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Evidence of ethnic discrimination in the Swedish labor market using experimental data
Labour Economics 14 (2007) 716 – 729 www.elsevier.com/locate/econbase
Evidence of ethnic discrimination in the Swedish labor market using experimental data ☆ Magnus Carlsson, Dan-Olof Rooth ⁎ Kalmar University, 39182 Kalmar, Sweden Available online 6 May 2007
Abstract We present evidence of ethnic discrimination in the recruitment process by sending fictitious applications to real job openings. Applications with identical skills were randomly assigned Middle Easternor Swedish-sounding names and applications with a Swedish name receive fifty percent more callbacks for an interview. We extend previous analyses by adding register and interview information on firms/recruiters to the experimental data. We find that male recruiters and workplaces with fewer than twenty employees less often call applications with a Middle Eastern name for an interview. © 2007 Elsevier B.V. All rights reserved. JEL classification: J64; J71 Keywords: Ethnic discrimination; Exit from unemployment
1. Introduction There are reasons to expect that discriminatory behavior exists among Swedish employers. Longitudinal attitude surveys of the general public show evidence of negative attitudes toward immigrants in general and surveys among potentially discriminated groups also point in this direction (see Lange, 2000; FSI, 2004).1 These studies indicate that discrimination is worst ☆ We thank two anonymous referees, Per Johansson, Olof Åslund, participants at seminars in Kalmar, Gothenburg, Stockholm, Växjö University and at the 2006 EALE Conference in Prague for valuable comments. Klara Johansson provided excellent research assistance. A research grant from the Swedish Council for Working Life and Social Research is gratefully acknowledged. The data used in the article are available from the authors upon request. ⁎ Corresponding author. E-mail addresses: [email protected] (M. Carlsson), [email protected] (D.-O. Rooth). 1 See also Arai and Vilhelmsson (2004), Edin and Lagerström (2006), Rooth (2002) and Åslund and Rooth (2005) for studies on ethnic discrimination in Sweden.
0927-5371/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.labeco.2007.05.001
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against individuals with a Middle Eastern background. Also, unemployment rates for immigrants born in the Middle East are found to be several times higher than for natives, indicating that ethnic discrimination exists in the recruitment process. Whether this is true is the main question to be answered in this study. However, ethnic discrimination is just one possible explanation for native-immigrant differences in finding jobs. Another is (to the researcher) unobserved productive characteristics. Since researchers can seldom account for all differences in productive characteristics between groups it is a difficult task to empirically identify the extent of ethnic discrimination in the labor market. To circumvent this difficulty researchers have relied on using field experiments specifically designed to test for discrimination in recruitment.2 The correspondence testing methodology implies that the researcher sends two equal applications to advertised job openings with the only difference being the name of the applicant; one with a native-sounding name and the other a foreign-sounding name. Hence, this methodology ensures that productive characteristics on the supply side are held constant. Ethnic discrimination is quantified by the difference in the number of callbacks for interview between the two ethnic groups. Contrary to most previous studies using this methodology we proceed with collecting company and, especially, recruiter information through official registers and by conducting interviews with the recruiters. Hence, while some studies use variation in application information to analyze how discrimination varies across application attributes (see Bertrand and Mullainathan, 2004), we use the same application attributes for both applicants to analyze which firm/workplace and recruiter characteristics that are correlated with the ethnic difference in the callback rate to interview.3 Our experimental data was collected between May 2005 and February 2006 by sending two applications; one with a Swedish-sounding male name and one with a Middle Eastern-sounding male name, to job openings in twelve different occupations in the Stockholm and Gothenburg labor market areas. In total we replied to 1552 job ads posted at the home-page of the Swedish Employment Agency. It was found that the callback rate of applications with a Swedish-sounding male name was fifty percent higher than for the ones with a Middle Eastern-sounding name. Interestingly, despite the fact that low level occupations have the largest share of immigrant employees we found that the relative callback rate for Swedish-sounding named applications is higher in lower level occupations than in higher level occupations. Further, regression analysis shows that the ethnic difference in callbacks is related to the sex of the recruiter and to the number of employees at the workplace. The remainder of this paper is outlined as follows. Section 2 presents the methodology used and the design of the experiment. Section 3 describes the collected data. In Section 4 an analysis is performed using register and interview data on which workplace and recruiter characteristics influence the ethnic difference in the probability of being called for an interview. Section 5 concludes. 2. Experimental design4 The experiment was conducted between May 2005 and February 2006. During this period all employment advertisements in selected occupations found on the webpage of the Swedish
2
A great number of situation tests have been conducted in Europe and the US in recent decades and a clear majority of studies find discrimination against minorities, see Riach and Rich (2002) for a summary of these experiments. 3 Ideally, one could combine the two approaches within one research design. This approach is left for future research. 4 See Riach and Rich (2004) for a discussion on ethical considerations of the method.
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employment agency were collected.5 All in all, 3104 applications were sent to 1552 employers. Callbacks for interviews were received via telephone, email or ordinary post. To minimize inconvenience to the employer invitations were promptly declined. 2.1. Choice of occupations To make satisfactory progress in the collection of cases it was necessary that the demand for labor was high in the chosen occupations. In addition, we wanted the skill requirement and the share of immigrants to vary across occupations. Hence, the selected occupations were both skilled and semi/unskilled and included a relatively high as well as a low ratio of immigrants.6 In the end the experiment was restricted to twelve occupations and the two major cities of Sweden: Stockholm and Gothenburg.7 The selected occupations were computer professionals, business sales assistants, four categories of teachers (preschool, math/science and language in upper level compulsory school, and upper secondary school), accountants, nurses, construction workers, restaurant workers, shop sales assistants and motor-vehicle drivers.8 2.2. Construction of applications The applications had to be realistic and yet not refer to any real persons. Also, since the competition from other applicants was considerable the testers had to be well qualified. A number of real life (written) applications available on the webpage of the Swedish employment agency were used as templates and adjusted and calibrated for our purposes. Applicants had identical human capital within occupations and were on average 25–30 years old, had two to four years of work experience in the same occupation as the job applied for and had obtained their education in the same type of school, but at different locations. The application consisted of a quite general biography on the first page and a detailed CV of education and work experience on the second page.9 For each occupational category three applications with a somewhat different layout were constructed. Two of the applications were randomly drawn and given a name: a Swedishsounding male name or a Middle Eastern-sounding male name. With the name followed also an email address, a telephone number and a postal address. The reason for using Middle Eastern names is that empirical studies indicate that discrimination is the worst against individuals with a Middle Eastern background. If this is true, analyzing this group gives an upper bound for discrimination in the hiring process against other ethnic groups.10
5
In approximately 20% of the job openings it was not possible to apply via email. These vacancies were excluded. This information was taken from the occupational register, Statistics Sweden, 2003. 7 We also started an experiment in Malmö, the third largest city in Sweden, but there were not enough jobs available to make satisfactory progress in the collection of cases. 8 A total of 62 cleaning jobs were also collected but we only received five callbacks, one firm calling both and four firms calling only the Swedish-named application for interview. Due to too little variation in callbacks these cases could not be used in the regression analysis. Cleaning is therefore discarded, which did not change the overall results. 9 See Carlsson and Rooth (2006) for an example of an application. 10 One could vary the names to include more immigrant backgrounds but our strategy has been to focus on firm/recruiter characteristics keeping the supply side information constant. 6
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The choice of names is crucial since ethnicity is only signaled through the name. Fortunately, there is a clear distinction between typical Swedish names and Middle Eastern names. Three of the most frequent Swedish and Middle Eastern names occurring in Sweden were selected and these were randomly assigned to resumes in order to avoid name effects.11 An email address and a telephone number (including an automatic answering service) were registered at a large Internet provider and a phone company for each fictitious applicant. To prevent any invitations being lost or returned to the employer real postal addresses were also included in the resumes. The addresses were chosen to signal that the respondents lived in the same neighborhood. The constructed resumes were then sent to the employer in alternating order with a one-day delay in between. 2.3. Firm/workplace and recruiter information The collected workplaces were identified in Statistics Sweden's business (workplace) register using company ID's (so called CFAR-no, see www.scb.se).12 We then obtained information on the number of employees at the workplace and at the firm (if more than one workplace at the firm), on the number of workplaces at the firm, whether public sector employer and the share of male employees at the firm.13 A variable measuring the share of immigrants living in the municipality where the workplace is located was also constructed and from the job ads we obtained information on the sex of the recruiter. We also collected information by interviewing recruiters.14 Thereby we gained additional information about the workplace as regards the share of employees born in another European country or in a non-European country, personnel turnover (the number of new recruitments relative to the number of employees), the ethnicity of the recruiter, whether the workplace had a personnel department for recruitment and whether the workplace had a diversity plan for equal rights work with respect to ethnicity. The interview also included a question measuring explicit attitudes towards hiring a qualified Middle Eastern minority applicant. Only one workplace responded that they would not hire an applicant with a Middle Eastern background, all others that they would. Hence, their reported explicit attitudes depart from their actual behavior. 3. Descriptive results The last row of Table 1 gives the aggregated results of the experiment. From the first column it is evident that the two applications were sent to 1552 different job openings. Since correspondence testing only focuses on the first step of the hiring process, being called for interview, thus neglecting the second step of who actually gets the job, there are four possible interview outcomes: neither invited, both invited, or only the majority or minority individual being invited for an 11 We used the Swedish first names Erik, Karl and Lars and the last names Andersson, Pettersson and Nilsson and the Middle Eastern first names Ali, Reza and Mohammed and the last names Ameer, Hassan and Said, from the name statistics register, Statistics Sweden (2005). A sensitivity analysis showed that neither different names (within ethnicity) nor different resumes (within occupations) had a statistically significant effect on the probability of being invited. These results are available upon request. 12 Company ID's were identified from combining information on firm name and address found in the job ad with information in Statistics Sweden's business register available on the Internet. 13 In sixty-three percent of the cases the firm only has one workplace and, hence, firm and workplace form the same unit. 14 These interviews did not include firms not inviting any application for an interview. Due to time constraints it was decided upon early in the project not to interview this group of firms. Later on the availability to match our data with firm/workplace registers at Statistics Sweden appeared.
