Estimation of

Cocaine

Availability

1996-1999

Office

of National

Drug Control Policy
ii

December 2000

Estimation of

Cocaine Availability

1996-1999

Prepared for:

Office of National Drug Control Policy

Office of Programs, Budget, Research and Evaluations

under HHS contract no. 282-98-0006

Prepared by:

Abt Associates, Inc.

1110 Vermont Avenue, NW

Suite 610

Washington, DC 20005-3522
iii

December 2000
iv

Contents

Acknowledgements ..............................................................................................................................vi

Disclaimer ...................................................................................................................................vi

Public Domain Notice .................................................................................................................vi

Executive Summary..............................................................................................................................1

1. Introduction ................................................................................................................................1

2. The STAR Model........................................................................................................................2

Overview ......................................................................................................................................2

STAR Stage and Transition Details .............................................................................................5

Potential Production Versus Actual Production.................................................................5

Actual Production ..............................................................................................................8

Stage 1: Net Cultivation From Previous Growing Year ....................................................8

Stage 2: Net Cultivation in Current Year...........................................................................8

Stage 3: Net Leaf ...............................................................................................................8

Stage 4: Base Availability..................................................................................................8

Stage 5: Cocaine Availability at Labs................................................................................8

Stage 6: Cocaine Departing South America ......................................................................9

Stage 7A: Non-U.S./South American Markets ................................................................10

Stage 7B: Transshipment Area ........................................................................................10

Stage 8: Cocaine Availability at U.S. Border Entry Regions ..........................................11

Stage 9: Cocaine Availability at Domestic Retail Areas .................................................22

3. Applying the STAR Model to Existing Estimates of Cocaine Availability .........................26

Global Accounting Scenario ......................................................................................................27

Modified Global Accounting Scenario.......................................................................................28

Consumption Scenario ...............................................................................................................29

U.S.-Residual Scenario ..............................................................................................................31

Summary ....................................................................................................................................32

4. STAR Model Estimates (1996-1999).......................................................................................33

Actual Production Availability Estimates ..................................................................................33

Consumption-Based Availability Estimates...............................................................................34

Cocaine Departing South America ..................................................................................35

Summary ....................................................................................................................................38

5. Summary of STAR Estimates (1996-1999) ............................................................................40

6. Limitations of the STAR Model and Directions for Improvement......................................42

7. Conclusions ...............................................................................................................................43

References............................................................................................................................................44

Appendix A: Coca Cultivation and Potential Production Data......................................................46
v

Appendix B: STAR Model for Cultivation and Base Production Stages.......................................51

Appendix C: Application of the Border and Domestic Allocation Models, 1996-1998. ...............55

Appendix D: Sources For Enhanced Seizure Data ..........................................................................61

Information in FDSS Data..........................................................................................................61

Information in EPIC BLISS Data...............................................................................................63

Information in Customs Seizure Data ........................................................................................65

Information in Coast Guard Seizure Data ..................................................................................68

Appendix E: Technical Details of the Border Allocation Model ....................................................69

Transportation Costs ..................................................................................................................69

Transportation Cost Smoothing Model ......................................................................................71

Outliers.............................................................................................................................72

Technical Details of Border Allocation Model ..........................................................................74

Limitations of the Model............................................................................................................76

Appendix F: Technical Details of U.S. Domestic Allocation Model ...............................................78

Limitations of the Model............................................................................................................79
vi

Acknowledgements

Mary Layne, Patrick Johnston, and William Rhodes, and the staff of Abt Associates, Inc.,

conducted and reported this research for ONDCP, under contract number 282-98-0006, Task

order Number 13. Michael Cala, Office of Programs, Budget, Research, and Evaluation,

ONDCP, serves as the Project Director for this research.

The following people provided thoughtful comments in the development of the paper: Adrian

(Zeke) Wolfberg, Defense Intelligence Agency; Scott Decker, Professor of Criminology and

Criminal Justice, University of Missouri-St. Louis; Senior Special Agent Frederick Stacey, U.S.

Customs Service, Ruth Berg, Principal Analyst, GRC International, and Anne-Marie Bruen,

Associate, Abt Associates, Inc. John Lavin, Senior Programming Analyst, Abt Associates, Inc.

provided expert GIS programming support and Caben Chester, Analyst, Abt Associates, Inc.

contributed invaluable research support. Additionally, we gratefully acknowledge the substantive

comments provided on the final draft, by the following: Peter Felsted, Carolyn Hoffman-Travers,

Patrick Gartin, and Mark Eiler; Larry Keister Patrick Tarr; and Maurice Renfret.

The following agencies provided data for this project and their cooperation is especially

recognized: Drug Enforcement Administration, Defense Intelligence Agency, Crime and

Narcotics Center, National Guard Bureau, the U.S. Interdiction Coordinator's office, U.S.

Customs Service, Office of Naval Intelligence Counterdrug Division, and the U.S. Coast Guard.

Disclaimer

Information presented in this publication reflects the views of the authors and does not

necessarily reflect the official policy or position of the Federal Government.

Public Domain Notice

All information appearing in this report is in the public domain and may be reproduced without

permission from ONDCP or the authors. Citation of the source is appreciated. This report is

available online at <www.WhiteHouseDrugPolicy.gov>, by selecting “Publications”.

NCJ 187081
1

Executive Summary

The 1998 National Drug Control Strategy specified five goals and thirty-two supporting

objectives that will guide the government’s anti-drug program over the next decade. The

Strategy’s five goals amount to reducing the supply of and the demand for illicit drugs by 50

percent by year 2007. The nation’s ability to meet these goals depends on its efficacy at reducing

drug availability through source country programs, transit zone interdiction, and domestic law

enforcement.

Having adopted this assessment for monitoring the success of the nation’s anti-drug programs,

one critical input -- the topic of this paper -- is a reliable estimate of cocaine availability at

various points in cocaine’s flow from source to the United States. This report discusses a new

model – the Sequential Transition and Reduction (STAR) model -- that goes well beyond

predecessor flow models and provides the best current basis for measuring the flow of cocaine

from producer nations, through the transit zones, across the nation’s borders, and throughout the

U.S.

The STAR Model takes a systems approach and breaks cocaine movement down into a series of

"stages" based on the cultivation, production, transportation, and marketing of the product. A

stage is a step in the course of the flow process, associated with a geographic area. The figure

below provides a simple schematic of stages.

Illustration of discreet availability stages in cocaine’s movement from source to street

After setting the stage-structure to the flow, cocaine availability at each stage was estimated by

triangulating between three dynamic existing processes: 1) estimation of coca cultivation based

on overhead imagery, 2) estimation of cocaine departing South America based on tabulation of

Stage 9 - Domestic retail areas

Stage 8 - Domestic border areas

Stage 7 a - Non-US/Latin America markets

b - Transshipment areas

Stage 6 - South American departure areas

Stage 5 - Cocaine HCl labs

Stage 4 - Cocaine base @ growing areas

Stage 3 - Net coca leaf @ growing areas

Stage 2 - Net coca cultivation @ growing areas

(current year)

Stage 1 - Net coca cultivation @ growing areas

(previous year)
2

movement events, and 3) estimation of US consumption based on prevalence estimates and

cocaine price/purity trends. The model transitions availability at one stage to the next through

comversions or reductions based on data from multiple sources including, the Federal-wide Drug

Seizure System (FDSS), the National Household Survey on Drug Abuse (NHSDA), the

Consolidated Counterdrug Data Base (CCDB), the Arrestee Drug Abuse Monitoring (ADAM)

Program, the CNC coca cultivation figures, and DEA's System to Retrieve Information on Drug

Evidence (STRIDE).

Table 1 summarizes cocaine availability estimates developed by the STAR model for the period

1996-1999. Estimates for Stages 1-5 are based on coca cultivation figures and estimates for

Stages 6-9 are based on domestic consumption figures. The gray line represents a discontinuity

between stages 5 and 6, and highlights the lack of historical estimates of cocaine consumption for

South America.

Table 1

STAR Estimates of the Availability of Cocaine and Coca Precursors

Stage Description 1996 1997 1998 1999

1 Net Cultivation (haa) from Previous Growing Year 214,800 209,700 194,100 190,800

2 Net Cultivation (ha) from Current Growing Year 209,700 194,100 190,800 183,000

3 Dry Coca Leaf (mtb

) 306,782 267,663 239,435 203,305

4 Base (mt) 887 803 759 687

5 HCl Labs (mt) 841 774 702 666

6 Departure from South America (mt) 523 570 567 566

7A Non-U.S./LTAM Markets (mt) 69 73 91 108

7B Transshipment Area (mt) 382 385 376 336

8 Domestic Border Areas (mt) 333 337 338 301

9 Domestic Retail Areas (mt) 288 312 291 276

a hectares

b metric tons

In addition to the STAR Model, this document also reports the results of two new modeling

efforts, the Border Allocation Model and the Domestic Allocation Model. Development of the

Border Allocation Model and the Domestic Allocation Model are independent of the STAR

Model and are an attempt to describe the distribution of cocaine flow between stages, based a

minimization of transportation costs. That attempt at modeling the flow between stages should be

considered developmental and not a conclusive set of estimates.

The information presented in this paper will be useful to decision-makers interested in the

magnitude of cocaine at various locations of its flow from source to street. Other analysts will

also benefit from this research because it provides them with a connected and coherent set of

availability estimates to frame more detailed assessments of movement between stages. The

reader should be aware that various levels of uncertainty are present in each of the component
3

estimates integrated by the STAR Model; thus there is a level of uncertainty within the STAR

Model results. But this is to be expected. Drug smuggling is an illegal and covert activity, and

therefore not easily subject to controlled research conditions. Improvements in estimates will

only come through integration of multiple data sets, such as the STAR Model. Future efforts will

focus on this aspect of improving the model through integration of additional data sets.
1

1. Introduction

Estimation of cocaine supply in the U.S. is a difficult problem. The Global Accounting approach,

used since 1990 by ONDCP, starts with an estimate of coca cultivation provided by the Crime

and Narcotics Center (CNC) and sequentially reduces it by losses due to seizures, spoilage, and

non-U.S. consumption. This approach provides a useful macro approach to integrating multiple

data sources, such as consumption, seizure, and production estimates. A recently published

report on Global Accounting1 organized these data into zones (e.g., source, transit, and arrival) to

provide a general flow framework for quantifying cocaine at various stages of movement from

source to market.

ONDCP has funded research to expand upon this methodology. The STAR model, described

here, tracks the flow of cocaine hydrochloride (HCl) from cultivation in source country growing

regions, to consumption in the U.S. – although it could just as easily track backwards from U.S.

consumption to potential production estimates. It can incorporate various values – or scenarios --

and project the impact forward to U.S. consumption, backward to potential production, or to any

point in between. It contains a micro level component that makes cocaine flow projections by

geographic regions and conveyance types, while providing macro level estimates at various

stages.

Two new statistical modeling efforts -- the Border Allocation Model and the Domestic

Allocation Model -- are incorporated into STAR. These modeling efforts provide details about

the movement of cocaine arriving at the U.S. border and within U.S. borders, respectively. They

are described in detail in Section 2, along with a detailed presentation of the STAR model and its

data sources. Section 3 employs the STAR model to examine variations on the Global

Accounting methodology. Section 4 recalculates cocaine availability estimates for the 1996-1999

period by using a new approach. Section 5 summarizes the various availability estimates

presented in the paper. Section 6 presents limitations of the STAR model and recommendations

for its improvement. Section 7 draws conclusions.

 

1 DCI Crime and Narcotics Center (CNC), Defense Intelligence Agency, April 2000. Cocaine: A Global

Accounting for 1999.
2

2. The STAR Model

Overview

The STAR model incorporates various cocaine availability estimates into a cohesive, connected

model. The model hinges on the notion of a transition of cocaine from one stage – estimate of

drug (or drug precursor) availability, distributed within a specific geographic region – to the next.

The transition is a computational link between stages that converts drug (or drug precursor)

availability at one stage to availability at another stage, and includes reductions (seizures, losses,

etc.). Table 2 details stages and transitions between stages (including reductions), and lists data

sources utilized in STAR. Although the table presents stages in numerical order, the model is not

necessarily applied sequentially from stage 1 to stage 9. For example, the model could just as

easily begin at stage 9 and work back -- adding in reductions -- to a potential production number.

Alternatively, the model could begin with event-based data2 and work backward or forward. The

important point is that the model is flexible and not bound to any specific ordering of stages.

The model is comprised of nine stages and eight transitions. Stages 1 through 4 are production

stages within the growing areas, and Stages 5 through 8 track cocaine HCl from Andean labs to

the streets of the U.S. and non-U.S. destinations. Figure 1 depicts the geographical areas

involved in each stage.

Stage 1 begins with net coca cultivation, in each growing area, from the previous growing year.

The transition to Stage 2 – net cultivation in the current year – is the net change, calculated as: the

amounts from Stage 1, plus new growth, minus eradication and field abandonment. The

eradication figures used in this research are the "effective" eradication figures calculated from the

estimation of coca crop cultivation.

Stage 3 is net cultivation converted into net leaf amounts, via calculations performed in

Transition 2/3. These calculations utilize leaf yield estimates and compensate for leaf losses,

including both licit consumption and leaf seizures. Stage 4 is the amount of coca base available

from the net leaf, calculated by using the leaf-to-base conversion factors assumed in Transition

3/4. Stage 5 represents cocaine availability at the HCl labs, and Transition 4/5 links cocaine base

from growing regions to the HCl labs. Stage 6 describes cocaine HCl availability at South

American departure points.

At Stage 7 the flow branches into two parts. The transition from 6 to 7A is the amount of cocaine

exported from South America that moves toward non-U.S./LTAM markets (e.g. Europe and

Canada). The transition from 6 to 7B is the amount of cocaine exported from South America that

moves toward the U.S. markets.

 

2 Event-based data are derived from a database of drug movements in the transit zone. These data are

described in detail later in this section.
3

Table 2

STAR Model Stages and Transitions

Stage Transition and Reductions Data Sources

Stage 1 - Net cultivation in previous year CNC

Transition 1/2 Eradication,

abandonment, new growth

CNC

Stage 2 – Net cultivation in current year

Transition 2/3 Coca leaf yield, leaf

reductions

Operation

Breakthrough,

CNC

Stage 3 - Net leaf availability at growing areas

Transition 3/4 Alkaloid content, base lab

processing efficiency

Operation

Breakthrough

Stage 4 - Base availability at growing areas

Transition 4/5 Base movement to

cocaine HCl labs, base seizures

IACM

Stage 5 - Cocaine produced at labs

Transition 5/6 Cocaine HCl movement

from labs to South American (SOAM)

departure areas, SOAM cocaine seizures

and consumption

IACM, CNC

Stage 6 - Cocaine availability at SOAM

departure areas

Transition 6/7A Transit zone seizures, in

non-U.S. bound corridors

IACM, Interpol

Stage 7A - Availability at non-U.S./Latin

America markets

Transition 6/7B Transit zone seizures

and consumption in U.S. bound corridors

IACM, CCDB

Stage 7B- Availability at U.S transshipment

corridors.

Transition 7B/8 Cocaine subsequent

movement to U.S., domestic border

seizures

FDSS, EPIC,

Customs seizures

Stage 8 - Cocaine availability at domestic,

border-entry regions

Border

Allocation

Model

Transition 8/9 Cocaine domestic

movement, domestic reductions

FDSS, Domestic

Allocation

Model

Stage 9 - Cocaine availability at domestic retail

areas

ONDCP
4

Figure 1

Geographic Areas of STAR Stages

Stage 8 is cocaine available at U.S. border regions. Stage 8 uses the Border Allocation Model to

apportion cocaine amounts to U.S. border entry regions, by conveyance types3. Transition 7B/8

subtracts border seizures.

