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Paris June 23-25 2010 Ferentillo July 8-9 2010

DO DIFFERENCES IN LEVELS OF PESTICIDES' MRLS AFFECT TRADE: THE CASE OF POME FRUITS F. DEMARIA & S. DROGUÉ (INRA-AGROPARISTECH, FRANCE UNIVERSITY OF CALABRIA, ITALY). Paris June 23-25 2010 Ferentillo July 8-9 2010. Background. Objectives of the study.

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Paris June 23-25 2010 Ferentillo July 8-9 2010

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  1. DO DIFFERENCES IN LEVELS OF PESTICIDES' MRLS AFFECT TRADE: THE CASE OF POME FRUITS F. DEMARIA & S. DROGUÉ (INRA-AGROPARISTECH, FRANCE UNIVERSITY OF CALABRIA, ITALY) Paris June 23-25 2010Ferentillo July 8-9 2010

  2. Background

  3. Objectives of the study • Assess the impact of differences in the MLRs of pesticides on pome fruits (apples and pears) and related processed products on trade competitiveness

  4. Why Pome fruits ? Most consumed fruit in the US & in Europe (with oranges) Unlike other fruits, easily shipped Temperated area product -> main producers are developed and emerging countries then they are less price differences

  5. Measurement of Non Tariff Barriers to Trade Different measures of NTBs have been suggested in the literature (Beghin & Bureau 2001; Disdier et al. 2007) • Frequency and coverages type measure • Quantity impact measures • Price comparison measures • Price effect based on import demand elasticities

  6. Why MLRs of pesticides ? (1) • Maximum Residue Limits or Maximum Level of pesticides are established in most countries to safeguard consumer health and to promote Good Agricultural Practice (GAP) in the use of insecticides, fungicides, herbicides and other agricultural compounds. • These MRLs vary from country to country depending on the pesticides available, the crops being treated and the way the pesticides are used. Food exporters must comply with these MRLs as a condition of market access.

  7. Why MLR of pesticides ? (2) • International harmonization of MLRs does not exist at a global level. • National authorities keep the sovereignity in fixing these limits. Therefore these legal limits can vary widely from a country to another. • Standards and regulations differ across importing countries and this heterogeneity will cause standards and regulation to act as a potential NTMs and thus impede trade.

  8. Literature on MLR of pesticides (1) • Otsuki and Wilson and Sewadeh 2001 • Wilson and Otsuki and Majumdar 2003 • Wilson and Otsuki 2004 • Wilson and Otsuki 2004 • Xion and Beghin 2010 • Level of Afaltoxins • Level of tetracycline • Level of chlorpyrifos pesticides • Level of Aflatoxins • Level of Aflatoxins

  9. Literature on MLR of pesticides (2) • These studies share a common result that more stringent food safety standards set by developed countries tend to deter trade supporting the view of standard as trade barriers.

  10. Hypothesis, model and results

  11. Main actors • Exporters are: • EU • Argentina • Brazil • China • Chile • New Zealand • South Africa They have been chosen on the basis of market share (first exporters) • Importers are: • USA • Japan • Mexico • Korea • Australia • Russian Federation • Canada They have been chosen based on the level of imports and level of per capita consumption

  12. APPLES

  13. PEARS

  14. Multiple Markets

  15. As many regulations as countries (1)

  16. As many regulations as countries (2) • Countries’ regulation still differ a lot • Most major exporters are developed countries then they must • Comply with their own domestic standards • Meet different requirements across destination market • We assume that a country which imposes tight levels to its producers will be more capable to export to countries with stringent regulations

  17. Our Contribution We consider: • All pesticides regulated in the countries of the sample (more or less 750). • Differences in the regulation between importing and exporting countries. • Time variation of the MLR variable. • Zero trade.

  18. MLR INDEX (1) • MLR = Weighted average (MLR_importer – MLR_exporter). Weight is import of pesticides • This allows us to take into account the differences between the legislation of the importer and the exporter

  19. The basic model • We use a gravity model to estimate the impact of MLR regulations on trade of apples, pear and related processed products. The basic model has the following specification :

  20. Definitions of variables

  21. MLR INDEX (2) • A positve β suggests that MLR of pesticides is trade-impeding: the lower the tolerant level is, the less the bilateral trade flows are

  22. Databases • UNCOMTRADE (trade data at HS6 level) • WBDI (GDPs) • CEPII (Distance, applied tariffs) • FAS USDA (MLRs of pesticides in ppm) • TRASPARENCY.ORG (Index of corruption)

  23. Results (1)

  24. POISSON NBR ZIP ZNB MLR EU -2.299*** -1.678* -2.299** -2.082*** [0.695] [0.912] [0.900] [0.671] MLR SAF -0.408 -2.496* 0.539 -1.559** [0.810] [1.337] [1.032] [0.725] MLR NZ -3.841** -9.753*** -2.848 -6.201*** [1.735] [1.064] [1.886] [1.814] MLR CHN 0.275 2.019* -0.081 1.944*** [1.161] [1.119] [0.580] [0.416] MLR CHL -2.612*** -5.331*** -1.838** -1.567*** [0.775] [1.307] [0.828] [0.509] MLR BRA -0.985*** -1.520** -0.911* -1.322*** [0.237] [0.722] [0.507] [0.405] MLR ARG -0.33 -5.214*** 0.217 -0.613 [1.242] [1.915] [1.227] [1.447] Results (2)

  25. Robustness • MLR of importing countries in place of MLR INDEX • MLR time invaring • Heckman two steps • Hurdle double models

  26. Comments (1) • The first table presents the results with 6 different methods of estimations on the entire sample. • The MLR coefficient is negative and significative, meaning that regulations on pesticides may have a positive impact on trade

  27. Comments (2) • MLR is interacted with 7 dummy variables that respectively take the value 1 if exporter are (i) EU, (ii) South Africa, (iii) New Zealand, (iv) China, (v) Chile, (vi) Brazil, (vii) Argentina • Our analysis suggests that MLR is impeding trade only for China, while in the other cases MLR of pesticides enhance trade

  28. Conclusion (1) • The results indicate a positive effect of MLRs imposed by importing countries on pome fruits exports • A 1% increase in regulatory stringency – tighter restrictions on MLRs - leads to an increase of exports of pome fruits by 0.92% This could be seen as confidence of consumers

  29. Conclusion (2) • This result suggests that contrary to what is commonly accepted, the stringency of regulations in the area of hazardous substances (like pesticides) is not trade-impeding but rather trade-enhancing. • It seems that it acts as a proof of trustability in the safety product. Consumers are more confident in products coming from developed areas known for being more stringent than from developing areas known for being laxer.

  30. Conclusion (3) • Costs of compliance are not a significant problem because these countries must comply with their own domestic regulations that are already stringent. • Then food safety standards may affect negatively the competitiveness of developping countries and positively that of developed countries.

  31. Develpoments • Consider both MLR of pesticides for importing and exporting countries • Add other covariates in the gravity model • Re-run the gravity model with a new data on MLR of pesticides and replace the missing value of the codex with 100 in place of 75 • Check for endogeneity problems

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