ints in time: September 2014 (before Proposition 47 was implemented),
October 2015 (one year following the passage of Proposition 47), and July 2016 (2.5 years following the passage of Proposition 47). Probation uses somewhat different categories of


igible Proposition 47 probationers; to assist with outreach efforts by posting Proposition 47 signage in probation supervision offices; to train deputy probation officers to provide supervised individuals with general information; and to refer indivi


passing of Proposition 47 because of their criminal justice status are no longer receiving those services. However, they represent a very small subset of the potential population affected by Proposition 47 that may be receiving DMH services, given th


et al., 2001; Hubbard et al., 1989; Simpson et al., 1997).

Table 3. 8. DPH Proposition 47  Cases, November 4, 2014 , to September 30, 2016
Proposition 47 cases  318
Remained in treatment after Proposition 47  154
Discharge d after remaining in t


ere is some controversy
regarding court-mandated treatment due to its potential coercive nature, research suggests that court-mandated substance-use treatment may be just as effective as treatment that is not court-mandated. For example, several stu


mation (e.g., disability and insurance status). Once data are matched across departments, a unique identifier can be assigned (identifiable information can be removed before analysis).
Set up in 2007, the ELP can track individuals longitudinally, al


ore inclusive than the ELP; it will include information from the courts, prosecution, and defense as well as from law enforcement and probation departments in Los Angeles County. JAIMS includes information from the Proactive Information Exchange syst


anagement system using the same software product as CWMDM with future plans to link its master data management to CWMDM. JAIMS will deploy the ISAB master data management with links to CWMDM once ISAB master data management is fully operational; ther


is eligible, anyone with a felony arrest or conviction prior to November 2014 was included, which likely means that the database overestimates the number of individuals who are qualified for relief. For example, prior criminal history that would prec


ilized to provide data analytics to support agency workload analysis, service integration, and program evaluation. Integration between JAIMS and CWMDM would allow for measurement of service utilization. The data analytics and statistics are still in


t had identified 196 Post-Release
Community Supervision offenders and 827 mandatory supervision offenders released from supervision as a result of Proposition 47 (Riverside County Community Corrections Partnership Executive Committee, 2016). The Riv


ely following Proposition 47, but perceived changes in prosecution charging (e.g., increased filing of felony drug sales cases under HS 11366). Of the 40 courts reporting complete data in the first six months of 2015, misdemeanor filings increased by


1 (the years before and after Public Safety Realignment was enacted). Calls for
service increased about 5 percent from the year prior to Proposition 47, although there
does not appear to be a trend break at the time of Proposition 47. LASD also has


e county
departments lacked the infrastructure to readily monitor workload changes and then translate
those changes to fiscal impacts. Although caseload information is available, translating caseloads to workload was reported to be relatively diffi


tion 47
for county departments. To do so, we provided assessments of changes in workload resulting from Proposition 47 for the individual departments, as well as estimates of costs obtained from several different departments, such as the PD and APD.


eles County’s efforts: arrests (including by offense type); changes in jail bookings and populations; probation caseload changes; increases in cases handled by the DA, PD, and APD; and changes in drug court participants. At the same time, there is ex


to draw a sample of cases and ultimately individuals to follow over time.
1 With respect to identifying a cohort of new Proposition 47
cases, ISAB agencies have developed a Proposition 47 data strategy and logic model, as discussed in Chapter Three.


the agency (e.g., training sessions, community outreach, administrative responsibilities). This approach addresses many of the limitations of other assessment methods that were based on aggregate case counts or weighted caseloads (Gramckow, 2012). W


ed by a program, policy change, or activity, as opposed to the consequences of
that workload. County departments are able to directly influence outputs, and they can represent measures of what is most directly salient for county agencies. Outcome me


ber or percentage of individuals with identified need or
risk profile referred to appropriate services, by service type or program provider; and
number or percentage of inmates released from custody with supervision or service
referral. Possible o


r drug-related crimes—including sales,
possession for sales, transportation, and possession of narcotics while armed with a
firearm—remain unchanged.
• Forgery crimes: Proposition 47 amended PC 473, adding a new subdivision (b) that makes the offe


sued to another person, without consent, with the intent to
defraud.
A nonexhaustive list of superseding priors is provided below.
• PC 37: treason
• PC 128: perjury causing the execution of an innocent person
• PC 182: conspiracy to commit a cr


n
• PC 311.1 (F/M): sending, bringing, or possessing obscene material depicting a person under age 18, with the intent to distribute or exhibit
• PC 311.2(b): sending, bringing, or possessing obscene material depicting a person under age 18, with t


d Party
Cat 1
Cases ** Cat 2
Cases** Cat 3
Cases** Assumed Item  DPD III  Paralegal  ITC ITC ITC Ware -
house
Worker II  IT
Technical
Support
Analyst I  IT Super -
visor  N/A
14,934  37,241  5,218  Monthly Budgeted Salary FY 2016–
2017 $12,


gal services that
originate with Proposition 47 intake.*Category 3 Proposition 47  Case
Time CalculationAverage time to complete
expungement .
**Case totals as of September 5, 2016.
  Filing only done on Proposition 47 –eligible cases .

Propos


  5 minutes 2–5
minutes

    5.4   Court filing          X

    5.5   Court proceeding  5–20
minutes

  6
    Process 6 :
Proposition  47 data
compilation


11 Category 2 Proposition 47  Case Time Calculation: High end of determined range.
12 Category 3 Proposition 47  Case Time Calculation: Average time to complete an
expungement.
13 Monthly will be updated on the first Monday of the following mo


OTES: Leftmost vertical red line represents timing of Assembly Bill  109, and rightmost vertical red line represents
timing of Proposition 47 . Seasonal adjustment calculated by the authors, with details on methodology provided below.
0
  20
  4


and assess changes in workload and resource needs following policy changes moving forward.
Step One: Identifying the Scope of the Study
At the outset of the study, stakeholders need to determine the scope of the study that is
feasible given time a


on steps performed and not solely on statutory requirements. It should also be recognized that, even when different professions participate in the same processing step, they may have different time requirements.
The key pieces of information needed


t in which the study will take place. Below, we outline the relevant potential data collection methods.
Self-Reported Time Logs
Self-reported time log methods require county department staff to keep a daily record of the
time spent on various task


rt can be estimated for each case type by
summing the time spent by each agency (and within each agency, each profession) in each major processing step. The total time spent is then divided by the total number of cases recorded at
each processing s


ons/Crime_Data_Dec2015.pdf
Buchen, Lizzie, and Michael Males, “Proposition 47: Estimating Potential County-Level Savings
and Jail Population Reductions,” Center on Juvenile and Criminal Justice, October 2014. As of December 19, 2016:  http://www.cj


ry 3, 2017:
http://www.sandiegocounty.gov/content/dam/sdc/probation/CCPdocs/CCP_Meeting_Presentation_02-16-15.pdf
County of San Diego, Public Safety Group, Adopted Operational Plan Fiscal Years 2015–16
and 2016–17, San Diego, Calif.: County of San


erced
Treatment for Drug-Abusing Offenders,” Federal Probation, Vol. 62, 1998, pp. 3–10.
Federal Bureau of Investigation, “Crime in the United States, 2015,” undated. As of March 8,
2017: https://ucr.fbi.gov/crime-in-the-u.s/2015/crime-in-the-u.s


d Study
Report, Madison, Wisc.: National Council on Crime and Delinquency, 2006.
Judicial Council of California, “Early Impacts of Proposition 47 on the Courts ,” Operations and
Programs Division, Criminal Justice Services, March 2016. As of Dece


.
———, ISAB’s Proposition 47 Data Analytics Strategy, provided to RAND on December 7,
2016c.
Los Angeles County Sheriff’s Department, “About the Los Angeles County Sheriff’s
Department,” web page, 2010. As of February 22, 2017:
http://www.la-she


015),” County of Los Angeles Department of Auditor-Controller, April 5, 2016a. As of December 21, 2016: http://file.lacounty.gov/bc/q2_2016/cms1_242181.pdf
89 ———, “Proposition 47: Status of Tracking Methods and Metrics (Board Agenda April 12,
201


 Street-Level Officers,” Criminal Justice Review, Vol. 26, No. 1, 2001,  pp. 17–37.
Smith, Brad W., Kenneth J. Novak, James Frank, and Christopher Lowenkamp, “Explaining
Police Officer Discretionary Activity,” Criminal Justice Review, Vol. 30, No.


hington, D.C.: U.S. Department of Justice Office of Community Oriented Policing Services, 2012.
Wu, Fei, and Max Stevens, “The Services Homeless Single Adults Use and Their Associated
Costs: An Examination of Utilization Patterns and Expenditures


SOCIAL AND ECONOMIC WELL-BEING
Forensic Familial and
Moderate Stringency  DNA Searches
Policies and Practices in the United States,
England, and Wales
Tepring Piquado, Carl F. Matthies, Lucy Strang, James M. Anderson
Prepared for the National Insti


s research was conducte d in the Justice Policy Program within RAND
Social and Economic Well-Being. The program fo cuses on such topics as access to justice,
policing, corrections, drug policy, and court system reform, as well as other policy conce


s for Local and State DNA Database Matches Among
Responding LDIS Laboratories, 2010 and 2018 (Prorated) ................................................. 26
Table 3.3. Familial Database Search  (FDS) of the California and Texas Arrestee Databases


ith familial
DNA testing policies to develop a more in-depth understanding of the effect of familial and
moderate stringency DNA testing. One case study ex amined a state where familiar searching and
moderate stringency match reporting is explic i


 Federal Bureau of Investigation, “Frequently Asked Questions on CODIS and NDIS, Questions 25 and 28,”
undated.  x Of responding LDIS laboratories, 42 percent ar e using probabilistic genotyping software to
interpret DNA profiles derived from crime


ty estimate thresholds
if specific loci are searched at moderate stri ngency. SDIS laboratories were focused on hiring
additional personnel because curren t staff struggle to keep pace with DNA testing and database
management responsibilities.


source forensic profile that has already been uploaded
to NDIS without producing any exact matches through rou tine searches.

