1. Jan FidrmucJarko Fidrmuc Brunel University, CEDI, CEPR and WDI University of Munich, Comenius University and CESifo Economic and Monetary Union: 10 Years of Success? November 27 - 28, 2008 Mendel University, Brno, Czech Republic Foreign Languages and Trade

3. 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%

4. 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

5. 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%

6. 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)

7. 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

8. 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

9. English: Native and Foreign Language (good/very good skills)

10. German: Native and Foreign Language (good/very good skills)

11. French: Native and Foreign Language (good/very good skills)

12. Russian: Native and Foreign Language (good/very good skills)

13. 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

14. 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

15. 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

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

17. 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

18. Results: EU 15 Variable(1)(2)(3)(4)(5) Intercept14.841 *** 15.175 *** 15.415 *** 15.318 *** 14.678 *** GDP1.004 *** 0.897 *** 0.885 *** 0.880 *** 0.895 *** Distance-0.772 *** -0.748 *** -0.761 *** -0.750 *** -0.668 *** Contiguity0.499 *** 0.471 *** 0.491 *** 0.364 *** 0.157 *** Official languages English0.908 *** 0.543 *** 0.570 *** 0.662 *** 0.775 *** German0.556 *** 0.581 *** 0.853 *** 0.841 *** 0.667 *** French0.150 ** 0.186 ** 0.1010.2950.788 *** Swedish0.1580.279 *** 0.235 ** 0.323 *** -2.974 *** Dutch-0.344 *** -0.263 *** -0.340 *** -0.180 ** 0.150 *** Proficiency: English1.152 *** 1.074 *** 0.944 *** 1.022 *** French0.0800.065-0.321 German-0.408 *** -0.274 *** 0.102 Italian8.724 *** 11.687 *** Spanish8.938 *** 12.071 *** Swedish19.793 *** N1470 *** Adjusted R 2 0.9720.974 0.9750.980

19. 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

20. 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%

21. Results: NMS/AC Variable(1)(2)(3)(4) Intercept19.838 *** 19.372 *** 17.119 *** 17.145 *** GDP0.571 *** 0.573 *** 0.566 *** 0.566 *** Distance-1.039 *** -1.024 *** -0.817 *** -0.820 *** Former Federation2.278 *** 2.292 *** 1.478 *** 1.471 *** Contiguity0.543 *** 0.531 *** 0.650 *** 0.654 *** Proficiency: English5.074 *** 5.182 *** 5.188 *** German13.381 * 13.239 * Russian3.748 *** 3.745 *** Hungarian-0.309 N1254 Adjusted R 2 0.8470.8500.858

22. 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

23. Results: EU29 Variable(1)(2)(3)(4)(5) Intercept19.177 *** 18.808 *** 18.853 *** 18.694 *** 18.669 *** GDP0.865 *** 0.875 *** 0.879 *** 0.884 *** 0.884 *** Distance-1.055 *** -1.042 *** -1.045 *** -1.034 *** -1.028 *** Former Federation2.472 *** 2.465 *** 1.948 *** 1.978 *** 2.034 *** Contiguity0.318 *** 0.324 *** 0.338 *** 0.326 *** 0.267 *** Official languages English0.916 *** 0.669 *** 0.699 *** 0.709 *** 0.746 *** German0.599 *** 0.601 *** 0.931 *** 0.911 *** 0.854 *** French0.0480.0690.0760.0880.150 Swedish0.1500.174 *** 0.146 *** 0.168 *** -2.176 *** Dutch-0.617 *** -0.618 *** -0.655 *** -0.624 *** -0.554 *** Greek2.272 *** 2.294 *** 2.282 *** 2.297 *** 2.327 *** Proficiency: English0.763 ** 0.658 *** 0.688 *** 0.597 *** French-0.064-0.028-0.030 German-0.465 *** -0.424 *** -0.318 ** Russian1.675 *** 1.627 *** 1.623 *** Italian1.532 *** 1.606 *** Spanish3.582 *** 4.362 *** Swedish12.824 *** Hungarian3.679 *** N5634 Adjusted R 2 0.930 0.931

24. 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

25. 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

26. 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

27. Non-linear Effect: EU15

28. 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

29. Results: EU 15, Quantile Regressions

30. 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

31. 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)

32. 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