Estimation of 

Cocaine Availability

1996-2000

Executive Office of the President

Office of National Drug Control Policy

MARCH 2002
ii
iii

Estimation of

Cocaine Availability

1996-2000

Prepared for:

Office of National Drug Control Policy

Office of Planning and Budget

Under HHS Contract No. 282-98-0006

Prepared by:

Abt Associates, Inc.

1110 Vermont Avenue, NW

Suite 610

Washington, DC 20005-3522

March 2002

NCJ 192335

PO 3265
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Contents

Acknowledgements..............................................................................................................................ix

Disclaimer...................................................................................................................................ix

Public Domain Notice.................................................................................................................ix

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

Stages in the STAR Model ..........................................................................................................4

STAR Model Estimates .............................................................................................................10

Consumption-Based Availability Estimates ..............................................................................12

Cocaine Departing South America..................................................................................12

Conclusion .................................................................................................................................14

Data Used in the STAR Model ..........................................................................................................16

Potential Production and Actual Production..............................................................................16

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

Movements in the Transit Zone .................................................................................................18

Enhanced Seizure Database .......................................................................................................20

Border Allocation Model ...........................................................................................................23

Domestic Seizures......................................................................................................................28

Domestic Allocation Model.......................................................................................................28

Limitations of the STAR Model and Directions for Improvement................................................32

References...........................................................................................................................................33

Tables

Table 1 - STAR Model Stages and Transitions......................................................................................3

Table 2 - Transition and Stages, 1996-2000.........................................................................................11

Table 3 - Seizure Rate for Cocaine Bound to U.S., 1996-2000 ...........................................................13

Table 4 – Cocaine for Non-U.S./LTAM Consumption (pure metric tons) ..........................................13

Table 5 - Cultivation and Potential Production Estimates, 1996-2000 ................................................16

Table 6 - Event-Based Cocaine Amounts Departing South America By Transit Corridor,

 1996-2000 (bulk metric tons) .........................................................................................................19

Table 7 - Border Seizures, 1991-2000 (bulk metric tons) ....................................................................22

Table 8 - Border Seizures (bulk metric tons): Average Over Years, 1991-2000 ................................25

Table 9 - Border Seizures (percent): Average Over Years, 1991-2000 ..............................................25

Table 10 - Non-Border, Domestic Seizures, By Census Division, 1991-2000 (bulk metric tons).......30

Table 11 - Percent of Cocaine From Border Entry Regions to Census Divisions, 2000......................31
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Figures

Figure 1 - Geographic Areas of STAR Stages .......................................................................................5

Figure 2 - Availability at U.S. Bound Transshipment Corridors and Non-U.S./

 LTAM Markets (Stage 7) ..................................................................................................................7

Figure 3 - U.S. Border Entry Regions ....................................................................................................8

Figure 4 - Domestric Retail Areas..........................................................................................................9

Figure 5 - Equivalent Market Loss Rate...............................................................................................13

Figure 6 - Actual Production and Consumption-Bases Estimates (pure metric tons) ..........................15

Figure 7 - Potential Cocaine Production, 1990-2000 (mt) ...................................................................18

Figure 8 - Seizures at the U.S. Border, 1991-2000 (bulk metric tons).................................................21

Figure 9 - Smoothed Seizures in Border Entry Regions, 1991-1999 (bulk metric tons)......................23

Figure 10 - Border Allocation Model Amounts by Region (pure metric tons), 1991-2000.................26

Figure 11 - Border Allocation Model: Amounts by Conveyance (pure metric tons), 1991-2000.......28

Appendix A – Coca Cultivation and Potential Production Data Tables.....................................A-1

A1 – Leaf Yield Factors, By Growing Area (metric tons of leaf per hectare) ..................................A-1

A2 – Leaf to Base Conversion Factors, By Growing Area ...............................................................A-2

A3 – Bolivia Cultivation and Potential Production Data ..................................................................A-3

A4 – Columbia Cultivation and Potential Production Data ..............................................................A-4

A5 – Peru Cultivation and Potential Production Data .......................................................................A-5

Appendix B – STAR Model for Cultivation and Base Production Stages Tables......................B-1

B1 – 1996 STAR Model for Production Stages ................................................................................B-2

B2 – 1997 STAR Model for Production Stages ................................................................................B-3

B3 – 1998 STAR Model for Production Stages ................................................................................B-4

B4 – 1999 STAR Model for Production Stages ................................................................................B-5

Appendix C – Application of the Border and Domestic Allocation Models, 1996-2000 Tables

