exploring the economic determinants of

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Volume 4, Issue 2 2012 Article 3

Exploring the Economic Determinants of Immigration

Attitudes

Beth Miller, University of Missouri - Kansas City

Published on behalf of the Policy Studies Organization

Miller, Beth (2012) "Exploring the Economic Determinants of Immigration Attitudes,"Poverty &

Public Policy: Vol. 4: Iss. 2, Article 3.

DOI: 10.1515/1944-2858.3

2012 Policy Studies Organization
http://www.ipsonet.org/http://www.psocommons.org/http://www.ipsonet.org/http://www.psocommons.org/ppphttp://www.psocommons.org/ppp


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Exploring the Economic Determinants of

Immigration AttitudesBeth Miller, University of Missouri - Kansas City

Abstract

This study explores the economic determinants of attitudes toward immigration across forty-

seven countries. The extant research addressing public opinion on immigration tends to concentrate

on Anglo attitudes toward immigrants in the United States or attitudes toward immigrants in

Western Europe. Thus, empirical evidence currently draws from a limited pool including advanced

industrial democracies. To more accurately and completely generalize statements regarding support

for economic theories of self-interest in regards to immigration, cross-national research outside of

the advanced industrial arena is necessary. Merging individual-level survey data from Pews Global

Attitudes Project with country-level economic data, the study finds that self-assessed poverty

plays a significant role in structuring immigration attitudes independent of the effect of objective

measures of economic status (education, employment, and income). Further, national economic

conditions also play a role in shaping immigration attitudes across countries.

KEYWORDS: immigration, economic sources, economy, poverty


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Introduction

Our system of integration is working worse and worse, because we have toomany foreigners on our territory and we can no longer manage to find themaccommodation, a job, a school (French President Nicolas Sarkozy).1 Suchattitudes toward immigration have been echoed by political leaders across theglobe. In the most recent presidential contest in the United State, the Republicanfrontrunner Mitt Romney stated that he was for self-deportation in whichpeople decide they can do better by going home because they cant find workhere because they dont have legal documentation to allow them to work here.

2

This persistent focus on immigration among political elites serves as a potentreminder that understanding the factors that influence immigration attitudesshould be a high priority for political researchers.

Political researchers have not been negligent in exploring the sources ofimmigration attitudes. A large body of research addresses opposition toimmigration in the United States and Western Europe. As discussed below,researchers argue that economic forces, including ones personal financialsituation and the countrys financial success, structure immigration attitudes.According to this perspective, opposition to immigration stems from economiccompetition with immigrants over limited financial resources.

The goal of the present study is to explore the role of individual-level andcountry-level economic factors in structuring immigration attitudes outside of theadvanced industrial democracies. Such research is necessary to generalize supportfor economic theories of self-interest and competition. Using survey datacollected by the Pew Research Centers Global Attitudes Project in 2007, the

present study finds that economic perceptions continue to influence publicopinion on immigration.

3Moreover, the present study demonstrates the important

role that the national economic context can play in guiding immigration attitudes.

Economic Sources of Immigration Attitudes

Researchers have argued that ones position in a countrys economic systemdrives attitudes toward immigrants. Specifically, the self-interest hypothesisfocuses on competition over limited resourcesjobs, social welfare benefits, and

1Alex Felton, Too Many Immigrants in France, Sarkozy Says, CNN, March 7, 2012,http://www.cnn.com.2 Michael Levenson and Matt Viser, Romney swings hard at Gingrich in debate,Boston Globe,January 24, 2012, http://www.boston.com.3 The Pew Research Center and the Pew Global Attitudes Project bear no responsibility for theanalyses or interpretations of the data presented here.

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Miller: Economic Sources of Immigration Attitudes

Published by De Gruyter, 2012


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educationas the source of opposition to immigration.4

A substantial body ofresearch provides support for the connection between economic vulnerability andattitudes toward immigration. Researchers find that support for immigration is

lower among lower-income workers, blue collar workers in occupationsfrequented by immigrants, and individuals with negative perceptions of theirpersonal financial situation.5 Thus, I propose that opposition to immigration willbe greater among individuals more vulnerable to competition from immigrants(the less educated, individuals with few financial resources, and the unemployed)than among individuals less susceptible to competition (H1).

However, there is considerable debate over whether immigration attitudesstem solely from ones personal economic situation or whether immigrationattitudes are structured by the health of ones country.6 Recent cross-nationalresearch finds that ones perception of the national economic situation influencesopposition to immigration.

