discrimination research paper

Introduction: The Case for Discrimination Research

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• Rosita Fibbi 4 ,
• Arnfinn H. Midtbøen 5 &
• Patrick Simon 6  

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Increasing migration-related diversity in Europe has fostered dramatic changes since the 1950s, among them the rise of striking ethno-racial inequalities in employment, housing, health, and a range of other social domains. These ethno-racial disadvantages can be understood as evidence of widespread discrimination; however, scholarly debates reflect striking differences in the conceptualization and measurement of discrimination in the social sciences. Indeed, what discrimination is, as well as how and why it operates, are differently understood and studied by the various scholarships and scientific fields. It is the ambition of this book to summarize how we frame, study, theorize, and aim at combatting ethno-racial discrimination in Europe.

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European societies are more ethnically diverse than ever. The increasing migration-related diversity has fostered dramatic changes since the 1950s, among them the rise of striking ethno-racial inequalities in employment, housing, health, and a range of other social domains. The sources of these enduring inequalities have been a subject of controversy for decades. To some scholars, ethno-racial gaps in such outcomes are seen as transitional bumps in the road toward integration, while others view structural racism, ethnic hostility, and subtle forms of outgroup-bias as fundamental causes of persistent ethno-racial inequalities. These ethno-racial disadvantages can be understood as evidence of widespread discrimination; however, scholarly debates reflect striking differences in the conceptualization and measurement of discrimination in the social sciences.

What discrimination is, as well as how and why it operates, are differently understood and studied by the various scholarships and scientific fields. A large body of research has been undertaken over the previous three decades, using a variety of methods – qualitative, quantitative, and experimental. These research efforts have improved our knowledge of the dynamics of discrimination in Europe and beyond. It is the ambition of this book to summarize how we frame, study, theorize, and aim at combatting ethno-racial discrimination in Europe.

1.1 Post-War Immigration and the Ethno-racial Diversity Turn

Even though ethnic and racial diversity has existed to some extent in Europe (through the slave trade, transnational merchants, and colonial troops), the scope of migration-related diversity reached an unprecedented level in the period following World War II. This period coincides with broader processes of decolonization and the beginning of mass migration from non-European countries, be it from former colonies to the former metropoles (from the Caribbean or India and Pakistan to the UK; South-East Asia, North Africa or Sub-Saharan Africa to France) or in the context of labor migration without prior colonial ties (from Turkey to Germany or the Netherlands; Morocco to Belgium or the Netherlands, etc.).

The ethnic and racial diversity in large demographic figures began in the 1960s (Van Mol and de Valk 2016 ). At this time, most labor migrants were coming from other European countries, but figures of non-European migration were beginning to rise: in 1975, 8% of the population in France and the UK had a migration background, half of which originated from a non-European country. By contrast, in 2014, 9.2% of the population of the EU28 had a migration background from outside of Europe (either foreign born or native-born from foreign-born parent(s)), and this share reached almost 16% in Sweden; 14% in the Netherlands, France, and the UK; and between 10 and 13% in Germany, Belgium, and Austria. The intensification of migration, especially from Asia and Africa, has heightened the visibility of ethno-racial diversity in large European metropolises. Almost 50% of inhabitants in Amsterdam and Rotterdam have a “nonwestern allochthon ” background (2014), 40% of Londoners are black or ethnic minorities (2011), while 30% of Berliners (2013) and 43% of Parisians (metropolitan area; 2009) have a migration background. The major facts of this demographic evolution are not only that diversity has reached a point of “super-diversity” (see Vertovec 2007 ; Crul 2016 ) in size and origins, but also that descendants of immigrants (i.e., the second generation) today make up a significant demographic group in most European countries, with the exception of Southern Europe where immigration first boomed in the 2000s.

The coming of age of the second generation has challenged the capacity of different models of integration to fulfill promises of equality, while the socio-cultural cohesion of European societies is changing and has to be revised to include ethnic and racial diversity. Native-born descendants of immigrants are socialized in the country of their parents’ migration and, in most European countries, share the full citizenship of the country where they live and, consequently, the rights attached to it. However, an increasing number of studies show that even the second generation faces disadvantages in education, employment, and housing that cannot be explained by their lack of skills or social capital (Heath and Cheung 2007 ). The transmission of penalties from one generation to the other – and in some cases an even higher level of penalty for the second generation than for the first – cannot be explained solely by the deficiencies in human, social, and cultural capital, as could have been the case for low-skilled labor migrants arriving in the 1960s and 1970s. Indeed, the persistence of ethno-racial disadvantages among citizens who do not differ from others except for their ethnic background, their skin color, or their religious beliefs is a testament to the fact that equality for all is an ambition not yet achieved.

Citizenship status may represent a basis for differential treatment. Undoubtedly, citizenship status is generally considered a legitimate basis for differential treatment, which is therefore not acknowledged as discrimination. Indeed, in many European countries, the divide between nationals and European Union (EU) citizens lost its bearing with the extension of social rights to EU citizens (Koopmans et al. 2012 ). Yet, in other countries, and for non-EU citizens, foreign citizenship status creates barriers to access to social subsidies, health care, specific professions, and pensions or exposure to differential treatment in criminal justice. In most countries, voting rights are conditional to citizenship, and the movement to expand the polity to non-citizens is uneven, at least for elections of representatives at the national parliaments. Notably, in countries with restrictive access to naturalization, citizenship status may provide an effective basis for unequal treatment (Hainmueller and Hangartner 2013 ). The issue of discrimination among nationals, therefore, should not overshadow the enduring citizenship-based inequalities.

The gap between ethnic diversity among the population and scarcity of the representation of this diversity in the economic, political, and cultural elites demonstrate that there are obstacles to minorities entering these positions. This picture varies across countries and social domains. The UK, Belgium, or the Netherlands display a higher proportion of elected politicians with a migration background than France or Germany (Alba and Foner 2015 ). Some would argue that it is only a matter of time before newcomers will take their rank in the queue and access the close ring of power in one or two generations. Others conclude that there is a glass ceiling for ethno-racial minorities, which will prove as efficient as that for women to prevent them from making their way to the top. The exception that proves the rule can be found in sports, where athletes with minority backgrounds are often well represented in high-level competitions. The question is how to narrow the gap in other domains of social life, and what this gap tells us about the structures of inequalities in European societies.

1.2 Talking About Discrimination in Europe

Discrimination is as old as human society. However, the use of the concept in academic research and policy debates in Europe is fairly recent. In the case of differential treatment of ethnic and racial minorities, the concept was typically related to blatant forms of racism and antisemitism, while the more subtle forms of stigmatization, subordination, and exclusion for a long time did not receive much attention as forms of “everyday racism” (Essed 1991 ). The turn from explicit racism to more subtle forms of selection and preference based on ethnicity and race paved the way to current research on discrimination. In European societies, where formal equality is a fundamental principle protected by law, discrimination is rarely observed directly. Contrary to overt racism, which is explicit and easily identified, discrimination is typically a hidden part of decisions, selection processes, and choices that are not explicitly based on ethnic or racial characteristics, even though they produce unfair biases. Discrimination does not have to be intentional and it is often not even a conscious part of human action and interaction. While it is clear that discrimination exists, this form of differential treatment is hard to make visible. The major task of research in the field is thus to provide evidence of the processes and magnitude of discrimination. Beyond the variety of approaches in the different disciplines, however, discrimination researchers tend to agree on the starting point: stereotypes and prejudices are nurturing negative perceptions, more or less explicit, of individuals or groups through processes of ethnicization or racialization, which in turn create biases in decision-making processes and serve as barriers to opportunities for these individuals or groups.

Although the concepts of inequality, discrimination, and racism are sometimes used interchangeably, the concept of discrimination entails specificities in terms of social processes, power relations, and legal frameworks that have opened new perspectives to understand ethnic and racial inequalities. The genealogy of the concept and its diffusion in scientific publications still has to be studied thoroughly, and we searched in major journals to identify broad historical sequences across national contexts. Until the 1980s, the use of the concept of discrimination was not widespread in the media, public opinion, science, or policies. In scientific publications, the dissemination of the concept was already well advanced in the US at the beginning of the twentieth century in the aftermath of the abolition of slavery to describe interracial relations. In Europe, there is a sharp distinction between the UK and continental Europe in this regard. The development of studies referring explicitly to discrimination in the UK has a clear link to the post-colonial migration after World War II and the foundation of ethnic and racial studies in the 1960s. However, the references to discrimination remained quite limited in the scientific literature until the 1990s – even in specialized journals such as Ethnic and Racial Studies , New Community and its follower Journal for Ethnic and Migration Studies , and more recently Ethnicities  – when the number of articles containing the term discrimination in their title or keywords increased significantly. In French-speaking journals, references to discrimination were restricted to a small number of feminist journals in the 1970s and became popular in the 1990s and 2000s in mainstream social science journals. The same held true in Germany, with a slight delay in the middle of the 2000s. Since the 2000s, the scientific publications on discrimination have reached new peaks in most European countries.

The year 2000 stands as a turning point in the development of research and public interest in discrimination in continental Europe. This date coincides with the legal recognition of discrimination by the parliament of the EU through a directive “implementing the principle of equal treatment between persons irrespective of racial or ethnic origin,” more commonly called the “Race Equality Directive.” This directive put ethnic and racial discrimination on the political agenda of EU countries. This political decision contributed to changing the legal framework of EU countries, which incorporated non-discrimination as a major reference and transposed most of the terms of the Race Equality Directive into their national legislation. The implementation of the directive was also a milestone in the advent of the awareness of discrimination in Europe. In order to think in terms of discrimination, there should be a principle of equal treatment applied to everyone, regardless of their ethnicity or race. This principle of equal treatment is not new, but it has remained quite formal for a long time. The Race Equality Directive represented a turning point toward a more effective and proactive approach to achieve equality and accrued sensitivity to counter discrimination wherever it takes place.

