《国际政治经济学》文献资料（Political Economy of Migration）Educated Preferences

Attitudes Toward Immigration in Europe 409 spond,respectively,to whether the person had completed only primary or basic schooling,secondary schooling,or tertiary education).39 For each of the fifty-one origin countries we were thus able to compute the proportion of immigrants to Europe in each education category.Here we present the main results of this analy- sis;more detailed results are available in are separate supplement to this article.40 As expected,we found that the proportion of low-(high-)skilled immigrants is sharply decreasing (increasing)in origin country gross domestic product(GDP) per capita.In the case of immigrants from European origins,the correlation between origin GDP per capita and the proportion of low(high)education immigrants is -0.22 (0.16).This pattern is even more pronounced for immigration from non- European origins,where the respective correlations are -0.49 and 0.72.Parsing the data another way,if we take the average per capita GDP among origin coun- tries in each subsample (that is,European and non-European)as the dividing line between"richer"and "poorer"countries,the skill differences among immigrants from each category are substantial.For instance,the proportion of immigrants from poorer non-European countries that have low (high)educational levels is 0.50 (0.21),compared to 0.21 (0.48)for immigrants from richer countries.The differ- ences between the skill levels of immigrants from richer and poorer nations are stark.Table 1 reports the summary measures of the skill attributes of different categories of immigrants. Thus,if concerns about labor-market competition are critical determinants of immigration preferences,given the large gap in average skills between immi- grants from richer and poorer countries,we should expect that respondent skill levels should have a substantially different effect on answers to the ESS questions about immigration from richer and poorer countries.Respondent skill levels should have a large and positive effect on support for immigration from poorer countries, since these are predominantly low-skilled immigrants who compete for jobs with low-skilled natives.This is in line with the proposition tested in previous studies. But respondent skill levels should have a substantially smaller,and perhaps even a negative,effect on support for immigration from richer countries,since these are predominantly high-skilled immigrants who are substitutes(rather than com- plements)to native workers with high skills.In Table 1 we have reported educa- tion levels of natives(the ESS sample)to compare with those of different types of immigrants.By this simple measure,immigrants from poorer countries(both from within and outside Europe)are,on average,less skilled than the ESS natives,while immigrants from richer countries are more highly skilled than natives.While these relationships can vary according to the education levels of natives within each particular ESS country,the large skill gap between immigrants from richer versus 39.These categories match the EDUCATIONAL ATTAINMENT measure in the ESS data that we employ below with the exception that van Tubergen also includes PHDs in the high education category rather than coding them separately. 40.This supplement is available at the authors'Web site at (http://www.people.fas.harvard.edu/ ~jhainm/research.htmI).It provides a detailed breakdown of education levels in each ESS country and compares these with education levels of immigrants to ESS countries using the van Tubergen data

spond, respectively, to whether the person had completed only primary or basic schooling, secondary schooling, or tertiary education!+ 39 For each of the fifty-one origin countries we were thus able to compute the proportion of immigrants to Europe in each education category+ Here we present the main results of this analy￾sis; more detailed results are available in are separate supplement to this article+ 40 As expected, we found that the proportion of low- ~high-! skilled immigrants is sharply decreasing ~increasing! in origin country gross domestic product ~GDP! per capita+ In the case of immigrants from European origins, the correlation between origin GDP per capita and the proportion of low ~high! education immigrants is
0+22 ~0+16!+ This pattern is even more pronounced for immigration from non￾European origins, where the respective correlations are