7As a result, I consider whether immigration attitudes

are determined by ones perception of the countrys economic health.Specifically, I suggest that negative attitudes toward immigration will be drivenby negative appraisals of the countrys financial situation (H2).

Other researchers concentrate on the objective financial situation in acountry as the source of immigration attitudes. This research suggests thatopposition to immigration generally increases as the countrys economic situationdeteriorates. Negative immigration attitudes emerge when economic growthrecedes or unemployment increases.8 As a result, I consider the possibility that the

4Jack Citrin, et al., Public Opinion Toward Immigration Reform: The Role of EconomicMotivations, The Journal of Politics 59 (3) (1997): 858-881; Victoria M. Esses, Lynne M.

Jackson, and Tamara L. Armstrong, Intergroup Competition and Attitudes toward Immigrantsand Immigration: An Instrumental Model of Group Conflict, Journal of Social Issues 54 (4)(1998): 699-724; Jason E. Kehrberg, Public Opinion on Immigration in Western Europe:Economics, Tolerance, and Exposure, Comparative European Politics 5 (2007): 264-281; GallyaLahav, Immigration and Politics in the New Europe: Reinventing Borders (Cambridge, UK:Cambridge University Press, 2004); Lincoln Quillian, Prejudice as a Response to PerceivedGroup Threat: Population Composition and Anti-Immigrant and Racial Prejudice in Europe,American Sociological Review 60 (4) (1995): 586-612; Paul M. Sniderman, Louk Hagendoorn,and Markus Prior, Predisposing Factors and Situational Triggers: Exclusionary Reactions toImmigrant Minorities,American Political Science Review 98 (1) (2004): 35-49.5 Citrin et al., Public Opinion Toward Immigration Reform; Kehrberg, Public Opinion onImmigration in Western Europe; Kenneth F. Scheve, and Matthew J. Slaughter, Labor MarketCompetition and Individual Preferences over Immigration Policy,Review of Economics and

Statistics 83 (1) (2001): 133-145.6John Sides and Jack Citrin, European Opinion about Immigration: The Role of Identities,Interests and Information,British Journal of Political Science 37 (2007): 477-504.7 Sides and Citrin, European Opinion about Immigration.8Joel S. Fetzer, Public Attitudes Toward Immigration in the United States, France, and Germany(Cambridge, UK: Cambridge University Press, 2000); Kehrberg, Public Opinion on Immigrationin Western Europe.

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actual economic health of the country is an important determinant of immigrationattitudes. Thus, opposition to immigration will be higher in countries with afragile financial situation (high unemployment and low growth) as compared to

countries with a stable financial situation (H3).

Research Design

To examine these hypotheses, the present study relies on survey data from the2007 Global Attitudes Project (PGAP) sponsored by the Pew Research Center forthe People and the Press.9 This cross-national survey was fielded from April 6 toMay 29, 2007 in 47 countries.

Sampling

PGAP sampled adults (n=45,239) from countries in North America (UnitedStates, Mexico, and Canada), South America (Argentina, Bolivia, Brazil, Chile,Peru, and Venezuela), Western Europe (France, Germany, Great Britain, Italy,Spain, Sweden, and Turkey), Eastern Europe (Bulgaria, Czech Republic, Poland,Russia, Slovakia, and Ukraine), Sub-Saharan Africa (Ethiopia, Ghana, IvoryCoast, Kenya, Mali, Nigeria, Senegal, South Africa, Tanzania, and Uganda), theMiddle East and North Africa (Egypt, Israel, Jordan, Kuwait, Lebanon, Morocco,Pakistan, and the Palestinian Territories), and Asia (Bangladesh, China, India,Indonesia, Japan, Malaysia, and South Korea). Table 1 illustrates the sample size,mode of interview, and sampling design for each country. The questionnaire was

administered face-to-face in most countries, but was administered as a phonesurvey in the United States, Canada, France, Germany, Great Britain, Sweden, theCzech Republic, and Slovakia. In most countries, the sample was a nationallyrepresentative sample, but in some countries it was disproportionately urban.10

9Ideally, newer survey data would be used to examine the relationship between economic factorsand immigration attitudes. However, this survey is the only survey that includes income, self-assessed poverty, and immigration attitudes and samples respondents from 47 countries.10 The differences in sampling methods (face-to-face vesus phone and national sample versusurban) stem from the inability to reach respondents for a national sample and the telephone accessacross countries.

