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Who supports Whom? Citizens’ support for affirmative action policies in recruitment processes toward four underrepresented groups

March 2022

DOI:10.31235/osf.io/ezw72

Authors:

Celine Teney

Freie Universität Berlin

Giuseppe Pietrantuono

Freie Universität Berlin

Preprints and early-stage research may not have been peer reviewed yet.

In the haze of identity politics, Affirmative Action Policies (AAP) are a highly contested instrument. Despite AAP’s divisive character, we still know very little on why some people oppose AAP, while others support such regulations. We provide new evidence from a survey experiment in Germany, where we asked respondents to what extent they would support the introduction of a hypothetical regulation favoring – by equal qualifications – members of an underrepresented group in the recruitment process for a managing position. We randomly varied the APP’s target group between women, persons with an immigrant background, native East Germans, and persons from a non-academic household. Our study shows that being a member of the AAP-targeted group increases the support for such a regulation significantly and substantially. Additionally, we find group-based differences in the rationales for supporting a specific AAP: While especially women tend to show solidarity to members of other underrepresented groups, East Germans and persons from a non-academic household respond in compliance with the group competition theory. We also find evidence for increased support for AAP if the targeted group is perceived as disadvantaged. By contrast, our results do not back up the idea that prejudice affects AAP support.

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Who supports Whom? Citizens’ support for affirmative action policies in

recruitment processes toward four underrepresented groups

Teney, Céline (celine.teney@FU-berlin.de)

Pietrantuono, Giuseppe (giuseppe.pietrantuono@FU-berlin.de)

Möhring, Katja (moehring@uni-mannheim.de)

March 2022

Abstract

In the haze of identity politics, Affirmative Action Policies (AAP) are a highly contested

instrument. Despite AAP’s divisive character, we still know very little on why some people

oppose AAP, while others support such regulations. We provide new evidence from a

survey experiment in Germany, where we asked respondents to what extent they would

support the introduction of a hypothetical regulation favoring – by equal qualifications –

members of an underrepresented group in the recruitment process for a managing

position. We randomly varied the APP’s target group between women, persons with an

immigrant background, native East Germans, and persons from a non-academic

household. Our study shows that being a member of the AAP-targeted group increases the

support for such a regulation significantly and substantially. Additionally, we find group-

based differences in the rationales for supporting a specific AAP: While especially women

tend to show solidarity to members of other underrepresented groups, East Germans and

persons from a non-academic household respond in compliance with the group

competition theory. We also find evidence for increased support for AAP if the targeted

group is perceived as disadvantaged. By contrast, our results do not back up the idea that

prejudice affects AAP support.

Keywords: Affirmative action policies, attitudes, survey experiment, Germany

Introduction

Identity politics have recently gained salience in the public debates of most Western

European countries. Recently, this debate has focused on the extent to which traditional

left-wing parties should orient their political agenda toward measures targeting

structural inequality faced by particular social groups (Abou-Chadi et al., 2021).

Proponents of such a political (re)orientation conceive group-based rights as a necessary

and complementary tool to individual-based rights for redressing group-based unequal

participation in various societal arenas. By contrast, opponents argue that traditional left-

wing parties should strive to fight social inequality primarily within the whole population

regardless of group memberships. Accordingly, allocating particular rights to individuals

because of common group membership is assumed to lead to a fragmentation of the

society into subgroups defined by particular identities perceived as constitutive and

ultimately weaken social cohesion.

This heated debate around the relevance of identity politics for the traditional left-wing

parties tends to discuss various instruments to redress inequality, including gender-

neutral restrooms in public buildings, the implementation of a gender-sensitive language,

or the introduction of a quote in favor of underrepresented groups in leading positions.

The latter instrument is an example of affirmative action policies (AAP): AAP imply that

members of underrepresented groups are given preference over others if equally

qualified in selection processes for leadership positions and/or public offices and are thus

group-based policies aiming at equality of outcomes (Harrison et al., 2006). As AAP are

often perceived as a zero-sum game, they compose an essential topic of contestation

between proponents and opponents of identity politics.

In Europe, the public debate on identity politics discusses women, LGTBIQ, and persons

with an immigrant background as main underrepresented groups (Abou-Chadi et al.,

2021). In the German context, two other underrepresented groups have been receiving

attention in this debate. First, persons born in former GDR (hereafter “East Germans”) are

still largely underrepresented at key positions in various societal arenas more than thirty

years after the reunification (see interview of Mau in Rennefanz, 2019; Mau, 2019).

Accordingly, this might help explain the feeling of being left behind and the perception of

lack of representation shared by a part of the East Germans. Second, social origin is a

particularly important determinant in educational and professional achievement in

Germany (Consiglio and Sologon, 2022), leading to an underrepresentation of persons

with low socio-economic background in leading positions (see Table 3).

The public debate has been not only discussing the relevance of identity politics

instruments in the political agenda of traditional left-wing parties, but also which

underrepresented groups should be entitled to benefit from regulations aiming at

improving their representation in influential positions. Against this backdrop, the lack of

empirical studies on citizens´ attitudes toward AAP for underrepresented groups is

striking. On the one hand, various studies highlighted the relative large tolerance toward

underrepresented groups among the German population (e.g., Mau et al., 2020; Teney and

Rupieper, 2021). Moreover, public opinion toward redistributive politics (e.g., Reeskens

and van der Meer, 2019; Schwander and Vlandas, 2020) and perceived social inequality

(e.g., Mijs, 2021) has received much attention from scholars. On the other hand, we know

very little about citizens´ support for concrete policies targeting underrepresented groups

for redressing group-based inequality apart from support for gender quota regulations

for supervisory boards, which yet concern a very small group of individuals in the highest

ranking positions of specific large companies (Möhring et al., 2019; Möhring and Teney,

2020).

Investigating citizens’ support for AAP for underrepresented groups is not only of societal

relevance but also composes an interesting scientific inquiry. Analysing support for AAP

can help us understand the interaction between key attitudinal dimensions underpinning

the support and real-life policies. Previous research focussing on equal outcome policies

for African Americans in the US suggests that support for AAP is based on the interplay of

identification with the target group, the own level of prejudice against this group, and the

perception of unequal opportunities faced by the target group (Krysan, 2000). However,

as most previous research has largely focussed on the US context and AAP aiming at equal

opportunities for African Americans, results cannot be generalized to other contexts and

other underrepresented groups. With our study, we can test these previous results in

another context and for diverse target groups which are not only defined by ethnicity.

Against this background, we investigate the determinants of support for implementing a

hypothetical AAP in recruitment processes for leading positions. Based on preregistered

survey experiments (preregistration reference removed for sake of anonymised peer review)

conducted with a YouGov online panel among the working population in Germany, we can

compare the level of support for AAP for women, persons with an immigrant background,

persons born in East Germany, and persons from a low socio-economic background.1

More specifically, we designed a survey experiment composed of vignettes introducing a

hypothetical regulation favouring one of the four target groups by equal qualification in

the recruitment process for a leading position. The target groups are randomly assigned

to the vignettes. Respondents are first asked to evaluate four vignettes in total. Then, they

are invited to answer several attitudinal items that might explain support for and

opposition to AAP measures. For our survey, we oversampled women, persons with an

immigrant background, East Germans, and persons from a low socio-economic

background (hereafter “sampling groups”). This sampling design enables us to investigate

support for AAP among members of AAP target groups and to incorporate three

dimensions of analysis. First, we can compare differences in support for AAP depending

on the vignettes´ target group. Second, we can compare the level of support for AAP of

respondents belonging to these target groups. Third, we can investigate the power of

various attitudinal factors in explaining these differences in the level of support for AAP.

We focus on the factors that have shown to play a key role in the support for policies

aiming at redressing ethnic inequality: group-based interest and group identity, prejudice

and belief about discrimination and source of inequality(Dixon et al., 2017; Sniderman et

1 We refrain from including LGTBIQ as fifth target group in our vignette experiment. The

introduction of a quote favouring members of LGTBIQ by equal qualification in recruitment

process for leading positions would require the indication of the sexual orientation in the

candidates´ application. The implementation of such a regulation would be therefore much more

challenging for the LGTBIQ than the other underrepresented groups of our survey experiment. In

order to provide vignettes that are as realistic as possible, we decided against the inclusion of

LGTBIQ as fifth AAP target group.

al., 1999; Weeks and Baldez, 2014). We assess the extent to which these factors can

explain (1) vignette target group differences in support for AAP and (2) differences in

support for AAP between respondents belonging to these target groups.

We find that being a member of the AAP-targeted group increases the support for the

introduction of a quota-regulation significantly and substantially. Additionally, we find

group-based differences in the rationales for supporting a specific AAP: While especially

women tend to show solidarity to members of other underrepresented groups, East

Germans and persons from a non-academic household respond in compliance with the

group competition theory. We also find evidence for increased support for AAP if the

targeted group is perceived as disadvantaged. By contrast, our results do not back up the

idea that prejudice affects AAP support.

This paper is structured as follows: first, we present our hypotheses to explain (1)

differences in support for AAP among respondents belonging to different target groups

and (2) differences in support for AAP depending on the vignettes´ target groups. Then,

we describe the design of our survey experiment before presenting our results and

concluding.

Group-based support for AAP

How do (perceived) group differences explain support for AAP? A rational choice

approach is frequently used to explain policy support by individuals, whereby own

interest is assumed to follow an individuals’ socio-demographic characteristics, hence

their membership in socially defined groups as gender, age, and origin. According to this

group-based interest perspective, individuals who perceive the benefit of a policy for

members of their own group are more likely to support it. Consequently, members of the

target groups tend to favour AAP policies that are believed to help their own demographic

group (Harrison et al., 2006). The body of empirical research on support for AAP

consistently provided evidence confirming this group interest-based explanation (e.g.,

Harrison et al., 2006; Möhring et al., 2019; Scarborough et al., 2019).

Thus, we expect respondents belonging to a target group to support AAP for their own target

group to a significantly larger extent than respondents not belonging to the target group

(Hypothesis 1).

Second, members of a target group might differ from members of the non-target group in

their support for AAP for other target groups. On the one hand, members of an

underrepresented group might be more likely than non-members to identify and feel

solidarity with other underrepresented groups. They might indeed perceive similarities

in their disadvantaged position with members of other underrepresented groups. This, in

turn, would lead members of an underrepresented group to support AAP for other target

groups to a more considerable extent than non-members. We know from US studies that

members of underrepresented groups are more likely to support AAP targeted at other

underrepresented groups than members of the majority group (i.e., white men)

(Bolzendahl and Coffé, 2020; Kane and Whipkey, 2009; Scarborough et al., 2019, 2019).

However, findings on support for AAP from the US context might not be generalizable to

other countries. Indeed, most US studies on support for AAP focus on African Americans

as AAP target group (Harrison et al., 2006; Krysan, 2000). However, the socio-structural

situation of Afro Americans is hardly comparable to the socio-structural case of

underrepresented groups in Germany or other European countries. This leads us to

formulate our second hypothesis:

Respondents belonging to one target group are more likely to support AAP for the other

target groups than respondents not belonging to any target group (Hypothesis 2a).

Alternatively, we could also expect respondents of a target group to oppose AAP for other

target groups to a larger extent than respondents not belonging to any target groups.

Indeed, based on group competition theory (Blumer, 1958; Bobo, 2000), respondents

might perceive members of AAP target groups as competitive threats for valued (but

scarce) social resources, statuses and privileges. Indeed, a recent survey among East

Germans and German Muslims showed that East Germans perceive social upwards

mobility of German Muslims as a threat against their own status (Foroutan et al., 2019).

As AAP tend to be considered a zero-sum conflict, members of a target group might

evaluate the implementation of an AAP for another target group as a threat for their own

group. As the number of leading positions is limited, there is not only a conflict between

target and non-target group members but between members of different target groups.

Potential conflicts between members of underrepresented groups might even be more

intense as they are outsiders competing for scarce free job positions that established non-

target group members do not yet occupy. Thus, the introduction of AAP for various target

groups in recruitment processes for leading positions implies that the absolute benefit

from such a regulation to the members of one target group directly depends on the total

number of underrepresented groups included in the regulation (and their demographic

size) concerning the number of free job positions. This leads us to formulate a competing

hypothesis to Hypothesis 2a:

Respondents belonging to one target group are less likely to support AAP for the other target

groups than respondents not belonging to any target groups (Hypothesis 2b).

