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The Role of Language in Shaping International Migration

The Role of Language in Shaping International Migration Abstract This article examines the importance of language in international migration from multiple angles by studying the role of linguistic proximity, widely spoken languages, linguistic enclaves and language‐based immigration policy requirements. To this aim we collect a unique data set on immigration flows and stocks in 30 OECD destinations from all world countries over the period 1980–2010 and construct a set of linguistic proximity measures. Migration rates increase with linguistic proximity and with English at destination. Softer linguistic requirements for naturalisation and larger linguistic communities at destination encourage more migrants to move. Linguistic proximity matters less when local linguistic networks are larger. Previous literature has shown that both fluency in the language of the destination country and the ability to learn it quickly play a key role in the transfer of existing human capital to foreign countries and generally boost immigrant’s success in destination countries’ labour markets, see Kossoudji (1988), Dustmann (1994), Dustmann and van Soest (2001, 2002), Chiswick and Miller (2002, 2007, 2010), Dustmann and Fabbri (2003) and Bleakley and Chin (2004). By exploiting differences in adult English proficiency between immigrants from non‐English‐speaking source countries who arrive as young children versus the others, Bleakley and Chin (2004, 2010) find that linguistic competence is a key variable in explaining immigrant’s disparities in terms of educational attainment, earnings and social outcomes. Recent studies show that it is easier for a foreigner to acquire a language if his native language is linguistically closer to the language to be learned (Chiswick and Miller, 2005; Isphording and Otten, 2011). This suggests that the ability to learn and speak a foreign language quickly might be an important factor in the potential migrants’ decision. Besides, a ‘widely‐spoken’ native language in the destination country can be a pull factor in international migration. Two different forces may explain that migration pattern. First, as some ‘widely spoken’ languages are often taught as second languages in schools in many source countries, immigrants are more likely to move to destinations where those languages are spoken in order to lower the costs associated with skill transferability. Second, foreign language proficiency may be valued in the labour market of the source country (European Commission, 2002). A recent article by Toomet (2011) finds that knowledge of English is associated with a 15% wage premium in the Estonian labour market. Thus, learning and practising ‘widely spoken’ languages in destination countries may serve as a pull factor especially for temporary migrants. Although the role of language in international migration is clearly important, this is, to our knowledge, the first study that disentangles this relationship from multiple angles by studying the role of linguistic proximity, widely spoken language, linguistic communities and language‐based policy requirements at destination. Previous evidence on the determinants of migration was limited to including a control for sharing a common language. Only two studies employ some more sophisticated linguistic measures. Belot and Hatton (2012) use the number of nodes on the linguistic tree between two languages to construct a linguistic proximity measure. Likewise, Belot and Ederveen (2012) employ the linguistic proximity index proposed by Dyen et al. (1992) to show that cultural barriers explain patterns of migration flows better than traditional economic variables in a sample limited to developed countries. In addition we importantly contribute to the literature on determinants of migration by collecting a unique data set on annual migration stocks and flows for 30 OECD destinations from all world source countries for the period 1980–2010. Moreover, we construct a new set of refined indicators of the linguistic proximity between two languages based on information from an encyclopaedia of languages (Ethnologue) and relate them to country‐pairs on the basis of either the first official, any other official or the major local language in each country. In the article, we first use the linguistic indices to examine the relevance of linguistic proximity between origin and destination countries in the decision to migrate and find that emigration rates are higher among countries whose languages are more similar. Migration flows to a country with the same first official language as opposed to one with the most distant language are around 20% higher in models that include a large set of socio‐economic and genetic distance controls as well as time and country dummies. The implied differences range from 19% to 35% when using instead either the distance between the major languages in each country or the maximum proximity between any of the official and main languages (if multiple) in both countries. This result is highly robust to the use of two alternative continuous measures of proximity developed by linguists: the Levenshtein distance, which relies on phonetic dissimilarity of words in two languages for all world languages, and the Dyen index, based on the similarity between samples of words among Indo‐European languages. Using these indices the implied increase in emigration rates to countries with similar language as opposite to linguistically distant countries ranges between 14% and 20%. In the context of other determinants of migration, our results show that the effect of linguistic proximity is larger than that of countries that are neighbours or share historical past and of higher (or lower) unemployment rates in origin (or destination) but smaller than the pull effect of income and ethnic networks in destinations. Finally, we use the information embedded in our linguistic proximity measure to study whether sharing a particular level of the linguistic tree matters incrementally more than another. Second, to investigate whether potential migrants prefer a destination with a ‘widely spoken’ language, such as English, as its local language, we estimate separate coefficients on linguistic distance for English and non‐English‐speaking destinations. We find that linguistic proximity matters more for the latter. Migrants’ pre‐migration exposure to English may temper the relevance of linguistic proximity when studying migration to English‐speaking destinations. Further, returns to English proficiency may be high in linguistically distant countries and in turn fuel temporary migration from those countries to English‐speaking destinations. We also explore these models separately for countries with low educational attainment. Finally, we find that stricter linguistic requirements for naturalisation deter migration flows whereas larger communities with similar linguistic background at destination encourage more migrants to move. These controls do not affect our main results on the linguistic proximity though linguistic distance matters less when the size of the local linguistic network is large. 1. A Model of International Migration To introduce our empirical specification we present a model of migration across different destinations. This model follows the ‘human capital investment’ theoretical framework (Sjastaad, 1962) and its recent applications in Ortega and Peri (2009) and Grogger and Hanson (2011). We assume that a potential immigrant maximising his utility chooses to locate in the country where his utility is the highest among all available destinations. The utility that migrant k, currently living in i, attains by moving to j is logarithmic and given by: Ukij=(ykj−ckij)λexp(εkij),(1) where ykj − ckij is the difference between income in destination j, ykj (which can be defined in line with Harris and Todaro (1970) as wage times the probability of finding a job, y = we) and the cost of migrating from the home country i to j, ckij. We can write the probability of individual k from country i choosing a country j among J possible destinations as: Pr(jk/ik)=PrUijk=max(Uki1,Uki2,…,UkiJ).(2) Assuming that ɛkij follows an i.i.d. extreme value distribution and λ > 0, and using the approximation that, ln(yj − cij) ≈ lnyj − (cij/yj), we apply the results in McFadden (1974) to write the log odds of migrating to destination country j versus staying in the source country i as follows: lnMijPi=lnmij≈λ(lnyj−lnyi)−λCij=λ(lnwkj+lnekj−lnwki−lneki)−λCij,(3) where Mij are flows of individuals from i to j; Pi are the stayers; mij is the emigration rate from i to j and Cij are migration costs expressed as a proportion of destination income, Cij = (cij/yj). The probability of migration depends on the difference between income related to staying at home country i or migrating abroad j adjusted for costs of migration (e.g. psychological and direct out‐of‐pocket costs and those associated with imperfect skill transferability). Equation (3) relies on the assumption that the relative probabilities of two alternative locations only depend on the characteristics of those two alternatives. Since the empirical analysis of our study includes only OECD destinations, we only need that the IIA holds for these countries (McFadden, 1974; Grogger and Hanson, 2011). As a way to test the IIA assumption across OECD destinations, we re‐estimate models by excluding one destination at a time. Results are stable and thus suggest that such an assumption is plausible here.1 2. Data Construction 2.1. International Migration Data For the analysis we have constructed a new data set on immigration flows and stocks of foreigners in 30 OECD destination countries from 223 source countries for the years 1980–2010. The data set was collected by writing to selected national statistical offices for the majority of the OECD countries to request detailed yearly information on immigration flows and foreign population stocks by source country in their respective country. For three countries, Korea, Mexico and Turkey (and partly Japan), we obtained the data from the OECD International Migration Database. The first version of the migration data set covered 22 OECD destination and 129 source countries over the period of years 1989–2000 (see Pedersen et al. 2008). For the purpose of this study we added data from eight additional destination countries – Czech Republic and Slovakia, Hungary, Poland, Ireland, Turkey, South Korea and Mexico – and extended the number of countries of origin to cover the entire world. Further, we prolonged the time period covered by the data to include the years 1980–9 and 2001–10. Our international migration data set presents substantial progress over that used in past research on determinants of migration.2 First, our data cover annually both migration flows and foreign population stocks.3 Second, the data are more comprehensive with respect to destinations, origins and time due to our own effort with data gathering from particular statistical offices. For an overview of comprehensiveness of observations of flows and stocks across all destination countries over time, see the Appendix Tables A1 and A2 respectively. It is apparent that the data become more comprehensive over time and thus missing observations become less of a problem for more recent years. In our data set, as in the other existing data sets, different countries use different definitions of an ‘immigrant’ and draw their migration statistics from different sources.4 In particular for foreign population stock, we preferably use the definition based on country of birth to determine the origin of migrants. See the Appendix Tables A3 and A4 for a detailed overview of definitions and sources for data on immigration flows and foreign population stock respectively. 2.2. Linguistic Distance We use three different linguistic distance indices for our analyses: (i) a newly constructed Linguistic proximity index, described below and based on information from Ethnologue; (ii) the Levenshtein distance developed by the Max Planck Institute for Evolutionary Anthropology; and (iii) the Dyen linguistic proximity measure proposed by Dyen et al. (1992). We create a measure that captures the linguistic proximity between two languages based on information from Ethnologue (Lewis, 2009). The linguistic proximity index ranges from 0 to 1 depending on how many levels of the linguistic family tree the languages of both the destination and the source country share. To construct the index, we first define a set of increasing weights: the first equal to 0.1 if two languages are related at the most aggregated linguistic tree level, for example Indo‐European versus Uralic (Finnish, Estonian, Hungarian); the second equal to 0.15 if two languages belong to the same second linguistic tree level, for example Germanic versus Slavic languages; the third equal to 0.20 if two languages belong to the same third linguistic tree level, for example Germanic West versus Germanic North languages; and the fourth equal to 0.25 if both languages belong to the same fourth level of linguistic tree family, for example Scandinavian West (Icelandic) versus Scandinavian East (Danish, Norwegian and Swedish), German versus English, or ItaloWest (Italian, French, Spanish, Catalan and Portuguese) versus RomanceEast (Romanian). Then, we construct the linguistic proximity index as a sum of those four weights to capture the maximum number of shared linguistic family tree’s branches; we set the index equal to 0 if two languages do not belong to any common language family and equal to 1 if the two countries have a common language. Thus, the linguistic proximity index equals 0.1 if two languages are only related at the most aggregated level of the linguistic, for example Indo‐European languages; 0.25 if two languages belong to the same first and second‐linguistic tree level, for example Germanic languages; 0.45 if two languages share up to the third linguistic tree level, for example Germanic North languages; and 0.7 if both languages share the first four levels, for example Scandinavian East (Danish, Norwegian and Swedish). We use this measure to build a matrix that contains metrics of proximity between any pair of languages from our destination–source pairs and provides a better adjusted and smoother indicator of proximity than the standard dummy for common language used in most of the literature. To link the linguistic proximity measure to country‐pairs we initially use the first official language in each country. Figure 1 shows the distribution of the linguistic proximity among country‐pairs employed in the baseline analysis of the study and for which we do not have missing observations in the control variables – the distribution is essentially the same without this restriction. Around 42.5% of country‐pair observations do not share any branch of the tree and 36% are only related at the most aggregated level. Only in around 4% of the observations do both countries share the same language, whereas the proportions of observations that are related at the second, third and fourth level of the linguistic tree stand at 8%, 6.7% and 2.7% respectively. Fig. 1. Open in new tabDownload slide Distribution of Country‐pairs by Linguistic Proximity Index Notes. The linguistic index equals 0 if two languages do not belong to any common language family, 0.1 if they are only related at the level 1; 0.25 at level 2; 0.45 at level 3 and 0.7 at level 4. The index equals 1 if the two countries have a common language. The sample includes country‐pairs in the baseline model in column (8) of Table 2. Unbalanced panel of 223 origin countries to 30 OECD destinations for period of 1980–2010. Fig. 1. Open in new tabDownload slide Distribution of Country‐pairs by Linguistic Proximity Index Notes. The linguistic index equals 0 if two languages do not belong to any common language family, 0.1 if they are only related at the level 1; 0.25 at level 2; 0.45 at level 3 and 0.7 at level 4. The index equals 1 if the two countries have a common language. The sample includes country‐pairs in the baseline model in column (8) of Table 2. Unbalanced panel of 223 origin countries to 30 OECD destinations for period of 1980–2010. Figure 2 presents more detailed information about the distribution of migration flows by the linguistic tree level the origin and destination country share. As seen in Table 1, on average, a total number of about 1,077 people migrate from specific origin to another OECD destination country per year. During the period of 1980–2010, there were in total about 110 million people migrating to another OECD country: among them about 14.6 million people migrated to countries that share the same first official language and about 40 million migrated to countries whose first official languages did not have any level in common with that of their country of origin. The largest proportion of migrants, around 45 million, migrated to countries whose languages share only the most aggregate linguistic tree family and about 1.6, 7 and 2.1 million to countries sharing the second, third and fourth level of the linguistic tree respectively. The overall pattern is not that different when looking at flows by major language spoken, though more migrants are moving to countries with major languages very distant from theirs. When all official and main languages are considered, the flows to destinations with a common language are strikingly large. Of course this is in part due common colonial past, which we take into account in our empirical specification.5 Table 1 Descriptive Statistics, Definitions and Sources Variables . Definition . Source . Observation . Mean . SD . Min . Max . Ln Emigration Rate Ln(migration inflow from i to j per source population) Own data collection, see Tables A1 and A3 100,519 −5.02481 2.5311 −14.041 4.7055 Flowij Migration inflow from i to j Own data collection, see Tables A1 and A3 102,472 1,077 6,874 0 946,167 Ln Stock of Migrants_t−1 Ln(foreign population stock from i in j per source population) t−1 Own data collection, see Tables A2 and A4 77,192 −3.1663 2.8741 −12.217 6.5749 Stockij Foreign population stock from i in j Own data collection, see Tables A2 and A4 82,277 18,922 169,793 0 12,100,000 Linguistic Proximity Linguistic proximity index between i and j countries using their main official language. Own calculation based on Ethnologue, see Data section 215,040 0.139397 0.24967 0 1 Linguistic Proximity All Linguistic proximity index set at the maximum proximity between two countries using any of their official and main languages Own calculation based on Ethnologue, see Data section 215,040 0.25353 0.32499 0 1 Linguistic Proximity Major Linguistic proximity index between i and j countries using language spoken by majority Own calculation based on Ethnologue, see Data section 215,040 0.08262 0.19175 0 1 Dyen Dyen linguistic proximity between i and j countries using their main official language based on the similarity of samples of words from each language Dyen et al.(1992) 100,608 414.3834 277.7419 110.6 1,000 Dyen All Dyen linguistic proximity set at the maximum proximity between two countries using any of their official and main languages Dyen et al.(1992) 147,264 490.5674 299.5442 112.8 1,000 Dyen Major Dyen linguistic proximity between i and j countries using language spoken by majority Dyen et al.