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Table 1: Description of the sample

Country Sample size Mode Sample design Country Sample size Mode

Argentina 800 Face-to-face National Lebanon 1,000 Face-to-fac

Bangladesh 1,000 Face-to-face National Malaysia 700 Face-to-fac

Bolivia 834 Face-to-face Largely urban Mali 700 Face-to-fac

Brazil 1,000 Face-to-face Largely urban Mexico 828 Face-to-fac

Bulgaria 500 Face-to-face National Morocco 1,000 Face-to-fac

Canada 1,004 Telephone National Nigeria 1,128 Face-to-fac

Chile 800 Face-to-face National Pakistan 2,008 Face-to-fac

China 3,142 Face-to-face Largely urban Palestinian ter. 808 Face-to-fac

Czech Republic 900 Telephone National Peru 800 Face-to-fac

Egypt 1,000 Face-to-face National Poland 504 Face-to-fac

Ethiopia 710 Face-to-face National Russia 1,002 Face-to-fac

France 1,004 Telephone National Senegal 700 Face-to-facGermany 1,000 Telephone National Slovakia 900 Telephone

Ghana 707 Face-to-face National South Africa 1,000 Face-to-fac

Great Britain 1002 Telephone National South Korea 718 Face-to-fac

India 2,043 Face-to-face Largely urban Spain 500 Face-to-fac

Indonesia 1,008 Face-to-face National Sweden 1,000 Telephone

Israel 900 Mixed National Tanzania 704 Face-to-fac

Italy 501 Face-to-face National Turkey 971 Face-to-fac

Ivory Coast 700 Face-to-face Largely urban Uganda 1,122 Face-to-fac

Japan 762 Face-to-face National Ukraine 500 Face-to-fac

Jordan 1,000 Face-to-face National United States 2,026 Telephone

Kenya 1,000 Face-to-face National Venezuela 803 Face-to-fac

Kuwait 500 Mixed National

Source. Pew Global Attitudes Project, 2007.



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Measures

Immigration attitudes. The dependent variable in the analysis derives from a

question asking respondents whether we should restrict and control entry ofpeople into our country more than we do now. Respondents were given thefollowing response options: completely disagree, mostly disagree, mostly agree,and completely agree.

Individual-level economic factors. To test the first two hypotheses, fiveindividual-level economic measures are included in the analysis. The first fourmeasuresincome, employment, education, and povertycapture theindividuals position in the economy. Specifically, respondents were asked toidentify where their household fell in a list of incomes including all wages,salaries, pensions, and other incomes in the household. Using responses to this

question, respondents within a country were divided into three income categories:bottom third, middle third, and top third. Respondents were also asked to indicatetheir employment situation. From responses to this variable, four categories ofemployment were created: employed, retired, unemployed, and not job seeking(students, housewife, houseman, disability, maternity leave, etc.). Employed is thecomparison category in the analyses that follow. Education is measured as thehighest level of education attained by the respondent: no formal education,incomplete primary education, complete primary education, incomplete secondaryeducation, complete secondary education, some college, college degree, andgraduate degree.

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While these measures go a long way to determining an individuals

position in the economy, I supplement these objective measures with a subjectivemeasure of financial vulnerability. In countries with a larger safety net,unemployment or little reported income may not equate to financial vulnerabilityas much as in countries without a large safety net. Moreover, self-reported incomemay not adequately assess ones financial position in developing countries as itmay in developed countries. As a result, a three-item index of poverty (=0.86) isincluded as an additional measure of ones financial status. Specifically,respondents were asked have there been times during the last year when you didnot have enough money to (1) buy food your family needed, (2) pay for medicaland health care your family needed, and (3) buy clothing your family needed. Asdiscussed earlier, the expectation is that certain types of individuals will feel more

economically insecure than other individuals. Individuals with fewer financialresources, less education, unemployed may feel the most threatened by

11 An alternative measure of education was also consideredthe age when the respondentcompleted schooland the effect of education on immigration remains consistent across the twomeasures.

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immigrants and will be the most likely to hold negative attitudes towardimmigration (H1).

Finally, a sociotropic measure capturing the respondents assessment of

the current economic situation in their country is also included in the analysis.Specifically, respondents were asked thinking about our economic situation, howwould you describe the current economic situation in our country? This measureranges from very bad (1) to very good (4). As the economic situation in a countryworsens, more and more individuals may find themselves in competition withimmigrants for jobs and services. Thus, negative assessments of the nationaleconomy may generate negative attitudes toward immigration (H2).

