Supplementary Materials

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Supplementary Materials

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S1: Literature overview of field experiments on ethnic discrimination in the Dutch labor market

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Table S1: Overview of field experiments on ethnic discrimination in the Dutch labor market

Publication Research design Minority groups Gender ^a Target population Observation

s (n) ^b Discrimin

ation? Discriminatio n ratio ^{c d} Bovenkerk, 1977 In-person audit +

correspondence test Surinamese origin,

Spanish origin Male +

Female Newspaper

advertisements 556 Yes 1.3 (gen.)

Büyükbozkoyum, Stamatiou, & Stolk, 1991

Correspondence test Turkish origin Male Newspaper

advertisements 84 No 0.9 (gen.)

Bovenkerk, Gras, &

Ramsoedh, 1995

In-person audit +

correspondence test Moroccan origin,

Surinamese origin Male +

Female Newspaper

advertisements 554 (study 1) + 598 (study 2)

Yes 1.3 (MOR)

1.3 (SUR)

Dolfing & Van Tubergen, 2005

In-person audit Moroccan origin Male +

Female

Internships 336 Yes -

Derous, 2007 Correspondence test Moroccan origin Male +

Female - 744 Yes -

Altintas, Maniram, &

Veenman, 2009

Correspondence test + online cv-test

Moroccan origin, Chinese origin

Male Newspaper

advertisements + Online job portals + Online CV database

468 (study 1) + 665 clicks (study 2)

No -

Andriessen, Nievers, Faulk,

& Dagevos, 2010 / Andriessen, Nievers, Dagevos, & Faulk, 2012

Correspondence test

+ in-person audit Moroccan origin, Turkish origin, Surinamese origin, Antillean origin

Male +

Female Online job portals 2,680 Yes 1.2 (men.)

1.1 (MOR) 1.2 (TUR) 1.2 (SUR) 1.2 (ANT) Andriessen, 2012 Correspondence test

+ in-person audit Moroccan origin, Turkish origin, Surinamese origin, Antillean origin

Male +

Female Employment

agencies + Online job portals

263 (study 1) + 460 (study 2)

Yes 1.6 (gen.)

1.1 (MOR) 1.8 (TUR) 1.6 (SUR) 2.1 (ANT) Derous, Ryan, & Nguyen,

2012

Correspondence test Arabic origin Male + Female

- 768 Yes -

Blommaert, Coenders, &

Van Tubergen, 2014

Online cv-test Moroccan origin Male +

Female Online CV database 636 Yes 1.5 (gen.)

Andriessen, Van der Ent, Van der Linden, & Dekker, 2015

Correspondence test Moroccan origin, Surinamese Hindustani origin

Male +

Female Online job portals 528 (study 1) + 436 (study 2)

Yes 1.8 (MOR)

1.5 (SUR)

Dirkzwager, Blokland, Nannes, & Vroonland, 2015

Correspondence test Non-Western migrant

origin Male Online job portals 1,152 Yes 1.8 – 2.1 (gen.)

Panteia, 2015 Online cv-test Moroccan origin,

Turkish origin, Surinamese origin, Antillean origin, Polish origin

Male Online CV database 1,346 Yes 2.0 (MOR)

2.2 (TUR) 1.9 (SUR) 1.8 (ANT) 2.0 (POL)

Abubaker & Bagley, 2017 Correspondence test Arabic origin Male Online job portals 430 Yes 1.3 (alg.)

Van den Berg, Blommaert, Bijleveld, & Ruiter, 2017

Correspondence test Non-Western migrant origin

Male Online job portals 520 Yes 3.0 – 5.0 (gen.)

Thijssen, Coenders & Lancee,

2019 Correspondence test 35 Western and non-

Western migrant origin groups, including:

Moroccan origin, Turkish origin, Polish origin, Bulgarian origin

Male +

Female Online job portals 4,211 Yes 1.3 (gen.)

1.2 (W) 1.4 (NW) 1.5 (MOR) 1.4 (TUR) 1.2 (POL) 1.3 (BUL)

a Please note that in case researchers applied with male and female candidates this does not necessarily indicate that a balanced experimental design was used. In a balanced experimental design, gender is randomly assigned to job applications. However, in many field experiments researchers used an unbalanced experimental design in which, for example, male candidates applied only for jobs in manufacturing or construction and female candidates applied only for jobs in health care or education.

b Unless indicated otherwise, N refers to the number of fictitious applications sent

c The discrimination ratio per group is calculated by dividing the callback rate of native-majority candidates by the callback rate of candidates with a migrant origin. Unfortunately, not all studies provided enough information to calculate the discrimination ratio. For several studies, it was possible to calculate a discrimination ratio per origin group investigated. Please note that there are major methodological differences between studies with regard to the ethnic minority groups or occupations included, the proportion of male or female applicants, design choices (in-person audit or correspondence test), research sample (newspaper advertisements or online job portals), study size, et cetera.

d Abbreviations: - = none or insufficient information available, gen. = all minority groups, MOR = Moroccan origin, TUR = Turkish origin, SUR = Surinamese origin, ANT = Antillean origin, POL = Polish origin, BUL = Bulgarian origin, W = Western migrant origin, NW = Non-Western migrant origin.

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S2: Descriptive information

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Table S2: Ethnic groups examined in the field experiment, number of applications per group, and classification by region of origin