C1 – Border Seizures (pure metric tons) ...........................................................................................C-1

C2 – Domestic Seizures (pure metric tons) .......................................................................................C-1

C3 – Calculation of Cocaine Availability at Transshipment Areas, 1996-2000

 (pure metric tons) .........................................................................................................................C-2

C4 – Border Allocation Distributions ................................................................................................C-2

C5 – Distribution of Cocaine in Stages 7B and 8 (pure metric tons) ................................................C-3

C6 – Results of the Domestic Allocation Model ...............................................................................C-4

C7 – Distribution of Cocaine in Stages 8 and 9 (pure metric tons) ...................................................C-6

Appendix D – Enhanced Seizure Data ...........................................................................................D-1

Border Seizures .................................................................................................................................D-1

Information in FDSS Data .................................................................................................................D-3

Information in EPIC BLISS Data ......................................................................................................D-5

Information in Customs Seizure Data ...............................................................................................D-7

Information in Coast Guard Seizure Data .......................................................................................D-10
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Appendix E – Technical Details of the Border Allocation Model ............................................... E-1

Transportation Costs .......................................................................................................................... E-1

Transportation Cost Smoothing Model ............................................................................................. E-4

Technical Details of Border Allocation Model ................................................................................. E-6

Limitations of the Model.................................................................................................................. E-10

Appendix E - Tables

Table E1 – Transportation Costs by Region and Conveyance ($ per kilogram): Average

 Over Years, 1991-1998 ............................................................................................................. E-3

Table E2 – Examples of Cost Outliers ($ per kilogram): Florida by Commercial Air .................... E-5

Table E3 – Smoothed Transportation Costs by Region and Conveyance ($ per kg):

 Average Over Years, 1991-1993 ............................................................................................... E-6

Table E4 – Residuals By Region and Conveyance (metric tons): Averae Over Years,

 1991-1998. ............................................................................................................................... E-10

Appendix E – Figures

Figure E1 – ROI Data Extraction Process ......................................................................................... E-2

Appendix F - Technical Details of U.S. Domestic Allocation Model ........................................... F-1

Limitations of the Model ................................................................................................................... F-2
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Acknowledgements

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

order Number 13, by Mary Layne, Patrick Johnston, and William Rhodes of Abt Associates, Inc.

Michael Cala, Office of Planning and Budget, ONDCP, serves as the Project Director for this

research.

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.

Thanks to Ms. Bonnie Robinson, OPB Program Support Specialist, for providing extensive editing

and publishing corrections to facilitate the final printing of this report and posting of the electronic

version.

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. The cut-off date for data used for

this project was June 30, 2001.

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”.
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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 updates the

Sequential Transition and Reduction (STAR) model through 2000, providing 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 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.

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 1 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 this research, the model begins at

stage 9 and works backward -- adding in reductions -- to a U.S. consumption-based estimate of

cocaine that departs South America. It also simultaneously begins at stage 1 and works forward

-- subtracting reductions – to a cultivation -based estimate of.
2

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 estimates (Rhodes, 2002).

The STAR model applies sequential transitions through a serious of matrix operations1. This

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

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

connecting flows, as well as flow amounts2.

 

1 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 ,

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

subarea.

2 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.
3

Table 1 - 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, base seizures

Operation

Breakthrough

Stage 4 - Base availability at growing areas

Transition 4/5 Base movement to

cocaine HCl labs, lab seizures IACM

Stage 5 - Cocaine produced at labs

Transition 5/6 Cocaine HCl movement

from labs to 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

Stages in the STAR Model

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.

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 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 conversions 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).
5

Figure 1 - Geographic Areas of STAR Stages

Stage 1: Net Cultivation From Previous Growing Year

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 Dry 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 available dry leaf amounts, measured in metric tons. The

transition from Stage 2 and Stage 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 dry leaf amounts, by growing region. Transition

3/4 applies leaf-to-cocaine conversion factors (detailed in table A2, Appendix A) for each

growing region.
6

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). The STAR model apportions base from 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), and source country consumption (based on a preliminary assessment).

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

Figure 2 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

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

apportioned in the same proportion as flows in the event-based data.
7

Figure 2 - 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 3 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

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.
8

Figure 3 - 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. Both the Enhanced Seizure

Database and 8 the Border Movement Model are described in the Section 2.

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 4 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 (described in Section 2).
9

Figure 4 - Domestric Retail Areas
10

STAR Model Estimates

The STAR model is used to generate annual stage-by-stage cocaine availability estimates for

1996-2000. First, availability estimates are 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 terminating at the amount available to

depart South America. South American cocaine consumption amounts are preliminary.

