nicolas sauter munich graduate school of economis

Nicolas Sauer: Talking Trade 1/14

Nicolas SauterMunich Graduate School of Economis

Talking TradeLanguage Barriers in Intra-Canadian Commerce

FIW-Forschungskonferenz 2008


Outline

1. Motivation2. Estimation

– Data– Empirical Strategy

3. Results– Baseline Results– Instrumental Variables– Robustness/Sensitivity

4. Summary


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!


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.


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.


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


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


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%.



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 ={



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



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



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



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.


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.


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


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

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tom

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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.

nicolas sauter munich graduate school of economis

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