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Talking Trade. Language Barriers in Intra-Canadian Commerce. Nicolas Sauter Munich Graduate School of Economis. FIW-Forschungskonferenz 2008. Outline. 1. Motivation 2. Estimation Data Empirical Strategy 3. Results Baseline Results Instrumental Variables Robustness/Sensitivity - PowerPoint PPT Presentation
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Nicolas Sauer: Talking Trade 1/14
Nicolas SauterMunich Graduate School of Economis
Talking TradeLanguage Barriers in Intra-Canadian Commerce
FIW-Forschungskonferenz 2008
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Outline
1. Motivation2. Estimation
– Data– Empirical Strategy
3. Results– Baseline Results– Instrumental Variables– Robustness/Sensitivity
4. Summary
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Is There a Language Barrier to Trade?
● Motivation○ Estimation○ Results
• Ample empirical evidence from gravity models: – Sharing a common mother tongue as well as higher literacy increase
trade (Mélitz, 2008, EER).– High bilateral calling tariffs reduce trade (Fink et al, 2005, JIE)
• Problem 1: Poor proxies for “knowledge of language(s)”: – “Common official language”, “% speakers with a common mother
tongue”, “% English speakers”.– Potentially biased in cross-section: institutional, cultural, legal effects.
• Problem 2: Many goods do not require knowledge of the others’ language(s) for trade (e.g. oil, rice).
• If they do, one translator should suffice!
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Hypotheses● Motivation○ Estimation○ Results
Hypothesis 1: Industries that require more cross-border communication in order to export their products trade more between Canadian provinces that know the other's language(s).
Hypothesis 2: Direct (oral) communication imposes a larger barrier to trade than indirect (written) communication.
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Data Sources
○ Motivation● Estimation○ Results
• Provincial trade flows at industry-level10 provinces & 3 territories, 36 industries
• Census 2001: Knowledge of Languages
Language commonality: probability that two randomly chosen people are able to speak with each other in English, French, or ‘Chinese’.
• Canadian Input-Output tables at industry-levelCommunication-intensity: share of telecommunications as well as
postal services inputs in total inputs.
These are proxies for the need to communicate directly / indirectly with the trading partner.
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Size of the Language-Trade Effect
ln ln( )ijk ij k 1 ijk 2 k ij 3 k ij ijkT prod trans dist c lang
ln Tijk – log of trade transk – transport-intensity of industry k
distij – distance
ck – communication-intensity of industry k
langij – language commonality
○ Motivation● Estimation○ Results
Bilateral trade (Tijk) between province i and j in industry k is modeled as:
ProductionProduction
GDP GDP
jkik
i jijkprod
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Baseline Estimates
(1) (2) (3) (4) (5)
ln(trans*dist) -0.409*** -0.415*** -0.413*** -0.409*** -0.409***(0.119) (0.123) (0.123) (0.119) (0.119)
prod 8.647*** 7.854*** 7.862*** 8.646*** 8.647***(0.965) (0.933) (0.932) (0.967) (0.965)
telecom*lang 20.23** 36.67*** 20.12** 20.22*(8.943) (10.98) (8.840) (10.96)
post*lang 2.654 -24.29***(7.884) (8.296)
telecom*GDPpc 234.6(3065)
telecom*religion -0.0379(20.12)
Observations 3330 3261 3261 3330 3330F-statistic 60.71 63.91 62.75 59.18 59.14R2 0.41 0.41 0.41 0.41 0.41
○ Motivation○ Estimation● Results
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Size of the Language-Trade Effect
When shifting from the 25th to the 75th percentile of the distribution of langij, trade volume increases for an average communication-intensive industry by:
%ΔTijk≈ 100*β3*Δx = 100 *β3*(lang75ij –lang25
ij)*telecomk=2.64%.
For a communication-intensive industry like ‘health services’ the increase in trade amounts to 6.94%.