Demographic factors. Finally, the analysis also includes demographic controlspreviously argued to influence immigration attitudes: age and gender. Age ismeasured as the respondents age at their last birthday. Gender is coded as zero

for female and one for male. Previous research on immigration attitudes finds thatopposition to immigration is most prevalent among older individuals and men.12

Contextual factors.13

On the other hand, a countrys financial condition may alsoinfluence immigration attitudes. Two country-level economic factors are includedin the analysis: gross domestic product (GDP) per capita

14and unemployment.

The data for both measures are from 2007 figures maintained by the World Bank.Because of the skewed nature of GDP, the measure was logged before inclusionin the analysis. As expected, there is a significant amount of variation in bothunemployment (2%43%) and GDP ($178$40,707) across the 47 countries. Ifthe economic threat thesis is valid, then a shrinking economy may put natives in

direct competition with immigrants thereby producing more negative feelingstoward immigration (H3).

12Jack Citrin, Beth Reingold, and Donald P. Green, American Identity and the Politics of EthnicChange, The Journal of Politics 52 (4) (1990): 1124-1154; Robert M. Kunovich, SocialStructural Position and Prejudice: An Exploration of Cross National Differences in RegressionSlopes, Social Science Research 33 (1) (2004): 20-44; Quillian, Prejudice as a Response toPerceived Group Threat; Peer Scheepers, Merove Gijsberts, and Marcel Coenders, EthnicExclusion in European Countries: Public Opposition to Civil Rights for Legal Migrants as aResponse to Perceived Ethnic Threat,European Sociological Review 18 (1) (2002): 17-34.13

Data for the Palestinian Territories is not available; thus, these respondents are excluded fromthe contextual analysis.14 As an alternative measure of the economic health of a country, I examined the UNs humandevelopment index. This measure is highly correlated with GDP (r=0.96) so both could not beincluded in the analysis. The results are nearly identical, so I included the results of the analysiswith GDP instead of the human development index. The analysis with the human developmentindex is available from the author.

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Results

Respondents were generally supportive of restricting immigration: 7% completelydisagreed, 19% mostly disagreed, 38% mostly agreed, and 36% completelyagreed with restricting and controlling the entry of people into their country.Table 2 reports the mean value on the dependent variable for each country.

Table 2: Immigration attitudes across countries

Country Restrictingimmigration

Country Restrictingimmigration

Argentina 2.94 Lebanon 2.97Bangladesh 3.21 Malaysia 3.46Bolivia 3.06 Mali 3.16Brazil 2.99 Mexico 2.92

Bulgaria 2.79 Morocco 3.25Canada 2.81 Nigeria 3.08Chile 3.04 Pakistan 3.29China 2.61 Palestinian Territories 2.44Czech Republic 3.13 Peru 2.66Egypt 2.90 Poland 2.61Ethiopia 2.98 Russia 3.10France 2.89 Senegal 3.29Germany 2.70 Slovakia 2.83Ghana 2.93 South Africa 3.46

Great Britain 3.13 South Korea 2.22India 3.26 Spain 3.08Indonesia 3.23 Sweden 2.55Israel 3.06 Tanzania 3.48Italy 3.37 Turkey 3.23Ivory Coast 3.56 United States 3.18Japan 2.54 Uganda 2.99Jordan 2.96 Ukraine 2.84Kenya 3.31 Venezuela 3.07Kuwait 2.76

Note. Table entries are mean values for each country ranging from 1 (positive attitudes toward

immigration) to 4 (negative attitudes toward immigration).

To explore the economic determinants of attitudes toward immigration, Ifirst consider whether individual-level economic factors are predictive ofimmigration attitudes. After examining the individual-level economic

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determinants of immigration attitudes, I then consider the effects of the contextualfactors on immigration attitudes. As the dependent variable is ordinal and rangesfrom 1 to 4, all models are analyzed as ordered probit models with maximum

likelihood estimation and include robust standard errors. The models alsoincorporate a few demographic variables as controls and the sample is weightedby the PGAP variable weight.

Economic Factors

Table 3 presents the results of the individual-level analysis. In general, personaland sociotropic factors influence attitudes toward immigration, but not necessarilyin the way predicted. Contrary to the first hypothesis (H1), reported householdincome increases negative attitudes toward immigration; however, reportedincome may not be the most accurate predictor of ones financial situation in a

number of countries. As a result, an alternative measure of financialvulnerabilityself-assessed povertyis also included in the model and has a

Table 3: Economic sources of immigration attitudes

Model 1

Coeff. s.e.

