Insights from Book Translations on the International Diffusion of Knowledge

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

This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/

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

ii 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

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.

Nicholas Bloom

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.

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.

iii 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 Rodr´ıguez-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 international 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 detailed information 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 do consumer 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. Shaw Fellowship.

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

ix 3.1 Data construction ...... 31 3.1.1 Translation data ...... 31 3.1.2 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 ...... 82 4.2.1 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

x 4.3.6 Translations published in more developed countries decrease less with physical distance ...... 96 4.4 Speed of translations ...... 98 4.5 Conclusions ...... 99 4.6 Figures and tables ...... 102

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 ...... 132 5.5.1 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

xi 5.6.3 Regression analysis by book subfield ...... 147 5.7 The effect on influential titles ...... 149 5.8 Conclusions and discussion ...... 153 5.9 Figures and tables ...... 156

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 3 177 A.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 ...... 216 C.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

xii 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: closest original country ...... 111

xiii 5.1 Before/after analysis: The effect of the collapse of Communism on translations ...... 164 5.2 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 ...... 168 5.5 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

xiv 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 ...... 159 5.5 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

xv 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

xvi 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 Rodr´ıguez-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 used proxies 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 different aspects 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 as trade, migration, and foreign direct investment (FDI) increase knowledge spillovers (e.g., MacGarvie, 2006, Branstetter, 2006, Agrawal, Kapur and McHale, 2008, and the relationships between geography and 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 and Verspagen, 2008, Singh and Marx, 2011). The answers to such questions could inform domestic policy that encourages or impedes research, trade, immigration, or FDI. They may also have implications for the theoretical modeling of knowledge spillovers and for predictions about income convergence between 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 flows of 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 4 CHAPTER 1. INTRODUCTION

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 between distance 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 with distance. 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 for trade 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 information costs, 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). 6 CHAPTER 1. INTRODUCTION

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. 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, which suggests 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 varied across 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 idea flows 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-oriented Satellite 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 from Communist 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 8 CHAPTER 1. INTRODUCTION

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 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 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 titles varied 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 collapse of 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, translation flows 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, and suppressing 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 feedback mechanism 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 referred to 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 such as 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 knowledge to 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 the organization, 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 growth highlights, 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 the drivers 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 14 CHAPTER 2. THE ECONOMICS OF IDEAS

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 model is 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 than their 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 2.2. THE ECONOMIC THEORY OF IDEAS 15

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 increasing returns 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 development, 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. 16 CHAPTER 2. THE ECONOMICS OF IDEAS

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 Rodr´ıguez-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. 2.2. THE ECONOMIC THEORY OF IDEAS 17

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 Rodr´ıguez-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 to adopt 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 18 CHAPTER 2. THE ECONOMICS OF IDEAS

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 Rodr´ıguez-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 costly and 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. 20 CHAPTER 2. THE ECONOMICS OF IDEAS

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 no effect 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) 22 CHAPTER 2. THE ECONOMICS OF IDEAS

doesn’t draw an explicit distinction between “pure” ideas and ideas embodied in capital goods. This distinction is important, because embodied ideas may not provide the 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 country B, 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 24 CHAPTER 2. THE ECONOMICS OF IDEAS

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 for knowledge 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 time involved 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, an economic 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 26 CHAPTER 2. THE ECONOMICS OF IDEAS

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. WHEN WILL TRANSLATIONS OCCUR? 27

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 28 CHAPTER 2. THE ECONOMICS OF IDEAS

may experience greater demand for translations from each other because their preferences favor each other’s ideas. At the same time, such similarities may mean they 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 occurring is 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 calculation of 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 2.5. WHEN WILL TRANSLATIONS OCCUR? 29

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 a wider 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 translations. 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 30 CHAPTER 2. THE ECONOMICS OF IDEAS

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, which could 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 mutual interests 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 the Universal 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 a country receiving a translation and Unesco listing the translation in the IT, the IT reports the year in which such translations were published, not just the year in which they were reported. I attribute them to the former and disregard the latter. 2Philology and Linguistics were a separate (and very small) category prior to 1970, and then were 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 an intermediate language. In these cases, I consider the idea ﬂow to be from the original language to the ﬁnal language, with the intermediate language just part of the mechanism. I thus count these as translations from the original language to the target language, and disregard the intermediate language. 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 1993 to 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 is necessary 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 not substantially 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 the book. 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 approximation is 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 either counts 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 the ordering within each group of titles alphabetically by author means this method is unlikely to bias my sample with respect the original or target language, the main dimensions of interest that vary within such a group. 3.1. DATA CONSTRUCTION 35

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

trans trans of the translating country in each of the three altitude zones by alt0 , alt100 , and trans orig alt800 respectively, and the equivalent proportions in the original country by alt0 , orig orig alt100 , and alt800 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%. 36 CHAPTER 3. DATA

AltitudeDist =

h trans orig trans orig trans orig i 1 − min(alt0 , alt0 ) + min(alt100 , alt100 ) + min(alt800 , alt800 ) (3.1)

Note this distance measure takes the value 0 if the original and translating country both lie entirely in the same altitude zone (e.g., below 100 meters above sea level). Conversely, if the two countries lie entirely in diﬀerent altitude zones (e.g., the original country lies entirely below 100 meters above sea level, and the translating country lies entirely between 100 and 800 meters), the distance between them is 1. In general, the measure captures the proportion of the two countries that lie in the same zone. To illustrate, the altitude proﬁle distance between the Netherlands (almost entirely low-lying) and France (largely intermediate altitude, with some high and some low)) is 0.75, whereas the distance between the Netherlands and Switzerland (largely high altitude) is 0.99.

Biome region proﬁle distance

Biome data are originally from the World Wildlife Fund (WWF) Terrestrial Ecore- gions of the World dataset, which capture global terrestrial vegetation biodiversity patterns. The data classify land into one of 14 biome types:

1. tropical and subtropical moist broadleaf forests 2. tropical and subtropical dry broadleaf forests 3. tropical and subtropical coniferous forests 4. temperate broadleaf and mixed forests 5. temperate conifer forests 6. boreal forests/taiga 7. tropical and subtropical grasslands, savannas and shrublands 3.1. DATA CONSTRUCTION 37

8. temperate grasslands, savannas and shrublands 9. ﬂooded grasslands and savannas 10. 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

th trans the i biome region by biomei and the fraction of the original country that falls th orig into the i biome region by biomei . Then the biome region distance between the original and translating countries is given by:

14 X trans orig BiomeDist = 1 − min(biomei , biomei ) (3.2) i=1

Climate region proﬁle distance

I use an aggregate version of the Köppen 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. 38 CHAPTER 3. DATA

3.1.3 Cultural distance data

Religious distance

My data on religious distance are generated from the data on the religious distribution of the population of each country used by Alesina et al. (2003). Note these distributions are for one point in time only, so the measures of religious distance I use do not vary by year. I aggregate up religions to the following categories:

1. Atheist 2. Anglican 3. African Christian 4. East Asian religions 5. Eastern Orthodox 6. Indian religions 7. Jewish 8. Muslim 9. Oriental Orthodox 10. Protestant 11. Roman Catholic 12. Christian not elsewhere classiﬁed 13. Numerous “traditional” religions that I regard as distinct from each other.

The primary measure of religious distance I use is the probability a randomly chosen person from the original country and a randomly chosen person from the translating country have a different religion, as classified into the categories above. For some countries, a (usually small) proportion of the population has missing religion. I assume alternatively that a randomly chosen person with missing religion a) is always of a different religion to any person from the other country (main specification) or b) 3.1. DATA CONSTRUCTION 39

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 results are 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 used by 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 - O¨ıl - 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

2 × CommonNodesij LinguisticDistij = 1 − (3.3) Nodesi + Nodesj 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

r 15 − CommonNodes LinguisticDistF earon = ij (3.4) ij 15 3.1. DATA CONSTRUCTION 41

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 the two 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 Kogut and 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 and institutions 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 values in society are caring for others and the quality of life.” My measure of cultural distance based on Hofstede’s cultural dimensions is given by

4 " k k 2 # 1 X (Ii − Ij ) HofstedeDistij = (3.5) 4 V ark k=1

k where i and j denote countries, k denotes the dimension, Ii 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 3.1. DATA CONSTRUCTION 43

elements derived from earlier work in the social sciences. The first of Schwartz’s dimensions is “conservatism”, defined as “a cultural emphasis on maintenance of 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 individuals independently 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 translating 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 1949 to 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 expected given 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 3.3 suggests 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 3.2. MAIN PATTERNS 47

across languages, especially titles with the potential to be of interest internationally. Economies of scale in idea production (e.g. resulting from a thriving intellectual community) 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: the largest 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. The percentage 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 or Russian, 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 occurred with 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. MAIN PATTERNS 49

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 landlocked countries 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 autocratic countries 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. 3.2. MAIN PATTERNS 51

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 with the 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, Muslim countries 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 from French 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 from English.

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 3.2. MAIN PATTERNS 53

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 book age 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 they translate. 54 CHAPTER 3. DATA

3.3 Figures and tables

Figure 3.1: Translations from English, French, German, and Russian by country

Western Europe

Notes: Translations into only the languages that are oﬃcial in the translating country are included. 3.3. FIGURES AND TABLES 55

Notes: Translations into only the languages that are oﬃcial in the translating country are included. 56 CHAPTER 3. DATA

Central and Eastern Europe

Notes: Translations into only the languages that are oﬃcial in the translating country are included. 3.3. FIGURES AND TABLES 57

Notes: Translations into only the languages that are oﬃcial in the translating country are included. 58 CHAPTER 3. DATA

Asia and the Middle East

Notes: Translations into only the languages that are oﬃcial in the translating country are included. 3.3. FIGURES AND TABLES 59

America

Notes: Translations into only the languages that are oﬃcial in the translating country are included. 60 CHAPTER 3. DATA

Africa

Notes: Translations into only the languages that are oﬃcial in the translating country are included. 3.3. FIGURES AND TABLES 61

Australasia

Notes: Translations into only the languages that are oﬃcial in the translating country are included.

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

Notes: Translations into only the major languages of the translating country are included. 3.3. FIGURES AND TABLES 63

Figure 3.3: Translations per capita by country and field

Notes: Translations into only the major languages of the translating country are included. 64 CHAPTER 3. DATA

Notes: Translations into only the major languages of the translating country are included. 3.3. FIGURES AND TABLES 65

Figure 3.4: Age at translation of non-fiction titles by original language for 1998-2000 Translations from four major original languages 66 CHAPTER 3. DATA 91% 97% 92% 93% 88% 89% 88% 90% 88% 92% 94% 93% 96% 97% 98% 97% 97% 96% 95% 96% 95% 97% 97% 96% Religion Religion 90% 97% 93% 86% 79% 86% 84% 87% 88% 89% 95% 94% 97% 99% 98% 97% 96% 97% 95% 96% 96% 96% 99% 98% Literature Literature Arts Arts 80% 82% 79% 81% 69% 83% 83% 81% 81% 79% 81% 79% 84% 84% 82% 87% 79% 87% 88% 85% 87% 84% 84% 82% 92% 99% 96% 88% 79% 84% 84% 92% 91% 94% 96% 96% 97% 99% 95% 93% 94% 91% 97% 96% 97% 99% 99% Philosophy Philosophy 80% 92% 85% 83% 73% 79% 77% 74% 77% 80% 84% 84% 88% 94% 89% 93% 86% 89% 87% 86% 88% 88% 88% 88% History History Field of translations Field of translations Field 66% 75% 75% 57% 57% 67% 62% 56% 50% 58% 85% 86% 79% 90% 85% 82% 74% 79% 81% 71% 69% 73% 89% 91% Social Science Social Science Social 85% 96% 85% 72% 68% 80% 80% 81% 82% 81% 92% 93% 92% 97% 89% 92% 86% 89% 90% 90% 92% 90% 95% 95% Applied Science Applied Science Applied 73% 84% 86% 74% 73% 80% 72% 68% 63% 61% 82% 86% 86% 93% 91% 91% 85% 87% 83% 87% 82% 82% 91% 90% Countries primarily translate into their own languages Natural Science Natural Science Natural 85% 93% 88% 81% 74% 82% 79% 80% 81% 83% 92% 92% 93% 96% 94% 94% 90% 92% 91% 90% 91% 92% 95% 95% Table 3.1: Total Total Panel A: % of titles translated into the most widespread language of the translating country translating of the language widespread most the into translated A: % of titles Panel Years Total 1949 1954 1959 1964 1969 1974 1979 1984 1989 1994 1999 country translating of the language official an into translated % of titles B: Panel Years Total 1949 1954 1959 1964 1969 1974 1979 1984 1989 1994 1999 Notes: The sample includes, for each year, data from all available countries. 3.3. FIGURES AND TABLES 67 Total 7,918 14,254 24,476 26,505 32,812 31,994 50,506 57,064 58,041 72,392 82,582 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 458,544 373 792 6.3% 4.7% 5.6% 5.3% 6.2% 6.7% 4.9% 5.5% 6.2% 6.2% 7.0% 7.5% 1,288 1,654 2,190 1,582 2,774 3,553 3,597 5,052 6,190 29,045 Religion 4,260 7,496 49.7% 53.8% 52.6% 55.6% 48.7% 45.7% 45.4% 47.1% 51.6% 51.1% 51.8% 47.9% 13,616 12,902 14,989 14,518 23,787 29,460 29,637 37,508 39,537 227,710 Literature 233 517 754 Arts 5.2% 2.9% 3.6% 3.1% 3.9% 4.6% 4.9% 5.9% 5.0% 5.4% 5.8% 6.4% 1,023 1,521 1,566 2,992 2,839 3,139 4,176 5,299 24,059 363 477 912 5.5% 4.6% 3.3% 3.7% 4.2% 4.9% 5.3% 5.2% 4.8% 5.7% 6.2% 6.8% 1,124 1,624 1,699 2,603 2,734 3,321 4,504 5,634 24,995 Philosophy Field of translations Field 765 7.4% 9.7% 8.7% 8.3% 9.0% 8.9% 8.1% 8.2% 6.0% 7.7% 6.7% 6.5% 1,243 2,035 2,396 2,905 2,585 4,145 3,409 4,441 4,830 5,369 34,123 History Distribution of translations by field 9.1% 7.6% 7.8% 1,092 1,638 2,657 3,007 4,370 4,760 6,346 6,091 5,262 5,490 6,468 10.3% 13.8% 11.5% 10.9% 11.3% 13.3% 14.9% 12.6% 10.7% 47,181 Social Science Social Table 3.2: 487 6.2% 8.3% 9.8% 9.1% 9.2% 9.2% 1,506 2,026 2,607 2,980 2,950 4,661 6,038 5,987 8,032 10.6% 10.6% 10.6% 10.3% 11.1% 13.9% 11,513 48,787 Applied Science Applied 345 585 4.9% 4.4% 4.1% 4.9% 6.8% 6.8% 7.3% 6.3% 5.2% 4.6% 3.9% 3.1% 1,188 1,792 2,233 2,334 3,198 2,940 2,657 2,800 2,572 22,644 Natural Science Natural Years Total 1949 1954 1959 1964 1969 1974 1979 1984 1989 1994 1999 Notes: This tableincludes, for presents each the year, number data from and all percentage available countries. of translations each year that fell into each ﬁeld. The sample 68 CHAPTER 3. DATA apeicue aafo l vial onre.Tedt npbiain foiia ilsaefo Unesco. The from are ﬁeld. each titles into original fell of that publications 1999 on in data published The translations countries. and available titles all original from of data percentage includes the sample presents table This Notes: 1999 3.3: Table % of non-fiction % overall books itiuino rnltosrltv odsrbto foiia ilsb il for field by titles original of distribution to relative translations of Distribution Original titles Translations Original titles Translations Science Natural 8.6% 6.0% 6.1% 3.1% Applied Science 21.8% 26.9% 15.5% 13.9% Science 32.8% 15.0% 23.3% Social 7.8% History 14.3% 12.5% 10.1% Field of translations 6.5% Philosophy 13.1% 4.9% 3.5% 6.8% 12.3% 9.2% 6.6% 6.4% Arts Literature 28.9% 47.9% Religion 14.4% 8.4% 5.9% 7.5% 3.3. FIGURES AND TABLES 69

Table 3.4: Most translated original languages by field

Panel A: All years Field of translations

Total Natural Science Applied Science Social Science History Philosophy Arts Literature Religion Rank English English English English English English English English English 1 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 2 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 3 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 4 9.2% 6.5% 7.5% 9.1% 9.3% 5.5% 6.9% 6.7% 5.3% Italian Czech Czech Serbo-Croatian Italian Ancient Greek Russian Spanish Latin 5 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 6 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 7 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 8 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 9 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 10 1.0% 0.9% 0.9% 1.4% 1.5% 0.8% 1.4% 0.9% 1.7%

Panel B: 1959 Field of translations Total Natural Science Applied Science Social Science History Philosophy Arts Literature Religion Rank English English Russian Russian English English English English French 1 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 2 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 3 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 4 9.2% 9.1% 7.4% 7.8% 12.7% 12.4% 14.9% 6.5% 8.3% Italian French Czech Serbo-Croatian Italian Ancient Greek Italian Spanish Italian 5 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 6 2.5% 2.3% 1.3% 3.4% 2.3% 5.0% 5.2% 2.6% 5.3% Spanish Polish Danish Romanian Latin Italian Serbo-Croatian Chinese Ancient Greek 7 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 8 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 9 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 10 1.4% 0.8% 0.9% 1.6% 1.7% 1.0% 1.2% 1.6% 1.0%

