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INSIGHTS FROM BOOK TRANSLATIONS

ON THE INTERNATIONAL DIFFUSION OF KNOWLEDGE

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF ECONOMICS

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Isabelle Yin Fong Sin

May 2011



http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/df340nb1179

© 2011 by Isabelle Yin Fong Sin. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-

Noncommercial 3.0 United States License.

ii


http://purl.stanford.edu/df340nb1179

http://purl.stanford.edu/df340nb1179





I certify that I have read this dissertation and that, in my opinion, it is fully adequate

in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Ran Abramitzky, Primary Adviser



Nicholas Bloom



Avner Greif

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in

electronic format. An original signed hard copy of the signature page is on file in

University Archives.

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iv



Abstract

Increases in the stock of ideas possessed by societies are central to modern economic

growth. The implications of idea flows are striking: Klenow and Rodrıguez-Clare

(2005) estimate world production would be just 6% of its current level if countries

did not share ideas. Yet, although theoretical economists have studied ideas and their

diffusion extensively, empirical studies are scarce because ideas are inherently difficult

to measure. Previous empirical studies of idea flows have tended to use proxies such

as trade flows, foreign direct investment, migration, and patent citations. However,

with the exception of the latter, these measures are not pure idea flows, and do not

capture the key properties of ideas, namely non-rivalry and disembodiedness.

My research proposes a novel measure of idea flows, namely book translations,

and uses it to study the factors that affect the international diffusion of ideas. Book

translations are an attractive way to quantify idea flows because they are both non-

rival and disembodied; they are a pure measure of idea flows rather than a by-product

of a process such as trade or migration, and their key purpose is to make the ideas

contained in the book accessible to speakers of another language.

In chapter 2, I outline the economics literature on ideas and their diffusion. I

motivate and discuss book translations as a measure of idea flows, and provide a

framework for thinking about when translations are likely to occur.

I describe the translation data in chapter 3. The source of the data is an inter-

national bibliography of translations collected by the United Nations Educational,

v



Scientific, and Cultural Organization. From this bibliography, I compile a data set of

over 2 million translations published in 80 countries since the 1949, including detailedinformation on each title translated. I then document the main patterns of translation

flows.

In chapter 4, I employ a gravity framework to study how distance affects

translation flows between countries. This sheds light both on the barriers to

international idea diffusion and on the underlying causes of the negative relationship

between distance and trade. Translations differ from trade in that they have zero

transportation costs, but they are subject to similar search and information costs

and costs of forming contracts. I estimate a gravity model where bilateral translation

flows vary with the sizes of the countries and the distance between them, and find the

elasticity of translations with respect to distance to be between -0.3 and -0.5 for the

1990s; these values are significantly smaller than the equivalent elasticity for trade

found in the literature, suggesting a significant role for transportation costs in the

distance effect on trade. In addition, I present several pieces of evidence that suggest

supply-side frictions play a larger role in the distance effect on translations than doconsumer preferences. For instance, the speed with which titles are translated, which

is likely to largely capture supply frictions as opposed to demand factors, decreases

significantly with distance.

Finally, in joint work with Ran Abramitzky (chapter 5), I study how the

collapse of the Communist regime in Eastern Europe at the close of the 1980s

affected the international diffusion of ideas. We show that while translations

between Communist languages decreased by two thirds with the collapse, Western-

to-Communist translations increased by a factor of seven and reached Western levels.

Convergence was full in economically-beneficial fields such as sciences and only partial

in culturally-beneficial fields such as history. The effects were larger for more Western-

oriented countries. These findings help us understand how institutions shape the

vi



international diffusion of knowledge and demonstrate the importance of preferences

in determining the type of ideas that diffuse into a country.

vii



Acknowledgements

First and foremost, I am indebted to my advisor, Ran Abramitzky, for his enthusiasm,

patience, and encouragement, as well as his incisive comments and sage counsel

throughout the evolution of this dissertation research.

I am also grateful to everyone who provided helpful comments and suggestions on

this research, including (but not limited to) my advisors, Avner Greif, Nick Bloom,

Jim Fearon and Kalina Manova, Manuel Amador, Kamran Bilir, Aaron Bodoh-Creed,

Albie Bollard, Tim Bresnahan, Elan Dagenais, Doireann Fitzgerald, Regina Grafe,

Paul Gregory, Caroline Hoxby, Nir Jaimovich, Seema Jayachandran, Pete Klenow,

Naomi Lamoreaux, Ed Leamer, Aprajit Mahajan, Neale Mahoney, Roy Mill, Joel

Mokyr, Nathan Nunn, John Pencavel, Luigi Pistaferri, Gary Richardson, Robert

Staiger, Alessandra Voena, Romain Wacziarg, Gui Woolston, Gavin Wright, my

classmates at Stanford, and numerous seminar participants.

I thank the Index Translationum team, especially Alain Brion, Mauro Rosi, and

Marius Tukaj, for providing me with a digital version of the recent translation data.

I gratefully acknowledge financial support from the Ric Weiland Graduate

Fellowship, the John E. Rovensky Fellowship, and the B.F. Haley and E.S. ShawFellowship.

viii



Contents

Abstract v

Acknowledgements viii

1 Introduction 1

2 The Economics of Ideas 10

2.1 The importance of ideas in historical perspective . . . . . . . . . . . . 10

2.2 The economic theory of ideas . . . . . . . . . . . . . . . . . . . . . . 12

2.2.1 One-country models . . . . . . . . . . . . . . . . . . . . . . . 13

2.2.2 Multiple-country models . . . . . . . . . . . . . . . . . . . . . 16

2.3 What types of ideas could be important? . . . . . . . . . . . . . . . . 18

2.4 Measuring idea flows empirically . . . . . . . . . . . . . . . . . . . . . 20

2.4.1 Embodied idea flows . . . . . . . . . . . . . . . . . . . . . . . 20

2.4.2 Pure idea flows: Patent citations and translations . . . . . . . 22

2.5 When will translations occur? . . . . . . . . . . . . . . . . . . . . . . 24

2.5.1 Translations versus bilingualism . . . . . . . . . . . . . . . . . 25

2.5.2 The decision to translate . . . . . . . . . . . . . . . . . . . . . 27

2.5.3 Implications for translation flows . . . . . . . . . . . . . . . . 28

3 Data 31

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3.1 Data construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.1.1 Translation data . . . . . . . . . . . . . . . . . . . . . . . . . 313.1.2 Physical environment data . . . . . . . . . . . . . . . . . . . . 35

3.1.3 Cultural distance data . . . . . . . . . . . . . . . . . . . . . . 38

3.2 Main patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.2.1 Countries primarily translate books into their main languages 44

3.2.2 Translations occur in a wide range of fields . . . . . . . . . . . 44

3.2.3 English is the most translated original language . . . . . . . . 46

3.2.4 Germany overtook the USSR as the biggest translating country 49

3.2.5 Bigger, richer countries that trade less translate more . . . . . 49

3.2.6 Western Europe translates quickly . . . . . . . . . . . . . . . . 51

3.3 Figures and tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4 The Gravity of Ideas 77

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

4.2 Empirical strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 824.2.1 Original languages and target languages and countries for

gravity model . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.2.2 Matching original languages to countries . . . . . . . . . . . . 86

4.3 How distances affect translations . . . . . . . . . . . . . . . . . . . . 87

4.3.1 The negative distance effect . . . . . . . . . . . . . . . . . . . 88

4.3.2 The negative distance effect over time . . . . . . . . . . . . . . 92

4.3.3 Translations of different types of books are affected differently

by physical distance . . . . . . . . . . . . . . . . . . . . . . . 93

4.3.4 Countries with similar physical environments translate more

from each other . . . . . . . . . . . . . . . . . . . . . . . . . . 94

4.3.5 Countries with similar cultures translate more from each other 95

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4.3.6 Translations published in more developed countries decrease

less with physical distance . . . . . . . . . . . . . . . . . . . . 964.4 Speed of translations . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99


5 The Collapse of Communism 113

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

5.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

5.2.1 The flow of book translations across countries . . . . . . . . . 120

5.2.2 Translation of influential titles . . . . . . . . . . . . . . . . . . 122

5.3 Historical context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

5.3.1 A brief timeline of the collapse of Communism . . . . . . . . . 123

5.3.2 Publishing and censorship under Communism . . . . . . . . . 125

5.4 Empirical strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

5.5 The effect of the collapse on total translations . . . . . . . . . . . . . 1325.5.1 Changes in overall translation patterns . . . . . . . . . . . . . 133

5.5.2 Changes in translations from Western and Communist

languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

5.5.3 Changes in translations in Soviet and Satellite countries . . . 137

5.5.4 Convergence in translation flows or catching up on stocks? . . 139

5.5.5 The collapse of Communism did not affect original publications

of books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

5.5.6 Further robustness checks . . . . . . . . . . . . . . . . . . . . 142

5.6 The effect by book field . . . . . . . . . . . . . . . . . . . . . . . . . . 144

5.6.1 Graphical evidence . . . . . . . . . . . . . . . . . . . . . . . . 145

5.6.2 Regression analysis by book field . . . . . . . . . . . . . . . . 146

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5.6.3 Regression analysis by book subfield . . . . . . . . . . . . . . 147

5.7 The effect on influential titles . . . . . . . . . . . . . . . . . . . . . . 1495.8 Conclusions and discussion . . . . . . . . . . . . . . . . . . . . . . . . 153


6 Conclusions and Discussion 171

6.1 The cost of multiple languages . . . . . . . . . . . . . . . . . . . . . . 172

6.2 The effect of translations on economic outcomes . . . . . . . . . . . . 176

A Appendices for Chapter 3 177A.1 Appendix figures and tables . . . . . . . . . . . . . . . . . . . . . . . 177

B Appendices for Chapter 4 197

B.1 Appendix figures and tables . . . . . . . . . . . . . . . . . . . . . . . 197

C Appendices for Chapter 5 205

C.1 Appendix figures and tables . . . . . . . . . . . . . . . . . . . . . . . 205

C.2 Degrees of transition . . . . . . . . . . . . . . . . . . . . . . . . . . . 216C.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216

C.2.2 Empirical strategy and results . . . . . . . . . . . . . . . . . . 217

C.3 Influential titles appendix . . . . . . . . . . . . . . . . . . . . . . . . 219

C.4 Keyword list appendix . . . . . . . . . . . . . . . . . . . . . . . . . . 227

C.5 Example title appendix . . . . . . . . . . . . . . . . . . . . . . . . . . 230

C.6 Most translated titles appendix . . . . . . . . . . . . . . . . . . . . . 236

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List of Tables

3.1 Countries primarily translate into their own languages . 66

3.2 Distribution of translations by field . . . . . . . . . . . . . . 67

3.3 Translations and original titles by field . . . . . . . . . . . 68

3.4 Most translated original languages by field . . . . . . . . 69

3.5 Most translated original language by country . . . . . . . 70

3.6 Correlates of translation flows into a country . . . . . . 72

3.7 Correlates of translation flows into a country ctd . . . 73

3.8 Correlates of speed of translation, 1998-2000 . . . . . . . . 74

3.9 Correlates of speed of translation, 1998-2000 ctd . . . . . 75

4.1 Closer countries translate more from each other . . . . . 104

4.2 Countries with more similar physical environments and

cultures translate more from each other . . . . . . . . . . 106

4.3 Translations into and out of more developed countries

decrease less with physical distance . . . . . . . . . . . . . . 108

4.4 Translations occur faster between closer countries, 1998-

2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

4.5 Translations occur faster between closer countries: clos-

est original country . . . . . . . . . . . . . . . . . . . . . . . . . 111

xiii



5.1 Before/after analysis: The effect of the collapse of

Communism on translations . . . . . . . . . . . . . . . . . . . . 1645.2 Difference-in-differences analysis: The effect of the

collapse of Communism on translations . . . . . . . . . . . . 165

5.3 Convergence analysis: The effect of the collapse of Com-

munism on translations of recent versus older Western

titles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

5.4 Total publications: The effect of the collapse of Com-

munism on total book publications . . . . . . . . . . . . . . . 1685.5 Title/author-level analysis . . . . . . . . . . . . . . . . . . . . 169

6.1 Average access to titles under four counterfactuals . . 174

A.1 Major countries of the top 100 languages . . . . . . . . . . 191

A.2 Countries and years with translation data . . . . . . . . . . 194

B.1 Closer countries translate more from each other (OLS) 199

B.2 The effect of trade on translations over time: consistent

countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

B.3 The effect of trade on translations over time: all

available countries . . . . . . . . . . . . . . . . . . . . . . . . . . 203

C.1 Pages translated . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

C.2 The Bertrand et al. critique . . . . . . . . . . . . . . . . . . . 209

C.3 Degree of reform . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

C.4 Secondary languages . . . . . . . . . . . . . . . . . . . . . . . . 212

C.5 Translations by book field . . . . . . . . . . . . . . . . . . . . 214

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List of Figures

3.1 Translations from English, French, German, and Russian

by country . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.2 Translations by country and field . . . . . . . . . . . . . . . 61

3.3 Translations per capita by country and field . . . . . . . . 63

3.4 Age at translation . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.1 Changes over time in the correlation between distance

and translations . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

4.2 The negative correlation between geographic distance

and translations by field . . . . . . . . . . . . . . . . . . . . . . 103

5.1 Translation dates of three influential titles . . . . . . . . 156

5.2 Map of Communist and Western Europe . . . . . . . . . . . . 157

5.3 Translations in Communist and Western Europe . . . . . . 158

5.4 The effects over time of the collapse of Communism on

translations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1595.5 The effects of the collapse on translations of recent

and older titles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

5.6 Translations by field . . . . . . . . . . . . . . . . . . . . . . . . 161

5.7 Effects of the collapse by field . . . . . . . . . . . . . . . . . 162

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5.8 Effects of the collapse on translations from English by

subfield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

A.1 Original languages by book field and continent . . . . . . 178

A.2 Age at translation by country and original language . . 183

B.1 Changes over time in the relationship between distances

and translations . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

C.1 The effects over time of the collapse of Communism on

translations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206

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Chapter 1

Introduction

“...every country must be much benefited, which by means of early translations,

possesses itself of the fruits of the labours of foreign nations.”

William Hamilton, translator of Berthollet’s Art of Dyeing

quoted from Mokyr (2002)

Increases in the aggregate stock of knowledge possessed by societies lie at the heart of

modern economic growth and intellectual development (Kuznets, 1966, Mokyr, 2002).

For example, Klenow and Rodrıguez-Clare (2005) estimate that world GDP would

be just 6% of its current level if countries did not share ideas. Nevertheless, while

theoretical research on ideas and their diffusion abounds (e.g., various work by Paul

Romer and Chad Jones), the inherent difficulty in measuring ideas means empirical

research has been much more limited.

This inherent difficulty is mainly due to two attributes of ideas: non-rivalry and

disembodiedness. Ideas are non rival, meaning the use of an idea by one party in no

way limits its simultaneous use by others. This non-rivalry is key because it means

advances in knowledge in one country may cause technological spillovers in other

countries to which the knowledge diffuses. Ideas may be embodied or disembodied,

1



2 CHAPTER 1. INTRODUCTION

but flows of disembodied ideas are more interesting because these are the flows

that generate spillovers. In contrast, previous empirical research has largely usedproxies for idea flows such as trade, foreign direct investment (FDI), and migration.

Although this literature offers many important insights, these proxies measure idea

flows embodied in something else, and thus the idea flows they capture may not cause

spillovers.

In this dissertation I propose a new measure of idea flows, namely book

translations, and use it to study the factors that affect the international diffusion

of ideas. The purest measure of idea flows used previously is patent citations.1 Book

translations offer a complementary measure of knowledge diffusion that captures a

broader definition of knowledge. Although much of the endogenous growth literature

has implicitly or explicitly restricted its attention to technological ideas narrowly

defined (e.g. Romer, 1990), a much wider range of ideas are likely to be important

for economic and intellectual development. Book translations encompass a broader

set of ideas than purely technological knowledge. They are also generated by a quite

different process to patent citations, meaning the two measures capture differentaspects of knowledge flows.

As a measure of international idea flows, book translations have a number of

appealing features. Translations, as distinct from the physical books that are

translated, are a measure of pure flows of disembodied ideas. The impetus behind

translations is the desire to make the ideas contained in the books accessible to

1An extensive literature uses patent citations to study questions such as whether flows such astrade, migration, and foreign direct investment (FDI) increase knowledge spillovers (e.g., MacGarvie,2006, Branstetter, 2006, Agrawal, Kapur and McHale, 2008, and the relationships between geographyand knowledge spillovers (e.g., Jaffe, Trajtenberg and Henderson, 1993, Bottazzi and Peri, 2003,Thompson and Fox-Kean, 2005, MacGarvie, 2005, Griffith, Lee and van Reenen, 2007, Criscuolo andVerspagen, 2008, Singh and Marx, 2011). The answers to such questions could inform domestic policythat encourages or impedes research, trade, immigration, or FDI. They may also have implicationsfor the theoretical modeling of knowledge spillovers and for predictions about income convergencebetween countries.



3

speakers of other languages, thus translations do not primarily occur as the by-product

of some other process such as trade or migration. Importantly, translations are flowsof non-rival ideas: a country can translate a book and use its ideas without affecting

the use of the ideas by other countries. Conveniently, book translations are readily

quantifiable and classifiable by the type of idea they contain, and they capture a broad

range of technological, organizational, cultural and social ideas that are important

for economic growth and intellectual advancement.

However, book translations face some limitations as a measure of idea flows.

Because the process of translating and publishing a book is time-consuming, book

translations do not capture very new ideas. They only capture flows of ideas between

speakers of different languages, and they only capture codifiable ideas, meaning they

exclude tacit knowledge. Finally, some people are multilingual, so have access to the

ideas in books before the books are translated into their native language.

In order to study book translations empirically, I construct a new data set

from an international bibliography of translations collected by the United Nations

Educational, Scientific and Cultural Organization (Unesco). The data set contains

information on every translation published between 1980 and 2000 for up to 80

countries each year, including information on the country and year in which the

translation was published, the subject of the book, the original and target languages,

the length of the book, the author, and the original and translated titles of the book.

This amounts to over two million translations. For every fifth year from 1949 to 1979,

I also digitized from hard copy a representative sample of the translations published

annually in each country, including information on the country and year of translation,

the subject of the book, the original and target languages, and the length of the book.

This amounts to approximately 100,000 translations. For specific sub-samples I also

collected additional information such as the year in which the original book was first

published, the sub-field of the book, and the author’s political views. These data




allow me to draw a detailed picture of the changing translation flows between a wide

range of countries for the period 1949 to the present.

In Chapter 2, I motivate my study of international idea flows with an overview

of the economics of ideas and their diffusion between countries. I also discuss

in more detail the qualities of ideas that the theoretical literature suggests to be

important for growth, and motivate the use of book translations as an empirical

measure of international idea flows. Finally, I provide a framework for thinking at

the microeconomic level about when translations are likely to occur.

In Chapter 3, I describe the data collection, and document a range of patterns of

the international flow of translations and their historical trends. I find the most

translated language is English, which accounted for over 46% of all translations

between 1949 and 2000, rising from 31% in 1959 to 61% in 1999. The most prolific

translating country in 1959 was the Soviet Union, which translated nearly 5,000 titles

that year. By 1999, after the collapse of communism in Eastern Europe and the

disintegration of the USSR, Germany had risen to be the top translating country

with nearly 10,000 translations annually; it was followed by Spain, France, and Japan.European countries translate more on average than countries on other continents, and

Muslim countries (such as Albania, Egypt, and Turkey) translate 73% fewer titles than

Roman Catholic countries (such as Peru, France, and Slovakia), even after controlling

for population, income and openness defined as trade as a fraction of GDP.

I also find that in the years 1998-2000, the bulk of translated books were translated

within 10 years of publication, though the speed with which titles are translated varies

considerably by original language and translating country. English and Italian books

are translated faster than French and German books on average; richer countries and

countries that trade more translate faster.

In Chapter 4, I study how distance affects translation flows. By studying the

relationship between measures of distance between countries and translation flows, I



5

shed light on an important type of impediment to the free international diffusion of

ideas. I also shed light on the factors underlying the negative relationship betweendistance and trade in goods. Translations have many commonalities with trade in

goods, but differ in that they are exempt from all costs related to physical relocation.

The relationship between distance and translations is thus informative about the

aspect of the distance effect in trade that is not driven by transportation costs.

Translations (and similarly trade) may decrease with distance for both supply and

demand reasons. Supply frictions such as distance-varying search and information

costs and costs of negotiating contracts could cause translations to decrease withdistance. Translations (or trade) may also fall off with distance because distance

is correlated with tastes, meaning closer countries cater better than more distant

countries to local tastes in books (or products).

I first estimate a gravity model of translation flows, in which the flow between

two countries depends on the sizes of the two countries and on the distance between

them. I estimate the elasticity of translations with respect to distance to be -0.3 to

-0.5 during the 1990s, which is considerably smaller than the equivalent elasticity fortrade found in the literature, which usually ranges from -1.08 to -1.24.2 The difference

between these estimates suggests transportation costs play a substantial role in the

distance effect on trade.

I next conduct several tests to study the relative roles of supply-side frictions and

consumer tastes in the distance effect on translations. Results suggest an important

role for search and information costs and a lesser role for demand factors. First, the

distance effect decreased between 1949 and 1999. This is consistent with informationcosts, which declined substantially over this period, being an important limiting factor

for translations; this result stands in contrast with the puzzling result in the trade

literature that distance did not become less important over this period. Second, the

2Disdier and Head (2008).




distance effect is larger in the fields of natural and applied science, where tastes are

less important, than in the fields of arts, literature and philosophy, which have ahigher cultural component. If geographically correlated tastes were the major driving

factor, the opposite would be true. Third, cultural distance between countries does

inhibit translation flows, but accounts for relatively little of the overall distance effect,

suggesting non-cultural factors play a large role. Finally, the speed with which titles

are translated, which likely captures supply frictions as opposed to demand factors,

also decreases significantly with distance.

My results have important implications for the international diffusion of ideas.They suggest that, despite the fact ideas have no transportation costs, idea flows

are hindered both by geographic distance and cultural distance between countries.

Furthermore, idea flows into less developed countries are hindered more by distance

than idea flows into more developed countries. This relationship works against income

convergence between rich and poor countries: the countries that can benefit most from

catch-up growth by adopting foreign ideas seem to face greater frictions in accessing

these ideas. However, the inhibiting effect of distance has decreased over time, whichsuggests that even the barriers surrounding less developed countries may be lower in

the future.

In Chapter 5, I use the translation data (with Ran Abramitzky3) to investigate

how institutions shaped the international diffusion of ideas in one of the largest events

in modern history, the collapse of Communism in Eastern Europe. We study how the

collapse affected translation flows within Communist Europe and between Western

and Communist Europe, how the effects varied across countries, and how they variedacross book fields.

The collapse of Communism was an important historical event and is thus worth

studying for its own sake, but our research also sheds light on a range of broader

3Both coauthors contributed equally to every aspect of the work involved in this paper.



7

issues. The collapse was a large shock that swiftly moved countries from nearly

complete isolation from Western ideas to full openness. Because our measure of ideaflows captures a broad range of ideas, the paper sheds light on the type of ideas

most likely to be affected by policy changes that reduce information restrictions. In

particular, we can examine whether the collapse of Communism had a stronger effect

on ideas that contain more “useful knowledge” for economic development than on

“less-useful” knowledge with more cultural content.

To shed further light on the role of preferences in the flow of ideas, first we compare

translation patterns in the Soviet countries with patterns in the more western-orientedSatellite countries. Second, we test the degree of convergence in translation flows

between Eastern and Western Europe post collapse.

We find that the collapse of Communism resulted in a sevenfold increase in

translations of Western European titles in the Satellite countries, suggesting a huge

increase in the inflow of Western ideas, and a threefold decrease in translations

of Communist titles, suggesting a decline in the flow of ideas between Communist

countries. These patterns also imply a substitution of Satellite countries away fromCommunist ideas and towards Western ideas.

Furthermore, we find evidence consistent with a surprising degree of cultural

convergence of Satellite countries and Western Europe. Given censorship was lifted

with the collapse of Communism, remaining differences in translation patterns likely

reflect differences in tastes for certain ideas between Eastern and Western Europe.

Since the end of Communism in Eastern Europe, the traditionally more Western-

looking Satellite countries have increased their translations of Western European titles

to Western levels. We interpret this convergence to the West as evidence that Satellite

preferences were either similar to Western ones or became like them quickly following

the collapse.

We find both an increase in Satellites’ translations of older titles and a jump




in translations of newer titles. These findings are consistent with both catching up

on the stock of ideas that were missed out on under Communism and a convergencebetween Satellite countries and Western Europe in the diffusion of new Western ideas.

In contrast, we find that the collapse of Communism had little effect on Western

translations in Soviet countries, suggesting the diffusion of Western ideas into these

countries was much more limited. The difference between the effect for the Soviet

and the Satellite countries suggests preferences play an important role in determining

the ideas that diffuse into a country.

We also find the effects of the collapse of Communist translations of Western titlesvaried significantly by field. The effects were larger and convergence to the West was

greater in fields that can be considered more “economically useful”, such as applied

science and economics, more ideological fields, such as philosophy and religion, and

for more threatening titles. They were smaller for less “economically useful” fields,

such as arts, and more “objective” fields, such as the exact sciences. Finally, we study

the translation patterns of a sample of particularly influential titles. We show most of

these titles were not translated anywhere in Communist Europe prior to the collapseof Communism, and with the collapse their translation increased dramatically in

Communist but not Western Europe. This affirms that the collapse of Communism

resulted in a genuine increase in access to important Western ideas within Communist

Europe.

A key lesson from our study is that incentives play a major role in shaping the

international flow of knowledge. Distortion of these incentives by institutions can

have long-lasting effects that can only be remedied by institutional change.

To summarize, in this dissertation I introduce a new measure of the flow of ideas

between countries, namely book translations. I assemble a novel data set of over two

million translations in 80 countries over the period 1949-2000. I use this measure

to study questions such as how physical and cultural distances between countries



9

shape the international diffusion of ideas, and what the role of institutions is in

shaping the international diffusion of knowledge. I find that, even today, translationflows decrease significantly with both physical and cultural distance, although this

relationship was stronger in earlier decades. Physical distance matters more for

less developed translating countries, and inhibits both the quantity and speed of

translations. Overall, my results are consistent with search and information costs

being a major driving force behind translation flows. Furthermore, I find (with

Ran Abramitzky) that the Communist regime in Eastern Europe severely distorted

international idea flows, artificially inflating flows between Communist countries, andsuppressing flows of ideas from the West into the Communist Bloc. The strong inflow

of economically-useful Western ideas into the Satellite countries, but not the Soviet

countries, upon the collapse of Communism suggests preferences play a strong role in

determining the types of ideas that diffuse into a country. Finally, this study of book

translations generates insights into the economic consequences of linguistic divisions

between populations.



Chapter 2

The Economics of Ideas and their

International Diffusion

2.1 The importance of ideas in historical

perspective

Even a brief contemplation of the sweep of human history will inevitably lead us to

agreement with Mokyr’s (2002) claim that “the central phenomenon of the modern

age is that as an aggregate we know more.” This claim is also echoed elsewhere in

the economic history literature. Economic historians including Kuznets, Rosenberg,

David, Jones and Mokyr have long held the viewpoint that increases in human

knowledge are central to modern economic growth. For instance, Kuznets (1966)

wrote, “We may say that certainly since the second half of the 19th century, the

major source of economic growth in the developed countries has been science-based

technology – in the electrical, internal combustion, electronic, nuclear, and biological

fields, among others.”

A focal historical period for studying idea-driven growth is the Industrial

10



2.1. THE IMPORTANCE OF IDEAS IN HISTORICAL PERSPECTIVE 11

Revolution and the enlightenment movement that preceded it. Mokyr (2002) explains

much of the Industrial Revolution as resulting from a change in the feedbackmechanism between two distinct types of useful knowledge. The first type of useful

knowledge is propositional knowledge about natural phenomena and regularities; the

second type is instructional or prescriptive knowledge, or techniques. Both types of

knowledge may reside in the minds of people or in storage devices such as books

from which they can be retrieved. Learning or diffusion of knowledge involves the

transmission of existing knowledge from one person or storage device to another. The

union of all propositional knowledge held by people or devices in a society is referredto as omega; the union of all techniques is referred to as lambda. A new discovery

adds to the set lambda or omega.

For any technique or element of lambda to exist, someone (though not necessarily

the person implementing the technique) must know enough about the elements of

omega upon which the technique is based to make the technique possible. More

broadly, each set of omega-knowledge possessed by a society makes possible many

sets of techniques, or lambdas. Which lambda is realized will depend on factors suchas the culture and institutions of the society, which affect the preferences and priorities

of agents and the rewards and penalties associated with suggesting new techniques.

Prior to 1800, serendipity played the dominant role in technological progress.

Techniques had narrow bases in omega, and thus the flow-on effects of new discoveries

ran into diminishing returns and petered out without ever leading to sustained

increases in technological progress. Furthermore, inventions based on limited

knowledge of the natural phenomena behind them were often treated with suspicion

by the public, making the spread of their use difficult.

Mokyr (2002) argues that the reason the Industrial Revolution didn’t die out

after 1820 in the manner of all previous episodes of growth is that the scientific

revolution of the seventeenth century and the Industrial Enlightenment of the



12 CHAPTER 2. THE ECONOMICS OF IDEAS

eighteenth century had broadened the epistemic base in omega of techniques in

lambda, allowing the feedback mechanism between the two types of useful knowledgeto switch from negative to positive. The Industrial Enlightenment affected the two

types of knowledge and their interactions in several ways. First, it reduced the

cost of accessing best-practice artisanal techniques. Second, by the generalization of

techniques it improved understanding of why they worked, thus broadening their bases

in omega. Third, it improved communication and interactions between the people who

understood the propositional knowledge and those who used the techniques. The time

of the Industrial Revolution was also the time of a knowledge revolution, in which theorganization, storability, accessibility and communicability of information in omega

advanced greatly. Much knowledge that was previously unwritten was codified in

books, and many scientific and technical works switched from being written in Latin

to being written in the vernacular, thus becoming accessible to the users of lambda

knowledge. The diffusion of knowledge between European countries was facilitated

by rapid translations of key works, and considerable movement of skilled individuals

between countries. This historical perspective on the role of knowledge in growthhighlights, among other things, the importance for further technological advancement

of how knowledge is stored and diffused both within and between societies.

2.2 The economic theory of ideas

Macroeconomic models such as Solow’s classic growth model, in which technological

change is absent or exogenous, explain growth through the accumulation of physical

and human capital. However, history reveals a long-term trend of accelerating growth

that cannot be explained by similarly increasing levels of capital (e.g., Jones and

Romer, 2010). An increasing rate of technological change is therefore required to

explain this accelerating growth rate. An exogenous and exogenously accelerating rate



2.2. THE ECONOMIC THEORY OF IDEAS 13

of technological change is obviously an unsatisfactory explanation for this historical

fact, so the economics profession has delved deeper into the nature of ideas and thedrivers of technological progress within the economic machine to look for an answer.

A likely candidate explanation for the acceleration in growth has emerged in the non-

rival nature of knowledge and the positive feedback between population and ideas.

2.2.1 One-country models

The central advance of endogenous growth models over the previous, exogenous

growth models was to recognize that technological change is determined within

the economic system and occurs as the result of actions taken by individuals and

firms, rather than being independent of economic activity. The first macroeconomic

models to include endogenous growth abstracted from the complication of interactions

between countries in their formal modeling. Such models can therefore be considered

to capture economies that are totally closed off from the rest of the world, or multiple-

country worlds in which countries are perfectly integrated. Although neither scenario

is the most interesting case, such models nevertheless provide important insights into

the role of ideas in economic growth.

In a seminal paper, Romer (1990) lays out clearly aspects of how to think

about ideas in a growth context that became widespread in later literature. Romer

essentially adds endogenous technological change to a neoclassical growth model,

where technological change is formulated as the invention of new ways to combine

raw materials to make producer durables. Once invented, ideas are excludable in

production, which provides firms with an incentive divert resources away from final

goods production and towards innovation. However, they are non-rival and are not

excludable in their use to produce further ideas, which produces a spillover effect.

Importantly, the research sector has increasing returns to scale in its two inputs,

human capital and the existing knowledge stock. The transmission of ideas within




the economy is assumed to be complete and free. In this model, economic growth is

ultimately driven by the accumulation of ideas. A further prediction of the modelis that a larger stock of human capital, not just more people, translates into more

research and thus faster growth.

An important debate that has arisen in choosing characteristics macroeconomic

models of growth ought to have is that of scale effects. Models such as Romer (1990),

Aghion and Howitt (1992) and Grossman and Helpman (1991) exhibit “strong” scale

effects, meaning the long-run growth rate of the economy increases with the scale

of the economy. In contrast, in models such as Jones (1995), Kortum (1997) and

Segerstrom (1998) exhibit only “weak” scale effects. That is, the long run level of per

capita income increases with scale or, equivalently, the economy has increasing returns

to scale. Jones (2005) argues that the predictions of strong scale effects have been

shown empirically untrue. For instance, strong scale effects are difficult to reconcile

with the empirical facts that research effort has grown over time, but US growth rates

have remained relatively stable for over a century. Weak scale effects, however, which

are largely synonymous with idea-based growth models, seem more plausible thantheir alternatives.

Several key properties of knowledge have emerged in the growth theory literature:

it is non-rival, may be excludable, is disembodied, and its accumulation responds to

incentives. (See, for example, work by Romer, Jones and Helpman.) Non-rivalry

is arguably the most important property of ideas for endogenous growth. It is the

technological characteristic that the use of an idea by one party in no way limits its

simultaneous use by others. This property generates spillovers and increasing returns

by the duplicability argument. That is, consider a production process that uses as

inputs both traditional rival inputs such as physical capital and labor, and knowledge,

which is non-rival. Then, supposing all rival inputs can be perfectly duplicated, a

doubling of the rival inputs will double output because the same knowledge can be




used in both identical plants at the same time. If, in addition, knowledge is doubled,

then output will more than double, hence the production function exhibits increasingreturns in all its inputs. Another consequence of the non-rivalry of knowledge is that

the total value to society of an idea is increasing in the size of the society, because

it can be used by more people or firms simultaneously (Jones and Romer, 2010).

This means there are advantages to integrating populations into as large groups as

possible.

Knowledge may or may not be excludable. It may be able to be protected

through secrecy, or through the patent system in an appropriate institutional setting.How quickly and fully ideas diffuse depends on the incentives inventors have to

exclude others from their inventions, which depend on institutions. In choosing their

institutions, societies face a trade-off between the stronger incentives for innovation

provided by high excludability, and the efficient utilization of existing ideas provided

by low excludability.

Knowledge itself is disembodied. A knowledge flow that is embodied in a capital

good may not produce a spillover; and if it does, it might be a pricing or pecuniary

externality rather than a technological one (Jaffe and Trajtenberg, 1999). Human

capital is ideas embodied in people. Unlike the ideas themselves, human capital is rival

because a person working on one project is limited in her ability to simultaneously

work on another.

Finally, knowledge accumulation responds to incentives. Research and devel-

opment, for example, is the purposeful generation of new knowledge. It comes

at an opportunity cost, and thus responds to incentives (Helpman, 2004). The

adoption of foreign technologies depends on institutions and the incentives they create

(Romer, 2010). Even learning-by-doing, by which an additional output, knowledge,

is generated by a production process, responds to the incentives that reward this

additional output.




2.2.2 Multiple-country models

The models discussed previously implicitly assume either zero or perfect international

idea diffusion. However, an intermediate scenario seems much more likely. At this

point, it is relevant to note that most research and development occurs in relatively

few industrialized countries. For instance, the G-7 countries accounted for 84% of

world research and development spending in 1995, compared with only 64% of world

GDP. As a consequence of this uneven distribution, foreign sources of technology

account for over 90% of domestic productivity growth in most countries (Keller, 2004).

Thus the extent to which ideas diffuse internationally has first order implications for

income disparities and convergence between countries (Helpman, 2004). Furthermore,

the institutions that encourage or inhibit idea diffusion and technology adoption could

play a major role in explaining the when and where of catch-up growth or its absence

(Jones and Romer, 2010).

Klenow and Rodrıguez-Clare (2005) document suggestive empirical evidence

of international knowledge externalities. Essentially, the growth rate a country

experiences depends not only on its own investment in physical and human capital,

but also on the growth rates and incomes of other countries. First, the 1970s saw

a worldwide growth slowdown that affected both rich and poor countries, but that

didn’t occur at a time of decreasing investment in human or physical capital. Second,

growth has generally accelerated over the 19th and 20th centuries, with an increase

in the speed countries transition out of poverty. This suggests rapid growth is being

made possible by the adoption of existing technologies from foreign countries. Third,

“growth miracles” always occur in countries with incomes well below the richest

countries, again suggesting the importance of foreign technology adoption. Fourth,

over the period 1950 to 1980, richer OECD countries grew faster than poor ones

despite the fact they were investing at a faster rate. Fifth, differences in investment

rates between countries are much more persistent than differences in growth rates.




Sixth, across countries, high investment rates are a far better predictor of high levels

of income than of high growth rates of income.

Although this evidence is far from definitive, it does suggest economists ought

to take the international diffusion of ideas seriously. Indeed, considerable theoretical

work has been done in this area, such as Nelson and Phelps (1966), Parente and

Prescott (1994), Romer (1994), Howitt (2000), Lucas (2009), Eaton and Kortum

(1996, 1997, 1999), Kortum (1997), Keller (2004) and Romer (2010). Klenow and

Rodrıguez-Clare (2005) calibrate a model of the international diffusion of knowledge

that builds off previous models, particularly Eaton and Kortum (1999). In order to

be consistent with the evidence, they focus on a model in which, in steady state,

all countries grow at the same rate because of international technology spillovers,

and policy difference across countries result in TFP level differences, not growth

rate differences. In a simple version of their model, there exists a world frontier

in technology, growth in which is determined by a weighted aggregate of worldwide

research activity. The research efforts of individual countries determine how close to

that frontier they will get. “Research”, which encompasses both R&D and efforts toadopt foreign technology, is more effective at increasing productivity the further is the

country from the world technology frontier. However, even in the absence of research

effort, some technology adoption from abroad occurs. They assume international

technology spillovers decrease in distance, and they capture additional barriers to

technology adoption in a country-specific “R&D tax”.

Some useful insights are derived from their model and calibration. For one thing,

large cross-differences in institutional or policy barriers to technology adoption are

required to make the model fit the data. In addition, the calculated benefit from the

world being connected is huge: the calibrated model suggests world GDP would be

a mere 6% of its current value if international idea diffusion were absent. Because

knowledge diffusion is costly, differences in levels of investment in knowledge creation




are likely to be an important explanation for cross-country differences in income.

2.3 What types of ideas could be important?

Much of the theoretical and empirical literature on ideas implicitly or explicitly

restricts its attention to technological ideas defined relatively narrowly. Romer (1990),

for instance, takes ideas in his model to be ways to combine raw materials to make

finished products. However, a much broader set of ideas is likely to be important

for economic growth and development, as various remarks scattered through the

literature acknowledge. For example, Jones (2005) comments, “The patent system

and research universities are examples of institutions [that increase welfare relative

to the competitive equilibrium outcome by increasing research], but there is little

reason to think we have found the best institutions – after all, these institutions

are themselves ideas.” Along a similar vein, Klenow and Rodrıguez-Clare (2005)

state, “Knowledge diffusion, broadly construed, could include imitation of successful

institutions and policies in other countries.” Finally, Romer (1994) discusses how new

goods might be actual physical goods, or they might be intangibles such as processes

to manufacture goods or monitor inventories.

Romer (2010) goes further in acknowledging the breadth of relevant ideas: he

explicitly models two types of ideas, which he refers to as “technological ideas” and

“rules”. Technological ideas are the type of idea explicitly modeled in much of the

previous literature: instructions on how to rearrange inanimate objects. The oral

rehydration formula is one such idea. Rules, in contrast, are specifications of how

people interact with each other. As an example, Romer cites the specific set of

phrases used by pilots and air traffic controllers to communicate unambiguously with

each other. This is clearly not a traditional technological recipe, but it is an idea

with a demonstrably large potential to improve the safety and thus efficiency of air



2.3. WHAT TYPES OF IDEAS COULD BE IMPORTANT? 19

traffic. The two types of ideas share many similarities. Both are non-rival and offer

potential benefits from being shared internationally. The adoption of either is costlyand is affected by incentives. In Romer’s model, which includes idea diffusion between

countries, productivity in a country depends both on the local stock of technological

ideas and on local rules. In addition, rules affect how likely foreign technological

ideas are to be adopted locally, because they determine the degree of excludability

the inventors hold, and the incentives local firms have to adopt the ideas. Rules also

have the potential to be adopted from overseas, though this may be difficult if it

requires the agreement of large numbers of people.

One potential reason for the relative neglect of non-technological ideas in the

literature may be that, compared with technological ideas, they remain a vague

concept and one that is even more difficult to measure empirically. Theoretical

discussions have long acknowledged the importance of institutions for economic

performance (e.g., North, 1990, among others), but only more recently has the

empirical literature begun to provide evidence along these same lines. A notable

example is Acemoglu, Johnson and Robinson (2001), who use differences in the

mortality rates faced by European colonists, which affected the colonial institutions

they established, and thus current institutions through institutional persistence, and

estimate large effects of institutions on income per capita. Although the institutions of

a country are heavily path-dependent and knowledge of an improvement over existing

institutions by no means guarantees its adoption, such knowledge is a necessary

condition for its adoption. As a result, the diffusion of ideas about institutions has

an important role in institutional change and development.




2.4 Measuring idea flows empirically

Despite the obvious importance of idea flows for growth and development, their

empirical study has been limited (see, for example, various work by Mokyr and

Romer). A key reason is likely to be the inherent difficulty in measuring flows of ideas.

One strand of the literature has focused on measuring the effect on productivity of

international flows that are hypothesized to embody ideas, such as trade in capital

goods and foreign direct investment. Another strand of the literature has focused on

studying the determinants of technological spillovers as measured by a more direct

indicator, patent citations.

2.4.1 Embodied idea flows

Several main channels for international idea diffusion have been hypothesized in the

literature, though the evidence for their effects on productivity has been mixed, as

discussed in Keller (2004). Import flows, especially of intermediate goods, are one

means by which countries may gain new technology from overseas sources. This is a

relatively weak form of technological diffusion, because the receiving country doesn’t

necessarily acquire the knowledge to produce the technology itself, just the finished

product. The empirical literature, summarized by Keller (2004), suggests a significant

role for import flows in bringing technology that increases TFP into a country.

However, care must be taken in interpreting greater flows of goods and services

between countries as indicative of greater flows of ideas. As Romer (2010) points

out, when developing countries gain the knowledge to manufacture goods that were

invented in developed countries and therefore begin production of these goods for

themselves, trade may actually decrease. Far from being an undesirable situation,

this is in fact a clear demonstration of the gains from globalization. Thus, high



2.4. MEASURING IDEA FLOWS EMPIRICALLY 21

levels of conventional trade flows may actually indicate inefficiently low international

diffusion of ideas.

The second potential channel for technology diffusion is exports. The mechanism

here is less immediately obvious than in the case of imports, but may operate

because firms benefit from dealing with international customers that possess more

advanced knowledge. For instance, the international customer may require higher

quality standards than domestic customers, and may provide the domestic firm with

information on how to meet those standards. Although case studies find support for

such a role of exports, the econometric evidence is consistent with exports having noeffect on domestic TFP (Keller, 2004). These two channels suggest that policies that

affect trade, especially imports, may have implications for the rate at which a country

adopts foreign technology.

The third channel is foreign direct investment. Multinational firms tend to be

the most R&D-intensive (e.g., Criscuolo, Haskel and Slaughter, 2010), hence they

offer a large potential for technology diffusion between the countries where the

parent company and the subsidiaries operate. The parent company may share firm-specific technology with its subsidiaries in other countries, high-quality inputs may

be provided to the subsidiaries, or labor training may generate learning externalities.