Ethnic origin Number of applications Region of origin

Absolute Percentage Broad classification Detailed classification

The Netherlands 1,115 26.5 Native-Majority Native-Majority

Albania 108 2.6 Western migrant origin Eastern European or

Russian origin

Belgium 67 1.6 Western migrant origin Western European or

American origin

Bulgaria 213 5.1 Western migrant origin Eastern European or

Russian origin

China 55 1.3 Non-Western migrant

origin Southeast or East Asian origin

Dutch Antilles 48 1.1 Non-Western migrant

origin Latin American origin

Egypt 63 1.5 Non-Western migrant

origin

Middle Eastern and North African origin

Ethiopia 57 1.4 Non-Western migrant

origin South and Central African origin

France 57 1.4 Western migrant origin Western European or

American origin

Germany 49 1.2 Western migrant origin Western European or

American origin

Greece 49 1.2 Western migrant origin Western European or

American origin

India 68 1.6 Non-Western migrant

origin South Asian origin

Indonesia 68 1.6 Western migrant origin Southeast or East Asian

origin

Iran 70 1.7 Non-Western migrant

origin

Middle Eastern and North African origin

Iraq 54 1.3 Non-Western migrant

origin Middle Eastern and North African origin

Italy 54 1.3 Western migrant origin Western European or

American origin

Japan 64 1.5 Western migrant origin Southeast or East Asian

origin

Lebanon 42 1.0 Non-Western migrant

Macedonia 46 1.1 Western migrant origin Eastern European or

Russian origin

Malaysia 61 1.5 Non-Western migrant

origin

Southeast or East Asian origin

Mexico 57 1.4 Non-Western migrant

Morocco 431 10.2 Non-Western migrant

Nigeria 50 1.2 Non-Western migrant

Norway 52 1.2 Western migrant origin Western European or

American origin

Pakistan 50 1.2 Non-Western migrant

origin South Asian origin

Poland 241 5.7 Western migrant origin Eastern European or

Russian origin

Romania 50 1.2 Western migrant origin Eastern European or

Russian origin

Russia 57 1.4 Western migrant origin Eastern European or

Russian origin

South Korea 56 1.3 Non-Western migrant

origin Southeast or East Asian origin

Spain 54 1.3 Western migrant origin Western European or

American origin

Surinam 66 1.6 Non-Western migrant

Turkey 424 10.1 Non-Western migrant

Uganda 58 1.4 Non-Western migrant

United Kingdom 57 1.4 Western migrant origin Western European or

American origin

United States 52 1.2 Western migrant origin Western European or

American origin

Vietnam 48 1.1 Non-Western migrant

origin Southeast or East Asian origin Source: GEMM, 2019

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Table S3: Descriptive statistics (N= 4,211 applications)

Variable Proportion / Mean

Positive response from an employer 0.377

Characteristics of applicants Ethnicity 1

Migrant origin 0.735

Ethnicity 2

Native-majority 0.265

Western migrant origin 0.318

Non-Western migrant origin 0.418

Ethnicity 3

Western European or American origin 0.117

Eastern European or Russian origin 0.170

Latin American origin 0.041

South Asian origin 0.028

Southeast or East Asian origin 0.084

Middle Eastern and North African origin 0.257

South and Central African origin 0.039

Ethnicity 4 (N=2.538) ^a

Polish origin 0.095

Bulgarian origin 0.084

Surinamese origin 0.026

Antillean origin 0.019

Moroccan origin 0.167

Turkish origin 0.170

Information: Picture included 0.514

Information: Grade included 0.507

Information: Social skills included 0.506

Information: Performance included 0.497

Number of information manipulations (min. = 0, max. = 4; SD = 1,019) 2.024

Female 0.494

Religiosity 0.478

Characteristics of job openings Occupation

Cooks 0.193

Electrician 0.043

Plumber 0.025

Carpenter 0.039

Receptionist 0.102

Hairdresser 0.042

Shop assistant 0.116

Payroll clerk 0.161

Software developer 0.141

Sales representative 0.139

Occupation type

Lower level of education and relatively few interpersonal skills 0.300

Lower level of education and relatively more interpersonal skills 0.259

Higher level of education and relatively few interpersonal skills 0.302

Higher level of education and relatively more interpersonal skills 0.139

Region

Groningen 0.016

Friesland 0.020

Drenthe 0.015

Overijssel 0.052

Flevoland 0.021

Gelderland 0.113

Utrecht 0.130

Noord-Holland 0.224

Zuid-Holland 0.194

Zeeland 0.014

Noord-Brabant 0.157

Limburg 0.045

G31 0.472

G4 0.218

Perceived advertisement fit

Underqualified 0.071

Fit 0.793

Overqualified 0.137

a The analysis that examines the effect of ethnicity 4 only includes native-majority candidates and candidates of Moroccan, Turkish, Polish, Bulgarian, Surinamese, and Antillean origin; all other ethnic groups are excluded.

Source: GEMM, 2019

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S3: External validity of the findings

A major advantage of a field experimental research design over other research designs (e.g. survey and laboratory studies) is the possibility to determine the causal effect of ethnic origin in real hiring situations (Gaddis 2018; Pager 2007). Field experiments thus combine a high degree of internal validity with a high degree of external validity. Despite of this, it is still important to pay attention to the external validity of the findings of field experiments (Di Stasio and Lancee, 2020; Thijssen, 2020).

First, because our aim was to develop high quality CVs and cover letters in order to generate enough responses from employers, we could not investigate whether less qualified candidates face similar levels of discrimination in the labor market. For example, several laboratory studies (Dovidio and Gaertner 2000;

Hodson, Dovidio, and Gaertner 2002) find evidence that ethnic or racial minorities face higher levels of discrimination when applicants are not clearly qualified for the job. Thus, because we applied with well- qualified candidates, this might have led to an underestimation of the degree of ethnic discrimination in our study. It is nonetheless important to note that all our analyzes do control for the “fit” between the fictitious applicant and the requirements listed in the job advertisement (cf. Weichselbaumer 2017).

Second, the selection of occupations in this study does not fully reflect the total population of jobs in the Dutch labor market (Statistics Netherlands 2015). As is shown in Table S4, certain sectors of the labor market are overrepresented and other sectors are underrepresented in the field experiment. In particular, we investigated a relatively large share of occupations in the accommodation and food service activities, trade, and information and communication sectors. Table S4 further shows that occupations in the public sector (public administration and government services, education and health and welfare care) are underrepresented. The main reason for this is that it is difficult to apply with fictitious applicants in the public sector because of its stricter application procedures and mandatory professional registrations. Previous research (Midtbøen 2016; Zschirnt and Ruedin 2016), however, provides tentative evidence that there is less discrimination in the public sector than in the private sector. Theoretically, this would imply that our estimates of ethnic discrimination might be overestimated due to the under-representation of jobs in the public sector. Additional statistics (not shown) further indicate that our field experiment tends to include jobs in sectors with a slightly higher percentage of persons with a (non-western) migrant background. Theoretically, however, greater shares of ethnic minorities could be related with either decreased (indicative of positive intergroup contact)(Allport 1954) or increased (indicative of group threat) (Blalock 1967) hiring discrimination against ethnic minorities (see e.g. Pecoraro and Ruedin 2020).