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Jobs
Neither At least Equal Only Swedish- Only M. Eastern- Callback rates χ2 Invited one invited Treatment sounding sounding SwedishM. EasternRelative (Swedish)/ name invited name invited sounding name sounding name (Middle Eastern)
(1)
(2)
Computer professionals 106 71 42 16 Teachers (math and science) a Business sales assistants 278 164 Preschool Teachers 184 64 Accountants 186 155 Nurses 150 95 Teachers — upper 64 41 secondary school 60 26 Teachers (language)a Construction workers 64 44 Restaurant workers 140 128 Motor-vehicle drivers 78 59 Shop sales assistants 200 167 Total number of jobs 1552 1030
(3)
(4)
(5)
(6)
(4 + 5)/(1)
(4 + 6)/(1)
35 26 114 120 31 55 23
9 17 57 76 10 30 10
14 7 39 36 14 20 11
12 2 18 8 7 5 2
0.22 0.57 0.35 0.61 0.13 0.33 0.33
0.20 0.45 0.27 0.46 0.09 0.23 0.19
1.10 1.26 1.28 1.33 1.41 1.43 1.75
0.2 2.8 7.7⁎⁎⁎ 17.8⁎⁎⁎ 2.3 9.0⁎⁎⁎ 6.2⁎⁎
34 20 12 19 33 522
9 7 3 6 5 239
19 12 8 13 24 217
6 1 1 0 4 66
0.47 0.30 0.08 0.24 0.14 0.29
0.25 0.12 0.03 0.08 0.04 0.20
1.87 2.38 2.75 3.17 3.22 1.50
6.8⁎⁎⁎ 9.3⁎⁎⁎ 5.4⁎⁎ 13.0⁎⁎⁎ 14.3⁎⁎⁎ 83.7⁎⁎⁎
Notes: The null hypothesis is “Both individuals are treated unfavorably equally often”, that is, (5) = (6). The critical value of the χ2 at the one percent level of significance is 6.63 (⁎⁎⁎) and at the five percent level of significance is 3.84 (⁎⁎). (a) Upper level of compulsory school.
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Table 1 Aggregated results for the correspondence testing
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interview. In 1030 cases neither application was invited and in the remaining 522 cases at least one of the two applications was invited for interview. Both applications were invited in 239 cases, while only the Swedish-named application was invited in 217 cases and only the Middle Easternnamed application in 66 cases. From this information the callback rate of Swedish and Middle Eastern-named applications, respectively, as well as the relative callback rate have been calculated. The design of the experiment ensures that a difference in callbacks between Middle Easternand Swedish-named applications is due to firms/recruiters using ethnicity as a decision variable in the process of selecting who to call for interview. We find that the callback rates are significantly lower for applications with a Middle Eastern name attached to it. On average, they are fifty percent lower compared to applications with a Swedish-sounding name.15 Table 1 also gives the same type of data description for each of the twelve job categories separately. The relative callback rate is the highest for shop sales assistants, motor-vehicle drivers, restaurant workers and construction workers, while for computer professionals, teachers in math and science and accountants the relative callback rate is not significantly different from one. The relative callback rates for the other occupations lie in between these two groups. This indicates a negative relation between the relative callback rate and the skill requirement for the job. This is confirmed when calculating correlations between the relative callback rate, the average skill requirement of the occupation and the average percentage of immigrants employed in each occupation (see Table A1 in the appendix for the data).16 There is a negative correlation (− 0.72 and statistically significant) between the relative callback rate and the occupational skill level, implying that there is less unequal treatment in highly-skilled occupations. One explanation for this finding may be that highly skilled job tasks are often very specific in nature and, hence, individual productivity is more evident from reading a highly skilled individual's application when compared to that of a low-skilled individual. Bovenkerk et al. (1995) and Goldberg et al. (1996) find similar results for Holland and Germany, respectively, i.e. a higher level of unequal treatment occurs in low and semi-skilled jobs compared to high-skilled jobs. The relative callback rate and the percentage of immigrants employed in the occupation are positively correlated (0.40 but not statistically significant). The interpretation is that firms in occupations with a higher percentage of immigrants employed tend to call the minority group for interview to a lesser extent. A possible reason why these firms still employ immigrants might be because few majority group members look for jobs in these occupations/firms. When these firms are then confronted with a majority and a minority applicant the preference is given to the one belonging to the majority. 4. Empirical analysis In this section we analyze the ethnic difference in the probability to be invited for interview and which, if any, workplace and recruiter characteristics are associated with this probability difference. First, we analyze the 2878 applications and 1439 firms for which we have data on recruiter and workplace characteristics.17 Second, we condition on that the workplace/recruiter has called at least one applicant for interview, analyzing 960 applications. These results are probably less flawed by a 15
In the empirical analysis the ethnic difference in callback rates was not found to be statistically significant between the Stockholm and Gothenburg labor market areas. We therefore only discuss the merged data for the two cities. 16 Information on the share of immigrants in each occupation is taken from aggregate data covering the employed population in Sweden, see Statistics Sweden (2003). 17 Hence, we have successfully identified 93 percent of the workplaces in the registers.
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misclassification of the dependent variable, which might occur when firms receive a great number of applications.18 It is then probably quite random who gets selected into the screening process. We therefore suspect that at least some firms that were found not to invite any application for interview would call our applicant(s) if they had actually read the applications. Hence, by restricting the sample to only firms that have called at least one applicant for interview we answer a somewhat different question but might get a “cleaner” picture of which factors influence the ethnic difference in callbacks for interview. Finally, we analyze a subset of 674 applications for which we have additional interview information on workplace and recruiter characteristics. 4.1. Ethnic differences in the probability to be invited for interview In this section ethnic differences as regards the probability of being called for interview are being analyzed, using probit regressions (reporting marginal effects). The data include 1439 observations for Middle Eastern- and Swedish-named applications, respectively, giving a total of 2878 observations. We start out by regressing the callback dummy on only the ethnic indicator variable and then on occupation fixed effects and these interacted with the ethnic dummy. The first column of Table 2 reveals that applications with a Middle Eastern name attached to it have a ten percentage point lower probability of being called for interview compared to applications with a Swedish name.19 When allowing for separate effects of a Middle Eastern name on the callback rate for each occupation in introducing occupation fixed effects and interacting the ethnic dummy variable with these occupation indicators, we find some variation in that the effect is not significant within the group of computer professionals. For the other occupations we find a significant negative effect of a Middle Eastern name on the callback rate that varies between six and fourteen percentage points. 4.2. Factors influencing ethnic differences in callbacks for interview To investigate which, if any, of the obtained firm/workplace and recruiter variables that are correlated with the ethnic difference in callbacks we estimate probit regressions by including these variables into the “interaction” model of Table 2. Specifically, we regress the callback dummy on the workplace/recruiter characteristics and the interaction of those characteristics with the ethnic dummy. To what extent the included characteristics are associated with ethnic differences in callbacks is captured by the interaction effect, reported as a marginal effect.20 Only the interaction coefficients are reported in Table 3.21
18 Anecdotal evidence and the extremely low callback rates in some of our investigated occupations indicate that this is the case for some occupations. 19 In Section 3 we reported the relative callback rate, which was fifty percent lower for applicants with Middle Eastern names than for Swedish-named applicants. In this section the ten percentage point difference in callback rates between the two ethnic groups is in focus. 20 These are the estimated marginal changes in the probability for the continuous variables and estimated discrete changes for dummy variables. 21 The model also controls for occupation fixed effects and the interaction of those with the ethnic dummy as well as fixed effects for the size of the firm and the number of workplaces within firms. These latter variables could not be used in the analysis when interacted with an ethnic dummy since there was no variation in the outcome variable in some of the cells. This is our best strategy to control for unobserved characteristics affecting the ethnic difference in callbacks. The full set of results is available upon request.
