 2 6
High 5 1 244    Evaluation of the Jinan City Water Ecological Development Implementation Plan
• RCP 8.5: It is characterized by increasing greenhouse gas emissions over time repre-
sentative for scenarios in the literature leading to high greenh


ent the effects of climatic variation on water supply in the Jinan WEAP model, we estimated relationships between historical precipitation, temperature, and inflows. Using regression analysis, we estimated relationships between climatic variables and


RCP 8.5
1
0 2 3 4
–100 –50 0 50 100 150 200 250
Change in annual precipitation (mm)Change in average annual temperature
(degrees Celsius)Climate scenarios: change from 2000 to 205046    Evaluation of the Jinan City Water Ecological Development Imp


tal, we thus developed 324 futures, which we then used to evaluate 16 diff  erent man-
agement strategies, including the system as currently confi  gured. To facilitate the evaluation of the WEAP model almost 5,000 times, we distributed the runs over


ectors more than offset these declines.
Under the High Demand projection, steep total increases in demand are seen from 2015
to 2030. The increases then lessen through 2050 (Figure 5.3). In this projection, total demand exceeds 2,800 MCM by 2050. Ta


n and a range between no change and 30 percent lower than Current Demand for the Low Demand projection.
Sources of Supply at Selected Demand Nodes
The WEAP model simulates how demand at each node is met by supply over time. To briefly
summarize how


ir. In 2030, additional supply is available from the Yuqing WTP, which increases because of greater available alloca-tion from the South-North Yangtze River Transfer. Th  e declining supply of industrial water, particularly in the High Demand project


020 2030 2040 2050Low Demand High DemandSources
Jiyang Groundwater
South-North TransferTuhaiYellow River
Figure 5.11
Sources of Supply for Zhangqiu Agricultural Demand Node for Historical Climate Projection
RAND RR1682-5.110 100 200 300 400 Supply (m


Agricultural Sector
Under the Historical Climate projection, there is a small amount of projected unmet demand
in the agricultural sector for all three demand projections beginning in 2024 (Figure 5.16). The pattern of unmet demand mirrors the histo


f months in which flow is below
the threshold of 4.35 MCM per month. The figure shows that there is an immediate divergence
in results among the three future demand projections and the Current Demand projections, with the future demand projections


 time under the assumption
of Large Initial Groundwater Storage for the three future demand scenarios and Current Demand. Under the Current Demand projection, groundwater is in steady state and only devi -
ates small amounts each year until 2030, wh


 GW Storage: Groundwater storage change
(billion m3)
–5–4–3–2–10
–400–300–200–1000100200Demand
projection
Current
LowMediumHigh64    Evaluation of the Jinan City Water Ecological Development Implementation Plan
ing unlimited groundwater. However, a


Initial Groundwater Storage and Low Initial Groundwater Storage
RAND RR1682-5.21Year2010 2020 2030 2040 2050 2015 2025 2035 2045Low Initial GW Storage: Groundwater storage change
(million m3)Large Initial GW Storage: Groundwater storage change
(mil


provides insight into the ability of Jinan’s current water management infrastructure to meet potential future demands under a changing climate. We also defined those futures in which performance would not be acceptable with respect to key metrics, s


demands increase more than the Medium Demand projection.
Figure 6.2 again shows unmet residential demand for all three demand projections across
climates for the Full Yellow River Allocation from 2015 to 2050, with the average unmet demand for the 2


l Development Implementation Plan
We now classify outcomes based on unmet demand. For purposes of example, we define
futures to be vulnerable if average unmet residential demand from 2031 to 2050 is higher than
5 percent of demand. 1 Any other thre


 2045 2050 2031–2050Demand
projection
Low
MediumHigh
NOTE: X symbols indicate results for the historical climate projection and the /f_illed circles indicate results for the
climate change projections.
RAND RR1682-6.5
Figure 6.6
Average Unmet Indus


ocation: Average annual unmet demand (2031–2050)
 (million m3)
0100200300400
NOTE: The red X symbols indicate futures in which the system is vulnerable and the /f_illed green circles indicate
futures in which the system is not vulnerable, depending


undwater Storage
Current 50% Current 50% Current 50%Low Demand High Demand Medium DemandYellow River
Allocation:
0100200300
District
Changqing
Jinan Urban Center
JiyangLicheng
PingyinShanghe
ZhangqiuLow Initial GW Storage: Groundwater
storage change


ections    77
For the predominantly urban basins in Figure 6.12, the range of climate projections has
a significant effect on the ending groundwater level. The 2050 change in groundwater stor -
age, for example, ranges from –1.4 billion to 0.3 billi


seen in all demand and Yellow River allotment futures except the Low Demand, Full Yellow River Allocation futures. The climate effect is very strong as well, with no shortages in the wettest climate but significant shortages in drier climates (see Fi


included in the IP and discussion with JWRB, two projects were identified as having the greatest potential effect on the system: a pipeline connecting the Duzhang Reservoir to the Langmaoshan Reservoir, 80    Evaluation of the Jinan City Water Ecolog


er Management (SAGM) policy represents an aggressive response to declining spring levels at the four largest springs in central Jinan: Baotu (Baotu Quan), Black Tiger (Heihuquan), Pearl (Zhenzhuquan), and Five Dragon (Wulongquan). The decision rule c


(residential and industrial sectors) or climate effects on demand (agricultural sector) by 2035. For each sector, efficiency standards were evaluated at a 100-percent implementation level (full implementation) and a 50-percent implementation level.


of IP projects
3 IPR In addition to IP, includes increase of reuse rates in districts with
WTPs specified to 40 percent of total district WTP capacity by 2020 and 60 percent of total district WTP capacity by 2030
4 IPR + SAGM In addition to IPR, inc


e effective in reducing unmet demand across all climates and allocations of the Yellow River. In some future scenarios that include the wastewater reuse strategy, residential reuse rates (as a percentage of total residential demand) actually decline.


s unmet demand by about 75 percent in the High Demand projection.
As with Figure  7.2, Figure  7.4 summarizes unmet demand in the industrial sector
for select strategies across all futures. Th  e red symbols are those results that exceed the 30-per


t Strategies for Historical Climate
RAND RR1682-7.50 15
10
5 20 25 30 Unmet demand (million m3)
Year Year2010 2020 2030 2040 2050 2010 2020 2030 2040 2050Medium Demand High Demand
Strategy
IPR + EffAll
IPR + 50Eff + WQIPR
Completion of IPIPR + 50Ef


ntation Plan
High Demand projection as well. The strategies with only half of the efficiency improvements
(for example, IPR + 50Eff) have a positive but smaller effect on groundwater levels over time (not shown). Groundwater levels are stabilized un


re for the residential sector except under High Demand or
Low Initial Groundwater Storage conditions. In these cases, the strategies that include reuse and also the residential efficiency strategy reduce vulnerabilities significantly. Improving indu


te change include ground saturation, vegetation and ground cover, upstream water withdrawals and diversions, and evaporation rates. In addition, future flooding will be shaped by other factors besides climate change, such as changes in popula -
tion


nt (whether it is a flood, a rainstorm, or an earthquake) is an estimate of the likelihood of the event. A flood with a 50-year return period is one that has a 2-percent chance of occurring each year. We will use a 50-year river stage as a benchmark,


 statistical model, s=f(p) , that can be used to predict river stage s in meters
given a particular precipitation intensity p , where intensity is defined as the amount of precipi -
tation that falls in a given time period (in this case, 24 hours).


and stage:
 si=–3.6 x  10–5pi2+ 0.023 pi+ 23.3.  Eq. 8.1
This
precipitation-stage model predicts, for example, that a precipitation of 230 mm
would lead to a 1-in-50-year water level of 26.7m. The model’s R-squared value is 0.43 and its P-value is


ating river stage return periods, but we use the annual maximum 24-hour precipitation data shown in Figure 8.5. Here, the lognormal model provides the best fit to the data.
5
Figure 8.6 shows the intensity of 24-hour precipitation return periods from


ion.
7 Th  e future return periods for the 1-in-20-
year, 24-hour precipitation event in the East Asia region is shown on the left panel of Figure 8.8. Each box plot summarizes the return periods predicted by 14 global climate models, in two diff  e


rs)
2046–2065
Years2081–2100Scenarios
B1
A1BA2
Full model rangeCentral 50%
intermodel range
Median
B1 A1B A2
Maximum Return Period 17 17 15
Mean Return Period 11 9 9
Minimum Return Period 8 6 8
RAND  RR1682-8.8100    Evaluation of the Jinan City Wate


ssible future conditions. Finally, we set out to demonstrate the value to JWRB of the Jinan WEAP model as a management and decision support tool and train JWRB’s technical staff on the refinement and use of the model for future work. In this chapter,


imulation model of the Jinan water system is only an approximation of the real physical system. The degree to which the model is a “good” representation of reality depends on the quality and quantity of data available to calibrate it, and an understa


s but very significant shortages in drier climates.
•
 Jina
n’s springs are vulnerable across all futures. In wet climates, low spring flows each year
occur for around three months on average; in dry climates, low flows appear about eight months eac


sectors and active management of groundwater withdrawals. Efficiency mea -
sures will counterbalance increases in population and industrial activities. Strategies to increase water reuse and improve the water quality in the Xiaoqing River could help


t stream flow data at the same location. This would enable the estima -
tion of pollutant loadings to the system that then could serve as a measure of effectiveness of water pollution control strategies implemented in the future.
Models
• The Jinan W


ta-gathering process, RAND created a master data inventory table to track the availability of data relative to various components of the Jinan WEAP model. This spreadsheet has been shared with JWRB, and JWRB has contributed directly to the spreadshee


physical infrastructure used to store
and distribute water across the model area
5. obser
vational data related to management of water releases from reservoirs, treatment
plants, and other water-related infrastructure.
To assess the potential outco


. The PCA, which RAND digitized, provide numerous tables and historical data that are directly applicable to the WEAP model. Additionally, Zhao (2015) provides tables and other data along with high-level information on Jinan’s groundwater hydrology t


 Jina
n river flood prevention monitoring station information
7. a summ
ary description of the Xiaoqing River after the 2006 treatment project.
Water Demand
We obtained detailed data for historical  water demand sectors for various geographical regi


 in the PCA (Jinan Water Resources Bureau, 2009–2014). Information regarding the volume and propor -
tion of these demands that are met through groundwater withdrawals is also available in these appendix tables.
Some historical data for correlates o


mand] &  11 [Consumption]), Monthly  —
Jiyang 2008–2013 PCA
(Tables 10 [Demand] &  11 [Consumption]), Monthly; 2011–2014 Jiyang Township Water Resources Communiqué  JWRB projections included in the third tranche
Shanghe 2008–2013 PCA  (Tables 10 [D


/
Pressure Head Requirements  NotesProjected Growth or Change in
Demand Correlates or Usage
Springs/Spring Zones1998–2004: Jinan Spring Water Geography, Annual; Additional journal articlesSome spring flow and level data received in third data tranche