How do perceived disadvantage and prejudice explain support for AAP?

Support for AAP depends on the perception that members of a target group are unfairly

disadvantaged in the respective societal arena. Indeed, if respondents consider members

of a target group as not disadvantaged or responsible for their disadvantaged situation,

they are unlikely to support AAP. The perception that group members are unfairly treated

or structurally disadvantaged is indeed a strong determinant of support for AAP for ethnic

minorities (Harrison et al., 2006; Krysan, 2000) and for diversity policies on the labour

market for women and ethnic minorities (Scarborough et al., 2019). Moreover, the level

of perceived disadvantage faced by women and persons with an immigrant background

on the labour market can partly explain differences in support for AAP for women and

persons with an immigrant background (Möhring and Teney, 2021). In addition to the

perception of disadvantage faced by members of the target groups, we will also assess the

extent to which respondents evaluate members of target groups to be as committed in

their job as non-members of target groups. The perceived job commitment of members of

target groups, in turn, might also explain the different levels of support for AAP depending

on the target groups. Indeed, respondents might perceive members of a target group as

disadvantaged in the labour market but consider them responsible for their situation (i.e.,

because of a perceived lack of commitment). Such respondents are likely to oppose AAP

for the corresponding target group, as they would consider members of the target group

to control their labour market situation. Such an argument has been made in the debate

on the introduction of a quota for women in CEOs of DAX-noted companies (e.g., Terjesen

and Sealy, 2016): qualified women are perceived to be on average less willing to hold such

top leading positions due to lower career orientation. This leads us to derive our third

hypothesis:

Differences in support for AAP depending on vignette target groups are partly explained by

different levels of perceived disadvantage and perceived commitment of the target groups

on the labour market (Hypothesis 3).

Ethnic and racial prejudices are significantly related to lower support for relative informal

solidarity toward immigrants (van Oorschot, 2006) and welfare programs targeted at

ethnic minorities (Harell et al., 2016). This argument might be extended to other

underrepresented groups. An assumed lack of competency to execute leading positions

ascribed to specific groups might drive disapproval of AAP targeted at these groups.

(Möhring and Teney, 2021) point to the relevance of prejudice against women and against

persons with an immigrant background in explaining target group differences in support

for AAP with prejudice being negatively related to support for AAP. Thus, the fact that

prejudice varies along the AAP target groups might explain differences in support for AAP

for different target groups. We operationalize prejudice against the four target groups by

evaluating members of the target groups as not possessing the necessary competencies

and qualities for holding a management position. More precisely, we asked respondents

to assess the competence of a hypothetical supervisor if he/she were a member of one of

our four target groups compared to a supervisor not belonging to any target groups.

Accordingly, we formulate our last hypothesis as follow:

Differences in support for AAP depending on vignette target groups are partly explained by

different levels of prejudice against the target groups (Hypothesis 4).

Data and Method

Sample

We use original data that we collected with a YouGov online panel in July 2021. The

sample consists of 2,676 individuals enrolled in the labour forces in Germany at the time

of the survey. As we focused on attitudes toward AAP of respondents belonging to one of

four groups that would benefit from an AAP, we oversampled respondents of those

groups, in the following labelled as sampling groups (SG). By analogy to our AAP target

groups, we define four sampling groups: Women, persons with a migration background,

native East Germans (i.e., born in one of the former GDR states), and respondents from a

non-academic background. We define respondents as having a migration background if

they are foreign-born, have at least one foreign-born parent, or have non-German

citizenship. We classify respondents as having a non-academic background if both their

parents did not graduate from university or technical college.

The experimental design

To assess support for AAP toward our four target groups, we developed vignettes on a

hypothetical AAP regulation (Ref. to preregistration removed for sake of anonymised peer

review). We varied four dimensions of the vignettes: In the first dimension, we randomly

assigned the target group benefitting from the hypothetical AAP (individuals with a

migration background, women, native East Germans, or persons from a non-academic

background). Second, we varied the level of the labor market segregation faced by the

target group (highly underrepresented or underrepresented). Third, the vignettes differ

in the sector of activity in which the AAP will apply (private sector or public service).

Lastly, we randomized the management level of the position targeted by the regulation

(upper management, middle management, or senior positions). The vignettes read as

follows:

[Members of target group] are [segregation level] in executive positions

[sector of activity] in Germany. Therefore, an independent commission

proposes the following regulation: Employers should be legally obliged to give

preference to [members of target group] in application procedures for

[position’s management level] with equal qualifications.

Our outcome variable refers to the degree to which respondents support or oppose such

regulation. We measured support for an AAP on an 11-point Likert scale ranging from 0

(fully oppose) to 10 (fully support). We assigned each respondent randomly to four

versions of the vignette. In total, we assessed the support for 10,704 vignettes. We use a

full factorial design for drawing the sample of vignettes from the entire vignette universe:

we considered all possible combinations of the dimensions’ levels when drawing a

random sample of vignettes.

Operationalization

To test our hypotheses, we use three further questions as mediator variables. First, we

asked to what extent respondents think that people from our four target groups are

disadvantaged in applying for management positions in Germany. The respondent could

indicate their opinion on a five-point scale ranging from “not at all” to “very strongly” for

each group. Second, we asked the respondents to rate the leadership competence and

presented an antagonistic pair of individuals. We presented four antagonistic pairs

(woman vs man; a person with vs without migration background; a native east vs west

German; a person from a non-academic vs academic household) and asked on an 11-point

Likert scale whether they think that the first person (a member of a target group) is more

competent (value of -5) or the member of the majority (value of +5). A value of 0

corresponds to the opinion that neither the members of the target group nor the members

of the majority are considered more competent. The values in-between could be used to

nuance the rating. Third, in analogy, we asked how respondents rate the performance of

the four pairs asking whether they think that the first person (a member of a target group)

performs better in the same position (value of -5) or the member of the majority (value of

+5) Perceived competence and level of performance (or lack thereof) combined measure

prejudice.

We furthermore control for respondents’ age, educational level, employment sector,

whether they fulfil a managerial role, whether they are married/live in a civil partnership,

and whether they have children or not. We included these controls mainly to capture the

non-random assignment of the mediators in our analysis. We provide summary statistics

in the Supportive Information Tables S1 and S2 for the whole sample and by survey group,

respectively.

Analytic strategy

The units of analysis are the vignettes (with respondents’ evaluation of the vignette as

outcome). We employ ordinary linear least squares regression to regress the rating

outcomes on sets of vignette-specific indicators and individual-level controls and cluster

the standard errors by the respondent.

Results

General Support for AAP

Figure 1 depicts the overall support for our four hypothetical AAP (the corresponding

regressions coefficients can be found in Table 3, Model 1.). AAP targeting women and

persons from a non-academic household find significantly more support than those aimed

at individuals with a migration background or native East Germans. Note that the

predictions plotted in Figure 1 represent the preference to support an AAP by

respondents not belonging to any of our target groups.

We gain a more precise insight by subdividing our analysis along with our target groups.

Figure 2 illustrates the differences in support for our four hypothetical AAP by the target

groups. We plotted the predicted probabilities for members of a specific target group

against respondents not belonging to this target group (i.e., male vs female, individuals

with vs without migration background, native East vs West Germans, and persons from

an academic vs non-academic household).

Figure 2 shows that the primary overall trend from Figure 1 is replicated within all target

groups: Non-members of the target group (in dark red) support AAP aiming at women

and individuals from non-academic households to a higher level in comparison to

regulations for migrants or East Germans. For members of a target group (in dark green),

we find some variation to this pattern; most importantly, we see that members of a target

group differ substantially in the support for the AAP targeting the own group compared

to non-members. However, this preference for the own group is less pronounced for

persons from a non-academic household.

Differences in support for AAP between respondents belonging to different target

groups

Table 1 provides the main effects for supporting the four proposed AAPs separately. It

shows that being a member of the AAP-targeted group increases the support for such a

regulation significantly and substantially (see regression coefficients for “sampling

groups (SG)”). This finding aligns with the group-based interest perspective and our

Hypothesis 1. Additionally, we note that women are more prone to support any AAP

proposed. To a lesser extent, this also holds for respondents with a migration background:

They support all AAP, but the one aimed at native East Germans. In contrast, native East

Germans and individuals from non-academic households support only AAP targeting their

own group. We can interpret those different rationales for supporting a specific AAP along

with our Hypotheses 2a and 2b: While women and interviewees with a migration

background tend to show solidarity to members of other underrepresented groups, East

Germans and persons from a non-academic household respond in compliance with the

group competition theory.

Notably, for all four AAP groups, the vignette dimensions cannot explain any differences

in support for the AAP. Solely the support for an AAP targeting native East Germans

decreases if the regulation affects the private sector (opposed to the public service). This

effect is statistically significant but small in magnitude (0.26 on an 11-point scale). In the

same vein, our controls are not predictive for respondents support toward a specific AAP.

Only age is negatively correlated with the support of an AAP aiming at migrants, and

higher levels of education slightly decreases the support for AAP targeting women.2

Perceived Disadvantage and prejudice

We hypothesized that the support for an AAP might be mediated by the perceived

disadvantage members of an underrepresented group face and prejudice. The latter is

measured as the attested competence and the willingness to perform at a high level or a

perceived lack of those components (see section “Operationalization“). With respect to

perceived disadvantage, we first give an overview of how disadvantage of different

underrepresented groups is evaluated by the respondents, and then assess with data of

the German Socio-Economic Panel (SOEP) how these evaluations match the real

representation of underrepresented groups in leading positions in Germany. Table 2 gives

the averages of the perceived disadvantage scores divided by SG for the different target

groups. The second column includes the values for respondents not belonging to any

target group, i.e., West-German men from academic households without migration

background. Those respondents perceive women and persons with a migration

background as slightly disadvantaged in applying for managerial positions (recall: we

2 The results are not altered by the introduction of indicators for the sequence with which we presented the vignettes

to the respondents (see Table S5—S7 in the Supporting Information).

measure perceived disadvantage on a five-point scale; 1 = not disadvantaged at all, 5 =

very strongly disadvantaged).

In contrast, native East Germans and individuals from non-academic households are not

seen as largely disadvantaged. Overall, we see only little variation on the perception of

group-based disadvantages in the recruitment process for managerial positions (Table 2).

We can back-up these observations by comparing the individual perceived disadvantage

with objective measurements of disadvantage. The SOEP entails different measurements

that quantify whether and to which degree different groups are excluded from managerial

positions. Following Holst and Friedrich (2017), Table 3 summarizes different ways of

identifying employees in the private sector with managerial functions. The first two make

use of the self-reported occupational position. Further, we define a manager following the

ISCO-08 and the ISCO-88 scale. The Classification of Occupations (KldB, 2010) developed

by the Federal Employment Agency offers a third possibility of operationalization. Lastly,

we operationalize managers with the help of the Erikson-Goldthorpe-Portocarero scheme

(Brauns et al., 2000). This scheme is based on the ISCO-88 or ISCO-08 occupational

classification; the occupations are scaled using the ISEI index (Ganzeboom and Treiman,

1996) based on their socio-economic occupational status. This results in 11 classes, the

first two of which identify managerial and highly skilled workers.

To gasp to which extend members of a minority are disadvantaged in the labor market

when it comes to positions with a managerial function, we can compare the proportion of

individuals belonging to one of our target groups with a managerial role with the ratio of

these groups in the full-time employee population over 18. Independently of the

measurement used in Table 3, we observe that the percentage of target group members

as full-time employees is always higher than the ratio with a managerial role. This fact

indicates clearly that members of the target groups are underrepresented in managerial

positions. Compared to the assessment of our respondents not belonging to any target

group, we observe that also native East Germans and persons from a non-academic

household are underrepresented in positions with a managerial role. Even though this is

at a lower level than for people with a migration background and for women. In this sense,

the respondents of our survey did a good job mapping the hierarchy of

underrepresentation, placing women and migrants on top and native East Germans and

individuals from non-academic backgrounds at the bottom. How does the four target

groups' perceived disadvantage (or lack thereof) affect the support for AAPs targeted at

these groups?