Parallel cocaine availability estimates for stages (6-9) are 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.

Table 2 summarizes cocaine availability estimates developed by the STAR model for the period

1996-2000. 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 the mean of

cultivation estimates and domestic consumption estimates. The STAR model makes all

calculations in pure amounts, using two purity figures – one for import purity3 and retail purity

(Rhodes, 2002) for domestic amounts.

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

growing areas by integrating all consumption and seizure losses into the production estimation

process. Tables B1 through B5 in Appendix B show the detailed data for the stage availability

estimates for the period 1996-2000, 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 estimates are 100 to 150 metric tons less than

the potential production estimates. Similar to the potential production estimates, there is a

downward trend.

 

3 Obtained from the DEA estimate for Colombia.
11

Table 2 - Transition and Stages, 1996-2000

TRANSITION STAG E 1996 1997 1998 1999 2000

1 to 2 STAGE 1: Previous Year Net Cultivation (ha) 214,800 209,700 194,179 190,878 183,075

New Growth (ha) 29,099 37,170 39,770 36,400 26,000

STAGE 2: Current Year Net Cultivation (HA) 209,700 194,179 190,878 183,075 184,922

Transition Accounts For:

Eradication (ha) 14,560 30,554 33,096 42,553 21,245

Abandonment (ha) 19,640 22,137 9,975 1,650 2,910

2 to3 STAGE2: Net Cultivation (ha) 209,700 194,179 190,878 183,075 184,922

Dry Leaf Yield Factors

STAG E 3: Net Dry Coca Leaf 

(MT)

304,055 262,495 238,760 210,452 207,081

Transition Accounts For:

Leaf Not Harvested (ha.): 1% 2,097 1,942 1,909 1,831 1,849

Licit Leaf Consumption (MT) 23,300 23,300 23,300 23,300 23,300

Leaf Seizures (MT) 175 197 227 220 107

3 to 4 STAG E 3: Net Dry Coca Leaf 

(MT)

304,055 262,495 238,760 210,452 207,081

Processing Efficiency

STAGE 4: Coca Base (MT) 896 805 775 726 736

4 to 5 STAGE 5: HCl Labs (Pure MT) 851 776 718 703 721

Transition Accounts For:

Base Seizures (MT) 45 29 57 22 15

Seizures from Labs (Pure MT) 0 0 0 0 0

5 to 6 STAGE 6: Departure Areas (Pure MT) 602 616 600 554 537

Transition Accounts For:

Spoilage (Pure MT): 1% 9 8 7 7 7

Seizures (Pure MT) 40 35 36 42 77

South American consumption (Pure MT) 100 100 100 100 100

DEPARTURE AREAS (MI DPOINT OF CULTIVATION/CONSUMPTION 

APPROACHES)

579 592 575 577 519

6 to7 STAGE 6: Departure Areas (Pure MT) 555.2 568.8 549.6 600.7 500.4

Colombia 555.2 471.3 444.7 390.3 452.7

Peru-Ecuador 0.0 46.4 34.9 148.5 37.0

Bolivia 0.0 51.0 70.0 61.9 10.6

7ADepart To ward U.S. Markets (Pure MT) 389.4 344.4 345.0 343.7 327.0

Transition Accounts For:

Transit Seizures (Pure MT) 45.6 74.0 69.7 62.3 71.1

Transit Country Consumption (Pure MT): 3% 9.6 9.1 9.2 9.1 8.3

7BDepart for non-US Markets (Pure MT): 90.9 109.7 99.8 145.2 72.7

Transition Accounts For:

Transit Seizures (Pure MT) 19.7 31.6 25.8 40.4 21.2

7 to 8 STAGE 7: Transshipment Areas (Pure MT) 389.4 344.4 345.0 343.7 327.0

MX/CENTAM 162.4 136.5 155.7 157.2 156.0

Caribbean 149.0 157.8 143.4 136.8 112.9

Direct to U.S. 77.9 50.1 46.0 49.7 58.2

STAGE 8: Entering U.S.(Pure 

MT)

338.7 295.0 305.2 290.5 279.4

Transition Accounts For:

Border Seizures (Pure MT) 50.7 49.4 39.9 53.2 47.7

8 to 9 STAGE 8: Entering U.S.(Pure 

MT)