3 Federal Bureau of Investigation, “CODIS-NDIS Statistics,” September 2


tremel y partial profile would be disregarded unless it
related to a serious offense such as murder or  sexual assault, for which every DNA result is
scrutinized for its value and potential  contribution to th e investigation.
Conclusion and Impli


 e fficacy, we know little about familial searching or
moderate stringency search  policies or practices.
A number of regulatory and res ource constraints may limit its application and/or adversely
impact other avenues of investigation. Where pol


ble offense and all
convicted offenders.2
Following practices originally used in Engla nd, U.S. forensic DNA databases have steadily
increased in size and scope through revision of stat e statutes to expand the class of individuals
for whom a DN


tations,
Y-STR profiles are the same for all male first- degree relatives of the same paternal lineage
(i.e., a father and his sons will all have the same Y-STR profiles, as will their sons). Y-STR
profiles are the preferred method of confirming k


 close matches . 4 one allele at each locus. Some state policies also stipul ate that these partial matches suggestive of
kinship must be discovered by happenstance, or routine moderate stringency searches, rather
than through a deliberate familial


ing Post-Conviction
Litigants’ Access to DNA Database Searches to Prove Innocence,” Boston College Law Review , Vol. 60, No. 2,
2019, p. 709–751.
9 Daniel Schorn, “A Not So Perfect Match: How Near DNA Match Can Incriminate Relatives of Criminals,”


 of this report, we provide an overview of forensic  DNA laws, regulations, policies,
and practices that govern the use of fam ilial DNA or moderate stringency searches.
SDIS and LDIS Laboratory Survey
We invited all SDIS and LDIS administrators


d the
implications of this policy option.
The topics discussed during the interviews were
 processes on the use of familial moderate stringency searches and the resulting partial
matches
 stakeholders involved in authorizing, conducting, and


ve tool. Familial searches
were first used in England in 2002 and led police to the perpetrator of th ree cold-case homicides
through the DNA of his son, who committed auto theft and whose DNA was already in the database.
1
Moderate stringency sea


 as models, the director of the Texas
Department of Public Safety asked the SDIS administrators to devise a procedure for familial
search in response to a series of unsolved sexual assaults of elderly women. With the assistance
of attorneys from t


019.
9 Utilizing DNA Technology to Solve Cold Cases Act of 2011, H.R. 3361, 112th Congress (2011).
10 Federal Bureau of Investigation, “Labor atory Services: Famili al Searching,” 2011.   11 several states have explicitly prohibited its us e. The c


 U.S. Department of Justice, NCJ 230183, 2010.
13 Nelson, 2010.
14 The White House, Office of the Pre ss Secretary, “Fact Sheet: Investments to Reduce the Rape Kit Backlog and
Combat Violence Against Women,” press release, March 16, 2015.
15 J.


y exogenous timing of state da tabase expansions, she finds
that the combined deterrent and probative effects of adding fel ony convicts decreased violent and
volume property crime rates. A 12-percent increase in database size (i.e., the approximat


21 Stephen P. Myers et al., “Searching for First-Degree  Familial Relationships in California’s Offender DNA
Database: Validation of a Like lihood Ratio-Based Approach,” Forensic Science International: Genetic


ch is believed to be the first conviction using familial search techniques
in Australia.31
Granja and Machado review the use of familial searching in the United Kingdom and
Poland.32 They argue that the use of familial searching depends in part on


34 Aaron Opoku Amankwaa and Carole McCartney, “ The Effectiveness of the U .K. National DNA Database,”
Forensic Science International: Synergy, Vol. 1, 2019, pp. 45– 55.
16 assault kits, “touch DNA”). None of the SDIS laboratories report ed


ate matches 909 956 –22%
Moderate and partial SDIS/NDIS
matches confirmed 330 140 –69%
Total matches 1,753 2,371   35%b
a The third column is a percent change in a percentage, derived by taking, for example, exact matches 2010/total
matches 2010


ns.36
Figure 3.5 shows 25 of 26 LDIS laboratories that responded to  the survey currently use
CODIS 20 STRs. Eighteen of the responding LDIS laboratories conduct Y-STR testing as well,
with another four relying on cont ract laboratories for th e


laboratories reported that they
shared some suspect profiles with SDIS in accordance with the law.
As a means of ensuring that our understanding of CODIS terminology was consistent with
LDIS laboratories, our survey asked LDIS labor atories wheth


 trace DNA samples in property crimes had
increased the fraction of forensic partials and forensic mixtures in LDIS and SDIS databases.
LDIS laboratories were asked to report the number of forensic partials, forensic mixtures, and forensic profiles


of LDIS Laboratories Using Fortuitous Partial Matches or
Familial Searches to Aid Investigations

LDIS laboratories that use fortuitous partia l matches were asked whether they conducted
Y-STR or mtDNA testing to confir m the likelihood of kinshi


s portion of the surv ey. Seven of eight res ponding LDIS laboratories
replied that it was either “too time-consuming”  or “required data they do not collect.” One
responding LDIS laboratory commented on the defi nition of “confirmed,” stating that


 in the
case of California), some portion of which are not exact matches. Thus, these SDIS laboratory
administrators have dealt exte nsively with moderate stringency matches. Both Texas and
California were early adopters of familial searching as w


s from the investigating and prosecuti ng agencies. The request forms describe the
conditions under which the familial search will be conducted and require applicants’ signatures
to attest that they unders tand and agree to the terms.43
The famili


S administrator, the laboratory information management
specialist, the DNA technical lea d, and three other SDIS laboratory personnel. In California,
there are seven people on the FDS committee, including the state CODIS administrator, DNA
technic


tative from the office of the district attorney. The offender name is revealed at the conclusion of an informational session, afte r making sure investigators and prosecutors
understand the significance of the prospective FD S match and the proper w


o cover it, and Interviewee 1 noted th at it was only after the technique had been
employed on a number of occasions that discus sions around legislation emerged. The Protection
of Freedom Act 2012 sets out the legal framework re lating to the coll


dited and have authorization to take genetic material and
generate a profile that is loaded and stored in the National DNA Database.
When a law enforcement agency wishes to c onduct a familial DNA search with a crime-stain
profile that is suitabl


onsiderations. For exampl e, if you have an offender in his 30s [with a
profile in the National DNA Database], and th e offense happened 15 years ago, what are the
chances that a child of that offender would be  20 or 25 now? Those sorts of calcula


he lowest one we’ve found is 1,100th on the list.” In some cases, if the
DNA profile has distinction—for example, some unusual components—a relevant hit may move
closer to the top of the list. Adding age and ge ographical parameters was also report


 is a probabilistic thing.” Furthermore,  the interviewee stated that the efficacy of a
familial inquiry will be significantly lower when  looking at sibling relationships rather than
parent-child relationships, noting the similarity in the DNA of


. Several interviewees hi ghlighted the relative
expense of running a familial inquiry (Interviewees  1 and 6). In terms of contributing costs,
Interviewee 1 noted that cold cas es may be investigated by retired detectives who have been
hired as c


rom a
policing perspective, then, the argument could be made that conducting an investigation with the
use of a familial DNA search is an extensi on of normal police operations. However, the
interviewee raised two matters of concern. First, for th


e are a number of estimates available on the
proportion of young Black men from urban areas in  the National DNA database, which suggests
that they constitute between one-quarter and one-third of profiles in the database. The 38 interviewee also de


hnology has advanced and become more
sensitive, increasingly mixed and partial samples are being gathered and used in investigations.
Initially, these searches were predominantly us ed in sexual offense cases, but they are now
increasingly common


butor.
In this area, Interviewee 2 described the National DNA Database St rategy Board as taking
the lead role in minimizing ri sk associated with using mixe d samples by establishing strong
elimination procedures. The inte rviewee noted that th


, criminal justice policymakers are hampered from adopting evidence-based familial
DNA practices by our lack of knowledge about the us e and effect of this evidence. Our project’s
related studies sought to incr ease that knowledge base.
Our findi


sins), and even sixth-de gree relatives (second cousins,
once removed).3 The genetic genealogy databases can also provide matches with more distant
cousins.4 These trends raise issues of privacy, bias, and efficacy.5 Additionally, while familial
s


h at exist within the crim inal justice systems will
result in racially biased genetic population surveillance. In California, for example, Black people
account for about 20 percent of felony arrestees,  but only 6 percent of  the state’s populatio


d Marper v. the United Kingdom (European Court of Human Rights, 2008).
15 National DNA Database Strategy Board Annual Report 2012–13 , Gov.UK, Home Office, 2013.  44 may also be the case that the exoneration of  Black and Latinx people could be poss


ther than identifying a few
suspects based on an investigation of a crime, it can subject large numbers of people to

16 Schorn, April 1, 2007.
17 Carpenter v. United States, 138 S. Ct. 2206 (2018


Matches, 2009.
21 Ram, 2011.
22 L. Gusmao et al., “DNA Commission of the International Society of Forensic Genetics (ISFG): An Update of the
Recommendations on the Use of Y-STRs in Forensic Analysis,” International Journal of Legal Medicine , Vol. 12


other investigative leads have been exhausted,”  convenes a committee to review familial search
requests and results,27 and uses supplemental test ing of Y-chromosome DNA.28 Depending on

25James Ra


orcement the inexact nature  of the investigative lead in searches that
yielded candidates and counseled caution. Thus , concerns about pr ivacy and government
overreach implied by the policies seem genuine.
Additionally, given the p ace of techn


rate stringency searching
and reporting of partial match results. Additionally, it is our recommendation for the future
improvement of policies that this informati on should be made publicly available online.
Overall, this report sought to  provid


, p. 36. As of June 12, 2019:
https://cbssacramento.files. wordpress.com/2018/07/crime- in-california-2017c2a0.pdf
———, “Crime in California 2017,” Table 37, Di spositions of Adult Felony Arrests,
1982–2017, p. 49. As of June 12, 2019: https://cbs


m Sleeper’ Is
Winning over Skeptics,” Los Angeles Times , October 25, 2016.
Gierlack, Keith, Shara Williams, Tom LaTourette , James M. Anderson, Lauren A. Mayer, and
Johanna Zmud, License Plate Readers for Law Enforcement: Opportunities and
Obsta


 of Arrestee DNA Expungement,” University of Pennsylvania Law
Review Online, Vol. 164, No. 51, 2015, pp. 51–60.
Kennett, Debbie, “Using Genetic Genealogy Databa ses in Missing Persons Cases and to Develop
Suspect Leads in Violent Crimes,” Forensic


loads/system/uploads/attachment_data/file/252885/NDNAD_Annual_Report_2012-13.pdf
National Police Chiefs’ C ouncil and Home Office, National DNA Database Annual Report, 2017
to 2018, London: Home Office, 2019. As of June 11, 2019: https://assets.pub


res, CODIS Master Document
List, DRN: CO-MDL,” 2012. As of June 11, 2019: https://txdpslabs.qualtraxcloud.com/ShowDocument.aspx?ID=43050
Thomas, Laura, “Nothing to Hide, Something to Fear: The Use of Partial DNA Matching in
Criminal Investigations


CORPORATION
The Energy Implications
of Drones for Package Delivery
A Geographic Information System Comparison
Timothy R. GuldenLimited Print and Electronic Distribution Rights
This document and trademark(s) contained herein are protected by law. Thi


following areas: energy consumption, infrastructure requirements, aerial congestion, privacy, and noise. The other forthcoming RAND publications include the following:
•  What’s the Buzz on Delivery Drones? (Welser and Xu, 2016)
•  What’s the Buzz


 that division. Collectively, their contributions reflect pooled grants from a range of sources, including corporations, trade and professional associations, individuals, government agencies, and private foundations.
 v Contents
Preface .....