Substantially, with Table 1, we can state that the support for AAP is higher if a group is

perceived as disadvantaged in applying for managerial positions. This effect is sizeable

and significant for all four target groups and aligns with our theoretical assumptions

(Hypothesis 3).

Next to the perceived disadvantage, we also hypothesized that prejudice against a target

group might mediate the support for an AAP. We measured prejudice as the attested

competence and the willingness to perform at a high level or a perceived lack of those

components. Descriptively, we observe only little variation for those two measures (see

Table 4. Note: a 0 means that the two groups have the same competency or willingness to

perform. A positive value means that a member of a target group is attested to have more

competence or willingness to perform; a negative value points to the perception that a

member of the majority group has more competence or willingness to perform). We see

that all values are slightly negative for our control group (i.e., respondents not belonging

to any target group). However, they are very close to 0, so that we can imply that the

majority of respondents do not consider varying levels of competence or performance

between social groups.

In our multivariate model (see Table 1), only the AAP targeting women is affected

positively by the competence ascribed to members of our target groups. In other words:

If women are seen as more competent in a leadership position than men, this leads to

higher support for a regulation aiming at women. Nonetheless, this effect is relatively

small in size. The perceived level of performance affects only the support for AAP towards

migrants: If respondents perceive migrants to perform better than non-migrants, they

support AAP aimed at migrants to a higher extend. Again, this effect is statistically

significant but small in magnitude. Thus, we can only partly confirm our theoretical

assumptions formulated in Hypothesis 4.3 Overall, the lack of variation in both measures

of perceived competence and performance explains the non-significant effect between

these attitudinal measures and support for the corresponding AAP.

3 In Tables S8—S12 of the Supporting Information we introduced summarizing scales for our three mediating

measurements. We constructed these scales using principal component analysis. The results are not altered when

we specify our models with the summarizing scales instead of the separate items.

We illustrate the differences in the predicted support for a specific AAP by the respective

sampled groups in Figure 4. We plotted results from two models, with and without the

inclusion of our three mediating variables (i.e., perceived disadvantage on the labour

market, perceived level of performance and perceived level of competence). Graphically

we see again that these three measurements combined only partly explain the differences

in support for AAP.

Discussion and Conclusion

This study examined support for AAP targeting four underrepresented groups in the

German labor market. We exploited a survey experiment randomly varying the target

group for a hypothetical regulation in the recruitment process for a managing position

favoring either women, persons with an immigrant background, native East Germans, or

persons from a non-academic household. We argued that the extent to which individuals

support an AAP is contingent first on their (ascribed) group membership, second on the

level of perceived disadvantage of the target group, and third, on level of prejudice held

against the target group.

Generally, our results show that AAP targeting women and persons from non-academic

households are supported to a larger extent than regulations aiming at East Germans and

persons with an immigrant background. This could hinge on the fact that being East

German or having a migration background speaks to regional membership in a broader

sense, whereas being a woman or from a non-academic household are ascriptions broader

distributed in the general population. Furthermore, welfare chauvinism could explain low

support for regulations aiming at persons with a migration background. Another plausible

explanation points to a more general problem of identity politics: the requirement of clear-

cut criterion for group membership. In the case of East German – and to a lesser degree for

persons with a migration background – the definition of who should belong to the target group

is not uncontested and clear-cut, as boundaries between some categories of distinctions tend to

blur (i.e., native East and West Germans).

Our finding pinpoints consistently that being a member of the AAP-targeted group

increases the support for such a regulation aligning with the group-based interest

perspective. Notably, we find that while especially women are more prone to support any

AAP proposed, native East-Germans and individuals from a non-academic household

support only AAP targeting their own group. Additionally, the support for AAP is higher if

a group is perceived as disadvantaged in applying for managerial positions. Lastly, we find

only partially evidence for the hypothesized link between prejudice against an

underrepresented group and the support for an AAP aiming at this group.

One important question that arises from these results is why women show more

solidarity towards other underrepresented groups than members of other minorities. We

can enqueue this finding in line with a “modern gender gap” in political attitudes

(Goossen, 2020). According to this gender gap, women are generally more in favor of left

parties and policies, including support for a comprehensive redistribution of resources

and welfare provision (e.g., Inglehart and Norris, 2003; Inglehart, 2018; Shorrocks and

Grasso, 2020). From a socio-psychological perspective, another plausible explanation

could be found in the higher level of empathy women generally show compared to men

(Mestre et al., 2009), especially towards ethnic and cultural minorities (Cundiff and

Komarraju, 2008).

These findings have clear implications regarding inequality and its perception in society.

Following Mijs (2021) and Mijs and Savage (2020), the rise of inequality and belief in

meritocracy have gone hand in hand. The authors argue that individuals consent to

inequality as they are convinced that societal success reflects a meritocratic process. The

more unequal a society is, the more individuals tend to explain success in meritocratic

terms and less through non-meritocratic factors such as ascribed characteristics. In this

sense, the underrepresentation of minorities in managerial positions is coupled with a

feeling of deservingness (as also backed up by our results) legitimizing the

underrepresentation itself. AAP can help overcome this misperception and actively

contribute to an equality of outcomes.

Clearly, more work is needed to identify individual motivations to support AAP in other

contexts and for other minorities. This study focused on one particular case of AAP

(recruitment process for leading positions). However, there are many ways in various

societal areas and sectors of activity where AAP could be implemented. Further work is

also necessary to better ascertain the mechanisms through which support for AAP is

amplified or reduced and to assess whether the underlying mechanism we find here also

applies when looking at support toward other types of AAP.

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Figures and tables for: “Who supports Whom? Citizens’ support for affirmative action

policies in recruitment processes toward four underrepresented groups”

(in order of appearance)

Figure 1: Main Effects Support for AAP. (The figure is based on Model 1, Table S3 of the Supporting

Information).

Note: The figure shows results for fully interacted models based on Table S4 of the Supporting Information.

Figure 2: Main Effects Support for AAP by Sampling Group.

Vignette dimension: target group

Women

Migrant

East-German

Non-Academic

Intercept

0.79

0.98

2.76***

3.44***

(0.61)

(0.66)

(0.68)

(0.63)

Vign.: Private Sector

0.09

0.20

-0.26*

0.23

(0.12)

(0.13)

Vign.: Strongly Underrepr.

-0.04

0.20

0.10

0.17

(0.12)

(0.13)

Vign.: Middle Mngm.

0.01

-0.28

0.14

0.07

(0.15)

(0.16)

Vign.: Low Mngm.

-0.20

-0.26

0.24

-0.07

(0.15)

(0.16)

(0.17)

SG: Women

0.92***

0.52**

0.60**

0.57**

(0.17)

(0.18)

(0.19)

(0.18)

SG: Non-Academi c

0.13

0.07

-0.03

0.82***

(0.19)

(0.20)

(0.21)

SG: E ast-GER

0.36

-0.02

1.42***

-0.45

(0.20)

(0.23)

(0.24)

(0.23)

SG: Migrant

0.45*

1.31***

0.41

0.51*

(0.21)

(0.22)

Women dis.

1.34***

(0.08)

Mig. dis.

0.95***

(0.08)

East-GER dis.

0.54***

(0.09)

Non-Acad. dis.

0.67***

(0.09)

Comp. Women

0.22**

(0.08)

Comp. Mig.

0.09

(0.06)

Comp . East-GER

-0.03

(0.09)

Comp. Non-Acad.

0.10

(0.08)

Perf. Women

-0.00

(0.07)

Perf. Mig.

0.14*

(0.06)

Perf. East-GER

0.13

(0.08)

Perf. Non-Acad.

-0.00

(0.08)

Age

-0.00

-0.02**

-0.01

(0.01)

Public/NGO

0.23

0.29

0.07

0.28

(0.17)

(0.18)

(0.19)

(0.18)

Mngm. Role

0.28

0.02

0.08

-0.06

(0.18)

(0.19)

(0.20)

Education middle

-0.51

0.01

-0.08

-0.41

(0.35)

(0.36)

(0.38)

(0.37)

Education high

-0.75*

0.07

-0.48

-0.65

(0.36)

(0.39)

(0.37)

Living w. Partner

0.20

-0.16

0.13

0.20

(0.17)

(0.19)

Children

0.12

0.26

0.09

0.01

(0.18)

(0.20)

(0.21)

(0.20)

0.24

0.21

0.10

Adj. R2

0.23

0.21

0.09

Num. obs.

2279

2160

2161

2148

Clustered standard errors in parantheses. ∗∗∗p < 0.001; ∗∗p < 0.01; ∗p < 0.05

Table 1: Main Results by AAP Group.

Overall

Academic,

West-Ger,

male

Female Non-Acad . East-GER Migrant

N = 2,676

N = 457

N = 1,289

N = 1,496

N = 550

N = 626

Perceived Disadvantage (1-5 scale):

Women

3.37

3.86

3.6

3.42

3.41

3.47

Migrants

3.58

3.45

3.73

3.55

3.51

3.83

Born in East-GER

3.09

2.85

3.16

3.18

3.21

3.17

Born in Non-Acad. HH

2.94

2.73

2.99

2.98

3.46

2.83

Table 2: Means in perceived disadvantages for the different AAP groups. Means of the control group

(e.g., respondents not belonging to any target group) are bold. (See also descriptive statistics, Table

S1 and S2 of the Supporting Information).

Measurement Total Women

Mig.

Background Born Eas t-GER Non-Acad. HH

rel.

abs.

rel.

abs.

rel.

abs.

rel.

abs.

Occupational Position (Highly Qualified

Professional) 2,080 32% 656 20% 415 15% 321 66% 1,371

Occupational Position (Managerial)

330

23%

16%

63%

208

ISCO-88 (for 2017)

539

31%

166

20%

107

14%

70%

379

ISCO-08

589

30%

178

14%

11%

68%

398

KldB 2010

450

31%

139

19%

15%

70%

313

EGP-88 (for 2017)

1,410

27%

379

20%

286

16%

229

62%

877

EGP-08

1,490

25%

366

18%

273

14%

202

61%

902

Full-time employee population over 18

6,370

40%

2,496

25%

1,625

16%

1,006

74%

4,685

Table 3: Managers in the private sector according to different operationalizations.

Source: SOEP v. 36; own calculations.

Overall

Academic,

West-Ger,

male

Female Non-Acad . East-GER Migrant

N = 2,676

N = 457

N = 1,289

N = 1,496

N = 550

N = 626

Competency (centered, scale -5 – 5 ):

Men vs. Women

0.15

-0.15

0.33

0.15

0.25

No Mig. v s. Mig. Background

-0.14

-0.29

-0.02

0.04

-0.24

0.15

Born in West-GER vs. East-GER

0.14

-0.08

0.22

0.14

0.41

0.21

Born in Acad. Vs. Non-Acad. HH

0.07

-0.16

0.15

0.29

0.10

0.15

Performance (centered, scale -5 – 5 ):

Men vs. Women

0.25

-0.01

0.53

0.33

0.17

0.39

No Mig. v s. Mig. Background

0.03

-0.10

0.16

0.04

-0.10

0.36

Born in West-GER vs. East-GER

0.10

-0.08

0.19

0.14

0.40

0.12

Born in Acad. Vs. Non-Acad. HH

0.20

-0.01

0.29

0.18

0.36

Table 4: Means in ascribed competency and performance for the different AAP groups. Means of the

control group (e.g., respondents not belonging to any target group) are bold. (See also descriptive

statistics, Table S1 and S2 of the Supporting Information).

Note: OLS regressions with clustered standard errors for respondents. The figure is based on the Models in Table 1; wit h and

without the inclusion of the mediating Variables.

Figure 3: Support for AAP, Perceived Disadvantage, Competency, and Performance.