338.7 295.0 305.2 290.5 279.4

Florida 171.7 134.5 150.8 147.3 125.2

Gulf of Mexico 4.1 2.0 3.6 3.8 3.9

NorthEast 9.2 6.0 6.4 7.3 6.0

Other U.S. 3.0 3.8 4.0 4.7 5.1

Puerto Rico/Virgin Islands 3.3 27.3 2.4 1.6 1.9

Southwest Border 147.4 121.4 137.9 125.8 137.1

STAGE 9: Retail U.S. (Pure MT) 300.9 275.0 267.0 271.0 259.0

Transition Accounts For: 0.0

Domestic Seizures (Pure MT) 37.8 20.0 38.1 19.5 20.4

Import Purity 86.0% 86.0% 86.0% 84.2% 81.7%

Retail Purity 69.5% 66.5% 68.3% 62.8% 57.5%
12

Consumption-Based Availability Estimates

This approach incorporates historical consumption estimates (Stage 9) as the starting point and

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

availability estimates for stages 6-9 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.

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.

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. 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.).

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

5 details the approach and Table 3 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 under-estimation.
13

Figure 5 - 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 3 - Seizure Rate for Cocaine Bound to U.S., 1996-2000

Variable Description 1996 1997 1998 1999 2000

A TZ Seizures 45.6 74.0 69.7 62.3 71.1

B U.S. Border Seizures 50.7 49.4 39.9 53.2 47.7

X Flow Toward U.S. 389.4 344.4 345.0 343.7 327.0

(A+B)/X TZ & AZ Seizure Rate 25% 36% 32% 34% 36%

Using the Equivalent Loss Rate and data shown in Table 3, non-U.S./LTAM consumption is

calculated and results shown in Table 4. 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.

Table 4 – Cocaine for Non-U.S./LTAM Consumption (pure metric tons)

Description 1996 1997 1998 1999 2000

Non-U.S. seizures (2-year average) 19.7 31.6 25.8 40.4 21.2

Flow to non-U.S. 110.6 141.3 125.6 185.7 93.9

Net non-U.S./LTAM consumption 90.9 109.7 99.8 145.2 72.7
14

The STAR model develops two estimates of cocaine departing South America; actual production

based on cultivation estimates and U.S. consumption based. Figure 8 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, although we used recently

developed estimates by CNC. 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.

Conclusion

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 stages4. The

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

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.

 

4 In addition to the STAR Model, this document also updates 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 describe the distribution of cocaine flow

arriving at the U.S border by conveyance and mode, followed by distribution within the U.S.
15

Figure 6 - Actual Production and Consumption-Bases Estimates (pure metric tons)

301

339

389

555

751

896

275

295

344

569

676

805

267

305

345

550

618

775

271

290

344

601

603

726

259.00

279.36

327.03

500.36

621

736.01

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 ar eas

(Stage 4)

2000

1999

1998

1997

1996
16

Data Used in the STAR Model

Potential Production and 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. Actual production is used in the STAR model.

Table 5 summarizes the stage-by-stage summary of potential production estimates for each year

(see Appendix A for details). Over the period 1996-2000, 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.

Table 5 - Cultivation and Potential Production Estimates, 1996-2000

Stage Description 1996 1997 1998 1999 2000

1 Previous Net Cultivation (ha) 214,800 209,700 194,100 190,800 183,000

2 Net Cultivation (ha) 209,700 194,100 190,800 183,000 231,900

3 Dry Coca Leaf Yield (mt) 333,603 294,242 265,498 230,383 328,325

4 Base Production (mt) 950 875 825 760 777

5 HCl Production (mt) 950 875 825 760 777

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
17

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, the recent Operation Breakthrough results for Colombia make it clear that Colombia is

not only a major cocaine producer, but also a leading coca cultivator.

Figure 7 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,

strength of the herbicide, and timing of the harvest). The Breakthrough (DEA 1994, DEA 1997,

DEA 2001) 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 recovering5.

Figure 7 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 75% by 2000. Applying time-series techniques to the raw data could reduce

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

 

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

Eighteenth Edition, Mid-Year Review, p. 2.
18

Estimates of Cocaine Departing South America Using EventBased 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.

Figure 7 - Potential Cocaine Production, 1990-2000 (mt)

0

100

200

300

400

500

600

700

800

900

1000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Peru

Total 

Bolivia

Colombian
19

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

the amount of cocaine that departs South America along major trafficking routes6. 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.

Table 6 includes event-based estimates of cocaine departing South America for 1996 through

20007. 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.