Panel C: 1999 Field of translations

Total Natural Science Applied Science Social Science History Philosophy Arts Literature Religion Rank English English English English English English English English English 1 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 2 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 3 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 4 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 5 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 6 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 7 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 8 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 9 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 10 0.8% 1.4% 0.4% 0.9% 0.9% 0.7% 0.9% 0.7% 1.1%

Notes: This table presents the ten most translated original languages and their percentages of total translations for each ﬁeld of translations. Panel A presents data in total for every ﬁfth year from 1949 to 1999; Panel B presents data for 1959; Panel C presents data for 1999. 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%

Central and Eastern Europe 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 Romanian Romanian 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%

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 Machiguenga 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 English 75.4% 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 country and its percentage of total translations for the country. Data in the “Total” column are for every fifth year from 1949 to 1999, where available. 72 CHAPTER 3. DATA o emn,bt0frSizrad tnaderr r lsee ttecutylvl seik eoesgicneat: significance denote Asterisks level. country value the the takes at it clustered years example, are are For The errors and language. Standard 25+, widespread year. aged Switzerland. most population for a p their the 0 * of in for country but country Germany, defined primary for the are a variables is 1 into country variable education translating flowing The The the onwards. translations 1999. whether 1964 to predicting years 1949 for results from available year regression only fifth OLS every presents are included table This Notes: Countries Observations R-Squared Year fixed effects Country is the main country of their main language Average years of schooling Fraction of population with post-school education GDP per capita (ln) Population (ln) Variable Dependent variable: of number translations into an official language of the country (ln) < .0 *p ** 0.10, < .5 * p *** 0.05, al 3.6: Table < 0.01. orltso rnlto lw noacountry a into flows translation of Correlates 1.276*** 0.638*** (0.204) (0.125) 0.357 449 Yes (1) 75 onr stemi onr ftermi language main their of country main the is Country 2.051*** 1.401*** 0.530*** (0.430) (0.185) (0.118) 0.543 449 Yes (2) 75 1.830*** 9.597*** (0.549) (2.947) (0.177) 0.373 0.281 269 Yes (3) 48 1.840*** 1.699*** 0.613*** (0.425) (3.471) (0.275) (0.158) -3.820 0.624 269 Yes (4) 48 1.469*** 0.374*** 0.479*** (0.506) (0.063) (0.164) 0.519 269 Yes (5) 48 sa niao for indicator an is 1.801*** 1.387*** 0.578*** (0.438) (0.414) (0.173) (0.116) 0.616 0.035 269 Yes (6) 48 3.3. FIGURES AND TABLES 73

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 most widespread 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 country of their most widespread language. For example, it takes the value 1 for Germany, but 0 for Switzerland. Standard errors are clustered at the country level. Asterisks denote significance at: * p<0.10, ** p<0.05, *** p<0.01. 74 CHAPTER 3. DATA r endfrtepplto gd2+ bevtosaewihe ytenme ftasain otiuigt the to contributing p translations * of at: number significance denote the Asterisks by level. weighted variables country education the are The at Observations clustered Italian. included. are p and are 25+. errors *** German country Standard French, aged translating English, speed. population the are of of the measure included regressions language these languages for official in original defined observation an The An into are country. translated translation. translating titles of a speed non-fiction in the Only predicting language regressions original OLS an of is results presents table The Notes: Countries Observations R-Squared Original language fixed effects Average years of schooling Fraction of population with post-school education GDP per capita (ln) Population (ln) Variable Dependent variable: < 0.01. al 3.8: Table -0.573*** (0.106) (0.047) -0.007 0.192 163 No (1) 50 orltso pe ftasain 1998-2000 translation, of speed of Correlates -0.612*** Median age of titles at time of translation (ln) (0.099) (0.047) -0.011 0.299 163 Yes (2) 50 -2.667*** (0.883) (0.091) 0.165 0.125 Yes (3) 29 97 -0.465*** (0.786) (0.125) (0.083) -0.361 0.231 0.078 Yes (4) 29 97 -0.125*** (0.033) (0.090) 0.181 0.064 Yes (5) 29 97 -0.459** (0.062) (0.180) (0.090) -0.016 0.230 0.071 Yes (6) 29 97 0.166*** (0.019) (0.010) -0.003 0.326 164 No (7) Proportion of titles translated within 2 years of first publication 50 0.172*** (0.019) (0.010) -0.003 0.384 164 Yes (8) 50 -0.043*** 0.956*** (0.219) (0.015) 0.295 Yes (9) 29 98 0.126*** -0.030** 0.330** (0.143) (0.025) (0.014) 0.395 (10) Yes 29 98 < .0 *p ** 0.10, 0.040*** (0.008) (0.016) -0.024 0.293 (11) Yes 29 98 < 0.137*** (0.012) (0.031) (0.015) -0.026 0.05, 0.386 0.008 (12) Yes 29 98 3.3. FIGURES AND TABLES 75 50 Yes 164 (10) 0.028 0.014 0.032 0.462 -0.006 -0.044 -0.035 -0.030 -0.007 -0.098 -0.016 0.020* 0.066* (0.012) (0.029) (0.042) (0.037) (0.049) (0.041) (0.071) (0.067) (0.054) (0.041) (0.078) (0.074) (0.058) 0.128*** 50 (9) Yes 164 0.027 0.001 0.451 -0.016 -0.014 -0.061 (0.010) (0.025) (0.041) (0.034) (0.047) (0.043) (0.052) (0.061) (0.030) (0.037) 0.023** 0.077** -0.135** 0.112*** -0.107*** 50 (8) Yes 164 0.033 0.433 (0.009) (0.020) (0.039) (0.031) 0.022** 0.113*** 0.089*** 50 (7) Yes 164 0.012 0.041 0.058 0.418 (0.008) (0.028) (0.040) (0.035) 0.119*** 50 (6) Yes 164 0.002 0.399 (0.009) (0.019) (0.032) 0.068** 0.171*** Proportion of titles translated within 2 years of first publication first 2 years of within translated titles of Proportion 50 (5) Yes 163 0.089 0.078 0.065 0.176 0.384 0.290 0.327 0.399 -0.118 -0.193 -0.268 -0.014 (0.063) (0.166) (0.194) (0.220) (0.236) (0.178) (0.410) (0.268) (0.326) (0.203) (0.281) (0.356) (0.246) -0.123* -0.479*** 50 (4) Yes 163 0.193 0.095 0.404 0.278 0.253 0.392 -0.194 -0.328 (0.055) (0.147) (0.188) (0.198) (0.249) (0.184) (0.309) (0.233) (0.146) (0.193) -0.127** 0.675*** -0.411*** 50 (3) Yes 163 0.363 -0.265 (0.211) (0.056) (0.131) (0.197) -0.413* -0.131** -0.335** 50 (2) Yes 163 0.341 -0.327 -0.219 (0.045) (0.120) (0.195) (0.155) -0.079* -0.407*** Median age of titles at time of translation (ln) translation of time at titles of age Median 50 (1) Yes 163 0.328 -0.041 (0.045) (0.096) (0.164) -0.429** -0.602*** Correlates of speed of translation, 1998-2000 continued Table 3.9: Atheist Orthodox Eastern religions Indian Muslim Other Protestant Africa Asia Europe Dependent variable: Dependent Variable (ln) Population (ln) capita per GDP Landlocked (ln) Openness: Exports/GDP (ln) Openness: Imports/GDP Catholic Roman religion: Omitted America continent: Omitted effects fixed language Original R-Squared Observations Countries 76 CHAPTER 3. DATA r egtdb h ubro rnltoscnrbtn otemaueo pe.Sadr rosaecutrda the at clustered are errors Standard speed. Observations of country. measure the p the of * to religion at: widespread contributing significance most translations denote the of Asterisks for Italian. included. number level. are and are country dummies German the country French, religion translating by English, The the weighted are of landlocked. are included regressions language being these languages official country in original an observation the The An into for country. translated translation. translating titles of a speed non-fiction in the Only predicting language regressions original OLS an of is results presents table The Notes: < .0 *p ** 0.10, < .5 * p *** 0.05, < 0.01. Landlocked sadummy a is 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 relationship between 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 understood.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 supply and 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

3Disdier and Head (2008). 80 CHAPTER 4. THE GRAVITY OF IDEAS

various costs of negotiating contracts.4 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).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 forming a contract between parties in different countries. These costs may vary with distance, and include the time and legal costs of negotiating and enforcing the contract, direct and indirect costs related to transacting between currencies, and the costs of overcoming any language barriers that exist between the parties. 5Blum and Goldfarb (2006) suggest this factor plays a significant role in the distance effect for taste-dependent digital goods. 6Disdier and Head (2008). 7MacGarvie (2005) and Griffith, Lee and van Reenen (2007). 4.1. INTRODUCTION 81

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 affect translations 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 demand that 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 that translations 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 82 CHAPTER 4. THE GRAVITY OF IDEAS

now. The speed of translations thus likely reflects frictions such as information and contracting costs, rather than tastes. I find that distance substantially decreases the speed 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

α1 α2 α3 Tij = α0Yi Yj Dij ηij (4.1)

where ηij is a multiplicative error term with E(ηij|Yi,Yj,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:

φ βit γjt transijt = αdistije e ijt (4.2) the more familiar linearized form of which is:

0 ln(transijt) = α + φln(distij) + βit + γjt + νijt (4.3)

0 where α ≡ ln(α) and νijt ≡ 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 βs are time-varying fixed effects for target language-in-country, the γs 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 84 CHAPTER 4. THE GRAVITY OF IDEAS

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 for them 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. EMPIRICAL STRATEGY 85

of ideas even if they were non-zero. For instance, Peru has no reason to translate into Czech, and, if it were to publish such translations, they could not be read by more than a trivial number of Peruvians anyway. In my primary specification, I thus include translations into a language in a country only if the language is (de facto) official in the whole of the translating country, suggesting such translations indicate an inflow of ideas to a settled and non-trivial group in the country. This means translations into the regional or immigrant languages of a country are generally not considered. However, it means I do include translations into some languages that don’t have a considerable body of native speakers who communicate primarily in that language in the country. This is usually because the language is official for heritage purposes and out of respect for a minority language group that has largely been assimilated into the dominant language group.

For the purpose of both original and target languages, I group dialects of a language together, and I aggregate languages to the macrolanguage level as they are coded by the ISO 639-3 classiﬁcation system.8

A macrolanguage is deﬁned by Ethnologue as “multiple, closely related individual languages that are deemed in some usage contexts to be a single language.” For example, Bosnian, Croatian, and Serbian are the three languages that constitute the macrolanguage Serbo-Croatian. One such usage context as described by Ethnologue is the literature or the writing system; thus individual languages within a macrolanguage may be identical or virtually identical in written form, and if they are not they are likely to be highly mutually intelligible in written form. Consequently, it seems appropriate to group them together as a single language for translation purposes. In addition, in some cases the IT does not distinguish between translations from (or into) individual languages of a macrolanguage, so separating them would not be possible.

8Available from http://www.sil.org/iso639-3/. 86 CHAPTER 4. THE GRAVITY OF IDEAS

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, 88 CHAPTER 4. THE GRAVITY OF IDEAS

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 countries translate 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 4.3. HOW DISTANCES AFFECT TRANSLATIONS 89

positively and signiﬁcantly correlated with translation ﬂows with elasticities of 0.72 and 0.75 respectively. The elasticity of translations with respect to the population of 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 90 CHAPTER 4. THE GRAVITY OF IDEAS

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 4.3. HOW DISTANCES AFFECT TRANSLATIONS 91

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 translating countries 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.

9Disdier and Head (2008). 92 CHAPTER 4. THE GRAVITY OF IDEAS

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 coefficient on 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. HOW DISTANCES AFFECT TRANSLATIONS 93

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, which tend 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 from one 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 largest inhibitory 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 94 CHAPTER 4. THE GRAVITY OF IDEAS

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 titles with 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 4.3. HOW DISTANCES AFFECT TRANSLATIONS 95

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 region profiles 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 96 CHAPTER 4. THE GRAVITY OF IDEAS

and information costs, play a large role in the distance effect. The process of globalization over the past half century has made the world smaller 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 correlation between 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 translations 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 and translating countries, in order to see descriptively how trade in ideas (translations) are correlated with trade in goods. Note the coefficients on these variables in particular should not be interpreted causally because of reverse causality and unobserved heterogeneity. For instance, trade flows between countries may cause an increase in idea flows and thus an increase in translations, but translations may increase understanding and decrease transaction costs, thus increasing trade. Trade in manufactured goods and translations may also be complements. The coefficients on both imports and exports are positive and significant, and are similar in magnitude: a 10% higher flow of trade in either direction corresponds to a 2.4% higher translation flow. The direction of this effect is consistent with the causality stories running in either direction. One interesting point to note is that inclusion of these two trade variables eliminates the negative correlation between distance and translations. However, as Appendix Tables B.2 and B.3 show, this was not the case prior to the 1990s. 4.3. HOW DISTANCES AFFECT TRANSLATIONS 97

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 world knowledge 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). 98 CHAPTER 4. THE GRAVITY OF IDEAS

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 GDPs per 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 linguistic distance 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 English are attributed to the USA for European translating countries, whereas in the second case they are attributed to the UK for these countries. 12The lack of power in these regressions makes identifying significant effects of interest difficult. 100 CHAPTER 4. THE GRAVITY OF IDEAS

costs related to negotiating contracts and search and information costs. In addition, both translations and trade may decline with distance because consumer tastes are geographically 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. 4.5. CONCLUSIONS 101

My results also have 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 is a force 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, which suggests that even the barriers surrounding less developed countries may be lower in the future. 102 CHAPTER 4. THE GRAVITY OF IDEAS

4.6 Figures and tables

Figure 4.1: The negative correlation between geographic distance and translations decreased over time

Notes: This figure shows the 95% confidence interval of the coefficient on geographic distance in regressions of the number of translations (ln) on distance (ln) and other controls as in column (4) of Table 4.1, run separately by year. The solid blue line is for the consistent set of 9 countries for which data are available each year; the dashed red line is for all the countries for which data are available in any one year. 4.6. FIGURES AND TABLES 103

Figure 4.2: The negative correlation between geographic distance and translations by field

