Language barriers to foreign trade:
evidence from translation costs∗
Alejandro Molnar†
Vanderbilt University
PRELIMINARY DRAFT
November 9, 2013
Abstract
[Foreign trade involves tasks that may be subject to language barriers, such as researching
foreign markets, communicating with counterparties and marketing products to foreign
consumers. Language skills have a wage premium that is determined by local and worldwide
supply and demand for language services, and the premium is specific to each country and
pair of languages. I construct a novel measure of language skill premia based on professional
rates for translation services from an online market. The skill premium measure relies on
the bi-directional nature of the translation cost data to control both for difficulties inherent in
defining a unit of account (as the per word “piece-rates” common in the translation industry
do not embody equal amounts of work across languages) and the skilled-wage component of
rates. I develop an estimation strategy based on overlaps in ethnolinguisitic populations to
estimate the effect of the language skill premium as a cost barrier to trade, net of confounders
such as trade by shared ethnic populations. I find that accounting for country and languagespecific language barriers yields a three-fold increase in the estimated effect of language on
foreign trade, relative to current estimates based on a shared common language.]
JEL: F1, F14, F23, Z13
Keywords: Gravity equation, common language, language barriers.
∗ I am grateful to Tim Bresnahan, Jon Levin and Kalina Manova for guidance and encouragement.
Special thanks to Ran Abramitzky, Kyle Bagwell, Emilio Depetris Chauvin, Bernardo Díaz de Astarloa,
Doireann Fitzgerald, Gordon Hanson, Han Hong, Paul Ma and Andrés Rodríguez-Clare for helpful
comments and discussions. All remaining errors are my own.
† Department of Economics, Vanderbilt University, 415 Calhoun, Nashville, TN 37204 , e-mail:
alejandro.i.molnar@vanderbilt.edu.
1
1
Introduction
In this paper I study the relationship between the languages that are spoken in a country
and the country’s patterns of foreign trade. Foreign trade involves tasks that may
be subject to language barriers, such as researching foreign markets, communicating
with counterparties and marketing products to foreign consumers. Language skills
have a wage premium that is determined by local and worldwide supply and demand
for language services, and the premium is specific to each country and pair of
languages. The main idea in this paper is to exploit variation in country-specific prices
for translation services across different pairs of languages to recover country-specific
measures of language skill premia. I construct a novel measure of these premia based
on professional rates for translation services from an online market. The skill premium
measure relies on the bi-directional nature of the translation cost data to control both for
difficulties inherent in defining a unit of account (as the per word “piece-rates” common
in the translation industry do not embody equal amounts of work across languages) and
the skilled-wage component of rates. I develop an estimation strategy based on overlaps
in ethnolinguisitic populations to estimate the effect of the language skill premium as a
cost barrier to trade, net of confounders such as trade by shared ethnic populations. I
find that accounting for country and language-specific language barriers yields a threefold increase in the estimated effect of language on foreign trade, relative to current
estimates based on a shared common language.
2
Online markets for translation services
In this section I describe the offline and online markets for translation services, as well as
the price data available from online translation markets and how it contains information
on skill premia for a specific form of human capital that is used intensively in foreign
trade.
The task of translation is typically carried out by a single individual. A translator’s
physical productivity can be thought of as the pace at which a source text is translated
into a target text of a given quality. A translator’s productivity is text-dependent, as
translation work may require domain-specific knowledge in addition to language skills,
and text may vary in difficulty, requiring additional time (e.g. for research) to produce a
translation of a given quality.
The typical translator is a freelance worker, but relationships with the demand side
of the translation market may be in-house as well as arms length. Relationships may
2
be long- or short-term, and occur directly or through intermediaries called translation
agencies. Agencies provide demand risk-sharing among teams of individual translators,
as well as reputation services (on reputation intermediaries in online labor markets, see
Stanton and Thomas, 2011).
The price of translation work is called a “translation rate” and is quoted in a unit
of text that can be easily counted on a computer: words in “spacing” languages (i.e.
those in which words are separated by white space such as English) and characters
in non-spacing languages (e.g. Simplified Chinese). Translation rates are specific to
a directed language pair (e.g. Spanish to English) and are almost always expressed
in units of the source text, as quoting per unit in the target language provides bad
incentives for the translator. As freelancers, translators negotiate rates bilaterally with
potential clients and will usually quote client-specific rates based on the current state of
demand for their services and the attributes of the client: for example, translators may
quote higher rates for rushed jobs or highly technical jobs, or lower rates when providing
quantity discounts or attempting to establish a relationship with a client that may be a
future source of demand. Offline demand for a translator can come from professional
listing services, translation agencies, reputation and word of mouth. A further source of
demand can be outsourcing from other translators, and these translators provide editing
and monitoring and may or may not disclose to the final client that the translation was
outsourced. Most demand from these offline sources is specific to a translator’s country
of residence.
Online platforms specifically designed to intermediate global translation markets
started in 1999 (proz.com) and 2002 (translatorscafe.com). These platforms introduced
new forms of market organization (e.g. procurement auctions, explicit reputation metrics
for both sides of the market) and greatly facilitated the meeting of supply and demand
across borders. To do business on these platforms, translators must create a profile and
report the minimum translation rate at which they are willing to work on each language
pair in which they work. These rates are not revealed to potential clients, but screen the
jobs that a translator will see on a job listings dashboard when logged into the site. Since
this screening rate is set prior to and independently of the attributes of listed translation
jobs, it can be thought of as a reservation wage (in units of work, rather than time) for
each translator. The second largest of these online markets, translatorscafe.com, discloses
the average of this minimum translation rate for all translators in a language pair that
are located in a particular country.
There is substantial variation in these average rates both across language pairs within
country and across countries within language pair. The determinants supply and
3
demand for translation services that give rise to variation across language pairs within
country arises are reasonably straightforward: for example, demand may depend on
the languages used by trade partners, foreign tourists, and on the country’s interest in
cultural products produced in foreign languages, and these in turn may depend on the
country’s ethnolinguistic composition. Supply follows from each country’s endowment
of people with language skills that enable work in each specific language pair (which
may be relatively fixed in the short term), and an opportunity cost of time for this type of
work, common across all possible translation pairs (e.g. the wage for the bundle of skills
that translators possess net of their language-specific skills). Variation across countries in
the same language pair is less straightforward, as a law of one price might be expected to
hold in online markets. One reason for cross country variation in prices is that translation
services are not homogeneous and may require country-specific knowledge (e.g. on the
legal environment, pop culture, slang or vernacular) for which translators in different
countries are imperfect substitutes. A second reason follows from the microstructure of
the translation industry and the fact that translators have limited capacity and spend a
fraction of their time unemployed and waiting for the arrival of the next job. Accepting
a low-paying job removes the translator from the market until the job is completed,
and therefore may preclude accepting a job that arrives later with a higher pay. Online
translators face such arrival processes for potential jobs from both online and offline
sources, and a simple search model suggests that they should set a reservation wage for
accepting an online job that depends on the opportunity cost of removing themselves
from the offline market for a period of time.
A further idiosyncratic factor of this industry is that the unit of work in which prices
are expressed is not constant across languages. Different western languages may differ
in their use of articles, contractions and compound words, so that texts that are supposed
to convey the same “meaning” would be counted at different lengths depending on the
language in which they are written. For instance, the English phrase “it is not so simple"
consists of 5 words, whereas the same phrase in Spanish “no es tan sencillo" consists of
4.1 This applies even more clearly to rate comparisons between spacing and non-spacing
languages, where rates are not expressed in words.