Economic sources

Income 0.025*** 0.009Retired 0.076*** 0.028Unemployed 0.080*** 0.027Not job seeking 0.055*** 0.016Education 0.050*** 0.004

Poverty 0.122*** 0.017Sociotropic 0.014* 0.008

ControlsAge 0.001 0.001

Gender 0.023* 0.014

Threshold 1 1.559*** 0.044

Threshold 2 0.727*** 0.044

Threshold 3 0.283*** 0.044

Log likelihood 41,529.65

2 302.50

Prob>2


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positive effect on opposition to immigration. As poverty increases, respondentsare more likely to support restrictions on entry.

Similarly, employment situation is significantly related to support for

restricting immigration. Respondents who are retired, unemployed, or not jobseeking are more likely to support restricting immigration than employedrespondents. Education is negatively related to opposition to immigration suchthat highly educated respondents hold more positive attitudes toward immigrationthan their less educated counterparts.

Contrary to expectation (H2), positive assessments of the countrysfinancial situation generate negative attitudes toward immigration. Individualsevaluating their countrys financial situation as very good are more likely tosupport restricting entry.

Turning to the control variables, age is not significantly related toimmigration attitudes, but gender is related to support for restricting entry.

Specifically, men are more supportive of restrictions on immigration than women.In sum, support for the first two hypotheses is mixed. On the one hand,

individuals with higher incomes and individuals positively assessing the nationaleconomy offer greater support for restrictions. On the other hand, self-assessedpoverty, less education, and unemployment are all related to support forrestrictions.

Importantly, we can also compare the differential effect of each economicfactor on immigration attitudes. Table 4 illustrates the predicted probabilities ofopposition to immigration for each economic factor. The variable of interest waschanged from its minimum value to its maximum value while all other variablesin the model were held constant at their mean value.

Table 4: Predicted probabilities of negative immigration attitudes (individual-level factors)

Predicted probability of strong support

for restrictions

Income 0.019Retired 0.025Unemployed 0.044Not job seeking 0.026Education 0.138Poverty 0.045Sociotropic 0.009

Note. Table entries are predicted probabilities from the ordered probit results in Table 3. Thevariable of interest is changed from the minimum value to the maximum value while holding allother variables in the model constant at their mean value.

In doing so, one can quickly surmise that the effect of education onimmigration attitudes eclipses the effect of all other economic determinants.Education reduces the probability of strongly supporting restrictions by 14%. The

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probability of completely agreeing with restrictions increases by 3% for retiredrespondents, 3% for respondents not seeking a job, and 4% for unemployedrespondents. Self-assessed poverty increases support for restrictions by 5%. A

high income and positive sociotropic assessments increase support for restrictionsby


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Table 5: Economic and contextual sources of immigration attitudes

Model 4Coeff. s.e.

Economic sourcesIncome 0.001 0.009

Retired 0.067** 0.028Unemployed 0.091*** 0.027Not job seeking 0.050*** 0.016Education 0.031*** 0.004

Poverty 0.069*** 0.017Sociotropic 0.009 0.008

Contextual sourcesUnemployment rate 0.010*** 0.001Per capita GDP (logged) 0.066*** 0.005

ControlsAge 0.001** 0.001Gender 0.022 0.014

Threshold 1 2.003*** 0.059

Threshold 2 1.156*** 0.058

Threshold 3 0.128** 0.058

Log likelihood 40,295.23

2 625.89

Prob>2


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economic factors. To put these predicted probabilities in perspective, anunemployed respondent with no formal education experiencing high rates ofpoverty is 16% more likely to support restricting immigration than an employed,

highly educated respondent with little self-assessed poverty. In contrast, arespondent living in a country with low economic growth and high unemploymentis 28% more likely to oppose immigration than a respondent living in afinancially stable country.

Table 6: Predicted probabilities of negative immigration attitudes

Predicted probability of

strong support for

restrictions

Individual-level sourcesIncome NSRetired 0.018

Unemployed 0.050Not job seeking 0.024Education 0.084Poverty 0.029Sociotropic NS

Contextual sourcesUnemployment 0.150Per capita GDP 0.130

Note. Table entries are predicted probabilities from the ordered probit results in Table 5. Thevariable of interest is changed from the minimum value to the maximum value while holding allother variables in the model constant at their mean value.