Table 6 - Event-Based Cocaine Amounts Departing South America By Transit Corridor,

1996-2000 (bulk metric tons)

1996 1997 1998 1999 2000

Caribbean 174.5 138.4 160.3 220 200

Mexico/Central America 341.7 250.7 318.6 277 424

Direct to U.S. 91.2 43.9 51.4 15 19

Non-U.S. Destinations 42.8 62.6 64.5 75 104

Unknown 2.5 - 1.0 - -

Total 652.7 495.6 595.8 587.0 747.6

 

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

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

published in the IACM. See Cala, 1999.
20

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 1991-2000 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.

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)8, 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

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

seizures. Appendix D details the method used to create the enhanced seizure data.

Figure 8 presents a plot of total border seizures for the years 1991-2000. The figure shows that

while there are fluctuations from year to year, they have remained fairly level. The chart also

includes a two-year moving average line, to smooth year-to-year variations. Table 8 details

border seizures by conveyance types.

In Figure 9, smoothed seizure (three-year moving average for southwest border and two-year

moving average for all other areas9) figures are plotted by region, for the period 1992 through

2000. Seizures on the southwest border (the solid line at the top of the figure) remained relatively

constant until 1999, when there was an increase. Seizures in Florida (the dotted line at the top of

the chart) have declined over most of the period, with an increase in 2000. Seizures in Puerto

Rico/Virgin Islands have steadily increased.

 

8 BLISS data were unavailable for 1999 and 2000, instead the southwest border seizure figures from IACM

publications (DIA 2000, DIA 2001) were used.

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

year.
21

Figure 8 - Seizures at the U.S. Border, 1991-2000 (bulk metric tons)

0

10

20

30

40

50

60

70

80

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Bulk Metric Tons

Two Year Moving Average
22

Table 7 - Border Seizures, 1991-2000 (bulk metric tons)

1991 1992 1993 1994 1995 1996 1997 1998 199

Commercial Air 5.5 6.2 7.7 7.4 9.6 6.1 6.3 3.0 6.Commercial Maritime 28.5 23.4 21.5 21.5 10.5 22.2 25.0 14.4 13Commercial Vehicle 3.4 7.3 5.4 2.9 8.1 7.7 5.6 7.4 - Noncommercial Air 6.9 4.1 5.1 2.6 0.6 0.9 0.5 0.0 0.Noncommercial Maritime 9.3 4.2 7.2 10.4 24.4 12.3 11.8 8.7 6.Noncommercial Vehicle 7.0 11.5 8.6 9.5 11.5 8.8 7.3 11.2 - 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 - Land 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 37Total (export quality mt) 61.9 59.8 55.8 55.6 68.4 59.0 57.4 46.4 63

a Southwest border figures for 1999 and 2000 were obtained from IACM publications (DIA 2000, DIA 2001) and land incorporates commercial vehicle,

noncommercial vehicle, pedestrian, and rail.

- indicates no data available.
23

Figure 9 - Smoothed Seizures in Border Entry Regions, 1991-1999 (bulk metric tons)

0

5

10

15

20

25

30

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Bulk Metric Ton

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.
24

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.

Tables 7 and Table 8 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 ten-year period, 39% of total seizures occurred at the southwest border

(SWB) and 39% at the Florida border. In terms of conveyances, 34% of the seizures occurred

from land-based-conveyances (commercial/noncommercial vehicle, pedestrian, and rail), while

commercial marine ships, noncommercial marine, commercial air, and noncommercial air

accounted for 33%, 19%, 11%, 4%, respectively.

Table 9 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 1991-2000 – 48% of cocaine destined for the U.S. arrives at Florida via

commercial marine conveyances and 38% arrives at the southwest border via land-based

conveyances. Note that the distribution of cocaine amounts (Table 9) differs considerably from

the distribution of cocaine seizures (Table 8). 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 22% 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 10 plots the amount of cocaine arriving at each border region for the period 1991-2000.

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

border. Taking the ten-year period as a whole, quantities arriving at the southwest border have
25

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.