Empirical evidence for such effects is mixed, though some studies find significant

effects that are economically important in magnitude. Furthermore, spillovers seem

to be larger in high-tech industries (Keller, 2004).

Knowledge may also diffuse between countries when carried by migrants and

temporary visitors. In addition to codifiable knowledge, people may carry tacit

knowledge, which may be strongly complementary to physical technology. This

channel suggests a strong geographic element to knowledge diffusion, because the

cost of moving people is highly correlated with distance.

In line with much of the empirical literature that he summarizes, Keller (2004)




doesn’t draw an explicit distinction between “pure” ideas and ideas embodied in

capital goods. This distinction is important, because embodied ideas may not providethe technological spillovers that are essential in theoretical models of endogenous

growth (e.g. Jaffe and Trajtenberg, 1999).

2.4.2 Pure idea flows: Patent citations and translations

The primary measure of pure idea flows in the literature is patent citations. The

literature on patent citations considers a patent to indicate a piece of technological

knowledge, and a patent citation indicates “a given bit of knowledge being useful in

the development of a descendant bit” (Jaffe and Trajtenberg, 1999). That is, patent

citations proxy for spillovers from R&D. Patent citations can therefore be used to

measure how geographic and other factors affect knowledge diffusion. The answers

such studies can provide have important implications for how technological change

and growth ought to be modeled in theoretical papers, and may guide the formation

of appropriate policy in the areas of science and technology (Jaffe and Trajtenberg,

1999).

A series of papers by Jaffe, Trajtenberg and Henderson (1993, 1996, 1999) find

a number of regularities of patent citations. Citations are localized both within and

between countries. That is, patents are much more likely to be cited by other patents

from the same region of the country than from other regions of it, and by patents from

the same country than from foreign countries. However, this localization effect fades

over time and any local advantage is eventually eliminated. This pattern of fading

localization over time can be explained by the two competing forces of knowledge

diffusion and obsolescence. Knowledge diffuses geographically over time, hence it

reaches closer locations more quickly, but at the same time at any location that

might use it, it passes through a natural life cycle from discovery, to usefulness, to

obsolescence. There are also some country-specific patterns to patent citations. For



2.4. MEASURING IDEA FLOWS EMPIRICALLY 23

example, the Japanese tend to cite a new patent quickly. Citation flows also tend to

be bidirectional. That is, if there is a large citation flow from country A to countryB, then there is likely to also be a large citation flow from country B to country A.

More recently, MacGarvie (2005) discusses some of the correlates of international

patent citation flows. Geographic distance, which MacGarvie interprets as a proxy

for difficulty of communication, inhibits citation flows, though this negative effect

decreased over the period 1980 to 1995. Lack of a common language inhibits citation

flows; import flows and FDI are positively correlated with citation flows.

Patent citations have many desirable qualities as a measure of idea flows, but they

also have some well-known limitations. First, the overall fraction of research output

that is ever patented is small. The decision to patent an invention is a strategic one;

in many circumstances, secrecy is viewed as a preferable means to protect a discovery,

or the discovery may not be considered worth protecting at all. Second, as stated by

Jaffe, Trajtenberg and Henderson (1993), “Ex post, the vast majority of patents are

seen to generate negligible private (and probably social) returns.” Third, incentives to

patent differ by time and place, and with the type of invention, which implies caution

must be used in interpreting comparisons between quantities of patents or patent

citations in different countries or periods, or the presence or absence of citations to

any particular patent. Finally, patent citations are limited to measuring the spread

of scientific and technical knowledge.

Book translations, the measure of the diffusion of ideas that I introduce in this

dissertation, have several attractive qualities for this purpose. They capture a broad

range of types of ideas, including technical, social, and organizational; in the parlance

of Romer (2010), they include both “technological ideas” and “rules”. Translations

themselves are non-rival and disembodied, thus they have the potential to create the

externalities that are key to growth in the theoretical literature. Besides not being

embodied, translations do not occur as a side effect of some other activity: a book




is translated in order for people who speak the target language to gain access to

the ideas contained in the book, thus occurs as the direct result of the desire forknowledge to spread. Unlike patents, there are no strategic considerations involved

in the translation of books. Finally, book translations have a natural quantification,

and they are classifiable by type.

Naturally, book translations also have some limitations as a measure of idea flows.

They only capture idea flows between languages, so cannot measure, for instance, idea

flows between the US and Britain. By their nature, the ideas they can transmit must

be codifiable, thus they miss flows of tacit knowledge. Because of the length of timeinvolved in writing and publishing a book, they tend not to capture very new ideas.

Finally, some people are multi-lingual, thus have access to ideas that are published

in languages other than their own before the books are translated.

2.5 When will translations occur?

A microeconomic perspective

The diffusion of ideas between the groups that create them is critical because it

enables a much greater rate of idea accumulation, and makes certain ideas available

to societies that lack the ability to generate them domestically. At the same time, it

decreases the duplication of effort involved in the creation of ideas.

However, language barriers hinder the diffusion of ideas between linguistically

distinct groups. This is especially true for ideas captured in books, which can

only be read by people who speak the language in which the books are written.

Despite the limitations inherent in using written language to capture ideas, books

are an important means for storing and transmitting many types of ideas between

individuals separated by space or time: a book may detail a technology itself or



2.5. WHEN WILL TRANSLATIONS OCCUR? 25

provide information that allows a technology to be adapted to new circumstances or

used most fully or efficiently; it may capture ideas on how to organize a society, aneconomic or political system, or a firm, or explain how an agent can exploit such an

existing system; it may contain, in the parlance of Mokyr (2002), “propositional

knowledge” about natural phenomena and regularities, which forms a basis for

the discovery of new technologies; it may contain ideas such as literature that are

consumption goods in themselves, or suggest activities such as arts, sports or hobbies

to consumers that improve their mapping from material consumption of goods and

services to utility; or it may contain many other sorts of ideas besides.

2.5.1 Translations versus bilingualism:

The individual’s viewpoint

Although language barriers are an impediment to the spread of ideas captured in

books, they can be overcome either through translation or through bilingualism.

Consider the choice faced by an individual endowed with some native language who

is interested in a particular idea described in a book. A book capturing the idea may

have been published in his native language, in which case he will read the original

with no difficulty. However, it may be the case that the idea was written in a foreign

language, and no adequate substitute was originally written in his native language. If

the foreign book has not been translated into the individual’s native language, he has

the options of learning the language in which the book was written and reading it in

the original, or obtaining the idea in some other form (such as from a bilingual person

who has read the original), from which he may receive it in an incomplete, diluted

or distorted version. If, on the other hand, the foreign book has been translated into

his native language, he may choose to read the translation, which is likely to be less

costly for him to read and fully understand, but which may lack some information




or nuance that was lost in translation, or he may still become bilingual and read the

original.

Clearly the decision to learn a foreign language is unlikely to be made on the

basis of one book, but will be driven by a much wider range of factors including the

lifetime possibilities for reading in the foreign language, and the other opportunities

and experiences in life that are offered by knowledge of the language. Learning the

foreign language relative to reading translations might be more or less attractive for

a number of reasons. Learning a foreign language is more attractive if the person’s

native language has fewer speakers, and thus offers less in terms of books (and life

opportunities more generally, such as work, travel, cultural enrichment, romance etc).

Similarly, learning a particular foreign language is more attractive if the language has

many speakers, particularly educated or wealthy speakers, and thus offers more books

(and life opportunities). A foreign language is also more attractive if it is useful in

more spheres of life. For example, if it is spoken widely in the person’s country, or

if a country that uses the language is economically important (in terms of trading

relationships, migrant relationships, as a holiday destination, etc) to the person’s

country.

A foreign language is also more attractive if more titles in the person’s field of

interest are originally written in it, and if fewer of these are translated into the person’s

native language. Finally, a foreign language that is easier (and thus less costly) to

learn from the native language is also more attractive.

Conversely, reading in translation may be relatively more attractive if the book

is in a field where the reading experience is relatively important in comparison with

the idea content, such as is the case with literature.




2.5.2 The decision to translate

A publisher is more likely to translate a title if it expects demand for the translation

to be high enough to make the endeavor profitable, or if the translation fits some

non-profit agenda of the publisher. Potential non-profit agendas that drive book

translations might include cultural or educational purposes, “social good” motives

where the publisher accounts for externalities that are likely to arise from the

translation, and personal or ideological objectives such as proselytizing.

Whether demand is sufficiently high is likely to be affected by a number of

factors. The size, wealth, and education level of the population that speaks the

target language are clearly important, because increases along any of these dimensions

increase the potential market size. However, the same factors are likely to mean the

target population is creating more of its own titles, which may be sufficiently good

substitutes for translated titles that the translations become unnecessary. Thus it

is ex ante unclear whether potential demand will increase or decrease with the size,

wealth, and education level of the target population.

Demand for translations is likely to decrease with the extent to which speakers

of the target language (who are interested in the particular field) are bilingual in the

original language, because such people have the option of reading the originals.

Demand for translations in a particular field is likely to be greater the greater is the

differentiation of subject matter across languages within the field. For instance, it may

be that all organic chemistry books written in different languages are relatively good

substitutes for each other, whereas philosophy books written in different languages

tend to be derived from different traditions and are very poor substitutes. In the

low differentiation case, it is more likely there will exist a title written in the target

language that is a good substitute for the translation, thus making the translation

unnecessary.

Pairs of countries whose needs, interests, or viewpoints are more closely aligned




may experience greater demand for translations from each other because their

preferences favor each other’s ideas. At the same time, such similarities may meanthey tend to generate original titles that contain ideas that are close substitutes, thus

making translations redundant. Thus it is conceptually unclear whether translations

will increase or decrease with similarity between the countries.

In addition to these demand-side considerations, supply-side considerations are

likely to play a role in determining translations. Importantly, a publisher can only

translate a title once it knows the title exists. This might occur serendipitously, or

may be the result of costly search. The chance of a serendipitous discovery occurringis increasing in the degree of interactions between the populations of the original and

target languages. At the extreme, where there two languages coexist in the same

country, and especially where they are geographically mixed, the chance is very high.

In the case where costly search is involved, it seems likely search efforts of a publisher

will be directed towards languages that publish a lot of original titles, and that are

able to test titles in large domestic markets.

A second supply-side consideration is transaction costs. A publisher’s calculationof whether translating a title is likely to be profitable will also account for the costs of

completing the transaction, which may vary with the culture or country of the original

title, and also with differences in culture or business practice between the original and

translating countries. Greater physical communication costs between the countries,

such as those caused by being in different time zones, may also decrease the likelihood

of translation.

2.5.3 Implications for translation flows

The mechanisms outlined above suggest the size of the target language population,

both in terms of numbers and in terms of wealth and education, should matter

for translation flows for several reasons. A larger population has less reason to be




bilingual and thus may translate more (bilingualism effect). They also offer a bigger

potential market for copies of each title translated, thus may attract translations of awider range of titles (market size effect). However, they are likely to also produce more

original titles that act as substitutes for potential translations and that may crowd

translations out of the market (substitution effect). At small sizes, it seems likely the

bilingualism and market size effects will dominate and translations will increase with

population. However, the substitution effect may become relatively more important

at larger sizes, and there may actually be a size beyond which the substitution effect

dominates and translations decrease with the size of the target language population.The size of the original language population must also be important for transla-

tions. An economically small linguistic group will not produce a large number of titles

that have the potential to be translated, so outward translations are likely to increase

with the size of the language group, at least up to a point. However, foreigners also

have less incentive to learn a small language, so if they want to read titles written

in such a language they must first translate them, whereas they may instead learn a

larger language to read it in the originals.

When we consider differences across fields in propensities to translate, the

predictions are again ambiguous. Fields in which the viewpoints of original titles

differ more across languages may be translated more because domestic substitutes

for foreign titles are less frequently available, or may be translated less because

the interests of potential readers differ more across language groups. However, in

fields where internationally books tend to be concentrated in a lingua franca (such as

English in many academic disciplines) we unambiguously expect fewer translations

relative to original publications.

Various types of similarities or close relationships between linguistic groups may

increase or decrease translation flows. Close relationships (such geographic proximity,

trading relationships, mutual membership of international treaties, etc) between two




linguistic groups foster bilingualism between them, making translations less necessary.

They also tend to align the types of original titles produced by the two groups, whichcould reduce translations through substitution, or increase them through enhancing

relevance. Additionally, these relationships could generate demand for the ideas of the

other group by increasing awareness of the existence of the ideas. Such relationships

also mean publishers are more likely to be aware of titles to potentially translate, and

face relatively low communication and transaction costs for doing so.

Along similar lines, cultural and other similarities between groups decrease the

costs of translation, and may either increase translations because of greater mutualinterests or decrease them because of the availability of domestic substitutes.

Finally, if two languages are sufficiently similar that the cost of learning one for a

person who speaks the other is very low, the prevalence of bilingualism may reduce

the need for translations.



Chapter 3

Data: Construction and main

patterns

Because translations are largely unstudied by economists, I begin by describing the

translation data and data collection, and documenting the patterns of translations

between countries and their historical trends. In this chapter I also describe the

additional data used in chapter 4.

3.1 Data construction

3.1.1 Translation data

The data on translations I use are derived from Unesco’s Index Translationum (IT), an

international bibliography of the translations published in a wide range of countries

over the periods 1932 to 1940 and 1948 to the present. In the majority of cases,

these bibliographical entries are acquired by Unesco from the central depository of

the translating country, which, under the law of legal deposit, receives copies of every

31



32 CHAPTER 3. DATA

book published in the country and intended for circulation.1

Titles are categorized into fields according to the nine main categories of theUniversal Decimal Classification (UDC) system: General; Philosophy (including Psy-

chology); Religion and Theology; Law, Social Sciences, Education; Natural and Exact

Sciences; Applied Sciences; Arts, Games, Sports; Literature (including books for

children)2; History Geography, Biography (including memoirs and autobiographies).

The bibliographic entry for each translation includes information on the country,

city, and year in the which the translation was published, the language of the original

title and the target language into which it was translated, the field (UDC class) of the title, the number of pages or volumes of the title, the author, and the original

and translated titles of the book.3

Digital translation data: 1979 to 2000

For approximately the period 1979 to 2000, I acquired the IT from Unesco in digital

format. Prior to 1979, these data do not exist in digital form. Beyond 2000, there

are still translations reported for some countries, but in many cases reporting of the

translations published in these years is clearly still incomplete. I do not use data from

countries in years where translations are incompletely reported. The digital record for

each translation includes the full bibliographic record for the translation, and usually

bibliographic details of the original title.

1Note that although there may be a delay of several years between the national depository of acountry receiving a translation and Unesco listing the translation in the IT, the IT reports the yearin which such translations were published, not just the year in which they were reported. I attributethem to the former and disregard the latter.

2Philology and Linguistics were a separate (and very small) category prior to 1970, and thenwere combined with Literature. I group them with Literature for all years for consistency.

3In a few instances, the IT reports that a title was translated from its original language via anintermediate language. In these cases, I consider the idea flow to be from the original language tothe final language, with the intermediate language just part of the mechanism. I thus count theseas translations from the original language to the target language, and disregard the intermediatelanguage.



3.1. DATA CONSTRUCTION 33

In the regressions that aim to capture contemporary translation patterns, I use

translation data from two points in time, the first being the annual average for 1993to 1995, and second being the annual average for 1998 to 2000. This period is

short enough to likely have a relatively constant relationship between translations

and distances throughout. In addition, the two points in time fit into the pattern of

every fifth year that I use for examining historical trends in translations, as described

below. Finally, including two periods as opposed to just one allows more precise

estimates of the relationships of interest. The averaging process reduces noise in

the data, while limiting the number of time-varying fixed effects required, which isnecessary to be able to feasibly estimate the PML model I use, as described in Section

4.2 of chapter 4.4 At the same time, this maximizes the number of countries in the

sample: if data are available for a country for only one or two years in either of the

three-year periods, I use the average translations for those one or two years.

Hand-collected translation data: 1949 to 1979

For translations prior to 1979, the IT exists only in hard copy format. The total

number of translations listed in the IT in one year is often thirty or forty thousand;

to make digitization manageable, I restrict my digitization effort to every fifth year

from 1949 to 1979. I choose to begin my sample period with 1949 because Unesco

only began systematic data collection in 1948. Specifically, Unesco did not compile

translations for the period 1941-47, and the pre-war data (1932 to 1940) were collected

by a different institution and are not entirely comparable. Because some countries

do not report their translations to Unesco every year, and in order to maximize

the geographical coverage of my historical translation data, where the exact year of

interest was not available for a country but the preceding or following year was, I

4Using just the years 1994 and 1999, instead of the averages as described here, does notsubstantially alter the results, though it increases the standard errors on the estimates.



34 CHAPTER 3. DATA

substitute that year instead.5 The years for which I have translation data for each

country are listed in Appendix Table A.2.Within each country, year and field, I take a 100, 50, 20, 10 or 5 percent sample

of entries.6 This amounts to approximately 100,000 records in total. I choose the

percentage to give me approximately 100 titles (or collect data on all translations

where the total number is fewer than 100) in total for each country-year-field group. In

all subsequent work I weight observations according to the inverse of their probability

of being sampled. For each entry I sample, I record the reporting country, original

and target languages, UDC category, year of publication and number of pages of thebook.

For the historical translation series, I combine these newly-digitized data with

digitized data provided by Unesco for every fifth year from 1979 to 1999.

Speed of translation data

The bibliographic entries in the IT in general do not include the year in which the

original title was first published. In order to investigate the factors driving the speed

with which titles are translated, I randomly sampled 20 non-fiction titles translated

from each of English, French, German and Italian in each country (or took all titles

where the total number of translations in the country from the original language was

fewer than 20) in the period 1998-2000. This amounts to a total of approximately

2,900 titles. For each sampled title I used online sources such as Worldcat and the

5Where data exist for consecutive years, they are very highly correlated, so this approximationis unlikely to have a significant effect on the results.

6My sample within each country-year-category group is pseudo-random in the following sense.Entries in the IT are identified by an entry number which starts at one each volume and eithercounts up throughout the whole volume, or restarts from one at the start of each new country entry.If I am taking a one in n sample, I sample every title whose identification number is a multiple of n . I do this instead of taking a genuinely random sample for speed of data entry, and because theordering within each group of titles alphabetically by author means this method is unlikely to biasmy sample with respect the original or target language, the main dimensions of interest that varywithin such a group.




European Library to establish the year in which the original title was first published,

and thus construct its age at translation.

3.1.2 Physical environment data

I generate three measures of difference in physical environment based on differences

in the altitude profiles of original and translating countries, the biome region profiles

of the countries, and the climate region profiles of the countries. All three measures

are generated from the Center for International Earth Science Information Network’s

data set “National Aggregates of Geospatial Data Collection: Population, Landscape,

and Climate Estimates, Version 2 (PLACE II)”.7

For biome region and climate region, if information is missing for some fraction of

the land area of the country, I rescale the non-missing data to sum to 100%.8 Altitude

data are effectively never missing.

Altitude profile distance

For each country, I calculate the proportion of the total land area that falls into

each of three altitude zones: sea level to 100 meters above sea level, 100 meters to

800 meters, and 800 or more meters above sea level. These cutoffs were chosen to

give a wide distribution across countries of proportion in each of the three regions.

Globally, on average countries fall 35 percent into the lowest zone, 46 percent into

the intermediate zone, and 19 percent into the highest zone. Denote the proportions

of the translating country in each of the three altitude zones by alttrans0 , alttrans100 , and

alttrans800 respectively, and the equivalent proportions in the original country by altorig0 ,

altorig100 , and alt

orig800 respectively. Then I define the altitude profile distance between

the countries to be:

7Available online at http://sedac.ciesin.columbia.edu/place/.8Note the fraction of the country with missing data is never greater than 5%.




8. temperate grasslands, savannas and shrublands

9. flooded grasslands and savannas10. montane grasslands and shrublands

11. tundra

12. Mediterranean forests, woodlands and scrub

13. deserts and xeric shrublands

14. mangroves

Biome region profile distance is defined analogously to altitude region profile

distance. Specifically, denote the fraction of the translating country that falls into

the ith biome region by biometransi and the fraction of the original country that falls

into the ith biome region by biomeorigi . Then the biome region distance between the

original and translating countries is given by:

BiomeDist = 1 −14i=1

min(biometransi , biomeorigi ) (3.2)

Climate region profile distance

I use an aggregate version of the Koppen Climate Classification, in which land is

classified as falling into one of five climate regions:

1. tropical

2. polar

3. temperate

4. cold

5. dry

I then define climate region profile distance analogously to biome region profile

distance, but summing over these five categories instead of the 14 for biome region.




has the same religion as a person from the other country with missing religion (results

not presented). The two distance measures are highly correlated and regression resultsare unaffected. As a second alternative measure, I use an indicator variable for the

most widespread religion in the two countries being the same. Because this last

measure uses less of the variation in the data, regression results using it tend to be

weaker statistically, but point in the same direction.

Linguistic distance

My primary measure of linguistic distance is based on the linguistic tree measure usedby Fearon (2003)9. This measure of linguistic distance is intended to capture how

long ago the two languages split from each other, which proxies for both the degree

of dissimilarity of the languages, and the cultural distance that has evolved between

the speakers of the languages.

My primary distance measure is generated as follows. First, each language is

classified as in the 16th edition of Ethnologue. For example, Spanish is classified as

follows:

- Indo-European

- Italic

- Romance

- Italo-Western

- Western

- Gallo-Iberian

- Ibero-Romance

- West-Iberian

- Castilian

- Spanish

9Whom I thank for kindly sharing his data with me.



40 CHAPTER 3. DATA

and French is classified as:

- Indo-European

- Italic

- Romance

- Italo-Western

- Western

- Gallo-Iberian

- Gallo-Romance

- Gallo-Rhaetian

- Oıl

- French

Each of these categories (e.g., Gallo-Iberian) is considered a node on the language

tree. I define the distance between two languages as

LinguisticDistij = 1 −2 × CommonNodesij

Nodesi + Nodes j(3.3)

where i and j denote the two languages, CommonNodes is the number of nodes

they have in common (e.g., 6 in the case of Spanish and French), and Nodes is the

number of nodes the individual language has (e.g., 10 in the case of Spanish). The

distance between a language and itself is thus 0, and two entirely unrelated languages

are distance 1 apart. In general two languages are further apart the smaller is their

common ancestry relative to their overall evolution. French and Spanish, for instance,

are somewhat related with a distance of 0.4 (= 1 – 12/20).As an alternative measure of linguistic distance, I use the exact measure used by

Fearon (2003), which is given by

LinguisticDistFearonij =

15 − CommonNodesij

15(3.4)




The significance of 15 here is that this is the maximum number of nodes any one

language has in Ethnologue’s classification scheme. The main difference between thetwo measures is that related languages with relatively few nodes in the language tree,

such as Czech and Slovak, are considered relatively close according to my primary

measure (0.2 for Czech and Slovak), but less close according to Fearon’s measure

(0.86 for Czech and Slovak). According to both measures, 80 percent of language

pairs worldwide are distance 1 from each other. The two measures yield similar

results in the regressions.

Genetic distance

I use Spolaore and Wacziarg’s (2009) measure of genetic distance. This distance

is defined at the country-pair level and captures the time elapsed since the two

populations’ last common ancestors. Where the population of a country consists

of more than one genetically distinct group, the population-weighted average over

the different groups is used.

Hofstede’s (1980, 2001) cultural distance measure

My first survey-based measure of cultural distance is the variance-adjusted average

of Hofstede’s (1979, 1980, 1982, 1983, 2001) four cultural dimension measures:

power distance, uncertainty avoidance, individualism, and masculinity.10 These four

dimensions were generated from surveys of 88,000 IBM employees in 53 different

countries. They relate especially to values in the workplace, but are closely tied in to

basic anthropological and societal issues (Hofstede and Bond, 1984).

The first dimension is “power distance”, defined as “the extent to which less

powerful members of institutions and organizations accept that power is distributed

10This method of combining Hofstede’s dimensions was used previously by studies including Kogutand Singh (1988) and Ng, Lee and Soutar (2007).



42 CHAPTER 3. DATA

unequally.” The second dimension is “uncertainty avoidance”, or “the extent to

which people feel threatened by ambiguous situations, and have created beliefs andinstitutions that try to avoid these.” The third dimension is a continuum that ranges

from “individualism”, or “a situation in which people are supposed to look after

themselves and their immediate family only,” to “collectivism”, or “a situation

in which people belong to in-groups or collectivities which are supposed to look

after them in exchange for loyalty.” The fourth dimension is a continuum between

“masculinity”, or “a situation in which the dominant values in society are success,

money, and things,” and “femininity”, or “a situation in which the dominant valuesin society are caring for others and the quality of life.”

My measure of cultural distance based on Hofstede’s cultural dimensions is given

by

HofstedeDistij =1

4

4k=1

(I ki − I k j )2

V ark

(3.5)

where i and j denote countries, k denotes the dimension, I ki is country i ’s value for

dimension k , and V ark is the variance across countries of the index for dimension k .

Differences between countries in these dimensions reflect differences in values,

priorities, and accepted norms. Such differences may hinder translation flows from

the supply side. Furthermore, they may mean original titles written in the countries

are likely to encompass more different world views, which may make them more

demanded in translation because they have no domestic substitutes, or less demanded

because the ideas they contain are less acceptable.

Schwartz’s (1994, 1999) cultural distance measure

My second survey-based measure of cultural distance is based on Schwartz’s (1994,

1999) seven cultural value dimensions. Schwartz’s framework is theory-driven, with




elements derived from earlier work in the social sciences. The first of Schwartz’s

dimensions is “conservatism”, defined as “a cultural emphasis on maintenanceof the status quo, propriety, and restraint of actions or inclinations that might

disrupt the solidary group or the traditional.” Conservatism stands in opposition

to two types of autonomy: autonomy in ideas and thought, called “intellectual

autonomy”, and autonomy in feelings and emotions, called “affective autonomy”.

Intellectual autonomy is defined as “a cultural emphasis on the desirability of

individuals independently pursuing their own ideas and intellectual directions.”

Affective autonomy is “a cultural emphasis on the desirability of individualsindependently pursuing affectively positive experience,” such as pleasure, or an

exciting or varied life.

The next dimension is “hierarchy”, or “a cultural emphasis on the legitimacy of

an unequal distribution of power, roles and resources,” which has clear commonalities

with Hofstede’s power distance dimension. Hierarchy stands in opposition to

“egalitarianism”, or “a cultural emphasis on transcendence of selfish interests in favour

of voluntary commitment to promoting the welfare of others.”

The next is “mastery”, meaning “a cultural emphasis on getting ahead through

active self-assertion,” which opposes “harmony”, defined as “a cultural emphasis on

fitting harmoniously into the environment.”

I follow the approach of Ng, Lee and Soutar (2007), and construct an average

distance on Schwartz’s dimensions analogously to my average using Hofstede’s

dimensions, but where the sum is instead over the seven dimensions.



44 CHAPTER 3. DATA

3.2 Main patterns

3.2.1 Countries primarily translate books into their main

languages

Table 3.1 shows that every year and across book fields, countries on average primarily

translate into their most widespread languages. Panel A shows that between 74 per

cent and 93 per cent of titles each year are translated into the main language of the

translating country. This percentage varies somewhat by field, with social science the

lowest at 66 per cent, and philosophy, religion, and literature the highest at 92, 91

and 90 per cent respectively.

When including translations into any language that is widespread in the trans-

lating country, as in Panel B, these percentages become even higher: between 90 per

cent and 96 per cent each year.

Overall these patterns suggest that the primary reason countries translate

is to make books written in foreign languages accessible to their populations.

Consequently, we can view translation flows as driven by the demand a country has

for foreign ideas, and not by certain countries imposing their ideas on others. Note,

however, that there are exceptions to this generalization. For example, the Soviet

Union before the collapse of Communism had a significant program translating its

own titles into languages such as English, Spanish and French for export to Western

Europe and America.

3.2.2 Translations occur in a wide range of fields

Table 3.2 shows the distribution of translations by field each year; it reveals that

translations occur right across fields, and in both fiction and non-fiction. The most

translated field is literature, with 49.7% of all translations; this value was relatively



3.2. MAIN PATTERNS 45

steady over the period 1949-1999. Applied science is the next most translated field,

with 10.6% of translations overall, and an increase in importance from 6.2% in 1949to 13.9% in 1999. Social science is next largest at 10.3%. The other fields range from

4.9% to 7.4%.

Table 3.3 compares the distribution across fields of translations with that of all

books published. Overall, literature and philosophy titles are most over-translated

relative to original publications; social science is particularly under-translated,

and natural science and history are somewhat under-translated. When restricting

attention to non-fiction titles, applied science is also translated more than expectedgiven the total publication of titles in the field.

The importance of translations in a field relative to total books published is

likely to depend on a number of factors. Translations will occur less the greater

are the foreign language ability of the audience for the field, and the willingness of

the audience to read in foreign languages. For instance, scientific audiences may be

better at reading English than audiences for popular literature, suggesting popular

literature will be translated relatively more than scientific titles; indeed, Table 3.3suggests this is the case.

Translations will occur more in fields where books written in different languages

are poorer substitutes for each other. For instance, it may be that all organic

chemistry textbooks are relatively close substitutes, so such books are translated

infrequently, whereas philosophy titles written in different languages are derived from

different traditions and thus are very poor substitutes for each other, hence are

translated a lot. Such forces are consistent with the low translation rate of natural

science and the high translation rates of philosophy, literature and religion.

The extent to which the same ideas are relevant in different countries will also

affect the relative importance of translations. For example, history books generally

focus on a specific region of the world, and countries tend to be more interested in



46 CHAPTER 3. DATA

their own history than that of foreign countries, thus we expect and indeed find that

history books are relatively less translated.

3.2.3 English is the most translated original language

I next examine the languages out of which titles are translated. Table 3.4 shows

the ten most translated original languages by field overall, and for the two years

1959 and 1999. Even in 1959, English was the most translated original language

overall and in most fields; Table 3.5, which gives the most translated original language

in each country by year, shows that the dominance of translations from English

appears to some extent on every continent, suggesting it is a worldwide phenomenon.

Furthermore, translations from English as a percentage of total translations grew

from 31% in 1959 to 61% in 1999.

Part of this increase can be attributed to the decline of translations from Russian

and other Eastern European languages caused by the collapse of Communism, as

documented in chapter 4 of this dissertation. In 1959, Russian was the second most

translated language, accounting for 18.5% of translations overall, and with particular

strengths in the fields of natural, applied and social sciences. By 1999, its share had

decreased to 1.3%, making it the seventh most translated language.

French fell from the third most translated language at 12.9%, above German with

9.2%, to the fourth, with particularly large decreases in philosophy, arts, and religion.

German moved up from fourth position to third overall, but showed large decreases

in the fields philosophy and religion. The other top 10 languages in 1959 were Italian,

Czech, Spanish, Chinese, Latin and Polish; by 1999, Czech, Chinese and Polish had

dropped out of the top 10, to be replaced by Swedish, Dutch and Danish.

One salient pattern that emerges is that translations are concentrated quite

strongly in a relatively small number of original languages. An important contributing

factor to this is likely to be that original titles are also relatively concentrated




across languages, especially titles with the potential to be of interest internationally.

Economies of scale in idea production (e.g. resulting from a thriving intellectualcommunity) and publication (publishing a book involves a relatively large fixed cost

relative to the cost of printing each copy) may be one reason original titles are more

concentrated across languages than is income. The concentration of originals across

original languages may also be partly because speakers of small languages writing in

areas of international interest learn larger languages and publish in them in order to

reach a wider readership. A similar pattern of concentration across languages appears

in another measure of idea creation, namely research and development spending: thelargest 7 industrialized countries accounted for 84% of world R&D spending in 1995,

which is considerably larger than their 64% share of world GDP.11

Table 3.5 and Figure 3.1 paint further details of the original languages from

which various countries translate. Total translations in most Western European

countries were on the rise over the second half of the twentieth century. With the

exceptions of East Germany, the United Kingdom, Malta and Portugal, each Western

European country translated most from English every year data were available. Thepercentage of translations from English was relatively steady in most cases, though

for some countries it increased mildly over time. French and German were also

heavily translated in most of these countries; Russian was translated very little. The

United Kingdom translated most from French, and Portugal from Spanish, French,

or English.

The source languages of translations published by Central and Eastern European

countries were more mixed: many translated most out of their own languages orRussian, though by 1999 most were translating most out of English. Several of

these countries show two distinct periods when translation patterns changed. First,

after the death of Stalin in 1953 and with the Khrushchev Thaw that followed,

11Keller (2004).



48 CHAPTER 3. DATA

translations from Russian declined somewhat in importance and translations from

English increased. Second, a similar change of much greater magnitude occurredwith the collapse of Communism in 1989. These changes are less evident in Turkey,

Yugoslavia and Albania, which were outside the sphere of Soviet influence for most

of this period.

Most South American countries translated most from English; French was also

translated heavily. Over the period, German replaced French as most translated in

USA. Total translations grew steadily in American countries such as the USA, Canada

and Brazil, but fell in Argentina.

English and French were the languages most translated in Africa. English was

most translated in most of Asia, and grew considerably percentage-wise in the

prominent case of Japan, which also increased its total translations steadily over

the period. East Asia translates mostly from English, and to a modest degree from

French and German. India and Israel translated considerably from Russian until the

collapse of communism, at which point such translations fell off.

Appendix Figure A.1 shows translations by continent and field from each of the

languages English, French, German, and Russian over time. A few main patterns

are evident. The dominance of English as an original language holds across fields,

continents and time. Russian is an important source of natural science titles and, to

a lesser extent, applied science titles, especially in America. However, translation of

other Russian titles is relatively limited outside Eastern Europe in the Communist

era. French and German are relatively similarly translated, though French tends to

be more translated in Western Europe and German in Eastern Europe, especially

in the fields of natural, applied and social science, and philosophy. The most

dramatic change over time is the collapse in translations from Russian and increase in

translations from English, French and German in Eastern Europe upon the collapse

of Communism; these changes occurred right across fields.




3.2.4 Germany overtook the USSR as the biggest translating

country

Figure 3.2 shows that the largest translator in 1959 was the USSR, which translated

nearly 5,000 titles that year, followed by Germany at slightly under 3,000. By 1999

and after the disintegration of the USSR, Germany had risen to first place with

nearly 10,000 translations published, followed by Spain, France, and Japan. Figure

3.3 shows that on a per capita basis, the Scandinavian countries, with their small

populations and distinct languages, were among the heaviest translators. In 1959 they

were accompanied in the top spots by Israel, and in 1999 by Monaco and Estonia.

The distribution of translations across fields also differs significantly by country.

For example, in 1999, although France’s total translations exceeded Japan’s by over

30%, non-fiction translations in the two countries were very similar.

3.2.5 Bigger, richer countries that trade less translate more

Next I study the correlation between various characteristics of a country and the

number of titles it translates. Tables 3.6 and 3.7 present the results of regressions

predicting the (ln) number of titles a country translates into its official languages,

using data from every fifth year from 1949 to 1999. These tables reveal, as expected,

that more populous and richer countries translate more titles. The elasticity of

translations with respect to population is in the region of 0.6. The fact this elasticity

is less than 1 implies a doubling in population corresponds to less than a doubling in

translations. This seems reasonable when we recall that translations, as opposed to

copies of the translated book, are non-rival goods, so any increase in translations as

population increases means each individual in the population has potential access to

more translated titles. GDP per capita is even more strongly related to translations,

with an elasticity of around 1.3. This suggests translations are something of a luxury



50 CHAPTER 3. DATA

good, with the diversity of translated titles demanded by a country increasing more

than proportionately with income.The main country of a language, such as Germany (for German), but not

Switzerland, translates considerably more than the secondary countries of the

language. This could be a result of such countries tending to have larger publishing

industries, and translating titles that are exported to other countries that speak the

same language. For example, many books for sale in Australia and New Zealand were

published in the US or UK.

Columns 3 to 6 of Table 3.6 show that countries with more educated populations,as measured by (i) the proportion of adults aged 25+ with post-school education or

(ii) the average years of schooling of the population aged 25+, translate more, but

this is entirely due to their higher incomes.12

Table 3.7 shows that landlocked countries translate considerably more than

countries with a coastal border. This seems counterintuitive given we usually consider

landlocked countries to be more isolated. However, column 8 of Table 3.7 suggests

this effect is driven by cross-continent differences. Specifically, most of the landlockedcountries in the sample are European, and European countries tend to be heavy

translators, yet landlocked European countries translate no more than European

countries with coastlines. More open countries, as measured by trade as a percentage

of GDP, actually translate less. This effect is statistically significant when using

exports to measure trade, but is smaller in magnitude and insignificant when using

imports. Again, this effect is driven by variation between continents.

Columns 4 and 5 of Table 3.7 suggest that more democratic and less autocraticcountries translate more, though the effects are not statistically significant.13 The

magnitude of the coefficients suggests a country with the highest level of democracy

12Education data are from Cohen and Soto (2007).13The democracy and autocracy variables are from the Polity IV data set; both vary on a scale of

0 to 10.




will translate 65% more than a country with the lowest level of democracy.

The number of official languages in a country is not significantly correlated withthe number of titles the country translates. This is perhaps surprising given each title

translated in a country with more languages becomes available to only some fraction

of the population, so more translations are required to generate the same access to

foreign titles. However, this effect may be counterbalanced by the fact each translated

title faces a smaller market in the country, so translation of any individual title is less

worthwhile.

Even after controlling for population, GDP per capita, and openness, Muslimcountries translate less than other countries. For instance, relative to Roman

Catholic countries, they translate 73% fewer titles. European countries translate more

than African, Asian, Middle Eastern and American countries, and Pacific countries

translate less.

3.2.6 Western Europe translates quickly

I next document patterns in the data on age of translated titles. In the years 1998-

2000, the bulk of translated books were translated within 10 years of publication,

with a mode of 1 to 2 years, but some countries systematically translate faster than

others. Figure 3.4 shows speed varies also considerably by original language. On

average, English and Italian titles are translated relatively quickly, and German and

French titles more slowly; these patterns vary substantially across countries, as shown

by Appendix Figure A.2. For each translating country, this figure shows a kernel

density plot of the age of titles at translation for translations from English, French,

German and Italian. Only original languages with at least 15 translations in the

country during the time period of interest are plotted.

Western European countries generally translate these four languages quickly, but

even within Western Europe there is significant variation: Spain is relatively slow,



52 CHAPTER 3. DATA

France, Germany and the Netherlands tend to be quick; Switzerland translates quickly

from French, but relatively slowly from English; Germany translates quickly fromFrench and English, but slowly from Italian.

Central and Eastern Europe tends to translate more slowly than Western Europe,

though this pattern is not universal. For instance, Estonia translates relatively quickly

from English, although it translates very slowly from French and German. Asia

translates relatively slowly, but much faster from English than from other languages.

Canada translates extremely quickly from English and very quickly from French,

but much slower from German and Italian. Chile translates extremely slowly fromEnglish.

Richer countries that trade more translate faster

Table 3.8 presents regressions predicting the median age of titles translated, or the

proportion of titles translated that are no older than two years at the translating

country-original language level. I weight observations by the number of translations

contributing to the speed of translation variable, and cluster standard errors at the

country level. One robust result is that richer countries translate faster. Columns

1 to 6 show a country with 10% higher GDP per capita translates books with a

median age that is 5 to 6 percent lower; columns 7 to 12 show that 1 to 2 percentage

points more of translations in such a country are no more than two years old. This

is consistent with richer countries have better access to communication technologies,

as suggested by other results such as the lower inhibiting effect of distance on the

quantity of translations for richer countries.

The fraction of the population with post-school education and average years of

schooling of the country’s population are also positively correlated with speed of

translation, though most of these effects act through higher income levels. Perhaps

surprisingly, more populous countries do not translate faster, and may in fact




translate slower (though these results are mostly not statistically significant). A

potential explanation is that countries with larger populations have more ideas(books) produced (published) internally, making foreign books less important.

Columns 1 and 6 of Table 3.9 show that landlocked countries translate significantly

faster than non-landlocked countries. Much of this effect acts through trade: countries

that trade more relative to their GDP, especially import more, translate faster, and

controlling for exports or imports as a fraction of GDP decreases the coefficient on

landlocked and turns it insignificant. Across specifications, a 10 percent increase in

imports as a fraction of GDP corresponds to a 3 to 4 percent decrease in median bookage at translation. The importance of trade for translation speed could suggest that

countries for which trade is more important are more outward-looking, and either

more interested in foreign countries, or more aware of what is currently occurring in

them.

Countries where the dominant religion is an Indian or other religion may translate

somewhat slower than Roman Catholic, atheist, Eastern Orthodox, Muslim or

Protestant countries. Continents do not differ significantly in how quickly theytranslate.



54 CHAPTER 3. DATA

3.3 Figures and tables

Figure 3.1: Translations from English, French, German, and Russian bycountry

Western Europe

Notes: Translations into only the languages that are official in the translating country areincluded.



3.3. FIGURES AND TABLES 55




56 CHAPTER 3. DATA

Central and Eastern Europe




58 CHAPTER 3. DATA

Asia and the Middle East





America




60 CHAPTER 3. DATA

Africa





Australasia


Figure 3.2: Translations by country and field

Notes: Translations into only the major languages of the translating country are included.



62 CHAPTER 3. DATA





Figure 3.3: Translations per capita by country and field




64 CHAPTER 3. DATA





Figure 3.4: Age at translation of non-fiction titles by originallanguage for 1998-2000Translations from four major original languages



66 CHAPTER 3. DATA

T

a b l e 3 . 1 : C o u n t r i e s p r i m a r

i l y t r a n s l a t e i n t o t h e i r

o w n l a n g u a g e s

P a n e l A : %

o f t i t l e s t r a n s l a

t e d i n t o t h e m o s t w i d e s p r e a d l a n g u a g e o f t h e t r a n s l a t i n g c o u n t r y

F i e l d o f t r a n s l a t i o n s

Y e a r s

T o t a l

N a t u r a l S c i e n c e A p p l i e d S c i e n c e S

o c i a l S c i e n c e

H i s t o r y

P h i l o s o p h y

A r t s

L i t e r a t u r e

R e l i g i o n

T o t a l

8 5 %

7 3 %

8 5 %

6 6 %

8 0 %

9 2 %

8 0 %

9 0 %

9 1 %

1 9 4 9

9 3 %

8 4 %

9 6 %

7 5 %

9 2 %

9 9 %

8 2 %

9 7 %

9 7 %

1 9 5 4

8 8 %

8 6 %

8 5 %

7 5 %

8 5 %

9 6 %

7 9 %

9 3 %

9 2 %

1 9 5 9

8 1 %

7 4 %

7 2 %

5 7 %

8 3 %

8 8 %

8 1 %

8 6 %

9 3 %

1 9 6 4

7 4 %

7 3 %

6 8 %

5 7 %

7 3 %

7 9 %

6 9 %

7 9 %

8 8 %

1 9 6 9

8 2 %

8 0 %

8 0 %

6 7 %

7 9 %

8 4 %

8 3 %

8 6 %

8 9 %

1 9 7 4

7 9 %

7 2 %

8 0 %

6 2 %

7 7 %

8 4 %

8 3 %

8 4 %

8 8 %

1 9 7 9

8 0 %

6 8 %

8 1 %

5 6 %

7 4 %

9 2 %

8 1 %

8 7 %

9 0 %

1 9 8 4

8 1 %

6 3 %

8 2 %

5 0 %

7 7 %

9 1 %

8 1 %

8 8 %

8 8 %

1 9 8 9

8 3 %

6 1 %

8 1 %

5 8 %

8 0 %

9 4 %

7 9 %

8 9 %

9 2 %

1 9 9 4

9 2 %

8 2 %

9 2 %

8 5 %

8 4 %

9 6 %

8 1 %

9 5 %

9 4 %

1 9 9 9

9 2 %

8 6 %

9 3 %

8 6 %

8 4 %

9 6 %

7 9 %

9 4 %

9 3 %

P a n e l B : %

o f t i t l e s t r a n s l a

t e d i n t o a n o f f i c i a l l a n g u a g e o f t h e t r a n s l a t i n g c

o u n t r y


Y e a r s

T o t a l

N a t u r a l S c i e n c e A p p l i e d S c i e n c e S

o c i a l S c i e n c e

H i s t o r y

P h i l o s o p h y

A r t s

L i t e r a t u r e

R e l i g i o n

T o t a l

9 3 %

8 6 %

9 2 %

7 9 %

8 8 %

9 7 %

8 4 %

9 7 %

9 6 %

1 9 4 9

9 6 %

9 3 %

9 7 %

9 0 %

9 4 %

8 4 %

9 9 %

9 7 %

1 9 5 4

9 4 %

9 1 %

8 9 %

8 5 %

8 9 %

9 9 %

8 2 %

9 8 %

9 8 %

1 9 5 9

9 4 %

9 1 %

9 2 %

8 2 %

9 3 %

9 5 %

8 7 %

9 7 %

9 7 %

1 9 6 4

9 0 %

8 5 %

8 6 %

7 4 %

8 6 %

9 3 %

7 9 %

9 6 %

9 7 %

1 9 6 9

9 2 %

8 7 %

8 9 %

7 9 %

8 9 %

9 4 %

8 7 %

9 7 %

9 6 %

1 9 7 4

9 1 %

8 3 %

9 0 %

8 1 %

8 7 %

9 1 %

8 8 %

9 5 %

9 5 %

1 9 7 9

9 0 %

8 7 %

9 0 %

7 1 %

8 6 %

9 7 %

8 5 %

9 6 %

9 6 %

1 9 8 4

9 1 %

8 2 %

9 2 %

6 9 %

8 8 %

9 6 %

8 7 %

9 6 %

9 5 %

1 9 8 9

9 2 %

8 2 %

9 0 %

7 3 %

8 8 %

9 7 %

8 4 %

9 6 %

9 7 %

1 9 9 4

9 5 %

9 1 %

9 5 %

8 9 %

8 8 %

9 9 %

8 4 %

9 9 %

9 7 %

1 9 9 9

9 5 %

9 0 %

9 5 %

9 1 %

8 8 %

9 9 %

8 2 %

9 8 %

9 6 %

N o t e s : T h e s a m p l e i n c l u d e s , f o r e a c h y e a r , d a t a f r o m

a l l a v a i l a b l e c o u n t r i e s .