1 The
phrase in English can be contracted to “it’s not so simple" or “it isn’t so simple", both of which
are also 4 words long. On average, text in Spanish tends to be longer than equivalent text in English.
German uses long compound words, and a famous example that arose from a state legislature was
the “Rindeischetikettierungsueberwachungsaufgabenuebertragungsgesetz", or the “Law on delegation of
duties for supervision of beef labeling". If the amount of work required from a translation is not a function
of the number of words but of the amount of “meaning” conveyed in the text, we should expect, all else
equal, that translation rates out of German be higher than out of English, and these higher than out of
Spanish.
4
To develop a comparable measure across languages, I assume the following structure
for observed translation rates, which are quoted in US dollar cents per source word or
character:
r abc = δ a δ(a,b)c η abc
(1)
where r abc is the observed average translation rate from source language a to target
language b for translators located in country c, δ a is a scaling factor specific to the
source language intended to absorb differences between languages in the amount of
work embodied in translating a word or character, and δ(a,b)c is an undirected pair and
country effect. I normalize log δ(eng,spa)USA = 0 and regress log r abc on source language
fixed effects and undirected pair and country fixed effects, so average rates by source
language relative to the rate on this specific pair are absorbed by source effects, and
remaining variation in rates is captured by the δ(a,b)c term. For example, the value
of δ(eng,spa) ARG is constructed from the average of rates for English to Spanish (net of
the English source language effect) and Spanish to English (net of the Spanish source
language effect) for translators located in Argentina.
After adjusting for source effects, a country’s average translation rate on a language
pair remains a nominal quantity. Equal nominal rates can represent widely different
resource costs for countries with different average wage levels for skilled labor. For
example, the average English to French rate is 10 USD cents per word for translators
located in France, 13 cents in Côte d’Ivoire, 12 cents in Algeria, 10 cents in Morocco, 8
cents in Cameroon and 7 cents in Senegal. As France’s GDP per capita is approximately
40 times that of Senegal, the resource cost of employing a person to overcome English
to French language barriers is presumably substantially higher as a per-person share of
Senegal’s economy than it is for the French economy, and much more so for the economy
of Cote d’Ivoire.2
To obtain a measure of variation across language pairs that is net of wages, I regress
\
log
δ(a,b)c on GDP per capita in country c, and take the residuals of this regression as
my final measure of real language skill premia. Figure ?? plots this exercise, plotting
\
log
δ(a,b)c in blue for language pairs that involve a country’s most widely spoken
language, and green otherwise. Some of the highest language skill premia include
English-French in Cote d’Ivoire, English-Swahili in Tanzania, and Italian-Japanese in
2 Similarly,
translation rates between Scandinavian languages are relatively high, presumably because
most speakers of these languages reside in high wage countries. A reasonable prior is that language
barriers between Scandinavian counties are relatively low.
5
Japan. Some of the lowest include English-Farsi in Afghanistan, English-Khmer in
Cambodia, Kazakh-Russian in Kazakhstan and English-Albanian in Greece. Figure 2
plots a subset of this data, narrowing in on countries for which English is not the
most widely spoken language and the skill premium for English and the country’s most
widely spoken language.
2.1
Why language could matter in foreign trade
Trade requires communication, which can give rise to language barriers. Languageintensive tasks that are essential to trade include researching foreign markets, adapting
and marketing products to foreign consumers,3 and communication and contracting
between importing and exporting firms. Language may affect the foreign direct
investment decisions of multinational firms, e.g. the location of regional headquarters,
and trade in final and intermediate goods may follow from such decisions.
Empirical estimates of trade costs acknowledge the role of language (?), usually
estimated by inclusion of a binary variable for whether two countries share an official
language. The size of language barriers can be expected to depend on the languageintensity of specific tasks involved in trade, and the cost of hiring workers within a
country with the required language skills.
3
Empirical evidence on language barriers to trade
In this section I describe how my measure of language skill premia explains trade flows
between countries by including the language skill premia described in Section 2 in
standard trade gravity estimation frameworks from the trade literature. I also describe
an empirical strategy to estimate a causal effect of language barriers on trade.
In order to include the language and country-specific adjusted translation rates in
a standard gravity equation framework, I map the adjusted rates to country pairs in
two ways. I define the top pair country-pair specific rate as the average of the rates
between the most widely spoken languages in each country. I also assign a value of 1 to
3 Firms make design or product choices in response to language barriers, an example of which are the
text-free assembly manuals that accompany furniture sold across many national markets by Swedish firm
IKEA. To sidestep individual language costs, IKEA incurs the cost of high quality design in assembly
manuals (and perhaps product adaptation) to avoid ambiguity and the use of written text. Not all retail
products that require instruction manuals or assembly instructions in a language other than that of design
or manufacture are suitable to, or have sufficient scale to afford, IKEA’s economy of words. See Kelly and
Zetzsche (2012) for examples of how translation services are employed in trade.
6
the translation rate observed dummy for the pair.4 I construct a fractional or populationweighted adjusted translation rate by applying the above procedure to every language
population within a country combined with every language population in a partner
country. The translation rate measure is obtained from the weighted average of every
cell with a combination of languages, where the weight is the product of the marginal
population measures in each country. Since rates for most potential language pairs are
not observed, the measure of population for which the rate is observed is counted in the
continuous translation rate observed variable.5
Additional linguistic variables include the measure of the population in a country
pair that speak the same language, i.e. the probability that a person picked at random
from one country would be able to speak with a person picked at random from the
other. This is equivalent to the main measure used in the preceding work on the effect
of language on trade by Melitz (2008) and Melitz and Toubal (2012). I include the log of
this variable and a dummy for when countries share no speakers of a common language.
I also include the dummy variable for common official language, which is the usual control
for language used in almost all prior empirical work that estimates the gravity equation.
Table 1 reports OLS estimates for three specifications of the standard gravity
equations for the subset of country pairs with positive trade flows. The first column
includes only the standard common language dummy, the second includes the fractional
adjusted rate measures and the third only the rates for the top pair of languages.6
From the estimates in Column (2), a 1 percent increase in the adjusted translation rate
between the languages of a pair of countries is associated with a 0.6 percent decline
in bilateral trade. Column (3) presents a weaker, non-significant estimate of the same
effect for the most widely spoken language for each country in the pair. In both cases
the magnitude of estimates for distance, contiguity and colonial relationships all decline
substantially after inclusion of the larger set of language skill premium and linguistic
overlap measures. The magnitude for “common official language” is almost halved,
but inclusion of the measures of ethnolinguistic overlap means that this binary variable
identifies purely the “official” status of any common languages.
Table 2 presents results from an exponential regression on the same data, which
allows inclusion of pairs with zero trade that are dropped due to the log transformation,
and is a favored empirical method in the trade literature because gravity equation
4 If
language pair data for only one country is observed, I include the rate and count it as observed.
translation rates in the data that are plotted in Figure ?? do not map to any ethnic population
(e.g. English to Spanish in Norway) and are therefore not used in gravity equation estimates.
6 Results are robust to netting GDP per capita from nominal rates with a quadratic term or a local
polynomial regression.
