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

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%

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

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%

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)

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

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:

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

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

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

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

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

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

Communicative Probability EU15NMS/ACsEU29 English German 725 French 513

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

Results: EU 15

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

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%

Results: NMS/AC

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

Results: EU29

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

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

Results: Non-linear Effect EU15 Variable(1)(2)(3)(4)(5) Intercept GDP Distance Contiguity included but not reported Official languages English0.908 *** *** *** *** *** German0.556 *** *** *** French0.150 * *** *** Swedish0.158 ** *** *** *** *** Dutch *** *** *** ****** Proficiency: English5.157 *** *** *** *** French1.119 *** *** ** German *** *** ** Italian *** *** Spanish *** *** Swedish *** Proficiency (Quadratic): English *** *** *** *** French *** *** ** German3.230 *** *** *** Italian *** *** Spanish ** Swedish *** N1470 Adjusted R

Non-linear Effect: EU15

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

Results: EU 15, Quantile Regressions

OLSQ10Q25Q50Q75Q90Test Income *** *** *** *** *** *** Distance *** *** *** *** *** *** 0.94 Contiguity *** *** *** *** *** *** 7.06 Eng. off. lang *** *** *** ** *** *** 5.10 Eng. proff *** *** *** *** *** 9.46 Intercept *** *** *** *** *** *** N 1800 Pseudo R 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 EU15 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 (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

Position of German in CEECs?