, Hui, Linshang, Luo, Jinshui, Nandasha, Qiji, Sha, Shangdong, Shangzhong, Tuhai, Tuma,
Xiaoqing, Yudai, and Yufu rivers are contained in this file. All stations include an inflow and outflow district to aid in their placement in the schematic. The


chuan inflowMonthly total discharge 2011–2014
Xiujiang 3 Beifeng, Dazhan and  Duozhuang inflowAnnual mean flow, monthly and annual reservoir inflow 1977–2014
(total coverage)
Hui 2 Chentun, Zhanxiaozhuang Monthly total discharge 2011–2014
Anluan 1


16, for Dazhan, Duozhuang, Duzhang, Jianshuiquan, Xinglin, and Xinglong reservoirs. To complete the catalog of available information for the five reservoir transfer projects, reservoir operational data for Longquan reservoir, including volume elevati


ective WEAP schematic counterparts. The table containing data on these links has not been fully reviewed to identify gaps, but most data needed appear to be available.
Capacities and locations of water treatment and purification plants are only avai


on Plan
Based on our in-person discussions with JWRB staff during the November 2015 and Janu -
ary 2016 workshops, we understand that construction of most of the major elements of the IP
are either complete or under way. However, our understanding o


ollows:
dsy=y−2009
2015−2009*ds2015( )+y−2014
2015−2009*ds2009( )
for each year from 2009 to 2015. Therefore, projected demand ds(2014) for 2014 in sector s is
given by:
ds2014( )= 5 / 6( ) ds(2015)+ 1/ 6( ) ds(2009).126    Evaluation of the Jinan C


et (green bars in Figure D.1) was either similar or much better than the model fit using the Shanghe precipitation data set (blue bars). Therefore, the Jinxiuch -
uan data were used to generate regression fits for all river headflows to maintain simp


3 + 10883.9p + 16497.5t 115 0.70
Yudai 1401650 + 117519p – 68225.2t 16 0.89
Yufu 499044 + 59438.2p – 58419.2t 66 0.69Regression Models Used in Climate Projections    129
regression model in Table D.1. The regression model does a good job of capturing


CM Downscaled Data Portal (CCAFS, 2014a; Ramirez, 2008). As described by the data providers, the Delta Method follows the following steps:
1. Gathering of baseline data (current climates corresponding to WorldClim).
2. Gath
ering of full GCM timeseri


(2013) estimated potential effects of climate change on water and agricultural production in the Yellow River basin. Changes in agricul -
tural demand were assessed in the Yellow River basin for three climate change scenarios drawn from the IPCC Four


 Changes in Annual Mean Temperature, Precipitation, and Agricultural Water Demand from 2010 to 2030
IPCC Climate
ScenarioAverage Change in Temperature by
2030 (degrees Celsius)Average Change in
Precipitation by 2030 (%)Change in Agricultural
Wat


, 2013.
a For the Low Demand projection, simulations were only run for management strategies 1 to 6 in Table 7.1.136    Evaluation of the Jinan City Water Ecological Development Implementation Plan
Table F.2
Identification and Ownership of Global Cli


E: Blue shading indicates representative climate scenarios.
RAND RR1682-G.1Global emissions
scenario
historical
rcp2_6
rcp4_5
rcp6_0
rcp8_51
0 2 3 4
–100 –50 0 50 100 150 200 250
Change in annual precipitation (mm)Change in average annual temperatu


Collins, Matthew, Reto Knutti, Julie M. Arblaster, Jean-Louis Dufresne, Thierry Fichefet, Pierre
Friedlingstein, Xuejie Gao, William J. Gutowski, Tim Johns, Gerhard Krinner, Mxolisi Shongwe, Claudia Tebaldi, Andrew J. Weaver, and Michael Wehner, “L


Vishal K. Mehta, Ben Wright, and Grantley Pyke, Developing Robust Strategies for Climate Change and Other Risks: A Water Utility Framework , Santa Monica, Calif.: RAND Corporation, RR-977-
WRF, 2014. As of December 14, 2016: http://www.rand.org/pubs/


ator Summary , 2015.
Jinan Statistics Bureau, Jinan City Statistical Yearbook 2012 , China Statistics Press, 2013.
———, Jinan City Statistical Yearbook 2013 , China Statistics Press, 2014.
Jinan Water Resources Bureau, “Jinan City Water Resources Pub


y A., Joan F. Kenny, Susan S. Hutson, John K. Lovelace, Nancy L. Barber, and Kristin S. Linsey, Estimated Use of Water in the United States in 2010 , Reston, Va.: U.S. Geological Survey, Circular
1405, 2014. As of November 2015: http://dx.doi.org/10


gement , CRC Press, November 15, 2004.
Taylor, Karl E., Ronald J. Stouffer, and Gerald A. Meehl, “An Overview of CMIP5 and the Experiment Design,” Bulletin of the American Meteorological Society , Vol. 93, No. 4, April 2012, pp. 485–498.
UNESA— See U


egies to help Jinan meet its long-term water resources goals. RAND’s approach uses well-tested methods of decision support, starting with building a shared understanding of the nature of the decision, metrics to evaluate progress toward goals, key un


STEPHANIE RENNANE, SAMANTHA CHERNEY
Who Settles in Workers’
Compensation?
An Analysis of How Trends in Claim
Settlements Relate to Workers’ Compensation Benefit Changes in Oregon
Workplace injury is costly to workers and firms (Leigh, 2011). Worker


to avoid the uncertainty about the judge or jury’s decision on the case. In the case of workers’ compensation, parties may opt for settlement to avoid the uncertainty of how long disability payments may continue when the path to recovery is uncertain


holder conversations, the increase in the frequency of settlements could, instead, indicate that claims administrators and attorneys sought to gain more information about the policy change by “testing out the system” and to reduce their uncertainty a


s
about the anticipated duration of disability and the workers’ rate of discount (or time preference). However, this ignores the very real dimensions of uncertainty for the worker. (Hunt and Barth, 2010, p. 4)
These dimensions include whether the wo


g the claim process. For accepted claims, workers may opt for a claim disposition agreement (CDA). Under a CDA, workers release all rights to present and future benefits in return for a payment up front, typically provided in a lump sum. The CDA sett


hen a worker is injured or experi -
ences illness due to work, a workers’ compensation claim is initiated, using either the Worker’s and Physician’s Report of Worker’s Compensation (com-pleted by a doctor) or the Report of Job Related Illness or Inju


ht shows the trends as a percentage of all indemnity claims in the injury year.
There are several patterns of note. First, in
all injury-year cohorts, there is a higher overall frequency of settlements among claims with a
FIGURE 1
Trends in CDA Set


“The majority of workers in our sample did not work before a settlement and did not return to work within one year after a settlement.” Savych also examined various worker and claim characteristics, and found benefit payments were suspended, were mo


Scheduled and unscheduled injuries had differ-ent rating procedures prior to 2005: For scheduled injuries, the extent of impairment was determined relative to the injured body part; for unscheduled injuries, the extent of impairment was determined re


y type of settlement was the disputed claim settlement. By comparing claims before and after the policy change (in 1989 and 1991), the authors found that the
introduction of CDAs sped resolution and
reduced TTD duration in a small group of claims: a


h efforts resulted in only two interviews. While response to such requests is typically low, the fact that we were analyzing a policy change from nearly 15 years ago limited our ability to reach out to actors who were actively working in the Oregon w


r; preinjury weekly wage; occupation and industry at the time of injury; injury characteristics, including body part, nature of the injury, and the injury event; claim characteristics, including date of injury, date of claim closure, and total values


as viewed as a significant change in policy. In particular, both noted that the work disability provisions in the new law were significant and drew a lot of attention soon after the law was passed. Both respondents indicated that this change meant th


l claimants, regardless of whether the claim ended in settlement or closure.
Furthermore, because the data set includes infor-
mation only on claims with indemnity payments, it is outside the scope of our research to examine the effects of settlement


ervable charac-teristics of claims that settled or claimed PPD before or after the policy change. Statistically significant changes in other claim characteristics would suggest that there were other shifts in the case mix of claims that settled or re


istribution well, it tends to underestimate higher-value PPD awards. Figure 4 compares the predicted propensity score for the sample of claims after 2005 when we were able Empirical Approach
Next, we used a logistic regression analysis to analyze whe


that may be likely to affect the value of the PPD award: preinjury weekly wage, body part of injury, and probability of receiving work disability. To test whether the relationship between these factors and settlements changed after 2005, we also incl


sig-nificantly affected the likelihood of settlement after SB 757. There are several potential explanations for the lack of an effect. First, workers may not have been aware of the policy change. Other actors in the system, such as attorneys and cla


 the marked increase in initial settlements after 2005 resulted from actors testing out the system to under-stand how the changes may work may well be correct and is worth exploring with further research.
Limitations
These findings present a prelimin


ide the scope of this analysis,
we also speculated on several potential explana-tions for the lack of changes in settlement behavior. First, workers and attorneys may face uncertainty about the expected value of the permanent disabil-ity award, and


, and dispute processes.
5  Oregon Workers' Compensation Board, undated.
6  See DCBS, 2011, and DCBS, 2016, for more details on SB 757.
7  This restriction excludes only approximately 1 percent of
claims in the data set.18
Bibliography
Barton, Sa


ted States,” Millbank Quarterly , Vol. 89, No. 4,
December 2011, pp. 728–772.
Morgan, James N., Marvin Snider, and Marion G. Sobol, Lump
Sum Redemption Settlements and Rehabilitation: A Study of Workmen’s Compensation in Michigan , Ann Arbor, Mich.


ttlement
State of Oregon, “Claimant Attorney Fees,” webpage, undated. As
of November 19, 2019: https://www.oregon.gov/dcbs/reports/compensation/disputes/Pages/attorney-fees.aspx19
Stuart, Elizabeth A., “Matching Methods for Causal Inference:
A Revi


ited.
Permission is given to duplicate this
document for personal use only, as
long as it is unaltered and complete.
Permission is required from RAND
to reproduce, or reuse in another
form, any of our research documents
for commercial use. For


er
and more prosperous.
RAND is nonprofit, nonpartisan, and committed to the public interest. RAND Ventures is
a vehicle for investing in policy solutions. Philanthropic contributions support our ability to
take the long view, tackle tough and oft


Evaluation of North
Carolina’s Pathways from Prison to Postsecondary Education Program
Lois M. Davis, Michelle C. Tolbert
CORPORATION
Limited Print and Electronic Distribution Rights
This document and trademark(s) contained herein are protected by l


 RAND Cor -
poration, in partnership with RTI International, was chosen to evaluate the Pathways demonstration project. This report focuses specifically on the evaluation of the North Carolina Pathways Program.
These findings should be of interest to


................ 19
Student Selection Criteria, Recruitment, and Enrollment  ....................................... 21
In-Prison Educational Activities, Supports, and Prerelease Statistics  .......................... 23
Prerelease from the In-Prison


implementation of the in-prison component of the three pilot Pathways Programs in Michigan, New Jersey, and North Carolina. The second phase of the eval -
uation focused on the community component. The Laughing Gull Foundation and Vera provided fundi


 strengthen the PSE and reentry services available to individuals incarcerated in North Carolina’s correctional system.
The Pathways Program was led by the NCDPS Office of Reentry Programs and
Services in collaboration with the NCDPS Community Corre


