dotterweich and Park,” UCLA Law Review, Vol. 28, No. 3, February 1981,


 General Economic Crime Sentence Increases Following Sarbanes-Oxley: Letter of Frank Bowman to U.S. Sentencing Commission,” Federal Sentencing Reporter, Vol. 15, No. 4, April 2003a, pp. 284–290.
———, “Editor’s Observations: The Sarbanes-Oxley Act and


rred and Non Prosecution Agreements, Washington, D.C., December 28, 2005. As of September 18, 2014: http://www.corporatecrimereporter.com/deferredreport.htm
Cuomo, Anthony A., “Mens Rea and Status Criminality,” Southern California Law Review, Vol. 40


ersity of Virginia School of Law, undated. As of October 22, 2014: http://lib.law.virginia.edu/Garrett/plea_agreements/home.php
———, “Federal Organizational Prosecution Agreements,” last updated October 27, 2014. As of October 27, 2014: http://lib.la


ction_873_report1.pdf
———, Report by the Interagency Suspension and Debarment Committee on Federal Agency Suspension and Disbarment Activities, c. 2012. As of September 30, 2014: https://isdc.sites.usa.gov/files/2014/02/ isdc_section_873_fy_2011_repo


1, 46 N.W. 735 (1890).
People v. John H. Woodbury Dermatological Institute, 192 N.Y. 454 (1908).
People v. Roby, 52 Mich. 577, 18 N.W. 365 (1884).
People ex rel. Price v. Sheffield Farms-Slawson-Decker Co., 225 N.Y. 25, 121 N.E. 474 (Ct. App. N.Y. 19


eptember 20, 2014: http://www.shearman.com/files/Publication/ bb1a7bff-ad52-4cf9-88b9-9d99e001dd5f/Presentation/ PublicationAttachment/590a9fc7-2617-41fc-9aef-04727f927e07/ FCPA-Digest-Jan2012.pdf
Shepherd, William, “A Country Overburdened by Too Man


).
“The Kindness of Cronies,” New York Times, editorial, May 25, 2008. As of September 20, 2014: http://www.nytimes.com/2008/05/25/opinion/25sun3.html
The King v. Inhabitants of Clifton, 101 Eng. Rep. 280 (K.B. 1794).
The Queen v. Birmingham & Glouce


20, Dumping or Destruction of Interstate Produce, Section 491, Destruction or Dumping of Farm Produce Received in Interstate Commerce by Commission Merchants, etc.; Penalty, 1976.
———, Title 7, Agriculture, Chapter 21, Tobacco Statistics, Section 503


mitted, 1976.
———, Title 15, Commerce and Trade, Chapter 53, Toxic Substances Control, Subchapter I, Control of Toxic Substances, Section 2614, Prohibited Acts, 1976.
———, Title 15, Commerce and Trade, Chapter 53, Toxic Substances Control, Subchapter


eat and Dairy Products, Subchapter I, Examination of Animals, Meats, and Meat and Dairy Products, Section 90, 1976.
———, Title 21, Food and Drugs, Chapter 9, Federal Food, Drug, and Cosmetic Act, Subchapter II, Definitions, Section 321, Definitions;


avigable Waters and of Harbor and River Improvements Generally, Subchapter III, New York Harbor, Harbor of Hampton Roads, and Harbor of Baltimore, Section 449, Disposition of Dredged Matter; Persons Liable; Penalty, 1976.
———, Title 33, Navigation an


014: http://www.icpsr.umich.edu/icpsrweb/NACJD/studies/24084
———, Federal Justice Statistics Program: Defendants in Federal Criminal Cases: Terminated, 2000, Ann Arbor, Mich.: Inter-university Consortium for Political and Social Research, March 8, 20


uded, 1995, Ann Arbor, Mich.: Inter-university Consortium for Political and Social Research, March 8, 2011q. As of October 27, 2014: http://www.icpsr.umich.edu/icpsrweb/NACJD/studies/24006
———, Federal Justice Statistics Program: Suspects in Federal


nter-university Consortium for Political and Social Research, March 8, 2011aa. As of October 27, 2014:
http://w w w.icpsr.umich.edu/icpsr web/NACJD/studies/24193
———, Federal Justice Statistics Program: Suspects in Federal Criminal Matters Concluded,


nnual Report, c. 2009. As of September 20, 2014: http://www.ussc.gov/research-and-publications/annual-reports-sourcebooks/2009/ annual-report-2009
———, Federal Sentencing Guidelines Manual, Washington, D.C., 2011. As of September 29, 2014: http://www


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TERRORIS


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sts are in touch with political, social, economic, and technological developments in and around the region.
Krishna B. Kumar, a senior economist at RAND and the director of its Labor and Popu- lation unit, is the principal investigator of this study.


3.9. Country Rankings for Components of the NRI Usage Sub-Index. . . . . . . . . . . . . . . . . . . . . . . . .
3.10. Country Scores for the World Bank’s Knowledge Economy Index andKnowledgeIndex......................................................


his model by the variety of intermediate goods used in production) is the engine of growth. Though the models are intended to isolate the effects of human capital and technology, in reality knowl- edge is likely to be embodied in both technologies a


e economy performance, as this will allow the GCC countries to learn lessons from the experience of other countries.
In particular, we have used a variety of indicators, such as the International Telecommu- nication Union’s (ITU’s) ICT Development In


In Chapter Two of this report, we present a simple framework of the various constituents of the knowledge economy. In Chapter Three, we assess the performance of GCC countries in these areas in a cross-country context based on the above-mentioned i


capital such as brands), universities (fundamental research), think tanks and applied research institutes (applied research), and the government (processes related to governance and regulation), which we collectively call orga- nizational capital.6


lack and Lynch (2005) for a recent reference.
7 See Sobel (2002) for a discussion and a critical perspective on social capital.
 these pillars into a single index that can be compared across countries and sub-indices that can be compared within a cou


ries tend to display significant income inequalities, and (2) they tend to have a relatively large expatriate population (which means that using either the total population or national population as a reference population in any per capita statistics


ts of the use of ICTs. Wireless infrastructure offers mobility; associates devices with people, not places; and appears to funda- mentally change what ICT is capable of providing to innovators and users.
 in the table, ranging from 124.2 percent in B


much as 89.6 percent in Qatar (which has the highest figure in the table). A similar picture emerges for households with Internet, ranging from 27.7 percent in Oman to 84 percent in Qatar, but Korea outperforms all other countries with 96.8 percent


ixed telephone, mobile cellular and fixed broadband Internet services—and computed as a percentage of average Gross National Income (GNI) per capita. As a comprehensive benchmarking tool, the IPB monitors the relative price of ICT services and provid


 ItU, 2011a.
Sub IDI—Access Rank
17
14 115 20 10
65
7 23
Sub IDI—Use Rank
4 18 124 23 1
50
11 17
Sub IDI—Skills Rank
1 35 116 27 2
92
36 12
  Bahrain
 45
 36
 47
 51
 Kuwait
Oman
60
60
54
86
Qatar
44
28
42
98
Saudi arabia
46
43
44
67
United arab emir


in enrollment and ICT indicators is particularly stark for the UAE, as shown in Figure 3.4. Even though education data are not available for all years, the uneven and generally lower slopes of their trends relative to the ICT trend is readily apparen


ent or service), and a lack of ICT skills are often cited as the top barriers to ICT uptake or reasons for not having an Internet connection at home (European Commission, 2009). A recent U.S. Federal Communi- cations Commission (FCC) survey also high


y within individual sectors, this is masked at the aggregate level by a movement into industries in which the economy is less productive.
Table 3.5
Barriers Limiting Internet/PC Use by Residents—Qatar, 2008
Barrier to ICT Adoption
high cost of Intern


When all available countries are included, instead of only the GCC countries and the countries used for comparison in this paper, Qatar too drops below the trend line. Also note that we do not control for other factors beyond per capita income that c