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Table 2 The probability of callback for an interview Variable
Model 1
Model 2 Marginal effect on callback when Difference in marginal effect when having a Swedish name having a Middle Eastern name
Middle Eastern−0.10⁎⁎⁎ [0.01] sounding name Occupation Computer professionals Business sales assistants Teachers (math/science) Accountants Preschool Teachers Nurses Teachers (language) Teachers secondary school Construction workers Restaurant workers Shop sales assistants Motor-vehicle drivers
0.14⁎⁎⁎ [0.06] 0.37⁎⁎⁎ [0.10] − 0.07 [0.04] 0.40⁎⁎⁎ [0.06] 0.15⁎⁎ [0.06] 0.26⁎⁎⁎ [0.09] 0.14⁎ [0.08] 0.07 [0.08] −0.13⁎⁎ [0.04] − 0.06 [0.05] 0.04 [0.07]
− 0.01 [0.05] − 0.06⁎⁎⁎ [0.02] − 0.08⁎ [0.04] − 0.07⁎⁎ [0.03] − 0.10⁎⁎⁎ [0.02] − 0.08⁎⁎⁎ [0.02] − 0.13⁎⁎ [0.04] −0.11⁎⁎⁎ [0.03] −0.11⁎⁎⁎ [0.03] − 0.12⁎⁎ [0.04] − 0.14⁎⁎⁎ [0.03] − 0.14⁎⁎⁎ [0.03]
Marginal effects (percentage points). Notes: This table reports marginal effects on the probability of being invited for an interview using probit regressions, N = 2878. Column 1 reports the marginal effect of a Middle Eastern name without any controls. Model 2 and column 2 report occupational marginal effects for applications with a Swedish name, while column 3 reports the difference in the occupational marginal effect for applications with a Middle Eastern name relative to the effect in column 2. ⁎, ⁎⁎, and ⁎⁎⁎ denote the ten, five and one percent significance level, respectively. Reported standard errors (in brackets) are adjusted for clustering on workplace.
The workplace and recruiter characteristics can be divided into three broad categories: recruiter information (the sex and ethnicity of the recruiting person and whether the workplace has a personnel department), composition of employees (the share of male employees at the firm and the share of employees at the workplace that are born in another European country or in a nonEuropean country) and workplace characteristics (the number of employees at the workplace, personnel turnover at the workplace, whether public sector employer, having a diversity plan for work on equal rights with respect to ethnicity and the share of immigrants living in the municipality where the workplace is located). Variable descriptions and descriptive statistics of these variables are found in the appendix, see Table A2.22 Using the full data of 2878 observations we find two variables that are associated with treating the Middle Eastern-named applications differently (column 1). First, male recruiters appear to treat applications with a Middle Eastern name more negatively. The probability of being called for interview is another six percentage points lower for Middle Eastern-named applications when a male, as opposed to a female, is responsible for whom to call for interview. Second, the differential treatment varies with the number of employees at the workplace. The probability of a callback is five percentage points lower for the Middle Eastern-named application if applying to jobs at workplaces with less than twenty employees as compared to applying to jobs at workplaces with more than twenty employees. The estimate for the fraction of males at the firm indicates that firms with more males treat the Middle Eastern-named applications more favorably; 22
Due to space limitations these are not commented upon.
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Table 3 Factors influencing the ethnic difference in callbacks Interaction Variable
Recruiter information Male responsible Native responsible Personnel department Composition of employees Fraction of males Fraction of immigrants born in Europe: 0% in Europe: 1–10% outside Europe: 0% outside Europe: 1–10% Workplace characteristics Number of employees: 0–19 Personnel turnover Public sector employer Diversity plan Immigrants in municipality less than average Occupational fixed effects Firm size fixed effects
Model 1
Model 2
Model 3
Model 4
All work places
Work places inviting at least one applicant
Work places participating in survey
Work places participating in survey
N = 2878
N = 960
N = 674
N = 674
− 0.06⁎⁎ [0.03]
− 0.14 [0.09]
− 0.21⁎ [0.12]
−0.23⁎⁎ [0.13] 0.04 [0.12] −0.05 [0.11]
0.04 [0.05]
0.20 [0.17]
0.09 [0.24]
0.09 [0.24] −0.00 [0.10 −0.09 [0.12] −0.05 [0.11] 0.10 [0.08]
− 0.05⁎ [0.03]
− 0.17⁎⁎ [0.09]
− 0.17⁎ [0.11]
0.01 [0.04]
0.03 [0.11]
− 0.06 [0.15]
0.01 [0.03]
0.06 [0.06]
0.13⁎ [0.06]
−0.18⁎ [0.11] 0.13⁎ [0.08] −0.07[0.15] −0.13 [0.14] 0.12⁎ [0.06]
Yes Yes
Yes Yes
Yes Yes
Yes Yes
Marginal effects (percentage points). Notes: This table reports the interaction effects between the Middle Eastern name dummy and different firm and workplace characteristics on the probability of being invited for interview. ⁎, ⁎⁎, and ⁎⁎⁎ denote the ten, five and one percent significance level respectively. Reported standard errors (in brackets) are adjusted for clustering on workplace.
however, this effect is not precisely estimated. Neither the variable Public sector employer nor Immigrants in municipality less than average are associated with an ethnic difference in callbacks. Next we repeat the analysis of column 1 but condition on that the firm called at least one application for interview (see column 2). We are interested in whether the quantitative conclusions as regards the association between the workplace and recruiter characteristics and the ethnic difference in callbacks for interview change when using this restriction on the sample. The point estimates of all variables increase, but so do the standard errors. Hence, we now only find one variable that is associated with an ethnic difference in callbacks. Conditional on having called at least one applicant for interview, workplaces with less than twenty, compared to workplaces with more than twenty, employees are associated with a seventeen percentage point lower probability of calling applications with a Middle Eastern name to an interview. The point estimates for the fraction of males at the firm and the sex of the recruiter at the workplace are large but estimated with low precision. Finally, we also include workplace characteristics that were obtained by interviewing recruiters. This information is only available for employers that invited at least one applicant for interview. A total of 337 employers (674 applications) participated in the survey, i.e. a response rate of seventy percent. Replicating the analysis of the second column and using this smaller sub-
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sample of the data we find that the magnitude of the estimates increases, indicating that recruiters participating in the survey are somewhat selective (see column 3). Next we add the full set of workplace and recruiter characteristics to the model, see column 4. In this final specification we find four variables that are both statistically and economically significant. Male recruiters and workplaces with less than twenty employees appear to treat applications with a Middle Eastern name more negatively compared to female recruiters and workplaces with more than twenty employees. Conditional on having called at least one applicant for interview the probability to be called for interview is twenty-three percentage points lower for Middle Eastern-named applications when a male, as opposed to a female, is responsible for whom to call for interview. Also, workplaces with less than twenty compared to workplaces with more than twenty employees are associated with an eighteen-percentage-point lower probability of calling applications with a Middle Eastern name to interview. Somewhat surprisingly, we find that workplaces located in a municipality with relatively many immigrants appear to treat applications with a Middle Eastern name less favorably compared to firms located in a municipality with relatively few immigrants.23 Personnel turnover at the workplace is also associated with an ethnic difference in callbacks for interview. An increase in the share of new hirings by ten percentage points is associated with a 1.3 percentage-point higher probability to be called for interview for Middle Eastern-named compared to Swedish-named applications. Other interesting workplace and recruiter characteristics, such as the workplace having a written plan for their work on equal rights as regards ethnicity, the ethnicity of the recruiter, the share of employees born in or outside Europe, or whether the workplace has a personnel department were not found to be correlated with an ethnic difference in callbacks for interview. 4.3. Interpretation of results How can we interpret the results of this experiment? Existing theories of preference and statistical discrimination24 could all explain the observed ethnic difference in callbacks. But to what extent are they consistent with our other findings, as the variation across occupations in relative callbacks and the ethnic specific “effect” on the callback for certain workplace/recruiter characteristics? One line of statistical discrimination theories implies that minority members are discriminated against because employers view their productivity with less precision. Since both education and work experience are easily verified from the application the probability of such statistical discrimination is minimized. However, we cannot rule out the possibility that workplaces/recruiters use ethnicity as a proxy for unobservable individual productivity. The extent to which our applications have left out skills important in the hiring process is likely to vary across occupations and hence we should expect a difference in relative callbacks between Middle Eastern-and Swedish-named applications across occupations, which is exactly what we find. However, such a result is also in line with Beckerian employee and/or customer discrimination, see Becker (1957). The occupations included in this experiment vary to what degree they have customer contact and also as regards what type of customer contact. In fact, we find the highest relative callbacks for restaurant workers and shop sale assistants, occupations with frequent customer contacts. However, accountants and business sales assistants probably also have quite a 23
This result is similar to the one that the relative callback rate is higher in occupations that, on average, have many immigrants employed, see Section 3. 24 See Altonji and Blank, 1999, for a thorough review of these theories and Heckman (1998) for a discussion of identification issues involved in situation testing.