Cross-National Comparisons

With 47 countries included in the analysis, it is possible to consider the importantquestion of whether the sources of immigration attitudes are comparable acrosscountries. Figures 15 present scatterplots comparing a countrys mean value onthe dependent variable to the countrys mean value on the relevant individual-level and contextual factors.

Of the individual-level economic factors, education, employment status,and self-assessed poverty are the most consistent predictors of negative attitudestoward immigration. As a result, Figure 1 presents the mean value on theimmigration measure by the average education level for each country in thesample. As the average education level in a country increases, support forrestricting immigration declines. While this relationship exists for most countries,there are a few countries where respondents hold negative attitudes towardimmigration, yet the average level of education in the country is quite high. For

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example, respondents from the United States, India, and Israel have high levels ofeducation, but negative attitudes toward immigration.

Figure 1. Relationship between education level and opposition to immigration

Note. Mean value of immigration attitudes by average level of education across the sample ofcountries.

Figure 2 displays the mean value on the immigration measure by theaverage number of unemployed respondents across the sample of countries. Asdescribed above, unemployed respondents tend to support restrictions onimmigration to a greater degree than employed respondents. This positive effectappears in Figure 2, yet there are a few discrepancies. For example, respondentsfrom Italy, Tanzania, and Malaysia are supportive of restrictions, yet thepercentage of unemployed respondents in these countries is generally low.

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Figure 2. Relationship between employment status and opposition to immigration

Note. Mean value of immigration attitudes by mean number of unemployed respondents acrossthe sample of countries.

Beyond education and employment status, an important individual-leveldeterminant of immigration attitudes is self-assessed poverty. Figure 3 illustrates

the relationship between self-assessed poverty and immigration attitudes. Whilethe relationship between poverty and immigration attitudes is generally positivewith increased poverty corresponding to negative attitudes, there are clearinconsistencies within the sample. Many of the countries in Western Europe andthe United States score high on supporting restrictions on immigration, yet self-assessed poverty is very low in these countries. In contrast, poverty is positivelyrelated to negative attitudes toward immigration within the African countries. Forexample, respondents in Kenya, South Africa, Tanzania, and the Ivory Coast havethe highest levels of anti-immigration attitudes and such negative attitudes arecoupled with high levels of self-assessed poverty.

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Figure 3. Relationship between self-assessed poverty and opposition to immigration

Note. Mean value of immigration attitudes by mean value of self-assessed poverty across thesample of countries.

Given the weak correlation between immigration attitudes and individual-level economic factors, the multivariate analysis also incorporates contextualelements (unemployment rate and economic growth). As a result, Figure 4illustrates the mean value on the immigration measure by the unemployment rateacross the sample of countries. Because the unemployment rate in most countriesranges from 5% to 20%, there is substantial clustering; however, the positivepattern for unemployment and opposition to immigration remains evident.Consistent with this pattern, respondents from South Africa offer very negativeattitudes toward immigration and South Africa has one of the highestunemployment rates of all countries in the sample. However, Italysunemployment rate is relatively low though respondents from Italy strongly

support restricting entry into their country.

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Figure 4. Relationship between unemployment rate and opposition to immigration

Note. Mean value of immigration attitudes by unemployment rate across the sample of countries.The extreme outlier (Kenya with an unemployment rate of 43%) was dropped to ensure that therelationship was visible for the remaining countries.

Similarly, Figure 5 illustrates the negative effect of economic growth onsupport for restrictions. As GDP increases, support for restrictions generallydeclines. This pattern is evident for countries like Sweden and Japan. Bothcountries have a high level of economic growth and respondents hold positiveimmigration attitudes. However, a number of the advanced industrial democraciesexperience substantial economic growth (e.g., the United States and the UnitedKingdom), yet respondents in these countries oppose immigration.

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Figure 5. Relationship between GDP and opposition to immigration

Note. Mean value of immigration attitudes by GDP (logged) across the sample of countries.