T a b l e 3 . 2 : D i s t r i b u t i o n o f t r a n s l a t i o n s b y

f i e l d


Y e a r s

N a t u r a l S c i e n c e A p p l i e d S c i e n c e

S o c i a l S c i e n c e

H i s t o r y

P h i l o s o p h y

A r t s

L i t e r a t u r e

R e l i g i o n

T o t a l

2 2 , 6

4 4

4 8 , 7

8 7

4 7 , 1

8 1

3 4 , 1

2 3

2 4 , 9

9 5

2 4 , 0

5 9

2 2 7 , 7

1 0

2 9 , 0

4 5

4 5 8 ,

5 4 4

4 .

9 %

1 0 . 6

%

1 0 . 3

%

7 . 4

%

5 . 5

%

5 . 2

%

4 9 . 7

%

6 . 3

%

1 0 0 . 0

%

3

4 5

4 8 7

1 , 0

9 2

7 6 5

3 6 3

2 3 3

4 , 2

6 0

3 7 3

7 , 9

1 8

4 .

4 %

6 . 2

%

1 3 . 8

%

9 . 7

%

4 . 6

%

2 . 9

%

5 3 . 8

%

4 . 7

%

1 0 0 . 0

%

5

8 5

1 , 5

0 6

1 , 6

3 8

1 , 2

4 3

4 7 7

5 1 7

7 , 4

9 6

7 9 2

1 4 , 2

5 4

4 .

1 %

1 0 . 6

%

1 1 . 5

%

8 . 7

%

3 . 3

%

3 . 6

%

5 2 . 6

%

5 . 6

%

1 0 0 . 0

%

1 , 1 8 8

2 , 0

2 6

2 , 6

5 7

2 , 0

3 5

9 1 2

7 5 4

1 3 , 6

1 6

1 , 2

8 8

2 4 , 4

7 6

4 .

9 %

8 . 3

%

1 0 . 9

%

8 . 3

%

3 . 7

%

3 . 1

%

5 5 . 6

%

5 . 3

%

1 0 0 . 0

%

1 , 7 9 2

2 , 6

0 7

3 , 0

0 7

2 , 3

9 6

1 , 1

2 4

1 , 0

2 3

1 2 , 9

0 2

1 , 6

5 4

2 6 , 5

0 5

6 .

8 %

9 . 8

%

1 1 . 3

%

9 . 0

%

4 . 2

%

3 . 9

%

4 8 . 7

%

6 . 2

%

1 0 0 . 0

%

2 , 2 3 3

2 , 9

8 0

4 , 3

7 0

2 , 9

0 5

1 , 6

2 4

1 , 5

2 1

1 4 , 9

8 9

2 , 1

9 0

3 2 , 8

1 2

6 .

8 %

9 . 1

%

1 3 . 3

%

8 . 9

%

4 . 9

%

4 . 6

%

4 5 . 7

%

6 . 7

%

1 0 0 . 0

%

2 , 3 3 4

2 , 9

5 0

4 , 7

6 0

2 , 5

8 5

1 , 6

9 9

1 , 5

6 6

1 4 , 5

1 8

1 , 5

8 2

3 1 , 9

9 4

7 .

3 %

9 . 2

%

1 4 . 9

%

8 . 1

%

5 . 3

%

4 . 9

%

4 5 . 4

%

4 . 9

%

1 0 0 . 0

%

3 , 1 9 8

4 , 6

6 1

6 , 3

4 6

4 , 1

4 5

2 , 6

0 3

2 , 9

9 2

2 3 , 7

8 7

2 , 7

7 4

5 0 , 5

0 6

6 .

3 %

9 . 2

%

1 2 . 6

%

8 . 2

%

5 . 2

%

5 . 9

%

4 7 . 1

%

5 . 5

%

1 0 0 . 0

%

2 , 9 4 0

6 , 0

3 8

6 , 0

9 1

3 , 4

0 9

2 , 7

3 4

2 , 8

3 9

2 9 , 4

6 0

3 , 5

5 3

5 7 , 0

6 4

5 .

2 %

1 0 . 6

%

1 0 . 7

%

6 . 0

%

4 . 8

%

5 . 0

%

5 1 . 6

%

6 . 2

%

1 0 0 . 0

%

2 , 6 5 7

5 , 9

8 7

5 , 2

6 2

4 , 4

4 1

3 , 3

2 1

3 , 1

3 9

2 9 , 6

3 7

3 , 5

9 7

5 8 , 0

4 1

4 .

6 %

1 0 . 3

%

9 . 1

%

7 . 7

%

5 . 7

%

5 . 4

%

5 1 . 1

%

6 . 2

%

1 0 0 . 0

%

2 , 8 0 0

8 , 0

3 2

5 , 4

9 0

4 , 8

3 0

4 , 5

0 4

4 , 1

7 6

3 7 , 5

0 8

5 , 0

5 2

7 2 , 3

9 2

3 .

9 %

1 1 . 1

%

7 . 6

%

6 . 7

%

6 . 2

%

5 . 8

%

5 1 . 8

%

7 . 0

%

1 0 0 . 0

%

2 , 5 7 2

1 1 , 5

1 3

6 , 4

6 8

5 , 3

6 9

5 , 6

3 4

5 , 2

9 9

3 9 , 5

3 7

6 , 1

9 0

8 2 , 5

8 2

3 .

1 %

1 3 . 9

%

7 . 8

%

6 . 5

%

6 . 8

%

6 . 4

%

4 7 . 9

%

7 . 5

%

1 0 0 . 0

%

1 9 8 9

1 9 9 4

1 9 9 9

T o t a l

1 9 4 9

1 9 5 4

1 9 5 9

1 9 8 4

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

N o t e s : T h i s t a b l e p r e s e n t s t h e n u m b e r a n d p e r c e n

t a g e o f t r a n s l a t i o n s e a c h y e a r

t h a t f e l l i n t o e a c h fi e l d . T h e s

a m p l e

i n c l u d e s , f o r e a c h y e

a r , d a t a f r o m a l l a v a i l a b l e c o u n t r i e s .



68 CHAPTER 3. DATA

T a b l e 3 . 3 : Di s t ri b uti on oft ra

n s lati on s relati vet o

di s

t ri b uti on of o ri gi nalti t

le s byfi eldf o r

1 9 9 9

N a t ur a l

S c i e n c e

A p pl i e d

S c i e n c e

S o c i a l

S c i e n c e

Hi s t

or y

P h i l o s o ph y

Ar t s

L i t e r a t ur e

R e l i gi on

T r a n s l a t i on s

3 .1 %

1 3 . 9 %

7 . 8 %

6 . 5

%

6 . 8 %

6 .4 %

4 7 . 9 %

7 . 5 %

Or i gi n a l t i t l e s

6 .1 %

1 5 . 5 %

2 3 . 3 %

1 0 .1 %

3 . 5 %

6 . 6 %

2 8 . 9 %

5 . 9 %

T r a n s l a t i on s

6 . 0 %

2 6 . 9 %

1 5 . 0 %

1 2 . 5 %

1 3 .1 %

1 2 . 3 %

1 4 .4 %

Or i gi n a l t i t l e s

8 . 6 %

2 1 . 8 %

3 2 . 8 %

1 4 . 3 %

4 . 9 %

9 .2 %

8 .4 %

F i e

l d of t r a n s l a t i on s

% o v

e r a l l

% of

n on-f i c t i on

b o ok

s

N o t e

s : Th i s t a b l e pr e s en t s t h e p er c en t a g e of or i gi n al t i t l e s an d t r an s l a t i on s p u b l i s h e d i n1 9 9 9 t h a t f e

l l i n t o e a ch fi el d .Th e

s am p

l ei n cl u d e s d a t af r om al l a v ai l a b

l e c o un t r i e s .Th e d a t a on p u b l i c a t i on s of or i gi n al t i t l e s ar ef r om

Un e s c o.




Table 3.4: Most translated original languages by field

Panel A: All years Field of translations

Rank Total Natural Science Applied Science Social Science History Philosophy Arts Literature Religion

English English English English English English English English English

46.6% 44.2% 53.9% 33.4% 37.3% 50.3% 38.5% 51.1% 36.2%

French Russian German Russian French German French French French

10.8% 22.5% 12.6% 23.5% 14.9% 14.5% 14.5% 10.5% 14.6%

Russian German Russian French German French German Russian German

9.3% 8.4% 9.8% 9.2% 11.9% 13.6% 13.7% 6.7% 12.3%

German French French German Russian Russian Italian German Italian

9.2% 6.5% 7.5% 9.1% 9.3% 5.5% 6.9% 6.7% 5.3%

Itali an Czech Czech Ser bo-Croatian Italian Ancient Greek Russian Spanish Latin

2.7% 3.5% 2.6% 3.7% 3.1% 2.8% 6.6% 2.5% 5.3%

Spanish Hungarian Swedish Italian Spanish Latin Spanish Italian Ancient Greek

2.1% 1.9% 1.8% 1.9% 2.7% 2.5% 3.1% 2.3% 3.4%

Swedish Italian Italian Romanian Latin Italian Hungarian Swedish Hebrew

1.7% 1.7% 1.7% 1.8% 1.7% 2.2% 2.3% 2.2% 2.7%

Czech Romanian Hungarian Czech Hungarian Spanish Dutch Danish Spanish

1.2% 1.1% 1.2% 1.7% 1.6% 1.0% 1.5% 1.1% 2.5%

Latin Swedish Dutch Spanish Serbo-Croatian Chinese Swedish Polish Sanskrit

1.0% 1.1% 1.0% 1.5% 1.5% 1.0% 1.4% 1.0% 1.9%

Danish Danish Danish Hungarian Swedish Dutch Czech Czech Dutch

1.0% 0.9% 0.9% 1.4% 1.5% 0.8% 1.4% 0.9% 1.7%

Panel B: 1959 Field of translations


English English Russian Russian English English English English French

30.7% 34.4% 34.1% 40.7% 30.0% 24.7% 26.1% 34.0% 25.8%

Russian Russian English English French German French Russian English

18.5% 27.5% 28.8% 21.7% 16.4% 20.2% 20.6% 13.9% 22.3%

French German German German German French Russian French German

12.9% 12.0% 11.4% 8.3% 13.1% 18.1% 15.5% 12.8% 15.9%

German Czech French French Russian Russian German German Latin

9.2% 9.1% 7.4% 7.8% 12.7% 12.4% 14.9% 6.5% 8.3%

Itali an French Czech Ser bo-Croatian Italian Ancient Greek Italian Spanish It alian

2.6% 5.7% 6.0% 4.3% 3.0% 6.7% 5.4% 3.7% 6.5%

Czech Bulgarian Bulgarian Czech Norwegian Latin Czech Italian Russian

2.5% 2.3% 1.3% 3.4% 2.3% 5.0% 5.2% 2.6% 5.3%

Spanish Polish Danish Romanian Latin Ital ian Serbo-Cr oatian Chi nese Ancient Greek

2.4% 1.4% 1.1% 2.1% 1.8% 3.4% 1.7% 1.9% 4.8%

Chinese Romanian Romanian Polish Swedish Danish Hungarian Polish Hebrew

1.5% 1.3% 1.1% 1.9% 1.7% 1.6% 1.2% 1.7% 2.8%

Latin Dutch Swedish Chinese Czech Chinese Danish Danish Dutch

1.5% 0.8% 1.0% 1.6% 1.7% 1.4% 1.2% 1.6% 1.5%

Polish Latin Polish Bulgarian Danish Bulgarian Dutch Romanian Norwegian

1.4% 0.8% 0.9% 1.6% 1.7% 1.0% 1.2% 1.6% 1.0%

Panel C: 1999 Field of translations


English English English English English English English English English

61.2% 62.6% 70.7% 57.6% 45.5% 63.4% 48.9% 64.7% 46.7%

German German German French French German French French French

9.4% 9.0% 12.3% 10.4% 14.2% 12.4% 13.1% 7.7% 10.8%

French French French German German French German German German

8.9% 7.9% 5.9% 10.0% 13.8% 10.4% 13.0% 6.9% 10.3%

Italian Russian Italian Spanish Spanish Italian Italian Italian Italian

3.1% 2.5% 1.9% 2.4% 5.0% 2.6% 6.7% 2.5% 6.9%

Spanish Italian Swedish Italian Italian Ancient Greek Spanish Spanish Latin

2.5% 1.7% 1.4% 2.4% 3.8% 2.0% 5.7% 2.5% 4.2%

Swedish Danish Spanish Finnish Latin Latin Dutch Swedish Spanish

1.4% 1.7% 1.1% 1.4% 1.5% 1.8% 1.3% 1.8% 2.8%

Russian Estonian Danish Russian Russian Spanish Danish Russian Arabic

1.3% 1.6% 1.0% 1.4% 1.5% 1.1% 1.2% 1.6% 1.7%

Dutch Spanish Finnish Swedish Polish Dutch Catalan Japanese Hebrew

0.9% 1.5% 0.7% 1.1% 1.2% 0.7% 1.1% 1.0% 1.7%

Latin Macedonian Dutch Dutch Dutch Chinese Russian Dutch Ancient Greek

0.8% 1.4% 0.7% 1.1% 1.0% 0.7% 1.0% 1.0% 1.7%Danish Swedish Russian Danish Danish Russian Swedish Norwegian Sanskrit

0.8% 1.4% 0.4% 0.9% 0.9% 0.7% 0.9% 0.7% 1.1%10

6

7

8

9

2

3

4

5

8

9

10

1

4

5

6

7

10

1

2

3

1

2

3

4

5

6

7

8

9

Notes: This table presents the ten most translated original languages and their percentagesof total translations for each field of translations. Panel A presents data in total for everyfifth year from 1949 to 1999; Panel B presents data for 1959; Panel C presents data for1999. The sample includes, for each year, data from all available countries.



70 CHAPTER 3. DATA

Table 3.5: Most translated original language by country

Year

Country Total 1949 1959 1969 1979 1989 1999

Western Europe

Austria English English English English English English English

58.6% 51.5% 58.1% 58.4% 55.8% 68.1% 60.1%

Belgium English English English English

49.6% 57.1% 38.9% 47.7%

Switzerland English English English English

47.3% 45.8% 46.2% 49.8%

East Germany Russian Russian Russian

36.7% 38.0% 30.5%

Germany English English English English English English

60.2% 43.1% 55.3% 59.2% 58.2% 64.8%

Denmark English English English English English English English

57.7% 57.4% 63.3% 58.8% 53.6% 62.1% 62.4%

Spain English English English English English English English

48.8% 54.9% 45.5% 33.7% 46.5% 50.1% 53.2%

Finland English English English

56.0% 51.4% 62.6%France English English English English English English English

60.4% 60.8% 48.5% 52.5% 59.8% 65.8% 60.0%

UK French French French

25.0% 25.0% 27.2%

Greece English English English English

39.2% 31.0% 49.7% 31.3%

Iceland English English English English

55.1% 41.1% 53.2% 65.7%

Italy English English English English English English English

47.4% 45.2% 40.6% 36.4% 49.7% 47.4% 59.6%

Malta English English Italian

40.0% 66.7% 33.3%

Netherlands English English English

67.5% 63.8% 73.8%

Norway English English English English English English English

64.5% 61.9% 68.4% 69.2% 56.0% 62.8% 69.9%

Portugal English Spanish French

37.2% 39.0% 37.9%

Sweden English English English English English

65.1% 55.8% 64.4% 62.5% 67.9%


Albania Albanian Russian Albanian Albanian Albanian English

26.6% 52.8% 63.2% 48.9% 35.0% 27.2%

Bulgaria Russian Russian Russian Russian Russian Russian English

32.9% 82.2% 48.7% 37.9% 38.4% 27.6% 68.2%

Czechoslovakia Czech Russian Czech Czech Czech

25.6% 27.4% 28.7% 25.1% 29.4%

Hungary English Russian Russian Hungarian Hungarian English English

27.0% 67.0% 24.6% 41.0% 38.1% 32.5% 55.4%

Poland English Russian English English English English English

42.2% 48.9% 21.9% 28.0% 17.9% 34.8% 65.2%

Romania Romanian Russian Romanian R omanian R omanian English

32.5% 43.9% 40.8% 48.0% 33.9% 44.2%

USSR Russian Russian Russian Russian Russian

50.6% 43.7% 44.1% 54.3% 52.1%

Turkey English English English English

52.9% 42.9% 46.7% 55.7%

Yugoslavia Serbo-Croatian Russian English Serbo-Croatian Serbo-Croatian English

26.4% 38.5% 24.6% 26.6% 24.8% 28.8%

Australasia

Australia English French German Ancient Greek

19.2% 22.2% 33.3% 33.3%

New Zealand English English Tokelauan

59.0% 70.6% 50.0%

Notes: This table presents the most translated original language in each translating countryand its percentage of total translations for the country. Data in the “Total” column are forevery fifth year from 1949 to 1999, where available.




Most translated original language by country continued

Year

Country Total 1949 1959 1969 1979 1989 1999

America

Argentina English English English English English

52.9% 45.1% 57.9% 56.9% 52.9%

Brazil English English English English English English

64.6% 48.7% 56.1% 68.2% 66.8% 66.3%

Canada English English English English

73.7% 75.7% 73.6% 72.6%

Chile English English English

51.5% 65.9% 50.9%

Colombia English English English English

75.5% 79.7% 66.7% 80.9%

Peru Spanish Spanish English English Portuguese Machiguen

39.6% 72.2% 36.4% 39.3% 25.0% 26.3%

Suriname Saramaccan Aukan

20.4% 33.3%

US French French French German German German German

22.6% 31.9% 32.9% 26.8% 22.3% 20.8% 19.3%

Venezuela English English English

40.4% 55.6% 54.5%

Africa

Egypt English English English English

77.1% 80.3% 75.0% 77.0%

Madagascar English French

50.0% 42.9%

Tunisia French French French French

64.9% 62.5% 75.0% 100.0%

Asia

China English English English

70.8% 80.2% 65.3%

India English English English English English

37.3% 44.9% 35.7% 29.9% 35.3%

Israel English English English English English English

53.5% 35.5% 33.5% 53.2% 75.3% 79.2%

Japan English English English English English English

69.1% 46.9% 58.6% 54.1% 69.2% 80.5%

South Korea English English English English English English75.4% 62.6% 50.8% 76.4% 72.5% 75.9%

Kuwait English English English English

36.6% 43.8% 31.2% 60.0%

Sri Lanka English English English English English

60.1% 73.7% 47.4% 45.7% 59.7%

Myanmar English English English English

81.2% 82.1% 36.2% 90.5%

Malaysia English English English English

80.1% 81.8% 78.1% 77.5%

Pakistan English English English Urdu Arabic

56.1% 62.2% 29.9% 50.0% 44.4%

Philippines English English English English

67.7% 58.3% 92.3% 60.0%

Saudi Arabia Arabic English English Arabic

43.8% 100.0% 100.0% 50.0%

Singapore English Malay English English Chinese

50.0% 100.0% 87.5% 71.4% 33.3%

Syria English English English English

41.5% 47.1% 34.4% 51.7%

Thailand English English English English

88.7% 79.8% 91.5% 88.3%

Vietnam French French French

42.7% 56.9% 35.6%

Notes: This table presents the most translated original language in each translating countryand its percentage of total translations for the country. Data in the “Total” column are forevery fifth year from 1949 to 1999, where available.



72 CHAPTER 3. DATA

T a b l e 3 . 6 : C

o r relate s oft ran s lati onfl o w s i nt oa c o unt ry

D e p e n d e n t v ar i a b l e : n u m b er o f t r a n s l a t i o n

s i n t o a n o f f i c i a l l a n g u a g e o f t h e c o u n

t r y ( l n )

V a r i

a b l e

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

P o p ul a t i on ( l n )

0 . 6 3 8 * * *

0 . 5 3 0 * * *

0 .2 8 1

0 . 6 1 3 * * *

0 .4 7 9 * * *

0 . 5 7 8 * * *

( 0 .1 2 5 )

( 0 .1 1 8 )

( 0 .1 7 7 )

( 0 .1 5 8 )

( 0 .1 6 4 )

( 0 .1 7 3 )

GDP p e r c a pi t a ( l n )

1 .2 7 6 * * *

1 .4 0 1 * * *

1 . 6 9 9 * * *

1 . 3 8 7 * * *

( 0 .2 0 4 )

( 0 .1 8 5 )

( 0 .2 7 5 )

( 0 .4 1 4 )

F r a c

t i on of p o p ul a t i on wi t h p o s t - s c h o ol e d

u c a t i on

9 . 5 9 7 * * *

- 3 . 8 2 0

( 2 . 9 4 7 )

( 3 .4 7 1 )

A v e r a g e y e a r s of s c h o ol i n g

0 . 3 7 4 * * *

0 . 0 3 5

( 0 . 0 6 3 )

( 0 .1 1 6 )

C o u

n t r yi s t h e m a i n c o un t r y of t h e i r m a i nl a n g u a g e

2 . 0 5 1 * * *

1 . 8 3 0 * * *

1 . 8 4 0 * * *

1 .4 6 9 * * *

1 . 8 0 1 * * *

( 0 .4 3 0 )

( 0 . 5 4 9 )

( 0 .4 2 5 )

( 0 . 5 0 6 )

( 0 .4 3 8 )

Y e a r f i x e d e f f e c t s

Y e s

Y e s

Y e s

Y e s

Y

e s

Y e s

R - S q u ar e d

0 . 3 5 7

0 . 5 4 3

0 . 3 7 3

0 . 6 2 4

0 .

5 1 9

0 . 6 1 6

O b s er v a t i o n s

4 4 9

4 4 9

2 6 9

2 6 9

2

6 9

2 6 9

C o u n t r i e s

7 5

7 5

4 8

4 8

4 8

4 8

N o t e

s : Th i s t a b l e pr e s en t s OL S r e g

r e s s i onr e s ul t s pr e d i c t i n g t r an s

l a t i on s fl o wi n gi n t o a c o un t r yi n a y e ar .Th e y e ar s

i n cl u

d e d ar e e v er yfi f t h y e ar f r om1 9 4 9 t o1 9 9 9 .Th e e d u c a t i on v ar i a

b l e s ar e d efi n e d f or t h e p o p ul a t i on a g e d 2 5 + , an d ar e

onl y

a v ai l a b l ef or y e ar s 1 9 6 4 on w ar d s .Th e v ar i a b l e C o u n t r y i s t h e m a i n c o u n t r y o f t h e i r m a i n l a n g u a

g ei s ani n d i c a t or f or

wh e t

h er t h e t r an s l a t i n g c o un t r yi s t h

e pr i m ar y c o un t r y of t h ei r m o s t

wi d e s pr e a d l an g u a g e.F or ex am

pl e ,i t t ak e s t h e v al u e

1 f or

G er m an y , b u t 0 f or S wi t z er l an d

. S t an d ar d er r or s ar e cl u s t er e d

a t t h e c o un t r yl e v el .A s t er i s k s d en o t e s i gni fi c an c e a t :

* p <

0 .1 0 ,* * p < 0 . 0 5 ,* * * p < 0 . 0 1 .




Table 3.7: Correlates of translation flows into a country continued

Dependent variable: number of translations into an official language of the country (ln)

Variable (1) (2) (3) (4) (5) (6) (7) (8)

Population (ln) 0.563*** 0.532*** 0.576*** 0.490*** 0.490*** 0.493*** 0.525*** 0.692***

(0.119) (0.124) (0.126) (0.131) (0.134) (0.153) (0.134) (0.101)

GDP per capita (ln) 1.382*** 1.669*** 1.541*** 1.600*** 1.649*** 1.669*** 1.265*** 0.980***

(0.185) (0.216) (0.222) (0.236) (0.236) (0.212) (0.347) (0.303)

Landlocked 0.852*** 0.890*** 0.923*** 0.892*** 0.879*** 0.886*** 0.720* 0.098

(0.289) (0.304) (0.298) (0.291) (0.298) (0.301) (0.373) (0.285)

Openness: Exports/GDP (ln) -0.354* -0.452* -0.452* -0.359** -0.244 -0.053

(0.179) (0.231) (0.238) (0.178) (0.188) (0.137)

Openness: Imports/GDP (ln) -0.169

(0.187)

Level of democracy 0.050

(0.047)

Level of autocracy -0.056

(0.061)

Number of official languages 0.012

(0.016)

Omitted religion: Roman Catholic

Atheist -0.197 0.664

(0.566) (0.618)

Eastern Orthodox 0.297 -0.042

(0.457) (0.378)

Indian religions -0.873 0.421

(0.758) (1.011)

Muslim -1.298* -0.506

(0.700) (0.763)

Other -0.324 0.984

(0.447) (0.791)

Protestant 0.171 0.335

(0.579) (0.338)

Omitted continent: America

Africa -0.312

(0.904)

Asia -0.600

(0.768)

Europe 1.652***

(0.475)

Pacific -2.652***

(0.578)

Country is the main country of their main language 2.089*** 1.908*** 1.952*** 1.867*** 1.874*** 1.890*** 1.725*** 0.665

(0.422) (0.409) (0.410) (0.423) (0.425) (0.410) (0.436) (0.433)

Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes

Dummy for democracy is undefined Yes

Dummy for autocracy is undefined Yes

R-Squared 0.559 0.586 0.568 0.587 0.587 0.588 0.630 0.756

Observations 449 341 338 337 337 341 341 341

Countries 75 73 73 72 72 73 73 73

Notes: This table presents OLS regression results predicting translations flowing into a

country in a year. The years included are every fifth year from 1949 to 1999.Landlocked

is a dummy for the country being landlocked. The religion dummies are for the mostwidespread religion of the country. The variable Country is the main country of their

main language is an indicator for whether the translating country is the primary countryof their most widespread language. For example, it takes the value 1 for Germany, but0 for Switzerland. Standard errors are clustered at the country level. Asterisks denotesignificance at: * p<0.10, ** p<0.05, *** p<0.01.



74 CHAPTER 3. DATA

T a b l e 3 . 8 : C o r relate s of s peed oft

ran s lati on ,1 9 9 8 -2 0 0 0

D e p e n d e n t v ar i a b l e :

V a r i a b l e

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

( 1 0 )

( 1 1 )

( 1 2 )

P o p ul a t i o

n ( l n )

- 0 . 0 0 7

- 0 . 0 1 1

0 .1 2 5

0 . 0 7 8

0 . 0 6 4

0 . 0 7 1

- 0 . 0 0 3

- 0 . 0 0 3

- 0 . 0 4 3 * * *

-

0 . 0 3 0 * *

- 0 . 0 2 4

- 0 . 0 2 6

( 0 . 0 4 7

)

( 0 . 0 4 7 )

( 0 . 0 9 1 )

( 0 . 0 8 3 )

( 0 . 0 9 0 )

( 0 . 0 9 0 )

( 0 . 0 1 0 )

( 0 . 0 1 0 )

( 0 . 0 1 5 )

( 0 . 0 1 4 )

( 0 . 0 1 6 )

( 0 . 0 1 5 )

GDP p e r

c a pi t a ( l n )

- 0 . 5 7 3 *

* *

- 0 . 6 1 2 * * *

- 0 .4 6 5 * * *

- 0 .4 5 9 * *

0 .1 6 6 * * *

0 .1 7 2 * * *

0

.1 2 6 * * *

0 .1 3 7 * * *

( 0 .1 0 6

)

( 0 . 0 9 9 )

( 0 .1 2 5 )

( 0 .1 8 0 )

( 0 . 0 1 9 )

( 0 . 0 1 9 )

( 0 . 0 2 5 )

( 0 . 0 3 1 )

F r a c t i on

of p o p ul a t i on wi t h p o s t - s c h o ol e d u c a t i on

-2 . 6 6 7 * * *

- 0 . 3 6 1

0 . 9 5 6 * * *

0 . 3 3 0 * *

( 0 . 8 8 3 )

( 0 . 7 8 6 )

( 0 .2 1 9 )

( 0 .1 4 3 )

A v e r a g e

y e a r s of s c h o ol i n g

- 0 .1 2 5 * * *

- 0 . 0 1 6

0 . 0 4 0 * * *

0 . 0 0 8

( 0 . 0 3 3 )

( 0 . 0 6 2 )

( 0 . 0 0 8 )

( 0 . 0 1 2 )

Or i gi n a l

l a n g u a g e f i x e d e f f e c t s

N o

Y e s

Y e s

Y e s

Y e s

Y e s

N o

Y e s

Y e s

Y e s

Y e s

Y e s

R - S q u ar e d

0 .1 9 2

0 .2 9 9

0 .1 6 5

0 .2 3 1

0 .1 8 1

0 .2 3 0

0 . 3 2 6

0 . 3 8 4

0 .2 9 5

0 . 3 9 5

0 .2 9 3

0 . 3 8 6

O b s er v a

t i o n s

1 6 3

1 6 3

9 7

9 7

9 7

9 7

1 6 4

1 6 4

9 8

9 8

9 8

9 8

C o u n t r i e s

5 0

5 0

2 9

2 9

2 9

2 9

5 0

5 0

2 9

2 9

2 9

2 9

M e d i a n a g e o f t i t l e s a t t i m e o f t r a n s l a t i o n ( l n )

P r o p or t i o n o f t i t l e s t r a n s l a t e d w i t h i n2 y e ar s o f f i r s t p u b l i c a t i o n

N o t e

s : Th e t a b l e pr e s en t s r e s ul t s of O

L S r e gr e s s i on s pr e d i c t i n g t h e s p

e e d of t r an s l a t i on.An o b s er v a t i oni n t h e s er e gr e s s i on s

i s an

or i gi n al l an g u a g ei n a t r an s l a t i n g c o un t r y.Th e or i gi n al l an g u a g e s i n cl u d e d ar eEn gl i s h ,F r en ch , G er m an an d I t al i an.

Onl y

n on-fi c t i on t i t l e s t r an s l a t e d i n t o an offi ci al l an g u a g e of t h e t r an

s l a t i n g c o un t r y ar ei n cl u d e d .Th e e d u c a t i on v ar i a b l e s

ar e d efi n e d f or t h e p o p ul a t i on a g e d

2 5 +. O b s er v a t i on s ar e w ei gh t e

d b y t h en um b er of t r an s l a t i on

s c on t r i b u t i n g t o t h e

m e a s ur e of s p e e d . S t an d ar d er r or s ar

e cl u s t er e d a t t h e c o un t r yl e v el .

A s t er i s k s d en o t e s i gni fi c an c e a t :

* p < 0 .1 0 ,* * p < 0 . 0 5 ,

* * * p < 0 . 0 1 .




T

a b l e 3 . 9 : C o r r e l a t e s o f s p e e d o f t r a n s l a t i o n , 1 9 9 8 - 2 0 0 0 c o n t i n u e d

D e p e n d e n t v a r i a b l e :

V a r i a b l e

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

( 1

0 )

P o p u l a t i o n ( l n )

- 0 . 0

4 1

- 0 . 0

7 9 *

- 0 . 1

3 1 * *

- 0 . 1

2 7 * *

- 0 . 1

2 3 *

0 . 0

0 2

0 . 0

1 2

0 . 0

2 2 * *

0 . 0

2 3 * *

0 . 0 2 0 *

( 0 . 0

4 5 )

( 0 . 0

4 5 )

( 0 . 0 5 6 )

( 0 . 0

5 5 )

( 0 . 0

6 3 )

( 0 . 0

0 9 )

( 0 . 0

0 8 )

( 0 . 0

0 9 )

( 0 . 0

1 0 )

( 0 . 0

1 2 )

G D P p e r c a p i t a ( l n )

- 0 . 6

0 2 * * *

- 0 . 4

0 7 * * *

- 0 . 3

3 5 * *

- 0 . 4

1 1 * * *

- 0 . 4

7 9 * * *

0 . 1

7 1 * * *

0 . 1

1 9 * * *

0 . 1

1 3 * * *

0 . 1

1 2 * * *

0 . 1 2

8 * * *

( 0 . 0

9 6 )

( 0 . 1

2 0 )

( 0 . 1 3 1 )

( 0 . 1

4 7 )

( 0 . 1

6 6 )

( 0 . 0

1 9 )

( 0 . 0

2 8 )

( 0 . 0

2 0 )

( 0 . 0

2 5 )

( 0 . 0

2 9 )

L a n d l o c k e d

- 0 . 4

2 9 * *

- 0 . 3

2 7

- 0 . 2

6 5

- 0 . 1

9 4

- 0 . 1

9 3

0 . 0

6 8 * *

0 . 0

4 1

0 . 0

3 3

0 . 0

2 7

0 . 0

2 8

( 0 . 1

6 4 )

( 0 . 1

9 5 )

( 0 . 1 9 7 )

( 0 . 1

8 8 )

( 0 . 1

9 4 )

( 0 . 0

3 2 )

( 0 . 0

4 0 )

( 0 . 0

3 9 )

( 0 . 0

4 1 )

( 0 . 0

4 2 )

O p e n n e s s : E x p o r t s / G D P

( l n )

- 0 . 2

1 9

0 . 0

5 8

( 0 . 1

5 5 )

( 0 . 0

3 5 )

O p e n n e s s : I m p o r t s / G D P

( l n )

- 0 . 4 1 3 *

- 0 . 3

2 8

- 0 . 2

6 8

0 . 0

8 9 * * *

0 . 0

7 7 * *

0 . 0 6 6 *

( 0 .

2 1 1 )

( 0 . 1

9 8 )

( 0 . 2

2 0 )

( 0 . 0

3 1 )

( 0 . 0

3 4 )

( 0 . 0

3 7 )

O m i t t e d r e l i g i o n : R o m a n

C a t h o l i c

A t h e i s t

0 . 1

9 3

0 . 0

8 9

- 0 . 0

1 6

0 . 0

1 4

( 0 . 2

4 9 )

( 0 . 2

3 6 )

( 0 . 0

4 7 )

( 0 . 0

4 9 )

E a s t e r n O r t h o d o x

0 . 0

9 5

0 . 0

7 8

- 0 . 0

1 4

- 0 . 0 0 6

( 0 . 1

8 4 )

( 0 . 1

7 8 )

( 0 . 0

4 3 )

( 0 . 0

4 1 )

I n d i a n r e l i g i o n s

0 . 4

0 4

0 . 0

6 5

- 0 . 1

3 5 * *

- 0 . 0 4 4

( 0 . 3

0 9 )

( 0 . 4

1 0 )

( 0 . 0

5 2 )

( 0 . 0

7 1 )

M u s l i m

0 . 2

7 8

0 . 1

7 6

- 0 . 0

6 1

- 0 . 0 3 5

( 0 . 2

3 3 )

( 0 . 2

6 8 )

( 0 . 0

6 1 )

( 0 . 0

6 7 )

O t h e r

0 . 6

7 5 * * *

0 . 3

8 4

- 0 . 1

0 7 * * *

- 0 . 0 3 0

( 0 . 1

4 6 )

( 0 . 3

2 6 )

( 0 . 0

3 0 )

( 0 . 0

5 4 )

P r o t e s t a n t

0 . 2

5 3

0 . 2

9 0

0 . 0

0 1

- 0 . 0 0 7

( 0 . 1

9 3 )

( 0 . 2

0 3 )

( 0 . 0

3 7 )

( 0 . 0

4 1 )

O m i t t e d c o n t i n e n t : A m e r

i c a

A f r i c a

- 0 . 1

1 8

0 . 0

3 2

( 0 . 2

8 1 )

( 0 . 0

7 8 )

A s i a

0 . 3

2 7

- 0 . 0 9 8

( 0 . 3

5 6 )

( 0 . 0

7 4 )

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P r o p o r t i o n o f t i t l e s t r a n s l a t e d w i t h i n 2 y e a r s o f f i r s t p u b l i c a t i o n



76 CHAPTER 3. DATA

N o t e




i s an

or i gi n al l an g u a g ei n a t r an s l a t i n g c o un t r y.Th e or i gi n al l an g u a g e s i n cl u d e d ar eEn gl i s h ,F r en ch , G er m an an d I t al i an.

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* * * p < 0 . 0 1 .



Chapter 4

The Gravity of Ideas: How

distance affects translations

4.1 Introduction

One important way ideas are stored and diffuse between individuals separated by

space or time is through books. However, this diffusion may be hindered by language

barriers, which place a natural limit on the spread of ideas stored in books, and

could hinder the diffusion of most types of ideas. Translations are an important

means by which ideas captured in books are able to diffuse across language barriers;

book translations thus provide a tractable empirical measure of the flow of ideas

between linguistic groups (see the introduction to this dissertation for a discussion of

translations as a measure of the flow of ideas).

The objective of this paper is to shed light on the determinants of translation

flows between countries. This objective has two main motivations: the first is to gain

insight into the factors that encourage or inhibit the international diffusion of ideas.

Although the ideas contained in books are non-rival, geographic and cultural distance

77



78 CHAPTER 4. THE GRAVITY OF IDEAS

and other types of dissimilarities between countries may inhibit translation flows, for

instance by increasing search and transaction costs. By studying the relationshipbetween measures of distance between countries and translation flows, I shed light on

an important type of impediment to the free international diffusion of ideas.

The second is to shed light on the factors underlying the negative relationship

between distance and trade in goods. This negative relationship is a robust finding

in international economics, but the driving factors behind it remain imperfectly un-

derstood.1 The most obvious contributing factor to the relationship is transportation

costs, but an increasing literature demonstrates that transportation costs account for

only a fraction of the total distance effect.2 For example, in a recent paper, Feyrer

(2011) uses time-varying exogenous variation in effective distance generated by the

temporary closure of the Suez Canal to estimate that only half the elasticity of trade

with respect to distance is driven by transportation costs.

An alternative approach to shedding light on the factors underlying the distance

effect in trade is to consider trade in goods for which specific costs are known to be

absent. Blum and Goldfarb (2006) study how distance affects trade in digital goods

consumed over the internet, which have no transportation, time, or distribution costs.

In this paper I consider another setting in which transportation, time, and much of

the distribution costs are negligible by studying how distance affects the translation

of books. Specifically, only a single copy of the title, in digital or hard copy form,

must travel between the countries in order for a translation to occur. Translations

thus have effectively zero transportation costs, both direct (freight, insurance) and

indirect (e.g. holding cost for the goods in transit). Translations are also largely free

from several other trade costs (discussed in Anderson and van Wincoop (2004): they

avoid border-related costs, policy barriers such as tariffs and quotas, and many legal

1e.g., Disdier and Head (2008), Blum and Goldfarb (2006), Feyrer (2011).2Anderson and van Wincoop (2004).



4.1. INTRODUCTION 79

and regulatory costs. In general, none of the costs related to the physical movement

of goods apply to book translations.

I use data on translations published in a large number of countries for the period

1949 to 2000, which I digitized from Unesco’s Index Translationum. The data are

described in detail in chapter 3 of this dissertation.

I study the effect of physical distance on translation flows within an augmented

gravity framework, where the translation flow between a pair of countries is affected

by characteristics of the two countries (such as GDP) and the distance between them

(section 4.3.1). Even though translations have zero transportation costs, I find they

decrease significantly with distance. However, I estimate the elasticity of translations

with respect to distance to be 0.3 to 0.5 during the 1990s, which is considerably

smaller than the equivalent elasticity for trade found in the literature, which usually

ranges from 1.08 to 1.24.3 Under the assumption that the non-transportation costs

faced by translations vary with distance in the same way as the equivalent costs for

trade, the magnitudes of these coefficients suggest that roughly half to three quarters

of the elasticity of trade with respect to distance is the result of transportation costs;

comparisons with results using a more similar estimation method, from Santos Silva

and Tenreyro (2006), decrease this range to a third to three fifths. Although my

method is very different, these results are comparable to Feyrer’s (2011) estimate

that half the elasticity of trade with respect to distance is the result of transportation

costs.

Translations (and similarly trade) may decrease with distance for both supplyand demand reasons. Supply frictions such as the search and information costs of

identifying titles worth translating are likely to increase with distance, as are the





various costs of negotiating contracts.4 Translations (or trade) may also fall off with

distance because distance is correlated with tastes, meaning closer countries caterbetter than more distant countries to local tastes in books (or products).5

Furthermore, these supply and demand factors may not remain constant over

time. I next examine how the relationship between distance and translations changed

over the latter half of the twentieth century (section 4.3.2). I find it fell significantly

over the period 1949-1999, especially in the last two decades. This result contrasts

with the puzzling finding that the effect of distance on trade in goods did not

decline over this period, instead remaining high.6

However, it is consistent with

the finding that the effect of distance on patent citations has fallen over time;7 both

the translations and patent citation results are easily explicable by the decrease over

time in communication and information costs.

In comparisons across fields of the effect of distance on translations, I find the

distance effect is larger for titles in exact and applied science than for titles in fields

where taste ought to play a larger role, such as philosophy, arts and literature (section

4.3.3). This suggests a greater importance for contracting or search and information

costs relative to consumer tastes in driving the distance effect. These results also

demonstrate that distance matters even for translations of “economically useful”

titles, not just for titles that may be considered largely consumption goods.

To further explore the roles of contracting costs, search and information costs, and

tastes, I add controls for religious distance, linguistic distance, genetic distance, and

4That is, both trade in goods and translations are subject to the costs that are related to forminga contract between parties in different countries. These costs may vary with distance, and includethe time and legal costs of negotiating and enforcing the contract, direct and indirect costs relatedto transacting between currencies, and the costs of overcoming any language barriers that existbetween the parties.

5Blum and Goldfarb (2006) suggest this factor plays a significant role in the distance effect fortaste-dependent digital goods.

6Disdier and Head (2008).7MacGarvie (2005) and Griffith, Lee and van Reenen (2007).




survey measures of differences in cultural values, all of which are expected to capture

some element of cultural differences (section 4.3.5). Cultural differences could affecttranslations through the supply channel by increasing contracting costs, or through

the demand channel by decreasing the similarity of taste for books. Religious and

linguistic distance and Hofstede’s (1980, 2001) survey measure of cultural distance are

negatively related to translations, but physical distance remains important even when

these controls are added. Furthermore, adding these controls reduces the elasticity

of translations with respect to physical distance by at most a quarter. This suggests

that cultural differences contribute to distance-varying contracting costs or to demandthat prefers titles written in nearby countries, but that other distance-varying costs

play a larger role.