5 Some
7
regressions are motivated by multiplicative structural models and estimates from the
exponential regression framework are robust to heteroscedasticity in the error term for
the parameters of interest in these models. Both this framework and the framework I
will employ below to instrument for the language measures cannot include importer and
exporter fixed effects, so I include instead a “remoteness” measure (used for example by
Baldwin and Harrigan (2011) and Manova and Zhang (2012), see discussion in Head and
Mayer (2013)). The main coefficient estimates on this sample are similar in magnitude,
but the estimates from the Column (3) specification are now significant. Common official
language becomes non-significant, and the inclusion of the full set of language variables
has a smaller effect on the coefficients for geographical covariates such as distance and
contiguity.
The language barrier estimates from Tables 1 and 2 cannot be given the causal
interpretation of a trade cost because linguistic overlap can be correlated with trade
through other channels, in particular ethnic trade (Rauch and Trindade, 2002). If
trade creates additional demand for translation services on the relevant language pairs
and this increases translation rates, this would lead trade to be positively correlated
with observed translation rates, biasing upwards (in this case, towards attenuation) the
coefficient estimates for translation rates as a trade cost in a gravity equation.
I use data on the overlaps of ethnolinguistic populations to develop a shifter for
the relative scarcity of language skills and estimate a causal effect of country-specific
language skills on trade. To describe the empirical strategy, consider the example of
trade between the United Kingdom and either Vietnam or Thailand. The population of
Vietnamese and Thai speakers in the UK is small and similar in magnitude, but there
is one large difference between these language pairs from the perspective of the UK
market for language services: the existence of the US as a majority English-speaking
country with a large population of Vietnamese speakers. There is no similar example
for the Thai language. The existence of the US as a country where Vietnamese and
English speakers overlap has an effect on the world market for language services in this
language pair. In particular, if the ethnolinguistic composition of the US has a larger
effect on supply than on demand for English-Vietnamese language services, this is likely
to decrease the English-Vietnamese language skill premium in the UK.
Based on this idea, I define an ethnic overlap instrument in the following manner:
for a given “language in country" pair (e.g. English in the UK, Vietnamese in Vietnam),
the probability that a speaker of each of these languages would coexist in an country
other than that of the language pair with a speaker of the other language. That is, I
take the measure of all English speakers worldwide with the exception of the UK, and
8
calculate the conditional probability that if they were to meet a fellow resident at random
from their country, that resident would be a speaker of Vietnamese. This conditional
probability is close to zero for most English speakers worldwide, except for the US where
it is about 0.02, and the US constitutes a large fraction of worldwide English-speakers. I
then do the same for Vietnam, where the measure is almost zero in neighboring countries
with Vietnamese speaking residents, but a large fraction of Vietnamese speakers outside
the UK-Vietnam pair reside in the US, where their probability of meeting an English
speaker is close to 1. I construct the instrument as the product of both of these
probabilities.
As this instrument coarsens the variation in the data to a language-pair level (since
foreign overlap will be a similar value for most countries, e.g. the instrument has
a similar value for English-Vietnamese pairs that involve Vietnam and Great Britain,
Australia or New Zealand), the estimated effect might be expected to be similar to that
from an OLS regression of trade flows on a language pair (but not country) specific
measure. However, whereas the average global rate may be driven by trade flows on
a particular country (e.g. the abundance of Vietnamese speakers in the US affects both
the worldwide supply and demand for English-Vietnamese language services, as well
as trade between the US and Vietnam through a direct ethnic channel, see Rauch and
Trindade, 2002), the instrument specifically excludes the correlation between trade flows
and a country pair’s common language fraction (e.g., in the case of US to Vietnam trade,
the instrument takes on a low value, whereas the common language fraction takes on a
relatively high value).
The fact that I do not observe language skill premia in all language pairs presents
a hurdle to straightforward instrumental variables estimation, as the common practices
of selecting a sample or interacting unobserved or censored values of a regressor with a
dummy variable (as I do in the results presented in Tables 1 and 2) are only valid under
exogeneity assumptions. When a censored regressor is endogenous, 2SLS estimation
can lead to a bias that amplifies the magnitudes of estimated coefficients, as discussed
by Rigobon and Stoker (2007). 7 .
As detailed in Appendix Appendix B, I implement the estimation strategy of
Chernozhukov, Rigobon, and Stoker (2010) to estimate the effect of language skill premia
on trade using the ethnolinguistic overlap instrument. Results are in Table 3, both for the
“conditional on positive” linear regression specification and the exponential regression.
The bottom row in the table presents results for the first stage: for the language
7 In
fact, estimating Columns 2 and 3 of Table 1 by 2SLS with the ethnolinguistic overlap instrument
does lead to implausibly large effects of language skill premia on trade flows
9
pair of a specific country, ethnolinguistic overlap elsewhere in the world predicts a
lower median adjusted translation rate, which suggest that the effects of ethnolinguistic
overlap through the expansion of supply dominates the effects through expansion of
demand. Any effect from the ethnolinguistic overlap for the common pair in question
is absorbed by the common language fraction variable, which is included in the second
stage. The main coefficient estimates for the effect of language skill premia on trade
are larger in magnitude than in the specifications of Tables 1 and 2, which is consistent
with the main concern for endogeneity in those regressions being attenuation from the
reverse the effect of trade on translation costs. The magnitudes from the exponential
regression specification imply that predicted trade for a pair of countries sharing a
common language is 5.2 times that of a pair of countries not sharing a common language
and having the largest language skill premium observed in the data, relative to a 1.7times increase estimated using only the common language dummy. This is a threefold increase in the estimated effect of language on foreign trade. Results are robust
across specification that use alternative thresholds for translator data quality and when I
include data from a second source (translationdirectory.com, an online listing directory
for translators, where incentives to price revelation are not as clear).
[To be done: Pairs through English as a lingua franca. Classification of products (e.g.
more differentiated products, contract-intense, R&D intense, etc.). Preliminary results
show monotonic results between product differentiation or technological component and
impact of measure. Alternative instruments based on migration flows and linguistic
cleavages (e.g. Shastry, 2012). List of potential alternative channels, e.g. Warcziarg-style
covariates, and Guiso, Sapienza, and Zingales (2009). “Splinter” the gravity equation
into population-proportional subcells to use the population-weighted language barrier
measure between country pairs while addressing endogenous censoring.]
4
Conclusions
This paper develops a novel measure of language barriers between countries based on
prices for translation services, which reflect the market premium on scarce language
skills, and estimates the impact of this measure on trade flows between countries. The
paper’s main result is that the conventional practice of controlling for language with
a “common official language” dummy omits a large share of the effect of language
on foreign trade. Understanding the proper role of language contributes to our
understanding of the barriers to trade and economic integration that some countries
may face: much as ‘landlocked” countries trade less, firms in countries with low
10
endowments of foreign language skills may face additional hurdles to carrying out
the multiple activities involved in foreign trade, and its domestic firms may need
to rely on the initiative of foreign partners to overcome these barriers. Additional
knowledge on the component factors that are regularly proxied by “distance” in
standard gravity applications may reduce the relevance attributed to this catch-all
variable, and increase our understanding of the nuanced factors that affect foreign trade.
As an institutional and cultural endowment, the abundance of language skills may have
broader implications for other flows such as the transmission of technological knowledge
and cultural values.
11
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12
Tables and figures
[To do: table of main languages, language iso codes, number of translators, average rate
against English, Spanish, French, Chinese, English in the US.]
log avg. undirected pair translation rate net of source f.e.