0+49 and 0+72+ Parsing the data another way, if we take the average per capita GDP among origin coun￾tries in each subsample ~that is, European and non-European! as the dividing line between “richer” and “poorer” countries, the skill differences among immigrants from each category are substantial+ For instance, the proportion of immigrants from poorer non-European countries that have low ~high! educational levels is 0+50 ~0+21!, compared to 0+21 ~0+48! for immigrants from richer countries+ The differ￾ences between the skill levels of immigrants from richer and poorer nations are stark+ Table 1 reports the summary measures of the skill attributes of different categories of immigrants+ Thus, if concerns about labor-market competition are critical determinants of immigration preferences, given the large gap in average skills between immi￾grants from richer and poorer countries, we should expect that respondent skill levels should have a substantially different effect on answers to the ESS questions about immigration from richer and poorer countries+ Respondent skill levels should have a large and positive effect on support for immigration from poorer countries, since these are predominantly low-skilled immigrants who compete for jobs with low-skilled natives+ This is in line with the proposition tested in previous studies+ But respondent skill levels should have a substantially smaller, and perhaps even a negative, effect on support for immigration from richer countries, since these are predominantly high-skilled immigrants who are substitutes ~rather than com￾plements! to native workers with high skills+ In Table 1 we have reported educa￾tion levels of natives ~the ESS sample! to compare with those of different types of immigrants+ By this simple measure, immigrants from poorer countries ~both from within and outside Europe! are, on average, less skilled than the ESS natives, while immigrants from richer countries are more highly skilled than natives+ While these relationships can vary according to the education levels of natives within each particular ESS country, the large skill gap between immigrants from richer versus 39+ These categories match the educational attainment measure in the ESS data that we employ below with the exception that van Tubergen also includes phds in the high education category rather than coding them separately+ 40+ This supplement is available at the authors’ Web site at ^http:00www+people+fas+harvard+edu0 ;jhainm0research+htmI&+ It provides a detailed breakdown of education levels in each ESS country and compares these with education levels of immigrants to ESS countries using the van Tubergen data+ Attitudes Toward Immigration in Europe 409

TABLE 1.Education levels of immigrants from richer/poorer countries and natives Proportion of immigrants with Difference: Average of Average immigrants- Immigrant source Low Middle High education Standard average of countries' educationeducation education Observations deviation natives3 Richer European countries 0.286 0.384 0.330 187 2.044 0.785 0.263 Poorer European countries 0.487 0.334 0.179 133 1.692 0.757 -0.089 Richer countries outside Europe 0.212 0.307 0.481 101 2.269 0.792 0.488 Poorer countries outside Europe 0.500 0.293 0.207 209 1.707 0.791 -0.074 Education levels of natives (full ESS sample) 0.402 0.414 0.184 41988 1.781 0.737 Notes:1.Richer/poorer European/non-European source countries are defined as countries that fall above/below the sample mean in the respective GDP per capita distribution of the fifty-one European/non-European origin countries available in the International File of Immigration Surveys Database(Van Tubergen 2004).See the Web supplement to this article for more detailed analysis and additional tests of the differences in education levels among immigrants from richer and poorer source countries. 2.The average education score is computed as the mean of a discrete attainment variable coded:low education =1, middle education 2,and high education 3. 3.Differences are assessed using two-sample t-tests(two-tailed)with uncqual variances assumed.All differences in means are significant at the 0.99 confidence level. poorer nations is abundantly clear and the implications are straightforward:if labor- market concerns are critical,the effects of individual skills levels on attitudes toward these different categories of immigrants should be markedly different.This is a simple,critical test for the labor-market competition account of anti-immigration sentiments. Asummary of the ESS data on immigration preferences is reported in Table 2.41 On average,survey respondents prefer European immigrants to non-Europeans (holding wealth constant),as perhaps we might expect,and they prefer immi- grants from richer countries to those from poorer countries (holding "European- ness"constant).42 The most preferred immigrants are thus those from richer European nations;the least preferred are from poorer countries outside Europe. Many different forces may be shaping these general preferences,of course,but it 41.Following the official ESS recommendation,we applied the design weight(DWEIGHT)to all estimations that examine single countries (all country-specific averages and probit estimations)and both the design weight and the population weight(PWEIGHT)to all estimations where data are pooled across countries (full sample averages and probit estimations).See the ESS guidelines "Weighting European Social Survey Data"at (http://ess.nsd.uib.no/files/WeightingESS.pdf).Accessed 10 Novem- ber2006. 42.Difference-of-mean tests indicate that these differences for both the Europe versus outside com- parisons and for both of the rich versus poor comparisons are highly significant (the lowest t-value in the four tests is 8.98),although the substantive differences are of course rather small