Stage 9 is the amount of cocaine available for consumption in various consumption regions

within the country. Transition 8/9 incorporates the Domestic Allocation Model to describe

cocaine movement from border entry regions to consumption regions4 and accounts for domestic

seizures.

The STAR model applies sequential transitions through a serious of matrix operations5. This

matrix formulation has several advantages: algebraic conciseness, ability to project assumptions

 

3 An overview of the model appears later in this section, and technical details are provided in Appendix D.

4 An overview of the model appears later in this section, and technical details are provided in Appendix F.

5 At each of the eight stages, there is a transition matrix that transforms the input into the predicted output.

At stage 1, v1 = v0 ∗ M1, where “*” denotes matrix multiplication. At stage 2, v2 = v1 ∗ M2. At stage 3,

v3 = v2 ∗ M3, and so on. The complete model can be written

v8 = v0 ∗ M = v0 ∗ M1 ∗ M2 ∗ M3 ∗ M4 ∗ M5 ∗ M6 ∗ M7 ∗ M8 ,
5

at any stage on predicted flows at subsequent stages, and ability to gauge transition probabilities

connecting flows, as well as flow amounts. The model was programmed using the matrix

programming language of SAS/IML (SAS Institute, 1990), a program with powerful facilities for

simulating alternative flow scenarios.

At most of the transitions, the matrix formulation is an accounting framework incorporating

availability estimates. These “accounting transitions” simply apply available data. However, at

stages 6, 8, and 9 the model is more than an accounting device. At these stages, the model

affords a comparison and potential reconciliation of alternate availability estimates. Thus, at

stage 6, it estimates the inconsistency in cocaine availability estimates by comparing potential

production with event-based estimates of cocaine departing South America. At stage 8, it

compares predicted outputs derived from potential production, event-based data, and the Border

Allocation Model. At stage 9, it judges the difference in availability estimates by incorporating

domestic consumption estimates6.

STAR Stage and Transition Details

The matrix formulation allows for differing assumptions – or scenarios – to be introduced at any

transition and then carried forward (or backward) for comparisons with other scenarios.

Although the following discussion presents stages in sequential order, the model does not have to

be applied sequentially. Section 4 details how the model was used to yield estimates for 1996-

1999, while the intent of this section is to provide specific details about data, stages, and

transitions. The section also discusses both the Border Allocation Model and Domestic

Allocation Model.

Potential Production Versus Actual Production

This paper makes a distinction between potential and actual production. Potential cocaine

production is calculated, by year, beginning with hectares under coca cultivation and then

multiplying by the leaf yield, alkaloid content, and base processing efficiency figures. These

figures measure availability for world consumption, assuming all coca hectares are converted to

cocaine product. Actual cocaine production is calculated by using the same conversion rates, but

subtracts losses that occur during the process, such as leaf spoilage, licit consumption, and base

and HCl seizures.

Table 3 summarizes the stage-by-stage summary of potential production estimates for each year

(see Appendix A for details). Over the period 1996-1999, potential production has decreased 50-

75 metric tons per year. These figures are worst-case estimates of cocaine availability in the

Andean countries because they do not account for known losses such as consumption, or leaf,

base, and HCl seizures. The STAR model expands on these estimates in order to calculate the

actual availability of cocaine for export from South America.

 

where v0 denotes gross hectares by growing area and v8 denotes cocaine consumed by U.S. geographical

subarea.

6 W. Rhodes, M. Layne, P. Johnston, L. Hozik, What America’s Users Spend on Illegal Drugs, 1988-1998,

June 2000.
6

Table 3

Cultivation and Potential Production Estimates, 1996-1999

Stage Description 1996 1997 1998 1999

1 Previous Net Cultivation (ha) 214,800 209,700 194,100 190,800

2 Net Cultivation (ha) 209,700 194,100 190,800 183,000

3 Dry Coca Leaf Yield (mt) 333,603 294,242 265,498 230,383

4 Base Production (mt) 950 875 825 765

5 HCl Production (mt) 950 875 825 765

For Colombia, Peru and Bolivia, estimates of the quantity of coca under cultivation are developed

by CNC, using survey methods similar to those used by agricultural organizations estimating the

size of licit crops. A survey is designed using statistically-based sampling techniques, ensuring

that an adequate number of samples are collected over randomly selected areas, as well as

sampling of known growing regions. Selected areas are then imaged, using satellites and aerial

photography. Using these images, region-specific coca crop estimates are developed.

Throughout the 1990's, Colombia was assumed to be cultivating the poorer yielding variety of

cocaine, E. coca var ipadu and was using processing techniques as efficient as Bolivia and Peru.

However, recent research makes it clear that Colombia is not only a major cocaine producer, but

also a leading coca cultivator. This new data has been used to revise historical Colombian

production estimates back to 1995. The STAR model incorporates the revised data as of March

2000.

Figure 2 depicts changes in the distribution of Andean potential production. Note that the figure

includes two lines for Colombia, the lower one representing earlier Colombian estimates and the

higher one representing data as of March 2000. Revision of the Colombian conversion figures

caused the total potential production figures to increase by nearly 200 metric tons per year, but a

downward trend still remains.

These adjustments highlight the difficulty in maintaining consistent trends during periods of

dynamic changes, such as the rapid increases in Colombian cultivation. The statistical nature of

the imaging process allows standard errors to be calculated, which measures a portion of the

uncertainty in the cultivation estimates. However, additional uncertainty is introduced by

extrapolating the cultivation figures into potential production estimates. Uncertainties include the

detection of new growing areas and eradication estimation (maturity of the eradicated crop,
7

strength of the herbicide, and timing of the harvest). The Breakthrough7 estimates provide the

crop yield data and processing efficiency data to calculate the potential production from the crop

cultivation estimates. These Breakthrough estimates are refined, as updated data becomes

available. All of these estimates are snapshots in time, and must therefore be periodically

updated. One example of a changing trend is that there have been reports that Peru’s coca

industry may be recovering8.

Figure 2 also shows that while production in Bolivia and Peru has dropped, Colombian

production has soared. Accounting for only 25% of total coca cultivation in 1995, Colombia’s

contribution grew to 68% by 1999. Applying time-series techniques to the raw data could reduce

what appears to be considerable random variation from year to year.

Figure 2

Potential Cocaine Production, 1990-1999 (mt)

0

100

200

300

400

500

600

700

800

900

1000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

Potential Production

Peru

Bolivia

Unrevised Colombian

Revised Colombian

Total Revised 

 

7 Drug Enforcement Administration, 1994, Operation Breakthrough: Coca Cultivation and Cocaine

Production in Bolivia. Drug Enforcement Administration, 1997, Operation Breakthrough: Coca

Cultivation and Cocaine Production in Peru.

8 Defense Intelligence Agency, 1999. Interagency Assessment of Cocaine Movement: August 1999

Eighteenth Edition, Mid-Year Review, p. 2.
8

Actual Production

The STAR model expands on potential production in order to calculate the actual availability of

cocaine for export from South America. This is done by subtracting losses such as spoilage, licit

consumption, and seizures.

Stages 1 through 4 occur in each of the eighteen Andean growing regions (Guaviare, West

Caqueta, East Caqueta, Norte de Santander, San Lucus, Arauca, Putamayo, and Macarena in

Colombia; Upper Huallaga Valley, Central Huallaga Valley, Lower Huallaga Valley, Aguaytia,

Pachitea, Apurimac, Cusco, and other Peruvian growing areas in Peru; Chapare, and

Yungas/Apolo/Other in Bolivia). From these regions, coca base moves to cocaine HCl

production labs (Stage 5), through base movement corridors.

Stage 1: Net Cultivation From Previous Growing Year

The STAR model starts with the estimates of hectares under cultivation. Stage 1 simply

represents the previous year’s net cultivation estimates

Stage 2: Net Cultivation in Current Year

Stage 2 represents the current year’s net cultivation in each of the eighteen growing areas.

Transition 1/2 is the computational link between the previous year’s net cultivation and the

current year’s net cultivation. The computation considers new growth, field abandonment, and

eradication.

Stage 3: Net Leaf

Stage 3 is the amount of net leaf yielded from coca plants, by growing region. The transition

between Stages 2 and 3 applies leaf yield factors (shown in table A1, Appendix A) to transform

the amount of net cultivation into potential leaf amounts, measured in metric tons. Colombian

leaf yields represent amounts of wet leaf, whereas Peruvian and Bolivian leaf yields are for dry

leaf. Transition 2/3 includes reductions for licit leaf consumption (obtained from the

International Narcotics Control Strategy Report (INCSR)), leaf seizures, and for leaf not

harvested – which is assumed to be one percent of mature hectares.

Stage 4: Base Availability

Stage 4 is the amount of base created from net leaf amounts, by growing region. Transition 3/4

applies leaf-to-cocaine conversion factors (detailed in table A2, Appendix A) for each growing

region.

Stage 5: Cocaine Availability at Labs

Stage 5 measures the amount of cocaine produced at labs. Transition 4/5 follows coca base from

growing regions to labs through base corridors of movement as defined in the IACM publications

(beginning in 1997). In 1997, three base corridors of movement were identified (northeast, south,

and northwest) and, in 1998, a fourth was added (west). The STAR model apportions base from
9

growing regions to labs by the percentages of observed movement in the IACM. Reductions in

the transition include cocaine base seizures.

Transitions 4/5 and 5/6 must be considered tentative for several reasons. First, data on

movements of base and cocaine within the source countries are incomplete. Second, data on

losses due to base spoilage and source country consumption are fragmentary, imprecise, or

nonexistent. Finally, Transition 4/5 assumes that base movement corridors are independent of

growing areas, and Transition 5/6 assumes that HCl movement corridors are independent of lab

locations. Neither assumption is realistic. Nonetheless, it is useful to begin to model these two

transitions, as base and HCl movement may become more detectable in the future.

Stage 6: Cocaine Departing South America

Transition 5/6 is the link between cocaine labs and South American departure points, through

HCl corridors of movement as defined in the IACM publications. The model apportions the flow

of HCl from labs to departure points by the percentages of observed South American cocaine

movement described in the IACM.

Reductions taken in this transition include source country seizures and spoilage (assumed to be

one percent). Source country consumption is not accounted for because estimates are tentative

and exist for 1999 only.

Estimates of Cocaine Departing South America Using Event-Based Data on Cocaine

Movements in the Transit Zone

The IACM uses an event-based, interagency consensus methodology to quantify cocaine

movement through the transit zone. Event-based data in the Consolidated Cocaine Database

(CCDB) combines two efforts: the Interagency Counterdrug Performance Assessment

Workgroup (ICPAWG) and the IACM. The ICPAWG -- established in 1992 to measure the

performance of international drug interdiction -- maintains a database of known drug movements

in the transit zone, with a destination of either the U.S. or Canada. Known events are designated

by expert participants of an interagency working group on the basis of the following information:

(1) seizure or observation of drugs; (2) observation of activity that could not be reasonably

attributed to anything other than drug smuggling; (3) reliable intelligence.

In 1996, the interagency group developed a cocaine flow assessment methodology to determine

the amount of cocaine that departs South America along major trafficking routes9. Three types

of uncertainty exist in the data: uncertainty in the amount of cocaine transported, uncertainty in

the existence of the event, and uncertainty about how much cocaine remains undetected. For

example, if the quantity of cocaine recorded in the database for movements from South America

to Florida come exclusively from seizures, then one can assume with a high degree of certainty

that more cocaine was moved but not detected. This type of uncertainty is important because it

can be used to show that cocaine movement via commercial means is underestimated.

 

9 The results are included in the transit zone section of the IACM publications.
10

Table 4 includes event-based estimates of cocaine departing South America for 1996 through

199810. Part of the variability from year to year in these numbers is attributable to evolving

methodology. There is considerable uncertainty in the magnitude of the IACM estimates, but the

stable trend in the estimate of cocaine departing South America correlates well with other supply

indicators. The stable, event-based estimates indicated a disconnect with the older Colombian

cultivation estimates.

Table 4

Event-Based Cocaine Amounts Departing South America

By Transit Corridor, 1996-1999 (bulk metric tons)

1996 1997 1998 1999

Caribbean 174.5 138.4 160.3 220

Mexico/Central America 341.7 250.7 318.6 277

Direct to U.S. 91.2 43.9 51.4 15

Non-U.S. Destinations 42.8 62.6 64.5 75

Unknown 2.5 - 1.0 -

Total 652.7 495.6 595.8 587.0

Stage 7A: Non-U.S./South American Markets

Figure 3 shows the split of the flow between that moving toward U.S. markets and that moving

toward non-U.S./LTAM markets (primarily Europe). Stage 7A is the amount of cocaine that

departs South America and successfully arrives at non-U.S./LTAM markets. Seizures in nonU.S. bound corridors are included in the transition.

Stage 7B: Transshipment Area

This stage is the amount of cocaine that departs South America towards the United States.

Transition 6/7B apportions cocaine from South American departure points through corridors of

movement, via specific conveyances (noncommercial and commercial air, noncommercial and

commercial maritime). Two assumptions are made: cocaine leaving from Colombia transits all

three corridors; cocaine leaving from departure points in Peru, Ecuador or Bolivia transits through

 

10 Movement events from the CCDB were used for the calculations, and they differ slightly from figures

published in the IACM. See Cala, 1999.
11

Mexico/Central America (MX/CA) only. Flows among corridors and conveyances are

apportioned in the same proportion as flows in the event-based data.

Figure 3

Availability at U.S.-Bound Transshipment Corridors and Non-U.S./LTAM Markets (Stage 7)

During Transition 6/7B, event-based data is incorporated, which describes cocaine departing

South America by corridor and conveyance combinations. Reductions taken in the transition

include transit seizures and transit country consumption, which is assumed to be three percent of

the flow.

Ideally, Transition 6/7B would include conveyance combinations. In the Mexican/Central

American corridor, the most prevalent combination is to use noncommercial maritime to get part

of the way through the transit zone and then to use land conveyance to travel the rest of the way.

There are some secondary movement events listed in the CCDB, but they were not included in

STAR.

Stage 8: Cocaine Availability at U.S. Border Entry Regions

Stage 8 is the amount of cocaine that successfully passes into the U.S., by border entry regions.

Figure 4 illustrates the U.S. border entry regions used in the model. Transition 7B/8 converts the

amount of cocaine passing through the transit zone -- by movement corridor and conveyance type

-- into amounts entering U.S. borders by geographic region and by conveyance type. It is
12

assumed that shipments passing through the Mexican/Central American corridor terminate at the

southwest border and that shipments in the Caribbean and Direct to U.S. corridors are distributed

in proportion to border seizures and conveyance combinations.

Figure 4

U.S. Border Entry Regions

Reductions taken during this transition account for seizures at the border using an Enhanced

Seizure Database created for the STAR model. At stage 8 the Border Movement Model provides

estimates of cocaine arriving to U.S. regions, by conveyance type.

Enhanced Seizure Database

To determine reproducible domestic and border seizure amounts, an Enhanced Seizure Database

was created, based on a variety of seizure databases. DEA’s Federal Drug Seizure System

(FDSS) for calendar years 1991-1998 provided the bulk of the data for this effort. FDSS data

contain no duplicate records -- each seizure in the FDSS is uniquely identified by a Federal Drug

Identifying Number (FDIN), eliminating the risk of double counting. The FDSS includes federal

and federally-supported cocaine seizures of 500 grams or more. The Enhanced Seizure database

only includes those FDSS seizures that were above the threshold set by the FDSS system.
13

FDSS contains limited details about each seizure, so the FDSS data was augmented with agencyspecific seizure data. Customs seizure data includes country of origin and more detailed

information about conveyance. Other supplementary data came from the Coast Guard, the El

Paso Intelligence Center (EPIC) Border/Land Interdiction Seizure System (BLISS), and the

CCDB. The EPIC data covers seizure events occurring at the United States/Mexican border and

up to 150 miles inside the United States. Appendix D details specific variables from each of

these data sources. The FDSS data was used as the “master” when conflicting data appeared

across databases. The exception to this is that EPIC data are employed for southwest border

seizures.