as from the Geo-Registered Map ........................................... 4
  Figure 2.3. Delivery Areas Overlaid with 2010 U.S. Census Block-Level Data ........................... 5
  Figure 2.4. Synthetic Delivery Districts Derived from Block-Le


bined the estimated energy used by trucks and drones to produce an estimate of total energy consumption under the drone scenario.
We found that the 20-percent drone scenario would require 13 fewer truck routes, 468 fewer
truck miles, and 46.8 fewe


focus on larger items and difficult delivery situations. Drones and trucks are quite different with respect to their weight, energy needs, and optimal routing strategies. This raises the question of how such a shift might be expected to change the ov


ning 20-percent more people) and repeated the mileage and diesel consumption estimate for these areas. Finally, we compared the energy content of the diesel saved with  the electricity used by the drones to find the energy consumption of the two deli


day. We therefore assume that district boundaries are drawn primarily based on the population that they contain. This assumption is used in subsequent steps to define a set of hypothetical delivery areas covering the metropolitan region.
Figure  2.3


s might require deviations from the great circle distance (GCD) shortest path. For the sake of simplicity, we will use GCDs, assuming that such deviations would not be required for most flight paths.
Figure  2.6.  Estimating  Drone  Stops  as  a  Ra


 miles
Truck   fuel  use  (10   MPG )   459.6   gallons   412.8   gallons   46.8   fewer  gallons
Drone   stops   0  stops    1,550  stops    1,550  more  drone  stops
Drone   miles   0  miles    9,406  miles    9,406  more  drone  miles
D


nt of
delivery stops could be serviced by drones instead of trucks. We conducted the analysis by
constructing a simple simulation model using real data for an actual city: Minneapolis, Minnesota. While the numbers and calculations in this prelimina


” data set, 2010. As of September 17,
2017: https://www.census.gov/geo/maps-data/maps/block/2010/
Welser, William, IV, and Jia Xu, What’s the Buzz on Delivery Drones? Santa Monica, Calif.:
RAND Corporation, PE-230-RC, 2016. As of September 12, 201


SOCIAL AND ECONOMIC WELL-BEING
What America’s Users
Spend on Illegal Drugs, 2006–2016
Appendixes
Gregory Midgette, Steven Davenport,
Jonathan P. Caulkins, Beau Kilmer
Prepared for the Office of National Drug Control PolicyLimited Print and Electro


g the number of cocaine, heroin, and methamphetamine users, and Appendix D presents survey-based data about the size of marijuana purchases. Appendix E extends the consumption estimates based on the limited set of drug use indicators available for 20


....................................... 33 Table E.4. Methamphetamine Consumption and Use Indicators, 2006–2017 ............................. 35

   vii Abbreviations
ADAM Arrestee Drug Abuse Monitoring
ASI Addiction Severity Index
CBSA core-ba


co;  Denver ; Las Vegas; Phoenix; Salt Lake City; Tucson, Arizona; Rio Arriba
County, New Mexico
Northeast: Albany, N ew York; Boston; Manhattan , New York City; Philadelphia
Pacific:  Anchorage, Alaska; Honolulu; Los Ang eles; Portland, Oregon;


r  because of  admissions rules or availability constraints, the former ’s reported
treatment rate in TEDS will be twice as large as the latter. However, we expect that such a situation is unlikely.   9 substance or substances leading to admissions


verall prevalence over several years of data (e.g., 0.10 percent between 2008 and 2010), even small changes in the number of positive tests observed in any area result in relatively large changes in prevalence rates entering our models. At this point


oisoning rate per 10,000 people (cocaine, heroin), and NSDUH reported
past-year use rates for drug d in county a in time t.
• I represents six region indicator variables, where each county a belongs to exactly one
region A and A contains at least


ariables, and drug use by region interaction terms, we chose a final specification for each drug model empirically. We selected models based on whether any coefficients in the group were significant in the saturated model and refined each specificati


il later
in this appendix.
Updates to the Logistic Regression Model
One may expect that the demographic profile of cocaine, heroin, and methamphetamine users
has changed in the time since the original model described in Kilmer et al. (2014b) wa


st two reasons. First, the fact that data in ADAM counties are different from the rest of the United States suggests the two groups may not be equivalent, and that the estimated coefficient based on ADAM data does not accurately represent national da


f low-reporting agencies from the county-level aggregate data (National Archive of Criminal Justice Data, 2019). However, the coverage indicator provided in the UCR county file cannot be used reliably to scale to a nationally representative count of


Opiates  Methamphetamine
CDC overdose cocaine × region 6  1.458

0.829
CDC overdose cocaine × region 7  1.766
 1.178
CDC overdose psychostimulant  × region 2    −56.487a

  19.573
CDC overdose psychostimulant  × region 3


y and transform upper and lower bound 95-percent confidence intervals for each using the same basic formula:
𝑅𝑅A
JK%=expMlogisticG𝑅𝑅AH±seElogisticG𝑅𝑅AH×1.96IR
1+expMlogisticG𝑅𝑅AH±seElogisticG𝑅𝑅 AH×1.96IR.
At this point, we have a predicted value fo


an four past-month use days), misreporting due to responses clustering along these intervals could influence our consumption estimates, though the direction of the potential bias is difficult to guess.
Although there is a decent-sized literature abo


as reported a false negative. Although the third reason can be an issue, there is also the possibility of a false positive or that the user misreported use days. The bias because of the specificity and sensitivity of the urinalysis test can work in b


 activity that occurred further in the past and may not be tied to current drug use. However,
this exclusion had very little effect on our estimates.   23 Table C.3. Arrests per Arrestee
Drug  1–10 Use Days
in Past Month  11 or More  Use Days


 past month  42,672 8,513 17,489
4–10 use days in past month  121,116 13,325 24,657
Fewer  than 4 use days in past month 204,734 12,811 21,930
 Although these individuals are not otherwise criminally involved, they are CDUs. Similar to
what was d


t. Examining overdose deaths without accounting for the impact of fentanyl likely grossly overstates use, as many of the recorded deaths likely also involve fentanyl. The relationship between overdose deaths and consumption lies somewhere between all


hetamine.
The consumption model used in this report draws on the relationships among the various
drug use indicators available as the foundation for our estimates. We also assume by necessity that these relationships are stable over time, as we ext


rcise to avoid the suggestion of confidence that may be associated with a more complicated method. The upper and lower points in the ranges defined in the following figures do not incorporate statistical uncertainty.
   31 Cocaine
Table E.2. Cocain


pters Five and Six, an
increase in the potency of marijuana available and the proliferation of THC consumption methods other than smoking suggest measures of consumption by weight are potentially spurious. Implicit in these estimates is the assumpti


y 17, 2019:  https://www.icpsr.umich.edu/icpsrweb/content/NACJD/guides/ucr.html   39 Reuter, Peter H., “Seizure of Drugs,” in Rosalyn Carson-DeWitt, ed., Encyclopedia of Drugs and
Alcohol, Vol. III, revised ed., New York, Macmillan, 2001, pp. 1022–1


What America’s Users
Spend on Illegal Drugs, 2006–2016
Gregory Midgette, Steven Davenport,
Jonathan P. Caulkins, Beau Kilmer
CORPORATION
Limited Print and Electronic Distribution Rights
This document and trademark(s) contained herein are protected


economic well-being of populations and communities throughout the world. This research was conducted in the Social and Behavioral Policy Program within RAND Social and Economic Well-Being. The program focuses on such topics as risk factors and preven


hat drug overdose deaths in 2018 exceeded 68,000, of which more than 47,000 involved opioids—close to the number of AIDS deaths at their peak in 1995 (CDC, 2011). Although heroin, prescription opioids, and synthetic opioids (such as fentanyl) receive


t consumption grew in 2016 among a stable number of users as price per pure gram declined.
•
 He
roin consumption increased 10 percent per year between 2010 and 2016.
Whereas most heroin consumed in the United States comes from poppies grown in Mexi


 but they have a very specific and nuanced meaning that is vulnerable to misinterpretation. They reflect only one source of uncertainty: a 95-percent confidence interval surrounding the share of adult male arrests involving a positive drug test.
4 Th


124 121 123 129 143 145 146
NOTES: The lower and higher ends of the ranges for cocaine, heroin, and methamphetamine reflect
only one source of uncertainty: a 95-percent confidence interval surrounding the share of adult male arrest events involving


7). Figuring out how data from these new sources can be mined to develop insights about the markets should be a priority.
With respect to the other drugs, bringing back the ADAM program or some ver -
sion of it would be enormously useful, particularl


ths at their peak in 1995 (CDC, 2011). Although heroin, prescription opioids, and synthetic opioids (such as fentanyl) receive most of the attention, deaths involving methamphetamine and cocaine are both on the rise. Marijuana continues to receive at


can be done. Numbers of chronic users can be estimated more accurately than spending, which in turn is subject to less uncertainty than estimates of quantities (weights) consumed. Likewise, there is a stronger foundation of evidence for marijuana tha


d C provide additional information about our approach to estimating the number of cocaine, heroin, and metham-phetamine users, and Appendix D presents survey-based data about the size of marijuana purchases. Appendix E extends the consumption estimat


untry for 2000–2016.
3 We extrapolate estimates for 2014–2016
by imposing the associations estimated for 2000–2013 on observed covariate values for 2014–2016.
2.
 Pr
oject the share of positive drug tests among adult male arrest events in all
count


s two criteria. For
each drug, we estimated models that optimize the amount of variation in county-level prevalence that is associated with variation in the included covariates. Among candidate models, several may produce similar predicted prevalenc


(based on urinalysis and self-report information)Average across annual estimates, 2000–2003, 2007–2013Calculate for three groups: 4–10, 11–20, and 21 or more use days in past month
Multiplying by F1 yields the number of adult male CDU arrest events w


tanding warrants has
relatively little effect.
c Based on ONDCP’s (Rhodes et al., 2012; Kilmer et al., 2014b) assumption that occasional users of cocaine, heroin, and methamphetamine in NSDUH
should be multiplied by four because of underreporting.1


 to the CDU total generates an estimate of the number of adult male CDUs in the country.
Figures in Chapters Two, Three, and Four include error bands to visualize the
scale of uncertainty underlying our best estimates. These uncertainty ranges refl


urvey both indicate an increase in use among those sample frames. TEDS data show that cocaine admissions dropped linearly between 2006 and 2015. TEDS is strongly related to prevalence in ADAM historically and is likely to
6  The impact of fentanyl i


has changed since ADAM was terminated in 2013, we may understate the true number of chronic users. Given that the composition of treatment admissions and overdose decedents is now more female and less concentrated in a few big cities than was the his


 data series that underlie the CDU estimates. Past-
month heroin users in NSDUH, heroin treatment admissions, and heroin overdose deaths all show increases starting in 2006 or 2007. The figure is dominated by over -
dose deaths with evidence of heroi


gest that about two-thirds of chronic heroin users are in the group that uses 21 or more days per month (Table 2.5), a considerably larger propor -
tion than for cocaine.
Methamphetamine
Figure 2.5 and Table 2.6 present estimates of the number of chr


sts to the FBI, meth -
amphetamine gets combined with “other dangerous drugs.”
These issues dramatically increase the uncertainty concerning the model results, which is captured in the very large error bands (Kilmer et al., 2014a, p. 26).
Figure 2.