(1992) 66,976 371.2698 260.8502 110.6 1,000 Levenshtein Levenshtein linguistic distance between i and j countries using their main official language Max Planck Institute for Evolutionary Anthropology 208,320 87.63829 23.59133 0 106.39 Levenshtein All Levenshtein linguistic distance set at the maximum proximity between two countries using any of their official and main languages Max Planck Institute for Evolutionary Anthropology 212,160 78.29216 30.35295 0 106.39 Levenshtein Major Levenshtein linguistic distance between i and j countries using language spoken by majority Max Planck Institute for Evolutionary Anthropology 194,880 91.60195 18.60136 0 106.4 Common Language A dummy for common language between i and j countries Own calculation based on Ethnologue, see Data section 215,040 0.0516369 0.22129 0 1 Ln Destination GDPperCap_t−1 Ln GDP per capita, PPP (constant 2005 international $) in destination j, t−1 WDI, World Bank 196,224 10.0149 0.43263 8.6204 11.2134 Ln Origin GDPperCap_t−1 Ln GDP per capita, PPP (const 2005 international $) in origin i, t−1 WDI, World Bank 151,770 8.5001 1.2706 4.9418 11.7221 Ln (Origin GDPperCap_t−1)2 Ln GDP per capita, PPP (const 2005 intern $) in origin i squared, t−1 WDI, World Bank 151,770 73.8664 21.5499 24.4212 137.4071 Ln Public Expenditure Ln Public social expenditure as a percentage of GDP in destination j, t−1 OECD SOCX Database 184,576 2.8722 0.4834 0.5306 3.5752 Ln Destination UnemplRate_t−1 Ln Unemployment, total (% of total labour force) in destination j, t−1 WDI, World Bank 173,152 1.8385 0.5532 0.4055 3.1739 lnsubUj1 Unemployment rate in j, t−1, substituted by country average in case of missing values WDI, World Bank and own calculation 208,320 1.8538 0.5436 0.4055 3.1739 Ujmissing A dummy indicating a missing value for unemployment rate in j WDI, World Bank and own calculation 215,040 0.1948 0.3960 0 1 Ln Origin UnemplRate_t−1 Ln Unemployment, total (% of total labour force) in origin i, t−1 WDI, World Bank 74,460 1.9603 0.7094 −1.6094 4.0860 lnsubUi1 Unemployment rate in i, t−1, substituted by country average in case of missing values WDI, World Bank and own calculation 174,840 1.9796 0.8117 −1.6094 4.0860 Uimissing A dummy indicating a missing value for unemployment rate in i WDI, World Bank and own calculation 215,040 0.6537 0.4758 0 1 Ln Population Ratio, t−1 Ln Share of population in destination j per population in country i, t−1 WDI, World Bank 196,740 8.4127 2.8426 −3.0912 17.2586 Ln Distance in kilometres Ln Distance between capitals of destination j and origin i in km Own extension of CEPII 212,128 8.5916 0.8880 2.2741 9.8839 Neighbouring Dummy Dummy variable for neighbouring countries Own extension of CEPII 215,040 0.0183 0.1341 0 1 Historical Past Dummy Dummy variable for countries ever in colonial relationship Own extension of Rose (2004) 215,040 0.0202 0.1408 0 1 Ln Origin Political Rights_t−1 Ln of Freedom House Index – Political Rights in origin i Freedom in the World Scores 167,880 1.0700 0.7461 0 1.9459 Ln Origin Civil Rights_t−1 Ln of Freedom House Index – Civil Liberties in origin i Freedom in the World Scores 167,880 1.1379 0.6456 0 1.9459 Dominant Genetic Distance Dominant genetic distance between plurality groups, current match Spolaore and Wacziarg (2009) 207,360 933.9762 720.1979 0 2,760 Weighted Genetic Distance Weighted genetic distance, current match Spolaore and Wacziarg (2009) 184,512 941.7764 651.6594 0 2,777.695 Variables . Definition . Source . Observation . Mean . SD . Min . Max . Ln Emigration Rate Ln(migration inflow from i to j per source population) Own data collection, see Tables A1 and A3 100,519 −5.02481 2.5311 −14.041 4.7055 Flowij Migration inflow from i to j Own data collection, see Tables A1 and A3 102,472 1,077 6,874 0 946,167 Ln Stock of Migrants_t−1 Ln(foreign population stock from i in j per source population) t−1 Own data collection, see Tables A2 and A4 77,192 −3.1663 2.8741 −12.217 6.5749 Stockij Foreign population stock from i in j Own data collection, see Tables A2 and A4 82,277 18,922 169,793 0 12,100,000 Linguistic Proximity Linguistic proximity index between i and j countries using their main official language. Own calculation based on Ethnologue, see Data section 215,040 0.139397 0.24967 0 1 Linguistic Proximity All Linguistic proximity index set at the maximum proximity between two countries using any of their official and main languages Own calculation based on Ethnologue, see Data section 215,040 0.25353 0.32499 0 1 Linguistic Proximity Major Linguistic proximity index between i and j countries using language spoken by majority Own calculation based on Ethnologue, see Data section 215,040 0.08262 0.19175 0 1 Dyen Dyen linguistic proximity between i and j countries using their main official language based on the similarity of samples of words from each language Dyen et al.(1992) 100,608 414.3834 277.7419 110.6 1,000 Dyen All Dyen linguistic proximity set at the maximum proximity between two countries using any of their official and main languages Dyen et al.(1992) 147,264 490.5674 299.5442 112.8 1,000 Dyen Major Dyen linguistic proximity between i and j countries using language spoken by majority Dyen et al.(1992) 66,976 371.2698 260.8502 110.6 1,000 Levenshtein Levenshtein linguistic distance between i and j countries using their main official language Max Planck Institute for Evolutionary Anthropology 208,320 87.63829 23.59133 0 106.39 Levenshtein All Levenshtein linguistic distance set at the maximum proximity between two countries using any of their official and main languages Max Planck Institute for Evolutionary Anthropology 212,160 78.29216 30.35295 0 106.39 Levenshtein Major Levenshtein linguistic distance between i and j countries using language spoken by majority Max Planck Institute for Evolutionary Anthropology 194,880 91.60195 18.60136 0 106.4 Common Language A dummy for common language between i and j countries Own calculation based on Ethnologue, see Data section 215,040 0.0516369 0.22129 0 1 Ln Destination GDPperCap_t−1 Ln GDP per capita, PPP (constant 2005 international $) in destination j, t−1 WDI, World Bank 196,224 10.0149 0.43263 8.6204 11.2134 Ln Origin GDPperCap_t−1 Ln GDP per capita, PPP (const 2005 international $) in origin i, t−1 WDI, World Bank 151,770 8.5001 1.2706 4.9418 11.7221 Ln (Origin GDPperCap_t−1)2 Ln GDP per capita, PPP (const 2005 intern $) in origin i squared, t−1 WDI, World Bank 151,770 73.8664 21.5499 24.4212 137.4071 Ln Public Expenditure Ln Public social expenditure as a percentage of GDP in destination j, t−1 OECD SOCX Database 184,576 2.8722 0.4834 0.5306 3.5752 Ln Destination UnemplRate_t−1 Ln Unemployment, total (% of total labour force) in destination j, t−1 WDI, World Bank 173,152 1.8385 0.5532 0.4055 3.1739 lnsubUj1 Unemployment rate in j, t−1, substituted by country average in case of missing values WDI, World Bank and own calculation 208,320 1.8538 0.5436 0.4055 3.1739 Ujmissing A dummy indicating a missing value for unemployment rate in j WDI, World Bank and own calculation 215,040 0.1948 0.3960 0 1 Ln Origin UnemplRate_t−1 Ln Unemployment, total (% of total labour force) in origin i, t−1 WDI, World Bank 74,460 1.9603 0.7094 −1.6094 4.0860 lnsubUi1 Unemployment rate in i, t−1, substituted by country average in case of missing values WDI, World Bank and own calculation 174,840 1.9796 0.8117 −1.6094 4.0860 Uimissing A dummy indicating a missing value for unemployment rate in i WDI, World Bank and own calculation 215,040 0.6537 0.4758 0 1 Ln Population Ratio, t−1 Ln Share of population in destination j per population in country i, t−1 WDI, World Bank 196,740 8.4127 2.8426 −3.0912 17.2586 Ln Distance in kilometres Ln Distance between capitals of destination j and origin i in km Own extension of CEPII 212,128 8.5916 0.8880 2.2741 9.8839 Neighbouring Dummy Dummy variable for neighbouring countries Own extension of CEPII 215,040 0.0183 0.1341 0 1 Historical Past Dummy Dummy variable for countries ever in colonial relationship Own extension of Rose (2004) 215,040 0.0202 0.1408 0 1 Ln Origin Political Rights_t−1 Ln of Freedom House Index – Political Rights in origin i Freedom in the World Scores 167,880 1.0700 0.7461 0 1.9459 Ln Origin Civil Rights_t−1 Ln of Freedom House Index – Civil Liberties in origin i Freedom in the World Scores 167,880 1.1379 0.6456 0 1.9459 Dominant Genetic Distance Dominant genetic distance between plurality groups, current match Spolaore and Wacziarg (2009) 207,360 933.9762 720.1979 0 2,760 Weighted Genetic Distance Weighted genetic distance, current match Spolaore and Wacziarg (2009) 184,512 941.7764 651.6594 0 2,777.695 Open in new tab Table 1 Descriptive Statistics, Definitions and Sources Variables . Definition . Source . Observation . Mean . SD . Min . Max . Ln Emigration Rate Ln(migration inflow from i to j per source population) Own data collection, see Tables A1 and A3 100,519 −5.02481 2.5311 −14.041 4.7055 Flowij Migration inflow from i to j Own data collection, see Tables A1 and A3 102,472 1,077 6,874 0 946,167 Ln Stock of Migrants_t−1 Ln(foreign population stock from i in j per source population) t−1 Own data collection, see Tables A2 and A4 77,192 −3.1663 2.8741 −12.217 6.5749 Stockij Foreign population stock from i in j Own data collection, see Tables A2 and A4 82,277 18,922 169,793 0 12,100,000 Linguistic Proximity Linguistic proximity index between i and j countries using their main official language. Own calculation based on Ethnologue, see Data section 215,040 0.139397 0.24967 0 1 Linguistic Proximity All Linguistic proximity index set at the maximum proximity between two countries using any of their official and main languages Own calculation based on Ethnologue, see Data section 215,040 0.25353 0.32499 0 1 Linguistic Proximity Major Linguistic proximity index between i and j countries using language spoken by majority Own calculation based on Ethnologue, see Data section 215,040 0.08262 0.19175 0 1 Dyen Dyen linguistic proximity between i and j countries using their main official language based on the similarity of samples of words from each language Dyen et al.(1992) 100,608 414.3834 277.7419 110.6 1,000 Dyen All Dyen linguistic proximity set at the maximum proximity between two countries using any of their official and main languages Dyen et al.(1992) 147,264 490.5674 299.5442 112.8 1,000 Dyen Major Dyen linguistic proximity between i and j countries using language spoken by majority Dyen et al.(1992) 66,976 371.2698 260.8502 110.6 1,000 Levenshtein Levenshtein linguistic distance between i and j countries using their main official language Max Planck Institute for Evolutionary Anthropology 208,320 87.63829 23.59133 0 106.39 Levenshtein All Levenshtein linguistic distance set at the maximum proximity between two countries using any of their official and main languages Max Planck Institute for Evolutionary Anthropology 212,160 78.29216 30.35295 0 106.39 Levenshtein Major Levenshtein linguistic distance between i and j countries using language spoken by majority Max Planck Institute for Evolutionary Anthropology 194,880 91.60195 18.60136 0 106.4 Common Language A dummy for common language between i and j countries Own calculation based on Ethnologue, see Data section 215,040 0.0516369 0.22129 0 1 Ln Destination GDPperCap_t−1 Ln GDP per capita, PPP (constant 2005 international $) in destination j, t−1 WDI, World Bank 196,224 10.0149 0.43263 8.6204 11.2134 Ln Origin GDPperCap_t−1 Ln GDP per capita, PPP (const 2005 international $) in origin i, t−1 WDI, World Bank 151,770 8.5001 1.2706 4.9418 11.7221 Ln (Origin GDPperCap_t−1)2 Ln GDP per capita, PPP (const 2005 intern $) in origin i squared, t−1 WDI, World Bank 151,770 73.8664 21.5499 24.4212 137.4071 Ln Public Expenditure Ln Public social expenditure as a percentage of GDP in destination j, t−1 OECD SOCX Database 184,576 2.8722 0.4834 0.5306 3.5752 Ln Destination UnemplRate_t−1 Ln Unemployment, total (% of total labour force) in destination j, t−1 WDI, World Bank 173,152 1.8385 0.5532 0.4055 3.1739 lnsubUj1 Unemployment rate in j, t−1, substituted by country average in case of missing values WDI, World Bank and own calculation 208,320 1.8538 0.5436 0.4055 3.1739 Ujmissing A dummy indicating a missing value for unemployment rate in j WDI, World Bank and own calculation 215,040 0.1948 0.3960 0 1 Ln Origin UnemplRate_t−1 Ln Unemployment, total (% of total labour force) in origin i, t−1 WDI, World Bank 74,460 1.9603 0.7094 −1.6094 4.0860 lnsubUi1 Unemployment rate in i, t−1, substituted by country average in case of missing values WDI, World Bank and own calculation 174,840 1.9796 0.8117 −1.6094 4.0860 Uimissing A dummy indicating a missing value for unemployment rate in i WDI, World Bank and own calculation 215,040 0.6537 0.4758 0 1 Ln Population Ratio, t−1 Ln Share of population in destination j per population in country i, t−1 WDI, World Bank 196,740 8.4127 2.8426 −3.0912 17.2586 Ln Distance in kilometres Ln Distance between capitals of destination j and origin i in km Own extension of CEPII 212,128 8.5916 0.8880 2.2741 9.8839 Neighbouring Dummy Dummy variable for neighbouring countries Own extension of CEPII 215,040 0.0183 0.1341 0 1 Historical Past Dummy Dummy variable for countries ever in colonial relationship Own extension of Rose (2004) 215,040 0.0202 0.1408 0 1 Ln Origin Political Rights_t−1 Ln of Freedom House Index – Political Rights in origin i Freedom in the World Scores 167,880 1.0700 0.7461 0 1.9459 Ln Origin Civil Rights_t−1 Ln of Freedom House Index – Civil Liberties in origin i Freedom in the World Scores 167,880 1.1379 0.6456 0 1.9459 Dominant Genetic Distance Dominant genetic distance between plurality groups, current match Spolaore and Wacziarg (2009) 207,360 933.9762 720.1979 0 2,760 Weighted Genetic Distance Weighted genetic distance, current match Spolaore and Wacziarg (2009) 184,512 941.7764 651.6594 0 2,777.695 Variables . Definition . Source . Observation . Mean . SD . Min . Max . Ln Emigration Rate Ln(migration inflow from i to j per source population) Own data collection, see Tables A1 and A3 100,519 −5.02481 2.5311 −14.041 4.7055 Flowij Migration inflow from i to j Own data collection, see Tables A1 and A3 102,472 1,077 6,874 0 946,167 Ln Stock of Migrants_t−1 Ln(foreign population stock from i in j per source population) t−1 Own data collection, see Tables A2 and A4 77,192 −3.1663 2.8741 −12.217 6.5749 Stockij Foreign population stock from i in j Own data collection, see Tables A2 and A4 82,277 18,922 169,793 0 12,100,000 Linguistic Proximity Linguistic proximity index between i and j countries using their main official language. Own calculation based on Ethnologue, see Data section 215,040 0.139397 0.24967 0 1 Linguistic Proximity All Linguistic proximity index set at the maximum proximity between two countries using any of their official and main languages Own calculation based on Ethnologue, see Data section 215,040 0.25353 0.32499 0 1 Linguistic Proximity Major Linguistic proximity index between i and j countries using language spoken by majority Own calculation based on Ethnologue, see Data section 215,040 0.08262 0.19175 0 1 Dyen Dyen linguistic proximity between i and j countries using their main official language based on the similarity of samples of words from each language Dyen et al.(1992) 100,608 414.3834 277.7419 110.6 1,000 Dyen All Dyen linguistic proximity set at the maximum proximity between two countries using any of their official and main languages Dyen et al.(1992) 147,264 490.5674 299.5442 112.8 1,000 Dyen Major Dyen linguistic proximity between i and j countries using language spoken by majority Dyen et al.(1992) 66,976 371.2698 260.8502 110.6 1,000 Levenshtein Levenshtein linguistic distance between i and j countries using their main official language Max Planck Institute for Evolutionary Anthropology 208,320 87.63829 23.59133 0 106.39 Levenshtein All Levenshtein linguistic distance set at the maximum proximity between two countries using any of their official and main languages Max Planck Institute for Evolutionary Anthropology 212,160 78.29216 30.35295 0 106.39 Levenshtein Major Levenshtein linguistic distance between i and j countries using language spoken by majority Max Planck Institute for Evolutionary Anthropology 194,880 91.60195 18.60136 0 106.4 Common Language A dummy for common language between i and j countries Own calculation based on Ethnologue, see Data section 215,040 0.0516369 0.22129 0 1 Ln Destination GDPperCap_t−1 Ln GDP per capita, PPP (constant 2005 international $) in destination j, t−1 WDI, World Bank 196,224 10.0149 0.43263 8.6204 11.2134 Ln Origin GDPperCap_t−1 Ln GDP per capita, PPP (const 2005 international $) in origin i, t−1 WDI, World Bank 151,770 8.5001 1.2706 4.9418 11.7221 Ln (Origin GDPperCap_t−1)2 Ln GDP per capita, PPP (const 2005 intern $) in origin i squared, t−1 WDI, World Bank 151,770 73.8664 21.5499 24.4212 137.4071 Ln Public Expenditure Ln Public social expenditure as a percentage of GDP in destination j, t−1 OECD SOCX Database 184,576 2.8722 0.4834 0.5306 3.5752 Ln Destination UnemplRate_t−1 Ln Unemployment, total (% of total labour force) in destination j, t−1 WDI, World Bank 173,152 1.8385 0.5532 0.4055 3.1739 lnsubUj1 Unemployment rate in j, t−1, substituted by country average in case of missing values WDI, World Bank and own calculation 208,320 1.8538 0.5436 0.4055 3.1739 Ujmissing A dummy indicating a missing value for unemployment rate in j WDI, World Bank and own calculation 215,040 0.1948 0.3960 0 1 Ln Origin UnemplRate_t−1 Ln Unemployment, total (% of total labour force) in origin i, t−1 WDI, World Bank 74,460 1.9603 0.7094 −1.6094 4.0860 lnsubUi1 Unemployment rate in i, t−1, substituted by country average in case of missing values WDI, World Bank and own calculation 174,840 1.9796 0.8117 −1.6094 4.0860 Uimissing A dummy indicating a missing value for unemployment rate in i WDI, World Bank and own calculation 215,040 0.6537 0.4758 0 1 Ln Population Ratio, t−1 Ln Share of population in destination j per population in country i, t−1 WDI, World Bank 196,740 8.4127 2.8426 −3.0912 17.2586 Ln Distance in kilometres Ln Distance between capitals of destination j and origin i in km Own extension of CEPII 212,128 8.5916 0.8880 2.2741 9.8839 Neighbouring Dummy Dummy variable for neighbouring countries Own extension of CEPII 215,040 0.0183 0.1341 0 1 Historical Past Dummy Dummy variable for countries ever in colonial relationship Own extension of Rose (2004) 215,040 0.0202 0.1408 0 1 Ln Origin Political Rights_t−1 Ln of Freedom House Index – Political Rights in origin i Freedom in the World Scores 167,880 1.0700 0.7461 0 1.9459 Ln Origin Civil Rights_t−1 Ln of Freedom House Index – Civil Liberties in origin i Freedom in the World Scores 167,880 1.1379 0.6456 0 1.9459 Dominant Genetic Distance Dominant genetic distance between plurality groups, current match Spolaore and Wacziarg (2009) 207,360 933.9762 720.1979 0 2,760 Weighted Genetic Distance Weighted genetic distance, current match Spolaore and Wacziarg (2009) 184,512 941.7764 651.6594 0 2,777.695 Open in new tab Fig. 2. Open in new tabDownload slide Distribution of Migration Flows by Linguistic Proximity Index Based on Ethnolinguistic Tree for Years 1980–2010 Notes. Migration flows are expressed in thousands of migrants. Unbalanced panel of 223 origin countries to 30 OECD destinations for period of 1980–2010. Fig. 2. Open in new tabDownload slide Distribution of Migration Flows by Linguistic Proximity Index Based on Ethnolinguistic Tree for Years 1980–2010 Notes. Migration flows are expressed in thousands of migrants. Unbalanced panel of 223 origin countries to 30 OECD destinations for period of 1980–2010. In addition to our index, we employ two continuous measures of linguistic distance between countries developed by linguists. The first one is the Levenshtein linguistic distance produced by the Max Planck Institute for Evolutionary Anthropology, which relies on phonetic dissimilarity of words in two languages. Linguists choose a core set of the 40 more common words across languages describing everyday life and items; then, express them in a phonetic transcription called ASJP code and finally compute the number of steps needed to move from one word expressed in one language to that same word expressed in the other language. For a detailed description of the method, see Bakker et al. (2009).6 In our country sample, the index ranges from 0 (when the two languages are the same) to a maximum of 106.39 (for the distance between Laos and Korea). The second one is a linguistic proximity measure proposed by Dyen et al. (1992), a group of linguists who built a continuous index between zero and 1,000 of the distance between Indo‐European languages based on the similarity of samples of words from each language. The index increases with similarity between languages and it is equal to 1,000 when the two languages are the same.7 The correlation between all linguistic indices is above 0.9, while the correlation of these indices with measures of the genetic distance of the population of two countries, a basic control included in our models below, ranges only between 0.13 and 0.06. 