I next allow the elasticity of translations with respect to distance to vary by the

level of development of the translating country, as measured by GDP per capita or

urbanization (section 4.3.6). I find a strong differential effect: the effect of distance

is 89% weaker for a translating country on the 75th percentile of GDP per capita

among countries in my data than for a country on the 25th

percentile. The fact thattranslations flowing into poorer countries are more affected by distance may have

important implications for the international diffusion of knowledge. Specifically, it

suggests that countries that are further from the world knowledge frontier, and thus

that can benefit most from adopting ideas that already exist elsewhere, are actually

less able to access these ideas.

Finally, to further explore the role of supply-side frictions relative to taste in the

relationship between distance and translations, I study the speed with which titles

are translated (section 4.4). Time is required for foreign publishers to discover that

a title exists and is worth translating, and to negotiate the rights to translate it; the

greater the supply-side frictions, the longer this will take. However, a title in enough

demand to be worth translating in five years time is likely to also be worth translating




now. The speed of translations thus likely reflects frictions such as information and

contracting costs, rather than tastes. I find that distance substantially decreases thespeed with which translations are published: a 50 per cent increase in the distance

between the countries corresponds to a 3 percentage point decrease in the proportion

of translations published that are of titles originally published no more than two years

previously. Because the speed and quantity of translations are intimately related,

this suggests that information and contracting costs also play a substantial role in

the effect of distance on quantity of translations.

4.2 Empirical strategy

To shed light on the determinants of translation flows and the factors underlying

the negative relationship between distance and trade, in section 4.3 I investigate the

relationship between geographic distance and translations, which are not subject to

any trade costs related to physically moving goods. To decompose the distance effect

I find, I add further controls for various types of distance or dissimilarity between the

countries.

The basic specification is a gravity model in multiplicative form estimated using

the pseudo-maximum-likelihood (PML) estimator recommended by Santos Silva and

Tenreyro (2006) for the gravity equation specifically and constant-elasticity models in

general. Santos Silva and Tenreyro consider a constant elasticity model of the form

T ij = α0Y α1

i

Y α2

j

Dα3

ij

ηij (4.1)

where ηij is a multiplicative error term with E (ηij|Y i, Y j, Dij) = 1 and where ηij

is assumed to be statistically independent of the regressors. They show that if

ηij is heteroskedastic in a manner that depends on the regressors, then lnηij is

not independent of the regressors, and thus linearizing equation (x) and estimating



4.2. EMPIRICAL STRATEGY 83

it by OLS leads to inconsistent estimates. In fact, they demonstrate that this

heteroskedasticity is usually present and substantial in gravity models of trade,so estimating the relationship in multiplicative form using their PML estimator is

preferred. The nature of translation data suggests such heteroskedasticity is also likely

to be present here, so I use their PML estimation technique. A further advantage

of this method is that it has no difficulty with observations where the value of the

dependent variable is zero.

The equation I thus estimate is:

transijt = αdistφijeβiteγ jtijt (4.2)

the more familiar linearized form of which is:

ln(transijt) = α + φln(distij) + β it + γ jt + ν ijt (4.3)

where α ≡ ln(α) and ν ijt ≡ ln(ijt). Here transijt is the number of translations into

language-in-country pair i , from language j , in year t , distij is the geographic distance

between the main country of language j and the country denoted by i , the β s are

time-varying fixed effects for target language-in-country, the γ s are time-varying fixed

effects for original language, and is a error term with mean 1. The coefficient of

interest is φ, which is the elasticity of translation flows with respect to geographic

distance.

I run specifications where I control for the population and GDP per capita of the

original and translating countries instead of including time-varying origin and target

fixed effects. However, Anderson and van Wincoop (2003) show such a specification

is likely to result in a biased estimate of the coefficient on distance because it suffers

omitted variable bias. In the translation context, it does not account for the average

barriers to translation from all possible original languages faced by a country. These




are likely to be correlated with average distance from potential original countries,

and thus with the distance from any one original country, so failing to account forthem introduces bias. Thus my preferred specification includes time-varying origin

and target fixed effects.

I also augment this model by including measures of non-physical distance between

the countries, such as differences in culture.

4.2.1 Original languages and target languages and countries

for gravity model

An observation in the gravity equation I estimate is an original language, a target

language in a country, and a year. Two questions then arise. First, from what set of

original languages should translations be included, and should this vary by translating

country? Second, into which target language or languages in each country should

translations be included?

I do not allow the set of original languages to vary by translating country. That is,

each target language in a translating country in a year contributes the same number of

observations to the regression, one for each language in a set of original languages that

does not vary by country. The advantage of this method is that it does not impose

any priors about which countries will translate from which languages. However, it

does mean many measured translations flows are zero. The set of original languages

out of which I consider translations in my primary specification is the set of the most

widely spoken 100 languages as listed by Ethnologue. From these languages, I drop

those out of which translations are never published.

One option for target languages in translating countries would be to include an

observation for translations into each possible language in every country. However,

most of these flows will be zero. In fact, most would also not signify a relevant transfer




4.2.2 Matching original languages to countries

Identifying which translations flows are occurring between which populations is

complicated by the lack of a one-to-one mapping between languages and countries.

Although the bibliographic entries in the Index Translationum identify the country

in which the translation was published, one of their limitations is that the country in

which the original title was published is not given, only the language in which it was

originally written. In order to examine the relationship between physical distance

and translation flows, I need to attribute translations to original countries.

Conceptually, there are several ways to think about what this attribution might

want to capture. One option is that titles diffuse through linkages with the country in

which they are written. Thus ideally the relevant “distance” for an original language

that is spoken in multiple countries is some weighted average of the distances to those

countries. However, if most of the titles published in the language originate in one

of those countries, the distance to that country seems a sensible approximation. An

alternative is that all countries where a particular language is spoken widely (even if

they do not generate many original works in the language) act as distributors of the

titles written in that language. Thus to be close to English, for example, a country

need not be close to the US or the UK, but may be close to a smaller English-speaking

nation such as Australia or New Zealand.

I thus use two alternative strategies to attribute translations to original countries.

In my central specification, I attribute all translations from a language to the “main”

country of the language as listed by Ethnologue, with the exception of English, which

I attribute to the USA rather than the UK in my main specification, based on the

much greater GDP of the USA relative to the UK. However, I note that results are

robust to attributing English to the UK, or to dropping it entirely. In my alternative

specification, for each original language I compile a list of major countries of the

language. A language is classified as major in a country based on how widespread its



4.3. HOW DISTANCES AFFECT TRANSLATIONS 87

native speakers are in the country, the population of native speakers in the country

relative to worldwide, and whether the language is national or official in the country.Very small countries (e.g. Monaco) are not counted as major unless they are the main

country of the language. The major countries for each original language are listed

in Appendix A. I then set the original country of translations in country C from

language L to be language L’s major country that is physically closest to country C.

4.3 How distances affect translations

In this section, I study how bilateral translation flows are affected by distance between

countries. This is a setting with many commonalities with trade in goods, but in which

transportation, time, and much of the distribution costs are negligible. Specifically,

because only a single copy of the title must travel between the countries in order

for a translation to occur, translations have effectively zero transportation costs,

both direct and indirect. They are also largely free from border-related costs, policy

barriers such as tariffs and quotas, and many legal and regulatory costs. That is,

translations face zero costs related to the physical movement of goods. Translations

are, however, expected to be subject to all the costs of contracting between parties

in different countries, plus search and information costs, that trade in goods face

and that may vary with distance. In addition, both trade and translations may occur

more between closer countries because consumers in these countries have more similar

tastes, thus more demand for each other’s books or products. Studying how distance

affects translations thus sheds light on the factors beyond transportation costs that

contribute to the negative relationship between distance and trade.

My estimation framework is an augmented gravity model, in which (directional)

translations between two countries depend on the economic sizes of the countries, and

the physical distance between them. I assume a constant elasticity functional form,




and estimate the model by PML as described in section 4.2. I add controls such as

the cultural distance between the countries to measure the extent to which countriestranslate more from their neighbors because they are more culturally similar to them.

4.3.1 The negative distance effect: Neighboring countries

translate more from each other than from distant

countries

In a basic gravity specification with physical distance as the only distance measure,

presented in Table 4.1, I find a strong negative correlation between the number of titles

translated and the physical distance between the original and translating countries.

Appendix Table B.1 presents the same specifications, but uses OLS and predicts the

natural log of the number of translations plus 1. The strong negative correlation

is again present, though the coefficients on distance are smaller in magnitude. The

data used in these regressions are a panel of the years 1994 and 1999, a short enough

period that we expect the relationship between distance and translations to have

remained relatively constant. Column 1 presents the basic gravity specification where

the number of translations flowing from one country to another depends on the

populations of the two countries, their GDP per capita, and the distance between

them. For each target language in a translating country, we consider translations

from the same set of original languages, namely those of the 100 languages most

widely spoken in the world that are ever translated. For each of the 56 countries with

translation data in at least one of the two years, we consider translations into each

of the languages that are official in the whole of the country. To generate distance

measures, I assign each original language to its main country as described in section

4.2.2.

As expected, the population and GDP per capita of the translating country are




positively and significantly correlated with translation flows with elasticities of 0.72

and 0.75 respectively. The elasticity of translations with respect to the populationof the original country is 1.1; the elasticity with respect to the GDP per capita of

the original country is 3.3. This strong relationship between wealth of the original

country and translations suggests the creation of ideas with international relevance is

very much concentrated in rich countries, whereas less rich countries tend to consume

ideas created elsewhere. The OLS version of this regression, presented in column 1 of

Appendix Table B.1, shows these basic covariates have moderate explanatory power:

the R-squared in this regression is 0.15.Column 2 of Table 4.1 adds controls for colonization relationships between the

original country and the translating country in either direction. There are relatively

few of these in the data, particularly because the translating country must have at

least one official language that differs from the language of the colonizer in order

for the pair to appear in the data, and neither direction of colonizing relationship is

significantly correlated with translation flows.

In these first two specifications, the elasticity of translations with respect to

geographic distance is -0.9, suggesting a 10% increase in the distance between two

countries corresponds to a 9% decrease in translation flows between them. However,

Anderson and van Wincoop (2003) show such a specification is likely to suffer omitted

variable bias as explained in section 4.2, so in column 3 I add in time-varying fixed

effects for original language and target language-translating country pairs. The

elasticity falls in magnitude to -0.47, but remains significant.

Next I add controls for the original and translating countries being contiguous,

and the original language being widely spoken in the translating country. Both are

associated with significantly higher translations, but their inclusion doesn’t eliminate

the relationship between distance and translations. The interpretation of these two

effects is similar. Sharing a land border with a country suggests the populations will




both interact more, implying lower search and transaction costs of translating from

each other, and have more similar tastes, implying greater demand for translations.Similarly, mixing geographically with a group that speaks a foreign language can be

expected to stimulate translations from both the demand and the supply sides.

Columns 5 to 10 of Table 4.1 run the same specification as column 4, but vary

the sample of original languages and translating countries in a number of ways.

Column 5 restricts the original languages to those in the top 100 that can be

unambiguously attributed to a single country, which eliminates many of the large

original languages such as English, German and Spanish. The elasticity of translations

with respect to distance increases in magnitude to -1.1 in this specification. Column

6 restricts original languages to the four main “research languages”, namely English,

French, German and Japanese. The magnitude of the correlation is similar, though

significance decreases because of the much smaller sample size. Column 7 uses all

of the top 100 original languages, but attributes each to the country in which it is

widely spoken that is geographically closest to the translating country (as explained

in section 4.2.2), instead of to its main country. The coefficient on distance falls

slightly in magnitude, which suggests geographic proximity to a secondary country

of a language may be a less-than-perfect substitute for geographic proximity to the

main country of the language for the purpose of enhancing idea flows. Column 8

differs from column 4 in that it restricts the sample of target languages in translating

countries to those where the translating country is the main country of the target

language. For example, it includes translations into German in Germany, but excludes

translations into German in Switzerland. The coefficient of interest is unaffected.

Column 9 instead includes all the target languages for each translating country

that are (i) official in at least part of the translating country, (ii) spoken natively

by at least 500,000 people in the country, and (iii) spoken by at least 5% of the

country’s population. Results are again largely unaffected. Finally, column 10 looks




at translation flows only within Europe. That is, it includes original languages in

the top 100 that have a European country as their main country, and translatingcountries that are European. The coefficient on distance increases slightly.

Overall, it seems that in the 1990s a 10 percent increase in distance corresponded

to roughly a 3 to 5 percent decrease in translations, despite translations having

zero transportation costs. This suggests there are significant distance-varying costs

involved in translation, which may relate to search and information, or to the costs of

forming contracts. Geographic correlation of tastes that causes demand to decrease

with distance may also contribute to the distance effect.

This elasticity of 0.3 to 0.5 is significantly lower than those found in the literature

on trade in goods, which generally range from 1.08 to 1.24.9 Under the (admittedly

strong) assumption that the non-transportation costs faced by translations vary with

distance in the same way as the equivalent costs for trade, the magnitudes of these

coefficients suggest that roughly half to three quarters of the elasticity of trade with

respect to distance is the result of transportation costs. However, this comparison

may be confounded by the use of PML estimation in this paper. Where Santos Silva

and Tenreyro (2006) use PML as opposed to OLS to estimate the distance effect on

trade in a model with importer and exporter fixed effects, their coefficient falls from

-1.3 to -0.75. This lower elasticity estimate for trade suggests a third to three fifths

of the distance effect in trade is due to transportation costs. These estimates are in

the same range as the value of a half found by Feyrer (2011) using a very different

approach.





4.3.2 The negative correlation between physical distance and

translations decreased over time

To gain further insight into the causes of the effect of distance on translations, I next

estimate how this effect changed over time. Figure 4.1 illustrates how the correlation

between physical distance and translations changed over time. These correlations are

coefficients from regressions of translations on geographic distance, origin and target

fixed effects, and controls as in column 4 of Table 4.1, run separately for each fifth year

from 1949 to 1999. The figure presents the 95% confidence interval of the coefficienton translations for two different sets of translating countries: the solid blue lines are

for the consistent set of 9 countries for which data are available every year; the dashed

red lines are for all the countries for which data are available in the particular year.

In each case, the magnitude of the negative correlation decreased significantly over

the period 1949 to 1999, particularly over the last two decades.

This contrasts with the changes seen in the distance effect in trade, which,

according to Disdier and Head’s (2008) meta-analysis of the results from many

papers, rose mid-century and has remained persistently high since. The decrease

in the inhibitory effect of physical distance on translations over time is consistent

with several causal mechanisms. For instance, the ease of international travel and

communication decreased over this period, and their costs fell. This could have

both weakened the relationship between distance and the search, information, and

transaction costs of translation, and stimulated interest in geographically distant

cultures. If search and information costs are higher on average for books than for

goods, this could explain why the distance effect decreased for translations but not

for trade. Note MacGarvie (2005) similarly finds a decrease in the effect of distance

on patent citations over the period 1980-1995, which is also consistent with such a

change in information costs.




4.3.3 Translations of different types of books are affected

differently by physical distance

There are a number of reasons to expect translations of titles in different fields to

be affected differently by physical distance. On the demand side, fields differ in the

extent to which their ideas are region-specific. For instance, history titles frequently

focus on a particular region of the world, thus are likely to be of more interest to

countries in that region. Similarly, religion titles tend to relate to a specific religion,

and thus will be of more interest in countries where that religion is widespread, whichtend to be geographically clustered. Conversely, many natural science ideas (such

as ideas in physics and chemistry) are equally relevant anywhere in the world. In

addition, the degree to which titles written in different languages are substitutes for

each other varies by field. In fields with high substitutability, there may be no reason

to translate from very distant languages because nearby languages are sufficient to

meet demand, thus if costs rise with distance translations may fall off quickly with

distance. In fields with low substitutability, a specific idea can only be sourced fromone language, so distance is likely to play a lesser role in determining translation

flows.

Figure 4.2 shows the coefficients and 95% confidence intervals of the coefficient on

physical distance when translations in each field are regressed on physical distance

and other controls as in column 4 of Table 4.1. Physical distance and translations

are negatively correlated for all fields of translation, though the magnitude of the

correlation varies across fields. Perhaps surprisingly, physical distance has the largestinhibitory effect on translations in the fields of natural science and applied science,

and the smallest in philosophy and arts.

These results by field demonstrate that distance matters even for translations of

“economically useful” titles such as titles in natural and applied science, not just for




titles that may be considered largely consumption goods, such as many philosophy,

arts, or fiction titles. Furthermore, the fact distance has a greater effect for titleswith less of a cultural or taste component suggests taste differences that increase

with distance may have a lesser role in driving the distance effect on translations

relative to supply-side frictions.

4.3.4 Countries with similar physical environments translate

more from each other

The negative relationship between translation flows and physical distance could be

driven by several factors, all of which apply to trade in goods to some extent: search

and information costs involved in identifying foreign titles worth translating; costs

of negotiating rights to translate a title; and tastes for ideas that differ more widely

between more distant countries.

One reason tastes for ideas may be more similar in neighboring regions is that

physical environment (such as climate, terrain, the types of plants that will grow

etc) tends to be more similar in neighboring regions, and the physical environment in

which a society lives might affect the types of ideas that are relevant or interesting to

its members. To estimate the importance of this effect, I augment the basic gravity

model with the difference between countries in altitude profile, biome region profile,

and climate region profile. Column 1 of Table 4.2 presents the results from this

regression. Differences in altitude profile and biome region profile significantly inhibit

translation flows, but together these three differences explain only a modest 6.5% of

the negative correlation between physical distance and translations. The coefficient

on altitude profile difference suggests that, relative to two countries with the same

altitude profiles, two countries with altitude profiles that are only 90% similar will

translate 8% less from each other. However, much of this correlation can be shown




to be attributable to the cultural differences that are correlated with altitude profile

differences (columns 2 to 6). A similar increase in the difference in biome regionprofiles corresponds to a 3% decrease in translations.

4.3.5 Countries with similar cultures translate more from

each other

Cultural differences that are correlated with physical distance could cause translations

to decrease with distance for two main reasons. On the demand side, cultural

similarity could imply similar preferences, meaning the countries have higher demand

for each other’s books. On the supply side, cultural similarity could lead to greater

trust and understanding, which reduce transaction costs. In columns 2 to 8 of

Table 4.2, I thus add controls for various measures of cultural distance: religious

distance, linguistic distance, genetic distance, and survey-based measures of cultural

differences. With the exception of genetic distance, these cultural distance measures

each significantly inhibit translation flows; the effects are particularly strong for

linguistic and religious distance. A 10 percentage point decrease in the probability

a randomly chosen individual from the translating country has the same religion as

a randomly chosen individual from the original country corresponds to a 10 to 18

percent decrease in translations. A 10 percentage point increase in distance between

the languages corresponds to a 2 to 7 percent decrease in translations. Finally, a one-

standard-deviation increase in cultural distance as measured by Hofstede’s (1980)

survey measures corresponds to an 8 to 16 percent decrease in translations.

Note however that adding these controls reduces the elasticity of translations

with respect to physical distance by at most a quarter. This suggests that cultural

differences contribute to distance-varying contracting costs or to demand that prefers

titles written in nearby countries, but that other distance-varying costs, such as search




and information costs, play a large role in the distance effect.

The process of globalization over the past half century has made the worldsmaller in many ways; international travel has become cheaper and faster, and

global communications have improved beyond measure. The forces that have allowed

distant cultures to mingle more easily may have decreased cultural barriers to the

flow of ideas. Also plausible is that globalization has caused a reactionary increase in

nationalism that may have actually decreased receptiveness to foreign ideas. It is thus

unclear theoretically how the relationship between cultural distances and translation

flows will have changed over time. Appendix Figure B.1 shows the negative correlationbetween religious distance and translations tended to increase between 1949 and 1999,

while the correlation between linguistic distance and translations tended to decrease.10

4.3.6 Translations published in more developed countries

decrease less with physical distance

To yield further insight into the drivers of the negative correlation between trans-

lations and distance, in Table 4.3 I allow the effect of distance to differ by various

characteristics of the original or translating country. In column 1, I allow the effect of

distance to differ by the wealth of the translating country. I find the effect of distance

is 89% weaker for a translating country on the 75th percentile of GDP per capita

10In column 9 of Table 4.2, I add controls for trade flows in each direction between the original andtranslating countries, in order to see descriptively how trade in ideas (translations) are correlatedwith trade in goods. Note the coefficients on these variables in particular should not be interpretedcausally because of reverse causality and unobserved heterogeneity. For instance, trade flowsbetween countries may cause an increase in idea flows and thus an increase in translations, buttranslations may increase understanding and decrease transaction costs, thus increasing trade. Tradein manufactured goods and translations may also be complements. The coefficients on both importsand exports are positive and significant, and are similar in magnitude: a 10% higher flow of tradein either direction corresponds to a 2.4% higher translation flow. The direction of this effect isconsistent with the causality stories running in either direction. One interesting point to note isthat inclusion of these two trade variables eliminates the negative correlation between distance andtranslations. However, as Appendix Tables B.2 and B.3 show, this was not the case prior to the1990s.




among countries in my data relative to the 25th percentile, or 70% weaker for a country

with urbanization rate on the 75th

percentile relative to the 25th

percentile (column 3).More developed countries can differ from less developed countries across a multitude

of dimensions, making it difficult to establish the causal mechanism behind these

results. For instance, communication technologies tend to be more advanced, reliable,

and widespread in richer countries, which could reduce search and information costs.

However, the fact that translations published in poorer countries are more affected

by distance has potentially important implications for the international diffusion of

knowledge. Specifically, it suggests that countries that are further from the worldknowledge frontier, and thus that can benefit most from adopting ideas that already

exist elsewhere, are actually less able to access these ideas.

Similarly, distance is significantly less important for translations of titles origi-

nating in wealthier countries. The effect of distance is 49% weaker for translations

from original countries on the 75th percentile of GDP per capita relative to the 25th

percentile (column 2); it is 52% weaker for a country with urbanization rate on the

75th

percentile relative to the 25th

percentile (column 4).

Distance is also significantly less important for translating countries that are

more democratic, as shown in column 5. The effect of distance is 44% weaker

for a translating country on the 75th percentile of democracy relative to a country

on the 25th percentile. Such a relationship could be observed if more democratic

countries were less threatened by ideas that differed more from their own than were

less democratic countries. The level of democracy in the original country is not

significantly correlated with the strength of the relationship between physical distance

and translations (column 6).




4.4 Speed of translations

I finally turn to study the speed with which original titles are translated from four

of the most-translated languages, namely English, French, German and Italian. In

terms of access to foreign ideas, speed of translation matters because it determines

whether a country can benefit immediately from foreign knowledge, or whether it

must wait 10 or more years for it benefits. Thus the speed with which a country

translates titles is informative about the country’s access to new ideas.

The speed with which titles are translated also sheds light on the role of frictions

relative to taste in the correlation between distance and translations. Speed of

translation is likely to reflect frictions such as information and contracting costs rather

than consumers’ tastes for books. Once an original title is published, it takes time

for foreign publishers to learn that the title exists and that it is worth translating,

and then to negotiate the rights to translate it. Greater search, information and

contracting costs will slow this process down. However, if demand for a title is

expected to be high enough to make translation profitable five years after the original

was published, the same is likely to be true at the time of original publication. Thus

delays in translating an original title are more likely to reflect supply-side frictions

than demand factors (such as consumer tastes).

I use data on translations published between 1998 and 2000 to estimate a gravity-

type model in which the proportion of translations occurring within two years of

the book’s original publication is explained by the characteristics of the original and

translating countries and the distance between them. I find distance and speed of

translation are negatively correlated. Table 4.4 presents these results where original

languages are attributed to their main countries; Table 4.5 presents these results when

original languages are attributed to the country where they are widely spoken that



4.5. CONCLUSIONS 99

is physically closest to the translating country, as described in section 4.2.2.11 This

correlation is statistically significant when controlling for the populations and GDPsper capita of the original and translating countries (column 1 in both tables), but loses

significance due to lack of power when origin and target fixed effects are included. A

50 per cent increase in the distance between the countries corresponds to a decrease

of 2.5 percentage points in the proportion of translations that were translated within

two years of first being published when original languages are attributed to their main

countries, and a 3 percentage point decrease when original languages are attributed

to their closest major countries. Columns 2 to 9 of these tables suggest linguisticdistance may decrease the speed of translation and distance may affect less the speed

with which richer countries translate.12

The negative effect of distance on speed of translation suggests translations face

significant supply-side frictions such as information and contracting costs that increase

with distance. These frictions likely play a substantial role in the negative relationship

between quantity of translations and distance.

4.5 Conclusions

In this paper, I study how flows of book translations between countries are correlated

with the physical distance between the countries. Understanding this relationship

is both important for understanding the impediments to the international diffusion

of ideas, and informative about the underlying causes of the negative relationship

between distance and trade in goods. Unlike goods, translations do not face any of the

costs related to physical relocation, though they too are subject to frictions such as the

11The most salient difference between the two is that in the first case translations from Englishare attributed to the USA for European translating countries, whereas in the second case they areattributed to the UK for these countries.

12The lack of power in these regressions makes identifying significant effects of interest difficult.




costs related to negotiating contracts and search and information costs. In addition,

both translations and trade may decline with distance because consumer tastes aregeographically correlated. Studying the determinants of translation flows is thus

informative on the drivers beyond transportation costs of the negative relationship

between trade in goods and distance.

I estimate a gravity-type model in which translation flows are affected by

characteristics of the original and translating countries (such as GDP per capita)

and the distance between them. I find an elasticity of translations with respect to

distance of between -0.3 and -0.5 for the 1990s, which is substantially smaller than

the corresponding elasticity for trade estimated in the literature, suggesting a sizeable

fraction of the distance effect in trade is due to transportation costs.

Several pieces of more refined analysis of the relationship between translations and

distance are consistent with an important role for search and information costs and

a lesser role for demand factors in the negative relationship between translations and

distance. First, the distance effect decreased between 1949 and 1999 for translations

but not trade; this is consistent with information costs, which may be higher for

translations than goods because books are more heterogeneous, and which almost

certainly fell over this period, being an important factor for driving translations.

Second, the distance effect is larger in the fields of natural and applied science, where

tastes are less important, than in the fields of arts, literature and philosophy, which

have a higher cultural component. This is the opposite to what we would expect

if geographically correlated tastes were the main driving factor behind the distance

effect. Third, cultural distance between countries does inhibit translation flows, but

accounts for relatively little of the overall distance effect, suggesting non-cultural

factors play a large role. Finally, the speed with which titles are translated, which is

likely to largely capture supply frictions as opposed to demand factors, also decreases

significantly with distance.





Figure 4.1: The negative correlation between geographic distance andtranslations decreased over time

Notes: This figure shows the 95% confidence interval of the coefficient on geographic distancein regressions of the number of translations (ln) on distance (ln) and other controls as incolumn (4) of Table 4.1, run separately by year. The solid blue line is for the consistentset of 9 countries for which data are available each year; the dashed red line is for all thecountries for which data are available in any one year.




Figure 4.2: The negative correlation between geographic distance andtranslations by field

Notes: This figure shows the point estimate and 95% confidence interval of the coefficienton geographic distance in regressions of the number of translations (ln) on distance (ln) andother controls as in column (4) of Table 4.1, run separately by book field. Data are for the

years 1994 and 1999.




T a b l e 4 . 1 : C l o s e r c o u n t r i e s t r a n s l a t e m o r e f r o m

e a c h o t h e r

D e p e n d e n t v a r i a b l e : n u m b e r o f t r a n s l a t i o n s ( l n )

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

( 1 0 )

O r i g i n a l l a n g u a g e s :

t o p 1 0 0

t o p 1 0 0

t o p 1 0 0

t o p 1 0 0

u n a m b i g u o u

s o r i g i n a l

c o u n t r y

m a i n

r e s e a r c h

l a n g u a g e s

t o p 1 0 0 ,

a s s i g n e d t o

n e a r e s t m a j o r

c o u n t r y

t o p 1 0 0

t o p 1 0 0

E u r o p e a n o f

t o

p 1 0 0

T a r g e t l a n g u a g e s f o r e a c h t r a n s

l a t i n g c o u n t r y :

o f f i c i a l i n

w h o l e

c o u n t r y

o f f i c i a l i n

w h o l e

c o u n t r y

o f f i c i a l i n

w h o l e

c o u n t r y

o f f i c i a l i n

w h o l e

c o u n t r y

o f f i c i a l i n

w h o l e

c o u n t r y

o f f i c i a l i n

w h o l e

c o u n t r y

o f f i c i a l i n

w h o l e

c o u n t r y

o f f i c i a l i n

w h o l e c o u n t r y ,

t r a n s l a t i n g

c o u n t r y i s m a i n

c o u n t r y

o f f i c i a l a n d

w i d e s p r e a d

i n c o u n t r y

o f f i c i a l i n

w

h o l e

c o u n t r y ,

E u

r o p e a n

c o u n t r y

V a r i a b l e

P h y s i c a l d i s t a n c e b e t w e e n o r i g i n a l a n d t r a n s l a t i n g

- 0 . 8

8 8 * * *

- 0 . 8

8 2 * * *

- 0 . 4

7 3 * * *

- 0 . 3

4 1 * * *

- 1 . 0

8 8 * * *

- 0 . 3

2 6 *

- 0 . 2

5 4 * * *

- 0 . 3

1 1 * * *

- 0 . 3

6 0 * * *

- 0 .

5 6 4 * * *

c o u n t r i e s ( l n )

( 0 . 1

1 1 )

( 0 . 1

1 0 )

( 0 . 0

7 1 )

( 0 . 0

6 2 )

( 0 . 1

1 2 )

( 0 . 1

8 3 )

( 0 . 0

5 0 )

( 0 . 0

6 5 )

( 0 . 0

6 1 )

( 0 . 0

8 4 )

P o p u l a t i o n o f t r a n s l a t i n g c o u n t r y ( l n )

0 . 7

2 2 * * *

0 . 7

3 2 * * *

( 0 . 0

7 9 )

( 0 . 0

8 2 )

G D P p e r c a p i t a o f t r a n s l a t i n g c

o u n t r y ( l n )

0 . 7

4 5 * * *

0 . 7

6 2 * * *

( 0 . 1

1 6 )

( 0 . 1

2 3 )

P o p u l a t i o n o f o r i g i n a l c o u n t r y ( l n )

1 . 0

9 9 * * *

1 . 0

9 0 * * *

( 0 . 0

6 4 )

( 0 . 0

6 4 )

G D P p e r c a p i t a o f o r i g i n a l c o u n t r y ( l n )

3 . 3

2 4 * * *

3 . 3

0 9 * * *

( 0 . 1

5 0 )

( 0 . 1

4 8 )

O r i g i n a l c o u n t r y c o l o n i s e d t r a n

s l a t i n g c o u n t r y

0 . 0

4 8

( 0 . 4

4 3 )

T r a n s l a t i n g c o u n t r y c o l o n i s e d o r i g i n a l c o u n t r y

- 0 . 5

9 5

( 0 . 5

0 3 )

O r i g i n a l a n d t r a n s l a t i n g c o u n t r i e s a r e c o n t i g u o u s

0 . 5

3 1 * * *

0 . 4

7 9 * * *

0 . 3

3 9 * * *

0 . 4

6 2 * * *

0 . 5

3 7 * * *

0 . 5

3 0 * * *

0 . 3 8 9 * * *

( 0 . 1

0 1 )

( 0 . 1

5 3 )

( 0 . 1

1 8 )

( 0 . 0

9 3 )

( 0 . 1

0 6 )

( 0 . 1

0 0 )

( 0 . 1

0 8 )

O r i g i n a l l a n g u a g e i s w i d e s p r e a

d i n t r a n s l a t i n g c o u n t r y

1 . 0

9 5 * * *

1 . 0

8 4 * * *

1 . 1

2 2 * * *

- 0 . 1

3 3

1 . 1

6 5 * * *

0 . 8 6 5 * * *

( 0 . 1

5 7 )

( 0 . 1

8 1 )

( 0 . 1

7 6 )

( 0 . 3

5 4 )

( 0 . 1

6 9 )

( 0 . 1

8 1 )

O r i g i n a l c o u n t r y i s t r a n s l a t i n g c o u n t r y

- 2 . 6

8 8 * * *

- 2 . 6

9 4 * * *

3 . 5

6 4 * * *

2 . 8

4 6 * * *

2 . 1

7 0 * * *

3 . 0

1 1 * * *

3 . 6

5 5 * * *

1 . 8

7 4 * * *

2 . 0 5 6 * * *

( 0 . 6

2 4 )

( 0 . 6

2 4 )

( 0 . 2

9 4 )

( 0 . 3

1 7 )

( 0 . 3

3 6 )

( 0 . 2

7 8 )

( 0 . 4

2 2 )

( 0 . 2

9 0 )

( 0 . 3

6 7 )

T i m e - v a r y i n g t a r g e t l a n g u a g e / c

o u n t r y f i x e d e f f e c t s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

T i m e - v a r y i n g o r i g i n a l l a n g u a g e f i x e d e f f e c t s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s


1 2 , 4

3 4

1 2 , 4

3 4

1 3 , 2

6 2

1 3 , 2

6 2

9 , 2

0 5

5 1 7

1 3 , 2

6 2

6 , 6

3 7

1 5 , 1

8 7

2 , 3

1 6

T r a n s l a t i n g c o u n t r i e s

5 6

5 6

5 8

5 8

5 8

5 8

5 8

3 7

5 6

3 1




N o t e s : T h i s t a b l e p r e s e n t s t h e r e s u l t s o f P M L r e g r e

s s i o n s ( a s d e s c r i b e d i n s e c t i o n 4 . 2 ) o f t h e n u m b e r o f t r a n s l a t i o n s f r o m

a n o r i g i n a l l a n g u a g e

t o a t a r g e t l a n g u a g e i n a t r a n s l a t i n g c o u n t r y i n a y e a r . T h e s a

m e o r i g i n a l l a n g u a g e s a r e i n c l u d

e d f o r

e v e r y t a r g e t l a n g u a g e ; z e r o v a l u e s a r e i n c l u d e d i n t

h e e s t i m a t i o n , a s a l l o w e d b y t h e P M L p r o c e d u r e . T h e s e t o f o r i g i n a l

l a n g u a g e s a n d t a r g e t l a n g u a g e / c o u n t r i e s i n c l u d e d v a r y b y c o l u m n . T h e y e a r s i n c l u d

e d a r e 1 9 9 4 a n d 1 9 9 9 .

T h e o r i g i n a l l a n g

u a g e s i n c o l u m n s 1 - 4 , 8 a n d 9 a r e a l l t h o s e l a n g u a g e s i n t h e 1 0 0 m

o s t w i d e l y s p o k e n l a n g u a g e s w o r l d w i d e

t h a t a r e e v e r t r a n s l a t e d ( t o p 1 0 0 l a n g u a g e s ) . T h e o

r i g i n a l l a n g u a g e s i n c o l u m n 4 a r e t h o s e o f t h e t o p 1 0 0 l a n g u a g e s t h a n

c a n u n a m b i g u o u s l y b e a s s i g n e d t o a s i n g l e o r i g i n a l c o

u n t r y . T h e o r i g i n a l l a n g u a g e s i n

c o l u m n 5 a r e t h e f o u r m a j o r “ r e s e a r c h

l a n g u a g e s ” , n a m e l y

E n g l i s h , F r e n c h , G e r m a n a n d J a

p a n e s e . T h e o r i g i n a l l a n g u a g e s i n c o l u m n 7 a r e t h e t o p 1 0 0 l a n g

u a g e s ,

b u t t h e o r i g i n a l c o u

n t r y u s e d f o r e a c h l a n g u a g e i s

t h e g e o g r a p h i c a l l y c l o s e s t c o u n t r y w h e r e t h e l a n g u a g e i s w i d e s p r e a d ,

r a t h e r t h a n t h e m a i n c o u n t r y o f t h e l a n g u a g e . T h e o

r i g i n a l l a n g u a g e s i n c o l u m n 1 0 a r e t h o s e i n t h e t o p 1 0 0 l a n g u a g e

s t h a t

a r e E u r o p e a n .

T h e t a r g e t l a n g u a g e s i n c o l u m n s 1 - 7 a r e t h e l a n g u a g e s t h a t a r e o ffi c i a l i n t h e w h o l e o f t h e t r a n s l a t i n g c o u n t r y

. T h e

t a r g e t l a n g u a g e s i n c o l u m n 8 a r e t h e l a n g u a g e s t h a t a r e o ffi c i a l i n t h e w h o l e o f t h e t r a n s l a t i n g c o u n t r y , a n d f o r w h i c h t h e

t r a n s l a t i n g c o u n t r y

i s t h e m a i n c o u n t r y o f t h e l a n g u

a g e ( e . g . G e r m a n i n G e r m a n y ,

b u t n o t G e r m a n i n S w i t z e r l a n d ) . T h e

t a r g e t l a n g u a g e s i n

c o l u m n 9 a r e t h e l a n g u a g e s t h a

t a r e 1 ) o ffi c i a l i n a t l e a s t p a r t

o f t h e t r a n s l a t i n g c o u n t r y , 2 ) s

p o k e n

n a t i v e l y b y a t l e a s t

5 0 0 , 0 0 0 p e o p l e i n t h e c o u n t r y , a n d 3 ) s p o k e n b y a t l e a s t 5 % o f

t h e c o u n t r y ’ s p o p u l a t i o n . T h e

t a r g e t

l a n g u a g e s i n c o l u m n

1 0 a r e t h e l a n g u a g e s t h a t a r e o ffi c i a l i n t h e w h o l e o f t h e t r a n s l a t i n g c o u n t r y , f o r E u r o p e a n c o u

n t r i e s

o n l y . S t a n d a r d e r r o r s

a r e r o b u s t . A s t e r i s k s d e n o t e s i g n i fi c a n c e a t : * p < 0 . 1 0 , * * p < 0 . 0

5 , * * * p < 0 . 0 1 .




T a b l e 4 .2 : C o unt ri e s wi thm o re s i mi la rphy s i calen vi r on

ment s and c ult u re s t ran

s latem o ref r om

ea ch othe r

D e p e n d

e n t v ar i a b l e : n u m b er o f t r a n s l a t i o n s ( l n )

V a r i a b l e

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

P h y s i c a l d i s t a n c e b e t w e e n or i gi n a l a n d t r a n s l a t i n g c o un t

r i e s ( l n )

- 0 . 3 1 9 * * *

- 0 .2 7 2 * * *

- 0 .2 4 7 * * *

- 0 .2 8 8 * * *

- 0 .2 4 3 * * *

- 0 .2 8 7 * * *

- 0 .1 6 3

- 0 . 3 3 5 * * *

0 . 0 7 6

( 0 . 0 8 7 )

( 0 . 0 7 4 )

( 0 . 0 6 7 )

( 0 . 0 8 3 )

( 0 . 0 8 5 )

( 0 . 0 9 2 )

( 0 .1 1 4 )

( 0 .1 1 0 )

( 0 . 0 6 9 )

Di f f e r e

n c e b e t w e e n a l t i t u d e pr of i l e s of or i gi n a l a n d t r a n s l a t i n g

- 0 . 7 8 8 * * *

- 0 . 7 2 0 * * *

- 0 . 3 6 9 *

- 0 . 3 5 3

- 0 . 0 6 9

- 0 .2 6 6

- 0 .1 6 8

- 0 . 7 8 2 * * *

- 0 .1 4 4

c o un t r i e s

( 0 .2 1 3 )

( 0 .2 0 3 )

( 0 .2 2 2 )

( 0 .2 2 4 )

( 0 .1 8 0 )

( 0 .2 6 9 )

( 0 .2 1 8 )

( 0 .2 4 5 )

( 0 .1 9 4 )

Di f f e r e

n c e b e t w e e n c l i m a t e r e gi on pr of i l e s of or i gi n a l a n d

0 .1 8 8

0 .1 1 2

0 . 0 8 5

0 .1 0 7

0 .1 4 0

0 .1 2 8

0 .1 0 3

0 .2 7 0

0 .2 0 0

t r a n

s l a t i n g c o un t r i e s

( 0 .2 0 1 )

( 0 .1 6 8 )

( 0 .1 5 5 )

( 0 .1 6 2 )

( 0 .1 8 6 )

( 0 .1 8 1 )

( 0 . 3 2 7 )

( 0 .2 4 0 )

( 0 .1 4 6 )

Di f f e r e

n c e b e t w e e n b i om e r e gi on pr of i l e s of or i gi n a l a n d

- 0 . 3 0 7 * * *

- 0 .2 9 0 * * *

- 0 .2 5 8 * * *

- 0 .2 3 4 * * *

- 0 .1 9 2 *

- 0 .2 0 0 * *

- 0 . 3 9 0 * * *

- 0 .2 6 4 * *

- 0 .4 4 2 * * *

t r a n

s l a t i n g c o un t r i e s

( 0 .1 0 1 )

( 0 . 0 8 1 )

( 0 . 0 8 1 )

( 0 . 0 8 6 )

( 0 .1 0 4 )

( 0 . 0 9 8 )

( 0 .1 3 8 )

( 0 .1 1 3 )

( 0 . 0 7 7 )

R e l i gi o

u s d i s t a n c e

-1 .2 7 5 * * *

-1 . 0 2 9 * * *

-1 . 0 4 0 * * *

-1 . 8 3 3 * * *

-1 . 0 9 8 * * *

- 0 . 7 9 4 * * *

( 0 .2 1 1 )

( 0 .1 9 9 )

( 0 .1 9 8 )

( 0 .1 6 4 )

( 0 .2 3 6 )

( 0 .1 7 9 )

L i n g ui s t i c d i s t a n c e

- 0 . 6 8 0 * * *

- 0 . 6 8 9 * * *

- 0 .2 3 2 * *

- 0 . 6 3 1 * * *

- 0 .4 4 2 * * *

( 0 .1 4 4 )

( 0 .1 4 8 )

( 0 .1 0 6 )

( 0 .1 4 5 )

( 0 .1 4 1 )

G e n e t i c d i s t a n c e

0 .1 3 0

0 .4 6 1 *

0 . 0 6 9

0 . 0 7 6

( 0 .1 2 9 )

( 0 .2 7 2 )

( 0 .1 4 0 )

( 0 .1 2 5 )

H of s t e d e ' s c ul t ur a l d i s t a n c e

- 0 . 0 7 5 * * *

- 0 .1 6 3 * * *

( 0 . 0 1 7 )

( 0 . 0 2 7 )

S c h w a r t z' s c ul t ur a l d i s t a n c e

0 . 0 0 2

- 0 . 0 2 0

( 0 . 0 2 8 )

( 0 . 0 2 9 )

I m p or t s i n t o t a r g e t c o un t r yf r om or i gi n a l c o un t r y ( l n )

0 .2 4 5 * * *

( 0 . 0 3 9 )

E x p or t s f r om t a r g e t c o un t r yi n t o or i gi n a l c o un t r y ( l n )

0 .2 4 0 * * *

( 0 . 0 4 4 )

Or i gi n a l a n d t r a n s l a t i n g c o un t r i e s a r e c on t i g u o u s

0 . 5 2 8 * * *

0 . 3 7 7 * * *

0 . 3 4 2 * * *

0 . 3 3 2 * * *

0 .2 0 6 * * *

0 . 3 3 5 * * *

0 . 5 6 8 * *

*

0 . 5 1 2 * * *

0 .1 8 3 * *

( 0 . 0 9 6 )

( 0 .1 0 0 )

( 0 .1 0 0 )

( 0 .1 0 0 )

( 0 . 0 6 8 )

( 0 .1 1 1 )

( 0 . 0 7 7 )

( 0 .1 0 5 )

( 0 . 0 7 8 )

Or i gi n a l l a n g u a g e i s wi d e s pr e a d i n t r a n s l a t i n g c o un t r y

1 .1 6 8 * * *

1 . 0 8 4 * * *

1 .2 4 6 * * *

1 .2 4 7 * * *

1 .1 6 5 * * *

1 .2 8 9 * * *

1 .2 6 2 * *

*

1 .1 2 8 * * *

1 .2 2 1 * * *

( 0 .1 5 7 )

( 0 .1 5 2 )

( 0 .1 4 2 )

( 0 .1 4 3 )

( 0 .1 6 5 )

( 0 .1 6 9 )

( 0 .1 5 5 )

( 0 .1 9 7 )

( 0 .1 4 0 )

Or i gi n a l c o un t r yi s t r a n s l a t i n g c o un t r y

2 . 3 4 0 * * *

1 . 9 7 1 * * *

1 . 9 7 6 * * *

1 . 9 4 0 * * *

1 . 9 0 0 * * *

2 . 0 3 5 * * *

2 . 3 5 9 * *

*

2 .4 7 1 * * *

6 . 0 4 8 * * *

( 0 . 3 4 1 )

( 0 .2 9 0 )

( 0 .2 9 2 )

( 0 .2 9 5 )

( 0 .2 8 1 )

( 0 . 3 3 2 )

( 0 . 3 3 1 )

( 0 . 3 9 3 )

( 0 . 7 4 9 )

T i m e - v

a r yi n g t a r g e t l a n g u a g e / c o un t r yf i x e d e f f e c t s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

T i m e - v

a r yi n g or i gi n a l l a n g u a g e f i x e d e f f e c t s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

D umm y v a r i a b l e s f or i m p or t s a r e z e r o a n d f or e x p or t s a r e z e r o

Y e s

O b s er v

a t i o n s

1 3 ,2 6 2

1 3 ,2 6 2

1 3 ,1 2 4

1 2 , 9 8 8

5 , 3 1 8

4 , 0 9 5

5 ,4 0 3

4 , 0 9 5

1 2 , 6 4 1

T r a n s l a

t i n g c o u n t r i e s

5 8

5 8

5 8

5 8

3 6

3 3

3 6

3 3

5 7




N o t e s : T h i s t a b l e p r

e s e n t s t h e r e s u l t s o f P M L r e g r e s s i o n s ( a s d e s c r i b e d i n s e c t i o n 4 . 2 ) o f t h e n u m b e r o f t r a n s l a t i o n s f r

o m a n

o r i g i n a l l a n g u a g e t o

a t a r g e t l a n g u a g e i n a t r a n s l a t i n

g c o u n t r y i n a y e a r . T h e s a m e o r i g i n a l l a n g u a g e s a r e i n c l u d e d f o r

e v e r y

t a r g e t l a n g u a g e ; z e r o v a l u e s a r e i n c l u d e d i n t h e e s t i m

a t i o n , a s a l l o w e d b y t h e P M L p r o c e d u r e . T h e o r i g i n a l l a n g u a g e s

a r e a l l

t h o s e l a n g u a g e s i n t h e m o s t w i d e l y s p o k e n 1 0 0 l a n g u a g e s w o r l d w i d e t h a t a r e e v e r t r a n s l a t e d . T h e t a r g e t l a n g u a g e / c o u

n t r i e s

i n c l u d e d a r e a l l t h e

l a n g u a g e s t h a t a r e o ffi c i a l i n t h

e w h o l e o f t h e t r a n s l a t i n g c o u n

t r y . T h e y e a r s i n c l u d e d a r e 1 9 9

4 a n d

1 9 9 9 . T h e a l t i t u d e p r o fi l e , c l i m a t e r e g i o n p r o fi l e , a n d

b i o m e r e g i o n p r o fi l e d i ff e r e n c e

v a r i a b l e s a r e a l l c o n s t r u c t e d t o v a r y

b e t w e e n 0 ( n o o v e r l a p i n p r o fi l e s ) a n d 1 ( i d e n t i c a l p r o fi l e s ) .