Border Seizures

Seizures at the border (arriving from foreign countries) were classified by conveyance types

(noncommercial and commercial air, noncommercial and commercial maritime, noncommercial

and commercial vehicle, rail and pedestrians) and geographic region (Florida, Gulf Coast,

Northeast, Southwest Border, Puerto Rico/Virgin Islands, and Rest of U.S. – including Ports of

Entry (POE) along the Canadian border)11.

EPIC has traditionally accounted for border seizures. There is a definitional difference in

seizures at the southwest border and at all other border areas. EPIC’s definition of a southwest

border extends 150 miles into the U.S., since the drugs likely came from Mexico. In Florida, by

contrast, the border does not extend inland, although it would seem just as plausible that the drugs

came across the Florida border. This issue points to the need for a consistent definition of a

border seizure.

To identify a border seizure, and to classify it by conveyance type and by geographic region:

1. Seizures on the high seas were excluded from FDSS data because they are included in transit

zone seizures.

2. To identify seizures along the southwest border, information from EPIC was used. Any car,

four-wheel drive, motorcycle, pickup truck, recreational vehicle, towed vehicle, or van was

classified as a noncommercial vehicle. Additionally, if the “type” variable indicated

“intrusion by vehicle at border (not POE)” or “vehicle at POE” the conveyance was classified

as a noncommercial vehicle. Conveyance was assigned as commercial vehicle for tanker

truck, bus, tractor trailer, trailer, or wrecker. If the type variable indicated “on foot at border”

or “pedestrian at POE”, then the conveyance was assigned as pedestrian. And finally, if

conveyance type was train, the seizure was assigned to the rail conveyance category.

3. To categorize maritime border seizures, Customs information was checked, specifically for

whether the conveyance arrived from non-U.S. locations. If so, and if the conveyance was

listed as a commercial vessel, then commercial maritime was assigned. If conveyance was

listed as a fishing or private vessel, then noncommercial maritime was assigned. Coast Guard

 

11 Our border seizures figures differ from those reported by EPIC in the IACM. A description of their

methodology was unavailable.
14

and CCDB information was used to identify maritime seizures that occurred outside of ports

of entry.

4. To categorize border seizures from air conveyances, Customs information was checked to

determine if the conveyance arrived from non-U.S. locations. If so, and if the conveyance

was listed as commercial air, mail, or express consignment, then commercial air was

designated as the conveyance type. If conveyance was listed as private aircraft, then

noncommercial air was designated. CCDB data were consulted for air conveyance seizures.

5. Finally, 113 border seizures that were classified by Customs as “other” or “no transport

involved” were examined individually, to determine if they were border seizures.

Figure 5 presents a plot of total border seizures for the years 1991-1999. The figure shows that

overall, border seizures have decreased 31.5%, from 61.9 metric tons in 1991 to 42.4 metric tons

in 1999. The chart also includes a two-year moving average line to smooth year-to-year

variations. Table5 details border seizures by conveyance types.

In Figure 6, smoothed seizure (three-year moving average for southwest border and two-year

moving average for all other areas12) figures are plotted by region, for the period 1992 through

1999. Seizures on the southwest border (the solid line at the top of the figure) have remained

relatively constant over the period. Seizures in Florida (the dotted line at the top of the chart)

have declined over the period, while seizures in Puerto Rico/Virgin Islands have steadily

increased.

 

12 A two-year moving average for the southwest border still yielded considerable variation from year to

year.
15

Figure 5

Seizures at The U.S. Border, 1991-1999 (export quality metric tons)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

1991 1992 1993 1994 1995 1996 1997 1998

Year

Bulk Metric Tons

Two Year Moving Average
16

Table 5

Border Seizures, 1991-1999 (export quality metric tons)

1991 1992 1993 1994 1995 1996 1997 1998 1999a

Commercial Air 5.5 6.2 7.7 7.4 9.6 6.1 6.3 3.0 7.0

Commercial Maritime 28.5 23.4 21.5 21.5 10.5 22.2 25.0 14.4 5.1

Commercial Vehicle 3.4 7.3 5.4 2.9 8.1 7.7 5.6 7.4 9.4

Noncommercial Air 6.9 4.1 5.1 2.6 0.6 0.9 0.5 0.0 0.0

Noncommercial Maritime 9.3 4.2 7.2 10.4 24.4 12.3 11.8 8.7 7.2

Noncommercial Vehicle 7.0 11.5 8.6 9.5 11.5 8.8 7.3 11.2 13.7

Pedestrian 1.4 3.2 0.2 1.4 3.6 0.9 0.9 1.7 -

Rail 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -

Total (export quality mt) 61.9 59.8 55.8 55.6 68.4 59.0 57.4 46.4 42.4

a Figures for 1999 obtained from EPIC for seizures at ports of entry (9.4 metric tons), between ports of entry (3.6 metric tons), and at traffic stops/checkpoints (10.1

metric tons). These port of entry seizures were placed into the commercial vehicle category while the other two types of seizures were placed the into

noncommercial vehicle category.

- indicates no data available.
17

Figure 6

Smoothed Seizures in Border Entry Regions, 1991-1999 (export quality metric tons)

0

5

10

15

20

25

30

1991 1992 1993 1994 1995 1996 1997 1998

Year

Bulk Metric Tons

Florida

Gulf Coast

NorthEast

Other

RQVQ

SW Border

Note: RQ/VQ designates Puerto Rico and U.S. Virgin Islands

Note: RQVQ designate the area of Puerto Rico and U.S. Virgin Islands.

Border Allocation Model

The Border Allocation Model was developed to allocate the cocaine entering the U.S.(Stage 8)

among the border entry regions. In particular, the model predicts the percentage of cocaine

arriving at specific regions, by specific conveyance types. Cocaine amounts are then obtained by

multiplying the percentages by the estimated total. The proportions can be employed in the

allocation of amounts based on any estimate of the amount of cocaine arriving to the U.S. For

example, using percentages generated by the Border Allocation Model, cocaine amounts

estimated via event-based data can be allocated to specific U.S. border regions and conveyances

(after subtracting transit zone seizures and consumption). Any amount that the STAR model

incorporates (including potential production estimates) can be distributed into conveyance/border

region combinations. Appendix E provides details about the methodology.

The Border Allocation Model uses data on U.S. border seizures and on the costs smugglers pay to

transport cocaine from Colombia to the U.S. Data on U.S. border seizures were obtained from

the Enhanced Seizure Database, and data pertaining to smuggler transportation costs were

obtained from Customs Reports of Investigation.
18

Tables 6 and Table 7 show the average number of metric tons seized, and the percentage of the

total amount seized, for each conveyance and border region combination. Note that seizures from

land conveyances in Florida, Gulf Coast, Northeast, and Puerto Rico/Virgin Islands (PR/VI) are

impossible and these region-conveyance combinations therefore contain structural zeros. This

contrasts with observed zeros (such as that obtained for Gulf Coast, commercial air) where the

region-conveyance combination is feasible, but no occurrences were observed.

Averaging over the eight year period, 45% of total seizures occurred at the southwest border

(SWB) and 34% at the Florida border. In terms of conveyances, 31% of the seizures occurred

upon commercial marine ships, while noncommercial vehicle, noncommercial marine,

commercial vehicle, commercial air, and noncommercial air accounted for 28%, 16%, 12%, 10%,

and 4%, respectively.

Table 8 shows how the Border Allocation Model allocates the total cocaine quantity arriving at

U.S. borders to specific border regions and conveyance types. The model predicts that –

averaged over the years 1991-1998 – 48% of cocaine destined for the U.S. arrives at Florida via

commercial marine conveyances and 37% arrives at the southwest border via commercial and

noncommercial vehicles. Note that the distribution of cocaine amounts (Table 8) differs

considerably from the distribution of cocaine seizures (Table 7). This is because estimates of

cocaine amounts are not simply proportional to seizures. For example, even though cocaine

seizures for Florida via commercial marine are only 21% of total seizures, the proportion of the

total amount transported through this region-conveyance combination is 48%. This occurs

because transportation costs were relatively high in this case ($3,568 compared to the mean of

$3,111), which, assuming constant total transportation costs, implies that the probability of

seizure, and therefore seizure costs, were relatively low. Thus the amount seized was a relatively

low percentage of the amount shipped to Florida via commercial marine.

Figure 7 plots the amount of cocaine arriving at each border region for the period 1991-1998.

The model indicates that most cocaine entering the U.S. does so via Florida and the southwest

border. Taking the eight-year period as a whole, quantities arriving at the southwest border have

increased at the expense of quantities arriving at Florida. All other regions have remained fairly

constant, with the exception of PR/VI, for which the model predicted a jump from 11 metric tons

in 1996 to 42 metric tons in 1997.
19

Table 6

Border Seizures (bulk metric tons): Average Over Years 1991-1998

Border Region Noncom

Vehicle

Commercial

Vehicle

Noncom

Air

Commercial

Air

Noncom

Marine

Commercial

Marine Total

Florida - - 1.0 4.6 3.5 14.1 23.1

Gulf Coast - - 0.0 0.0 0.8 1.5 2.3

Northeast - - 0.0 1.3 0.0 2.9 4.1

PR/VI - - 0.9 0.2 4.6 1.3 7.0

SWB 18.8 8.3 0.5 0.3 2.2 0.8 30.8

Rest of U.S. 0.0 0.0 0.2 0.1 0.0 0.3 0.7

Total 18.8 8.3 2.6 6.5 11.0 20.9 70.0

- indicates not applicable
20

Table 7

Border Seizures (percent): Average Over Years 1991-1998

Border

Region

Noncom

Vehicle

Commercial

Vehicle

Noncom

Air

Commercial

Air

Noncom

Marine

Commercial

Marine Total

Florida - - 1.5 6.8 5.1 20.7 34.0

Gulf Coast - - 0.0 0.0 1.1 2.2 3.4

Northeast - - 0.0 1.9 0.0 4.2 6.1

PR/VI - - 1.3 0.2 6.8 1.9 10.2

SWB 27.6 12.2 0.7 0.4 3.2 1.2 45.3

Rest of U.S. 0.0 0.0 0.3 0.2 0.0 0.5 1.0

Total 27.6 12.2 3.8 9.5 16.2 30.7 100.0

- indicates not applicable

Table 8

Percent Allocation of Cocaine By Border Region and Conveyance: Average Over Years 1991-1998

Border

Region

Noncom

Vehicle

Commercial

Vehicle

Noncom

Air

Commercial

Air

Noncom

Marine

Commercial

Marine

Florida - - 0.3 2.2 1.3 47.9

Gulf Coast - - 0.0 0.0 0.2 1.7

Northeast - - 0.0 0.5 0.0 3.3

PR/VI - - 0.2 0.1 1.5 1.2

SWB 7.8 28.8 0.2 0.1 0.5 0.9

Rest of U.S. 0.0 0.8 0.1 0.0 0.0 0.4

- indicates not applicable
21

Figure 7

Border Allocation Model: Amounts by Region (pure metric tons)

0

50

100

150

200

250

300

1991 1992 1993 1994 1995 1996 1997 1998

year

Pure Metric Tons

Florida

Gulf Coast

Northeast

PR/VI

SWB

Rest of U.S.

Figure 8 plots model estimates by conveyance type. Conveyance types of choice appear to be

commercial vehicle and commercial marine. Although it is likely that noncommercial air actually

plays a large role in transporting cocaine, the model does not capture this because the typical

flight stops just short of the U.S.- Mexican border. Figure 8 shows that, over the eight year

period, conveyance by commercial vehicle has increased at the expense of conveyance by

commercial marine: commercial vehicle increased by 78% (from 91 to 162 metric tons) and

commercial marine decreased by 29% (from 286 to 203 metric tons). These estimates are

consistent with Colombian drug lords allowing Mexico-based trafficking organizations to play an

increasing role in shipping cocaine to the U.S. Indeed, taking Figures 7 and 8 together, it would

appear that there has been a shift in smuggling from Florida via commercial marine to the

southwest border, via commercial vehicle. Appendix C (Table C6) presents detailed estimates for

each year.

Results of the Border Allocation Model indicate a higher proportion of cocaine flow to the

Florida destination than current intelligence assessments. The results of the Border Allocation

Model should be seen as developmental and not a conclusive result. But the model does provide

an interesting perspective. The current intelligence assessment consistently underestimates

smuggling via commercial conveyances, which would probably be the primary means of

smuggling into the Florida corridor. Further research is needed in the critical border region to

determine the more correct estimate.
22

Figure 8

Border Allocation Model: Amounts by Conveyance (pure metric tons)

0.00

50.00

100.00

150.00

200.00

250.00

300.00

1991 1992 1993 1994 1995 1996 1997 1998

Year

Pure Metric Tons

Com Air

Com Marine

Com Vehicle

NonCom Air

NonCom Marine

NonCom Vehicle

Stage 9: Cocaine Availability at Domestic Retail Areas

Cocaine at this stage represents the amount of cocaine arriving to U.S. consumption regions from

U.S. border entry regions. Figure 9 depicts domestic retail markets, which have been broken

down into ten main regions. Transition 8/9 incorporates domestic (non-border) cocaine seizures.

The arrows in the figure depict routes taken from border entry regions, based on the results of the

Domestic Allocation Model.
23

Figure 9

Domestic Retail Areas

Domestic Seizures

The Enhanced Seizure Database was also used to quantify domestic seizures within the United

States. Table 9 shows the annual domestic seizures allocated by census regions.
24

Table 9

Non-Border, Domestic Seizures, By Census Divisions, 1991-1998 (export quality metric tons)

1991 1992 1993 1994 1995 1996 1997 1998 1999a

New England 4.5

(7%)

0.1

(0%)

0.1

(0%)

0.1

(0%)

0.0

(0%)

0.0

(0%)

0.1

(0%)

0.1

(0%)

Mid Atlantic 10.9

(17%)

4.0

(7%)

3.7

(8%)

6.0

(10%)

3.0

(6%)

5.5

(10%)

5.3

(18%)

10.4

(19%)

East North Central 1.0

(2%)

1.5

(2%)

.8

(2%)

4.1

(7%)

1.4

(3%)

1.5

(3%)

2.9

(9%)

4.5

(8%)

West North Central 0.3

(0%)

0.5

(1%)

1.0

(2%)

1.3

(2%)

0.6

(1%)

0.2

(0%)

0.4

(1%)

0.8

(1%)

South Atlantic 9.6

(15%)

14.0

(23%)

5.3

(12%)

5.4

(9%)

6.4

(14%)

9.9

(18%)

4.9

(16%)

8.0

(14%)

East South Central 1.2

(2%)

1.6

(3%)

1.2

(3%)

2.7

(4%)

0.5

(1%)

1.4

(3%)

0.4

(1%)

0.4

(1%)

West South Central 12.3

(19%)

10.2

(17%)

14.3

(32%)

14.0

(23%)

14.0

(30%)

12.2

(22%)

9.6

(32%)

15.5

(28%)

Mountain 2.6

(4%)

3.7

(6%)

1.6

(4%)

4.4

(7%)

4.7

(10%)

8.3

(15%)

0.6

(2%)

4.7

(8%)

Pacific 18.2

(28%)

23.1

(38%)

12.4

(28%)

20.2

(33%)

13.7

(29%)

10.1

(19%)

4.0

(13%)

6.6

(12%)

PR/VI 3.5

(5%)

2.7

(4%)

4.3

(10%)

3.1

(5%)

2.3

(5%)

5.2

(10%)

2.2

(7%)

4.7

(8%)

Total (export quality mt) 64.1 61.4 44.7 61.3 46.6 54.3 30.4 55.7 40.0

a Census division breakdowns unavailable at this time.
25

Domestic Allocation Model

To allocate cocaine entering the U.S. to consumption regions, the Domestic Allocation Model

was created. The premise of the model is consistent with the classic operations research

transportation problem: given the quantities of cocaine entering the domestic market at the six

border regions, and given the quantities demanded in each of the ten U.S. census divisions, it is

assumed traffickers determine the allocation that satisfies demand in all divisions while

minimizing total transportation costs. Standard linear programming techniques were used to

solve this problem. Appendix E provides details of the model.