Supporting Information

“Who supports Whom? Citizens’ support for aﬃrmative action policies

in recruitment processes toward four underrepresented groups”

Contents

S1 Descriptive Statistics S2

S2 Additional Results S4

S3 Robustness Check Main Tables S6

S3.1VignetteSequence ...................................... S6

S3.2 Summarizing Scales for Perceived Disadvantage, Competence, and Performance . . . . S10

S4 Subgroup Analysis S13

S4.1SupportforAAPbyGender................................. S13

S4.2 Support for AAP by Academic Household . . . . . . . . . . . . . . . . . . . . . . . . . S15

S4.3 Support for AAP by East-Germans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S17

S4.4 Support for AAP by Migration Background . . . . . . . . . . . . . . . . . . . . . . . . S19

S1 Descriptive Statistics

Variable Obs. Mean Std. Dev. Min. Max

Outcome:

Support for Quota Regulation 10,704 4.81 3.35 0 10

Vignette Dimensions:

AAP Group 10,704 2.51 1.12 1 4

Migrants 2,634 1 – – –

Women 2,720 2 – – –

Born in East-GER 2,641 3 – – –

Born in a Non-Acad. HH 2,709 4 – – –

Employment Sector 10,704 0.51 0.50 0 1

Public 5,246 0 – – –

Private 5,458 1 – – –

Segregation Status 10,704 0.50 0.50 0 1

Underrepresented 5,373 0 – – –

Strongly Underrep. 5,331 1 – – –

Position 10,704 2.0 0.82 1 3

Top Management 3,622 1 – – –

Middle Management 3,527 2 – – –

Low Management 3,555 3 – – –

Respondants Background (Sampling Group):

Female 10,704 0.48 0.50 0 1

Non-Academic Background 10,704 0.56 0.50 0 1

East-German Background 10,704 0.21 0.40 0 1

Migration Background 10,704 0.23 0.42 0 1

Middle Eastern Background 10,704 0.08 0.27 0 1

East-European Background 10,704 0.08 0.27 0 1

Attitudes:

Perceived Disadvantage (1-5 scale):

Women 10,096 3.37 1.01 1 5

Migrants 9,824 3.58 1.09 1 5

Born in East-GER 9,748 3.09 1.06 1 5

Born in a Non-Acad. HH 9,496 2.94 1.0 1 5

Competency (centered, -5 – 5 scale)

Men vs. Woman 10,704 0.15 1.54 -5 5

No Mig. vs. Mig. Background 10,704 -0.14 1.82 -5 5

Born in West-GER vs. East-GER 10,704 0.14 1.48 -5 5

Born in Acad. HH vs. Non-Acad. HH 10,704 0.07 1.39 -5 5

Performance (centered, -5 – 5 scale)

Men vs. Woman 10,704 0.25 1.70 -5 5

No Mig. vs. Mig. Background 10,704 0.03 1.81 -5 5

Born in West-GER vs. East-GER 10,704 0.10 1.51 -5 5

Born in Acad. HH vs. Non-Acad. HH 10,704 0.20 1.50 -5 5

Socio Demographics:

Age 10,704 47.29 13.83 18 85

Employment Sector 9,776 0.31 0.46 0 1

(Pivate vs. Public)

Managerial Role (0/1) 10,504 0.29 0.45 0 1

Education 10,600 2.52 0.63 1 3

Living with Partner (0/1) 10,580 0.60 0.50 0 1

Children (0/1) 10,704 0.51 0.50 0 1

Table S1: Descriptive Statistics.

White, academic, Female Non-Academic East-German Migrant

W-German male

(N= 1828) (N= 5156) (N= 5984) (N= 2200) (N= 2504)

Variable Mean SD Mean SD Mean SD Mean SD Mean SD

Outcome:

Support for Quota Regulation 3.88 3.41 5.45 3.23 5.00 3.28 5.10 3.27 5.52 3.36

Vignette Dimensions:

AAP Group 2.50 1.13 2.51 1.12 2.51 1.11 2.52 1.11 2.50 1.10

Employment Sector 0.53 0.50 0.51 0.50 0.50 0.50 0.51 0.50 0.51 0.50

Segregation Status 0.51 0.50 0.49 0.50 0.50 0.50 0.50 0.50 0.50 0.50

Position 2.02 0.80 2.00 0.82 1.99 0.82 2.01 0.82 1.99 0.83

Attitudes:

Perceived Disadvantage (0-5 scale):

Women 3.86 1.04 3.60 0.95 3.42 0.96 3.41 0.92 3.47 0.99

Migrants 3.45 1.16 3.73 1.02 3.55 1.07 3.51 1.06 3.83 1.01

Born in East-GER 2.85 1.07 3.16 1.06 3.18 1.05 3.21 0.95 3.17 1.07

Born in a Non-Acad. HH 2.73 0.99 2.99 0.99 2.98 0.95 3.46 0.90 2.83 0.99

Competency (centered, -5 – 5 scale)

Men vs. Woman -0.15 1.32 0.33 1.60 0.33 1.75 0.15 1.44 0.25 1.84

No Mig. vs. Mig. Background -0.29 1.65 -0.02 1.78 0.04 1.88 -0.24 2.05 0.15 1.67

Born in West-GER vs. East-GER -0.08 1.3 0.22 1.48 0.14 1.56 0.41 1.53 0.21 1.55

Born in Acad. HH vs. Non-Acad. HH -0.16 1.31 0.15 1.41 0.29 1.51 0.10 1.40 0.15 1.64

Performance (centered, -5 – 5 scale)

Men vs. Woman -0.01 1.41 0.53 1.84 0.33 1.75 0.17 1.68 0.39 1.96

No Mig. vs. Mig. Background -0.10 1.62 0.16 1.82 0.04 1.88 -0.10 1.98 0.36 1.83

Born in West-GER vs. East-GER -0.08 1.29 0.19 1.56 0.14 1.56 0.40 1.78 0.12 1.54

Born in Acad. HH vs. Non-Acad. HH -0.01 1.40 0.29 1.54 0.29 1.51 0.18 1.57 0.36 1.63

Socio Demographics:

Age 49.8 13.01 43.63 14.34 47.85 13.84 47.71 13.09 42.08 13.45

Employment Sector 0.32 0.47 0.36 0.48 0.29 0.45 0.26 0.44 0.36 0.48

(Pivate vs. Public)

Managerial Role (0/1) 0.45 0.50 0.20 0.40 0.21 0.41 0.28 0.45 0.31 0.46

Education 2.89 0.35 2.47 0.63 2.25 0.65 2.44 0.56 2.59 0.60

Living with Partner (0/1) 0.63 0.48 0.55 0.50 0.59 0.49 0.62 0.49 0.60 0.49

Children (0/1) 0.52 0.5 0.48 0.50 0.53 0.50 0.61 0.49 0.47 0.50

Table S2: Descriptive Statistics by Sampling Group.

S2 Additional Results

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Intercept 3.05∗∗∗ 4.02∗∗∗

−0.87∗∗ 3.09∗∗∗ 3.08∗∗∗

−0.16

(0.15) (0.42) (0.28) (0.15) (0.15) (0.48)

AAP: Women 1.26∗∗∗ 1.27∗∗∗ 1.23∗∗∗ 1.26∗∗∗ 1.26∗∗∗ 1.23∗∗∗

(0.11) (0.11) (0.11) (0.11) (0.11) (0.11)

AAP: Born in East-GER 0.19 0.20 0.13 0.19 0.18 0.15

(0.12) (0.11) (0.11) (0.12) (0.11) (0.11)

AAP: Non-Academic HH 1.23∗∗∗ 1.24∗∗∗ 1.14∗∗∗ 1.23∗∗∗ 1.22∗∗∗ 1.14∗∗∗

(0.11) (0.11) (0.11) (0.11) (0.11) (0.11)

Private Sector 0.06 0.05 0.04 0.06 0.05 0.04

(0.07) (0.07) (0.07) (0.07) (0.07) (0.07)

Strongly Underrepr. 0.16∗0.16∗0.11 0.17∗0.17∗0.13

(0.07) (0.07) (0.07) (0.07) (0.07) (0.07)

Middle Mngm. −0.02 −0.02 −0.03 −0.02 −0.02 −0.04

(0.08) (0.08) (0.08) (0.08) (0.08) (0.08)

Low Mngm. −0.01 −0.01 −0.01 −0.02 −0.02 −0.02

(0.08) (0.08) (0.08) (0.08) (0.08) (0.08)

SG: Women 1.05∗∗∗ 0.98∗∗∗ 0.70∗∗∗ 0.99∗∗∗ 1.01∗∗∗ 0.66∗∗∗

(0.13) (0.13) (0.13) (0.13) (0.13) (0.13)

SG: Non-Academic 0.32∗0.25 0.26∗0.32∗0.34∗∗ 0.17

(0.13) (0.15) (0.12) (0.13) (0.13) (0.14)

SG: East-GER 0.53∗∗ 0.51∗∗ 0.33∗0.57∗∗∗ 0.56∗∗∗ 0.30

(0.16) (0.17) (0.16) (0.16) (0.16) (0.16)

SG: Migrant 0.91∗∗∗ 0.85∗∗∗ 0.73∗∗∗ 0.86∗∗∗ 0.86∗∗∗ 0.64∗∗∗

(0.16) (0.16) (0.15) (0.16) (0.16) (0.15)

Women dis. 0.54∗∗∗ 0.51∗∗∗

(0.08) (0.08)

Mig. dis. 0.30∗∗∗ 0.28∗∗∗

(0.07) (0.07)

East-GER dis. 0.22∗∗ 0.25∗∗∗

(0.07) (0.07)

Non-Acad. dis. 0.24∗∗ 0.25∗∗∗

(0.07) (0.07)

Comp. Women 0.14∗∗ 0.14∗

(0.05) (0.06)

Comp. Mig. 0.13∗∗ 0.05

(0.04) (0.05)

Comp. East-GER −0.02 0.00

(0.06) (0.06)

Comp. Non-Acad. −0.08 −0.01

(0.07) (0.06)

Perf. Women 0.01 −0.06

(0.05) (0.05)

Perf. Mig. 0.17∗∗∗ 0.07

(0.04) (0.05)

Perf. East-GER −0.02 −0.03

(0.05) (0.06)

Perf. Non-Acad. −0.06 −0.06

(0.06) (0.06)

Age −0.01∗

−0.01∗

(0.01) (0.01)

Public/NGO 0.20 0.18

(0.14) (0.13)

Mngm. Role 0.07 0.15

(0.15) (0.14)

Education middle −0.34 −0.13

(0.26) (0.26)

Education high −0.53∗

−0.38

(0.26) (0.26)

Living w. Partner −0.03 0.07

(0.14) (0.13)

Children 0.11 0.22

(0.15) (0.14)

R20.08 0.08 0.17 0.08 0.08 0.18

Adj. R20.08 0.08 0.17 0.08 0.08 0.18

Num. obs. 8188 8188 8188 8188 8188 8188

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S3: Main Results Full Sample.