R e

l i g i o u s

d i s t a n c e i s t h e

p r o b a b i l i t y a r a n d o m l y c h o s e n p e r s o n

f r o m t h e t r a n s l a t i n g

c o u n t r y a n d a r a n d o m l y c h o s e

n p e r s o n f r o m t h e o r i g i n a l c o u n

t r y h a v e t h e s a m e r e l i g i o n . S t a

n d a r d

e r r o r s a r e r o b u s t . A

s t e r i s k s d e n o t e s i g n i fi c a n c e a t : *

p < 0 . 1 0 , * * p < 0 . 0 5 , * * * p < 0 . 0 1 .




Table 4.3: Translations into and out of more developed countries

decrease less with physical distance

Dependent variable: number of translations (ln)

Variable (1) (2) (3) (4) (5) (6)

Physical distance (ln) * GDP per capita of translating country (ln) 0.696***

(0.083)

Physical distance (ln) * GDP per capita of original country (ln) 0.306***

(0.060)

Physical distance (ln) * urbanization of translating country (fraction) 1.894***

(0.330)

Physical distance (ln) * urbanization of original country (fraction) 0.990***

(0.340)

Physical distance (ln) * level of democracy of translating country 0.049*

(0.025)

Physical distance (ln) * level of democracy of original country 0.026

(0.016)Physical distance between original and translating countries (ln) -6.958*** -3.260*** -1.669*** -1.027*** -0.737*** -0.545***

(0.791) (0.563) (0.220) (0.231) (0.239) (0.164)

Difference between altitude profiles of original and translating -0.329 -0.455** -0.382* -0.396* -0.368 -0.406*

countries (0.221) (0.226) (0.231) (0.231) (0.224) (0.222)

Difference between climate region profiles of original and 0.119 0.110 0.077 0.051 0.106 0.119

translating countries (0.172) (0.165) (0.174) (0.177) (0.166) (0.163)

Difference between biome region profiles of original and translating -0.300*** -0.254*** -0.244*** -0.231*** -0.237*** -0.235***

countries (0.091) (0.088) (0.088) (0.088) (0.088) (0.087)

Religious distance -1.022*** -1.027*** -1.012*** -1.007*** -1.046*** -1.022***

(0.199) (0.201) (0.204) (0.207) (0.204) (0.200)

Linguistic distance -0.691*** -0.640*** -0.678*** -0.732*** -0.695*** -0.692***

(0.149) (0.147) (0.150) (0.148) (0.150) (0.149)

Genetic distance 0.106 0.213* 0.128 0.207* 0.141 0.179

(0.149) (0.125) (0.121) (0.126) (0.134) (0.126)

Original and translating countries are contiguous 0.312*** 0.310*** 0.333*** 0.280*** 0.346*** 0.322***

(0.091) (0.101) (0.101) (0.101) (0.101) (0.102)

Original language is widespread in translating country 1.306*** 1.243*** 1.301*** 1.259*** 1.251*** 1.231***

(0.138) (0.140) (0.150) (0.140) (0.143) (0.144)Original country is translating country 1.032*** 1.393*** 1.574*** 1.697*** 1.932*** 1.925***

(0.245) (0.266) (0.269) (0.272) (0.289) (0.293)

Time-varying target language/country fixed effects Yes Yes Yes Yes Yes Yes

Time-varying original language fixed effects Yes Yes Yes Yes Yes Yes

Observations 12,706 12,438 12,988 12,988 12,611 12,428

Translating countries 56 58 58 58 56 58

Notes: This table presents the results of PML regressions (as described in section 2) of the number of translations from an original language to a target language in a translatingcountry in a year. The same original languages are included for every target language;zero values are included in the estimation, as allowed by the PML procedure. The originallanguages are all those languages in the most widely spoken 100 languages worldwide that

are ever translated. The target language/countries included are all the languages that areofficial in the whole of the translating country. The years included are 1994 and 1999.The altitude profile, climate region profile, and biome region profile difference variables areall constructed to vary between 0 (no overlap in profiles) and 1 (identical profiles). Religious

distance is the probability a randomly chosen person from the translating country and arandomly chosen person from the original country have the same religion. Democracy ismeasured on a scale of 0 to 10. Standard errors are robust. Asterisks denote significanceat: * p<0.10, ** p<0.05, *** p<0.01.




T a b l e

4 . 4 : T r a n s l a t i o n s o c c u r f a s t e r b e t w e e n c l o s e r c o u n t r i e s , 1 9 9 8 - 2 0 0 0

D e p e n d e n t v a r i a b l e : p r o p o r t i o n o f t i t l e s t r a n s l a t e d w i t h i n 2 y e a r s o f f i r s t p u b l i c a t i o n

V a r i a b l e

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

P h y s i c a l d i s t a n c e b e t w e e n o r i g i n a l a n d t r a n s l a t i n g c o u n t r i e s ( l n )

- 0 . 0

5 1

* * *

- 0 . 0

3 1

- 0 . 0

3 1

- 0 . 0

1 8

0 . 0

0 3

- 0 . 9

9 3

- 5 . 3

0 8

- 0 . 3

9 9

- 0

. 3 2 8

( 0 . 0 1

7 )

( 0 . 0

4 9 )

( 0 . 0

5 2 )

( 0 . 0

4 8 )

( 0 . 0

5 0 )

( 0 . 8

5 8 )

( 3 . 5

7 9 )

( 0 . 2

6 3 )

( 0 . 3

0 0 )

P o p u l a t i o n o f t r a n s l a t i n g c o u

n t r y ( l n )

0 . 0 1

5 *

( 0 . 0 0

8 )

G D P p e r c a p i t a o f t r a n s l a t i n g c o u n t r y ( l n )

0 . 1

3 1 * * *

( 0 . 0 1

3 )


- 0 . 2 1

9 * *

( 0 . 1 0

2 )


0 . 1 9

2

( 0 . 3 0

3 )

R e l i g i o u s d i s t a n c e

0 . 1

4 2

0 . 1

8 5

0 . 1

9 3

( 0 . 1

3 7 )

( 0 . 1

3 3 )

( 0 . 1

3 5 )

L i n g u i s t i c d i s t a n c e

- 0 . 2

3 2

- 0 . 2 2 5

( 0 . 1

3 9 )

( 0 . 1

4 0 )


- 0 . 1 8 9

( 0 . 2

5 8 )

P h y s i c a l d i s t a n c e ( l n ) * G D P

p e r c a p i t a o f t r a n s l a t i n g c o u n t r y ( l n )

0 . 1

0 1

( 0 . 0

9 1 )


p e r c a p i t a o f o r i g i n a l c o u n t r y ( l n )

0 . 5

3 6

( 0 . 3

6 5 )

P h y s i c a l d i s t a n c e ( l n ) * u r b a

n i z a t i o n o f t r a n s l a t i n g c o u n t r y ( f r a c t i o n )

0 . 5

1 0

( 0 . 3

6 4 )


n i z a t i o n o f o r i g i n a l c o u n t r y ( f r a c t i o n )

0 . 3

9 7

( 0 . 4

0 7 )


0 . 0 2

1

0 . 0

9 3

0 . 1

0 9 *

0 . 1

0 0

0 . 1

0 9 *

0 . 1

0 9 *

0 . 0

7 4

0 . 1

0 8 *

0 . 0

7 6

( 0 . 0 4

3 )

( 0 . 0

6 4 )

( 0 . 0

6 1 )

( 0 . 0

6 2 )

( 0 . 0

6 1 )

( 0 . 0

6 4 )

( 0 . 0

6 8 )

( 0 . 0

6 1 )

( 0 . 0

6 6 )

O r i g i n a l l a n g u a g e i s w i d e s p r e a d i n t r a n s l a t i n g c o u n t r y

0 . 2

6 7 * * *

0 . 3

1 4 * * *

0 . 3

2 4 * * *

0 . 3

8 4 * * *

0 . 3 7

6 * * *

0 . 3

1 0 * * *

0 . 3

1 7 * * *

0 . 3

0 9 * * *

0 . 3 1 0 * * *

( 0 . 0 7

6 )

( 0 . 1

0 0 )

( 0 . 0

9 3 )

( 0 . 0

8 5 )

( 0 . 0

8 3 )

( 0 . 0

9 9 )

( 0 . 0

7 4 )

( 0 . 1

0 1 )

( 0 . 0

8 7 )

T a r g e t l a n g u a g e / c o u n t r y f i x e

d e f f e c t s

N o

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s


N o

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

R - S q u a r e d

0 . 4 5

8

0 . 6

9 7

0 . 7

0 3

0 . 7

1 5

0 . 7

1 7

0 . 7

0 6

0 . 7

0 3

0 . 7

0 3

0 . 7

0 1


1 8 0

1 8 4

1 8 4

1 8 4

1 8 4

1 8 0

1 8 4

1 8 4

1 8 4


5 0

5 1

5 1

5 1

5

1

5 0

5 1

5 1

5 1




N o t e




i s an

or i gi n al l an g u a g e an d a t ar g e t l an g u a g ei n a t r an s l a t i n g c o un t r y.Th e or i gi n al l an g u a g e s i n cl u d e d ar eEn gl i s h ,F r en ch ,

G er m

an an d I t al i an. Onl yn on-fi c t i on t i t l e s t r an s l a t e d i n t o an offi c

i al l an g u a g e of t h e t r an s l a t i n g c o un t r y ar ei n cl u d e d .

L a n d

l o c k e d i s a d umm yf or t h e c o un t

r y b ei n gl an d l o ck e d .Th er el i gi on d ummi e s ar ef or t h em o s t wi d e s pr e a d r el i gi on of t h e

c o un

t r y. O b s er v a t i on s ar e w ei gh t e d b y t h en um b er of t r an s l a t i on s c on t r i b u t i n g t o t h em e a s ur e of s p

e e d . S t an d ar d er r or s

ar e c

l u s t er e d a t t h e c o un t r yl e v el .A s t er i s k s d en o t e s i gni fi c an c e a t : *

p < 0 .1 0 ,* * p < 0 . 0 5 ,* * * p < 0 . 0 1

.




T a b l e 4 . 5 : T r a n s

l a t i o n s o c c u r f a s t e r b e t w e e n c l o s e r c o u n t r i e s ,

1 9 9 8 - 2 0 0 0 : a s s i g n i n g o r i g i n a l

l a n g u a g e t o c l o s e s t c o u n t r y w h e r e i t i s s p o k e n w i d e l y

D e p e n d e n t v a r i a b l e : p r o p o r t i o n o f t i t l e s t r a n s l a t e d w i t h i n 2 y e a r s o f f i r s t p u b l i c a t i o n

V a r i a b l e

( 1

)

( 2 )

( 3 )

( 4 )

(

5 )

( 6 )

( 7 )

( 8 )

( 9 )

P h y s i c a l d i s t a n c e b e t w e e n o r i g i n a l a n d t r a n s l a t i n g c o u n t r i e s ( l n )

- 0 . 0 6

0 * * *

- 0 . 0

4 3

- 0 . 0

4 2

- 0 . 0

5 1 *

- 0 .

0 4 6

- 0 . 6

8 3 *

- 0 . 0

3 4

- 0 . 2

1 6

- 0 . 0

1 5

( 0 . 0

1 3 )

( 0 . 0

3 3 )

( 0 . 0

3 3 )

( 0 . 0

2 9 )

( 0 . 0 2 8 )

( 0 . 3

8 1 )

( 0 . 1

8 6 )

( 0 . 1

4 3 )

( 0

. 0 4 2 )

P o p u l a t i o n o f t r a n s l a t i n g c o u

n t r y ( l n )

0 . 0

1 5

( 0 . 0

0 9 )

G D P p e r c a p i t a o f t r a n s l a t i n g c o u n t r y ( l n )

0 . 1

1 8

* * *

( 0 . 0

1 5 )


0 . 0

5 4

* * *

( 0 . 0

1 0 )


- 0 . 1

6 9 *

( 0 . 0

9 2 )

R e l i g i o u s d i s t a n c e

0 . 1

2 9

0 . 1

7 3

0 . 1 6 2

( 0 . 1

1 2 )

( 0 . 1

1 0 )

( 0 . 1 1 1 )

L i n g u i s t i c d i s t a n c e

- 0 . 2

7 0 * *

- 0 . 3

0 3 * * *

( 0 . 1

2 6 )

( 0 . 1 0 2 )


0 . 0 5 0

( 0 . 1 1 3 )


p e r c a p i t a o f t r a n s l a t i n g c o u n t r y ( l n )

0 . 0

6 9 *

( 0 . 0

4 0 )


p e r c a p i t a o f o r i g i n a l c o u n t r y ( l n )

- 0 . 0

0 1

( 0 . 0

1 8 )


n i z a t i o n o f t r a n s l a t i n g c o u n t r y ( f r a c t i o n )

0 . 2

6 2

( 0 . 2

0 0 )


n i z a t i o n o f o r i g i n a l c o u n t r y ( f r a c t i o n )

- 0 . 0

2 9

( 0

. 0 3 8 )


- 0 . 0

0 1

0 . 0

8 2

0 . 0

9 8 *

0 . 0

9 5 *

0 . 0 9 3

0 . 1

0 9 *

0 . 0

8 2

0 . 0

9 6

0

. 0 8 9

( 0 . 0

4 1 )

( 0 . 0

5 9 )

( 0 . 0

5 4 )

( 0 . 0

5 6 )

( 0 . 0 5 6 )

( 0 . 0

5 6 )

( 0 . 0

5 9 )

( 0 . 0

6 0 )

( 0

. 0 6 1 )

O r i g i n a l l a n g u a g e i s w i d e s p r e a d i n t r a n s l a t i n g c o u n t r y

0 . 2

3 0

* * *

0 . 2

8 8 * * *

0 . 3

1 8 * * *

0 . 3

6 7 * * *

0 . 3 8

1 * * *

0 . 3

7 6 * * *

0 . 2

9 1 * * *

0 . 3

5 6 * * *

0 . 2

9 8 * * *

( 0 . 0

5 0 )

( 0 . 0

8 5 )

( 0 . 0

7 2 )

( 0 . 0

7 4 )

( 0 . 0 6 7 )

( 0 . 0

9 4 )

( 0 . 0

8 4 )

( 0 . 0

8 7 )

( 0

. 0 8 4 )

T a r g e t l a n g u a g e / c o u n t r y f i x e

d e f f e c t s

N o

Y e s

Y e s

Y e s

Y

e s

Y e s

Y e s

Y e s

Y e s


N o

Y e s

Y e s

Y e s

Y

e s

Y e s

Y e s

Y e s

Y e s

R - S q u a r e d

0 . 5

2 9

0 . 7

0 9

0 . 7

1 3

0 . 7

3 0

0 . 7 3 3

0 . 7

2 2

0 . 7

0 9

0 . 7

1 4

0

. 7 1 3


1 8

0

1 8 4

1 8 4

1 8 4

1

8 4

1 8 0

1 8 4

1 8 4

1 8 4


5 0

5 1

5 1

5 1

5

1

5 0

5 1

5 1

5 1




N o t e

s : Th i s t a b l e d u pl i c a t e s t h er e g

r e s s i on s i nT a b l e4 .4 , ex c e p t h e

r e t h e or i gi n al c o un t r y u s e d f or

e a ch l an g u a g ei s t h e

g e o gr a ph i c al l y cl o s e s t c o un t r y wh er e

t h el an g u a g ei s wi d e s pr e a d ,r a t h er t h an t h em ai n c o un t r y of t h

el an g u a g e. S t an d ar d

er r or

s ar e cl u s t er e d a t t h e c o un t r yl e

v el .A s t er i s k s d en o t e s i gni fi c an c

e a t : * p < 0 .1 0 ,* * p < 0 . 0 5 ,* * * p < 0 . 0 1 .



Chapter 5

The Effects of the Collapse of

Communism on the Diffusion of

Knowledge (with Ran Abramitzky)

5.1 Introduction

Economists and economic historians have long recognized the importance of knowl-

edge and ideas for growth and development.1 Indeed, much of the “new” growth

theory highlights idea accumulation as key to explaining accelerating growth.2

Moreover, the international sharing of ideas plays a huge role: according to one

estimate3, world GDP would be just 6% of its current level if countries did not share

ideas.

Nevertheless, there is little empirical work on the international flows of ideas4 for

1See, for example, Kuznets (1966), Mokyr (2002, 2009, 2010), Romer (1990, 1993), Grossman andHelpman (1991), Jones (2005), Klenow and Rodrıguez-Clare (2005), and Jones and Romer (2010).

2See, for example, Romer (1986, 1990), Helpman (2004), and Jones (2001, 2005).3Klenow and Rodrıguez-Clare (2005)4Paul Romer makes this point forcefully in his 2010 paper.

113



114 CHAPTER 5. THE COLLAPSE OF COMMUNISM

two main reasons. First, ideas are challenging to measure. Second, it is challenging

to capture the two main properties of ideas, namely non-rivalry and disembodiment.5

Vitally, ideas are non-rival, meaning the use of an idea by one party in no way affects

its simultaneous use by another; this non-rivalry drives technological spillovers.6 Ideas

are also disembodied; an idea that is embodied in a purchased piece of equipment

may not generate a technological spillover.7

We address these challenges by suggesting a new measure of the international

flow of ideas and a setting in which to study how policies and institutions shape

the international diffusion of ideas. Given the importance of ideas for growth, it isimperative to understand how their spread can be affected by policy and institutional

changes.

Specifically, we use book translations as a measure of the international flow of

ideas. Translations are an attractive measure of the diffusion of ideas because they, as

opposed to the physical books that contain them, are both non-rival and disembodied,

and their key purpose is to transmit written ideas, information or knowledge between

speakers of different languages. In the absence of translation, many ideas stored inwords might never leave the language or country in which they were conceived. Of

course, book translations are not the only way societies gain new knowledge8, but they

are an important channel for the flow of ideas between linguistically distinct groups,

and are both quantifiable and classifiable by field and specific content. Moreover, the

types of ideas captured by translations are broad, ranging from technical ideas (such

5Note that measures such as trade, migration, and foreign direct investment are informative inmany ways, but they measure embodied flows of ideas, which are not as such non-rival.

6See, for example, Romer (1986, 1990, 2010), Helpman (2004), and Jones and Romer (2010).7See, for example, Jaffe and Trajtenberg (1999).8An alternative measure is patent citations, which track the diffusion of particular technological

knowledge across disciplines and geographical space (see, for example, Jaffe, Trajtenberg andHenderson, 1993, Jaffe and Trajtenberg, 1999, 2002, and Jaffe, Trajtenberg and Fogarty, 2000).Foreign research and development (R&D) was also suggested as a measure of knowledge spillovers(Coe and Helpman, 1995, Keller, 2002), as well as international trade and foreign direct investment(Keller, 2004, 2009). Book translations are a complementary measure.




as in physics or engineering books), to ideas that are essentially social or cultural

(such as in books on religion, philosophy, or literature). Finally, empirical analysisof translations is possible because systematic data on translations can be generated

from national bibliographies.

The setting we propose is the collapse of Communism in Eastern Europe, which is a

natural place to identify the effect of policy on idea flows. The collapse of Communism

was a large shock that swiftly moved countries from nearly complete isolation from

Western ideas to full openness. Because our measure of idea flows captures a broad

range of ideas, this paper sheds light on the type of ideas most likely to be affectedby policy changes that reduce information restrictions. In particular, we can examine

whether the collapse of Communism had a stronger effect on ideas that contain more

“useful knowledge” (as coined by Mokyr, 2002) for economic development than on

“less-useful” knowledge with more cultural content.

More broadly, we examine how economic incentives shape the international

diffusion of knowledge, which economic historians view as one of the most crucial

economic phenomena of all (see various work by Joel Mokyr). The wider lesson fromour paper is that when these incentives are seriously impaired by institutions, this

can have severe effects that are only remedied as institutional change occurs.

This study of the Communist regime and its collapse in Eastern Europe is not only

a natural context for the study of international idea flows, but it also contributes to

our understanding of this highly important episode in history. First, this is the first

study to assess how Communism affected idea flows.9 Second, while it is known that

9There is a literature that documents and explains the transition of Eastern European countriesfrom Communism into market economies (e.g. Blanchard, 1994, 1996, 1997, Aghion and Blanchard,1994, Frye and Mansfield, 2003). There is also a literature exploring the “natural experiment”created by the collapse of Communism in Eastern Europe and elsewhere to learn about individuals’preferences and behavior (e.g. Munich, Svejnar and Terrell, 2005, Fuchs-Schundeln and Schundeln,2005, Alesina and Fuchs-Schundeln, 2007, Fuchs-Schundeln, 2008, Abramitzky, 2008). However, thispaper is the first to test the effect of the collapse of Communism on the flow of information andideas.




Communist Europe had low inflows of Western knowledge and ideas (e.g. Garton Ash,

1995, Harrison, 2003, 2005), the role of differences in preferences for ideas betweenEast and West has never been clear. Instead, the emphasis is typically on the stronger

censorship of Western ideas in Eastern Europe. Our empirical strategy sheds light

on the role of preferences. To the extent we see convergence in translation rates to

Western levels post collapse, we can conclude that Eastern preferences were either

similar to Western ones or became like them quickly following the collapse. If there

was no convergence despite the end of censorship, then we can conclude that Eastern

European preferences for ideas differ from Western preferences.We begin by comparing translation patterns in former Communist countries before

and after the collapse. To account for possible general changes in translations over the

1980s and 1990s, we also compare translation patterns in Communist countries with

those in Western European countries. To shed further light on the role of preferences

in the flow of ideas, we first compare translation patterns in the Soviet countries

with patterns in the more western-oriented Satellite countries. Second, we test the

degree of convergence in translation flows between Eastern and Western Europe postcollapse. We then test the effect of the collapse and the degree of convergence to the

West of book translations in different fields, to better understand what type of ideas

are more likely to increase once information restrictions are lifted.

We use newly-collected data on almost 800,000 book translations for the period

1980 to 2000. The data were extracted from Unesco’s Index Translationum (IT), an

international bibliography of the translations published annually in a wide range of

countries.

We present four main sets of results. First, we use graphs and regression analysis

and show that when Communism collapsed the overall flow of translations from

Western Europe into the Soviet satellites increased by a factor of seven. At the

same time, we document an offsetting two-third decrease in Communist-to-Satellite




translations. These large magnitudes emphasize just how much the flow of ideas was

affected by the collapse of Communism. In contrast, translations of Western titles intothe former Soviet countries, which had less Western orientation than the Satellites,

barely increased. We further show that these findings are not driven by changes in

the publishing industry that allowed a larger total number of books to be published.

In fact, the total number of books published in Communist countries didn’t increase

with the collapse of Communism, and may have actually declined. Another striking

pattern that emerges is that Western European countries translated very little from

Communist languages, both before and after the collapse of the Eastern Bloc. Second,we show that whereas the Satellite countries converged to Western countries in their

level of translations of Western titles, Soviet countries did not. This suggests that

non-Soviet Eastern Europe has similar preferences for ideas to the West but the

former Soviet Union does not. The Satellite countries not only started to catch up

on translation of older titles (stocks), but they also increased their rate of translation

of current titles (flows) and converged to Western levels of these translations. This

suggests both a convergence in the flow of new ideas, and a catching up on the stock of ideas. Interestingly, even in the Satellite countries, translations of Communist titles

remained higher that in the West.

Third, we show that the effect of Communism’s collapse was larger for the more

“ideological” book fields. Translations of titles in fields such as religion, philosophy,

and the social sciences, were highly suppressed under Communism because they were

perceived as especially threatening to the Communist regime. For instance, religion

was considered an enemy of the Communist regime and was firmly suppressed under it.

Once Communism collapsed, translations of titles in religion increased dramatically,

especially Christian titles. Similarly, translations in philosophy and the social sciences

(especially economics) jumped post collapse. In contrast, the study of exact sciences

was strongly supported by Communist governments, and was important for the




USSR’s international standing. Such translations, especially in mathematics, geology

and physics, increased relatively little from the West when Communism collapsed,and decreased the most of any field between Communist countries.

At the same time, given censorship was lifted with the collapse of Communism,

remaining differences between Eastern and Western Europe post collapse are likely

to reflect differences in tastes between East and West. We find that translations

of Western titles in the fields of applied science and social science fully converged

to their levels in the West. In contrast, translations of Western titles in the fields

of history and arts did not converge to their levels in the West. That is, fields

that contain more “useful knowledge” and lend themselves more directly to economic

development converged more than fields that contain more cultural information and

are relatively culture-specific, which suggests economically-beneficial foreign ideas are

the most likely to be adopted.

Finally, we conduct title- and author-level analyses to test how the collapse of

Communism affected translations of especially important titles, namely titles that

were considered highly influential in the West, and a sample of Western Europe’s

most translated titles. For this purpose, we augment our translation data on these

titles with more detailed information on the book and its author. We find that most of

these titles were not published in translation anywhere in Communist Europe prior to

the collapse of Communism, but after the collapse Eastern Europe translated them at

rates more comparable to Western Europe. This suggests a genuine increase in access

to important Western titles in Communist Europe, both through the main languages

of the countries and through secondary languages such as Russian. Furthermore,

we examine the translation of titles whose authors voiced anti-Communist opinions,

titles published in the Communist era, and those written by Nobel laureates, all of

which were more likely to pose threats to the Communist regime than other important

titles. Such titles were translated at lower rates in Eastern Europe pre collapse, and




experienced larger increases in translation post collapse than did other influential

titles.

Our findings are consistent with a dramatic increase in the flow of Western ideas

into former Communist countries when Communism collapsed, and with a decline

in the flow of ideas between Communist countries. The effect of the collapse of

Communism on the flow of ideas reflected both high suppression of idea flows during

the Cold War and East/West differences in preferences for ideas. For example, the

higher effect of the collapse on translations in philosophy and economics relative to

exact sciences illustrates the role of severe suppression. The convergence in Western

ideas between the more Western-oriented Satellite countries and Western Europe,

and the lack of convergence between the more Russian-oriented Soviet countries

and Western Europe illustrates differences in tastes for Western ideas. Similarly,

the remaining differences in translations between East and West in some fields, and

between Soviet and the West in all fields, illustrate how cultural differences persisted

even after Communism collapsed.10

This paper proceeds as follows. In Section 5.2 we present the data on book

translations and explain the construction of our measures of idea flows. Section 5.3

briefly outlines the historical context of publishing in Communist Europe and of the

collapse of Communism. Section 5.4 describes our empirical strategy for examining

the effect of the collapse of Communism on book translations. Section 5.5 presents

results on the effect of the collapse of Communism on the total flow of translations.

Section 5.6 presents results breaking translations down by book field. Section 5.7

presents our analysis of the effect of the collapse on influential titles. Section 5.8

discusses further translations as a measure of the diffusion of ideas and concludes.

10This illustration is consistent with the literature showing how history can shape culture (e.g.Greif, 1994, Guiso, Sapienza and Zingales, 2008, Nunn and Wantchekon, forthcoming, and Fletcherand Iyigun, 2010; see also the surveys by Tabellini, 2010 and Nunn, 2009 on the historical origins of culture).




5.2 Data

5.2.1 The flow of book translations across countries

The translation data are extracted from Unesco’s Index Translationum (IT), an

international bibliography of the translations published in a wide range of countries.

These data originate at the national level through the law of legal deposit, which

specifies that every book published that is intended for circulation must be submitted

to the national depository. The national depository then compiles a list of the

publications that are translations, and submits this list to Unesco, which standardizes

the entries across countries to form the IT.

Titles in the IT are categorized according to the nine main categories of the

Universal Decimal Classification (UDC) system: General (0.1% of translations

in the period 1980-2000); Philosophy (including Psychology, 5.3%); Religion and

Theology (5.7%); Law, Social Sciences, Education (8.5%); Natural and Exact Sciences

(4.2%); Applied Sciences (11.4%); Arts, Games, Sports (5.2%); Literature (including

books for children, 52.3%)11; History, Geography, Biography (including memoirs and

autobiographies, 6.6%).12

The bibliographic entry for each translation includes information on the country,

city, and year in the which the translation was published, the language of the original

title and the target language into which it was translated, the field (UDC class) of

the title, the number of pages or volumes of the title, the author, and the original

and translated titles of the book.

We use data on the translations in Communist countries (our group of interest)and Western European countries (our comparison group) over the period 1980 to

2000, which comprise approximately 800,000 translations. We limit our Communist

11Literature also includes the very small category Philology and Linguistics.12For a detailed description of the subfields that make up each UDC field, see https://www.

unido.org/library/help/udc.html.



5.2. DATA 121

countries to European countries that were part of the Eastern Bloc and that were

Warsaw Pact members in the 1980s, meaning they were under heavy Soviet controlpre-collapse because Soviet troops were permitted to be stationed within their

borders. Our Communist countries are thus seven former Soviet countries (Russia,

Belarus, Estonia, Latvia, Lithuania, Moldova, and the Ukraine), Bulgaria, the Czech

Republic, Hungary, Poland, Romania, and Slovakia.13 The Western European

countries in our sample are: Austria, Belgium, Switzerland, Denmark, Spain, Finland,

France, Iceland, Italy, the Netherlands, Norway, Portugal, and Sweden. Results are

unchanged if we include the USA in the group of Western countries. We include eachcountry only in the years it reported consistently, resulting in an unbalanced panel.

Note that Germany is excluded from the analysis because our data do not allow us to

distinguish whether a translation after unification was in East or West Germany, and

in any case the country post collapse was a single market with a common language.

The UK is also excluded because it stopped reporting its translations to Unesco in

1990. However, we do use translations from all Western and Communist languages

flowing to these countries, including translations from English.Creation of translation series over time for some of these countries is complicated

by the fact they only became separate countries upon the upheaval of interest in

the middle of our period of study. Prior to 1992, the USSR as a whole reported

its translations; prior to 1993, Czechoslovakia as a whole reported its translations.

Our data provide a rare opportunity to nevertheless allocate the idea flows to the

constituent countries. Specifically, we allocate the translations reported by the USSR

and Czechoslovakia to one of their constituent countries based on the city in whicheach translation was published.

We note that the translations reported are only those that were submitted to

13We omit Yugoslavia because it escaped the Soviet sphere in the Tito-Stalin split of 1948, andAlbania because it withdrew from the Warsaw pact in 1968; thus in our period of interest they wereno longer politically aligned with the Soviet Union.




the central depository of the country. In particular, this excludes samizdat , the

illegal books published under the Communist regime. The exclusion of these titlesis unfortunate, but is unlikely to affect our analysis. The number of samizdat

translations produced under Communism is not available, but they were likely only

a small fraction of total translations. These illegal publications were largely political

magazines and bulletins defending human rights and criticizing repression. Although

some were poems and books, both locally written by dissidents and translated from

foreign publications, the large personal risk involved in owning such books meant

their circulation was limited, and the ideas contained therein were not available tothe general populace.

5.2.2 Translation of influential titles

To test the effect of the collapse of Communism on the most influential titles,

we extract from the Index Translationum data the translation patterns of titles

considered important and influential in the West. The titles selected, listed inAppendix C.3, are those given in any one of three lists. The first is the Central

and East European Publishing Project’s (CEEPP) list of the 100 books that have

been most influential in the West since 1945. This list was assembled in 1994, and

appeared in Garton Ash (1995). The second is the Modern Library’s list of the

100 best non-fiction books of the 20th century published in English.14 The third is

National Review’s best 100 non-fiction books of the 20th century.15 A considerable

number of titles appear in more than one of these lists. We include only titles that were

originally published before 1985 (to allow them enough time to have been translated

before the collapse), and we omit all titles that were not translated in any of our

14The “Board’s List”, available at http://www.randomhouse.com/modernlibrary/

100bestnonfiction.html.15http://www.nationalreview.com/100best/100_books.html




when Gorbachev came to power in the USSR. Among the reforms he instituted,

perhaps the most important two were perestroika , restructuring of the economy andpolitical system, and glasnost , openness in the media and culture. Through these

sets of gradual reforms, the Soviet Union began to move in the direction of a market

economy, with a decrease in centralization and the emergence of private firms, and the

increase in the freedom of people to express their views on a range of topics without

fear of retribution.

An important consequence of glasnost was that people could now openly air

their dissatisfaction with the Communist regime. This freedom spread to the Sovietsatellites, and was likely a contributing factor in revolutions that heralded the fall of

the Berlin Wall and the collapse of the Communist regimes in the Satellite countries

in the last few months of 1989.

The Communist USSR held together for nearly a further two years, though the

power of the Soviet Communists was waning and nationalism in the Soviet republics

was on the rise. Late in 1991, a conservative coup in Russia aimed at preventing

the disintegration of the Soviet Union was staged. Its unintended effect was just the

opposite; the USSR was officially dissolved.

The Communist countries had many commonalities, but there was heterogeneity

between them in the extent to which they had a Western orientation. The former

Soviet countries had a more Russian orientation, the preferences of their consumers

favored Western ideas less, and they maintained stronger ties with Russia and

demonstrated less effort or desire to integrate with Western Europe. However, the

three Baltic states of the Soviet Union, Latvia, Lithuania and Estonia, were more

similar to the Satellites than they were to the Soviet nations. Historically, they were

relatively recent additions to the USSR (annexed in 1940), and had always maintained

their more Western feeling. They were the first among the Soviet nations to declare

their independence from the Soviet Union. Furthermore, their independent streak was



5.3. HISTORICAL CONTEXT 125

highlighted when, upon the collapse of the Soviet Union, they were the only three

Soviet states not to join the Commonwealth of Independent States (CIS), the loosealliance of independent countries that succeeded the USSR. Since the disintegration

of the USSR, the former Communist countries have coalesced into two trading blocs:

the Russia-focused CIS countries in one, and the Western-centered non-CIS countries,

including the Baltic states, in the other. For this reason, our main analysis groups

the three Baltic states with the Satellites, but we note that the results are similar

when excluding them from the analysis or when assigning them to a separate group.

Figure 5.2 is a map showing the Soviet countries, Satellites plus Baltic states, andthe Western European countries in our analysis.

5.3.2 Restricting information flows:

Publishing and censorship under Communism

Prior to Gorbachev’s reforms, book publishing in the Soviet Union16 was a state-

run industry that produced vast numbers of books with little regard for consumer

demand.17 All publishers were owned and operated by the government, and each had

its own subject area or field in which it enjoyed a complete monopoly. Book prices,

like other prices and wages in the publishing industry, were strictly controlled; each

subject had a designated price range, chosen to ensure the subjects the government

intended to be widely read were available at low cost. Selection of the titles published

was centrally coordinated and crafted according to the government’s grand plan.18

Central to the organization of the Soviet publishing system was the conception

of publishing as an ideological activity. Reading was viewed as a way in which the

16We discuss the publishing and censorship system of the Soviet Union, which is the one bestunderstood by Western scholars and observers during the Communist period. The publishingindustries of the other Communist countries varied in their exact details, but were similar in theirprinciples.

17Skelly and Stabnikov (1993).18Walker (1978).




social consciousness of individuals was shaped, thus full state control over the material

published and its availability to citizens was vital. Profits and publishing in order tomeet demand were considered less important, though periodically concern surfaced

in Soviet publishing circles about the shortages of books in specific fields.

The process determining the exact titles printed in any year was complex and

centrally planned to a high degree. USSR-level and republic-level authorities decided

on the proportion of total books published in the coming year that would be in

each subject area, and assigned printing capacity, paper, and binding materials to

individual publishers. Working within these bounds and other specifications given tothem, publishers compiled their own lists of planned printings, each item on which

then received an approval, rejection, or other recommendation from a “coordinating”

central authority. Considerations for the coordinating authority were maintaining the

subject monopolies of the printing houses, avoiding duplication of subject matter, and

economy in the use of paper, which was often in short supply.

Additional centralized planning occurred that was related to the publication

of translations.19 Foreign titles were selected for translation by utilizing experts

employed for the purpose at home, representatives located in numerous countries

abroad, and foreign visiting experts such as scientists. The representatives located

abroad reviewed tens of thousands of new books annually. They then bought copies

of the most important titles from local bookshops, and mailed them back to their

publishers in the USSR.20

Censorship of books intended for sale in the USSR was the domain of Glavlit

(occasionally referred to by its full name, the “Chief Administration for the Protection

of State Secrets in the Press attached to the Council of Ministers of the USSR”).

Editors of publishing houses were expected to use their good sense in selecting titles

19Walker (1978).20Bernstein et al. (1971).



5.3. HISTORICAL CONTEXT 127

for publication, but the corrected galley-proofs (granki ) then had to be perused by

Glavlit “both for the mention of prohibited topics and for the observance of politicallines and nuances” (Walker, 1978, page 66) before publication could occur.

Censorship of translations followed a somewhat different, but undoubtedly no less

rigorous, process, explained by Walker (1978):

“The importance of careful and vigilant selection by Soviet publishers in

choosing works for translation from foreign languages has been frequently

stressed by Party and government, and is visible in a number of specialregulations applying to the publication of translations. A publishing-

house considering translation of a foreign work must, unless there is a

special need for speedy publication, obtain at least two recommendations

for the translation from scholarly institutions or specialists, and secure the

agreement of the appropriate chief editorial office in the State Committee

for Publishing before submitting details of the work for ‘coordination’ to

the State Committee or (in the case of scientific and technical works) tothe State Scientific and Technical Library.”

Between 1986 and 1991, control over the publishing industry moved out of

state hands. State-owned publishing houses were joined by a multitude of other

ownership structures, competition entered the industry, and the focus shifted away

from producer-led publishing to consumer-led publishing. The monopoly system of

publishers was scrapped; price controls and many state subsidies were terminated.Through the reforms, firms, organizations, and institutions gained the right to

publish, and Russian authors and publishers gained the right to freely buy or sell

rights, including in transactions with international parties.21

21Skelly and Stabnikov (1993).




5.4 Empirical strategy:

OLS and difference-in-differences estimates

Communism may have affected idea flows through its effects on the supply of ideas

and on the demand for ideas. On the supply side, the political agenda and censorship

depressed certain ideas and promoted others. Most notably, the Communist regime

depressed ideas centered around the capitalist ideology and promoted pro-communist

ideas. On the demand side, Communism may have shaped preferences for ideas (e.g.

for Communist ideas) and such preferences may or may not have changed with the

collapse of Communism (Alesina and Fuchs-Schundeln, 2007).

Our most basic identification strategy examines the effect of the collapse as a

whole, acting through either supply or demand channels. Specifically, we compare

translation flows in Communist countries before and after the collapse, where the

effect of the collapse depends on both the supply and demand sides. We then consider

a number of “counterfactuals” that shed light on the specific roles played by supply

and demand factors. First, we compare translation patterns in Soviet relative to

Satellite countries. While censorship suppressed Western ideas in both, Satellite

countries have always been more Western in their orientation and might have had

greater pent-up demand for translations. Differences in the effect of the collapse

between these two regions would depend on differences in their preferences for Western

and Eastern ideas. Second, we compare translation patterns in Eastern relative to

Western Europe. The premise here is that there were no censorship post collapse,

so that the degree of convergence between East and West post collapse reflected

remaining East/West differences in the demand for ideas. Finally, we repeat the

comparisons above by the type of ideas, such as translations of various book fields,

and translations of titles that posed more or less threat to the regime.

All of our regressions examine the change in translation patterns in former




Communist countries post collapse, and take a variation of the following form:

Y it = β 0 + β 1Postt + β 2X it + it (5.1)

where Y it is the (log) number of book translations in country i in year t .22 Postt

is a dummy variable for the years 1991 and onwards,23 and its coefficient measures

the change in translation patterns post collapse. Our control variables X it include

population and real GDP per capita. In some specifications, we include country fixed

effects to account for differences across countries that are constant over time.

We also estimate difference-in-differences models that compare the pre- and post-

collapse translation flows into Communist countries with flows into Western European

countries. The inclusion of Western European countries as a comparison group

accounts for other common factors that may have affected translation patterns over

the sample period 1980-2000. The basic difference-in-differences specification is:

Y it = β 0 + β 1Communisti × Postt + β 2Communisti + β 3Postt + β 4X it + it (5.2)

where Y it and Postt are as before, Communisti is a dummy variable for whether the

translating country was a former Communist country, and Communisti × Postt is

the interaction between these two variables. The coefficient on the latter variable

measures the effect of the collapse of Communism on translations into Communist

countries (relative to into Western European countries). In addition to specifications

that control for population and GDP and include country fixed effects, we also run

specifications with year fixed effects to absorb changes over time that are common to

all regions.