Table 8 - Border Seizures (bulk metric tons): Average Over Years, 1991-2000

Border Region Land Noncom

Air

Commercial

Air

Noncom

Marine

Commercial

Marine Total

Florida - 0.0 4.3 4.4 13.1 22.6

Gulf Coast - 0.0 0.0 0.6 1.3 1.9

Northeast - 0.0 1.2 0.0 2.4 3.6

PR/VI - 0.7 0.3 4.7 1.3 7.1

SWB 19.6 0.4 0.2 1.7 0.7 22.7

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

Total 19.6 2.1 6.3 11.4 19.2 58.5

- indicates not applicable

Table 9 - Border Seizures (percent): Average Over Years, 1991-2000

Border

Region

Land Noncom

Air

Commercial

Air

Noncom

Marine

Commercial

Marine Total

Florida - 1.4 7.4 7.4 22.4 38.6

Gulf Coast - 0.0 0.0 1.0 2.2 3.2

Northeast - 0.0 2.1 0.0 4.1 6.2

PR/VI - 1.2 0.6 7.9 2.3 12.0

SWB 33.5 0.7 0.4 3.0 1.2 38.8

Rest of U.S. 0.0 0.3 0.3 0.0 0.6 1.2

Total 33.5 3.6 10.7 19.4 32.8 100.0

- indicates not applicable
26

Table 10 - Percent Allocation of Cocaine By Border Region and Conveyance: Average

 Over Years, 1991-2000

Border Region Land Noncom

Air

Commercial

Air

Noncom

Marine

Commercial

Marine Total

Florida - 0.3 2.0 1.4 47.6 51.3

Gulf Coast - 0.0 0.0 0.2 1.6 1.8

Northeast - 0.0 0.5 0.0 3.1 3.6

PR/VI - 0.2 0.1 1.5 1.0 2.8

SWB 38.3 0.1 0.1 0.5 0.7 39.7

Rest of U.S. 0.0 0.0 0.0 0.0 0.7 0.8

Total 2.7 54.7 38.2 0.6 3.6 100.0

- indicates not applicable

Figure 10 - Border Allocation Model Amounts by Region (pure metric tons), 1991-2000

0

50

100

150

200

250

300

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Pure Metric Tons

Florida

Gulf Coas

Northeast

PR/VI

SWB

Rest Of U
27

Figure 11 plots model estimates by conveyance type. Conveyance types of choice appear to be

land-based conveyances 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 11 shows that, over the ten year

period, conveyance by land has increased at the expense of conveyance by commercial marine:

land-based conveyances increased by 25% (from 118 to 158 metric tons) and commercial marine

decreased by 53% (from 272 to 177 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 10 and 11 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.
28

Figure 11 - Border Allocation Model: Amounts by Conveyance (pure metric tons), 1991-

2000

0

50

100

150

200

250

300

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Pure Metric Tons

Com Air

Com Marine

Land Conveyances

NonCom Marine

NonCom Air

Domestic Seizures

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

States. Table 10 shows the annual domestic seizures allocated by census regions.

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.
29

Table 11 shows, for each border entry region, the percentage of cocaine moved to each

consumption region in 200 (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 traffickers10.

 

10 Drug Enforcement Administration, August 1997, Changing Dynamics of the U.S. Cocaine Trade.
30

Table 10 - Non-Border, Domestic Seizures, By Census Division, 1991-2000 (bulk metric tons)

1991 1992 1993 1994 1995 1996 1997 1998 

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 64.1 61.4 44.7 61.3 46.6 54.3 30.4 55.7 

a Census division breakdowns unavailable at this time.
31

Table 11 - Percent of Cocaine From Border Entry Regions to Census Divisions, 2000

Florida Gulf Coast Northeast Rest of U.S.

Puerto Rico

Virgin Islands

Southwest

Border

New England 5% 0 100% 100% 36% 0

Mid Atlantic 43% 0 0 0 0 0

E. North Central 9% 100% 0 0 0 23%

W. North Central 0 0 0 0 0 8%

S. Atlantic 36% 0 0 0 100% 0

E. South Central 7% 0 0 0 0 0

W. South Central 0 0 0 0 0 15%

Mountain 0 0 0 0 0 11%

Pacific 0 0 0 0 0 42%
32

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.
33

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.

Defense Intelligence Agency. Interagency Assessment of Cocaine Movement: Year End Review,

February 1998.

Defense Intelligence Agency. Interagency Assessment of Cocaine Movement: Year End Review,

March 1999.

Defense Intelligence Agency. Interagency Assessment of Cocaine Movement: Year End Review,

February 2000.

Defense Intelligence Agency. Interagency Assessment of Cocaine Movement: Year End Review,

February 2001.

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, 2001, Operation Breakthrough: Coca Cultivation and

Cocaine Base Processing in Colombia..

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.
34

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,

1988-1999. January 2002. Abt Associates Inc. Report prepared for ONDCP.

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.
A-1

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 2000

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
A-2

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

Puerto Leg 959:3

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
A-3

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
A-4

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 
A-5

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 
A-6
B-1

Appendix B: STAR Model for Cultivation and Base

Production Stages
B-2

Table 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 
B-3

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
B-4

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 
B-5

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
B-6
C-1

Appendix C: Application of the Border and

Domestic Allocation Models, 1996-2000.