Figure 1: Adjusted translation cost and per capita GDP
ita−jpn−JPN
1
jpn−swe−SWE
eng−khm−USA
eng−kur−USA
eng−tam−MYS
hun−nld−NLD
nld−tur−NLD
eng−lao−USA
.5
0
−.5
eng−swa−CAN
eng−som−USA
eng−srp−ITA
fin−fra−FIN
deu−nor−DEU
deu−jpn−AUT
deu−jpn−DEU
deu−fin−FIN
eng−tur−CYP
eng−pan−GBR
fin−ita−FIN
ita−nor−ITA
dan−fra−FRA
dan−deu−DEU
eng−est−USA
ell−eng−CAN
fra−jpn−CAN
eng−tur−NLD
deu−vie−DEU
eng−isl−ISL
fra−nor−FRA
eng−nep−USA
fas−fra−FRA
deu−ita−CHE
dan−nor−DNK
dan−ita−DNK
ces−eng−IRL
eng−hbs−GBR
amh−eng−USA
eng−ita−FIN
eng−swa−USA
eng−zlm−GBR
jpn−rus−UKR
eng−swa−TZA
deu−swe−DEU
bos−eng−GBR
fra−tur−FRA
eng−fin−SWE
eng−spa−FIN
deu−fra−MAR
eng−zho−VNM
rus−srp−SRB spa−zho−ARG
eng−mar−USA
fra−jpn−FRA
dan−eng−CAN
eng−kat−USA
nor−swe−SWE
eng−urd−USA
ita−jpn−ITA
dan−swe−DNK
deu−swe−SWE
eng−vie−GBR
eng−uzb−USA
eng−mya−USA
eng−gle−IRL
eng−lat−USA
eng−fin−DEU
eng−kat−GBR
eng−fin−ITA
deu−fin−DEU
eng−ita−GRC
ara−eng−AUS
eng−urd−GBR
ell−fra−CYP
eng−tha−GBR
fra−hat−USA
nld−pol−NLD
ind−zho−IDN
eng−jpn−ITA
eng−kur−GBR
dan−deu−DNK
eng−lit−USA
eng−vie−AUS
fra−swe−SWE
deu−jpn−JPN
ara−eng−ITA
nld−rus−NLD
deu−eng−UKR
eng−kor−IND
deu−fra−CHE
deu−fra−CAN
jpn−spa−ESP
eng−uzb−GBR
fra−spa−CHE
dan−fra−DNK
eng−swe−CHE
nld−swe−NLD
ell−fra−BEL
eng−mkd−USA
eng−nor−CAN
dan−spa−DNK
eng−hin−USA
eng−por−BEL
nld−por−BEL
eng−lat−GBR
fra−swe−FRA
eng−jpn−AUS
aze−eng−USA
eng−hat−USA
ara−eng−MYS
ara−eng−SGP
eng−zul−ZAF
deu−eng−FIN
eng−fra−CIV
ell−eng−CYP
fra−rus−CHE
deu−eng−BEL
fra−rus−DEU
ell−nld−GRC
deu−ell−DEU
jpn−zho−JPN
ara−fra−GBR
ara−fra−ARE
hbs−ita−ITA
eng−ita−CAN
fin−spa−FIN
eng−spa−AUS
eng−ron−ISR
eng−nor−USA
ita−swe−SWE
fin−swe−SWE
deu−tur−DEU
eng−ind−AUS
eng−swe−NOR
eng−hin−CAN
eng−hin−GBR
dan−ita−ITA
dan−nor−NOR
dan−swe−SWE
eng−tha−USA
ara−eng−USA
ara−eng−DZA
eng−fin−FIN
ben−eng−USA
ara−deu−DEU
deu−eng−CHE
deu−eng−JPN
deu−fra−LUX
eng−fra−CHE
eng−fra−CAN
deu−nld−NLD
fra−jpn−JPN
ces−deu−AUT
eng−mon−USA
eng−hrv−USA
eng−pus−GBR
eng−ita−MLT eng−zho−ITA
bos−eng−USA
dan−eng−DNK
eng−nor−IRL
eng−lit−IRL
eng−guj−USA
nor−swe−NOR
eng−est−GBR
ara−eng−QAT
cat−eng−USA
eng−heb−GBR
bul−eng−CAN
eng−nor−GBR
hin−kan−IND
hin−tam−IND
eng−pan−USA
eng−swe−CAN
eng−swe−DEU
eng−tur−DEU
fra−por−CAN
eng−fin−USA
deu−zho−DEU
ben−eng−GBR
ara−fra−USA
fra−zho−FRA
ita−srp−ITA
fra−pol−FRA
deu−eng−LUX
eng−rus−DNK
eng−vie−DEU
bul−eng−GRC
deu−rus−BEL
deu−nld−FRA
eng−jpn−CAN
eng−jpn−GBR
eng−fra−LUX
jpn−spa−CHL
deu−fra−AUT
jpn−kor−USA
deu−fra−FRA
eng−hbs−USA
deu−hrv−DEU
fra−spa−PRI
ara−eng−BEL
deu−eng−CZE
spa−swe−SWE
eng−fra−AUS
eng−hye−USA
ara−ita−EGY
ita−zho−ITA
eng−ita−AUS
deu−ita−DEU
eng−urd−CAN
nor−spa−ESP
rus−spa−USA
ron−rus−ROU
eng−heb−USA
fra−heb−ISR
fin−swe−FIN
eng−rus−JPN
deu−rus−DEU
eng−swe−SWE
ara−eng−GBR
eng−kor−AUS
eng−srp−USA
deu−eng−CAN
eng−jpn−USA
deu−fra−BEL
deu−fra−USA
eng−fra−DZA
eng−nld−NLD
deu−nld−DEU
eng−tgk−USA
eng−tgl−USA
fra−nld−NLD
fra−nld−FRA
eng−hun−USA
eng−ron−IRL
eng−fra−JPN
eng−fra−ARE
deu−spa−USA
ita−tur−ITA
nld−spa−NLD
jpn−spa−USA
fra−spa−CAN
eng−urd−PAK
eng−lit−GBR
ita−slv−ITA
ces−eng−CAN
dan−eng−NOR
nld−swe−SWE
eng−lat−ITA
dan−spa−ESP
eng−sqi−USA
ara−urd−PAK
nor−rus−RUS
fra−srp−FRA
ces−spa−CZE
eng−pan−CAN
deu−rus−AUT
eng−swe−USA
eng−fin−GBR
eng−swe−GBR
eng−kor−USA
ara−eng−CAN
ara−eng−DEU
eng−pol−CAN
eng−pol−IRL
eng−pol−USA
eng−por−CAN
eng−fas−USA
eng−zho−AUS
ita−sqi−ITA
deu−eng−AUS
deu−eng−AUT
deu−fas−IRN
eng−mlt−MLT
eng−swe−ITA
dan−eng−ITA
eng−jpn−KOR
ces−deu−SVK
deu−fra−DEU
eng−nld−USA
deu−nld−BEL
eng−nld−GBR
ell−fra−USA
fra−ron−BEL
fra−zho−CAN
fra−hin−IND
fra−nld−DEU
hun−nld−HUN
deu−eng−DNK
eng−jpn−JPN
eng−jpn−DEU
eng−fra−DEU
eng−fra−ZAF deu−eng−HUN
ara−ita−ITA
eng−lav−USA
eng−prs−GBR
ita−swe−ITA
eng−spa−CAN
eng−jpn−NZL
ara−spa−ESP
fra−spa−URY
eng−swa−KEN hin−mal−IND
eng−ita−JPN