The first step to mobilize against discrimination is to launch awareness-raising campaigns to create a new consciousness of the existence of ethno-racial disadvantages. The denial of discrimination is indeed a paradoxical consequence of the extension of formal equality in post-war democratic regimes. Since racism is morally condemned and legally prohibited, it is expected that discrimination should not occur and, thus, that racism is incidental. Incidentally, an opinion survey conducted in 2000 for the European Union Monitoring Center on Racism and Xenophobia (which was replaced in 2003 by the Fundamental Rights Agency [FRA]), showed that only 31% of respondents in the EU15 at the time agreed that discrimination should be outlawed. However, the second Eurobarometer explicitly dedicated to studying discrimination in 2007 found that ethnic discrimination was perceived as the most widespread (very or fairly) type of discrimination by 64% of EU citizens (European Commission 2007 ). Almost 10 years later, in 2015, the answers were similar for ethnic discrimination but had increased for all other grounds except gender. Yet, there are large discrepancies between countries, with the Netherlands, Sweden, and France showing the highest levels of consciousness of ethnic discrimination (84%, 84%, and 82%, respectively), whereas awareness is much lower in Poland (31%) and Latvia (32%). In Western Europe, Germany (60%) and Austria (58%) stand out with relatively lower marks (European Commission 2015 ).

These Eurobarometer surveys provide useful information about the knowledge of discrimination and the attitudes of Europeans toward policies against it. However, they focus on the representation of different types of discrimination rather than the personal experience of minority members. To gather statistics on the experience of discrimination is difficult for two reasons: (1) minorities are poorly represented in surveys with relatively small samples in the general population and (2) questions about experiences of discrimination are rarely asked in non-specific surveys. Thanks to the growing interest in discrimination, more surveys are providing direct and indirect variables that are useful in studying the personal experiences of ethno-racial disadvantage.

The European Social Survey, for example, has introduced a question on perceived group discrimination (which is not exactly a personal self-reported experience of discrimination, see Chap. 4 ). In 2007 and 2015, the FRA conducted a specialized survey on discrimination in the 28 EU countries, the Minorities and Discrimination (EU-MIDIS) survey, to fill the gap in the knowledge of the experience of discrimination of ethnic and racial minorities. The information collected is wide ranging; however, only two minority groups were surveyed in each EU country, and the survey is not representative of the population.

Of course, European-wide surveys are not the main statistical sources on discrimination. Administrative statistics, censuses, and social surveys at the national and local levels in numerous countries bring new knowledge of discrimination, either with direct measures when this is the main topic of data collection or more indirectly when they provide information on gaps in employment or education faced by disadvantaged groups. The key point is to be able to identify the relevant population category in relation to discrimination, as we know that ethno-racial groups do not experience discrimination to the same extent. Analyses of immigrants or the second generation as a whole might miss the significant differences between – broadly speaking – European and non-European origins. Or, to put it in a different way, between white and non-white or “visible” minorities. Countries where groups with a European background make up most of the migration-related diversity typically show low levels of discrimination, while countries with high proportions of groups with non-European backgrounds, especially Africans (North and Sub-Saharan), Caribbean people, and South Asians, record dramatic levels of discrimination.

1.3 Who Is Discriminated Against? The Problem with Statistics on Ethnicity and Race

Collecting data on discrimination raises the problem of the identification of minority groups. Migration-related diversity has been designed from the beginning of mass migration based on place of birth of the individuals (foreign born) or their citizenship (foreigners). In countries where citizenship acquisition is limited, citizenship or nationality draws the boundary between “us” and “the others” over generations. This is not the case in countries with more open citizenship regimes where native-born children of immigrants acquire by law the nationality of their country of residence and thus cannot be identified by these variables. If most European countries collect data on foreigners and immigrants, a limited number identify the second generation (i.e., the children of immigrants born in the country of immigration). The question is whether the categories of immigrants and the second generation really reflect the population groups exposed to ethno-racial discrimination. As the grounds of discrimination make clear, nationality or country of birth is not the only characteristic generating biases and disadvantages: ethnicity, race, or color are directly involved. However, if it seems straightforward to define country of birth and citizenship, collecting data on ethnicity, race, or color is complex and, in Europe, highly sensitive.

Indeed, the controversial point is defining population groups by using the same characteristics by which they are discriminated against. This raises ethical, political, legal, and methodological issues. Ethical because the choice to re-use the very categories that convey stereotypes and prejudices at the heart of discrimination entails significant consequences. Political because European countries have adopted a color-blind strategy since 1945, meaning that their political philosophies consider that racial terminologies are producing racism by themselves and should be strictly avoided (depending on the countries, ethnicities receive the same blame). Legal because most European countries interpret the provisions of the European directive on data protection and their transposition in national laws as a legal prohibition. Methodological because there is no standardized format to collect personal information on ethnicity or race and there are several methodological pitfalls commented in the scientific literature. Data on ethnicity per se are collected in censuses to describe national minorities in Eastern Europe, the UK, and Ireland, which are the only Western European countries to produce statistics by ethno-racial categories (Simon 2012 ). The information is collected by self-identification either with an open question about one’s ethnicity or by ticking a box (or several in the case of multiple choices) in a list of categories. None of these questions explicitly mention race: for example, the categories in the UK census refer to “White,” “black British,” or “Asian British” among other items, but the question itself is called the “ethnic group question.”

In the rest of Europe, place of birth and nationality of the parents would be used as proxies for ethnicity in a limited number of countries: Scandinavia, the Netherlands, and Belgium to name a few. Data on second generations can be found in France, Germany, and Switzerland among others in specialized surveys with limitations in size and scope. Moreover, the succession of generations since the arrival of the first migrants will fade groups into invisibility by the third generation. This process is already well advanced in the oldest immigration countries, such as France, Germany, Switzerland, and the Netherlands. Asking questions about the grandparents and the previous generations is not an option since it would require hard decisions to classify those with mixed ancestry (how many ancestors are needed to belong to one category?), not to mention the problems in memory to retrieve all valuable information about the grandparents. This is one of the reasons why traditional immigration countries (USA, Canada, Australia) collect data on ethnicity through self-identification questions.

The discrepancies between official categories and those exposed to discrimination have fostered debates between state members and International Human Rights Organizations – such as the UN Committee for the Elimination of Racial Discrimination (CERD), European Commission against Racism and Intolerance (ECRI) at the Council of Europe, and the EU FRA – which claim that more data are needed on racism and discrimination categorized by ethnicity. The same applies to academia and antiracist NGOs where debates host advocates and opponents to “ethnic statistics.” There is no easy solution, but the accuracy of data for the measurement of discrimination is a strategic issue for both research and policies.

1.4 Discrimination and Integration: Commonalities and Contradictions

How does research on discrimination relate to the broader field of research on immigrant assimilation or integration? On one hand, assimilation/integration and discrimination are closely related both in theory and in empirical studies. Discrimination hinders full participation in society, and the persistence of ethnic penalties across generations contradicts long-term assimilation prospects. On the other hand, both assimilation and integration theory tend to assume that the role of discrimination in shaping access to opportunities will decrease over time. Assimilation is often defined as “the decline of ethnic distinction and its corollary cultural and social difference” (Alba and Nee 2003 , 11), a definition that bears an expectation that migrants and their descendants will over time cease to be viewed as different from the “mainstream population,” reach parity in socioeconomic outcomes, and gradually become “one of us.” In the canonical definition, integration departs from assimilation by considering incorporation as a two-way process. Migrants and ethnic minorities are expected to become full members of a society by adopting core values, norms, and basic cultural codes (e.g., language) from mainstream society, while mainstream society is transformed in return by the participation of migrants and ethnic minorities (Alba et al. 2012 ). The main idea is that convergence rather than differentiation should occur to reach social cohesion, and mastering the cultural codes of mainstream society will alleviate the barriers to resource access, such as education, employment, housing, and rights.

Of course, studies of assimilation and integration do not necessarily ignore that migrants and ethnic minorities face penalties in the course of the process of acculturation and incorporation into mainstream society. In the landmark book, Assimilation in American Life , Milton Gordon clearly spelled out that the elimination of prejudice and discrimination is a key parameter for assimilation to occur; or to use his own terms, that “attitude receptional” and “behavioral receptional” dimensions of assimilation are crucial to complete the process (Gordon 1964 , 81). Yet, ethnic penalties are believed to be mainly determined by human capital and class differences and therefore progressively offset as education level rises, elevating the newcomers to conditions of the natives and reducing the social distance between groups. Stressing the importance of generational progress, assimilation theory thus tends to consider discrimination as merely a short-run phenomenon.

The main blind spots in assimilation and integration theories revolve around two issues: the specific inequalities related to the ethnicization or racialization of non-white minorities and the balance between the responsibilities of the structures of mainstream society and the agencies of migrants and ethnic minorities in the process of incorporation. Along these two dimensions, discrimination research offers a different perspective than what is regularly employed in studies of assimilation and integration.

Discrimination research tends to identify the unfavorable and unfair treatment of individuals or groups based on categorical characteristics and often shows these unfair treatments lie in the activation of stereotypes and prejudices by gatekeepers and the lack of neutrality in processes of selection. In this perspective, what has to be transformed and adapted to change the situation are the structures – the institutions, procedures, bureaucratic routines, etc. – of mainstream society, opening it up to ethnic and racial diversity to enable migrants and ethnic minorities to participate on equal footing with other individuals, independent of their identities. By contrast, in studies of assimilation and integration, explanations of disadvantages are often linked to the lack of human capital and social networks among migrants and ethnic minorities, suggesting that they have to transform themselves to be able to take full part in society. To simplify matters, studies of assimilation and integration often explain persistent disadvantages by pointing to characteristics of migrants and ethnic minorities, while discrimination research explains disadvantages by characteristics of the social and political system.