’s Pathways from Prison to Postsecondary Education Program
cates, diplomas, and degrees; gainful employment, either part-time or full-time, and
increased earnings; and reductions in recidivism, including fewer community supervi -
sion violations and


ered in prison were for only three majors (business administration, computer information technology [IT], Summary    xiii
and entrepreneurship), which did not always align with students’ career interests. Sev -
eral students changed their majors once


elp support the family.
Having a Pathways navigator and trusted persons of authority was impor -
tant.  Most interviewees reported feeling that the Pathways navigator role was essential
and was an important source of support for many of the students


nsitional housing managers, and other community service providers. State admin -
istrations had to provide 25-percent match funding and staff time to plan, imple -
ment, and manage the program; agree to such policy changes as inmate transfers to
Pat


 impact on how NCDPS approaches higher education in prison and reentry planning. Pathways laid the groundwork for and showed what reentry planning should look like. It also has led to more coordi -
nation among prisons and PPOs and community resource


ed in developing the logic model and in assessing the in-prison component of the Pathways Program in North Carolina. We also appreciate the thoughtful insights provided by our technical reviewers, including Matthew Mizel of RAND, Susan Turner of the


individuals who participated in PSE programs. Davis et al. (2013) estimated that participation in college or PSE programs
reduced an individual’s risk of recidivating by 16  per centage points compared with
those who did not participate in correcti


-year public; two-year and less than two-year private nonprofit; and four-year, two-year, and less than two-year private for-profit colleges).
A more recent survey of all 50 state correctional education directors by Davis
et al. (2014) found that o


nical education (CTE) programs
and apprenticeships that lead to industry-recognized credentials to credit-  bea ring
programs that lead to postsecondary degrees (e.g., associate and bachelor’s degrees or higher) (Davis et al., 2014; Erisman and Con


tructured to enable incarcerated stu -
dents to earn credentials and, ultimately, college degrees?
• Who are the key stakeholders and what are their roles in supporting these types of programs?
• What other types of supports do incarcerated students


tering college and complet -
ing their PSE through the two years following their release.6 To increase educational
persistence and completion rates among participants, the three pilot states also pro -
vided extensive case planning both in prison an


m in the pilot state, specifically (1) certifying the cre -
dentials of corrections educators and the curricula used in corrections education programs for the purposes of granting credits to students for those courses after release; (2) signing artic


s.
PSE programs funded by the state were primarily CTE. The NCDPS Division of
Adult Correction (DAC) (now known as the Division of Adult Corrections and Juve -
nile Justice) provided CTE and other education services to incarcerated individuals thro


litate the reentry process. Instead, it was up to the individual being released from prison to develop his or her own plan and determine his or her next steps, meaning that if an inmate was organized, he or she would have developed a reentry plan; ot


s*Academic and Instructional Supports
• participants living in shared quarters*• computer training, access to computer lab and
monitored/limited internet*
• trust-fund incentives for meeting performance goals*• flexible visitation schedule and fina


roved quality of life in neighborhoods
impacted by crime and incarceration
• skilled workforce• taxpaying citizensSocietal Impacts10    Evaluation of North Carolina’s Pathways from Prison to Postsecondary Education Program
reentry councils (LRCs). N


ate sources) and 10  pe rcent in-kind resources to be distributed over the four years of the demonstration
project.
10 Some of these eligibility requirements (e.g., scores on assessments) were later relaxed during the selection pro -
cess to ensure


elony
drug convictions. The time required to clear up these challenges led to the decision to supplement with Pathways
funds in order to get these students enrolled in college.12    Evaluation of North Carolina’s Pathways from Prison to Postseconda


hed LRCs in the three designated release communities. It was also planned for the LRCs to use the funds to customize services based on the students’ needs and available community resources, including employment opportu -
nities, transportation, housi


his section, we briefly summa -
rize the methods for each phase of the evaluation.14    Evaluation of North Carolina’s Pathways from Prison to Postsecondary Education Program
In-Prison Component Evaluation
As noted earlier, the evaluation of the in-p


eptions about the program’s strength and weaknesses; (5) plans for continuing their PSE upon release from prison; (6) views about support needs; and (7) their advice for improving the program. All focus group discussions were conducted in a private c


esigning, imple -
menting, and overseeing the Pathways Program. This allowed us to get a higher-level perspective of the overall approach North Carolina took to the Pathways Program, stakeholders’ assessments of critical factors and challenges, adjus


opped out of the program to get their perspectives on factors that influenced their decisions, their assessments of the program, their educational plans, and recommendations for improving the program.
Analysis
We qualitatively analyzed the interview


 Pathways Program
unfolded, it is important to examine how the in-prison component was designed and implemented, including student selection, recruitment and enrollment, courses and other services provided, adjustments made, and lessons learned. The


y Community College served Albemarle; AB-Tech served Craggy; and Pamlico Community College served Pamlico. Male inmate students needing developmental courses were to be housed at either Avery-Mitchell or Mountain View facilities to complete these cou


 in heating, ventilation, and air con -
ditioning (HVAC); electrical wiring; commercial cleaning; welding; furni -
ture upholstery; horticulture; food services and culinary arts; carpentry; and
masonry.
4
Student Selection Criteria, Recruitment, an


d to abide by any postrelease and/or probation conditions after exiting from prison (NCDPS, 2014).
7 The Pathways Performance Incentive process was based on weekly progress reports from the community col -
lege instructors who completed an incentive


er the course of the in-prison component of the Pathways Program, a total of
3,750 credits and 259 certificates were earned by Pathways students.
In-Prison Educational Activities, Supports, and Prerelease Statistics
North Carolina’s Pathways Progra


  2. 2 indicates that a total of 3,750 credits
were earned during the in-prison component of the program. Certificates earned by North Carolina Pathways students while in prison were in entrepreneurship, business administration, computer IT, and dev


rollment Processes
In the first year, North Carolina experienced some challenges with its selection, recruit -
ment, and enrollment processes and made adjustments as needed. North Carolina had less than six months from the time of the award of the gr


 -
ferred to another correctional facility or promoted to minimum custody)
13 during the
application process or risk losing some individuals who wanted to be in the program but were in the process of being moved to another correctional facility.
Des


internet access created some security issues. For example, when access was first provided, email communication with college instructors not affiliated with the program was inadver -
tently made available to incarcerated students. As soon as the probl


e students should just be grateful to be in the program. These differing views created some tension between the two groups. Program administrators at times found it challenging to accommodate students’ education and study needs without making them fe


gram was embraced by the state’s central leadership. This support helped set the tone that rehabilitation, and the Pathways Program in particular, was a priority. For example, a senior state administrator in North Carolina noted that Path -
ways need


the Pathways student population viewed doing so as rewarding, the students did present challenges. For example, according to one col -
lege administrator, instructors suspected that some students struggled with attention-deficit/hyperactivity disorde


cult for some family members to The North Carolina Pathways Program’s In-Prison Component   31
visit the student while he or she was incarcerated and, as discussed in Chapter Three,
presented a difficult decision for some students as to whether to r


o the general services provided to all of their reentry clients (e.g., job referrals, housing assis-34    Evaluation of North Carolina’s Pathways from Prison to Postsecondary Education Program
tance, bus cards, counseling, program referrals), the LRC


their families while managing and seeking treatment for any substance abuse, depression, anxiety, or other mental health issues. As one community college administrator noted, Pathways students were a microcosm of the range of issues that affect most


fund services (e.g., reentry assistance and tuition assis -
tance) and for a Pathways navigator in each of the release communities. The majority of the Pathways students said that they would not have been able to stay committed to the program if it w


y the Pathways students as having as many services and supports, the other LRCs were able to maintain the same level of financial support to the students in their communities through the end of the initiative.
Even within the same community, student


ng.
In one of the release communities, the housing manager was well respected by the
students. He would help them with transportation issues and encourage them to stay in the program. However, that was not the case in all the release communities. Ma


lancing work and school. Many of the students, therefore, worked additional hours so that they could purchase their own cars.
Lessons Learned
The implementation of the community and reentry component of the Pathways Pro -
gram, including the challen


at same PPO, though, noted that it was not appropriate for him to interact with the students at the college, because it could inadvertently stigmatize the students. He believed that that role was better suited for the Pathways navigator.
In another


 and Reentry Component   41
The Pathways navigators, PPOs, and instructors also pointed out that many of
the students were reluctant to ask for help. As one navigator described,
Some of the participants wait until the last minute to come and talk ab


ed that she felt like she was failing as a mother. Another student commented that “[b]alancing all my responsibili -
ties is hard. Something has to give. I feel very overwhelmed.”42    Evaluation of North Carolina’s Pathways from Prison to Postsecond


ed to pay full-time bills with part-time jobs.
Many students agreed that the incentives initially promised were more of a
distraction—particularly when they did not come to fruition—than a help. Rather,
the students recommended focusing on the uni


make sure that the Pathways Pro -
gram, or something similar, would be made available to other incarcerated students. If that happened, they had the following advice for other incarcerated individuals consid -
ering enrolling in such a program:
• “Yo


in-prison education component to eliminate duplication among participating colleges and provide more transfer options for students after their release. He and other staff agreed that many of the Pathways students struggled with being far away from ho


he position could be strengthened. One college administrator recommended embedding the Pathways navigator role at the college or having the navigator be a shared position between the LRC and college. Similarly, a navigator noted the importance of hav


and Snyder, 2014). Research has shown that returning citizens are particularly vulner -
able in the first few weeks of release from prison, with peak rates for recidivism occur -
ring in the days and weeks immediately following release (National Rese


 research has long recognized the important role of family in successful
reentry. Like the Pathways students, many individuals depend on family members or other relatives or friends for financial, housing, and emotional support upon release from pri


on career paths that would lead to postsecondary certifications, diplomas, and/or AAS degrees (versus simply taking courses to bide time while in prison)
• enabled many individuals to stay focused on education and made having infrac -
tions while inc


e asked them about their education experience prior to Pathways, many reported that they had earned several Conclusions and Recommendations   51
other occupational credentials while incarcerated. However, Pathways was different in
that it allowed th


me growing” effect desired as part of the demonstration project. That is, one intended outcome of the in-prison component was that the Pathways Program would motivate other individu -
als in prison who were not selected for Pathways to earn their GED


 opportunity.
Communication Takes Time and Is Critical to Program Success
Another critical lesson learned from both the in-prison and community components was the need for regular communication among staff and students. At various points, there were


ition than those students who had to change to a different college upon release.
The Pathways students also expressed appreciation for meeting with success teams
prior to release. These meetings gave them the opportunity to get to know the reentry s


d over time. This proved to be problematic for the implementers of the community compo -
nent of the Pathways Program (e.g., program staff, reentry staff, headquarters staff). For Pathways students, this eroded their trust and introduced uncertainty