poorer nations is abundantly clear and the implications are straightforward: if labor￾market concerns are critical, the effects of individual skills levels on attitudes toward these different categories of immigrants should be markedly different+ This is a simple, critical test for the labor-market competition account of anti-immigration sentiments+ Asummary of the ESS data on immigration preferences is reported in Table 2+ 41 On average, survey respondents prefer European immigrants to non-Europeans ~holding wealth constant!, as perhaps we might expect, and they prefer immi￾grants from richer countries to those from poorer countries ~holding “European￾ness” constant!+ 42 The most preferred immigrants are thus those from richer European nations; the least preferred are from poorer countries outside Europe+ Many different forces may be shaping these general preferences, of course, but it 41+ Following the official ESS recommendation, we applied the design weight ~dweight! to all estimations that examine single countries ~all country-specific averages and probit estimations! and both the design weight and the population weight ~pweight! to all estimations where data are pooled across countries ~full sample averages and probit estimations!+ See the ESS guidelines “Weighting European Social Survey Data” at ^http:00ess+nsd+uib+no0files0WeightingESS+pdf&+ Accessed 10 Novem￾ber 2006+ 42+ Difference-of-mean tests indicate that these differences for both the Europe versus outside com￾parisons and for both of the rich versus poor comparisons are highly significant ~the lowest t-value in the four tests is 8+98!, although the substantive differences are of course rather small+ TABLE 1. Education levels of immigrants from richer/poorer countries and natives Proportion of immigrants with Immigrant source countries1 Low education Middle education High education Observations Average education score2 Standard deviation Difference: Average of immigrants— average of natives3 Richer European countries 0+286 0+384 0+330 187 2+044 0+785 0+263 Poorer European countries 0+487 0+334 0+179 133 1+692 0+757
0+089 Richer countries outside Europe 0+212 0+307 0+481 101 2+269 0+792 0+488 Poorer countries outside Europe 0+500 0+293 0+207 209 1+707 0+791
0+074 Education levels of natives (full ESS sample) 0+402 0+414 0+184 41988 1+781 0+737 Notes: 1+ Richer0poorer European0non-European source countries are defined as countries that fall above0below the sample mean in the respective GDP per capita distribution of the fifty-one European0non-European origin countries available in the International File of Immigration Surveys Database ~Van Tubergen 2004!+ See the Web supplement to this article for more detailed analysis and additional tests of the differences in education levels among immigrants from richer and poorer source countries+ 2+ The average education score is computed as the mean of a discrete attainment variable coded: low education 1, middle education 2, and high education 3+ 3+ Differences are assessed using two-sample t-tests ~two-tailed! with unequal variances assumed+ All differences in means are significant at the 0+99 confidence level+

Attitudes Toward Immigration in Europe 411 TABLE 2.Immigration preferences by source:Full ESS sample Dichotomous variables Allow Allow Allow Allow Standard Immigration from none a few some many Missing Total Mean deviation Richer European 4.048 11.936 17.946 6.336 2.035 42302 0.603 0.489 countries 9.57% 28.22% 42.42% 14.98% 4.81% Poorer European 3,617 13.759 18.306 4.904 1,717 42302 0.572 0.495 countries 8.55% 32.53% 43.27% 11.59% 4.06% Richer countries 4.466 13,178 17,351 5.256 2.050 42302 0.562 0.496 outside Europe 10.56% 31.15% 41.02% 12.43% 4.85% Poorer countries 4.316 14.670 17.127 4.364 1.826 42302 0.531 0.499 outside Europe 10.20% 34.68% 40.49% 10.32% 4.32% Notes:Cases weighted by DWEIGHT and PWEIGHT. 1.For dichotomous variables:1 allow many/some;0 allow few/none is interesting to note that they clash rather directly with a simple labor-market competition story in at least one clear way:since the average ESS respondent is more highly skilled than the average immigrant from poorer countries inside Europe, but has an even greater skill advantage over the average immigrant from poorer countries outside Europe,the distributional effects (on their own)would imply that the latter should be more preferred than the former on average. Table 3 reports immigration preferences by country of respondent.Here we just provide the mean of each dichotomous dependent variable (indicating whether respondents supported immigration from each different source),and we have ranked the ESS countries according to per capita GDP.Overall,Sweden