Notes: This figure shows the point estimate and 95% confidence interval of the coefficient on geographic distance in regressions of the number of translations (ln) on distance (ln) and other controls as in column (4) of Table 4.1, run separately by book field. Data are for the years 1994 and 1999. 104 CHAPTER 4. THE GRAVITY OF IDEAS 31 Yes Yes (10) 2,316 whole whole (0.084) (0.108) (0.181) (0.367) country top 100 top country, country, 0.389*** 0.865*** 2.056*** European European official in in official -0.564*** European of European 56 (9) Yes Yes 15,187 (0.061) (0.100) (0.169) (0.290) top 100 top 0.530*** 1.165*** 1.874*** in country in -0.360*** widespread widespread official and and official 37 (8) Yes Yes 6,637 -0.133 (0.065) (0.106) (0.354) (0.422) country top 100 top 0.537*** 3.655*** official in in official -0.311*** translating translating whole country, country, whole country is main is main country 58 (7) Yes Yes whole whole 13,262 (0.050) (0.093) (0.176) (0.278) country country top 100, top 3.011*** 0.462*** 1.122*** official in in official -0.254*** assigned to to assigned nearest major major nearest 58 (6) Yes Yes 517 main main whole whole (0.118) (0.183) (0.181) country -0.326* research research 0.339*** 1.084*** official in in official languages 58 (5) Yes Yes 9,205 whole whole (0.112) (0.153) (0.336) country country 0.479*** 2.170*** s original s original official in in official -1.088*** unambiguou 58 (4) Yes Yes whole whole 13,262 (0.062) (0.101) (0.157) (0.317) country top 100 top 0.531*** 1.095*** 2.846*** official in in official -0.341*** 58 (3) Yes Yes whole whole 13,262 (0.071) (0.294) country top 100 top 3.564*** official in in official -0.473*** 56 (2) 0.048 whole whole -0.595 12,434 (0.110) (0.082) (0.123) (0.064) (0.148) (0.443) (0.503) (0.624) country top 100 top 0.732*** 0.762*** 1.090*** 3.309*** official in in official -0.882*** -2.694*** 56 (1) whole whole 12,434 (0.111) (0.116) (0.079) (0.064) (0.150) (0.624) country top 100 top 0.722*** 0.745*** 1.099*** 3.324*** official in in official -0.888*** -2.688*** Closer countries translate more from each other Table 4.1: Dependent variable: number of translations (ln) numbertranslations of variable: Dependent languages: Original country: translating for each languages Target Variable translating and original between distance Physical (ln) countries (ln) country of translating Population (ln) country of translating capita per GDP (ln) country of original Population (ln) country of original capita per GDP country translating colonised country Original country original colonised country Translating contiguous are countries translating and Original country translating in is widespread language Original country is translating country Original effects fixed language/country target Time-varying effects fixed language original Time-varying Observations countries Translating 4.6. FIGURES AND TABLES 105 0.01. < 0.05, *** p < 0.10, ** p < The original languages in columns 1-4, 8 and 9 are all those languages in the 100 most widely spoken languages worldwide The target languages in columns 1-7 are the languages that are official in the whole of the translating country. The Standard errors are robust. Asterisks denote significance at: * p Notes: This table presentsan the original results language of toevery PML a target regressions target language; (as language described zero inlanguages in a values and section translating are target 4.2) country included language/countries in of included in a the vary the by year. number estimation, column. of Thethat as translations The same are allowed from years original ever by included languages translated the arecan are (top 1994 PML unambiguously included 100 be and procedure. for assigned languages). 1999. tolanguages”, The The a namely single set original English, original languages of country. French, German in original Thebut and original column the Japanese. languages 4 in original The are column countryrather original those 5 than languages are used of the the in the for four main column top major each country 7are 100 “research of are European. language the languages the is language. top than the 100 The languages, geographically original languages closest in countrytarget column where 10 languages are in the those column language intranslating 8 is the country are top is widespread, the 100 the languages languagestarget main that that languages country are in of official the columnnatively in language by 9 the (e.g. at are whole least German of thelanguages 500,000 in the languages in people Germany, translating that column but country, in 10 are not andonly. the are for German 1) country, the which in official and languages the Switzerland). in 3) that spoken The at are by least official at part in least of the 5% whole the of of translating the the country, country’s translating 2) population. country, spoken for The European target countries 106 CHAPTER 4. THE GRAVITY OF IDEAS ahother each 4.2: Table Translating countries Observations Dummy variables for imports are zero and for exports are zero Time-varying original language fixed effects Time-varying target language/country fixed effects Original country is translating country Original language is widespread in translating country Original and translating countries are contiguous Exports from target country into original country (ln) Imports into target country from original country (ln) Schwartz's cultural distance culturalHofstede's distance Genetic distance Linguistic distance Religious distance translating countries Difference between biome region profiles of original and translating countries Difference between climate region profiles of original and countries Difference between altitude profiles of original and translating Physical distance between original and translating countries (ln) Variable Dependent variable: of number translations (ln) onre ihmr iia hsclevrnet n utrstasaemr from more translate cultures and environments physical similar more with Countries -0.307*** -0.788*** -0.319*** 2.340*** 1.168*** 0.528*** (0.341) (0.157) (0.096) (0.101) (0.201) (0.213) (0.087) 13,262 0.188 Yes Yes (1) 58 -1.275*** -0.290*** -0.720*** -0.272*** 1.971*** 1.084*** 0.377*** (0.290) (0.152) (0.100) (0.081) (0.168) (0.203) (0.074) (0.211) 13,262 0.112 Yes Yes (2) 58 -0.680*** -1.029*** -0.258*** -0.247*** 1.976*** 1.246*** 0.342*** -0.369* (0.292) (0.142) (0.100) (0.144) (0.199) (0.081) (0.155) (0.222) (0.067) 13,124 0.085 Yes Yes (3) 58 -0.689*** -1.040*** -0.234*** -0.288*** 1.940*** 1.247*** 0.332*** (0.295) (0.143) (0.100) (0.129) (0.148) (0.198) (0.086) (0.162) (0.224) (0.083) 12,988 -0.353 0.130 0.107 Yes Yes (4) 58 -0.075*** -1.833*** -0.243*** 1.900*** 1.165*** 0.206*** -0.232** -0.192* (0.281) (0.165) (0.068) (0.017) (0.272) (0.106) (0.164) (0.104) (0.186) (0.180) (0.085) 0.461* -0.069 5,318 0.140 Yes Yes (5) 36 -0.631*** -1.098*** -0.287*** 2.035*** 1.289*** 0.335*** -0.200** (0.332) (0.169) (0.028) (0.140) (0.145) (0.236) (0.098) (0.181) (0.269) (0.092) (0.111) -0.266 4,095 0.002 0.069 0.128 Yes Yes (6) 33 -0.163*** -0.390*** 2.359*** 1.262*** 0.568*** (0.331) (0.155) (0.077) (0.027) (0.138) (0.327) (0.218) (0.114) -0.168 -0.163 5,403 0.103 Yes Yes (7) 36 -0.782*** -0.335*** 2.471*** 1.128*** 0.512*** -0.264** (0.393) (0.197) (0.105) (0.029) (0.240) (0.245) (0.113) (0.110) -0.020 4,095 0.270 Yes Yes (8) 33 -0.442*** -0.794*** -0.442*** 6.048*** 1.221*** 0.240*** 0.245*** 0.183** (0.749) (0.140) (0.078) (0.044) (0.039) (0.125) (0.141) (0.179) (0.077) (0.146) (0.194) (0.069) 12,641 -0.144 0.076 0.200 0.076 Yes Yes Yes (9) 57 4.6. FIGURES AND TABLES 107 0.01. < is the probability a randomly chosen person 0.05, *** p < Religious distance 0.10, ** p < The altitude profile, climate region profile, and biome region profile difference variables are all constructed to vary Notes: This table presents theoriginal results language of to PML a regressions targettarget (as language language; described in zero in a values section translating arethose 4.2) country included languages in of in in a the the the year. estimation, number most of asincluded The widely translations allowed same by spoken are from original the 100 an all languages PML languages are procedure. the worldwide1999. included that languages The for are original that ever every languages translated. are are The all official target in language/countries thebetween whole 0 of (no overlap the infrom translating profiles) the country. and translating 1 The country (identicalerrors years profiles). and are included robust. a are randomly Asterisks 1994 denote chosen and significance person at: from * the p original country have the same religion. Standard 108 CHAPTER 4. THE GRAVITY OF IDEAS

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 translating country 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 original languages 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 are official 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 are all 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 a randomly chosen person from the original country have the same religion. Democracy is measured on a scale of 0 to 10. Standard errors are robust. Asterisks denote significance at: * p<0.10, ** p<0.05, *** p<0.01. 4.6. FIGURES AND TABLES 109 51 (9) Yes Yes 184 0.397 0.076 0.701 -0.328 (0.300) (0.407) (0.066) (0.087) 0.310*** 51 (8) Yes Yes 184 0.510 0.703 -0.399 0.108* (0.263) (0.364) (0.061) (0.101) 0.309*** 51 (7) Yes Yes 184 0.536 0.074 0.703 -5.308 (3.579) (0.365) (0.068) (0.074) 0.317*** 50 (6) Yes Yes 180 0.101 0.706 -0.993 0.109* (0.858) (0.091) (0.064) (0.099) 0.310*** 51 (5) Yes Yes 184 0.003 0.193 0.717 -0.225 -0.189 0.109* (0.050) (0.135) (0.140) (0.258) (0.061) (0.083) 0.376*** 51 (4) Yes Yes 184 0.185 0.100 0.715 -0.018 -0.232 (0.048) (0.133) (0.139) (0.062) (0.085) 0.384*** 51 (3) Yes Yes 184 0.142 0.703 -0.031 0.109* (0.052) (0.137) (0.061) (0.093) 0.324*** 51 (2) Yes Yes 184 0.093 0.697 -0.031 (0.049) (0.064) (0.100) 0.314*** 50 (1) No No 180 0.192 0.021 0.458 0.015* (0.017) (0.008) (0.013) (0.102) (0.303) (0.043) (0.076) -0.219** 0.131*** 0.267*** -0.051*** Translations occur faster between closer countries, 1998-2000 Table 4.4: Dependent variable: proportion of titles translated within 2 years of first publication first 2 years of within translated titles of proportion variable: Dependent Variable (ln) countries translating and original between distance Physical (ln) country of translating Population (ln) country of translating capita per GDP (ln) country of original Population (ln) country of original capita per GDP distance Religious distance Linguistic distance Genetic (ln) country of translating capita per * GDP (ln) distance Physical (ln) country of original capita per * GDP (ln) distance Physical (fraction) country of translating * urbanization (ln) distance Physical (fraction) country of original * urbanization (ln) distance Physical contiguous are countries translating and Original country translating in is widespread language Original effects fixed language/country Target effects fixed language Original R-Squared Observations countries Translating 110 CHAPTER 4. THE GRAVITY OF IDEAS onr.Osrain r egtdb h ubro rnltoscnrbtn otemaueo pe.Sadr errors Standard p speed. * of included. at: measure significance are the denote country to Asterisks contributing translating level. translations the country of of the number at language the French, clustered official by English, are weighted are an included are into languages Observations translated original country. regressions The titles these country. non-fiction in translating observation Only a An Landlocked in translation. language Italian. of target and speed a the German and predicting language regressions original OLS an of is results presents table The Notes: sadmyfrtecutybiglnlce.Terlgo ume r o h otwdsra eiino the of religion widespread most the for are dummies religion The landlocked. being country the for dummy a is < .0 *p ** 0.10, < .5 * p *** 0.05, < 0.01. 4.6. FIGURES AND TABLES 111 51 (9) Yes Yes 184 0.089 0.713 -0.015 -0.029 (0.042) (0.038) (0.061) (0.084) 0.298*** 51 (8) Yes Yes 184 0.262 0.096 0.714 -0.216 (0.143) (0.200) (0.060) (0.087) 0.356*** 51 (7) Yes Yes 184 0.082 0.709 -0.034 -0.001 (0.186) (0.018) (0.059) (0.084) 0.291*** 50 (6) Yes Yes 180 0.722 0.069* 0.109* (0.381) (0.040) (0.056) (0.094) -0.683* 0.376*** 51 (5) Yes Yes 184 0.162 0.050 0.093 0.733 -0.046 (0.111) (0.028) (0.102) (0.113) (0.056) (0.067) 0.381*** -0.303*** 51 (4) Yes Yes 184 0.173 0.730 0.095* (0.029) (0.110) (0.126) (0.056) (0.074) -0.051* -0.270** 0.367*** 51 (3) Yes Yes 184 0.129 0.713 -0.042 0.098* (0.033) (0.112) (0.054) (0.072) 0.318*** 51 (2) Yes Yes 184 0.082 0.709 -0.043 (0.033) (0.059) (0.085) 0.288*** 50 (1) No No 180 0.015 0.529 -0.001 (0.013) (0.009) (0.015) (0.010) (0.092) (0.041) (0.050) -0.169* 0.118*** 0.054*** 0.230*** -0.060*** Translations occur faster between closer countries, 1998-2000: assigning original Dependent variable: proportion of titles translated within 2 years of first publication first 2 years of within translated titles of proportion variable: Dependent Variable (ln) countries translating and original between distance Physical (ln) country of translating Population (ln) country of translating capita per GDP (ln) country of original Population (ln) country of original capita per GDP distance Religious distance Linguistic distance Genetic (ln) country of translating capita per * GDP (ln) distance Physical (ln) country of original capita per * GDP (ln) distance Physical (fraction) country of translating * urbanization (ln) distance Physical (fraction) country of original * urbanization (ln) distance Physical contiguous are countries translating and Original country translating in is widespread language Original effects fixed language/country Target effects fixed language Original R-Squared Observations countries Translating Table 4.5: language to closest country where it is spoken widely 112 CHAPTER 4. THE GRAVITY OF IDEAS oe:Ti al ulctstergesosi al .,ecp eeteoiia onr sdfrec agaei the is language Standard p each * language. for the at: of used significance country country denote main Asterisks original the the level. than country here rather the except widespread, at is 4.4, clustered Table language are the in errors where regressions country the closest duplicates geographically table This Notes: < .0 *p ** 0.10, < .5 * p *** 0.05, < 0.01. 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 knowledge 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 and Helpman (1991), Jones (2005), Klenow and Rodr´ıguez-Clare(2005), and Jones and Romer (2010). 2See, for example, Romer (1986, 1990), Helpman (2004), and Jones (2001, 2005). 3Klenow and Rodr´ıguez-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 is imperative 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 in words 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 in many 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 and Henderson, 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. 5.1. INTRODUCTION 115

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 analysis of 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 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” (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 from our 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 countries from 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. Münich, Svejnar and Terrell, 2005, Fuchs-Schündelnand Schündeln, 2005, Alesina and Fuchs-Schündeln,2007, Fuchs-Schündeln,2008, Abramitzky, 2008). However, this paper is the first to test the effect of the collapse of Communism on the flow of information and ideas. 116 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 between East 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 post collapse. 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 5.1. INTRODUCTION 117

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 into the 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 118 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 5.1. INTRODUCTION 119

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 Fletcher and Iyigun, 2010; see also the surveys by Tabellini, 2010 and Nunn, 2009 on the historical origins of culture). 120 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 control pre-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 each country 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 which each 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, and Albania because it withdrew from the Warsaw pact in 1968; thus in our period of interest they were no longer politically aligned with the Soviet Union. 122 CHAPTER 5. THE COLLAPSE OF COMMUNISM

the central depository of the country. In particular, this excludes samizdat, the illegal books published under the Communist regime. The exclusion of these titles is 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 to the 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 in Appendix 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 5.3. HISTORICAL CONTEXT 123

sample countries in the period 1980-2000. This leaves us with a total of 161 titles. For each of these titles, we used various online sources to establish the publication date of the original book, determine whether the author expressed explicitly anti- Communist views, and whether he or she was a Nobel laureate. To illustrate, one of our inﬂuential titles is Isaiah Berlin’s 1969 book, “Four Essays on Liberty”. Berlin was a philosopher and historian of ideas, was one of the leading liberal thinkers of the 20th century, and featured prominently in the intellectual and ideological battle against Communism during the Cold War. His book, originally written in English, was translated before 1989 by Western European countries, but was only translated after the collapse of Communism in former Communist countries. Similarly, F.A. von Hayek’s “The Road to Serfdom”, an inﬂuential exposition of classical liberalism and libertarianism, was translated widely in Western Europe in the early 1980s, but not in Communist Europe until 1989. In contrast, Karl Marx’s “Das Kapital” was translated prior to the collapse in both Communist and Western countries. The translation dates in Western and Communist Europe of these three titles are illustrated in Figure 5.1.

5.3 Historical context

5.3.1 A brief timeline of the collapse of Communism

In the early 1980s, the Soviet Union and its satellites were all Communist countries with centrally planned economies, in which the ruling (and only) party, the Communist Party under some name or other, interfered in virtually all aspects of its citizens’ lives. Eastern Europe was isolated from Western Europe by the Iron Curtain, which hindered the movement of both people and information. The changes that would result in the fall of Communism began in the late 1980s 124 CHAPTER 5. THE COLLAPSE OF COMMUNISM

when Gorbachev came to power in the USSR. Among the reforms he instituted, perhaps the most important two were perestroika, restructuring of the economy and political 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 Soviet satellites, 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 loose alliance 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, and the 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 best understood by Western scholars and observers during the Communist period. The publishing industries of the other Communist countries varied in their exact details, but were similar in their principles. 17Skelly and Stabnikov (1993). 18Walker (1978). 126 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 to meet 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 to them, 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 political lines 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 special regulations 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) to the 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). 128 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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-Schündeln,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 5.4. EMPIRICAL STRATEGY 129

Communist countries post collapse, and take a variation of the following form:

Yit = β0 + β1P ostt + β2Xit + it (5.1)

22 where Yit is the (log) number of book translations in country i in year t. P ostt 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 Xit 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:

Yit = β0 + β1Communisti × P ostt + β2Communisti + β3P ostt + β4Xit + it (5.2)

where Yit and P ostt are as before, Communisti is a dummy variable for whether the translating country was a former Communist country, and Communisti × P ostt 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 P ost variables, namely post-1989, post-1990, and post-1992, does not substantially alter the results (not presented). 130 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 between countries 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 across different 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. 5.4. EMPIRICAL STRATEGY 131

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 the collapse 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 Eastern interest 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. 132 CHAPTER 5. 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 Communist language nor a Western European language. 26Translations from English show very similar changes over time to translations from all Western European 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 next subject 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 country i in year t. 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 P ostt 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, but this 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 per capita are identified off within-country correlation between population and translations. 134 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 esternLangj), and and its converse, a dummy for 29 the translation being from a Communist language (CommunistLangj):

Yijt =β1aP ostt × W esternLangj + β1bP ostt × CommunistLangj

+ β2aW esternLangj + β2bCommunistLangj + β3Xit + ijt (5.3)

where Yijt 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 P ostt ×

W esternLangj and P ostt × CommunistLangj, whose coeﬃcients measure the eﬀect of the collapse of Communism on translations from Western or Communist languages

into Communist countries. Our control variables Xit include population, and GDP

28Results are unchanged when we focus on translations from the major languages only, namely from English and Russian. 29Note CommunistLang + W esternLang = 1, so our speciﬁcation is fully interacted with respect to the original language of the translation. 5.5. THE EFFECT OF THE COLLAPSE ON TOTAL TRANSLATIONS 135

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 β1a to be positive. The expected sign of β1b 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 estimate the following regression:

Yijt =β1aCommunisti × P ostt × W esternLangj

+ β1bCommunisti × P ostt × CommunistLangj

+ β2aCommunisti × W esternLangj + β2bCommunisti × CommunistLangj

+ β3aP ostt × W esternLangj + β3bP ostt × CommunistLangj

+ β4aW esternLangj + β4bCommunistLangj + β5Xit + ijt (5.4) where the variables of interest in these speciﬁcations are the interactions

Communisti ×P ostt ×W esternLangj and Communisti ×P ostt ×CommunistLangj, 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%. 136 CHAPTER 5. THE COLLAPSE OF COMMUNISM

Because translations tended to increase in Western Europe during the 1990s, the difference-in-difference estimates presented in Table 5.2 are generally smaller than 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 above these 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 excluded in the speciﬁcations where these controls are included. 5.5. THE EFFECT OF THE COLLAPSE ON TOTAL TRANSLATIONS 137

P ostt with CommunistLangj 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:

Yijt =β1aSatellitei × P ostt × W esternLangj

+ β1bSatellitei × P ostt × CommunistLangj

+ β2aP ostt × W esternLangj + β2bP ostt × CommunistLangj

+ β3aSatellitei × W esternLangj + β3bSatellitei × CommunistLangj

+ β4aW esternLangj + β4bCommunistLangj + β5Xit + ijt (5.5)

where Satellitet is a dummy variable for whether the translating country is a Satellite country. The main coeﬃcients of interest are β1a and β1b, 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 countries show similar patterns to those in the Soviet nations, and translations in the non-Slavic countries are similar to in the Soviet satellites. However, the Slavic/non-Slavic diﬀerence is less pronounced than the Soviet/satellite diﬀerence. Similarly, the Orthodox countries behave more like the Soviet nations and the Catholic countries more like the satellites, though the distinction here is smaller again. 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, and Hungary. 138 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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:

Yijt =β1a1Communisti × P ostt × W esternLangj

+ β1a2Communisti × Satellitei × P ostt × W esternLangj

+ β1b1Communisti × P ostt × CommunistLangj

+ β1b2Communisti × Satellitei × P ostt × CommunistLangj

+ β2aP ostt × W esternLangj + β2bP ostt × CommunistLangj

+ β3a1Communisti × W esternLangj

+ β3a2Communisti × Satellitei × W esternLangj

+ β3b1Communisti × CommunistLangj

+ β3b2Communisti × Satellitei × CommunistLangj

+ β4aW esternLangj + β4bCommunistLangj + β5Xit + ijt (5.6)

The main coeﬃcients of interest are now β1a2 and β1b2.