○ Motivation○ Estimation● Results
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Instrumental Variables Approach
Instrument: Legal language status (predetermined and unaffected by the trade flow in 2001)
ln ln( )
ijkk
ijk ij k 1 ijk 2 k ij 3 k ij ijk
k ij ij k ijk ij
T prod trans dist c lang
c lang c legal u
βx
ij
- 2 if both provinces enforce special language rights
- 1 if one province enforces special language rights
- zero otherwise
legal ={
○ Motivation○ Estimation● Results
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IV Estimates
(1) (2) (3) (4)
ln(trans*dist) 2.034** 1.259 -0.0801 0.903(0.993) (1.020) (1.024) (1.150)
prod 8.657*** 9.591*** 7.858*** 8.591***(0.895) (0.896) (0.865) (0.852)
telecom*lang 38.73*** 23.91** 32.26**(11.24) (11.41) (14.93)
post*lang 13.38 -13.89(12.62) (18.61)
Observations 3327 2554 3258 2492F-stat 62.76 80.94 63.18 79.13R2 0.411 0.492 0.410 0.501
1st-stage F stat 184.6 155.3 95.041st-stage p. R2 0.435 0.423 0.4241st-stage F stat 103.4 92.531st-stage p. R2 0.434 0.423
○ Motivation○ Estimation● Results
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Sample Sensitivity
Full Sample Only Provinces 1998 Sample
Telecommunication Serviceslang_ij 18.17* 19.30** 24.44***
(9.313) (9.161) (9.325)3409 2554 3072
work_ij 18.33** 17.03**(7.519) (7.333)3409 2554
Postal Serviceslang_ij 0.833 3.039 6.658
(8.204) (8.795) (7.057)3340 2492 3021
work_ij 2.509 4.205(6.697) (7.087)3340 2492
○ Motivation○ Estimation● Results
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Bias from Zero Trade Flows? – Poisson Estimates
(1) (2) (3) (4)
ln(trans*dist) -0.974*** -0.972*** -0.979*** -0.969***(0.254) (0.254) (0.254) (0.254)
prod 7.029*** 6.916*** 7.001*** 6.831***(1.496) (1.668) (1.494) (1.645)
telecom*lang 19.30*** 23.36*** 31.65***(6.062) (5.930) (7.622)
post*lang 11.82 -15.68*(7.356) (8.586)
Observations 5610 5466 5454 5310Pseudo-R2 0.891 0.908 0.892 0.910
○ Motivation○ Estimation● Results
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Summary
• I identify one mechanism that could justify the empirical evidence for the language barrier to trade in gravity models.
• The language barrier is larger for industries, which require more direct communication in order to trade their products.
• Particularly service industries trade more between provinces with a high proportion of same-language speakers.
• Inability to speak the other’s language is a comparative disadvantage for exports and imports of services.
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References
Anderson, James E. and Eric van Wincoop (2004): “Trade Costs," Journal of Economic Literature, 42:3, 691-751.
Fink, Carsten, Aaditya Mattoo and Ileana Cristina Neagu (2005): “Assessing the Impact of Communication Cost on International Trade," Journal of International Economics, 67, 428-445.
Melitz, Jacques (2008): Language and Foreign Trade," European Economic Review, 52:4,667-699.
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Mother Tongues vs. Knowledge of Languages
Mother Tongue neither
English/French
Know English
only
Know French
only
Know English & French
Know Neither English nor
FrenchPopulation
Canada 17.6% 67.5% 13.3% 17.7% 1.5% 30,007,094Alberta 16.0% 92.0% 0.1% 6.9% 1.1% 2,974,807British Columbia 24.3% 90.3% 0.0% 7.0% 2.7% 3,907,738Manitoba 19.9% 89.7% 0.1% 9.3% 0.8% 1,119,583New Brunswick 1.7% 56.5% 9.2% 34.2% 0.1% 729,498Newfoundland/Labrador 1.1% 95.7% 0.0% 4.1% 0.1% 512,930Northwest Territories 19.0% 90.4% 0.1% 8.4% 1.0% 37,360Nova Scotia 3.0% 89.7% 0.1% 10.1% 0.1% 908,007Nunavut 70.8% 83.0% 0.1% 3.8% 13.1% 26,745Ontario 23.7% 85.9% 0.4% 11.7% 2.1% 11,410,046Prince Edward Island 1.5% 87.9% 0.1% 12.0% 0.0% 135,294Quebec 10.0% 4.6% 53.8% 40.8% 0.8% 7,237,479Saskatchewan 12.2% 94.5% 0.0% 5.1% 0.3% 978,933Yukon Territory 9.5% 89.4% 0.2% 10.1% 0.3% 28,674
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Industry Mean Trade Telecoms services
Postal Services
Transport Services
in million $ in % in % in %
Fishery 6.78 0.05 - 0.68Metal 90.31 0.07 0.02 2.18Paper 60.41 0.09 0.05 3.94Petroleum and Coal 102.60 0.09 0.01 0.59Fuels 680.76 0.10 0.03 0.11Lumber, Wood 35.25 0.14 0.04 2.14Beverages and Tobacco 17.75 0.15 0.07 0.76Residential Construction 74.65 0.15 0.07 0.76Leather 29.74 0.19 0.19 0.94Textiles 22.58 0.19 0.15 0.54
Retail 25.63 1.01 1.87 0.07Finance, Insurance 143.51 1.07 0.56 0.02Utilities 43.39 1.13 0.68 1.01Educational services 3.32 1.41 0.71 0.05Wholesale 140.05 2.27 1.08 0.13Communication services 45.52 2.43 5.17 0.11Health 3.70 2.52 0.45 0.05misc. Services 51.25 2.82 2.32 0.39Transport & Storage 74.00 4.21 1.22 0.88Professional services 152.78 5.30 2.55 0.69
Input Shares of by Industry, 2001
Bot
tom
10
Top
10
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Non-parametric scatterplot smoother
Royston, Patrick and Nicholas J.Cox (2005): “A multivariable scatterplot smoother,“ Stata Journal, 5:3, 405-412.
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