nal
Programs
There are numerous reentry stressors that can affect students’ decisions about whether
to complete their education program out in the community, work full-time, or return home or move closer to family. Housing, employment, and transpor


 was more likely to understand where flexibility was needed to accommodate the chal -
lenges the students faced in going to school full-time, working, and trying to reunite with family.
Family Plays a Key Role in Students’ Decisions and Success
From


changed majors upon release from prison. In addition, there was recognition that it would have been helpful to allow students to earn certificates while incarcerated so that they would have those certificates when released.
Not all the credits transf


would relieve the stress of trying to go to college full-time while needing to work full-time.
• Include enough release communities in the program so students can live near their families and other supports.
• Invest in reentry infrastructure to ensu


 three pillars.
In addition, NCDPS has set up a PSE advisory committee as a result of Pathways
that continues to discuss what they want prison education to entail in North Carolina. This has led to several colleges receiving grants (from a regional


f material support as needed.
• Provide mentoring, tutoring, and comprehensive supports and services to stu -
dents once enrolled.
• Evaluate the progress of students academically, in employment and earnings, and in crime desistance.62    Evaluation


se actions to ensure that educational programming could proceed as planned
• commitment from the director of the state parole or postrelease community supervision agency and a detailed plan from the head of the supervision agency for how the agency w


prison resources included department of corrections programming staff, cor -
rectional staff, educational staff, case management staff, and supervisory staff to facili -64    Evaluation of North Carolina’s Pathways from Prison to Postsecondary Educat


ustice costs• reduced crime and increased public safety• improved quality of life in neighborhoods
impacted by crime
• skilled workforce• taxpaying citizensSocietal Impacts
NOTE: The dashed line indicates that the services and components are supplem


le assessment opportunities• academic and occupational training
• remedial education
• assessment-driven placement
• noninstructional supports
• classroom instruction enhanced by com -
puter technology
• behavior management by students sign -
ing beh


ministrators/Program Staff/Instructors
Project Leaders: Lois Davis (PI) and Robert Bozick (co-PI)
Tel. 310-393-0411
Email: lmdavis@rand.org; rbozick@rand.org
1. PURPOSE OF THE STUDY
The RAND Corporation, a non-profit research institution in Santa Mo


bservation checklist to guide our data collection.
Our interest is in understanding what instructional methods are being used, the variety of instructional activities that occur, whether individualized learning is occurring, the degree to which stud


premises—electronic records will be protected by password and/or encryption—and will not be shared with anyone outside of this project. After completion of the study, all written materials will be destroyed 12 months after the report has been complet


ptions about program quality, usefulness, and effectiveness
Focus Group Questions
Overview of Focus Group [5 minutes]
Thank you for coming today. I want to introduce myself and the others working
on this project. My name is _____ and this is ______


our plans for enrolling and continuing your education upon release?
o How do you think the program will help you meet those goals?
•	What do you think will be your biggest challenges when released and how might the program help you address those chal


e Depart -
ment may receive a copy of the overall results at the end of the study but will not be able to identify you personally from the copy they receive. You should know that if you indicate plans to harm yourself, to harm someone else, or to esc


in the focus group discussion today , we
have arranged for you to participate in an alternative activity (e.g., study hall or computer lab time). You are free to go to that activity now.78    Evaluation of North Carolina’s Pathways from Prison to Po


is is a research project being conducted by the RAND Corporation; it is
not being conducted by the North Carolina Department of Public Safety (DPS). RAND will keep confidential your comments. DPS staff will not get a copy of your answers. The Depart


oration, 1776 Main Street, Santa Monica, CA, 90407-2138, or by phone at (866) 697-5620, or by emailing hspcinfo@rand.org.82    Evaluation of North Carolina’s Pathways from Prison to Postsecondary Education Program
RAND Corporation
EV ALUATION OF THE


s) key to supporting this program. The interview will take between 60-90 minutes.
The information provided from your comments will be used by the evaluation
team to evaluate the outcomes of the NC Pathways Program. The findings will be summarized in


udy. De-identified data may be kept after the study indefinitely.
8. VOLUNTARY PARTICIPATIONYour participation in the study and interviews is completely voluntary. You
may refuse to participate, refuse to answer any question in the interview , or s


on-instructional supports that complement classroom activities (e.g., Pathways Navigator, computers, transporta-tion, childcare, housing)
§	Other reentry services
§	Views regarding program’s strengths/weaknesses and addi-tional support needed
§	For t


lities, and other resources supporting program
•	Changes in partners and stakeholders to support students in the community
§	Perceptions of the program’s strengths and weaknesses
§	Update on data collection
o Program outcomes and impact
§	Transition


er 2005.
Gorgol, Laura E., and Brian A. Sponsler, Unlocking Potential: Results of a National Survey of
Postsecondary Education in State Prisons , Washington, D.C.: Institute for Higher Education Policy,
May 2011.90    Evaluation of North Carolina’s


nt provided to RAND.
———, “Unlocking Potential: Pathways from Prison to Postsecondary Education, Request for
Proposals,” 2012b, unpublished document provided to RAND.
———, Pathways from Prison to Postsecondary Education Project , New York, 2014.
Vis


Michael Dworsky, Stephanie Rennane, Nicholas BrotenWage Loss Monitoring for
Injured Workers inCalifornia’s Workers’ Compensation System
2013 Injury Year Findings (First Interim Report)CORPORATIONLimited Print and Electronic Distribution Rights
This


e loss for all injured workers in the state.
RAND Institute for Civil Justice
The RAND Institute for Civil Justice (ICJ) is dedicated to improving the civil justice system
by supplying policymakers and the public with rigorous and nonpartisan rese


..................................................................................... 61
Table A.7a. Demographic Characteristics of Injured Workers with Indemnity Claims .............. 62
Table A.7b. Firm and Employment Characteristics of Injured W


ating worker counterfactual outcomes, and alignment of economic incentives and resources to support the research.
The California Department of Industrial Relations (DIR) has established a project that will
provide regular monitoring of earnings los


 into account when estimating
wage loss. The concept of wage loss relies on comparison of a worker’s realized earnings after
injury with the worker’s potential earnings in the (hypothetical) absence of the injury.
Researchers have taken numerous a


n ” period for reporting in tables.  xii Figure S.1 . Trends in Relative Earnings in Second Year After Injury, 2005 –2013 Injuries

SOURCE : 2005 –2013 Workers’ Compensation Information System ( WCIS ) and  Employment Development
Department (EDD)


 WCIS -EDD data.
NOTE: Relative earnings are defined as the ratio of injured worker earnings to potential earnings over the second full
year following the quarter of injury.
 xiv Labor Market Outcomes Vary by Industry and N ature of Injury
Afte


ed here
to include Los Angeles County, Orange County, Riverside County, San Bernardino County, and Imperial County) (see Figure S.3).  xv Figure S.3 . Trends in Relative Earni ngs by Region and Cumulative Injury Status, 2005 –2013
Injuries with Ind


 claims that have not yet fully resolved. Given the slow pace of medical recovery and claim resolution for severe injuries, data on earnings losses several years after injury  are necessary to directly assess the
long-term impact of permanently disa


ups, Especially
Cumulative Injury Cases in Southern California
While we were  unable to disentangle particular policy impacts in our analyses of overall
trends, our analysis of key subgroups revealed some notable differences in wage loss and labor


tions
AB Assembly Bill
AMA  American Medical Association
CHSWC  Commission on Health and Safety and Workers’ Compensation
DEU Disability Evaluation Unit
DIR California Department of Industrial Relations
DWC  Division of Workers’ Compensation


y disabled workers were found to be highly sensitive to economic conditions at the time of injury: Permanently disabled workers’ labor market outcomes deteriorated sharply during the Great Recession of 2008–2009 and showed little sign of recovery thr


ement rates, a major reform
package known as Senate Bill (SB) 863 was enacted late in 2012 (DIR, 2017b). SB 863 included changes to the methods used for calculating final disability ratings that increased disability ratings for the majority of perma


e overall objective of this project is to provide workers’ compensation policymakers in
California with a basis for regular and ongoing monitoring of earnings loss and injured worker economic outcomes. DIR has contracted with RAND to carry out a thr


te. In addition to monitoring trends in earnings losses, a key component of this project is the development of a data set that can be used for more evaluation of specific policy changes in the workers’ compensation system when it is possible to defin


eriod, and, thus, they receive only medical care. However, three in ten injured
workers will receive some form of indemnity benefits as compensation for a longer spell of work
absence or for permanent disability.
10 Ultimately, around 15 percent of


ornia workers’ compensation benefits in calendar year 2015 (National Academy of Social
Insurance, 2017).
  6 Recent Developments in California Workers’ Compensation
The data used in this study include injuries occurring on or after January 1, 2005


27 and SB 228. AB 749, enacted in 2002,
also provided for increases in TTD and PPD benefits that were phased in between 2004 and 2006. A summary of the
provisions of these laws is available on the DIR website at http://www.dir.ca.gov/dwc/reform03.h


s such as these are unique to injured workers, other trends associated
with an economic expansion—such as increased rates of job turnover, transition, and wage growth—will affect all workers in the economy. In the following chapters, we describe how


itional modeling effort far beyond the scope of this monitoring report is necessary to say how the magnitude of earnings loss would evolve over time in the absence of policy changes.
Earnings loss estimates provide a single summary measure of the e


t
that neither the insurance industry nor state workers’ compensation agencies have mechanisms in place to collect individual-level data on outcomes for injured workers after their involvement with the workers’ compensation system ends. This informa


oss for earlier injury years
• comparison of differences in earnings loss levels and changes across subgroups of
injuries.
Updates to this monitoring report will be produced in the summer of 2018 (covering 2014–
2015 injuries) and in the summer of


onic Data Interchange versions of the First Report of Injury (FROI) and Subsequent Report of Injury (SROI). In 2007, DIR began collecting transaction-level medical treatment data at the claim level, including procedures, diagnoses, and payments. In a


ase.
Unfortunately, compliance with reporting standards remains imperfect, and the data reported
on the SROI are less complete than what are reported by the FROI; the most recent assessment
published by the Division of Workers’ Compensation (DWC)


be captured in the Base Wage File. The Base Wage File represents the most accurate and comprehensive possible source of data on quarterly wage and salary earnings in California. The industries covered by UI are virtually identical to the industries c


s and employment because it is impossible to observe what an injured worker’s labor market outcomes would have been if he or she had not been injured. To illustrate this problem,
imagine that we had data only on an injured worker’s earnings before a


ve continued to resemble the earnings of control workers who had the same employer, same job tenure, and very similar trends in prior earnings. In effect, this method uses data to tell us how each injured worker’s earnings would have evolved over tim


timate true earnings losses. On the other hand, assuming that earnings would continue to grow at some constant rate requires the fairly strong assumption that the worker would not have experienced any other voluntary or involuntary disruptions in
em


lance
between the observable characteristics of the injured workers and their control group, comparing  17 post-injury outcomes between injured and control workers should deliver valid estimates of the
causal effects of injury—i.e., earnings loss.


group of key covariates relevant for our study
that can be observed for both injured and control workers: the quarterly earnings trajectory prior to the injury, job tenure, and employment status. We required that the control workers fall into
the s


bove, it is easier to find matched control workers at large employers with a larger donor pool. We construct ed sampling weights to produce weighted
estimates that were representative of the sample of workers with complete records (step 3) on the


 injury, injured worker
earnings started to converge toward control worker earnings, with injured workers earning
approximately $500 less than control workers by the eighth quarter after injury.