Gender Household Place of Birth Migration Background

Base-Line Women Men Acad. Non-Acad. East-GER West-GER Yes No

Intercept 3.15∗∗∗ 4.37∗∗∗ 3.05∗∗∗ 3.50∗∗∗ 3.15∗∗∗ 2.99∗∗∗ 3.32∗∗∗ 4.71∗∗∗ 2.95∗∗∗

(0.14) (0.19) (0.18) (0.19) (0.16) (0.30) (0.15) (0.28) (0.15)

AAP: Women 1.27∗∗∗ 1.59∗∗∗ 0.98∗∗∗ 0.93∗∗∗ 1.55∗∗∗ 1.97∗∗∗ 1.10∗∗∗ 0.23 1.59∗∗∗

(0.10) (0.14) (0.14) (0.15) (0.13) (0.21) (0.11) (0.20) (0.11)

AAP: Born in East-GER 0.14 0.05 0.23 −0.09 0.33∗1.88∗∗∗

−0.30∗∗

−1.09∗∗∗ 0.51∗∗∗

(0.10) (0.14) (0.14) (0.15) (0.13) (0.22) (0.11) (0.21) (0.11)

AAP: Non-Academic HH 1.19∗∗∗ 1.07∗∗∗ 1.31∗∗∗ 0.59∗∗∗ 1.67∗∗∗ 1.64∗∗∗ 1.08∗∗∗ 0.00 1.54∗∗∗

(0.10) (0.13) (0.14) (0.15) (0.12) (0.21) (0.11) (0.20) (0.11)

Private Sector −0.00 −0.06 0.05 0.00 0.00 −0.08 0.01 −0.13 0.05

(0.06) (0.08) (0.09) (0.10) (0.08) (0.12) (0.07) (0.13) (0.07)

Strongly Underrepr. 0.15∗0.17∗0.14 0.20∗0.09 0.15 0.14 0.19 0.13

(0.06) (0.09) (0.09) (0.10) (0.08) (0.13) (0.07) (0.13) (0.07)

Middle Mngm. −0.06 −0.19 0.06 −0.08 −0.05 −0.02 −0.05 −0.16 −0.03

(0.07) (0.10) (0.11) (0.12) (0.09) (0.17) (0.08) (0.16) (0.08)

Low Mngm. −0.09 −0.22∗0.02 −0.10 −0.10 0.07 −0.14 −0.16 −0.08

(0.07) (0.10) (0.10) (0.12) (0.09) (0.16) (0.08) (0.16) (0.08)

SG: Women 1.10∗∗∗ 1.22∗∗∗ 1.04∗∗∗ 0.90∗∗∗ 1.16∗∗∗ 1.20∗∗∗ 1.07∗∗∗

(0.11) (0.18) (0.14) (0.24) (0.13) (0.24) (0.13)

SG: Non-Academic 0.27∗0.22 0.31 0.29 0.25 0.74∗∗ 0.12

(0.11) (0.16) (0.16) (0.25) (0.13) (0.24) (0.13)

SG: East-GER 0.47∗∗∗ 0.34 0.61∗∗ 0.37 0.56∗∗ 0.17 0.51∗∗∗

(0.14) (0.19) (0.20) (0.22) (0.17) (0.50) (0.14)

SG: Migrant 0.87∗∗∗ 0.93∗∗∗ 0.79∗∗∗ 0.48∗1.19∗∗∗ 0.48 0.90∗∗∗

(0.14) (0.18) (0.21) (0.21) (0.18) (0.48) (0.14)

R20.08 0.06 0.04 0.06 0.10 0.08 0.09 0.07 0.08

Adj. R20.08 0.06 0.04 0.05 0.10 0.08 0.09 0.07 0.07

Obs. 10704 5156 5548 4720 5984 2200 8504 2504 8200

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S4: Main Results by Sampling Group.

S3 Robustness Check Main Tables

S3.1 Vignette Sequence

In this ﬁrst set of robustness checks, we run Table 1 in the main text and Table S3 and S4 of

the supportive information including dummies for the sequence of the vignettes presented to the

respondents.We can state that the sequence does not alter the results. The coeﬃcients of our predictive

variables remain stable also under this model speciﬁcation.

Women Migrant East-German Non-Academic

Intercept 0.98 0.72 2.51∗∗∗ 3.76∗∗∗

(0.61) (0.66) (0.68) (0.64)

Second Vig. −0.28 0.27 0.31 −0.21

(0.16) (0.16) (0.17) (0.17)

Third Vig. −0.34∗0.18 0.47∗∗

−0.44∗∗

(0.15) (0.16) (0.17) (0.17)

Fourth Vig. −0.19 0.66∗∗∗ 0.28 −0.50∗∗

(0.16) (0.16) (0.17) (0.17)

Private Sector 0.08 0.21 −0.26∗0.22

(0.12) (0.13) (0.13) (0.13)

Strongly Underrepr. −0.03 0.20 0.10 0.15

(0.12) (0.13) (0.13) (0.13)

Middle Mngm. 0.00 −0.29 0.13 0.06

(0.15) (0.15) (0.16) (0.16)

Low Mngm. −0.20 −0.26 0.23 −0.08

(0.15) (0.15) (0.16) (0.17)

SG: Women 0.92∗∗∗ 0.52∗∗ 0.60∗∗ 0.57∗∗

(0.17) (0.18) (0.19) (0.18)

SG: Non-Academic 0.14 0.08 −0.03 0.82∗∗∗

(0.19) (0.20) (0.20) (0.21)

SG: East-GER 0.37 −0.02 1.45∗∗∗

−0.44

(0.20) (0.23) (0.24) (0.23)

SG: Migrant 0.44∗1.32∗∗∗ 0.41 0.51∗

(0.21) (0.22) (0.22) (0.22)

Women dis. 1.34∗∗∗

(0.08)

Mig. dis. 0.95∗∗∗

(0.08)

East-GER dis. 0.55∗∗∗

(0.09)

Non-Acad. dis. 0.66∗∗∗

(0.09)

Comp. Women 0.22∗∗

(0.08)

Comp. Mig. 0.09

(0.06)

Comp. East-GER −0.03

(0.09)

Comp. Non-Acad. 0.09

(0.08)

Perf. Women 0.00

(0.07)

Perf. Mig. 0.14∗

(0.06)

Perf. East-GER 0.13

(0.08)

Perf. Non-Acad. 0.00

(0.07)

Age −0.00 −0.02∗∗

−0.01 −0.01

(0.01) (0.01) (0.01) (0.01)

Public/NGO 0.24 0.31 0.07 0.28

(0.17) (0.18) (0.19) (0.18)

Mngm. Role 0.27 0.02 0.07 −0.06

(0.18) (0.19) (0.20) (0.20)

Education middle −0.51 −0.02 −0.09 −0.43

(0.35) (0.35) (0.38) (0.37)

Education high −0.74∗0.05 −0.49 −0.67

(0.36) (0.36) (0.39) (0.37)

Living w. Partner 0.20 −0.17 0.12 0.20

(0.17) (0.18) (0.19) (0.19)

Children 0.12 0.28 0.09 0.01

(0.18) (0.20) (0.22) (0.20)

R20.24 0.22 0.10 0.10

Adj. R20.23 0.21 0.09 0.09

Num. obs. 2279 2160 2161 2148

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S5: Main Results by AAP Group including Moderator Variables, Controlling for Vignette Sequence.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Intercept 3.07∗∗∗ 4.04∗∗∗

−0.85∗∗ 3.11∗∗∗ 3.10∗∗∗

−0.14

(0.16) (0.42) (0.28) (0.16) (0.16) (0.48)

AAP: Women 1.26∗∗∗ 1.27∗∗∗ 1.23∗∗∗ 1.26∗∗∗ 1.26∗∗∗ 1.23∗∗∗

(0.11) (0.11) (0.11) (0.11) (0.11) (0.11)

AAP: Born in East-GER 0.19 0.20 0.13 0.19 0.18 0.15

(0.12) (0.11) (0.11) (0.12) (0.11) (0.11)

AAP: Non-Academic HH 1.23∗∗∗ 1.24∗∗∗ 1.14∗∗∗ 1.23∗∗∗ 1.22∗∗∗ 1.14∗∗∗

(0.11) (0.11) (0.11) (0.11) (0.11) (0.11)

Second Vig. −0.02 −0.02 −0.02 −0.02 −0.02 −0.02

(0.05) (0.05) (0.05) (0.05) (0.05) (0.05)

Third Vig. −0.04 −0.04 −0.04 −0.04 −0.04 −0.04

(0.05) (0.05) (0.05) (0.05) (0.05) (0.05)

Fourth Vig. −0.02 −0.02 −0.02 −0.02 −0.02 −0.02

(0.06) (0.06) (0.06) (0.06) (0.06) (0.06)

Private Sector 0.06 0.05 0.04 0.06 0.05 0.04

(0.07) (0.07) (0.07) (0.07) (0.07) (0.07)

Strongly Underrepr. 0.16∗0.16∗0.11 0.17∗0.17∗0.13

(0.07) (0.07) (0.07) (0.07) (0.07) (0.07)

Middle Mngm. −0.02 −0.02 −0.03 −0.02 −0.02 −0.04

(0.08) (0.08) (0.08) (0.08) (0.08) (0.08)

Low Mngm. −0.01 −0.01 −0.00 −0.02 −0.02 −0.02

(0.08) (0.08) (0.08) (0.08) (0.08) (0.08)

SG: Women 1.05∗∗∗ 0.98∗∗∗ 0.70∗∗∗ 0.99∗∗∗ 1.01∗∗∗ 0.66∗∗∗

(0.13) (0.13) (0.13) (0.13) (0.13) (0.13)

SG: Non-Academic 0.32∗0.25 0.26∗0.32∗0.34∗∗ 0.17

(0.13) (0.15) (0.12) (0.13) (0.13) (0.14)

SG: East-GER 0.53∗∗ 0.51∗∗ 0.33∗0.57∗∗∗ 0.56∗∗∗ 0.30

(0.16) (0.17) (0.16) (0.16) (0.16) (0.16)

SG: Migrant 0.91∗∗∗ 0.85∗∗∗ 0.73∗∗∗ 0.86∗∗∗ 0.86∗∗∗ 0.64∗∗∗

(0.16) (0.16) (0.15) (0.16) (0.16) (0.15)

Women dis. 0.54∗∗∗ 0.51∗∗∗

(0.08) (0.08)

Mig. dis. 0.30∗∗∗ 0.28∗∗∗

(0.07) (0.07)

East-GER dis. 0.22∗∗ 0.25∗∗∗

(0.07) (0.07)

Non-Acad. dis. 0.24∗∗ 0.25∗∗∗

(0.07) (0.07)

Comp. Women 0.14∗∗ 0.14∗

(0.05) (0.06)

Comp. Mig. 0.13∗∗ 0.05

(0.04) (0.05)

Comp. East-GER −0.02 0.00

(0.06) (0.06)

Comp. Non-Acad. −0.08 −0.01

(0.07) (0.06)

Perf. Women 0.01 −0.06

(0.05) (0.05)

Perf. Mig. 0.17∗∗∗ 0.07

(0.04) (0.05)

Perf. East-GER −0.02 −0.03

(0.05) (0.06)

Perf. Non-Acad. −0.06 −0.06

(0.06) (0.06)

Age −0.01∗

−0.01∗

(0.01) (0.01)

Public/NGO 0.20 0.18

(0.14) (0.13)

Mngm. Role 0.07 0.15

(0.15) (0.14)

Education middle −0.34 −0.13

(0.26) (0.26)

Education high −0.53∗

−0.38

(0.26) (0.26)

Living w. Partner −0.03 0.07

(0.14) (0.13)

Children 0.11 0.22

(0.15) (0.14)

R20.08 0.08 0.17 0.08 0.08 0.18

Adj. R20.07 0.08 0.17 0.08 0.08 0.18

Num. obs. 8188 8188 8188 8188 8188 8188

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S6: Main Results Full Sample controlling for Vignette Sequence.

Gender Household Place of Birth Migration Background

Base-Line Women Men Acad. Non-Acad. East-GER West-GER Yes No

Intercept 3.14∗∗∗ 4.30∗∗∗ 3.09∗∗∗ 3.51∗∗∗ 3.13∗∗∗ 3.01∗∗∗ 3.28∗∗∗ 4.74∗∗∗ 2.93∗∗∗

(0.14) (0.20) (0.18) (0.19) (0.17) (0.30) (0.15) (0.29) (0.16)

AAP: Women 1.28∗∗∗ 1.59∗∗∗ 0.98∗∗∗ 0.93∗∗∗ 1.55∗∗∗ 1.98∗∗∗ 1.10∗∗∗ 0.23 1.59∗∗∗

(0.10) (0.14) (0.14) (0.15) (0.13) (0.21) (0.11) (0.20) (0.11)

AAP: Born in East-GER 0.14 0.05 0.23 −0.09 0.33∗1.88∗∗∗

−0.30∗∗

−1.09∗∗∗ 0.51∗∗∗

(0.10) (0.14) (0.14) (0.15) (0.13) (0.23) (0.11) (0.21) (0.11)

AAP: Non-Academic HH 1.19∗∗∗ 1.07∗∗∗ 1.31∗∗∗ 0.59∗∗∗ 1.67∗∗∗ 1.65∗∗∗ 1.08∗∗∗ 0.01 1.54∗∗∗

(0.10) (0.13) (0.14) (0.15) (0.12) (0.21) (0.11) (0.20) (0.11)

Second Vig. 0.04 0.13 −0.05 0.03 0.05 −0.01 0.08 0.04 0.03

(0.05) (0.07) (0.07) (0.07) (0.07) (0.11) (0.05) (0.10) (0.05)

Third Vig. −0.00 0.05 −0.06 −0.03 0.00 −0.05 0.03 −0.07 0.02

(0.05) (0.07) (0.06) (0.07) (0.07) (0.10) (0.05) (0.09) (0.05)