3. Empirical Model Specification On the basis of (3), our econometric model assumes that emigration rates to one destination are driven by differences in wages, employment rates between origin and destination countries, and the costs of migration: ln(mijt)=γ1+γ2ln(gdpjt−1)+γ3ln(gdpit−1)+γ4ln(ujt−1)+γ5ln(uit−1)+γ6ln(psejt−1)+γ7ln(sijt−1)+γ8Lij+γ9Dij+γ10FHit−1+γ11lrjt−1+γ12ln(pijt−1)+δj+δi+θt+εijt,(4) where mijt denotes gross flows of migrants from country i to country j divided by the population of the country of origin i at time t, where i = 1, … , 223; j = 1, … , 30 and t = 1, … , 31. As in previous studies we proxy wages by GDP per capita and employment prospects in the sending and receiving countries by unemployment rates, ujt and uit. In some models, we introduce the level of GDP per capita in the source country in a quadratic form, ln(gdpit−1)2, as a means to test for the non‐linearity effects found by previous works (Chiquiar and Hanson, 2005; Hatton and Williamson, 2005, 2011, Clark et al., 2007; Pedersen et al., 2008; Belot and Hatton, 2012; Docquier and Rapoport, 2012). The hypothesis behind this line of work is that extreme poverty constrains the ability to cover costs of migration and, as income levels rise beyond extreme poverty, migration increases. However, after GDP reaches a certain level, migration could again decrease because the economic incentives to migrate to other countries decline.8 In addition, Borjas (1999) argues that generous social security payment structures may play a role in migrants’ decision making. Potential emigrants take into account both the probability of being unemployed and the generosity of welfare benefits in the destination country that constitute a substitute of earnings during the period of job search.9 We include public social expenditure as percentage of GDP, psejt−1, as a proxy for the ‘welfare magnet’ among explanatory variables. We expect costs associated with migration to be larger with physical, cultural and linguistic distance between countries but to fall with the existence of migration networks (i.e. networks of family members, friends and people of the same origin that already live in a host country). In addition migration costs may depend on specific destination and origin factors (such as immigration laws in destinations or credit market constraints at origin). In our empirical specification, we use the total foreign population from country i living in country j per population of the source country i, sijt, to control for the network of migrants that has been shown to play an important role in lowering the direct and psychological migration costs (Massey et al., 1993; Munshi, 2003). Additionally, in the robustness analyses we control for the total stock of migrants with the same linguistic background as a migrant from a particular origin to be able to account for the effect of linguistic enclaves on the propensity to migrate. Matrix Lij includes measures of linguistic distance between countries described in IIIB to test the main hypothesis of the study, namely whether larger language barriers increase migration costs for an individual by setting hurdles to the transfer of her skills and her integration in the receiving society. Even with recent improvements in communication technologies, the continued globalisation of the economy and declining costs of transportation, physical and cultural distance are bound to raise the direct cost of migration. To control for the effect of distance, matrix Dij includes the following variables: Log Distance in Kilometres between the capital areas in the sending and receiving countries; Neighbour Country which takes a value of 1 if the two countries are neighbours; and Historical Past Dummy, with value 1 for countries ever sharing historical past. Past common history might decrease the cultural distance between countries, and increase the information available about the potential destination country. Further, we include measures of the genetic distance between populations of both countries in our regressions as an additional control for cultural factors that could be confounded with our linguistic distance indices,. These indices, provided by Roman Wacziarg, are based on the work by Cavalli‐Sforza et al. (1994) and have already been employed in other contexts to study, for example, cross‐country differences in development (Spolaore and Wacziarg, 2009). A detailed explanation of how the indices were constructed can be found in these two publications. The ‘dominant’ genetic distance measures, for each pair of countries, the distance between the ethnic groups with the largest shares of population in each country. It increases with the differences between two populations and takes a zero if the distributions of alleles in both populations are identical. In one model specification, we also include Log Trade Volume, which is defined as the (log) total trade values (both imports and exports) for all country‐pairs. The import and export value are collected from the Direction of Trade Statistics and are expressed in nominal US dollar prices. We expect that the business ties represented by the volume of trade to have (positive) effects on international migration. Moreover, this variable is often considered as an indicator of globalisation and cultural proximity. Matrix FHi includes a couple of indices from Freedom House, which aim to separately measure the degree of freedom in political rights and civil liberties in each country. Each variable takes on values from one to seven, from the highest degree of freedom to the lowest. Violated political rights and civil liberties may increase migration outflows in a given country. On the other hand, political restrictions may also impede outmigration. Further to account for differences on how much language matters for policy in each country, we build a time‐varying index on language requirements for naturalisation in each destination lrjt−1 as detailed in the results section. Finally, we include a variable that captures the relative population size in destination with respect to origin, pijt−1, in order to control for demographic developments. All variables used in the estimations, except dummy variables and the linguistic proximity indices, are expressed in logarithms. Table 1 contains definitions, sources and summary statistics of all variables. In order to account for what information was available to the potential migrant at the time the migration decision was made, the relative differences in economic development and employment between origin and destination countries are lagged by one period. More importantly, there might be a problem of reverse causality if migration flows impact both earnings and employment. Lagging the economic explanatory variables and treating them as predetermined is one way to reduce the risks of reverse causality in the model. Since the stock is just a function of previous stock plus migration flows minus out‐migration, we also lag it and assume that the lagged stock is predetermined with respect to the current migration flows. All specifications contain a set of year dummies, θt, in order to control for common idiosyncratic shocks over the time period and robust Hubert/White/sandwich standard errors clustered at each pair of destination and source countries. Models also contain country of destination and country of origin fixed effects, δj and δi, separately to capture unobserved characteristics of immigration policy practices in each destination country, credit market constraints in origins, as well as climate, openness towards foreigners or culture in each country, among other things. Some previous literature includes pair‐wise fixed effects to capture (unobserved) traditions, historical and cultural ties between a particular pair of destination and origin countries. We cannot include them here since they would be collinear with our linguistic proximity variables of interest. However, as described above, we use a number of explanatory variables that help to control for the historical and cultural ties between countries. We add a one to each observation of immigration flows and foreign population stocks prior to constructing emigration and stock rates, so that once taking logarithms we do not discard the ‘zero’ observations (only around 4.5% in our data).10 Even though most previous studies on migration determinants have used linear models with log‐transformed variable, a few have chosen count models to fit the non‐negative dependent variable (e.g. Belot and Ederveen, 2012 use negative binomial; Simpson and Sparber, 2013 use Tobit and Poisson count models). In Table 2 below we present Poisson estimates of our baseline model in column (9).11 Table 2 Language Proximity and Migration Rates to 30 OECD Destination Countries from All World Countries of Origin for 1980–2010 Variables . OLS . OLS . OLS . FE . FE . FE . FE . FE . Betas . Poisson . FE . FE . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . (9) . (10) . (11) . (12) . Linguistic Proximity 3.271*** – 3.343*** 0.774*** 0.766*** 0.960*** 0.732*** 0.209*** 0.020*** 0.508*** 0.222*** 0.201*** (0.147) (0.215) (0.123) (0.123) (0.146) (0.123) (0.066) (0.121) (0.084) (0.065) Common Language Dummy 2.929*** −0.095 – – – – – – – −0.019 – (0.169) (0.254) (0.094) Ln Stock of Migrants_t−1 – – – – – – – 0.669*** 0.760*** 0.693*** 0.669*** 0.659*** (0.009) (0.020) (0.009) (0.009) Ln Destination GDPperCapPPPj_t−1 – – – 0.564*** 0.570*** 0.803*** 0.916*** 1.723*** 0.202*** 1.608*** 1.723*** 1.633*** (0.135) (0.134) (0.229) (0.165) (0.132) (0.481) (0.132) (0.134) Ln Origin GDPperCapPPPi_t−1 – – – −0.268*** 1.353*** 1.087** 1.404*** 0.072 0.037 0.224 0.071 0.199 (0.051) (0.286) (0.547) (0.315) (0.267) (0.695) (0.267) (0.277) Ln (Origin GDPperCapPPPi_t−1)2 – – – – −0.102*** −0.050 −0.104*** −0.011 −0.097 −0.016 −0.011 −0.028* (0.017) (0.031) (0.019) (0.016) (0.045) (0.016) (0.017) Ln Destination Public Social Expenditure_t−1 – – – – – 0.315** 0.384*** 0.576*** 0.056*** −0.020 0.577*** 0.566*** (0.142) (0.112) (0.101) (0.282) (0.101) (0.099) Ln Destination UnemplRate_t−1 – – – – – −0.064* −0.078*** −0.051** −0.010** −0.141** −0.051** −0.027 (0.036) (0.028) (0.025) (0.068) (0.025) (0.026) Ln Origin UnemplRate_t−1 – – – – – 0.124*** 0.030 0.054*** 0.017*** 0.191*** 0.054*** 0.049** (0.026) (0.024) (0.021) (0.054) (0.021) (0.021) lnTrade_t−1 – – – – – – – – – – – 0.122*** (0.011) Ln Population Ratio_t−1 – – – 1.218*** 1.439*** 1.168*** 1.330*** 0.582*** 0.550*** −0.081 0.583*** 0.630*** (0.101) (0.112) (0.172) (0.119) (0.101) (0.288) (0.101) (0.102) Ln Distance in kilometres – – – −1.069*** −1.071*** −1.004*** −1.084*** −0.390*** −0.145*** −0.377*** −0.390*** −0.257*** (0.048) (0.048) (0.048) (0.049) (0.030) (0.044) (0.030) (0.030) Neighbouring Dummy – – – 0.230 0.227 0.193 0.134 −0.198** −0.122 −0.198** −0.226*** (0.171) (0.171) (0.158) (0.166) (0.082) (0.127) (0.082) (0.081) Historical Past Dummy – – – 1.942*** 1.943*** 1.427*** 1.899*** 0.261*** 0.075 0.266*** 0.154* (0.181) (0.181) (0.195) (0.184) (0.092) (0.100) (0.098) (0.088) Dominant Genetic Distance – – – −0.0002** −0.0002** −0.00003 −0.0002* 0.00003 0.009 0.001*** 0.00003 0.00002 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Ln Origin Freedom Political Rightsi_t−1 – – – 0.027 0.040 0.146*** 0.022 0.017 0.005 0.094 0.017 0.025 (0.027) (0.027) (0.037) (0.029) (0.023) (0.101) (0.023) (0.023) Ln Origin Freedom Civil Rightsi_t−1 – – – −0.084** −0.087*** −0.174*** −0.135*** −0.074*** −0.019*** −0.063 −0.074*** −0.061** (0.034) (0.033) (0.041) (0.036) (0.028) (0.070) (0.028) (0.028) Dummies for substituted unemployment No No No No No No Yes Yes Yes Yes Yes Yes Destination & Origin FE No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant −5.574*** −5.257*** −5.580*** −6.772*** −14.830*** −18.742*** −18.621*** −23.576*** −5.613 −23.577*** −25.040*** (0.044) (0.040) (0.046) (1.684) (2.249) (4.028) (2.616) (2.167) (6.581) (2.167) (2.248) Observations 100,519 100,519 100,519 84,874 84,874 41,621 74,797 51,257 51,257 51,257 51,257 50,561 Adjusted R2 0.111 0.076 0.111 0.756 0.756 0.774 0.764 0.899 0.899 0.899 0.902 Variables . OLS . OLS . OLS . FE . FE . FE . FE . FE . Betas . Poisson . FE . FE . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . (9) . (10) . (11) . (12) . Linguistic Proximity 3.271*** – 3.343*** 0.774*** 0.766*** 0.960*** 0.732*** 0.209*** 0.020*** 0.508*** 0.222*** 0.201*** (0.147) (0.215) (0.123) (0.123) (0.146) (0.123) (0.066) (0.121) (0.084) (0.065) Common Language Dummy 2.929*** −0.095 – – – – – – – −0.019 – (0.169) (0.254) (0.094) Ln Stock of Migrants_t−1 – – – – – – – 0.669*** 0.760*** 0.693*** 0.669*** 0.659*** (0.009) (0.020) (0.009) (0.009) Ln Destination GDPperCapPPPj_t−1 – – – 0.564*** 0.570*** 0.803*** 0.916*** 1.723*** 0.202*** 1.608*** 1.723*** 1.633*** (0.135) (0.134) (0.229) (0.165) (0.132) (0.481) (0.132) (0.134) Ln Origin GDPperCapPPPi_t−1 – – – −0.268*** 1.353*** 1.087** 1.404*** 0.072 0.037 0.224 0.071 0.199 (0.051) (0.286) (0.547) (0.315) (0.267) (0.695) (0.267) (0.277) Ln (Origin GDPperCapPPPi_t−1)2 – – – – −0.102*** −0.050 −0.104*** −0.011 −0.097 −0.016 −0.011 −0.028* (0.017) (0.031) (0.019) (0.016) (0.045) (0.016) (0.017) Ln Destination Public Social Expenditure_t−1 – – – – – 0.315** 0.384*** 0.576*** 0.056*** −0.020 0.577*** 0.566*** (0.142) (0.112) (0.101) (0.282) (0.101) (0.099) Ln Destination UnemplRate_t−1 – – – – – −0.064* −0.078*** −0.051** −0.010** −0.141** −0.051** −0.027 (0.036) (0.028) (0.025) (0.068) (0.025) (0.026) Ln Origin UnemplRate_t−1 – – – – – 0.124*** 0.030 0.054*** 0.017*** 0.191*** 0.054*** 0.049** (0.026) (0.024) (0.021) (0.054) (0.021) (0.021) lnTrade_t−1 – – – – – – – – – – – 0.122*** (0.011) Ln Population Ratio_t−1 – – – 1.218*** 1.439*** 1.168*** 1.330*** 0.582*** 0.550*** −0.081 0.583*** 0.630*** (0.101) (0.112) (0.172) (0.119) (0.101) (0.288) (0.101) (0.102) Ln Distance in kilometres – – – −1.069*** −1.071*** −1.004*** −1.084*** −0.390*** −0.145*** −0.377*** −0.390*** −0.257*** (0.048) (0.048) (0.048) (0.049) (0.030) (0.044) (0.030) (0.030) Neighbouring Dummy – – – 0.230 0.227 0.193 0.134 −0.198** −0.122 −0.198** −0.226*** (0.171) (0.171) (0.158) (0.166) (0.082) (0.127) (0.082) (0.081) Historical Past Dummy – – – 1.942*** 1.943*** 1.427*** 1.899*** 0.261*** 0.075 0.266*** 0.154* (0.181) (0.181) (0.195) (0.184) (0.092) (0.100) (0.098) (0.088) Dominant Genetic Distance – – – −0.0002** −0.0002** −0.00003 −0.0002* 0.00003 0.009 0.001*** 0.00003 0.00002 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Ln Origin Freedom Political Rightsi_t−1 – – – 0.027 0.040 0.146*** 0.022 0.017 0.005 0.094 0.017 0.025 (0.027) (0.027) (0.037) (0.029) (0.023) (0.101) (0.023) (0.023) Ln Origin Freedom Civil Rightsi_t−1 – – – −0.084** −0.087*** −0.174*** −0.135*** −0.074*** −0.019*** −0.063 −0.074*** −0.061** (0.034) (0.033) (0.041) (0.036) (0.028) (0.070) (0.028) (0.028) Dummies for substituted unemployment No No No No No No Yes Yes Yes Yes Yes Yes Destination & Origin FE No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant −5.574*** −5.257*** −5.580*** −6.772*** −14.830*** −18.742*** −18.621*** −23.576*** −5.613 −23.577*** −25.040*** (0.044) (0.040) (0.046) (1.684) (2.249) (4.028) (2.616) (2.167) (6.581) (2.167) (2.248) Observations 100,519 100,519 100,519 84,874 84,874 41,621 74,797 51,257 51,257 51,257 51,257 50,561 Adjusted R2 0.111 0.076 0.111 0.756 0.756 0.774 0.764 0.899 0.899 0.899 0.902 Notes OLS estimates with and without fixed effects. Dependent variable: Ln(Emigration Rate). All models include year dummies. Robust standard errors clustered at the country‐pair level in parentheses. Columns (7)–(12) use substituted unemployment rates whenever there is a missing information on unemployment rates. Substituted unemployment indicates that missing unemployment rates were set at the mean per country. Column (8) shows beta coefficients of continuous variables in column (7). ***p < 0.01, **p < 0.05, *p < 0.1. Open in new tab Table 2 Language Proximity and Migration Rates to 30 OECD Destination Countries from All World Countries of Origin for 1980–2010 Variables . OLS . OLS . OLS . FE . FE . FE . FE . FE . Betas . Poisson . FE . FE . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . (9) . (10) . (11) . (12) . Linguistic Proximity 3.271*** – 3.343*** 0.774*** 0.766*** 0.960*** 0.732*** 0.209*** 0.020*** 0.508*** 0.222*** 0.201*** (0.147) (0.215) (0.123) (0.123) (0.146) (0.123) (0.066) (0.121) (0.084) (0.065) Common Language Dummy 2.929*** −0.095 – – – – – – – −0.019 – (0.169) (0.254) (0.094) Ln Stock of Migrants_t−1 – – – – – – – 0.669*** 0.760*** 0.693*** 0.669*** 0.659*** (0.009) (0.020) (0.009) (0.009) Ln Destination GDPperCapPPPj_t−1 – – – 0.564*** 0.570*** 0.803*** 0.916*** 1.723*** 0.202*** 1.608*** 1.723*** 1.633*** (0.135) (0.134) (0.229) (0.165) (0.132) (0.481) (0.132) (0.134) Ln Origin GDPperCapPPPi_t−1 – – – −0.268*** 1.353*** 1.087** 1.404*** 0.072 0.037 0.224 0.071 0.199 (0.051) (0.286) (0.547) (0.315) (0.267) (0.695) (0.267) (0.277) Ln (Origin GDPperCapPPPi_t−1)2 – – – – −0.102*** −0.050 −0.104*** −0.011 −0.097 −0.016 −0.011 −0.028* (0.017) (0.031) (0.019) (0.016) (0.045) (0.016) (0.017) Ln Destination Public Social Expenditure_t−1 – – – – – 0.315** 0.384*** 0.576*** 0.056*** −0.020 0.577*** 0.566*** (0.142) (0.112) (0.101) (0.282) (0.101) (0.099) Ln Destination UnemplRate_t−1 – – – – – −0.064* −0.078*** −0.051** −0.010** −0.141** −0.051** −0.027 (0.036) (0.028) (0.025) (0.068) (0.025) (0.026) Ln Origin UnemplRate_t−1 – – – – – 0.124*** 0.030 0.054*** 0.017*** 0.191*** 0.054*** 0.049** (0.026) (0.024) (0.021) (0.054) (0.021) (0.021) lnTrade_t−1 – – – – – – – – – – – 0.122*** (0.011) Ln Population Ratio_t−1 – – – 1.218*** 1.439*** 1.168*** 1.330*** 0.582*** 0.550*** −0.081 0.583*** 0.630*** (0.101) (0.112) (0.172) (0.119) (0.101) (0.288) (0.101) (0.102) Ln Distance in kilometres – – – −1.069*** −1.071*** −1.004*** −1.084*** −0.390*** −0.145*** −0.377*** −0.390*** −0.257*** (0.048) (0.048) (0.048) (0.049) (0.030) (0.044) (0.030) (0.030) Neighbouring Dummy – – – 0.230 0.227 0.193 0.134 −0.198** −0.122 −0.198** −0.226*** (0.171) (0.171) (0.158) (0.166) (0.082) (0.127) (0.082) (0.081) Historical Past Dummy – – – 1.942*** 1.943*** 1.427*** 1.899*** 0.261*** 0.075 0.266*** 0.154* (0.181) (0.181) (0.195) (0.184) (0.092) (0.100) (0.098) (0.088) Dominant Genetic Distance – – – −0.0002** −0.0002** −0.00003 −0.0002* 0.00003 0.009 0.001*** 0.00003 0.00002 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Ln Origin Freedom Political Rightsi_t−1 – – – 0.027 0.040 0.146*** 0.022 0.017 0.005 0.094 0.017 0.025 (0.027) (0.027) (0.037) (0.029) (0.023) (0.101) (0.023) (0.023) Ln Origin Freedom Civil Rightsi_t−1 – – – −0.084** −0.087*** −0.174*** −0.135*** −0.074*** −0.019*** −0.063 −0.074*** −0.061** (0.034) (0.033) (0.041) (0.036) (0.028) (0.070) (0.028) (0.028) Dummies for substituted unemployment No No No No No No Yes Yes Yes Yes Yes Yes Destination & Origin FE No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant −5.574*** −5.257*** −5.580*** −6.772*** −14.830*** −18.742*** −18.621*** −23.576*** −5.613 −23.577*** −25.040*** (0.044) (0.040) (0.046) (1.684) (2.249) (4.028) (2.616) (2.167) (6.581) (2.167) (2.248) Observations 100,519 100,519 100,519 84,874 84,874 41,621 74,797 51,257 51,257 51,257 51,257 50,561 Adjusted R2 0.111 0.076 0.111 0.756 0.756 0.774 0.764 0.899 0.899 0.899 0.902 Variables . OLS . OLS . OLS . FE . FE . FE . FE . FE . Betas . Poisson . FE . FE . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . (9) . (10) . (11) . (12) . Linguistic Proximity 3.271*** – 3.343*** 0.774*** 0.766*** 0.960*** 0.732*** 0.209*** 0.020*** 0.508*** 0.222*** 0.201*** (0.147) (0.215) (0.123) (0.123) (0.146) (0.123) (0.066) (0.121) (0.084) (0.065) Common Language Dummy 2.929*** −0.095 – – – – – – – −0.019 – (0.169) (0.254) (0.094) Ln Stock of Migrants_t−1 – – – – – – – 0.669*** 0.760*** 0.693*** 0.669*** 0.659*** (0.009) (0.020) (0.009) (0.009) Ln Destination GDPperCapPPPj_t−1 – – – 0.564*** 0.570*** 0.803*** 0.916*** 1.723*** 0.202*** 1.608*** 1.723*** 1.633*** (0.135) (0.134) (0.229) (0.165) (0.132) (0.481) (0.132) (0.134) Ln Origin GDPperCapPPPi_t−1 – – – −0.268*** 1.353*** 1.087** 1.404*** 0.072 0.037 0.224 0.071 0.199 (0.051) (0.286) (0.547) (0.315) (0.267) (0.695) (0.267) (0.277) Ln (Origin GDPperCapPPPi_t−1)2 – – – – −0.102*** −0.050 −0.104*** −0.011 −0.097 −0.016 −0.011 −0.028* (0.017) (0.031) (0.019) (0.016) (0.045) (0.016) (0.017) Ln Destination Public Social Expenditure_t−1 – – – – – 0.315** 0.384*** 0.576*** 0.056*** −0.020 0.577*** 0.566*** (0.142) (0.112) (0.101) (0.282) (0.101) (0.099) Ln Destination UnemplRate_t−1 – – – – – −0.064* −0.078*** −0.051** −0.010** −0.141** −0.051** −0.027 (0.036) (0.028) (0.025) (0.068) (0.025) (0.026) Ln Origin UnemplRate_t−1 – – – – – 0.124*** 0.030 0.054*** 0.017*** 0.191*** 0.054*** 0.049** (0.026) (0.024) (0.021) (0.054) (0.021) (0.021) lnTrade_t−1 – – – – – – – – – – – 0.122*** (0.011) Ln Population Ratio_t−1 – – – 1.218*** 1.439*** 1.168*** 1.330*** 0.582*** 0.550*** −0.081 0.583*** 0.630*** (0.101) (0.112) (0.172) (0.119) (0.101) (0.288) (0.101) (0.102) Ln Distance in kilometres – – – −1.069*** −1.071*** −1.004*** −1.084*** −0.390*** −0.145*** −0.377*** −0.390*** −0.257*** (0.048) (0.048) (0.048) (0.049) (0.030) (0.044) (0.030) (0.030) Neighbouring Dummy – – – 0.230 0.227 0.193 0.134 −0.198** −0.122 −0.198** −0.226*** (0.171) (0.171) (0.158) (0.166) (0.082) (0.127) (0.082) (0.081) Historical Past Dummy – – – 1.942*** 1.943*** 1.427*** 1.899*** 0.261*** 0.075 0.266*** 0.154* (0.181) (0.181) (0.195) (0.184) (0.092) (0.100) (0.098) (0.088) Dominant Genetic Distance – – – −0.0002** −0.0002** −0.00003 −0.0002* 0.00003 0.009 0.001*** 0.00003 0.00002 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Ln Origin Freedom Political Rightsi_t−1 – – – 0.027 0.040 0.146*** 0.022 0.017 0.005 0.094 0.017 0.025 (0.027) (0.027) (0.037) (0.029) (0.023) (0.101) (0.023) (0.023) Ln Origin Freedom Civil Rightsi_t−1 – – – −0.084** −0.087*** −0.174*** −0.135*** −0.074*** −0.019*** −0.063 −0.074*** −0.061** (0.034) (0.033) (0.041) (0.036) (0.028) (0.070) (0.028) (0.028) Dummies for substituted unemployment No No No No No No Yes Yes Yes Yes Yes Yes Destination & Origin FE No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant −5.574*** −5.257*** −5.580*** −6.772*** −14.830*** −18.742*** −18.621*** −23.576*** −5.613 −23.577*** −25.040*** (0.044) (0.040) (0.046) (1.684) (2.249) (4.028) (2.616) (2.167) (6.581) (2.167) (2.248) Observations 100,519 100,519 100,519 84,874 84,874 41,621 74,797 51,257 51,257 51,257 51,257 50,561 Adjusted R2 0.111 0.076 0.111 0.756 0.756 0.774 0.764 0.899 0.899 0.899 0.902 Notes OLS estimates with and without fixed effects. Dependent variable: Ln(Emigration Rate). All models include year dummies. Robust standard errors clustered at the country‐pair level in parentheses. Columns (7)–(12) use substituted unemployment rates whenever there is a missing information on unemployment rates. Substituted unemployment indicates that missing unemployment rates were set at the mean per country. Column (8) shows beta coefficients of continuous variables in column (7). ***p < 0.01, **p < 0.05, *p < 0.1. Open in new tab 4. Results 4.1. Linguistic Proximity Table 2 first shows results from the most parsimonious model that only includes the linguistic proximity index and a constant to the full specification. Columns (1) and (2) in Table 2 show that our linguistic proximity index alone accounts for 11% of the variance in world migration rates, whereas the common language dummy used in the previous literature only explains around 7%. When both are included in the same model in column (3), only the linguistic proximity index is significant in a sample that encompasses around 100,000 observations. The remaining columns in Table 2 include the basic pull and push factors as well as country and time fixed effects as given by (4). The coefficient of linguistic proximity is positive and highly significant in all specifications. Thus, other things being equal, emigration flows between two countries are larger the closer their languages are. As expected, the coefficient decreases in size as more controls are added to the model; in particular it shrinks from around 0.73 to around 0.2 when migrant’s stocks are included in columns (7)–(12). The latter suggests that a large network from the same origin may alleviate the pressure of learning the local language to assimilate to the new labour markets and society. We test the effects of ‘linguistic networks’ on migration and linguistic proximity in depth in subsection 4.4. Column (6) adds measures of social expenditure in destination as well as unemployment rates in origin and destination. Source country unemployment rates impose the largest restriction with respect to the number of missing observations. By including unemployment variables the number of observations halves. In order to take advantage of the large sample of migration flows and stocks we have gathered, for the remaining models we create an alternative measure of unemployment where we substitute missing observations by the mean unemployment over the sample and include dummy indicators where unemployment is missing (columns 7–12). The coefficient of linguistic proximity in the model with migration stocks and unemployment in column (8), our baseline specification for the rest of the article, is 0.209 and it is highly significant at 1%. It implies that emigration flows to a country with the same language as opposed to a country with the most distant language should be around 20% higher, ceteris paribus. Thus, emigration rates to France from Benin where French is the first official language should be around 18% higher than those from Zambia’s with a linguistic index of 0.1 with respect to France or 6% higher than those from Sao Tome, with a linguistic index of 0.7, ceteris paribus.12 Similarly to previous studies such as Bauer et al. (2005), Clark et al. (2007), Pedersen et al. (2008), McKenzie and Rapoport (2010) and Beine et al. (2011) we find network effects to be an important determinant of subsequent migration. Results in column (8) indicate that a 10% increase in the stock of migrants from a certain country is associated with an increase of around 6.7% in the emigration rate from this country, ceteris paribus.13 Further a 10% increase in the GDP of the destination country is associated with an increase in emigration rates to that country of around 17%. While the level of GDP per capita in origin enters negatively and significantly in column (4) as expected, we test for the presence of non‐linearities found by the previous literature in the remaining columns of Table 2. Estimates in columns (5) and (7) conform to the hypothesis that emigration rates increase with per capita income for very low levels, in this case up to annual levels of income below $800, and then decrease as the economic incentives to migrate diminish.14 Once the stock of migrants from the country of origin is included in the models, migration rates clearly decrease with income per capita in the source country. A possible explanation for this finding is that the networks of friends and ethnic fellows can help to alleviate the poverty constraints to migrate as suggested by, for example, Hatton and Williamson (2005, 2011) and Pedersen et al. (2008). Emigration rates are significantly higher from countries with relatively high unemployment rates and lower to destinations with high unemployment, other things being the same. In line with the theoretical framework proposed by Borjas (1999), we find that the coefficients to public social expenditure are positive and significant. This runs contrary to some existent empirical evidence (Zavodny, 1997; Wadensjö, 2007; Pedersen et al., 2008; among others) and more in line with other works reviewed by Giulietti and Wahba (2013). At any rate social expenditures would only be relevant for migrants as long as they are entitled to receive them but some of the OECD countries provide universal benefits to anybody eligible regardless of nationality.15 Population ratio, common history and shorter distance are significantly associated with stronger emigration flows. In our preferred specification in column (8), having a past historical tie increases the emigration rates to a destination by around 26%. Lack of political liberties seems to increase outmigration but coefficients fail to be significant in most specifications. Conversely, controlling for political rights, emigration rates are larger from countries with better civil rights. Some of these rights are associated with lower barriers to out‐migration and geographic mobility. Finally, findings regarding linguistic proximity are robust to the inclusion of measures of genetic distance which are only significant and negative (as expected) in models that do not include stocks (columns 4–7) but not once network controls are added. This suggests that language on its own affects migration costs beyond any ease derived from moving to a destination where people may look or be culturally more similar to the migrant.16 Our findings are also robust to the inclusion of bilateral trade volume, in column (12), which is often considered an indicator of globalisation and cultural proximity. The volume of trade is associated with larger migration flows. To sum up, we find that linguistic proximity has an important role in migration. Sharing the same language versus not sharing any level of the linguistic family tree has an effect on immigration flows equivalent to an increase of 12% in destination country GDP. To get a better sense of the relative importance of the linguistic proximity compared to other pull and push factors, we include in column (9) the standardised beta coefficients of column (8) which give us a measure of the changes in standard deviations of migration rates resulting from a change of 1 SD in each factor. This is of course not a perfect measure since we may attach very different meaning to similarly relatively sized changes across different factors. An increase of 1 SD in the existing stock of migrants is associated with a 0.76 SD increase in migration rates. A similar increase in the income per capita of the destination country increases migration to this country by 0.2 SDs, whereas the implied impact of linguistic proximity is just a tenth of that, around 0.02 SDs. At any rate the impact of having closer languages is larger than that of countries having higher (or lower) unemployment rates in origin (or destination) but less than half of the pull implied from larger social expenditures in destination. 4.2. Robustness To test the robustness of our results, in Table 3 we use a set of alternative measures of linguistic distance. The first three columns in Table 3 present the baseline model estimated first with our index of linguistic proximity and then, in column (2) with the Levenshtein index (divided by 100) and in column (3) with the Dyen index (divided by 1,000), all calculated for the first official language in each country. Given that the Levenshtein index is defined in terms of distance as opposed to proximity between languages, the significant negative estimate in column (2) indicates that emigration rates are larger to countries with closer languages. The coefficient implies that emigration rates to countries with similar languages should be around 15% higher than to those with an index of around 100 (quite dissimilar). Similarly, in column (3) the coefficient for the Dyen index is significantly positive and implies that emigration rates from a country with the same language (and a Dyen index of 1,000) are around 18% larger than those from a country with a rather dissimilar language (the minimum of around 100 in our sample of Indo‐European languages). Because the Dyen index covers only Indo‐European languages, our number of observations in the most complete models presented in the article is reduced significantly from over 51,000 to only close to 28,000. It is interesting that the size of the implied effect with the Dyen is remarkably similar to those obtained with the other two indices, even though the sample is smaller and restricted to countries that are likely more homogenous. Table 3 Robustness Checks: Alternative Measures of Linguistic Proximity (Dyen and Levenshtein Linguistic Indexes and/or Controls for Multiple Official Languages) and Migration rates To OECD Countries Linguistic proximity/Distance measured by: . First official language . All official and main languages . Major language . Linguistic proximity . Levenshtein . Dyen . Ling. prox . Levenshtein . Dyen . Linguistic proximity . Levenshtein . Dyen . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . (9) . Linguistic Proximity 0.209*** −0.144* 0.203*** 0.192*** −0.199*** 0.333*** 0.355*** −0.218** 0.225** (0.066) (0.076) (0.077) (0.054) (0.058) (0.066) (0.085) (0.099) (0.096) z‐score [0.020]*** [−0.013]* [0.022]*** [0.024]*** [−0.023]*** [0.039]*** [0.027]*** [−0.016]** [0.023]** Constant −23.576*** −23.373*** −18.884*** −23.712*** −23.297*** −18.885*** −23.517*** −24.027*** −16.534*** (2.167) (2.201) (3.136) (2.171) (2.174) (2.495) (2.167) (2.231) (3.996) Observations 51,257 49,709 27,495 51,257 50,865 38,612 51,257 48,016 18,906 Adjusted R2 0.899 0.899 0.900 0.899 0.899 0.904 0.899 0.898 0.902 Linguistic proximity/Distance measured by: . First official language . All official and main languages . Major language . Linguistic proximity . Levenshtein . Dyen . Ling. prox . Levenshtein . Dyen . Linguistic proximity . Levenshtein . Dyen . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . (9) . Linguistic Proximity 0.209*** −0.144* 0.203*** 0.192*** −0.199*** 0.333*** 0.355*** −0.218** 0.225** (0.066) (0.076) (0.077) (0.054) (0.058) (0.066) (0.085) (0.099) (0.096) z‐score [0.020]*** [−0.013]* [0.022]*** [0.024]*** [−0.023]*** [0.039]*** [0.027]*** [−0.016]** [0.023]** Constant −23.576*** −23.373*** −18.884*** −23.712*** −23.297*** −18.885*** −23.517*** −24.027*** −16.534*** (2.167) (2.201) (3.136) (2.171) (2.174) (2.495) (2.167) (2.231) (3.996) Observations 51,257 49,709 27,495 51,257 50,865 38,612 51,257 48,016 18,906 Adjusted R2 0.899 0.899 0.900 0.899 0.899 0.904 0.899 0.898 0.902 Notes Dependent variable: Ln(Emigration Rate); numbers in brackets and italic show z‐scores (beta coefficients). Controls included: stock of migrants, economic variables, distance variables, year dummies and destination and origin country fixed effects. Robust standard errors clustered at the country‐pair level. ***p < 0.01, **p < 0.05, *p < 0.1. Open in new tab Table 3 Robustness Checks: Alternative Measures of Linguistic Proximity (Dyen and Levenshtein Linguistic Indexes and/or Controls for Multiple Official Languages) and Migration rates To OECD Countries Linguistic proximity/Distance measured by: . First official language . All official and main languages . Major language . Linguistic proximity . Levenshtein . Dyen . Ling. prox . Levenshtein . Dyen . Linguistic proximity . Levenshtein . Dyen . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . (9) . Linguistic Proximity 0.209*** −0.144* 0.203*** 0.192*** −0.199*** 0.333*** 0.355*** −0.218** 0.225** (0.066) (0.076) (0.077) (0.054) (0.058) (0.066) (0.085) (0.099) (0.096) z‐score [0.020]*** [−0.013]* [0.022]*** [0.024]*** [−0.023]*** [0.039]*** [0.027]*** [−0.016]** [0.023]** Constant −23.576*** −23.373*** −18.884*** −23.712*** −23.297*** −18.885*** −23.517*** −24.027*** −16.534*** (2.167) (2.201) (3.136) (2.171) (2.174) (2.495) (2.167) (2.231) (3.996) Observations 51,257 49,709 27,495 51,257 50,865 38,612 51,257 48,016 18,906 Adjusted R2 0.899 0.899 0.900 0.899 0.899 0.904 0.899 0.898 0.902 Linguistic proximity/Distance measured by: . First official language . All official and main languages . Major language . Linguistic proximity . Levenshtein . Dyen . Ling. prox . Levenshtein . Dyen . Linguistic proximity . Levenshtein . Dyen . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . (9) . Linguistic Proximity 0.209*** −0.144* 0.203*** 0.192*** −0.199*** 0.333*** 0.355*** −0.218** 0.225** (0.066) (0.076) (0.077) (0.054) (0.058) (0.066) (0.085) (0.099) (0.096) z‐score [0.020]*** [−0.013]* [0.022]*** [0.024]*** [−0.023]*** [0.039]*** [0.027]*** [−0.016]** [0.023]** Constant −23.576*** −23.373*** −18.884*** −23.712*** −23.297*** −18.885*** −23.517*** −24.027*** −16.534*** (2.167) (2.201) (3.136) (2.171) (2.174) (2.495) (2.167) (2.231) (3.996) Observations 51,257 49,709 27,495 51,257 50,865 38,612 51,257 48,016 18,906 Adjusted R2 0.899 0.899 0.900 0.899 0.899 0.904 0.899 0.898 0.902 Notes Dependent variable: Ln(Emigration Rate); numbers in brackets and italic show z‐scores (beta coefficients). Controls included: stock of migrants, economic variables, distance variables, year dummies and destination and origin country fixed effects. Robust standard errors clustered at the country‐pair level. ***p < 0.01, **p < 0.05, *p < 0.1. Open in new tab The coefficient of 0.203 in column (3) implies that the difference in emigration rates to an English‐speaking country from Nepal (with a Dyen of 157 with respect to English) as compared to those from Zambia (with a score of 1,000) should be around 17%. The difference between migrants from either Argentina (with an index of 240) or Austria (with an index of 578) with respect to someone from Zambia should be in order of 15% and 8.5% respectively.17 Table 3 includes one row with standardised coefficients for each model. They confirm the closeness of results across the three linguistic indices since 1 SD change in each of them results in between 0.022 and 0.013 SD changes in migration rates. Next, we extend the set of linguistic measures to take into account the existence of multiple official and main (first and second most‐spoken, even if not official) languages and calculate the maximum proximity between two countries using any of those languages. Figure 1 shows that the distribution for this index when measuring the linguistic proximity with the Ethno‐linguistic tree shifts towards more closeness between countries compared to the distribution when only first official languages are taken into account. Using all official and main languages, the percentage of country‐pair observations that have no branch in common shrinks to 22, while both countries share at least one common official language in 10% of the cases. The proportions are also higher at all other levels: over 38% at level 1; 14% at level 2; 8% at level 3 and 7% at level 4. The main reason for this change is that many former colonies retain the official language of their colonising power (i.e. English, French and Portuguese) among their official languages. The literature has shown that migrants from different linguistic backgrounds self‐select to different areas within destination countries with multiple languages according to the most widely used language in each area. Chiswick and Miller (1995), one of the most prominent examples of this line of research, show how migrants to Canada self‐select to the province whose language is closer to their own because that enhances their labour market returns. Finally, with the same methodology we construct an index of linguistic proximity using instead the language most extensively used in the country (the ‘major’ language) even if in some countries it is not among the official ones. Not surprisingly, the linguistic proximity index is equal to zero for the majority of country‐pairs (58%) and around 28% only share the first branch of the linguistic tree. Just 2% of the observations share a common major language and the proportions for the other levels are also lower than for first official languages (2.25% at level 4, 4% at level 3 and 5% at level 2). The coefficients of the linguistic proximity when using the two alternative criteria, shown in columns (4)–(9) of Table 3, are significant and positive. Yet the size of coefficients reveals some small differences with respect to indices based on the first official language. For our linguistic proximity index, the explained differences in emigration rates when using all official and main languages in column (4) and, particularly when using major languages in column (7) (where the standardised coefficient increases to 0.027), are a bit larger. When using the Levenshtein index, the coefficient is the largest when using major languages in column (8) (in part due to the fact that languages tend to be more dissimilar on average) but the standardised coefficient is the largest when taking into account all official languages in column (5). When looking at results with the Dyen it is important to understand that the size and composition of the sample varies tremendously. In fact the sample size when using all official languages in column (6) is twice as large as when using the major language in column (9), since many of the latter are not Indo‐European. The standardised coefficient is the largest in column (6), where a 1 SD change in the Dyen implies an increase of almost 0.04 SDs in migration rates.18 4.3. The Role of Widely Spoken Languages Our linguistic proximity index does not capture the importance of some widely spoken Indo‐European languages (particularly English) in the media, in business or as a choice of second language in schools (see the Eurobarometer study on languages by European Commission, 2006). Further, if foreign language proficiency is an important part of human capital in the labour market of source countries (European Commission, 2002 on language proficiency as an essential skill for finding a job in home countries), returns to proficiency in widely spoken languages may be particularly high in countries which are linguistically distant from the widely spoken language. Thus, learning, practising and improving the skills of ‘widely spoken’ languages in ‘native’ countries could serve as a pull factor, especially for temporary migrants who take this skill back home. Models in Table 4 include separate indicators of linguistic proximity for non‐English and for English‐speaking destinations to examine the role of English as one of the most widely spoken languages in the world. If there is some advantage from knowing English as a second language, we expect that the linguistic proximity between native languages should matter more for non‐English‐speaking destinations than for the others. Results in Table 4, column (1), show that the linguistic proximity index is a strong predictor of emigration rates towards non‐English‐speaking destinations. The coefficient for English destinations is much smaller than that for non‐English destinations and it is statistically insignificant. This is consistent with the hypothesis that people migrate to destinations with a widely spoken language even if their mother languages are linguistically far from that language. The finding is similar in column (2) when we use the linguistic proximity of the major language in the country instead. As a matter of fact, the coefficient to non‐English‐speaking destination is even larger that when the first official language is employed in column (1). In column (3), we use the proximity index for the closest pair among all official and main languages of each country. The coefficient for English destinations is now slightly larger and statistically significant at the 10% level, but still lower than that for non‐English‐speaking destinations. This is probably related to the fact that English and other colonial languages are (when not first) likely second or third official languages in many countries where they are not necessarily neither majoritarian nor widely known by the whole population but are taught in schools.19 Table 4 The Role of English as Widely Spoken Language, Education and Migration Rates to OECD Countries . All countries . Countries with low levels of education . All countries . First official . Major . All official and main . First official . Major . All official and main . First official . First official . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . Linguistic Proximity 0.244*** −0.014 (0.067) (0.126) ln Non‐English destination 0.363*** 0.509*** 0.225*** 0.271* −0.176 0.368*** (0.073) (0.082) (0.059) (0.144) (0.287) (0.099) ln English destination 0.061 0.108 0.150* 0.025 0.108 0.227** (0.095) (0.147) (0.083) (0.123) (0.237) (0.100) Origin Tertiary Education_t 0.109*** 0.099*** (0.022) (0.022) Linguistic Proximity × Tertiary Education_t 0.094** (0.043) Other controls Yes Yes Yes Yes Yes Yes Yes Yes Constant −23.579*** −23.579*** −23.686*** −41.942*** −41.629*** −41.945*** −23.650*** −23.725*** (2.174) (2.174) (2.172) (4.498) (4.498) (4.496) (2.210) (2.208) Observations 51,257 51,257 51,257 11,079 11,079 11,079 50,497 50,497 Adjusted R2 0.899 0.899 0.899 0.890 0.890 0.889 0.899 0.899 . All countries . Countries with low levels of education . All countries . First official . Major . All official and main . First official . Major . All official and main . First official . First official . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . Linguistic Proximity 0.244*** −0.014 (0.067) (0.126) ln Non‐English destination 0.363*** 0.509*** 0.225*** 0.271* −0.176 0.368*** (0.073) (0.082) (0.059) (0.144) (0.287) (0.099) ln English destination 0.061 0.108 0.150* 0.025 0.108 0.227** (0.095) (0.147) (0.083) (0.123) (0.237) (0.100) Origin Tertiary Education_t 0.109*** 0.099*** (0.022) (0.022) Linguistic Proximity × Tertiary Education_t 0.094** (0.043) Other controls Yes Yes Yes Yes Yes Yes Yes Yes Constant −23.579*** −23.579*** −23.686*** −41.942*** −41.629*** −41.945*** −23.650*** −23.725*** (2.174) (2.174) (2.172) (4.498) (4.498) (4.496) (2.210) (2.208) Observations 51,257 51,257 51,257 11,079 11,079 11,079 50,497 50,497 Adjusted R2 0.899 0.899 0.899 0.890 0.890 0.889 0.899 0.899 Notes Dependent variable: Ln(Emigration Rate). Linguistic proximity measured by our linguistic proximity index. A country with low education is below the 25th percentile in gross secondary school enrolment rates for a given year. Tertiary education is measured by gross enrolment rates. Controls included: stock of migrants, economic variables, distance variables, year dummies and destination and origin country fixed effects. Robust standard errors clustered at the country‐pair level. ***p < 0.01, **p < 0.05, *p < 0.1. Open in new tab Table 4 The Role of English as Widely Spoken Language, Education and Migration Rates to OECD Countries . All countries . Countries with low levels of education . All countries . First official . Major . All official and main . First official . Major . All official and main . First official . First official . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . Linguistic Proximity 0.244*** −0.014 (0.067) (0.126) ln Non‐English destination 0.363*** 0.509*** 0.225*** 0.271* −0.176 0.368*** (0.073) (0.082) (0.059) (0.144) (0.287) (0.099) ln English destination 0.061 0.108 0.150* 0.025 0.108 0.227** (0.095) (0.147) (0.083) (0.123) (0.237) (0.100) Origin Tertiary Education_t 0.109*** 0.099*** (0.022) (0.022) Linguistic Proximity × Tertiary Education_t 0.094** (0.043) Other controls Yes Yes Yes Yes Yes Yes Yes Yes Constant −23.579*** −23.579*** −23.686*** −41.942*** −41.629*** −41.945*** −23.650*** −23.725*** (2.174) (2.174) (2.172) (4.498) (4.498) (4.496) (2.210) (2.208) Observations 51,257 51,257 51,257 11,079 11,079 11,079 50,497 50,497 Adjusted R2 0.899 0.899 0.899 0.890 0.890 0.889 0.899 0.899 . All countries . Countries with low levels of education . All countries . First official . Major . All official and main . First official . Major . All official and main . First official . First official . (1) . (2) . (3) . (4) . (5) . (6) . (7) . (8) . Linguistic Proximity 0.244*** −0.014 (0.067) (0.126) ln Non‐English destination 0.363*** 0.509*** 0.225*** 0.271* −0.176 0.368*** (0.073) (0.082) (0.059) (0.144) (0.287) (0.099) ln English destination 0.061 0.108 0.150* 0.025 0.108 0.227** (0.095) (0.147) (0.083) (0.123) (0.237) (0.100) Origin Tertiary Education_t 0.109*** 0.099*** (0.022) (0.022) Linguistic Proximity × Tertiary Education_t 0.094** (0.043) Other controls Yes Yes Yes Yes Yes Yes Yes Yes Constant −23.579*** −23.579*** −23.686*** −41.942*** −41.629*** −41.945*** −23.650*** −23.725*** (2.174) (2.174) (2.172) (4.498) (4.498) (4.496) (2.210) (2.208) Observations 51,257 51,257 51,257 11,079 11,079 11,079 50,497 50,497 Adjusted R2 0.899 0.899 0.899 0.890 0.890 0.889 0.899 0.899 Notes Dependent variable: Ln(Emigration Rate). Linguistic proximity measured by our linguistic proximity index. A country with low education is below the 25th percentile in gross secondary school enrolment rates for a given year. Tertiary education is measured by gross enrolment rates. Controls included: stock of migrants, economic variables, distance variables, year dummies and destination and origin country fixed effects. Robust standard errors clustered at the country‐pair level. ***p < 0.01, **p < 0.05, *p < 0.1. Open in new tab Broadly speaking, research based on micro‐data unveils two polar types of migrants: on the one hand, low skilled manual workers in jobs that are not filled by the natives in the destination country and, on the other hand, high skilled professionals (in IT or science, among other fields) (Belot and Hatton, 2012; Docquier and Rapoport, 2012; for an overview). Since language plays a key role in a successful transfer of immigrants’ home country education and skills to foreign labour markets (Kossoudji, 1988; Dustmann, 1994; Dustmann and van Soest, 2001, 2002; Chiswick and Miller, 2002, 2007, 2010; Dustmann and Fabbri, 2003; Bleakley and Chin, 2004), the relevance of linguistic proximity and knowledge of widely spoken language is likely to differ across these various groups of migrants with different needs of skill transferability. Ideally we would like to have individual level data in order to explore these (likely) heterogenous effects of language, but unfortunately a data set at the level of the individual migrant is not currently available. Keeping in mind potential problems of ecological inference driven by differential selection across countries (Docquier et al., 2007), the large number of destination and origin countries in our data set allow us to analyse the migration patterns for groups of countries with different levels of educational attainment. In columns (4)–(6) in Table 4, we restrict the sample to countries in the lowest quartile in gross secondary education enrolment rates by year. Results seem to support the hypothesis that linguistic proximity and knowledge of a widely spoken language are less relevant for migrants with lower average skills. The coefficient for non‐English destinations in column (4) is lower than in column (1) and barely significant, whereas the coefficient for English‐speaking destinations remains statistically insignificant. When we use linguistic proximity between major languages, we find no effect of linguistic proximity for both English and non‐English‐speaking destinations. Conversely, both coefficients are positive, significant and larger than in column (3) when we employ the closest pair among all official and main languages of each country.20 With the lack of individual data it is difficult to tease out the competing mechanisms behind this finding. A large proportion of countries in this restricted sample are former colonies and some of the widely spoken languages in the world turn up among their official languages. Thus, even if potential migrants do not learn those formally as an additional foreign language, they may have received basic instruction in English (or other major languages). In fact among the country‐pairs with an English‐speaking destination in this restricted sample, close to 60% of the observations share English as a common official language (as opposed to 40% in the complete sample). Further, a positive selection within those countries will make the average migrant more likely to have received some education in English. In columns (7) and (8), we add tertiary enrolment rate into our model as a proxy for country’s level of education and we find that for the sample of all countries, those origins with higher tertiary enrolment rates have larger migration outflows. This is in line with the human capital investment theoretical framework prediction that more educated individuals are more mobile. The relevance of linguistic proximity is robust to the inclusion of tertiary education in column (7) and it increases with the level of gross tertiary enrolment in column (8). The latter likely exposes an increasing need for skill transferability. 4.4. Linguistic Networks and Policy A potential concern when interpreting our findings is to what extent linguistic proximity interacts with migration policy, which has been shown to be an important factor to explain migration flows (Ortega and Peri, 2009; Mayda, 2010). We contacted several specialists in the field and went over legislation in an attempt to gather a comparable index to account for the strictness of language requirements at entry. Given the heterogeneity of schemes across countries (skilled and unskilled workers; economic, spouse or student visas, among others),21 we followed the advice of those experts who suggested naturalisation policy requirements would be easier to measure in a homogeneous way. We have combined existing information gathered by previous research (Weil, 2001; Waldrauch, 2006; Joppke, 2007; Goodman, 2010a; country official websites and data from the project EUDO Citizenship Observatory, among others) and we have read all the pertinent legislation on citizenship by country available in the eudo‐citizenship.eu website. We have created a time‐varying index that measures whether countries have any language requirement in the naturalisation process, whether the requirement is formal (i.e. written test) or informal and whether it has changed in each of the 30 OECD destinations for the 1980–2010 period. We include this index in the models in Table 5 (columns 1 and 2). Results show that, ceteris paribus, migration flows are smaller in countries with higher linguistic requirements for naturalisation, but the coefficient of linguistic proximity and its significance remain unaltered, even when we include its interaction with the policy requirement in column (2). Table 5 The Role of Policy, Networks and Linguistic Networks on Linguistic Distance and Migration Rates to OECD Countries . (1) . (2) . (3) . (4) . Policy . Policy . Linguistic networks at the 4th level of the linguistic tree . Linguistic networks at the 3rd level of the linguistic tree . Linguistic Proximity 0.205*** 0.244** 0.311*** 0.467*** (0.066) (0.096) (0.079) (0.085) Linguistic Requirement (Policy)_t −0.249*** −0.240*** (0.027) (0.031) Linguistic Requirement (Policy)_t × Linguistic Proximity −0.065 (0.107) Linguistic networks_t−1 0.040*** 0.027** (0.011) (0.011) Linguistic Networks_t−1 × Linguistic Proximity −0.035** −0.065*** (0.017) (0.017) Ln Stock of Migrants_t−1 0.671*** 0.671*** 0.655*** 0.661*** (0.009) (0.009) (0.010) (0.010) Constant −23.374*** −23.374*** −23.847*** −23.770*** (2.134) (2.134) (2.163) (2.165) Observations 51,233 51,233 51,147 51,112 Adjusted R2 0.899 0.899 0.899 0.899 . (1) . (2) . (3) . (4) . Policy . Policy . Linguistic networks at the 4th level of the linguistic tree . Linguistic networks at the 3rd level of the linguistic tree . Linguistic Proximity 0.205*** 0.244** 0.311*** 0.467*** (0.066) (0.096) (0.079) (0.085) Linguistic Requirement (Policy)_t −0.249*** −0.240*** (0.027) (0.031) Linguistic Requirement (Policy)_t × Linguistic Proximity −0.065 (0.107) Linguistic networks_t−1 0.040*** 0.027** (0.011) (0.011) Linguistic Networks_t−1 × Linguistic Proximity −0.035** −0.065*** (0.017) (0.017) Ln Stock of Migrants_t−1 0.671*** 0.671*** 0.655*** 0.661*** (0.009) (0.009) (0.010) (0.010) Constant −23.374*** −23.374*** −23.847*** −23.770*** (2.134) (2.134) (2.163) (2.165) Observations 51,233 51,233 51,147 51,112 Adjusted R2 0.899 0.899 0.899 0.899 Notes Dependent variable: Ln(Emigration Rate). Linguistic proximity measured by our linguistic proximity index. Controls included: stock of migrants, economic variables, distance variables, year dummies and destination and origin country fixed effects. Linguistic networks measured on the fourth (column 1) and third (column 2) level of the linguistic family tree. Linguistic requirement for naturalisation in destination countries is coded as 1 formal test; 0.5 informal test and 0 none. Robust standard errors clustered at the country‐pair level. ***p < 0.01, **p < 0.05, *p < 0.1. Open in new tab Table 5 The Role of Policy, Networks and Linguistic Networks on Linguistic Distance and Migration Rates to OECD Countries . (1) . (2) . (3) . (4) . Policy . Policy . Linguistic networks at the 4th level of the linguistic tree . Linguistic networks at the 3rd level of the linguistic tree . Linguistic Proximity 0.205*** 0.244** 0.311*** 0.467*** (0.066) (0.096) (0.079) (0.085) Linguistic Requirement (Policy)_t −0.249*** −0.240*** (0.027) (0.031) Linguistic Requirement (Policy)_t × Linguistic Proximity −0.065 (0.107) Linguistic networks_t−1 0.040*** 0.027** (0.011) (0.011) Linguistic Networks_t−1 × Linguistic Proximity −0.035** −0.065*** (0.017) (0.017) Ln Stock of Migrants_t−1 0.671*** 0.671*** 0.655*** 0.661*** (0.009) (0.009) (0.010) (0.010) Constant −23.374*** −23.374*** −23.847*** −23.770*** (2.134) (2.134) (2.163) (2.165) Observations 51,233 51,233 51,147 51,112 Adjusted R2 0.899 0.899 0.899 0.899 . (1) . (2) . (3) . (4) . Policy . Policy . Linguistic networks at the 4th level of the linguistic tree . Linguistic networks at the 3rd level of the linguistic tree . Linguistic Proximity 0.205*** 0.244** 0.311*** 0.467*** (0.066) (0.096) (0.079) (0.085) Linguistic Requirement (Policy)_t −0.249*** −0.240*** (0.027) (0.031) Linguistic Requirement (Policy)_t × Linguistic Proximity −0.065 (0.107) Linguistic networks_t−1 0.040*** 0.027** (0.011) (0.011) Linguistic Networks_t−1 × Linguistic Proximity −0.035** −0.065*** (0.017) (0.017) Ln Stock of Migrants_t−1 0.671*** 0.671*** 0.655*** 0.661*** (0.009) (0.009) (0.010) (0.010) Constant −23.374*** −23.374*** −23.847*** −23.770*** (2.134) (2.134) (2.163) (2.165) Observations 51,233 51,233 51,147 51,112 Adjusted R2 0.899 0.899 0.899 0.899 Notes Dependent variable: Ln(Emigration Rate). Linguistic proximity measured by our linguistic proximity index. Controls included: stock of migrants, economic variables, distance variables, year dummies and destination and origin country fixed effects. Linguistic networks measured on the fourth (column 1) and third (column 2) level of the linguistic family tree. Linguistic requirement for naturalisation in destination countries is coded as 1 formal test; 0.5 informal test and 0 none. Robust standard errors clustered at the country‐pair level. ***p < 0.01, **p < 0.05, *p < 0.1. Open in new tab In addition to providing information and affective support for the newcomer, a large stock of migrants from the same origin is likely associated with lower pressure to learn the local language immediately after arrival as it facilitates the existence of ‘language enclaves’ within destination countries. In that regard, the relevant community for a newcomer may be the one composed by individuals that share the same linguistic background. We have constructed two indicators that measure the size of the linguistic networks: the total stock of migrants that share the same level of the linguistic tree (either at level 3 or at level 4). The coefficient of those indicators in columns (3) and (4) of Table 5 imply that a larger linguistic community significantly attracts more migrants to a destination. In addition, we include the interaction of the size of the linguistic network with the index of linguistic proximity. The coefficient is negative and significant indicating that linguistic distance matters less when the size of the linguistic community (potentially, the linguistic enclave) is large in the destination country. 