20


Injury
Total quarterly earnings for injured workersTotal quarterly earnings for control workersQuarterly Earnings
(Real 2014 Dollars)   21 analysis of the closeness of earnings between injured and control workers during the pre-injury
period.
Ear


injured and control workers in order to interpret the relative measures that we focus on in our presentation of results. We do note, however, that total employment or at-injury employment outcomes may be of greater interest for analyzing certain poli


ing that our data suggest long-term earnings losses for workers whose injuries did not result in permanent disability, other studies have documented comparable long- term earnings losses for workers deemed not to be permanently disabled by the worker


s, we focus on relative measures that present injured worker earnings and employment as percentages of the same measure for the comparison control workers. All our measures are constructed so that higher values represent better outcomes for injured w


 terms,
but it has the limitation that it does not provide any context for how the loss in earnings compares with a worker’s earnings potential: A $10,000 loss may represent a moderate work
interruption for a worker earning $100,000 per year but a


,NO K
MQJ
J
K∑ ]M,NS K
MQJ.								(4)
Absolute and relative measures of return to work can be defined analogously by replacing the
indicator for employment anywhere with indicators for employment at the at-injury employer.
Assessing Statistical


2013 for all injured workers. We then
describe the composition or case mix of injured workers in California between 2005 and 2013, including demographics, job characteristics, receipt of indemnity benefits, and selected injury
characteristics. Chan


le and local economic factors, change the composition of the labor
force over time, leading to changes in the size of the covered workforce and affecting the pool of
workers at risk of experiencing an injury. In particular, 2013 coincided with a ti


able
disability rating data are subject to significant data lags, and we do not examine disability ratings in this report.
Analyses that incorporate disability rating data are planned for the final policy report under this project, by which time th


n 2005–2007 to 78 percent in 2010–2012. The trend toward higher
job tenure at injury began to reverse in 2013: The share of injured workers with one year or more  32 of job tenure fell slightly to 76 percent among 2013 injuries. A shift toward injur


ely 97 to 98 percent of what they would have earned
without an injury.
The second line on the chart shows trends for workers who received indemnity payments but
did not have a permanent disability. Relative earnings for this group range between 8


reasing shares of female injured workers, increasing age, and declining pre-injury wage persist among workers with indemnity
benefits as in the overall analysis sample. In all years, however, workers with indemnity benefits tend to be older, are sli


er
outcomes began to improve in 2013. However, we caution that these improvements could be due
in part to changes in the composition of workers in 2013 or to more workers entering the system with lower-severity claims. In particular, we note that f


s associated with wage loss among permanently
disabled workers in previous studies, these analyses have generally been guided by specific
research questions centered on benefit adequacy and have not encompassed the broader population of workers wit


10a–A.10g in the appendix. As described in Chapter 2, we also tested for
statistically significant differences between group-specific trends and overall average trends. However, the majority of these tests for trend breaks by industry and time perio


jured at very large firms (those with
more than 5,000 workers) and 8.3 percentage points lower than average for workers injured at
very small firms (those with ten or fewer workers). While the concentration of public-sector employers in the largest


ase in 2010, approaching the trend for the first and third wage quartiles by 2013. The first
and third wage quartiles trended  between approximately 85 percent and 75 percent between 2005
and 2013.24
Figure 4.3 . Earnings Loss by Pre -Injury Earni


c injuries, cumulative injuries, multiple injuries, and other injuries. Specific injuries account for about four in five claims, and so the trend for specific injuries tracks with the overall average closely. Workers with cumulative injuries experien


Injuries

SOURCE:  2005– 2013 WCIS -EDD data.
NOTES: Relative earnings are defined as the ratio of injured worker earnings to counterfactual earnings in the
second full year after injury (5–8 quarters after quarter of injury). The gray line ind


 reflect relative earnings for the indicated
subgroup.
Cumulative Injuries in Southern California
Across all of these subgroup analyses, one of the most striking patterns is the low relative
earnings for workers with cumulative injuries. This fac


up analyses here demonstrate that these gains were not shared equally
among all workers. Workers in industries that were especially hard-hit by the recession
continued to experience larger earnings losses than workers in other industries. Workers w


verage worker who is involved in the workers’ compensation system . The fact that post-
termination claims may contribute to the cumulative trauma trends provides another reason why
the wage loss trends in this report should not be interpreted as a


 W e found lower relative earnings and
employment rates for workers who received indemnity payments, and we did not observe any improvement in labor market outcomes for workers with permanent injuries in 2013, compared with workers with permanent in


th cumulative injuries in particular as a group that
warrants further attention in this region.
Based on these findings, we conclude with two main policy conclusions.
First, this initial monitoring report provides a benchmark against which to me


 workers in the quarter of injury.
3. Screen out control candidates whose job tenure at the at-injury firm did not match the injured worker’s tenure. We defined tenure as the number of consecutive quarters prior to
injury with positive earnings at


ing on consecutive tenure at the at-injury firm mitigates this concern.
Match quality can be evaluated using statistics that summarize the distance between the
injured and control workers. In order to evaluate the quality of our match, we produce t


by Injury Cohort
Imbalance  2005– 2007
Injuries  2008– 2009
Injuries  2010– 2012
Injuries  2013
Injuries
Average  0.95%  1.16%  0.57%  0.35%
Average absolute  5.22%  5.21%  3.88%  3.59%
Average elementwise absolute  11.96%  10.91%  9.53%


we defined categories of workers by age, gender, region, self-insured status, injury year, and quartiles of WCIS weekly wages. Weights are defined as the inverse proportion of workers in our sample with complete records on each of these variables and


s across the four time
periods using the following specification:
Relative 	Earnings ^=𝛼+∑ 𝛽^E
b<c𝟏{Calendar 	quarter 	of	𝑞=𝑟}+
𝛽k:𝟏{Calendar 	year	of	𝑞>=𝑄12008 }+
𝛽5k𝟏{Calendar 	year	of	𝑞>=𝑄12010 }+
𝛽5r𝟏{Calendar 	year	of	𝑞>=𝑄12013 }+𝜀^          (


esis tests, and thus uncorrected
inference would run a high risk of identifying false positives (i.e., Type I errors) in terms of group differences. Coefficients for these interaction terms are shown in Tables A.10a–A10e.
Supplementary  Results
T


 Permanent Differences in Relative Earnings by Region and Benefit Type
 Region  Indemnity  PPD Indemnity, Not PPD
Bay Area  0.0263  0.0078  0.0115
 (0.0031)  (0.0049)  (0.0028)
 [0.0000]**  [0.1219]  [0.0002]**

Central Coast  0.0004  –


Full tenure  0.0082  0.0169  0.0022
 (0.0008)  (0.0007)  (0.0011)
 [0.0000]**  [0.0000]**  [0.0514]

 NOTE S: Table shows linear regression coefficients from Equation 7 , standard errors in parentheses, and unadjusted
p-values i


Upper extremities  –0.0096  –0.0137  –0.0034
 (0.0033)  (0.0033)  (0.0048)
 [0.0066]  [0.0002]**  [0.4891]

NOTE: Table shows linear regression coefficients, standard errors in parentheses, and unadjusted p-values in
brackets. ** ind


ptember 2007a. As of June 29, 2018: http://www.dir.ca.gov/dwc/reform03.htm
California Department of Industrial Relations, “Assembly Bill 749,” September 2007b. As of
June 29, 2018: http://www.dir.ca.gov/dwc/AB749.htm
California Department of Indus


 UC DATA/Survey Research Center, 2007.
Pace, Nicholas M., and Julia Pollak, Provider Fraud in California Workers’ Compensation:
Selected Issues , Santa Moni ca, Calif .: RAND Corporation, RR-1703-DIR, 2017. As of June
5, 2018:
https://www.rand.or


ions, "Injury Description Table—
Part/Nature/Cause," undated. As of August 13, 2018:
www.wcio.org/Document%20Library/InjuryDescriptionTablePage.aspx
Workers’ Compensation Insurance Rating Bureau of California, Analysis of Changes in
Indemnity Cla


CORPORATION
RAND Model of
Automated Vehicle Safety (MAVS)
Model Documentation
Nidhi Kalra and David G. GrovesLimited Print and Electronic Distribution Rights
This document and trademark(s) contained herein are protected by law. This representation o


ies might shape these factors to improve safety now and over time. In particular, this model was used in the 2017 report The Enemy of Good: Estimating the Cost of Waiting for Nearly Perfect Automated Vehicles to assess how safe highly automated vehic


hese issues. Each year, RAND’s president draws on this generous gift to help RAND research and outreach teams extend the impact of completed research.
Support for this project is also provided, in part, by the income earned on client -funded
resear


redict the pace of
future diffusion or the rate of learning of HAVs. Nor does it predict how safety will evolve among non-HAVs. Predicting technological diffusion, human behavior, and future interactions of new technology in real-world transportatio


are enormously grateful to
Kenneth Kuhn at RAND and Kara Kockelman at the University of Texas for their reviews. We thank Charles Zwick for his generous support of RAND through the Zwick Impact Awards, without which this report and related materials


-diverse) miles that HAVs drive, the more potential there is for improving the state of the art in HAV safety performance. This means that near-term choices about when HAVs are introduced will also

1


ese factors may interact and result in different road safety outcomes over time.
Importantly, the Model of Automated Vehicle Safety (MAVS) does not predict the pace of
future diffusion, the rate of learning of HAVs, or how safety will evolve among


t?
• How do we define a future with HAVs and a future without HAVs?
• How do we measure road safety?
• How has future road safety been assessed in the literature?
What Is a Highly Automated Vehicle and What Is Not ?
The 2016 SAE International ta


enhanced by active
safety systems.

The human driver is entirely responsible for driving, even if such features as active
electronic stability cont rol are available and engaged.
1 Driver assistance  The sustained and operational design domain


n driving automation system
or feature thereof is designed to function.”