Table 10 shows, for each border entry region, the percentage of cocaine moved to each

consumption region in 1998 (values for other years are shown in Appendix C, Table C6). Taking

these estimates at face value, one could conclude that cocaine smuggled in at the Gulf Coast,

Northeast, and Rest of U.S. stays in that general area, while shipments through Florida, Puerto

Rico and the southwest border go to other regions. In particular, 90% of the southwest border’s

imported cocaine is distributed to areas beyond the southwest border, reflecting the increased role

of Mexico-based traffickers13.

Table 10

Percent of Cocaine From Border Entry Regions to Census Divisions, 1998

Florida Gulf Coast Northeast Rest of U.S.

Puerto Rico

Virgin Islands

Southwest

Border

New England 7 0 100 100 0 0

Mid Atlantic 43 0 0 0 0 0

E. North Central 10 100 0 0 0 23

W. North Central 00 0 0 0 8

S. Atlantic 34 0 0 0 100 0

E. South Central 70 0 0 0 0

W. South Central 0 0 0 0 0 15

Mountain 0 0 0 0 0 12

Pacific 0 0 0 0 0 43

 

13 Drug Enforcement Administration, August 1997, Changing Dynamics of the U.S. Cocaine Trade.
26

3. Applying the STAR Model to Existing

Estimates of Cocaine Availability

In this section, the STAR model is used to compare estimates of cocaine availability published in

Cocaine: A Global Accounting for 1999 (CNC and DIA, April 2000). The STAR model is used

to explore variations – or scenarios – using data found in the referenced report. Table 11

describes the scenarios that are examined and indicates which of the three availability estimates --

South American Supply, U.S. Consumption, and non-United States and non-Latin American

(non-U.S./LTAM) consumption – are estimated. The estimates are averaged for the 1998-1999

period, following the methodology used in the Global Accounting report.

The first scenario reviews Global Accounting results. It uses South American production and

consumption and U.S. consumption as data inputs and produces an estimate of cocaine

consumption for non-U.S./LTAM markets. Cocaine availability at each stage is reported as a

range, based on the uncertainty of the measurement. Note that as cocaine losses are subtracted at

each transition, the low estimates of losses are subtracted from the high estimate of cocaine

availability.

The second scenario modifies the first approach by subtracting the low estimate of losses from

the low availability estimate and the high estimate of losses from the high availability estimate.

Other slight modifications are included.

The third scenario – the Consumption Approach -- incorporates three consumption estimates

(U.S., non-U.S./LTAM markets, and South America) into the STAR Model and produces an

estimate of cocaine production to meet the worldwide consumption and seizure losses. The

estimates for non-U.S./LTAM markets and for South America are reported by CNC in their

Global Accounting report14.

The fourth scenario -- the U.S. Residual Approach – incorporates data for South American

production and consumption and consumption for non-U.S./LTAM. It generates an estimate of

cocaine remaining for U.S. consumption.

 

14 These consumption estimates were based on country studies, correlation of prevalence rates with U.S.

consumption, and analyst judgement.
27

Table 11

Estimation Scenarios

Quantities and Data Sources

Scenario South American Supply U.S. Consumption Non U.S./LTAM Consumption

Global Accounting CNC production and

consumption estimates

ONDCP consumption

estimates

Estimated

Modified Global Accounting CNC production and

consumption estimates

ONDCP consumption

estimates

Estimated

Consumption Estimated ONDCP consumption

estimates

CNC consumption estimates

U.S. Residual CNC production and

consumption estimates

Estimated CNC consumption estimates

Global Accounting Scenario

In this scenario, the known quantities are South American production and consumption, and

domestic consumption; the unknown component is the Non-U.S./Latin American consumption

Table 12 reports the results of this methodology, which begins with an estimated range of

potential cocaine and sequentially reduces it by seizures. The range in the potential production

estimate is due to uncertainty in the crop imagery process and the Colombian base processing

efficiency. Ranges in seizures are due to many uncertainties, such as cocaine purity.

Note that the referenced report subtracts the low loss estimates from the high availability figures

(and the high losses from the low availability), which results in an ever-widening estimate of

cocaine available for non-U.S./LTAM consumption. The Stage 6 estimate is calculated by

beginning with the Stage 5 estimate, then subtracting the source zone seizures and consumption.

The Stage 7b estimate is calculated by beginning with the Stage 9 estimate, then adding domestic

and border seizures. As the table shows, the estimate of Non-U.S./LTAM consumption is quite

large, ranging from 0 metric tons to 300 metric tons. The approach may be accurate in its

treatment of uncertainty, but its utility to decision-makers may be limited.
28

Table 12

Global Accounting Scenario, 1998-1999 (pure metric tons)

Stage Description Transition Operations Low High Source

5 HCl Labs 655 915 CNC

Source Zone Seizures1 (65) (50) IACM

Source Zone Consumption (175) (120) CNC

6 Departure areas 415 745 Calculated

Non-LTAM Seizures1 (40) (30) IACM

7A Non-U.S./LTAM markets 110 (300) Estimated

Transit Zone Seizures1 (60) (50) IACM

Transit Zone Consumption (25) (15) CNC

7B U.S. Transshipment

Corridors 400 350

Arrival Zone Seizures (50) (45) EPIC

8 Entering the U.S. 350 305

Domestic Seizures (30) (25) FDSS

9 Retail U.S. 320 280 ONCDP

1

Includes purity range and other factors

Source: DCI Crime and Narcotics Center (CNC), Defense Intelligence Agency, April 2000. Cocaine: A Global

Accounting for 1999

Modified Global Accounting Scenario

This scenario modifies the previous one by subtracting the high losses from the high availability

estimates (and the low losses from the low availability). It begins with cocaine production

estimate and sequentially reduces it by subsequent losses including South American and U.S.

consumption to yield an estimate of the flow to non-U.S./LTAM markets.

The loss estimates differ slightly in this modified approach. The only uncertainty in the seizure

figures is due to a range in purity. Also, the earlier approach rounded-off the loss figures, but this

scenario does not.
29

Table 13 presents the results and shows that the estimate of cocaine consumption in the nonU.S./LTAM market is between 11 and 138 metric tons. This estimate is an improvement over the

prior scenario approach since it provides a narrower range. From this scenario, one would

conclude that a coherent and consistent set of cocaine flow measures is available for the 1998-99

period, using currently-available production and consumption estimates. But is the 11-138 metric

ton estimate for non-U.S./LTAM consumption logical? A higher estimate would require either a

lower U.S. or South American consumption estimate, or a higher production figure. To explore

these questions, additional scenarios are tested.

Table 13

Modified Global Accounting Scenario, 1998-1999 (pure metric tons)

Description Transition Operations Low High Source

HCl Labs 655 915 CNC

Source Zone Seizures1 (61.6) (69.3) IACM

Source Zone Consumption (120) (175) CNC

Departure areas 473.4 670.7

Non-LTAM Seizures1 (38.4) (43.2) IACM

Non-U.S./LTAM

markets (11.2) (137.6) Estimated

Transit Zone Seizures1 (59.2) (66.6) IACM

Transit Zone Consumption (15) (25) CNC

U.S. Transshipment

Corridors 349.6 398.3

Arrival Zone Seizures (44.8) (50.4) EPIC

Entering the U.S. 304.8 347.9

Domestic Seizures (24.8) (27.9) FDSS

Retail U.S. 280 320 ONCDP

1

Includes only purity range

Consumption Scenario

This scenario begins with U.S. consumption and works back to cocaine availability at South

American labs. Estimates for South American and transit country consumption are incorporated,

as well as amounts flowing to non-U.S./LTAM markets.
30

The results are displayed in Table 14. The estimated amount, availability at HCl labs falls toward

the high end of the range reported for potential production. The higher consumption estimate for

non-U.S./LTAM drives the higher production estimate. These estimates are certainly possible,

but raise the question of uncertainly in the other estimates.

Table 14

Consumption Scenario, 1998-1999 (pure metric tons)

Description Transition Operations Low High Source

HCl Labs 788.8 1002.4 Estimated

Source Zone Seizures1 (61.6) (69.3) IACM

Source Zone Consumption (120) (175) CNC

Departure areas 607.2 758.1

Non-LTAM Seizures1 (38.4) (43.2) IACM

Non-U.S./LTAM

markets (145) (225) CNC

Transit Zone Seizures1 (59.2) (66.6) IACM

Transit Zone Consumption (15) (25) CNC

U.S. Transshipment

Corridors 349.6 398.3

Arrival Zone Seizures (44.8) (50.4) EPIC

Entering the U.S. 304.8 347.9

Domestic Seizures (24.8) (27.9) FDSS

Retail U.S. 280 320 ONCDP

1

Includes only purity range

The column showing the low range estimates is very similar to the high range estimates for the

Modified Global Accounting scenario. Both of these scenarios provide a consistent set of figures

in which non-U.S./LTAM consumption is roughly 140 metric tons.
31

U.S.-Residual Scenario

This fourth and final scenario uses South American production and consumption for nonU.S./LTAM markets. It calculates an estimate of cocaine remaining for U.S. consumption. Table

15 reports the results. The residual amount remaining, after all consumption and seizures are

subtracted from the production, ranged between 146 and 233 metric tons. This figure is at least

1/3 less than the independent estimate of domestic consumption (which itself contains

uncertainty). If the results of this scenario were accepted, one would conclude that U.S.

consumption was equivalent to non-U.S./LTAM consumption, which seems unlikely.

Table 15

U.S. Residual Scenario, 1998-1999 (pure metric tons)

Description Transition Operations Low High Source

HCl Labs 655 915 CNC

Source Zone Seizures1 (61.6) (69.3) IACM

Source Zone Consumption (120) (175) CNC

Departure areas 473.4 670.7

Non-LTAM Seizures1 (38.4) (43.2) IACM

Non-U.S./LTAM

markets (145) (225) CNC

Transit Zone Seizures1 (59.2) (66.6) IACM

Transit Zone Consumption (15) (25) CNC

U.S. Transshipment

Corridors 215.8 310.9

Arrival Zone Seizures (44.8) (50.4) EPIC

Entering the U.S. 171.0 260.5

Domestic Seizures (24.8) (27.9) FDSS

Retail U.S. 146.2 232.6 Estimated

1

Includes only purity range
32

Summary

The STAR Model was used to test four scenarios for combining existing 1998-1999 estimates of

cocaine production, seizures, and consumption. Table 16 summarizes results. The upper bound

figures for the Modified Global Accounting Scenario and the lower-bound figures for the

Consumption Scenario provided the most consistent combination of input figures. In these

scenarios, production was approximately 800-900 metric tons, South American consumption was

175 metric tons, non-U.S./LTAM consumption was 140 metric tons, and U.S. consumption was

approximately 300 metric tons. For the other scenarios, different combinations of input data

provided outputs inconsistent with other the other independent estimates.

This scenario analysis could only be conducted for the average annual period of 1998-1999.

Historical data was not available for many of the foreign consumption estimates used by the

model. In the next section, non-U.S./LTAM consumption will be approximated each year to

permit a historical trend in cocaine flow to be calculated.

Table 16

Scenario Results

Amounts and Data Sources

Scenario South American Supply U.S. Consumption Non-U.S./LTAM

Consumption

Global Accounting Input production: 615 to 955 mt

Input consumption: 120 to 175 mt

Input: 280 to 320 mt Output: 10 to 300 mt

Modified Global

Accounting

Input production: 615 to 955 mt

Input consumption: 120 to 175 mt

Input: 280 to 320 mt Output: 11 to 138 mt

Consumption Input consumption: 120 to 175 mt

Output production: 789 to 1002 mt

Input: 280 to 320 mt Input: 145-225 mt

U.S. Residual Input production: 615 to 955 mt

Input consumption: 120 to 175 mt

Output: 146 to 233 mt Input: 145-225 mt
33

4. STAR Model Estimates (1996-1999)

In this section, the STAR model is used to generate annual stage-by-stage cocaine availability

estimates for 1996-1999. First, availability estimates will be developed by beginning with the

previous year's coca cultivation estimate (Stage 1), then moving forward through the production

stages, incorporating losses along the way. These annual availability estimates terminate at Stage

5, since there are no historical consumption estimates for non-U.S./LTAM. Next, cocaine

availability estimates for the other stages (6-9) is determined by beginning with the domestic

consumption estimate (Stage 9) and working backward through prior movement stages (by

adding in losses between stages). These two approaches, cultivation-based and domestic

consumption-based, will then be compared with a third estimate of cocaine availability, the eventbased estimate of cocaine departing South America developed by the IACM. No uncertainties

will be calculated for these estimates, but there are inherent uncertainties in each component.

Reductions in uncertainty will be gained by integration of additional data sets.

Actual Production Availability Estimates

The STAR model was used to calculate the cocaine available for export from South America

growing areas by integrating all consumption (except South American) and seizure losses into the

production estimation process. Tables B1 through B4 in Appendix B show the detailed data for

the stage availability estimates for the period 1996-1999, from cultivation (Stage 1) to the base

availability at each growing area (Stage 4). Based on the calculations in tables B1 through B4,

the actual base production can be estimated for each year. These figures are summarized in table

17.

Table 17

Estimation of Cocaine Available for Export From South America

Stage Description 1996 1997 1998 1999

1 Previous Net Cultivation (ha) 214,800 209,700 194,100 190,800

2 Net Cultivation (ha) 209,700 194,100 190,800 183,000

3 Dry Coca Leaf (mt) 306,782 267,663 239,435 203,305

4 Base (mt) 887 803 759 687

Base Seizures (mt) (45) (29) (57) (21)

5 HCl (mt) 841 774 702 666

HCl Seizures (mt) (47) (58) (74) (53)

5+ Available for World Consumption

(mt)

795 715 628 613
34

These estimates are 100 to 150 metric tons less than the potential production estimates. Similar

to the potential production estimates (refer to Table 3), there is a downward trend.

The purity of seizures is normally of "export quality”. Table 18 details cocaine purity for 1996-

1999. The STAR model makes all calculations in pure amounts.

Table 18

Cocaine Purity for Export Quality

1996 1997 1998 1999

Purity 83% 83.1% 81.9% 81.9%

Consumption-Based Availability Estimates

This approach incorporates historical consumption estimates (Table 19) as the starting point and

works backward to an estimated amount of cocaine that departs South America. Because there

are no annual estimates of South American cocaine consumption, cocaine availability estimates

for stages 5-9 will be based on the U.S. consumption figures America (Appendix C presents step

by step details of the calculations). The model estimates transit zone country consumption at

three-percent of the flow through the region.

