1. Jan FidrmucJarko Fidrmuc Brunel University, CEDI, CEPR and WDI University of Munich, Comenius University and CESifo 2 nd FIW-Research Conference „International Economics“ Vienna University of Economics December 12, 2008 Foreign Languages and Trade

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, T ijt, and output of i and j, y it and y jt,, measured in nominal US$ Distance between i and j: d ij Common border and common history dummies: b ij and f ij

12. Gravity Model (cont’d) Common official-language dummies: L dij Communication probabilities: P fij 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 1. English 2. Languages spoken by at least 10% of population in at least 3 countries German, French, Russian 3. Languages spoken by at least 4% of population in at least 3 countries Italian, Spanish, Hungarian, Swedish

14. Communicative Probability EU15NMS/ACsEU29 English 221117 German 725 French 513

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: 1. 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 2. 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 Variable(1)(2)(3)(4)(5) Intercept GDP Distance Contiguity included but not reported Official languages English0.908 *** 1.369 *** 1.672 *** 1.749 *** 1.601 *** German0.556 *** 0.661 *** 0.0300.0150.325 *** French0.150 * 0.292 *** 0.4000.5141.003 *** Swedish0.158 ** 0.362 *** 0.256 *** 0.279 *** 17.057 *** Dutch-0.344 *** -0.283 *** -0.404 *** -0.286 ****** 0.030 Proficiency: English5.157 *** 6.005 *** 6.008 *** 5.178 *** French1.119 *** 1.220 *** 0.040 ** German-2.633 *** -2.499 *** -1.108 ** Italian46.564 *** 33.852 *** Spanish10.856 *** 11.446 *** Swedish80.606 *** Proficiency (Quadratic): English-3.580 *** -4.481 *** -4.580 *** -3.690 *** French-1.552 *** -1.712 *** -0.872 ** German3.230 *** 3.172 *** 1.571 *** Italian-748.687 *** -461.089 *** Spanish-75.874 ** -52.094 Swedish-857.98 *** N1470 Adjusted R 2 0.9720.9750.9770.9780.983

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 50 th percentile (median regression) Effect of foreign languages not due to outliers

27. Results: EU 15, Quantile Regressions

28. OLSQ10Q25Q50Q75Q90Test Income 0.895 *** 0.962 *** 0.931 *** 0.874 *** 0.836 *** 0.795 *** 26.15 Distance -0.694 *** -0.464 *** -0.695 *** -0.709 *** -0.787 *** -0.852 *** 0.94 Contiguity 0.643 *** 0.673 *** 0.483 *** 0.687 *** 0.591 *** 0.319 *** 7.06 Eng. off. lang. 0.488 *** 1.088 *** 0.890 *** 0.433 ** 0.426 *** 0.400 *** 5.10 Eng. proff. 0.549 *** 0.3040.340 *** 0.697 *** 0.426 *** 0.272 *** 9.46 Intercept-21.313 *** -27.083 *** -23.557 *** -20.109 *** -17.209 *** -14.193 *** 22.42 N 1800 Pseudo R 2 0.9180.7380.7350.7220.7160.714ND

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?