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 5.5. THE EFFECT OF THE COLLAPSE ON TOTAL TRANSLATIONS 139

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. A comparison 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 shows that 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 effects in the regression equation (equivalent to column 3 of Table 5.2). The effects are similar and more precisely estimated, but there it is not possible to compare Communist translations with the Western level of translations. 33We present this figure for the difference-in-differences specification, but the equivalent graph for the OLS specification looks nearly identical. 140 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 translations by 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 convergence in 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. 5.5. THE EFFECT OF THE COLLAPSE ON TOTAL TRANSLATIONS 141

stocks. Both translations of stocks and flows of Western titles show large increases in Communist Europe upon the collapse of Communism. This suggests Communist countries 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 from Unesco’s online data on book production available at http://stats.uis.unesco.org/unesco/. They are available pre and post collapse for only a subset of our countries, namely the Communist countries Belarus, Bulgaria, Estonia, Hungary, Latvia, Poland, Romania and the Ukraine, and the Western European countries Belgium, Denmark, Finland, France, Iceland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland. Note, however, that these data are only available at an aggregate level and a large number of years are missing, which precludes using them to conduct more complex analysis. 142 CHAPTER 5. THE COLLAPSE OF COMMUNISM

and may have actually declined. Specifically, the coefficient of interest, which is the coefficient on Post in the OLS specifications and on P ost × Communism in the difference-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. Because we 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 techniques 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 5.5. THE EFFECT OF THE COLLAPSE ON TOTAL TRANSLATIONS 143

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 factors as 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. 144 CHAPTER 5. THE COLLAPSE OF COMMUNISM

Accounting for Russian-speaking populations in other Communist countries

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 translations into Russian as well as into the main language for all the Communist countries instead of just the Soviet countries makes no difference (results not presented). 5.6. THE EFFECT BY BOOK FIELD 145

over time graphically for each of the eight book fields Philosophy, Religion, Social Science, Exact Science, Applied Science, Arts, Literature, and History. We then run difference-in-difference regression specifications for each of the eight fields separately, to test which fields were affected the most. Finally, we disaggregate further each of the eight fields by searching for the most commonly used keywords in the book titles, grouping these keywords by subfield such as mathematics, physics and chemistry, and testing the effect of the collapse of Communism on each subfield.

5.6.1 Graphical evidence

The eight panels of Figure 5.6 show translation patterns for each book field. Trans- lations from the West into Communist countries jump in all fields, and in Satellite countries converge to or even overtake Western translations in every field but Arts and History. It is interesting to note that translations of Religion titles were almost non-existent before the collapse. This is consistent with religion being considered an enemy of Communism, and with the fact religious freedom was severely restricted in most Communist countries.39 When Communism collapsed, Satellites’ translations of Religion books increased dramatically from Western European languages, and somewhat from other Communist sources. The rapidity of the increase suggests demand for these translations existed under the Communist regime, but was unable to be satisfied. Soviet countries’ translations of religious books from Western European languages, however, increased only a little, again reflecting their Russian rather than Western orientation. In contrast, Communist countries before the collapse already translated Exact Science titles in levels similar to the West, although most of their translations came from fellow Communist countries. This is consistent with research in Exact Science receiving a lot of governmental support under the Communist regime, probably

39Riasanovsky and Steinberg (2005). 146 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 Science in 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 the number of translations is positive (extensive margin), and an OLS regression that estimates the log number of translations given the number of translations is non-zero (intensive margin). Appendix Table C.5 presents the coeﬃcients on the interactions of interest in both regressions. The results tell 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 languages and 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 under Communism, 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 to Soviet satellites (figure not presented), the relative positions of the subjects are similar for the two types of Communist countries, though the points for the Soviet countries are all shifted to the left. 148 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 on each 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 added related 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 translated from Western languages to titles translated from English in a difference-in-differences specification pooling all fields increases the coefficient of interest from 1.34 to 1.78; subsequently restricting to translations with non-missing original titles decreases it slightly to 1.62. These changes are small relative to the standard errors on the coefficient estimates. 43Note our searches also capture variant forms and spellings of the keywords (e.g. British and American 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 and Exact Science: mathematics, physics, chemistry, biology, geology; for Applied Science: computers, business, medical, engineering, food, gardening. We do not present results from subfield keyword searches in the fields Arts, Games and Sports, Literature, History, Geography, and Biography, or Philosophy and Psychology because they are largely uninformative. 45Notice individual titles might be captured by more than one search, in which case they are attributed to both. 46The primary reasons why these percentages were not higher were that many titles are uninformative about the subject of the book (e.g. “Nowhere to Hide” by Susan Francis is an Englishwoman’s story of her life in Iraq in the time of Saddam Hussein), and many others contain only keywords that appear in multiple contexts (e.g. the keyword “rights” appears in Thomas Paine’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 constituent subfields. The coefficients of interest are the interactions of the subfield fixed effects with the P ost × 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. Medical titles 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 period about 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 150 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 whole period 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 the two 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

Yijt =αi + γiP ostt + β1P ostt × Communistj × AntiComm Authori

+ β2P ostt × Communistj + β3Communistj × AntiComm Authori

+ β4Communistj + ijt (5.7)

where Yijt 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

48 Europe. P ostt 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 β1, which tests the extent to which the translations of anti-Communist authors 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 similar results (not shown). 48Note this cutoff date of 1989 for “post” differs to the 1991 used in the analysis of the total number of translations. The reason we prefer the 1989 cutoff for the analysis of individual titles is that by 1989 Gorbachev’s reforms had greatly reduced the Communist regime’s restrictions on information flows, so we don’t want to attribute a translation published in 1989 to the pre-collapse period. The results are qualitatively similar when using 1991 as the first “post” year, but they are sometimes 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 and 1990 and using 1991 as the first “post” year, the results are unchanged and highly significant. We also note that the results from the analysis of the total number of translations discussed in equations 1-6 are robust to defining post as 1989 onwards, but there we choose the 1991 cutoff because we test for an average effect and because Communism did not collapse in the Satellites until 1991. 152 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 the titles 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 the Communist 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 most translated 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 5.8. CONCLUSIONS AND DISCUSSION 153

immediate relevance to the recent shift away from Communism.

5.8 Conclusions and discussion

Despite the importance of the international diffusion of ideas for economic growth, idea flows have received limited attention from empirical economists because they are inherently difficult to measure. Furthermore, many proxies for idea flows such as trade, migration, and foreign direct investment do not capture the properties of ideas upon which hinge the predictions of the new growth theory literature, namely non-rivalry and disembodiment. We tackle this empirical challenge by introducing book translations as a measure of non-rival, disembodied idea flows between Eastern and Western European countries. We use this measure to study how the flow of ideas transmitted by translations was affected by the collapse of Communism in Eastern Europe, which is an attractive setting to study how policy and institutional changes affect idea flows. 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 from Communist 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, these differences likely reflected 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 find 154 CHAPTER 5. THE COLLAPSE OF COMMUNISM

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 both catching 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 flowing more 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 less than 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.

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, which precludes 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 international 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 own language, thus an increase in translations into his native language implies a reduced cost of access to information. 156 CHAPTER 5. THE COLLAPSE OF COMMUNISM

5.9 Figures and tables

Figure 5.1: Translation dates of three influential titles 5.9. FIGURES AND TABLES 157

Figure 5.2: Map of Communist and Western Europe 158 CHAPTER 5. THE COLLAPSE OF COMMUNISM

Figure 5.3: Translations in Communist and Western Europe

Notes: This ﬁgure shows translations from Western European and Communist languages in the former Soviet countries, the Satellite countries, and Western European countries. The values are averages over the countries in the regions, and include translations into the main language of the country only. 5.9. FIGURES AND TABLES 159

Figure 5.4: The effects over time of the collapse of Communism on translations

Translations from Western European languages

Translations from Communist languages

Figure 5.5: The effects of the collapse on translations of recent and older titles

Flows: Translations of titles 15 years old or newer

Stocks: Translations of titles older than 15 years

Notes: This ﬁgure shows translations of recent (top panel) and older (bottom panel) titles from Western European languages in the former Soviet countries, the Satellite countries, and Western European countries. The values are averages over the countries in the regions, and include translations into the main language of the country only. 5.9. FIGURES AND TABLES 161

Figure 5.6: Translations by field

Notes: See the notes for Figure 5.3. 162 CHAPTER 5. THE COLLAPSE OF COMMUNISM

Figure 5.7: Effects of the collapse by field

Notes: This figure plots the coefficients (x100) on Communisti × P ostt × W esternLangj (x axis) and Communisti × P ostt × CommunistLangj (y axis) from equation (5.4) (with controls for log population and GDP per capita) run separately for each subject. The dependent variable is the log of translations plus one. These coefficients (approximately) measure the percentage change in Communist translations caused by the collapse of Communism. 5.9. FIGURES AND TABLES 163

Figure 5.8: Effects of the collapse on translations from English by subfield

Notes: The regressions that give rise to these coefficients are difference-in-differences regressions comparing Communist with Western Europe, run by field as described in Section 5.4. 164 CHAPTER 5. THE COLLAPSE OF COMMUNISM (9) Yes Yes Yes Yes 511 0.206 0.288 0.880 -2.930 (0.333) (0.336) (0.453) (0.484) (0.353) (1.810) 0.806** 1.168*** -1.421*** (8) Yes Yes Yes Yes 511 0.091 0.661 0.893* 0.989* (0.483) (0.452) (0.445) (0.375) (0.552) (0.156) 1.271** -1.154** 0.717*** (7) Yes Yes Yes Yes 511 0.270 0.559 0.425 (0.411) (0.274) (0.330) (0.445) 1.741*** -1.776*** (6) Yes Yes Yes Yes 511 0.869 0.691* (0.179) (0.186) (0.331) (2.096) -4.953** 1.761*** -1.160*** (5) Yes Yes 511 0.422 (0.113) (0.226) (0.290) (0.091) 2.014*** 1.494*** 0.549*** -0.945*** country, year, original language (Western or Communist) (Western language original year, country, (4) Yes Yes 511 0.245 (0.259) (0.179) 1.589*** -1.370*** (3) Yes 256 0.740 1.266* (0.267) (0.616) (3.242) 0.799** -8.621** (2) 256 0.356 (0.216) (0.397) (0.092) 0.926*** 1.716*** 0.624*** country, year country, (1) 256 0.439 0.028 (0.293) 0.01. < is a dummy for 1991 onwards. Standard errors, in parentheses, are clustered at the Post 0.05, *** p < 0.10, ** p < Before/after analysis: The effect of the collapse of Communism on translations Table 5.1: Dependent variable: log number of translations number log variable: Dependent Post with: languages interacted original Western from Translations Post * post country Satellite with: Communistlanguages interacted original from Translations Post * post country Satellite controls: Other (ln) capita per GDP Real (ln) Population dummy language original Western dummy language original Communist language original Western * country Satellite language original * Communist country Satellite effects fixed Country language original Western * effects fixed Country language original * Communist effects fixed Country R-Squared Observations is a: An observation Notes: All columns arefrom OLS the regressions paper; usinganalysis annual columns data are 4-6 for Russia, the estimate Belarus,Poland, period Romania, equation Estonia, 1980-2000. and (5.3); Latvia, Slovakia. Columns Lithuania,5.3.1). We columns 1-3 include Moldova, estimate 7-9 the The the equation three estimate Communist Ukraine, (5.1) Balticlanguage and equation countries Bulgaria, of Western in (5.5). the the original the Czech Satellite country languages The Republic, countries only. are (see Hungary, countries given explanation used in in Section in footnote 25. the We include translations into the main country level. * p 5.9. FIGURES AND TABLES 165 Yes Yes Yes Yes Yes Yes Yes 964 (10) 0.508 0.195 0.932 (0.407) (0.337) (0.507) (0.469) -1.251** 1.137*** (9) Yes Yes Yes Yes Yes Yes Yes 964 0.192 0.864 0.135 0.121 0.943 -1.159 -0.276 (0.607) (0.530) (0.129) (0.784) (0.720) (0.191) (8) Yes Yes Yes Yes Yes 964 0.409 0.221 0.925 -0.086 (0.361) (0.325) (0.154) (0.492) (0.469) (0.172) 0.379** -1.354** 1.183*** (7) Yes Yes Yes 964 0.110 0.687 0.158 0.573 0.764 (0.511) (0.410) (0.905) (0.678) (0.150) (0.482) (0.354) (0.471) (0.502) (0.172) -0.880* 1.777** -0.369** 1.337*** 1.907*** -3.249*** (6) Yes Yes 1.102 0.559 0.641 1,000 -0.050 -0.102 -0.117 (0.296) (0.323) (1.056) (1.076) (0.125) (0.431) (0.435) (0.436) (0.395) (0.157) 0.321** 1.741*** 1.846*** -3.371*** -1.659*** (5) Yes Yes Yes Yes Yes Yes Yes 964 0.928 (0.256) (0.292) 1.428*** -1.009*** (4) Yes Yes Yes Yes Yes Yes Yes 964 0.138 0.124 0.942 (0.344) (0.129) (0.469) (0.191) 0.799** -1.349*** (3) Yes Yes Yes Yes Yes 964 0.921 -0.084 (0.233) (0.153) (0.267) (0.174) 0.380** 1.361*** -1.095*** (2) Yes Yes Yes 964 0.043 0.673 (0.269) (0.498) (0.135) (0.206) (0.424) (0.160) -0.437** 1.897*** 2.583*** -1.739*** -0.582*** (1) Yes Yes 0.579 1,000 -0.117 (0.283) (0.484) (0.125) (0.235) (0.331) (0.157) 0.321** 1.268*** 1.775*** -2.608*** -1.253*** Difference-in-differences analysis: The effect of the collapse of Communism on Dependent variable: log number of translations of translations number log variable: Dependent languages in: original Western from Translations * post country Communist * post country Satellite country Communist country Satellite Post Communistlanguages in: original from Translations * post country Communist * post country Satellite country Communist country Satellite Post controls: Other dummy language original Western dummy language original Communist controls GDP and Population language original Western * effects fixed Country language original * Communist effects fixed Country language original Western * trends time Country-specific language original * Communist trends time Country-specific language original Western * effects fixed Year language original * Communist effects fixed Year R-Squared Observations or Communist) (Western language original year, country, is a An observation translations Table 5.2: 166 CHAPTER 5. THE COLLAPSE OF COMMUNISM nScin531.TeCmuitadWsenoiia agae r ie nfont 5 eicuetasain into translations p include * We capita. level. per 25. country GDP footnote the real in at given clustered of only. are and country languages population the original explanation of of Western (see countries language the and The Satellite Italy, Communist main the Iceland, Slovakia. in The the France, countries and Baltic Finland, 5.3.1). Estonia, three Romania, Spain, the Section Belarus, Poland, include Denmark, We in Russia, Hungary, Switzerland, are Sweden. Belgium, Republic, and analysis Portugal, Austria, Czech Norway, the are Netherlands, the the in used from used Bulgaria, countries (5.4) countries equation Ukraine, European Communist estimate Western the 1-5 The Columns Moldova, (5.6). group. Lithuania, comparison equation Latvia, the Communist estimate as with 6-10 Europe 1980-2000, Western columns period and the interest paper; for of data region annual the using regressions as OLS Europe difference-in-differences are columns All Notes: < .0 *p ** 0.10, Post onr-pcfi ietrends time Country-specific < sadmyfr19 onwards. 1991 for dummy a is .5 * p *** 0.05, < 0.01. r ier tnaderr,i aetee,are parentheses, in errors, Standard linear. are ouainadGPcontrols GDP and Population r h logs the are 5.9. FIGURES AND TABLES 167 Yes Yes Yes 482 (10) 0.920 (0.325) 1.254*** (9) Yes Yes Yes 482 0.573 0.940 -0.219 (0.385) (0.139) Population and GDP (8) Yes Yes 482 0.076 0.908 (0.305) (0.173) 1.331*** are linear. Standard errors, in (7) Yes 482 0.453 (0.352) (0.597) (0.178) -1.061* -0.335* 1.960*** Stocks: titles older than 15 years Stocks: older titles (6) 500 0.269 0.01. -0.027 (0.283) (0.484) (0.125) 1.263*** -2.029*** < is a dummy for 1991 onwards. (5) Yes Yes Yes 482 0.943 (0.325) 1.408*** Post 0.05, *** p Country-specific time trends < (4) Yes Yes Yes 482 0.236 0.957 0.727* (0.385) (0.139) 0.10, ** p < (3) Yes Yes 482 0.934 (0.305) (0.173) 1.485*** 0.530*** (2) Yes 482 0.119 0.610 (0.352) (0.597) (0.178) 2.114*** -1.997*** Flows: titles 15 years old and 15 years newer Flows: titles (1) 500 0.478 (0.283) (0.484) (0.125) 1.417*** 0.428*** -2.966*** Convergence analysis: The effect of the collapse of Communism on translations of are the logs of population and of real GDP per capita. Dependent variable: log number of translations from a Western original language original Western a from of translations number log variable: Dependent country Post * Communist country Communist Post controls GDP and Population effects fixed Country trends time Country-specific effects fixed Year R-Squared Observations year country, is a An observation Table 5.3: recent versus older Western titles Notes: All columns2000, are with difference-in-differences Communist OLSvariable Europe regressions for as (equation columns the (5.2)) 1-5to region using is Table of annual 5.2 translations data for interest of theinclude for and recent Communist translations titles, the Western and into Europe and period Western the countries for as main 1980- used. columns language the The 6-10 of comparison Western the is originalparentheses, group. country translations are languages only. of are clustered The given older at dependent in the titles. footnote country See 25. level. the * We notes p controls 168 CHAPTER 5. THE COLLAPSE OF COMMUNISM r egu,Dnak iln,Fac,Iead tl,Ntelns owy otgl pi,See,adSwitzerland. and p Sweden, * Spain, level. used Portugal, country Norway, countries the European Netherlands, are at Western Italy, used the countries Iceland, and Communist France, Ukraine, The Post Finland, the (5.2)). and the Denmark, (equation Romania only Belgium, Poland, Europe using interest are Western Latvia, regressions of Hungary, is OLS region Estonia, group the before/after Bulgaria, comparison where are Belarus, regressions the 1-4 OLS and Columns difference-in-differences countries 1980-2000. are Communist 5-9 period is columns the (5.1)); for (equation data countries annual Communist use columns All Notes: publications 5.4: Table An observationAn is a country, year Observations R-Squared Year fixed effects Country-specific time trends Country fixed effects Population and GDP controls Post Communist country * CommunistPost country Dependent variable: log number of translations from a Western original language sadmyfr19 onwards. 1991 for dummy a is oa ulctos h feto h olpeo omns nttlbook total on Communism of collapse the of effect The publications: Total < .0 *p ** 0.10, -2.966*** 0.428*** 1.417*** (0.125) (0.484) (0.283) 0.478 500 (1) < Flows: titles newer 15 years old and onr-pcfi ietrends time Country-specific .5 * p *** 0.05, -1.997*** 2.114*** (0.178) (0.597) (0.352) 0.610 0.119 482 Yes (2) 0.530*** 1.485*** < (0.173) (0.305) 0.934 482 Yes Yes 0.01. (3) (0.139) (0.385) 0.727* 0.957 0.236 482 Yes Yes Yes (4) r ier tnaderr,i aetee,aeclustered are parentheses, in errors, Standard linear. are 1.408*** (0.325) 0.943 482 Yes Yes Yes (5) -2.029*** 1.263*** (0.125) (0.484) (0.283) -0.027 0.269 500 (6) Stocks: titles Stocks: older 15 years than 1.960*** -0.335* -1.061* (0.178) (0.597) (0.352) 0.453 482 Yes (7) 1.331*** (0.173) (0.305) 0.908 0.076 482 Yes Yes (8) (0.139) (0.385) -0.219 0.940 0.573 482 Yes Yes Yes (9) 1.254*** (0.325) 0.920 (10) 482 Yes Yes Yes 5.9. FIGURES AND TABLES 169 (9) Yes Yes Yes 952 238 0.818 (0.086) (0.209) (0.107) (0.061) (0.148) (0.076) -0.280* 0.262*** 0.568*** 0.332*** -0.536*** -0.406*** (8) Yes Yes Yes 952 238 0.806 -0.435 (0.051) (0.456) (0.036) (0.323) 0.932** 0.490*** -0.795*** Most translated titles (7) Yes Yes Yes 952 238 0.804 (0.051) (0.036) 0.502*** -0.800*** (6) Yes Yes Yes 644 161 0.730 (0.066) (0.202) (0.047) (0.143) -0.257* 0.463*** 0.579*** -0.504*** title, pre/post, Communist/West pre/post, title, (5) Yes Yes Yes 644 161 0.732 -0.171 (0.116) (0.068) (0.164) (0.048) 0.436*** 0.505*** -0.501*** Influential titles (4) Yes Yes Yes 644 161 0.723 (0.063) (0.044) 0.524*** -0.531*** (3) Yes Yes Yes 828 207 0.817 -0.076 0.326* (0.058) (0.195) (0.041) (0.138) 0.352*** -0.495*** (2) Yes Yes Yes 828 207 0.829 (0.056) (0.176) (0.040) (0.125) 0.278*** 1.001*** -0.444*** -0.562*** Influential authors (1) Yes Yes Yes 828 207 0.815 (0.055) (0.039) author, pre/post, Communist/West pre/post, author, 0.380*** -0.501*** Title/author-level analysis: The effect of the collapse of Communism on the Sample: Dependent variable: log number of countries translating the author/title + 1 author/title the translating of countries number log variable: Dependent country Post * Communist author Anti-Communist * country Post * Communist laureate * Nobel country Post * Communist 1917-44 * Published country Post * Communist 1945-85 * Published country Post * Communist country Communist author Anti-Communist * country Communist laureate * Nobel country Communist 1917-44 * Published country Communist 1945-85 * Published country Communist Post effects Author fixed * post effects Author fixed effects fixed Title * post effects fixed Title R-Squared Observations of authors Number of titles Number is a: An observation Table 5.5: translation of influential titles/authors, and the most translated titles 170 CHAPTER 5. THE COLLAPSE OF COMMUNISM egu.W nld rnltosit h anlnug ftecutyol,pu noRsini h oitcountries. Soviet the in Russian into Poland, plus Republic, only, Czech country the the and p period Austria, * of Bulgaria, Norway, “pre” parentheses. language are Denmark, The in France, main used level. given Spain, the title countries are are into the errors Communist and used at translations Standard countries run The collapse include Western are We The 4-9 pre/post 1989-2000. columns Estonia. Belgium. the is level; Europe and author Western Belarus, to period the and Slovakia, at “post” interest aggregated Romania, run of the data are region 1-3 1980-88; the using Columns as is regressions group. Europe comparison Communist OLS the with as (5.7)), difference-in-differences (equation are level Europe columns Communist/Western All Notes: < .0 *p ** 0.10, < .5 * p *** 0.05, < 0.01. 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 ideas written in books, but quantifying how language barriers aﬀect access to books remains informative about 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 the same 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 174 CHAPTER 6. CONCLUSIONS AND DISCUSSION