5. Conclusions In this article, we construct a new data set on migration flows into and stocks of foreigners in 30 OECD destination countries from 223 source countries for the years 1980–2010 in order to study the role of language in shaping international migration. Specifically, we investigate how linguistic distance, the presence of a widely spoken language at destination, linguistic requirements in immigration policy and the existence of linguistic enclaves in destinations are related to migration flows. Besides collecting the largest international migration data set to date, we construct our own linguistic proximity measure, based on information from Ethnologue (Lewis, 2009). We find that migration rates are higher between countries whose first official languages are closer. The result holds when we instead use either the proximity between the most commonly used language in each country or the minimum distance between any of the multiple official and main languages in both countries. This finding is also highly robust to the use of two continuous distance measures developed by linguists (the Dyen and the Levenshtein indices) and to the inclusion of a number of variables that capture cultural, historical and trade ties between countries, such as genetic distance, dummies for common historical past and common border, distance and bilateral trade ties. This suggests that language itself affects migration costs beyond the effects of cultural homogeneity or physical proximity between origin and destination countries. In the context of traditional economic push and pull factors found in the literature, the impact of linguistic proximity on migration flows between two countries is lower than that of ethnic networks or destination GDP per capita level but much stronger than that of unemployment rates. To investigate the role of English, a widely spoken language, in migration, we estimate separate coefficients on linguistic proximity for English and non‐English‐speaking destinations and we find that linguistic proximity matters more for the latter group. Pre‐migration exposure to English by the average migrant probably weakens the relevance of linguistic proximity indicators to English‐speaking destinations. We also find that linguistic proximity (particularly to English‐speaking destinations) is less relevant for migrants coming from countries with low levels of secondary enrolment. Overall, there is more emigration from countries with higher levels of tertiary education, and the importance of linguistic proximity increases with the level of tertiary education at origin. This may reflect the increasing need for skill transferability for highly skilled migrants. Finally, we investigate the role of immigration policy and linguistic enclaves on migration. Immigration policy with stricter requirements of language proficiency may affect migration flows and the impact of linguistic proximity. To test this, we create a time‐varying language requirement index for naturalisation for our 30 destinations for 1980–2010. Results show that even though migration flows are smaller in countries with higher requirements, the relevance of linguistic proximity remains unaltered. Further, migration rates are significantly larger in destinations with larger size of the linguistic community, where the pressure to learn the local language immediately after arrival is likely to be lower. Our estimates reveal that the linguistic proximity matters significantly less when the size of the linguistic community (the linguistic enclave) is large in the destination country. Our research contributes to the understanding of the determinants of the direction of migration flows across countries and highlights the importance of migration costs as obstacles to greater international migration. Governmental policies aimed at promoting instruction of foreign languages both at origins and destinations can foster the recruitment and mobility of international workers. Some existing empirical literature suggests that the presence of foreign workers and ethnic diversity in the workplace, especially among the high skilled, tend to foster innovation at the firm level and that lowering costs of communication and cross‐cultural exchanges is key to transforming the ethnic diversity of the workforce into the firm’s competitive advantage (Parrotta et al., 2014a, b). International workers with better knowledge of the destination language or widely spoken languages facilitate the global interchange of skills and stimulate the overall economic performance. Appendix A: International Migration Dataset – Data Coverage, Definitions and Sources Table A1 Country‐year Coverage Migration Flows Dest . AUS . AUT . BEL . CAN . CHE . CZE . DEU . DNK . ESP . FIN . FRA . GBR . GRC . HUN . IRL . ISL . ITA . JPN . KOR . LUX . MEX . NLD . NOR . NZL . POL . PRT . SVK . SE . TUR . USA . Year 2010 208 190 217 198 135 193 203 113 183 144 208 179 184 141 194 213 212 124 148 212 194 197 2009 205 190 184 214 194 141 193 203 113 183 26 139 209 178 188 201 58 141 128 198 202 212 123 150 212 192 200 198 2008 204 190 182 214 194 143 194 203 113 183 120 21 142 208 178 187 198 57 146 126 195 202 213 205 143 212 192 196 196 2007 206 190 93 214 194 147 193 203 113 183 124 19 191 128 2 178 181 197 28 142 126 197 202 213 205 126 211 192 195 197 2006 206 190 96 214 194 142 193 202 108 183 120 34 190 133 2 178 182 195 10 139 193 202 213 205 128 208 192 193 193 2005 203 190 85 214 194 142 191 203 66 183 107 114 121 2 178 185 10 10 137 187 202 213 205 124 208 192 193 195 2004 203 190 71 214 194 146 191 203 57 183 107 109 108 2 178 183 10 10 135 193 202 213 205 118 208 192 193 204 2003 201 189 70 214 195 142 191 203 57 183 127 107 121 2 178 180 10 10 127 191 202 213 205 114 208 192 194 204 2002 198 189 70 214 194 141 191 203 57 183 128 99 110 2 178 182 10 10 123 198 192 213 205 126 208 192 193 204 2001 198 189 70 214 194 115 84 203 57 183 130 106 117 2 178 181 10 10 116 197 192 213 205 114 208 192 194 204 2000 200 189 70 214 180 110 83 203 59 183 129 111 118 2 178 182 15 10 124 197 192 213 205 113 208 192 194 204 1999 198 189 70 214 180 108 193 203 58 183 118 110 114 2 178 181 15 123 191 192 213 205 114 208 159 172 204 1998 193 189 70 214 180 122 193 203 59 183 117 116 188 114 2 178 182 14 120 191 192 213 16 144 208 166 171 204 1997 192 189 55 214 179 111 193 203 39 183 118 48 183 114 2 178 179 14 110 194 192 213 14 144 208 164 172 204 1996 195 189 55 214 176 114 193 203 58 183 118 52 205 116 2 178 178 14 108 191 191 213 14 144 208 167 165 203 1995 187 55 214 176 117 193 203 39 183 118 54 203 117 2 178 48 15 110 187 192 213 13 144 165 165 203 1994 186 55 214 179 106 193 203 39 183 118 27 205 119 2 178 32 14 103 186 192 213 13 144 164 203 1993 180 48 214 178 97 193 203 39 183 39 205 106 2 178 32 14 99 185 192 213 11 143 168 204 1992 182 48 214 174 189 203 45 183 45 205 111 2 178 32 14 105 174 191 213 11 143 157 205 1991 171 48 213 158 172 203 42 183 49 206 104 2 178 32 11 95 160 191 213 11 148 206 1990 168 48 213 156 44 203 42 183 38 200 102 2 178 32 12 100 163 190 213 10 144 205 1989 155 48 213 154 105 203 42 183 31 97 2 178 32 11 93 164 192 213 10 142 205 1988 150 25 213 159 105 203 42 183 38 100 2 178 32 11 94 158 192 213 138 205 1987 159 27 213 155 105 203 183 29 99 2 178 32 7 93 161 192 213 136 205 1986 153 27 213 154 105 203 183 33 103 178 32 7 191 213 138 205 1985 155 27 213 154 105 203 183 35 95 18 32 7 116 213 134 205 1984 154 27 213 151 105 203 183 18 205 213 126 205 1983 166 27 213 152 105 203 183 18 205 213 123 205 1982 161 27 213 154 105 203 18 205 213 121 205 1981 27 213 154 105 203 18 205 213 123 204 1980 27 213 105 203 205 213 119 202 Dest . AUS . AUT . BEL . CAN . CHE . CZE . DEU . DNK . ESP . FIN . FRA . GBR . GRC . HUN . IRL . ISL . ITA . JPN . KOR . LUX . MEX . NLD . NOR . NZL . POL . PRT . SVK . SE . TUR . USA . Year 2010 208 190 217 198 135 193 203 113 183 144 208 179 184 141 194 213 212 124 148 212 194 197 2009 205 190 184 214 194 141 193 203 113 183 26 139 209 178 188 201 58 141 128 198 202 212 123 150 212 192 200 198 2008 204 190 182 214 194 143 194 203 113 183 120 21 142 208 178 187 198 57 146 126 195 202 213 205 143 212 192 196 196 2007 206 190 93 214 194 147 193 203 113 183 124 19 191 128 2 178 181 197 28 142 126 197 202 213 205 126 211 192 195 197 2006 206 190 96 214 194 142 193 202 108 183 120 34 190 133 2 178 182 195 10 139 193 202 213 205 128 208 192 193 193 2005 203 190 85 214 194 142 191 203 66 183 107 114 121 2 178 185 10 10 137 187 202 213 205 124 208 192 193 195 2004 203 190 71 214 194 146 191 203 57 183 107 109 108 2 178 183 10 10 135 193 202 213 205 118 208 192 193 204 2003 201 189 70 214 195 142 191 203 57 183 127 107 121 2 178 180 10 10 127 191 202 213 205 114 208 192 194 204 2002 198 189 70 214 194 141 191 203 57 183 128 99 110 2 178 182 10 10 123 198 192 213 205 126 208 192 193 204 2001 198 189 70 214 194 115 84 203 57 183 130 106 117 2 178 181 10 10 116 197 192 213 205 114 208 192 194 204 2000 200 189 70 214 180 110 83 203 59 183 129 111 118 2 178 182 15 10 124 197 192 213 205 113 208 192 194 204 1999 198 189 70 214 180 108 193 203 58 183 118 110 114 2 178 181 15 123 191 192 213 205 114 208 159 172 204 1998 193 189 70 214 180 122 193 203 59 183 117 116 188 114 2 178 182 14 120 191 192 213 16 144 208 166 171 204 1997 192 189 55 214 179 111 193 203 39 183 118 48 183 114 2 178 179 14 110 194 192 213 14 144 208 164 172 204 1996 195 189 55 214 176 114 193 203 58 183 118 52 205 116 2 178 178 14 108 191 191 213 14 144 208 167 165 203 1995 187 55 214 176 117 193 203 39 183 118 54 203 117 2 178 48 15 110 187 192 213 13 144 165 165 203 1994 186 55 214 179 106 193 203 39 183 118 27 205 119 2 178 32 14 103 186 192 213 13 144 164 203 1993 180 48 214 178 97 193 203 39 183 39 205 106 2 178 32 14 99 185 192 213 11 143 168 204 1992 182 48 214 174 189 203 45 183 45 205 111 2 178 32 14 105 174 191 213 11 143 157 205 1991 171 48 213 158 172 203 42 183 49 206 104 2 178 32 11 95 160 191 213 11 148 206 1990 168 48 213 156 44 203 42 183 38 200 102 2 178 32 12 100 163 190 213 10 144 205 1989 155 48 213 154 105 203 42 183 31 97 2 178 32 11 93 164 192 213 10 142 205 1988 150 25 213 159 105 203 42 183 38 100 2 178 32 11 94 158 192 213 138 205 1987 159 27 213 155 105 203 183 29 99 2 178 32 7 93 161 192 213 136 205 1986 153 27 213 154 105 203 183 33 103 178 32 7 191 213 138 205 1985 155 27 213 154 105 203 183 35 95 18 32 7 116 213 134 205 1984 154 27 213 151 105 203 183 18 205 213 126 205 1983 166 27 213 152 105 203 183 18 205 213 123 205 1982 161 27 213 154 105 203 18 205 213 121 205 1981 27 213 154 105 203 18 205 213 123 204 1980 27 213 105 203 205 213 119 202 Notes Columns: destination countries, Rows: year, Cell: numbers of source countries, for which we have observations on the number of migrants for particular year. Open in new tab Table A1 Country‐year Coverage Migration Flows Dest . AUS . AUT . BEL . CAN . CHE . CZE . DEU . DNK . ESP . FIN . FRA . GBR . GRC . HUN . IRL . ISL . ITA . JPN . KOR . LUX . MEX . NLD . NOR . NZL . POL . PRT . SVK . SE . TUR . USA . Year 2010 208 190 217 198 135 193 203 113 183 144 208 179 184 141 194 213 212 124 148 212 194 197 2009 205 190 184 214 194 141 193 203 113 183 26 139 209 178 188 201 58 141 128 198 202 212 123 150 212 192 200 198 2008 204 190 182 214 194 143 194 203 113 183 120 21 142 208 178 187 198 57 146 126 195 202 213 205 143 212 192 196 196 2007 206 190 93 214 194 147 193 203 113 183 124 19 191 128 2 178 181 197 28 142 126 197 202 213 205 126 211 192 195 197 2006 206 190 96 214 194 142 193 202 108 183 120 34 190 133 2 178 182 195 10 139 193 202 213 205 128 208 192 193 193 2005 203 190 85 214 194 142 191 203 66 183 107 114 121 2 178 185 10 10 137 187 202 213 205 124 208 192 193 195 2004 203 190 71 214 194 146 191 203 57 183 107 109 108 2 178 183 10 10 135 193 202 213 205 118 208 192 193 204 2003 201 189 70 214 195 142 191 203 57 183 127 107 121 2 178 180 10 10 127 191 202 213 205 114 208 192 194 204 2002 198 189 70 214 194 141 191 203 57 183 128 99 110 2 178 182 10 10 123 198 192 213 205 126 208 192 193 204 2001 198 189 70 214 194 115 84 203 57 183 130 106 117 2 178 181 10 10 116 197 192 213 205 114 208 192 194 204 2000 200 189 70 214 180 110 83 203 59 183 129 111 118 2 178 182 15 10 124 197 192 213 205 113 208 192 194 204 1999 198 189 70 214 180 108 193 203 58 183 118 110 114 2 178 181 15 123 191 192 213 205 114 208 159 172 204 1998 193 189 70 214 180 122 193 203 59 183 117 116 188 114 2 178 182 14 120 191 192 213 16 144 208 166 171 204 1997 192 189 55 214 179 111 193 203 39 183 118 48 183 114 2 178 179 14 110 194 192 213 14 144 208 164 172 204 1996 195 189 55 214 176 114 193 203 58 183 118 52 205 116 2 178 178 14 108 191 191 213 14 144 208 167 165 203 1995 187 55 214 176 117 193 203 39 183 118 54 203 117 2 178 48 15 110 187 192 213 13 144 165 165 203 1994 186 55 214 179 106 193 203 39 183 118 27 205 119 2 178 32 14 103 186 192 213 13 144 164 203 1993 180 48 214 178 97 193 203 39 183 39 205 106 2 178 32 14 99 185 192 213 11 143 168 204 1992 182 48 214 174 189 203 45 183 45 205 111 2 178 32 14 105 174 191 213 11 143 157 205 1991 171 48 213 158 172 203 42 183 49 206 104 2 178 32 11 95 160 191 213 11 148 206 1990 168 48 213 156 44 203 42 183 38 200 102 2 178 32 12 100 163 190 213 10 144 205 1989 155 48 213 154 105 203 42 183 31 97 2 178 32 11 93 164 192 213 10 142 205 1988 150 25 213 159 105 203 42 183 38 100 2 178 32 11 94 158 192 213 138 205 1987 159 27 213 155 105 203 183 29 99 2 178 32 7 93 161 192 213 136 205 1986 153 27 213 154 105 203 183 33 103 178 32 7 191 213 138 205 1985 155 27 213 154 105 203 183 35 95 18 32 7 116 213 134 205 1984 154 27 213 151 105 203 183 18 205 213 126 205 1983 166 27 213 152 105 203 183 18 205 213 123 205 1982 161 27 213 154 105 203 18 205 213 121 205 1981 27 213 154 105 203 18 205 213 123 204 1980 27 213 105 203 205 213 119 202 Dest . AUS . AUT . BEL . CAN . CHE . CZE . DEU . DNK . ESP . FIN . FRA . GBR . GRC . HUN . IRL . ISL . ITA . JPN . KOR . LUX . MEX . NLD . NOR . NZL . POL . PRT . SVK . SE . TUR . USA . Year 2010 208 190 217 198 135 193 203 113 183 144 208 179 184 141 194 213 212 124 148 212 194 197 2009 205 190 184 214 194 141 193 203 113 183 26 139 209 178 188 201 58 141 128 198 202 212 123 150 212 192 200 198 2008 204 190 182 214 194 143 194 203 113 183 120 21 142 208 178 187 198 57 146 126 195 202 213 205 143 212 192 196 196 2007 206 190 93 214 194 147 193 203 113 183 124 19 191 128 2 178 181 197 28 142 126 197 202 213 205 126 211 192 195 197 2006 206 190 96 214 194 142 193 202 108 183 120 34 190 133 2 178 182 195 10 139 193 202 213 205 128 208 192 193 193 2005 203 190 85 214 194 142 191 203 66 183 107 114 121 2 178 185 10 10 137 187 202 213 205 124 208 192 193 195 2004 203 190 71 214 194 146 191 203 57 183 107 109 108 2 178 183 10 10 135 193 202 213 205 118 208 192 193 204 2003 201 189 70 214 195 142 191 203 57 183 127 107 121 2 178 180 10 10 127 191 202 213 205 114 208 192 194 204 2002 198 189 70 214 194 141 191 203 57 183 128 99 110 2 178 182 10 10 123 198 192 213 205 126 208 192 193 204 2001 198 189 70 214 194 115 84 203 57 183 130 106 117 2 178 181 10 10 116 197 192 213 205 114 208 192 194 204 2000 200 189 70 214 180 110 83 203 59 183 129 111 118 2 178 182 15 10 124 197 192 213 205 113 208 192 194 204 1999 198 189 70 214 180 108 193 203 58 183 118 110 114 2 178 181 15 123 191 192 213 205 114 208 159 172 204 1998 193 189 70 214 180 122 193 203 59 183 117 116 188 114 2 178 182 14 120 191 192 213 16 144 208 166 171 204 1997 192 189 55 214 179 111 193 203 39 183 118 48 183 114 2 178 179 14 110 194 192 213 14 144 208 164 172 204 1996 195 189 55 214 176 114 193 203 58 183 118 52 205 116 2 178 178 14 108 191 191 213 14 144 208 167 165 203 1995 187 55 214 176 117 193 203 39 183 118 54 203 117 2 178 48 15 110 187 192 213 13 144 165 165 203 1994 186 55 214 179 106 193 203 39 183 118 27 205 119 2 178 32 14 103 186 192 213 13 144 164 203 1993 180 48 214 178 97 193 203 39 183 39 205 106 2 178 32 14 99 185 192 213 11 143 168 204 1992 182 48 214 174 189 203 45 183 45 205 111 2 178 32 14 105 174 191 213 11 143 157 205 1991 171 48 213 158 172 203 42 183 49 206 104 2 178 32 11 95 160 191 213 11 148 206 1990 168 48 213 156 44 203 42 183 38 200 102 2 178 32 12 100 163 190 213 10 144 205 1989 155 48 213 154 105 203 42 183 31 97 2 178 32 11 93 164 192 213 10 142 205 1988 150 25 213 159 105 203 42 183 38 100 2 178 32 11 94 158 192 213 138 205 1987 159 27 213 155 105 203 183 29 99 2 178 32 7 93 161 192 213 136 205 1986 153 27 213 154 105 203 183 33 103 178 32 7 191 213 138 205 1985 155 27 213 154 105 203 183 35 95 18 32 7 116 213 134 205 1984 154 27 213 151 105 203 183 18 205 213 126 205 1983 166 27 213 152 105 203 183 18 205 213 123 205 1982 161 27 213 154 105 203 18 205 213 121 205 1981 27 213 154 105 203 18 205 213 123 204 1980 27 213 105 203 205 213 119 202 Notes Columns: destination countries, Rows: year, Cell: numbers of source countries, for which we have observations on the number of migrants for particular year. Open in new tab Table A2 Country‐year Coverage Migration Stocks Dest . AUS . AUT . BEL . CAN . CHE . CZE . DEU . DNK . ESP . FIN . FRA . GBR . GRC . HUN . IRL . ISL . ITA . JPN . KOR . LUX . MEX . MEX . NLD . NOR . NZL . POL . PRT . SVK . SE . TUR . USA . Year 2010 209 191 171 192 201 193 179 173 209 175 192 26 209 213 209 176 150 199 107 2009 209 209 185 194 172 190 201 112 191 171 180 208 175 190 201 27 26 207 213 209 177 145 199 185 133 2008 209 209 187 194 171 192 201 112 191 127 177 178 175 192 199 28 26 209 213 176 144 199 183 133 2007 209 209 178 194 168 193 200 112 191 128 174 174 175 188 198 25 26 207 213 179 142 199 133 2006 199 209 184 210 194 168 193 200 112 193 193 148 189 173 43 175 189 195 25 23 207 213 211 174 143 199 96 2005 209 209 182 194 166 139 201 112 193 204 97 191 165 175 189 183 25 23 10 208 213 173 138 199 96 2004 208 209 181 194 165 139 201 112 193 101 189 162 172 188 18 25 23 10 208 213 171 137 199 96 2003 208 209 181 194 163 138 201 112 193 100 190 156 172 188 18 25 23 207 213 167 149 199 96 2002 208 209 181 194 161 138 201 99 193 100 158 177 172 186 42 25 23 207 213 201 167 148 199 96 2001 190 207 181 190 194 163 138 201 99 193 97 154 172 187 42 19 12 206 213 199 166 142 199 96 2000 207 191 176 195 161 138 201 99 193 102 207 163 172 184 122 19 137 201 206 213 163 140 199 196 132 1999 206 174 195 164 138 201 99 193 162 87 163 172 185 42 19 12 202 204 213 157 136 111 96 1998 206 174 195 158 138 201 99 193 104 161 172 38 42 19 12 204 213 154 144 111 96 1997 204 55 195 152 138 201 99 193 100 189 159 172 189 42 19 12 204 212 151 144 111 96 1996 192 55 201 195 153 138 201 63 193 90 205 157 36 65 50 18 19 12 204 212 52 150 139 111 96 1995 202 55 195 150 138 201 58 193 85 205 146 65 50 37 19 12 200 212 150 140 111 96 1994 49 55 195 145 137 201 58 193 87 205 66 50 18 19 12 9 212 146 107 126 1993 49 48 195 137 201 58 193 87 205 66 50 18 19 12 9 212 139 104 126 1992 49 48 194 132 201 58 193 82 205 66 185 18 17 12 9 212 129 101 126 1991 168 48 180 194 117 201 58 193 70 205 2 43 184 16 15 12 9 212 51 125 98 126 1990 49 70 48 194 118 201 57 193 76 205 60 42 15 82 9 212 120 100 12 127 1989 48 194 118 201 57 134 204 60 12 8 9 212 121 98 125 1988 194 118 201 57 134 204 60 12 3 8 9 212 119 98 125 1987 194 118 201 57 131 204 60 12 4 8 9 212 118 97 125 1986 75 42 194 118 201 57 125 204 2 60 12 9 8 9 212 75 115 94 125 1985 194 118 201 57 124 204 60 42 9 212 109 95 125 1984 194 118 201 191 204 60 12 9 187 103 89 125 1983 194 118 201 204 60 12 9 187 100 125 1982 194 118 201 204 60 12 193 83 85 125 1981 81 47 42 194 118 201 204 2 59 12 189 75 98 125 1980 64 194 116 201 204 42 79 190 90 95 128 Dest AUS AUT BEL CAN CHE CZE DEU DNK ESP FIN FRA GBR GRC HUN IRL ISL ITA JPN KOR NLD NOR NZL POL PRT SE TUR USA Dest . AUS . AUT . BEL . CAN . CHE . CZE . DEU . DNK . ESP . FIN . FRA . GBR . GRC . HUN . IRL . ISL . ITA . JPN . KOR . LUX . MEX . MEX . NLD . NOR . NZL . POL . PRT . SVK . SE . TUR . USA . Year 2010 209 191 171 192 201 193 179 173 209 175 192 26 209 213 209 176 150 199 107 2009 209 209 185 194 172 190 201 112 191 171 180 208 175 190 201 27 26 207 213 209 177 145 199 185 133 2008 209 209 187 194 171 192 201 112 191 127 177 178 175 192 199 28 26 209 213 176 144 199 183 133 2007 209 209 178 194 168 193 200 112 191 128 174 174 175 188 198 25 26 207 213 179 142 199 133 2006 199 209 184 210 194 168 193 200 112 193 193 148 189 173 43 175 189 195 25 23 207 213 211 174 143 199 96 2005 209 209 182 194 166 139 201 112 193 204 97 191 165 175 189 183 25 23 10 208 213 173 138 199 96 2004 208 209 181 194 165 139 201 112 193 101 189 162 172 188 18 25 23 10 208 213 171 137 199 96 2003 208 209 181 194 163 138 201 112 193 100 190 156 172 188 18 25 23 207 213 167 149 199 96 2002 208 209 181 194 161 138 201 99 193 100 158 177 172 186 42 25 23 207 213 201 167 148 199 96 2001 190 207 181 190 194 163 138 201 99 193 97 154 172 187 42 19 12 206 213 199 166 142 199 96 2000 207 191 176 195 161 138 201 99 193 102 207 163 172 184 122 19 137 201 206 213 163 140 199 196 132 1999 206 174 195 164 138 201 99 193 162 87 163 172 185 42 19 12 202 204 213 157 136 111 96 1998 206 174 195 158 138 201 99 193 104 161 172 38 42 19 12 204 213 154 144 111 96 1997 204 55 195 152 138 201 99 193 100 189 159 172 189 42 19 12 204 212 151 144 111 96 1996 192 55 201 195 153 138 201 63 193 90 205 157 36 65 50 18 19 12 204 212 52 150 139 111 96 1995 202 55 195 150 138 201 58 193 85 205 146 65 50 37 19 12 200 212 150 140 111 96 1994 49 55 195 145 137 201 58 193 87 205 66 50 18 19 12 9 212 146 107 126 1993 49 48 195 137 201 58 193 87 205 66 50 18 19 12 9 212 139 104 126 1992 49 48 194 132 201 58 193 82 205 66 185 18 17 12 9 212 129 101 126 1991 168 48 180 194 117 201 58 193 70 205 2 43 184 16 15 12 9 212 51 125 98 126 1990 49 70 48 194 118 201 57 193 76 205 60 42 15 82 9 212 120 100 12 127 1989 48 194 118 201 57 134 204 60 12 8 9 212 121 98 125 1988 194 118 201 57 134 204 60 12 3 8 9 212 119 98 125 1987 194 118 201 57 131 204 60 12 4 8 9 212 118 97 125 1986 75 42 194 118 201 57 125 204 2 60 12 9 8 9 212 75 115 94 125 1985 194 118 201 57 124 204 60 42 9 212 109 95 125 1984 194 118 201 191 204 60 12 9 187 103 89 125 1983 194 118 201 204 60 12 9 187 100 125 1982 194 118 201 204 60 12 193 83 85 125 1981 81 47 42 194 118 201 204 2 