Fourth Vig. 0.01 0.08 −0.07 −0.04 0.04 −0.04 0.03 −0.07 0.05

(0.05) (0.07) (0.07) (0.08) (0.07) (0.11) (0.05) (0.10) (0.06)

Private Sector −0.00 −0.05 0.05 −0.00 0.00 −0.08 0.01 −0.14 0.05

(0.06) (0.08) (0.09) (0.10) (0.08) (0.12) (0.07) (0.13) (0.07)

Strongly Underrepr. 0.15∗0.17∗0.14 0.20∗0.09 0.15 0.13 0.18 0.13

(0.06) (0.09) (0.09) (0.10) (0.08) (0.13) (0.07) (0.13) (0.07)

Middle Mngm. −0.06 −0.19 0.06 −0.08 −0.05 −0.03 −0.05 −0.16 −0.03

(0.07) (0.10) (0.11) (0.12) (0.09) (0.17) (0.08) (0.16) (0.08)

Low Mngm. −0.09 −0.22∗0.02 −0.10 −0.10 0.07 −0.14 −0.16 −0.08

(0.07) (0.10) (0.10) (0.12) (0.09) (0.16) (0.08) (0.16) (0.08)

SG: Women 1.10∗∗∗ 1.22∗∗∗ 1.04∗∗∗ 0.90∗∗∗ 1.16∗∗∗ 1.20∗∗∗ 1.07∗∗∗

(0.11) (0.18) (0.14) (0.24) (0.13) (0.24) (0.13)

SG: Non-Academic 0.27∗0.22 0.31 0.29 0.25 0.74∗∗ 0.12

(0.11) (0.16) (0.16) (0.25) (0.13) (0.24) (0.13)

SG: East-GER 0.47∗∗∗ 0.34 0.61∗∗ 0.37 0.56∗∗ 0.17 0.51∗∗∗

(0.14) (0.19) (0.20) (0.22) (0.17) (0.50) (0.14)

SG: Migrant 0.87∗∗∗ 0.93∗∗∗ 0.79∗∗∗ 0.48∗1.19∗∗∗ 0.48 0.90∗∗∗

(0.14) (0.18) (0.21) (0.21) (0.18) (0.48) (0.14)

R20.08 0.06 0.04 0.06 0.10 0.08 0.09 0.07 0.08

Adj. R20.08 0.06 0.04 0.05 0.10 0.07 0.09 0.07 0.07

Obs. 10704 5156 5548 4720 5984 2200 8504 2504 8200

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S7: Main Results by Target Group, Contolling for Vignette Sequence.

S3.2 Summarizing Scales for Perceived Disadvantage, Competence, and Performance

We use a polychoric principal component analysis (PCA) to construct a summarizing scale for three

constructs: i) preceived disadvantage of target groups, ii) competence of the target groups, and iii)

performance of the target groups. To extract the principal components, polychoric PCA uses the lin-

ear combinations of the polychoric correlation matrix of the input variables, rather than the variables

themselves (Olsson 1979).

First, to create the perceived disadvantage scale, we extract the ﬁrst principal component, which

accounts for 59% of the total variance (Eigenvalue = 2.37). For the higher-order components the

explanatory power drops sharply: The second component accounts for 18% (Eigenvalue = 0.70), the

third component for 14% (Eigenvalue = 0.54), and the fourth component for 10% of the total variance

(Eigenvalue = 0.38). We rescale the ﬁrst principal component to have a mean zero and standard

deviation of 0.5 for interpretability. Tabel S8 presents the polychonic correlation matrix for perceived

disadvantages.

Women dis. Mig. dis. East-GER dis. Non-Acad. dis.

Women dis. 1

Mig. dis. 0.57 1

East-GER dis. 0.52 0.36 1

Non-Acad. dis. 0.45 0.37 0.47 1

Table S8: Polychonic Correlation Matrix for Perceived Disadvantages.

Second, in analogy, to create the competence scale, we extract the ﬁrst principal component, which

accounts for 60% of the total variance (Eigenvalue = 2.42). The second component accounts for 15%

(Eigenvalue = 0.60), the third component for 14% (Eigenvalue = 0.55), and the fourth component for

11% of the total variance (Eigenvalue = 0.44). We rescale the ﬁrst principal component to have a mean

zero and standard deviation of 0.5 for interpretability. Tabel S9 presents the polychonic correlation

matrix for competence.

Comp. Women Comp. Mig. Comp. East-GER Comp. Non-Acad.

Comp. Women 1

Comp. Mig. 0.41 1

Comp. East-GER 0.44 0.46 1

Comp. Non-Acad. 0.51 0.45 0.55 1

Table S9: Polychonic Correlation Matrix for Competence.

Third, to create the performance scale, we extract the ﬁrst principal component, which accounts for

59% of the total variance (Eigenvalue = 2.35). The second component accounts for 16% (Eigenvalue

= 0.64), the third component for 14% (Eigenvalue = 0.56), and the fourth component for 11% of the

total variance (Eigenvalue = 0.45). We rescale the ﬁrst principal component to have a mean zero and

standard deviation of 0.5 for interpretability. Tabel S10 presents the polychonic correlation matrix for

performance.

Comp. Women Comp. Mig. Comp. East-GER Comp. Non-Acad.

Comp. Women 1

Comp. Mig. 0.45 1

Comp. East-GER 0.40 0.38 1

Comp. Non-Acad. 0.51 0.45 0.51 1

Table S10: Polychonic Correlation Matrix for Performance.

In the following we run Tables 1 (main text) and Table S3 with the summarizing scales instead of our

moderator variables.

S10

Women Migrant East-German Non-Acad.

Intercept 5.42∗∗∗ 4.21∗∗∗ 3.75∗∗∗ 5.64∗∗∗

(0.60) (0.61) (0.63) (0.57)

Second Vig. −0.24 0.30 0.18 −0.21

(0.16) (0.17) (0.17) (0.16)

Third Vig. −0.38∗0.24 0.45∗

−0.44∗∗

(0.16) (0.17) (0.17) (0.17)

Fourth Vig. −0.17 0.53∗∗ 0.20 −0.53∗∗

(0.16) (0.17) (0.18) (0.16)

Private Sector 0.09 0.21 −0.28∗0.18

(0.13) (0.13) (0.13) (0.13)

Strongly Underrepr. −0.01 0.19 0.16 0.10

(0.13) (0.13) (0.14) (0.13)

Middle Mngm. −0.12 −0.29 0.13 0.13

(0.16) (0.16) (0.16) (0.16)

Low Mngm. −0.14 −0.29 0.31 −0.03

(0.16) (0.16) (0.16) (0.16)

SG: Women 1.33∗∗∗ 0.51∗∗ 0.48∗0.41∗

(0.18) (0.19) (0.19) (0.18)

SG: Non-Academic 0.18 −0.03 −0.01 0.60∗∗

(0.21) (0.21) (0.21) (0.20)

SG: East-GER 0.10 −0.29 1.32∗∗∗

−0.33

(0.23) (0.24) (0.24) (0.23)

SG: Migrant 0.45∗1.41∗∗∗ 0.49∗0.29

(0.23) (0.23) (0.23) (0.21)

Disadvantage Scale 2.27∗∗∗ 2.01∗∗∗ 1.75∗∗∗ 2.24∗∗∗

(0.17) (0.19) (0.19) (0.18)

Comepetence Scale 0.70∗0.44 0.08 0.50∗

(0.30) (0.26) (0.28) (0.23)

Performance Scale −0.23 0.01 −0.08 −0.29

(0.28) (0.26) (0.27) (0.23)

Age −0.01 −0.03∗∗

−0.01 −0.01

(0.01) (0.01) (0.01) (0.01)

Public/NGO 0.11 0.32 0.07 0.23

(0.18) (0.19) (0.20) (0.18)

Mngm. Role 0.34 −0.03 0.13 0.08

(0.19) (0.19) (0.20) (0.20)

Education middle −0.42 −0.02 0.14 −0.29

(0.39) (0.38) (0.40) (0.36)

Education high −0.62 0.32 −0.41 −0.72∗

(0.39) (0.39) (0.40) (0.36)

Living w. Partner 0.17 −0.17 0.07 0.26

(0.19) (0.19) (0.19) (0.18)

Children 0.18 0.38 0.14 0.14

(0.20) (0.21) (0.22) (0.20)

R20.20 0.20 0.14 0.17

Adj. R20.19 0.19 0.13 0.16

Num. obs. 2094 2008 2018 2068

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S11: Main Results by AAP Group including Summarazing Scales for Perceived Disadvanteage,

Competence, and Performance.

S11

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Intercept 3.07∗∗∗ 4.04∗∗∗ 3.41∗∗∗ 3.12∗∗∗ 3.11∗∗∗ 4.11∗∗∗

(0.16) (0.42) (0.15) (0.16) (0.16) (0.40)

AAP: Women 1.26∗∗∗ 1.27∗∗∗ 1.23∗∗∗ 1.26∗∗∗ 1.26∗∗∗ 1.24∗∗∗

(0.11) (0.11) (0.11) (0.11) (0.11) (0.11)

AAP: Born in East-GER 0.19 0.20 0.15 0.18 0.18 0.16

(0.12) (0.11) (0.11) (0.12) (0.12) (0.11)

AAP: Non-Academic HH 1.23∗∗∗ 1.24∗∗∗ 1.15∗∗∗ 1.22∗∗∗ 1.23∗∗∗ 1.15∗∗∗

(0.11) (0.11) (0.11) (0.11) (0.11) (0.11)

Second Vig. −0.02 −0.02 −0.02 −0.02 −0.02 −0.02

(0.05) (0.05) (0.05) (0.05) (0.05) (0.05)

Third Vig. −0.04 −0.04 −0.04 −0.04 −0.04 −0.04

(0.05) (0.05) (0.05) (0.05) (0.05) (0.05)

Fourth Vig. −0.02 −0.02 −0.02 −0.02 −0.02 −0.02

(0.06) (0.06) (0.06) (0.06) (0.06) (0.06)

Private Sector 0.06 0.05 0.04 0.06 0.05 0.03

(0.07) (0.07) (0.07) (0.07) (0.07) (0.07)

Strongly Underrepr. 0.16∗0.16∗0.11 0.16∗0.16∗0.12

(0.07) (0.07) (0.07) (0.07) (0.07) (0.07)

Middle Mngm. −0.02 −0.02 −0.03 −0.01 −0.02 −0.03

(0.08) (0.08) (0.08) (0.08) (0.08) (0.08)

Low Mngm. −0.01 −0.01 −0.01 −0.01 −0.01 −0.02

(0.08) (0.08) (0.08) (0.08) (0.08) (0.08)

SG: Women 1.05∗∗∗ 0.98∗∗∗ 0.75∗∗∗ 1.02∗∗∗ 1.02∗∗∗ 0.69∗∗∗

(0.13) (0.13) (0.12) (0.13) (0.13) (0.13)

SG: Non-Academic 0.32∗0.25 0.25∗0.29∗0.31∗0.15

(0.13) (0.15) (0.12) (0.13) (0.13) (0.14)

SG: East-GER 0.53∗∗ 0.51∗∗ 0.28 0.52∗∗ 0.52∗∗ 0.22

(0.16) (0.17) (0.16) (0.16) (0.16) (0.16)

SG: Migrant 0.91∗∗∗ 0.85∗∗∗ 0.74∗∗∗ 0.88∗∗∗ 0.89∗∗∗ 0.65∗∗∗

(0.16) (0.16) (0.15) (0.16) (0.16) (0.15)

Disadvantage Scale 2.08∗∗∗ 2.10∗∗∗

(0.12) (0.12)

Comepetence Scale 0.43∗∗ 0.45∗

(0.15) (0.19)

Performance Scale 0.29 −0.16

(0.15) (0.19)

Age −0.01∗

−0.01∗

(0.01) (0.01)

Public/NGO 0.20 0.18

(0.14) (0.13)

Mngm. Role 0.07 0.14

(0.15) (0.14)

Education middle −0.34 −0.13

(0.26) (0.26)

Education high −0.53∗

−0.37

(0.26) (0.26)

Living w. Partner −0.03 0.08

(0.14) (0.13)

Children 0.11 0.19

(0.15) (0.14)

R20.08 0.08 0.17 0.08 0.08 0.17

Adj. R20.07 0.08 0.17 0.08 0.08 0.17

Num. obs. 8188 8188 8188 8188 8188 8188

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S12: Main Results Full Sample.