 those who traded them for some other good or service, or those who produced their own).
The estimates presented in this chapter come with an important caveat. Cocaine,
heroin, and methamphetamine expenditure data are extremely limited in the Unite


ated for the 2000–2003 period and the 2007–2013 period.
3.
 Use t
he change between the periods 2000–2003 and 2007–2013 for ADAM-II
counties to extrapolate the linear growth in expenditures between these periods in the larger set of ADAM-I counties


end and 130 percent more than what methamphetamine users spend because of the concentration of expenditures among daily/near-daily users. Average spending on
Table 3.1
Average Monthly Cocaine Expenditures (nominal dollars)
Year21 or More Days in
Pa


3.
We adjust the annual expenditure estimate for inflation using the Bureau of
Labor Statistics’ CPI-U, which increased by 24 percent from 2006 to 2018 (Bureau of Labor Statistics, 2019). Note that trends in expenditure estimates for all three drug


OTE: Figure does not include expenditures by non-CDUs.0102030405060Cocaine expenditure (billions, 2018 dollars)
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016Light
WeeklyMore than weeklyDaily/near-daily
Table 3.5
Cocaine Expenditures by User


arter-based acquisitions were more or less common than the 1.125 figure would sug -
gest, it is a simple exercise to modify this assumption. For example, if one-fifth of product consumed was acquired via in-kind transfers, then the factor would be 1.


(percent)Heroin
MethCocaine36    What America’s Users Spend on Illegal Drugs, 2006–2016
to have fallen 30 percent since 2010. Although fentanyl-laced heroin, methamphet -
amine, and cocaine have been reported, we cannot assess the impact of fentanyl


As was discussed in Chapter Two, the range depicted by the “higher” and “lower” estimates reflects only a portion of the uncertainty in these estimates.Figure 4.3
Cocaine Consumption Estimates
0100200300400500600
2006 2007 2008 2009 2010 2011 2012 20


18; Orens et al., 2018; and Oregon Liquor Control Commission, 2019).
As will be discussed in greater detail in the next chapter, these changes might well
have influenced the average quantity consumed per day of use and even threatened how meaningfu


available (through 2013), and (3) the remaining adult non–criminal justice–involved population, using an adjustment factor discussed later in this chapter. We also present estimates for infrequent marijuana users who report consuming in the past year


report 11–20 days of use per month), and (5) daily/near-daily users (those who report 21 or more days of use in the past month).
Table 5.1 provides estimates of the number of marijuana users by user group
(both unadjusted and adjusted). The unadjust


ily (21
or more days of use). For users who report using blunts but not marijuana, unless they later clarified that their blunt use did not include marijuana, the maximum number of their blunt-use days was taken as their number of marijuana-use days


and Related Conditions
(NESARC) data provided parameters for average numbers of joints consumed per day, estimated separately for each past-month user group: occasional (1.68), weekly (1.92), more than weekly (1.92), and daily/near-daily users (3.87


stantially beyond the traditional smoked products, such as joints and blunts. In Washington state’s legal market, extract-based products, such as edibles, vaping oils, and waxes, accounted for more than 20 percent of sales by the latter part of 2016


er groups (i.e., one to three, four to ten, 11–20, and 21 or more past-month use days). Each group-specific RASTUD is multiplied by the number of use days reported by that group’s past-month buyers  who did not report reselling . Multiplying by
12 c


res may be underreported to a larger extent than use. For example, respondents may feel comfortable admitting to use but not to purchasing, or they may misremember or underreport the number of days they pur -
chased in the past month or how much they


.5 percent higher than the RASTUD-based series.Figure 5.3
Two NSDUH-Based Series Related to Marijuana Expenditures
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 201604080Expenditure (billions, 2018 dollars)20Legacy replication
RASTUD-based estima


t-month use days) increased from 9.7 million to 11.1 million (a 14-percent increase). That group’s RASTUD increased from $8.39 to $8.92 (or 6 percent), and the proportion who recently purchased marijuana for personal consumption increased from 68.7 p


tment generates an estimate of $49.2 billion of spending for 2014, which is close to what is reported in Table 5.3 ($53.7 billion).
Extrapolating from Washington State
Caulkins et al. (2019) estimates that Washington state’s 755,000 NSDUH-identified


ative approaches to estimat -
ing market size that may transcend these challenges—though, in some cases, only if the right additional data collection is undertaken.
Changes in Marijuana Markets
Marijuana markets have been changing rapidly due to in


was consumed in the United States by combust -
ing dried plant material in joints, pipes, or bongs. Often, only the parts of the plant that contained higher concentrations of THC (notably the flowers or buds) were used, and other material (leaves, tr


l
market, where extract-based products, such as edibles, vaping oils, and dabs, accounted for roughly one-third of sales in mid-2017 (see Chapter Five). It is no longer reasonable to treat nonflower consumption as unimportant.
The next few sections


AUSID reports (e.g., Rhodes et al., 2001, and Kilmer et al., 2014a) by estimating the number of days of use broken down by type of user, and then multiplying those figures by an estimate of the average number of grams consumed per day of use. Those r


 users in Washington state in 2013 sug -
gest that daily/near-daily users consumed between 1.3 and 1.9 grams per use day, with
a best estimate close to 1.6 grams (Kilmer et al., 2013). A similar survey conducted for the Colorado Department of Revenu


that weight consumed per
day of use has changed, and data are insufficient to settle the matter empirically. Hence, we do not think it is safe to make any particular assumption about stability or change in consumption per day of use and do not view


f THC purchased is better defined, but focusing on it would fail to
record material that is grown by the user, given to or shared with others by the grow -
ers, or obtained by barter (such as when workers in the illegal marijuana industry are paid i


ariation can be seen in Figure 6.1 by plotting data series for two other fentanyl-related measures for the entire country and by geographic region: (1) total mortality attributed to synthetic opioids, derived from the CDC’s Multiple Cause of Death re


 Atlantic
906030
0120
2013 2014 2015 2016 2013 2014 2015 2016West North CentralPacific
1,000
500
0New England
2013 2014 2015 2016West South Central
1,000
500
066    What America’s Users Spend on Illegal Drugs, 2006–2016
deaths to fentanyl consumption


ude 100-kilogram seizures of cocaine. Measurement of the Modern Marijuana and Opioid Markets    67
Let Wd equal the weight of drug d consumed in the United States and Wdi equal
the weight of drug d consumed in region i . Because heavy users dominate


ty card programs, and other sources (Caulkins, 2016, and Kilmer and Pacula, 2017). Figuring out how data from these new sources can be aggregated and manipulated to develop scientifi -
cally based insights should be a priority for those who want bett


endence , Vol. 117, No. 1,
August 2011, pp. 59–61.
Caulkins, Jonathan P., “Cannabis Policy Research Agenda,” comments presented at the Cannabis
Science and Policy Summit 2016, New York, April 2016. As of May 17, 2019: https://www.youtube.com/watch


t Drugs: 1981–2007 , Alexandria, Va.: Institute for Defense Analyses, October
2008.
Gallet, Craig A., “Can Price Get the Monkey Off Our Back? A Meta-Analysis of Illicit Drug
Demand,” Health Economics , Vol. 23, No. 1, January 2014, pp. 55−68.
Gmel


LCB, 2013. As of May 17, 2019: https://www.rand.org/pubs/research_reports/RR466.html
Kilmer, Beau, Steven Davenport, Rosanna Smart, Jonathan P. Caulkins, and Gregory Midgette,
After the Grand Opening: Assessing Cannabis Supply and Demand in Washingt


2005 , Santa Monica, Calif.: RAND
Corporation, MG-829-MPF/NIDA, 2009. As of May 17, 2019: https://www.rand.org/pubs/monographs/MG829.html
Office of National Drug Control Policy, National Drug Control Strategy: Data Supplement 2016 ,
Washington D.C.


anna, Jonathan P. Caulkins, Beau Kilmer, Steven Davenport, and Greg Midgette,
“Variation in Cannabis Potency and Prices in a Newly Legal Market: Evidence from 30 Million Cannabis Sales in Washington State,” Addiction , Vol. 112, No. 12, December 201


CORPORATION
International Commercial
Drone Regulation and Drone Delivery Services
Therese JonesLimited Print and Electronic Distribution Rights
This document and trademark(s) contained herein are protected by law. This representation of RAND
intell


ergy Implications of Drones for Package Delivery: A Geographic Information
System Comparison (Gulden, 2017)
•  Design Perspectives of Delivery Drones  (Xu, 2017)
•  Small Unmanned Aerial System Certification and Traffic Management Systems  (Kuhn,


ry
Due to a sharp increase in drone use internationally, countries are struggling to incorporate
drones into their aviation regulatory frameworks. This report draws on a literature review and
discussions with subject-matter experts to summarize


  guidelines  or  require  licensing,  registration,  and  insurance,
but  upon  following
proper  procedures  it  is  straightforward  to  operate
a  commercial  delivery  drone.   •  Costa  Rica
•  Iceland
•  Italy •  Norway
•  Sweden


s are struggling to adapt current laws to new technology. To address this issue, this report summarizes national-level commercial drone regulations across the world, discusses the regulatory approaches taken by different countries, and examines how s


allow drones at all for commercial use.
2.  Effective ban: Countries have a formal process for commercial drone licensing,
but requirements are either impossible to meet or licenses do not appear to have
been approved.
3.  Requirement for constan


s, but legal obstacles—such as the inability to procure proper certification or insurance—make these rules into an effective ban.    4 Table  2. 1.  Drone  Legislation  Approaches,   by  Country
Approach Definition  Countries
Outright  b an Count


art 107, which governs drones under 55 lb. As of August 24, 2017, almost exactly one year after its enactment, seven companies have been given Part 107.31 exemptions, allowing them to operate BVLOS.
2 In October 2017, President
Donald Trump advocate


 of
experimental BVLOS technology. The United Arab Emirates regulations explicitly list parcel delivery as a possible use.
While other countries have drafted or enacted drone legislation, it is largely vague
because of a lack of need, capacity, or


g/
Training
Requirements
for  Pilots  Regulations  Regarding
Registration,
Operational
Requirements  Insurance
Required  Distribution
of  Goods
Allowed  Obstacles  to
Delivery  Drone  Use
(if  any)
Costa  Rica  No Authorizati