eng−spa−ITA
eng−spa−GTM eng−jpn−CHN
jpn−spa−JPN
fra−ita−CHE
fra−por−GBR
ita−nld−NLD
dan−eng−GBR
eng−fra−AUT
hrv−ita−ITA
fra−ita−CAN
nor−spa−NOR
dan−eng−USA
eng−zho−USA
deu−ron−DEU
cat−deu−DEU
ita−mlt−MLT
ita−por−GBR
eng−vie−USA
eng−heb−CAN
eng−zho−GBR
eng−mkd−MKD
eng−sqi−GBR
bul−ita−BGR
eng−rus−POL
eng−kor−CAN
ara−eng−ARE
eng−tur−CAN
eng−swe−FIN
eng−zho−PHL
fra−tur−TUR
eng−kor−KOReng−rus−ITA
eng−kor−GBR
eng−fas−GBR
eng−nor−DNK
eng−srp−CAN
ara−fra−CAN
deu−por−DEU
spa−tur−TUR
deu−eng−DEU
fin−ita−ITA
eng−fra−DNK
eng−fra−IRL
ell−eng−USA
eng−nld−AUS
eng−nld−DEU
eng−nld−FRA
eng−hun−CAN
eng−hun−DEU
fra−rus−CAN
eng−hbs−MKD
bul−eng−GBR
eng−spa−CHE
deu−spa−AUT
eng−spa−IRL
deu−spa−DEU
eng−spa−DEU
deu−ukr−DEU
eng−hrv−GBR
ind−jpn−JPN
ell−spa−ESP
nld−spa−BEL
nld−spa−ESP
deu−ita−GBR
eng−ita−IND eng−tgl−PHL
fra−ita−DEU
ell−ita−GRC
eng−mkd−SRB
dan−eng−SWE
eng−spa−BEL
ces−eng−USA
eng−ita−BEL
eng−ita−DEU
eng−rus−ARE
eng−jpn−MYS
ara−rus−UKR
rus−zho−UKR
spa−tur−ESP
eng−zho−THA
eng−zlm−USA
eng−swe−DNK
nld−swe−BEL
eng−zho−CAN
eng−rus−CAN
eng−rus−IRL
fra−rus−USA
eng−zho−NZL
eng−tur−FRA
eng−tur−GBR
eng−ita−CHE
por−rus−RUS
por−ron−ROU
eng−tur−USA
eng−swe−IRL
eng−por−USA
eng−por−FRA
eng−por−GBR
fra−por−FRA
nld−por−NLD
spa−zho−ESP
deu−hun−AUT
deu−hun−DEU
eng−rus−NOR
ara−eng−CHE
eng−spa−DNK
eng−mal−USA
eng−hbs−MNE
deu−eng−ITA
eng−slv−ITA
eng−sqi−ITA
deu−eng−GBR
eng−ilo−PHL
eng−kor−CHN
eng−yor−NGA
eng−hat−HTI
ara−deu−MAR
deu−eng−MLT
eng−slv−USA
eng−pus−USA
eng−fra−BEL
eng−fra−USA
eng−fra−MARdeu−tha−THA
eng−fra−FRA
eng−fra−GBR
eng−fra−MLT
ell−eng−GBR
eng−nld−BEL
eng−nld−CAN
eng−por−AUS
ara−fra−CHE
fra−zho−USA
fra−slv−SVN
fra−lit−LTUara−eng−SAU
fin−fra−FRA
eng−fra−MYS
fra−nld−BEL
ces−nld−CZE
eng−ita−BRA
deu−eng−GRC
eng−fra−ITA
eng−fra−GRC
deu−ell−GRC
eng−hin−ARE
ell−fra−FRA
eng−spa−SWE
eng−spa−USA
eng−yid−USA
eng−san−IND
eng−ukr−CAN
fra−spa−USA
eng−slv−GBR
rus−zho−RUS
ita−nld−ITA
deu−por−BRA
deu−ita−AUT
deu−ita−ITA
fra−spa−LUX
deu−spa−CAN
nld−spa−FRA
rus−spa−DEU
ell−spa−GBR
fra−spa−SEN
rus−ukr−USA
jpn−zho−USA
jpn−zlm−MYS
hin−jpn−INDeng−jpn−PHL
jpn−tha−THA eng−ita−ROU
eng−jpn−SGP
ell−eng−DEU
ces−fra−CZE
ces−fra−FRA
eng−ron−GBR
ita−nld−BEL
ita−spa−CAN
ita−lit−ITA
ita−spa−USA
eng−kur−TUR
fin−rus−FIN
eng−ind−USA
cat−eng−GBR
cat−deu−ESP
eng−zlm−MYS
dan−rus−DNK
dan−pol−DNK
eng−rus−DEU
eng−rus−NZL
fra−vie−VNM
eng−ind−GBR
nld−rus−BEL
ara−eng−FRA
ara−eng−YEM
eng−zho−DEU
eng−pol−AUS
ara−fra−FRA
eng−pol−GBR
ara−fra−EGY deu−mkd−MKD
ara−fra−DZA
ron−spa−ESP
fra−rus−BEL
fra−rus−FRA
eng−tgk−TJK
deu−zho−CHN
deu−tur−TUR
deu−srp−DEU
eng−por−IRL
pol−spa−ESP
eng−lav−GBR
nld−por−PRT
eng−prs−USA
deu−eng−USA
deu−eng−IRL
deu−eng−ARG
cat−rus−ESP
eng−fra−SGP
eng−fra−CHN
eng−fra−MUS
ita−zho−CHN
bul−eng−USA
ell−fra−GRC
fra−nld−USA
eng−hin−IND
eng−hrv−CAN
por−spa−USA
por−spa−FRA
eng−kur−CAN
ind−zlm−MYS
eng−spa−PRI
deu−eng−ROU
fra−ukr−FRA
fra−spa−FRA
ell−spa−GRC
eng−fra−KEN
eng−ita−FRA
eng−ita−GBR
eng−ita−ITA
eng−ita−USA
fra−ita−FRA
fra−ita−GBR
ita−rus−GBR
fin−rus−RUS
eng−lat−CAN
eng−pus−PAK
eng−sqi−ALB
heb−rus−USA
rus−swe−SWE
eng−sqi−MKD
ces−deu−DEU
fra−spa−DOM
fra−spa−DEU
eng−ron−CAN
pol−rus−USA
eng−tur−AZE
deu−ita−USA
fra−ron−CAN
fra−ron−FRA
aze−eng−TUR
afr−eng−ZAF
cat−fra−FRA
rus−zho−CHN
eng−slk−CAN
eng−ind−MYS
eng−zho−IDN
eng−guj−IND
eng−slk−GBR
eng−rus−USA
eng−rus−GBR
deu−eng−DOM
rus−swe−RUS
eng−heb−ISR
deu−heb−ISR
ara−fra−BEL
ara−fra−JOR
deu−pol−DEU
eng−est−EST
deu−slv−SVN
heb−spa−ISR
eng−ron−USA
ara−spa−EGY
pol−por−POL
ita−rus−ITA
ara−deu−EGY
eng−pus−AFG
eng−hbs−CANeng−zho−CHE
eng−hbs−ITA
eng−eus−ESP
deu−spa−VEN
eng−ukr−ITA
eng−fas−DEU
deu−ita−FRA
eng−lit−DEU
deu−eng−NZL
eng−jav−IDN
eng−kan−IND
deu−hin−IND
deu−eng−BRA
eng−hau−NGA
eng−tel−IND
deu−eng−ZAF
eng−nep−NPL
deu−eng−MEX
spa−swe−ESP
eng−nld−IRL