In contrast, common to Level 3 (conditional driving automation), Level 4 (high driving
automation), and Level 5 (full driving automation) is that the vehicle is responsibl


ehicle crashes.
MAVS can be used to explore safety in any of these terms: The model equations we describe
refer only to a generic performance rate, which can reflect any of these metrics, and the model equations remain the same regardless. Similarl


is, incidents per VMT) of both non-HAVs and HAVs to change in this time frame based on users’ inputs. The model does assume that VMT and safety rates will change smoothly over time, but, in reality, there will likely be punctuated periods during whi


adoption of those technologies. Going a step further than prior work, Li and Kockelman (2016) estimates both the types and severity of crashes that could be avoided by each type of technology, as well as the economic benefit of those savings. In rec


the full effect of many new technologies deeply uncertain.
Second, these existing estimates also compare the marginal benefits of a technology with the
safety performance of current vehicles and drivers. However, the benefit of a vehicle with a pa


s
suggest, for example, that from 2014 to 2044, total VMT could grow between 0.53 percent and 0.65 percent annually (Federal Highway Administration, 2016). While these bounds are themselves uncertain and include potential changes from HAV use,
8 the


s2069 = 0.7 x 10–8.
Figure 3.2. Historical Fatality Rates and Two Scenarios of Future Fatality Rates

Highly Autom ated Vehicle Safety Rate over Time
Calculating the number of safety events f is straightforward once annual VMT and safety
rates


unction reasonably approximates more-detailed scenarios of HAV diffusion that have been developed by others, such as Bansal and Kockelman (2017) and Litman (2017). A standard logistic function is described by Equation 4.2:

 𝑓(𝑥) =
,
,IJKL . (4.2)


centage of baseline miles that will be
driven by HAVs, we consider how VMT may change as a result of the diffusion of HAVs.
HAVs are widely expected to increase miles traveled because they lower the cost of travel, can provide travel opportunities


re with HAVs as they
would in a future without HAVs.11 In other words, s n,x = s x as defined in Equation 3.2 in
Chapter Three. However, the number of safety incidents from non-HAVs will be lower than in
the baseline future because non–highly auto


ents as a metric for safety because it is unclear what standards of risk are
used and whether they are homogenous across all test drivers or developers.   22 existing vehicles (Tesla, undated). Tesla calls this “fleet learning”—that is, when an enti


15  (NHTSA, 2017 ). The figures
in this report are based on the earlier 2015 estimat e.  23 Figure 4.3. Scenario of the Relative Fatality Rate of HAV s as a Function of Cumulative Miles Driven
by HAVs

NOTE: This figure assumes that the initial


ded during their time on the road. Therefore, this case is best
thought of as a useful computational bookend rather than a bookend of the actual future of fleet improvement.  26 Figure 4.5. HAV  Fatalities with Perfect and with No Fleet Improvement


uction in the rate? t0.99 1 trillion  1 trillion  1 trillion
How upgradeable is the existing HAV fleet?  w 0.5 0.5 0.5

The baseline future without HAVs reflects a compounding annual growth rate of 0.6 percent
with a 30-percent decline in fatali


ance sooner than in the other scenarios.
18
This illustration is not to suggest that early diffusion of lower-performing technology will
unfold as it has in this scenario, or that any of these scenarios will indeed occur. As just one example, pub


f roads ; and, most of all, behavior changes, such as changes in seat belt
use, distracted driving, or driving while intoxicated.
• The timing and extent of HAV  diffusion. This input will be shaped by regulatory
choices, such as whether to have s


 𝑏 = 𝑥 H'− 𝑚𝑥 H . (A.4)
 We can further solve for x’
a and x’ 1–a by taking the inverse of the logistic function:

 𝑥 =	−ln,2x($)
x$ . (A.5)

Because of the symmetry of the standard logistic function, this simplifies to

 𝑚 =-	RS	(<KO
O)
$<KO


 𝑅𝑡= 𝑅 h+ (𝑅 i− 𝑅 h)𝑒2f(Kkl	(Q)
mQ) . (B.4)

   39 References
ABI Research, “ABI Research Anticipates Accelerated Diffusion of Automotive Software Over-
the-Air Updates with Nearly 180 Million New SOTA-Enabled Cars Shipping Between 2016
and 202


ipping the Trickiest Thing About Self-Driving Cars,” Wired,
November 10, 2015. As of January 17, 2017:  https://www.wired.com/2015/11/ford-self -driving-car-plan-google/
Eccles, K., F. Gross, M. Liu, and F. Council, Crash Data Analyses for Vehicle-


merce Committee, Subcommittee on Digital Commerce and Consumer Protection, Santa Monica, Calif.: RAND Corporation, CT-463, February 14, 2017. As of September 8, 2017: https://www.rand.org/pubs/testimonies/CT463.html
Kalra, Nidhi, and David G. Groves


ry 17, 2017:
https://www.transportation.gov/sites/dot.gov/files/docs/ AV%20policy%20guidance%20PDF.pdf
———, 2016 Fatal Motor Vehicle Crashes: Overview, Washington, D.C.: U.S. Department of
Transportation, DOT HS 812 456, October 2017. As of Octob


.house.gov/meetings/IF/IF17/20170214/ 105548/HHRG-115-IF17-MState -W000791-20170214.pdf
Waymo, Report on Autonomous Mode Disengagements for Waymo Self-Driving Vehicles in
California, Mountain View, Calif., December 2016.  https://www.dmv.ca.gov/por


SOCIAL AND ECONOMIC WELL-BEING
Access to Medical
Treatment for InjuredWorkers in California
Year 2 Annual Report
Kandice A. Kapinos, Madeline B. Doyle, Mark Barrett, Cheryl K. Montemayor,
Andrew W. Mulcahy, Denise D. QuigleyLimited Print and Electr


for High -Quality Research and
Analysis (available at www.rand.org/standards ). We thank Philip Armour (RAND Corporation)
and Frank W. Neuhauser (University of California , Berkeley)  for serving as reviewers on this
report. Irina Nemirovsky and G


... ..............................  45

    viii Summary
The California’s Workers’ Compensation (WC) program provides  medical care and
indemnity (e.g ., wage-replacement) benefits to workers who suffer on -the-job injuries and
illnesses. The


the state of California have adequate access to care. We found about 70,000 unique
providers treating injured workers, which is significant relative to the 110,000 licensed
physicians and surgeons in the state  (Medical Board of California, 2016)


ion Schedule
(MTUS) guidelines; 31  percent reported not knowing what the MTUS guidelines were.
Excluding those who did not know what the MTUS guidelines were,  only 27 percent reported
regular use of the guidelines. A pproximately 83 percent of


cond, util ization and payments were relatively stable across most provider types, except
acupuncturists, occupational medicine physicians, and physical therapists, all of whom
experienced statistically significant increases in the number of claims


occupational medicine
OT/PT occupational therapist or physical therapist
ORTHO orthopedic medicine
RFA request for authorization
TAG Technical Advisory Group
TIN Taxpayer Identification Number
UR utilization review
WC Workers’ Compensat


ot conducting causal
analyses. In other words, although we are reporting averages and medians over time, we are not
examining the extent to which any changes are attribu table to policy or other changes.
 2 Outline of Report
The report is orga


ploratory and
descriptive; it is not a causal investigation of the effects of those reforms. Moreover, because of
the inherent difficulties in obtaining a representative sample of physicians and adequate response
rates, we recommend that future s


note that there are novel questions relevant to newer policies and
requirements.
Table 2-1. Provider Survey Domains and Corresponding Survey Items
Survey Doma ins Corresponding Survey Items
Total patient volume  Q1–4
About your WC practice  Q


actice was in a m etropolitan or nonmetropolitan  statistical
 6 area; and (3) whether the physician had five or fewer injured worker claims (“low volume ”) or
six or more (“high volume”) during the period .
We administered the survey to a total


r,
change in provider employment status (e.g., retirement) , change in busin ess address, or other
reasons. Researchers received 247 completed surveys across the two surv ey waves, with
approximately 57  percent of those received in the first surv


location
and about 29 percent practiced in two or three locations. Physicians were asked to provide an
approximation of the proportion of their patients’ primary co verage sources within each of the
following categories: Medicare, Medicaid, commer


SOURCE: Physician Experience Survey on Treating Injured Wor kers, fielded by the RAND Corporation.
Treating New WC Patients
Approximately  70 percent of survey respondents reported having limits on which new WC
patients they accept ed to treat (


electe d each benefit or
wrote in response s. The percentages presented represent the number of respondents who selected
each advantage or wrote in another advantage  or disadvantage divided by the number of
respondents who answered this question


ee Figure 2-5).

 13 Figure 2-5. Physician’s Easy Access to MTUS Guidelines

SOURCE: Physician Experience Survey on Treating Injured Workers, fielded by the RAND Corporation.
NOTE: Numbers and percentages  are rounded.
Referrals and Au


ds an d speaking to
a peer reviewer  (see Figure 2-7).
Among physicians who requested authorizations, about 16 percent reported that none of their
RFAs were denied on medical necess ity grounds , and an additional 19 percent of physicians
indicat


) indicated that the IBR process is never or only occasionally effective in resolving
payment issues in a timely and reasonable manner.
WC physicians can also file a lien with the WC A ppeal Board for issues related to
entitlement of billed amoun


tients, for a wide
variety of reasons, but most commonly because of  (1) issues with authorizations and UR denials
(46 percent), (2) paperwork and/or administrative issues (39 percent), and (3) payment levels
(23 percent).
About 60 percent repo


 863 in 2012 to improve
access and  give the DWC  more oversight.
One study found that use of network providers increased from 5 5 percent of injuries from
2000 to 2002 to 8 0 percent of injuries from 2009 to 2011, but more recent analysis using


ci an or provider in their
networks using the following categories: primary treating physician (PTP), orthopedic medicine
(ORTHO), chiropractic medicine (DC), occupational medicine (OCCM), acupuncture medicine
(LAC), psychology (PSYCH), pain speci


0002001 to
40004001 to
80008001 to
1200012001 to
1600016001 to
2000020000 +Number of MPNs
Number of Unique Providers Within Network
 21 We also examined the number of unique provider locations (addresses) within each MPN and
found that, on average,


ctor/
Accupuncture
Emergency
Orthopedic
/other surgery
Podiatry
Behavioral
health
Physical therapy
and rehab
Other MDs
RN/PA/Other
providers
Other
Percentage of Providers Participating in
11+ MPNsMean Number of MPNs
 23 Figure 3-4. Number of Unique


(population of 58,792)  (California Health Care Foundati on,
2017). The total number s of PCPs in these counties range from 2 to 16. Overall, the physician
supply in these counties is low. Injured workers participating in MPNs in these counties,
h


per 100,000
population
Specialists per
100,000 population
Occupational
Medicine
Orthopedic
Medicine
Pain Specialty
Medicine
Physical Medicine &
Rehabilitation
Psychiatry
Psychology
All Other
Lake  42 60 — 1 — — — 2 17
Lassen


counties or
could seek care for those services out of network. Future analysis of the claims data that includes
details on MPN participation should be conducted  to understand this better.
Table 3-4. Summary  of the Total Number of Providers per


on that payment for billed m edical services is denied. Each medical bill includes
information on the injured worker and provider, diagnosis and procedure codes, the provider’s
charges, actual payments to providers, and codes e xplaining the reason


ether changes over time are
“true” trends or whether they are an artifact of the data changes. One significant change is that
the rate of matching first reports of injuries to medical records  has improved over time . In an
earlier RA ND study, we


rn   X
SOURCE: RAND  analysis of WCIS data .
Table 4-2. Main Analytic Sample Sizes

2012  2013  2014  2015
Service Year Sample
Number of Claims  846,649    852,260    909,227    843,162
Number of Bill Lines  23,003,501  22,875,062  2


ample siz es are relatively large  (Benjamin et al., 2018 ).
In some analyses, we stratified by important measures for which there may be variation in
access. In those analyses, we examined vari ation by (1) provider specialty —categories  are lis


%
Emergency medicine
physicians  84.60  74.89  69.28  63.42  –21.18  –25%
Family medicine and general
practice physicians  280.72  249.93  271.55  239.40  –41.33  –15%
Internal medicine physicians  75.83  91.28  88.74  73.49  –2.34  –3%
Neur


cian’s
permanent and stationary reports, consultation reports requested by the WC Appeals Board, and
several medical legal codes.
Table 4-8. Percentage of Injured Workers with Non -Zero Utilization by Service Category and Year
(2012–2015)
Servi


ED settings.