ation,
Operational
Requirement s Insurance
Required Distribution
of  Goods
Allowed  Obstacles  to  Delivery
Drone  Use  (if  any)
Argentina        Commercial  use
banned
Brazil Possible   under
25  kg  if
authorized;;
must  a


name  and  address ;;
limited  to  100   m  in
altitude
Belgium  Yes  Drone  pilot
license  required  Manufacturer’s
certificate,  regist ration
required;;  maximum
height  90   m  for
commercial   use Yes Transport  of
mail  or


e;;
certificate  if  <
  25  kg  Required  to  d isplay  ID
that  transmits/
records  data  in  real
time;;  license  plate  and
declaration  of  vehicle
competence  required     LOS  operations
Latvia Yes         Requires  a  flight


ion
of  Goods
Allowed  Obstacles  to   Delivery
Drone  Use  (if  any)
Switzerland  May  grant
exemptions   May  grant
exemptions   None  required
<  30  kg  None  required  <   30  kg  Yes  LOS  operations
Ukraine           >  20  kg


required   for
commercial  use;;
costly  (up  to  $4,000)  Registration   required   for
commercial  use,  manual
and  log  book  needed    LOS  operations    17 Country LOS
Operations
Required Flight  Permitted
Over  Populated
Areas


ing  course ;;
air  traffic  control
license  or  flight
crew  license
required  Registration,   operations
manual,  airworthiness
certificate   required  Yes
Qatar     Permission  required
Saudi  Arabia        Ban   19 Country L


for countries with existing drone legislation, laws are constantly being
reevaluated: Almost all of the laws listed were written or amended within the past two
years. While drone laws almost always move toward a more permissive approach to regulati


ugust 13, 2017. As of August 28, 2017:
https://www.casa.gov.au/aircraft/landing-page/flying-drones-australia
Szal, Andy, “Amazon Could Test More Drone Deliveries with Australia Expansion,”
Manufacturing.net, May 24, 2017. As of August 28, 2017: h


ugust 29, 2017:
http://www.mincom.gov.bn/dca/_layouts/15/start.aspx#//dca/Theme/Home.aspx
Cambodia
Markert, Francis, “Drone Regulations in Cambodia,” Drone Traveller blog, April 18, 2017. As
of August 29, 2017:
https://drone-traveller.com/drone-


dla Bez
Pilota,” CAA/S-SLS-010-0/2012, March 1, 2012. As of August 28, 2017: http://webdav.agents.fel.cvut.cz/data/teaching/bep/smernice_caa-s-sls-010-0-2012.pdf
Denmark
Njord Law, “Njord Insight: New Danish Drone Rules in 2016 and 2017,” January


atory-response-pub-68218
Indonesia
Hanafiah Ponggawa & Partners, “Updated Drone Regulations in Indonesia: Now, a Specific Rule
for a Specific Drone,” July 12, 2016. August 29, 2017:
http://hprplawyers.com/updated-drone-regulations-in-indonesia-no


ar,” 2017. As of August 29, 2017:
https://www.airport-uav.com/drone-rules/legislation-africa/regulations-madagascar/
Malawi “Malawi and Unicef Launch Drone Air Corridor,” BBC News, June 29, 2017. As of August 29,
2017:
http://www.bbc.com/news/wor


ry-of-pizza-in-New-Zealand/4271479363638/
Civil Aviation Authority of New Zealand, “Part 101 CAA Consolidation,” March 10, 2017. As
of August 29, 2017: https://www.caa.govt.nz/rules/Rule_Consolidations/Part_101_Consolidation.pdf
Norway
Luftfa rts


drone-control-network-paving-way-for-commercial-boom/
“Russia Introduces Obligatory Drone Registration,” RT, December 31, 2015. As of August 28,
2017: https://www.rt.com/politics/327536-russian-introduces-obligatory-registration-of/
Saudi Arabia


uary 1, 2017. As of August 29, 2017:
http://www.tcaa.go.tz/files/Air%20Navigation%20Services/Aeronatical%20Information%20Circulars/AIC%2005%20-%202017%20Unmanned%20Aircraft%20Systems.pdf
Thailand
Barrow, Richard, “Quick Look at the New Drone Law i


-RC, 2017. As of October 5, 2017: https://www.rand.org/pubs/research_reports/RR1718z1.html
Kuhn, Kenneth, Small Unmanned Aerial System Certification and Traffic Management Systems ,
Santa Monica, Calif.: RAND Corporation, PE-269-RC, 2017. As of Nov


www.rand.org
RR-2129-RF9780833 098962ISBN-13 978-0-8330-9896-2ISBN-10 0-8330-9896-9
54550$45.50Policymakers, program practitioners, and investors who want to achieve the greatest possible benefits from the
resilience projects that they support lack


d.orgFor more information on this publication, visit www.rand.org/t/RR2129
Library of Congress Cataloging-in-Publication Data  is available for this publication.
ISBN: 978-0-8330-9896-2
Published by the RAND Corporation, Santa Monica, Calif.
© Copyri


s conducted in the RAND Infrastructure Resilience and
Environmental Policy program, which performs analyses on urbanization and other stresses.
This includes research on infrastructure de velopment; infrastructure financing; energy policy;
urban p


Table 4.18. Runoff by Restoration and Fire in Conklin et al. (2015) .............................................106
Table 4.19. Other Studies That Estimate the Relationship Between Restoration and Runoff ........107
Table 4.20. Value of Water Runoff


ro vide a systematic, “structural” frame-
work for assessing resilience inter ventions that ultimately create benefits and costs within a
system such as a community or city. The model is a means to systemically account for the returns to resilience


to the future.
The RDVM is a flexible approach that helps users think about inter ventions in a structured,
systems-focused way, and it guides users in how to use data to pro vide evidence of the di vi-
dend.  In real-world settings with limited da


 vidend, and the limitations of the model. While we belie ve that the
inclusi ve wealth conceptualization of systems for e valuating resilience projects is a promising
way forward, the need to generate and use extensi ve quantities of data in compl


ill allow for the construction of portfolios that can, potentially, make all stakeholders winners with the appropriate choices of projects with portfolios. The budget constraints that arise in portfolio analysis pro vide additional limitations that t


S i e r r a  N e vada
Adapti ve Management Project, and Blue Forest Conser vation. In particular, Phong Tran at
ISET; Simone Lombardini and Rob Fuller at Oxfam; N ick W obbrock at Blue Forest Con-
servation; Sasank Vemuri and Elma Morsheda at the A


e the case.
Developed to help o vercome these issues, the Resilience Di vidend Valuation Model (RDVM)
documented in this report is a modeling framework designed to help analysts and practitioners estimate the realized or potential resilience di vi


isk of loss from an outside shock or stressor and any co-benefits the project or in vest-
ment produces.
Howe ver, as noted by Anderies, et al.:
The resilience lens is useful for making suggestions about broad categories of in vestment
such as in th


 of an ad verse e vent. As such, the resil-
ience di vidend is not equi valent to the value of increased resilience of the system, though it
theoretically includes these potential net benefits.
Valuing the resilience di vidend requires that we under


 estimates of the resilience di vidend can be calculated either before (ex ante) or after (ex post)
they are implemented.4This report is not designed to be a users’ guide for the RDVM. We ha ve produced a separate
guidethat focuses on when and how


 length of time it takes for the system to rebalance once the stressor has been remo ved. C.S. Holling
3 was the first to transfer these ideas from the physical sciences to the bio-
logical sciences. Holling’s distinction from the physical sciences i


ow quickly can the system get back to “normal” functioning—understanding that “normal” may look different after the e vent than before it—labeled
restorati ve capacity. These ideas are laid out implicitly in Francis and Bekera
7 and explicitly
in R


e, may remo ve some responses to risk that may be
beneficial. In particular, if the focus is solely on what happens after a disaster occurs, there is a risk that strategies or actions to reduce vulnerabilities will be under valued or ignored entirel


n, 1998 Community The process through which mediating structures (schools, peer
groups, family) and activity settings moderate the impact of oppressive systems
Paton, 2001 Community The capability to bounce back and to use physical and economic
res


Arup International De velopment.
14 NIST (2015). Disaster resilie nce framework .9and the long-standing notion of cities as ‘systems of systems.’”15 Howe ver, Arup re views stud-
ies on subsystems rather than on the system as a whole. This lea ves th


approach disasters
as problems of risk management within a systems-of-systems framework combined with some form of either risk management or resilience. The major differences are the detail and connec-tions among the different systems and subsystems


es into account the three major elements of resilience: adaptation, absorption, and reco very. Additionally, Norris et al. do not
recognize that post-e vent functioning from one e vent is the pre-e vent functioning for the next
event. This circular


they use to produce goods and ser vices and, ultimately, community well-being.  How flexible
the system is, inherently, and how it is enhanced through the mitigating acti vities of firms
and indi vidual households determines the system’s inherent l


licitly discusses the objecti ves of increasing resilience. It is paramount when
beginning an exercise aimed at increasing resilience to know what the goals are. These goals tie into the metrics that will measure progress toward increasing resilienc


ng of a community. By making this distinction between vulner-
ability and resilience, these potential complementarities may be lost. One key point the Cutter work recognizes is the link between resilience and sustainability. Sustainability is a large


tion &
     infrastructure in placeCommunity
Other
systems
Hospital
systemElectric power
service
Water
serviceLocal emergency
Mgmt.system
SOURCE: Bruneau et al. (2003). Used with permission.
Figure 2.6. Bruneau et al. (2003) Framework
RESILENT
COMMUN


 vide a le vel playing field for all participants to the decisionmaking process.
To obtain accurate tradeoffs, the spillo ver of resilience in one subsystem to the other subsys-
tems needs to be incorporated with the direct effect on the subsystem.


d to the role of adapti ve beha viors and substitutability when discussing the resiliency of systems. Furthermore,
when taking a system-of-systems approach, the interdependencies across subsystems play an important role in how the entire system resp


 relationships between
capital stocks, goods and ser vices, and the value of goods and ser vices to stakeholders in an
economic system o ver time. Capital stocks are durable resources that can be used together to
produce a stream of goods and ser


 the capital stocks is gi ven by state transition
equations  Kn  = fn  (K1, K2,..., KN )An. This portfolio of stocks is broadly defined and can
include man-made, natural, human, and social stock le vels.3 Changes in the quantity and qual-
ity of ca


ion mechanism.
Our experience to date has suggested that many resilience projects view changes in this alloca-
tion mechanism as key sub-objecti ves in the inter vention. The pro vision of information has the
ability to change how people respond an


silience is defined as one minus the probability of a switch into
the bad state, and valued as the change in the net present value of future flows associated with
a change in a related resilience “stock.”9 The approach was used by Walker, et al. in


end
This framework can be adapted to our definition of the resilience di vidend. Let social pref-
erences be represented by a utility function U
(C(t)), where C(t) is a vector of valued flows
that accrue o ver time t, and assume that this vector of


oods and ser vices produced from these stocks,
and the well-being that these flows create. Each of these elements is assumed to be functionally related, mimicking a system-of-systems approach, and can be affected either directly or indi-rectly by re


nd ser vices that cannot be valued quantitati vely can be discussed qualitati vely.
Goods a nd services  are the tangible and intangible items that are produced within a system.
The capital stocks are the durable inputs used in the production relat


o pri vate or public insurance. Then the willingness to pay for the in vestment
would be equal to the damage to the home, as the household would be indifferent between paying for the in vestment
and the home being damaged, irrespecti ve of what act


 of a
developing country with a large agricultural economy. For simplicity, we assume households in
this economy only produce one crop. Due to a lack of fertilizer, households face a stressor in the form of declining land fertility. In this example


duals in volved.
Such changes, relati ve to BAU, would ha ve to be estimated or simulated in order to quantify
the resilience di vidend associated with these acti vities.
Social Welfare Function
The social welfare fu nction reflects the full social


puts, howe ver, would require
empirical data. Furthermore, we note that in the ex post case, the resultant (partial) resilience dividend is conditional on what actually happened and thus does not include the expected
value associated with all poten


and organizations
or go vernments.
Because of their properties, the proper valuation of a change in the pro vision of a public
good is to sum the benefits that accrue to e very stakeholder affected. Many public goods
are not traded in a market, ma