Discussion

This study is one of the first to examine immigration attitudes outside of theadvanced industrial democracies. Despite substantial cross-national variation inthe effect of economic and contextual factors on immigration attitudes, there aresome consistent patterns. Individual-level economic factors play a modest role instructuring immigration attitudes among respondents in the survey. Of theeconomic factors, education, employment status, and self-assessed poverty playthe most consistent role in producing negative attitudes toward immigration.Opposition to immigration is driven by respondents with less education andunemployed respondents. Additionally, respondents finding it difficult to feed,

clothe, and purchase medical care for their families are hostile towardimmigration. However, the contextual elements also influence attitudes towardimmigration and exceed the effects found for the individual-level economicfactors. Respondents living in countries with high unemployment or loweconomic growth are more likely to support restrictions on immigration.

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While these general patterns emerge, one cannot overemphasize the cross-national variation evident in the scatterplots. Importantly, the cross-nationalcomparisons indicate that the factors traditionally identified as insignificant in

studies of American and European attitudes toward immigrationones financialvulnerability and the countrys economic healthmay be more applicable outsidethese regions where unemployment and economic growth vary more dramatically.Further research is needed to disentangle factors specific to each country fromfactors common across countries. Economic factors may serve to explainimmigration attitudes in the developing world whereas symbolic factorsideology, media coverage, elite rhetoric, racial tolerance, national identity, etc.may be most critical in the advanced industrial democracies. Such research isparticularly important given the substantial variation across countries for theeconomic and contextual elements.

The results of this study are important for a variety of reasons. The most

apparent role for research of this kind is to inform the way researchers think abouttheories of self-interest and immigration attitudes. Current research on this topictends to concentrate on public opinion in the advanced industrial democracies ofthe United States and Western Europe. Yet, the results of the present study runcontrary to prior research on immigration concluding that economic concernshave little effect on immigration attitudes in the United States (Citrin et al. 1997)and Western Europe (Sides and Citrin 2007; Kehrberg 2007). As such, failing toexplore public opinion in the developing world may lead researchers tounderestimate the importance of economic theories of self-interest to publicopinion. Additionally, the present study indicates that self-reported income maybe an inappropriate measure to capture financial vulnerability as compared to

questions specifically addressing issues of poverty. Studying immigration cross-nationally is also practically important as immigration policy is once again on thetable and individual attitudes on immigration are likely to be critical elements insuch policy discussions. Thus, the present research furthers our understanding ofimmigration attitudes and, indirectly, decisions on immigration policy.

References

Citrin, J., D.P. Green, C. Muste, and C. Wong. 1997. Public Opinion towardImmigration Reform: The Role of Economic Motivations. The Journal of

Politics 59 (3): 858-881.Citrin, J., B. Reingold, and D.P. Green. 1990. American Identity and the Politics

of Ethnic Change. The Journal of Politics 52 (4): 1124-1154.

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Esses, V.M., L.M. Jackson, and T.L. Armstrong. 1998. Intergroup Competitionand Attitudes toward Immigrants and Immigration: An InstrumentalModel of Group Conflict. Journal of Social Issues 54 (4): 699-

724.Fetzer, J.S. 2000. Public Attitudes toward Immigration in the UnitedStates, France, and Germany. Cambridge, UK: Cambridge UniversityPress.

Kehrberg, J.E. 2007. Public opinion on Immigration in Western Europe:Economics, Tolerance, and Exposure. Comparative European Politics 5:264-281.

Kunovich, R.M. 2004. Social Structural Position and Prejudice: An Explorationof Cross National Differences in Regression Slopes. Social ScienceResearch 33(1): 20-44.

Lahav, G. 2004. Immigration and Politics in the New Europe: ReinventingBorders. Cambridge, UK: Cambridge University Press.

Quillian, L. 1995. Prejudice as a Response to Perceived Group Threat:Population Composition and Anti-immigrant and Racial Prejudice inEurope.American Sociological Review 60(4): 586-612.

Scheepers, P., Gijsberts, M., and Coenders, M. 2002. Ethnic Exclusion inEuropean Countries: Public Opposition to Civil Rights for Legal Migrantsas a Response to Perceived Ethnic Threat.European Sociological Review18 (1): 17-34.

Scheve, K.F., and M.J. Slaughter. 2001. Labor Market Competition andIndividual Preferences over Immigration Policy. Review of Economicsand Statistics 83 (1): 133-145.

Sides, J., and J. Citrin. 2007. European Opinion about Immigration: The Role of

Identities, Interests and Information.British Journal of Political Science37: 477-504.

Sniderman, P.M., L. Hagendoorn, and M. Prior. 2004. Predisposing Factors andSituational Triggers: Exclusionary Reactions to Immigrant Minorities.American Political Science Review 98 (1): 35-49.

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