Foreign Languages and Trade Economic and Monetary Union

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

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

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

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

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

Our Contribution ¢ First to study effect of native and foreign (learned) languages alike l 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 ¢

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 ¢

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

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

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

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

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

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

Communicative Probability ¢ 1. 2. 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 l 3. German, French, Russian Languages spoken by at least 4% of population in at least 3 countries l Italian, Spanish, Hungarian, Swedish

Communicative Probability EU 15 NMS/ACs EU 29 English 22 11 17 German 7 2 5 French 5 1 3

Results: EU 15 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 ¢ l Most country pairs’ at/close to zero

Results: EU 15 Variable (1) Intercept 14. 841 *** 15. 175 *** 15. 415 *** 15. 318 *** 14. 678 *** 1. 004 *** 0. 897 *** 0. 885 *** 0. 880 *** 0. 895 *** Distance -0. 772 *** -0. 748 *** -0. 761 *** -0. 750 *** -0. 668 *** Contiguity 0. 499 *** 0. 471 *** 0. 491 *** 0. 364 *** 0. 157 *** English 0. 908 *** 0. 543 *** 0. 570 *** 0. 662 *** 0. 775 *** German 0. 556 *** 0. 581 *** 0. 853 *** 0. 841 *** 0. 667 *** French 0. 150 ** 0. 186 ** 0. 101 0. 788 *** Swedish 0. 158 0. 279 *** 0. 235 ** 0. 323 *** -2. 974 *** -0. 263 *** -0. 340 *** -0. 180 ** 0. 150 *** 1. 152 *** 1. 074 *** 0. 944 *** 1. 022 *** GDP (2) (3) (4) (5) Official languages Dutch -0. 344 *** 0. 295 Proficiency: English French 0. 080 German -0. 408 0. 065 -0. 321 -0. 274 *** 0. 102 Italian 8. 724 *** 11. 687 *** Spanish 8. 938 *** 12. 071 *** 19. 793 *** *** Swedish N 1470 1470 Adjusted R 2 0. 974 0. 975 0. 980

Results: EU 15, magnitude ¢ Consider column (5) Average effect in EU 15: 25% increase due to English proficiency (22% average communicative probability) l 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 l NL-S trade increased 1. 7 times and NLUK trade more than doubled l

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

Results: NMS/AC Variable (1) Intercept 19. 838 *** 19. 372 *** 17. 119 *** 17. 145 *** 0. 571 *** 0. 573 *** 0. 566 *** -1. 039 *** -1. 024 *** -0. 817 *** -0. 820 *** Former Federation 2. 278 *** 2. 292 *** 1. 478 *** 1. 471 *** Contiguity 0. 543 *** 0. 531 *** 0. 650 *** 0. 654 *** 5. 074 *** 5. 182 *** 5. 188 *** GDP Distance (2) (3) (4) Proficiency: English German 13. 381 Russian 3. 748 Hungarian * *** 13. 239 3. 745 -0. 309 N 1254 Adjusted R 2 0. 847 0. 850 0. 858 * ***

Results: EU 29 Weaker results ¢ English significant but impact less powerful than in either EU 15 or NMS/AC ¢ l Average English proficiency (17%) raises trade by 11% French not significant and German negative impact ¢ Remaining languages significant ¢

Results: EU 29 Variable (1) Intercept 19. 177 *** 18. 808 *** 18. 853 *** 18. 694 *** 18. 669 *** 0. 865 *** 0. 879 *** 0. 884 *** -1. 055 *** -1. 042 *** -1. 045 *** -1. 034 *** -1. 028 *** Former Federation 2. 472 *** 2. 465 *** 1. 948 *** 1. 978 *** 2. 034 *** Contiguity 0. 318 *** 0. 324 *** 0. 338 *** 0. 326 *** 0. 267 *** English 0. 916 *** 0. 669 *** 0. 699 *** 0. 709 *** 0. 746 *** German 0. 599 *** 0. 601 *** 0. 931 *** 0. 911 *** 0. 854 *** French 0. 048 0. 069 Swedish 0. 150 0. 174 *** 0. 146 *** 0. 168 *** -2. 176 *** GDP Distance (2) (3) (4) (5) Official languages 0. 076 0. 088 0. 150 Dutch -0. 617 *** -0. 618 *** -0. 655 *** -0. 624 *** -0. 554 *** Greek 2. 272 *** 2. 294 *** 2. 282 *** 2. 297 *** 2. 327 *** 0. 763 ** 0. 658 *** 0. 688 *** 0. 597 *** Proficiency: English French -0. 064 -0. 028 German -0. 465 *** -0. 424 *** -0. 318 ** Russian 1. 675 *** 1. 627 *** 1. 623 *** Italian 1. 532 *** 1. 606 *** Spanish 3. 582 *** 4. 362 *** 12. 824 *** 3. 679 *** Swedish Hungarian -0. 030 N 5634 5634 Adjusted R 2 0. 930 0. 931