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

Table C1

Border Seizures (pure metric tons)

Region 1996 1997 1998 1999 2000

Florida (21.2) (17.1) (13.3) (13.5) (19.9)

Gulf of Mexico (1.9) (1.9) (0.5) (.3) (0.1)

North East (3.8) (2.8) (1.8) (0.8) (1.9)

Puerto Rico/

Virgin Islands

(7.4) (0.8) (1.3) (5.8) (6.4)

Rest of U.S. (0.1) (10.4) (4.3) (.8) (.5)

Southwest Border (14.5) (14.6) (16.9) (30.1) (18.8)

Sum (48.9) (47.7) (38.0) (51.7) (47.8)

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)

1999 (3.9) (0.9) (5.5) (.5) (2.9) (1.3) (4.8) (5.2) (0.4)) (25.4)

2000 (1.1) (0.6) (3.4) (0.3) (3.5) (6.2) (3.2) (9.8) (1.0) (29.0)
C-2

2. Calculate the macro availability estimates for Stages 7B-9

Table C3 - Calculation of Cocaine Availability at Transshipment Areas,

1996-2000, (pure metric tons)

Stage or Transition Description 1996 1997 1998 1999 2000

Stage 9 Retail U.S. 288.0 312.0 291.0 301 278

Transition 8/9 domestic seizures (45.1) (25.0) (45.8) (25.4) (29.0)

Stage 8 Entering U.S. 333.1 337.0 336.8 326.4 307

Transition 7/8 Border seizures (48.9) (47.7) (38.0) (51.7) (47.8)

Stage 7B Transshipment areas 382.0 384.8 374.8 378.2 354.8

3. Run the Border Allocation Model for each year and determine the distribution of cocaine arriving

from Stage 7B.

Table C4 - Border Allocation Distributions

Year Region NCVEH COMVEH LAND 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%

1999 Florida 0.00% 0.00% 1.20% 1.71% 43.94% 48.86%

GOMX 0.00% 0.00% 0.00% 0.00% 1.26% 1.26%

NorthEast 0.00% 0.00% 0.55% 0.00% 2.01% 2.56%

PR/VI 0.00% 0.00% 0.04% 1.75% 0.31% 2.09%

Rest of US 0.00% 0.00% 0.03% 0.00% 0.00% 1.62%

SWB 45.61% 0.00% 0.00% 0.00% 0.00% 45.61%

2000 Florida 0.00% 0.00% 1.22% 1.74% 44.6% 47.56%

GOMX 0.00% 0.00% 0.00% 0.00% 1.25% 1.25%

NorthEast 0.00% 0.00% 0.55% 1.73% 1.99% 2.54%

PR/VI 0.00% 0.00% 0.04% 0.00% 0.30% 2.08%

Rest of US 0.00% 0.00% 0.04% 0.00% 1.82% 1.86%

SWB 44.71% 0.00% 0.00% 0.00% 0.00% 44.71%
C-3

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

Table C5 (Cont’d)

Distribution of Cocaine in Stages 7B and 8 (pure metric

tons)

1999 2000

Region Stage 7B Border

Seizures

Stage 8 Stage 7B Border

Seizures

Stage 8

Florida 177.2 (13.6) 163.6 168.6 (19.9) 148.8

GOMX 4.8 (.3) 4.6 4.4 (.1) 4.3

North East 9.7 (0.8) 8.9 9.0 (1.0) 7.1

PR/VI 7.9 (5.8) 2.1 7.4 (6.4) .9

Rest of US 6.1 (0.8) 5.4 6.6 (0.5) (6.1)

SWB 172.5 (30.6) 141.9 158.6 (18.9) 139.8

Sum 378.2 (51.7) 326.4 354.8 (47.8) 307.0
C-4

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%

1999 New England 5% 100% 100%

Mid Atlantic 42%

E. North Central 13% 100% 20%

W. North Central 8%

S. Atlantic 34% 100%

E. South Central 7%

W. South Central 15%

Mountain 12%

Pacific 44%
C-5

Table C6 (Cont’d)

Results of the Domestic Allocation Model

Year Census region Florida Gulf of Mexico North East Other

U.S.