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large amount of customer contact, even if of a different type, but for these occupations we do not find an ethnic difference in callbacks for interviews. Further, the results do not support the existence of employee discrimination in our sample of firms. Adding the share of immigrant employees at the workplace did not show any correlation with ethnic differences in the callback for an interview. If anything, the results point in the other direction. The relative callback is the highest in occupations that, on average, have many immigrants employed, indicating a selection rule where firms choose who to call for an interview so that it corresponds to the population base rate (see Bertrand and Mullainathan, 2004). The result that men in recruiting positions, compared to women in the same positions, are less likely to call the Middle Eastern-named application for an interview indicates that employer/ recruiter preference discrimination is at play too. This result is in line with the fact that men in Sweden are found to have stronger negative attitudes toward immigrants than women do, see Swedish Integration Board (2005). 5. Conclusion This is the first study relating to Sweden which examines the extent of differential treatment in the hiring process due to ethnicity by means of correspondence testing, an experimental method specifically designed for this purpose. Our aggregated results show that for equivalent applications the interview callback rate is twenty-nine percent when having a Swedish name and twenty percent when having a Middle Eastern name attached to it. This difference is exclusively attributed to the name manipulation. How important this difference in callbacks is for ethnic differences in finding jobs is difficult to say. However, the results imply that Swedishnamed applicants get called to interview three times for every ten jobs they apply to, while Middle Eastern applicants need to apply up to fifteen jobs to achieve the same number of callbacks. The impact of this differential treatment depends on the availability of jobs. Only when jobs are scarce is this effect likely to be strong. Previous analyses using this experimental method have only to a minor extent attempted to answer what factors characterize firms that treat minority and majority applicants differently. We have extended the analysis by adding register and interview information to the experimental data. These results show that the probability to be called for an interview is much lower for Middle Eastern-named applications when a male, as opposed to a female, is responsible for whom to call for an interview. Also, the differential treatment varies with the number of employees at the workplace, if the workplace has a high degree of personnel turnover and if it is located in a municipality with relatively few immigrants. There may be several reasons for the first two results. Larger workplaces and those with a higher personnel turnover may have a more comprehensive recruitment process and thus reduce statistical discrimination. Another explanation might be that these companies use recruitment practices that call more individuals to interviews, i.e. decide on average to call all applicants with similar merits above a certain threshold.25 Interestingly, workplaces that have ethnic diversity plans, the share of immigrant employees at the workplace and the ethnicity of the recruiter were not found to be correlated with the difference in callbacks. 25
The observation that larger firms and firms with higher personnel turnover are less likely to treat the Middle Eastern applicant differently may potentially have an impact on the estimated relative callbacks. Firms that had many job openings during the period of investigation were included only once. Hence, firms with many job openings will have a lower weight in our sample compared to their weight in employing people in the labor market.
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The results do not give one but rather several explanations for the existence of an ethnic difference in callbacks for interviews, indicating that anti-discrimination legislation must fight discrimination broadly. Hence, in order to decrease ethnic discrimination in the hiring process there might be scope for reinforcing the legislation in this respect. Appendix A Description of variables included in the statistical analysis Variable Recruiter information Male responsible Native responsible Personnel department Composition of employees Males Immigrants born in Europe: 0% in Europe: 1–10% in Europe: 11–100% outside Europe: 0% outside Europe: 1–10% outside Europe: 11–100% Workplace characteristics Employees : 0–19 Personnel turnover Public sector Diversity plan Immigrants in municipality less than average
Occupational fixed effects Firm size fixed effects
Description Equals 1 if a male is responsible for recruitment at workplace, else 0 Equals 1 if a native is responsible for recruitment at workplace, else 0 Equals 1 if workplace has a personnel department, else 0 Share of males at the firm Equals 1 if workplace has 0 percent immigrants born in another European country other than Sweden, else 0 Equals 1 if workplace has 0 percent immigrants born in a non-European country, else 0
Equals 1 if the number of employees at workplace is between 0 and 19, else 0 Number of persons employed per year relative to the number of employees at workplace Equals 1 if workplace is public sector employer, else 0 Equals 1 if workplace works with equal rights w.r.t. ethnicity, else 0 Equals 1 if the share of immigrants in the municipality where the workplace is located is less than the average share in the sample, else 0. Information taken from the Swedish Population Register (RTB), Statistics Sweden. Dummy variables assigned to 1 if the job applied to belong to the particular occupation, else 0 Dummy variables assigned to 1 if firm has only one workplace, else 0, and dummy variables controlling for whether the firm has 0–9, 10–99 or more than 100 employees
Table A1 Relative callback rates, immigrant shares and skill requirements
Computer professionals Teachers — upper level of compulsory school (Math) Business sales assistants Preschool Teachers Accountants Nurses Teachers — upper secondary school Teachers — upper level of Compulsory school (Lang.) Motor-vehicle drivers
Relative callback rate
Fraction Immigrants (%)
Skill requirement
1.10 1.26
8 9
3 3
1.28 1.33 1.41 1.43 1.75 1.87
6 7 7 10 8 9
2 3 3 3 3 3
2.17
12
2 (continued on next page )
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Table (continued) A1 (continued )
Construction workers Restaurant workers Shop sales assistants Total
Relative callback rate
Fraction Immigrants (%)
Skill requirement
2.38 2.75 3.22 1.50
5 20 8 10
2 1 2 –
Notes: The relative callback rate is taken from Table 1. Information on the fraction of immigrants and skill requirement in each occupational category is taken from Statistics Sweden (2003). The required skill/level of education in each occupation is defined as 3 = higher education. 2 = secondary education and 1 = primary education. Table A2 Descriptive statistics Variable
Recruiter information Male responsible Native responsible⁎ Personnel department⁎ Work place composition of employees Fraction of males† Fraction of immigrants born in Europe: 0%⁎ in Europe: 1–10%⁎ in Europe: 11–100%⁎ outside Europe: 0%⁎ outside Europe: 1–10%⁎ outside Europe: 11–100%⁎ Work place characteristics Employees : 0–19 Personnel turnover⁎ Public sector Diversity plan⁎ Immigrants in municipality less than average Occupational distribution Computer professionals Business sales assistants Teachers (math and science) Accountants Preschool Teachers Nurses Teachers — upper secondary school Teachers (language) Construction workers Restaurant workers Shop sales assistants Motor-vehicle drivers Firm size Employees : 0–9† Employees : 10–99† Employees : 100-†
Only Swedishsounding name invited
Only M. Easternsounding name invited
Both invited
Neither invited
Total
(1)
(2)
(3)
(4)
(5)
0.55 0.89 0.20
0.54 0.93 0.18
0.37 0.88 0.25
0.59
0.55 0.89 0.22
0.45
0.52
0.38
0.50
0.48
0.34 0.33 0.33 0.40 0.29 0.31
0.38 0.30 0.32 0.32 0.42 0.25
0.38 0.29 0.32 0.42 0.31 0.27
0.57 0.28 (0.52) 0.26 0.18 0.25
0.51 0.38 (0.77) 0.22 0.12 0.32
0.50 0.28 (0.55) 0.36 0.16 0.22
0.06 0.17 0.03 0.07 0.17 0.09 0.05 0.09 0.04 0.04 0.11 0.05
0.19 0.27 0.03 0.08 0.12 0.07 0.03 0.10 0.02 0.02 0.07 0.00
0.25 0.34 0.41
0.34 0.32 0.34
0.37 0.31 0.32 0.40 0.32 0.28 0.59
0.24
0.57 0.29 (0.57) 0.19 0.16 0.24
0.04 0.23 0.07 0.04 0.33 0.13 0.04 0.04 0.03 0.01 0.02 0.03
0.07 0.15 0.02 0.15 0.06 0.09 0.04 0.02 0.04 0.12 0.15 0.05
0.07 0.17 0.03 0.12 0.12 0.09 0.04 0.04 0.04 0.09 0.12 0.05
0.31 0.22 0.47
0.39 0.33 0.28
0.35 0.32 0.33
0.13
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Table (continued) A2 (continued ) Variable
Firm size Number of work places equals one†
Only Swedishsounding name invited
Only M. Easternsounding name invited
Both invited
Neither invited
Total
(1)
(2)
(3)
(4)
(5)
0.56
0.63
0.54
0.67
0.63
Notes: This table reports the mean characteristics for workplaces that invited only the applicant with a Swedish name (column 1, N = 204), only the applicant with a Middle Eastern name (column 2, N = 59), both applicants (column 3, N = 221), neither of the applicants (column 4, N = 1014) and finally the average characteristics (column 4, N = 1498). For continuous variables standard deviations are reported in parenthesis. (⁎) indicate survey data where the number of observations is 140, 40, 161, 0 and 341 in each column, respectively. All variables are measured at the workplace level except variables marked with (†), which are measured at the firm level.