Table 6.1: Average access to titles under four counterfactuals: number and % of titles written

Counterfactual: One Multiple Multiple Multiple language languages languages, languages, translations bilingualism Assumption:

All books are 640,326 66,766 69,463 163,127 interesting 100% 10.4% 10.8% 25.5%

Only books that are ever 11,204 3,517 6,214 7,366 translated are 100% 31.4% 55.5% 65.7% interesting

Notes: The columns of this table present the average person’s access to titles under four alternative counterfactual scenarios and two alternative assumptions about what constitutes an interesting title. The counterfactuals are as follows: i) everyone speaks the same language, 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 no translations 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 language that gives them the greatest additional access to titles. The ﬁrst row assumes all titles published are interesting; the second row assumes only titles that are ever translated are interesting. Each cell in the table gives the number and percentage of total interesting titles the 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 in the 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. 176 CHAPTER 6. CONCLUSIONS AND DISCUSSION

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 on economic 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 positive effect 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

177 178 APPENDIX A. APPENDICES FOR CHAPTER 3 Original languages by book field and continent Figure A.1: Western Europe: Austria, Denmark, France, Germany (East and West), Italy, Norway, Portugal, and Spain A.1. APPENDIX FIGURES AND TABLES 179 Yugoslavia Eastern and Central Europe: Albania, Bulgaria, Czechoslovakia, Hungary, Poland, Romania, the USSR, and 180 APPENDIX A. APPENDICES FOR CHAPTER 3 mrc:UA rzl n Peru and Brazil, USA, America: A.1. APPENDIX FIGURES AND TABLES 181 Asia and the Middle East: India, Israel, Japan, and South Korea 182 APPENDIX A. APPENDICES FOR CHAPTER 3 fia gp n Tunisia and Egypt Africa: A.1. APPENDIX FIGURES AND TABLES 183

Figure A.2: Age at translation of non-fiction titles by country and original language for 1998-2000

Western Europe

Notes: Translations into only the languages that are oﬃcial in the translating country are included. 184 APPENDIX A. APPENDICES FOR CHAPTER 3

Notes: Translations into only the languages that are oﬃcial in the translating country are included. A.1. APPENDIX FIGURES AND TABLES 185

Central and Eastern Europe

Notes: Translations into only the languages that are oﬃcial in the translating country are included. 186 APPENDIX A. APPENDICES FOR CHAPTER 3

Notes: Translations into only the languages that are oﬃcial in the translating country are included. A.1. APPENDIX FIGURES AND TABLES 187

Notes: Translations into only the languages that are oﬃcial in the translating country are included. 188 APPENDIX A. APPENDICES FOR CHAPTER 3

Asia and the Middle East

Notes: Translations into only the languages that are oﬃcial in the translating country are included. A.1. APPENDIX FIGURES AND TABLES 189

America

Notes: Translations into only the languages that are oﬃcial in the translating country are included. 190 APPENDIX A. APPENDICES FOR CHAPTER 3

Africa

Notes: Translations into only the languages that are oﬃcial in the translating country are included. A.1. APPENDIX FIGURES AND TABLES 191

Table A.1: Major countries of the top 100 languages

Language Major countries Akan* Ghana Amharic Ethiopia Arabic* Saudi Arabia, Algeria, Chad, Bahrain, Oman, Egypt, Iraq, Yemen, Libya, Morocco, Syria, United Arab Emirates, Jordan, Sudan, Tunisia, Lebanon, Palestine, Kuwait, Qatar, Western Sahara, Israel Assamese India Azerbaijani* Iran, Azerbaijan Baluchi* Pakistan Bavarian Austria, Germany Belarusian Belarus Bengali Bangladesh, India Bulgarian Bulgaria Burmese Myanmar Catalan Spain Cebuano Philippines Central Khmer Cambodia Chinese* China, Malaysia, Singapore Czech Czech Republic Dutch* Netherlands, Belgium English UK, Australia, Canada, Ireland, New Zealand, USA, Belize, Jamaica Filipino Philippines French France, Belgium, Canada, Switzerland Fulah* Senegal, Cameroon, Nigeria, Guinea German* Germany, Switzerland, Austria, Liechtenstein Gujarati India Haitian Haiti Hausa Nigeria, Benin, Niger Hindi India Hmong* China Hungarian Hungary, Romania Igbo Nigeria Iloko Philippines Italian Italy Japanese Japan 192 APPENDIX A. APPENDICES FOR CHAPTER 3

Language Major countries Javanese Indonesia Kannada India Kazakh Kazakhstan Kikuyu Kenya Kinyarwanda Rwanda Konkani* India Korean South Korea, North Korea Kurdish* Iraq, Iran, Turkey Lahnda* Pakistan Lombard Italy Maithili India Malagasy* Madagascar Malay* Malaysia, Indonesia Malayalam India Marathi India Modern Greek Greece, Cyprus Neapolitan Italy Nepali Nepal Nyanja Malawi Oriya India Oromo* Ethiopia Panjabi India Persian* Iran, Afghanistan Polish Poland Portuguese Portugal, Brazil Pushto* Pakistan, Afghanistan Quechua* Peru, Bolivia Rajasthani* India Romanian* Romania, Moldova Russian Russia, Israel, Kazakhstan, Kyrgyzstan Serbo-Croatian* Serbia and Montenegro, Bosnia and Herzegovina, Croatia Shona Zimbabwe Sindhi Pakistan, India Sinhala Sri Lanka Somali Somalia, Ethiopia Spanish Spain, Argentina, Bolivia, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Peru, Puerto Rico, Uruguay, Venezuela A.1. APPENDIX FIGURES AND TABLES 193

Language Major countries Swedish Sweden Tamil India, Sri Lanka Tatar Russia Telugu India Thai* Thailand Turkish Turkey Turkmen Turkmenistan, Iran Uighur China Ukrainian Ukraine Urdu Pakistan, India Uzbek* Uzbekistan, Tajikistan, Afghanistan Vietnamese Vietnam Xhosa South Africa Yoruba Nigeria Zulu 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. 194 APPENDIX A. APPENDICES FOR CHAPTER 3 cln 9916 9418 951999 1995 1989 1984 2004 1999 1999 1999 1994 1994 1994 1989 1989 1990 1984 1984 2004 1979 1999 1979 1999 1974 1994 1994 1969 1988 2004 1964 1985 1999 1979 1979 1994 1964 1974 1999 2004 1989 1959 1969 1994 1989 1999 1984 1954 1999 1964 1989 1984 1994 1979 1994 1959 1985 1969 1979 1989 2004 1974 1989 1974 1984 1999 1969 1984 1979 1994 1964 1979 1974 1989 1959 1959 2004 1969 2003 1984 1954 1954 1999 1964 1999 1979 1949 1959 1994 1959 1994 1988 1954 1989 1984 1949 1984 1989 1979 1979 1984 1974 India 1979 1969 1984 1969 1974 Iceland 1964 1999 1979 1964 1969 1959 Hungary 2004 1994 1959 1964 1954 Greece 1999 1989 1959 1949 1994 Germany 1984 1954 1989 1979 France 1984 1974 Finland 1979 1969 2004 Estonia 1974 1964 1999 1999 Egypt 1969 1959 1994 1994 Germany 1964 1954 East 1989 1989 1989 1959 1984 Denmark 1984 1984 1954 1979 1979 Czechoslovakia 1979 1949 1974 1974 Republic 1984 1974 Czech 1969 1969 1979 1969 Croatia 1964 1964 1974 1964 1959 Colombia 1959 1969 1959 China 1964 1954 1959 1949 Chile 1954 Canada 1950 Bulgaria Brazil Belgium Belarus Bangladesh Austria Australia Argentina Albania al A.2: Table onre n er ihtasaindata translation with years and Countries 9419 2004 1999 1994 2004 1999 1994 2004 1999 1994 A.1. APPENDIX FIGURES AND TABLES 195 2003 1999 1994 1994 1999 1994 1999 2003 1995 19991994 2004 19991994 2004 1999 2004 1994 1999 2003 KuwaitLatvia Lithuania Macedonia MadagascarMalaysiaMaltaMoldova Monaco 1964 1969 1974 1979 1979 1984 1984 1988 1989 1994 1999 2004 1984 1989 1994 1980 1984 1989 IranIsraelItalyJapanKazakhstan 1949 1950 1954 1954 1954 1959 1959 1959 1964 1964 1964 1959 1969 1969 1969Morocco 1974 1974 1974 1979Mozambique 1979 1979Myanmar 1984 1984 1970 1989Namibia 1994Netherlands 1988 1999New 1994 Zealand 2004 1994 1999Norway 1999 2003 Pakistan 2004 PeruPhilippinesPolandPortugalRomania 1949Russia 1954Saudi Arabia 1959Serbia and 1964 Montenegro 1964 1969 1969 1974 1949 1974 1979 1954 1979 1955 1984 1959 1984 1960 1989 1964 1989 1954 1964 1994 1969 1979 1959 1964 1969 1955 1999 1974 1964 1969 1974 1959 2004 1979 1979 1964 1984 1984 1969 1989 1984 1988 1974 1994 1979 1979 1995 1979 1999 1984 1984 1999 1984 2004 1985 1989 1989 2004 1989 1989 1980 1994 1994 1994 1994 1984 1999 1999 1998 2004 2004 1980 2004 1984 2003 1989 1994 1994 1999 2003 1979 1984 2004 1989 1998 2004 196 APPENDIX A. APPENDICES FOR CHAPTER 3 oe:Ti al it h er o hc rnlto aaaeaalbefrec country. each for available are data translation which for years the lists table This Notes: ntdAa mrts19 992003 1999 1994 1989 1984 1979 1974 1989 1984 2003 2003 1979 2000 1999 2003 2000 1993 1998 1994 1989 1989 1994 1989 1984 1984 1989 1969 1984 1989 1979 1979 1975 1989 1984 1964 2004 1979 1984 1965 1974 1969 1984 1979 1959 1999 1974 1979 1994 1969 1999 1964 1979 1994 1964 1994 1959 1974 1989 1959 1989 1969 1984 1954 1984 1964 1979 2004 1984 1950 1979 1959 1999 1979 1974 1994 1969 1964 1995 1998 1959 1988 1994 1954 1984 1989 1949 1979 1983 Yugoslavia 1974 1979 Germany West 2004 1969 1974 Vietnam 1998 1999 1964 1969 1994 1994 Venezuela 1959 1964 1989 1989 Uruguay Emirates 1984 1984 Arab United 1979 1988 1979 Ukraine 1974 1984 1974 1969 1979 USSR 1969 1964 1974 1964 US 1959 1970 1959 1964 UK 1954 1959 Turkey 1949 Tunisia 1960 Thailand Syria Switzerland Sweden Suriname Lanka Sri Spain Korea South Africa South Slovenia Republic Slovak Singapore 9519 2003 1999 1995 2004 1999 1994 Appendix B

Appendices for Chapter 4

B.1 Appendix ﬁgures and tables

197 198 APPENDIX B. APPENDICES FOR CHAPTER 4

Figure B.1: Changes over time in the relationship between physical environment differences or cultural distances and translations

