Illicit Agricultural Trade

Peyton FerrierEconomic Research Service, USDA Washington, DC

2007 Crime and Population Dynamics Workshop Queenstown, MDJune 5th 2007

These opinions do not express the views of the USDA.

This work is supported by PREISM (Program for Research on the Economics of Invasive Species Management).

Why USDA Cares? Two Risks

1. SPS (Sanitary and Phytosanitary) Risk– USDA regulated for invasive species

• Plant Protection Act of 2000, Animal Health Protection Act of 2002

– Large Potential Effects • Office of Technology Assessment (OTA, 1993) estimates of

invasive species at $97 billion from 1906 to 1991• During the 1990’s, APHIS spending on emergency eradication

programs increased from $ 232 million to $10.4 billion annually• Exotic New Castle disease in California, $160 million to eradicate,

depopulation of more than 3 million birds

2. Resource Risk– US FWS (endangered, over-harvested species) regulated

• CITES and Endangered Species Act.

– Illegal wildlife trade estimated at $7-20 billion globally (Interpol)• Second largest type of illegal trade after narcotics

Research Questions

What goods are smuggled?What are the origins?How much comes in?How responsive to price?

“Any effort to describe the international wildlife trade must unfortunately begin with the recognition that this cannot be done with any accuracy” (TRAFFIC, Roe et al, 2002)

“..though enforcement personnel know a great deal about what illegal trade activities occur locally, there is less understanding of illegal trade activity nationally, or what might be occurring at other ports…..” (USFWS)

Two Papers Here

1. Illicit Agricultural Trade – Theoretical, premised on price effects of

sudden bans

2. Description of Illicit Agricultural and Wildlife Trade and its Regulation

– Descriptive, based on USDA and US FWS data.

Close to Here…..The Emerald Ash Borer Beetle

• In 2003, a Michigan nursery broke quarantine and shipped infested trees to Prince Georges County, MD.

• After three years of eradication effort, the EAB was again detected in 2006

• Sales of firewood and ash products are still under quarantine from PG county.

Examples of intercepted goods

Citrus Cutting with Citrus Canker Intercepted in CaliforniaBoneless Chicken Feet from TaiwanLive Giant African Snails

Distinctive Features

• Restrictions (Quarantines, Trade Bans):– vary dramatically across many different goods– are often country or region specific – are sudden and disruptive

• Illegal trade: – often co-exists with legal trade– may have poor public awareness of, concern for risk– is technically uncomplicated

• Trans-shipping and mis-manifesting

– Involves uncertainty over risk magnitudes (invasibility, health risk).

Distinctive Features

• Difficult-to-quantify externalities:– depend on small, imprecisely-measured risk

probabilities of an invasive species establi– values of abstract goods such as

biodiversity and habitat preservation

• Focus is types of goods smuggled, volume of smuggling, more than lost tax revenue or consumer welfare effects.

Economic Model of Agricultural and Wildlife Smuggling

• Demand Side – Driven by the price difference in excess of

ordinary trade costs following a trade ban

• Supply Side – Driven by risk preferences of exporters, fines

and punishments, and the probability of getting caught

S1

S2S3

D3 D2

D1

ExDem1 =

(ExSup2+ ExSup3)

ExSup2

ExSup3

21

31

P1

Market 1 Market 2 Market 3

ExDem1 ExSup3

Smuggler’s Payoff = ΔP1-ΔP2

Free Market Equilibrium

A pest detection causesa ban on imports from country 2

31

Smuggling if this price difference is greater than the cost of smuggling

Ordinary Shipping Costs

Market 2 Restricted

The Demand for Smuggled Goods

Smuggling replaces all banned trade

ΔP1-ΔP2

(ΔP1 –ΔP2)*

Amount of Smuggling

Demand for smuggled goods

Smuggled Goods

Reduced Imports

Demand increases as demand and supply are more inelastic(less responsive to price) for any trade partner

The Supply for Smuggled Goods

121212 ,,,, fPUPU ieCE

Certainty Equivalent

Utility from P2

Expected Utility of getting P1

Coefficient of risk aversion

fine if caught costs to

smuggle

12122121 ,,,,,

~~

fPPUU CEe

Firms will smuggle if φi is less than some threshold so that utility under the risky scenario is higher:

~

The Supply of Smuggled Goods

ΔP1-ΔP2

(ΔP1 –ΔP2)*Amount of Smuggling

Demand(ΔP1-ΔP2)

Smuggled Goods

12122121 ,,,,,,~

fPPFMPPS TBTB

Distribution of Risk CoefficientsNumber of

Potential Traders

Supply of Smuggled Goods

Supply(ΔP1-ΔP2)

Background on Data

• Interdictions – goods being sold illegally and intercepted in U.S. markets– USDA (SITC) - Smuggling and Interdiction Trade

Compliance

• Inspections – goods found at ports and refused entry by inspectors– APHIS PPQ 280 and USFWS LEMIS

• Random Inspections – goods randomly inspected with varying intensity– (AQIM) Agricultural Quarantine Inspection Monitoring