Third, although we have tried to select occupations that vary widely with regard to level and field of education, occupations in the lowest (e.g. waitress, bartender, warehouse worker, cleaner) and highest segments (e.g. lawyer, doctor, manager, academic researcher) of the labor market fall outside the scope of this study. One important reason for this is that most of these job openings are not advertised on online job portals but are distributed via informal (mouth to mouth, advertisements in public places) or formal (professional organizations, LinkedIn) social networks. A second important reason for this is the difficulty of developing realistic educational and occupational careers for highly specialized occupations (e.g. academics, lawyers or doctors) or management positions without arousing to much suspicion among employers.

Fourth, it is important to emphasize that we focused on job seekers at the start of their working careers (aged 23–25 years, ± 4 years work experience). The motivation for this was twofold. A practical argument for this choice was related to the difficulty of creating realistic careers for older job seekers. A theoretical argument

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for investigating this relatively young population is that various studies (Blau and Duncan 1967; Luijkx and Wolbers 2009; Pais 2013) show that the start of a person’s career is a critical moment, with potentially lasting (negative) consequences for one’s future employment prospects. Indeed, long-term unemployment spells at the start of people's career can have a scarring effect on their labor market outcomes later in life (Luijkx and Wolbers 2009). All in all, this stresses the importance of gaining more insight into the social barriers of people at the start of people’s career.

A final limitation with regard to the external validity of this study is related to its focus on the first phase of the hiring process - that is, the screening of potential candidates for a job interview. Although a few studies show that discrimination mainly occurs in the first phases of the hiring process (Blommaert et al. 2014;

Zegers de Beijl 2000), we could not examine the level of discrimination against job seekers with a migrant origin during job interviews or during negotiations about employment conditions. Furthermore, our study could not investigate the extent to which people with a migrant background face discrimination in the workplace, in promotions, or in terminations of employment contracts.

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Table S4: Statistics per sector: employed persons in the labor force and fictitious applicants in the field experiment

Sector (NACE-code) Labor Force Survey 2015 Field experiment

Absolute Percentage Absolute Percentage

A Agriculture, Forestry and Fishing 179,231 2.33 13 0.31

B Mining and Quarrying 13,839 0.18 0 0.00

C Manufacturing 776,785 10.11 259 6.15

D Electricity, Gas, Steam, and Air Conditioning Supply 26,033 0.34 19 0.45

E Water Supply; Sewerage, Waste Management and Remediation Activities 30,620 0.40 10 0.24

F Construction 401,593 5.23 345 8.19

G Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles 1,232,935 16.05 830 19.71

H Transportation and Storage 356,429 4.64 63 1.50

I Accommodation and Food Service Activities 352,430 4.59 909 21.59

J Information and Communication 261,521 3.41 606 14.39

K Financial and Insurance Activities 267,011 3.48 56 1.33

L Real Estate Activities 64,417 0.84 48 1.14

M Professional, Scientific and Technical Activities 588,030 7.66 379 9.00

N Administrative and Support Service Activities 430,921 5.61 159 3.78

O Public Administration and Defense; Compulsory Social Security 494,661 6.44 16 0.38

P Education 561,417 7.31 48 1.14

Q Human Health and Social Work Activities 1,281,099 16.68 146 3.47

R Arts, Entertainment and Recreation 171,904 2.24 94 2.23

S Other Service Activities 180,629 2.35 211 5.01

T Activities of Households as Employers 6,400 0.08 0 0.00

U Activities of Extraterritorial Organizations and Bodies 2,155 0.03 0 0.00

Source: Statistics Netherlands, 2015 & GEMM, 2019

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S4: Additional results

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Table S5: Linear probability model estimating the effect of ethnic origin on callback (full table)

Model 1 Model 2 Model 3 Model 4

Native-Majority Ref. Ref. Ref. Ref.

Migrant origin -0.108^***

(0.017)

Western migrant origin -0.079^***

(0.019)

Non-Western migrant origin -0.129^***

(0.018)

Western European or American origin -0.067^**

(0.025)

South Asian origin -0.076^~

(0.045)

Eastern European or Russian origin -0.084^***

(0.024)

Southeast or East Asian origin -0.089^**

(0.029)

Latin American origin -0.130^***

(0.037)

South and Central African origin -0.131^***

(0.038)

Middle Eastern and North African origin -0.143^***

(0.020)

Polish origin -0.056

(0.038)

Bulgarian origin -0.109^**

(0.039)

Surinamese origin -0.095^~

(0.056)

Antillean origin -0.184^**

(0.067)

Moroccan origin -0.151^***

(0.026)

Turkish origin -0.141^***

(0.026)

Male Ref. Ref. Ref. Ref.

Female 0.023

(0.014) 0.022

(0.014) 0.020

(0.018)

No grade included Ref. Ref. Ref. Ref.

Grade included 0.002

(0.014)

0.002 (0.014)

-0.002 (0.018)

No performance included Ref. Ref. Ref. Ref.

Performance included 0.006

(0.014) 0.006

(0.014) 0.008

(0.014) 0.006

(0.018)

No warmth included Ref. Ref. Ref. Ref.

Warmth included 0.020

(0.014) 0.020

(0.014) 0.012

(0.018)

No picture included Ref. Ref. Ref. Ref.

Picture included 0.083^***

(0.014) 0.083^***

(0.014) 0.082^***

(0.014) 0.100^***

(0.018)

Not openly religious Ref. Ref. Ref. Ref.

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Openly Religious 0.008

(0.014) 0.007

(0.014) 0.008

(0.014) 0.014

(0.018) Occupation fixed effects

Software developer Ref. Ref. Ref. Ref.