NRI is based on 71 variables spread over three sub-indices: environment, readiness, and usage. These variables are a combination of quantitative data collected by international orga- nizations and data from the Executive Opinion Survey, conducted an


and engineers).
SOUrCe: weF/InSeaD, 2011.
 environment and infrastructure categories.8 The infrastructure component of this sub-index includes human capital, which explains why the infrastructure performance of GCC countries is worse here than on ind


d by developing new applica- tions, new products and services, and new ways of delivering them.
Could it be that GCC countries are too highly focused on the hydrocarbon sector and government services, and that ICT usage for knowledge addition is cons


e the readiness sub-index, however, the government usage ranking is worse for most countries, espe- cially Saudi Arabia and the UAE.11
Again, it is particularly worrying that GCC countries perform relatively worse in the business component of this in


into account, which are crucial for being able to exploit the benefits of ICT and maximize its impact on economic growth and development. Indeed, the KEI takes into account whether the environment is conducive to using knowledge effectively for econo


“protecting investors,” and “enforcing contracts.” This is very worrying, as these factors are all key to developing entrepreneurship and a dynamic business environment in which the knowledge economy can prosper.
Value Addition
A symptom of the imbal


se complementary factors. In Chapter Three, we presented data to support this theme by examining KBE indices developed by various organizations, focusing on the compo- nents of these indices. Without these complementary inputs and a balanced knowledg


ward the energy and real-estate sectors (van Welsum, 2011). It is therefore important to further liberalize the fixed, mobile, and Internet and broadband markets and roll out high-speed broadband networks. This should be accompanied by complementary


nsa, Chanont, and Adrian Peralta-Alva, “The U.S. Financial Sector’s Value Added: Trends Now and Then,” National Economic Trends, Federal Reserve Bank of St. Louis, June 2009.
Basu, Susanto, and David N. Weil, “Appropriate Technology and Growth,” The


tudy No. 1: Market Openness, Trade Liberalisation and Innovation Capacity in the Finnish Telecom Equipment Industry,” OECD Trade Policy Working Paper No. 73, 2008.
Lightfoot, Michael, “Promoting the Knowledge Economy in the Arab World,” SAGE Open, Au


epublic of Korea: Transition to a Knowledge-Based Economy, Report No. 20346-KO, Washington, D.C.: The International Bank for Reconstruction and Development/The World Bank, June 29, 2000.
World Bank, Korea as a Knowledge Economy: Evolutionary Process


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and complete. Permission is required from RAND to reproduce, or reuse in another form, any of our research documents for commercial use. For information on reprint and linking permissions, please see the RAND permissions page (http://www.rand.org/pub


y and cost, by using trends based on historical statistics of hydrology and best-estimate forecasts of other important factors, such as demand, regulatory conditions, and the likely increases in new
1 SWAN serves as the voluntary technical advisory g


eeting in-stream flow requirements (IFRs). The WEAP model evaluated the current management baseline and five other response packages made up of different water-management strategies. The proof-of-concept analysis considers the notional costs of these


nts that are to the left of the agricultural supply reliability threshold and below the urban supply reliability threshold (i.e., the shaded area) are vulnerable. In addition to those within the shaded area, some points above the shaded region in the


five response packages—those in which agricultural efficiency improves to the greatest extent. Urban supply reliability also improves the most for the three response packages in which agricultural efficiency improves the most— declining to less than


, nor does it demonstrate all aspects of RDM. The analysis under way for the CWP Update 2013 will address some but not all of these issues. Specifically, it will evaluate a larger set of scenarios to span a wider range of plausible future conditions,


ing environment
 xxi
Chapter One. Introduction
California faces significant challenges in ensuring that its water resources successfully meet diverse needs across the state in the coming decades. Escalating needs due to population and economic growth


rth Lahontan Sacramento River
San Francisco Bay
San Joaquin River
Central Coast
Tulare Lake
South Lahontan
SOURCE: DWR.
NOTE: Red area indicates the connected portion of the North Coast hydrologic region. The blue, green, and orange/brown areas corre


ology and best-estimate forecasts of other important factors, such as demand, regulatory conditions, and the likely increases in new supply from investments. Given increasing recognition that the past is no longer a good predictor of future climate a


vironmental Protection Agency, and the World Bank (Lempert, Kalra, et al., forthcoming).
  5
RDM offers a novel approach to understanding the vulnerabilities of proposed strategies and identifying factors under the planners’ and resource managers’ co


ng Analysis
The RDM process begins at the top of Figure 2.1 with a participatory scoping activity in which stakeholders and decisionmakers define their objectives and metrics, strategies that could be used to meet these objectives, the uncertainties


can usefully be considered to be decision-relevant scenarios in a decision support process because they focus decisionmakers’ attention on the uncertain future conditions most important to the challenges they face and help facilitate discussions reg


lly, for a decision analysis, scenario discovery can thus reduce the number of individual futures that need to be simulated to evaluate trade-offs among different decisions.
9
Chapter Three. Scope of the Proof-of-Concept Analysis
This chapter outline


ed in the Study
  Uncertainties or Scenario Factors (X)
Demographic and land-use scenarios, which describe changes in
• population
• household factors
• employment factors
• climate sequences, which describe changes
in temperature and precipitation
R


esent major demographic and land-use trends; and (3) evaluate the effects of water- management responses. The model includes 86 demand nodes, which are grouped into four broad categories: agriculture, urban, managed wetlands, and IFRs. The model atte


arch Center for Global Change medium-
resolution (miroc32med) global climate model (Japan)
• Max Planck Institute ECHAM5 GCM (MPI-ECHAM5) (Germany)
• National Center for Atmospheric Research Community Climate System Model,
version 3.0 (NCAR-CCSM3) (U


nsive Growth (Figure 3.1). Each has a different pattern in population, land use, irrigated crop area, environmental requirements, and water conservation.
Figure 3.1. Demographic and Land-Use Scenarios from the California Water Plan Update 2009
SOURCE


otal reservoir storage reaches a low in 2046. Once again, results in other cases differ significantly.8
 8 Similar graphics have been generated in an accompanying interactive workbook for a variety of other outcomes. 16
Figure 3.2. Annual Supply, Dem


d municipal water*
Surface storage: CALFED and state Surface storage: regional and local
    NOTE: Asterisks indicate strategies evaluated in this analysis. CALFED = California Bay-Delta Program.
The proof-of-concept analysis considered an even small


mates of the area of irrigated landscaping, the required water use for landscaping, and the evapotranspiration requirements of the total landscape over time. Increased efficiency was modeled using the same process as for agricultural water-use effici


o increase linearly between 2005 and 2020, with exception of conjunctive use, which was modeled to be completely effective all at once at 2020.
Table 3.5 summarizes the current management baseline and five different response packages that reflect inc


reases
This response package represents the combination of increases in both previous management strategies.
Aggressive Infrastructure
This response package represents further increases in use of recycled water and in conjunctive management. The high


supply results (across the 39 scenarios) in Figure 4.1 shows slightly declining supply and demand and that total agricultural demand is generally larger than supply during all time periods—the median amount is about 10 percent higher in the first de


 reliability, leading to fewer missed IFRs (bigger symbols) but at the expense of urban supply reliability.
 27
Figure 4.3. Urban and Agricultural Supply Reliability Across Scenarios Under the Current Management Baseline
NOTE: Each symbol shows resul


bility (75 percent). As measured by the total vulnerability metric, the current management baseline is vulnerable in 15 of the 39 scenarios (38 percent).
Table 4.1. Percentage of Vulnerable Futures in the Current Management Baseline
  Performance Met


ation
• temperature and precipitation in summer months
• temperature and precipitation in winter months
• temperature and precipitation from 2040 to 2050.
We next use the PRIM algorithm (see Chapter Two) to define a decision-relevant scenario that le


lnerable. Note that the Hot and Dry composite scenario does not describe 25 percent of the vulnerable outcomes. Because there are only 12 unique climate sequences used to generate 36 futures, each combination of temperature trend and change in precip


d reduce vulnerabilities. Figure 5.1 shows, as an example, how the Increased Efficiency response package improves urban and agricultural supply reliability results across the futures evaluated for Chapter Four and, in some cases, converts the outcome