References Altonji, J., Blank, R., 1999. Race and Gender in the Labor Market. In: Ashenfelter, O., Card, D. (Eds.), Handbook of Labor Economics. Elsevier, Amsterdam, pp. 3143–3259. Arai, M., Vilhelmsson, R., 2004. Unemployment-Risk Differentials between Immigrant and Native Workers in Sweden. Industrial Relations 43, 690–698. Åslund, O., Rooth, D., 2005. Do Shifts in Attitudes Affect Labour Market Discrimination: Swedish Experiences After 9–11. Journal of Population Economics 18 (4), 602–629. Becker, Gary S., 1957. The Economics of Discrimination. University of Chicago Press, Chicago. Bertrand, M., Mullainathan, S., 2004. Are Emily and Greg More Employable Than Lakisha and Jamal? Field Experiment on Labor Discrimination. American Economic Review 991–1014. Bovenkerk, F., Gras, M., Ramsoedh, D., 1995. Discrimination against migrant workers and ethnic minorities in access to employment in the Netherlands. International Migration Papers, vol. 4. International Labour Office, Geneva. Carlsson, M., Rooth, D., 2006. Evidence of Ethnic Discrimination in the Swedish Labor Market Using Experimental Data. IZA Discussion Paper, vol. 2281, IZA, Bonn. Edin, P.-A., Lagerström, J., 2006. Blind Dates: Quasi-Experimental Evidence on Discrimination. IFAU Working Paper, vol. 4, IFAU, Uppsala. FSI, 2004. Några frågor om invandringen och invandrare/flyktingar. Forskningsgruppen för Samhälls-och Informationsstudier, Stockholm. Goldberg, A., Mourinho, D., Kulke, U., 1996. Labour market discrimination against foreign workers in Germany. International Migration Papers, vol. 7. International Labour Office, Geneva. Heckman, J., 1998. Detecting Discrimination. Journal of Economic Perspectives 12, 101–116. Lange, A., 2000. Diskriminering, integration och etniska relationer. Integrationsverket, Norrköping. Riach, P.A., Rich, J., 2002. Field Experiments of Discrimination in the Market Place. Economic Journal 112, F480–F518. Riach, P.A., Rich, J., 2004. Deceptive Field Experiments of Discrimination: Are They Ethical? Kyklos 57, 457–470. Rooth, D.-O., 2002. Adopted Children in the Labour Market — Discrimination or Unobserved Characteristics? International Migration Quarterly Review 40 (1), 71–98. Statistics Sweden, 2003. Swedish Occupational Register. Statistics Sweden, Stockholm. Statistics Sweden, 2005. Swedish Name Register. Statistics Sweden, Stockholm. Swedish Integration Board, 2005. Integrationsbarometern, Norrköping.
Evidence of ethnic discrimination in the Swedish labor market using experimental data ☆ Magnus Carlsson, Dan-Olof Rooth ⁎ Kalmar University, 39182 Kalmar, Sweden Available online 6 May 2007
Abstract We present evidence of ethnic discrimination in the recruitment process by sending fictitious applications to real job openings. Applications with identical skills were randomly assigned Middle Easternor Swedish-sounding names and applications with a Swedish name receive fifty percent more callbacks for an interview. We extend previous analyses by adding register and interview information on firms/recruiters to the experimental data. We find that male recruiters and workplaces with fewer than twenty employees less often call applications with a Middle Eastern name for an interview. © 2007 Elsevier B.V. All rights reserved. JEL classification: J64; J71 Keywords: Ethnic discrimination; Exit from unemployment
1. Introduction There are reasons to expect that discriminatory behavior exists among Swedish employers. Longitudinal attitude surveys of the general public show evidence of negative attitudes toward immigrants in general and surveys among potentially discriminated groups also point in this direction (see Lange, 2000; FSI, 2004).1 These studies indicate that discrimination is worst ☆ We thank two anonymous referees, Per Johansson, Olof Åslund, participants at seminars in Kalmar, Gothenburg, Stockholm, Växjö University and at the 2006 EALE Conference in Prague for valuable comments. Klara Johansson provided excellent research assistance. A research grant from the Swedish Council for Working Life and Social Research is gratefully acknowledged. The data used in the article are available from the authors upon request. ⁎ Corresponding author. E-mail addresses: [email protected] (M. Carlsson), [email protected] (D.-O. Rooth). 1 See also Arai and Vilhelmsson (2004), Edin and Lagerström (2006), Rooth (2002) and Åslund and Rooth (2005) for studies on ethnic discrimination in Sweden.
0927-5371/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.labeco.2007.05.001
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against individuals with a Middle Eastern background. Also, unemployment rates for immigrants born in the Middle East are found to be several times higher than for natives, indicating that ethnic discrimination exists in the recruitment process. Whether this is true is the main question to be answered in this study. However, ethnic discrimination is just one possible explanation for native-immigrant differences in finding jobs. Another is (to the researcher) unobserved productive characteristics. Since researchers can seldom account for all differences in productive characteristics between groups it is a difficult task to empirically identify the extent of ethnic discrimination in the labor market. To circumvent this difficulty researchers have relied on using field experiments specifically designed to test for discrimination in recruitment.2 The correspondence testing methodology implies that the researcher sends two equal applications to advertised job openings with the only difference being the name of the applicant; one with a native-sounding name and the other a foreign-sounding name. Hence, this methodology ensures that productive characteristics on the supply side are held constant. Ethnic discrimination is quantified by the difference in the number of callbacks for interview between the two ethnic groups. Contrary to most previous studies using this methodology we proceed with collecting company and, especially, recruiter information through official registers and by conducting interviews with the recruiters. Hence, while some studies use variation in application information to analyze how discrimination varies across application attributes (see Bertrand and Mullainathan, 2004), we use the same application attributes for both applicants to analyze which firm/workplace and recruiter characteristics that are correlated with the ethnic difference in the callback rate to interview.3 Our experimental data was collected between May 2005 and February 2006 by sending two applications; one with a Swedish-sounding male name and one with a Middle Eastern-sounding male name, to job openings in twelve different occupations in the Stockholm and Gothenburg labor market areas. In total we replied to 1552 job ads posted at the home-page of the Swedish Employment Agency. It was found that the callback rate of applications with a Swedish-sounding male name was fifty percent higher than for the ones with a Middle Eastern-sounding name. Interestingly, despite the fact that low level occupations have the largest share of immigrant employees we found that the relative callback rate for Swedish-sounding named applications is higher in lower level occupations than in higher level occupations. Further, regression analysis shows that the ethnic difference in callbacks is related to the sex of the recruiter and to the number of employees at the workplace. The remainder of this paper is outlined as follows. Section 2 presents the methodology used and the design of the experiment. Section 3 describes the collected data. In Section 4 an analysis is performed using register and interview data on which workplace and recruiter characteristics influence the ethnic difference in the probability of being called for an interview. Section 5 concludes. 2. Experimental design4 The experiment was conducted between May 2005 and February 2006. During this period all employment advertisements in selected occupations found on the webpage of the Swedish
2
A great number of situation tests have been conducted in Europe and the US in recent decades and a clear majority of studies find discrimination against minorities, see Riach and Rich (2002) for a summary of these experiments. 3 Ideally, one could combine the two approaches within one research design. This approach is left for future research. 4 See Riach and Rich (2004) for a discussion on ethical considerations of the method.
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employment agency were collected.5 All in all, 3104 applications were sent to 1552 employers. Callbacks for interviews were received via telephone, email or ordinary post. To minimize inconvenience to the employer invitations were promptly declined. 2.1. Choice of occupations To make satisfactory progress in the collection of cases it was necessary that the demand for labor was high in the chosen occupations. In addition, we wanted the skill requirement and the share of immigrants to vary across occupations. Hence, the selected occupations were both skilled and semi/unskilled and included a relatively high as well as a low ratio of immigrants.6 In the end the experiment was restricted to twelve occupations and the two major cities of Sweden: Stockholm and Gothenburg.7 The selected occupations were computer professionals, business sales assistants, four categories of teachers (preschool, math/science and language in upper level compulsory school, and upper secondary school), accountants, nurses, construction workers, restaurant workers, shop sales assistants and motor-vehicle drivers.8 2.2. Construction of applications The applications had to be realistic and yet not refer to any real persons. Also, since the competition from other applicants was considerable the testers had to be well qualified. A number of real life (written) applications available on the webpage of the Swedish employment agency were used as templates and adjusted and calibrated for our purposes. Applicants had identical human capital within occupations and were on average 25–30 years old, had two to four years of work experience in the same occupation as the job applied for and had obtained their education in the same type of school, but at different locations. The application consisted of a quite general biography on the first page and a detailed CV of education and work experience on the second page.9 For each occupational category three applications with a somewhat different layout were constructed. Two of the applications were randomly drawn and given a name: a Swedishsounding male name or a Middle Eastern-sounding male name. With the name followed also an email address, a telephone number and a postal address. The reason for using Middle Eastern names is that empirical studies indicate that discrimination is the worst against individuals with a Middle Eastern background. If this is true, analyzing this group gives an upper bound for discrimination in the hiring process against other ethnic groups.10
5
In approximately 20% of the job openings it was not possible to apply via email. These vacancies were excluded. This information was taken from the occupational register, Statistics Sweden, 2003. 7 We also started an experiment in Malmö, the third largest city in Sweden, but there were not enough jobs available to make satisfactory progress in the collection of cases. 8 A total of 62 cleaning jobs were also collected but we only received five callbacks, one firm calling both and four firms calling only the Swedish-named application for interview. Due to too little variation in callbacks these cases could not be used in the regression analysis. Cleaning is therefore discarded, which did not change the overall results. 9 See Carlsson and Rooth (2006) for an example of an application. 10 One could vary the names to include more immigrant backgrounds but our strategy has been to focus on firm/recruiter characteristics keeping the supply side information constant. 6
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The choice of names is crucial since ethnicity is only signaled through the name. Fortunately, there is a clear distinction between typical Swedish names and Middle Eastern names. Three of the most frequent Swedish and Middle Eastern names occurring in Sweden were selected and these were randomly assigned to resumes in order to avoid name effects.11 An email address and a telephone number (including an automatic answering service) were registered at a large Internet provider and a phone company for each fictitious applicant. To prevent any invitations being lost or returned to the employer real postal addresses were also included in the resumes. The addresses were chosen to signal that the respondents lived in the same neighborhood. The constructed resumes were then sent to the employer in alternating order with a one-day delay in between. 2.3. Firm/workplace and recruiter information The collected workplaces were identified in Statistics Sweden's business (workplace) register using company ID's (so called CFAR-no, see www.scb.se).12 We then obtained information on the number of employees at the workplace and at the firm (if more than one workplace at the firm), on the number of workplaces at the firm, whether public sector employer and the share of male employees at the firm.13 A variable measuring the share of immigrants living in the municipality where the workplace is located was also constructed and from the job ads we obtained information on the sex of the recruiter. We also collected information by interviewing recruiters.14 Thereby we gained additional information about the workplace as regards the share of employees born in another European country or in a non-European country, personnel turnover (the number of new recruitments relative to the number of employees), the ethnicity of the recruiter, whether the workplace had a personnel department for recruitment and whether the workplace had a diversity plan for equal rights work with respect to ethnicity. The interview also included a question measuring explicit attitudes towards hiring a qualified Middle Eastern minority applicant. Only one workplace responded that they would not hire an applicant with a Middle Eastern background, all others that they would. Hence, their reported explicit attitudes depart from their actual behavior. 3. Descriptive results The last row of Table 1 gives the aggregated results of the experiment. From the first column it is evident that the two applications were sent to 1552 different job openings. Since correspondence testing only focuses on the first step of the hiring process, being called for interview, thus neglecting the second step of who actually gets the job, there are four possible interview outcomes: neither invited, both invited, or only the majority or minority individual being invited for an 11 We used the Swedish first names Erik, Karl and Lars and the last names Andersson, Pettersson and Nilsson and the Middle Eastern first names Ali, Reza and Mohammed and the last names Ameer, Hassan and Said, from the name statistics register, Statistics Sweden (2005). A sensitivity analysis showed that neither different names (within ethnicity) nor different resumes (within occupations) had a statistically significant effect on the probability of being invited. These results are available upon request. 12 Company ID's were identified from combining information on firm name and address found in the job ad with information in Statistics Sweden's business register available on the Internet. 13 In sixty-three percent of the cases the firm only has one workplace and, hence, firm and workplace form the same unit. 14 These interviews did not include firms not inviting any application for an interview. Due to time constraints it was decided upon early in the project not to interview this group of firms. Later on the availability to match our data with firm/workplace registers at Statistics Sweden appeared.