S12

S4 Subgroup Analysis

S4.1 Support for AAP by Gender

Women Men Women Men Women Men

Intercept 4.10∗∗∗ 3.04∗∗∗ 4.97∗∗∗ 3.92∗∗∗

−0.34 −0.74∗

(0.23) (0.19) (0.58) (0.58) (0.49) (0.35)

AAP: Women 1.68∗∗∗ 0.94∗∗∗ 1.68∗∗∗ 0.96∗∗∗ 1.62∗∗∗ 0.95∗∗∗

(0.17) (0.15) (0.16) (0.15) (0.16) (0.14)

AAP: Born in East-GER 0.07 0.28 0.07 0.30 0.01 0.22

(0.17) (0.15) (0.17) (0.15) (0.17) (0.15)

AAP: Non-Academic HH 1.12∗∗∗ 1.32∗∗∗ 1.12∗∗∗ 1.33∗∗∗ 1.08∗∗∗ 1.20∗∗∗

(0.17) (0.15) (0.16) (0.15) (0.16) (0.15)

Private Sector 0.00 0.10 0.00 0.10 0.03 0.05

(0.10) (0.10) (0.10) (0.10) (0.10) (0.09)

Strongly Underrepr. 0.23∗0.11 0.23∗0.11 0.22∗0.04

(0.10) (0.10) (0.10) (0.10) (0.10) (0.09)

Middle Mngm. −0.22 0.12 −0.21 0.12 −0.21 0.10

(0.12) (0.12) (0.12) (0.12) (0.12) (0.11)

Low Mngm. −0.07 0.03 −0.06 0.02 −0.07 0.04

(0.13) (0.11) (0.13) (0.11) (0.13) (0.11)

SG: Non-Academic 0.32 0.34 0.35 0.17 0.28 0.26

(0.20) (0.17) (0.22) (0.21) (0.18) (0.16)

SG: East-GER 0.48∗0.60∗∗ 0.51∗0.54∗0.32 0.31

(0.24) (0.22) (0.25) (0.23) (0.22) (0.22)

SG: Migrant 1.04∗∗∗ 0.79∗∗∗ 0.97∗∗∗ 0.75∗∗ 0.77∗∗∗ 0.66∗∗

(0.22) (0.23) (0.22) (0.24) (0.21) (0.21)

Women dis. 0.24∗0.78∗∗∗

(0.12) (0.11)

Mig. dis. 0.52∗∗∗ 0.14

(0.10) (0.09)

East-GER dis. 0.35∗∗∗ 0.15

(0.10) (0.10)

Non-Acad. dis. 0.24∗0.22∗

(0.11) (0.10)

Age −0.01 −0.01

(0.01) (0.01)

Public/NGO 0.26 0.14

(0.19) (0.19)

Mngm. Role 0.12 0.03

(0.23) (0.19)

Education middle −0.39 −0.27

(0.42) (0.33)

Education high −0.40 −0.64

(0.42) (0.34)

Living w. Partner −0.10 0.02

(0.19) (0.20)

Children −0.05 0.24

(0.23) (0.20)

R20.07 0.04 0.08 0.04 0.17 0.14

Adj. R20.07 0.04 0.07 0.04 0.16 0.14

Obs. 3548 4640 3548 4640 3548 4640

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S13: Main Results Gender I.

S13

Women Men Women Men Women Men

Intercept 4.08∗∗∗ 3.07∗∗∗ 4.11∗∗∗ 3.06∗∗∗ 0.20 −0.10

(0.23) (0.19) (0.23) (0.19) (0.73) (0.64)

AAP: Women 1.65∗∗∗ 0.94∗∗∗ 1.67∗∗∗ 0.95∗∗∗ 1.59∗∗∗ 0.97∗∗∗

(0.16) (0.15) (0.17) (0.15) (0.16) (0.14)

AAP: Born in East-GER 0.06 0.28 0.05 0.28 0.00 0.24

(0.17) (0.15) (0.17) (0.15) (0.17) (0.15)

AAP: Non-Academic HH 1.13∗∗∗ 1.31∗∗∗ 1.12∗∗∗ 1.30∗∗∗ 1.08∗∗∗ 1.21∗∗∗

(0.16) (0.15) (0.16) (0.15) (0.15) (0.15)

Private Sector 0.03 0.10 0.01 0.09 0.03 0.05

(0.10) (0.10) (0.10) (0.10) (0.10) (0.09)

Strongly Underrepr. 0.23∗0.12 0.23∗0.13 0.22∗0.06

(0.10) (0.10) (0.10) (0.10) (0.10) (0.09)

Middle Mngm. −0.23 0.12 −0.21 0.12 −0.22 0.09

(0.12) (0.12) (0.12) (0.12) (0.12) (0.11)

Low Mngm. −0.09 0.02 −0.06 0.01 −0.08 0.02

(0.13) (0.11) (0.13) (0.11) (0.12) (0.11)

SG: Non-Academic 0.35 0.31 0.31 0.37∗0.23 0.18

(0.20) (0.17) (0.20) (0.17) (0.20) (0.19)

SG: East-GER 0.59∗0.61∗∗ 0.47 0.63∗∗ 0.35 0.24

(0.24) (0.22) (0.24) (0.22) (0.23) (0.23)

SG: Migrant 0.96∗∗∗ 0.77∗∗∗ 0.98∗∗∗ 0.75∗∗ 0.68∗∗ 0.56∗

(0.22) (0.23) (0.22) (0.23) (0.21) (0.22)

Women dis. 0.22 0.75∗∗∗

(0.12) (0.11)

Mig. dis. 0.49∗∗∗ 0.13

(0.10) (0.09)

East-GER dis. 0.38∗∗∗ 0.19

(0.10) (0.10)

Non-Acad. dis. 0.23∗0.23∗

(0.11) (0.10)

Comp. Women 0.18∗∗ 0.12 0.16∗0.12

(0.07) (0.08) (0.07) (0.09)

Comp. Mig. 0.22∗∗ 0.07 0.12 0.02

(0.07) (0.05) (0.08) (0.06)

Comp. East-GER −0.09 0.01 −0.08 0.04

(0.09) (0.07) (0.10) (0.08)

Comp. Non-Acad. −0.20∗

−0.00 −0.11 0.06

(0.09) (0.09) (0.09) (0.08)

Perf. Women 0.01 0.01 −0.01 −0.09

(0.07) (0.07) (0.07) (0.08)

Perf. Mig. 0.14 0.18∗∗∗ 0.01 0.08

(0.07) (0.05) (0.08) (0.07)

Perf. East-GER 0.04 −0.06 0.04 −0.07

(0.08) (0.07) (0.09) (0.07)

Perf. Non-Acad. −0.07 −0.05 −0.02 −0.10

(0.09) (0.07) (0.08) (0.08)

Age −0.01 −0.01

(0.01) (0.01)

Public/NGO 0.32 0.08

(0.19) (0.18)

Mngm. Role 0.18 0.14

(0.21) (0.18)

Education middle −0.17 −0.06

(0.38) (0.34)

Education high −0.35 −0.31

(0.39) (0.35)

Living w. Partner 0.06 0.07

(0.18) (0.18)

Children 0.10 0.31

(0.21) (0.19)

R20.08 0.05 0.07 0.05 0.18 0.15

Adj. R20.08 0.04 0.07 0.04 0.17 0.15

Obs. 3548 4640 3548 4640 3548 4640

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S14: Main Results Gender II.

S14

S4.2 Support for AAP by Academic Household

Non-Acad. Acad. Non-Acad. Acad. Non-Acad. Acad.

Intercept 3.10∗∗∗ 3.38∗∗∗ 3.46∗∗∗ 5.29∗∗∗

−0.51 −0.86∗

(0.19) (0.20) (0.49) (0.84) (0.37) (0.40)

AAP: Women 1.59∗∗∗ 0.90∗∗∗ 1.59∗∗∗ 0.92∗∗∗ 1.53∗∗∗ 0.91∗∗∗

(0.15) (0.17) (0.15) (0.17) (0.14) (0.16)

AAP: Born in East-GER 0.37∗

−0.02 0.38∗

−0.00 0.34∗

−0.12

(0.16) (0.17) (0.16) (0.17) (0.15) (0.16)

AAP: Non-Academic HH 1.77∗∗∗ 0.61∗∗∗ 1.77∗∗∗ 0.62∗∗∗ 1.65∗∗∗ 0.58∗∗∗

(0.15) (0.17) (0.15) (0.16) (0.14) (0.16)

Private Sector 0.04 0.07 0.05 0.06 0.05 0.02

(0.09) (0.11) (0.09) (0.11) (0.09) (0.10)

Strongly Underrepr. 0.06 0.24∗0.07 0.23∗0.05 0.16

(0.09) (0.11) (0.09) (0.11) (0.09) (0.10)

Middle Mngm. −0.05 −0.00 −0.05 −0.02 −0.06 −0.01

(0.11) (0.13) (0.11) (0.13) (0.10) (0.13)

Low Mngm. −0.02 −0.03 −0.02 −0.04 −0.05 0.02

(0.11) (0.13) (0.11) (0.13) (0.11) (0.13)

SG: Women 1.01∗∗∗ 1.13∗∗∗ 0.99∗∗∗ 1.01∗∗∗ 0.70∗∗∗ 0.75∗∗∗

(0.17) (0.20) (0.17) (0.21) (0.16) (0.19)

SG: East-GER 0.64∗∗ 0.42 0.64∗∗ 0.43 0.42∗0.27

(0.21) (0.26) (0.22) (0.26) (0.20) (0.25)

SG: Migrant 1.27∗∗∗ 0.51∗1.26∗∗∗ 0.44 1.06∗∗∗ 0.32

(0.21) (0.23) (0.22) (0.24) (0.21) (0.22)

Women dis. 0.54∗∗∗ 0.55∗∗∗

(0.11) (0.12)

Mig. dis. 0.27∗∗ 0.30∗∗

(0.08) (0.10)

East-GER dis. 0.04 0.45∗∗∗

(0.10) (0.10)

Non-Acad. dis. 0.33∗∗∗ 0.12

(0.10) (0.11)

Age −0.00 −0.02∗

(0.01) (0.01)

Public/NGO 0.27 0.12

(0.18) (0.20)

Mngm. Role 0.15 0.02

(0.20) (0.21)

Education middle −0.30 −0.91

(0.28) (0.76)

Education high −0.45 −1.18

(0.29) (0.71)

Living w. Partner −0.20 0.20

(0.18) (0.21)

Children 0.22 −0.05

(0.19) (0.23)

R20.10 0.05 0.11 0.06 0.18 0.17

Adj. R20.10 0.05 0.11 0.05 0.18 0.16

Obs. 4396 3792 4396 3792 4396 3792

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S15: Main Results Non-Academic I.

S15

Non-Acad. Acad. Non-Acad. Acad. Non-Acad. Acad.

