1. Illicit Agricultural Trade Peyton Ferrier Economic Research Service, USDA Washington, DC 2007 Crime and Population Dynamics Workshop Queenstown, MD June 5 th 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).

2. 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

3. 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)

4. 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.

5. 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.

6. Examples of intercepted goods Citrus Cutting with Citrus Canker Intercepted in California Boneless Chicken Feet from Taiwan Live Giant African Snails

7. 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).

8. 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.

9. 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

10. S1S1 S 2 S3S3 D3D3 D 2 D1D1 ExDem 1 = (ExSup 2 + ExSup 3 ) ExSup 2 ExSup 3 21 21 31 31 P1P1 Market 1Market 2Market 3 ExDem 1 ExSup 3 Smuggler’s Payoff = ΔP 1 -ΔP 2 Free Market Equilibrium A pest detection causes a ban on imports from country 2 3131 Smuggling if this price difference is greater than the cost of smuggling Ordinary Shipping Costs Market 2 Restricted

11. The Demand for Smuggled Goods Smuggling replaces all banned trade ΔP1-ΔP2ΔP1-ΔP2 (ΔP 1 –ΔP 2 )* 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

12. The Supply for Smuggled Goods Certainty Equivalent Utility from P 2 Expected Utility of getting P 1 Coefficient of risk aversion fine if caught costs to smuggle Firms will smuggle if φ i is less than some threshold so that utility under the risky scenario is higher:

13. The Supply of Smuggled Goods ΔP1-ΔP2ΔP1-ΔP2 (ΔP 1 –ΔP 2 )* Amount of Smuggling Demand( ΔP 1 -ΔP 2 ) Smuggled Goods Distribution of Risk Coefficients Number of Potential Traders Supply of Smuggled Goods Supply( ΔP 1 -ΔP 2 )

14. 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

15. Pros and Cons of Different Data Type Traits InterdictionsTargeted Inspections Random Inspections USDA, SITC USFWS, LEMIS USDA, PPQ 280 USDA-APHIS, AQIM Non-BiasedNo Yes LargeYes No Covers All Goods NoYes Identifies Intent to smuggle YesNoYes

16. APHIS Interdictions Data Table (3) SITC Plant Product Interdictions RankCountryShipmentsValueWt. (lbs)Top Three Items 1China338$1,169,561801,332 Szechuan Pepper, Citrus- based spice and Burdock 2India140$116,84251,895 Corn/Millet, Citrus-based spice and Curcurbit 3Mexico125$192,46233,098 Citrus-based spice, Lemon grass and Ruda, 4Thailand64$69,26371,932 Citrus-based spice, Kaffir Lime and Szechuan Pepper 5Korea33$154,01774,585 Corn/Millet, lentil and Citrus-based spice Total897$2,193,8031,170,664 Szechuan Pepper, Corn/Millet and Citrus Products

17. APHIS Interdictions Data Table (2) SITC Fruit Product Interceptions (2002-06) RankCountryValueWeight (lbs)Top Three Items 1Mexico$94,42669,840Tejocotes, Avocados, Hog plums 2China$75,04439,437 Bael Fruit, Garlic Stems, Ya Pears 3Thailand$8,0472,776Bael Fruit, Wood Apple, Krasang 4Bangladesh$7,6772,110Satakora, Citrus 5Asia (Unknown)$18,4689,522Citrus, Longans, Wood Apple Total$556,447209,049Bael Fruit, Tejocotes, Avocadoes

18. APHIS Interdictions Data Table (5)-Total Interdicted Material (2002-06) RankCountry Value of Interdicted Material Percentage of Ag Imports 1China$ 2,342,6400.02914% 2Japan$ 374,5620.01815% 3India$ 281,7240.00677% 4South Korea$ 232,8000.02413% 5Mexico$ 207,2410.00056%

19. Table (8) Total Refused Imports from 2000-04 (min. 100 refusals) RankCountryTotalRefused% Refused 1Mexico13,4133,77228.1% 2Canada108,1451,5601.4% 3China16,6931,1386.8% 4Philippines44,9777281.6% 5Hong Kong65,6655910.9% 6Russia2,71156220.7% 7Unknown1,42852436.7% 8Thailand30,1494731.6% 9Italy46,8074060.9% 10South Africa21,4383411.6% USFWS Inspections Data

20. Table (7): Number of Wildlife Shipments Refused CategoryTotal RefusedTotalPercentPrimary Uses Reptiles5,16390,5425.7%Leather Products, Shoes Corals1,12320,1445.6%Raw and Live Coral Birds2,08250,2234.1%Live, Feathers, Trophies Echinoderms742,3233.2%Bodies and Shells Mammals4,996223,3492.2%Medicinals, Skins, Ivory Fish1,656148,0541.1%Caviar, Live Fish and Meat Mollusks1,750157,0671.1%Shells for Jewelry All Others504133,2900.4%

21. USFWS Inspections Data Table (9) - Value of Legal and Illegal Wildlife Trade (US FWS) Illegal TradeLegal Trade Year Value Refused % with No Value Value Cleared % with No Value % of Total Refused 2000$10.7 M26.40%$1.7 B11.00%0.6% 2001$7.1 M22.20%$1.5 B10.10%0.5% 2002$ 4.5 M21.80%$ 1.4 B8.80%0.3% 2003$4.4 M28.50%$1.5 B9.20%0.3% 2004$4.1 M27.10%$1.8 B6.60%0.2% Total$ 30.7 M $8.8 B 0.4%

22. 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

23. 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

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26. USDA Inspections Data Table (6) Total Refusals Without Resale of Shipments of Agricultural Goods RankOriginShipments Total Quant (kgs).Top Three Goods 1Mexico5982,774,204 Mango (70), Papaya (70), Cilantro (23) 2Netherlands237428,416Various Cut Flowers 3 Israel * 228*572,649Various Cut Flowers 4Thailand18150,162 Orchid (151), Dracaena (Bamboo,17), and Litchi (10) 5China162827,493 Szechuan Pepper (36), Mustard Greens (14) and Ya Pear (8) *May have come from a few very large shipments

27. Size of Price Differences In general, the price change is smaller if supply and demand (anywhere) is more elastic. Proportion consumed in domestically for each country