Cook 0.038

(0.028) 0.037

(0.028) 0.036

(0.028) 0.029

(0.036)

Electrician -0.088^*

(0.043) -0.089^*

(0.043) -0.088^*

(0.043) -0.128^* (0.054)

Payroll clerk -0.358^***

(0.025) -0.359^***

(0.025) -0.357^***

(0.025) -0.379^***

(0.033)

Plumber -0.066

(0.053) -0.069

(0.066)

Receptionist -0.309^***

(0.029) -0.311^***

(0.029) -0.310^***

(0.029) -0.345^***

(0.037)

Sales representative -0.266^***

(0.027) -0.268^***

(0.027) -0.290^***

(0.035)

Sales assistant -0.300^***

(0.028) -0.300^***

(0.028) -0.304^***

(0.037)

Hairdresser -0.047

(0.044) -0.047

(0.044) -0.049

(0.044) -0.055

(0.056)

Carpenter -0.084^~

(0.044) -0.083^~

(0.044) -0.084^~

(0.044) -0.100^~ (0.055) Period fixed effects

yearmonth=201610 0.112

(0.138) 0.107

(0.137) 0.111

(0.137) 0.025

(0.150)

(0.054) 0.025

(0.054) 0.028

(0.054) -0.026

(0.064)

yearmonth=201612 0.125^*

(0.061) 0.126^*

(0.061) 0.124^*

(0.061) 0.084

(0.087)

yearmonth=201701 Ref. Ref. Ref. Ref.

(0.039) 0.008

(0.039) 0.010

(0.039) 0.045

(0.053)

(0.037) 0.047

(0.037) 0.098^~ (0.051)

(0.039)

0.028 (0.039)

0.030 (0.039)

0.014 (0.052)

yearmonth=201705 0.097^*

(0.040) 0.094^*

(0.040) 0.097^*

(0.040) 0.100^~ (0.053)

yearmonth=201706 0.059^~

(0.036) 0.058

(0.036) 0.060^~

(0.036) 0.048

(0.047)

yearmonth=201707 0.069^~

(0.039) 0.068^~

(0.039) 0.069^~

(0.039) 0.057

(0.052)

yearmonth=201708 -0.021

(0.054) -0.026

(0.054) 0.059

(0.075)

yearmonth=201709 -0.025

(0.041) -0.026

(0.041) -0.025

(0.058)

(0.041) 0.029

(0.041) 0.028

(0.041) 0.009

(0.054)

(0.042) -0.004

(0.042) -0.000

(0.042) 0.032

(0.054)

(0.054) 0.032

(0.054) 0.034

(0.055) 0.045

(0.068)

(0.044) 0.054

(0.045) 0.057

(0.045) 0.049

(0.057)

yearmonth=201802 0.062 0.045 0.051 0.058

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(0.053) (0.053) (0.054) (0.065)

yearmonth=201803 -0.005

(0.046) -0.013

(0.046) -0.010

(0.046) -0.009

(0.054)

yearmonth=201804 -0.088

(0.103) -0.092

(0.103) -0.090

(0.103) -0.087

(0.106) Region fixed effects

Utrecht Ref. Ref. Ref. Ref.

Groningen -0.100^~

(0.057) -0.102^~

(0.057) -0.103^~

(0.057) -0.128^~ (0.068)

Friesland -0.052

(0.054) -0.053

(0.054) -0.054

(0.055) -0.111^~ (0.066)

Drenthe -0.001

(0.067) -0.001

(0.067) -0.003

(0.067) -0.057

(0.085)

Overijssel -0.059^~

(0.036) -0.059^~

(0.036) -0.061^~

(0.036) -0.069

(0.045)

Flevoland 0.027

(0.054) 0.028

(0.054) 0.024

(0.054) -0.077

(0.068)

Gelderland -0.070^*

(0.028) -0.072^*

(0.028) -0.071^*

(0.028) -0.076^* (0.037)

Noord-Holland -0.050^*

(0.024) -0.050^*

(0.024) -0.051^*

(0.024) -0.062^~ (0.032)

Zuid-Holland -0.062^*

(0.025) -0.063^*

(0.025) -0.064^*

(0.025) -0.066^* (0.033)

Zeeland -0.061

(0.064) -0.062

(0.064) -0.025

(0.085)

Noord-Brabant -0.054^*

(0.026) -0.056^*

(0.026) -0.057^*

(0.026) -0.080^* (0.033)

Limburg -0.054

(0.040) -0.054

(0.040) -0.055

(0.040) -0.046

(0.051) Perceived advertisement fit

Fit Ref. Ref. Ref. Ref.

Underqualified -0.101^***

(0.025) -0.101^***

(0.025) -0.115^***

(0.033)

Overqualified 0.058^**

(0.021) 0.057^**

(0.021) 0.058^* (0.026)

Constant 0.557^***

(0.044)

0.563^***

(0.044)

0.561^***

(0.044)

0.580^***

(0.056)

R² 0.140 0.142 0.143 0.161

N 4,211 4,211 4,211 2,538

Standard errors in parentheses. ~ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001 (two-sided).

Note: The dependent distinguishes between a positive response from an employer and a negative or no response from an employer. Ref. = reference category. Model 1 examines differences in callbacks between native-majority candidates and candidates with a migrant origin.

Model 2 examines differences in callbacks between native-majority candidates and candidates with a Western or a non-Western migrant origin. Model 3 examines differences in callbacks between native-majority candidates and candidates with a Western European or American origin, South Asian, Eastern European or Russian origin, Southeast or East Asian origin, Latin American origin, South and Central African origin, or a Middle Eastern and North African origin. Model 4 examines differences in callbacks between native-majority candidates and candidates with a Moroccan, Turkish, Bulgarian, Polish, Surinamese, and Antillean origin.

Source: GEMM, 2019

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Table S6. Logistic regression model estimating the effect of ethnic origin on callback

Model 1 Model 2 Model 3 Model 4

Native-Majority Ref. Ref. Ref. Ref.

Migrant origin -0.523^***

(0.081)

(0.094)

(0.089)

Western European or American origin -0.324^**

(0.122)

South Asian origin -0.358^~

(0.214)

Eastern European or Russian origin -0.402^***

(0.115)

Southeast or East Asian origin -0.427^**

(0.138)

Latin American origin -0.639^***

(0.189)

South and Central African origin -0.660^***

(0.196)

Middle Eastern and North African origin -0.706^***

(0.099)

Polish origin -0.269

(0.179)

Bulgarian origin -0.528^**

(0.191)

Surinamese origin -0.470

(0.289)

Antillean origin -0.933^**

(0.355)

Moroccan origin -0.756^***

(0.134)

Turkish origin -0.704^***

(0.134)

Male Ref. Ref. Ref. Ref.