Table 19

Cocaine Domestic Consumption (pure metric tons)

1996 1997 1998 1999

Consumption 288 312 291 276

In the STAR model, cocaine availability from the transshipment area (Stage 7B) can be estimated

by adding the domestic and border seizures to the domestic consumption. Combining the

domestic consumption estimates with the domestic and border seizures results in estimates of

cocaine available in the transshipment areas. Table 20 details these figures.
35

Table 20

Calculation of Cocaine Availability at Transshipment Areas, (pure

metric tons)

Stage or Transition Description 1996 1997 1998 1999

Stage 9 Retail U.S. 288.0 312.0 291.0 276.0

Transition 8/9 domestic seizures (45.1) (25.0) (45.8) (25.4)

Stage 8 Entering U.S. 333.1 337.0 336.8 301.4

Transition 7/8 Border seizures (48.9) (47.7) (38.0) (34.7)

Stage 7B Transshipment areas 382.0 384.8 374.8 336.1

Cocaine Departing South America

To estimate cocaine availability at South American departure areas (Stage 6), the STAR model

assumes that all of the cocaine entering the Southwest Border originates in the Mexico/Central

America corridor, and that cocaine entering other border areas is divided between the Caribbean

corridor and the Direct-to-U.S. corridor proportional to the event-based estimate of cocaine

departing South America. Table 21 shows the distribution of cocaine from Stage 7B to

transshipment corridors. It is assumed that 3% of the cocaine in the transshipment area is

consumed locally. Adding the consumption losses and the seizure losses to the Stage 7B estimate

results in the estimate for the component of Stage 6 (bound for the U.S.).
36

Table 21

Distribution of Cocaine By Transshipment Corridor in Stages 6 and 7B

(pure mt)

Mexico/Central

America

Caribbean Direct to U.S. Total

1996 Stage 7B 169.1 135.1 70.6 374.8

3% consumption (5.2) (4.2) (9.4)

Transit Zone seizures (24.9) (4.8) (14.3) (44.0)

Stage 6 (toward U.S.) 199.2 144.0 84.9 428.2

1997 Stage 7B 152.5 176.1 56.2 384.8

3% consumption (4.7) (5.4) (10.2)

Transit Zone seizures (46.6) (10.2) (14.7) (71.5)

Stage 6 (toward U.S.) 203.8 191.8 70.8 466.4

1998 Stage 7B 169.1 156.0 49.7 374.8

3% consumption (5.2) (4.8) (10.1)

Transit Zone seizures (44.0) (9.5) (12.9) (66.3)

Stage 6 (toward U.S.) 218.3 170.3 62.6 451.2

Cocaine departing South America (Stage 6) splits between amounts headed towards nonU.S./LTAM markets (Stage 7A) and towards the U.S.(Stage 7B) market. There are no historical

estimates of non-U.S./LTAM consumption. The STAR Model develops its own historical

estimates, based on calculating the equivalent loss-rate. This assumes that the ratio of U.S.-

bound arrival and transit zone seizures to U.S.-bound flow is equal to the ratio of nonU.S./LTAM-bound arrival and transit zone seizures to non-U.S./LTAM U.S.-bound flow. Figure

10 details the approach and Table 22 presents the results. A two-year moving average was used

to smooth non-U.S./ LTAM seizures, which are highly variable from year to year.

Assumption of an equivalent loss-rate is a simplistic approach. Additional data is needed to

further refine the annual magnitude of cocaine smuggled to foreign markets. Event-based

estimates also provide an under-estimation.
37

Figure 10

Equivalent Market Loss Rate

FLOW TO NON-LTAM MARKETS (Y)

FLOW TO US MARKETS (X)

TZ Seizures (C)

AZ Seizures (D)

TZ Seizures (A)

AZ Seizures (B)

A + B = C + D

 X Y

Table 22

Seizure Rate for Cocaine Bound to U.S., 1996-1999

Variable Description 1996 1997 1998 1999

A TZ seizures (44.0) (71.5) (66.3) (60.6)

B Border Seizures (48.9) (47.7) (38.0) (34.7)

X Toward US 382.0 384.8 374.8 336.1

(A+B)/X TZ & AZ seizure rate 21% 26% 23% 23%

Using the Equivalent Loss Rate and data shown in Table 22, non-U.S./LTAM consumption is

calculated and results shown in Table 23. The trend in equivalent market loss estimates appear

reasonable and have been increasing, which agrees with increased South American consumption

and constant U.S. demand. This is a preliminary measure and will require further research.
38

Table 23

Cocaine for Non-U.S./LTAM Consumption

(pure metric tons)

Description 1996 1997 1998 1999

Non-U.S. seizures (2-year average) (18.7) (24.9) (27.4) (31.9)

Flow to non-US (87.5) (97.6) (118.6) (140.1)

Net non-U.S./LTAM consumption (68.8) (72.7) (91.2) (108.1)

Summary

This section has presented two estimates using the STAR model; actual production and

consumption-based estimates of cocaine departing South America. Figure 11 presents estimates

of the two. The consumption-based estimates can only be carried back to Stage 6 because of the

lack of historical estimates of South American consumption estimates. Thus there is a disconnect

between the actual production estimates and the consumption-based estimates of cocaine

departing South America, represented by the dashed line between stages 5 and 6.
39

Figure 11

Actual Production and Consumption-Based Estimates (pure metric tons)

288

333

382

523

841

887

312

337

385

570

774

803

291

337

375

567

702

759

276

301

336

565

666

687

0 100 200 300 400 500 600 700 800 900 1000

Retail U.S.

(Stage 9)

U.S. Border

(Stage 8)

Transshipment areas

(Stage 7b)

SOAM departure areas

(Stage 6)

HCl Labs

(Stage 5)

Base at growing areas

(Stage 4)

1999

1998

1997

1996
40

5. Summary of STAR Estimates (1996-1999)

This section summarizes the four differing measures of availability discussed in this paper.

1. Potential cocaine produced at HCl labs based on the potential production estimate (Stage

5).

2. Estimate of cocaine available for South American consumption and export based on

actual cocaine production (Stage 5).

3. The STAR model's estimate of cocaine departing South America based on the domesticconsumption estimate (Stage 6).

4. The IACM estimate of cocaine departing South America, based on event-based data

(Stage 6).

Figure 12 compares the four estimates described above. As expected, the potential production

estimates exceed all other estimates every year. A better measure of cocaine availability, based

on cultivation, is actual production. This set of estimates is approximately 150 metric tons less

than the potential production estimate, due to the seizure losses (however, South American

consumption has not been subtracted from this estimate). This estimate follows the same

declining trend as the potential production estimate.

Figure 12

Comparison of Differing Estimates of Cocaine Departing South America ( pure metric tons)

0

200

400

600

800

1,000

Cocaine departing SOAM (pure mt)

1996 1997 1998 1999

Potential production (INCSR)

Net actual production-based (STAR)

Domestic consumption-based (STAR)

Event-based (IACM)
41

The third estimate, domestic consumption-based, is different in both magnitude and trend, from

the previous estimate. This estimate has been stable at 500-600 metric tons over the past four

years. During the 1996-1997 period, the two STAR model estimates (production-based and

consumption-based), were 150-300 metric tons apart. But during the 1998-99 period, the two

STAR model estimates were only 50 metric tons apart.

The fourth estimate, the IACM event-based estimate, is consistent with the STAR model

production-based estimate for every year except 1997. Both estimates show a stable availability

over the 1996-99 period, and a magnitude of 525-650 metric tons.
42

6. Limitations of the STAR Model and Directions

for Improvement

While these results give insights into detailed patterns of flow, the STAR model has important

limitations. Some, but not all, of these deficiencies can be ameliorated by refining the stages and

classifications of the model, by incorporating additional data, and by undertaking data

improvement and alternative estimation procedures, such as modeling the dynamics of cultivation

data. Two more difficult problems remain:

1. The model includes no time dimension. It takes time to grow crops, process them into

cocaine, transport the product to destination countries, and distribute that product within

destination countries. This temporal dimension is highly relevant to understanding the flow

of cocaine, but it is difficult to know whether cocaine detected in transit this year was grown

and processed earlier in the year or grown and processed last year, and stored in a stockpile.

2. The model is static rather than dynamic and thus lacks economic perspective. For example,

decisions by farmers in South America to cultivate or not to cultivate cocaine are influenced

by trends in the demand for cocaine in the United States, but the model incorporates no

feedback mechanisms by which market conditions in the U.S. can affect supply, or viceversa. The model includes no calculus for predicting future cocaine flows based on current

trends in either demand or supply. Flicker and Nilsson (1996) developed a dynamic

economic model based on the assumption that the cocaine market is “demand-driven,” i.e.,

that opportunities to produce and transport cocaine are so plentiful, and profit margins so

favorable, that substitute cartels of producers quickly arise to replace cartels that are put out

of business or that can no longer enforce monopolistic controls over production and

distribution. Flicker and Nilsson provide very useful inferences about the dynamics of the

cocaine trade; similar approaches would increase the STAR’s utility.

3. The enhanced seizure data used in the STAR model may differ from existing agency seizure

estimates. The primary reason is differences in definitions and access to data. Interagency

cooperation is needed to make existing data available and standardize definitions for

categorizing seizures.
43

7. Conclusions

Available data are an imperfect reflection of true cocaine flows, but the STAR model provides a

means of incorporating differing data within a cohesive structure. Analysts are able to examine

detailed flow results as each estimate is carried forward or backward, inspect inconsistencies, and

evaluate the impact of each estimate. It provides a setting for more detailed analysis of specific

transition points. The Border Allocation model is an illustration of this, but the STAR model can

accommodate and would benefit from additional modeling efforts. The model is a power

platform for expressing specific research findings within the context of other analysis and

estimates.

The STAR model has been used to examine the integration of various data sets. It indicates that a

consumption-based estimate of cocaine availability provides a consistent approach for integrating

the various data sets. The STAR model provided estimates of cocaine availability over the 1996-

1999 period, which correlate well with the independent IACM estimate of cocaine availability.

Further research is needed to reduce the uncertainty in the estimates, and complete the flow

spectrum with estimates of South American and European cocaine consumption.
44

References

Cala, M., Comparison of IACM Published Figures With Those Extracted From a Historical Data

Base, October 1999. Office of National Drug Control Policy.

Crime and Narcotics Center and Drug Enforcement Administration, February, 1999, Colombian

Coca Yields: An Update.

Defense Intelligence Agency, Interagency Assessment of Cocaine Movement, February 1997,

Thirteenth Edition.

Defense Intelligence Agency, Interagency Assessment of Cocaine Movement: March 1999 Year

End Review.

Defense Intelligence Agency, 1998. Interagency Assessment of Cocaine Movement: February

1998 Year End Review.

Drug Enforcement Administration, 1994, Operation Breakthrough: Coca Cultivation and

Cocaine Production in Bolivia.

Drug Enforcement Administration, 1997, Operation Breakthrough: Coca Cultivation and

Cocaine Production in Peru.

Drug Enforcement Administration, August 1997, Changing Dynamics of the U.S. Cocaine Trade.

Fahrmeir L. and Tutz G. Multivariate Statistical Modeling Based on Generalized Linear Models,

Springer, New York, 1994.

Flicker, M. and K. Nilsson, 1996. A Dynamic Targeting Model of the Cocaine Trade for Policy

Makers and Intelligence Analysts. Unpublished MA thesis. Naval Postgraduate School.

Hillier, F. and G. Lieberman, Introduction to Operations Research, New York: Holden-Day,

1980, Chap. 4.

Judge G.G., Griffiths W.E., Hill R.C., Lutkepohl H, Lee T-C. The Theory and Practice of

Econometrics, Second Edition. Wiley, New York, 1985.

Layne, M., Rhodes, W., Chester, C., The Cost of Doing Business for Cocaine Smugglers, March

2000, Abt Associates Inc. Report prepared for U.S. Customs Service.

McCullagh P. and Nelder J.A. Generalized Linear Models, Second Edition. Chapman and Hall,

London, 1989.

Rhodes, W., Layne, M., Johnston, P., Hozik, L. What America’s Users Spend on Illegal Drugs,

1988-1998. May 2000, Abt Associates Inc. Report prepared for ONDCP.
45

Rhodes, W., D. Hunt, and L. Truitt, Simeone, R., 1997, A Plan for Estimating the Number of

“Hardcore” Drug Users in the United States, Abt Associates Inc.

SAS Institute Inc., 1990. SAS/IML Software: Usage and Reference. Version 6, 1st edition. Cary,

NC: SAS Institute.

SAS Institute Inc., 1990, SAS/STAT User’s Guide. Version 6, Fourth Edition. Cary, NC: SAS

Institute.

Substance Abuse and Mental Health Services Administration, 1997, Uniform Facility Data Set

(UFDS): Data for 1995 and 1980-1995. Rockville, MD: Office of Applied Statistics.
46

Appendix A: Coca Cultivation and Potential

Production Data

Table A1

Leaf Yield Factors, By Growing Area (metric tons of leaf per hectare)

1996 1997 1998 1999

Colombia (wet leaf)

Guaviare 4.7 4.7 4.7 4.7

W. Caqueta 4.1 4.1 4.1 4.1

E. Caqueta 4.7 4.7 4.7 4.7

Norte de Santander 3.9 3.9 3.9 3.9

San Lucas 4.1 4.1 4.1 4.1

Arauca 4.7 4.7 4.7 4.7

Putamayo 3.9 3.9 3.9 3.9

Macarena 4.7 4.7 4.7 4.7

Peru (dry leaf)

Upper Hallaga Valley 2.1 2.1 2.1 2.1

Aguaytia 1.7 1.7 1.7 1.7

Pachitea 2.1 2.1 2.1 2.1

Central Hallaga Valley 1.6 1.6 1.6 1.6

Lower Hallaga Valley 1.3 1.3 1.3 1.3

Apurimac 2.6 2.6 2.6 2.6

Cusco .9 .9 .9 .9

Other 1.2 1.2 1.2 1.2

Bolivia (dry leaf)

Chapare 1.86 1.78 1.64 1.19

Yungas/ Apolo .91 .97 .99 .96

Other 1.00 1.00 1.00 1.00
47

Table A2

Leaf To Base Conversion Factors, By Growing Area

(metric tons of leaf per metric tons of cocaine base)

All Years

Colombia (wet leaf)

Guaviare 959:1

W. Caqueta 959:1

E. Caqueta 1028:1

Norte de Santander 959:1

San Lucas 959:1

Arauca 959:1

Putamayo 1050:1

Macarena 959:1

Peru (dry leaf)

Upper Hallaga Valley 400:1

Aguaytia 400:1

Pachitea 400:1

Central Hallaga Valley 400:1

Lower Hallaga Valley 400:1

Apurimac 400:1

Cusco 400:1

Other 400:1

Bolivia (dry leaf)

Chapare 363:1

Yungas/ Apolo 312:1

Other 312:1
48

Table A3

Bolivia Cultivation and Potential Production Data

Year Stage Chapare Yungas Other Sum

1995 net cult (ha) 33,700 14,200 700 48,600

1996 new growth (ha) 6,800 400 0 7,200

eradication (ha) (7,500) (200) 0 (7,700)

abandonment (ha) 0 0 0 0

net change (ha) (700) 200 0 (500)

2 net cult (ha) 33,000 14,400 700 48,100

3 MT leaf(dry) 61,300 13,100 700 75,100

4 & 5 HCl (mt) 169 42 2 213

1997 new growth (ha) 5,570 0 0 5,570

eradication (ha) (7,026) (400) (400) (7,826)

abandonment (ha) 0 0 0 0

net change (ha) (1,456) (400) (400) (2,256)

2 net cult (ha) 31,544 14,000 300 45,844

3 MT leaf(dry) 0

4 & 5 HCl (mt) 155 44 1 199

1998 new growth (ha) 3,620 200 0 3,820

eradication (ha) (11,621) 0 0 (11,621)

abandonment (ha) 0 0 0 0

net change (ha) (8,001) 200 0 (7,801)

2 net cult (ha) 23,543 14,200 300 38,043

3 MT leaf(dry) 0

4 & 5 HCl (mt) 106 45 1 152

1999 new growth (ha) 500 300 0 800

eradication (ha) (15,353) 0 0 (15,353)

abandonment (ha) (1,150) (500) 0 (1,650)

net change (ha) (16,003) (200) 0 (16,203)

