jan fidrmucjarko fidrmuc brunel university, cedi, cepr and wdi university of munich, comenius...
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Jan Fidrmuc Jarko Fidrmuc
Brunel University, CEDI, CEPR and WDI
University of Munich, Comenius University and CESifo
2nd FIW-Research Conference „International Economics“Vienna University of EconomicsDecember 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 languageE.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 tooNon-official indigenous languagesForeign languages
Introduction (cont’d)
Rauch (1999, 2001), Rauch and Trindade (2002), Bandyopadhyay, Coughlin and Wall (2008)Ethnic-networks increase tradeRauch 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 languagesLanguage 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 alikeTrade 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
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
ijt
F
fijff
D
dijddijijijjtitjtitijt PLfbdyyT ,,4321
Gravity Model (cont’d)
Common official-language dummies: Ldij
Communication probabilities: Pfij Time-varying country dummies:
Country-specific time-invariant and time-varying omitted variables
Country-specific measurement problems
ijt
F
fijff
D
dijddijijijjtitjtitijt PLfbdyyT ,,4321
Communicative Probability
Probability that two random individuals from two different countries speak the same language
1. English2. 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
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 15Variable (1) (2) (3) (4) (5) (6) GDP 1.004 *** 0.897 *** 0.885 *** 0.880 *** 0.895 *** 1.007 ***
Distance -0.772 *** -0.748 *** -0.761 *** -0.750 *** -0.668 *** -0.754 ***
Contiguity 0.499 *** 0.471 *** 0.491 *** 0.364 *** 0.157 *** 0.478 ***
Official Languages
English 0.908 *** 0.543 *** 0.570 *** 0.662 *** 0.775 *** 0.786 ***
German 0.556 *** 0.581 *** 0.853 *** 0.841 *** 0.667 *** 0.336 ***
French 0.150 ** 0.186 ** 0.101 0.295 0.788 *** -0.033
Swedish 0.158 0.279 *** 0.235 ** 0.323 *** -2.974 *** 0.218 **
Dutch -0.344 *** -0.263 *** -0.340 *** -0.180 ** 0.150 *** -0.287 ***
Proficiency English 1.152 *** 1.074 *** 0.944 *** 1.022 *** French 0.080 0.065 -0.321 German -0.408 *** -0.274 *** 0.102 Italian 8.724 *** 11.687 *** Spanish 8.938 *** 12.071 *** Swedish 19.793 *** Cumulative EFG 0.396 ***
N 1470 1470 1470 1470 1470 1470 Adjusted R2 0.972 0.974 0.974 0.975 0.980 0.973
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
Variable (1) (2) (3) (4) (5)
GDP 0.571 *** 0.573 *** 0.566 *** 0.566 *** 0.574 **
Distance -1.039 *** -1.024 *** -0.817 *** -0.820 *** -1.001 ***
Former Federations 2.278 *** 2.292 *** 1.478 *** 1.471 *** 2.299 ***
Contiguity 0.543 *** 0.531 *** 0.650 *** 0.654 *** 0.538 ***
Proficiency:
English 5.074 *** 5.182 *** 5.188 ***
German 13.381 * 13.239 *
Russian 3.748 *** 3.745 ***
Hungarian -0.309
Cumulative 4.978 ***
N 1254 1254 1254 1254 1254
Adjusted R2 0.847 0.850 0.858 0.858 0.850
Results: EU29
Weaker results English significant but impact less
powerful than in either EU15 or NMS/ACAverage English proficiency (17%)
raises trade by 11% French not significant and German
negative impact Remaining languages significant
Results: EU29
Variable (1) (2) (3) (4) (5) (6)
GDP 0.987 *** 0.767 *** 0.769 *** 0.773 *** 0.773 *** 0.843 ***
Distance -1.038 *** -1.029 *** -1.035 *** -1.025 *** -1.019 *** -1.028 ***
Former Federations 2.460 *** 2.455 *** 1.961 *** 1.988 *** 2.043 *** 2.466 ***
Contiguity 0.320 *** 0.325 *** 0.339 *** 0.328 *** 0.270 *** 0.317 ***
EU 0.263 *** 0.235 *** 0.216 *** 0.218 *** 0.210 *** 0.246 ***
Official languages
English 0.931 *** 0.715 *** 0.739 *** 0.752 *** 0.786 *** 0.802 ***
German 0.566 *** 0.571 *** 0.910 *** 0.893 *** 0.838 *** 0.337 ***
French 0.037 0.056 0.230 0.240 0.295 -0.160
Greek 2.319 *** 2.333 *** 2.316 *** 2.330 *** 2.359 *** 2.333 ***
Swedish 0.140 ** 0.162 *** 0.134 ** 0.153 ** -2.156 *** 0.162 **
Dutch -0.621 *** -0.622 *** -0.638 *** -0.610 *** -0.543 *** -0.614 ***
Proficiency:
English 0.664 *** 0.569 *** 0.595 *** 0.508 ***
French -0.315 -0.283 -0.276
German -0.470 *** -0.436 *** -0.330 **
Russian 1.603 *** 1.559 *** 1.557 ***
Italian 1.637 *** 1.706 ***
Spanish 2.645 ** 3.444 ***
Swedish 12.635 ***
Hungarian 3.577 ***
Cumulative 0.386 ***
N 5634 5634 5634 5634 5634 5634
Adjusted R2 0.930 0.930 0.931 0.931 0.931 0.930
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 EU29Except Russian: U-shaped in NMS/AC
Results: Non-linear Effect EU15Variable (1) (2) (3) (4) (5)
Intercept GDP Distance Contiguity included but not reported
Official languages
English 0.908 *** 1.369 *** 1.672 *** 1.749 *** 1.601 ***
German 0.556 *** 0.661 *** 0.030 0.015 0.325 ***
French 0.150 * 0.292 *** 0.400 0.514 1.003 ***
Swedish 0.158 ** 0.362 *** 0.256 *** 0.279 *** 17.057 ***
Dutch -0.344 *** -0.283 *** -0.404 *** -0.286 *** 0.030
Proficiency:
English 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 ***
Swedish 80.606 ***
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 ***
Italian -748.687 *** -461.089 ***
Spanish -75.874 ** -52.094
Swedish -857.98 ***
N 1470 1470 1470 1470 1470
Adjusted R2 0.972 0.975 0.977 0.978 0.983
Robustness: EU15
Results potentially driven by outliersCountry pairs with especially
high/low trade Effect of English proficiency
highest around 50th percentile (median regression)
Effect of foreign languages not due to outliers
Results: EU 15, Quantile Regressions
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Q5 Q15 Q25 Q35 Q45 Q55 Q65 Q75 Q85 Q95
Quantile Regression
OLS
Results: EU 15, Quantile Regressions
OLS Q10 Q25 Q50 Q75 Q90 Test
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 *** 0.304 0.340 *** 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 1800 1800 1800 1800 1800 1800
Pseudo R2 0.918 0.738 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 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 tradeCommon currency costly (OCA
theory) Improving English proficiency does
not require abandoning national languages
Large gains possible at little cost