Female 0.110

(0.069) 0.109

(0.069) 0.107

(0.069) 0.096

(0.090)

No grade included Ref. Ref. Ref. Ref.

Grade treatment 0.011

(0.069)

0.008 (0.069)

0.010 (0.069)

-0.011 (0.090)

No performance included Ref. Ref. Ref. Ref.

Performance treatment 0.030

(0.069) 0.034

(0.069) 0.040

(0.069) 0.031

(0.090)

No warmth included Ref. Ref. Ref. Ref.

Warmth treatment 0.096

(0.069) 0.097

(0.069) 0.058

(0.090)

No picture included Ref. Ref. Ref. Ref.

Picture treatment 0.411^***

(0.070) 0.410^***

(0.070) 0.404^***

(0.071) 0.495^***

(0.093)

Not openly religious Ref. Ref. Ref. Ref.

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Religion 0.037

(0.069) 0.032

(0.069) 0.037

(0.070) 0.070

(0.090) Occupation fixed effects

Software developer Ref. Ref. Ref. Ref.

Cook 0.139

(0.118) 0.133

(0.119) 0.132

(0.119) 0.096

(0.157)

(0.179) -0.372^*

(0.179) -0.548^* (0.227)

(0.134) -1.723^***

(0.134) -1.719^***

(0.134) -1.837^***

(0.175)

Plumber -0.268

(0.221) -0.283

(0.222) -0.285

(0.222) -0.296

(0.282)

(0.149) -1.448^***

(0.149) -1.444^***

(0.149) -1.631^***

(0.197)

(0.129) -1.199^***

(0.129) -1.200^***

(0.129) -1.315^***

(0.169)

(0.140) -1.370^***

(0.140) -1.369^***

(0.140) -1.383^***

(0.182)

Hairdresser -0.221

(0.180) -0.221

(0.180) -0.230

(0.180) -0.272

(0.231)

Carpenter -0.358^~

(0.186) -0.352^~

(0.186) -0.354^~

(0.568) 0.546

(0.570) 0.568

(0.571) 0.180

(0.711)

(0.311) 0.136

(0.312) 0.151

(0.313) -0.159

(0.413)

yearmonth=201612 0.635^*

(0.286) 0.637^*

(0.287) 0.626^*

(0.288) 0.452

(0.410)

yearmonth=201701 Ref. Ref. Ref. Ref.

(0.201) 0.043

(0.201) 0.055

(0.202) 0.257

(0.273)

(0.194) 0.248

(0.194) 0.250

(0.194) 0.517^* (0.263)

(0.203)

0.159 (0.204)

0.171 (0.204)

0.106 (0.278)

yearmonth=201705 0.479^*

(0.200) 0.466^*

(0.201) 0.479^*

(0.201) 0.517^~ (0.272)

yearmonth=201706 0.307^~

(0.183) 0.303^~

(0.183) 0.311^~

(0.183) 0.279

(0.250)

yearmonth=201707 0.357^~

(0.193) 0.350^~

(0.194) 0.355^~

(0.194) 0.322

(0.265)

yearmonth=201708 -0.065

(0.265) -0.095

(0.265) -0.096

(0.266) 0.319

(0.367)

yearmonth=201709 -0.136

(0.224) -0.142

(0.224) -0.140

(0.224) -0.121

(0.314)

(0.214) 0.154

(0.214) 0.147

(0.214) 0.049

(0.292)

(0.219) -0.017

(0.221) 0.001

(0.222) 0.178

(0.283)

(0.258) 0.163

(0.260) 0.170

(0.262) 0.249

(0.332)

(0.218) 0.265

(0.220) 0.285

(0.223) 0.268

(0.290)

yearmonth=201802 0.313 0.232 0.259 0.313

(16)

(0.252) (0.253) (0.256) (0.318)

yearmonth=201803 -0.025

(0.234) -0.059

(0.234) -0.044

(0.234) -0.016

(0.277)

yearmonth=201804 -0.391

(0.594) -0.408

(0.594) -0.401

(0.594) -0.368

(0.613) Region fixed effects

Groningen -0.512^~

(0.297) -0.532^~

(0.298) -0.544^~

(0.298) -0.704^~ (0.360)

Friesland -0.248

(0.266) -0.262

(0.267) -0.271

(0.267) -0.572

(0.349)

Drenthe -0.023

(0.289) -0.021

(0.290) -0.030

(0.289) -0.282

(0.377)

Overijssel -0.298^~

(0.176) -0.299^~

(0.176) -0.310^~

(0.177) -0.345

(0.227)

Flevoland 0.134

(0.255) 0.134

(0.256) 0.115

(0.256) -0.363

(0.355)

Gelderland -0.346^*

(0.140) -0.354^*

(0.140) -0.353^*

(0.141) -0.375^* (0.181)

Utrecht Ref. Ref. Ref. Ref.

(0.121) -0.240^*

(0.121) -0.248^*

(0.121) -0.304^~ (0.160)

(0.124) -0.310^*

(0.124) -0.312^*

(0.124) -0.326^* (0.163)

Zeeland -0.294

(0.305) -0.298

(0.305) -0.300

(0.306) -0.126

(0.385)

(0.129) -0.279^*

(0.129) -0.285^*

(0.130) -0.403^* (0.170)

Limburg -0.259

(0.186) -0.260

(0.186) -0.265

(0.186) -0.221

(0.242) Perceived advertisement fit

Fit Ref. Ref. Ref. Ref.

(0.153) -0.561^***

(0.153) -0.559^***

(0.154) -0.654^**

(0.210)

(0.105) 0.290^**

(0.105) 0.292^**

(0.105) 0.295^* (0.130)

Constant 0.240

(0.216)

0.275 (0.216)

0.269 (0.216)

0.341 (0.282)

Pseudo R² 0.111 0.112 0.113 0.128

Note: The dependent distinguishes between a positive response from an employer and a negative or no response from an employer. Ref. = reference category. Model 1 examines differences in callbacks between native-majority candidates and candidates with a migrant origin.