Notes: This figure shows the 95% confidence interval of the coefficients on various physical environment dissimilarity and cultural distance measures in regressions of the number of translations (ln) on various distances and other controls as in column (4) of Table 4.2, run separately by year. The solid blue line is for the consistent set of 9 countries for which data are available each year; the dashed red line is for all the countries for which data are available in any one year. B.1. APPENDIX FIGURES AND TABLES 199 31 Yes Yes (10) 0.746 2,316 whole whole (0.035) (0.060) (0.204) (0.308) country top 100 top country, country, 0.652** -0.447** 0.186*** European European official in in official -0.203*** European of European 56 (9) Yes Yes 0.524 15,187 (0.007) (0.029) (0.089) (0.093) top 100 top 0.191** -0.231** 0.079*** in country in -0.120*** widespread widespread official and and official 37 (8) Yes Yes 0.695 6,637 (0.013) (0.045) (0.161) (0.226) country top 100 top 0.171*** 0.636*** official in in official -0.145*** -0.525*** translating translating whole country, country, whole country is main is main country 58 (7) Yes Yes 0.027 0.570 whole whole -0.002 13,262 (0.008) (0.035) (0.133) (0.145) country country top 100, top 0.131*** official in in official -0.110*** assigned to to assigned nearest major major nearest . . 58 (6) Yes Yes 517 main main 0.193 0.070 0.862 whole whole (0.080) (0.178) (0.326) country research research official in in official languages -0.374*** . . 58 (5) Yes Yes 0.372 9,205 whole whole (0.005) (0.032) (0.073) country country 0.127*** 0.233*** s original s original official in in official -0.039*** unambiguou 58 (4) Yes Yes 0.027 0.570 whole whole -0.002 13,262 (0.008) (0.035) (0.133) (0.145) country top 100 top 0.131*** official in in official -0.110*** 58 (3) Yes Yes 0.568 whole whole -0.038 13,262 (0.008) (0.060) country top 100 top official in in official -0.129*** 56 (2) No No 0.083 0.153 whole whole 12,434 (0.008) (0.004) (0.005) (0.002) (0.006) (0.076) (0.072) (0.064) country top 100 top -0.164** 0.042*** 0.072*** 0.030*** 0.131*** 0.224*** official in in official -0.114*** 56 (1) No No 0.150 whole whole 12,434 (0.008) (0.004) (0.005) (0.002) (0.006) (0.064) country top 100 top 0.043*** 0.072*** 0.030*** 0.133*** official in in official -0.116*** -0.174*** Closer countries translate more from each other (OLS) Table B.1: Dependent variable: ln number of translations + 1 + numbertranslations ln of variable: Dependent languages: Original country: translating for each languages Target Variable and original between distance Physical (ln) countries translating (ln) country of translating Population (ln) country of translating capita per GDP (ln) country of original Population (ln) country of original capita per GDP country translating colonised country Original country original colonised country Translating contiguous are countries translating and Original country translating in is widespread language Original country is translating country Original effects fixed language/country target Time-varying effects fixed language original Time-varying R-Squared Observations countries Translating 200 APPENDIX B. APPENDICES FOR CHAPTER 4 e h oe oTbe1frfrhrdtis tnaderr r out seik eoesgicnea:*p * at: significance denote Asterisks PML. using robust. of are instead errors 1) + Standard ln(translations predicts details. and further OLS for uses 1 but Table 1, Table p to in notes results the the See duplicates table This Notes: < .5 * p *** 0.05, < 0.01. < .0 ** 0.10, B.1. APPENDIX FIGURES AND TABLES 201 17 Yes Yes Yes 1999 0.122 0.215 1,769 -0.451 1.564* (0.137) (0.426) (0.306) (0.139) (0.330) (0.259) (0.236) (0.095) (0.089) (0.102) (0.887) (1.770) 0.207** -0.354** -0.243** 0.370*** 0.382*** 9.348*** -0.973*** -0.899*** 17 Yes Yes Yes 1994 0.174 0.056 1,769 -0.500 -0.016 -0.094 0.194* 1.749* (0.154) (0.423) (0.363) (0.159) (0.342) (0.277) (0.400) (0.101) (0.103) (0.133) (1.001) (2.057) 0.831** 0.221** -0.606** -0.437*** -1.368*** 17 Yes Yes Yes 1989 0.165 0.306 1,769 -0.056 -0.046 -0.050 (0.114) (0.359) (0.221) (0.228) (0.313) (0.241) (0.262) (0.083) (0.089) (0.153) (0.959) (1.677) 0.491** -0.821** 0.994*** 0.298*** -0.442*** -1.108*** -1.251*** 17 Yes Yes Yes 1984 0.112 0.307 1,769 -0.632 -0.013 -0.088 (0.111) (0.408) (0.257) (0.238) (0.345) (0.281) (0.336) (0.079) (0.085) (0.170) (0.795) (1.447) 0.473** -0.790** 1.205*** 0.340*** 8.701*** -0.634*** -1.297*** 17 Yes Yes Yes 1979 0.067 0.304 0.121 0.043 1,769 -0.228 -0.024 -0.038 (0.153) (0.535) (0.233) (0.282) (0.327) (0.323) (0.444) (0.037) (0.081) (0.192) (0.639) (1.797) -0.770** 1.171*** 0.115*** -0.630*** -1.478*** 17 Yes Yes Yes 1974 0.208 0.013 0.795 1,769 -0.273 -0.488 -0.207 0.165* 0.169* (0.115) (0.413) (0.207) (0.244) (0.331) (0.284) (0.335) (0.097) (0.087) (0.191) (0.585) (1.642) 0.485** 0.839** -0.694*** -1.695*** 17 Yes Yes Yes 1969 0.255 0.302 0.148 0.023 0.408 2.299 1,769 -0.441 -0.144 (0.111) (0.131) (0.401) (0.240) (0.238) (0.322) (0.336) (0.333) (0.086) (0.246) (0.746) (1.728) -0.671* 0.269** -2.111*** -0.550*** 17 Yes Yes Yes 1964 0.034 0.025 0.032 1,769 -0.124 -0.359 -0.200 0.232* (0.148) (0.398) (0.284) (0.258) (0.310) (0.326) (0.324) (0.119) (0.080) (0.221) (0.626) (1.576) -0.896** -0.763** 1.025*** -0.723*** -1.347*** The effect of trade on translations over time: consistent countries Table B.2: Dependent variable: number of translations (ln) numbertranslations of variable: Dependent Variable (ln) countries translating and original between distance Physical translating and of original profiles altitude between Difference countries and of original profiles region climate between Difference countries translating and of original profiles region biome between Difference countries translating distance Religious distance Linguistic distance Genetic country original from country target into Imports country original into country target from Exports contiguous are countries translating and Original country translating in is widespread language Original country is translating country Original effects fixed language/country Target effects fixed language Original zero are for exports and zero are for imports variables Dummy Observations countries Translating 202 APPENDIX B. APPENDICES FOR CHAPTER 4 rmtetasaigcutyadarnol hsnpro rmteoiia onr aetesm eiin Standard religion. same the have country original p the * from at: person significance chosen denote Asterisks randomly year. a robust. column each are and Each profiles). used errors (identical country are country. countries 1 translating original target translating and the The same profiles) the The from in target of overlap header. procedure. The included whole (no column PML translated. 0 are the the between ever in the languages in given are by as original official that year, allowed are same worldwide different languages as that a The 100 for languages estimation, results from spoken year. the regression the widely translations a presents all most of in in are the number included country in the included are languages of translating language/countries those values 4.2) a all section zero in are in language; language languages described target target (as every a regressions PML to for of language results original the an presents table This Notes: h liuepol,ciaergo rfie n im einpol ieec aibe r l osrce ovary to constructed all are variables difference profile region biome and profile, region climate profile, altitude The < .0 *p ** 0.10, eiiu distance Religious < .5 * p *** 0.05, stepoaiiyarnol hsnperson chosen randomly a probability the is < 0.01. B.1. APPENDIX FIGURES AND TABLES 203 51 Yes Yes Yes 1999 0.269 0.053 5,898 -0.000 0.190* 0.170* (0.107) (0.254) (0.198) (0.102) (0.217) (0.167) (0.151) (0.060) (0.059) (0.092) (0.236) (0.915) -0.386** 0.364*** 0.216*** 1.167*** 7.576*** -0.535*** -0.787*** 54 Yes Yes Yes 1994 0.012 0.188 0.044 6,461 -0.264 0.210* 3.443* (0.093) (0.295) (0.218) (0.116) (0.275) (0.227) (0.186) (0.057) (0.069) (0.126) (0.205) (1.951) -0.459** 0.234*** 0.213*** 1.166*** -0.457*** -0.753*** 46 Yes Yes Yes 1989 6,177 -0.002 -0.212 -0.023 (0.082) (0.231) (0.164) (0.178) (0.224) (0.178) (0.165) (0.047) (0.058) (0.093) (0.217) (0.664) 0.477*** 0.570*** 0.208*** 0.333*** 1.124*** 5.704*** -0.417*** -1.221*** -0.517*** 51 Yes Yes Yes 1984 0.374 0.175 0.008 0.132 6,555 -0.077 (0.088) (0.343) (0.178) (0.203) (0.252) (0.180) (0.140) (0.051) (0.065) (0.107) (0.264) (1.102) 0.413*** 0.271*** 0.742*** 6.605*** -0.447*** -1.274*** -0.771*** 49 Yes Yes Yes 1979 0.046 0.180 0.228 0.472 6,178 0.086* 0.235* (0.101) (0.337) (0.202) (0.223) (0.285) (0.210) (0.203) (0.034) (0.050) (0.121) (0.287) (1.157) 0.656*** 0.112*** 4.210*** -0.510*** -0.841*** -1.153*** 30 Yes Yes Yes 1974 0.109 0.071 0.857 3,456 -0.124 -0.400 -0.022 -0.087 0.382* (0.115) (0.415) (0.189) (0.198) (0.304) (0.287) (0.187) (0.079) (0.074) (0.156) (0.548) (1.444) 0.798*** 0.256*** -0.598*** -1.816*** 33 Yes Yes Yes 1969 0.232 0.312 0.076 0.003 0.041 0.557 3,829 -0.211 (0.106) (0.374) (0.200) (0.210) (0.299) (0.256) (0.238) (0.082) (0.068) (0.149) (0.614) (1.776) -0.614** 0.225*** 7.619*** -0.430*** -1.486*** 34 Yes Yes Yes 1964 0.113 0.159 0.223 0.064 4,590 -0.031 -0.731 0.122* (0.191) (0.550) (0.283) (0.232) (0.319) (0.307) (0.346) (0.095) (0.073) (0.163) (0.607) (1.629) 0.701** -0.803** 6.933*** -0.882*** -1.765*** The effect of trade on translations over time: all available countries Table B.3: Dependent variable: number of translations (ln) numbertranslations of variable: Dependent Variable (ln) countries translating and original between distance Physical translating and of original profiles altitude between Difference countries and of original profiles region climate between Difference countries translating and of original profiles region biome between Difference countries translating distance Religious distance Linguistic distance Genetic country original from country target into Imports country original into country target from Exports contiguous are countries translating and Original country translating in is widespread language Original country is translating country Original effects fixed language/country Target effects fixed language Original zero are for exports and zero are for imports variables Dummy Observations countries Translating 204 APPENDIX B. APPENDICES FOR CHAPTER 4 rmtetasaigcutyadarnol hsnpro rmteoiia onr aetesm eiin Standard religion. same the have country original p the * from at: person significance chosen denote Asterisks randomly a robust. each are and profiles). used errors (identical country are 1 translating column countries and the Each profiles) target from available in country. overlap All original (no translating header. The 0 the column between target of the procedure. The included whole in PML translated. are the given ever the as languages in are year, by original official that different allowed are same worldwide a year. languages as that The for 100 languages results estimation, from spoken year. the regression the widely translations a presents all most of in in are the number included country in the included are languages of translating language/countries those values 4.2) a all section zero in are in language; language languages described target target (as every a regressions PML to for of language results original the an presents table This Notes: h liuepol,ciaergo rfie n im einpol ieec aibe r l osrce ovary to constructed all are variables difference profile region biome and profile, region climate profile, altitude The < .0 *p ** 0.10, eiiu distance Religious < .5 * p *** 0.05, stepoaiiyarnol hsnperson chosen randomly a probability the is < 0.01. Appendix C

Appendices for Chapter 5

C.1 Appendix ﬁgures and tables

205 206 APPENDIX C. APPENDICES FOR CHAPTER 5

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

Translations from Western European languages

Translations from Communist languages

Notes: The coefficients plotted are from the estimation of a version of equation (5.6) in which the post dummy and its interactions have been replaced by year dummies (for 1989- 2000) and their equivalent interactions. Country fixed effects and controls for population and GDP per capita are also included. The top two figures show coefficients and 95% confidence intervals on interactions of the year dummies with Western translations in Soviet countries (left panel) and in Satellite countries (right panel). The Western level line is the negative of the coefficient on Soviet (left panel) or Satellite (right panel). The lower two figures show the equivalent for translations from Communist languages. C.1. APPENDIX FIGURES AND TABLES 207 Yes Yes Yes Yes Yes Yes Yes 963 (10) 0.928 -0.965 -0.106 (0.385) (0.319) (0.565) (0.514) 0.833** 0.719** (9) Yes Yes Yes Yes Yes Yes Yes 963 0.176 0.770 0.094 0.100 0.939 -1.067 -0.422 (0.622) (0.535) (0.135) (0.755) (0.696) (0.186) (8) Yes Yes Yes Yes Yes 963 0.920 -0.076 -0.156 0.317* (0.343) (0.314) (0.161) (0.550) (0.512) (0.163) -1.050* 0.752** 0.767** (7) Yes Yes Yes 963 0.089 0.763 0.757 -0.738 -0.082 0.882* (0.479) (0.374) (0.967) (0.753) (0.160) (0.558) (0.440) (0.489) (0.519) (0.158) 0.973** 1.985** -0.380** 1.699*** -3.285*** (6) Yes Yes 999 0.167 1.197 0.319 0.592 -0.025 -0.147 (0.274) (0.301) (1.160) (1.178) (0.139) (0.500) (0.504) (0.498) (0.442) (0.140) 0.301** -3.226** 1.377*** 1.795*** -1.473*** (5) Yes Yes Yes Yes Yes Yes Yes 963 0.926 (0.248) (0.304) 1.396*** -0.995*** (4) Yes Yes Yes Yes Yes Yes Yes 963 0.095 0.102 0.938 (0.343) (0.135) (0.461) (0.186) 0.716** -1.362*** (3) Yes Yes Yes Yes Yes 963 0.918 -0.155 0.318* (0.211) (0.159) (0.267) (0.162) 1.352*** -1.056*** (2) Yes Yes Yes 963 0.027 0.664 (0.252) (0.503) (0.145) (0.200) (0.431) (0.143) 1.822*** 2.490*** -1.648*** -0.619*** -0.442*** (1) Yes Yes 999 0.538 -0.147 (0.258) (0.521) (0.138) (0.224) (0.354) (0.140) 0.301** 1.223*** 1.778*** -2.398*** -1.240*** Pages translated: The effect of the collapse of Communism on the number of book Dependent variable: log number of pages translated of pages number log variable: Dependent languages in: original Western from Translations * post country Communist * post country Satellite country Communist country Satellite Post Communistlanguages in: original from Translations * post country Communist * post country Satellite country Communist country Satellite Post controls: Other dummy language original Western dummy language original Communist controls GDP and Population language original Western * effects fixed Country language original * Communist effects fixed Country language original Western * trends time Country-specific language original * Communist trends time Country-specific language original Western * effects fixed Year language original * Communist effects fixed Year R-Squared Observations or Communist) (Western language original year, country, is a An observation Table C.1: pages translated 208 APPENDIX C. APPENDICES FOR CHAPTER 5 r ier tnaderr,i aetee,aecutrda h onr ee.*p * level. country only. the languages country at original Baltic clustered the Western three are of and parentheses, the language in Communist include main errors, We more The the Standard into or used. linear. translations 49 5.3.1). countries are Western include are Section We and that in Communist titles 25. explanation the onwards. from from footnote (see (5.4) for pages in equation 5.2 countries given includes Table estimate Satellite variable are 1-5 to dependent the Columns notes The in the group. countries See comparison (5.6). the only. equation as long estimate Europe pages 6-10 Western Communist with columns and 1980-2000, interest paper; period of the the for region data annual the using as regressions OLS Europe difference-in-differences are columns All Notes: ouainadGPcontrols GDP and Population r h oso ouainado elGPprcapita. per GDP real of and population of logs the are < .0 *p ** 0.10, < onr-pcfi ietrends time Country-specific .5 * p *** 0.05, Post sadmyfr1991 for dummy a is < 0.01. C.1. APPENDIX FIGURES AND TABLES 209