    53
         27
         27
         27
Outside the Hot and Dry Composite Scenario
26 4 4 0 0 0
      The implementation of response packages will also change the nature of conditions to which the system is resilient. Figure 5.3 illustrates this e


st response packages—Increased Efficiency, Moderate Increases, and Aggressive Infrastructure—are all equally effective at reducing the number of vulnerable futures. These response packages have identical levels of efficiency with which water would be


itments response package would be the most cost-effective. If the likelihood were between 12 and 37 percent, however, the Increased Efficiency response package would be the most cost-effective. Lastly, if likelihoods were greater than 37 percent, the


ckages and did not consider the value of outcomes beyond the reductions in vulnerabilities. It also did not demonstrate how iteration through the RDM steps could help identify increasingly robust response packages.
Its treatment of future climate unc


to, Calif.: August 2006. As of May 10, 2013: http://www.calwater.ca.gov/content/Documents/library/WUE/2006_WUE_Public_Final.pdf
California Department of Water Resources, California Water Plan Update 2009: Integrated Water Management, Bulletin 160-09,


ew Analytic Method for Finding Policy-Relevant Scenarios,” Global Environmental Change Part A: Human and Policy Dimensions, Vol. 17, No. 1, February 2007, pp. 73–85. As of May 10, 2013: http://www.rand.org/pubs/reprints/RP1244.html
Groves, David G.,


 15, No. 22, November 2002, pp. 3237–3251.
Means, Edward III, Maryline Laugier, Jennifer Daw, Laurna Kaatz, and Marc Waage, Decision Support Planning Methods: Incorporating Climate Change Uncertainties into Water Planning, San Francisco, Calif.: Wate


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re protection and homeland security, transportation and energy policy, and environmen- tal and natural resource policy.
Questions or comments about this report should be sent to the project leader, John Hollywood (John_Hollywood@rand.org). For more i


. . . . . . . . . . . . . . Nashville, Tennessee: Integrating Crime and Traffic Crash Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . Baltimore, Maryland: Crash-Crime Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Risk Assessment Instruments for Domestic Violence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Risk Assessment Instruments for Mental Health . . . . .


perations
PredictivePolicingPitfalls ......................................................................... 118 Pitfall 1: Focusing on Prediction Accuracy Instead of Tactical Utility. . . . . . . . . . . . . . . . . . . . . 119 Pitfall 2: Relying


of this research was to develop a reference guide for departments interested in predictive policing, providing assessments of both the most promising technical tools for making predictions and the most promising tactical approaches for acting on the


offenders, perpetrators’ iden- tities, and victims, respectively. We found a near one-to-one correspondence between conventional crime analysis and investigative methods and the more recent “predictive analytics” methods that mathematically extend or


hot spot identification)
Basic regression models created in a spreadsheet program
Assumption of increased risk in areas immediately surrounding a recent crime
Graphing/mapping the frequency of crimes in a given area by time/date (or specific events)


marizes methods to identify groups—and, in some cases, individuals—who are likely to become victims of crime. These methods mirror those used to predict where and when crimes will occur, as well as some of the methods used to predict who is most like


against the predicted crime (or help solve past crimes). The type of intervention will vary with the situation and the department charged with intervening. Figure S.1 shows three broad types of interventions (lower right of figure). They are, from s


ound as if the computer can foretell the future. Although much news coverage promotes the meme that predictive policing is a crystal ball, these algorithms predict the risk of future events, not the events themselves. The com- puter, as a tool, can d


eries that are as “accurate” as possible (i.e., to design an analysis in which as many future crimes as possible fall inside areas predicted to be high-risk, thus confirming that these areas are high-risk). One way to accomplish this is to designate


e very act of labeling areas and people as worthy of further law enforcement attention inherently raises concerns about civil liberties and privacy rights . Labeling areas as “at-risk” appears to pose fewer problems because, in that case, individuals


ting infor- mation the system can provide; and, of course, the types of analyses and predictions the system can support and under what conditions.
Advice for Vendors and Developers
The list of questions for purchasers doubles as guidance on desired c


olicing approaches employed in their departments: the Metropolitan Nashville Police Department in Nashville, Tennessee; the Vancouver Police Department in Vancouver, British Columbia; the Glendale Police Department in Glendale, Arizona; the East Oran


sk terrain modeling
SARA Scanning, Analysis, Response, and Assessment (model)
SASSI Substance Abuse Subtle Screening Inventory SEATS Signal Extraction in ARIMA Time Series SOM self-organizing map
SPCS State Plane Coordinate System
SPSS Statistical Pa


to make predictions in support of crime prevention. These tools greatly increase police departments’ reliance on information technology (IT) to collect, maintain, and ana- lyze those data sets, however.
These analytical tools, and the IT that support


ort of math can’t be done . . . but humans are not nearly as random as we think. . . . In a sense, crime is just a physical process, and if you can explain how offenders move and how they mix with their victims, you can understand an incredible amoun


luding community-oriented policing and intelligence-led policing.5
In 2008, Chief Bratton worked closely with the acting director of the Bureau of Justice Assistance, James H. Burch II, and the acting director of the National Institute of Justice (NI


ong others, have covered PredPol specifically and predictive policing in general.9 Much of the coverage implied (or directly stated) that the software could literally predict where crime would occur and used as an example the arrest of two suspects i


nd crime mapping, and it developed the Crime Mapping Research Center to respond to that need. Later renamed the Mapping and Analysis for Public Safety program, it serves as a vehicle for the NIJ to provide research, training, and periodic conferences


of both the most promising technical tools for making predictions and the most promising tactical approaches to act on those predictions. More broadly, this guide is intended to put predictive policing in the context of other modern, proactive polic


tified promising practices (technical and operational) for each “node” in the taxonomy. We also identified some overall pitfalls (and mitigations) and policy issues that need to be addressed.
The Nature of Predictive Policing: This Is Not Minority Re


n assessing predictive methods, we found a near one-to-one correspondence between conventional crime analysis and investigative methods and the more recent “predictive analytics” methods that mathematically extend or automate the earlier methods. The


edictive Technologies: Predicting Crimes
   Problem
Identify areas at increased risk
Using historical crime data
Using a range of additional data (e.g., 911 calls, economics)
Accounting for increased risk from a recent crime
Determine when areas will


ith other crimes)
Locating areas both near and between crimes in a series
Manual requests and review of sensor data
Predictive Analytics (large data demand and high complexity)
Computer-assisted queries and analysis of intelligence and other database


 in such incidents are, by definition, at risk
Predictive Analytics (large data demand and high complexity)
Advanced models to identify crime types by hot spot; risk terrain analysis
Advanced crime-mapping tools to generate crime locations and identi


s for combining data from disparate sources to form a cohesive picture of high-risk places, individuals, and groups. We address some specific technical methods for making sense of noisy and conflicting data in Chapter Four.
Analysis
Chapters Two thro


ying it out—from the command level to tactical officers—will need information to execute the intervention successfully. Thus, a critical part of any intervention plan is providing information that creates the needed situational awareness among office


tegic approaches). Although this is true for addressing the questions of when, where, what, and who, the methods used will differ with the context and the goal:
• Hot spot analysis, statistical regression, data mining, and near-repeat methods are gen


s examined here, existing techniques were adapted to support predictive policing. For example, classical statistical methods can be used to produce heat maps, which are simple, color-coded grids depicting the intensity of crime activity in a given ar


ependent on the initial data set and the partition- ing of the map. Nevertheless, our discussion here begins by illustrating some simple approaches using grid mapping. We then focus on two more mathematically rigorous hot spot methods: covering ellip


ime risk or natural variation over the six months of data (i.e., “bad luck” in those months).
Typically, grid maps are prepared using GIS programs, but this is not always nec- essary. Figure 2.1 was actually prepared using Excel.4
4 For this analysis


ering ellipses and, in this case, convex hulls.
While using the two-step approach will generally reveal in the densest hot spots of significance, the elliptical clustering method has two major deficiencies:
• These methods require the number of cover


ains only appropriate occurrences of crime.
Single and Dual Kernel Density Estimation
Kernel density estimation (KDE) is another approach for identifying hot spots. The intuitive idea is to spread out each crime’s expected contribution to future crim


nal Terrorist Groups,” Journal of the Operational Research Society, Vol. 55, No. 4, April 2004.
7 Readers who are interested in a more mathematical discussion of KDE can refer to Walter Zucchini, Applied Smoothing Techniques, Part 1: Kernel Density E