seems to be the most pro-immigrant country across the board,while Hungary is the most anti- immigrant.Interestingly,respondents in Germany and Italy,nations often regarded as fertile soil for chauvinism and antiforeigner movements(such as the Republi- kaner and the National Democratic Party in Germany or the Lega Nord party in Italy),appear to look more favorably on immigration,in general,than citizens in many other European nations.Other countries yield less of a surprise as,for exam- ple,Austria,with its strong right-wing party (the Freiheitlichen),shows rather low support for immigration.Another interesting result is that respondents in Den- mark appear to differentiate most strongly between types of immigrants,prefer- ring"rich"over"poor"immigrants by larger margins than respondents elsewhere. (Given the recent success of the right-wing Folkeparti in Denmark,campaigning largely on opposition to poor immigrants,perhaps this should not be surprising.) The general pattern in preferences is again rather inconsistent with the labor- market competition argument.Assuming the skill level of the average respondent is increasing in per capita GDP across these countries,we should expect that

is interesting to note that they clash rather directly with a simple labor-market competition story in at least one clear way: since the average ESS respondent is more highly skilled than the average immigrant from poorer countries inside Europe, but has an even greater skill advantage over the average immigrant from poorer countries outside Europe, the distributional effects ~on their own! would imply that the latter should be more preferred than the former on average+ Table 3 reports immigration preferences by country of respondent+ Here we just provide the mean of each dichotomous dependent variable ~indicating whether respondents supported immigration from each different source!, and we have ranked the ESS countries according to per capita GDP+ Overall, Sweden seems to be the most pro-immigrant country across the board, while Hungary is the most anti￾immigrant+ Interestingly, respondents in Germany and Italy, nations often regarded as fertile soil for chauvinism and antiforeigner movements ~such as the Republi￾kaner and the National Democratic Party in Germany or the Lega Nord party in Italy!, appear to look more favorably on immigration, in general, than citizens in many other European nations+ Other countries yield less of a surprise as, for exam￾ple, Austria, with its strong right-wing party ~the Freiheitlichen!, shows rather low support for immigration+ Another interesting result is that respondents in Den￾mark appear to differentiate most strongly between types of immigrants, prefer￾ring “rich” over “poor” immigrants by larger margins than respondents elsewhere+ ~Given the recent success of the right-wing Folkeparti in Denmark, campaigning largely on opposition to poor immigrants, perhaps this should not be surprising+! The general pattern in preferences is again rather inconsistent with the labor￾market competition argument+ Assuming the skill level of the average respondent is increasing in per capita GDP across these countries, we should expect that TABLE 2. Immigration preferences by source: Full ESS sample Dichotomous variables1 Immigration from Allow none Allow a few Allow some Allow many Missing Total Mean Standard deviation Richer European 4,048 11,936 17,946 6,336 2,035 42302 0+603 0+489 countries 9+57% 28+22% 42+42% 14+98% 4+81% Poorer European 3,617 13,759 18,306 4,904 1,717 42302 0+572 0+495 countries 8+55% 32+53% 43+27% 11+59% 4+06% Richer countries 4,466 13,178 17,351 5,256 2,050 42302 0+562 0+496 outside Europe 10+56% 31+15% 41+02% 12+43% 4+85% Poorer countries 4,316 14,670 17,127 4,364 1,826 42302 0+531 0+499 outside Europe 10+20% 34+68% 40+49% 10+32% 4+32% Notes: Cases weighted by dweight and pweight+ 1+ For dichotomous variables: 1 allow many0some; 0 allow few0none+ Attitudes Toward Immigration in Europe 411

412 International Organization TABLE 3.Immigration preferences by source:Individual ESS countries Means of dichotomous dependent variables Favor immigration from Richer Poorer Richer Poorer countries countries European European outside outside GDP per Country countries countries Europe Europe Observations! capita- Luxembourg 0.52 0.51 0.49 0.47 1370 56290 Norway 0.62 0.66 0.54 0.60 2017 35132 freland 0.68 0.68 0.62 0.64 1964 30100 Denmark 0.69 0.56 0.59 0.46 1415 29306 Switzerland 0.69 0.73 0.63 0.69 1947 28128 Austria 0.43 0.39 0.37 0.35 2063 28009 Netherlands 0.54 0.58 0.50 0.56 2312 27071 Belgium 0.61 0.62 0.55 0.56 1843 26435 Germany 0.65 0.64 0.61 0.59 2841 26067 France 0.57 0.57 0.48 0.51 1448 25318 Finland 0.50 0.46 0.41 0.40 1940 25155 Italy 0.69 0.65 0.68 0.62 1141 24936 United Kingdom 0.56 0.53 0.51 0.49 2020 24694 Sweden 0.79 0.87 0.75 0.85 1900 24525 Israel 0.74 0.58 0.72 0.55 2261 20597 Spain 0.55 0.51 0.53 0.49 1557 19965 Portugal 0.43 0.39 0.43 0.38 1405 17310 Greece 0.33 0.16 0.27 0.14 2459 16657 Slovenia 0.69 0.59 0.64 0.57 1452 16613 Czech Republic 0.66 0.54 0.65 0.51 1262 13997 Hungary 0.30 0.16 0.24 0.12 1531 12623 Poland 0.68 0.59 0.66 0.57 1971 9935 Source:World Development Indicators 2003.Cases weighted by DWEIGHT. Notes:1.Mean number of observations for the four dependent variables. 