, an inter vention may slow or re verse depreciation of a stock, such
as increased maintenance of a man-made stock, or augment its quality, such as an in vest-
ment in education affecting human capital and augmenting o verall labor producti vity.
•


that is producti ve since it helps achie ve certain outcomes once it is put to use. Social
capital arises from social networks, and these networks themsel ves produce social capital.
Trust and reciprocity in volved in the relationships within these


.
For consumers, goods and ser vices pro vide welfare through the generation of a gap between
willingness to pay and what is actually paid, which is termed consumer surplus. Direct benefits from a resilience project should thus be calculated as the


 generate income and produce more sa vings.
Beneﬁts Related to the Allocation Mechanism
Although the direct benefits and co-benefits are more or less comprehensi ve categories, the
importance of the link between beha viors and benefits is importan


ws a graphical example of how one might approach the ex post e valuation of a
resilience project that creates co-benefits and has experienced a shock prior to the e valuation.
The well-being, or measure of social welfare, for the (obser vable) worl


he project and BAU cases gi ven
shocks that actually happened. This approach, while practical, may ignore rele vant
shocks in the future, which may be different from shocks experienced in the past.
In the case of forward-looking ex ante cases, we c


 a poten-
tially complex e valuation. For ex ante settings, simulation modeling can often pro vide
estimates of the project and BAU paths.
For additional details about data collection and estimation, readers are referred to the
practitioner’s guid


e
change, PovertyHousehold/
CommunityNo estimate Data limitations mean only a partial RD
can be estimated, but there is evidence that the project changed behaviors and certain capital stocks with beneﬁts to the communities. Obtaining a full RD wou


RD of a project before
it is complete, it is advisable to use a pre-project estimate approach on disaster risk reduction, broad-based development for community resilience, and economic development investments, and it is important to deﬁne a counterf


o a treatment-and-control framework.
The resilience di vidend is defined as the difference in outcomes between inter ventions that
take a resilience view and those that do not. Outcomes are typically related to the ability of
“people to better with


 others, help their neighbors, and engage in search-and-rescue efforts. Furthermore, we ha ve evidence that indi viduals trust their community lead-
ers more after the inter vention, leading to increases in social capital. While we do not ha ve the


 a The effects on social capital can affect absorptive capacity, co-beneﬁts, and recovery/transformative
capacities. As such, we treat it as its own category in the exposition.
SOURCE: Authors’ calculation based on multivariate regression analysis.4


 Project
The systems being analyzed are certain Nepalese communities that primarily grow crops
and li vestock for income and face flood risk. The objecti ve of the analysis is to compare the
differences in well-being outcomes for households in comm


ssets that a household values in terms of information or
entertainment, such as tele visions, radios, or DVD players. Additionally, households ha ve
durable capital stocks of housing, livestock co ntainment, and storage . Essentially, increased own


ld well-being.
Well-Being
Measures of well-being in the model are closely related to the good and ser vices discussed
above. Damage  (including loss of crops, other property, and life) is a direct measure of well-
being after a flood, though the dat


heoretic framework is the dynamic feed-
backs that occur between outputs and capital stocks and among capital stocks themsel ves. As
households generate income, they may in vest in capital stocks. As households produce goods,
they may be deteriorat


n the “parallel trends” assumption that
the groups would ha ve otherwise turned out the same. Howe ver, when there is only one period
of data before the treatment period (as in this case), it is impossible to verify this assumption
other than thro


ccess to credit and sa vings in 201 3;
• other forms of income beyond agriculture in 201 3;
• whether they recei ved an early warning about the 2012 storm;
• what actions they took following the early warning in 2012;• opinion data on community leade


ousehold engaged in casual employment in 2010; whether the
household engaged in formal employment in 2010; whether the household owned a business in 2010; whether the household engaged in crop production in 2010; whether the household owned li vesto


ouseholds and not exploiting
the treatment/control group structure).
As shown in the second column of Table 4.4, results demonstrated the counterintuiti ve result
that only pri vate flood protection has a significant impact on crop losses7—but hou


pri vate and
public protection measures are substitutes for other actions (like changing crop patterns, moving li vestock, etc.) and that public and pri vate protections are complements to each other.
That is, if the community takes public protecti


teraction on social capital. In particular, Oxfam asked
households if, mo ving forward, they had trust in their community leaders in the face of the
next e vent. As was found in Oxfam’s effecti veness re view, inter vention households trust their


 from the flood of
2012. Thus, some differences in income may be dri ven by the flood and carried o ver to 201 3.
Additionally, we ha ve information on changes in soil fertility. One of the main dri vers of soil
fertility is erosion, which is asso


sets Between Intervention and BAU Households
AssetsDifference Between
Intervention and BAU 2010Difference Between
Intervention and BAU 2013Change in
Ownership  2010–2013
Oxen 0.16* -0.02 -0.18*
Cows 0.36*** 0.38*** 0.01Buffalo -0.19** -0.22*** -0.


kely not feasible for many projects. Indeed, for any large-scale project with implications that stretch into the future, such data cannot be compiled ex post. Future research (and data collection) might be better ser ved to identify those parts of th


lar threshold, making ex ante analysis valuable mo ving forward. 60Ideal ex post data structures should include data for at least four points in time: prior
to the intervention, after the intervention and before a shock, after a shock, and at some p


ther case studies presented in Chapter 4 and the lessons learned
from all the cases in Chapter 5. This case study is intended primarily to help readers under-
stand how to apply  the RDVM framework gi ven a set of data or information about a gi ven


ock from the storm compared to similar households (in the control group) that did not participate in the inter vention nor were exposed to the storm.
10 These benefits were
generated because the project changed farmers’ crop choices (or, in terms of


s’ analysis.
Oxfam Pakistan Intervention Background11
Oxfam conducted an inter vention in three districts in Pakistan in 2010–2011 through part-
nerships with the Sindh Agricultural and Forestry Workers Coordinating Organisation (SAFWCO) and the Balo


ion increases, build social
trust, and pro vide a means for resol ving disputes. This may change the allocation mechanism
or capital stocks within the system.
The inter vention acti vities took place in 2010–2011. There was a shock—se vere flooding


ocation mechanism or production process that is go verned by the
stock of a vailable resources. Oxfam’s inter vention aims to change both how and which crops
are grown. 65Figure 4.4. Mapping of the Pakistan System
Land
Assets
HumanCrops
LivestockAg


These “control” communities were chosen based on their proximity
to the treatment communities.
This sur vey design allows us to estimate the partial net resilience di vidend of the inter ven-
tion by comparing treatment and control households. Impor


ot affect all households. This allows us to esti-
mate the annual partial resilience di vidend for households not affected by the flood and those
that were, since treatment and control households are similar prior to the inter vention.
Our approach,


and in particular dairy production—was an important focus of Oxfam’s
intervention. Additionally, we know that households obtain income that is not related to farm-
ing but not the amount.
17 This is why we belie ve we are only estimating a portion o


 may be significant compared to crop income.69Results
We di vide our presentation of the results into two broad sections. First, using a partial measure
of well-being (agricultural income), we estimate the net annual value in 2012 of the resilience


zed the de velopment path between treatment and control
households without a shock. Alternati vely, the inter vention may be a le vel shift up rather than
a growth cur ve.
18 Because Oxfam only collected flood damage data, we ha ve no physical mea


as measured by a positi ve self-reported crop loss. Figure 4.6 presents
our conceptualization for this analysis. With only one year of data (2012), the estimate of the resilience di vidend for those households affected by the shock includes the tota


find no significant difference between treatment and control.72control group, but the reco very benefit was likely negati ve compared to households that did
not experience the shock. Note that this conclusion rests on the assumption that we ha ve ac


ds. Additionally, while the pre vious analysis takes the inter vention as an “all or noth-
ing” construct that estimates a verage effects, this analysis will allow us to better understand
which aspects of the inter vention pro vided value to the hou


crease income.
The last two practices require a little more explanation. Kitchen gardens do not necessarily
increase production but are used to grow higher- value crops for personal consumption. Thus,
it isn’t the quantity of production that appea


e di vidend. We are able to identify which
aspects of the inter vention increased income as well as which aspects were successfully incor-
porated into beha vior.
Conclusions and Lessons Learned
The Oxfam–Pakistan case shows that it is feasible to


one.   76Another challenge with estimating the resilience di vidend for those that experience the shock is
that we must condition on the obser ved event. Estimating the resilience dividend associated
with a particular shock is difficult , since the


the resilience di v
idend elements of interest.
Diamond Valley
This case study is based on the RDVM. It pro vides a demonstration of how RDVM can be
applied in real-world situations. The case can be read on its own, but it is part of the broader pac


e resilience di vidend that may result from
imposing a share-based rights system. As such, the proposed inter vention is an institutional
change (water rights structure and enforcement), which in turn changes beha vior of agricul-
tural producers (


ue of non-production-
based public goods related to community viability and stability. As such, the resilience di vi-
dend is really a measure of the total (pri vate and public) net benefits pro vided via the inter ven-
tion and achie ved by exercisi


 allows us to estimate the change in profitability to water
users, the Duke Uni versity analytical report did not calculate the secondary and tertiary values
associated with community viability and cohesion. These include the value of a voiding the


propriated by a factor of about 4 ½ when
considering all water rights in the basin (see Figure 4.7). Perennial yield, defined as the maxi-mum amount of groundwater that can be consumed in the long run without depleting the reservoir, is estimated at


withdrawal of groundwater.
Applying the Resilience Dividend Model to Estimate the Potential Dividend
for the Diamond Valley
The system under consideration is the quantity-based agricultural-hydrological system. The
GMP is the hypothetical inter ven