his approach will result in less compact crime hot spots than will using ellipses followed by taking the convex hull. That may not be a significant problem, however, depending on the selection of a basis function. (Specifically, basis functions with


rom sophisticated math- ematical analyses, making it easier for police officers to access the information they need to understand the current situation and determine how to respond. One crime analyst put it this way: “The output from the dashboard is


hematic Quadrat Map of Vehicle Crimes
Greater than 15 10–15
5–10
1–5
No crime
SOURCE: Eck et al., 2005, p. 25, Exhibit 10.
RAND RR233-2.5
12 Eck et al., 2005, pp. 25–26.
   the polygons more so than a real underlying risk. Analysts can choose whether


plus one-fourth the disorderly conduct calls last month plus some constant number). The model is called linear because, geometrically, these methods fit a line or plane to the relationship between the output and input variables. These methods can als


les that are (1) not well correlated with the output variable to begin with or (2) too highly correlated with the output variable. The analyst adjusts the filtering criteria and otherwise experiments with different variables to find a model with good


allow ana- lytical policing to move from reactive to proactive, but care should be taken to ensure the predictive models remain relevant. When some types or targets of crime cease to be profitable, the system may evolve in response, and new leading


lude not just robbery histo- ries but also the histories of five other Part 1 crimes and leading indicators, including disorderly conduct and suspicious activity calls. The disorderly conduct and suspicious activity calls have been further categorize


egression (e.g., “We predict an average of 1.24 robberies here next month”).
• Clustering methods subdivide records into groups in which the records are “simi- lar” mathematically. These models make predictions by stating that a future situ- ation wi


egorical value). A continuous variable in this case will be any numerical value that can take any value within a range (e.g., property value, temperature).
One also can choose techniques of varying complexity. At one extreme, there are simple methods


e of primary interest for predictive policing: regression, clustering, and classification. Regression methods were introduced earlier; here, we just mention that there are techniques within this family that are much more complicated than the basic me


ster perpetrators of serious sexual assaults using a technique called a self-organizing map (SOM).27 The SOM clusters observations with similar traits to form related groups. The authors were able to identify crimes commit- ted by the same individual


), given that a record has been assigned to a particular category (e.g., the “high-risk” category).
As mentioned, these data mining techniques include comparatively simple meth- ods, such as decision-tree algorithms, which run quickly and produce hum


either the average or plurality vote.33
These more complicated methods do offer the potential of improved accuracy, though it is far from guaranteed that they will perform much better on any particu- lar problem.34 They also usually have good running


study could be duplicated to identify leading indica- tors to be fed into a regression model.
Training and Testing a Model
It is worth noting that verifying the accuracy of a predictive model is usually a two- step process. Especially if there are ma


ctim.
To take advantage of this effect, Mohler et al. developed a “self-exciting process,” or “earthquake modeling” algorithm.38 This algorithm has been widely reported on by the media, as discussed in Chapter One. Although this algorithm uses sophis


ffe at Temple Univer- sity with a grant from the NIJ and is freely available.43 The calculator software is based on the concept of “communicability” of risk to nearby locations for a short amount of time. The method builds on a space-time clustering


le heuristic version of near-repeat meth- ods: Mark areas that have just seen a crime, especially a burglary, as being at elevated risk of another similar crime in the near future. This technique appears to work best with burglaries; other types of c


ries and the number of days the offender waits until the next offense. A forecast of snow might herald an increase in traffic collisions at specific intersections, especially if it is expected to occur during specific time frames.
Considerations of t


y 2007 to November 2009).
Preparing the heat maps involved simply creating new variables for the hour and day of the weeks in which the crimes reportedly occurred. We used pivot table features in Excel to create the tables showing the numbers of crim


sions of regression models on grids; the input data include probabilities that each grid cell had a particular spatiotemporal feature at a particular time. Here, a “spatiotemporal feature” can be a prior crime, general attribute (e.g., socioeconomic


ntry. The important seasonal factor is calculated by finding the quarterly average across all five years of data. Finally, the seasonally adjusted pat- tern is calculated by subtracting the quarterly seasonal factor from the original series for the c


 for this family of methods, including “geospatial predictive modeling,” which we find to be overly general. Risk terrain analysis is a bit more specific and descriptive of what these methods do.
 likely subject to high crime in the near future. Howe


, eds., Risk Terrain Modeling Compendium for Crime Analysis, Rutgers Center on Public Security, Newark, N.J.: Rutgers Center on Public Security, 2011.
54 Joel M. Caplan, Leslie W. Kennedy, and Joel Miller, “Risk Terrain Modeling: Brokering Criminolog


sk based on geographic attributes rather than merely extrapolate prior crime histories. Operationally, this means that these methods can predict new hot spots that are similar to other hot spots. The idea is that, even though the newly predicted hot


spot. For example, Figure 2.13 flags the most heavily populated areas of Washington, D.C. As for factors, the earlier caution about how the appearance of a predictive element in a model does not equal causation applies here, too. Note that many geos


s and the data supporting the forecasts. Examples of the latter might include recent crime locations and descriptions, major call locations and descriptions, locations of crime attractors, reports on persons of interest in an area, and recent field i


of local problems and conditions.3
Figure 3.1
Evidence-Based Policing Matrix
Results key: Non-significant finding Successful intervention Mixed results
Harmful intervention
Y: Specificity of prevention mechanism General Focused
Individuals
SOURCE: Cr


t spots that saw multiple robberies. These would include the four street blocks immedi- ately adjacent to the Metro station, which collectively saw 11 robberies over the period studied. Also of interest are Columbia Road NW and several adjacent stree


. This latter requirement stemmed from Koper’s finding that reductions in crime last for about two hours after the previous visit.
The results of the test were rather dramatic. Part 1 crime rates during the 90-day test were compared with the same cri


 the city, and the hot spot was reduced from the entire east end to 14 stores. Crime-specific intervention strategies could then be focused on understanding why these stores were robbed repeatedly.
Figure 3.5
Convenience Store Robberies in Chula Vist


s were largely basic statistical tools that could be suitable for a large fraction of departments.
Shreveport, Louisiana: Predictive Intelligence–Led Operational Targeting
The Shreveport Police Department is conducting an NIJ-funded experiment origin


usively, as practitioners preferred to focus on smaller spots. Predictive maps were updated monthly.
Staff in Districts 7 and 8 responded to the predictions by dispatching dedicated teams (two two-person cars plus a sergeant) for focused patrols in t


 over the controls while PILOT was running in Districts 7 and 8.
Memphis, Tennessee: Crime Reduction Utilizing Statistical History
Serious and violent crime was a problem in Memphis, Tennessee. In April 2009, Forbes Magazine ranked Memphis one of the


phis to counteract a sharp spike in gang-related gun violence, using IBM’s SPSS. Speaking about the program, John Williams of the Memphis Police Department said, “criminals leave a footprint,” adding that the SPSS software licensed by the Blue CRUSH


r metropolitan-area motels. Informed by Blue CRUSH data, the department tasked undercover units to investigate the motels. The investigation revealed drug sales, drug use, prostitution, underage prostitution, robberies, and a homicide. “Operation Hea


h rates of violations, specifically those leading to traffic collisions. The theory also posits that the traffic crime will attract other types of crime without persistent enforcement.19 The theory has led to a series of police interventions known as


hville,” Geography and Public Safety, Vol. 2, No. 3, June 2010.
22 Scott Silverii, “Changing the Culture and History of Policing,” Law Enforcement Today, October 9, 2012.
 goals, identifying hot spots, and deploying tactics during a series of compara


of this success to the program’s high-quality data, good analysis, and a department-wide emphasis on fighting crime
23 As mentioned earlier, the FBI UCR system collects data on serious crimes, such as murder and non-negligent manslaughter, forcible r