Model 2 examines differences in callbacks between native-majority candidates and candidates with a Western or a non-Western migrant origin. Model 3 examines differences in callbacks between native-majority candidates and candidates with a Western European or American origin, South Asian, Eastern European or Russian origin, Southeast or East Asian origin, Latin American origin, South and Central African origin, or a Middle Eastern and North African origin. Model 4 examines differences in callbacks between native-majority candidates and candidates with a Moroccan, Turkish, Bulgarian, Polish, Surinamese, and Antillean origin.

Source: GEMM, 2019

(17)

Table S7: Linear probability model estimating the effect of ethnic origin, information, and their interaction on callback (full table)

Model 1 Model 2 Model 3 Model 4 Model 5

Native-Majority Ref. Ref. Ref. Ref. Ref.

(0.020) -0.081^~

(0.042) -0.079^***

(0.019) -0.079^~

(0.042) -0.069 (0.077)

(0.018) -0.145^***

(0.039) -0.129^***

(0.018) -0.143^***

(0.039) -0.110 (0.075) Number of information manipulations

included 0.028^***

(0.007) 0.024^~ (0.014) Western migrant origin * No. of information

treatment included 0.002

(0.018) Non-Western migrant origin * No. of information

treatments included

0.008 (0.017)

No grade included Ref. Ref.

(0.014) -0.010 (0.027)

No performance included Ref. Ref.

(0.014) 0.014 (0.027)

No warmth included Ref. Ref.

(0.014) 0.001 (0.028)

No picture included Ref. Ref.

(0.014) 0.095^**

(0.029)

Western migrant origin * Grade included 0.014

(0.037)

Non-Western migrant origin * Grade included 0.017

(0.035)

Western migrant origin * Performance included -0.024

(0.037) Non-Western migrant origin * Performance

included 0.001

(0.035)

Western migrant origin * Warmth included 0.032

(0.037)

Non-Western migrant origin * Warmth included 0.020

(0.035)

Western migrant origin * Picture included -0.022

(0.038)

Non-Western migrant origin * Picture included -0.010

(0.036) Number of information treatments

No information treatments included Ref.

One information treatment included 0.056

(0.068)

Two information treatments included 0.062

(0.066)

Three information treatments included 0.074

(18)

(0.067)

Four information treatments included 0.156^~

(0.080) Western migrant origin * One information

treatment included -0.002

(0.085) Western migrant origin * Two information

treatments included -0.020

(0.083) Western migrant origin * Three information

(0.085) Western migrant origin * Four information

treatments included 0.006

(0.105) Non-Western migrant origin * One information

(0.083) Non-Western migrant origin * Two information

(0.081) Non-Western migrant origin * Three information

(0.083) on-Western migrant origin * Four information

(0.099)

Male Ref. Ref. Ref. Ref. Ref.

Female 0.020

(0.014) 0.020

(0.014) 0.022

(0.014) 0.020 (0.014)

Not openly religious Ref. Ref. Ref. Ref. Ref.

Openly religious 0.007

(0.014) 0.007

(0.014) 0.007 (0.014) Occupation fixed effects

Software developer Ref. Ref. Ref. Ref. Ref.

Cook 0.039

(0.028) 0.039

(0.028) 0.037

(0.028) 0.040 (0.028)

(0.043)

-0.088^* (0.043)

-0.089^* (0.043)

-0.086^* (0.043)

(0.025) -0.355^***

(0.026) -0.359^***

(0.025) -0.358^***

(0.025) -0.353^***

(0.026)

Plumber -0.075

(0.053) -0.075

(0.053) -0.069

(0.053) -0.070

(0.053) -0.074 (0.053)

(0.029)

-0.311^***

(0.029)

-0.311^***

(0.029)

-0.311^***

(0.029)

-0.310^***

(0.029)

(0.027) -0.267^***

(0.027) -0.268^***

(0.028) -0.267^***

(0.028)

(0.028) -0.299^***

(0.028) -0.300^***

(0.028) -0.296^***

(0.028)

Hairdresser -0.044

(0.044) -0.044

(0.044) -0.047

(0.044) -0.046

(0.044) -0.042 (0.044)

Carpenter -0.081^~

(0.044) -0.081^~

(0.044) -0.083^~

(0.136) 0.102

(0.136) 0.107

(0.137) 0.111

(0.136) 0.110 (0.136)

(0.055) 0.030

(0.055) 0.025

(0.054) 0.026

(0.054) 0.029 (0.055)

yearmonth=201612 0.132^*

(0.061) 0.133^*

(0.061) 0.126^*

(0.061) 0.128^*

(0.061) 0.133^* (0.062)

yearmonth=201701 Ref. Ref. Ref. Ref. Ref.

(19)

(0.039) 0.006

(0.039) 0.008

(0.039) 0.003 (0.039)

(0.038) 0.052

(0.038) 0.047

(0.037) 0.047

(0.037) 0.050 (0.038)

(0.040)

0.034 (0.040)

0.028 (0.039)

0.029 (0.039)

0.034 (0.040)

yearmonth=201705 0.095^*

(0.041) 0.095^*

(0.041) 0.094^*

(0.040) 0.095^*

(0.040) 0.095^* (0.041)

yearmonth=201706 0.060^~

(0.036) 0.060^~

(0.036) 0.058

(0.036) 0.059^~

(0.036) 0.059^~ (0.036)

yearmonth=201707 0.071^~

(0.039)

0.071^~ (0.039)

0.068^~ (0.039)

0.069^~ (0.039)

0.070^~ (0.040)

yearmonth=201708 -0.027

(0.054) -0.027

(0.054) -0.026

(0.054) -0.027

(0.054) -0.027 (0.054)

yearmonth=201709 -0.021

(0.041) -0.021

(0.041) -0.026

(0.041) -0.023 (0.041)

(0.041)

0.029 (0.041)

0.030 (0.041)

0.029 (0.041)

(0.042) 0.002

(0.042) -0.004

(0.042) 0.001 (0.042)

(0.055) 0.039

(0.055) 0.032

(0.054) 0.032

(0.054) 0.035 (0.055)

(0.045)

0.060 (0.045)

0.054 (0.045)

0.055 (0.045)

0.060 (0.045)

(0.053) 0.050

(0.053) 0.045

(0.053) 0.046

(0.053) 0.048 (0.053)

(0.046) 0.012

(0.046) -0.013

(0.046) -0.016

(0.047) 0.010 (0.046)

yearmonth=201804 -0.064

(0.102)

-0.062 (0.102)

-0.092 (0.103)

-0.101 (0.104)

-0.060 (0.103) Region fixed effects

Utrecht Ref. Ref. Ref. Ref. Ref.