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 Yes Country 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 aggregated to the pre/post collapse level, with Communist Europe as the region of interest and Western Europe 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 for the 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 original languages are given in footnote 25. We include translations into the main language of the country only. Population and GDP controls are the logs of population and of real GDP per capita. Standard errors, in parentheses, are clustered at the country level. * p<0.10, ** p<0.05, *** p<0.01. 210 APPENDIX C. APPENDICES FOR CHAPTER 5 translations C.3: Table An observationAn is a country, year, original language (Western or Communist) Observations R-Squared Country fixed effects * Communist original language Country fixed effects * Western original language Year fixed effects * Communist original language Year fixed effects * Western original language Population and GDP controls Communist original language dummy Western original language dummy Other controls: Trade and foreign exchange system reform Price liberalization Political competition Institutionalized democracy Translations from original interacted Communist languages with: Trade and foreign exchange system reform Price liberalization Political competition Institutionalized democracy Translations from Western original interacted languages with: Dependent variable: log number of translations ereo eom h feto h ereo olpeo omns nbook on Communism of collapse of degree the of effect The reform: of Degree -0.100*** 0.299*** (0.022) (0.031) 0.489 507 Yes Yes Yes (1) 0.436*** (0.043) (0.125) 0.085* 0.560 507 Yes Yes Yes Yes Yes (2) 0.139** (0.031) (0.046) -0.003 0.897 507 Yes Yes Yes Yes Yes Yes Yes (3) -0.117*** 0.336*** (0.023) (0.036) 0.501 507 Yes Yes Yes (4) 0.451** (0.060) (0.158) 0.553 0.031 507 Yes Yes Yes Yes Yes (5) (0.031) (0.060) 0.120* -0.006 0.894 507 Yes Yes Yes Yes Yes Yes Yes (6) -0.426*** 0.396** 1.091** (0.146) (0.376) (0.259) (0.110) 0.691 0.014 277 Yes Yes Yes (7) 1.324*** 0.428** -0.249* (0.148) (0.123) (0.263) (0.246) 0.744 0.313 277 Yes Yes Yes Yes Yes (8) 0.375** (0.236) (0.180) (0.172) (0.151) -0.210 0.903 0.278 0.119 277 Yes Yes Yes Yes Yes Yes Yes (9) C.1. APPENDIX FIGURES AND TABLES 211 Institutionalized are measures of aspects 0.01. < 0.05, *** p < 0.10, ** p < Trade and foreign exchange system reform are the logs of population and of real GDP per capita. Standard , and Price liberalization , Population and GDP controls Political competition , of the degree ofdetail reform in from communist Section centrally-planned C.2.1. economy to democratic market economy. They are described in democracy errors, in parentheses, are clustered at the country level. * p Notes: All columnsfor are 1989-2000. OLS The regressionsBulgaria, countries using the used Czech annual in Republic, data. theare Hungary, analysis given Poland, in Columns are Romania, footnote Russia, 1-6 and 25. Belarus, Slovakia. are We include Estonia, The for translations Latvia, Communist into the and Lithuania, the years Western main Moldova, original language the 1980-2000; of languages Ukraine, columns the country 7-9 only. are The variables 212 APPENDIX C. APPENDICES FOR CHAPTER 5 nomi n eodr languages secondary and main into C.4: Table An observationAn is a country, year, original language (Western or Communist) Observations R-Squared Year fixed effects * Communist original language Year fixed effects * Western original language Country-specific time trends * Communist original language Country-specific time trends * Western original language Country fixed effects * Communist original language Country fixed effects * Western original language Population and GDP controls Communist original language dummy Western original language dummy Other controls: Post Satellite country Communist country Satellite country * post Communist country * post Translations from original in: Communist languages Post Satellite country Communist country Satellite country * post Communist country * post Translations from Western original in: languages Dependent variable: log number of translations eodr agae:Teefc fteclas fCmuimo oktranslations book on Communism of collapse the of effect The languages: Secondary -1.181*** -2.388*** 1.904*** 0.343*** 1.390*** (0.134) (0.321) (0.163) (0.405) (0.230) (0.117) -0.040 1,001 0.656 Yes Yes (1) -0.602*** -1.665*** 2.569*** 1.932*** -0.317** (0.131) (0.373) (0.196) (0.122) (0.417) (0.207) 0.759 0.103 965 Yes Yes Yes (2) -0.968*** 1.532*** 0.347** (0.159) (0.217) (0.143) (0.192) -0.071 0.922 965 Yes Yes Yes Yes Yes (3) -1.217*** 1.074*** (0.191) (0.386) (0.129) (0.255) 0.940 0.186 0.164 965 Yes Yes Yes Yes Yes Yes Yes (4) -0.909*** 1.578*** (0.243) (0.233) 0.929 965 Yes Yes Yes Yes Yes Yes Yes (5) -1.252*** -2.640*** 2.027*** 0.343*** 0.940** (0.134) (0.383) (0.446) (0.173) (0.188) (0.803) (0.780) (0.445) (0.424) (0.117) -0.040 -0.177 1,001 0.670 0.107 0.363 0.688 Yes Yes (6) -2.425*** 2.183*** 1.430*** -0.290** -0.284** -0.473** 0.934** (0.137) (0.382) (0.450) (0.222) (0.129) (0.425) (0.602) (0.369) (0.392) (0.116) 0.785 0.394 0.130 0.547 965 Yes Yes Yes (7) 1.225*** -0.741** 0.346** (0.159) (0.242) (0.277) (0.144) (0.370) (0.388) -0.072 -0.325 0.924 0.419 965 Yes Yes Yes Yes Yes (8) 0.941*** -0.819* (0.191) (0.449) (0.463) (0.129) (0.342) (0.319) -0.560 0.941 0.188 0.166 0.196 965 Yes Yes Yes Yes Yes Yes Yes (9) 1.304*** -0.663** (0.245) (0.287) (0.378) (0.426) -0.344 0.930 0.379 (10) 965 Yes Yes Yes Yes Yes Yes Yes C.1. APPENDIX FIGURES AND TABLES 213 are the logs of population and of real GDP Population and GDP controls are linear. Standard errors, in parentheses, are clustered at the country level. * 0.01. < is a dummy for 1991 onwards. 0.05, *** p Post < Country-specific time trends 0.10, ** p < Notes: All columns are difference-in-differencesEurope OLS regressions as using the annual data region forthe the of period paper; interest 1980-2000, and columns with Communist Westernused. 6-10 Europe estimate as We include the equation comparison theand (5.6). Western group. three original Baltic See Columns languages countries 1-5 are thethe in given estimate country. notes in the equation to footnote (5.4) Satellite 25. Table from per countries 5.2 We (see capita. include for explanation translations the in into the Communist Section main 5.3.1). and and Western secondary The countries languages Communist of p 214 APPENDIX C. APPENDICES FOR CHAPTER 5 ilso oktranslations book of fields C.5: Table An observationAn is a country, year, original language (Western or Communist) Observations R-Squared Population and GDP controls Communist original languages Western original languages Communist original languages * post Western original languages * post Other controls: Western original languages Communist original languages Western original languages * post Communist original languages * post Translations countries from: in Communist Panel B: OLS predicting log number in the field, of translations where are translations non-zero (intensive margin) Observations Controls as in Panel B Western original languages * post Communist original languages * post Translations countries from: in Communist Panel ProbitA: predicting non-zero in the field translations (extensive margin) rnltosb okfed h feto h olpeo omns nvarious on Communism of collapse the of effect The field: book by Translations Natural Sci Natural Sci -1.256*** 2.445*** 0.992*** -0.287* -0.955* -0.767* (0.145) (0.167) (0.553) (0.438) (0.396) (0.375) (0.157) (0.339) 0.328* 0.684* 0.535 752 966 Yes Yes Yes Yes Applied Sci Applied Sci -0.497*** -1.907*** 3.134*** 2.067*** 1.139*** (0.165) (0.129) (0.580) (0.396) (0.338) (0.251) (0.265) (0.286) -0.362 0.709 0.184 0.399 748 966 Yes Yes Yes Yes -0.467*** -1.758*** -1.312*** Social Sci Social Sci 2.380*** 1.762*** 1.139*** 0.299** (0.148) (0.130) (0.543) (0.321) (0.372) (0.272) (0.284) (0.532) 0.606 0.221 824 966 Yes Yes Yes Yes -1.679*** 0.419*** 1.215*** -0.622** 1.144** 0.764** -0.249* (0.137) (0.124) (0.546) (0.552) (0.284) (0.226) (0.286) (0.279) -0.330 0.691 Arts Arts 750 966 Yes Yes Yes Yes -9.018*** Literature Literature 2.388*** 1.897*** -1.329** -0.641** -0.345* (0.192) (0.167) (0.483) (0.449) (0.256) (0.246) (0.473) -0.048 -4.633 0.682 953 966 Yes Yes Yes Yes . Philosophy Philosophy -2.291*** 0.354*** 2.176*** 1.434*** 1.154** (0.215) (0.119) (0.528) (0.447) (0.280) (0.300) (0.269) (0.259) -0.206 0.718 0.198 0.241 717 966 Yes Yes Yes Yes -2.164*** 2.074*** 0.889*** 2.003*** 0.839*** Religion Religion -0.223* (0.119) (0.166) (0.704) (0.417) (0.435) (0.171) (0.372) (0.251) 0.739 0.212 0.270 656 966 Yes Yes Yes Yes -1.551*** -0.841*** 1.739*** 1.198*** 1.133*** History History (0.146) (0.150) (0.406) (0.355) (0.318) (0.242) (0.342) (0.402) -0.174 -0.551 0.680 0.186 846 966 Yes Yes Yes Yes C.1. APPENDIX FIGURES AND TABLES 215 Population and are linear. Standard 0.01. < is a dummy for 1991 onwards. 0.05, *** p < Country-specific time trends Post 0.10, ** p < are the logs of population and of real GDP per capita. Notes: All columnswith are Communist Europe difference-in-differences as regressions thefor (equation region the (5.4)) of Communist interest and using andWe Western annual Western include countries Europe data used. translations as for into The the the the Communist comparison and main group. period Western language See 1980-2000, original of the languages notes the are to country given Table only. in 5.2 footnote 25. GDP controls errors, in parentheses, are clustered at the country level. * p 216 APPENDIX C. APPENDICES FOR CHAPTER 5

C.2 Comparing Communist countries that transitioned to diﬀerent degrees

This appendix uses several variables on the degree to which the former Communist countries transitioned into democratic market economies to test the prediction that countries that experienced greater such transitions also converged to Western translation patterns to a higher degree.

C.2.1 Data

We use four variables to measure the degree to which the Communist countries transitioned from communist, centrally-planned economies to democratic market economies, namely institutionalized democracy, political competition, price liberalization, and trade and foreign exchange system reform. The variables institutionalized democracy and political competition are from the Polity IV data set, described at and available from www.systemicpeace.org/ polity/polity4.htm. Institutionalized democracy is measured on a scale of 0 to 10, with greater values indicating more democratic political systems. Political competition captures the degree of regulation of participation and the competitiveness of participation in the political arena. It is measured on a scale of 1 to 10, where larger values denote more regulation and more competitiveness. These variables are available for all the Communist countries in our sample for each year 1980 to 2000. The variables price liberalization and textittrade and foreign exchange system reform were developed by The European Bank for Reconstruction and Development, and are available at www.ebrd.com/country/sector/econo/stats/index.htm. Each is measured on a scale from 1 to 4.33, where 1 indicates “most prices formally controlled by the government” and “widespread import and/or export controls or very C.2. DEGREES OF TRANSITION 217

limited legitimate access to foreign exchange” for the two variables respectively, and 4.33 indicates “standards and performance typical of advanced industrial economies: complete price liberalization with no price control outside housing, transport and natural monopolies” and “standards and performance norms of advanced industrial economies: removal of most tariﬀ barriers; membership in WTO”.1 These two variables are available for all the Communist countries in our sample for each year 1989 to 2000.

C.2.2 Empirical strategy and results

We run regressions that predict translations from Western European or Communist languages using a “degree of transition” variable fully interacted with Western European original language, plus controls. We include only the former Communist countries in these regressions, and run them for the years 1980-2000 or 1989-2000, depending on the availability of the “degree of transition” variable. For each “degree of transition” variable, described above, a higher value indicates a greater degree of transition. We control for price liberalization and trade and foreign exchange system reform in a single regression, which allows us to investigate which type of transition was more important for which type of translation. Appendix Table C.3 presents the results from OLS regressions that show the relationship between several types of reform in Communist countries and translations from Western European and Communist languages. The first of each group of three columns includes the additional controls population and GDP per capita only; here the coefficients of interest, on the reform variable interacted with the two types of original language, are identified both off between-country variation in the degree of transition and off average trends in transition over time. An important concern here

1These descriptions of the values are from www.ebrd.com/country/sector/econo/stats/ timeth.htm. 218 APPENDIX C. APPENDICES FOR CHAPTER 5

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 presence of 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 to a 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 the West 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) 220 APPENDIX C. APPENDICES FOR CHAPTER 5

• “The Outsider (L’Etranger)” (Albert Camus) • “Crowds and Power (Masse und Macht)” (Elias Canetti) • “In Cold Blood” (Truman Capote) • “Silent Spring” (Rachel Carson) • “The Everlasting Man” (G.K. Chesterton) • “Orthodoxy” (G.K. Chesterton) • “The Second World War” (Winston S. Churchill) • “Civilisation: A Personal View” (Kenneth Clark) • “The Pursuit of the Millennium: Revolutionary Millenarians and Mystical Anarchists of the Middle Ages” (Norman Cohn) • “The Idea of History” (R.G. Collingwood) • “The Great Terror” (Robert Conquest) • “Out of Africa” (Isak Dinesen) • “Purity and Danger: An Analysis of Concepts of Pollution and Taboo” (Mary Douglas) • “The Souls of Black Folk” (W.E.B. Du Bois) • “Taking Rights Seriously” (Ronald Dworkin) • “Henry James” (Leon Edel) • “Ideas and Opinions” (Albert Einstein) • “Relativity: The Special and the General Theory (Uber die spezielle und die allgemeine Relativitatstheorie” (Albert Einstein) • “Images and Symbols (Images et symboles)” (Mircea Eliade) • “The Civilizing Process (Uber den Prozess der Zivilisation)” (Norbert Elias) • “Selected Essays, 1917-1932” (T.S. Eliot) • “James Joyce” (Richard Ellmann) • “The Struggle for History (Combat pour l’histoire)” (Lucien Febvre) • “The Feynman Lectures on Physics” (Richard Phillips Feynman) C.3. INFLUENTIAL TITLES APPENDIX 221

• “Aspects of the Novel” (E.M. Forster) • “Madness and Civilization: A History of Insanity in the Age of Reason (Folie et déraison.Histoire de la folie àl’âgeclassique)” (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) 222 APPENDIX C. APPENDICES FOR CHAPTER 5

• “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 and Richard 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) C.3. INFLUENTIAL TITLES APPENDIX 223

• “The Structure of Scientiﬁc Revolutions” (Thomas S. Kuhn) • “The Book of Laughter and Forgetting (Kniha smichu a zapomneni)” (Milan Kundera) • “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) 224 APPENDIX C. APPENDICES FOR CHAPTER 5

• “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.3. INFLUENTIAL TITLES APPENDIX 225

• “Eminent Victorians” (Lytton Strachey) • “Natural Right and History” (Leo Strauss) • “The Origins of Totalitarian Democracy” (Jacob Leib Talmon) • “Working: People Talk About What They Do All Day and How They Feel About What They Do” (Studs Terkel) • “The Lives of a Cell: Notes of a Biology Watcher” (Lewis Thomas) • “On Growth and Form” (D’Arcy Thompson) • “The Making of the English Working Class” (E.P. Thompson) • “A Study of History” (Arnold J. Toynbee) • “The Guns of August (August 1914)” (Barbara Tuchman) • “The Autobiography of Mark Twain” (Mark Twain) • “Spheres of Justice” (Michael Walzer) • “Up from Slavery” (Booker T. Washington) • “The Double Helix: A Personal Account of the Discovery of the Structure of DNA” (James D. Watson) • “Ideas have Consequences” (Richard M. Weaver) • “Economy and Society (Wirtschaft und Gesellschaft)” (Max Weber) • “Black Lamb and Grey Falcon” (Rebecca West) • “Principia Mathematica” (Alfred North Whitehead and Bertrand Russell) • “Power Politics” (Robert James Martin Wight) • “In the American Grain” (William Carlos Williams) • “Sociobiology: The New Synthesis” (Edward O. Wilson) • “Philosophical Investigations (Philosophische Untersuchungen)” (Ludwig Wittgen- stein) • “Tractatus logico-philosophicus (Logisch-Philosophische Abhandlung)” (Lud- wig Wittgenstein) • “The Electric Kool Aid Acid Test” (Tom Wolfe) 226 APPENDIX C. APPENDICES FOR CHAPTER 5

• “Radical Chic and Mau-Mauing the Flak Catchers” (Tom Wolfe) • “The Right Stuﬀ” (Tom Wolfe) • “A Room of One’s Own” (Virginia Woolf) • “Black Boy” (Richard Wright) • “The Art of Memory” (Frances A. Yates) • “Balzac” (Stefan Zweig) • “The Constitution of Liberty (Die Verfassung der Freiheit)” (F.A. von Hayek) • “The Road to Serfdom” (F.A. von Hayek) • “Bureaucracy” (Ludwig von Mises) C.4. KEYWORD LIST APPENDIX 227

C.4 Keyword list appendix: Keywords contributing to each subﬁeld

This appendix lists the keywords that contribute to each subfield search. Note alternative forms and spellings of the words are also included (except where explicitly stated that they are excluded), and variations in capitalization and hyphenation are ignored. The total number of titles in the field or subfield is given in parentheses.