2 net cult (ha) 7,540 14,000 300 21,840

3 MT leaf(dry) 0

4 & 5 HCl (mt) 25 43 1 69
49

Table A4Colombia Cultivation and Potential Production Data

YEAR Stage Guaviare WCaqueta ECaqueta Caqueta Putumayo Norte de

Santander

San Lucas M

1995 net cult (ha) 28,700 15,600 6,600 1996 new growth (ha) 14,972 6,528 400 

eradication (ha) (5,072) (528) 0 abandonment (ha) 0 0 0 net change (ha) 9,900 6,000 400 

2 net cult (ha) 38,600 12,233 9,367 21,600 7,000 3 MT leaf(dry) 51,886 13,392 11,754 25,146 6,770 4 & 5 HCl (mt) 189 52 43 95 26 

1997 new growth (ha) 7,900 11,700 12,000 

eradication (ha) (17,450) (1,815) 0 abandonment (ha) 0 0 0 net change (ha) (9,550) 9,885 12,000 

2 net cult (ha) 29,050 18,691 12,794 31,485 19,000 3 MT leaf(dry) 39,049 20,461 16,055 36,516 18,377 4 & 5 HCl (mt) 142 80 59 138 71 

1998 new growth (ha) 7,450 11,800 11,100 2,800 2,800 

eradication (ha) (9,750) (3,900) 0 0 0 abandonment (ha) 0 0 0 0 0 net change (ha) (2,300) 7,900 11,100 2,800 2,800 

2 net cult (ha) 26,750 21,708 17,678 39,385 30,100 2,800 2,800 3 MT leaf(dry) 35,957 23,763 22,183 45,947 29,113 2,916 3,065 4 & 5 HCl (mt) 131 93 81 174 112 11 12 

1999 new growth (ha) 5,900 3,000 15,800 5,200 1,300 

eradication (ha) (4,600) (8,800) abandonment (ha) 0 0 net change (ha) 1,300 (5,800) 15,800 5,200 1,300 

2 net cult (ha) 28,050 14,600 19,000 33,585 45,900 8,000 4,100 3 MT leaf(dry) 37,705 15,983 23,843 39,826 44,394 8,330 4,488 

4 & 5 HCl (mt) 138 62 87 149 170 33 18 
50

Table A5Peru Cultivation and Potential Production DataYEAR Stage LHV CHV LHV/CHV UHV Aguaytia Pachitea Apurimac Cusco Other 1995 net cult (ha) 6,500 6,500 13,000 33,700 19,600 7,100 21,000 10,000 10,900 1996 net change (ha) (1,500) (1,500) (3,000) (4,300) (4,600) (900) (4,200) (1,000) (2,900) 

2 net cult (ha) 5,000 5,000 10,000 29,400 15,000 6,200 16,800 9,000 8,000 3 MT leaf(dry) 6,500 8,000 14,500 60,300 25,500 13,000 43,700 8,100 9,600 4 & 5 HCl (mt) 16 20 36 151 64 33 109 20 24 

1997 net change (ha) (2,200) (2,500) (4,700) (4,400) (6,600) (4,000) (4,200) (700) (1,000) 

2 net cult (ha) 2,800 2,500 5,300 25,000 8,400 2,200 12,600 8,300 7,000 3 MT leaf(dry) 3,600 4,000 7,600 52,500 14,300 4,600 35,300 7,500 8,400 4 & 5 HCl (mt) 9 10 19 131 36 12 88 19 21 

1998 net change (ha) (1,800) (1,400) (3,200) (4,000) (3,600) (900) (3,600) (800) (1,700) 

2 net cult (ha) 1,000 1,100 2,100 21,000 4,800 1,300 9,000 7,500 5,300 3 MT leaf(dry) 1,300 1,800 3,100 44,100 8,200 2,700 24,300 6,800 6,400 4 & 5 HCl (mt) 3 5 8 110 21 7 61 17 16 

1999 net change (ha) (100) (5,800) (3,900) (300) (900) 0 (1,300) 

2 net cult (ha) 2,000 15,200 900 1,000 8,100 7,500 4,000 3 MT leaf(dry) 2,500 31,100 900 2,100 21,100 6,700 4,800 4 & 5 HCl (mt) 6 78 2 5 53 17 12 
51

Appendix B: STAR Model for Cultivation and Base Production STable B1: 1996 STAR Model for Production Stages

STAGE 1 STAGE 2 STAGE 3 

Growing Area CO

UNTRY

Net

Cultivation

(ha)

Net

Change

(ha)

Net

Cultivation

(ha)

Leaf Not

Harvested

(ha.): 1%

Wet leaf

water

content

Effective

Wet Leaf

Yield

Effective

Dry Leaf

Yield

Licit Leaf

Consumption

(MT)

Leaf

Seizures

(MT)

Net Dry

Coca Leaf

(MT)

Guaviare CO 28,700 9,900 38,600 (386) 71.4% 4.7 1.3 0 0 51,367 W.Caqueta CO 0 12,233 12,233 (122) 73.3% 4.1 1.1 0 0 13,258 E.Caqueta CO 15,600 (6,233) 9,367 (94) 73.3% 4.7 1.3 0 0 11,637 Norte de Santander CO 0 0 0 0 73.3% 3.9 1.0 0 0 0 San Lucas CO 0 0 0 0 73.3% 4.1 1.1 0 0 0 Arauca CO 0 0 0 0 73.3% 4.7 1.3 0 0 0 Macarena CO 73.3% 4.7 1.3 Putamayo CO 6,600 400 7,000 (70) 75.2% 3.9 1.0 0 0 6,703 Upper HV PE 33,700 (4,300) 29,400 (294) 2.1 (3,114) (34) 56,518 Aguaytia PE 19,600 (4,600) 15,000 (150) 1.7 (1,589) (14) 23,642 Pachitea PE 7,100 (900) 6,200 (62) 2.1 (657) (7) 12,226 Central HV PE 6,500 (1,500) 5,000 (50) 1.6 (530) (5) 7,386 Lower HV PE 6,500 (1,500) 5,000 (50) 1.3 (530) (4) 5,902 Apurimac PE 21,000 (4,200) 16,800 (168) 2.6 (1,780) (25) 41,438 Cusco PE 10,000 (1,000) 9,000 (90) 0.9 (953) (4) 7,061 Other PE 10,900 (2,900) 8,000 (80) 1.2 (847) (5) 8,651 Chapare BO 33,700 (700) 33,000 (330) 1.9 (9,125) (65) 51,577 Yungas/ Apolo BO 14,900 200 15,100 (151) 0.9 (4,175) (12) 9,417 SUM 214,800 (5,100) 209,700 (2,097) (23,300) (176) 306,782 

Country Summaries

BO 48,600 (500) 48,100 (481) (13,300) (76) 60,993 CO 50,900 16,300 67,200 (672) 0 0 82,965 PE 115,300 (20,900) 94,400 (944) (10,000) (99) 162,824 

Andean Total 214,800 (5,100) 209,700 (2,097) (23,300) (176) 306,782 
52

Table B2: 1997 STAR Model for Production Stages

STAGE 1 STAGE 2 ST

Growing Area CO

UNTRY

Net

Cultivation

(ha)

Net

Change

(ha)

Net

Cultivation

(ha)

Leaf Not

Harvested

(ha.): 1%

Effective

Wet Leaf

Yield

Effective

Dry Leaf

Yield

Licit Leaf

Consumption

(MT)

Leaf Seizures

(MT)

Net Dr

Le

Guaviare CO 38,600 (9,550) 29,050 (291) 4.7 1.3 0 0 W.Caqueta CO 12,233 6,458 18,691 (187) 4.1 1.1 0 0 E.Caqueta CO 9,367 3,427 12,794 (128) 4.7 1.3 0 0 Norte de Santander CO 0 0 0 0 3.9 1.0 0 0 San Lucas CO 0 0 0 0 4.1 1.1 0 0 Arauca CO 0 0 0 0 4.7 1.3 0 0 Macarena CO 0.0 Putamayo CO 7,000 12,000 19,000 (190) 3.9 1.0 0 0 Upper HV PE 29,400 (4,400) 25,000 (250) 2.1 (3,634) (60) Aguaytia PE 15,000 (6,600) 8,400 (84) 1.7 (1,221) (16) Pachitea PE 6,200 (4,000) 2,200 (22) 2.09 (320) (5) Central HV PE 5,000 (2,500) 2,500 (25) 1.6 (363) (4) Lower HV PE 5,000 (2,200) 2,800 (28) 1.29 (407) (4) Apurimac PE 16,800 (4,200) 12,600 (126) 2.8 (1,831) (41) Cusco PE 9,000 (700) 8,300 (83) 0.9 (1,206) (8) Other PE 8,000 (1,000) 7,000 (70) 1.2 (1,017) (9) Chapare BO 33,000 (1,456) 31,544 (315) 1.78 (9,151) (42) Yungas/ Apolo BO 15,100 (800) 14,300 (143) 0.97 (4,149) (9) SUM 209,700 (15,521) 194,179 (1,942) 2

Country Summaries

BO 48,100 (2,256) 45,844 (458) (13,300) (51) CO 67,200 12,335 79,535 (795) 0 0 PE 94,400 (25,600) 68,800 (688) (10,000) (147) 1

Andean Total 209,700 (15,521) 194,179 (1,942) (23,300) (197) 2
53

Table B3: 1998 STAR Model for Production Stages

STAGE 1 STAGE 2 STAGE 3 

Growing Area CO

UNTRY

Net Cultivation

(ha)

Net

Change

(ha)

Net

Cultivation

(ha)

Leaf Not

Harvested

(ha.): 1%

Effective

Wet Leaf

Yield

Effective

Dry Leaf

Yield

Licit Leaf

Consumption

(MT)

Leaf

Seizures

(MT)

Net Dry Coca

Leaf (MT)

Guaviare CO 29,050 (2,300) 26,750 (268) 4.7 1.3 0 0 35,598 W.Caqueta CO 18,691 3,017 21,708 (217) 4.1 1.1 0 0 23,526 E.Caqueta CO 12,794 4,884 17,678 (177) 4.7 1.3 0 0 21,962 Norte de Santander CO 0 2,800 2,800 (28) 3.9 1.0 0 0 2,886 San Lucas CO 0 2,800 2,800 (28) 4.1 1.1 0 0 3,035 Arauca CO 0 0 0 0 4.7 1.3 0 0 0 Macarena CO 0.0 Putamayo CO 19,000 11,100 30,100 (301) 3.9 1.0 0 0 28,822 Upper HV PE 25,000 (4,000) 21,000 (210) 2.1 (4118) (62) 39,479 Aguaytia PE 8,400 (3,600) 4,800 (48) 1.71 (941) (11) 7,173 Pachitea PE 2,200 (900) 1,300 (13) 2.08 (255) (4) 2,418 Central HV PE 2,500 (1,400) 1,100 (11) 1.64 (216) (2) 1,568 Lower HV PE 2,800 (1,800) 1,000 (10) 1.3 (196) (2) 1,089 Apurimac PE 12,600 (3,600) 9,000 (90) 2.7 (1765) (35) 22,257 Cusco PE 8,300 (800) 7,500 (75) 0.91 (1471) (8) 5,278 Other PE 7,000 (1,700) 5,300 (53) 1.21 (1039) (8) 5,301 Chapare BO 31,544 (8,001) 23,543 (235) 1.64 (8231) (72) 29,922 Yungas/ Apolo BO 14,300 200 14,500 (145) 0.99 (5069) (22) 9,120 SUM 194,179 (3,300) 190,879 (1,909) (23,300) (227) 239,435 

Country Summaries

BO 45,844 (7,801) 38,043 (380) (13,300) (94) 39,042 CO 79,535 22,301 101,836 (1,018) 0 0 115,829 PE 68,800 (17,800) 51,000 (510) (10,000) (133) 84,565 

Andean Total 194,179 (3,300) 190,879 (1,909) (23,300) (227) 239,435 
54

Table B4: 1999 STAR Model for Production Stages

STAGE 1 STAGE 2 ST

Growing Area CO

UNTRY

Net

Cultivation

(ha)

Net

Change

(ha)

Net

Cultivation

(ha)

Leaf Not

Harvested

(ha.): 1%

Effective

Wet Leaf

Yield

Effective

Dry Leaf

Yield

Licit Leaf

Consumption

(MT)

Leaf Seizures

(MT)

Net Dr

Le

Guaviare CO 26,750 1,250 28,000 (280) 4.7 1.3 0 0 W.Caqueta CO 21,708 (7,108) 14,601 (146) 4.1 1.1 0 0 E.Caqueta CO 17,678 1,323 19,001 (190) 4.7 1.3 0 0 Norte de Santander CO 2,800 5,200 8,000 (80) 3.9 1.0 0 0 San Lucas CO 2,800 1,300 4,100 (41) 4.1 1.1 0 0 Arauca CO 0 1,100 1,100 (11) 4.7 1.3 0 0 Macarena CO 0 1,800 1,800 (18) 4.7 1.3 Putamayo CO 30,100 15,800 45,900 (459) 3.9 1.0 0 0 Upper HV PE 21,000 (5,065) 15,935 (159) 2.0 (4,794) (79) Aguaytia PE 4,800 (1,158) 3,642 (36) 1.0 (536) (9) Pachitea PE 1,300 (314) 986 (10) 2.1 (305) (5) Central HV PE 1,100 (265) 835 (8) 1.3 (153) (3) Lower HV PE 1,000 (241) 759 (8) 1.3 (139) (2) Apurimac PE 9,000 (2,171) 6,829 (68) 2.6 (2,616) (43) Cusco PE 7,500 (1,809) 5,691 (57) 0.9 (748) (12) Other PE 5,300 (1,278) 4,022 (40) 1.2 (710) (12) Chapare BO 23,543 (16,003) 7,540 (75) 1.2 (5252) (22) Yungas/ Apolo BO 14,500 (200) 14,300 (143) 1.0 (8048) (34) SUM 190,879 (7,838) 183,041 (1,830) (23,300) (220) 2

Country Summaries

BO 38,043 (16,203) 21,840 (218) (13,300) (56) CO 101,836 20,665 122,501 (1,225) 0 0 1PE 51,000 (12,300) 38,700 (387) (10,000) (164) 

Andean Total 190,879 (7,838) 183,041 (1,830) (23,300) (220) 2
55

Appendix C: Application of the Border and

Domestic Allocation Models, 1996-1998.

1. Calculate domestic and border seizures from the Enhanced Seizure Database:

Table C1

Border Seizures (pure metric tons)

Region 1996 1997 1998

Florida (21.2) (17.1) (13.3)

Gulf of Mexico (1.9) (1.9) (0.5)

North East (3.8) (2.8) (1.8)

Puerto Rico/

Virgin Islands

(7.4) (0.8) (1.3)

Rest of U.S. (0.1) (10.4) (4.3)

Southwest Border (14.5) (14.6) (16.9)

Sum (48.9) (47.7) (38.0)
56

Table C2

Domestic Seizures (pure metric tons)

Year New

England

Mid

Atlantic

E. North

Central

W. North

Central

S.

Atlantic

E. South

Central

W. South

Central

Mountain Pacific Sum

1996 (1.3) (1.2) (4.6) (6.9) (10.1) (8.4) (4.3) (8.2) (0.2) (45.1)

1997 (2.4) (0.3) (4.4) (0.5) (8.0) (3.3) (1.8) (4.1) (0.3) (25.0)

1998 (3.7) (0.4) (8.5) (3.9) (12.8) (5.4) (3.9) (6.6) (0.7) (45.8)
57

2. Calculate the macro availability estimates for Stages 7B-9

Table C3

Calculation of Cocaine Availability at Transshipment Areas

(pure metric tons)

Stage or Transition Description 1996 1997 1998 1999

Stage 9 Retail U.S. 288.0 312.0 291.0 276.0

Transition 8/9 domestic seizures (45.1) (25.0) (45.8) (25.4)

Stage 8 Entering U.S. 333.1 337.0 336.8 301.4

Transition 7/8 Border seizures (48.9) (47.7) (38.0) (34.7)

Stage 7B Transshipment areas 382.0 384.8 374.8 336.1

3. Run the Border Allocation Model for each year and determine the distribution of cocaine

arriving from Stage 7B.