Intercept 3.19∗∗∗ 3.38∗∗∗ 3.20∗∗∗ 3.37∗∗∗

−0.13 0.48

(0.19) (0.20) (0.19) (0.20) (0.59) (0.93)

AAP: Women 1.57∗∗∗ 0.90∗∗∗ 1.57∗∗∗ 0.90∗∗∗ 1.53∗∗∗ 0.90∗∗∗

(0.15) (0.17) (0.15) (0.17) (0.14) (0.15)

AAP: Born in East-GER 0.37∗

−0.01 0.35∗

−0.02 0.36∗

−0.11

(0.15) (0.17) (0.15) (0.17) (0.15) (0.16)

AAP: Non-Academic HH 1.74∗∗∗ 0.62∗∗∗ 1.73∗∗∗ 0.60∗∗∗ 1.63∗∗∗ 0.57∗∗∗

(0.15) (0.17) (0.15) (0.16) (0.14) (0.16)

Private Sector 0.03 0.07 0.02 0.08 0.05 −0.00

(0.09) (0.11) (0.09) (0.11) (0.09) (0.10)

Strongly Underrepr. 0.07 0.23∗0.08 0.24∗0.06 0.16

(0.09) (0.11) (0.09) (0.11) (0.09) (0.10)

Middle Mngm. −0.06 −0.02 −0.07 −0.01 −0.07 −0.04

(0.11) (0.13) (0.10) (0.13) (0.10) (0.12)

Low Mngm. −0.06 −0.03 −0.05 −0.02 −0.07 0.01

(0.11) (0.13) (0.11) (0.13) (0.11) (0.12)

SG: Women 0.94∗∗∗ 1.12∗∗∗ 0.90∗∗∗ 1.17∗∗∗ 0.65∗∗∗ 0.75∗∗∗

(0.17) (0.20) (0.17) (0.21) (0.17) (0.20)

SG: East-GER 0.61∗∗ 0.43 0.58∗∗ 0.50 0.38 0.26

(0.21) (0.26) (0.21) (0.26) (0.21) (0.25)

SG: Migrant 1.15∗∗∗ 0.52∗1.18∗∗∗ 0.49∗1.01∗∗∗ 0.26

(0.21) (0.23) (0.21) (0.23) (0.21) (0.22)

Women dis. 0.48∗∗∗ 0.53∗∗∗

(0.11) (0.12)

Mig. dis. 0.28∗∗ 0.29∗∗

(0.09) (0.11)

East-GER dis. 0.08 0.47∗∗∗

(0.09) (0.11)

Non-Acad. dis. 0.30∗∗ 0.13

(0.10) (0.11)

Comp. Women 0.20∗∗ 0.06 0.14 0.15

(0.06) (0.08) (0.07) (0.09)

Comp. Mig. 0.18∗∗∗ 0.01 0.12∗

−0.07

(0.05) (0.07) (0.06) (0.08)

Comp. East-GER −0.04 −0.05 −0.07 0.09

(0.07) (0.09) (0.08) (0.09)

Comp. Non-Acad. −0.03 −0.11 0.07 −0.09

(0.08) (0.10) (0.08) (0.10)

Perf. Women 0.11 −0.12 0.02 −0.17

(0.06) (0.08) (0.07) (0.09)

Perf. Mig. 0.19∗∗∗ 0.12 0.05 0.08

(0.05) (0.07) (0.06) (0.08)

Perf. East-GER 0.02 −0.09 0.02 −0.09

(0.06) (0.09) (0.07) (0.09)

Perf. Non-Acad. −0.08 −0.00 −0.12 −0.00

(0.07) (0.09) (0.07) (0.09)

Age −0.00 −0.02∗

(0.01) (0.01)

Public/NGO 0.22 0.09

(0.18) (0.19)

Mngm. Role 0.16 0.12

(0.19) (0.20)

Education middle −0.14 −0.61

(0.27) (0.82)

Education high −0.43 −0.78

(0.28) (0.77)

Living w. Partner −0.09 0.24

(0.17) (0.20)

Children 0.27 0.10

(0.18) (0.22)

R20.13 0.05 0.12 0.05 0.20 0.18

Adj. R20.13 0.05 0.12 0.05 0.19 0.17

Obs. 4396 3792 4396 3792 4396 3792

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S16: Main Results Non-Academic II.

S16

S4.3 Support for AAP by East-Germans

East West East West East West

Intercept 3.12∗∗∗ 3.16∗∗∗ 4.91∗∗∗ 3.96∗∗∗

−0.68 −0.75∗

(0.34) (0.16) (0.99) (0.45) (0.80) (0.30)

AAP: Women 1.84∗∗∗ 1.13∗∗∗ 1.86∗∗∗ 1.13∗∗∗ 1.79∗∗∗ 1.10∗∗∗

(0.25) (0.12) (0.24) (0.12) (0.23) (0.12)

AAP: Born in East-GER 1.76∗∗∗

−0.21 1.80∗∗∗

−0.20 1.60∗∗∗

−0.23

(0.26) (0.13) (0.26) (0.13) (0.26) (0.12)

AAP: Non-Academic HH 1.52∗∗∗ 1.17∗∗∗ 1.54∗∗∗ 1.18∗∗∗ 1.39∗∗∗ 1.10∗∗∗

(0.25) (0.12) (0.25) (0.12) (0.25) (0.12)

Private Sector −0.05 0.08 −0.00 0.07 −0.02 0.05

(0.15) (0.08) (0.15) (0.08) (0.14) (0.08)

Strongly Underrepr. 0.07 0.17∗0.05 0.17∗0.12 0.11

(0.15) (0.08) (0.15) (0.08) (0.15) (0.07)

Middle Mngm. −0.02 −0.01 0.00 −0.01 −0.03 −0.02

(0.19) (0.09) (0.19) (0.09) (0.18) (0.09)

Low Mngm. 0.15 −0.05 0.15 −0.06 0.17 −0.05

(0.19) (0.09) (0.19) (0.09) (0.19) (0.09)

SG: Women 0.90∗∗ 1.08∗∗∗ 0.67∗1.05∗∗∗ 0.68∗0.71∗∗∗

(0.28) (0.15) (0.29) (0.15) (0.28) (0.14)

SG: Migrant −0.02 0.99∗∗∗

−0.04 0.93∗∗∗ 0.06 0.80∗∗∗

(0.60) (0.16) (0.61) (0.17) (0.55) (0.16)

SG: Non-Academic 0.35 0.31∗0.11 0.28 0.31 0.24

(0.29) (0.15) (0.34) (0.17) (0.28) (0.14)

Women dis. 0.64∗∗ 0.53∗∗∗

(0.20) (0.09)

Mig. dis. 0.35∗0.27∗∗∗

(0.14) (0.07)

East-GER dis. 0.13 0.24∗∗

(0.16) (0.08)

Non-Acad. dis. 0.05 0.29∗∗∗

(0.17) (0.08)

Age −0.01 −0.01

(0.01) (0.01)

Public/NGO 0.20 0.24

(0.33) (0.15)

Mngm. Role −0.87∗0.29

(0.34) (0.16)

Education middle −0.62 −0.38

(0.83) (0.27)

Education high −0.75 −0.53

(0.81) (0.28)

Living w. Partner −0.01 −0.02

(0.31) (0.15)

Children −0.02 0.11

(0.34) (0.17)

R20.07 0.09 0.09 0.09 0.14 0.18

Adj. R20.06 0.09 0.08 0.09 0.13 0.18

Obs. 1636 6552 1636 6552 1636 6552

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S17: Main Results East-GER I.

S17

East West East West East West

Intercept 3.28∗∗∗ 3.19∗∗∗ 3.23∗∗∗ 3.18∗∗∗ 1.03 −0.26

(0.34) (0.16) (0.35) (0.16) (1.11) (0.53)

AAP: Women 1.79∗∗∗ 1.12∗∗∗ 1.85∗∗∗ 1.12∗∗∗ 1.73∗∗∗ 1.09∗∗∗

(0.25) (0.12) (0.25) (0.12) (0.23) (0.12)

AAP: Born in East-GER 1.78∗∗∗

−0.21 1.77∗∗∗

−0.21 1.65∗∗∗

−0.22

(0.26) (0.13) (0.27) (0.12) (0.26) (0.12)

AAP: Non-Academic HH 1.51∗∗∗ 1.17∗∗∗ 1.54∗∗∗ 1.16∗∗∗ 1.38∗∗∗ 1.10∗∗∗

(0.25) (0.12) (0.26) (0.12) (0.24) (0.12)

Private Sector −0.04 0.09 −0.09 0.08 0.03 0.04

(0.15) (0.08) (0.15) (0.08) (0.14) (0.07)

Strongly Underrepr. 0.09 0.17∗0.10 0.17∗0.10 0.12

(0.15) (0.08) (0.15) (0.08) (0.14) (0.07)

Middle Mngm. 0.04 −0.02 −0.01 −0.01 0.05 −0.04

(0.19) (0.09) (0.19) (0.09) (0.18) (0.09)

Low Mngm. 0.12 −0.05 0.13 −0.06 0.14 −0.06

(0.19) (0.09) (0.19) (0.09) (0.18) (0.09)

SG: Women 0.82∗∗ 1.02∗∗∗ 0.77∗∗ 1.05∗∗∗ 0.37 0.71∗∗∗

(0.28) (0.15) (0.29) (0.15) (0.29) (0.14)

SG: Migrant 0.10 0.94∗∗∗ 0.01 0.92∗∗∗ 0.05 0.70∗∗∗

(0.63) (0.16) (0.62) (0.17) (0.58) (0.16)

SG: Non-Academic 0.17 0.32∗0.27 0.37∗

−0.06 0.25

(0.29) (0.14) (0.30) (0.14) (0.34) (0.15)

Women dis. 0.57∗∗ 0.50∗∗∗

(0.20) (0.09)

Mig. dis. 0.32∗0.25∗∗∗

(0.15) (0.07)

East-GER dis. 0.19 0.29∗∗∗

(0.16) (0.08)

Non-Acad. dis. 0.12 0.26∗∗

(0.18) (0.08)

Comp. Women 0.09 0.16∗0.02 0.17∗∗

(0.09) (0.06) (0.11) (0.06)

Comp. Mig. 0.15 0.12∗0.17 0.01

(0.08) (0.05) (0.10) (0.06)

Comp. East-GER −0.04 −0.03 0.11 −0.02

(0.10) (0.07) (0.11) (0.07)

Comp. Non-Acad. 0.15 −0.14 0.11 −0.05

(0.13) (0.07) (0.14) (0.07)

Perf. Women 0.21∗

−0.04 0.19 −0.12∗

(0.10) (0.05) (0.11) (0.06)

Perf. Mig. 0.05 0.21∗∗∗

−0.12 0.14∗

(0.08) (0.05) (0.10) (0.06)

Perf. East-GER −0.06 −0.04 −0.12 −0.01

(0.10) (0.06) (0.11) (0.06)

Perf. Non-Acad. −0.02 −0.07 −0.08 −0.07

(0.12) (0.06) (0.13) (0.06)

Age −0.01 −0.01

(0.01) (0.01)

Public/NGO 0.20 0.21

(0.33) (0.14)

Mngm. Role −0.79∗0.37∗

(0.32) (0.15)

Education middle −0.61 −0.12

(0.67) (0.28)

Education high −0.85 −0.32

(0.66) (0.28)

Living w. Partner 0.04 0.10

(0.29) (0.14)

Children 0.16 0.20

(0.32) (0.15)

R20.09 0.09 0.08 0.09 0.18 0.20

Adj. R20.08 0.09 0.07 0.09 0.17 0.19

Obs. 1636 6552 1636 6552 1636 6552

Clustered standard errors in parantheses. ∗∗∗ p < 0.001; ∗∗ p < 0.01; ∗p < 0.05.

Table S18: Main Results East-GER II.

S18

S4.4 Support for AAP by Migration Background

Mig. No-Mig. Mig. No-Mig. Mig. No-Mig.

Intercept 4.50∗∗∗ 2.91∗∗∗ 4.82∗∗∗ 4.06∗∗∗ 0.12 −0.92∗∗

(0.32) (0.17) (0.76) (0.47) (0.69) (0.30)

AAP: Women 0.32 1.54∗∗∗ 0.36 1.55∗∗∗ 0.37 1.48∗∗∗

(0.24) (0.12) (0.23) (0.12) (0.23) (0.12)

AAP: Born in East-GER −1.04∗∗∗ 0.54∗∗∗

−1.02∗∗∗ 0.56∗∗∗

−0.97∗∗∗ 0.45∗∗∗

(0.24) (0.13) (0.24) (0.13) (0.23) (0.13)

AAP: Non-Academic HH 0.16 1.52∗∗∗ 0.16 1.54∗∗∗ 0.17 1.41∗∗∗

(0.23) (0.13) (0.23) (0.12) (0.21) (0.12)

Private Sector −0.07 0.10 −0.10 0.10 −0.07 0.08

(0.15) (0.08) (0.15) (0.08) (0.14) (0.08)

Strongly Underrepr. 0.35∗0.09 0.34∗0.10 0.26 0.07

(0.15) (0.08) (0.15) (0.08) (0.14) (0.07)

Middle Mngm. −0.19 0.02 −0.18 0.02 <