ate to withdraw water
for purposes other than domestic use.83Figure 4.8). Historically, irrigators in the basin ha ve grown Timothy hay and alfalfa on an
11-year rotation. While there may be some potential for a producer whose water rights are curt


ral equilibrium
28 Zeff, H., et al. (2016). Benefits, costs, a nd distributio nal impacts of a grou ndwater tradi ng program i n the Diamo nd Valley,
Nevada . New York: Rockefeller Foundation.
29 Possibilities for future  analysis include a sur vey


 di vidend.
There are se veral elements that are not included in the model due to a lack of data. First, we do
not include bankruptcies, monitoring costs, legal costs, or other financial costs that might be associated with a change of the system.
3


igators (i.e., o verdrawing water relati ve to their allotment) would
lead to increased pri vate profitability at the expense of others.
Empirical Methods
As mentioned earlier, a quantitati ve assessment of the expected benefits of a share-based,
r


oduction (lack of system di versity or a vailable substitutes). Readers
interested in additional technical details are referred to the original analysis.
38
For the purposes of this case study, we assume that modeling scenario (B), “sudden curtailme


me full curtailment to 35,000 acre-feet either during or
immediately after the planning horizon. Drawdown rates for project scenarios are either 3% or 6% per year, for a total of four project scenarios. BAU curtailment is in accordance with current


llion in scenario (E) and $8.2 million under scenario (F). 88Figure 4.9. Estimated Resilience Dividend from Agricultural Production, Net Present Value,
30-Year Time Horizon
$  -$10$20$30$40$50$60$70
Scenario E, 3% Scenario F, 3% Scenario E, 6% Scen


eping more water. 3% and 6% refer to assumed rate of decline of assignable water due to curtailment. Basinwide, there are 9 owners of senior rights only, 17 owners with mixed rights, and 40 with junior rights only. Baseline proﬁtability for senior ri


undwater management plan is
developed in the region, there would be e vidence of a community stability resilience di vidend
if any particular producer is willing to accept less profitability in exchange for keeping junior rightholders in production


 also likely to impair community stability and vitality.
Consideration of this alternati ve baseline highlights the short-run/long-run trade-offs between
profitability and the long-term sustainability of the community as it exists today. It also pr


he discount rate is used in net present value calculations to enable comparison of monetary flows at different points in
time. A positi ve discount rate suggests less weight on future flows. So, for example, the net present value of $100 next
year


e appropriate methods to detail the expected beha vioral changes associated with any
project inter vention.
Calculation of the resilience dividend may be highly dependent on assumptions made
about adaptive and transformative capacities of the agent


ti ve resilience di vidend for each of the four
scenarios examined, there were both “winners” and “losers” in terms of profitability. Thus, an overall social net benefit, when aggregated, does not necessarily imply that e veryone wins. We
recommend


ity element to the resilience di vidend. Although we currently lack such
information, any realized management plan that results in decreased net profitability for senior rightholders relati ve to the baseline, but for which there is support from thi


entify
each and e very element of the resilience di vidend at a detailed le vel, limited information and
data may preclude doing so. In these cases, aggregating a set of (perhaps ill-defined) benefits into a broader category (like the benefits from


d not the type of rights system in place (e.g., prior appropriation versus a shares-based system). Howe ver, the distribution of the di vidend varied significantly,
with mixed rightholders accounting for nearly 60 percent of the o verall gain for sc


. In addition to estimating a resilience di vi-
dend of forest restoration, we discuss some of the estimation and policy challenges that may arise in the de velopment of a forest resilience bond.
For this case study, the resilience di vidend would b


Unknown (Site Speciﬁc)Fire Suppression Cost $18-21Fire Structural Damage Unknown (Site Speciﬁc)Fire Water Quality/
Sedimentation$8.70
Social Beneﬁt Unknown (Site Speciﬁc)
SOURCE: Authors’ calculations.
This approach pro vides bounds for a partial qu


anagement. Throug h ecosystem restoratio n.
45 Vilsack, T. (2015). “The cost of fighting wildfires is sapping forest ser vice budget.” U.S. Forest Ser vice 2017.99Restoring forests to a more natural state may ha ve other benefits as well: there is po


lliot, W. J., Miller, M. E., and Enstice, N. (2016). “ Targeting forest management through fire and erosion modelling.”
International Jour nal of Wildla nd Fire , 25(8), 876-887.
49 We assume that the BAU case is the present situation of an unrestor


ecosystem restoration that selecti vely har vests different ages of trees depending on the
restoration’s goals.
Second, fires are a result of the fuel pro vided by trees and forest debris. We treat fire severity
as  “bad” because, while fires pre


pproach, we
would know what stakeholders are willing to pay for each of the goods and ser vices pro vided
by a forest, and could then estimate the value of the indi vidual capital stocks and, in aggregate,
the forest as a whole. Because we do not


ystem. 103There are two important outside forces—forest fires and precipitation—that we ha ve not
explicitly described in Figure 4.12. First, from the data presented in Figure 4.11, it appears that forest fires are determined outside the system, and


cult
to estimate and may be geographically dependent.
The impacts of fire suppressio n: The main focus of the restoration is not necessarily ecosystem
services but fire suppression costs. There are two sets of impacts to consider: First, what is th


S. Department of Agriculture, Forest Ser vice, Rocky Mountain Research Station.
pp. 141–148.
53 Lynch, D. L., and Mackes, K. (200 3). Costs for reduci ng fuels i n Colorado forest restoratio n projects . U. F. Ser vice, USDA
Forest Ser vice Proceed


t within the forest and is equal to a board that is one foot long by one
foot wide by one inch thick.
SOURCE: Wilm and Dunford (1948).
56 Wilm, H., and Dunford, E. (1948). “Effect of timber cutting on water a vailable for stream flow from a lodgepol


ra-tion to 0.5 cm 10 years out (2 cm of increased runoff translates into 0.06 acre-feet of water per acre of restoration and 0.5 cm translates into 0.01 acre-feet).
Other studies ha ve also estimated the impact of changes in forest structure on run


efficiency of 80% and a dam height of 300 feet, an acre-foot of water produces
approximately 244.8 kWh of energy or 0.245 MWh. Using data from the EIA, we estimate that the a verage day ahead price for wholesale electricity in 2016 was $ 30.31 per M


urn in California, this implies that the annual probability of an acre of forest burning in California is approximately 0.5 percent. If these forests were restored, the expected savings in fire suppression costs are $2.10–$2.50 per acre of fire in an


 differences
in runoff between restored and unrestored forest that would gi ve rise to changes in value for
water and electric utilities. These changes will be small in comparison to the sedimentation and potentially increased treatment costs. As s


al Damage Unknown (Site Speciﬁc)Fire Water Quality/Sedimentation $8.70Social Beneﬁt Unknown (Site Speciﬁc)
SOURCE: Authors’ Analysis.
In addition to the resilience di vidend estimates of a forest restoration project, there exist a
number of aspects


essed. Additionally, some work suggests that to realize these estimated ben-efits, 20%- 30% of the landscape would need to be restored.
ISET Da Nang, Vietnam
This case study is based on the RDVM. It pro vides a demonstration of how the RDVM can
be a


nam.”
Urba n Climate , 12, 85.
69 Throughout this analysis we use the term typhoon-resilient housing, as ISE T-Vietnam did in its cost-benefit analysis.
ISET also used the term storm-resistant housing in its housing inter vention. We chose to use t


t rate as of June 2015, and each inter vention household also had some sa vings. In
addition, ISE T’s pre-inter vention feasibility study found that households are willing to take
loans to in vest in resilient housing,71 suggesting that poor and ne


e outcomes of intervention and non-intervention population and their perceptions on trust in community to rely on each other in case of storm events; early warning reception; capacity to prepare and respond to events based on training; understanding


ntial to grow e ven
stronger due to climate change. Weather, combined with an increasing population and rapid economic de velopment, make Da Nang prone to flooding and disasters. The area has increas-
ingly experienced typhoon and flood e vents that


ented technical training for local builders and financial sa vings training for households, hosted community
risk management workshops, and created sa vings groups aimed at building good sa vings habits
and trust among participants. The objecti ve


lable for analysis. Howe ver, a number of potentially broader
social impacts ha ve been assessed. For example, 702 Women’s Union staff trained in commu-
nity-based disaster risk management and climate change, and at least 644 people in the target be


 very and transformational capacities in the broader
community. We cannot directly measure these aspects but understand there are changes in behavior that may affect social capital, which is discussed in the following sections.
Applying the RDVM to


 Financial capital  plays a critical role both in the
provision of housing capital and in the reco very process after a typhoon occurs. As ISE T has
shown, in vesting in typhoon-resistant housing is cost-effecti ve, but households may lack the
fin


k associated with the absorpti ve capacities is
typhoon protectio n. Resilient houses protect household members but can also shelter members
of the public  (e.g., neighbors, tra velers, fishermen) who lack resilient housing, which may build
commun


nducted a random selection household assessment87
fr o m Ma y t o J un e 20 1 2 tha t in cl uded dem ograp hi c an d eco n o mi c co n di ti o ns info rma ti o n , construction conditions of homes, household assets, and historical information regard


sehold le vels. These estimates would include things
like increased economic wealth due to increased income associated with the well-being of resilient households. They would include other human and social capital estimates at the com-munity le vel


ed? Housing, &  Human Capital (No electricity
or internet reduces access to information)
Disaster Response &
PreparednessMethod alerted of storm/s (TV,
Newspaper, Radio, neighbors etc.)Human & Social Capital
Did you move due to past storms (2009


atisfaction on as scale of
1-5 (very satisﬁed) with things like the following: success in your life, safety during storms and ﬂoods, feeling that you are part of the community, future securityHuman & Social Capital, well-being as it
relates to the


. ** Responses for this question were not included in the dataset from ISET.
SOURCE: Authors’ analysis.
For each of the questions outlined in this table, we ha ve identified where each fits within the
conceptual model elements. This process indicate


on the community for support in an e vent like Typhoon Nari; a follow-up
question could ask how much and what types of support they would anticipate recei ving.
Finally, se veral questions could be asked about the household perception of their well


ecti ve
can contribute to household well-being in a typhoon resilience system. The steps identified in this analysis can be used to consider similar analysis in future projects.
Socioeconomic data should be collected. More detailed information on t


both pre-project and post-project approaches for estimating the resilience di vi-
dend. It is a good example of a large-scale, broad-based resilience project, but the project design also makes calculating the resilience di vidend challenging.
Execut


t two strategies for estimating the resilience di vidend,
one based on a pre-project perspecti ve and one that looks forward to a post-project period when
it may be feasible to measure the realized benefits if data are collected to support measurem


baria project includes planning, preparation, and procurement in 201 3–2015 and infrastruc-
ture construction in 2016–2017. Data collection for the Mathbaria project—especially the output assessment at the household and community le vel—has not been


sical capital  includes changes to piped water, bridges, drainage, roads, and cyclone shel-
ters. This suite of infrastructure impro vements pro vides both pri vate and public benefits. Pri-
vate benefits come through in vestments in water and sewage


income, employment, and health quality. Income
is a measurable proxy for household utility, capturing changes in earnings that dri ve other
aspects of welfare. At the household le vel, employme nt will be correlated with income, but at a
communit


silience di vidend. The components can be di vided into three broad catego-
ries. The first set is in vestments that target disaster risk reduction and are unlikely to produce
co-benefits, such as community cyclone shelters. The second set is in ves