59 12 189 75 98 125 1980 64 194 116 201 204 42 79 190 90 95 128 Dest AUS AUT BEL CAN CHE CZE DEU DNK ESP FIN FRA GBR GRC HUN IRL ISL ITA JPN KOR NLD NOR NZL POL PRT SE TUR USA Notes Columns: destination countries, Rows: year, Cell: numbers of source countries, for which we have observations on the number of migrants for particular year. Open in new tab Table A2 Country‐year Coverage Migration Stocks Dest . AUS . AUT . BEL . CAN . CHE . CZE . DEU . DNK . ESP . FIN . FRA . GBR . GRC . HUN . IRL . ISL . ITA . JPN . KOR . LUX . MEX . MEX . NLD . NOR . NZL . POL . PRT . SVK . SE . TUR . USA . Year 2010 209 191 171 192 201 193 179 173 209 175 192 26 209 213 209 176 150 199 107 2009 209 209 185 194 172 190 201 112 191 171 180 208 175 190 201 27 26 207 213 209 177 145 199 185 133 2008 209 209 187 194 171 192 201 112 191 127 177 178 175 192 199 28 26 209 213 176 144 199 183 133 2007 209 209 178 194 168 193 200 112 191 128 174 174 175 188 198 25 26 207 213 179 142 199 133 2006 199 209 184 210 194 168 193 200 112 193 193 148 189 173 43 175 189 195 25 23 207 213 211 174 143 199 96 2005 209 209 182 194 166 139 201 112 193 204 97 191 165 175 189 183 25 23 10 208 213 173 138 199 96 2004 208 209 181 194 165 139 201 112 193 101 189 162 172 188 18 25 23 10 208 213 171 137 199 96 2003 208 209 181 194 163 138 201 112 193 100 190 156 172 188 18 25 23 207 213 167 149 199 96 2002 208 209 181 194 161 138 201 99 193 100 158 177 172 186 42 25 23 207 213 201 167 148 199 96 2001 190 207 181 190 194 163 138 201 99 193 97 154 172 187 42 19 12 206 213 199 166 142 199 96 2000 207 191 176 195 161 138 201 99 193 102 207 163 172 184 122 19 137 201 206 213 163 140 199 196 132 1999 206 174 195 164 138 201 99 193 162 87 163 172 185 42 19 12 202 204 213 157 136 111 96 1998 206 174 195 158 138 201 99 193 104 161 172 38 42 19 12 204 213 154 144 111 96 1997 204 55 195 152 138 201 99 193 100 189 159 172 189 42 19 12 204 212 151 144 111 96 1996 192 55 201 195 153 138 201 63 193 90 205 157 36 65 50 18 19 12 204 212 52 150 139 111 96 1995 202 55 195 150 138 201 58 193 85 205 146 65 50 37 19 12 200 212 150 140 111 96 1994 49 55 195 145 137 201 58 193 87 205 66 50 18 19 12 9 212 146 107 126 1993 49 48 195 137 201 58 193 87 205 66 50 18 19 12 9 212 139 104 126 1992 49 48 194 132 201 58 193 82 205 66 185 18 17 12 9 212 129 101 126 1991 168 48 180 194 117 201 58 193 70 205 2 43 184 16 15 12 9 212 51 125 98 126 1990 49 70 48 194 118 201 57 193 76 205 60 42 15 82 9 212 120 100 12 127 1989 48 194 118 201 57 134 204 60 12 8 9 212 121 98 125 1988 194 118 201 57 134 204 60 12 3 8 9 212 119 98 125 1987 194 118 201 57 131 204 60 12 4 8 9 212 118 97 125 1986 75 42 194 118 201 57 125 204 2 60 12 9 8 9 212 75 115 94 125 1985 194 118 201 57 124 204 60 42 9 212 109 95 125 1984 194 118 201 191 204 60 12 9 187 103 89 125 1983 194 118 201 204 60 12 9 187 100 125 1982 194 118 201 204 60 12 193 83 85 125 1981 81 47 42 194 118 201 204 2 59 12 189 75 98 125 1980 64 194 116 201 204 42 79 190 90 95 128 Dest AUS AUT BEL CAN CHE CZE DEU DNK ESP FIN FRA GBR GRC HUN IRL ISL ITA JPN KOR NLD NOR NZL POL PRT SE TUR USA Dest . AUS . AUT . BEL . CAN . CHE . CZE . DEU . DNK . ESP . FIN . FRA . GBR . GRC . HUN . IRL . ISL . ITA . JPN . KOR . LUX . MEX . MEX . NLD . NOR . NZL . POL . PRT . SVK . SE . TUR . USA . Year 2010 209 191 171 192 201 193 179 173 209 175 192 26 209 213 209 176 150 199 107 2009 209 209 185 194 172 190 201 112 191 171 180 208 175 190 201 27 26 207 213 209 177 145 199 185 133 2008 209 209 187 194 171 192 201 112 191 127 177 178 175 192 199 28 26 209 213 176 144 199 183 133 2007 209 209 178 194 168 193 200 112 191 128 174 174 175 188 198 25 26 207 213 179 142 199 133 2006 199 209 184 210 194 168 193 200 112 193 193 148 189 173 43 175 189 195 25 23 207 213 211 174 143 199 96 2005 209 209 182 194 166 139 201 112 193 204 97 191 165 175 189 183 25 23 10 208 213 173 138 199 96 2004 208 209 181 194 165 139 201 112 193 101 189 162 172 188 18 25 23 10 208 213 171 137 199 96 2003 208 209 181 194 163 138 201 112 193 100 190 156 172 188 18 25 23 207 213 167 149 199 96 2002 208 209 181 194 161 138 201 99 193 100 158 177 172 186 42 25 23 207 213 201 167 148 199 96 2001 190 207 181 190 194 163 138 201 99 193 97 154 172 187 42 19 12 206 213 199 166 142 199 96 2000 207 191 176 195 161 138 201 99 193 102 207 163 172 184 122 19 137 201 206 213 163 140 199 196 132 1999 206 174 195 164 138 201 99 193 162 87 163 172 185 42 19 12 202 204 213 157 136 111 96 1998 206 174 195 158 138 201 99 193 104 161 172 38 42 19 12 204 213 154 144 111 96 1997 204 55 195 152 138 201 99 193 100 189 159 172 189 42 19 12 204 212 151 144 111 96 1996 192 55 201 195 153 138 201 63 193 90 205 157 36 65 50 18 19 12 204 212 52 150 139 111 96 1995 202 55 195 150 138 201 58 193 85 205 146 65 50 37 19 12 200 212 150 140 111 96 1994 49 55 195 145 137 201 58 193 87 205 66 50 18 19 12 9 212 146 107 126 1993 49 48 195 137 201 58 193 87 205 66 50 18 19 12 9 212 139 104 126 1992 49 48 194 132 201 58 193 82 205 66 185 18 17 12 9 212 129 101 126 1991 168 48 180 194 117 201 58 193 70 205 2 43 184 16 15 12 9 212 51 125 98 126 1990 49 70 48 194 118 201 57 193 76 205 60 42 15 82 9 212 120 100 12 127 1989 48 194 118 201 57 134 204 60 12 8 9 212 121 98 125 1988 194 118 201 57 134 204 60 12 3 8 9 212 119 98 125 1987 194 118 201 57 131 204 60 12 4 8 9 212 118 97 125 1986 75 42 194 118 201 57 125 204 2 60 12 9 8 9 212 75 115 94 125 1985 194 118 201 57 124 204 60 42 9 212 109 95 125 1984 194 118 201 191 204 60 12 9 187 103 89 125 1983 194 118 201 204 60 12 9 187 100 125 1982 194 118 201 204 60 12 193 83 85 125 1981 81 47 42 194 118 201 204 2 59 12 189 75 98 125 1980 64 194 116 201 204 42 79 190 90 95 128 Dest AUS AUT BEL CAN CHE CZE DEU DNK ESP FIN FRA GBR GRC HUN IRL ISL ITA JPN KOR NLD NOR NZL POL PRT SE TUR USA Notes Columns: destination countries, Rows: year, Cell: numbers of source countries, for which we have observations on the number of migrants for particular year. Open in new tab Table A3 Inflows of Foreign Population: Definitions and Sources Migration flows to: . Definition of ‘foreigner’ based on . Source . Australia Country of birth Permanent and long‐term arrivals, Government of Australia, DIMA, Dept. of Immigration and Multicultural Affairs http://www.immi.gov.au/media/statistics/index.htm Austria Citizenship Population register, Statistik Austria (1997–2002), Wanderungsstatistik 1996–2001, Vienna Belgium Citizenship Population register. Institut National de Statistique. Canada Country of birth Issues of permanent residence permit. Statistics Canada – Citizenship and Immigration Statistics. Flow is defined as a sum of foreign students, foreign workers and permanent residents. http://www.cic.gc.ca/english/resources/statistics/facts2009/glossary.asp Czech Rep. Citizenship Permanent residence permit and long‐term visa, Population register, Czech Statistical Office Denmark Citizenship Population register. Denmark Statistics Finland Citizenship Population register. Finnish Central Statistical Office France Citizenship Statistics on long‐term migration produced by the ‘Institut national d’études démographiques (INED)’ on the base on residence permit data (validity at least one year) transmitted by the Ministry of Interior. Germany Citizenship Population register. Statistisches Bundesamt Greece Citizenship Labour force survey. National Statistical Service of Greece 2006–7 Eurostat Hungary Citizenship Residence permits, National Hungary statistical office. Iceland Citizenship Population register. Hagstofa Islands national statistical office. Ireland Country of birth Labour Force Survey. Central Statistical Office. Very aggregate, only very few individual origins Italy Citizenship Residence Permits. ISTAT Japan Citizenship Years 1988–2005: Permanent and long‐term permits. Register of Foreigners, Ministry of Justice, Office of Immigration. Years 2006–8: Permanent and long‐term permits. OECD Source International Migration data Korea Citizenship OECD Source International Migration data Luxembourg Citizenship Population register, Statistical Office Luxembourg Mexico Citizenship OECD Source International Migration data Netherlands Country of birth Population register, CBS New Zealand Last permanent residence Permanent and Long‐term ARRIVALS (Annual – Dec) Census, Statistics New Zealand Norway 1979–1984 country of origin Population register, Statistics Norway 1985–2009 citizenship Poland Country of origin Administrative systems (PESEL, POBYT), statistical surveys (LFS, EU‐SILC, Population censuses). Central Statistical Office of Poland Portugal Citizenship Residence Permit, Ministry of Interior Slovakia Country of origin Permanent residence permit and long‐term visa, Slovak Statistical Office Spain Country of origin Residence Permit, Ministry of Interior Sweden Citizenship Population register, Statistics Sweden Switzerland Citizenship Register of Foreigners, Federal Foreign Office of Switzerland Turkey Citizenship OECD Source International Migration data UK Citizenship Residence permits for at least 12 months. IPS – office for national statistics, and EUROSTAT US Country of birth US Census Bureau Current Population Survey (CPS); US Department of Homeland Security: Yearbook of Immigration Statistics. Persons obtaining Legal Permanent Resident Status by Region and Country of birth (www.dhs.gov/ximgtn/statistics/publications/LPR06.shtm) Migration flows to: . Definition of ‘foreigner’ based on . Source . Australia Country of birth Permanent and long‐term arrivals, Government of Australia, DIMA, Dept. of Immigration and Multicultural Affairs http://www.immi.gov.au/media/statistics/index.htm Austria Citizenship Population register, Statistik Austria (1997–2002), Wanderungsstatistik 1996–2001, Vienna Belgium Citizenship Population register. Institut National de Statistique. Canada Country of birth Issues of permanent residence permit. Statistics Canada – Citizenship and Immigration Statistics. Flow is defined as a sum of foreign students, foreign workers and permanent residents. http://www.cic.gc.ca/english/resources/statistics/facts2009/glossary.asp Czech Rep. Citizenship Permanent residence permit and long‐term visa, Population register, Czech Statistical Office Denmark Citizenship Population register. Denmark Statistics Finland Citizenship Population register. Finnish Central Statistical Office France Citizenship Statistics on long‐term migration produced by the ‘Institut national d’études démographiques (INED)’ on the base on residence permit data (validity at least one year) transmitted by the Ministry of Interior. Germany Citizenship Population register. Statistisches Bundesamt Greece Citizenship Labour force survey. National Statistical Service of Greece 2006–7 Eurostat Hungary Citizenship Residence permits, National Hungary statistical office. Iceland Citizenship Population register. Hagstofa Islands national statistical office. Ireland Country of birth Labour Force Survey. Central Statistical Office. Very aggregate, only very few individual origins Italy Citizenship Residence Permits. ISTAT Japan Citizenship Years 1988–2005: Permanent and long‐term permits. Register of Foreigners, Ministry of Justice, Office of Immigration. Years 2006–8: Permanent and long‐term permits. OECD Source International Migration data Korea Citizenship OECD Source International Migration data Luxembourg Citizenship Population register, Statistical Office Luxembourg Mexico Citizenship OECD Source International Migration data Netherlands Country of birth Population register, CBS New Zealand Last permanent residence Permanent and Long‐term ARRIVALS (Annual – Dec) Census, Statistics New Zealand Norway 1979–1984 country of origin Population register, Statistics Norway 1985–2009 citizenship Poland Country of origin Administrative systems (PESEL, POBYT), statistical surveys (LFS, EU‐SILC, Population censuses). Central Statistical Office of Poland Portugal Citizenship Residence Permit, Ministry of Interior Slovakia Country of origin Permanent residence permit and long‐term visa, Slovak Statistical Office Spain Country of origin Residence Permit, Ministry of Interior Sweden Citizenship Population register, Statistics Sweden Switzerland Citizenship Register of Foreigners, Federal Foreign Office of Switzerland Turkey Citizenship OECD Source International Migration data UK Citizenship Residence permits for at least 12 months. IPS – office for national statistics, and EUROSTAT US Country of birth US Census Bureau Current Population Survey (CPS); US Department of Homeland Security: Yearbook of Immigration Statistics. Persons obtaining Legal Permanent Resident Status by Region and Country of birth (www.dhs.gov/ximgtn/statistics/publications/LPR06.shtm) Open in new tab Table A3 Inflows of Foreign Population: Definitions and Sources Migration flows to: . Definition of ‘foreigner’ based on . Source . Australia Country of birth Permanent and long‐term arrivals, Government of Australia, DIMA, Dept. of Immigration and Multicultural Affairs http://www.immi.gov.au/media/statistics/index.htm Austria Citizenship Population register, Statistik Austria (1997–2002), Wanderungsstatistik 1996–2001, Vienna Belgium Citizenship Population register. Institut National de Statistique. Canada Country of birth Issues of permanent residence permit. Statistics Canada – Citizenship and Immigration Statistics. Flow is defined as a sum of foreign students, foreign workers and permanent residents. http://www.cic.gc.ca/english/resources/statistics/facts2009/glossary.asp Czech Rep. Citizenship Permanent residence permit and long‐term visa, Population register, Czech Statistical Office Denmark Citizenship Population register. Denmark Statistics Finland Citizenship Population register. Finnish Central Statistical Office France Citizenship Statistics on long‐term migration produced by the ‘Institut national d’études démographiques (INED)’ on the base on residence permit data (validity at least one year) transmitted by the Ministry of Interior. Germany Citizenship Population register. Statistisches Bundesamt Greece Citizenship Labour force survey. National Statistical Service of Greece 2006–7 Eurostat Hungary Citizenship Residence permits, National Hungary statistical office. Iceland Citizenship Population register. Hagstofa Islands national statistical office. Ireland Country of birth Labour Force Survey. Central Statistical Office. Very aggregate, only very few individual origins Italy Citizenship Residence Permits. ISTAT Japan Citizenship Years 1988–2005: Permanent and long‐term permits. Register of Foreigners, Ministry of Justice, Office of Immigration. Years 2006–8: Permanent and long‐term permits. OECD Source International Migration data Korea Citizenship OECD Source International Migration data Luxembourg Citizenship Population register, Statistical Office Luxembourg Mexico Citizenship OECD Source International Migration data Netherlands Country of birth Population register, CBS New Zealand Last permanent residence Permanent and Long‐term ARRIVALS (Annual – Dec) Census, Statistics New Zealand Norway 1979–1984 country of origin Population register, Statistics Norway 1985–2009 citizenship Poland Country of origin Administrative systems (PESEL, POBYT), statistical surveys (LFS, EU‐SILC, Population censuses). Central Statistical Office of Poland Portugal Citizenship Residence Permit, Ministry of Interior Slovakia Country of origin Permanent residence permit and long‐term visa, Slovak Statistical Office Spain Country of origin Residence Permit, Ministry of Interior Sweden Citizenship Population register, Statistics Sweden Switzerland Citizenship Register of Foreigners, Federal Foreign Office of Switzerland Turkey Citizenship OECD Source International Migration data UK Citizenship Residence permits for at least 12 months. IPS – office for national statistics, and EUROSTAT US Country of birth US Census Bureau Current Population Survey (CPS); US Department of Homeland Security: Yearbook of Immigration Statistics. Persons obtaining Legal Permanent Resident Status by Region and Country of birth (www.dhs.gov/ximgtn/statistics/publications/LPR06.shtm) Migration flows to: . Definition of ‘foreigner’ based on . Source . Australia Country of birth Permanent and long‐term arrivals, Government of Australia, DIMA, Dept. of Immigration and Multicultural Affairs http://www.immi.gov.au/media/statistics/index.htm Austria Citizenship Population register, Statistik Austria (1997–2002), Wanderungsstatistik 1996–2001, Vienna Belgium Citizenship Population register. Institut National de Statistique. Canada Country of birth Issues of permanent residence permit. Statistics Canada – Citizenship and Immigration Statistics. Flow is defined as a sum of foreign students, foreign workers and permanent residents. http://www.cic.gc.ca/english/resources/statistics/facts2009/glossary.asp Czech Rep. Citizenship Permanent residence permit and long‐term visa, Population register, Czech Statistical Office Denmark Citizenship Population register. Denmark Statistics Finland Citizenship Population register. Finnish Central Statistical Office France Citizenship Statistics on long‐term migration produced by the ‘Institut national d’études démographiques (INED)’ on the base on residence permit data (validity at least one year) transmitted by the Ministry of Interior. Germany Citizenship Population register. Statistisches Bundesamt Greece Citizenship Labour force survey. National Statistical Service of Greece 2006–7 Eurostat Hungary Citizenship Residence permits, National Hungary statistical office. Iceland Citizenship Population register. Hagstofa Islands national statistical office. Ireland Country of birth Labour Force Survey. Central Statistical Office. Very aggregate, only very few individual origins Italy Citizenship Residence Permits. ISTAT Japan Citizenship Years 1988–2005: Permanent and long‐term permits. Register of Foreigners, Ministry of Justice, Office of Immigration. Years 2006–8: Permanent and long‐term permits. OECD Source International Migration data Korea Citizenship OECD Source International Migration data Luxembourg Citizenship Population register, Statistical Office Luxembourg Mexico Citizenship OECD Source International Migration data Netherlands Country of birth Population register, CBS New Zealand Last permanent residence Permanent and Long‐term ARRIVALS (Annual – Dec) Census, Statistics New Zealand Norway 1979–1984 country of origin Population register, Statistics Norway 1985–2009 citizenship Poland Country of origin Administrative systems (PESEL, POBYT), statistical surveys (LFS, EU‐SILC, Population censuses). Central Statistical Office of Poland Portugal Citizenship Residence Permit, Ministry of Interior Slovakia Country of origin Permanent residence permit and long‐term visa, Slovak Statistical Office Spain Country of origin Residence Permit, Ministry of Interior Sweden Citizenship Population register, Statistics Sweden Switzerland Citizenship Register of Foreigners, Federal Foreign Office of Switzerland Turkey Citizenship OECD Source International Migration data UK Citizenship Residence permits for at least 12 months. IPS – office for national statistics, and EUROSTAT US Country of birth US Census Bureau Current Population Survey (CPS); US Department of Homeland Security: Yearbook of Immigration Statistics. Persons obtaining Legal Permanent Resident Status by Region and Country of birth (www.dhs.gov/ximgtn/statistics/publications/LPR06.shtm) Open in new tab Table A4 Stock of Foreign Population: Definitions and Sources Foreign population stock in: . Definition of ‘foreigner’ based on . Source . Australia Country of birth Census of Population and Housing, Australian Bureau of Statistics Austria Country of birth Statistics Austria, Population Census 2001 and Population Register 2001–9. For census year 1981 and 1991 definition by citizenship Belgium Citizenship Population register. Institut National de Statistique Canada Country of birth Census of Canada, Statistics Canada. www.statcan.ca/ Czech Rep. Citizenship Permanent residence permit and long‐term visa, Population register, Czech Statistical Office and Directorate of Alien and Border Police Denmark Country of origin Population register. Denmark Statistics Finland Country of birth Population register. Finnish Central Statistical Office France Country of birth Census. Residence permit. Office des migrations internationals Germany Citizenship Population register. Statistisches Bundesamt Greece Citizenship Labour force survey. National Statistical Service