Foreign Languages and Trade Technical University Ko šice , Herl’any, October 14-15, 2010

1. Foreign Languages and Trade Technical University Košice, Herl’any, October 14-15, 2010 Jarko Fidrmuc OeNB Vienna, Comenius University Bratislava, CESifo Munich, OEI Regensburg Jan Fidrmuc Brunel University London, CEPR, and CESifo The research was largely completed during Jarko Fidrmuc’s stay at the University of Munich. The opinions are those of the author and do not necessarily reflect the official viewpoint of the Oesterreichische Nationalbank or of the Eurosystem.We acknowledge CESIUK support from the Operational Program of Research and Development (OP VaV) in the framework of the European Regional Development Fund (ERDF).

2. Literature Review – I • Gravity models usually include also dummies for common languages as a control variables. • Helpman, Melitz and Rubinstein (2008) derive the gravity equation by in a model with heterogenous firms which stresses the link between productivity and export performance of firms. • Their empirical results indicate that common languages are an important part of fixed costs related to market entry.

3. Literature Review – II • 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)

4. Literature Review – III • 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. 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

6. Data–Foreign Languages • Special Eurobarometer 255: Europeans and their Languages, November - December 05. • Nationally representative surveys; only EU nationals included. • Respondents were asked on their language skills: • Native language(s), • up to 3 other languages that they speak well enough to have a conversation, • Self-assessed proficiency of foreign languages: basic (not used here), good, and very good.

7. Foreign Languages in Europe – I French (good/very good skills) English (good/very good skills)

8. Foreign Languages in Europe – II German (good/very good skills) Russian (good/very good skills)

9. Communicative Probability • Probability that two randomly selected individuals from two different countries can speak sufficiently well the same language • English • Languages spoken by at least 10% of population in at least 3 countries • German • French • Russian (only in Eastern Europe) • We compute the overall communication probability based of possible multiple knowledge of English, French, and German.

10. Communicative Probability

11. Average Cumulative Com. Probabilities–EU15 (English, German, and French)

12. Average Cumulative Com. Probabilities–EUROPA29(English, German, and French)

13. Data–Further Variables • Trade and GDP data are taken from the IMF (IFS and DOT) • All variables are converted to euro. • PISA test results in 2006 (because of country availability), • Public and private expenditures on education in 2000. • We cover EU15, new member states, and the candidate countries.

14. Gravity Model–Core Variables • Trade (in logs) between countries i and j, Tijt, • Log of output of i and j, yitand yjt,, measured in nominal EUR, • Distance between i and j, dij • Common border dummy, bij. • A dummy for former federations in CESEE, fij.

15. Gravity Model–Languages • Communication probabilities: Pfij • English, • English, French, German • Cumulative cummulative probability for English, French and German • In this version we do not include dummies for common languages.

16. Gravity Model–Panel Structure • Time-varying country dummies following Baldwin and Taglioni (2006): • Country-specific time-invariant and time-varying omitted variables • Country-specific measurement problems • This lowers the possible endogeneity problems.

17. OLS-Results for EU15

18. OLS-Results for EUROPA29

19. Controlling for Endogeneity • Proficiency in foreign languages and trade may be endogenous: • People learn foreign languages of their main economic partners (e.g. the rise of interest in Chinese courses in the last decade) • People forget languages which are not frequently used (e.g. Russian in CEECs) • OLS results may be biased.

20. Instrumental Strategy • We compare several sets of instrumental variables: • Language groups are our preferred instruments • We use dummies for countries with Germanic and Romanic languages. • Unlike common languages, language groups are not correlated with free trade areas (e.g. Germany and Norway, France and Spain). • Pisa tests results are valid but weak instruments. • Public expenditures on education are invalid instruments. • All instruments work worse for the CESEE.

21. IV-Results for EU15: Language Groups

22. First Stage Equation for EU15: Lang. Groups

23. IV-Results for EU15: PISA Variables

24. First Stage Eq. for EU15: PISA Instruments

25. IV-Results for EUROPA29: Language Groups

26. First Stage Eq. for EUROPA29: Lang. Groups

27. IV-Results for EUROP29: PISA Tests

28. First Stage Eq. for EUROPA29: PISA Tests

29. Economic Significance (EU15) • Consider the OLS and IV (Group) results: • Coefficients for English communication probability is 1.1-1.4. • Average effect in EU15: 27%-36% increase due to English proficiency (22% average communicative probability). • UK-IRL trade is increased 3 to four times because of English proficiency (97% communicative probability). • Furthermore, NL-UK trade is higher by factor 2.3 to 2.9 (76% com. prob.). • But also NL-S trade is increased 1.7-2.1 times (52% com. prob.).

30. Extensions and Sensitivity Analysis • The foreign language effects may be non-linear, but the effects are not robust. • Hump-shaped effect of English  diminishing returns to language skills. • Median regression confirms the robustness to outliers. • Quantile regression shows that the effects are highest for the lowest and highest quantiles (5th & 90th). • The results are largely confirmed also East Europe and EU27 despite a different language education history.

31. Non-linear Effect for EU15

32. Conclusions • Language proficiency has a strong effect on trade. • Large trade gains are possible through better foreign language education. • The gains are comparable to those of a monetary integration. • Improving English proficiency does not require abandoning national languages. • Large gains are possible at little cost.