22The trivially few observations with zero values are dropped.23We choose post-1991 because it is midway between the end of Communism in the Satellites

(late in 1989) and the collapse of the Soviet Union (late in 1991). Using alternative Post variables,namely post-1989, post-1990, and post-1992, does not substantially alter the results (not presented).




In both the basic regression and difference-in-differences model, the construction

of the dependent variable is complicated by the lack of a one-to-one mapping betweencountries and languages. We deal with this by only counting translations into the

“main” language for each country, defined as the most widely spoken language in

the country.24 In Section 5.5.6 we show the main results are robust to also including

translations into secondary languages, and to using the number of pages translated

as an alternative dependent variable.

After testing the effect of the collapse of Communism on overall translations

in Section 5.5.1, we investigate heterogeneity in the magnitude of the effect acrossdifferent types of idea to shed light on what sorts of ideas were more restricted during

the Communist era and on what determined the degree of convergence to the West

post-collapse. We begin in Section 5.5.2 by allowing the effect to differ for translations

from Western and Communist languages, expecting mainly translations from Western

languages, which weren’t originally written under a Communist government, to

increase after the collapse of Communism.

To shed light on what determined convergence between East and West, in Section

5.5.3 we test whether the effect of the collapse was bigger for the Satellite countries,

which had a more Western orientation, than for the Soviet countries. Then, in

Section 5.5.4, we test whether the convergence we document reflected catching up

in translating old titles (stocks) or a convergence to Western levels in translating

current titles (flows).

In Section 5.5.5, we show that the changes in translation patterns that occurred

were not simply driven by general changes in the book industry, as total publications

of original books in Communist countries did not increase after Communism’s

collapse.

24“Most widely spoken” is defined in terms of native speakers where these data are available,otherwise in terms of the language spoken at home or spoken on a day-to-day basis.




We test for heterogeneity of the effect across book fields in Section 5.6. To further

shed light on the role of isolation during the Cold War, we test whether the effect of thecollapse was bigger for more “ideological” fields, such as philosophy and economics,

and whether it was bigger for titles that were perceived to be more threatening to the

regime. We note that, given censorship was lifted with the collapse of Communism,

remaining differences between Eastern and Western Europe post collapse reflect either

pre-existing differences in tastes between East and West, or a lack of convergence in

their tastes post collapse. We find that in fields such as history and arts, translations

of Western titles did not converge to Western levels, suggesting a lack of Easterninterest in the Western version of these fields, perhaps because they are relatively

culture specific.

Section 5.7 analyzes the translation patterns of the most influential Western titles

of the 20th century. This analysis reveals that the increase in translations of Western

titles in Communist Europe involved important ideas. Furthermore, it allows us to

see whether specific titles were available in translation in any Communist language

before the collapse of Communism, which could mean countries were accessing the

titles through a secondary language. This might be particularly relevant for the

case of Russian, which could be read by many people in Communist Europe even

outside Russia. In fact, we find most of these titles were not translated anywhere

in Communist Europe before the collapse of Communism. Finally, we collected

additional information on these titles that allowed us to test the extent to which

translations of specific authors and titles considered particularly threatening to

Communism increased more than translations of other titles with the collapse of

Communism.




5.5 The effect of the collapse of Communism on

total translations

Figure 5.3 shows translations per million inhabitants in the Soviet countries, the

Satellites, and the Western European countries. For each set of countries, we show

translations from Communist languages and Western European languages.25,26

This figure shows that before the collapse of Communism, Western European

countries had much higher translation rates into their main language than Communist

countries, and these translations were almost entirely from Western European

languages. The Satellites translated more than the Soviet countries, and both sets

translated primarily from Communist languages.

However, in the few years around 1990, the patterns of translation for Communist

countries changed drastically. The Satellites’ translations of Western European titles

shot up to approach the level of translations of Western European countries, and their

translations of Communist titles fell away.

By the year 2000, the Satellites’ translation patterns had converged to those

of Western European countries to a remarkable degree, though they still showed a

slight bias towards translations from other former Communist countries. The Soviet

countries also experienced a fall in translations from Communist languages, but their

increase in translations from Western European languages was small and short-lived.

These translation patterns stand in contrast to translations from Western European

languages in Western European countries, which increased only gradually and by

25The Communist languages are: Armenian, Azerbaijani, Belarusian, Bulgarian, Czech, Estonian,Georgian, Hungarian, Kazakh, Kirghiz, Latvian, Lithuanian, Moldovan, Polish, Romanian, Russian,Slovakian, Tajik, Turkmen, Ukrainian, and Uzbek. The Western European languages are: Danish,Dutch, English, Finnish, French, Modern Greek, Icelandic, Irish, Italian, Maltese, Norwegian,Portuguese, Spanish, and Swedish. Note the German language is neither classified as a Communistlanguage nor a Western European language.

26Translations from English show very similar changes over time to translations from all WesternEuropean languages.



5.5. THE EFFECT OF THE COLLAPSE ON TOTAL TRANSLATIONS 133

much less over this period. Similarly, translations from Communist languages in

Western Europe, which were few, showed little change over the period. We nextsubject these patterns to regression analysis.

5.5.1 Changes in overall translation patterns

We first estimate a simple OLS regression as in equation (5.1), predicting the number

of book translations in countryi

in yeart . The first three columns of Table 5.1 present

the regression results. The first column is a basic specification with no additional

controls. The second column adds controls for log population and log GDP per capita.

The third column adds country fixed effects. We see that translations in Communist

countries rose when Communism collapsed. We note that the main coefficient in

the specification without controls is positive but statistically insignificant, but we

show next that this simply masks opposite patterns of translations from Western and

Communist languages. When controls for population and GDP per capita are added,

the coefficient on Postt is large and significant, even when country fixed effects are

included. Translations in Communist countries increased by 120% (e0.799 − 1) after

the collapse of Communism (column 3).

In column 2, where country fixed effects are not included, the coefficients on

population and GDP per capita have the expected positive sign and are significant,

indicating richer and more populous countries translate more. When country fixed

effects are included, the coefficient on population becomes large and negative, butthis is based on little variation, and is probably driven by the population decreases

that occurred in many of the Communist countries post collapse.27

27In the specifications with country fixed effects, the coefficients on population and GDP percapita are identified off within-country correlation between population and translations.




5.5.2 Changes in translations from Western and Communist

languages

We expect translations from Western languages to be differently affected by the

collapse of Communism to translations from Communist languages. Specifically,

if Communism indeed suppressed information flows from the West, we expect

translations from Western languages to increase after the collapse of Communism.

Moreover, to the extent Communist countries artificially translated more from each

other during Communism, we expect translations from Communist languages to

decrease with the collapse of Communism.

For this reason, we allow the effect of the collapse of Communism to differ

between translations from Western languages and those from Communist languages.28

Specifically, we include a dummy variable for whether the translation is from a

Western European language (W esternLang j), and and its converse, a dummy for

the translation being from a Communist language (CommunistLang j):29

Y ijt =β 1aPostt × W esternLang j + β 1bPostt × CommunistLang j

+ β 2aWesternLang j + β 2bCommunistLang j + β 3X it + ijt (5.3)

where Y ijt is the (log) number of book translations from either a Communist language

or a Western European language, and j denotes Communist or Western original

language. The variables of interest in these specifications are the interactions Postt×

W esternLang j and Postt×CommunistLang j, whose coefficients measure the effect

of the collapse of Communism on translations from Western or Communist languages

into Communist countries. Our control variables X it include population, and GDP

28Results are unchanged when we focus on translations from the major languages only, namelyfrom English and Russian.

29Note CommunistLang +WesternLang = 1, so our specification is fully interacted with respectto the original language of the translation.




per capita; we also include specifications that fully interact the dummies for whether

the original language is Communist or Western European with country fixed effects.Under the hypothesis that Communism suppressed information flows from Western

into Communist Europe, we expect β 1a to be positive. The expected sign of β 1b is less

clear, but is expected to be negative if Communist countries substituted Communist

translations for Western ones pre collapse.

We next estimate difference-in-differences regressions that use Western Europe as

the comparison group. To allow translation patterns to differ between translations

from Western languages and those from Communist languages, we in fact estimatethe following regression:

Y ijt =β 1aCommunisti × Postt × W esternLang j

+ β 1bCommunisti × Postt × CommunistLang j

+ β 2aCommunisti × WesternLang j + β 2bCommunisti × CommunistLang j

+ β 3aPostt × WesternLang j + β 3bPostt × CommunistLang j


where the variables of interest in these specifications are the interactions

Communisti×Postt×WesternLang j and Communisti×Postt×CommunistLang j ,

whose coefficients measure the effect of the collapse of Communism on translations

from Western or Communist languages into Communist countries (relative to into

Western European countries).

Column 4-6 of Table 5.1 present the OLS estimation results of regression equation

(5.3), and columns 1-5 in Table 5.2 present the difference-in-differences estimates of

equation (5.4). Table 5.1 suggests that translations by Communist countries from

Western languages increased dramatically, by 480% (e1.761−1), but translations from

fellow Communist countries fell sharply, by 69%.




Because translations tended to increase in Western Europe during the 1990s,

the difference-in-difference estimates presented in Table 5.2 are generally smallerthan the OLS estimates, but they are still economically large and statistically

significant. Specifically, the first column of Table 5.2 is a basic difference-in-

differences specification with no additional controls. We see that, as suggested by

the graphs, Communist translations from Western European languages rose by 260%

when Communism collapsed, whereas translations between Communist countries fell

by 71%. These large magnitudes demonstrate just how dramatically the types of

translated titles available in Eastern Europe shifted when Communism collapsed.

The second column of Table 5.2 shows that these effects are robust to controlling

for log population and log GDP per capita.30 The third column adds country

fixed effects interacted with Communist and Western original languages; the main

results hold and remain significant. The fourth column is the most demanding

specification. It allows translations from Communist languages and from Western

European languages to be on different linear time trends in each country, and identifies

the effect of the collapse of Communism off changes in translations over and abovethese time trends. The main results hold up, though the decrease in translations from

Communist languages decreases in significance. Note, however, that this specification

may in fact underestimate the effect of the collapse of Communism on translations

because the changes that constituted the collapse of Communism were many and

occurred over several years around the date we attribute to the collapse, so some of

these changes are likely falsely attributed to the time trends in this specification. The

fifth column includes both country and year fixed effects; the results are unchanged.

Moreover, column 3 of Table 5.2 also shows that Western countries did not

translate more Communist titles post collapse; the coefficient on the interaction of

30We do not have comparable population or GDP data for Iceland, thus this country is excludedin the specifications where these controls are included.




Postt with CommunistLang j is small and statistically insignificant.

5.5.3 Changes in translations in Soviet and Satellite

countries

We next examine how the difference in East/West orientation between Soviet and

Satellite countries reveals itself in their translation patterns. We note that as an

alternative measure for Western-orientation among Communist countries, we use the

distance of a country from Western Europe, which proxies for cultural distance from

the West. Results (not shown) suggest a similar pattern: Western-to-Communist

translations increased post collapse more in former Communist countries located

closer to Western Europe.31 Specifically, we estimate the following specification:

Y ijt =β 1aSatellitei × Postt × W esternLang j

+ β 1bSatellitei × Postt × CommunistLang j


+ β 3aSatellitei × WesternLang j + β 3bSatellitei × CommunistLang j


where Satellitet is a dummy variable for whether the translating country is a Satellite

country. The main coefficients of interest are β 1a and β 1b, which capture whether

translations from Western European and Communist languages respectively increased

31As a second alternative, we divide the Communist countries by whether they are Slavic or non-Slavic, and by whether they are primarily Catholic or Orthodox. Translations in the Slavic countriesshow similar patterns to those in the Soviet nations, and translations in the non-Slavic countriesare similar to in the Soviet satellites. However, the Slavic/non-Slavic difference is less pronouncedthan the Soviet/satellite difference. Similarly, the Orthodox countries behave more like the Sovietnations and the Catholic countries more like the satellites, though the distinction here is smalleragain. The Slavic countries are Russia, the Ukraine, Belarus, the Czech Republic, Slovakia, Poland,and Bulgaria. The Catholic countries are Lithuania, Poland, the Czech Republic, Slovakia, andHungary.




more in the Satellites than in the Soviet countries when Communism collapsed.

To examine these translation patterns relative to translation patterns in Western

Europe, we run the difference-in-differences version of this OLS regressions equation:

Y ijt =β 1a1Communisti × Postt × WesternLang j

+ β 1a2Communisti × Satellitei × Postt × WesternLang j

+ β 1b1Communisti × Postt × CommunistLang j

+ β 1b2Communisti × Satellitei × Postt × CommunistLang j


+ β 3a1Communisti × W esternLang j

+ β 3a2Communisti × Satellitei × W esternLang j

+ β 3b1Communisti × CommunistLang j

+ β 3b2Communisti × Satellitei × CommunistLang j


The main coefficients of interest are now β 1a2 and β 1b2.

Columns 7-9 of Table 5.1 present the results from estimating OLS equation (5.5),

and columns 6-10 of Table 5.2 present results from estimating difference-in-differences

equation (5.6). The OLS and the difference-in-differences estimates show similar

results, and again the magnitudes of the changes are generally greater in the OLS.

We see the increase in translations from Western European languages was larger

for the Satellites, and the decrease in translations from Communist languages was

insignificantly larger for the Soviet countries. Satellite translations of Western titles

increased by 390% in the difference-in-differences specification with population and

GDP controls and country fixed effects (620% in the OLS specification, i.e. increased

by a factor of seven), compared with 51% for Soviet translations (120% in the OLS




specification). In contrast, translations of Communist titles decreased by 68% (70%,

i.e. decreased by two thirds) for Satellites and 74% (76%) for Soviet countries. Acomparison of column 6 with column 7 reveals that differences in income can account

for some but not all of the difference between the post-Communism translation

experiences of the Soviet countries and those of the Satellites.

To test how the effect of the collapse of Communism changed over time and how

similar Eastern and Western Europe become, we run a version of column 7 of Table

5.2 that replaces Post and its interactions with year dummies (for each year 1989

and onwards) and their equivalent interactions. The top half of Figure 5.4 showsthat the positive effect of the collapse of Communism on translations from Western

Europe increases until about 1992, and then stabilizes, especially for the Satellite

countries. The lower half of Figure 5.4 shows that the negative effect of the collapse

on translations between Communist countries increases until 1991, at which time it

largely stabilizes.32,33

5.5.4 Convergence in translation flows or catching up on

stocks?

As mentioned earlier, Figure 5.3 suggests that translations of Western titles in

Satellite countries nearly converged to their levels in Western countries. We note

that this figure understates convergence because it doesn’t control for GDP, which

was lower in Communist countries. Indeed, column 7 of Table 5.2 shows that

Satellite translations of Western titles post collapse are actually greater than Western

32Appendix Figure C.1 shows the equivalent graph where we also include country fixed effectsin the regression equation (equivalent to column 3 of Table 5.2). The effects are similar and moreprecisely estimated, but there it is not possible to compare Communist translations with the Westernlevel of translations.

33We present this figure for the difference-in-differences specification, but the equivalent graph forthe OLS specification looks nearly identical.




translations of these titles after controlling for population and GDP.34 Translations of

Western titles by Soviet countries, however, increased to just 8% of such translationsby Western countries.35

Figure 5.4 illustrates the dynamics of how the translation of Western titles in

Satellite countries converged to and even surpassed Western levels, but in Soviet

countries did not. The figure also shows that translations of Communist titles fell

over several years in both Soviet and Satellite countries but remained higher than

their level in the West.

This convergence of Communist to Western countries could reflect a convergencein the rate of translation of new titles (flows), or a catching up on older titles missed

out on during the Communist era (stocks). We now examine this issue.

Our data set does not lend itself easily to infer the years in which the original titles

were published. However, for the years 1985, 1993 and 1996, we sampled over 1,400

translations from Western languages, identified their original dates of publication

from online sources, and used these to estimate the age distribution of translations

of Western titles.We define flows as titles translated within 15 years of their publication, but our

findings hold for other cutoffs (10, 20, 30 years). We find that such titles make up

the majority of translations in most fields.36 Across fields, the median percentage of

translations that were flows in Communist Europe was 58% in the pre period and

71% post; in Western Europe it was 78% in the pre period and 82% post. We adjust

the total number of translations using these percentages corresponding to each field,

and repeat our main analysis for both flows and stocks.Table 5.3 shows our difference-in-differences regressions separately for flows and

340.687 + 1.337 - 3.249 + 1.777<0.35Specifically, the coefficient on Communist countries for translations from Western languages is

-3.249, and its interaction with post is 0.687, so Soviet translations of Western titles remain at 8%(e−2.562) of Western levels.

36Literature is the primary exception, where flows account for roughly half the titles translated.




stocks. Both translations of stocks and flows of Western titles show large increases

in Communist Europe upon the collapse of Communism. This suggests Communistcountries both began catching up on older titles, and increased their rate of translation

of current titles. Moreover, Communist countries overtook the West in their

translation of both newer and older titles. This suggests both a convergence in the

flow of new ideas, and a catching up on older ideas.

To illustrate these phenomena graphically, Figure 5.5 replicates Figure 5.3 for

flows and stocks separately. The figure illustrates how the Satellite’s translations of

new titles almost converge to their Western levels even without controlling for GDP,and their translations of old titles overshoot the levels in the West.

5.5.5 The collapse of Communism did not affect original

publications of books

One potential concern is that the increases in Western translations post collapse were

driven by changes in the publishing industry that allowed a larger total number of

books to be published. If this were the case, then the increase in translations could

be mechanical rather than indicating an increased openness to Western ideas.

Table 5.4 presents OLS and difference-in-differences specifications such as in

equations (5.1) and (5.2) with the total number of original books published as the

dependent variable.37 The table shows that the total number of original books

published in Communist countries did not increase with the collapse of Communism,

37Book publication data are from the Unesco Statistical Yearbooks for the years 1985-99 and fromUnesco’s online data on book production available at http://stats.uis.unesco.org/unesco/. Theyare available pre and post collapse for only a subset of our countries, namely the Communistcountries Belarus, Bulgaria, Estonia, Hungary, Latvia, Poland, Romania and the Ukraine, andthe Western European countries Belgium, Denmark, Finland, France, Iceland, Italy, Netherlands,Norway, Portugal, Spain, Sweden, and Switzerland. Note, however, that these data are only availableat an aggregate level and a large number of years are missing, which precludes using them to conductmore complex analysis.




and may have actually declined. Specifically, the coefficient of interest, which is the

coefficient on Post in the OLS specifications and on Post × Communism in thedifference-in-differences specifications, is negative and small in most specifications.

5.5.6 Further robustness checks

Number of pages translated as an alternative dependent variable

For robustness, we use the number of pages translated as an alternative dependent

variable that captures the possibility that longer books contain more ideas. Becausewe are concerned that some of the short publications might not in fact be books, we

limit translations to titles of 49 pages or longer (the minimum length for a “book”

as defined by Unesco). Appendix Table C.1 shows that the results are similar when

using this alternative dependent variable.

The Bertrand et al. critique of difference-in-differences estimators

Bertrand, Duflo and Mullainathan (2004) show that difference-in-differences tech-

niques applied to data with more than two periods generate inconsistent standard

errors because they do not account for serial correlation of the outcomes. To address

this critique, we follow their recommended procedure and collapse our data down to

one pre-collapse and one post-collapse observation. The pre-collapse values of the

variables are the averages for the years 1980 to 1989, and the post-collapse values

are the averages for 1992 to 2000. We discard data from 1990 and 1991, considering

this the transition period. Appendix Table C.2 shows the equivalent difference-in-

differences regressions to Table 5.2, but run with only these two observations for

each country/original language pair. Our main results remain large and statistically

significant. Specifically, the increase in Satellite translations from Western European

languages is significant at the 1% or 5% level in every specification, and the decrease




in translations between Communist countries is significant at the 10% level or better

in every specification but one.

Comparing Communist countries that transitioned to different degrees

We showed that the collapse of Communism was stronger in the Western-oriented

Satellites, whose translations of Western titles converged to Western levels. More

generally, we expect the countries that transitioned more into democratic market

economies to have experienced greater convergence to the West, namely to have

experienced larger increases in translations from the West, and greater declines in

translations from the East. We show in Appendix Section C.2, which also describes

the data and empirical strategy used in this analysis, that Communist countries that

transitioned more away from Communism increased their translations of Western

European titles more. We note that a main disadvantage of using variation in

the degree of transition is that unlike the single exogenous event of Communism

collapsing, these reforms were outcomes likely deriving from many of the same factorsas translations.

Accounting for translations into countries’ secondary languages

As a robustness check, we also include translations into secondary languages. We

include as secondary languages all additional languages that are (de facto) official in

part or all of the country, or that are natively spoken by at least 5% of the population.

Note specifically that this includes Russian in many of the Communist countries. As

shown in Appendix Table C.4 this does increase translations of Western titles in

the Soviet countries post collapse, but they still lag behind such translations in the

Satellite countries.




Accounting for Russian-speaking populations in other Communist coun-

tries

Our main analysis shows Soviet countries lag behind both Satellite and Western

countries in their translations of Western titles post collapse. To create a lower

bound on these differences, we include translations into Russian in each of the Soviet

countries in addition to translations into the country’s main language. The results

(not presented) are very similar to the specifications that include translations into

secondary languages, shown in Appendix Table C.4.38

Accounting for the possibility of Russia translating for other Communist

countries

A potential concern is that many translations into Communist languages might

actually be published in Russia, the largest of the Communist countries and the

political center of Communist Europe, rather than in the home country, in which case

we would under-report the ideas flowing into the other Communist countries. That is,

the concern is that translations from, for instance, English into Czech are published

in Russia. To account for this possibility, we ran specifications including Russia’s

translations into other Communist languages as translations in the appropriate

Communist countries. In fact, the number of such translations was very low and

the results (not presented) are effectively unchanged.

5.6 The effect of the collapse by book field

In this section we investigate how the effect of the collapse of Communism on book

translations varied by field. First we show the change in translations per capita

38We note that the Satellite countries translate very few titles into Russian; including translationsinto Russian as well as into the main language for all the Communist countries instead of just theSoviet countries makes no difference (results not presented).




because it tended to be unthreatening to Communism and was vital for Soviet power

on the world stage. Perhaps surprisingly given the advanced state of Exact Sciencein Communist Europe, Western translations of Communist Exact Science titles were

always very low. When Communism collapsed, Exact Science translations between

Communist countries fell away, but were gradually replaced by translations from

Western European languages.

More generally, Satellite translations of Western titles converged to Western levels

in all fields except for Art and History. Before the collapse of Communism, differences

in translation rates between Eastern and Western Europe reflected both the effect of

censorship in the Communist countries and differences in tastes. However, when

the Communist regime collapsed official censorship was abolished, thus post-collapse

differences are likely indicative of consumer preferences that differ considerably

between the two halves of Europe. Titles in Arts and History seem likely to contain

pervasive culture-specific aspects, which makes differences in preferences probable

and explains the lack of convergence of their translations post collapse.

5.6.2 Regression analysis by book field

We next estimate our second specification from Table 5.2 separately for translations

in each of the eight fields. We run for each field a difference-in-differences regression

predicting the log of translations plus one.40 Figure 5.7 plots the coefficients on

the two interactions of interest against each other. The axes in the figure are the

coefficients of interest multiplied by 100, which can approximately be thought of as

40For each field we also run two separate regressions, a probit regression predicting whether thenumber of translations is positive (extensive margin), and an OLS regression that estimates the lognumber of translations given the number of translations is non-zero (intensive margin). AppendixTable C.5 presents the coefficients on the interactions of interest in both regressions. The resultstell a similar story.



5.6. THE EFFECT BY BOOK FIELD 147

percentage changes in translation when Communism collapsed.41

The figure shows that the change in translations from Western European languagesand the change from Communist languages are positively correlated across fields. This

suggests the types of ideas that were considered helpful or harmful to the Communist

regime tended to be the same whether the original language was Communist or

Western European.

The axes, which show the extent to which translations “rebounded” when

Communism collapsed, can be approximately thought of as the extent to which the

translation of such ideas was suppressed under Communism. Religion translations,in the top right hand corner of the graph, were most highly suppressed under

Communism. Natural Science translations, in the lower left hand corner, were the

most encouraged under Communism from both types of language. However, the

comparatively small increases in translations of Western Arts and History titles

likely reflect a lack of taste for these books in Eastern Europe rather than a lack

of suppression of them under Communism. Another subject of particular interest is

Social Science, which was relatively suppressed from Western European sources underCommunism, but was among the most encouraged from Communist languages. This

seems to suggest that Communist countries had their own version of Social Science,

but they substituted away from it and towards the Western version when Communism

collapsed.

5.6.3 Regression analysis by book subfield

While our translation data divide titles into eight aggregate fields, we disaggregate

further each of these eight fields by searching for the most commonly used keywords

41When we allow the effect of the collapse of Communism to differ for Soviet countries relative toSoviet satellites (figure not presented), the relative positions of the subjects are similar for the twotypes of Communist countries, though the points for the Soviet countries are all shifted to the left.




in the book titles, and grouping these keywords by subfields such as mathematics,

physics and chemistry. We then test the effect of the collapse of Communism oneach subfield. In order to consistently categorize books by keywords in their titles,

we focus on titles translated from English (74% of the titles translated from Western

European languages) for which the original title is non-missing (79% of these titles).42

To select the keywords for which we search in each field, we first identified the words

that appear most frequently in titles translated in that field (e.g. physics, chemistry,

earth, and universe). We then discarded those that select titles that are not primarily

on a consistent topic. To the remaining informative common keywords we addedrelated keywords that also returned consistent topics.43 We then aggregated our

keyword searches into cohesive subfields.44,45 The percentage of titles captured by

this process ranges from roughly 20% to 55% in the various fields.46 Appendix C.4

lists the keywords contributing to each subfield. Appendix C.5 gives examples of the

titles found by each keyword search.

42Our results for the subfields identified by keyword searches are not driven by the restrictions to

titles translated from English or with non-missing original titles. Restricting from titles translatedfrom Western languages to titles translated from English in a difference-in-differences specificationpooling all fields increases the coefficient of interest from 1.34 to 1.78; subsequently restricting totranslations with non-missing original titles decreases it slightly to 1.62. These changes are smallrelative to the standard errors on the coefficient estimates.

43Note our searches also capture variant forms and spellings of the keywords (e.g. British andAmerican spellings), and obvious typographical errors.

44The aggregated subfields for each field are as follows. For Religion and Theology: Christian,Judeo-Christian, Judaism, theology, Islam, Eastern religions; for Education, Social Science and Law:economics, communism, political science, sociology and anthropology, and education; for Natural andExact Science: mathematics, physics, chemistry, biology, geology; for Applied Science: computers,business, medical, engineering, food, gardening. We do not present results from subfield keywordsearches in the fields Arts, Games and Sports, Literature, History, Geography, and Biography, orPhilosophy and Psychology because they are largely uninformative.

45Notice individual titles might be captured by more than one search, in which case they areattributed to both.

46The primary reasons why these percentages were not higher were that many titles areuninformative about the subject of the book (e.g. “Nowhere to Hide” by Susan Francis is anEnglishwoman’s story of her life in Iraq in the time of Saddam Hussein), and many others containonly keywords that appear in multiple contexts (e.g. the keyword “rights” appears in ThomasPaine’s classic on democracy “Rights of Man” and the title “Human Rights Violations In Zaire”.)



5.7. THE EFFECT ON INFLUENTIAL TITLES 149

To test which subfields jumped the most post collapse, within each field we

run a difference-in-differences regression that compares the effects across constituentsubfields. The coefficients of interest are the interactions of the subfield fixed effects

with the Post × Communist variable.

The coefficients of interest and their confidence intervals are shown in Figure

5.8, which suggests that even within fields, certain subfields increased more post

collapse. We find that within the field of Exact Science, mathematics titles jumped

less than titles in geology, physics, chemistry and especially biology. Within the Social

Science field, books related to economics jumped the most post collapse. Medicaltitles jumped more than any other titles in the Applied Science field; engineering

titles jumped the least. Within the field of Religion, books with Christian-related

words in their titles jumped more post collapse than Eastern Religion books and

books with Jewish-related or Islamic-related words in their titles. Titles in the field

History, Geography and Biography were difficult to categorize by keyword because of

the manner in which such books are titled. However, we were able to isolate early

history titles (approximately the prehistoric period until the renaissance), a periodabout which we expect Western and Eastern Europe to largely agree, and indeed

Communist translations of this category increased very little.

5.7 The effect of the collapse on translations of

influential titles

Since we have a small number of observations in our analysis of influential titles,

we limit ourselves to a simple pre/post, Communist/West comparison. This means

we need to use the same set of countries in every year we include in order to draw

conclusions about relative changes in Eastern compared with Western Europe. Thus




because some countries have missing data for some years, we consider three alternative

sub-samples for which we have consistent data. Our preferred sample, using the wholeperiod 1980-2000, consists of translations in the Communist countries Bulgaria, the

Czech Republic, Poland, Romania, Slovakia, Estonia, and Belarus, and the Western

European countries Spain, France, Denmark, Norway, Austria, and Belgium. The

first alternative sample also includes Russia, but only uses the period 1980-1996. The

second alternative sample differs from the preferred sample in that it also includes

Finland, Lithuania, Latvia, Iceland, and Moldova, but only uses the periods 1980-89

and 1995-2000. We present results for the preferred sample only, but results for thetwo alternative samples are similar.

A glance at the countries that translated the influential titles in the pre and

post periods reveals their translation in the Communist countries greatly increased

after the collapse of Communism. Furthermore, the majority of these titles that

were so influential to Western European thought were not published in translation

anywhere in Communist Europe before the collapse of Communism. Specifically,

only 19% of the titles were translated in the period 1980-88 anywhere in Communist

Europe, compared with 61% in the period 1989-2000. Note this implies the collapse

of Communism didn’t merely cause Communist countries to translate into their own

languages titles they’d previously had access to in another Communist language,

such as Russian; it actually increased the titles available in any Communist language.

In contrast, Western Europe had already translated 72% of the titles in the pre

period. Our sample of the titles most frequently translated in Western Europe was

also strongly affected; 30% were translated in the Communist region in the pre period,

and 66% post.



5.7. THE EFFECT ON INFLUENTIAL TITLES 151

To formally test the effect of the collapse on influential titles, we run the following

title- and author-level difference-in-differences specification:47

Y ijt =αi + γ iPostt + β 1Postt × Communist j × AntiComm Authori

+ β 2Postt × Communist j + β 3Communist j × AntiComm Authori

+ β 4Communist j + ijt (5.7)

where Y ijt is the log of the number of countries translating title i or alternatively

author i (plus one). The dependent variable is defined over the two periods pre (1980-1988) and post (1989-2000) and the two regions Western Europe and Communist

Europe.48 Postt is a dummy for post Communism’s collapse, and AntiComm Authori

is a dummy for whether the author of title i voiced explicitly anti-Communist

opinions. We also include title (or author) fixed effects to test the effect of the

collapse within a title (or an author). We interact these title fixed effects with the

post dummy to allow each title to be translated differently post. The coefficient of

interest is β 1, which tests the extent to which the translations of anti-Communistauthors increased more than the translations of other authors post collapse.

As an alternative to examining the translation of influential titles, we examine

the translation of titles by influential authors. The authors we use are those with a

47OLS regressions that compare Communist countries before and after the collapse yield similarresults (not shown).

48Note this cutoff date of 1989 for “post” differs to the 1991 used in the analysis of the totalnumber of translations. The reason we prefer the 1989 cutoff for the analysis of individual titlesis that by 1989 Gorbachev’s reforms had greatly reduced the Communist regime’s restrictions oninformation flows, so we don’t want to attribute a translation published in 1989 to the pre-collapseperiod. The results are qualitatively similar when using 1991 as the first “post” year, but they aresometimes less significant because some anti-Communist authors were translated as early as 1989,e.g. von Hayek’s famous “The Road to Serfdom”. When dropping the two transition years 1989 and1990 and using 1991 as the first “post” year, the results are unchanged and highly significant. Wealso note that the results from the analysis of the total number of translations discussed in equations1-6 are robust to defining post as 1989 onwards, but there we choose the 1991 cutoff because we testfor an average effect and because Communism did not collapse in the Satellites until 1991.




book appearing on one of the three lists of influential titles given in Section 5.2.2. As

a second alternative that captures readership rather than critics’ views, we take thetitles most frequently translated in Western Europe in the period 1980-2000 (30 from

each field). Compared with the influential titles, these titles, listed in Appendix C.6,

are more likely to be classics or popular works, and less likely to be academic. We

run alternative specifications that replace the anti-Communist author variable with

dummies for whether the title was published during the Communist era and whether

it was published during the Cold War. The premise is that titles published during the

Communist era, especially during the Cold War, would be more threatening to theCommunist regime and thus more likely to be translated by Communist countries only

post collapse. We also run alternative specifications that test whether authors who

won the Nobel prize, and are thus potentially even more influential, were translated

more by Communist countries post collapse.

Table 5.5 presents the estimation results for our preferred sample of countries and

years. The first six columns are author- and title-level regressions of influential titles,

and the last three columns present results from title-level regressions for the mosttranslated titles. We find that overall Communist translation of titles and authors

considered influential in the West and of the most translated titles increased sharply

and significantly post collapse.

Furthermore, compared with other influential titles, titles written by Nobel

laureates and titles first published during the Communist period were both translated

less pre collapse and increased more post collapse. Similarly, titles whose authors

voiced anti-Communist opinions were translated less in Communist countries than

other influential titles pre collapse (significantly in the author specification), but their

translation increased more post collapse to the point they were actually translated

more than other titles. These patterns suggest such titles were more threatening

to the Communist regime, and later increased in popularity, likely because of their




both an increase in Satellites’ translations of older titles and a jump in translations

of newer titles, which reached Western levels. These findings are consistent with bothcatching up on the stock of ideas that were missed out on under Communism and a

convergence between Satellite countries and Western Europe in the diffusion of new

Western ideas. In contrast, we find that the collapse of Communism had little effect

on Western translations in Soviet countries, suggesting the diffusion of Western ideas

into these countries was limited.

The effects of the collapse of Communism varied substantially by book field.

Specifically, we find evidence consistent with some types of Western ideas flowingmore than others into Communist countries. First, Western ideas that were more

suppressed under Communism jumped more after the collapse. The translation of

religious and philosophy titles was heavily suppressed under Communism and jumped

substantially post collapse, but the translation of scientific titles was affected to

a much smaller degree. When focusing on a subset of titles considered the most

influential, we find titles whose authors voiced anti-Communist opinions, titles written

during the Communist era, and titles written by Nobel Laureates were translated lessthan other titles under Communism, and experienced larger increases in translation

post collapse.

Second, the degree of convergence to Western levels of translations varied

substantially across types of Western ideas. Whereas Satellites’ translations of

Western titles in the more scientific fields, which likely contain knowledge that is

more useful for economic development, reached their levels in Western Europe post

collapse, translations in Art and History, which are more cultural, did not increase

by as much.

A key lesson from our study is that incentives play a major role in shaping the

international flow of knowledge. Distortion of these incentives by institutions can

have long-lasting effects that can only be remedied by institutional change.



5.8. CONCLUSIONS AND DISCUSSION 155

Naturally, book translations have a number of limitations as a measure of the flow

of ideas. They only allow us to measure idea flows across language barriers, whichprecludes measuring idea flows between countries that share a language, or between

linguistically similar groups within a country. Furthermore, because of the length of

time it takes to write a book, they tend not to capture very new ideas. In addition,

some people are able to read multiple languages, so have access to ideas before they

are translated.49 Finally, ideas in books must by definition be codifiable as opposed

to tacit. That is, they must be able to be expressed in words and written down.

Despite these limitations, translations are an attractive measure of the interna-tional flow of ideas because they capture flows of non-rival, disembodied ideas, and

their key purpose is to transmit written ideas, information and/or knowledge between

languages. Moreover, they are both quantifiable and classifiable by field and specific

content, and thus lend themselves naturally to empirical work.

49However, it is reasonable to assume that such a person finds it less costly to read in his ownlanguage, thus an increase in translations into his native language implies a reduced cost of accessto information.





Figure 5.1: Translation dates of three influential titles




Figure 5.2: Map of Communist and Western Europe




Figure 5.3: Translations in Communist and Western Europe

Notes: This figure shows translations from Western European and Communist languages inthe former Soviet countries, the Satellite countries, and Western European countries. Thevalues are averages over the countries in the regions, and include translations into the mainlanguage of the country only.




Figure 5.4: The effects over time of the collapse of Communism ontranslations

Translations from Western European languages

Translations from Communist languages

Notes: The coefficients plotted are from the estimation of a version of equation (5.6) inwhich the post dummy and its interactions have been replaced by year dummies (for 1989-2000) and their equivalent interactions. Controls for population and GDP per capita are also

included. The top two figures show coefficients and 95% confidence intervals on interactionsof the year dummies with Western translations in Soviet countries (left panel) and inSatellite countries (right panel). The Western level line is the negative of the coefficient onSoviet (left panel) or Satellite (right panel). The lower two figures show the equivalent fortranslations from Communist languages.




Figure 5.6: Translations by field

Notes: See the notes for Figure 5.3.




Figure 5.7: Effects of the collapse by field

Notes: This figure plots the coefficients (x100) on Communisti × Postt ×WesternLang j(x axis) and Communisti × Postt × CommunistLang j (y axis) from equation (5.4) (withcontrols for log population and GDP per capita) run separately for each subject. Thedependent variable is the log of translations plus one. These coefficients (approximately)measure the percentage change in Communist translations caused by the collapse of

Communism.




Figure 5.8: Effects of the collapse on translations from English bysubfield

Notes: The regressions that give rise to these coefficients are difference-in-differencesregressions comparing Communist with Western Europe, run by field as described in Section5.4.




T a b l e 5 . 1 : B e f o r e / a f t e r a n a l y s i s : T h e e f f e c t o f t h e c o l l a p s e o f

C o m m u n i s m o n t r a n s l a t i o n s

D e p e n d e n t v a r i a b l e : l o g n u m

b e r o f t r a n s l a t i o n s

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

P o s t

0 . 4

3 9

0 . 9

2 6 * * *

0 . 7

9 9 * *

( 0 . 2

9 3 )

( 0 . 2

1 6 )

( 0 . 2

6 7 )

T r a n s l a t i o n s f r o m W e s t e r n

o r i g i n a l l a n g u a g e s i n t e r a c t e d w i t h :

P o s t

1 . 5

8 9 * * *

2 . 0 1 4

* * *

1 . 7

6 1 * * *

0 . 2

7 0

0 . 8

9 3 *

0

. 8 0 6 * *

( 0 . 2

5 9 )

( 0 . 2 2 6 )

( 0 . 1

7 9 )

( 0 . 2

7 4 )

( 0 . 4

8 3 )

( 0 . 3

3 3 )

S a t e l l i t e c o u n t r y * p o s t

1 . 7

4 1 * * *

1 . 2

7 1 * *

1 . 1

6 8 * * *

( 0 . 3

3 0 )

( 0 . 4

5 2 )

( 0 . 3

3 6 )

T r a n s l a t i o n s f r o m C o m m u n i s t o r i g i n a l l a n g u a g e s i n t e r a c t e d w i t h :

P o s t

- 1 . 3

7 0 * * *

- 0 . 9 4 5

* * *

- 1 . 1

6 0 * * *

- 1 . 7

7 6 * * *

- 1 . 1

5 4 * *

- 1

. 4 2 1 * * *

( 0 . 1

7 9 )

( 0 . 1 1 3 )

( 0 . 1

8 6 )

( 0 . 4

1 1 )

( 0 . 4

4 5 )

( 0 . 4

5 3 )


0 . 5

5 9

0 . 0

9 1

0 . 2

0 6

( 0 . 4

4 5 )

( 0 . 3

7 5 )

( 0 . 4

8 4 )

O t h e r c o n t r o l s :

R e a l G D P p e r c a p i t a ( l n )

1 . 7

1 6 * * *

1 . 2

6 6 *

1 . 4 9 4

* * *

0 . 6

9 1 *

0 . 9

8 9 *

0 . 2

8 8

( 0 . 3

9 7 )

( 0 . 6

1 6 )

( 0 . 2 9 0 )

( 0 . 3

3 1 )

( 0 . 5

5 2 )

( 0 . 3

5 3 )

P o p u l a t i o n ( l n )

0 . 6

2 4 * * *

- 8 . 6

2 1 * *

0 . 5 4 9

* * *

- 4 . 9

5 3 * *

0 . 7

1 7 * * *

- 2 . 9

3 0

( 0 . 0

9 2 )

( 3 . 2

4 2 )

( 0 . 0 9 1 )

( 2 . 0

9 6 )

( 0 . 1

5 6 )

( 1 . 8

1 0 )

W e s t e r n o r i g i n a l l a n g u a g e d u

m m y

Y e s

Y e

s

Y e s

Y e s

Y e s

Y e s

C o m m u n i s t o r i g i n a l l a n g u a g e d u m m y

Y e s

Y e

s

Y e s

Y e s

Y e s

Y e s

S a t e l l i t e c o u n t r y * W e s t e r n o

r i g i n a l l a n g u a g e

Y e s

Y e s

S a t e l l i t e c o u n t r y * C o m m u n i s t o r i g i n a l l a n g u a g e

Y e s

Y e s

C o u n t r y f i x e d e f f e c t s

Y e s

C o u n t r y f i x e d e f f e c t s * W e s t e r n o r i g i n a l l a n g u a g e

Y e s

Y e s

C o u n t r y f i x e d e f f e c t s * C o m m u n i s t o r i g i n a l l a n g u a g e

Y e s

Y e s

R - S q u a r e d

0 . 0

2 8

0 . 3

5 6

0 . 7

4 0

0 . 2

4 5

0 . 4 2 2

0 . 8

6 9

0 . 4

2 5

0 . 6

6 1

0 . 8

8 0


2 5 6

2 5 6

2 5 6

5 1 1

5 1 1

5 1 1

5 1 1

5 1 1

5 1 1

A n o b s e r v a t i o n i s a :

c o u n t r y , y e a r

c o u n t r y , y e a r , o r i g i n a l l a n g u a g e ( W e s t e r n o r C o m m u n i s t )

N o t e s : A l l c o l u m n s

a r e O L S r e g r e s s i o n s u s i n g a n n u a l d a t a f o r t h e p e r i o d 1 9 8 0 - 2 0 0 0 . C o l u m n s 1 - 3 e s t i m a t e e q u a t i o n ( 5 . 1 )

f r o m t h e p a p e r ; c o l u m n s 4 - 6 e s t i m a t e e q u a t i o n ( 5 . 3 ) ; c o l u m n s 7 - 9 e s t i m a t e e q u a t i o n ( 5 . 5 ) . T h e c o u n t r i e s u s e d

i n t h e

a n a l y s i s a r e R u s s i a ,

B e l a r u s , E s t o n i a , L a t v i a , L i t h u

a n i a , M o l d o v a , t h e U k r a i n e , B u l g a r i a , t h e C z e c h R e p u b l i c , H u

n g a r y ,

P o l a n d , R o m a n i a , a n d S l o v a k i a . W e i n c l u d e t h e t h r e e B a l t i c c o u n t r i e s i n t h e S a t e l l i t

e c o u n t r i e s ( s e e e x p l a n a t i o n i n S

e c t i o n

5 . 3 . 1 ) . T h e C o m m u

n i s t a n d W e s t e r n o r i g i n a l l a n g u

a g e s a r e g i v e n i n f o o t n o t e 2 5 . W e i n c l u d e t r a n s l a t i o n s i n t o t h e

m a i n

l a n g u a g e o f t h e c o u n t r y o n l y .

P o s t i s a d u m m y f o r 1 9 9 1 o n w a r d s . S t a n d a r d e r r o r s , i n p a r e n t h e s e s , a r e c l u s t e r e d

a t t h e

c o u n t r y l e v e l . * p < 0 . 1 0 , * * p < 0 . 0 5 , * * * p < 0 . 0 1 .