Groningen -0.102^~

(0.058) -0.102^~

(0.057) -0.101^~

(0.058) -0.105^~ (0.058)

Friesland -0.049

(0.055) -0.049

(0.055) -0.053

(0.054) -0.054

(0.054) -0.051 (0.055)

Drenthe 0.004

(0.067) 0.005

(0.067) -0.001

(0.067) 0.003 (0.067)

Overijssel -0.058

(0.036) -0.058

(0.036) -0.059^~

(0.036) -0.058 (0.036)

Flevoland 0.020

(0.054) 0.020

(0.054) 0.028

(0.054) 0.017 (0.054)

Gelderland -0.073^**

(0.028)

-0.073^**

(0.028)

-0.072^* (0.028)

-0.071^* (0.028)

-0.075^**

(0.028)

(0.024) -0.050^*

(0.024) -0.052^* (0.024)

(0.025) -0.063^*

(0.025) -0.064^**

(0.025)

Zeeland -0.062

(0.064)

-0.062 (0.064)

-0.059 (0.064)

-0.060 (0.063)

(0.026) -0.057^*

(0.026) -0.056^*

(0.026) -0.055^*

(0.026) -0.059^* (0.026)

Limburg -0.054

(0.040) -0.054

(0.040) -0.053

(0.040) -0.054 (0.040) Perceived advertisement fit

Fit Ref. Ref. Ref. Ref. Ref.

(20)

(0.025) -0.102^***

(0.025) -0.101^***

(0.025) -0.102^***

(0.025)

(0.021) 0.058^**

(0.021) 0.057^**

(0.021)

Constant 0.560^***

(0.044)

0.567^***

(0.050)

0.563^***

(0.044)

0.568^***

(0.049)

0.550^***

(0.073)

R² 0.138 0.138 0.142 0.142 0.139

N 4,211 4,211 4,211 4,211 4,211

Note: The dependent distinguishes between a positive response from an employer and a negative or no response from an employer. Ref. = reference category. Model 1 examines differences in callbacks between native- majority candidates and candidates with a Western or a non-Western migrant origin. Also, model 1 examines the effect of the number of information treatments included on callback. Model 2 includes the main effects of ethnic origin, the number of information manipulations included and their interaction. Model 3 examines differences in callbacks between native-majority candidates and candidates with a Western or a non-Western migrant origin and the effect of grade, performance, warmth, and picture on callback. Model 4 includes the main effects of ethnic origin, grade, performance, warmth, picture and the interactions between ethnic origin and the four information treatments. Model 5 includes the main effects of ethnic origin, dummies for the number of information treatments included and their interaction.

Source: GEMM, 2019

(21)

Table S8. Logistic regression model estimating the effect of ethnic origin, information, and their interaction on callback

Model 1 Model 2 Model 3 Model 4 Model 5

Native-Majority Ref. Ref. Ref. Ref. Ref.

(0.094) -0.399^~

(0.204) -0.378^***

(0.094) -0.391^~

(0.204) -0.348 (0.386)

(0.089) -0.725^***

(0.193) -0.631^***

(0.089) -0.725^***

(0.194) -0.539 (0.372) Number of information manipulations included 0.136^***

(0.034) 0.111^~ (0.066) Western migrant origin * No. of information

manipulations included 0.014

(0.089) Non-Western migrant origin * No. of information

manipulations included

0.049 (0.084)

No grade included Ref. Ref.

(0.069) -0.050 (0.131)

No performance included Ref. Ref.

(0.069) 0.065 (0.131)

No warmth included Ref. Ref.

(0.069) 0.001 (0.131)

No picture included Ref. Ref.

(0.070) 0.447^**

(0.140)

Western migrant origin * Grade included 0.069

(0.179)

Non-Western migrant origin * Grade included 0.083

(0.171)

Western migrant origin * Performance included -0.114

(0.179) Non-Western migrant origin * Performance

included 0.014

(0.171)

Western migrant origin * Warmth included 0.163

(0.180)

Non-Western migrant origin * Warmth included 0.102

(0.171)

Western migrant origin * Picture included -0.096

(0.186)

Non-Western migrant origin * Picture included -0.016

(0.178) Number of information treatments

No information treatments included Ref.

One information treatment included 0.265

(0.330)

Two information treatments included 0.295

(0.323)

Three information treatments included 0.352

(22)

(0.328)

Four information treatments included 0.721^~

(0.388) Western migrant origin*One information

treatment included 0.012

(0.426) Western migrant origin*Two information

(0.415) Western migrant origin*Three information

(0.425) Western migrant origin*Four information

(0.509) Non-Western migrant origin*One information

(0.410) Non-Western migrant origin*Two information

(0.400) Non-Western migrant origin*Three information

(0.407) Non-Western migrant origin* Four information

(0.488)

Male Ref. Ref. Ref. Ref. Ref.

Female 0.095

(0.069) 0.094

(0.069) 0.109

(0.069) 0.108

(0.069) 0.095 (0.069)

Not openly religious Ref. Ref. Ref. Ref. Ref.

Openly religious 0.030

(0.069) 0.031

(0.069) 0.032

(0.069) 0.034

(0.069) 0.031 (0.069) Occupation fixed effects

Software developer Ref. Ref. Ref. Ref. Ref.

