Illicit Agricultural Trade

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

2. Why USDA Cares? Two Risks • 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 • 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 • Illicit Agricultural Trade • Theoretical, premised on price effects of sudden bans • 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. Ordinary Shipping Costs Smuggler’s Payoff = ΔP1-ΔP2 S1 Smuggling if this price difference is greater than the cost of smuggling S2 S3 ExSup2 P1 21 ExDem1 31 ExSup3 ExDem1 = (ExSup2+ ExSup3) 31 ExSup3 D1 D2 D3 Market 1 Market 2 Market 2 Restricted Market 3 Free Market Equilibrium A pest detection causes a ban on imports from country 2

11. The Demand for Smuggled Goods Demand increases as demand and supply are more inelastic (less responsive to price) for any trade partner Smuggling replaces all banned trade Amount of Smuggling ΔP1-ΔP2 Reduced Imports (ΔP1 –ΔP2)* Demand for smuggled goods Smuggled Goods

12. The Supply for Smuggled Goods fine if caught Coefficient of risk aversion costs to smuggle Certainty Equivalent Utility from P2 Expected Utility of getting P1 Firms will smuggle if φi is less than some threshold so that utility under the risky scenario is higher:

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

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

16. APHIS Interdictions Data

17. APHIS Interdictions Data

18. APHIS Interdictions Data

19. USFWS Inspections Data

20. USFWS Inspections Data

21. USFWS Inspections Data

22. Some Very Basic Conclusions • Illegal trade in agricultural goods seems dominated by the trade in ethnic foods • Trade in wildlife goods seems dominated by the trade in luxury items • Illegal trade is not small • 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

24. Blank slide

26. USDA Inspections Data *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