 45 Figure 4-2. Median and Mean Wait Times from Injury to First Visit, by Year

SOURCE: RAND  analysis of WCIS data from 2012 to 2015 .
NOTE: Means in 2012, 2013, and 2014 are all statistically different from the 2015 values (


 not all WC claims are
reported into the system. Additionally, among reported cla ims, there is underreporting of initial
medical visits and of medical bills. As a result, our results are from WCIS -reported injuries and
medical bills only and can


a 36-percent dec line),
surgery (a 39-percent decline) , and DME  (a 57-percent decline)  all decreased significantly, while
the bills for “other or unknown ” service types increased significantly  (by 332 percent). These
changes largely correspon


ew WC patients they
accept (70 percent), which is high but consistent with the earlier WC surveys in both the state of
California and elsewhere  (Texas Medical Association, 2016 ). The primary reason for limiting
new WC patients was the administra


in the total number of providers by specialty listed in
an MPN, but more research is needed to determine the effect of MPN participation on access for
injured workers .
Analysis of WCIS Claims
Our analysis of WCIS claims data from 2012 to 2015 (


ough our analysis of the WCIS claims data primarily showed stable trends in the
numbers of providers, utilization, and payments, we highlight the following findings that
emerged across the analyses in this report that are worth further  study.
Fi


ative treatments, neurology, physical
medicine), drugs, and E&M  visits declined and the number of “other or unknown” services
increased.  These trends should be followed and investigated further to determine the causes of
these changes.
Althoug


alty=6;   ** emergency medicine ;
else if pps4 in ('207Q', '208D') then specialty=7;   ** family medicine general practice ;
else if pps4='207R'               then specialty=8; ** i nternal medicine ;
else if primary_taxonomy_nppes='207W00000X'


rs’ Compensation: Databases,
Publications, Reports and Schedules,” webpage, last updates October 2018. As of November
1, 2018:
https://www.dir.ca.gov/dwc/dwcrep.htm#3
California Health Care Foundation , “California Maps: How Many Primary Care an


nia Workers’ Compensation System, 2006 , Los Angeles: UCLA Center for He alth
Policy Research,  December 2006. As of November 1, 2018:
https://www.dir.ca.gov/dwc/AccessMedTreatmentReport2006/AccessToMedicalTreatmentIn
CAWC2006.pdf
Martin, P., Th


iation , Survey of Texas Physicians: Research Findings , Austin, Texas,
2016. As of November 1, 2018:
https://www.texmed.org/uploadedFiles/Curren t/2016_Advocacy/2016_Physician_Survey_Fi
ndings.pdf
UCLA Institute for Digital Research and Educati


Business Bribery Risk
Assessment
Karlyn D. Stanley, Elvira N. Loredo, Nicholas Burger,
Jeremy N. V. Miles, Clinton W. Saloga
CORPORATIONLimited Print and Electronic Distribution Rights
This document and trademark(s) contained herein are protected


symposium for global companies, Anti-Corruption Regulations in Emerging and Expeditionary Markets: New Markets, New Challenges.
This version of the report, RR-839-1-TII, replaces the earlier version, with corrected data for
Total Risk Score and Dom


ment officials, and governance and corruption measurement. Next, we interviewed U.S. regulators, multinational companies, international consulting firms, international non-profit organizations, and global law firms to understand the processes corpora


hank our external reviewer, Charles E. Duross, who heads the global
anti-corruption practice at the law firm of Morrison and Foerster. Mr. Duross most recently served as a Deputy Chief in the Fraud Section in the Criminal Division of the U.S. Depart


rmation on bribery risk can lead to less-than-adequate compliance programs, exposing firms to the potential risk of violating anti-corruption laws, or to an overly aggressive and costly approach. We have developed a new index, the TRACE Matrix, for b


r retain business.”
6 DOJ and SEC guidelines refer to this as the “business purpose
test.”7 Alexandra Wrage explained in her book, Bribery and Extortion: Undermining Business,
Governments and Security, that a bribe need not be cash.8 She provided e


The problem that our research addresses is that bribery of foreign government officials is
prohibited by the FCPA and other international anti-corruption laws, but businesses have inadequate measures to assess this risk on a country-specific basis


y and industry sector,  the business opportunity, potential business
partners, level  of involvement with governments, amount of government
regulation and oversight, and exposure to customs and  immigration in conducting
business affairs. When as


’s risks chang e over time.  The Bribery Act 2010 Guidance , London:
Ministry of Justice, March 2011, pp. 20 –31.   5 government, media, local business community and civil society effectively to promote
transparent procurement and investment polici


n earnings growth, which has  grown elevenfold since 1980.”
34 Shearman & Sterling LLP, FCPA Digest: Recent Trends and Patterns in the Enforcement of the Foreign Corrupt
Practices Act , Memorandum, January 2014, pp.  2–4.
35 Shearman & Sterling LL


ountry’s rank indicates its position relative to the other countries and territories included in the index.
41 There is no information available on a country basis

36 Elvira N.  Loredo , Kar lyn D. St


ss risk across thousands of intermediaries, I don’t know that you have the time or resources to look at all the specifics.” A general counsel stated, “We are trying to control the behavior of thousands of employees around the world. Someone is going


rement of the level of state ownership in businesses, the level of enforcement of anti-bribery laws and the industry sectors that are the subject of enforcement, and a government requirement for foreign businesses to have a local partner or agent. On


iness representatives we interviewed identified this domain as the “biggest piece missing from the CPI.”
47 The third domain, which addresses
government and civil service transparency, includes indicators concerning whether government budgets are pu


olders included U.S. regulators, global non-profit institutions, general counsel of global companies, partners at international consulting firms, and partners in global law firms who represent clients in FCPA investigations and compliance matters. We


example, the World Bank’s Worldwide Governance Indicators (WGI) uses a sophisticated data aggregation approach to combine nearly every known data source on corruption and governance, but the WGI is not designed specifically for business bribery risk


93) in Domain 1, business interactions with government.
• Thailand: CPI scores Thailand as 65 and the TRACE Matrix scores Thailand as 45. Thailand receives moderate scores across all four TRACE Matrix business-related domains.
• Indonesia: CPI sc


he target
audience and application: multinational firms managing business bribery risk.
2. The index should use publicly available data to allow users to examine the data that support the index.
56
3. The analytic process used to aggregate the und


mains and then aggregated to
produce an overall score.
Table 2.1. Domains and Their Respective Subdomains
Domain Subdomains
1.0 Business Interactions with Government  1.1 Contact with Government
1.2 Expectation of Paying Bribes
1.3 Regulator


 on risk (e.g., a high-quality civil service or strong non-governmental oversight), in which case it may not need an abundance of legal mechanisms to control corruption.
The fourth domain captures the role played by extra-governmental actors in moni


nce and corruption. We chose 64 indicators so the TRACE Matrix was not disproportionately driven by any one data source (e.g., WGI, Enterprise Survey).
60 Additionally, where many data sources had gaps in
country coverage, by selecting a range of in


How many laws address the United Nations Convention Against Corruption, Chapter 2, Article 5?”
Another challenge was presented by composite or aggregated data, which combines elements
of other indices to fully or partially construct scores (e.g., E


ocedure for calculating a total risk score was as follows: First, we standardized every
indicator and then calculated the average score for all items within a subdomain for each country to give subdomain scores. We then re-standardized subdomain sco


h of the value of our index comes from the ability for users to draw
on the underlying domain and subdomain scores, we first present a summary of the overall risk for the 197 countries in the TRACE Matrix. Figure 3.1 displays the top-line risk score


 190. Chad (83)
• 191. Cameroon (83)
• 192. Burundi (83)
• 193. Cambodia (88)
• 194. Uzbekistan (90)
• 195. Yemen (95)
• 196. Nigeria (95)
• 197. Angola (97)
B. Guide to the Country Risk Scores
Table D.1 in Appendix D presents the t


ption problem, but might also reflect a lower risk environment because of rigorous enforcement. India has a domain score of 43, which is fairly low risk. India’s score in subdomain 2.1 indicates a fairly good number and quality of anti-bribery laws.


 4. Conclusion
In this report, we have introduced the problem of global business bribery, discussed
international efforts to combat it, reported business views, and explained the new business
bribery risk indicators we have developed and modeled


69 Shearman & Sterling LLP, 2014, pp.  2–4.   27 governmental oversight. Each domain includes multiple subdomains that capture specific risk
factors and align with specific sources of data to measure risk.
We inves


hapter Three. Among other questions, we asked:
1. What countries do you operate in? How long have you operated in these countries?
2. Describe the process your company uses to perform country risk assessments.
a. What types of risks do you assess?


 take all the prophylactic measures we can.”
A senior compliance counsel at a multinational company that operates in approximately 40
countries and sells into approximately 200 countries through third parties outlined the factors her company looked


national consulting firm stated that, among his clients, “many companies
are only factoring in CPI.” He explained that many companies “take [CPI] and incorporate a bunch of internal information—product, sales, and so forth—easier things to rank from


e—specifically, whether it included government customers and commercial organizations that have government involvement. He also pointed to key factors such as management by expatriates or local management and whether there would be reliance on a dist


ou worry about FCPA, you include domestic corruption enforcement” in the calculation
of business bribery risk.
A partner in an international consulting firm echoed these observations. He stated that “the
biggest missing piece from CPI is the level


counsel stated, “We are trying to control the behavior of thousands of employees around the world. Someone is going to panic and pay a bribe. It should not happen but it does, even with good  33 employees and good companies.” He explained that a new


orld Bank Ease of Doing
Business http://www.doingbusiness.org  2013 Procedures to enforce a contract (number)
Procedures to register property (number)
Start- up procedures to register a business (number)
Labor Force Share of Government Employm


servants to recuse
themselves from policy decisions where their personal
interests may be affected.
Is there legislation criminalizing corruption?   36 United Nations Convention Against Corruption  Article -
Specific Laws  United Nations  Legal L


orldjusticeproject.org/rule -of-law-
index  2012

Domain 4: Capacity for Civil Society Oversight
     4.1 Quality and Freedom of Media
Freedom of the Press  Freedom House Freedom of the
Press  http://www.freedomhouse.org/report -
types


as follows. First, to ensure that the different variance of each item
did not influence its relative importance in the total score, each item within a domain was standardized to have a mean of 0 and standard deviation of 1. Simple aggregation at thi


mputation is a technique  designed to obtain unbiased parameter estimates in the presence of missing
data, and to provide information about the level of uncertainty in those data. A simplified explanation follows: For
each variable that has missing


ribery experts, and the experience of TRACE International, whose experts have over 20 years of experience in assessing business bribery risk. We decreased the weight of domain 2, anti-bribery laws and enforcement, based on the lack of reliable data c