Cook 0.143

(0.118) 0.143

(0.118) 0.133

(0.119) 0.135

(0.119) 0.152 (0.119)

(0.178)

-0.367^* (0.178)

-0.372^* (0.179)

-0.373^* (0.179)

-0.354^* (0.178)

(0.133) -1.697^***

(0.133) -1.723^***

(0.134) -1.722^***

(0.134) -1.692^***

(0.134)

Plumber -0.309

(0.221) -0.309

(0.221) -0.283

(0.222) -0.291

(0.222) -0.306 (0.221)

(0.149)

-1.441^***

(0.149)

-1.448^***

(0.149)

-1.446^***

(0.149)

-1.433^***

(0.149)

(0.129) -1.186^***

(0.129) -1.199^***

(0.129) -1.186^***

(0.129)

(0.140) -1.359^***

(0.140) -1.370^***

(0.140) -1.374^***

(0.140) -1.347^***

(0.140)

Hairdresser -0.207

(0.180) -0.205

(0.180) -0.221

(0.180) -0.215

(0.180) -0.193 (0.180)

Carpenter -0.337^~

(0.185) -0.336^~

(0.185) -0.352^~

(0.186) -0.351^~

(0.569) 0.521

(0.569) 0.546

(0.570) 0.568

(0.570) 0.555 (0.568)

(0.311) 0.165

(0.311) 0.136

(0.312) 0.147

(0.312) 0.157 (0.311)

yearmonth=201612 0.664^*

(0.287) 0.667^*

(0.287) 0.637^*

(0.287) 0.648^*

(0.287) 0.671^* (0.287)

yearmonth=201701 Ref. Ref. Ref. Ref. Ref.

(23)

(0.201) 0.034

(0.201) 0.043

(0.201) 0.042

(0.201) 0.016 (0.201)

(0.193) 0.272

(0.193) 0.248

(0.194) 0.251

(0.194) 0.260 (0.194)

(0.203)

0.189 (0.203)

0.159 (0.204)

0.166 (0.204)

0.189 (0.203)

yearmonth=201705 0.463^*

(0.200) 0.464^*

(0.200) 0.466^*

(0.201) 0.469^*

(0.201) 0.461^* (0.200)

yearmonth=201706 0.312^~

(0.182) 0.312^~

(0.182) 0.303^~

(0.183) 0.306^~

(0.183) 0.306^~ (0.183)

yearmonth=201707 0.363^~

(0.193)

0.364^~ (0.193)

0.350^~ (0.194)

0.354^~ (0.194)

0.355^~ (0.194)

yearmonth=201708 -0.106

(0.265) -0.106

(0.265) -0.095

(0.265) -0.100

(0.266) -0.113 (0.266)

yearmonth=201709 -0.117

(0.223) -0.118

(0.223) -0.142

(0.224) -0.143

(0.224) -0.132 (0.224)

(0.213)

0.154 (0.213)

0.154 (0.214)

0.157 (0.214)

0.149 (0.214)

(0.220) 0.011

(0.220) -0.017

(0.221) -0.016

(0.221) 0.006 (0.220)

(0.259) 0.196

(0.259) 0.163

(0.260) 0.166

(0.260) 0.175 (0.259)

(0.220)

0.294 (0.220)

0.265 (0.220)

0.270 (0.220)

0.290 (0.221)

(0.253) 0.251

(0.253) 0.232

(0.253) 0.235

(0.253) 0.238 (0.253)

(0.231) 0.071

(0.232) -0.059

(0.234) -0.070

(0.239) 0.060 (0.233)

yearmonth=201804 -0.279

(0.593)

-0.267 (0.594)

-0.408 (0.594)

-0.445 (0.598)

-0.258 (0.594) Region fixed effects

Utrecht Ref. Ref. Ref. Ref. Ref.

Groningen -0.528^~

(0.297) -0.524^~

(0.297) -0.532^~

(0.298) -0.529^~

(0.298) -0.540^~ (0.298)

Friesland -0.242

(0.266) -0.241

(0.266) -0.262

(0.267) -0.265

(0.267) -0.254 (0.266)

Drenthe -0.001

(0.289) 0.003

(0.290) -0.021

(0.290) -0.023

(0.290) -0.011 (0.291)

Overijssel -0.291^~

(0.176) -0.290^~

(0.176) -0.299^~

(0.176) -0.298^~

(0.176) -0.293^~ (0.176)

Flevoland 0.096

(0.255) 0.097

(0.255) 0.134

(0.256) 0.136

(0.256) 0.080 (0.255)

Gelderland -0.359^*

(0.140)

-0.358^* (0.140)

-0.354^* (0.140)

-0.351^* (0.141)

-0.370^**

(0.140)

(0.120) -0.240^*

(0.121) -0.237^*

(0.121) -0.249^* (0.121)

(0.123) -0.309^*

(0.123) -0.310^*

(0.124) -0.305^*

(0.124) -0.316^* (0.124)

Zeeland -0.306

(0.305)

-0.306 (0.305)

-0.298 (0.305)

-0.286 (0.306)

-0.301 (0.306)

(0.129) -0.285^*

(0.129) -0.279^*

(0.129) -0.275^*

(0.130) -0.295^* (0.129)

Limburg -0.259

(0.186) -0.259

(0.186) -0.260

(0.186) -0.254

(0.186) -0.258 (0.186) Perceived advertisement fit

Fit Ref. Ref. Ref. Ref. Ref.

(24)

(0.153) -0.564^***

(0.153) -0.561^***

(0.153) -0.564^***

(0.154) -0.567^***

(0.153)

(0.104) 0.294^**

(0.104) 0.290^**

(0.105) 0.289^**

(0.105) 0.293^**

(0.105)

Constant 0.260

(0.215)

0.308 (0.242)

0.275 (0.216)

0.313 (0.243)

0.227 (0.356)

Pseudo R² 0.109 0.109 0.112 0.113 0.110

N 4,211 4,211 4,211 4,211 4,211

Note: The dependent distinguishes between a positive response from an employer and a negative or no response from an employer. Ref. = reference category. Model 1 examines differences in callbacks between native- majority candidates and candidates with a Western or a non-Western migrant origin. Also, model 1 examines the effect of the number of information treatments included on callback. Model 2 includes the main effects of ethnic origin, the number of information manipulations included and their interaction. Model 3 examines differences in callbacks between native-majority candidates and candidates with a Western or a non-Western migrant origin and the effect of grade, performance, warmth, and picture on callback. Model 4 includes the main effects of ethnic origin, grade, performance, warmth, picture and the interactions between ethnic origin and the four information treatments. Model 5 includes the main effects of ethnic origin, dummies for the number of information treatments included and their interaction.

Source: GEMM, 2019