Both assimilation and integration theories have gradually opened up for including processes of ethnicization and racialization and the consequences of such processes on assimilation prospects. Most prominently, segmented assimilation theory (Portes and Rumbaut 2001 ; Portes and Zhou 1993 ) shifts the focus away from migrants’ adaptation efforts and to the forms of interaction between minority groups – and prominently the second and later generations – and the receiving society. In this variant of assimilation theory, societies are viewed as structurally stratified by class, gender, and race, which powerfully influence the resources and opportunities available to immigrants and their descendants and contribute to shaping alternative paths of incorporation. According to segmented assimilation theory, children of immigrants may end up “ascending into the ranks of a prosperous middle class or join in large numbers the ranks of a racialized, permanently impoverished population at the bottom of society” (Portes et al. 2005 , 1004), the latter outcome echoing worries over persistent ethnic and racial disadvantage. Another possible outcome is upward bicultural mobility (selective acculturation) of the children of poorly educated parents, protected by strong community ties.

The major question arising from these related fields of research – the literature on assimilation and integration, on the one hand, and the literature on discrimination, on the other – is whether the gradual diversification of Europe will result in “mainstream expansion,” in which migrants and their descendants over time will ascend the ladders into the middle and upper classes of the societies they live in, or whether we are witnessing the formation of a permanent underclass along ethnic and racial lines. This book will not provide the ultimate answer to this question. However, by introducing the main concepts, theories, and methods in the field of discrimination, as well as pointing out key research findings, policies that are enacted to combat discrimination, and avenues for future research, we hope to provide the reader with an overview of the field.

1.5 The Content of the Book

The literature on discrimination is flourishing, and it involves a wide range of concepts, theories, methods, and findings. Chapter 2 provides the key concepts in the field. The chapter distinguishes between direct and indirect discrimination as legal and sociological concepts, between systemic and institutional discrimination, and between discrimination as intentional actions, subtle biases, and what might be referred to as the cumulative effects of past discrimination on the present. Chapter 3 reviews the main theoretical explanations of discrimination from a cross-disciplinary perspective. Mirroring the historical development of the field, it presents and discusses theories seeking the cause of prejudice and discrimination at the individual, organizational, and structural levels.

Of course, our knowledge of discrimination depends on the methods of measurement, since the phenomenon is mainly visible through its quantification. Hence, Chapter 4 offers an overview of the strengths and weaknesses of available methods of measurement, including statistical analysis of administrative data, surveys among potential victims and perpetrators, qualitative in-depth studies, legal cases, and experimental approaches to the study of discrimination (including survey experiments, lab experiments, and field experiments).

Importantly, discrimination does not occur similarly in all domains of social life, and it takes different forms according to the domain in question (e.g., the labor market, education, housing, health services, and public services). Chapter 5 taps into the large body of empirical work that can be grouped under the heading “discrimination research” in order to provide some key findings, while simultaneously highlighting a distinction between systems of differentiation and systems of equality.

What happens when discrimination occurs? Chapter 6 addresses the consequences of unfair treatment for targeted individuals and groups, as well as their reaction to it. These individual and collective responses to discrimination are seconded by policies designed to tackle discrimination. However, antidiscrimination policies vary greatly across countries, and Chapter 7 provides an overview of the different types of policies against discrimination in Europe and beyond, both public policies and schemes implemented by organizations. The chapter also reflects on some of the key political and societal debates about the implementation and the future of these policies. Chapter 8 concludes on the future of discrimination research in Europe, stressing the main challenges ahead for a burgeoning scientific field.

Alba, R., & Foner, N. (2015). Strangers no more: Immigration and the challenges of integration in North America and Western Europe . Princeton: Princeton University Press.

Book   Google Scholar  

Alba, R., & Nee, V. (2003). Remaking the American mainstream: Assimilation and contemporary immigration . Cambridge: Harvard University Press.

Alba, R., Reitz, J. G., & Simon, P. (2012). National Conceptions of assimilation, integration, and cohesion. In M. Crul & J. H. Mollenkopf (Eds.), The changing face of world cities: Young adult children of immigrants in Europe and the United States (pp. 44–61). New York: Russel Sage.

Google Scholar  

Crul, M. (2016). Super-diversity vs. assimilation: How complex diversity in majority–minority cities challenges the assumptions of assimilation. Journal of Ethnic and Migration Studies, 42 (1), 54–68. https://doi.org/10.1080/1369183X.2015.1061425 .

Article   Google Scholar  

Essed, P. (1991). Understanding everyday racism: An interdisciplinary theory . Newbury Park: Sage.

European Commission. (2007). Discrimination in the European Union (Special Eurobarometer, Vol. 263). Brussels: European Commission.

European Commission. (2015). Discrimination in the EU in 2015 (Special Eurobarometer, Vol. 437). Brussels: European Commission.

Gordon, M. (1964). Assimilation in American life: The role of race, religion, and National Origins . New York: Oxford University Press.

Hainmueller, J., & Hangartner, D. (2013). Who gets a swiss passport? A natural experiment in immigrant discrimination. American Political Science Review, 107 (01), 159–187. https://doi.org/10.1017/S0003055412000494 .

Heath, A. F., & Cheung, S. Y. (Eds.). (2007). Unequal chances: Ethnic minorities in Western labour markets . Oxford: British Academy/Oxford University Press.

Koopmans, R., Michalowski, I., & Waibel, S. (2012). Citizenship rights for immigrants. National political processes and cross-national convergence in Western Europe, 1980–2008. American Journal of Sociology, 117 (4), 1202–2045. https://doi.org/10.1086/662707 .

Portes, A., & Rumbaut, R. (Eds.). (2001). Legacies: The story of the immigrant second generation . Los Angeles: University of California Press.

Portes, A., & Zhou, M. (1993). The new second generation: Segmented assimilation and its variants. The Annals of the American Academy of Political and Social Science, 530 , 74–96. https://doi.org/10.1177/0002716293530001006 .

Portes, A., Fernández-Kelly, P., & Haller, W. (2005). Segmented assimilation on the ground: The new second generation in early adulthood. Ethnic and Racial Studies, 28 (6), 1000–1040. https://doi.org/10.1080/01419870500224117 .

Simon, P. (2012). Collecting ethnic statistics in Europe: A review. Ethnic and Racial Studies, 35 (8), 1366–1391. https://doi.org/10.1080/01419870.2011.607507 .

Van Mol, C., & de Valk, H. (2016). Migration and immigrants in Europe: A historical and demographic perspective. In B. Garcés-Mascareñas & R. Penninx (Eds.), Integration processes and policies in Europe (IMISCOE Research Series). Cham: Springer.

Vertovec, S. (2007). Super-diversity and its implications. Ethnic and Racial Studies, 30 (6), 1024–1054. https://doi.org/10.1080/01419870701599465 .

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Fibbi, R., Midtbøen, A.H., Simon, P. (2021). Introduction: The Case for Discrimination Research. In: Migration and Discrimination. IMISCOE Research Series. Springer, Cham. https://doi.org/10.1007/978-3-030-67281-2_1

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Racial discrimination and health: a prospective study of ethnic minorities in the United Kingdom

• Ruth A. Hackett   ORCID: orcid.org/0000-0002-5428-2950 1 , 2 ,
• Amy Ronaldson 3 ,
• Kamaldeep Bhui 4 ,
• Andrew Steptoe 2 &
• Sarah E. Jackson 2  

BMC Public Health volume  20 , Article number:  1652 ( 2020 ) Cite this article

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Racism has been linked with poor health in studies in the United States. Little is known about prospective associations between racial discrimination and health outcomes in the United Kingdom (UK).

Data were from 4883 ethnic minority (i.e. non-white) participants in the UK Household Longitudinal Study. Perceived discrimination in the last 12 months on the basis of ethnicity or nationality was reported in 2009/10. Psychological distress, mental functioning, life satisfaction, self-rated health, physical functioning and reports of limiting longstanding illness were assessed in 2009/10 and 2011/12. Linear and logistic regression analyses adjusted for age, sex, income, education and ethnicity. Prospective analyses also adjusted for baseline status on the outcome being evaluated.

Racial discrimination was reported by 998 (20.4%) of the sample. Cross-sectionally, those who reported racial discrimination had a greater likelihood on average of limiting longstanding illness (odds ratio (OR) = 1.78, 95% confidence interval (CI) 1.49; 2.13) and fair/poor self-rated health (OR = 1.50; 95% CI 1.24; 1.82) than those who did not report racial discrimination. Racial discrimination was associated with greater psychological distress ( B  = 1.11, 95% CI 0.88; 1.34), poorer mental functioning ( B  = − 3.61; 95% CI -4.29; − 2.93), poorer physical functioning ( B  = − 0.86; 95% CI -1.50; − 0.27), and lower life satisfaction ( B  = − 0.40, 95% CI -0.52; − 0.27). Prospectively, those who reported racial discrimination had a greater likelihood on average of limiting longstanding illness (OR = 1.31, 95% CI 1.01; 1.69) and fair/poor self-rated health (OR = 1.30; 95% CI 1.00; 1.69), than those who did not report racial discrimination. Racial discrimination was associated increased psychological distress ( B  = 0.52, 95% CI 0.20; 0.85) and poorer mental functioning ( B  = − 1.77; 95% CI -2.70; − 0.83) over two-year follow-up, adjusting for baseline scores.