T a b l e 5 . 2 : D i f f e r e n c e - i n - d i f f e r e n c e s a n a

l y s i s : T h e e f f e c t o f t h

e c o l l a p s e o f C o m m u n i s m o n

t r a n s l a t i o n s

D e p e n d e n t v a r i a b l e : l o g n u m b

e r o f t r a n s l a t i o n s

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

( 1 0 )

T r a n s l a t i o n s f r o m W e s t e r n o

r i g i n a l l a n g u a g e s i n :

C o m m u n i s t c o u n t r y * p o s t

1 . 2 6 8 * * *

1 . 8 9 7 * * *

1 . 3 6 1 * * *

0 . 7 9 9 * *

1 . 4 2 8 * * *

- 0

. 0 5 0

0 . 6 8 7

0 . 4 0 9

0 . 1 9 2

0 . 5 0 8

( 0 . 2 8 3 )

( 0 . 2 6

9 )

( 0 . 2 3 3 )

( 0 . 3 4 4 )

( 0 . 2 5 6 )

( 0

. 2 9 6 )

( 0 . 5 1 1 )

( 0 . 3 6 1 )

( 0 . 6 0 7 )

( 0 . 4 0 7 )


1 . 7

4 1 * * *

1 . 3 3 7 * * *

1 . 1 8 3 * * *

0 . 8 6 4

1 . 1 3 7 * * *

( 0

. 3 2 3 )

( 0 . 4 1 0 )

( 0 . 3 2 5 )

( 0 . 5 3 0 )

( 0 . 3 3 7 )

C o m m u n i s t c o u n t r y

- 2 . 6 0 8 * * *

- 1 . 7 3 9

* * *

- 3 . 3

7 1 * * *

- 3 . 2 4 9 * * *

( 0 . 4 8 4 )

( 0 . 4 9

8 )

( 1

. 0 5 6 )

( 0 . 9 0 5 )

S a t e l l i t e c o u n t r y

1

. 1 0 2

1 . 7 7 7 * *

( 1

. 0 7 6 )

( 0 . 6 7 8 )

P o s t

0 . 3 2 1 * *

0 . 0 4

3

0 . 3 8 0 * *

0 . 1 3 8

0 . 3 2 1 * *

0 . 1 1 0

0 . 3 7 9 * *

0 . 1 3 5

( 0 . 1 2 5 )

( 0 . 1 3

5 )

( 0 . 1 5 3 )

( 0 . 1 2 9 )

( 0

. 1 2 5 )

( 0 . 1 5 0 )

( 0 . 1 5 4 )

( 0 . 1 2 9 )

T r a n s l a t i o n s f r o m C o m m u n i s t o r i g i n a l l a n g u a g e s i n :


- 1 . 2 5 3 * * *

- 0 . 5 8 2

* * *

- 1 . 0 9 5 * * *

- 1 . 3 4 9 * * *

- 1 . 0 0 9 * * *

- 1 . 6

5 9 * * *

- 0 . 8 8 0 *

- 1 . 3 5 4 * *

- 1 . 1 5 9

- 1 . 2 5 1 * *

( 0 . 2 3 5 )

( 0 . 2 0

6 )

( 0 . 2 6 7 )

( 0 . 4 6 9 )

( 0 . 2 9 2 )

( 0

. 4 3 1 )

( 0 . 4 8 2 )

( 0 . 4 9 2 )

( 0 . 7 8 4 )

( 0 . 5 0 7 )


0

. 5 5 9

0 . 1 5 8

0 . 2 2 1

- 0 . 2 7 6

0 . 1 9 5

( 0

. 4 3 5 )

( 0 . 3 5 4 )

( 0 . 4 6 9 )

( 0 . 7 2 0 )

( 0 . 4 6 9 )


1 . 7 7 5 * * *

2 . 5 8 3 * * *

1 . 8

4 6 * * *

1 . 9 0 7 * * *

( 0 . 3 3 1 )

( 0 . 4 2

4 )

( 0

. 4 3 6 )

( 0 . 4 7 1 )


- 0

. 1 0 2

0 . 5 7 3

( 0

. 3 9 5 )

( 0 . 5 0 2 )

P o s t

- 0 . 1 1 7

- 0 . 4 3 7 * *

- 0 . 0 8 4

0 . 1 2 4

- 0

. 1 1 7

- 0 . 3 6 9 * *

- 0 . 0 8 6

0 . 1 2 1

( 0 . 1 5 7 )

( 0 . 1 6

0 )

( 0 . 1 7 4 )

( 0 . 1 9 1 )

( 0

. 1 5 7 )

( 0 . 1 7 2 )

( 0 . 1 7 2 )

( 0 . 1 9 1 )


W e s t e r n o r i g i n a l l a n g u a g e d u m

m y

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

C o m m u n i s t o r i g i n a l l a n g u a g e

d u m m y

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

P o p u l a t i o n a n d G D P c o n t r o l s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

C o u n t r y f i x e d e f f e c t s * W e s t e r n o r i g i n a l l a n g u a g e

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

C o u n t r y f i x e d e f f e c t s * C o m m

u n i s t o r i g i n a l l a n g u a g e

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

C o u n t r y - s p e c i f i c t i m e t r e n d s *

W e s t e r n o r i g i n a l l a n g u a g e

Y e s

Y e s

C o u n t r y - s p e c i f i c t i m e t r e n d s *

C o m m u n i s t o r i g i n a l l a n g u a g e

Y e s

Y e s

Y e a r f i x e d e f f e c t s * W e s t e r n o r i g i n a l l a n g u a g e

Y e s

Y e s

Y e a r f i x e d e f f e c t s * C o m m u n i s t o r i g i n a l l a n g u a g e

Y e s

Y e s

R - S q u a r e d

0 . 5 7 9

0 . 6 7

3

0 . 9 2 1

0 . 9 4 2

0 . 9 2 8

0

. 6 4 1

0 . 7 6 4

0 . 9 2 5

0 . 9 4 3

0 . 9 3 2


1 , 0 0 0

9 6 4

9 6 4

9 6 4

9 6 4

1

, 0 0 0

9 6 4

9 6 4

9 6 4

9 6 4

A n o b s e r v a t i o n i s a c o u n t r y , y e a r , o r i g i n a l l a n g u a g e ( W e s t e r n o r C o m m u n i s t )




N o t e

s : Al l c ol umn s ar e d i ff er en c e-i n- d

i ff er en c e s OL S r e gr e s s i on s u s i n g

ann u al d a t af or t h e p er i o d 1 9 8 0 -2

0 0 0 , wi t h C omm uni s t

E ur o

p e a s t h er e gi on of i n t er e s t an d W

e s t er nE ur o p e a s t h e c om p ar i s on gr o u p. C ol umn s 1 - 5 e s t i m a t e e q u a t i on ( 5 .4 ) f r om t h e

p a p e

r ; c ol umn s 6 -1 0 e s t i m a t e e q u a t i on ( 5 . 6 ) .Th e C omm uni s t c o un t r i e s u s e d i n t h e an al y s i s ar e R u s s i a ,B el ar u s ,E s t oni a ,

L a t v

i a ,L i t h u ani a ,M ol d o v a , t h e Uk r ai n e ,B ul g ar i a , t h e Cz e ch R e p u

b l i c ,H un g ar y ,P ol an d , R om ani a , an d S l o v ak i a.Th e

W e s t er nE ur o p e an c o un t r i e s u s e d ar eA u s t r i a ,B el gi um , S wi t z er l an d ,D enm ar k , S p ai n ,F i nl an d ,F r an

c e ,I c el an d ,I t al y , t h e

N e t h

er l an d s ,N or w a y ,P or t u g al , an d S w e d en. W ei n cl u d e t h e t h r e eB al t i c c o un t r i e s i n t h e S a t el l i t e c o un t r i e s ( s e e ex pl an a t i on

i n S e c t i on 5 . 3 .1 ) .Th e C omm uni s t an d W e s t er n or i gi n al l an g u a g e s a

r e gi v eni nf o o t n o t e2 5 . W ei n c

l u d e t r an s l a t i on s i n t o

t h em ai nl an g u a g e of t h e c o un t r y onl y.P o s t i s a d umm yf or 1 9 9 1

on w ar d s .P o p u l a t i o n a n d GDP

c o n t r o l s ar e t h el o g s

of p o p ul a t i on an d of r e al GDP p er c a pi t a. C o u n t r y- s p e c i fi c t i m e t r e n d s ar el i n e ar . S t an d ar d er r or

s ,i n p ar en t h e s e s , ar e

cl u s t

er e d a t t h e c o un t r yl e v el .* p < 0

.1 0 ,* * p < 0 . 0 5 ,* * * p < 0 . 0 1 .




T a b l e 5 . 3 : C o n v e r g e n c e a n a l y s i s : T h e e f f

e c t o f t h e c o l l a p s e o f C

o m m u n i s m o n t r a n s l a t i o n s o f

r e c e n t v e r s u s o

l d e r W e s t e r n t i t l e s

D e p e n d e n t v a r i a b l e : l o

g n u m b e r o f t r a n s l a t i o n s f r o m a W e s t e r n o r i g i n a l l a n g u a g e

F l o w s : t i t l e s 1 5 y e

a r s o l d a n d n e w e r

S t o c k s : t i t l e s o l d e r t h a n 1 5 y e a r s

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

( 1

0 )

P o s t * C o m m u n i s t c o u

n t r y

1 . 4

1 7 * * *

2 . 1 1

4 * * *

1 . 4 8

5 * * *

0 . 7

2 7 *

1 . 4

0 8 * * *

1 . 2

6 3 * * *

1 . 9

6 0 * * *

1 . 3

3 1 * * *

0 . 5

7 3

1 . 2 5

4 * * *

( 0 . 2

8 3 )

( 0 . 3

5 2 )

( 0 . 3

0 5 )

( 0 . 3

8 5 )

( 0 . 3

2 5 )

( 0 . 2

8 3 )

( 0 . 3

5 2 )

( 0 . 3

0 5 )

( 0 . 3

8 5 )

( 0 . 3 2 5 )


- 2 . 9

6 6 * * * - 1 . 9

9 7 * * *

- 2 . 0

2 9 * * *

- 1 . 0

6 1 *

( 0 . 4

8 4 )

( 0 . 5

9 7 )

( 0 . 4

8 4 )

( 0 . 5

9 7 )

P o s t

0 . 4

2 8 * * *

0 . 1 1

9

0 . 5 3

0 * * *

0 . 2

3 6

- 0 . 0

2 7

- 0 . 3

3 5 *

0 . 0

7 6

- 0 . 2

1 9

( 0 . 1

2 5 )

( 0 . 1

7 8 )

( 0 . 1

7 3 )

( 0 . 1

3 9 )

( 0 . 1

2 5 )

( 0 . 1

7 8 )

( 0 . 1

7 3 )

( 0 . 1

3 9 )

P o p u l a t i o n a n d G D P c o n t r o l s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y

e s

C o u n t r y f i x e d e f f e c t s

Y e s

Y e s

Y e s

Y e s

Y e s

Y

e s

C o u n t r y - s p e c i f i c t i m e

t r e n d s

Y e s

Y e s


Y e s

Y

e s

R - S q u a r e d

0 . 4

7 8

0 . 6

1 0

0 . 9

3 4

0 . 9

5 7

0 . 9

4 3

0 . 2

6 9

0 . 4

5 3

0 . 9

0 8

0 . 9

4 0

0 . 9 2 0


5 0 0

4 8 2

4 8 2

4 8 2

4 8 2

5 0 0

4 8 2

4 8 2

4 8 2

4 8 2

A n o b s e r v a t i o n i s a c o

u n t r y , y e a r


a r e d i ff e r e n c e - i n - d i ff e r e n c e s O L S r e g r e s s i o n s ( e q u a t i o n ( 5 . 2 ) ) u s i n g a n n u a l d a t a f o r t h e p e r i o d

1 9 8 0 -

2 0 0 0 , w i t h C o m m u n i s t E u r o p e a s t h e r e g i o n o f i n t

e r e s t a n d W e s t e r n E u r o p e a s t h e c o m p a r i s o n g r o u p . T h e d e p e n d e n t

v a r i a b l e f o r c o l u m n s 1 - 5 i s t r a n s l a t i o n s o f r e c e n t t i t

l e s , a n d f o r c o l u m n s 6 - 1 0 i s t r a n s l a t i o n s o f o l d e r t i t l e s . S e e t h e

n o t e s

t o T a b l e 5 . 2 f o r t h e

C o m m u n i s t a n d W e s t e r n c o u n t r

i e s u s e d . T h e W e s t e r n o r i g i n a l l a n g u a g e s a r e g i v e n i n f o o t n o t e 2

5 . W e

i n c l u d e t r a n s l a t i o n s

i n t o t h e m a i n l a n g u a g e o f t h e c o u n t r y o n l y .

P o s t i s a d u m m y f o

r 1 9 9 1 o n w a r d s . P o p u

l a t i o n a n d

G D P

c o n

t r o l s a r e t h e l o g s

o f p o p u l a t i o n a n d o f r e a l G D P p e r c a p i t a .

C o u n

t r y - s p

e c i fi c

t i m e

t r e n

d s a r e l i n e a r . S t a n d a r d e r r o r s , i n

p a r e n t h e s e s , a r e c l u s t e r e d a t t h e c o u n t r y l e v e l . * p < 0 . 1 0 , * * p < 0 . 0 5 , * * * p < 0 . 0 1 .




T a b l e 5 .4 : T otal

p ubli cati on

s :

The

effe ct

of

the

c ollap s e

of

C omm uni s m

on

t otal

b o ok

p ubli cati on s

D e p

e n d e n t v a r i a b l e : l o gn um b e r of t r a n s l a t i on s f r om a W e s t e r n or i gi n a l l a n g u a g e

F

l o w s : t i t l e s 1 5 y e ar s ol d an d n e w e r

S t o c k s : t i t l e s ol d e r t h

an1 5 y e ar s

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

( 1 0 )

P o s t * C omm uni s t c o un t r

1 .4 1 7 * * *

2 .1 1 4 * * *

1 .4 8 5 * * *

0 . 7 2 7 *

1 .4 0 8 * * *

1 .2 6 3 * * *

1 . 9 6 0 * * *

1 . 3 3 1 * * *

0 . 5 7 3

1 .2 5 4 * * *

( 0 .2 8 3 )

( 0 . 3 5 2 )

( 0 . 3 0 5 )

( 0 . 3 8 5 )

( 0 . 3 2 5 )

( 0 .2 8 3 )

( 0 . 3 5 2 )

( 0 . 3 0 5 )

( 0 . 3 8 5 )

( 0 . 3 2 5 )

C om

m uni s t c o un t r

-2 . 9 6 6 * * * -1 . 9 9 7 * * *

-2 . 0 2 9 * * *

-1 . 0 6 1 *

( 0 .4 8 4 )

( 0 . 5 9 7 )

( 0 .4 8 4 )

( 0 . 5 9 7 )

P o s t

0 .4 2 8 * * *

0 .1 1 9

0 . 5 3 0 * * *

0 .2 3 6

- 0 . 0 2 7

- 0 . 3 3 5 *

0 . 0 7 6

- 0 .2 1 9

( 0 .1 2 5 )

( 0 .1 7 8 )

( 0 .1 7 3 )

( 0 .1 3 9 )

( 0 .1 2 5 )

( 0 .1 7 8 )

( 0 .1 7 3 )

( 0 .1 3 9 )

P o p ul a t i on a n d GDP c on t r ol s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

C o u

n t r f i x e d e f f e c t s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

C o u

n t r y- s p e c i f i c t i m e t r e n d s

Y e s

Y e s


Y e s

Y e s

R- S q u a r e d

0 .4 7 8

0 . 6 1 0

0 . 9 3 4

0 . 9 5 7

0 . 9 4 3

0 .2 6 9

0 .4 5 3

0 . 9 0 8

0 . 9 4 0

0 . 9 2 0

O b s e r v a t i on s

5 0 0

4 8 2

4 8 2

4 8 2

4 8 2

5 0 0

4 8 2

4 8 2

4 8 2

4 8 2

An o b s e r v a t i oni s a c o un t r , e a r

N o t e

s : Al l c ol umn s u s e ann u al d a t af or t h e p er i o d 1 9 8 0 -2 0 0 0 . C ol um

n s 1 -4 ar e b ef or e / af t er OL S r e gr

e s s i on s u s i n g onl y t h e

C om

m uni s t c o un t r i e s ( e q u a t i on ( 5 .1 ) ) ; c ol umn s 5 - 9 ar e d i ff er en c e-i n- d

i ff er en c e s OL S r e gr e s s i on s wh er e t h er e gi on of i n t er e s t

i s C o

mm uni s t c o un t r i e s an d t h e c om p ar i s on gr o u pi s W e s t er nE ur o p e ( e q u a t i on ( 5 .2 ) ) .Th e C omm un

i s t c o un t r i e s u s e d ar e

B el ar u s ,B ul g ar i a ,E s t oni a ,H un g ar y ,L a t vi a ,P ol an d , R om ani a an d t h e Uk r ai n e , an d t h e W e s t er nE ur o p e an c o un t r i e s u s e d

ar eB

el gi um ,D enm ar k ,F i nl an d ,F r a

n c e ,I c el an d ,I t al y ,N e t h er l an d s ,N or w a y ,P or t u g al , S p ai n , S w e d en , an d S wi t z er l an d .

P o s t

i s a d umm yf or 1 9 9 1 on w ar d s . C o u n t r y- s p e c i fi c t i m e t r e n d s ar e

l i n e ar . S t an d ar d er r or s ,i n p ar e

n t h e s e s , ar e cl u s t er e d

a t t h

e c o un t r yl e v el .* p < 0 .1 0 ,* * p <

0 . 0 5 ,* * * p < 0 . 0 1 .




T a b l e 5 . 5 :

T i t l e

/ a u t h o r - l e v e l a n a l y s i s :

T h e e f f e c t o f t h e c o l l a p s e o f C o m m u n i s m o n

t h e

t r a n s l a t i o n o f

i n f l u e n t i a l t i t l e s / a u t h o

r s , a n d t h e m o s t t r a n s l a

t e d t i t l e s

D e p e n d e n t v a r i a b l e : l o g n u m b e r o f c o u n t r i e s t r a n s l a t i n g t h e a u t h o r / t i t l e + 1

S a m p l e :

I n f l u e n t i a l a u t h o r s

I n f l u e n t i a

l t i t l e s

M o s t t r a n s l a t e d t i t l e s

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

P o s t * C o m m u n i s t c o u n t r y

0 . 3

8 0 * * *

0 . 2

7 8 * * *

0 . 3

5 2 * * *

0 . 5

2 4 * * *

0 . 4

3 6 *

* *

0 . 4

6 3 * * *

0 . 5

0 2 * * *

0 . 4

9 0 * * *

0 . 2

6 2 * * *

( 0 . 0

5 5 )

( 0 . 0

5 6 )

( 0 . 0

5 8 )

( 0 . 0

6 3 )

( 0 . 0 6

8 )

( 0 . 0

6 6 )

( 0 . 0

5 1 )

( 0 . 0

5 1 )

( 0 . 0

8 6 )


* A n t i - C o m m u n i s t a u t h o r

1 . 0

0 1 * * *

0 . 5

0 5 *

* *

0 . 9

3 2 * *

( 0 . 1

7 6 )

( 0 . 1 6

4 )

( 0 . 4

5 6 )


* N o b e l l a u r e a t e

0 . 3

2 6 *

0 . 5

7 9 * * *

( 0 . 1

9 5 )

( 0 . 2

0 2 )


* P u b l i s h e d 1 9 1 7 - 4 4

0 . 5

6 8 * * *

( 0 . 2

0 9 )


* P u b l i s h e d 1 9 4 5 - 8 5

0 . 3

3 2 * * *

( 0 . 1

0 7 )


- 0 . 5

0 1 * * * -

0 . 4

4 4 * * *

- 0 . 4

9 5 * * *

- 0 . 5

3 1 * * *

- 0 . 5

0 1 * * *

- 0 . 5

0 4 * * *

- 0 . 8

0 0 * * *

- 0 . 7

9 5 * * *

- 0 . 5

3 6 * * *

( 0 . 0

3 9 )

( 0 . 0

4 0 )

( 0 . 0

4 1 )

( 0 . 0

4 4 )

( 0 . 0 4

8 )

( 0 . 0

4 7 )

( 0 . 0

3 6 )

( 0 . 0

3 6 )

( 0 . 0

6 1 )

C o m m u n i s t c o u n t r y * A n t i - C o m m u n i s t a u t h o r

-

0 . 5

6 2 * * *

- 0 . 1 7

1

- 0 . 4

3 5

( 0 . 1

2 5 )

( 0 . 1 1

6 )

( 0 . 3

2 3 )

C o m m u n i s t c o u n t r y * N o b e l l a u r e a t e

- 0 . 0

7 6

- 0 . 2

5 7 *

( 0 . 1

3 8 )

( 0 . 1

4 3 )

C o m m u n i s t c o u n t r y * P u b l i s h e d 1 9 1 7 - 4 4

- 0 . 2

8 0 *

( 0 . 1

4 8 )

C o m m u n i s t c o u n t r y * P u b l i s h e d 1 9 4 5 - 8 5

- 0 . 4

0 6 * * *

( 0 . 0

7 6 )

P o s t

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

A u t h o r f i x e d e f f e c t s

Y e s

Y e s

Y e s

A u t h o r f i x e d e f f e c t s * p o s t

Y e s

Y e s

Y e s

T i t l e f i x e d e f f e c t s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

T i t l e f i x e d e f f e c t s * p o s t

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

R - S q u a r e d

0 . 8

1 5

0 . 8

2 9

0 . 8

1 7

0 . 7

2 3

0 . 7 3

2

0 . 7

3 0

0 . 8

0 4

0 . 8

0 6

0 . 8

1 8


8 2 8

8 2 8

8 2 8

6 4 4

6 4 4

6 4 4

9 5 2

9 5 2

9 5 2

N u m b e r o f a u t h o r s

2 0 7

2 0 7

2 0 7

N u m b e r o f t i t l e s

1 6 1

1 6 1

1 6 1

2 3 8

2 3 8

2 3 8

A n o b s e r v a t i o n i s a :

a u t h o r , p r e / p o s t , C o m m u n i s t / W e s t

t i t l e , p r e / p o s t , C o m m u n i s t / W e s t




N o t e

s : Al l c ol umn s ar e d i ff er en c e-i n- d i ff er en c e s OL S r e gr e s s i on s u s i n g d a t a a g gr e g a t e d t o t h e p

r e / p o s t c ol l a p s e an d

C om

m uni s t / W e s t er nE ur o p el e v el ( e

q u a t i on ( 5 . 7 ) ) , wi t h C omm uni s t

E ur o p e a s t h er e gi on of i n t er e s t

an d W e s t er nE ur o p e

a s t h

e c om p ar i s on gr o u p. C ol umn s 1

- 3 ar er un a t t h e a u t h or l e v el ; c o

l umn s 4 - 9 ar er un a t t h e t i t l el e v el .Th e“ pr e” p er i o d

i s 1 9

8 0 - 8 8 ; t h e“ p o s t ” p er i o d i s 1 9 8

9 -2 0 0 0 .Th e C omm uni s t c o un t

r i e s u s e d ar eB ul g ar i a , t h e Cz e

ch R e p u b l i c ,P ol an d ,

R om

ani a , S l o v ak i a ,B el ar u s , an d E s t

oni a.Th e W e s t er n c o un t r i e s u s e d ar e S p ai n ,F r an c e ,D enm ar k ,

N or w a y ,A u s t r i a , an d

B el gi um. W ei n cl u d e t r an s l a t i on s i n

t o t h em ai nl an g u a g e of t h e c o un t r y onl y , pl u s i n t o R u s s i ani n

t h e S o vi e t c o un t r i e s .

S t an d ar d er r or s ar e gi v eni n p ar en t h e s e s .* p < 0 .1 0 ,* * p < 0 . 0 5 ,* * * p < 0 . 0 1 .



Chapter 6

Conclusions and Discussion

In this dissertation, I tackle the difficult question of how to measure empirically the

international diffusion of ideas. I propose using book translations as a measure of

idea flows. The written word is an important storehouse for a wide range of types of

knowledge, and translations are a means by which such knowledge overcomes language

barriers. I compile a novel data set of international translation flows that span the

period 1949 to 2000, and document the major patterns of translations (Chapter

3). I show that, despite the absence of transportation costs, translation flows are

significantly inhibited by distance between countries, and present suggestive evidence

that an import driver of this effect is search and information costs that increase

in distance (Chapter 4). I then show (with Ran Abramitzky) how the collapse

of Communism in Eastern Europe dramatically altered international patterns of

translations, reducing intra-Communist translations and increasing translation flows

from Western countries into Eastern Europe (Chapter 5). This study demonstrates

the severe effects institutions such as Communism can have on the spread of ideas, as

well as the importance of preferences in determining the type of ideas that diffuse into

a country. I conclude this chapter by speculating on the cost of multiple languages

and the effect of translations on growth.

171



172 CHAPTER 6. CONCLUSIONS AND DISCUSSION

6.1 The cost of multiple languages

The existence of multiple languages and the necessity of translating between them

raise the interesting question of what is the cost imposed by language barriers in

terms of access to ideas, relative to the counterfactual of everyone speaking the

same language.1 The one-language world is an interesting counterfactual because it

minimizes barriers to idea diffusion and, once established, involves no more language-

learning than a situation where different populations speak different languages. The

languages spoken by people in different parts of the world are the result of many

historical events and processes, and it may well be the case that such a multi-language

situation is a highly inefficient equilibrium.

To estimate the cost of status quo in terms of access to written ideas, consider

the question of how many books the average person has access to under four different

scenarios. In the first scenario, everyone speaks the same language, thus every written

book is accessible to everyone. In the second scenario, people in different countries

speak different languages, and there is neither multilingualism nor translation of titles.

Here an individual only has access to the titles written in his own country. In the

third scenario, people in different countries speak different languages and there is

no multilingualism, but some titles are translated. Thus an individual has access to

those titles written in his country plus those translated into his language. In the

final scenario, people in different countries speak different languages natively and

there are no translations, but everyone also speaks a second language, namely the

language that gives him access to the most additional titles. Practically, this means

everyone who doesn’t speak English natively learns it as their second language, and

native English speakers learn German. However, original titles still cannot be read

1There are, of course, many more aspects to language and language barriers than access to ideaswritten in books, but quantifying how language barriers affect access to books remains informativeabout one aspect of the cost of multiple languages.



6.1. THE COST OF MULTIPLE LANGUAGES 173

by everyone because they are written in a range of different languages.

I consider access to titles in each scenario under two alternative assumptions

about what constitutes an interesting title. First, I assume that all titles published

are interesting. In both cases, I also assume the original titles that are written in

each country do not vary by scenario. Note this assumption could be violated if

potential market size or competition from other titles affect what books are actually

published. Using average annual data for 1995 to 1999 on original publications and

translations, and population data from 1999 for the 49 countries for which all these

are available, I find average access to titles is 640,326 when everyone speaks thesame language, compared with just 66,766 (10.4% of total titles) in the scenario with

multiple languages, 69,463 (10.8%) with multiple languages and translations, and

163,127 (25.5%) when everyone is bilingual. These (admittedly crude) calculations

demonstrate that access to titles is drastically reduced by the existence of multiple

languages, and translations do relatively little to combat this effect.

However, titles vary in importance, and it may be that most of the titles that are

not translated would be of no interest to people in foreign countries (and perhaps

of relatively little interest to people at home as well). I thus alternatively assume

the extreme case that only titles that are ever translated are of interest to anyone,

and that the number of titles translated out of a language is the maximum of the

number of titles translated from that language into any one target language in a

country. Then average access to titles falls to 11,204 in the one-language scenario,

3,517 (31.4%) in the multiple-language scenario, 6,214 (55.5%) with translations, and

7,366 (65.7%) with bilingualism.

Although under both assumptions universal bilingualism leads to higher access

to titles than translations, the cost is also likely to be much higher. Universal

bilingualism in the countries in the sample would imply 2.6 billion people must learn a

second language; this compares with just under 65,000 titles being translated. Under




Table 6.1: Average access to titles under four counterfactuals:

number and % of titles written

Counterfactual: Onelanguage

Multiplelanguages

Multiplelanguages,translations

Multiplelanguages,bilingualism

Assumption:

All books are 640,326 66,766 69,463 163,127interesting 100% 10.4% 10.8% 25.5%

Only books thatare ever 11,204 3,517 6,214 7,366translated are 100% 31.4% 55.5% 65.7%interesting

Notes: The columns of this table present the average person’s access to titles under fouralternative counterfactual scenarios and two alternative assumptions about what constitutesan interesting title. The counterfactuals are as follows: i) everyone speaks the samelanguage, and all books are written in this language; ii) everyone speaks only their native

language, books are distributed across languages as given by the data, and there are notranslations or multilingualism; iii) as case ii, but translations occur as given by the data;and iv) as case ii, but everyone is bilingual in their native language and the foreign languagethat gives them the greatest additional access to titles. The first row assumes all titlespublished are interesting; the second row assumes only titles that are ever translated areinteresting. Each cell in the table gives the number and percentage of total interesting titlesthe average person can read.

any reasonable assumptions, the cost of the translations is much lower.

Comparisons of either scenario with the case where there is only one language

reveal that the cost of the existence of multiple languages in terms of access to written

works is very large. However, there would be large costs of transitioning to a situation

where everyone speaks the same language natively, even ignoring the cultural heritage

losses this would likely entail.



6.1. THE COST OF MULTIPLE LANGUAGES 175

On the other hand, it seems we are increasingly moving towards using English

as the international lingua franca . Historically, lingua francas such as Arabic inthe Islamic Empire, Latin for European scholars until the eighteenth century, and

French within diplomatic circles have emerged organically within specific geographic

or social areas. English is already used widely in many parts of the world where it is

not spoken natively, and it may be that the increasing ease of global communication

through means such as the internet will encourage its spread to all corners of the

globe. Revealingly, translation patterns over the past half century show an increase

in the dominance of English as a source language, especially with the collapse of Communism in Eastern Europe, which caused the former Communist countries to

switch from predominantly translating from Russian to predominantly translating

from English. This suggests an increase in the extent to which titles of international

interest are written in English, though it remains an open question whether this trend

will continue to the point where all communication internationally occurs in English.

One force that may work in the opposite direction is the improvement in machine

translation. Although still far from perfect, the ability of computer programs

to translate between languages (and generate meaningful output) has increased

dramatically over recent years. This reduces the costs of translation, and thus the cost

of the existence of multiple languages. However, it seems unlikely that technology will

be able to fully bridge all language barriers in the foreseeable future, and thus language

differences are likely to remain a barrier to idea transmission, with implications for

economic development and intellectual advancement, for some time to come.




6.2 The effect of translations on economic

outcomes

A natural further question to ask is how translation flows affect important economic

outcomes such as GDP and economic growth. This is a challenging question to address

convincingly because of the obvious problems of reverse causality and unobserved

heterogeneity. For example, as countries become richer they inevitably translate

more, and it may be that countries with populations that are more inclined to read

both translate more and grow faster. In addition, the effect of translation flows oneconomic outcomes is expected to be distributed over a number of years after the

flow occurs, making it more difficult to identify empirically. To study the causal

effect of translations on growth will thus require an instrumental variable that affects

translation flows without affecting growth directly. Such a variable is difficult to find.

I thus leave questions of the effects of translations on economic outcomes for future

research.

However, the evidence I present in this dissertation is consistent with a positiveeffect of idea flows on growth. First, as shown in Table 3.7, there is a positive

correlation between inward translation flows and GDP per capita in the country.

Second, as I show in chapter 5, after the collapse of Communism (which drove

the largest change in translation patterns during the period I study), the Satellite

countries both increased their translations of Western titles more than the Soviet

countries, and had better growth outcomes over the following decade.



Appendix A

Appendices for Chapter 3

A.1 Appendix figures and tables

177



A.1. APPENDIX FIGURES AND TABLES 179

E a s t e r n a n d C e n t r a l E u r o p e : A l b a n i a , B u l g a r i a ,

C z e c h o s l o v a k i a , H u n g a r y , P

o l a n d , R o m a n i a , t h e U S S R , a n d

Y u g o s l a v i a



180 APPENDIX A. APPENDICES FOR CHAPTER 3

A m e r i c a: U S A ,B r a z i l , a n d P e r u




A s i a a n d t h e M i d d l e E a s t : I n d i a , I s r a e l , J a p a n , a n d S

o u t h K o r e a




A f r i c a: E g y p t a n d T u n i s i a




Figure A.2: Age at translation of non-fiction titles by country and

original language for 1998-2000

Western Europe





Asia and the Middle East





Africa





Table A.1: Major countries of the top 100 languages

Language Major countriesAkan* GhanaAmharic EthiopiaArabic* Saudi Arabia, Algeria, Chad, Bahrain, Oman, Egypt, Iraq,

Yemen, Libya, Morocco, Syria, United Arab Emirates, Jordan,Sudan, Tunisia, Lebanon, Palestine, Kuwait, Qatar, WesternSahara, Israel

Assamese IndiaAzerbaijani* Iran, AzerbaijanBaluchi* PakistanBavarian Austria, GermanyBelarusian BelarusBengali Bangladesh, IndiaBulgarian BulgariaBurmese MyanmarCatalan SpainCebuano PhilippinesCentral Khmer CambodiaChinese* China, Malaysia, SingaporeCzech Czech Republic

Dutch* Netherlands, BelgiumEnglish UK, Australia, Canada, Ireland, New Zealand, USA, Belize,

JamaicaFilipino PhilippinesFrench France, Belgium, Canada, SwitzerlandFulah* Senegal, Cameroon, Nigeria, GuineaGerman* Germany, Switzerland, Austria, LiechtensteinGujarati IndiaHaitian HaitiHausa Nigeria, Benin, NigerHindi India

Hmong* ChinaHungarian Hungary, RomaniaIgbo NigeriaIloko PhilippinesItalian ItalyJapanese Japan




Language Major countriesJavanese IndonesiaKannada IndiaKazakh KazakhstanKikuyu KenyaKinyarwanda RwandaKonkani* IndiaKorean South Korea, North KoreaKurdish* Iraq, Iran, TurkeyLahnda* PakistanLombard ItalyMaithili India

Malagasy* MadagascarMalay* Malaysia, IndonesiaMalayalam IndiaMarathi IndiaModern Greek Greece, CyprusNeapolitan ItalyNepali NepalNyanja MalawiOriya IndiaOromo* EthiopiaPanjabi India

Persian* Iran, AfghanistanPolish PolandPortuguese Portugal, BrazilPushto* Pakistan, AfghanistanQuechua* Peru, BoliviaRajasthani* IndiaRomanian* Romania, MoldovaRussian Russia, Israel, Kazakhstan, KyrgyzstanSerbo-Croatian* Serbia and Montenegro, Bosnia and Herzegovina, CroatiaShona Zimbabwe

Sindhi Pakistan, IndiaSinhala Sri LankaSomali Somalia, EthiopiaSpanish Spain, Argentina, Bolivia, Chile, Colombia, Costa Rica,

Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala,Honduras, Mexico, Nicaragua, Panama, Peru, Puerto Rico,Uruguay, Venezuela




Language Major countries

Swedish SwedenTamil India, Sri LankaTatar RussiaTelugu IndiaThai* ThailandTurkish TurkeyTurkmen Turkmenistan, IranUighur ChinaUkrainian UkraineUrdu Pakistan, IndiaUzbek* Uzbekistan, Tajikistan, AfghanistanVietnamese VietnamXhosa South AfricaYoruba NigeriaZulu South Africa

Notes: This table lists the “top 100” languages (aggregated to the macrolanguage level),and their “major” countries as described in section 3.1.1. Asterisks denote macrolanguages.




T a b l e A.2 : C o unt ri e s andyea r s wi tht ran s lati ondata

A

l b ani a

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0 0 4

A

r g e n t i n a

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

A

u s t r al i a

1 9 5 0

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

A

u s t r i a

1 9 4 9

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

B

an gl a d e s h

1 9 7 9

1 9 8 4

B

e l ar u s

1 9 9 4

1 9 9 9

2 0 0 4

B

e l gi um

1 9 7 9

1 9 8 4

1 9 8 8

1 9 9 4

1 9 9 9

2 0 0 4

B

r azi l

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

B

ul g ar i a

1 9 4 9

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0 0 4

C

an a d a

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0 0 4

C

h i l e

1 9 8 5

1 9 8 9

1 9 9 4

1 9 9 9

C

h i n a

1 9 5 9

1 9 6 4

1 9 6 9

C

ol om b i a

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

C

r o a t i a

1 9 9 4

1 9 9 9

2 0 0 4

C

z e c h R e p u b l i c

1 9 9 4

1 9 9 9

2 0 0 4

C

z e c h o s l o v ak i a

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

D

e nm ar k

1 9 4 9

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0 0 3

E

a s t G e r m an y

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

E

g y p t

1 9 5 9

1 9 7 9

1 9 8 5

1 9 8 8

1 9 9 4

1 9 9 9

E

s t oni a

1 9 9 4

1 9 9 9

2 0 0 4

F

i nl an d

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0 0 4

F

r an c e

1 9 4 9

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0 0 4

G

e r m an y

1 9 5 4

1 9 5 9

1 9 6 9

1 9 9 0

1 9 9 4

1 9 9 9

G

r e e c e

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

H

un g ar y

1 9 4 9

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0 0 4

I c e l an d

1 9 5 9

1 9 6 4

1 9 8 4

1 9 8 9

1 9 9 5

1 9 9 9

I n d i a

1 9 5 4

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9




I r a n

1 9 5 9

1 9 7 0

I s r a e l

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0

0 4

I t a l y

1 9 4 9

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 8

1 9 9 4

1 9 9 9

2 0

0 3

J a p a n

1 9 5 0

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 9 4

1 9 9 9

2 0

0 4

K a z a k h s t a n

1 9 9 4

1 9 9 9

2 0

0 3

K u w a i t

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0

0 4

L a t v i a

1 9 9 5

1 9 9 9

2 0

0 4

L i t h u a n i a

1 9 9 4

1 9 9 9

2 0

0 4

M a c e d o n i a

1 9 9 4

1 9 9 9

2 0

0 4

M a d a g a s c a r

1 9 8 4

1 9 8 9

1 9 9 4

M a l a y s i a

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 8

M a l t a

1 9 8 0

1 9 8 4

1 9 8 9

M o l d o v a

1 9 9 4

1 9 9 9

2 0

0 3

M o n a c o

1 9 9 9

M o r o c c o

1 9 8 4

M o z a m b i q u e

1 9 7 9

M y a n m a r

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

N a m i b i a

1 9 9 4

N e t h e r l a n d s

1 9 8 5

1 9 8 9

1 9 9 4

1 9 9 8

2 0

0 4

N e w Z e a l a n d

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

2 0

0 3

N o r w a y

1 9 4 9

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0

0 4

P a k i s t a n

1 9 6 4

1 9 6 9

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0

0 4

P e r u

1 9 5 5

1 9 6 0

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 8

1 9 9 5

1 9 9 9

2 0

0 4

P h i l i p p i n e s

1 9 8 0

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0

0 3

P o l a n d

1 9 4 9

1 9 5 4

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0

0 4

P o r t u g a l

1 9 5 4

1 9 5 9

1 9 6 4

1 9 8 0

1 9 8 4

1 9 9 4

2 0

0 4

R o m a n i a

1 9 5 5

1 9 5 9

1 9 6 4

1 9 6 9

1 9 7 4

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 4

1 9 9 9

2 0

0 4

R u s s i a

1 9 9 4

1 9 9 9

S a u d i A r a b i a

1 9 7 9

1 9 8 4

1 9 8 9

1 9 9 8

2 0

0 4

S e r b i a a n d M o n

t e n e g r o

2 0

0 3



C.1. APPENDIX FIGURES AND TABLES 207

T a b l e C . 1 : P a g e s

t r a n s l a t e d : T h e e f f e c t

o f t h e c o l l a p s e o f C o m m

u n i s m o n t h e n u m b e r o f b o o k

p a g e s t r a n s l a t e

d

D e p e n d e n t v a r i a b l e : l o g n

u m b e r o f p a g e s t r a n s l a t e d

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

( 1 0 )

T r a n s l a t i o n s f r o m W e s t e r n o r i g i n a l l a n g u a g e s i n :


1 . 2 2 3 * * * 1 . 8 2 2 * * *

1 . 3 5 2 * * *

0 . 7 1 6 * *

1 . 3 9 6 * * *

0 . 1 6 7

0 . 8 8 2 *

0 . 7 5 2 * *

0 . 1 7 6

0

. 8 3 3 * *

( 0 . 2 5 8 )

( 0 . 2 5 2 )

( 0 . 2 1 1 )

( 0 . 3 4 3 )

( 0 . 2 4 8 )

( 0 . 2 7 4 )

( 0 . 4 7 9 )

( 0 . 3 4 3 )

( 0 . 6 2 2 ) (

0 . 3 8 5 )


1 . 3 7 7 * * *

0 . 9 7 3 * *

0 . 7 6 7 * *

0 . 7 7 0

0

. 7 1 9 * *

( 0 . 3 0 1 )

( 0 . 3 7 4 )

( 0 . 3 1 4 )

( 0 . 5 3 5 ) (

0 . 3 1 9 )


- 2 . 3 9 8 * * * - 1 . 6 4 8 * * *

- 3

. 2 2 6 * *

- 3 . 2 8 5 * * *

( 0 . 5 2 1 )

( 0 . 5 0 3 )

( 1 . 1 6 0 )

( 0 . 9 6 7 )


1 . 1 9 7

1 . 9 8 5 * *

( 1 . 1 7 8 )

( 0 . 7 5 3 )

P o s t

0 . 3 0 1 * *

0 . 0 2 7

0 . 3 1 8 *

0 . 0 9 5

0 . 3 0 1 * *

0 . 0 8 9

0 . 3 1 7 *

0 . 0 9 4

( 0 . 1 3 8 )

( 0 . 1 4 5 )

( 0 . 1 5 9 )

( 0 . 1 3 5 )

( 0 . 1 3 9 )

( 0 . 1 6 0 )

( 0 . 1 6 1 )

( 0 . 1 3 5 )

T r a n s l a t i o n s f r o m C o m m u n i s t o r i g i n a l l a n g u a g e s i n :


- 1 . 2 4 0 * * * - 0 . 6 1 9 * * * - 1 . 0 5 6 * * * - 1 . 3 6 2 * * * - 0 . 9 9 5 * * * - 1 . 4 7 3 * * *

- 0 . 7 3 8

- 1 . 0 5 0 *

- 1 . 0 6 7

- 0 . 9 6 5

( 0 . 2 2 4 )

( 0 . 2 0 0 )

( 0 . 2 6 7 )

( 0 . 4 6 1 )

( 0 . 3 0 4 )

( 0 . 5 0 0 )

( 0 . 5 5 8 )

( 0 . 5 5 0 )

( 0 . 7 5 5 ) (

0 . 5 6 5 )


0 . 3 1 9

- 0 . 0 8 2

- 0 . 0 7 6

- 0 . 4 2 2

- 0 . 1 0 6

( 0 . 5 0 4 )

( 0 . 4 4 0 )

( 0 . 5 1 2 )

( 0 . 6 9 6 ) (

0 . 5 1 4 )


1 . 7 7 8 * * * 2 . 4 9 0 * * *

1 . 7 9 5 * * *

1 . 6 9 9 * * *

( 0 . 3 5 4 )

( 0 . 4 3 1 )

( 0 . 4 9 8 )

( 0 . 4 8 9 )


- 0 . 0 2 5

0 . 7 6 3

( 0 . 4 4 2 )

( 0 . 5 1 9 )

P o s t

- 0 . 1 4 7

- 0 . 4 4 2 * * *

- 0 . 1 5 5

0 . 1 0 2

- 0 . 1 4 7

- 0 . 3 8 0 * *

- 0 . 1 5 6

0 . 1 0 0

( 0 . 1 4 0 )

( 0 . 1 4 3 )

( 0 . 1 6 2 )

( 0 . 1 8 6 )

( 0 . 1 4 0 )

( 0 . 1 5 8 )

( 0 . 1 6 3 )

( 0 . 1 8 6 )


W e s t e r n o r i g i n a l l a n g u a g e d u m m y

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

C o m m u n i s t o r i g i n a l l a n g u a g e d u m m y

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

P o p u l a t i o n a n d G D P c o n t

r o l s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

C o u n t r y f i x e d e f f e c t s * W

e s t e r n o r i g i n a l l a n g u a g e

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

C o u n t r y f i x e d e f f e c t s * C o m m u n i s t o r i g i n a l l a n g u a g e

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

C o u n t r y - s p e c i f i c t i m e t r e n d s * W e s t e r n o r i g i n a l l a n g u a g e

Y e s

Y e s

C o u n t r y - s p e c i f i c t i m e t r e n d s * C o m m u n i s t o r i g i n a l l a n g u a g e

Y e s

Y e s

Y e a r f i x e d e f f e c t s * W e s t e r n o r i g i n a l l a n g u a g e

Y e s

Y e s

Y e a r f i x e d e f f e c t s * C o m m u n i s t o r i g i n a l l a n g u a g e

Y e s

Y e s

R - S q u a r e d

0 . 5 3 8

0 . 6 6 4

0 . 9 1 8

0 . 9 3 8

0 . 9 2 6

0 . 5 9 2

0 . 7 5 7

0 . 9 2 0

0 . 9 3 9

0 . 9 2 8


9 9 9

9 6 3

9 6 3

9 6 3

9 6 3

9 9 9

9 6 3

9 6 3

9 6 3

9 6 3

A n o b s e r v a t i o n i s a c o u n t

r y , y e a r , o r i g i n a l l a n g u a g e ( W e s t e r n o r C o m m u n i s t )




Table C.2: The Bertrand et al. critique: Two-period difference-in-differences

Dependent varable: log average number of translations

(1) (2) (3) (4) (5) (6)

Translations from Western original languages in:

Communist country * post 1.389*** 2.193*** 2.001*** 0.443* 1.224** 1.038*

(0.247) (0.239) (0.384) (0.258) (0.484) (0.516)

Satellite country * post 1.366*** 1.008** 1.084**

(0.294) (0.390) (0.514)

Communist country -2.665*** -1.542*** -3.403*** -3.123***

(0.481) (0.464) (1.062) (0.890)

Satellite country 1.066 1.729**

(1.087) (0.673)Post 0.271*** -0.055 0.080 0.271*** 0.052 0.113

(0.092) (0.106) (0.204) (0.094) (0.121) (0.201)

Translations from Communist original languages in:

Communist country * post -1.213*** -0.370* -0.562 -1.568*** -0.748* -0.934*

(0.212) (0.194) (0.356) (0.410) (0.429) (0.537)

Satellite country * post 0.512 0.154 0.230

(0.432) (0.305) (0.553)

Communist country 1.783*** 2.857*** 1.813*** 2.043***

(0.330) (0.413) (0.448) (0.490)

Satellite country -0.044 0.619

(0.409) (0.549)

Post -0.193* -0.556*** -0.422* -0.193* -0.450*** -0.389*

(0.110) (0.132) (0.232) (0.112) (0.145) (0.227)

Other controls:

Western original language dummy Yes Yes Yes Yes Yes Yes

Communist original language dummy Yes Yes Yes Yes Yes Yes

Population and GDP controls Yes Yes Yes Yes

Country fixed effects * Western original language Yes YesCountry fixed effects * Communist original language Yes Yes

R-Squared 0.641 0.755 0.982 0.698 0.838 0.986

Observations 104 100 100 104 100 100

An observation is a country, pre/post, original language (Western or Communist)

Notes: All columns are difference-in-differences OLS regressions using using data aggregatedto the pre/post collapse level, with Communist Europe as the region of interest and WesternEurope as the comparison group. Columns 1-3 estimate equation (5.4) from the paper;columns 4-6 estimate equation (5.6).“Pre” values are the average over the years 1980-89;“post” values are the average over the years 1992-2000. See the notes to Table 5.2 forthe Communist and Western countries used. We include the three Baltic countries in the

Satellite countries (see explanation in Section 5.3.1). The Communist and Western originallanguages are given in footnote 25. We include translations into the main language of thecountry only. Population and GDP controls are the logs of population and of real GDP percapita. Standard errors, in parentheses, are clustered at the country level. * p<0.10, **p<0.05, *** p<0.01.





a r e O L S r e g r e s s i o n s u s i n g a n n u a l d a t a . C o l u m n s 1 - 6 a r e f o r

t h e y e a r s 1 9 8 0 - 2 0 0 0 ; c o l u m n s 7

- 9 a r e

f o r 1 9 8 9 - 2 0 0 0 . T h e

c o u n t r i e s u s e d i n t h e a n a l y s i s a r e R u s s i a , B e l a r u s , E s t o n i a , L a t

v i a , L i t h u a n i a , M o l d o v a , t h e U k

r a i n e ,

B u l g a r i a , t h e C z e c h

R e p u b l i c , H u n g a r y , P o l a n d , R o m a n i a , a n d S l o v a k i a . T h e C o m m

u n i s t a n d W e s t e r n o r i g i n a l l a n g u a g e s

a r e g i v e n i n f o o t n o t e

2 5 . W e i n c l u d e t r a n s l a t i o n s i n t o t h e m a i n l a n g u a g e o f t h e c o u n t r y o n l y . T h e v a r i a b l e s I n s t i t u

t i o n a

l i z e d

d e m o c r a c y ,

P o

l i t i c a l

c o m p e

t i t i o n ,

P r i c e

l i b e r a

l i z a t i o n , a n d T r a

d e a n

d f o

r e i g n e x c

h a n g e

s y s t e m

r e f o

r m

a r e m e a s u r e s o f a

s p e c t s

o f t h e d e g r e e o f r e f o

r m f r o m c o m m u n i s t c e n t r a l l y - p l a n n e d e c o n o m y t o d e m o c r a t i c m

a r k e t e c o n o m y . T h e y a r e d e s c r i b e d i n

d e t a i l i n S e c t i o n C . 2 . 1 . P o p u

l a t i o n a n

d G D P c o n t r

o l s a r e t h e l o g s o f p o p u l a t i o n a n d o f r e a l G D P p e r c a p i t a . S t a

n d a r d

e r r o r s , i n p a r e n t h e s e s , a r e c l u s t e r e d a t t h e c o u n t r y l e v e l . * p < 0 . 1 0 , * * p < 0 . 0 5 , * * * p < 0 . 0 1 .