Pros and Cons of Different Data Type

Traits

Interdictions Targeted

Inspections

Random

Inspections

USDA,

SITC

USFWS,

LEMIS

USDA,

PPQ 280

USDA-APHIS,

AQIM

Non-Biased No No Yes

Large Yes Yes No

Covers All Goods

No Yes Yes

Identifies Intent to smuggle

Yes No Yes

APHIS Interdictions DataTable (3) SITC Plant Product Interdictions

Rank Country Shipments Value Wt. (lbs) Top Three Items

1 China 338 $1,169,561 801,332Szechuan Pepper, Citrus-

based spice and Burdock

2 India 140 $116,842 51,895Corn/Millet, Citrus-based

spice and Curcurbit

3 Mexico 125 $192,462 33,098Citrus-based spice, Lemon

grass and Ruda,

4 Thailand 64 $69,263 71,932

Citrus-based spice, Kaffir Lime and Szechuan

Pepper

5 Korea 33 $154,017 74,585Corn/Millet, lentil and

Citrus-based spice

  Total 897 $2,193,803 1,170,664

Szechuan Pepper, Corn/Millet and Citrus

Products

APHIS Interdictions Data

Table (2) SITC Fruit Product Interceptions (2002-06)  

Rank Country Value Weight (lbs) Top Three Items

1 Mexico $94,426 69,840 Tejocotes, Avocados, Hog plums

2 China $75,044 39,437Bael Fruit, Garlic Stems,

Ya Pears

3 Thailand $8,047 2,776 Bael Fruit, Wood Apple, Krasang

4 Bangladesh $7,677 2,110 Satakora, Citrus

5 Asia (Unknown) $18,468 9,522 Citrus, Longans, Wood Apple

  Total $556,447 209,049 Bael Fruit, Tejocotes, Avocadoes

APHIS Interdictions Data

Table (5)-Total Interdicted Material (2002-06)

Rank Country Value of Interdicted

MaterialPercentage of

Ag Imports

1 China $ 2,342,640 0.02914%

2 Japan $ 374,562 0.01815%

3 India $ 281,724 0.00677%

4 South Korea $ 232,800 0.02413%

5 Mexico $ 207,241 0.00056%

Table (8) Total Refused Imports from 2000-04 (min. 100 refusals)

Rank Country Total Refused % Refused

1 Mexico 13,413 3,772 28.1%

2 Canada 108,145 1,560 1.4%

3 China 16,693 1,138 6.8%

4 Philippines 44,977 728 1.6%

5 Hong Kong 65,665 591 0.9%

6 Russia 2,711 562 20.7%

7 Unknown 1,428 524 36.7%

8 Thailand 30,149 473 1.6%

9 Italy 46,807 406 0.9%

10 South Africa 21,438 341 1.6%

USFWS Inspections Data

USFWS Inspections DataTable (7): Number of Wildlife Shipments Refused

Category Total Refused Total Percent Primary Uses

Reptiles 5,163 90,542 5.7% Leather Products, Shoes

Corals 1,123 20,144 5.6% Raw and Live Coral

Birds 2,082 50,223 4.1% Live, Feathers, Trophies

Echinoderms 74 2,323 3.2% Bodies and Shells

Mammals 4,996 223,349 2.2% Medicinals, Skins, Ivory

Fish 1,656 148,054 1.1% Caviar, Live Fish and Meat

Mollusks 1,750 157,067 1.1% Shells for Jewelry

All Others 504 133,290 0.4%

USFWS Inspections DataTable (9) - Value of Legal and Illegal Wildlife Trade (US FWS)

  Illegal Trade Legal Trade  

YearValue

Refused% with

No ValueValue

Cleared% with

No Value% of Total

Refused

2000 $10.7 M 26.40% $1.7 B 11.00% 0.6%

2001 $7.1 M 22.20% $1.5 B 10.10% 0.5%

2002 $ 4.5 M 21.80% $ 1.4 B 8.80% 0.3%

2003 $4.4 M 28.50% $1.5 B 9.20% 0.3%

2004 $4.1 M 27.10% $1.8 B 6.60% 0.2%

Total $ 30.7 M   $8.8 B   0.4%

Some Very Basic Conclusions

1. Illegal trade in agricultural goods seems dominated by the trade in ethnic foods

2. Trade in wildlife goods seems dominated by the trade in luxury items

3. Illegal trade is not small

4. Illegal trade detected in inspections and interdiction data has a high likelihood of coming from Mexico or China

In other work ….

• Optimal Profiling with Learning – How random inspections can be used to improve

inspection targeting– Chris Costello, Mike Springborn, UC-Santa Barbara

• Port Shopping– Importers finding lax ports to avoid inspections– David Zilberman, UC-Berkeley

….That’s it

Blank slide

USDA Inspections Data Table (6) Total Refusals Without Resale of Shipments of Agricultural Goods

Rank Origin ShipmentsTotal Quant

(kgs). Top Three Goods

1 Mexico 598 2,774,204Mango (70), Papaya (70),

Cilantro (23)

2 Netherlands 237 428,416 Various Cut Flowers

3 Israel* 228* 572,649 Various Cut Flowers

4 Thailand 181 50,162Orchid (151), Dracaena

(Bamboo,17), and Litchi (10)

5 China 162 827,493

Szechuan Pepper (36), Mustard Greens (14) and Ya

Pear (8)

*May have come from a few very large shipments

Size of Price Differences

DSDS

PQ

PQ

NXP

3333

3111

1

1

21

0

2222

2

22

DSS

PQ

NXP

In general, the price change is smaller if supply and demand (anywhere) is more elastic.

Proportion consumed in domestically for

each country