720
Jobs
Neither At least Equal Only Swedish- Only M. Eastern- Callback rates χ2 Invited one invited Treatment sounding sounding SwedishM. EasternRelative (Swedish)/ name invited name invited sounding name sounding name (Middle Eastern)
(1)
(2)
Computer professionals 106 71 42 16 Teachers (math and science) a Business sales assistants 278 164 Preschool Teachers 184 64 Accountants 186 155 Nurses 150 95 Teachers — upper 64 41 secondary school 60 26 Teachers (language)a Construction workers 64 44 Restaurant workers 140 128 Motor-vehicle drivers 78 59 Shop sales assistants 200 167 Total number of jobs 1552 1030
(3)
(4)
(5)
(6)
(4 + 5)/(1)
(4 + 6)/(1)
35 26 114 120 31 55 23
9 17 57 76 10 30 10
14 7 39 36 14 20 11
12 2 18 8 7 5 2
0.22 0.57 0.35 0.61 0.13 0.33 0.33
0.20 0.45 0.27 0.46 0.09 0.23 0.19
1.10 1.26 1.28 1.33 1.41 1.43 1.75
0.2 2.8 7.7⁎⁎⁎ 17.8⁎⁎⁎ 2.3 9.0⁎⁎⁎ 6.2⁎⁎
34 20 12 19 33 522
9 7 3 6 5 239
19 12 8 13 24 217
6 1 1 0 4 66
0.47 0.30 0.08 0.24 0.14 0.29
0.25 0.12 0.03 0.08 0.04 0.20
1.87 2.38 2.75 3.17 3.22 1.50
6.8⁎⁎⁎ 9.3⁎⁎⁎ 5.4⁎⁎ 13.0⁎⁎⁎ 14.3⁎⁎⁎ 83.7⁎⁎⁎
Notes: The null hypothesis is “Both individuals are treated unfavorably equally often”, that is, (5) = (6). The critical value of the χ2 at the one percent level of significance is 6.63 (⁎⁎⁎) and at the five percent level of significance is 3.84 (⁎⁎). (a) Upper level of compulsory school.
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Table 1 Aggregated results for the correspondence testing
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interview. In 1030 cases neither application was invited and in the remaining 522 cases at least one of the two applications was invited for interview. Both applications were invited in 239 cases, while only the Swedish-named application was invited in 217 cases and only the Middle Easternnamed application in 66 cases. From this information the callback rate of Swedish and Middle Eastern-named applications, respectively, as well as the relative callback rate have been calculated. The design of the experiment ensures that a difference in callbacks between Middle Easternand Swedish-named applications is due to firms/recruiters using ethnicity as a decision variable in the process of selecting who to call for interview. We find that the callback rates are significantly lower for applications with a Middle Eastern name attached to it. On average, they are fifty percent lower compared to applications with a Swedish-sounding name.15 Table 1 also gives the same type of data description for each of the twelve job categories separately. The relative callback rate is the highest for shop sales assistants, motor-vehicle drivers, restaurant workers and construction workers, while for computer professionals, teachers in math and science and accountants the relative callback rate is not significantly different from one. The relative callback rates for the other occupations lie in between these two groups. This indicates a negative relation between the relative callback rate and the skill requirement for the job. This is confirmed when calculating correlations between the relative callback rate, the average skill requirement of the occupation and the average percentage of immigrants employed in each occupation (see Table A1 in the appendix for the data).16 There is a negative correlation (− 0.72 and statistically significant) between the relative callback rate and the occupational skill level, implying that there is less unequal treatment in highly-skilled occupations. One explanation for this finding may be that highly skilled job tasks are often very specific in nature and, hence, individual productivity is more evident from reading a highly skilled individual's application when compared to that of a low-skilled individual. Bovenkerk et al. (1995) and Goldberg et al. (1996) find similar results for Holland and Germany, respectively, i.e. a higher level of unequal treatment occurs in low and semi-skilled jobs compared to high-skilled jobs. The relative callback rate and the percentage of immigrants employed in the occupation are positively correlated (0.40 but not statistically significant). The interpretation is that firms in occupations with a higher percentage of immigrants employed tend to call the minority group for interview to a lesser extent. A possible reason why these firms still employ immigrants might be because few majority group members look for jobs in these occupations/firms. When these firms are then confronted with a majority and a minority applicant the preference is given to the one belonging to the majority. 4. Empirical analysis In this section we analyze the ethnic difference in the probability to be invited for interview and which, if any, workplace and recruiter characteristics are associated with this probability difference. First, we analyze the 2878 applications and 1439 firms for which we have data on recruiter and workplace characteristics.17 Second, we condition on that the workplace/recruiter has called at least one applicant for interview, analyzing 960 applications. These results are probably less flawed by a 15
In the empirical analysis the ethnic difference in callback rates was not found to be statistically significant between the Stockholm and Gothenburg labor market areas. We therefore only discuss the merged data for the two cities. 16 Information on the share of immigrants in each occupation is taken from aggregate data covering the employed population in Sweden, see Statistics Sweden (2003). 17 Hence, we have successfully identified 93 percent of the workplaces in the registers.
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misclassification of the dependent variable, which might occur when firms receive a great number of applications.18 It is then probably quite random who gets selected into the screening process. We therefore suspect that at least some firms that were found not to invite any application for interview would call our applicant(s) if they had actually read the applications. Hence, by restricting the sample to only firms that have called at least one applicant for interview we answer a somewhat different question but might get a “cleaner” picture of which factors influence the ethnic difference in callbacks for interview. Finally, we analyze a subset of 674 applications for which we have additional interview information on workplace and recruiter characteristics. 4.1. Ethnic differences in the probability to be invited for interview In this section ethnic differences as regards the probability of being called for interview are being analyzed, using probit regressions (reporting marginal effects). The data include 1439 observations for Middle Eastern- and Swedish-named applications, respectively, giving a total of 2878 observations. We start out by regressing the callback dummy on only the ethnic indicator variable and then on occupation fixed effects and these interacted with the ethnic dummy. The first column of Table 2 reveals that applications with a Middle Eastern name attached to it have a ten percentage point lower probability of being called for interview compared to applications with a Swedish name.19 When allowing for separate effects of a Middle Eastern name on the callback rate for each occupation in introducing occupation fixed effects and interacting the ethnic dummy variable with these occupation indicators, we find some variation in that the effect is not significant within the group of computer professionals. For the other occupations we find a significant negative effect of a Middle Eastern name on the callback rate that varies between six and fourteen percentage points. 4.2. Factors influencing ethnic differences in callbacks for interview To investigate which, if any, of the obtained firm/workplace and recruiter variables that are correlated with the ethnic difference in callbacks we estimate probit regressions by including these variables into the “interaction” model of Table 2. Specifically, we regress the callback dummy on the workplace/recruiter characteristics and the interaction of those characteristics with the ethnic dummy. To what extent the included characteristics are associated with ethnic differences in callbacks is captured by the interaction effect, reported as a marginal effect.20 Only the interaction coefficients are reported in Table 3.21
18 Anecdotal evidence and the extremely low callback rates in some of our investigated occupations indicate that this is the case for some occupations. 19 In Section 3 we reported the relative callback rate, which was fifty percent lower for applicants with Middle Eastern names than for Swedish-named applicants. In this section the ten percentage point difference in callback rates between the two ethnic groups is in focus. 20 These are the estimated marginal changes in the probability for the continuous variables and estimated discrete changes for dummy variables. 21 The model also controls for occupation fixed effects and the interaction of those with the ethnic dummy as well as fixed effects for the size of the firm and the number of workplaces within firms. These latter variables could not be used in the analysis when interacted with an ethnic dummy since there was no variation in the outcome variable in some of the cells. This is our best strategy to control for unobserved characteristics affecting the ethnic difference in callbacks. The full set of results is available upon request.