fra−lav−LVA
bul−fra−FRA
ell−fra−GBR
fra−tha−THA
deu−nld−AUT
nld−rus−RUS
eng−ukr−USA
eng−hun−SRB
eng−fra−MDG
eng−nld−ITA
deu−spa−ESP
ita−tur−TUR
deu−spa−MEX
eng−spa−MEX
eng−spa−GBR
eng−spa−ISR
fra−spa−MEX
bul−deu−DEU
fra−ita−BEL
deu−spa−CHE
eng−spa−JPN
fra−spa−BEL
ara−rus−RUS
eng−zho−SGP
eus−spa−ESP
eng−spa−SGP
slv−spa−SVN
hin−urd−INDdan−eng−PHL
fra−ita−ITA
ces−eng−GBR
ita−spa−GBR
eng−hbs−BIH
rus−tur−TUR
ita−lit−LTU
ita−slv−SVN
ita−spa−COL
fra−ita−MAR
ita−pol−GBR
eng−ind−IDN
dan−pol−POL
eng−rus−KAZ
ell−rus−GRC
ara−eng−KWT
ara−eng−LBY
ara−eng−LBN
deu−ind−IDN
ita−ron−ITA
eng−srp−GBR
afr−eng−GBR
ara−fra−LBN
eng−slv−SVN
ron−spa−ROU
eng−zlm−SGP
deu−slk−DEU
eng−rus−GRC
eng−heb−DEU
ita−ron−ROU
ara−fra−MAR
ara−fra−TUN
ara−heb−ISR
fra−pol−POL
eng−fas−CAN
ind−jpn−IDN
pol−spa−POL
eng−tur−TUR
hrv−slv−SVN
eng−mar−IND
cat−spa−USA
jpn−zho−CHN
eng−hun−GBR
deu−hun−ROU
fra−hun−FRA
eng−por−BRA
eng−por−DEU
ita−pol−POL
eng−rus−FIN
eng−fra−NZL
eng−fra−ISR
eng−fra−INDeng−spa−BOL
eng−fra−LBN
eng−fra−RWA
ell−eng−GRC
eng−hye−ARM
eng−zho−JPN
eng−zho−MYS
eng−ukr−DEU
aze−rus−AZE
eng−glg−GBR
hin−pan−IND
eng−spa−URY
eng−spa−ESP
eng−spa−CHN
deu−spa−ARG
bul−ita−ITA
fra−spa−ESP
fra−spa−VEN
ita−mkd−MKD
hun−ron−ROU
ita−ukr−ITA
bul−spa−ESP
eng−ita−IRL
pol−rus−POL
ara−eng−IRL
fra−ita−USA
eng−fra−MEX
ces−pol−CZE
lit−pol−LTU
pol−rus−UKR
zho−zlm−MYS
eng−lit−LTU
eng−vie−CAN
eng−fas−ARE
ita−spa−ITA
ita−spa−FRA
ita−spa−DEU
eng−spa−IND
cat−eng−ESP
eng−ita−EGY
cat−fra−ESP
eng−slk−USA
eng−tam−USA
eng−tam−IND
por−rus−PRT
bul−rus−BGR
deu−rus−USA
eng−rus−AUS
eng−rus−GEO
deu−rus−UKR
eng−rus−AZE
ces−ita−ITA
hun−slk−SVK
ara−eng−MAR
ara−eng−ISR
ara−eng−EGY
ara−eng−JOR
eng−pol−POL
deu−pol−POL
fra−zho−CHN
fra−ron−USA
ita−slk−ITA
rus−spa−ESP
rus−spa−RUS
spa−zho−CHN
deu−hun−HUN
eng−rus−LVA
eng−rus−ROU
eng−pus−CAN
fra−slk−SVK
ita−sqi−ALB
ara−eng−SYR
ita−pol−ITA
fra−por−BEL
por−spa−COL
bul−eng−BGR
deu−por−PRT
bul−eng−DEU
ben−eng−BGD
fra−por−BRA
bul−fra−BGR
eng−ukr−GBR
eng−hbs−SRB
bul−ell−GRC
eng−kat−GEO
eng−por−CHE
deu−ron−USA
deu−est−EST
bel−deu−BLR
deu−spa−URY
deu−lit−LTU
eng−nld−SUR
eng−hun−ITA
deu−lav−LVA
eng−hrv−ITA
eng−ind−DEU
eng−pol−ITA
cat−eng−DEU
deu−ell−GBR
eng−nep−IND
eng−zlm−PHL
eng−zho−KOR
deu−eng−CHN
eng−tgk−UZB
eng−som−KEN
deu−eng−KEN eng−mal−IND
eng−fra−DOM
eng−fra−HTI
eng−swe−MEX
eng−glg−ESP
fra−tur−USA
fas−fra−IRN
fra−por−USA
deu−fra−ITA
fra−por−DEU
fra−nld−ITA
fra−por−ITA
fra−rus−ITA
fra−ind−IDN
fra−rus−ISR
deu−fra−ARG
nld−pol−POL
eng−nld−ZAF
deu−eng−SVN
eng−spa−GRC
eng−spa−CUB
ita−por−ITA
hin−mar−IND
eng−guj−GBR
eng−ita−ARG
eng−ita−ZAF
ell−ita−ITA
est−rus−EST
eng−spa−COL
fra−spa−ITA
hun−spa−HUN
spa−swe−MEX
ara−spa−LBN
ara−spa−JOR
eng−spa−TTO
eng−ita−ISR
ces−eng−CZE
ces−deu−CZE
ces−eng−SVK
deu−slk−SVK
ita−spa−ARG
deu−ron−ROU
eng−ron−ROU
eng−ron−DEU
fra−slk−FRA
eng−jpn−IND
heb−rus−ISR
ita−rus−USA
eng−tam−LKA
eng−prs−CAN
amh−eng−ETH
hrv−ita−HRV
eng−rus−CHE
dan−eng−IND
eng−rus−BGR deu−rus−RUS
eng−rus−IND
ces−ita−CZEeng−rus−ISR
fra−rus−UKR eng−fas−IRN
eng−pol−DEU
ces−spa−ESP
eng−rus−KGZ
eng−rus−LTU
fra−rus−RUS
eng−zho−IRL
eng−hun−HUN
cat−ita−ESP
bul−deu−BGR eng−lav−LVA
eng−pan−IND
eng−zho−TWN
bel−eng−USA
eng−ind−CAN
eng−por−PRT
ben−eng−IND
fra−por−PRT
deu−eng−IND
deu−eng−ISR
deu−eng−HRV
deu−eng−POL
bul−rus−USA
rus−slk−SVK
hun−ita−ITA
glg−spa−ESP
aze−eng−AZE
ara−rus−EGY
por−spa−ESP
por−spa−PRT
ara−zho−EGY
eng−zho−RUS
eng−spa−CHL
eng−spa−CRI
eng−spa−VEN
eng−spa−ARG
eng−spa−PRY
ita−por−PRT
fra−spa−CHL
deu−hrv−HRV
eng−ita−CHN
hun−rus−HUN
deu−eng−RUS
deu−hrv−AUT
eng−kur−IRQ
eng−zho−CHN
eng−mon−MNG
eng−slk−SVK
ita−spa−ESP
eng−ron−ITA
por−spa−GBR
fra−ron−ROU
ben−hin−IND
eng−ita−HRV
eng−rus−RUS
eng−rus−UKRdeu−rus−BLR
eng−rus−EST
lav−rus−GBR
ara−eng−IND
rus−spa−ARG
ara−tur−EGY
ita−rus−RUS
eng−vie−VNM