Conclusions

UK adults belonging to ethnic minority groups who perceive racial discrimination experience poorer mental and physical health than those who do not. These results highlight the need for effective interventions to combat racial discrimination in order to reduce inequalities in health.

Peer Review reports

Discrimination is defined as the differential treatment of an individual based on a socially ascribed characteristic [ 1 ]. In the United Kingdom (UK), the 1965 Race Relations Act [ 2 ] outlawed discrimination on the grounds of colour, nationality and ethnic or national origins. Race remains a protected characteristic under contemporary equality law [ 3 ]. Despite this legislative effort, ethnic inequalities in education, work, health and criminal justice remain [ 4 ].

Discrimination on the basis of ethnic origin is regarded as the most common type of prejudice in Europe, with 64% of adults perceiving racial discrimination to be widespread in a survey of 27,718 people [ 5 ]. In Britain in 2017, 26% of a representative sample described themselves as racially prejudiced [ 6 ], and race continues to be the most common motivator for hate crime incidents [ 7 , 8 ]. Against the backdrop of the vote to leave the European Union (Brexit), hostility towards migrants and the growth in right-wing nationalist movements [ 9 ], these figures reflect a rise in reported racial discrimination in both the UK and Europe [ 5 , 6 ].

A growing body of research has investigated discrimination as a determinant of mental health [ 10 , 11 , 12 ] and to a lesser extent physical health [ 11 ]. In an early meta-analysis of 110 studies, discrimination was linked with poor mental health, including psychological distress and decreased life satisfaction [ 11 ]. A sub-set of 36 studies in the review investigated associations with physical health. Significant associations were detected in a pooled analysis with various outcomes including hypertension and acute cardiovascular responses to laboratory discrimination protocols. A more recent meta-analysis of 328 studies focusing on discrimination and mental health outcomes alone, again observed that those who perceived discrimination had poorer mental health [ 12 ]. This finding was also detected in an independent analysis of 211 cross-sectional studies linking racial discrimination with poor mental health [ 12 ].

Racism is a recognised social determinant of health and a driver of ethnic inequities in health [ 13 ]. It can be understood as a complex, organised system embedded in socio-political and historical contexts, that involves classifying ethnic groups into social hierarchies. These groups are ideologically assigned differential value, which drives disparities in access to power, resources and opportunities [ 14 , 15 ]. It occurs at both structural and individual levels (self-reported experiences of racial discrimination) [ 14 , 15 ].

Several reviews and meta-analyses have focused solely on perceived racial discrimination and health outcomes [ 13 , 16 , 17 , 18 ]. The largest study to date meta-analysed the results from 293 studies and assessed both mental and physical health outcomes [ 16 ]. In this analysis, racial discrimination was associated with poorer overall mental health including greater psychological distress, poorer life satisfaction and poorer general mental functioning in independent analyses. Racism was also linked with poorer general health and poorer physical health overall, though few effects remained significant when looking at specific physical health outcomes in separate analyses.

Racial discrimination at the structural and individual level is theorised to impact health through several mechanisms [ 15 ]. At the structural level racial discrimination may operate through the unfair allocation of societal resources that are determinants of health (e.g. education, employment, housing) [ 14 , 15 ] and through differential access to healthcare, as well as perceived poorer quality of care [ 19 ]. Another mechanism linking racial discrimination and health could be through the dysregulation of stress-related biological processes [ 20 ]. Frequent exposure to racial discrimination is a chronic stressor and has been linked with dysregulated cardiovascular, neuroendocrine and inflammatory processes [ 21 , 22 ] which in turn impact both physical and mental health. Individual health risk (e.g. smoking, alcohol consumption) could link perceived racial discrimination and health, as means of coping with or avoiding discrimination [ 23 , 24 ].

Although a growing number of studies have investigated the link between racial discrimination and health, there are still areas where more research is required. In the 2015 racism meta-analysis of almost 300 studies, only 9% of the data included were prospective [ 16 ]. The authors aimed to compare the effect sizes of the cross-sectional and prospective studies included in their review but were unable to conduct this analysis for the physical outcomes data, emphasising the need for more prospective studies on physical health outcomes in particular.

Further, the literature is dominated by United States (US)-based studies drawn from convenience samples [ 12 , 16 ]. In the latest racism and health meta-analysis, over one third of the articles included were drawn from student samples and only nine (2.7%) of the included studies were UK-based [ 16 ]. This is important as the makeup of ethnic minority groups in the UK differs from that of the US, with those of South Asian backgrounds forming the largest minority group [ 25 ]. In addition, all of the UK studies were cross-sectional in nature and focused on mental health, with physical outcomes such as the number of physical illnesses [ 26 ] and self-rated health [ 27 ] included in only two of the studies.

To date, one UK study has assessed the relationship between racial discrimination and health prospectively. In an analysis of the UK Household Longitudinal Study (UKHLS), the authors found that those who reported racial discrimination had poorer mental functioning scores 4 years later [ 28 ]. They also reported a dose-response relationship between the experience of racial discrimination and mental health, with those who reported racial discrimination at more than one timepoint over a 3-year period experiencing a greater deterioration in mental functioning.

Overall, there is a dearth of prospective evidence on the link between racial discrimination and health in UK samples, particularly in relation to physical health outcomes.

To address these gaps in the literature, the present study set out to assess cross-sectional and prospective associations between racial discrimination and health in a large community-dwelling UK population cohort. Specifically, we were interested in psychological distress, mental functioning and life satisfaction, as indicators of mental health, as well as self-rated health and physical functioning as markers of physical health, along with limiting longstanding illness as an indicator of impairment. We hypothesised that those who perceived racial discrimination would have poorer health across all measures both cross-sectionally and prospectively.

Study population

The current study uses data from UKHLS [ 29 ]. The study began in 2009/10 (wave 1) with follow-ups yearly. This study uses data from waves 1 (2009/10) and 3 (2011/12) of the data collection. The UKHLS consists of a representative sample of the UK population, as well as an ethnic minority boost sample [ 25 , 30 ]. In this study we use data from ‘extra 5 minutes sample’ of over 8000 individuals who had an additional 5 min of questions on issues of importance to ethnicity research including discrimination. The majority of this sample are drawn from ethnic minority groups ( n  = 6722), in addition to a smaller comparison group of white participants ( n  = 1428) [ 25 ]. We restricted our analyses to those who provided information on racial discrimination at wave 1 ( n  = 5707) and self-reported being of non-white ethnicity ( n  = 4883). The participants included in our study were significantly older ( p  = 0.002) and were less likely to have an educational qualification ( p  < 0.001) than those who did not provide data for the study. They were also more likely to be male ( p  < 0.001) and of South Asian ethnicity ( p  < 0.001) The groups did not differ on income ( p  = 0.136). All participants provided fully informed written consent and the University of Essex Ethics Committee granted ethical approval for UKHLS.

Racial discrimination

To measure perceived discrimination, participants were asked whether in the past 12 months, they had (a) felt unsafe, (b) avoided going to or being in, (c) been insulted, called names, threatened or shouted at, or (d) been physically attacked in 7 different settings 1) At school/college/work, 2) On public transport, 3) At or around bus or train stations, 4) In a taxi, 5) Public buildings such as shopping centres or pubs, 6) Outside on the street, in parks or other public places, or 7) At home. If they answered yes to any one of these questions, a follow-up question asked them to choose an attribution for the discrimination from a list of categories including ethnicity, nationality, age, and sex among others. Participants could choose multiple settings and attributions for the perceived discrimination. Those who attributed any experience of discrimination to their ethnicity or nationality are treated as cases of perceived racial discrimination in our analyses. Those who did not perceive any form of discrimination serve as the comparison group in our analyses. Those who reported other (non-racial) forms of discrimination were not included in the analysis. This measure has been used in previous investigations to look at the link between perceived discrimination and health outcomes [ 28 , 31 , 32 ].

Mental health outcomes

We included 3 mental health measures at waves 1 (2009/10) and 3 (2011/12). Psychological distress was assessed using the General Health Questionnaire (GHQ)-12 [ 33 ], in line with previous studies [ 31 , 32 ]. This tool has been validated as a screening tool to detect psychological distress in community samples [ 34 ]. This measure involved ratings of 12 statements including whether the participant had “ Been able to enjoy your normal day to day activities ” or whether they “ Felt constantly under strain ” with binary response options (yes/no). After totalling, the overall score ranged from 0 (least distressed) to 12 (most distressed). The Cronbach’s alpha for the scale was 0.99.

The 12-item short-form health survey (SF-12) mental component summary score was used to measure limitations caused by emotional, mental health and social functioning issues [ 35 ], in keeping with previous studies [ 31 , 32 ]. This tool has been validated for use as a measure of mental functioning in community samples [ 35 , 36 ]. Items included ratings of feelings experienced over the past 4 weeks such as “ Have you felt downhearted or blue ?” or “Accomplished less than you would like” . A total score ranging from 0 (low functioning) to 100 (high functioning) was derived using standard methods [ 37 ]. The Cronbach’s alpha for this scale was 0.98.

One item was used to assess participants’ life satisfaction by asking them how satisfied they were with their “life overall”, on a scale from 1 (completely dissatisfied) to 7 (completely satisfied) [ 38 ]. Single item measures of life satisfaction are widely used in survey studies [ 39 ] This measure has been used in previous investigations to assess the link between discrimination and life satisfaction [ 31 , 32 ].

Impairment outcome

Self-reported limiting longstanding illness at waves 1 (2009/10) and 3 (2011/12) was used as measure of impairment. It was measured using one item “Do you have any long-standing physical or mental impairment, illness or disability?...mean [ing] anything that has … or is likely to trouble you over a period of at least 12 months” with response options of yes or no. Self-reported limiting longstanding illness has been investigated in relation to perceived discrimination in other studies [ 40 , 41 ].