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Table 2 The probability of callback for an interview Variable
Model 1
Model 2 Marginal effect on callback when Difference in marginal effect when having a Swedish name having a Middle Eastern name
Middle Eastern−0.10⁎⁎⁎ [0.01] sounding name Occupation Computer professionals Business sales assistants Teachers (math/science) Accountants Preschool Teachers Nurses Teachers (language) Teachers secondary school Construction workers Restaurant workers Shop sales assistants Motor-vehicle drivers
0.14⁎⁎⁎ [0.06] 0.37⁎⁎⁎ [0.10] − 0.07 [0.04] 0.40⁎⁎⁎ [0.06] 0.15⁎⁎ [0.06] 0.26⁎⁎⁎ [0.09] 0.14⁎ [0.08] 0.07 [0.08] −0.13⁎⁎ [0.04] − 0.06 [0.05] 0.04 [0.07]
− 0.01 [0.05] − 0.06⁎⁎⁎ [0.02] − 0.08⁎ [0.04] − 0.07⁎⁎ [0.03] − 0.10⁎⁎⁎ [0.02] − 0.08⁎⁎⁎ [0.02] − 0.13⁎⁎ [0.04] −0.11⁎⁎⁎ [0.03] −0.11⁎⁎⁎ [0.03] − 0.12⁎⁎ [0.04] − 0.14⁎⁎⁎ [0.03] − 0.14⁎⁎⁎ [0.03]
Marginal effects (percentage points). Notes: This table reports marginal effects on the probability of being invited for an interview using probit regressions, N = 2878. Column 1 reports the marginal effect of a Middle Eastern name without any controls. Model 2 and column 2 report occupational marginal effects for applications with a Swedish name, while column 3 reports the difference in the occupational marginal effect for applications with a Middle Eastern name relative to the effect in column 2. ⁎, ⁎⁎, and ⁎⁎⁎ denote the ten, five and one percent significance level, respectively. Reported standard errors (in brackets) are adjusted for clustering on workplace.
The workplace and recruiter characteristics can be divided into three broad categories: recruiter information (the sex and ethnicity of the recruiting person and whether the workplace has a personnel department), composition of employees (the share of male employees at the firm and the share of employees at the workplace that are born in another European country or in a nonEuropean country) and workplace characteristics (the number of employees at the workplace, personnel turnover at the workplace, whether public sector employer, having a diversity plan for work on equal rights with respect to ethnicity and the share of immigrants living in the municipality where the workplace is located). Variable descriptions and descriptive statistics of these variables are found in the appendix, see Table A2.22 Using the full data of 2878 observations we find two variables that are associated with treating the Middle Eastern-named applications differently (column 1). First, male recruiters appear to treat applications with a Middle Eastern name more negatively. The probability of being called for interview is another six percentage points lower for Middle Eastern-named applications when a male, as opposed to a female, is responsible for whom to call for interview. Second, the differential treatment varies with the number of employees at the workplace. The probability of a callback is five percentage points lower for the Middle Eastern-named application if applying to jobs at workplaces with less than twenty employees as compared to applying to jobs at workplaces with more than twenty employees. The estimate for the fraction of males at the firm indicates that firms with more males treat the Middle Eastern-named applications more favorably; 22
Due to space limitations these are not commented upon.
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Table 3 Factors influencing the ethnic difference in callbacks Interaction Variable
Recruiter information Male responsible Native responsible Personnel department Composition of employees Fraction of males Fraction of immigrants born in Europe: 0% in Europe: 1–10% outside Europe: 0% outside Europe: 1–10% Workplace characteristics Number of employees: 0–19 Personnel turnover Public sector employer Diversity plan Immigrants in municipality less than average Occupational fixed effects Firm size fixed effects
Model 1
Model 2
Model 3
Model 4
All work places
Work places inviting at least one applicant
Work places participating in survey
Work places participating in survey
N = 2878
N = 960
N = 674
N = 674
− 0.06⁎⁎ [0.03]
− 0.14 [0.09]
− 0.21⁎ [0.12]
−0.23⁎⁎ [0.13] 0.04 [0.12] −0.05 [0.11]
0.04 [0.05]
0.20 [0.17]
0.09 [0.24]
0.09 [0.24] −0.00 [0.10 −0.09 [0.12] −0.05 [0.11] 0.10 [0.08]
− 0.05⁎ [0.03]
− 0.17⁎⁎ [0.09]
− 0.17⁎ [0.11]
0.01 [0.04]
0.03 [0.11]
− 0.06 [0.15]
0.01 [0.03]
0.06 [0.06]
0.13⁎ [0.06]
−0.18⁎ [0.11] 0.13⁎ [0.08] −0.07[0.15] −0.13 [0.14] 0.12⁎ [0.06]
Yes Yes
Yes Yes
Yes Yes
Yes Yes
Marginal effects (percentage points). Notes: This table reports the interaction effects between the Middle Eastern name dummy and different firm and workplace characteristics on the probability of being invited for interview. ⁎, ⁎⁎, and ⁎⁎⁎ denote the ten, five and one percent significance level respectively. Reported standard errors (in brackets) are adjusted for clustering on workplace.
however, this effect is not precisely estimated. Neither the variable Public sector employer nor Immigrants in municipality less than average are associated with an ethnic difference in callbacks. Next we repeat the analysis of column 1 but condition on that the firm called at least one application for interview (see column 2). We are interested in whether the quantitative conclusions as regards the association between the workplace and recruiter characteristics and the ethnic difference in callbacks for interview change when using this restriction on the sample. The point estimates of all variables increase, but so do the standard errors. Hence, we now only find one variable that is associated with an ethnic difference in callbacks. Conditional on having called at least one applicant for interview, workplaces with less than twenty, compared to workplaces with more than twenty, employees are associated with a seventeen percentage point lower probability of calling applications with a Middle Eastern name to an interview. The point estimates for the fraction of males at the firm and the sex of the recruiter at the workplace are large but estimated with low precision. Finally, we also include workplace characteristics that were obtained by interviewing recruiters. This information is only available for employers that invited at least one applicant for interview. A total of 337 employers (674 applications) participated in the survey, i.e. a response rate of seventy percent. Replicating the analysis of the second column and using this smaller sub-
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sample of the data we find that the magnitude of the estimates increases, indicating that recruiters participating in the survey are somewhat selective (see column 3). Next we add the full set of workplace and recruiter characteristics to the model, see column 4. In this final specification we find four variables that are both statistically and economically significant. Male recruiters and workplaces with less than twenty employees appear to treat applications with a Middle Eastern name more negatively compared to female recruiters and workplaces with more than twenty employees. Conditional on having called at least one applicant for interview the probability to be called for interview is twenty-three percentage points lower for Middle Eastern-named applications when a male, as opposed to a female, is responsible for whom to call for interview. Also, workplaces with less than twenty compared to workplaces with more than twenty employees are associated with an eighteen-percentage-point lower probability of calling applications with a Middle Eastern name to interview. Somewhat surprisingly, we find that workplaces located in a municipality with relatively many immigrants appear to treat applications with a Middle Eastern name less favorably compared to firms located in a municipality with relatively few immigrants.23 Personnel turnover at the workplace is also associated with an ethnic difference in callbacks for interview. An increase in the share of new hirings by ten percentage points is associated with a 1.3 percentage-point higher probability to be called for interview for Middle Eastern-named compared to Swedish-named applications. Other interesting workplace and recruiter characteristics, such as the workplace having a written plan for their work on equal rights as regards ethnicity, the ethnicity of the recruiter, the share of employees born in or outside Europe, or whether the workplace has a personnel department were not found to be correlated with an ethnic difference in callbacks for interview. 4.3. Interpretation of results How can we interpret the results of this experiment? Existing theories of preference and statistical discrimination24 could all explain the observed ethnic difference in callbacks. But to what extent are they consistent with our other findings, as the variation across occupations in relative callbacks and the ethnic specific “effect” on the callback for certain workplace/recruiter characteristics? One line of statistical discrimination theories implies that minority members are discriminated against because employers view their productivity with less precision. Since both education and work experience are easily verified from the application the probability of such statistical discrimination is minimized. However, we cannot rule out the possibility that workplaces/recruiters use ethnicity as a proxy for unobservable individual productivity. The extent to which our applications have left out skills important in the hiring process is likely to vary across occupations and hence we should expect a difference in relative callbacks between Middle Eastern-and Swedish-named applications across occupations, which is exactly what we find. However, such a result is also in line with Beckerian employee and/or customer discrimination, see Becker (1957). The occupations included in this experiment vary to what degree they have customer contact and also as regards what type of customer contact. In fact, we find the highest relative callbacks for restaurant workers and shop sale assistants, occupations with frequent customer contacts. However, accountants and business sales assistants probably also have quite a 23
This result is similar to the one that the relative callback rate is higher in occupations that, on average, have many immigrants employed, see Section 3. 24 See Altonji and Blank, 1999, for a thorough review of these theories and Heckman (1998) for a discussion of identification issues involved in situation testing.