2.GDP per capita,purchasing power parity in current international dollars for the year 2000. (average)attitudes would become markedly less supportive of immigration from richer versus poorer nations at higher levels of per capita GDP.While it does seem to be the case that the preference for immigrants from richer versus poorer nations is largest in ESS countries with the lowest levels of per capita GDP,that same preference still appears in many of the most developed ESS countries(such as Luxembourg,Denmark,Italy,United Kingdom,Germany,or Finland).In fact,in all countries except Sweden,the Netherlands,Norway,and Switzerland,richer immigrants are preferred to poorer ones or people (on average)are essentially indif- ferent between the two. Previous studies of opinion data on immigration have typically been severely constrained by the absence of good measures of key variables and theoretically relevant controls,since the surveys generating the data were not focused ex- plicitly on the immigration issue.The ESS allows us to overcome these problems

~average! attitudes would become markedly less supportive of immigration from richer versus poorer nations at higher levels of per capita GDP+ While it does seem to be the case that the preference for immigrants from richer versus poorer nations is largest in ESS countries with the lowest levels of per capita GDP, that same preference still appears in many of the most developed ESS countries ~such as Luxembourg, Denmark, Italy, United Kingdom, Germany, or Finland!+ In fact, in all countries except Sweden, the Netherlands, Norway, and Switzerland, richer immigrants are preferred to poorer ones or people ~on average! are essentially indif￾ferent between the two+ Previous studies of opinion data on immigration have typically been severely constrained by the absence of good measures of key variables and theoretically relevant controls, since the surveys generating the data were not focused ex￾plicitly on the immigration issue+ The ESS allows us to overcome these problems TABLE 3. Immigration preferences by source: Individual ESS countries Means of dichotomous dependent variables Favor immigration from Country Richer European countries Poorer European countries Richer countries outside Europe Poorer countries outside Europe Observations1 GDP per capita2 Luxembourg 0+52 0+51 0+49 0+47 1370 56290 Norway 0+62 0+66 0+54 0+60 2017 35132 Ireland 0+68 0+68 0+62 0+64 1964 30100 Denmark 0+69 0+56 0+59 0+46 1415 29306 Switzerland 0+69 0+73 0+63 0+69 1947 28128 Austria 0+43 0+39 0+37 0+35 2063 28009 Netherlands 0+54 0+58 0+50 0+56 2312 27071 Belgium 0+61 0+62 0+55 0+56 1843 26435 Germany 0+65 0+64 0+61 0+59 2841 26067 France 0+57 0+57 0+48 0+51 1448 25318 Finland 0+50 0+46 0+41 0+40 1940 25155 Italy 0+69 0+65 0+68 0+62 1141 24936 United Kingdom 0+56 0+53 0+51 0+49 2020 24694 Sweden 0+79 0+87 0+75 0+85 1900 24525 Israel 0+74 0+58 0+72 0+55 2261 20597 Spain 0+55 0+51 0+53 0+49 1557 19965 Portugal 0+43 0+39 0+43 0+38 1405 17310 Greece 0+33 0+16 0+27 0+14 2459 16657 Slovenia 0+69 0+59 0+64 0+57 1452 16613 Czech Republic 0+66 0+54 0+65 0+51 1262 13997 Hungary 0+30 0+16 0+24 0+12 1531 12623 Poland 0+68 0+59 0+66 0+57 1971 9935 Source: World Development Indicators 2003+ Cases weighted by dweight+ Notes: 1+ Mean number of observations for the four dependent variables+ 2+ GDP per capita, purchasing power parity in current international dollars for the year 2000+ 412 International Organization

Attitudes Toward Immigration in Europe 413 to a substantial degree,since it provides multiple measures of a wide array of critical socioeconomic,demographic,and attitudinal variables.In the next sec- tions we incorporate a large variety of these variables when estimating the proba- bility of support for different types of immigration among individual survey respondents.Our principal goal,which we address immediately in the next sec- tion,is to provide a rigorous new set of tests of the labor-market competition expla- nation for anti-immigration sentiments.We also investigate alternative explanations of attitudes toward immigration that focus on cultural conflict. Labor-Market Competition and Anti-Immigration Views? Benchmark Model To provide a basic test of the conventional labor-market competition argument, we estimate a series of probit models for the dichotomous dependent variables described above (indicating support for immigration from different types of source countries).We employ the two indicators of individual levels of education that have been applied as proxy measures of individual skill levels in previous studies: the first measure,YEARS OF SCHOOLING,simply counts the total number of years of full-time