, Baltimore County crime analysts mapped instances of burglaries, robberies, and auto theft alongside the detailed crash and crime hot spots from the first phase of the proj- ect. For each overlapping area, they used a kernel density function to depi


n the immediate area.
The AHS software looked for clusters of two or more IED events in small, user- specified areas (typically circles up to a 100-m radius) over a period of one to several weeks, with the time frame selected empirically to suit a pa


reported to be considerably better than what the units could achieve on their own.
Minneapolis, Minnesota: Micro Crime Hot Spots
“The little kids’ playground was the gang hangout,” remarked North Minneapolis Peavey Park employee Jeanie Kneath. Gang a


, effectively forecasting the next robbery using data on the prior robberies. It has also developed evidence-based strategies to predict the criminal activities and robbery targets of spe- cific suspects recently released from prison. This individual


ed the social fabric between neighbors, but it also opened new avenues for criminal opportunities. Clusters of foreclosed homes left abandoned are targets for vandalism and squatting; as homes fall into disrepair, quality of life in the neighborhood


ti- mately, the Charlotte-Mecklenburg Police Department had identified a problem using geospatial data analysis and exposed the underlying community trends; it then success- fully partnered with community organizations to address both components.
Cri


 and one involving alcohol.
The DWI Resource Center was founded in Albuquerque to reduce the social and economic costs associated with driving while intoxicated (DWI) through research and outreach. In 2005, the DWI Resource Center developed a sequenc


government agencies, some organizations have a greater capability to use the resource than do others. In 1997, Hartford, Connecticut, was one of 12 cities to be awarded a Comprehensive Communities Program grant by the U.S. Department of Justice, Bure


ive Policing: The Role of Crime Forecasting in Law Enforcement Operations
Police Actions
This chapter focused on the police intervention portion of the prediction-led polic- ing business process and, in some cases, criminal responses. The Center for


ely suspects in crimes of interest.
There are two important caveats involving predictions regarding potential offend- ers. The first is that, compared with predictions related to spatiotemporal crime tech- niques, methods for making predictions invol


ave yet to be resolved.
1 The film, based on the sci-fi novella by Philip K. Dick, was released in 2002. The story is set in 2054 and revolves around the activities of a recently established law enforcement agency known as PreCrime. The agency employ


der the Fourth Amendment of the U.S. Constitution
Discussions of this nature—about the balance between personal privacy and the state’s need to maintain public safety—are not new, but they have become increasingly important with advances in technolog


ty data as the potential basis for public-private partnerships similar to those the government maintains with major banks, airlines, and other organizations to protect against national security threats (e.g., identifying people who are financing terr


count when developing or using any intelligence system.12 The NCISP also recommended that state agencies consider the Association of Law Enforcement Intelligence Units’ (LEIU’s) Intelligence File Guidelines as a model for maintaining intelligence dat


a retains ownership and control over its data. Thus, the program clearly defines the system controls and centralizes responsibility for policies governing data dissemi- nation and privacy.
• Clear standards for classification: Only data classified as


aws in the United States, Canada, Europe, and other regions are structured around the widely accepted Fair Information Practice Principles. These principles were first established by the U.S. Department of Health, Education, and Welfare in 1973. The


ble for complying with measures which give effect to the principles stated above.16
The State of California has developed privacy laws based, in part, on the Fair Information Practice Principles, and it has used the framework to develop many state- s


, if met, triggers addi- tional screening) or positive (i.e., if all indicators are met, secondary screening can be minimized). Index or scoring methods typically characterize a risk level by summing indicator scores or by computing risk as the produ


or law enforcement are not yet certain, but it appears that some of these methods might help in building a case for monitoring a given individual or group more closely. Some of the more sophisticated mathematical methods are not likely to be universa


e. According to Dawn Clausius, a police intelligence analyst with the Olathe, Kansas, Police Depart- ment, these assessments may hold “mountains of untapped data for predictive polic- ing efforts.”22 Behavioral patterns from the LSI-R might be integr


ers include the Substance Abuse Subtle Screening Inventory (SASSI), the Beck Depression Inven- tory–II (BDI-II), the Minnesota Multiphasic Personality Inventory–2 (MMPI-2), the Addictions Severity Index (ASI), the Hare Psychopathy Checklist–Revised (


el characteristics of the same population of offenders would return the same results. Low inter-rater reliability indicates that vari- ous raters do not agree and that the scale is defective or the raters need to be retrained. Implementation of the L


struments should complement a series of other carefully and clinically informed appraisals and should not be used as a substitute for them when making an assessment about a prisoner.27
James Austin has published a useful paper on proper and improper


appropriate and applicable to the criminal element in their community.
Quebec, Canada: Assessing Criminogenic Risks of Gang Members
A great threat to public safety in Quebec is the presence of street gangs. Juvenile pros- titution networks, drug traf


 Koehler, and Richard Rosenfeld, “The Prediction of Violence and Homicide in Young Men,” Journal of Consult- ing and Clinical Psychology, Vol. 73, No. 6, 2005.
 96 Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations
homic


. Wilson, Susan C. Smith, John D. Markovic, and James L. LeBeau, Geospatial Technology Working Group Meeting Report on Predictive Policing, Scottsdale, Ariz., October 2009.
 Using Predictions to Support Investigations of Potential Offenders 97
bery)


and Patricia L. Brantingham, Organized Crime Detection in Co-offending Networks, working paper, Burnaby, B.C.: Simon Fraser University, c. 2011.
36 Jay S. Albanese, “Risk Assessment in Organized Crime: Developing a Market and Product-Based Model to D


kov shares several useful case studies of policing methods that use risk assessment methods to analyze organized crime. Like Zoutendijk, Tusikov concluded that there is ambiguity in the definitions and analysis
38 Stephen Schneider, Predicting Crime:


nvolved may immediately call a domestic violence counselor to guide
41 Natasha Tusikov, “Measuring Organised Crime–Related Harms: Exploring Five Policing Methods,” Crime, Law, and Social Change, Vol. 57, No. 1, February 2012.
42 For example, if A is


ier in this chapter, are much more rigid in their design but may be more statistically reliable and valid.46
According to Campbell’s research, women who have been threatened with a gun are 20 times more likely to be murdered, and women who have been


section, we review several methods currently in use to “predict” who likely com- mitted recent crimes of interest. The common approach is to assemble the available clues—pieces of data both about the crime and about past perpetrators in the area— an


 areas frequented by the subject and the types of crimes committed, can inform behavioral and appropriate responses to target the person or the activity.
Anchor Point Analysis, or Geographic Profiling
Geographic profiling is an analytic tool that det


cations to find the offender with the goal of locating him or her sooner rather than later.
It is important to note that one does not necessarily need specialized computer software to do a basic geospatial profiling analysis. Analysts can do reasonab


lly those with progressive analysts, responses to this information are often lackluster. Police agencies are unsure what to do with a forecast that “John Smith, a known dry- wall burglar, is likely to strike again this month in one of six possible ta


S tracking databases to see whether any tracked offenders were in the immediate vicinity of a crime. Facial recog- nition software, as it improves, will add yet another potential source to identify perpe- trators who commit crimes in front of surveil


igence information and whose car matched the vehicle description.51
• The Baltimore Police Department was faced with a homicide victim who was also a known gang member, with the only clue being a surveillance video that showed the likely perpetrators


GPS tracking devices.
• Interventions led by community or other nonprofit organizations are also in use. For example, Cure Violence (formerly Chicago Ceasefire) is widely known for intervening with individuals at high risk of involvement in gang-rela


t be the start of a longer-term decline in the national prison population. One of the proposed reasons is that better use of data and analytic tools to predict the potential for additional criminal behavior can improve a case management system’s abil


f the 85,000 youth remained arrest-free. “We are hoping that the use of predictive analytics will essentially put us out of business, with first- offending juveniles never returning to the incarceration system,” said Mark Greenwald of the Florida Dep


ity’s School of Criminology in British Columbia, and he founded a section of the Vancou- ver Police Department focused on these crime analysis methods. The concept of using geospatial and other tactical information to construct behavioral patterns ha