Score Domain 1:
Interactions with Government  Dom ain 2:
Anti-B ribery Laws
and Enforcement  Domain 3:
Governmental
and Civil Service
Transparency  Domain 4:
Capacity for Civil
Society Oversight
Variance
Pena lty Dom 1
Overall
Ri


in 3:
Governmental
and Civil Service
Transparency  Domain 4:
Capacity for Civil
Society Oversight
Variance
Pena lty Dom 1
Overall
Risk  d1.1 d1.2 d1.3 Dom 2
Overall
Risk  d2.1 d2.2 Dom 3
Overall
Risk  d3.1 d3.2 Dom 4
Overall
R


1 39 48 55 41 65 71 61 66 48 78 3.9
Zimbabwe  75 58 46 61 56 29 19 39 88 84 86 74 55 86 7.4

   58 Appendix E:  List of Reviewed Data Sources
Table E .1. List of Reviewed Data Sources
Source Indicator  Countries  Years  Website
Afrobarometer


iik.de/en/konfliktbarometer/index.html
Heritage Foundation  Index of Economic
Freedom  169 1995– 2014 http://www.heritage.org/index/
United Nations
Development
Programme  Human Development
Index 186 1990– 2013 http://hdr.undp.org/en/statistic


Competitiveness
Reports  148 2005– 2011 http://www.weforum.org/issues/global -competitiveness
World Economic Forum  Global Risks Report Global  2006– 2013 http://www.weforum.org/issues/global -risks
World Bank  Worldwide
Governance
Indicators


2 Brazil
153 Guinea -Bissau
154 Timor -Leste
155 Ethiopia
156 Afghanistan
157 Libya
158 Iran
159 Syrian Arab
Republic
160 Cote d'Ivoire
161 Tanzania, United
Republic of
162 Gabon
163 Mongolia
164 Laos
165 Congo
166 Sierra Leone


International Business Transactions, OECD Publishing, 2011.
Rao, Sumedh, “Critique of Government Assessment Applications,” Helpdesk Research Report,
Governance and Social Development Resource Centre, July 30, 2010.
R Core Team, R: A Language and


SOCIAL AND ECONOMIC WELL-BEING
2018 National Survey of
Health Attitudes
Appendix A: Survey Results Comparing
Populations by City Size
Katherine Grace Carman, Anita Chandra, Sarah Weilant, Carolyn Miller,
Margaret Tait
Prepared for the Robert Wood


ans need health care, do you think it  is easier or harder for them to get
the care they need than it is for White Americans, or is there not much of a difference? ............ 32
Q14b .When Latinos need health car e, do you think it is easier or h


izations impact the health of your
community. For each, please indicate what impact (positive, negative, or no impact) this group has on the health of your community. If the impact is both positive and negative, please ind icate
whether the overall


...................... 64
Q29. The next question asks about your health insurance or health coverage plans. In answering this
question, please exclude plans that pay for only one type of service (such as nursing home care, accidents, family plannin


itudes: Description and Top-Line Summary Data (Carman et al., 2019a) in Building a National Culture of Health: Background, Action Framework, Measures and Next Steps (Chandra et al., 2016), and at https://www.rwjf.org/en/cultureofhealth.html.
For th


ese cases, we note that chi-squared statistics were not calculated due to small cell sizes.
We offer no other analysis in this appendix beyond presenting the data as described. There is
a companion appendix examining differences for communities arr


8 0.7
All Groups  2.5 4.5 22.9 36.2 33.4 0.6
NOTE: Chi -square statistic  11.29  (p = 0.505)
  8 E. Neighborhood Options for Healthy Food and Exercise
Sample  1 (No Effect)  2 3 4 5 (Very
Strong
Effect)  Missing
Not a Town
(n = 971)  5.8 1


ives 0.7 0.4 0.4 0.5 0.5
Community safety  0.3 0.4 0.4 0.4 0.4
Race/ethnicity  . 0.2 0.3 0.6 0.4
Access to wellness technologies (Fitbit,
trackers etc.)  0.1 . 0.1 0.1 0.1
Refused  0.3 . 0.4 0.2 0.2
SOURCE: RAND.
NOTES: Chi -square statistic


16.86  (p = 0.155) .
  21 B. Second Highest Top Priority

Making sure
that the
disadvantaged
have an equal
opportunity to
be healthy.  Making sure
that healthy
foods are for
sale at
affordable
prices in
communities
where they
are no


24.3 3.2
NOTE: Chi -square statistic  16.31  (p = 0.178) .

E. Fifth  Highest Top Priority

Making sure
that the
disadvantaged
have an equal
opportunity to
be healthy.  Making sure
that healthy
foods are for
sale at
affordable
prices


0,000+)
(n = 1,901 ) 32.5 15.4 0.9
All Groups  33.8 15.2 0.8
SOURCE: Pew Research Center, 2012; revised by the RAND team to reflect health.
NOTE: Chi -square statistic  2.74 (p = 0.434) .
Q11b. Which of the following is the biggest reason that


.001 ).***  34 Q15a. In the U.S. today, people with lower incomes live on average 7.5 years less than
people with higher incomes. What do you think are the top three reasons why this is
the case? (Select three options)
The following tables each l


0 18.0
Midsized City
(50,000 –500,000 )
(n = 1,493)  81.8 18.2
Large City
(500,000+)
(n = 3,945)  82.6 17.4
All Groups  82.5 17.5
NOTE: Chi -square statistic  0.81 (p = 0.847) .
  37 I. Luck
Sample  Not Selected  Selected
Not a Town


.3
Small Town
(2,500 –50,000 ) 600 39.4
Midsized City
(50,000 –500,000 ) 1,142 39.6
Large City ( 500,000+) 3,057 37.2
NOTE: F-statistic  = 7.80 (p < 0.0 01).* **

   42 E. Elementary and High School Education
 Number of
Respondents Mean


 36.1 38.4 18.4 5.5 1.6
SOURCE: Chavis, Lee, and Acosta, 2008.
NOTE S: Chi- square statistic  61.96  (p < 0.001) .*** The order of questions 17A– 17P was randomized for each
respondent.

D. This Community Has Symbols and Expressions of Members


ent Leaders
Sample  Very
negative Somewhat
negative No
difference Somewhat
positive Very
positive Missing
Not a Town
(n = 971)  9.2 13.2 55.2 18.2 1.7 2.6
Small Town
(2,500 –50,000 )
(n = 749)  8.8 14.6 52.2 19.8 3.5 1.1
Midsized City


4
NOTE: Chi-square statistic  3.75 (p = 0.29) .

F. Ever served as an elected appointee or official
Sample  Yes No Missing
Not a Town
(n = 971)  7.8 89.9 2.3
Small Town
(2,500 –50,000 )
(n = 749)  8.6 89.1 2.4
Midsized City
(50,000 –


 12.81  (p = 0.383) .  60 Q21. Would you say that in general your health is excellent, very good, good, fair, or
poor?
Sample  Excellent  Very good Good Fair Poor  Missing
Not a Town
(n = 971)  9.2 36.3 35.5 14.1 3.9 1.0
Small Town
(2,500 –50


ause of a health issue for yourself
and/or others?
 Sample  Yes No Missing
Not a Town
(n = 971)  27.8 70.5 1.7
Small Town
(2,500 –50,000 )
(n = 749)  29.5 69.5 1.0
Midsized City
(50,000 –500,000 )
(n = 1,493)  25.8 72.3 2.0
Large City


of
government -sponsored assistance plan based on income or a disability [Medicaid name of state where
respondent lives]
Sample  Covered  Not covered  Not sure  Missing
Not a Town
(n = 971)  11.8 76.4 8.5 3.3
Small Town
(2,500 –50,000 )
(


 family member is ”: 0.25 (p = 0.969) . Chi-square statistic  for “No”: 0.08  (p
= 0.994) .
SOURCE : RAND .
NOTE: Because respondents were asked to select all that apply, the total may sum to
more than 100 percent .
Q31. How confident are you th


, Sarah Weilant, Carolyn Miller, and Margaret Tait,
2018 National Survey of Health Attitudes: Description and Top-Line Summary Data, Santa
Monica, Calif.: RAND Corporation, RR-2876-RWJF, 2019a. As of May 2, 2019:
https://www.rand.org/pubs/research


12.
Plough, Alonzo, Anita Chandra, Penny Bolla, Laura Leviton, Carolyn Miller, C. Tracy Orleans,
Tejal Shah, Margaret Tait, Matthew Trujillo, Joie Acosta, Katherine Carman, Tamara
Dubowitz, Laurie Martin, Christopher Nelson, Gery Ryan, Blair Smith


CORPORATION
What’s the Buzz?
The City-Scale Impacts of Drone Delivery
Andrew J. LohnLimited Print and Electronic Distribution Rights
This document and trademark(s) contained herein are protected by law. This representation of RAND
intellectual prope


nclude the following:
•Jia Xu’s research report on the design perspectives on delivery drones
•Kenneth Kuhn’s perspective on certification and traffic management of drones
•Therese Jones’s perspective on international drone regulation and drone deli


try and others, to understand what effects to anticipate and what options are available for ensuring the well-being of citizens without stifling innovation and business opportunities. The lack of precedent for large-scale delivery drones and the prop


, and noise also all benefit from an increased number of
drone centers. Drones delivering packages will depart from, and return to, their drone center,
which means that regions of a city near a delivery center should expect more drone activity over


on of drone delivery trip
𝐴!"#$ area of a city
𝐴! area of a delivery zone
𝐶! geometrical constant for truck deliveries
𝐶! geometrical constant for truck deliveries
𝐶! constant relating to aeronautical parameters
𝐶! constant relating to aeronaut


 benefits, convenience, and delivery of time-critical goods and services to difficult-to-reach places.
2 But there is also
potential for negative effects related to aerial congestion,3 privacy,4 noise,5 energy consumption,
and air pollution. Weighi


Acoustics , Vol. 74, No. 1, 2013; and  F. Farassat, “Linear Acoustic Formulas for Calculation of Rotating Blade
Noise,” AIAA Journal , Vol. 19, No. 9, 1981.
 2 white paper on its Uber Elevate service,6 but city-scale delivery drones provide diff


hough I am the sole author of this report, several people
contributed to the research contained herein, so I often employ we when describing research conducted or steps
taken.
 3 Organization of This Report
In the remainder of this report, we