Physical health outcomes

We included 2 measures of physical health that were assessed at waves 1 (2009/10) and 3 (2011/12). The SF-12 physical component summary score was used to measure limitations caused by deficits in physical functioning [ 35 ]. Participants were ask ed “Does your health now limit you a lot, limit you a little or not limit you at all?” in activities such “climbing stairs” or “moving a table, pushing a vacuum cleaner, bowling or playing golf”. Overall scores were derived using standard methods ranging from 0 (low functioning) to 100 (high functioning) [ 37 ]. The Cronbach’s alpha for the scale was 0.98. This tool has been validated for use as a measure of physical functioning in community samples [ 35 , 36 ].

A single item was used to assess self-rated health: “ Would you say your health is … poor/fair/good/very good/excellent?” In keeping with earlier work [ 31 , 32 , 42 ] self-rated health was dichotomised with 0 being “good/very good/excellent” and 1 being “poor/fair”. This single item measure has been shown to have good predictive validity for health outcomes [ 42 ].

Our analyses included covariates that are likely relevant to racial discrimination and physical and mental health. All covariates were assessed at wave 1. Age in years was included as a continuous variable. Self-reported sex was included and coded as male/female. Socioeconomic status is an important contributor to racial disparities in health [ 43 ]. Racial discrimination can compound these inequalities. Therefore, we included education as a 3-level variable, coded as 1 “university degree”, 2 “high school qualification” and 3 “no qualification”. Equivalised monthly household income was computed by dividing total household net income by the modified Organization for Economic Cooperation and Development (OECD) equivalence scale to account for the effects of household size and composition [ 44 ]. The UKHLS samples the 5 main ethnic minority groups in the UK [ 25 , 30 ]: Indian, Pakistani, Bangladeshi, Black African and Black Caribbean. Participants were asked “What is your ethnic group?” with response options standardised in line with the England and Wales 2011 Census [ 25 ]. Response options also accounted for those of “mixed backgrounds”. We included ethnicity as a 6-level variable with these 5 main UK minority groups and 1 additional category of non-white individuals from a range of other minority backgrounds including Chinese, Arab and mixed ethnic backgrounds among others. For our sensitivity analysis, we collapsed ethnicity into a 3-level variable with Indian, Pakistani and Bangladeshi participants coded as “South Asian” Black African and Black Caribbean participants coded as “Black” and other non-white participants coded as “Other”.

Statistical analyses

The characteristics of those who did and those who did not report racial discrimination at wave 1 were compared using Chi-squared tests for categorical variables and independent samples t-tests for continuous variables. Associations between racial discrimination and the mental and physical health measures were assessed using linear regression for continuous outcomes and binary logistic regression for categorical outcomes. For the mental health analyses, psychological distress, mental functioning and life satisfaction were the outcome variables. For the impairment analysis limiting longstanding illness was the outcome variable. For the physical health analyses, physical functioning and self-rated health were the outcome variables. Age, sex, household income, education and ethnicity at wave 1 were adjusted for in all analyses. Baseline (wave 1) score/status on the relevant outcome variable was included as an additional covariate in prospective analyses. Only those with complete case information at wave 1 ( n  = 4883) and wave 3 ( n  = 2833) were included in the analyses. We tested for interactions between racial discrimination and age, sex, income, education or ethnicity on the mental and physical health outcomes at both waves 1 and 3. No significant effects were detected. Thus, interaction terms were not included in our final reported models.

Results from linear regression analyses are presented as unstandardized B and 95% confidence intervals (95% CI). Results from binary logistic regression analyses are presented as odds ratios (ORs) and 95% CI. The level of significance was set at p  < 0.05. Unstandardized Bs and ORs rather than p values should be used to determine the strength of associations. All analyses were conducted using SPSS v.24.

Sensitivity analyses

To test the robustness of our findings, we conducted three sets of sensitivity analyses. In our first, we investigated whether a certain type of discriminatory experience (i.e. feeling unsafe, avoiding somewhere, being insulted or attacked) contributing to the measure of racial discrimination was driving the results. We tested this by removing each type of discriminatory experience from the exposure variable in turn, as has been done in previous investigations [ 31 , 32 , 40 ]. In the second sensitivity analysis, we assessed whether participants who were lost to follow-up differed from those who provided data at both waves, and tested whether this influenced the findings by conducting the cross-sectional analyses (wave 1) including only those who provided follow-up data at wave 3. In our final sensitivity analysis, we assessed whether the associations between racial discrimination and our health outcomes varied depending on ethnic group (South Asian, Black or Other), as there is currently limited evidence in this area outside of the US context [ 16 ].

A total of 4883 participants were included in our analysis and of these 998 (20.4%) reported ethnicity ( n  = 854) or nationality ( n  = 144) discrimination. The characteristics of the sample at wave 1 in relation to racial discrimination are displayed in Table  1 . Those who perceived racial discrimination were younger on average and were more likely to hold a university degree than those who did not perceive racial discrimination. There were no differences in sex or income, but reports of racial discrimination did vary by ethnic group. Those in the Indian (23.3%) and in the Other ethnic group (24%) were most likely to report experiences of racial discrimination. Further detail on the types of racial discrimination and the settings in which the racial discrimination occurred for the different ethnic groups can be found in Supplementary Table  1 .

Racial discrimination and mental health

The descriptive characteristics of the sample in relation to health outcomes are displayed in Table  2 . The mental health findings from the regression analyses are displayed in the upper panel of Table  3 . Cross-sectionally, those who reported racial discrimination had greater psychological distress ( B  = 1.11, 95% CI 0.88; 1.34, p  < 0.001), poorer mental functioning ( B  = − 3.61; 95% CI -4.29; − 2.93, p  < 0.001) and lower life satisfaction ( B  = − 0.40, 95% CI -0.52; − 0.27, p  < 0.001), than those who did not report racial discrimination, independent of covariates.

In prospective analyses, those who perceived racial discrimination had greater psychological distress 2 years later than those who did not perceive racial discrimination, independent of covariates and baseline psychological distress ( B  = 0.52, 95% CI 0.20; 0.85, p  = 0.002). We detected an association between racial discrimination and poorer mental functioning ( B  = − 1.77; 95% CI -2.70; − 0.83, p  < 0.001), independent of covariates and mental functioning at wave 1. In adjusted analyses, those who reported racial discrimination had slightly lower life satisfaction than those who did not report racial discrimination at follow-up (means = 4.77 vs 4.91), but this difference did not reach statistical significance ( p  = 0.102).

Racial discrimination, impairment and physical health

The impairment and physical health results are displayed in the lower panel of Table 3 . The cross-sectional findings suggest that independent of covariates, participants who perceived racial discrimination were significantly more likely on average to report having a limiting longstanding illness (OR = 1.78; 95% CI 1.49; 2.13, p  < 0.001), and were more likely on average to rate their health as fair/poor (OR = 1.50; 95% CI 1.24; 1.82, p  < 0.001) than those who did not perceive racial discrimination. Those who reported racial discrimination also had significantly poorer physical functioning ( B  = − 0.86; 95% CI -1.50; − 0.27, p  = 0.008) than those who did not report racial discrimination in adjusted analyses.

In prospective analyses, those who reported racial discrimination were significantly more likely on average to have a limiting longstanding illness 2 years later than those who did not report racial discrimination, independent of covariates and limiting longstanding illness at baseline (OR = 1.31; 95% CI 1.01; 1.69, p  = 0.039). A greater proportion of those who reported racial discrimination rated their health as fair/poor on average at follow-up than those who did not report racial discrimination (OR = 1.30; 95% CI 1.00; 1.69, p  = 0.048) in adjusted analyses. However, we failed to detect a prospective adjusted association between racial discrimination and physical functioning ( p  = 0.290).

In the first sensitivity analysis, removing each of the discriminatory experiences from the measure of racial discrimination in turn did not alter any of the cross-sectional results (Table  4 , upper panel). Prospectively, the association between racial discrimination and all the mental health measures and limiting longstanding illness remained the same regardless of the type of discriminatory experience removed from the measure (Table 4 , lower panel). For self-rated health, the association was fairly robust to the type of discriminatory experience, but was slightly attenuated when “feeling unsafe” was removed from the racial discrimination variable ( p  = 0.133). Again, for the most part, no significant prospective associations were detected for physical functioning except when “feeling unsafe” was removed from the racial discrimination variable ( p  = 0.027).

In the second sensitivity analysis (Supplementary Table  2 ), cross-sectional physical and impairment (lower panel) and mental health (upper panel) findings for those who provided complete data at wave 3 were similar to the full-sample at wave 1.

In our final sensitivity analysis (Supplementary Table  3 ), we assessed whether the associations between racial discrimination and our health outcomes varied depending on ethnic group (South Asian, Black, Other). For the cross-sectional analyses, the findings for psychological distress and mental functioning did not vary by ethnic group. However, for life satisfaction ( B  = − 0.23; 95% CI -0.47; 0.02, p  = 0.069), limiting longstanding illness (OR = 1.34; 95% CI 0.93; 1.92, p  = 0.113), physical functioning ( B  = 0.42; 95% CI -0.84; 1.68, p  = 0.511), and self-rated health (OR = 1.01; 95% CI 0.67; 1.53, p  = 0.955) the findings for the Black group were non-significant, with lower point estimates than when the ethnic groups were combined in the main analysis. For the prospective analyses, there was no group difference for the impairment and physical health outcomes. However, the findings for psychological distress ( B  = 0.32; 95% CI -0.18; 0.82, p  = 0.207), and mental functioning ( B  = − 1.37; 95% CI -2.83; 0.09, p  = 0.065), were not significant for the South Asian group, with lower point estimates than in the combined model. Interestingly, for life satisfaction, those in the Other ethnic group had significantly lower life satisfaction at wave 3 ( B  = − 0.39; 95% CI -0.69;-0.08, p  = 0.013), with greater point estimates than in the combined model. This finding remained non-significant for the South Asian and Black groups.