.
64 Grierson, 2003.
 112 Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations
his or her home area, but there will be a buffer of some distance around the resting site to maintain a safe distance from the site of the crim


rime Mapping and the Crimestat Program,” Geographical Analysis, Vol. 38, No. 1, January 2006; David Canter, Toby Coffey, Malcolm Huntley, and Christopher Missen, “Predicting Serial Killers’ Home Base Using a Decision Support System,” Journal of Quant


ness of these methods.
Predictive Policing Myths
As a term of art, predictive policing is relatively new, but it has received substantial attention. Therefore, it is important to dispel some commonly repeated myths about the field. Whether by reporte


owing in advance exactly which grid cells will have the most crime—equivalent to a crystal ball), and nothing (choosing locations to focus on at random).2
As shown, for vehicle thefts, the regression model did noticeably better than simple hot spot m


icing: The Role of Crime Forecasting in Law Enforcement Operations
In addition to cognitive processes, these functions require a great deal of social interaction to maintain accurate contextual knowledge of the crime environment, col- laborate with p


 Developers, and Policymakers 119
range from data deficiencies to assessment flaws and a failure to adequately evaluate the techniques used. In a recent article for the National Institute of Justice Journal, former RAND colleague Greg Ridgeway discus


nging to what you learned in statistics 101 (getting beyond statistics models); (3) assuming that one method works well for all problems; (4) trying to interpret too much (focus on accuracy and not trans- parency); (5) forsaking model simplicity for


y, standardized by the total study-area density. See Ned Levine, “The ‘Hottest’ Part of a Hotspot: Comments on ‘The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime,’” Security Journal, Vol. 21, No. 4, October 2008.
 Rock Creek Par


re has been some improvement, however.
When applying techniques, such as regression or any of the data mining variants, using common sense to vet the factors incorporated into the model will help avoid spu- rious relationships. It also is important t


he understanding that police responses to the predictions may have an effect on the outcomes.
On the intervention effectiveness side, for example, once a hot spot has been iden- tified and an intervention has been employed, follow-on analysis can be


as being at risk of recidivism. What should be done with a prediction that a parolee has a high risk of reoffending when that prediction—while much better than chance—is still far from definitive? The common answer to date has been that, because most


advanced tools, such as the NIJ-sponsored CrimeStat series.
Larger agencies with large volumes of incident and intelligence data to be ana- lyzed and shared with many officers will want to consider more sophisticated—and therefore more costly—systems


lete suite of predictive policing tools might work together to create shared situational awareness.
A Developer’s Guide to Predictive Policing
Here, we present findings for developers and vendors to help assist their technical and marketing efforts.


into the high hundreds of thousands or millions of dollars, plus annual maintenance fees of tens or hundreds of thousands of dollars. These costs are simply not affordable for most departments.
That said, we do understand that the investment in devel


efs and commanders was necessary for the success of predictive policing initiatives. “Mar- keting” efforts promoting the value of predictive products, combined with good prod- ucts that genuinely help solve cases or prevent crimes (and publicizing th


perations. That said, the architecture shown in Figure 5.5 has been tailored specifically to law enforcement based on our interviews and reviews of the types of systems currently being used. For more informa- tion on the original COINCOP concept, see


e.
• Network analytics tools: These tools show (and calculate) relationships between entities and events. Chapter Four discussed how queries across social network data can help identify suspects. Social network analytics can also help command- ers an


ary
HPP call for service previous op day
Other
Prowler/suspicious person Suspicious activity Tactical patrol
Traffic stop
Targeted crime probability prediction
Medium to high High
September 25, 2012
134 Predictive Policing: The Role of Crime Forecast


and focused deterrence policing approaches are built on the idea that relatively small numbers of offenders are responsible for or connected to large numbers of crimes. These strategies target key offenders, dis- couraging them from committing crimes


ample of police departments’ efforts to build on community relationships was trying to talk with as many people as possible—not to stop and frisk them but to explain that the police were trying to reduce crime in the area. Officers then asked
17 Eliz


systems. However, the key for agencies of all sizes is to think of the tools as providing situational awareness rather than crystal balls. The sys- tems should help agencies understand the where, when, and who of crime and identify the specific prob


s taught electrical engineering and computer sciences at The George Washington University, statistics at George Mason University, and mathematics at the U.S. Mili- tary Academy at West Point.
Brian McInnis
Brian McInnis (M.P.P. education policy, Peab


d Cor- rections Technology Center’s Information and Geospatial Technologies Center. His recent research has focused on improving information collection and analysis meth- ods to prevent acts of violence, ranging from violent crime to terrorism and in


 Charlie, and Colleen McCue, “Predictive Policing: What Can We Learn from Wal-Mart and Amazon About Fighting Crime in a Recession?” The Police Chief, November 2009.
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142 Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operati


iminology, Vol. 44, No. 5, 2004, pp. 641–658.
Bushman, Brad J., Morgan C. Wang, and Craig A. Anderson, “Is the Curve Relating Temperature to Aggression Linear or Curvilinear? Assaults and Temperature in Minneapolis Reexamined,” Journal of Personality


 of Chiefs of Police Law Enforcement Information Management Conference, Indianapolis, Ind., May 23, 2012.
City of Cincinnati, “Neighborhood Enhancement Program,” web page, undated. As of August 6, 2013:
http://www.cincinnati-oh.gov/community-developm


013:
http://geographicprofiling.com/products/rigel-profiler
Esri, “ArcGIS for Desktop,” web page, undated. As of August 6, 2013: http://www.esri.com/software/arcgis/arcgis-for-desktop
Estivill-Castro, Vladimir, and Ickjai Lee, “Data Mining Techniques


 a Smarter Planet: A Smarter Planet Blog, April 14, 2010. As of August 6, 2013: http://asmarterplanet.com/blog/2010/04/improving-juvenile-justice-for-the-state-of-florida.html
Grierson, Bruce, “The Hound of the Data Points,” Popular Science, March 21


 Law Enforcement Information Management Conference, Indianapolis, Ind., May 22, 2012.
Heaton, Brian, “Predictive Policing a Success in Santa Cruz, Calif.,” Government Technology, October 8, 2012. As of August 6, 2013: http://www.govtech.com/public-sa


lace: Units of Analysis in Geographic Criminology, New York: Springer, 2009, pp. 171–198.
Kanable, Rebecca, “Dig into Data Mining,” Officer.com, April 12, 2007. As of August 6, 2013: http://www.officer.com/article/10250000/dig-into-data-mining
Katran


in Java,” homepage, undated. As of August 6, 2013:
http://www.cs.waikato.ac.nz/ml/weka
Marshall, Mark A., “N-DEx: The National Information Sharing Imperative,” The Police Chief, Vol. 73, No. 6, 2006.
Maryland Network Against Domestic Violence, “What


ce Datasheet,” Atlanta, Ga., 2012. As of August 6, 2013: http://www.cdc.gov/violenceprevention/pdf/yv-datasheet-a.pdf
National Criminal Justice Association, Justice Information Privacy Guideline: Developing, Drafting and Assessing Privacy Policy for


“Local Spatial Autocorrelation Statistics: Distributional Issues and an Application,” Geographical Analysis, Vol. 27, No. 4, October 2005, pp. 286–306.
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rticle_archive/results/details?id=1530
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 “Stopping Crime Before It Starts,” Los Angeles Times, August 21, 2010. As of August 6, 2013:
http://articles.latimes.com/2010/aug/21/local/la-me-predictcrime-20100427-1
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Zetter, Kim, “U.


