Foreign Languages and Trade

1. Foreign Languages and Trade 2nd FIW-Research Conference „International Economics“Vienna University of EconomicsDecember 12, 2008

2. Introduction • Do languages affect trade? • Easier communication  lower transaction costs  greater trade • Trade analysis (gravity model) typically accounts for common official language • E.g. Rose (2000): common language increases trade by 50%

3. Introduction (cont’d) • Gravity models: official languages only • Dummy variables, not proficiency • Proficiency varies across countries • E.g. French in France, Belgium, Luxembourg, Switzerland, Canada,… • Other languages besides official ones matter too • Non-official indigenous languages • Foreign languages

4. Introduction (cont’d) • Rauch (1999, 2001), Rauch and Trindade (2002), Bandyopadhyay, Coughlin and Wall (2008) • Ethnic-networks increase trade • Rauch and Trindade (2002): ethnic Chinese networks in SE Asia increase trade by at least 60%

5. Introduction (cont’d) • Mélitz (2008) • Official and non-official indigenous languages • Language impact measured using dummy variables (if official or spoken by more than 20%) or communicative probability • Only indigenous languages (Ethnologue database)

6. Our Contribution • First to study effect of native and foreign (learned) languages alike • Trade often relies on communication in non-native languages • Unique extensive dataset on language proficiency in the EU • Non-linearity • Old vs new Europe • Role of English

7. Data • Special Eurobarometer 255: Europeans and their Languages, November - December 2005 • Nationally representative surveys; only EU nationals included • Mother’s tongue(s) and up to 3 other languages that they speak well enough to have a conversation • Self-assessed proficiency: basic, good, very good • Trade flows: 2001-07

8. English (good/very good skills) French (good/very good skills)

9. German (good/very good skills) Russian (good/very good skills)

10. Spanish (good/very good skills) Italian (good/very good skills)

11. Gravity Model • Gravity model methodology following Baldwin and Taglioni (2006) • Trade between i and j, Tijt, and output of i and j, yitand yjt,, measured in nominal US$ • Distance between i and j: dij • Common border and common history dummies: bijand fij

12. Gravity Model (cont’d) • Common official-language dummies: Ldij • Communication probabilities: Pfij • Time-varying country dummies: • Country-specific time-invariant and time-varying omitted variables • Country-specific measurement problems

13. Communicative Probability • Probability that two random individuals from two different countries speak the same language • English • Languages spoken by at least 10% of population in at least 3 countries • German, French, Russian • Languages spoken by at least 4% of population in at least 3 countries • Italian, Spanish, Hungarian, Swedish

14. Communicative Probability

15. Results: EU15 • Common official language and communicative probability raise trade • English especially important • Accounting for proficiency in English lowers official-language effect • French/German: weak/mixed results • Spanish/Italian/Swedish: seemingly strong effects • Most country pairs’ at/close to zero

16. Results: EU 15

17. Results: EU15, magnitude • Consider column (5) • Average effect in EU15: 25% increase due to English proficiency (22% average communicative probability) • UK-IRL trade increased 2.2 times because English is official language and 2.7 times because of English proficiency  5.8 fold increase overall • NL-S trade increased 1.7 times and NL-UK trade more than doubled

18. Results: NMS/AC • English proficiency raises trade • Large coefficient estimate  but proficiency is relatively low • Average impact: 77% increase (11% average communicative probability) • German and Russian also significant • Average impact of German: 30%

19. Results: NMS/AC

20. Results: EU29 • Weaker results • English significant but impact less powerful than in either EU15 or NMS/AC • Average English proficiency (17%) raises trade by 11% • French not significant and German negative impact • Remaining languages significant

21. Results: EU29

22. Results: Discussion • Possible explanations for weaker EU29 results: • Heterogeneity: EU15 vs NMS/AC • Trade between EU15 and NMS/AC still below potential • Different political, economic and linguistic legacy • NMS/AC have not yet reached their new linguistic equilibrium • Effect of languages not linear

23. Results: Non-linear Effect • Add squared communicative probability • Hump-shaped effect of English  diminishing returns • Peak at around 70% probability • Quadratic term not significant in NMS/AC and EU29 • French/German: weaker/negative effect • Other languages: quadratic terms not significant in NMS/AC and EU29 • Except Russian: U-shaped in NMS/AC

24. Results: Non-linear Effect EU15

25. Non-linear Effect: EU15

26. Robustness: EU15 • Results potentially driven by outliers • Country pairs with especially high/low trade • Effect of English proficiency highest around 50th percentile (median regression) • Effect of foreign languages not due to outliers

27. Results: EU 15, Quantile Regressions

28. Results: EU 15, Quantile Regressions

29. Conclusions • Language has a strong effect on trade • Countries with common official language trade more with each other • Proficiency in foreign languages also increases trade • Effects of languages different in EU15 and NMS/AC • Effect of languages seems non-linear (diminishing returns)

30. Conclusions (cont’d) • Universal proficiency in English could raise trade up to 2.7 times (EU15) • Rose: monetary unions  2-3 fold increase in trade • Common currency costly (OCA theory) • Improving English proficiency does not require abandoning national languages • Large gains possible at little cost

32. Position of German in CEECs?