In this large UK-based prospective sample of ethnic minority participants, we detected associations between racial discrimination and poorer health. Cross-sectionally, those who reported racial discrimination had a greater likelihood on average of limiting longstanding illness and poor self-rated health, than those who did not report racial discrimination. Racial discrimination was associated greater psychological distress, lower life satisfaction, and poorer physical and mental functioning. In prospective analyses, those who reported racial discrimination had a greater likelihood on average of limiting longstanding illness and poor self-rated health than those who did not report racial discrimination. Racial discrimination was associated with greater psychological distress and poorer mental functioning over a two-year follow-up period, regardless of baseline health. No significant prospective associations with physical functioning or life satisfaction were detected.

To our knowledge, this is the first prospective UK-based study to investigate both mental and physical health outcomes in relation to racial discrimination. One earlier analysis of the UKHLS found that those who reported racial discrimination had poorer mental functioning over a 1–4 year follow-up period [ 28 ]. The current study also found a prospective association between racial discrimination and poor mental functioning. Our study builds upon previous findings by additionally showing that this association is independent of baseline mental functioning. We also observed a prospective association with psychological distress, another marker of mental health, with those reporting racial discrimination experiencing an increase in psychological distress over time. We did not detect a prospective association between racial discrimination and poorer life satisfaction. Mean scores trended in this direction but the association did not reach statistical significance. A 2015 longitudinal analysis of the US-based Health and Retirement Study with over 6000 participants also failed to detect a prospective association between racial discrimination and decreases in life satisfaction [ 45 ], and pooled analyses have been unable to investigate prospective associations with life satisfaction due lack of sufficient evidence [ 12 , 16 ]. A possible explanation for this null finding, consistent with earlier work, is that racial discrimination is more strongly associated with negative mental health outcomes such as psychological distress than with positive outcomes such as life satisfaction [ 12 , 16 ]. Another potential reason for these findings relates to duration of follow-up, as review evidence suggests that a recent experience of racial discrimination may be more strongly associated with poor mental health and more weakly related to life satisfaction measures [ 16 ]. Our follow-up period of 2 years was relatively short which may have contributed to these results.

Reviews in the field [ 16 , 17 ] have highlighted the need for more prospective evidence, particularly for physical health outcomes [ 16 ]. We found that participants who reported racial discrimination were more likely to report having a limiting longstanding illness and poorer self-rated health, independent of baseline status. Meta-analytic evidence has demonstrated an association between racism and poor general health and worse physical health outcomes [ 16 ]. We built upon this predominately US-based data (a considerable portion of which used convenience sampling) to demonstrate prospective associations between racial discrimination and physical health outcomes in a representative sample of UK adults from ethnic minority groups. We failed to observe a prospective association between perceived racial discrimination and physical functioning, although participants who reported racial discrimination had slightly lower physical functioning scores prospectively than those who did not report racial discrimination. This lack of association may indicate that ongoing experiences of racial discrimination had already made an impact on physical functioning at the time of wave 1 survey, limiting the scope for further significant decreases in this measure over time, particularly as we took baseline physical functioning into account in our analyses. Another possibility, is that the etiological period involved for a decline in physical functioning may differ from that of mental functioning [ 14 ]. These outcomes were measured using the same tool (SF-12) but only mental functioning was significantly associated with racial discrimination over the follow-up period.

Review evidence based on US data suggests that associations between racial discrimination and health may vary depending on ethnic group [ 16 ]. In our sensitivity analysis, the cross-sectional results for life satisfaction and impairment and physical health outcomes were non-significant for the Black group. Prospectively the findings for psychological distress and mental functioning were non-significant for the South Asian group. Whereas, life satisfaction was found to significantly decline for the Other group over the follow-up period. Taken together these results suggest associations with health outcomes are strongest for South Asian and Other groups cross-sectionally, while prospectively racial discrimination appears to most consistently impact mental health outcomes in Black and Other ethnic groups. These findings should be interpreted with caution due to the likelihood that some of our analyses were underpowered.

In our cross-sectional analyses, we found that those who perceived racial discrimination had poorer mental health, with greater psychological distress, poorer mental functioning and lower life satisfaction. Previous work in UKHLS has demonstrated a cross-sectional association with psychological distress using pooled data across three waves of data collection [ 46 ]. To our knowledge no prior UK-based work has reported on cross-sectional associations with poor mental functioning and low life satisfaction. These findings are consistent with earlier work in other countries [ 12 , 16 , 45 ].

We detected links between racial discrimination and poor physical health and impairment. Specifically, we found that those who reported racial discrimination had poorer self-rated health, poorer physical functioning scores and a greater likelihood of having a limiting longstanding illness than those who did not report racial discrimination. Earlier work using the 1993/1994 UK-based Fourth National Survey of Ethnic Minorities survey reported associations between perceived racial discrimination and poor self-rated health [ 27 , 47 ] and limiting longstanding illness [ 47 ]. Our more recent findings from 2009/2010 suggest that these deleterious associations remain an issue for minorities in the UK.

We detected stronger associations between racial discrimination and health for cross-sectional than for prospective comparisons, in keeping with earlier evidence [ 16 ]. However, cross-sectional work cannot determine whether reports of racial discrimination stimulate poor mental and physical health or whether perceptions of racial discrimination are a manifestation of feeling suboptimal mentally or physically. Our prospective findings therefore add to the field in establishing that racial discrimination predicts poor mental and physical outcomes prospectively, net of baseline associations, supporting the hypothesis that racial discrimination has adverse consequences for future health.

With regard to the pathways through which racial discrimination negatively impacts health, there are several possibilities that could help explain our results. One mechanism linking racial discrimination and health may be through the dysregulation of stress-related biological processes. In response to perceived chronic discrimination, stress processes may be frequently activated, which over time may result in disturbances across multiple biological systems, in line with the theory of allostatic load [ 20 ]. Review evidence indicates discrimination is associated with heightened cardiovascular responses to stress [ 11 , 21 ], though it is unclear whether this translates into an increased risk for clinical hypertension [ 48 ]. Another biological mechanism that may link discrimination and health is through activation of the hypothalamic-pituitary-adrenal (HPA) axis. Several reviews have linked racial discrimination [ 21 , 49 , 50 ] with changes in various cortisol parameters, which in turn have been linked with poorer mental and physical health [ 51 , 52 ]. Deleterious changes in other biological processes such as heightened inflammation [ 22 ] and alterations in DNA methylation of stress-related genes [ 53 ] have been linked with discrimination in recent studies. Alterations in these stress-related biological processes offer a plausible link to negative changes in physical [ 54 , 55 ] and, mental health outcomes [ 51 , 56 ]. Racial discrimination has also been associated with disturbances in neurobiological processes, with alterations observed in brain areas such as the anterior cingulate cortex, prefrontal cortex and amygdala which overlap with pathways associated with poor mental health [ 57 ].

Individual health risk (e.g. smoking, alcohol consumption etc.) could link perceived racial discrimination and poor mental and physical health, either as a method of coping with the negative psychological effect of perceiving racial discrimination (e.g. excessive alcohol consumption as a coping mechanism) or as a barrier to engaging in healthy behaviours (e.g. avoiding a health service perceived to be discriminatory). Racial discrimination has been associated with smoking [ 23 , 58 , 59 ], excessive alcohol consumption [ 23 , 60 ], as well as substance abuse [ 61 , 62 ]. Review evidence has linked discrimination with poor sleep [ 63 ] as well as weight gain in prospective studies [ 24 ]. This individual health risk offers a plausible indirect pathway linking racial discrimination with both poor mental [ 64 , 65 ], as well as physical health outcomes [ 66 ].

Another possibility at the broader structural level is that racial discrimination may impact health through differential access to societal resources such as education, employment, welfare and criminal justice [ 14 , 15 ]. In the UK, a 2016 report documented persistent ethnic disparities in educational attainment, employment, access to fair pay and adequate housing, as well the over-representation of ethnic minorities in the criminal justice system [ 4 ]. Further, data from this report highlight inequalities in access to healthcare among ethnic minority groups [ 4 ]. While meta-analytic evidence indicates that racial discrimination is associated with more negative patient experiences of health services, as well as delaying/not getting healthcare and lack of treatment uptake [ 19 ]. As these factors are social determinants of health in of themselves [ 13 , 14 , 15 ], they may act as a pathway through which perceptions of racial discrimination can act to negatively influence health.

The results of the current study need to be assessed in terms of strengths and limitations. There is a dearth of prospective evidence on the link between racial discrimination and health in UK samples, particularly in relation to physical health. Our large sample of ethnic minority participants allowed us to examine changes in mental and physical health over 2 years, and demonstrated both cross-sectional and prospective associations. We also adjusted statistically for factors that potentially confound associations, including age, sex, socioeconomic status and ethnicity. Although controlling for covariates does not tease out the complexity of the relationships between perceived racial discrimination and these sociodemographic characteristics [ 43 ]. For example, socioeconomic status contributes to racial inequalities in health [ 43 ], while racial discrimination can compound these disparities and can be conceptualised as an indicator of structural racism [ 13 ]; statistical adjusting for socioeconomic status does not capture these relationships.

