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Shifts in attitudes and labor market discrimination: Swedish experiences after 9-11

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Abstract

We present longitudinal survey data suggesting that the terrorist attacks in the USA on September 11, 2001, changed attitudes toward certain minorities in Sweden. This finding is consistent with results in previous studies. To investigate whether this change in attitudes also affected the labor market situation of these minorities, we study unemployment exit around 9-11 using detailed data on the entire Swedish working-age population. Contrary to what may be expected from many theories of labor market discrimination, the time pattern of exits and entries for different ethnic groups, as well as difference-in-differences analyses, shows no sign of increased discrimination toward these minorities. A possible explanation for this result is that employers act rationally in their hiring decisions and do not respond to changes in attitudes toward immigrants as a group.

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Notes

  1. Such theories were initially formalized in Becker (1957).

  2. The FBI reported a 17-fold increase in anti-Muslim crimes in 2001, which is in line with figures from Los Angeles county and Chicago (Human Rights Watch 2002). In the first year after the attacks, the US Equal Employment Opportunity Commission (EEOC) received more than twice as many charges based on “Religion—Muslim” compared to the year preceding 9-11. The EEOC has also found 654 charges of employment discrimination with an alleged connection to 9-11. The figure includes “charges alleging discrimination related to the events of September 11, 2001, by individuals who are—or are perceived to be—Muslim, Arab, Afghani, Middle Eastern or South Asian, or individuals alleging retaliation related to the events of September 11, 2001” (EEOC 2003). It is worth noting that out of the 449 cases that had been resolved by October 1, 2002, 288 were “closed with no cause findings.”

  3. See for instance the International Social Survey Program at http://www.issp.org.

  4. The respondents were asked “What do you think about immigrants?” and had three positive, three negative, and one neutral alternative to choose from when answering the question. The positive and negative categories are then further aggregated into only one category.

  5. Unfortunately, there is no connection to demographic data of the respondents, thereby preventing a separate analysis of attitudes. The data we received from FSI were on a more aggregate level than in Fig. 1, which restricts us from expanding the figure in time.

  6. As Larsson (2003) discusses, it is not certain that the answers give a representative picture of the general experiences of Swedish Muslims after 9-11. First, the questionnaire was distributed via Islamic organizations mostly in Gothenburg. Thus, the survey had regional boundaries and reached only a particular part of the population of interest. Second, only 176 out of 450 distributed surveys were answered. Still, considering how widespread the feelings of increased exposure are, they are probably not fully limited to the responding group.

  7. See the Appendix B for a more formal presentation. How discrimination affects an unemployed person engaged in job search has been formalized in an increasing number of articles during the last 20 years. This literature focuses on equilibrium effects rather than on the (partial) effect on individual job search strategies (see for instance Altonji and Blank 1999). The purposes of these articles have been to extend the Becker (1957) model of discrimination and to show that wage differentials due to discrimination exist in equilibrium when frictions are included into the Becker model.

  8. We believe that discrimination due to changing preferences is the most likely mechanism in this context. It is, though, also possible that 9-11 increased the so-called statistical discrimination (see Altonji and Blank 1999). Statistical discrimination arises when employers have imperfect information about individuals and therefore use measures of group productivity. It is of course unknown to us which productivity characteristics employers consider to be important but hard to observe. One could argue that 9-11 did not provide information about many of the factors commonly connected to this type of discrimination, such as language skills or institutional knowledge. In the 9-11 context, changes in statistical discrimination could occur, e.g., if employers consider the risk that an individual is planning terrorist activities. Indeed, such an explanation can be regarded as far-fetched and requires that the terrorist activities would incur a cost to the employer. Statistical discrimination may, however, also operate through increased risk aversion. If hiring certain immigrant groups is considered risky and the events made employers more risk-averse, we would expect a decrease in job offers given to certain groups.

  9. This could reflect the cost of contacting potentially discriminatory employers. It may also be, for example, the discomfort of leaving one’s home.

  10. Section 5 discusses the data. See the Appendix A for a description of the grouping based on region of birth.

  11. If one believes that employers set out to discriminate Muslims only, the empirical analysis could be said to capture statistical discrimination of a group where many—but not all—are Muslim. We do not know how preferences are formed but note that the studies presented in Sect. 2 suggest some stereotyping in the attitudes.

  12. According to Statistics Sweden (1993), about 90% of those who are unemployed according to the labor force surveys are also registered at the employment offices.

  13. Roughly 10% of those included in the data have one or more dates (for transition) changed by our procedure.

  14. For Natives, we use a 10% random sample.

  15. Another contributing explanation may be that the figures include information on some spells starting before 1991. However, this is only the case for a very small fraction of the sample. Among Nordic immigrants included in HÄNDEL (who have the longest average stay in Sweden), less than 0.1% of the individuals have a spell that started before 1991.