education completed by the respondent;the second measure,which we label EDUCATIONAL ATTAINMENT,is a categorical indicator of the highest level of education attained by the respondent,adjusted by the ESS to allow for differ- ences between the various European educational systems so that the results are comparable across countries.43(See Table Al,p.438 for complete descriptive sta- tistics for all variables described here and used in the analysis). We include the standard socioeconomic and demographic control variables in an otherwise streamlined "benchmark"model.These variables include the respondent's AGE (in years),GENDER (1 female,0=male),and INCOME (mea- sured on a categorical scale from 1 to 12).44 We include whether the respondent is a NATIVE of his or her country of residence (1 born in country;0=foreign born),for obvious reasons.To account for "neighborhood"effects,we include a 43.The coding is:0=not completed primary education;1 completed primary or first stage of basic education;2=completed lower secondary or second stage of basic education;3 completed upper secondary;4=postsecondary,nontertiary:5=first stage of tertiary;and 6=completed second stage of tertiary education. 44.Since individual income is correlated with education,one could make the case for excluding it from the benchmark model when assessing aggregate effects of educational attainment on attitudes toward immigrants.Mayda 2006;and Scheve and Slaughter 2001a estimated models with and without an income control.We report estimations including income here but have replicated all the analysis after excluding the income variable-the results (available from the authors)are virtually identical. The coding for income is:1=less than€150 monthly;2=€150-30；3=300-500；4=500-1000； 5=1000-1500:6=1500-2000:7=2000-2500:8=2500-3000:9=3000-5000:10=5000-7500: 11=7500-10000:12=>10000

to a substantial degree, since it provides multiple measures of a wide array of critical socioeconomic, demographic, and attitudinal variables+ In the next sec￾tions we incorporate a large variety of these variables when estimating the proba￾bility of support for different types of immigration among individual survey respondents+ Our principal goal, which we address immediately in the next sec￾tion, is to provide a rigorous new set of tests of the labor-market competition expla￾nation for anti-immigration sentiments+ We also investigate alternative explanations of attitudes toward immigration that focus on cultural conflict+ Labor-Market Competition and Anti-Immigration Views? Benchmark Model To provide a basic test of the conventional labor-market competition argument, we estimate a series of probit models for the dichotomous dependent variables described above ~indicating support for immigration from different types of source countries!+ We employ the two indicators of individual levels of education that have been applied as proxy measures of individual skill levels in previous studies: the first measure, years of schooling, simply counts the total number of years of full-time education completed by the respondent; the second measure, which we label educational attainment, is a categorical indicator of the highest level of education attained by the respondent, adjusted by the ESS to allow for differ￾ences between the various European educational systems so that the results are comparable across countries+ 43 ~See Table A1, p+ 438 for complete descriptive sta￾tistics for all variables described here and used in the analysis!+ We include the standard socioeconomic and demographic control variables in an otherwise streamlined “benchmark” model+ These variables include the respondent’s age ~in years!, gender ~1 female, 0 male!, and income ~mea￾sured on a categorical scale from 1 to 12!+ 44 We include whether the respondent is a native of his or her country of residence ~1 born in country; 0 foreign born!, for obvious reasons+ To account for “neighborhood” effects, we include a 43+ The coding is: 0 not completed primary education; 1 completed primary or first stage of basic education; 2 completed lower secondary or second stage of basic education; 3 completed upper secondary; 4 postsecondary, nontertiary; 5 first stage of tertiary; and 6 completed second stage of tertiary education+ 44+ Since individual income is correlated with education, one could make the case for excluding it from the benchmark model when assessing aggregate effects of educational attainment on attitudes toward immigrants+ Mayda 2006; and Scheve and Slaughter 2001a estimated models with and without an income control+ We report estimations including income here but have replicated all the analysis after excluding the income variable—the results ~available from the authors! are virtually identical+ The coding for income is: 1 less than Y150 monthly; 2 Y150–30; 3 300–500; 4 500–1000; 5 1000–1500; 6 1500–2000; 7 2000–2500; 8 2500–3000; 9 3000–5000; 10 5000–7500; 11 7500–10000; 12 .10000+ Attitudes Toward Immigration in Europe 413