212 APPENDIX C. APPENDICES FOR CHAPTER 5

T a b l e C.4 : S e c onda rylan g ua ge s : Theeffe ct ofthe c ol

lap s e of C omm uni s m

onb o

okt ran s lati on s

i nt o

mai nand s e c onda rylan

g ua ge s

D e p e n d e n t v a r i a b l e : l o gn um b e r of t r a n s l a t i on s

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

( 9 )

( 1 0 )

Tr an

s l a t i on s f r om W e s t e r n or i gi n al l an g u a g e s i n :

C omm uni s t c o un t r y* p o s t

1 . 3 9 0 * * *

1 . 9 3 2 * * *

1 . 5 3 2 * * *

1 . 0 7 4

* * *

1 . 5 7 8 * * *

0 . 6 8 8

1 .4 3 0 * * *

1 .2 2 5 * * *

0 . 9 4 1 * * *

1 . 3 0 4 * * *

( 0 .2 3 0 )

( 0 .2 0 7 )

( 0 .1 9 2 )

( 0 .2 5 5 )

( 0 .2 3 3 )

( 0 .4 2 4 )

( 0 . 3 9 2 )

( 0 . 3 8 8 )

( 0 . 3 1 9 )

( 0 .4 2 6 )

S a t e l l i t e c o un t r y* p o s t

0 . 9 4 0 * *

0 . 5 4 7

0 .4 1 9

0 .1 9 6

0 . 3 7 9

( 0 .4 4 5 )

( 0 . 3 6 9 )

( 0 . 3 7 0 )

( 0 . 3 4 2 )

( 0 . 3 7 8 )

C omm uni s t c o un t r y

-2 . 3 8 8 * * * -1 . 6 6 5 * * *

-2 . 6 4 0 * * * -2 .4 2 5 * * *

( 0 .4 0 5 )

( 0 .4 1 7 )

( 0 . 7 8 0 )

( 0 . 6 0 2 )

S a t e l l i t e c o un t r y

0 . 3 6 3

0 . 9 3 4 * *

( 0 . 8 0 3 )

( 0 .4 2 5 )

P o s t

0 . 3 4 3 * * *

0 .1 0 3

0 . 3 4 7 * *

0 .1 6 4

0 . 3 4 3 * * *

0 .1 3 0

0 . 3 4 6 * *

0 .1 6 6

( 0 .1 1 7 )

( 0 .1 2 2 )

( 0 .1 4 3 )

( 0 .1 2 9 )

( 0 .1 1 7 )

( 0 .1 2 9 )

( 0 .1 4 4 )

( 0 .1 2 9 )

Tr an

s l a t i on s f r om C omm uni s t or i gi n al l an g u a g e s i n :

C omm uni s t c o un t r y* p o s t

-1 .1 8 1 * * * - 0 . 6 0 2 * * * - 0 . 9 6 8 * * * -1 .2 1 7 * * * - 0 . 9 0 9 * * * -1 .2 5 2 * * *

- 0 .4 7 3 * *

- 0 . 7 4 1 * *

- 0 . 8 1 9 *

- 0 . 6 6 3 * *

( 0 .1 6 3 )

( 0 .1 9 6 )

( 0 .2 1 7 )

( 0 . 3 8 6 )

( 0 .2 4 3 )

( 0 .1 8 8 )

( 0 .2 2 2 )

( 0 .2 7 7 )

( 0 .4 6 3 )

( 0 .2 8 7 )

S a t e l l i t e c o un t r y* p o s t

0 .1 0 7

- 0 .2 8 4 * *

- 0 . 3 2 5

- 0 . 5 6 0

- 0 . 3 4 4

( 0 .1 7 3 )

( 0 .1 1 6 )

( 0 .2 4 2 )

( 0 .4 4 9 )

( 0 .2 4 5 )

C omm uni s t c o un t r y

1 . 9 0 4 * * *

2 . 5 6 9 * * *

2 . 0 2 7 * * *

2 .1 8 3 * * *

( 0 . 3 2 1 )

( 0 . 3 7 3 )

( 0 .4 4 6 )

( 0 .4 5 0 )

S a t e l l i t e c o un t r y

- 0 .1 7 7

0 . 3 9 4

( 0 . 3 8 3 )

( 0 . 3 8 2 )

P o s t

- 0 . 0 4 0

- 0 . 3 1 7 * *

- 0 . 0 7 1

0 .1 8 6

- 0 . 0 4 0

- 0 .2 9 0 * *

- 0 . 0 7 2

0 .1 8 8

( 0 .1 3 4 )

( 0 .1 3 1 )

( 0 .1 5 9 )

( 0 .1 9 1 )

( 0 .1 3 4 )

( 0 .1 3 7 )

( 0 .1 5 9 )

( 0 .1 9 1 )

O t h e

r c on t r ol s :

W e s t e r n or i gi n a l l a n g u a g e d umm y

Y e s

Y e s

Y e s

Y e

s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

C omm uni s t or i gi n a l l a n g u a g e d umm y

Y e s

Y e s

Y e s

Y e

s

Y e s

Y e s

Y e s

Y e s

Y e s

Y e s

P o p ul a t i on a n d GDP c on t r ol s

Y e s

Y e s

Y e

s

Y e s

Y e s

Y e s

Y e s

Y e s

C o un

t r yf i x e d e f f e c t s * W e s t e r n or i gi n a l l a n g u a g e

Y e s

Y e

s

Y e s

Y e s

Y e s

Y e s

C o un

t r yf i x e d e f f e c t s * C omm uni s t or i gi n a l l a n g u a g e

Y e s

Y e

s

Y e s

Y e s

Y e s

Y e s

C o un

t r y- s p e c i f i c t i m e t r e n d s * W e s t e r n or i gi n a l l a n g u a g e

Y e

s

Y e s

C o un

t r y- s p e c i f i c t i m e t r e n d s * C omm uni s t or i gi n a l l a n g u a g e

Y e

s

Y e s

Y e a r f i x e d e f f e c t s * W e s t e r n or i gi n a l l a n g u a g e

Y e s

Y e s

Y e a r f i x e d e f f e c t s * C omm uni s t or i gi n a l l a n g u a g e

Y e s

Y e s

R- S q u a r e d

0 . 6 5 6

0 . 7 5 9

0 . 9 2 2

0 . 9 4 0

0 . 9 2 9

0 . 6 7 0

0 . 7 8 5

0 . 9 2 4

0 . 9 4 1

0 . 9 3 0


1 , 0 0 1

9 6 5

9 6 5

9 6 5

9 6 5

1 , 0 0 1

9 6 5

9 6 5

9 6 5

9 6 5

An o b s e r v a t i oni s a c o un t r y , y e a r , or i gi n a l l a n g u a g e ( W

e s t e r n or C omm uni s t )




N o t e s : A l l c o l u m n s a

r e d i ff e r e n c e - i n - d i ff e r e n c e s O L S r e g r e s s i o n s u s i n g a n n u a l d a t a f o r

t h e p e r i o d 1 9 8 0 - 2 0 0 0 , w i t h C o m m

u n i s t

E u r o p e a s t h e r e g i o n o f i n t e r e s t a n d W e s t e r n E u r o p

e a s t h e c o m p a r i s o n g r o u p . C o l u m n s 1 - 5 e s t i m a t e e q u a t i o n ( 5 . 4 ) f r o m

t h e p a p e r ; c o l u m n s

6 - 1 0 e s t i m a t e e q u a t i o n ( 5 . 6 ) . S e e t h e n o t e s t o T a b l e 5 . 2 f o r t h e C o m m u n i s t a n d W e s t e r n c o u

n t r i e s

u s e d . W e i n c l u d e t h

e t h r e e B a l t i c c o u n t r i e s i n t h e S a t e l l i t e c o u n t r i e s ( s e e e x p l a n a t i o n i n S e c t i o n 5 . 3 . 1 ) . T h e C o m m

u n i s t

a n d W e s t e r n o r i g i n a

l l a n g u a g e s a r e g i v e n i n f o o t n o t e

2 5 . W e i n c l u d e t r a n s l a t i o n s i n t o t h e m a i n a n d s e c o n d a r y l a n g u a g e s o f

t h e c o u n t r y . P o s t i s

a d u m m y f o r 1 9 9 1 o n w a r d s . P o

p u

l a t i o n a n

d G D P c o n

t r o l s a r e t

h e l o g s o f p o p u l a t i o n a n d o f r e a l G D P

p e r c a p i t a .

C o u n

t r y

- s p e c

i fi c

t i m e

t r e n

d s a r e l i n e a r .

S t a n d a r d e r r o r s , i n p a r e n t h e s e s

, a r e c l u s t e r e d a t t h e c o u n t r y l e

v e l . *

p < 0 . 1 0 , * * p < 0 . 0 5 ,

* * * p < 0 . 0 1 .




T a b l e C. 5 : T ran s lati on s by

b o ok

fi eld: The

effe ct

ofthe

c ollap s e

of C omm uni s m

on

va ri o u s

fi eld s ofb o okt ran s lati on s

P an e

l A : P r o b i t pr e d i c t i n gn on-z e r o t r an s l a t i on s i n t h e f i e l d ( e x t e n s i v e m ar gi n )

N a t ur a l S c i A p pl i e d S c i S o c i a l S c i

Ar t s

L i t e r a t ur e

P h i l o s o ph y

R e l i gi on

Hi s t or y

Tr an

s l a t i on s i n C omm uni s t c o un t r i e s f r om :

C om

m uni s t or i gi n a l l a n g u a g e s * p o s t

-1 .2 5 6 * * *

0 . 3 9 9

0 .2 2 1

- 0 . 3 3 0

- 9 . 0 1 8 * * *

0 .2 4 1

0 . 8 3 9 * * *

- 0 . 5 5 1

0 . 3 3 9

0 .2 8 6

0 . 5 3 2

0 .2 7 9

0 .4 7 3

0 .2 5 9

0 .2 5 1

0 .4 0 2

W e s t e r n or i i n a l l a n u a e s *

o s t

0 . 9 9 2 * * *

1 .1 3 9 * * *

1 .1 3 9

* * *

1 .2 1 5 * * *

-4 . 6 3 3

1 .4 3 4 * * *

2 . 0 0 3 * * *

1 .1 3 3 * * *

0 .1 5 7

0 .2 6 5

0 .2 8 4

0 .2 8 6

.

0 .2 6 9

0 . 3 7 2

0 . 3 4 2

C on t r ol s a s i nP a n e l B

Y e s

Y e s

Y e

s

Y e s

Y e s

Y e s

Y e s

Y e s


9 6 6

9 6 6

9 6 6

9 6 6

9 6 6

9 6 6

9 6 6

9 6 6

P an e

l B : OL S

r e d i c t i n

l o n um b e r of t r an s l a

t i on s i n t h e f i e l d , wh e r e t r an s l a t i on s ar e n

on-z e r o

i n t e n s i v e m ar i n

N a t ur a l S c i A p pl i e d S c i S o c i a l S c i

Ar t s

L i t e r a t ur e

P h i l o s o ph y

R e l i gi on

Hi s t or y

Tr an

s l a t i on s i n C omm uni s t c o un t r i e s f r om :

C om

m uni s t or i i n a l l a n u a e s *

o s t

- 0 . 7 6 7 *

- 0 . 3 6 2

-1 . 3 1 2

* * *

- 0 . 6 2 2 * *

- 0 . 6 4 1 * *

0 .1 9 8

0 . 8 8 9 * * *

- 0 . 8 4 1 * * *

0 . 3 7 5

0 .2 5 1

0 .2 7 2

0 .2 2 6

0 .2 4 6

0 . 3 0 0

0 .1 7 1

0 .2 4 2

W e s t e r n or i gi n a l l a n g u a g e s * p o s t

0 . 6 8 4 *

2 . 0 6 7 * * *

1 . 7 6 2

* * *

0 . 7 6 4 * *

1 . 8 9 7 * * *

2 .1 7 6 * * *

2 . 0 7 4 * * *

1 .1 9 8 * * *

( 0 . 3 9 6 )

( 0 . 3 3 8 )

( 0 . 3 7 2 )

( 0 .2 8 4 )

( 0 .2 5 6 )

( 0 .2 8 0 )

( 0 .4 3 5 )

( 0 . 3 1 8 )

C om

m uni s t or i i n a l l a n u a e s

2 .4 4 5 * * *

3 .1 3 4 * * *

2 . 3 8 0

* * *

1 .1 4 4 * *

2 . 3 8 8 * * *

1 .1 5 4 * *

0 .2 7 0

1 . 7 3 9 * * *

0 .4 3 8

0 . 3 9 6

0 . 3 2 1

0 . 5 5 2

0 .4 4 9

0 .4 4 7

0 .4 1 7

0 . 3 5 5

W e s t e r n or i i n a l l a n u a e s

- 0 . 9 5 5 *

-1 . 9 0 7 * * *

-1 . 7 5 8

* * *

-1 . 6 7 9 * * *

-1 . 3 2 9 * *

-2 .2 9 1 * * *

-2 .1 6 4 * * *

-1 . 5 5 1 * * *

( 0 . 5 5 3 )

( 0 . 5 8 0 )

( 0 . 5 4 3 )

( 0 . 5 4 6 )

( 0 .4 8 3 )

( 0 . 5 2 8 )

( 0 . 7 0 4 )

( 0 .4 0 6 )

O t h e

r c on t r ol s :

W e s t e r n or i i n a l l a n u a e s *

o s t

0 . 3 2 8 *

0 .1 8 4

0 .2 9 9 * *

0 .4 1 9 * * *

- 0 . 0 4 8

0 . 3 5 4 * * *

0 .2 1 2

0 .1 8 6

0 .1 6 7

0 .1 2 9

0 .1 3 0

0 .1 2 4

0 .1 6 7

0 .1 1 9

0 .1 6 6

0 .1 5 0

C om

m uni s t or i gi n a l l a n g u a g e s * p o s t

- 0 .2 8 7 *

- 0 .4 9 7 * * *

- 0 .4 6 7

* * *

- 0 .2 4 9 *

- 0 . 3 4 5 *

- 0 .2 0 6

- 0 .2 2 3 *

- 0 .1 7 4

( 0 .1 4 5 )

( 0 .1 6 5 )

( 0 .1 4 8 )

( 0 .1 3 7 )

( 0 .1 9 2 )

( 0 .2 1 5 )

( 0 .1 1 9 )

( 0 .1 4 6 )

W e s t e r n or i i n a l l a n u a e s

Y e s

Y e s

Y e

s

Y e s

Y e s

Y e s

Y e s

Y e s

C om

m uni s t or i i n a l l a n u a e s

Y e s

Y e s

Y e

s

Y e s

Y e s

Y e s

Y e s

Y e s

P o u

l a t i on a n d GDP c on t r ol s

Y e s

Y e s

Y e

s

Y e s

Y e s

Y e s

Y e s

Y e s

R- S q

u a r e d

0 . 5 3 5

0 . 7 0 9

0 . 6 0 6

0 . 6 9 1

0 . 6 8 2

0 . 7 1 8

0 . 7 3 9

0 . 6 8 0


7 5 2

7 4 8

8 2 4

7 5 0

9 5 3

7 1 7

6 5 6

8 4 6

An o

b s e r v a t i oni s a c o un t r , e a r , or i i n a l l a n u a

e W e s t e r n or C omm uni s t





a r e d i ff e r e n c e - i n - d i ff e r e n c e s r e g r e s s i o n s ( e q u a t i o n ( 5 . 4 ) ) u s i n g a n n u a l d a t a f o r t h e p e r i o d 1 9 8 0

- 2 0 0 0 ,

w i t h C o m m u n i s t E u

r o p e a s t h e r e g i o n o f i n t e r e s t a n d W e s t e r n E u r o p e a s t h e c o m p a r i s o n g r o u p . S e e t h e n o t e s t o T a b l e 5 . 2

f o r t h e C o m m u n i s t a n d W e s t e r n c o u n t r i e s u s e d . T h e C o m m u n i s t a n d W e s t e r n o r i g i n a l l a n g u a g e s a r e g i v e n i n f o o t n o t e 2 5 .

W e i n c l u d e t r a n s l a t i o n s i n t o t h e m a i n l a n g u a g e o f t

h e c o u n t r y o n l y .

P o s t i s a d u m m y f o r 1 9 9 1 o n w a r d s . P o p u

l a t i o

n a n

d

G D P

c o n

t r o l s a r e t h e l o g s o f p o p u l a t i o n a n d o f r e a

l G D P p e r c a p i t a .

C o u n

t r y - s p e

c i fi c

t i m e

t r e n

d s a r e l i n e a r . S t a

n d a r d

e r r o r s , i n p a r e n t h e s e s , a r e c l u s t e r e d a t t h e c o u n t r y l e v e l . * p < 0 . 1 0 , * * p < 0 . 0 5 , * * * p < 0 . 0 1 .




is that, because both Western translations and the degree of transition increase over

time in most countries, the effects in this specification may be driven by the presenceof two unrelated time trends. We thus add year fixed effects interacted with original

language in the second column of each group. The concern remains that we are

identifying off levels differences between countries, and countries differ across many

more dimensions than just their degree of transition away from Communism, so we

add country dummies interacted with original language in the third columns. Thus

in the final column of each group, the coefficient of interest is identified solely off

between-country differences in changes over time.The two variables directly related to the political system, institutionalized

democracy and political competition , are both positively and significantly related

to translations from Western European languages. These results suggest that

Communist countries that transitioned more away from Communism experienced

a higher jump in Western European translations. For instance, the regression with

country and year fixed effects shows an increase in institutionalized democracy score

from 7, the 25th percentile in 2000, to 9, the 75th percentile in 2000, corresponds toa 32% increase in translations from the West. The transition away from Communism

consisted of various broad-ranging reforms, and in columns 7 to 9 we test the relative

importance of two relevant reforms, namely price and trade deregulations. The

regressions suggest that while trade and foreign exchange system reform was a more

important driving force of increasing translations from Western European languages,

price liberalization was more important in reducing translations from Communist

languages. These results suggest that, while trade barriers kept translations from theWest artificially low, the Communist price control system kept between-Communist

translations artificially high.



C.3. INFLUENTIAL TITLES APPENDIX 219

C.3 Influential titles appendix

This appendix lists the titles that we include in our analysis of titles influential in the

West. The author is given in parentheses.

• “The Education of Henry Adams” (Henry Adams)

• “Eichmann in Jerusalem: A Report on the Banality of Evil” (Hannah Arendt)

• “The Origins of Totalitarianism (Elemente und Ursprunge totaler Herrschaft)”

(Hannah Arendt)

• “Memoirs (Memoires)” (Raymond Aron)

• “Social Choice and Individual Values” (Kenneth Arrow)

• “Mythologies (Mythologies)” (Roland Barthes)

• “The Second Sex (Le Deuxieme Sexe)” (Simone de Beauvoir)

• “The Cultural Contradictions of Capitalism” (Daniel Bell)

• “The End of Ideology” (Daniel Bell)

• “Four Essays on Liberty” (Isaiah Berlin)

• “The Hedgehog and the Fox” (Isaiah Berlin)

• “Russian Thinkers” (Isaiah Berlin)

• “Feudal Society (La Societe feodale)” (Marc Bloch)

• “The Historian’s Craft (Apologie pour l’histoire, ou, Metier d’historien)” (Marc

Bloch)

• “The Future of Democracy (Il Futuro della Democrazia)” (Norberto Bobbio)

• “The Mediterranean and the Mediterranean World in the Age of Philip II (La

Mediterranee et le monde mediterraneen a l’epoque de Philippe II)” (Fernand

Braudel)

• “I and Thou (Ich und Du)” (Martin Buber)

• “The Myth of Sisyphus and Other Essays (Le Mythe de Sisyphe)” (Tom Wolfe)

• “Notebooks 1935-1942” and “Notebooks 1943-1951” (Carnets) (Albert Camus)




• “Aspects of the Novel” (E.M. Forster)

• “Madness and Civilization: A History of Insanity in the Age of Reason (Folieet deraison. Histoire de la folie a l’age classique)” (Michel Foucault)

• “The Diary of a Young Girl (Het Achterhuis: Dagboekbrieven van 12 Juni 1942

– 1 Augustus 1944)” (Anne Frank)

• “The Golden Bough” (James George Frazer)

• “Civilization and Its Discontents (Das Unbehagen in der Kultur)” (Sigmund

Freud)

• “The Interpretation of Dreams (Die Traumdeutung)” (Sigmund Freud)• “Capitalism and Freedom” (Milton Friedman)

• “The Fear of Freedom/Escape from Freedom (Die Furcht vor der Freiheit)”

(Erich Fromm)

• “The Affluent Society” (John Kenneth Galbraith)

• “The Revolt of the Masses (La Rebelion de las masas)” (Jose Ortega y Gasset)

• “The Interpretation of Cultures” (Clifford Geertz)

• “Nations and Nationalism” (Ernest Gellner)• “Wealth and Poverty” (George Gilder)

• “The Presentation of Self in Everyday Life” (Erving Goffman)

• “Art and Illusion: A Study in the Psychology of Pictorial Representation” (E.H.

Gombrich)

• “The Mismeasure of Man” (Stephen Jay Gould)

• “Prison Notebooks (Quaderni del carcere)” (Antonio Gramsci)

• “Good-bye to all that” (Robert Graves)• “The Autobiography of Malcolm X” (Alex Haley)

• “A Mathematician’s Apology” (G.H. Hardy)

• “The Concept of Law” (H.L.A. Hart)

• “Being and Time (Sein und Zeit)” (Martin Heidegger)




• “Exit, Voice, And Loyalty: Responses to Decline in Firms, Organizations and

States” (Albert Hirschman)• “The American Political Tradition and the Men Who Made it” (Richard

Hofstadter)

• “Dialectic of Enlightenment (Philosophische Fragmente/Dialektik der Aufk-

laerung)” (Max Horkheimer and Theodor W. Adorno)

• “The Waning of the Middle Ages/The Autumn of the Middle Ages (Herfsttij

der Middeleeuwen)” (Johan Huizinga)

• “Brave New World” (Aldous Huxley)• “The Varieties of Religious Experience” (William James)

• “The Perennial Scope of Philosophy (Der philosophische Glaube)” (Karl

Jaspers)

• “Modern Times: A History of the World from the 1920s to the 1980s” (Paul

Johnson)

• “Memories, Dreams, Reflections (Erinnerungen, Traeume, Gedanken)” (Carl

Gustav Jung)• “The Castle (Das Schloss)” (Franz Kafka)

• “The Face of Battle” (John Keegan)

• “The Economic Consequences of the Peace” (John Maynard Keynes)

• “The General Theory of Employment, Interest, and Money” (John Maynard

Keynes)

• “The Conservative Mind: From Burke to Eliot” (Russell Kirk)

• “The God that Failed: Six Studies in Communism” (Arthur Koestler andRichard Crossman (eds))

• “Darkness at Noon (Sonnenfinsternis)” (Arthur Koestler)

• “Main Currents of Marxism (Glowne nurty marksizmu)” (Leszek Kolakowski)

• “On Being a Christian (Christ Sein)” (Hans Kueng)




• “The Structure of Scientific Revolutions” (Thomas S. Kuhn)

• “The Book of Laughter and Forgetting (Kniha smichu a zapomneni)” (MilanKundera)

• “The Savage Mind (La Pensee Sauvage)” (Claude Levi-Strauss)

• “A World on the Wane (Tristes Tropiques)” (Claude Levi-Strauss)

• “If This is a Man/Survival in Auschwitz (Se questo e un uomo)” (Primo Levi)

• “The Abolition of Man: Reflections on Education with Special Reference to the

Teaching of English in the Upper Forms of Schools” (C.S. Lewis)

• “Mere Christianity” (C.S. Lewis)• “On Aggression (Das Sogenannte Boese)” (Konrad Lorenz)

• “Man’s Fate (La Condition humaine)” (Andre Malraux)

• “The Last Lion: Winston Spencer Churchill” (William Manchester)

• “West with the Night” (Beryl Markham)

• “The Rise of the West: A History of the Human Community” (William H.

McNeill)

• “The Seven Storey Mountain” (Thomas Merton)• “The Captive Mind (Zniewolony umysl)” (Czeslaw Milosz)

• “Principia Ethica” (G.E. Moore)

• “Speak, Memory” (Vladimir Nabokov)

• “Behemoth: The Structure and Practice of National Socialism” (Franz Neu-

mann)

• “The Quest for Community: A Study in the Ethics of Order and Freedom”

(Robert Nisbet)• “Anarchy, State and Utopia” (Robert Nozick)

• “Nineteen Eighty-Four” (George Orwell)

• “Animal Farm” (George Orwell)

• “Collected Essays” (George Orwell)




• “Homage to Catalonia” (George Orwell)

• “The Gnostic Gospels” (Elaine Pagels)• “Studies in Iconology” (Erwin Panofsky)

• “Doctor Zhivago (Doktor Zhivago)” (Boris Pasternak)

• “The Great Transformation” (Karl Polanyi)

• “The Logic of Scientific Discovery (Logik der Forschung)” (Karl Popper)

• “The Open Society and its Enemies” (Karl Popper)

• “The ABC of Reading” (Ezra Pound)

• “A Theory of Justice” (John Rawls)• “The Lonely Crowd” (David Riesman)

• “The Joy of Cooking” (Irma S. Rombauer, Marion Rombauer Becker, and Ethan

Becker)

• “Philosophy and the Mirror of Nature” (Richard Rorty)

• “Love in the Western World (L’Amour et l’Occident)” (Denis de Rougemont)

• “Economics: An Introductory Analysis” (Paul Samuelson)

• “The Strategy of Conflict” (Thomas Schelling)• “The Messianic Idea in Judaism, and Other Essays on Jewish Spirituality”

(Gershom Scholem)

• “Small is Beautiful: Economics as if People Mattered” (Ernst Friedrich

Schumacher)

• “Capitalism, Socialism, and Democracy” (Joseph A. Schumpeter)

• “The Two Cultures and the Scientific Revolution” (C.P. Snow)

• “The Gulag Archipelago” (Alexander Solzhenitsyn)• “Ethnic America” (Thomas Sowell)

• “The Gate of Heavenly Peace” (Jonathan D. Spence)

• “The Autobiography of Alice B. Toklas” (Gertrude Stein)

• “The Politics of Cultural Despair” (Fritz Stern)



C.4. KEYWORD LIST APPENDIX 229

• Business (937): business, marketing, sales, salesman, selling

• Medical (5,986): doctor, patient, medicine, medical, medication, medical-surgical, treatment, disease, disorder, dental, obstetrics, radiology, psychia-

try, psychology, surgery, pediatric, physiology, physiotherapy, psychotherapy,

anatomy, diagnosis, clinic, birth, pregnancy, health, healthcare, remedy,

depression, nursing, heart, heartbeat, cancer

• Engineering (178): engineer

• Food (1,029): cookery, cookbook, cook, wine, winebook, winemaker, winespeak,

winetasting, winewise, food• Gardening (921): garden, plants, herbs, flowers




Biology

• “On the Origin of Species” (Charles Robert Darwin)

• “Animal Roundabout” (Johnny Morris)

• “The Tropical Rain Forest” (Arnold Newman)

• “The Major Transitions in Evolution” (John Maynard Smith)

Geology

• “Minerals and Rocks” (Keith Lye)

• “In the Weedy Rock Pool” (Helena Ramsay)

• “Geology of Phosphate Deposits”

• “Explorers Book, Rock and Minerals” (Steve Parker)

Applied Science

Computers

• “Inside Autocad” (Daniel Raker)

• “Microsoft Word at a Glance” (Jerry Joyce)

• “The Internet by E-mail” (Clay Shirky)

• “Connaitre Windows ’95”

Business

• “What They Don’t Teach You at Harvard Business School” (Mark H. MacCor-

mack)

• “Diagnostic Marketing” (C. Davis Fogg)

• “Marketing Management” (Philip Kotler)




C.6 Most translated titles appendix

This appendix lists the titles that we include in our analysis of the titles most

translated in the West, listed by field. The author is given in parentheses.

Philosophy and Psychology

• “Falling in Love (Innamoramento e Amore)” (Francesco Alberoni)

• “The Myth of Sisyphus and Other Essays (Le Mythe de Sisyphe)” (Tom Wolfe)

• “How to Stop Worrying and Start Living” (Dale Carnegie)

• “How to Win Friends and Influence People” (Dale Carnegie)

• “Discourse on the Method of Rightly Conducting One’s Reason and of Seeking

Truth in the Sciences (Discours de la methode pour bien conduire sa raison, et

chercher la verite dans les sciences)” (Rene Descartes)

• “Your Erroneous Zones” (Wayne W. Dyer)

• “The Sky’s the Limit” (Wayne Dyer)

• “In Praise of Folly (Morias Enkomion)” (Desiderius Erasmus)

• “The Interpretation of Dreams (Die Traumdeutung)” (Sigmund Freud)

• “The Art of Loving” (Erich Fromm)

• “To Have or to Be” (Erich Fromm)

• “Linda Goodman’s Love Signs” (Linda Goodman)

• “Sun Signs” (Linda Goodman)

• “You Can Heal Your Life” (Louise Hay)

• “Critique of Pure Reason (Kritik der reinen Vernunft)” (Immanuel Kant)

• “Groundwork/Foundations of the Metaphysics of Morals (Grundlegung zur

Metaphysik der Sitten)” (Immanuel Kant)

• “On Death and Dying” (Elisabeth Kbler-Ross)

• “Tao Te Ching (Dao De Jing)” (Laozi)



C.6. MOST TRANSLATED TITLES APPENDIX 239

Law, Social Science and Education

• “The Second Sex (Le Deuxieme Sexe)” (Simone de Beauvoir)

• “On Crimes and Punishments (Dei delitti e delle pene)” (Cesare Beccaria)

• “Crowds and Power (Masse und Macht)” (Elias Canetti)

• “H: Autobiography of a Child Prostitute and Heroin Addict (Wir Kinder vom

Bahnhof Zoo)” (Christiane F)

• “On War (Vom Kriege)” (Karl von Clausewitz)

• “I’m Your Mother (Moi, ta mere)” (Christiane Collange)

• “How to Parent” (Fitzhugh Dodson)

• “Macroeconomics” (Rudiger Dornbusch)

• “The Rules of Sociological Method (Les regles de la methode sociologique)”

(Emile Durkheim)

• “Grimm’s Fairy Tales (Kinder- und Hausmarchen/Grimms Marchen)” (Jacob

and Wilhelm Grimm)

• “Mein Kampf” (Adolf Hitler)

• “The Prince (Il Principe/De Principatibus)” (Niccol Machiavelli)

• “Capital (Das Kapital: Kritik der politischen Okonomie)” (Karl Marx)

• “The Communist Manifesto (Manifest der Kommunistischen Partei)” (Karl

Marx)

• “On Liberty” (John Stuart Mill)

• “Utopia” (Thomas More)

• “Emile, or On Education (Emile ou de l’Education)” (Jean-Jacques Rousseau)

• “The Social Contract, Or Principles of Political Right (Du contrat social ou

Principes du droit politique)” (Jean-Jacques Rousseau)

• “Economics: An Introductory Analysis” (Paul Anthony Samuelson)

• “Find out about Finland (Tiesitko taman Suomesta?)” (Seppo Sauri)

• “The Global Challege (Le Dfi mondial)” (Jean-Jacques Servan-Schreiber)




• “Go Ask Alice” (Beatrice Sparks)

• “The Art of War (Sunzi Bingfa/Sun Tzu Ping Fa)” (Sun Tzu)• “Democracy in America (De la democratie en Amerique)” (Alexis de Toc-

queville)

• “The Third Wave” (Alvin Toffler)

• “Kamasutra Vatsyayana”

• “Lowest of the Low (Ganz Unten)” (Gunter Wallraff)

• “A Room of One’s Own” (Virginia Woolf)

• “In God’s Name” (David Yallop)

Exact Science

• “A Field Guide in Color to Wild Flowers (Was bluht denn da?)” (Dietmar

Aichele and Alois Kosch)

• “Molecular Biology of the Cell” (Bruce Alberts)

• “Breakthroughs in Science” (Isaac Asimov)

• “The (New) Intelligent Man’s Guide to Science/ Asimov’s (New) Guide to

Science” (Isaac Asimov)

• “Please Explain” (Isaac Asimov)

• “The Universe: From Flat Earth to Quasar” (Isaac Asimov)

• “The Living Planet: A Portrait of the Earth” (David Attenborough)

• “Animal Tracks and Signs” (Preben Bang)

• “The Hamlyn Guide to Birds of Britain and Europe” (Bertel Bruun, Bruce

Campbell and Arthur Singer)

• “On the Origin of Species by Means of Natural Selection, or the Preservation

of Favoured Races in the Struggle for Life” (Charles Darwin)

• “The Selfish Gene” (Richard Dawkins)




• “Relativity: The special and the general theory (Uber die spezielle und die

allgemeine Relativitatstheorie” (Albert Einstein)• “Gorillas in the Mist” (Dian Fossey)

• “The Panda’s Thumb” (Stephen Jay Gould)

• “The Country Diary of an Edwardian Lady/Nature Notes for 1906” (Edith

Holden)

• “Introduction to Solid State Physics” (Charles Kittel)

• “The Making of Mankind” (Richard Leakey)

• “Biochemistry: The Molecular Basis of Cell Structure and Function” (AlbertLehninger)

• “Animal Days” (Desmond Morris)

• “The Naked Ape: A Zoologist’s Study of the Human Animal” (Desmond Morris)

• “A Field Guide to the Birds of Britain and Europe” (Roger Tory Peterson, Guy

Mountfort, and P.A.D. Hollom)

• “Natural History (Naturalis Historia)” (Pliny the Elder)

• “The Cosmic Connection: An Extraterrestrial Perspective” (Carl Sagan)• “Cosmos” (Carl Sagan)

• “Gemstones of the World (Edelsteine und Schmucksteine)” (Walter Schumann)

• “Einstein for Beginners” (Joseph A. Schwarcz)

• “Calculus” (Michael Spivak)

• “Gymnasiets Fysik” (Eve Staffensson)

• “The Man Who Calculated (O Homem que Calculava)” (Malba Tahan)

• “Physics” (Paul Allen Tipler)

Applied Science

• “Better Sight Without Glasses” (Harry Benjamin)

• “The Witch’s Handbook” (Malcolm Bird)




• “Dieter’s Guide to Weight Loss During Sex” (Richard Smith)

• “Baby and Child Care” (Benjamin Spock)• “The Baby Care Book” (Miriam Stoppard)

• “The Complete Scarsdale Medical Diet plus Dr. Tarnower’s Lifetime Keep-Slim

Program” (Herman Tarnower)

• “Der Kleine Doktor” (Alfred Max Vogel)

• “Running MS-DOS” (Van Wolverton)

Arts, Games and Sports

• “Stretching” (Bob Anderson)

• “ History of Modern Architecture (Storia dell’architettura moderna)” (Leonardo

Benevolo)

• “Linnea in Monet’s garden (Linnea i malarens tradgard)” (Christina Bjork)

• “Drawing on the Right Side of the Brain” (Betty Edwards)

• “How to Play Guitar” (Roger Evans)

• “The Paradoxicon: A Collection of Contradictory Challenges, Problematical

Puzzles and Impossible Illustrations” (Nicholas Falletta)

• “The Runner’s Handbook” (Bob Glover, Jack Shepherd and Shelly-lynn

Florence Glover)

• “The Story of Art” (Ernst Gombrich)

• “Anatomy and Figure Drawing” (Louise Gordon)

• “How to Recognize Gothic Art (Come riconoscere l’arte gotica)” (Maria Cristina

Gozzoli)

• “The Social History of Art” (Arnold Hauser)

• “The Photographer’s Handbook” (John Hedgecoe and Leonard Ford)

• “Frida, a Biography of Frida Kahlo” (Hayden Herrera)

• “The Art of Color (Kunst der Farbe)” (Johannes Itten)




Henry Lincoln)

• “The Discoverers” (Daniel Boorstin)• “My Last Sigh (Mon Dernier soupir)” (Luis Bunuel)

• “Commentarii de Bello Gallico” (Gaius Julius Caesar)

• “My Autobiography” (Charlie Chaplin)

• “Tracks” (Robyn Davidson)

• “The Story of Civilization” (Will and Ariel Durant)

• “The Diary of a Young Girl (Het Achterhuis: Dagboekbrieven van 12 Juni 1942

– 1 Augustus 1944)” (Anne Frank)• “Deeds of the Danes (Gesta Danorum)” (Saxo Grammaticus)

• “Varldshistoria : folkens liv och kultur” (Carl Grimberg)

• “Seven Years in Tibet” (Heinrich Harrer)

• “Memories, Dreams, Reflections (Erinnerungen, Traeume, Gedanken)” (Carl

Gustav Jung)

• “A Brief History of Finland (Katsaus Suomen Historiaan)” (Matti Klinge)

• “Freedom at Midnight (Cette nuit la liberte)” (Dominique Lapierre)• “Survival in Auschwitz/If this is a Man” (Primo Levi)

• “The Truce/The Reawakening (La Tregua)” (Primo Levi)

• “A Distant Mirror (Les croisades vues par les arabes)” (Amin Maalouf)

• “Out on a Limb” (Shirley MacLaine)

• “Rome, the first thousand years (Storia di Roma)” (Indro Montanelli)

• “The Diary of Anais Nin” (Anais Nin)

• “Homage to Catalonia” (George Orwell)• “Parallel Lives/Lives of the Noble Greeks and Romans” (Plutarch)

• “Ten Days that Shook the World” (John Reed)

• “The Conspiracy of Catiline (De coniuratione Catilinae/Bellum Catilinae)”

(Sallust)



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