  16. Figure 2 shows no long-term trend in exit rates. However, in the years preceding the fall of 1999 (period –24), exit rates increased steadily as shown inFig. A1.

    Fig. A1
    figure a

    Relative effects on exits from unemployment, 120-day periods. Index period:−1

    Full size image

  17. The full estimation results are presented in Table A1 in the Appendix A.

  18. This result can actually be seen in Fig. A2, since the confidence intervals for all estimates overlap the estimated effect in the “Sweden” group. The DD specification is a linear version of Eq. 1 where a DD dummy (group*post-9-11) is added. The coefficients for all individual variables are allowed to vary across groups (i.e., we include interactions in the model). Note also in Fig. 2 that the “Africa” group has a high peak around the month “−12”, i.e. the start of the pre-9-11 period in the DD analysis.

  19. It should be noted that the people in the sample are unemployed and, in the strict sense, do not belong to any particular occupation. Since searching for other types of jobs may very well be a response to the 9-11 changes, we have excluded this variable from the baseline specification.

  20. Of course, this conclusion assumes that the reservation wage effect does in fact not exactly cancel out the direct effect. If it does, we would not expect any differential impact on exit rates due to changed discrimination. As discussed in Sect. 4.1, several arguments suggest that this is unlikely to be the case. Furthermore, if opposing effects canceling each other out were the true story, the results would probably not be so robust across subgroups.

  21. A more extensive presentation of this analysis is included in a previous version that can be retrieved from the authors.

  22. For instance, the family of lognormal distributions and the family of Pareto distributions fulfill these requirements.

  23. If we assume that \({\left| {\frac{{dw*}}{{dk}}k} \right|} = {\left| {\frac{{dw*}}{{d\lambda {\left( {s*} \right)}}}\lambda {\left( {s*} \right)}} \right|}\) then it is required that \({\left| {\frac{{ds*}}{{dk}}\frac{k}{s}} \right|} > 1\) for the results in Van den Berg (1994) to hold. We thank Peter Fredriksson for pointing this out.

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We thank the responsible editor, two anonymous referees, Anders Forslund, Peter Fredriksson, Per Johansson, Maria Melkersson, Eva Mörk, Oskar Nordström Skans, Björn Öckert, participants at seminars at IFAU and SOFI, the Conference on Ethnic Minorities, Integration and Marginalisation at the Danish National Institute, and the 2004 Nordic Conference on the Effects of Labour Market Policy and Education for valuable comments. Åslund acknowledges financial support from the Jan Wallander and Tom Hedelius Foundation. A research grant from the Swedish Council for Working Life and Social Research is gratefully acknowledged by Rooth.

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Authors and Affiliations

  1. IFAU (Institute for Labour Market Policy Evaluation), Uppsala, Sweden

    Olof Åslund

  2. Kalmar University, Kalmar, Sweden

    Dan-Olof Rooth

Authors
  1. Olof Åslund
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  2. Dan-Olof Rooth
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Correspondence to Dan-Olof Rooth.

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Responsible editor: Olof Åslund

Appendices

Appendix A: Supplementary tables and figures

Variable:

Definition

Post-9-11:

1 in the period after 9-11, 0 in the preceding period

Female:

1 for females, 0 for males

Age:

Age on Dec. 31, 2000 for post-9-11, on Dec. 31, 1999 for pre-9-11

Age squared:

 

Cohabiting:

1 if married or cohabitant with children in common (with spouse)

Kid:

Child <18 years old in the household

Cohabiting*Female:

 

Kid*Female:

 

Education dummies:

Dummy for highest completed level; see Table 1 for further description

Country of origin dummies:

Birth “countries” as available in the IFAU database; see below

Days in current spell (interval dummies):

Dummies for the following intervals: 0–49, 50–99, 100–199, 200–299, 300–399, 400–

Days in previous spells (interval dummies):

Dummies for the following intervals: 0, 1–99, 100–299, 300–499, 500–999, 1,000–

Search category:

Search category according to HÄNDEL at the sampling date (see Ams 2002). Includes categories 11, 12, and 13 in investigations of the exit to employment. Specifications for entry to unemployment include all categories

Regions and countries of origin

Region

Countries included

Sweden

Sweden

Nordic

Finland, Denmark, Norway + Iceland

Western

GB + Ireland, Germany, Southern Europe (Greece + Italy + Spain + the Vatican + Monaco + Malta + San Marino), Other European countries (Andorra + Belgium + France + Liechtenstein + Luxemburg + the Netherlands + Switzerland + Austria), USA + Canada