, which limits our ability to calculate numerical estimates of the resilience
dividend.
Our approach using ADB-generated rate of return estimates is straightforward: we would
categorize estimated net benefits according to the resilience di vidend


ta. In addition, where feasible
the project should measure changes in outputs, since those will be realized on a shorter time horizon than outcomes. Household data, collected through sur veys, would support estimates
of the changes in well-being, i


81.1 TBD TBD TBDBridges 41.2 TBD TBD TBDCyclone shelters 69.9 TBD TBD TBDTotal 1,057.5
NOTES: Beneﬁts estimates from ADB 2014(a) and component summaries from ADB 2014(b). TBD indicates that
these estimates are to be determined given existing data th


ost-Project Resilience Dividend
Type of capital or good/service Outputs to measure Outcomes to measure
Physical capital Infrastructure built Avoided damage
Avoided mortality/morbidity
Human capital Residents trained
Municipal workers trained Worker


only for the resilience project but also for the
relevant BAU cases, whether they are for alternati ve investment or no in vestment.
Non-physical capital is difficult to incorporate. Economic analysis of infrastructure projects
is unlikely to addr


enarios that can be used. For instance, with the forest bond project, the BAU case could ha ve been either “do nothing” or the opposite extreme of clear cutting the entire forest.
In fact, any alternati ve forest management strategy could be used as


he lack of a benefit may be because the shock was larger than what had been planned for and the investment in protection was too small compared to the shock that was obser ved. Protection
measures are typically designed for either an entire distribu


 beha vior
to shift to something new—perhaps relaxing a constraint or introducing new opportunities—can be an indicator of changes in resilience capacities, and is also directly linked to the pro-duction or consumption of goods and ser vices. In Nep


e actions to reduce damage. This informa-
tion likely changed people’s beha vior by encouraging them to take pri vate mitigation actions,
but not all households in the inter ventions will take action. In the case of these households,
the resilience


ger term could help shed light on this process, but the ability
to determine a casual effect from the inter vention versus other inter ventions or circumstances
is complicated the further remo ved one is from project completion.
Regardless of data


ltural producers had choices of 1) buying or sell-
ing water rights; 2) extending the depth of their wells based on the state of the groundwater capital stock; and 3) ceasing production entirely. The hydro-economic model captured this
behavior using


ected for both project and BAU stakeholders if a casual
inference approach was to be used, or simulated in the case of a forward-looking project that has not yet been implemented.
These four time periods are important because they allow us to isolat


e cases, aggregation of various components of the resilience dividend into broad
categories of goods and services may be an effective empirical strategy. As noted through-
out this document, changes in the flows of valuable goods and ser vices are t


ssary to create a consistent model of a system. Howe ver, the relationships between these model elements are
complex and, in some cases, not well understood. Examples include how changes in institu-tions will ultimately change human beha vior, and t


alth founda-
tion of the RDVM relies hea vily on theories of value that predominate modern economics. In
particular, we assume that something (i.e., a good, ser vice, policy, or action) is valuable if and
only if an indi vidual would be willing to


ata structures that can best suit quantification of
the di vidend, the importance of beha vior in generating a resilience di vidend, and the limita-
tions of the model. While we belie ve that the inclusi ve wealth conceptualization of systems for
e


he RAND research team, the Rockefeller Foundation has defined
five characteristics of resilient systems.1 The following reproduces those characteristics and
their definitions:
• Aware: knowing your strengths, assets, liabilities and vulnerabilities


h which these changes might be
made is informational (as with awareness), as exposure to differing ideas or strategies may result in pri vate or public actions that are different than that which would ha ve been taken other-
wise. Another channel mi


d that has the potential to produce other beneﬁts not directly related to the shock or stressor
Resilience Lens An approach to project and portfolio development that
better accounts for system interdependencies and potential co-beneﬁts, with the ult


t h System Scie nce, 13(11), 2707-2716.
Anderies, J., Folke, C., Walker, B., and Ostrom, E. (201 3). “Aligning key concepts for global
change policy: Robustness, resilience, and sustainability.” Ecology a nd Society , 18(2), 8.
Arrow, K. J., Dasgup


i ve and acti ve learning in the adapti ve management of nutrients in shallow
lakes.” Canadian Jour nal of Agricultural Eco nomics/Revue ca nadienne d’agroeco nomie,
57(4), 555-57 3.
Brown, D., & Kulig, J. (1996/97). The concept of resiliency: The


and
Psychopathology, 5, 517–528.
Elliot, W. J., Miller, M. E., and Enstice, N. (2016). “ Targeting forest management through fire
and erosion modelling.” International Jour nal of Wildla nd Fire , 25(8), 876-887.
Eureka County (2016, September). Eu


 adaptation from a disaster risk perspecti ve.” Natural Hazards , 70(1), 609-627.
Longstaff, P. H., Security, Resilie nce, and Commu nicatio n in Unpredictable E nvironments Suc h
as T errorism, N atural Dis as ters, a nd Complex Tec hnology, Cambri


Commu nity Psyc hology, 41(1-2), 127-150.
Paton, D., and Johnston, D. (2001). Disasters and communities: Vulnerability, resilience, and
preparedness. Disaster Pre vention and Management, 10, 270-277.
Pawson, R. (200 3). “Nothing as practical as a go


alysis for typhoon resilient housing in Danang city, Vietnam.” Urba n Climate ,
12, 85-10 3.
Turner, B. L., Kasperson, R. R., Matson, P. A., McCarthy, J. J., Corell, R. W., Christensen, L.,
et al. (200 3). “A framework for vulnerability analysis i


the net benefits of a resilience project. We call the framework the Resilience Dividend Valuation Model (RDVM). We use the term resilience dividend to describe the net benefits associated with the absorption of shocks and stressors, the recovery path


Nicholas M. Pace, Lloyd DixonAssigning Responsibility
Following a Catastrophe
Alternatives to Relying Solely on Traditional
Civil LitigationCORPORATIONLimited Print and Electronic Distribution Rights
This document and trademark(s) contained herein


e that traditional civil
litigation might not be the best way to assign responsibility . We do not, however, address the
underlying question of whether an alternative to ordinary litigation is actually needed for any
particular type of adverse eve


e have argued that an approach better suited to handle
the flood of claims that arises fo llowing a major disaster is needed, one that minimizes the
generation of substantial legal and other transaction costs while extending liability protections


ility for an adverse event might be affixed upon or accepted by
individuals or entities through formal procedures (such as agency hearings), informal actions
(such as when a company voluntarily sets up a claim fac ility to pay requests for compensa


 Determinations
Agency determinations are nonadversarial  proceedings in which agency staff affix
responsibility on a party based on its internal review of relevant facts and controlling
regulations, statutes, and departmental guidelines and proce


t might  arise in the future, regardless of the party ’s
actions.
Frameworks
These processes must be viewed in the context of overarching frameworks  that reflect
legislatures ’ intentions as to how legal relationships between actors ought to be


s) with pneumoconiosis ( “black lung”), as well as to their surviving
dependents; it typically requires the operator of the last coal mine in which the
injured or diseased worker was employed for a year or more to  cover the cost of
those benefits


S. Senate issued its resolution of advice and consent to ratify the
treaty on July  31, 2003.
10 Pub. L. No.  101-380.
11 Pub. L. No.  85-256.  xvi Personnel Protection Act of 2003  and the National Smallpox Vaccination Program  made
possible  b


hose limitations.  Punitive damages continued to be prohibited, but carriers would now
be strictly liable (sometimes referred to as presumed liable ) for proven injury claims up to
about $153 ,000 (as of April 2017 ). Claims beyond that point would


overed persons)
NOTE : OSLTF  = Oil Spill Liability Trust Fund, created by OPA.
a A demise charter is a vessel lease in which all of the vessel’s operating expenses pass to the lessee .  xviii OPA was expressly enacted to address responsibility i


er o f licensees across the
country, this equates to a potential fund of about  $13 billion to address claims arising out of a
single incident, although the owner or operator of the power plant where the incident actually
occurred would be directl


itional tort lawsuit.
The NCVIA took a different approach, conferring no blanket immunity on manufacturers of
specified types of vaccines, but instead markedly narrowing the theories of liability that could
be used against them.  Claims for compe


be vital to the nation ’s public health
were at risk or currently inadequate.  The story behind OPA is not as straightforward :
Developing a comprehensive and rapid response to immediate needs following a spill was
arguably a motivation of at leas


  Although  the favored parties under OPA and Price –Anderson
continue to bear responsibility for the costs of compensation, remediation, and recovery (as well as a ssociated transactional expenses), such parties have the benefit of potentially
si


hly structured system
for considering a very s pecific type of claim and rules crafted for a singular purpose (such as
the NCVIA program), but it would also cover instances in which traditional tort litigation is the
sole option available to claim


st
a PRP for claims
previously paid  •!Not relevant; claimant does
not participate because its
claims have already been
resolved  Responsible party
(“claimant ” is the OSLTF)
OPA: Claim
resolution through
litigation  Civil lawsuit of c laim


allpox vaccine
acts: Agency claim
resolutions  Semiformal  internal
agency determination
of claims against the
United States  •!There are limits on
attorneys’ fees (assuming
no impact on the availability
of counsel) .
•!If the claim meets th


s to one year .
For PRPs:  Results of agency claim decisions  are
not relevant until trust fund reimbursement
action is initiated .
OPA: Agency -initiated
civil reimbursement
actions Civil lawsuit  brought by an
agency against a PRP for
clai


mination of a claim pres ented to it, such as when OPA
claimants proceed against the OSLTF to recover damages or recovery costs or when Swine Flu
Act and smallpox vaccine act  claimants seek compensation from the Secretary of Health and
Human Serv


are considered.  In contrast are semiformal  agency determinations in which the
decisionmaker has some sort of preexisting relationship with what might be thought of as an
interested party.  For example, claims submitted against the OSLTF under OPA


e PRP in district
court
For PRPs: Not as part of this
process, although defenses
could be put forth if an agency
seeks reimbursement through court action
OPA: Agency -
initiated  civil
reimbursement
actions Civil lawsuit
brought by an
ag


specifically address claim
procedures .
For PRPs:  Not directly relevant
following substitution of the
United States as defendant -payor  For claimants : The claimant
could request reconsideration
of the decision, although
appellate review wo