fra−hun−HUN
bel−rus−BLR
eng−ukr−UKR
eng−ukr−RUS
fra−ukr−UKR
por−spa−URY
por−spa−VEN
por−spa−MEX
eng−por−ZAF
eng−por−URY
eng−pol−DNKeng−spa−NOR
deu−spa−PER
deu−hbs−SRB
bul−eng−ITA
eng−tur−EGY
eng−sin−LKA
deu−rus−ARM
bel−eng−GBR
ces−eng−DEU
bih−eng−IND
deu−slk−CZE
deu−eng−PHL
eng−sqi−SRB
eng−kur−IRN
ara−eng−CHN
ara−eng−KEN
eng−lao−LAO
deu−vie−VNM
eng−rus−MEX
deu−eng−URY
eng−fas−TUR
eng−fra−SEN
eng−nld−NZL
eng−nld−IND
fra−glg−ESP
fra−ron−ITA
fra−spa−ECU
fra−hun−ROU
fra−spa−CUB
ell−sqi−GRC
eng−nld−CHN
eng−tha−THA
eng−urd−IND
por−spa−CHL
por−spa−BRA
eng−por−ITA
eng−spa−PER
eng−spa−PAN
eng−spa−NZL
eng−spa−HND
eng−spa−ECU
fra−spa−PER
fra−spa−ARG
fra−spa−COL
eng−fra−TUN
eng−nld−ISR
eng−slk−CZE
guj−hin−IND
ita−spa−AUS
ita−spa−BEL
spa−ukr−ESP
rus−spa−MEX
por−spa−DEU
spa−ukr−UKR
rus−spa−UKR
eng−spa−ZAF
eng−spa−DOM
eng−spa−PHL
ita−por−BRA
ita−spa−VEN
eng−slv−HRV
afr−eng−USA
ita−spa−CHL
ita−spa−PER ara−tur−TUR
ita−rus−UKR
ita−ukr−UKR
eng−slk−IRL
eng−ind−SGP
kat−rus−GEO
eng−rus−CZE
rus−tur−RUS
cat−spa−ESP
ara−eng−IRQ
ara−eng−TUN
hye−rus−ARM
ara−fra−SAU
ara−fra−ITA
ces−rus−UKR
cat−spa−DEU
ara−eng−SDN
ara−eng−PSE
eng−hun−ROU
por−ron−PRT
cat−ita−ITA
hin−tel−IND
hin−rus−IND
deu−ukr−UKR
eng−zho−IND
eng−vie−SGP
bul−spa−BGR
eng−uzb−UZB
ara−urd−IND
hun−ita−HUNjpn−kor−KOR
bel−eng−BLR
kor−zho−CHN
por−spa−ARG
lav−rus−LVA
eng−spa−SLV
fra−spa−CRI
slk−spa−ESP
ita−spa−URY
ita−spa−MEX
eng−ron−MDAeng−rus−ARM
eng−kaz−KAZ
pol−ukr−POL
eng−rus−UZB
eng−rus−BLR
eng−rus−ZAF
ara−eng−TUR
eng−fas−IND
ara−heb−EGY
ara−spa−MAR
lit−rus−LTU
ron−rus−MDA
por−spa−PER
pol−rus−RUS
eng−por−IND
eng−hbs−HRV
eng−ind−JPN
eng−rus−MNG
eng−tel−USA
eng−srp−GRC
eng−tir−ETH
eng−nld−PHL
eng−ukr−POL
eng−fas−AFG
eng−ita−MEX
deu−spa−CHL
deu−rus−ITA
deu−rus−MDA
eng−fra−PHL
fra−spa−MAR
fra−ron−MDA
fra−rus−MDA
fra−ita−ROU
fra−mkd−MKD
ell−rus−UKR
ces−slk−CZE
eng−spa−NIC
slk−spa−SVK
rus−ukr−UKR
rus−spa−BLR
ita−slk−SVK
hbs−ita−SRB
ita−rus−BLR
eng−rus−MDA
eng−rus−TJK rus−uzb−UZB
eng−ron−HUN
ces−slk−SVK
ces−rus−RUS
eng−rus−CHN
fra−rus−BLR
ces−rus−CZE
mon−rus−MNG
eng−rus−SRB
deu−rus−BGR
eng−sqi−GRC
hun−ron−HUN
vie−zho−VNM
rus−srp−RUS
ces−pol−POL
pol−ukr−UKRpol−rus−BLR
deu−spa−BOL
rus−ukr−RUS
eng−hun−SVK
heb−rus−UKR
kaz−rus−KAZ
fra−swa−KEN
eng−zlm−IDN
bul−ell−BGR
jpn−rus−RUS
eng−khm−KHM
−1
6
8
10
log country GDP per capita
12
Vertical axis: log of the average translation rates between a pair of languages (in both directions),
net of source language fixed effects). English-Spanish rates for translators located in the United
States are normalized to zero. Sample is coded by color: language pairs that involve a country’s
most widely spoken language in blue, language pairs that do not involve a country’s most widely
spoken language in green. Black line is linear fit.
13
Figure 2: Adjusted translation cost to English and per capita GDP
log avg. pair rate net of source f.e.
.5
ISL
DZA
PHL
TJK
ETH
−.5
CYP
MLT
KOR
SAU
MKD
FIN
DNK
QAT
SWE
NLD
AUT
JPN
ARE
DEU
FRA
MNE
MYS
PRI ITA
ALB
ISR
EST
IDN
NPL
MEX
SGP KWT
BIH
KAZ
LBY
LBN
SVN
TURBRA
GRC
BOL ARM
URY
ESP
LTU
MAR
JOR
EGY
POL
SYR
BGR
BGD
GEO
CUBSRB
COLSUR
CZE
ROU
HUN TWN
IRN
LVA
PRT
ARG CHLSVK
CHNAZE CRI VEN
MNG
RUS
VNM
UKR
LAO
LKA
HND
ECU PERPAN
IRQTUNDOM
PSE SDN
UZB
BLR
SLV
MDA
HTI
0
GTM
ZAF
AFG
YEM
IND
HRV
NIC
KHM
−1
6
8
10
log country GDP per capita
12
Vertical axis: log of the average translation rates between a country’s most widely spoken language
and English (undirected rates, i.e. combining rates where English is the source or the target
language), net of source language fixed effects, plotted for countries for which English is not the
most widely spoken language. English-Spanish rates for translators located in the United States
are normalized to zero. Black line is linear fit.