The study of racism is a complex and contested area of research [ 67 , 68 ] and our study was not without limitations. Our measure of perceived discrimination was not specifically tailored for racial discrimination, as participants in the could attribute their experience to other forms of discrimination as well (e.g. sexism, ageism). There is evidence that the exposure instrument can influence associations between racism and physical and mental health outcomes [ 16 ] . Participants were able to attribute multiple reasons for their report of discrimination, which could have helped to avoid priming and this measure has been used to assess racial discrimination in previous work [ 28 ]. However, it is possible that measures such as the Schedule of Racist Events scale [ 69 ] and the Perceived Racism Scale [ 70 ] with more specific items on racist degradation and experiences of racism in personal and professional contexts could have garnered different results. Further, the self-report individual measure of racial discrimination employed in our study does not capture the structural conditions that shape the varied ways in which racial discrimination operates [ 14 ]. We only assessed perceived racial discrimination at baseline in this study and did not investigate whether racial discrimination experiences were persistent or changed over time.

Racial discrimination was assessed by self-reports of experiences in the past year and was therefore subject to recall bias. Our findings reflect the perception of racial discrimination rather than objective encounters with racial discrimination. It is possible that objective encounters with racism and perceiving one’s self as the target of racial discrimination might have different consequences for health. Experimental studies involving exposure to discriminatory scenarios have been used to investigate the health impact of objective exposures to racial discrimination. However, these studies may not represent a gold standard for the study of the relationship between discrimination and health, as meta-analytic evidence indicates that exposure to a single negative event in a laboratory setting does not negatively influence health [ 12 ].

In conclusion, this study adds to the field by demonstrating cross-sectional and prospective relationships between racial discrimination and both mental and physical health outcomes. With the rise in racial discrimination in the UK [ 6 ] in the aftermath of the Brexit vote [ 9 ] our findings highlight the need to reduce racial discrimination, not only to promote equity, but also to potentially benefit mental and physical health and reduce health inequalities.

Racial discrimination is a complex system that involves assigning ethnic groups differential value, which drives disparities in access to power, resources and opportunities [ 14 , 15 ]. Due to its multi-faceted nature, occurring at both the structural and individual level multiple interventions will be required to tackle this pervasive determinant of health. Historically, raising awareness of racial discrimination has been necessary to promote activism to bring about legislative and social change to improve the position of ethnic minority groups. In terms of public health, there are calls to integrate research about racial discrimination and health into medical teaching in an attempt to tackle structural racism and to highlight the impact racial discrimination has on health [ 71 , 72 ]. As well as strategies to reduce the pervasiveness of racial discrimination in institutional contexts, action through social media may have benefits for individual health too. The Black Lives Matter campaign is an example of a recent social media movement which has drawn attention to the issue of racial discrimination. There is some evidence that campaigns may provide a source of empowerment, particularly in a time where ethnic minority youth participation in traditional civic engagement activities are in decline [ 73 ]. Evidence suggests the Twitter conversation remained Black-led [ 73 ] and that the majority of the 40 million plus tweets were supportive of the movement [ 73 , 74 ]. However, whether social media campaigns positively [ 73 ] or negatively impact minority health [ 75 ] remains the subject of debate. Further, it should be acknowledged that interventions to educate and raise awareness do not tackle the structural macro-level forces that shape the position of ethnic minorities in society. Although, more challenging to address, work is required to identify socio-political processes that generate racial discrimination so attempts can be made to mitigate its effects. Research into the pathways underlying the link between racial discrimination and health are required to develop policy and to target interventions in this field.

Availability of data and materials

The UKHLS datasets analysed during the current study are freely available in the UK Data Service repository https://ukdataservice.ac.uk/

Abbreviations

Confidence Interval

General Health Questionnaire-12

Hypothalamic-pituitary-adrenal

Organization for Economic Cooperation and Development

Short-form Health Survey-12

United Kingdom

The United Kingdom Household Longitudinal Study

United States

Alvarez-Galvez J, Salvador-Carulla L. Perceived discrimination and self-rated health in Europe: evidence from the European social survey (2010). PLoS One. 2013;8:e74252.

Article   CAS   Google Scholar  

The Race Relations Act. 1965. https://www.legislation.gov.uk/ukpga/1965/73/enacted .

The Equality Act. 2010. https://www.legislation.gov.uk/ukpga/2010/15/contents .

Equality and Human Rights Commission. Healing a Divided Britain: the need for a comprehensive race equality strategy. 2016.

Google Scholar  

European Union. Discrimination in the EU in 2015. 2015.

Kelley N, Khan O, Sharrock S. Racial prejudice in Britain today. London: NatCen Social Research and Runnymede Trust; 2017.

Home Office. Hate Crime, England and Wales, 2017/18. Statistical Bulletin 20/18. 2018.

Crown Office and Procurator Fiscal Service. Hate Crime in Scotland 2017–18. 2019. https://www.copfs.gov.uk/images/Documents/Statistics/Hate%20Crime%202017-18/Hate%20Crime%20in%20Scotland%202017-18.pdf .

Bhui K, Halvorsrud K, Nazroo J. Making a difference: ethnic inequality and severe mental illness. Br J Psychiatry. 2018;213:574–8.

Article   Google Scholar  

Goto JB, Couto PFM, Bastos JL. Systematic review of epidemiological studies on interpersonal discrimination and mental health. Cad Saude Publica. 2013;29:445–59.

Pascoe EA, Smart RL. Perceived discrimination and health: a meta-analytic review. Psychol Bull. 2009;135:531–54.

Schmitt MT, Branscombe NR, Postmes T, Garcia A. The consequences of perceived discrimination for psychological well-being: a meta-analytic review. Psychol Bull. 2014;140:921–48.

Williams DR, Mohammed SA. Discrimination and racial disparities in health: evidence and needed research. J Behav Med. 2009;32:20–47.

Krieger N. Measures of racism, sexism, heterosexism, and gender Binarism for health equity research: from structural injustice to embodied harm—an Ecosocial analysis. Annu Rev Public Health. 2020;41:37–62.

Williams DR, Mohammed SA. Racism and health I: pathways and scientific evidence. Am Behav Sci. 2013;57. https://doi.org/10.1177/0002764213487340 .

Paradies Y, Ben J, Denson N, Elias A, Priest N, Pieterse A, et al. Racism as a determinant of health: a systematic review and meta-analysis. PLoS One. 2015;10:e0138511.

Priest N, Paradies Y, Trenerry B, Truong M, Karlsen S, Kelly Y. A systematic review of studies examining the relationship between reported racism and health and wellbeing for children and young people. Soc Sci Med 1982. 2013;95:115–27.

Lee DL, Ahn S. The relation of racial identity, ethnic identity, and racial socialization to discrimination-distress: a meta-analysis of black Americans. J Couns Psychol. 2013;60:1–14.

Ben J, Cormack D, Harris R, Paradies Y. Racism and health service utilisation: a systematic review and meta-analysis. PLoS One. 2017;12:e0189900.

McEwen BS. Stress, adaptation, and disease. Allostasis and allostatic load. Ann N Y Acad Sci. 1998;840:33–44.

Lockwood KG, Marsland AL, Matthews KA, Gianaros PJ. Perceived discrimination and cardiovascular health disparities: a multisystem review and health neuroscience perspective. Ann N Y Acad Sci. 2018;1428:170–207.

Kershaw KN, Lewis TT, Diez Roux AV, Jenny NS, Liu K, Penedo FJ, et al. Self-reported experiences of discrimination and inflammation among men and women: the multi-ethnic study of atherosclerosis. Health Psychol Off J Div Health Psychol Am Psychol Assoc. 2016;35:343–50.

Borrell LN, Kiefe CI, Diez-Roux AV, Williams DR, Gordon-Larsen P. Racial discrimination, racial/ethnic segregation, and health behaviors in the CARDIA study. Ethn Health. 2013;18:227–43.

de CO B, Bastos JL, González-Chica DA, Peres MA, Paradies YC. Interpersonal discrimination and markers of adiposity in longitudinal studies: a systematic review. Obes Rev Off J Int Assoc Study Obes. 2017;18:1040–9.

McFall S, Nandi A, Platt L. Understanding society: UK household longitudinal study: user guide to ethnicity and immigration research. 4th ed. Colchester: University of Essex; 2017.

Heim D, Hunter SC, Jones R. Perceived discrimination, identification, social capital, and well-being: relationships with physical health and psychological distress in a U.K. minority ethnic community sample. J Cross-Cult Psychol. 2011;42:1145–64.

Bécares L, Nazroo J, Stafford M. The buffering effects of ethnic density on experienced racism and health. Health Place. 2009;15:700–8.

Wallace S, Nazroo J, Bécares L. Cumulative effect of racial discrimination on the mental health of ethnic minorities in the United Kingdom. Am J Public Health. 2016;106:1294–300.

University of Essex. Institute for Social and Economic Research, NatCen Social Research, Kantar Public. Understanding Society: Waves 1–7, 2009–2016 and Harmonised BHPS: Waves 1–18, 1991–2009. 9th Edition. UK data service; 2017.

Knies G. Understanding society: The UK household longitudinal study, Waves 1–7 (User Guide). University of Essex. Colchester: Institute for Social and Economic Research; 2017.

Hackett RA, Steptoe A, Jackson SE. Sex discrimination and mental health in women: a prospective analysis. Health Psychol Off J Div Health Psychol Am Psychol Assoc. 2019;38:1014–24.

Hackett RA, Steptoe A, Lang RP, Jackson SE. Disability discrimination and well-being in the United Kingdom: a prospective cohort study. BMJ Open. 2020;10:e035714.

Goldberg D, Williams P. A user’s guide to the general health questionnaire. Basingstoke: NFER-Nelson; 1988.