Table C4

Domestic Seizures (pure metric tons)

Year Region NCVEH COMVEH NCAIR COMAIR NCMAR COMMAR Sum

1996 Florida 0.00% 2.50% 1.18% 46.06% 49.75%

GOMX 0.03% 0.04% 0.13% 1.35% 1.55%

NorthEast 0.03% 0.52% 0.03% 2.79% 3.37%

PR/VI 0.00% 0.05% 2.34% 0.43% 2.82%

Rest of US 0.05% 0.00% 0.00% 0.14% 0.03% 0.57% 0.80%

SWB 7.89% 33.77% 0.00% 0.03% 0.03% 0.00% 41.71%

1997 Florida 0.00% 1.70% 1.26% 41.25% 44.20%

GOMX 0.00% 0.00% 0.02% 1.14% 1.16%

NorthEast 0.00% 0.47% 0.00% 2.11% 2.59%

PR/VI 0.00% 0.04% 1.95% 9.05% 11.05%

Rest of US 0.00% 0.00% 0.00% 0.12% 0.00% 1.24% 1.37%

SWB 6.27% 33.37% 0.00% 0.00% 0.00% 0.00% 39.64%

1998 Florida 0.00% 1.21% 1.73% 44.83% 47.76%

GOMX 0.00% 0.00% 0.00% 1.18% 1.18%

NorthEast 0.00% 0.51% 0.00% 1.88% 2.39%

PR/VI 0.00% 0.04% 1.66% 0.29% 1.99%

Rest of US 0.00% 0.00% 0.00% 0.03% 0.00% 1.53% 1.56%

SWB 5.72% 39.39% 0.00% 0.00% 0.00% 0.00% 45.11%
58

4. Multiply the Stage 7B estimate, for a given year, by the percentages shown in the step #3 table

and subtract the border seizures shown in Step #1; the result is the estimate for Stage 8.

Table C5

Distribution of Cocaine in Stages 7B and 8 (pure metric tons)

1996 1997 1998

Region Stage 7B Border

Seizures

Stage 8 Stage 7B Border

Seizures

Stage 8 Stage 7B Border

Seizures

Stage 8

Florida 190.1 (21.2) 168.8 170.1 (17.1) 153.0 179.0 (13.3) 165.7

GOMX 5.9 (1.9) 4.0 4.5 (1.9) 2.6 4.4 (0.5) 3.9

North

East

12.9 (3.8) 9.1 10.0 (2.8) 7.1 9.0 (1.8) 7.2

PR/VI 10.8 (7.4) 3.4 42.5 (0.8) 41.7 7.5 (1.3) 6.2

Rest of US 3.0 (0.1) 2.9 5.3 (10.4) (5.2) 5.8 (4.3) 1.6

SWB 159.4 (14.5) 144.9 152.5 (14.6) 137.9 169.1 (16.9) 152.2

Sum 382.0 (48.9) 333.1 384.8 (47.7) 337.0 374.8 (38.0) 336.8
59

5. Run the Domestic Allocation Model for each year and determine the distribution of cocaine

arriving from Stage 8.

Table C6

Results of the Domestic Allocation Model

Year Census region Florida Gulf of Mexico North East Other

U.S.

Puerto Rico

Virgin Islands

Southwest Border

1996 New England 6% 100% 100%

Mid Atlantic 41%

E. North Central 13% 100% 20%

W. North Central 8%

S. Atlantic 33% 100%

E. South Central 7%

W. South Central 15%

Mountain 12%

Pacific 44%

1997 New England 100% 49%

Mid Atlantic 36% 100% 36%

E. North Central 21% 100% 15%

W. North Central 9%

S. Atlantic 36% 15%

E. South Central 8%

W. South Central 16%

Mountain 13%

Pacific 47%

1998 New England 7% 100% 100%

Mid Atlantic 43%

E. North Central 10% 100% 23%

W. North Central 8%

S. Atlantic 34% 100%

E. South Central 7%

W. South Central 15%

Mountain 12%

Pacific 43%
60

6. Multiply Stage 8 estimate by table percentages shown in Step #5, and sum by census area.

Table C7

Distribution of Cocaine in Stages 8 and 9 (pure metric tons)

1996 1997 1998

Census region Stage 8 Domestic

seizures

Stage 9 Stage 8 Domestic

seizures

Stage 9 Stage 8 Domestic

seizures

Stage 9

New England 8.0 (3.7) 4.3 4.6 (2.4) 2.2 22.6 (1.3) 21.3

Mid Atlantic 65.9 (0.4) 65.5 94.4 (0.3) 94.1 69.9 (1.2) 68.7

E. North Central 61.6 (8.5) 53.1 55.4 (4.4) 51.1 54.8 (4.6) 50.2

W. North Central 13.5 (3.9) 9.6 12.2 (0.5) 11.7 12.1 (6.9) 5.1

S. Atlantic 59.1 (12.8) 46.4 53.6 (8.0) 45.6 58.4 (10.1) 48.2

E. South Central 12.5 (5.4) 7.1 11.5 (3.3) 8.2 11.4 (8.4) 3.0

W. South Central 25.0 (3.9) 21.1 22.6 (1.8) 20.8 22.3 (4.3) 18.0

Mountain 19.5 (6.6) 12.9 17.6 (4.1) 13.5 17.4 (8.2) 9.2

Pacific 71.8 (0.7) 71.1 65.0 (0.3) 64.7 64.3 (0.2) 64.1
61

Appendix D: Sources For Enhanced Seizure Data

Information in FDSS Data

FDIN

Drug Name

Weight in Grams

Date of Seizure

State

Southwest Border Flag - value is “Y” if seizure was made on southwest border

Conveyance Type:

Aircraft

Business

Cargo

Internal (body)

Mail

Other

Person

Residence

Unknown

Vehicle

Vessel

Location –varies by conveyance type:

Aircraft – airport or city

Business – street address

Cargo – airport or city

Internal (body) – airport or city

Mail –courier or city

Other – latitude/longitude or city

Person – city, street address, terminal name, or name of port of entry

Residence – street address, city

Unknown – lat/long or city

Vehicle – street address, city, name of port of entry, or Border Patrol checkpoint

Vessel – lat/long, city or name of port of entry

Conveyance ID –varies by conveyance type:

Aircraft – flight number or location of drugs in aircraft

Business – name of business

Cargo – bill of lading number, type of courier

Internal (body) – number of pellets or flight number

Mail – city or bill of lading number

Other – container number, street address, or business name

Person – flight number, license plate number, carry location in/on body

Residence – street address or location in house (room)
62

Unknown – various things that can’t be categorized

Vehicle – type of car, license plate number (with state)

Vessel – vessel name

Enforcement Activity:

Abandoned

Buy/Bust

Buy/Walk

Controlled delivery

Consent search

Eradication

Free sample

Interdiction

Clandestine laboratory

Other/unknown

Reverse undercover operation

Search warrant

Traffic stop

Undercover operation
63

Information in EPIC BLISS Data

DATE Date of Incident

TIME Time of Incident

DAY Day of Incident

ZONE EPIC defined Seizure Zones within the SWB States

AZ01 – Arizona state line to 113 degrees west

AZ02 – 113 degrees west to 111 degrees west

AZ03 – 111 DEGREES west to New Mexico state line

CA01 – Pacific Coast to 116 degrees west

CA02 – 116 degrees to Arizona state line

NM01 – New Mexico west of Texas

NM02 – New Mexico north of Texas

TX01 – Anthony, TX to 105 degrees west

TX02 – 105 degrees west to 102 degrees west

TX03 – 102 degrees west to 100 degrees west

TX04 - 100 degrees west to 99 degrees west

TX05 – 99 degrees west to 98 degrees west

TX06 – 98 degrees west to Texas Gulf coast

LOCATION City, State, Country

HWY Highway Seizure Location (if applicable)

T Type

A – Abandoned

I – Intrusion by vehicle at border (not POE)

N – Investigation

F – On foot at border (not POE)

O – Other

P – Pedestrian at POE

T – Traffic stop seizure

L – Train

U – Unknown

V – Vehicle at POE

K Kind

B – Between port-of-entry

P – Through port-of-entry

U - Unknown

ENTRY Entry zone (if known) CA01, etc.

TOT Number of Suspects Detained

S Sex (M-male or F-female)

R Race

BC Birth Country

CZ Citizenship

ST Vehicle Registration State

YEAR Year Vehicle Built
64

MAKE Vehicle Make

MODEL Vehicle Model

TYPE Vehicle Type

BUS. – Bus

CAR – Car

4WD – 4-Wheel Drive

MOR – Motorcycle

FOT – On foot

OTR – Other

PUC – Pickup truck with camper

PUT – Pickup truck without camper

REC – Recreational vehicle

STW – Station wagon

TNK – Tanker Truck

TXI – Taxi

TOW - Towed vehicle

TRC – Tractor/Trailer rig

TLR – Trailer

TRN – Train

TRK – Truck

VAN – Van

WRK - Wrecker

LOC Concealment Location

DRG Type of drug

AMOUNT Amount seized

MARKING Drug marking/packaging
65

Information in Customs Seizure Data

Port

Conveyance Type

Auto

Bus

Commercial air

Fishing vessel

Bicycle

Commercial truck

Train

Motorcycle

Other

Van

Private aircraft

Mail

Truck

Commercial vessel

Pedestrian

Private vessel

Express consignment

No transport involved

Discovery Date

Agency Participation:

Discovering

Seizing

Participated in seizure

Air Operations Branch

Itinerary Info:

In/Out Bound

Date

From

Conveyance Info:

Type

Searched?

Seized?

Vessel Name

Flight #
66

Search Type

Results

Abandoned

Blitz

Dog Alert

X-Ray

Enforcement Aid Used

Long-range night vision system

Non-airborne infrared sensor devices

Airborne radar system

Mobile 3-d radar

Airborne flir system

Airborne radio d/f equipment

Unattended ground/sea intrusion detection system

UHF scanner

Remote CCTV

Hand held night vision devices

Intel

Air intel

Marine units

C3

I

Other

Plane

Enforcement profile

Helicopter

Beeper

Transponder

U.S.CS fixed radar side

Buster (density detector)

Containerized

Place of Discovery

Place of Seizure

Qty
67

FDIN

Weight Determination code

Nbr of Packages

Pkg Type

Country of Origin

Export

Destination

Concealment Location

Body cavity (including swallowed)

On body

Clothing

Other body (including dead body)

Suitcase

Trunk (as in luggage)

Box

Other bag

Mail parcel

Cargo

Auto/truck

Vessel

Aircraft

Other (bus, train, motorcycle, etc.)

Camper

Within cargo container

Express consignment package

Not concealed

Concealed in Secret Compartment
68

Information in Coast Guard Seizure Data

Amount (lbs)

Date of seizure

Coast Guard District

Drug seized

Flag country

Location

State

Seizing unit

Vessel name

Vessel type

Information sources
69

Appendix E: Technical Details of the Border

Allocation Model

In this appendix the Border Allocation model is described in considerable detail. The model

utilizes data from the Enhanced Seizure database and data about fees smugglers receive to deliver

cocaine to the U.S.

Transportation Costs

As used in this report, transportation cost is the amount it costs to ship cocaine from the source

country to a particular U.S. border destination via a particular mode of transportation. This cost

does not include the cost of lost cargo due to seizure, which is addressed subsequently.

Transportation costs were obtained from Customs Reports of Investigations (ROIs) and from

seizure and intelligence reports15. Using Customs BRS text search capability, a query was

designed to extract those ROIs, intelligence reports, and seizure reports that contained explicit

transportation cost information for 1989 through 1999. 14,328 reports were retrieved. The

textual extraction programming language, PERL was employed – first to screen for references to

cocaine, and – next, to screen for data pertaining to transportation costs. The first and second

stages reduced the 14,328 reports to 6,131 and 836 reports respectively. The ROI data extraction

process is summarized in Figure D1.

In some cases, payments consist of a portion of the load (in-kind payment), with or without a cash

payment. Because these transactions are difficult to identify through the ROI extraction process,

and, therefore, would likely be under-represented, were excluded. Data prior to 1991 were also

excluded, the earliest year for our seizure data, leaving a total of 613 transportation cost

observations.

These 613 observations were categorized by geographical region (Florida, the southwest border,

and Rest of the U.S.) and by conveyance types (noncommercial and commercial air,

noncommercial and commercial marine, and noncommercial and commercial vehicle).

Transportation costs for “Rest of U.S.” were applied to the three regions that are identified in

seizure (but not in transportation) data: northeast, Puerto Rico/Virgin Islands, and Rest of U.S.

Table E1 summarizes the cost data in terms of the average cost per kilogram, for 1991-1998.

 

15 Layne, M., Rhodes, W., Chester, C., The Cost of Doing Business for Cocaine Smugglers, March 2000,

Abt Associates Inc. Report prepared for U.S. Customs Service.
70

Figure D1

ROI Data Extraction Process

All Customs Reports of Investigation

(1989-1999)

Reports With Transportation Data

836

Cocaine Reports

6,131

Reports From BRS Search Query

14,328

BRS Search Engine

PERL

PERL

Table E1

Transportation Costs by Region and Conveyance ($ per kilogram): Average Over Years 1991-1998

Border

Region

Noncom.

Vehicle

Commercial

Vehicle

Noncom.

Air

Commercial

Air

Noncom.

Marine

Commercial

Marine

Florida - - $2,796 $3,236 $2,852 $2,902

SWB $452 $870 $2,141 $3,647 $3,530 $3,716

Rest of U.S. $1,371 $2,875 $2,788 $2,892 $2,932 $3,304

- indicates not applicable
71

Transportation costs for land conveyances (commercial/non commercial vehicles) crossing the

southwest border are much lower than elsewhere, because they do not include the costs associated

with the air or sea journey from Colombia, only with the cost of driving the cocaine from Mexico

into the U.S.16. Costs for land conveyances were adjusted such that they represent the full cost of

shipping from Colombia to the U.S. The Mexican transportation cost adjustment problem is

complicated by the fact that Colombians pay Mexican traffickers in kind (generally 35 to 50

percent of the shipment) rather than in cash17.

Colombians pay Mexican traffickers up to one half a kilogram of cocaine for each kilogram

successfully delivered. Thus, the adjusted transportation cost of shipping 1 kilogram consists of

two components:

1. Cost of shipment from Colombia to Mexico: $1,40018.

2. The in-kind cost to the Colombians.

From the Colombian perspective, the in-kind cost of shipping one kilogram is:

Wholesale price in Colombia ($2,00019): .5 x $2,000

Transportation cost from Colombia to Mexico: .5 x $1,400

Total Colombian In-Kind Cost: $1,700

The two costs, when added together, created the adjusted the transportation cost of $3,100.

For land conveyance costs to the rest of the U.S. (i.e. from Canada), the transportation cost for

Colombia to Mexico ($1,400) was used, as no other estimate was available.

Transportation Cost Smoothing Model

The transportation cost data contained several figures that were inordinately high or low.

Because the Border Allocation Model is sensitive to very high or low cost values, the cost data

 

16 Costs for moving cocaine from Canada into the U.S. are higher, suggesting that poverty in Mexico leads

to lower prices for smuggling services.

17 During the late eighties Colombians were paying the Mexicans cash fees for transportation services.

One Mexican group shipped large quantities of Colombian-owned cocaine across the border to

warehouses. They refused to release the load to Colombian wholesale distributors until they were paid

their transportation fees. Over a three-month period in 1989, 40 metric tons were seized from various

warehouses in the U.S. (including 21 metric tons from a single warehouse in Sylmar, California – the

largest cocaine seizure in U.S. history). Since then, Mexicans have adopted an in-kind arrangement.