14
Table 1: Trade and language barriers. Gravity linear regression on positive trade flows
(1)
Log tradedo > 0
Adj. translation rate (fractional)
(2)
Log tradedo > 0
-0.603∗
[0.301]
(3)
Log tradedo > 0
0.370∗∗∗
[0.075]
Translation rate observed
(fractional)
Adj. translation rate (top pair)
-0.215
[0.242]
Translation rate observed (top pair)
0.362∗∗∗
[0.060]
Log fraction common language
0.054∗∗∗
[0.005]
0.055∗∗∗
[0.005]
No common language
-0.936∗∗∗
[0.064]
-0.938∗∗∗
[0.064]
Common official language
0.587∗∗∗
[0.049]
0.370∗∗∗
[0.055]
0.376∗∗∗
[0.055]
Log distance
-1.517∗∗∗
[0.021]
-1.401∗∗∗
[0.022]
-1.403∗∗∗
[0.022]
Contiguity
0.809∗∗∗
[0.102]
0.605∗∗∗
[0.100]
0.605∗∗∗
[0.100]
Colonial tie (ever)
0.266∗
[0.128]
0.065
[0.130]
0.090
[0.130]
Colonial tie (after 1945)
1.209∗∗∗
[0.169]
1.254∗∗∗
[0.169]
1.223∗∗∗
[0.169]
Common colonizer (after 1945)
0.726∗∗∗
[0.064]
0.614∗∗∗
[0.065]
0.609∗∗∗
[0.065]
Yes
23767
0.95
Yes
23767
0.95
Yes
23767
0.95
Exporter and importer f.e.
Observations
R2
Standard errors in brackets
∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
15
Table 2: Trade and language barriers. Gravity exponential regression on all trade flows
(1)
Tradedo
Adj. translation rate (fractional)
(2)
Tradedo
-0.609∗
[0.266]
(3)
Tradedo
-0.213∗
[0.094]
Translation rate observed
(fractional)
Adj. translation rate (top pair)
-0.506∗
[0.235]
Translation rate observed (top pair)
-0.095
[0.073]
Log fraction common language
0.010
[0.008]
0.008
[0.008]
No common language
-0.306∗
[0.128]
-0.284∗
[0.126]
-0.080
[0.109]
-0.184
[0.110]
-0.168
[0.107]
Log distance
-0.685∗∗∗
[0.038]
-0.677∗∗∗
[0.039]
-0.663∗∗∗
[0.038]
Contiguity
0.566∗∗∗
[0.115]
0.548∗∗∗
[0.108]
0.550∗∗∗
[0.110]
Colonial tie (ever)
-0.147
[0.135]
-0.231
[0.144]
-0.213
[0.143]
Colonial tie (after 1945)
0.263
[0.233]
0.303
[0.262]
0.312
[0.258]
Common colonizer (after 1945)
0.532∗∗
[0.163]
0.508∗∗
[0.162]
0.541∗∗∗
[0.162]
Yes
41412
-8.45e+09
Yes
41412
-8.36e+09
Yes
41412
-8.38e+09
Common official language
Log gdp and remoteness (o & d)
Observations
Log lik.
Standard errors in brackets
∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
16
Table 3: Two-stage estimates of language barrier effect
(1)
Log tradedo > 0
(2)
Tradedo
Adj. translation rate (top pair)
-0.670∗
[0.263]
-1.047∗∗∗
[0.283]
Log fraction common language
-0.004
[0.012]
0.016
[0.011]
No common language
-0.505∗∗
[0.157]
-0.389∗
[0.155]
Common official language
0.409∗
[0.180]
-0.068
[0.192]
Log distance
-1.162∗∗∗
[0.068]
-0.644∗∗∗
[0.044]
Contiguity
0.950∗∗∗
[0.196]
0.790∗∗∗
[0.108]
Colonial tie (ever)
0.243
[0.276]
-0.168
[0.178]
Colonial tie (after 1945)
0.864∗∗
[0.288]
0.245
[0.282]
Common colonizer (after 1945)
0.046
[0.410]
0.595
[0.406]
Yes
Yes
-.066
(0.021)
3554
0.71
-0.062
(0.021)
4418
0.71
Second stage:
Log gdp and remoteness (o & d)
First stage:
Log ethnolinguistic overlap
Observations
R2
Standard errors in parentheses for coefficient on adjusted translation rate (top pair)
is block bootstrapped at the country-pair level, and for first stage coefficient on log
ethnolinguistic overlap is bootstrapped. Conventional standard errors in brackets are
preliminary.
∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
17
Appendix A
Data sources
Translation rates: Baseline results are from average rates by language pair and country,
downloaded from translatorscafe.com on March 6, 2013 and discussed in the main text.
Rates are included and labeled as “observed” if there are at least five translators present
in a directed language-pair and country.
Trade flows: Trade flows for 2011 at the HS6 level are from the BACI dataset provided
by CEPII, which are in turn based on United Nations Comtrade data.
Gravity covariates: Distance, common official language and colonial link data are
from the CEPII Gravity Dataset.
Ethnolinguistic data: Counts of population by language group within country are
from Ethnologue, 16th Edition.
R & D and advertising intensity: From Kugler and Verhoogen (2012), the ratio of
advertising plus research and development expenditures to total sales, from the U.S.
Federal Trade Commission (FTC) 1975 Line of Business Survey. Measures for ISIC 4digit rev. 2 classification concorded to HS6.
Measure of horizontal differentiation: Classification due to Rauch (1999). SITC 4digit industries concorded to HS6.
Appendix B
Estimation with a censored endogenous regressor
I follow the method of Chernozhukov, Rigobon, and Stoker (2010) for estimation of a
linear conditional mean model with a bound-censored and endogenous regressor. To
describe the estimation approach, assume
∗
ln X = βL + D 0 δ + U
∗
0
L = Z π+V
U ∗ = γV ∗ + ε
∗
∗
(2)
(3)
(4)
where ε is mean independent of (V ∗ , L∗ ) and V ∗ is median independent of Z.
The dependent variable X stands for exports and L∗ is the uncensored language skill
premium, which is endogenous when γ 6= 0. D is a vector of standard gravity regressors
such as distance, and Z is a vector of instruments that includes D. We do not observe L∗
for all pairs of languages, so for all unobserved pairs I set the language skill premium at
its highest observed value L and assume that this is an upper censoring threshold such
that an observed, censored language skill premium L is given by
18
L=
L∗ if L∗ < L
L otherwise
(5)
I estimate equation (3) in a first stage by censored quantile regression, employing
the method of Chernozhukov and Hong (2002). Residuals from this first stage can be
used as a control function for inclusion in a second stage, which can be estimated on
the subsample above the censoring threshold. Construction of the control function for a
linear conditional mean model follows directly. Applying the control function approach
to the exponential conditional mean model for the gravity equation (as in Silva and
Tenreyro, 2006) requires additional assumptions. I modify equation (2) to
h
∗
E X | L , D, V
∗
i
∗
0
= exp βL + D δ + γV
∗
.
(6)
Inclusion of a control function γV ∗ in equation (2) is a stronger functional form
assumption, for which a sufficient condition is joint normality of (U ∗ , V ∗ ). As
censored quantile regressions are difficult to estimate with fixed effects, I replace
exporter and importer fixed effects with importer and exporter gross domestic products
and “remoteness” measures, following Baldwin and Harrigan (2011). Alternative
gravity estimation methods aimed at removing country fixed effects (e.g. tetrads, see
Hallak (2006) and Head and Mayer (2013)) are unsuitable in this context because they
pass censoring points through non-linear functions. I compute standard errors by
bootstrapping country pair observations across both stages. I do not resample translation
rate data, as I view the fixed effect regressions to net source language effects and countryspecific wages from nominal translation rates as a data construction step.
19