Puerto Rico

Virgin Islands

Southwest Border

2000 New England 5% 0 100% 100% 36% 0

Mid Atlantic 43% 0 0 0 0 0

E. North Central 9% 100% 0 0 0 23%

W. North Central 0 0 0 0 0 8%

S. Atlantic 36% 0 0 0 100% 0

E. South Central 7% 0 0 0 0 0

W. South Central 0 0 0 0 0 15%

Mountain 0 0 0 0 0 11%

Pacific 0 0 0 0 0 42%
C-6

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

Table C7 (Cont’d)

Distribution of Cocaine in Stages 8 and 9 (pure metric tons)

1999 2000

Census region Stage 8 Domestic seizures Stage 9 Stage 8 Domestic seizures Stage 9

New England 22.1 (3.9) 18.2 20.8 (1.1) 19.7

Mid Atlantic 68.5 (0.9) 67.6 64.4 (0.6) 63.8

E. North Central 53.7 (5.5) 48.2 50.5 (3.4) 47.2

W. North Central 11.8 (0.5) 11.4 11.1 (0.3) 10.8

S. Atlantic 57.2 (2.9) 54.2 53.8 (3.5) 50.3

E. South Central 11.2 (1.3) 9.9 10.5 (6.2) 4.3

W. South Central 21.9 (4.8) 17.1 20.6 (3.2) 17.4

Mountain 17.1 (5.2) 11.9 16.1 (9.8) 6.3

Pacific 63.0 (0.4) 62.6 59.2 (1.0) 58.3
D-1

Appendix D: Enhanced Seizure Data

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)1.

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, fourwheel 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

 

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

methodology was unavailable.
D-2

fishing or private vessel, then noncommercial maritime was assigned. Coast Guard 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.
D-3

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)

Unknown – various things that can’t be categorized

Vehicle – type of car, license plate number (with state)

Vessel – vessel name
D-4

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
D-5

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

MAKE Vehicle Make

MODEL Vehicle Model
D-6

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
D-7

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 #
D-8

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
D-9

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
D-10

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
E-1

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 reports1. 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 E1.

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

 

1 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.
E-2

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.

Figure E1

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
E-3

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

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.2. 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 cash3.

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: $1,4004.

2. The in-kind cost to the Colombians.

From the Colombian perspective, the in-kind cost of shipping one kilogram is:

 

2 Costs for moving cocaine from Canada into the U.S. are higher, suggesting that poverty in Mexico leads

to lower prices for smuggling services.

3 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.

4 Senior Special Agent Frederick J. Stacey, U.S. Customs Service, 1999.
E-4

Wholesale price in Colombia ($2,0005): .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

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) = /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  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.6

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

 

5 Ibid.

6 Fahrmeir and Tutz, 1994, pp.52-55
E-5

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 way7.

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.

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

 

7 McCullagh and Nelder, 1989, pp. 37-40
E-6

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

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)
E-7

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 stable8 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 = itjTt,:

E(Sitj) = Titj(K  Citj )/V

 = itjTt(K  Citj)/V (1)

In these expressions, Sitj and Citj are observed variables, while itj and K are parameters to be

estimated. Incidentally, the quantities Tt and V do not affect the estimates of itj, the key

parameters of interest.

 

8 Senior Special Agent Frederick J. Stacey, U.S. Customs Service.
E-8

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 ijs 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 itj be a parsimonious function of time, itj = fij(t).

Three simple polynomial functions were considered, ones that allowed itj to vary over time in a

constant, linear, or quadratic fashion:

itj = uij

itj = uij + vijt

itj = 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 itjs are probabilities,

it was desirable to constrain them to lie between zero and one. This was achieved by expressing

itj as a multivariate logistic function of an unconstrained parameter itj = (uij + vijt + wijt

2

), which

means itj took the form

itj = exp(itj)/{exp(itj)}

where the sum is over all ij9. (Actually, since only 27 of the 28 itj’s are estimated, the last 6t6,

was dropped, and the denominator changes from exp(itj) to {exp(itj) – exp(6t6) + 1}).

 

9 Judge et al., 1985, pp 770-77
E-9

The second modification was entirely technical. Since K is at least as large as the largest Citj, K

was estimated via the parameter , where K = max(Citj) + exp(). 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() Citj}/V 

(3)

V(Sitj) = 2

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 itj 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 itj. 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.
E-10

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:

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
E-11

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.
E-12
F-1

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 = CijXij

i=1 j=1

n

Subject to Xij = Si, for i=1, 2,..., m

j=1

m

 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,
F-2

Dj, is estimated as the fraction of the number of treatment clients1 in the census division divided

by the total amount of cocaine consumed in the U.S.2 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.

 

1 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.

2 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.