18 Senior Special Agent Frederick J. Stacey, U.S. Customs Service, 1999.

19 Ibid.
72

were smoothed by modeling and removing outliers. A suitable model for the cost data appeared

to be a multiplicative model (with no interaction) with coefficient of variation (standard deviation

divided by the mean) proportional to sample size.

E(Citj) = exp(Regioni + Conveyancej)

CV(Citj) = φ/√nitj

In these expressions, a cost observation from the ith region and jth conveyance at the tth time

period is represented by Citj. The mean and coefficient of variation of Citj are E(Citj) and CV(Citj),

and the number of data points in itjth combination is nitj. The constant φ is to be estimated. This

model represents a considerable simplification of the original cost data, and one which residual

analysis appears to support. It is worth noting, in passing, that the specification of the coefficient

of variation is not critical, in the sense that consistency and asymptotic normality are known to

hold, even under mis-specification.20

Outliers

The transportation cost data contained several costs that were inordinately high or low. These

outlying costs were detected, and subsequently removed, in the context of the multiplicative

model above. A cost observation was deleted if its residual was sufficiently large − the residual

being the difference between the observed cost and predicted cost given the region and

conveyance. Of course, in order to gauge the degree of discrepancy, it was necessary to know the

probability distribution of residual prices. For normal linear models, the standardized residuals

(residuals divided by their standard errors) follow a standard normal distribution and the

probability of a large residual is readily calculated. In the case of the above multiplicative model,

deviance residuals (which are approximately normally distributed under an assumed gamma

response) were used in an analogous way21.

By rejecting cost observations with large residuals, one hopes to exclude a high proportion of the

erroneous data and a low proportion of the genuine data. A quantile threshold was chosen such

that the probability of excluding genuine data was 0.01. Data were deleted in an iterated fashion

because our experiments with simulated data indicate that iteration increases the probability of

detecting outliers. This occurs because the distribution of deviance residuals in early iterations is

artificially dispersed because of the presence of inordinately extreme residuals which will be

absent from subsequent iterations. In this case, no further outliers could be detected after the

ninth iteration. Of the 613 cost observations, 82, or about 13%, were deleted. Given the 1%

probability of excluding genuine data, it can be inferred that approximately 12% of the cost data

were actually erroneous. Table E2 shows some examples of excluded data.

 

20 Fahrmeir and Tutz, 1994, pp.52-55

21 McCullagh and Nelder, 1989, pp. 37-40
73

Table E2

Examples of Cost Outliers ($ per kilogram): Florida by Commercial Air

Iteration Year Region Conveyance Reported Predicted Dev. Resid

1 1998 Florida ComAir 50 3,144 -3.8

1 1998 Florida ComAir 23,000 3,144 4.5

2 1998 Florida ComAir 417 2,963 -3.0

3 1998 Florida ComAir 640 3,076 -2.8

4 1998 Florida ComAir 7,900 3,104 2.7

7 1998 Florida ComAir 926 3,098 -2.6

Table E3 shows the smoothed conveyance costs (i.e., outliers removed) actually used in the

Border Allocation Model. The model implies, among other things, that Florida’s costs are

consistently 4% higher than other regions, and that commercial marine is 14% more expensive

than commercial air, 18% more expensive than noncommercial marine, and 19% more expensive

than noncommercial air.

Table E3

Smoothed Transportation Costs by Region and Conveyance ($ per kg): Average Over Years 1991-1998

Border Region Noncom.

Vehicle

Commercial

Vehicle

Noncom.

Air

Commercial

Air

Noncom.

Marine

Commercial

Marine

Florida - - $2,998 $3,136 $3,017 $3,568

Gulf Coast - - $2,882 $3,015 $2,900 $3,431

Northeast - - $2,882 $3,015 $2,900 $3,431

PR/VI - - $2,882 $3,015 $2,900 $3,431

SWB $3,067 $3,569 $2,875 $3,007 $2,893 $3,422

Rest of U.S. $3,075 $3,578 $2,882 $3,015 $2,900 $3,431

- indicates not applicable

Based on conveyance costs alone, the least expensive route into the U.S. is by noncommercial air

through the southwest border. What then prevents the entire cocaine flow destined for the U.S.

from entering via this route?

Consider two, possibly equilibrating forces. One is that, for a given region and conveyance, the

probability of detection − and therefore the cost of seizure − increases with the total quantity

shipped. Highly traveled routes probably attract larger quantities of U.S. enforcement assets, and

low-risk methods (e.g. flying at night) tend to be crowded out as more smugglers use them.

Another possible equilibrating force is the preference to choose a border close to the ultimate
74

U.S. market. However, this second possibility was not pursued because transportation costs

within the U.S. are negligible compared to external transportation costs.

Technical Details of Border Allocation Model

The model used here is essentially an economic one that assumes that smugglers choose to

minimize total transportation costs and thus, as a group, unwittingly equalize total transportation

costs across all routes (region-conveyance combinations) and times. It is assumed that the total

transportation cost for the ijth route at the tth time, Kitj, is the transportation cost, Citj, (the sum

required to ship cocaine from its source to the ith region in the U.S. via the jth conveyance type)

plus the seizure cost, Zitj (the cost associated with the cargo being seized). From the viewpoint of

a Colombian shipper, it is assumed that the cost of seizure is simply the replacement cost of the

lost cargo. This is just the probability of seizure, Pitj, times the cost of producing a metric ton, Vt.

Since costs of production have been reasonably stable22 over the last decade, Vt is taken to equal

V. The probability of being seized for the ijth route at the tth time is simply the expected amount

seized as a fraction of the amount shipped, E(Sitj)/Titj.

It is further assumed that Colombian shippers choose routes such that transportation costs are

equal across all region-conveyance combinations and times, that is, Kitj = K for all itj. This

behavioral assumption is based on the grounds that if one route were cheaper than others,

smugglers would increase activity through that route, thus increasing the likelihood of seizure and

increasing total transportation costs, until equality prevailed. Similarly, if smugglers expected

next year’s total transportation costs to be lower than this year’s, they would choose to store some

cocaine this year and ship it next year.

Summarizing the above assumptions algebraically, the total transportation cost associated with

the ith region, jth conveyance, and tth year can be expressed as:

K = Citj + Zitj 

 = Citj + PitjV

 = Citj + {E(Sitj)/Titj}V

Solving for E(Sitj) and writing the amount through the ijth route in a given year as a proportion of

the total amount during that year, Titj = βitjTt,:

E(Sitj) = Titj(K − Citj )/V

 = βitjTt(K − Citj)/V (1)

In these expressions, Sitj and Citj are observed variables, while βitj and K are parameters to be

estimated. Incidentally, the quantities Tt and V do not affect the estimates of βitj, the key

parameters of interest.

 

22 Senior Special Agent Frederick J. Stacey, U.S. Customs Service.
75

As it stands, with 217 parameters and 224 observations, model (1) is almost saturated. The 224

seizure observations result from the 28 routes (six regions times six conveyances minus eight

structural zeros) over eight years, and the 217 parameters result from estimating K plus 27 βijs in

each year (the 28th is 1 minus the sum of the first 27 since the 28 probabilities must sum to unity).

In passing, it is worth noting that even a fully saturated model (model (1) with eight distinct Kts)

is not entirely trivial inasmuch as it provides information that is far from obvious by an inspection

of the data. Nevertheless, high parameter models tend to over-fit the data at hand, this state of

affairs is improved by letting βitj be a parsimonious function of time, βitj = fij(t).

Three simple polynomial functions were considered, ones that allowed βitj to vary over time in a

constant, linear, or quadratic fashion:

βitj = uij

βitj = uij + vijt

βitj = uij + vijt + wijt

2 

In these expressions, u, v, and w are parameters to be estimated. When these expressions are

incorporated into model (1), the resulting models contain 28, 55, and 82 parameters respectively

(e.g. the quadratic model estimates 27 uijs, 27 vijs, 27 wijs, and K), all of which are considerable

simplifications over model (1) itself. A likelihood ratio test indicated that the quadratic function

was much preferred to the linear function (p<0.0001), while the linear function was similar to the

constant function (p=0.156). Thus the model (1) becomes:

E(Sitj) = (uij + vijt + wijt

2

)Tt(K − Citj)/V (2)

In fact, it was necessary to modify model (2) in two ways. Firstly, since the βitjs are probabilities,

it was desirable to constrain them to lie between zero and one. This was achieved by expressing

βitj as a multivariate logistic function of an unconstrained parameter αitj = (uij + vijt + wijt

2

), which

means βitj took the form

βitj = exp(αitj)/{exp(αitj)}

where the sum is over all ij23. (Actually, since only 27 of the 28 βitj’s are estimated, the last β6t6,

was dropped, and the denominator changes from exp(αitj) to {exp(αitj) – exp(α6t6) + 1}).

The second modification was entirely technical. Since K is at least as large as the largest Citj, K

was estimated via the parameter γ, where K = max(Citj) + exp(γ). In light of these modifications,

the final model was:

E(Sitj) = (exp(uij + vijt + wijt

2

)/{(exp[uij + vijt + wijt

2

])})Tt{max(Citj) + exp(γ) − Citj}/V 

(3)

V(Sitj) = σ2

 

23 Judge et al., 1985, pp 770-77
76

In these expressions, Sitj represents the kilograms seized from the ith region, jth conveyance and

tth year, with mean E(Sitj) and variance V(Sitj). Note that parameters such as uij actually represent

the sum of 27 parameter-dummy variable terms of the form uijIij, where Iij = 1 for the ijth regionconveyance and Iij = 0 otherwise. As previously noted, estimates of βitj are unaffected by the

inclusion of Tt, but for each year an estimate of Tt was obtained to produce estimates of Titj of the

form Titj = Tt x βitj. In this study, the estimate of Tt was obtained as the sum of (1) estimates of

pure cocaine consumed in the U.S., (2) pure cocaine seized inside the U.S., and (3) pure cocaine

seized at the U.S. border.

Model (3) was successfully fit via the method of least squares with the Gauss-Newton algorithm

using SAS’s NLIN procedure. The analysis of residual (the difference between observed and

predicted seizures) supported the adequacy of the model specification in various ways (Table E4).

First, the variance of the residuals was unrelated to the mean level of seizures, which vindicates

the assumption of constant variance. Second, residuals were small relative to seizure amounts,

which implies the model closely fit the observed seizure data. Third, there was no obvious

region-conveyance pattern in the residuals, which suggests that the model fit the data uniformly

well.

Table E4

Residuals By Region and Conveyance (metric tons): Average Over Years 1991-1998

Border

Region

Noncom.

Vehicle

Commercial

Vehicle

Noncom.

Air

Commercial

Air

Noncom.

Marine

Commercial

Marine

Florida - - 0.11 0.02 0.02 0.02

Gulf Coast - - -0.03 -0.04 0.02 0.04

Northeast - - -0.03 0.02 -0.03 0.00

PR/VI - - 0.15 0.00 0.12 0.30

SWB -0.10 -0.02 0.01 0.01 0.45 0.02

Rest of U.S. -0.04 -0.19 0.03 0.02 -0.03 -0.01

- indicates not applicable

Limitations of the Model

As a nonlinear economic model, the Border Allocation Model represents a new approach to

estimating cocaine availability at the U.S. border, and its estimates are strikingly different from

those that might be obtained from simpler models, such as those assuming proportionality

between seizures and flows. Nevertheless, the Border Allocation Model has important

limitations, both as a model and in terms of the data on which it is based. The following are some

of these limitations:
77

1. It was assumed that production costs for cocaine, V, and total transportation costs, K, have

been constant over the period 1991 through 1998. That is, it was assumed Vt = V and Kt = K

for all t. More accurate data is needed.

2. The method used to reconcile southwest border and Canadian transportation costs with

transportation costs in other regions is tenuous. In particular, the estimate used for the

Colombia-to-Mexico leg needs improvement, and an invariant 50% payment-in-kind is

undoubtedly an over-simplification.

3. It was noted that definitions of seizure are inconsistent, particularly at the southwest and

Florida borders. This inconsistency should be addressed.

4. The economic component of the model could be made more realistic. For example, the cost

of a seizure may be more involved than simply the replacement cost of lost cargo. Also, the

model may be insufficiently dynamic in that it implicitly assumes a market that instantly

equilibrates. However, it should be noted that the model is already complicated from a

statistical viewpoint (e.g. difficulties in convergence occurred with certain optimization

methods), and economic enhancements are likely to cause further complications.

Because the typical, noncommercial drug smuggling flight stops short of the U.S. border, the

model does not accurately reflect the contribution of noncommercial air. More generally, the

model may benefit by incorporating more realistic descriptions of the Colombia-to-U.S.

transportation routes.
78

Appendix F: Technical Details of U.S. Domestic

Allocation Model

The premise of the Domestic Allocation Model is consistent with a classic, operations research

transportation problem: given quantities of cocaine entering the domestic market at six border

regions, and given quantities demanded in each of ten U.S. divisions, it is assumed traffickers

determine the allocation that satisfies demand in all divisions while minimizing total

transportation costs. Standard linear programming techniques are used.

The general transportation problem is concerned with distributing a commodity from a group of

supply centers (sources), to a group of receiving centers (destinations), in such a way as to

minimize total distribution cost. In general, suppose that the ith source (i=1,2, ...., m) has a

supply of Si units to distribute to n destinations and the jth destination (j=1, 2, ... , n) has a

demand of Dj units to be received from the m sources. If Xij is the number of units to be

distributed from source i to destination j, then Si = Xij, and Dj = Xij.

Subject to these demand and supply constraints, it is assumed suppliers choose Xij in order to

minimize the total distribution cost, Z = f(Cij, Xij), where Cij is the distribution cost per unit.

For simplicity, it is further assumed that the distribution cost are proportional to the number of

units distributed, so that f(Cij, Xij) = CijXij. The Domestic Allocation Model now becomes a

standard linear programming problem, which is solved using the LP call in SAS IML:

m n

Minimize Z = Σ Σ CijXij

i=1 j=1

n

Subject to Σ Xij = Si, for i=1, 2,..., m

j=1

m

Σ Xij = Dj for j=1, 2,..., n

i=1

Xij > 0, for all i and j

In generic terms, the observed variables S, D, and C represent supply, demand and costs of

distribution. In our particular setting, Si is the amount of cocaine that passes through the ith U.S.

border region without being seized. This is obtained from the Border Allocation Model described

earlier as the estimated total flow into the ith region (summed over all conveyances) minus the

total amount seized, minus Federal non-border seizures. The demand at the jth census division,

Dj, is estimated as the fraction of the number of treatment clients24 in the census division divided

 

24 Substance Abuse and Mental Health Services Administration, 1997, Uniform Facility Data Set (UFDS):

Data for 1995 and 1980-1995. Rockville, MD: Office of Applied Statistics.
79

by the total amount of cocaine consumed in the U.S.25 The costs of distribution, Cij, is the cost of

shipping via U.S. interstate highways, including costs associated with risks of seizure en route.

This is assumed to be roughly proportional to the distance between origin and destination.

Limitations of the Model

While the model provides a plausible first-order method for allocating cocaine from border

regions to consumption areas, a fundamental flaw is its assumption that there are no barriers to

trade. As cocaine is illegal, transporting it involves considerable risk, and paying for taking on

this risk must surely dwarf the costs of gasoline. Further, cocaine transporters cannot simply

carry their goods to the nearest/cheapest city, but must go to a place where they have a buyer.

Finally, state and local seizures have not been accounted for. Consequently, our working

estimates may be significantly flawed.

 

25 Rhodes, W., Layne, M., Johnston, P., Hozik, L.1995. What America’s Users Spend on Illegal Drugs,

1988-1998. November 1999, Abt Associates Inc. Report prepared for ONDCP.