Results: Discussion ¢ 1. Possible explanations for weaker EU 29 results: Heterogeneity: EU 15 vs NMS/AC l l l 2. Trade between EU 15 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

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 EU 29 ¢ French/German: weaker/negative effect ¢ Other languages: quadratic terms not significant in NMS/AC and EU 29 ¢ l Except Russian: U-shaped in NMS/AC

Results: Non-linear Effect EU 15 Variable (1) (2) (3) (4) (5) Intercept GDP Distance Contiguity included but not reported Official languages English 0. 908 *** 1. 369 *** 1. 672 German 0. 556 *** 0. 661 *** 0. 030 French 0. 150 * 0. 292 *** 0. 400 Swedish 0. 158 ** 0. 362 *** 0. 256 *** 0. 279 *** -0. 344 *** -0. 283 *** -0. 404 *** -0. 286 * * 0. 030 5. 157 *** 6. 005 *** 6. 008 *** 5. 178 *** French 1. 119 *** 1. 220 *** 0. 040 ** German -2. 633 *** -2. 499 *** -1. 108 ** Italian 46. 564 *** 33. 852 *** Spanish 10. 856 *** 11. 446 *** 80. 606 *** Dutch *** 1. 749 1. 601 *** 0. 015 0. 325 *** 0. 514 1. 003 *** 17. 057 *** * Proficiency: English Swedish Proficiency (Quadratic): English -3. 580 -4. 481 *** -4. 580 *** -3. 690 *** French -1. 552 *** -1. 712 *** -0. 872 ** German 3. 230 *** 3. 172 *** 1. 571 *** -748. 687 *** -461. 089 *** Italian Spanish -75. 874 Swedish ** -52. 094 -857. 98 N 1470 1470 Adjusted R 2 0. 975 0. 977 0. 978 0. 983 ***

Non-linear Effect: EU 15

Robustness: EU 15 ¢ Results potentially driven by outliers l 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 ¢

Results: EU 15, Quantile Regressions

Results: EU 15, Quantile Regressions OLS Q 10 0. 895 *** Distance Contiguity Income Eng. off. lang. Eng. proff. Intercept Q 25 0. 962 *** -0. 694 *** 0. 643 *** 0. 488 *** 0. 549 *** 0. 304 -21. 313 *** -27. 083 Q 50 0. 931 *** -0. 464 *** 0. 673 *** 1. 088 *** Q 75 0. 874 *** -0. 695 *** 0. 483 *** 0. 890 *** 0. 340 *** -23. 557 *** Q 90 0. 836 *** -0. 709 *** 0. 687 *** 0. 433 ** 0. 697 *** -20. 109 *** Test 0. 795 *** 26. 15 -0. 787 *** -0. 852 *** 0. 94 0. 591 *** 0. 319 *** 7. 06 0. 426 *** 0. 400 *** 5. 10 0. 426 *** 0. 272 *** 9. 46 -17. 209 *** -14. 193 *** 22. 42 N 1800 1800 Pseudo R 2 0. 918 0. 735 0. 722 0. 716 0. 714 ND

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 EU 15 and NMS/AC ¢ Effect of languages seems non-linear (diminishing returns) ¢

Conclusions (cont’d) Universal proficiency in English could raise trade up to 2. 7 times (EU 15) ¢ Rose: monetary unions 2 -3 fold increase in trade ¢ l Common currency costly (OCA theory) Improving English proficiency does not require abandoning national languages ¢ Large gains possible at little cost ¢