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, regulators, financial ser- vices executives, accounting professionals and standard-setters, members of the business com- munity, and other stakeholders with broad interests in accounting standards, valuation, risk management, systemic risk, and the


er FOur
Accounting Standards and Prudential regulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bank Prudential Regulators and Capital Requirements: The Basics. . . . . . . . . . . . . .


idity pricing. Valuation of an asset based on degree of liquidity in a thinly traded market, rather than on the asset’s future earning power.
Liquidity pricing model. An economic model that seeks to explain the influence of valu- ation accounting on


oth the financial system and its underlying accounting information more robust.
Study Purpose and Methods
The purpose of this study was broadly to investigate FVA and HCA policy issues and evidence in the wake of the financial crisis, and to explore


to sell assets into the illiquid market to fulfill their own regulatory capital constraints. The theoretical result is the spread of risk across otherwise independent institutions, contributing to an accelerating spiral of devaluation that could culm


generic ways in which valuation approaches can be associated with systemic risk, an additional focal point of our study involved the observation that both FVA and HCA approaches are capable of generating poor-quality information when implemented poor


FVA caused the 2008 crisis and which of FVA or HCA is the “better” accounting approach are not the most useful questions to focus on. Based on our reading of the avail- able empirical evidence, we conclude that FVA was probably not a primary driver


hile reducing related risks to the financial system.
Governance
• Policymakers should consider new steps to strengthen institutional governance and control mechanisms that in turn support higher-quality FVA and HCA practices within financial firms. T


ernance that support the valuation process, or on the quality and recency of any reference market prices used in FVA.
• Policymakers and standard-setters should consider adding disclosure requirements to address situations in which market power and o


institutions, while imposing some common baseline assumptions on related practice.
4 Basel III is a global, voluntary regulatory standard on bank capital adequacy. See Basel Committee on Banking Supervi- sion (2010).
 Acknowledgments
We would like to


g standards are also deeply tied to the prudential regulation of finan- cial institutions (including banks) and, by extension, to assessing the adequacy of bank capital reserves. In sum, FVA and HCA are of central importance both to investors and reg


 would be more robust to these sorts of problems involving distorted market prices and financial risk.
The counterarguments made in favor of FVA are numerous. Advocates of FVA have sug- gested that fair value actually provides more transparency in ba


the potential to destabilize an entire financial system or market, as opposed to the simple risk of insolvency for any single financial institution or bank within the system. The crisis of 2008 is notably an example of a “systemic event,” in which t


s banks’ portfolios of loans, have to be viewed in this context. As we will endeavor to show, many of the concerns that have been raised in the recent debate over FVA are only peripherally connected to systemic risk issues.
8 Known in finance as “pru


ncome analysts (5); current and former government officials (7); current and former representatives from professional accounting organizations and standard-setting groups (6); accounting and finance executives at a range of bank (8) and nonbank finan


over FVA and HCA
The purpose of standardized accounting is to provide sufficient financial information to inves- tors, shareholders, creditors, managers, and others, so that each can make meaningful assess- ments about the value and sustainability of


VA in recent years.
  7
8 Fair Value Accounting, Historical Cost Accounting, and Systemic Risk
2009, responding to [then] proposed revisions to the FVA standards. We heard similarly strong sentiments about FVA and HCA expressed in our interviews, by


atile, less responsive to liquidity shock, less vulnerable to specula- tive run-up in asset prices, and less assumption-dependent than is sometimes the case for FVA valuation. For these reasons, HCA may provide particularly useful information in pric


mselves (in tracking their own status and managing portfolio risk), contractual counterparties (in assessing the solvency of their partners in finan- cial transactions), and equity analysts and economists (in assessing the financial status and risk o


 generally more consistent with promoting investor welfare. Others, however, have disputed those argu- ments, particularly by challenging whether FVA, as applied to nonliquid, nontrading assets, really does provide much incremental transparency.6
Reg


on non-market-traded assets involved many more embedded and opaque assumptions, making it more difficult for him to know how much confidence to put in the numbers. Another analyst observed that it is difficult for an outsider to assess how “good” the


n FVA clearly offers the relevant answer for what the value of the assets are at any given point in time. But for another firm that holds similar investments, but in which there is no intent to sell the assets at all, then the liquidation value of th


isagreement between partisans of FVA and HCA often involves their perspectives on volatility in asset prices, and their views on whether or not such volatility itself provides useful information for understanding asset (and firm) values. Because FVA


its hold-to-maturity assets, then day-to-day fluctuations in the value of the assets become far less rel- evant. It’s only when a forced liquidation of assets is threatened that spot market prices abruptly become the relevant metric for understanding


lid under GAAP.” Another respondent said, “Auditing firms are often not particularly strong in evaluating the models used in determining fair value for illiquid assets. Often, those models are much better understood by the firms and banks that apply


that banks are often overly reluctant when it comes to writing other-than-temporary impairments onto their books, and that bank examiners who are called upon to review the books of insolvent institutions often find serious deficiencies in accounting


les” (FASB, no date-a).
17 The difference in views was illustrated in the comments of two people we spoke to. One asserted that CDO values broadly fell by 60 percent or more at the height of the financial crisis but had mostly recovered back to par v


interviewees related a version of this story. Yet this perspective raises several questions. What is “systemic risk” in this context? What is the implication of suggesting that FVA causes, or contributes to, systemic risk? In context, is it sufficie


ising . . . a system.
• The potential for a modest economic shock to induce substantial volatility in asset prices, significant reductions in corporate liquidity, potential bankruptcies, and effi- ciency losses.
• The risk that a default by one marke


tial to “infect” French and German banks. But this is not the definition for “contagion” that we intend here. To the extent that French and German banks themselves hold Greek debt, then the French and German institutions are directly exposed to relat


y-driven.
Our interviews and the popular press notably provide several examples of possible liquid- ity pricing witnessed during the 2008 financial crisis. For example, in July of 2007, AAA-rated super-senior tranches of collateralized debt obligatio


way poorly aligned with fundamental values.
The Theoretical Relationship Between FVA, Liquidity Pricing, and Risk Contagion: The Industrial Bonds Example
So how does FVA relate to liquidity pricing and risk contagion in economic models? A simpli- fie


odels) suggests that FVA can indeed be a unique vector for risk contagion in some cir-
9 See, e.g., discussion in Adrian and Shin (2008).
 cumstances, at least under appropriate model assumptions. Those assumptions notably include the occurrence of l


 long-run assets until maturity (i.e., in the final time period specified by the model).
Put another way, the key result of the AC model framework is a contagion of risk that drives bankruptcy within the banking sector, and which would not have occur


uring a period of acute illiquidity, such that assets that were previously marked to market may instead
10 Again, a question that arises in passing is, Under what circumstances might there be “no short-term liquid market for selling” an asset (or in


quidity pricing, represent a strong exercise in logic: An attempt to formulate a set of well-defined conditions under which FVA itself might serve to drive systemically important risk contagion effects. The fact that the AC model succeeds in doing th


at whether SFAS 157-4 represented a “good” policy response at the height of the crisis is difficult to assess. In principle, more flexible application of FVA standards during a liquidity crunch might help to prevent some asset sales and a procyclical


pairments as being other than temporary, has strong implications for risk management and valuation practice within financial institutions. It also has broader implications for systemic risk.
HCA does not drive contagion of risk via eroding asset pric


eoretical case is much in line with what actually happened to many financial institutions during the S&L crisis of the 1980s. By exten- sion, FVA practice may be superior to HCA as a valuation mechanism in some circumstances but not others, or for so


tagion among banks might be best addressed through a modification of accounting standards, a modification of prudential regulatory requirements, or perhaps both.1 These two structural elements of the financial system are tied together, particularly i


l financed to meet their obligations to their creditors.
Two related points deserve mention. First is that the prudential regulation of banks gener- ally applies to depository institutions (i.e., commercial banks) and to bank-holding companies (conso


n FVA standards and bank capital requirements to consider, such that only some categories of bank assets (e.g., trading securities) but not others (e.g., loans held to maturity) are typically subject to FVA treatment and related markdowns.7 In theory


preferred stock and some other types of subordinated debt issued by the bank, which also have a relatively low priority for repayment in insolvency.9 Taken together, Tier 1 and Tier 2 capital represent the portion of value in a bank’s assets that cor


should occur.
10 Elliott (2009a, 2009b, 2010).
11 One of our interview respondents told us that the details of this process tend to be far more complicated than is sug-
gested by a simple summary of risk-weights by broad asset categories.
12 The FRBC


dmati et al. (2010).
Accounting Standards and prudential Regulation 31
      Banks with total assets < $10 billion Banks with total assets > $10 billion
  1234134123412341234
2006 2007 2008 2009 2010 Calendar quarters and years
 32 Fair Value Account


rather than through seeking new equity. See Admati et al. (2010); Elliott (2009a, 2009b); Roberts (2010).
    How Can Banks Respond to an FVA-Induced Capital Shortfall?
The situation becomes more complicated when we consider a decline in the value of