of Greece Hungary Citizenship National Hungary statistical office Iceland Country of birth Population register. Hagstofa Islands Ireland Country of birth Censuses, Statistical Office, Ireland Italy Citizenship Residence Permits. ISTAT Japan Citizenship Years 1980–99, Register of Foreigners, Ministry of Justice, Office of Immigration. Years 1999–2008 OECD Source Migration stat. Both sources based on permanent and long‐term permits Korea Citizenship 1986–8: Trends in international migration Outlook, OECD 1990–2008: OECD Source International Migration Database Luxembourg Citizenship Population register, Statistical office Luxembourg Mexico Country of birth 2005: Trends in international migration Outlook, OECD 2000: OECD Source International Migration Database Netherlands Citizenship Population register, CBS New Zealand Country of birth Census, Statistics New Zealand Norway Country background Population register, Statistics Norway Country background is the person’s own, their mother’s or possibly their father’s country of birth. Persons without an immigrant background only have Norway as their country background. In cases where the parents have different countries of birth, the mother’s country of birth is chosen. Poland Country of birth 2002 Census, residence permits, Statistics Poland Portugal Citizenship Residence Permit, Ministry of Interior, www.ine.pt Slovak Republic Country of origin Permanent residence permit and long‐term visa, Slovak Statistical Office Spain 1985–95 citizenship Residence Permit, Ministry of Interior 1996–2009 country of birth Sweden Country of birth Population register, Statistics Sweden Switzerland Citizenship Register of Foreigners, Federal Foreign Office Turkey Country of birth OECD Source International Migration Database United Kingdom Country of birth LFS, UK statistical office United States Country of birth US Census Bureau: 1990 and 2000 US census, the rest Current Population Survey (CPS) December. Data Ferret. Years 1980–9, 1991–3 from extrapolations by Tim Hatton (RESTAT) Foreign population stock in: . Definition of ‘foreigner’ based on . Source . Australia Country of birth Census of Population and Housing, Australian Bureau of Statistics Austria Country of birth Statistics Austria, Population Census 2001 and Population Register 2001–9. For census year 1981 and 1991 definition by citizenship Belgium Citizenship Population register. Institut National de Statistique Canada Country of birth Census of Canada, Statistics Canada. www.statcan.ca/ Czech Rep. Citizenship Permanent residence permit and long‐term visa, Population register, Czech Statistical Office and Directorate of Alien and Border Police Denmark Country of origin Population register. Denmark Statistics Finland Country of birth Population register. Finnish Central Statistical Office France Country of birth Census. Residence permit. Office des migrations internationals Germany Citizenship Population register. Statistisches Bundesamt Greece Citizenship Labour force survey. National Statistical Service of Greece Hungary Citizenship National Hungary statistical office Iceland Country of birth Population register. Hagstofa Islands Ireland Country of birth Censuses, Statistical Office, Ireland Italy Citizenship Residence Permits. ISTAT Japan Citizenship Years 1980–99, Register of Foreigners, Ministry of Justice, Office of Immigration. Years 1999–2008 OECD Source Migration stat. Both sources based on permanent and long‐term permits Korea Citizenship 1986–8: Trends in international migration Outlook, OECD 1990–2008: OECD Source International Migration Database Luxembourg Citizenship Population register, Statistical office Luxembourg Mexico Country of birth 2005: Trends in international migration Outlook, OECD 2000: OECD Source International Migration Database Netherlands Citizenship Population register, CBS New Zealand Country of birth Census, Statistics New Zealand Norway Country background Population register, Statistics Norway Country background is the person’s own, their mother’s or possibly their father’s country of birth. Persons without an immigrant background only have Norway as their country background. In cases where the parents have different countries of birth, the mother’s country of birth is chosen. Poland Country of birth 2002 Census, residence permits, Statistics Poland Portugal Citizenship Residence Permit, Ministry of Interior, www.ine.pt Slovak Republic Country of origin Permanent residence permit and long‐term visa, Slovak Statistical Office Spain 1985–95 citizenship Residence Permit, Ministry of Interior 1996–2009 country of birth Sweden Country of birth Population register, Statistics Sweden Switzerland Citizenship Register of Foreigners, Federal Foreign Office Turkey Country of birth OECD Source International Migration Database United Kingdom Country of birth LFS, UK statistical office United States Country of birth US Census Bureau: 1990 and 2000 US census, the rest Current Population Survey (CPS) December. Data Ferret. Years 1980–9, 1991–3 from extrapolations by Tim Hatton (RESTAT) Open in new tab Table A4 Stock of Foreign Population: Definitions and Sources Foreign population stock in: . Definition of ‘foreigner’ based on . Source . Australia Country of birth Census of Population and Housing, Australian Bureau of Statistics Austria Country of birth Statistics Austria, Population Census 2001 and Population Register 2001–9. For census year 1981 and 1991 definition by citizenship Belgium Citizenship Population register. Institut National de Statistique Canada Country of birth Census of Canada, Statistics Canada. www.statcan.ca/ Czech Rep. Citizenship Permanent residence permit and long‐term visa, Population register, Czech Statistical Office and Directorate of Alien and Border Police Denmark Country of origin Population register. Denmark Statistics Finland Country of birth Population register. Finnish Central Statistical Office France Country of birth Census. Residence permit. Office des migrations internationals Germany Citizenship Population register. Statistisches Bundesamt Greece Citizenship Labour force survey. National Statistical Service of Greece Hungary Citizenship National Hungary statistical office Iceland Country of birth Population register. Hagstofa Islands Ireland Country of birth Censuses, Statistical Office, Ireland Italy Citizenship Residence Permits. ISTAT Japan Citizenship Years 1980–99, Register of Foreigners, Ministry of Justice, Office of Immigration. Years 1999–2008 OECD Source Migration stat. Both sources based on permanent and long‐term permits Korea Citizenship 1986–8: Trends in international migration Outlook, OECD 1990–2008: OECD Source International Migration Database Luxembourg Citizenship Population register, Statistical office Luxembourg Mexico Country of birth 2005: Trends in international migration Outlook, OECD 2000: OECD Source International Migration Database Netherlands Citizenship Population register, CBS New Zealand Country of birth Census, Statistics New Zealand Norway Country background Population register, Statistics Norway Country background is the person’s own, their mother’s or possibly their father’s country of birth. Persons without an immigrant background only have Norway as their country background. In cases where the parents have different countries of birth, the mother’s country of birth is chosen. Poland Country of birth 2002 Census, residence permits, Statistics Poland Portugal Citizenship Residence Permit, Ministry of Interior, www.ine.pt Slovak Republic Country of origin Permanent residence permit and long‐term visa, Slovak Statistical Office Spain 1985–95 citizenship Residence Permit, Ministry of Interior 1996–2009 country of birth Sweden Country of birth Population register, Statistics Sweden Switzerland Citizenship Register of Foreigners, Federal Foreign Office Turkey Country of birth OECD Source International Migration Database United Kingdom Country of birth LFS, UK statistical office United States Country of birth US Census Bureau: 1990 and 2000 US census, the rest Current Population Survey (CPS) December. Data Ferret. Years 1980–9, 1991–3 from extrapolations by Tim Hatton (RESTAT) Foreign population stock in: . Definition of ‘foreigner’ based on . Source . Australia Country of birth Census of Population and Housing, Australian Bureau of Statistics Austria Country of birth Statistics Austria, Population Census 2001 and Population Register 2001–9. For census year 1981 and 1991 definition by citizenship Belgium Citizenship Population register. Institut National de Statistique Canada Country of birth Census of Canada, Statistics Canada. www.statcan.ca/ Czech Rep. Citizenship Permanent residence permit and long‐term visa, Population register, Czech Statistical Office and Directorate of Alien and Border Police Denmark Country of origin Population register. Denmark Statistics Finland Country of birth Population register. Finnish Central Statistical Office France Country of birth Census. Residence permit. Office des migrations internationals Germany Citizenship Population register. Statistisches Bundesamt Greece Citizenship Labour force survey. National Statistical Service of Greece Hungary Citizenship National Hungary statistical office Iceland Country of birth Population register. Hagstofa Islands Ireland Country of birth Censuses, Statistical Office, Ireland Italy Citizenship Residence Permits. ISTAT Japan Citizenship Years 1980–99, Register of Foreigners, Ministry of Justice, Office of Immigration. Years 1999–2008 OECD Source Migration stat. Both sources based on permanent and long‐term permits Korea Citizenship 1986–8: Trends in international migration Outlook, OECD 1990–2008: OECD Source International Migration Database Luxembourg Citizenship Population register, Statistical office Luxembourg Mexico Country of birth 2005: Trends in international migration Outlook, OECD 2000: OECD Source International Migration Database Netherlands Citizenship Population register, CBS New Zealand Country of birth Census, Statistics New Zealand Norway Country background Population register, Statistics Norway Country background is the person’s own, their mother’s or possibly their father’s country of birth. Persons without an immigrant background only have Norway as their country background. In cases where the parents have different countries of birth, the mother’s country of birth is chosen. Poland Country of birth 2002 Census, residence permits, Statistics Poland Portugal Citizenship Residence Permit, Ministry of Interior, www.ine.pt Slovak Republic Country of origin Permanent residence permit and long‐term visa, Slovak Statistical Office Spain 1985–95 citizenship Residence Permit, Ministry of Interior 1996–2009 country of birth Sweden Country of birth Population register, Statistics Sweden Switzerland Citizenship Register of Foreigners, Federal Foreign Office Turkey Country of birth OECD Source International Migration Database United Kingdom Country of birth LFS, UK statistical office United States Country of birth US Census Bureau: 1990 and 2000 US census, the rest Current Population Survey (CPS) December. Data Ferret. Years 1980–9, 1991–3 from extrapolations by Tim Hatton (RESTAT) Open in new tab Footnotes 1 " Results are available from authors. 2 " See data by Docquier and Marfouk (2006), OECD (2011), the World Bank (2011) and the United Nations (2008). 3 " Migration flow is the inflow of immigrants to a destination from a given origin in a given year. The definition usually covers immigrants coming for a period of half year or longer. Foreign population stock is a number of foreigners from a given country of origin living in a destination in a given year. The foreign population stock data are dated ultimo. 4 " Thus, our data set bears some problems related to different sources of migration data (censuses, registers or labour force surveys), different definitions of foreigner (country of birth and citizenship) and unbalanced nature of the data due to missing observations for some countries of destinations and origins. For example, Austria, Belgium, Germany, Luxembourg, the Netherlands, Switzerland and the Nordic countries use data based on population registers; the majority of Southern and Eastern European countries use data based on the number of residence permits issued; Australia, Canada, New Zealand and Poland use data from censuses; some countries such as Greece, the UK and the US use labour force surveys and others have information based on social security systems or other sources. In definitions of immigration flows some countries such as Australia, Canada, Ireland, the Netherlands, Poland and the US define an ‘immigrant’ by country of birth. Other countries such as New Zealand, Slovakia and Spain use definition by country of origin, while the rest of countries define an immigrant by citizenship. For immigration stock, the definition of immigrant population differs among countries as well, but for the majority of destinations we use the definition by country of birth. Australia, Austria, Canada, Denmark, Finland, France, Iceland, Ireland, Mexico, New Zealand, Norway, Poland, Slovakia, Spain, Sweden, Turkey, the UK and the US define immigrant stock by country of birth. A few countries like Belgium, Czech Republic, Germany, Greece, Hungary, Italy, Japan, Korea, Luxembourg, the Netherlands, Portugal and Switzerland define immigrant population by citizenship. 5 " When we split the sample by decades (not shown here), we observe that flows are increasing over time and that, despite our panel being somewhat unbalanced, this change is pretty proportional across all linguistic tree levels. 6 " The Levenshtein linguistic distance has been used, for example, to measure the difficulty in learning the local language among migrants to Germany (Isphording and Otten, 2011, 2013). To the best of our knowledge, we are the first to employ the Max Planck index in analyses of migration determinants. 7 " For application of the Dyen index in the context of determinants of migration, see Belot and Ederveen (2012). 8 " Lack of good South–South migration data may also account for this finding if individuals from the poorest countries migrate to close and relative poor countries. 9 " In fact a similar argument would warrant the inclusion of social expenditures in the country of origin into our empirical model. Unfortunately, as in previous research, data constraints preclude us from including this information as only the OECD provides good comparative data on social expenditures. 10 " This percentage is much lower than either the 95% of zero values that Simpson and Sparber (2013), who specifically discuss the ‘zero problem’ in migration data, face or the usually reported in the trade literature when estimating gravity models. 11 " We obtained similar estimates of the model using non‐linear least squares where the level of migration flows is explained by the exponential of the linear combination of all log‐transformed independent variables without imposing any restrictions between the mean and the variance as some count models require. 12 " As an additional test on whether the IIA assumption holds for OECD destinations, we restrict the data to study migration flows among OECD countries. The coefficient of the linguistic proximity index remains highly significant at 1% and increases to around 0.39. Results are available from the authors. 13 " If t−2 lags in the migration stock variable are used instead, its coefficients are slightly lower but the rest remains unchanged. Results are available from the authors. 14 " In column (6) that encompasses the smaller sample for which unemployment rates are available in origin, emigration rates increase, though very moderately, with GDP at origin. 15 " This is something we plan to investigate further in a separate article. 16 " Results are also robust to the use of a second index (‘weighed’) that takes into account within‐country subpopulations that are genetically distant and calculates the distance between both countries by taking into account the difference between each pair of genetic groups and weighting them by their shares. Estimates with this alternative ‘weighed’ genetic distance are available from the authors upon request. 17 " In separate estimates, we use the Dyen index and attach a zero value for the pairs of countries in which only one of the languages is Indo‐European. The estimated coefficient on this linguistic index of 0.146 is, not surprisingly, slightly smaller than when the sample is restricted to only Indo‐European countries (but still significant). Conversely, when we restrict the sample to countries with Indo‐European languages, the estimated coefficient of our index of linguistic proximity is larger than that obtained in Tables 2 and 3. 18 " As an additional robustness analysis we took advantage of the detailed information embedded in our linguistic proximity index and substituted (in the baseline model) the linguistic proximity index for an indicator of whether the languages of two countries share a particular level in the Ethno linguistic tree. We estimated the model separately for each level to see whether there are some non‐linearities in the relevance of linguistic proximity. Dummies for all levels of the linguistic family tree – except for the most aggregated (Indo‐European versus Uralic) and the second most aggregated (Germanic versus Slavic) – display a significant positive coefficient that increases up to the fourth level of the tree. When the analysis is restricted to the last fifteen years level 2 is also significant. The Table with those additional robustness tests is available from the authors upon request. 19 " Results are robust to including measures of the number of computers per capita in the source country to infer exposure to English or other languages through Internet. 20 " In fact the effect implied by standardised coefficients is also larger than when using the entire data set. The corresponding beta coefficients for linguistic proximity based on the closest pair among all official languages in column (3) are 0.022 and 0.015 for non‐English and English‐speaking destinations, respectively, whereas they almost double, to 0.044 and 0.026, when using the sample of countries with low secondary enrolment in column (6). 21 " When we used some basic information on entry requirements gathered by Goodman (2010b) for 15 EU countries, only available for a couple of years of the sample, our estimates on the effect of linguistic proximity were not altered. We also employed some general country classification on whether selective point systems were used or not in some countries (many of them English speaking). Results showed that linguistic distance mattered as much (in fact a bit more) in those countries without a point system. 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OpenURL Placeholder Text WorldCat Author notes " We are grateful to our anonymous referees and the editor in charge, for useful comments and suggestions, to participants at NORFACE and CReAM conference on Migration: Economic Change, Social Challenge in London, EEA conference in Oslo, WISE Symposium on Contemporary Labor Economics in Xiamen, IZA AM2 2010 in Bonn, First Tempo Conference on International Migration in Dublin and NORFACE, WB and CReAM conference on “Migration, Development, and Global Issues” in London, and to Bo Honoré for helpful discussions. Jan Bryla, Anne Lund, Enric Boix and Rasmus Steffensen provided excellent research assistance within the migration and linguistic data collection process. We also thank Ignacio Ortuño and Roman Wacziarg for providing the data on linguistic diversity and genetic distance, and Tim Hatton for extrapolated data on migration stocks to the US for years 1980–9. This research was funded in part by the NORFACE research programme on Migration in Europe–Social, Economic, Cultural and Policy Dynamics (MI3‐Migration: Integration, Impact and Interaction), by the Danish Research Council Project No. 09‐075719 (No. 0602‐01553B), from the Operational Programme Education for Competitiveness (No. CZ.1.07/2.3.00/20.0296), by a Czech Science Foundation grant (No. GA15‐23177S) and by an SGS Research grant (No. SP2014/136). The usual disclaimer applies. © 2015 Royal Economic Society http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economic Journal Oxford University Press

The Role of Language in Shaping International Migration

Economic Journal , Volume 125 (586) – Aug 1, 2015