Eastern Europe

Poland, The Baltic states (Estonia + Latvia + Lithuania), Eastern Europe 1 (Rumania + the former USSR + Bulgaria + Albania), Eastern Europe 2 (Hungary + the former Czechoslovakia)

Former Yugoslavia

Bosnia–Herzegovina, former Yugoslavia (Yugoslavia + Croatia + Macedonia + Slovenia)

Latin America

Mexico and Central America, Chile, Other South American countries (Argentina + Bolivia + Peru + Colombia + Uruguay + Ecuador + Guyana + Paraguay + Surinam + Venezuela)

Africa

African Horn (Ethiopia + Somalia + Sudan + Djibouti), Other African countries (all African countries not included in African Horn or North Africa)

Middle East + North Africa

North Africa + Middle East (Lebanon + Syria + Morocco + Tunisia + Egypt + Algeria + Israel + Palestine + Jordan + South Yemen + Yemen + the United Arab Emirates + Kuwait + Bahrain + Qatar + Saudi Arabia + Cyprus), Iran, Iraq, Turkey

Asia

East Asia (Japan + China + Korea + Hong Kong + Taiwan), Southeast Asia (Vietnam + Thailand + the Philippines + Malaysia + Laos + Burma + Indonesia + Singapore), Other Asian countries (Sri Lanka + Bangladesh + India + Afghanistan + Pakistan + Brunei + Bhutan + Kampuchea + the Maldives + Mongolia + Nepal + Oman + Sikkim)

The left column contains the regional grouping used in our analysis. The bold-faced names in the right column indicate the aggregations in the original IFAU data. We exclude “Oceanic” (e.g., Australia and New Zealand) countries from our analysis, since this category mixes very different countries. People from this part of the world constitute a very small part of the Swedish immigrant population

Appendix B: Discrimination in a standard search model

This appendix presents how discrimination following an attitude change affects the escape rates out of unemployment in a standard job search model (Mortensen 1986). We assume that the attitude change affects two factors: employers’ willingness to hire (the job offer arrival function) and the costs for job search.

1.1 Escape rates and discrimination

In this type of job search model, the escape rate out of unemployment is

$$\theta = \lambda {\left( {s*} \right)}{\left[ {1 - F{\left( {w*} \right)}} \right]},$$
(2)

where s* is the (endogenous) search intensity, λ(s*) is the function for wage offer arrivals, while [1−F(w*)] is the probability that a wage offer w from the cumulative wage distribution F(w*) is greater than the reservation wage (w*). First, changed preferences and attitudes could affect λ(s*), which means fewer job offers at a given search intensity. We assume that λ(s*)=λs*, where λ can be regarded as an efficiency parameter of search. Further, the cost (of search) function affecting the optimal level of search is assumed to be a simple convex function c(s)ks 2, where k is the parameter that shifts due to increased discrimination.

The next step is to analyze how a change in these two channels of discrimination affects the escape rate out of unemployment. Eq. 3 shows how the escape rate out of unemployment is affected by an increase in wage offer arrivals.

$$\frac{{d\theta }}{{d\lambda {\left( {s*} \right)}}} = {\left[ {1 - F{\left( {w*} \right)}} \right]} - \lambda {\left( {s*} \right)}F^{\prime } {\left( {w*} \right)}\frac{{dw*}}{{dk{\left( {s*} \right)}}}$$
(3)

For plausible assumptions regarding the shape of the wage offer distribution (see Van den Berg 1994), Eq. 3 is positive.Footnote 22 This implies that we would expect the escape rate out of unemployment to decrease for discriminated job searchers when employers decrease the availability of jobs to these individuals (the efficiency of search will now be lower).

How the escape rate out of unemployment is affected by an increase in the search cost parameter k is shown in Eq. 4.

$$\frac{{d\theta }}{{dk}} = \frac{{ds*}}{{dk}}\lambda {\left[ {1 - F{\left( {w*} \right)}} \right]} - \lambda s*F^{\prime } {\left( {w*} \right)}\frac{{dw*}}{{dk}}$$
(4)

The first term in Eq. 4 is the negative direct effect of an increase in the costs of search (ds*/dk is negative), while the second term is an indirect effect that lowers the reservation wage (dw*/dk is negative since the job searcher becomes less “choosy” when costs increase). As in Eq. 3, there are two opposing effects on the escape rate out of unemployment. Even if the sign of Eq. 4 is indeterminate, it is likely that the negative direct effect is greater than the counteracting indirect effect for the same reason that makes Eq. 3 positive.Footnote 23 Then, the hazard rate would decrease when the costs of search increases.

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Åslund, O., Rooth, DO. Shifts in attitudes and labor market discrimination: Swedish experiences after 9-11. J Popul Econ 18, 603–629 (2005). https://doi.org/10.1007/s00148-005-0036-9

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