Religion (13,321)

• Christian (1,794): jesus, christ, lord, angel, cross, crucify, pope, papacy, satan, evangelism, catholicism, protestantism, orthodoxy, baptism, lutheranism, anglicanism, methodism, adventism, pentecostalism, mormonism, jehovah • Judeo-Christian (1,819): bible, testament, genesis, god (excluding “gods”) • Theology (351): theology, religion • Islam (114): muslim, islam, koran, muhammad, allah • Judaism (126): judaism, jew, talmud, torah, yetzirah, kabbalah • Eastern Religions (439): tibet, tao, buddhism, dhammapada, zen, hindu, krishna, sikhism, jaina, zoroastrian, yin, yang, confucius, shinto, eastern (excluding titles that include the string “christ”)

Social Science (16,706)

• Communism (140): communism, socialism, marx • Economics (2,049): economics, economy, macroeconomics, macroeconomy, monetary, inflation, microeconomics, business, profit, trade, market, finance, marketing, capitalism, corporate, money, accounting, management • Political Science (591): politics, democracy, government 228 APPENDIX C. APPENDICES FOR CHAPTER 5

• Sociology and Anthropology (620): sociology, anthropology, culture, society • Education (1,084): child, learning, teaching, classroom

Exact Science (12,441)

• Mathematics (711): math, mathematics, matrix, calculus, number, geometry, algebra, equation, statistics, probability, pi • Physics (1,575): physics, thermodynamics, quantum, astrophysics, electricity, electromagnetic, geophysics, meteorology, newton, weather, astronomy, planets (plural form only), star, black hole, cosmos, universe, big bang, solar system, sun, space-time, space, relativity, einstein, feynman, quark • Chemistry (631): chemistry, biochemistry • Biology (3,024): biology, microbiology, cell, dna, biotechnology, zoology, genetics, evolution, darwin, species, botany, physiology, human body, animal, animal-watching, bird, birdfeeder, birdhouse, birdkeeper, birdlife, bird- watching, mammal, insect, butterfly, wildlife, cat, cat-watching, fish, fishing, whale, shark, dog, dog-watching, elephant, reptile, dolphin, moth, spider, creatures, bear, chimpanzee, kingfisher, snake, snake-keeping, horse, horselore, horse-watching, crocodile, tiger, amphibian, wolf, gorilla, ape, panda, fauna, from, porpoise, penguin, jaguar, alligator, flora, plant, tree, flower, forest, mushroom, herb, fern, wood, woodland • Geology (234): geology, earthquake, volcano, rock, mineral, tectonic

Applied Science (40,481)

• Computers (4,294): microsoft, windows, macintosh, programming, computer, internet, server, software, database, photoshop, dbase, excel, ms-dos, wordper- fect, lotus, autocad, linux, adobe 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 230 APPENDIX C. APPENDICES FOR CHAPTER 5

C.5 Example title appendix

This appendix gives a random sample of 4 titles found by each keyword search. The author is given in parentheses.

Religion

Christian

• “The Founder of Christianity” (Charles Harold Dodd) • “Jesus the Storyteller” (Jenny Wood) • “The Great Controversy Between Christ and Satan” (Ellen Gould White) • “Mother of Jesus in New Testament” (John MacHugh)

Judeo-Christian

• “God Help” (Della Tombleson) • “An Introduction to the Bible” (John William Rogerson) • “A Survey of the Old Testament” (Fred J. Greve) • “Mother of Jesus in New Testament” (John MacHugh)

Theology

• “Teach Yourself History of Religions” (Edwin Oliver James) • “Religions of the World” (Lynn Underwood) • “Zoroastrians: Their Religious Beliefs And Practices” (Mary Boyce) • “Religion without Revelation” (Julian Huxley) C.5. EXAMPLE TITLE APPENDIX 231

Islam

• “The Dagger of Islam” (John Laﬃn) • “An Introduction to Islam” (David Waines) • “The Suﬁ Orders in Islam” (J. Spencer Trimingham) • “Healing the Broken Family of Abraham. The Winning of Muslims” (Don MacCurry)

Judaism

• “The Essential Talmud” (Adin Steinsaltz) • “From Early Judaism to Early Church” (David Syme Russell) • “The Talmud” (Emanuel Oskar Menahem Deutsch) • “Christ on the Jewish Road” (Richard Wurmbrand)

Eastern Religions

• “Enlightenment of a Taoist Love Master” (Jolan Chang) • “Zen Antics” • “Zoroastrians: Their Religious Beliefs And Practices” (Mary Boyce) • “Dropping Ashes on the Buddha” (Seung Sahn)

Social Science

Communism

• “Ten Great Economists: From Marx to Keynes” (Joseph Alois Schumpeter) • “The Soul of Man Under Socialism” (Oscar Wilde) • “The Communist Party of the United States” 232 APPENDIX C. APPENDICES FOR CHAPTER 5

• “Considerations on Western Marxism: In the Tracks of Historical Materialism” (Perry Anderson)

Economics

• “Foreign Trade Dictionary” (A. Campo Grande) • “Financial Innovations and Market Volatility” (Merton H. Miller) • “Index Theory and Economic Reality” (Pal Koves) • “Successful Marketing Strategies for Nonproﬁt Organizations” (Barry J. MacLeish)

Political Science

• “A History of Political Theory” (George Holland Sabine) • “Economic Reforms in New Democracies” (Luiz Carlos Bresser Pereira) • “The Irony of Democracy” (Thomas R. Dye) • “Local Government: A Councillor’s Guide”

Sociology and Anthropology

• “The Logic of Science in Sociology” (Walter L. Wallace) • “Predicament of Culture” (James Cliﬀord) • “The Information Society” (Yoneji Masuda) • “Cross-Cultural Business Behavior: Marketing, Negotiating And Managing Across Cultures” (Richard R. Gesteland)

Education

• “Evaluacion: A Practical Guide For Teachers” (Terry D. Tenbrink) • “To a Very Special Teacher” (Pam Brown) C.5. EXAMPLE TITLE APPENDIX 233

• “T.E.T.: Teacher Eﬀectiveness Training” (Thomas Gordon) • “How to Really Love your Child” (Ross Campbell)

Exact Science

Mathematics

• “Calculus: One And Several Variables With Analytic Geometry” (Saturnino L. Salas) • “Fermat’s Last Theorem: Unlocking The Secret Of An Ancient Mathematical Problem” (Amir D. Aczel) • “Mathematical Methods in Medicine” (Richard Ernest Bellman) • “Diﬀerential And Diﬀerence Equations Through Computer Experiments” (Huseyin Kocak)

Physics

• “Physics” (David Bryant) • “Destiny Or Chance, Our Solar System And Its Place In The Cosmos” (Stuart Ross Taylor) • “Essentials of Modern Physics” (Virgilio Acosta) • “Continuum Mechanics of Electromagnetic Solids” (Gerard A. Maugin)

Chemistry

• “Physical Chemistry” (Gordon M. Barrow) • “Chemical Demonstrations” (Lee R. Summerlin) • “Radiation Chemistry Of Hydrocarbons” • “Organic Chemistry” (Robert Thornton Morrison) 234 APPENDIX C. APPENDICES FOR CHAPTER 5

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.5. EXAMPLE TITLE APPENDIX 235

• “The Ones Minute Sales Person” (Spencer Johnson)

Medical

• “Folk Medicine” (Deforest Clinton Jarvis) • “Practice Guideline for Treatment of Patients With Bipolar Disorder” • “Coronary Artery Disease” (Richard Gorlin) • “Advanced Pranic Healing” (Choa Kok Sui)

Engineering

• “Unit Operations of Chemical Engineering” (Warren L. MacCabe) • “Timber Engineering” • “Corrosion and Corrosion Control: An Introduction to Corrosion Science and Engineering” (Herbert Henry Uhlig) • “Handbook of Electronics Calculation for Engineers” (Milton Kaufman)

Food

• “The Art and Science of Cooking with Cannabis” (Adam Gottlieb) • “Foods that Harm, Foods that Heal” • “Yugoslav Cook-Book” (Miroslav Kutanjac) • “Cooking with Spices” (Jill Norman)

Gardening

• “The A.B.C. of House and Conservatory Plants” (Jocelyn Baines) • “The Weekend Gardener” (Max Davidson) • “Gardening Techniques” (Alan Titchmarsh) • “The Healing Herbs” (Michael Castleman) 236 APPENDIX C. APPENDICES FOR CHAPTER 5

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 méthode pour bien conduire sa raison, et chercher la véritédans 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 237

• “For Your Own Good (Am Anfang war Erziehung)” (Alice Miller) • “Essays (Essais)” (Michel Eyquem de Montaigne) • “Life After Life: The Investigation of a PhenomenonSurvival of Bodily Death” (Raymond Moody) • “The Antichrist (Der Antichrist)” (Friedrich Nietzsche) • “Beyond Good and Evil (Jenseits von Gut und Böse)”(Friedrich Nietzsche) • “The Gay Science (Die fröhliche Wissenschaft)” (Friedrich Nietzsche) • “Thus Spoke Zarathustra: A Book for All and None (Also Sprach Zarathustra: Ein Buch fürAlle und Keinen)” (Friedrich Wilhelm Nietzsche) • “Women Who Love Too Much” (Robin Norwood) • “Pensees” (Blaise Pascal) • “The Power of Positive Thinking” (Norman Vincent Peale) • “The Apology of Socrates (Apologia Sokratous)” (Plato) • “The Republic (Politeia)” (Plato)

Religion and Theology

• “Summary of Theology (Summa (Theologica))” (Thomas Aquinas) • “Of the City of God (De Civitate Dei)” (Augustine of Hippo) • “Confessions (Confessiones)” (Augustine of Hippo) • “My Utmost for his Highest” (Oswald Chambers) • “How to Read the New Testament” (Etienne Charpentier) • “How to Read the Old Testament (Pour lire l’Ancien Testament)” (Etienne Charpentier) • “Cosmos and History: The Myth of the Eternal Return (Le Mythe de l’eternel retour: arch´etypes et r´epetition)” (Mircea Eliade) • “A History of Religious Ideas (histoire des croyances et des idees religieuses)” (Mircea Eliade) 238 APPENDIX C. APPENDICES FOR CHAPTER 5

• “The Way (Camino or Consideraciones Espirituales)” (Saint Josemara Escriv de Balaguer) • “Gods, Men & Monsters from the Greek Myths” (Michael Gibson) • “Who’s who in Classical Mythology” (Michael Grant) • “Mythology: Timeless Tales of Gods and Heroes” (Edith Hamilton) • “The Parables of Jesus (Die Gleichnisse Jesu)” (Joachim Jeremias) • “On the Christian Family in the Modern World (Familiaris Consortio)” (Pope John Paul II) • “On Human Work (Laborem exercens)” (Pope John Paul II) • “Rich in Mercy (Dives in Misericordia)” (Pope John Paul II) • “Salviﬁci Doloris” (Pope John Paul II) • “The Imitation of Christ (De imitatione Christi)” (Thomas Kempis) • “When Bad Things Happen to Good People” (Harold Kushner) • “A Grief Observed” (Clive Staples Lewis) • “Mere Christianity” (Clive Staples Lewis) • “Liturgy of the Hours (Liturgia Horarum)” • “The Song of the Bird” (Anthony de Mello) • “Prayers (Prieres)” (Michel Quoist) • “Roman Missal (Missale Romanum)” • “Taize” (Brother Roger) • “Theologico-Political Treatise (Tractatus Theologico-Politicus)” (Benedictus de Spinoza) • “Heroes, Gods & Emperors from Roman Mythology” (Kerry Usher) • “Steps to Christ” (Ellen Gould White) • “Sadhana: A Way to God” (Anthony de Mello) 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 mère)”(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 Hausmärchen/Grimms Märchen)” (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 (TiesitkötämänSuomesta?)” (Seppo Sauri) • “The Global Challege (Le Dfi mondial)” (Jean-Jacques Servan-Schreiber) 240 APPENDIX C. APPENDICES FOR CHAPTER 5

• “Go Ask Alice” (Beatrice Sparks) • “The Art of War (Sunzi Bingfa/Sun Tzu Ping Fa)” (Sun Tzu) • “Democracy in America (De la démocratie en Amérique)” (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 bl¨uht 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) C.6. MOST TRANSLATED TITLES APPENDIX 241

• “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” (Albert Lehninger) • “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) 242 APPENDIX C. APPENDICES FOR CHAPTER 5

• “The One Minute Manager: The Quickest Way to Increase Your Own Prosperity” (Ken Blanchard and Spencer Johnson) • “The Tao of Love and Sex” (Jolan Chang) • “The Joy of Sex” (Alex Comfort) • “Diagnostic and Statistical Manual of Mental Disorders (DSM)” (The American Psychiatric Association (pub)) • “Fit for Life” (Harvey and Marilyn Diamond) • “Textbook of Medical Physiology” (Arthur Guyton and John E. Hall) • “Color Me Beautiful” (Carole Jackson) • “Folk Medicine: A Vermont Doctor’s Guide to Good Health” (DeForest Clinton Jarvis) • “Hugh Johnson’s Pocket Wine Book” (Hugh Johnson) • “The World Atlas of Wine” (Hugh Johnson) • “Ancient Secret of the Fountain of Youth” (Peter Kelder) • “The Complete Book of Pregnancy and Childbirth” (Sheila Kitzinger) • “Your Baby and Child” (Penelope Leach) • “The Care of House Plants” (David Longman) • “Yoga Self-Taught (J’apprends le Yoga)” (Andr Van Lysebeth) • “What’s Happening to Me?” (Peter Mayle) • “Men¨u,die sch¨onstenRezepte der Welt) • “The Peter Principle” (Laurence Peter) • “In Search of Excellence” (Thomas (Tom) Peters and Robert H. Waterman, Jr.) • “Dogs (Cani)” (Gino Pugnetti) • “Introduction to the Chakras” (Peter Rendel) • “The Man Who Mistook His Wife for a Hat and Other Clinical Tales” (Oliver Sacks) C.6. MOST TRANSLATED TITLES APPENDIX 243

• “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 tr¨adgard)”(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) 244 APPENDIX C. APPENDICES FOR CHAPTER 5

• “History of Art” (Horst Woldemar Janson) • “Techniques of the world’s great painters” (Waldemar Januszczak) • “Concerning the Spiritual in Art” (Wassily Kandinsky) • “Introducing Music” (Otto Karolyi) • “The Thames & Hudson Dictionary of Art Terms” (Edward Lucie-Smith) • “Architects’ Data (Bauentwurfslehre)” (Ernst Neufert) • “Renaissance and Renascences in Western Art” (Erwin Panofsky) • “Studies in Iconology” (Erwin Panofsky) • “On Architecture (De architectura)” (Marcus Vitruvius Pollio) • “What is the Name of This Book?” (Raymond Smullyan) • “The Book about Stretching” (Sven-Anders Solveborn) • “Hitchcock (Le Cinémaselon Alfred Hitchcock)” (Francois Truffaut) • “On Architecture (De architectura)” (Vitruvius) • “The Flying Dutchman (Der fliegende Holländer)”(Richard Wagner) • “Treatise on Painting (Trattato della pittura)” (Leonardo da Vinci)

Literature

• “Little Women” (Louisa May Alcott) • “The Divine Comedy (La Divina Commedia)” (Dante Alighieri) • “One Thousand and One Nights/Arabian Nights (Kitaab Alf Laila Wa-Laila)” • “Wuthering Heights” (Emily Bront) • “Alice in Wonderland” (Lewis Carroll) • “The Last of the Mohicans” (James Fenimore Cooper) • “The Life and Strange Surprizing Adventures of Robinson Crusoe of York, Mariner” (Daniel Defoe) • “Oliver Twist” (Charles Dickens) • “The Three Musketeers (Les Trois Mousquetaires)” (Alexandre Dumas, pre) C.6. MOST TRANSLATED TITLES APPENDIX 245

• “Madame Bovary” (Gustave Flaubert) • “The Prophet” (Khalil Gibran) • “Iliad (Ilias)” (Homer) • “The Odyssey” (Homer) • “The Metamorphosis (Die Verwandlung)” (Franz Kafka) • “The Jungle Book” (Rudyard Kipling) • “The Call of the Wild” (Jack London) • “White Fang” (Jack London) • “Moby-Dick; or, The Whale” (Herman Melville) • “Ivanhoe” (Sir Walter Scott) • “The Red and the Black (Le Rouge et le Noir)” (Stendhal) • “The Strange Case of Dr Jekyll and Mr Hyde” (Robert Louis Stevenson) • “Treasure Island” (Robert Louis Stevenson) • “Uncle Tom’s Cabin; or, Life Among the Lowly” (Harriet Beecher Stowe) • “Gulliver’s Travels/Travels into Several Remote Nations of the World, in Four Parts. By Lemuel Gulliver, First a Surgeon, and then a Captain of Several Ships” (Jonathan Swift) • “The Prince and the Pauper” (Mark Twain) • “The Adventures of Tom Sawyer” (Mark Twain) • “Journey to the Centre of the Earth” (Jules Verne) • “Michael Strogoﬀ: The Courier of the Czar (Michel Strogoﬀ)” (Jules Verne) • “Twenty Thousand Leagues Under the Sea (Vingt mille lieues sous les mers)” (Jules Verne) • “Aeneid (Aeneis)” (Virgil)

Geography, History and Biography

• “The Holy Blood and the Holy Grail” (Michael Baigent, Richard Leigh, and 246 APPENDIX C. APPENDICES FOR CHAPTER 5

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) • “Världshistoria: 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 liberté)”(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) C.6. MOST TRANSLATED TITLES APPENDIX 247

• “The Autobiography of Alice B. Toklas” (Gertrude Stein) • “Annals (Annales/Ab excessu divi Augusti)” (Tacitus) • “History of Rome (Ab urbe condita (libri))” (Livy) • “A Distant Mirror: The Calamitous 14th Century” (Barbara Tuchman) • “Julian” (Gore Vidal) • “Spain, a Brief History (Histoire de l’Espagne)” (Pierre Vilar) Bibliography

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