 $99
          34 Fair Value Accounting, Historical Cost Accounting, and Systemic Risk
Table 4.4
Bank Responds to Loss of CDO Value by Selling Loans
Bank Responds to Loss of Value by Selling Loans ($9), Reducing Liabilities ($9)
   Assets
CDO Loans
t


mortgage-backed assets at “fire sale” prices during the 2008 crisis.
But as we mentioned above, there are a host of practical reasons why banks would prefer to avoid recapitalizing, when the opportunity to sell assets is available as an alternative (


bank capital requirements involve calculations based on risk- weighted assets and favor less risky types of assets in the portfolio (e.g., cash) over more risky ones (e.g., loans), there may be additional pressure on banks to preferentially liquidat


s, under conditions of liquidity pricing. However, HCA does pose the problems of risk accumulation and of concealing unrecognized but other-than-temporary losses in the value of assets that nominally appear unchanged on bank balance sheets. This latt


icacy of prudential regulation for banks. There are some important and subtle implications for risk oversight here. To offer one example, earlier in this chapter we discussed the fact that a bank could respond to a sudden shortfall in its regulatory


ontribut-
25 In fact, the recent Basel III round of reforms to international bank capital standards did focus specifically on the latter point, and on increasing and strengthening institutional capital reserves. See overview discussion in Basel Commi


FVA by look- ing at changes in regulatory capital levels during the crisis, across a sample of large banking institutions. Importantly, large banking institutions tend to utilize FVA for a larger portion of their asset books and balance sheets, in pa


rite-downs. However, the observed effects were reportedly quite small, and did not appear to manifest on an industry-wide basis. Put another way, according to Badertscher et al., although a distressed bank may sometimes be forced to sell assets due t


ccounting Standards and prudential Regulation 39
 40 Fair Value Accounting, Historical Cost Accounting, and Systemic Risk
their regulatory capital constraints). In short, Bowen et al.’s findings mainly indicate that for banks during the crisis, chang


and regulatory capital requirements specifically accommodate concerns about liquidity risk by declining to apply FVA and market pricing for important categories of bank assets. Moreover, recent real- world experience seems to support the same conclus


- tion provided by banks, and suspicions about embedded and unacknowledged credit risks associated with their assets, may indeed have contributed to illiquidity and to the freeze-up in markets.33 In turn, loss of liquidity had very significant impact


regulators and is an appropriate yardstick for determining asset values. But when immediate liquidation is not contemplated or necessary, and when market illiquidity causes asset prices to deviate from underlying fundamental values, then FVA itself


(LDC) debt crisis of the 1970s. We selected these two cases, in part, because several of our interview respondents specifically pointed them out to us and talked about the similarities and differences between these episodes and the 2008 crisis. We al


n anticipated5 and the losses reduced. While some elements of this story are indeed accurate, a careful reading of the history of the S&L crisis does not support the superiority of one accounting approach over the other. Instead, it suggests the limi


or depositors from other types of institutions (such as banks). Unlike the thrifts, the banks did not face statutory inter- est rate caps and could offer much higher rates of return to depositors. The effects of competi- tion on the S&L industry, and


among S&L regulators.9 Prior to the 1980s, there were relatively few failures among thrifts, and those failures were not particularly costly to resolve. As a result, supervisory oversight of the thrifts was decentralized and generally did not examine


997).
11 This was an argument eerily parallel to some of the more recent arguments concerning FVA in the 2008 crisis, in set- ting out reasons why superficial accounting losses on paper weren’t in fact “real.” In the S&L context, one of our interview


Fund. (p. 5)
The GAO concluded that abuses in bank accounting practices, and particularly in the recognition of other-than-temporary impairments to assets, had contributed significantly to the failure of early warning mechanisms among prudential regu


ticular argues that S&L regulators during the 1980s were far less independent than FDIC or other federal bank regulators. Political appointees in the Reagan administration reportedly pushed for deregulation and forbearance toward S&Ls, while simultan


billion were in various degrees of default to lenders. For U.S. banks, the crisis was largest in connection with loans to Latin American and Caribbean countries. Sixteen of those countries (including Mexico, Brazil, Ven- ezuela, and Argentina) owed U


e evolution of this aspect of the LDC crisis, in the context of historical financial and banking crisis episodes more broadly, see Reinhart and Rogoff (2008).
26 World Bank World Debt Tables.
 50 Fair Value Accounting, Historical Cost Accounting, and


essful outcome in the case of the S&Ls? The answer likely does not lie in the role of the accounting standards. In both cases, evidence accumulated that the fair value of bank loans was actually far lower than that reported to regulators. The primary


ut down banks that would otherwise have been insolvent if forced immedi- ately to mark their LDC loans to reflect current prices and actual impairment. The argument presented to us on behalf of this regulatory strategy was that the Federal Reserve an


ckly, so as to limit the losses ultimately incurred?33 And ex post, will regulatory forbearance help in developing a viable debt restructuring plan for the Eurozone, or will it instead involve allowing further accumulation of bad loan assets, possibl


of the Greek government. See, e.g., discussion in Story, Thomas, and Shwartz (2010).
 Lessons from Historical episodes Involving Accounting Standards, Systemic Risk, and Financial Crisis 53
regulators have in responding post-crisis. The LDC episode,


in asset valuations, and contagion.
In the course of our interviews, respondents raised a series of concerns connected to the implementation of FVA and HCA and to problems in institutional governance and controls that can limit the fidelity of FVA an


after the 2008 financial crisis (Senior Supervisors Group, 2008, 2009). The Senior Supervisors Group (SSG)1 published multiple reports summarizing institutional risk management lessons learned from the crisis, based on intensive interviewing and roun


inesses actually performed relatively well through the crisis, in part because they carried out their respective valuation approaches effectively and carefully. By con- trast, firms that violated the integrity of their own valuation processes, regard


 specified in GAAP per ASC 820.
 58 Fair Value Accounting, Historical Cost Accounting, and Systemic Risk
Another interview respondent told us that oversight for mark-to-model approaches in FVA fre- quently involves auditors who are substantially less


though that probably represents only a small fraction of all instances in which FVA is used under ordinary circumstances,5 in a situation where acute liquidity pricing arguably obtains, the problems potentially become much larger,6 and the
4 With reg


d in which similar assets can be held in both places but subject to different forms of accounting treatment, the results can be both complex and perverse. One of our interview respondents suggested that a financial services firm that mixes its busine


ond the risks inherent to either of FVA or HCA taken alone. Second, the combination of the two accounting approaches has important institutional risk management and arbitrage implications to it, which again go beyond the risks associated with either


under specified circumstances. See discussion in Renders (2009). Here again, to the extent that assets can be moved between FVA and HCA treatment based on exigent circumstances and shifting investor intent, then concerns may arise about accounting t


res offer the best estimate of what assets are worth in liquidation, and therefore provide an appropriately conservative yardstick for gauging firm value. None of these arguments is con- clusive, and they focus more directly on institutional risk tha


ional participants who either own, or have the means and interest to acquire, a particular asset. Whenever ownership of a financial asset is concentrated in the hands of only a few players, those players may have a disproportionate effect on price, t


d to reflect any other than temporary impairment.
 Implementation and Risk: the Challenges to Doing FVA and HCA well 63
ation approach generates. These issues illustrate the challenge involved in implementing either of FVA or HCA well, together with


ciple. On a somewhat different note, the over- sight of prudential regulators in examining FVA and HCA information offers another possible lever for strengthening the valuation process. As one of our interview respondents noted, the prudential regula


, what seems most clear is that both FVA and HCA can produce useful information, that both approaches can be vulnerable to producing misinformation when not applied rigorously, and that both are capable of contributing to systemic risk under some cir


f acute market illiquidity raise basic questions about how FVA prices are supposed to be calculated, while HCA struggles because accounting for other-than-temporary impairment presumes that there is an objective way to distinguish between (transient)


r, advo- cates on both sides acknowledged that each of FVA and HCA could be appropriate and useful in some circumstances. It gradually became clear to us that at least some of the debate (on both sides) tends to involve straw man–type arguments. In p


tive regulatory intervention afterward to help financial institutions recover and restructure themselves. In both cases, the role of prudential regulators in monitoring accounting informa- tion, applying capital requirements, overseeing financial ins