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large amount of customer contact, even if of a different type, but for these occupations we do not find an ethnic difference in callbacks for interviews. Further, the results do not support the existence of employee discrimination in our sample of firms. Adding the share of immigrant employees at the workplace did not show any correlation with ethnic differences in the callback for an interview. If anything, the results point in the other direction. The relative callback is the highest in occupations that, on average, have many immigrants employed, indicating a selection rule where firms choose who to call for an interview so that it corresponds to the population base rate (see Bertrand and Mullainathan, 2004). The result that men in recruiting positions, compared to women in the same positions, are less likely to call the Middle Eastern-named application for an interview indicates that employer/ recruiter preference discrimination is at play too. This result is in line with the fact that men in Sweden are found to have stronger negative attitudes toward immigrants than women do, see Swedish Integration Board (2005). 5. Conclusion This is the first study relating to Sweden which examines the extent of differential treatment in the hiring process due to ethnicity by means of correspondence testing, an experimental method specifically designed for this purpose. Our aggregated results show that for equivalent applications the interview callback rate is twenty-nine percent when having a Swedish name and twenty percent when having a Middle Eastern name attached to it. This difference is exclusively attributed to the name manipulation. How important this difference in callbacks is for ethnic differences in finding jobs is difficult to say. However, the results imply that Swedishnamed applicants get called to interview three times for every ten jobs they apply to, while Middle Eastern applicants need to apply up to fifteen jobs to achieve the same number of callbacks. The impact of this differential treatment depends on the availability of jobs. Only when jobs are scarce is this effect likely to be strong. Previous analyses using this experimental method have only to a minor extent attempted to answer what factors characterize firms that treat minority and majority applicants differently. We have extended the analysis by adding register and interview information to the experimental data. These results show that the probability to be called for an interview is much lower for Middle Eastern-named applications when a male, as opposed to a female, is responsible for whom to call for an interview. Also, the differential treatment varies with the number of employees at the workplace, if the workplace has a high degree of personnel turnover and if it is located in a municipality with relatively few immigrants. There may be several reasons for the first two results. Larger workplaces and those with a higher personnel turnover may have a more comprehensive recruitment process and thus reduce statistical discrimination. Another explanation might be that these companies use recruitment practices that call more individuals to interviews, i.e. decide on average to call all applicants with similar merits above a certain threshold.25 Interestingly, workplaces that have ethnic diversity plans, the share of immigrant employees at the workplace and the ethnicity of the recruiter were not found to be correlated with the difference in callbacks. 25
The observation that larger firms and firms with higher personnel turnover are less likely to treat the Middle Eastern applicant differently may potentially have an impact on the estimated relative callbacks. Firms that had many job openings during the period of investigation were included only once. Hence, firms with many job openings will have a lower weight in our sample compared to their weight in employing people in the labor market.
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The results do not give one but rather several explanations for the existence of an ethnic difference in callbacks for interviews, indicating that anti-discrimination legislation must fight discrimination broadly. Hence, in order to decrease ethnic discrimination in the hiring process there might be scope for reinforcing the legislation in this respect. Appendix A Description of variables included in the statistical analysis Variable Recruiter information Male responsible Native responsible Personnel department Composition of employees Males Immigrants born in Europe: 0% in Europe: 1–10% in Europe: 11–100% outside Europe: 0% outside Europe: 1–10% outside Europe: 11–100% Workplace characteristics Employees : 0–19 Personnel turnover Public sector Diversity plan Immigrants in municipality less than average
Occupational fixed effects Firm size fixed effects
Description Equals 1 if a male is responsible for recruitment at workplace, else 0 Equals 1 if a native is responsible for recruitment at workplace, else 0 Equals 1 if workplace has a personnel department, else 0 Share of males at the firm Equals 1 if workplace has 0 percent immigrants born in another European country other than Sweden, else 0 Equals 1 if workplace has 0 percent immigrants born in a non-European country, else 0
Equals 1 if the number of employees at workplace is between 0 and 19, else 0 Number of persons employed per year relative to the number of employees at workplace Equals 1 if workplace is public sector employer, else 0 Equals 1 if workplace works with equal rights w.r.t. ethnicity, else 0 Equals 1 if the share of immigrants in the municipality where the workplace is located is less than the average share in the sample, else 0. Information taken from the Swedish Population Register (RTB), Statistics Sweden. Dummy variables assigned to 1 if the job applied to belong to the particular occupation, else 0 Dummy variables assigned to 1 if firm has only one workplace, else 0, and dummy variables controlling for whether the firm has 0–9, 10–99 or more than 100 employees
Table A1 Relative callback rates, immigrant shares and skill requirements
Computer professionals Teachers — upper level of compulsory school (Math) Business sales assistants Preschool Teachers Accountants Nurses Teachers — upper secondary school Teachers — upper level of Compulsory school (Lang.) Motor-vehicle drivers
Relative callback rate
Fraction Immigrants (%)
Skill requirement
1.10 1.26
8 9
3 3
1.28 1.33 1.41 1.43 1.75 1.87
6 7 7 10 8 9
2 3 3 3 3 3
2.17
12
2 (continued on next page )
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Table (continued) A1 (continued )
Construction workers Restaurant workers Shop sales assistants Total
Relative callback rate
Fraction Immigrants (%)
Skill requirement
2.38 2.75 3.22 1.50
5 20 8 10
2 1 2 –
Notes: The relative callback rate is taken from Table 1. Information on the fraction of immigrants and skill requirement in each occupational category is taken from Statistics Sweden (2003). The required skill/level of education in each occupation is defined as 3 = higher education. 2 = secondary education and 1 = primary education. Table A2 Descriptive statistics Variable
Recruiter information Male responsible Native responsible⁎ Personnel department⁎ Work place composition of employees Fraction of males† Fraction of immigrants born in Europe: 0%⁎ in Europe: 1–10%⁎ in Europe: 11–100%⁎ outside Europe: 0%⁎ outside Europe: 1–10%⁎ outside Europe: 11–100%⁎ Work place characteristics Employees : 0–19 Personnel turnover⁎ Public sector Diversity plan⁎ Immigrants in municipality less than average Occupational distribution Computer professionals Business sales assistants Teachers (math and science) Accountants Preschool Teachers Nurses Teachers — upper secondary school Teachers (language) Construction workers Restaurant workers Shop sales assistants Motor-vehicle drivers Firm size Employees : 0–9† Employees : 10–99† Employees : 100-†
Only Swedishsounding name invited
Only M. Easternsounding name invited
Both invited
Neither invited
Total
(1)
(2)
(3)
(4)
(5)
0.55 0.89 0.20
0.54 0.93 0.18
0.37 0.88 0.25
0.59
0.55 0.89 0.22
0.45
0.52
0.38
0.50
0.48
0.34 0.33 0.33 0.40 0.29 0.31
0.38 0.30 0.32 0.32 0.42 0.25
0.38 0.29 0.32 0.42 0.31 0.27
0.57 0.28 (0.52) 0.26 0.18 0.25
0.51 0.38 (0.77) 0.22 0.12 0.32
0.50 0.28 (0.55) 0.36 0.16 0.22
0.06 0.17 0.03 0.07 0.17 0.09 0.05 0.09 0.04 0.04 0.11 0.05
0.19 0.27 0.03 0.08 0.12 0.07 0.03 0.10 0.02 0.02 0.07 0.00
0.25 0.34 0.41
0.34 0.32 0.34
0.37 0.31 0.32 0.40 0.32 0.28 0.59
0.24
0.57 0.29 (0.57) 0.19 0.16 0.24
0.04 0.23 0.07 0.04 0.33 0.13 0.04 0.04 0.03 0.01 0.02 0.03
0.07 0.15 0.02 0.15 0.06 0.09 0.04 0.02 0.04 0.12 0.15 0.05
0.07 0.17 0.03 0.12 0.12 0.09 0.04 0.04 0.04 0.09 0.12 0.05
0.31 0.22 0.47
0.39 0.33 0.28
0.35 0.32 0.33
0.13
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Table (continued) A2 (continued ) Variable
Firm size Number of work places equals one†
Only Swedishsounding name invited
Only M. Easternsounding name invited
Both invited
Neither invited
Total
(1)
(2)
(3)
(4)
(5)
0.56
0.63
0.54
0.67
0.63
Notes: This table reports the mean characteristics for workplaces that invited only the applicant with a Swedish name (column 1, N = 204), only the applicant with a Middle Eastern name (column 2, N = 59), both applicants (column 3, N = 221), neither of the applicants (column 4, N = 1014) and finally the average characteristics (column 4, N = 1498). For continuous variables standard deviations are reported in parenthesis. (⁎) indicate survey data where the number of observations is 140, 40, 161, 0 and 341 in each column, respectively. All variables are measured at the workplace level except variables marked with (†), which are measured at the firm level.
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