Romppel M, Braehler E, Roth M, Glaesmer H. What is the general health Questionnaire-12 assessing?: dimensionality and psychometric properties of the general health Questionnaire-12 in a large scale German population sample. Compr Psychiatry. 2013;54:406–13.

Ware J, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34:220–33.

Cheak-Zamora NC, Wyrwich KW, McBride TD. Reliability and validity of the SF-12v2 in the medical expenditure panel survey. Qual Life Res. 2009;18:727–35.

Ware JE. How to score version 2 of the SF-12 health survey (with a supplement documenting version 1) edition: first. Lincoln, RI: Health Assessment Lab; 2002.

Lucas RE, Donnellan MB. Estimating the reliability of single-item life satisfaction measures: results from four National Panel Studies. Soc Indic Res. 2012;105:323–31.

Dolan P, Peasgood T, White M. Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being. J Econ Psychiatry. 2008;29:94–122.

Jackson SE, Hackett RA, Steptoe A. Associations between age discrimination and health and wellbeing: cross-sectional and prospective analysis of the English longitudinal study of ageing. Lancet Public Health. 2019;4:e200–8.

Jackson SE, Hackett RA, Grabovac I, Smith L, Steptoe A. Perceived discrimination, health and wellbeing among middle-aged and older lesbian, gay and bisexual people: a prospective study. PLoS One. 2019;14:e0216497.

DeSalvo KB, Bloser N, Reynolds K, He J, Muntner P. Mortality prediction with a single general self-rated health question. A meta-analysis. J Gen Intern Med. 2006;21:267–75.

Williams DR, Priest N, Anderson N. Understanding associations between race, socioeconomic status and health: patterns and prospects. Health Psychol Off J Div Health Psychol Am Psychol Assoc. 2016;35:407–11.

OECD. What are equivalence scales? 2005.

Sutin AR, Stephan Y, Carretta H, Terracciano A. Perceived discrimination and physical, cognitive, and emotional health in older adulthood. Am J Geriatr Psychiatry Off J Am Assoc Geriatr Psychiatry. 2015;23:171–9.

Nandi A, Luthra R, Benzeval M. Ethnic and racial harassment and mental health: Identifying sources of resilience (No. 2016–14). 2016. https://www.iser.essex.ac.uk/research/publications/working-papers/iser/2016-14 .

Karlsen S, Nazroo JY. Agency and structure: the impact of ethnic identity and racism on the health of ethnic minority people. Sociol Health Illn. 2002;24:1–20.

Dolezsar CM, McGrath JJ, Herzig AJM, Miller SB. Perceived racial discrimination and hypertension: a comprehensive systematic review. Health Psychol Off J Div Health Psychol Am Psychol Assoc. 2014;33:20–34.

Busse D, Yim IS, Campos B, Marshburn CK. Discrimination and the HPA axis: current evidence and future directions. J Behav Med. 2017;40:539–52.

Korous KM, Causadias JM, Casper DM. Racial discrimination and cortisol output: A meta-analysis. Soc Sci Med 1982. 2017;193:90–100.

Adam EK, Quinn ME, Tavernier R, McQuillan MT, Dahlke KA, Gilbert KE. Diurnal cortisol slopes and mental and physical health outcomes: a systematic review and meta-analysis. Psychoneuroendocrinology. 2017;83:25–41.

Hackett RA, Kivimäki M, Kumari M, Steptoe A. Diurnal cortisol patterns, future diabetes, and impaired glucose metabolism in the Whitehall II cohort study. J Clin Endocrinol Metab. 2016;101:619–25.

Santos HP, Nephew BC, Bhattacharya A, Tan X, Smith L, Alyamani RAS, et al. Discrimination exposure and DNA methylation of stress-related genes in Latina mothers. Psychoneuroendocrinology. 2018;98:131–8.

Wang X, Bao W, Liu J, OuYang Y-Y, Wang D, Rong S, et al. Inflammatory markers and risk of type 2 diabetes. Diabetes Care. 2013;36:166–75.

Chida Y, Steptoe A. Greater cardiovascular responses to laboratory mental stress are associated with poor subsequent cardiovascular risk status a meta-analysis of prospective evidence. Hypertension. 2010;55:1026–32.

Slavich GM, Irwin MR. From stress to inflammation and major depressive disorder: a social signal transduction theory of depression. Psychol Bull. 2014;140:774–815.

Berger M, Sarnyai Z. “More than skin deep”: stress neurobiology and mental health consequences of racial discrimination. Stress Amst Neth. 2015;18:1–10.

Bennett GG, Wolin KY, Robinson EL, Fowler S, Edwards CL. Perceived racial/ethnic harassment and tobacco use among African American young adults. Am J Public Health. 2005;95:238–40.

Sims M, Diez-Roux AV, Gebreab SY, Brenner A, Dubbert P, Wyatt S, et al. Perceived discrimination is associated with health behaviours among African-Americans in the Jackson heart study. J Epidemiol Community Health. 2016;70:187–94.

Gilbert PA, Zemore SE. Discrimination and drinking: A systematic review of the evidence. Soc Sci Med 1982. 2016;161:178–94.

Gibbons FX, Gerrard M, Cleveland MJ, Wills TA, Brody G. Perceived discrimination and substance use in African American parents and their children: a panel study. J Pers Soc Psychol. 2004;86:517–29.

Gibbons FX, Etcheverry PE, Stock ML, Gerrard M, Weng C-Y, Kiviniemi M, et al. Exploring the link between racial discrimination and substance use: what mediates? What buffers? J Pers Soc Psychol. 2010;99:785–801.

Slopen N, Lewis TT, Williams DR. Discrimination and sleep: a systematic review. Sleep Med. 2016;18:88–95.

Brennan PL, SooHoo S, Lemke S, Schutte KK. Alcohol use predicts 10-year depressive symptom trajectories in the health and retirement study. J Aging Health. 2016;28:911–32.

Baglioni C, Battagliese G, Feige B, Spiegelhalder K, Nissen C, Voderholzer U, et al. Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. J Affect Disord. 2011;135:10–9.

Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364:937–52.

Boyd R, Lindo E, Weeks L, McLemore M. On Racism: A New Standard For Publishing On Racial Health Inequities. Health Affairs Blog. 2020. https://www.healthaffairs.org/do/10.1377/hblog20200630.939347/full/ . Accessed 18 Aug 2020.

Kaplan JB, Bennett T. Use of race and ethnicity in biomedical publication. JAMA. 2003;289:2709–16.

Landrine H, Klonoff EA. The schedule of racist events: a measure of racial discrimination and a study of its negative physical and mental health consequences. J Black Psychol. 1996;22:144–68.

McNeilly MD, Anderson NB, Armstead CA, Clark R, Corbett M, Robinson EL, et al. The perceived racism scale: a multidimensional assessment of the experience of White racism among African Americans. Ethn Dis. 1996;6:154–66.

CAS   PubMed   Google Scholar  

Jee-Lyn García J, Sharif MZ. Black lives matter: a commentary on racism and public health. Am J Public Health. 2015;105:e27–30.

Williams DR, Cooper LA. Reducing racial inequities in health: using what we already know to take action. Int J Environ Res Public Health. 2019;16. https://doi.org/10.3390/ijerph16040606 .

Freelon D, McIlwain CD, Clark M. Beyond the Hashtags: #Ferguson, #Blacklivesmatter, and the online struggle for offline justice. SSRN scholarly paper. Rochester, NY: Social Science Research Network; 2016. https://papers.ssrn.com/abstract=2747066 . Accessed 8 Aug 2019.

Ince J, Rojas F, Davis CA. The social media response to black lives matter: how twitter users interact with black lives matter through hashtag use. Ethn Racial Stud. 2017;40:1814–30.

Umaña-Taylor AJ, Tynes BM, Toomey RB, Williams DR, Mitchell KJ. Latino adolescents’ perceived discrimination in online and offline settings: an examination of cultural risk and protective factors. Dev Psychol. 2015;51:87–100.

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Supplementary Table 1. Racial discrimination types and settings by ethnic group. Supplementary Table 2. Associations between racial discrimination and health outcomes (complete cases at wave 3). Supplementary Table 3. Cross-sectional and prospective associations between racial discrimination and health outcomes stratified by ethnic group

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Hackett, R.A., Ronaldson, A., Bhui, K. et al. Racial discrimination and health: a prospective study of ethnic minorities in the United Kingdom. BMC Public Health 20 , 1652 (2020). https://doi.org/10.1186/s12889-020-09792-1

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Promoting Diversity and Combatting Discrimination in Research Organizations: A Practitioner’s Guide

Diversity and Discrimination in Research Organizations

ISBN : 978-1-80117-959-1 , eISBN : 978-1-80117-956-0

Publication date: 1 December 2022

The essay is addressed to practitioners in research management and from academic leadership. It describes which measures can contribute to creating an inclusive climate for research teams and preventing and effectively dealing with discrimination. The practical recommendations consider the policy and organizational levels, as well as the individual perspective of research managers. Following a series of basic recommendations, six lessons learned are formulated, derived from the contributions to the edited collection on “Diversity and Discrimination in Research Organizations.”

• Inclusive work climate
• Lessons learned
• Policy recommendations
• Recommendations for actions
• Sexual harassment

Striebing, C. , Müller, J. , Schraudner, M. , Gewinner, I.V. , Morales, P.G. , Hochfeld, K. , Hoffman, S. , Kmec, J.A. , Nguyen, H.M. , Schneider, J. , Sheridan, J. , Steuer-Dankert, L. , O’Connor, L.T. and Vandevelde-Rougale, A. (2022), "Promoting Diversity and Combatting Discrimination in Research Organizations: A Practitioner’s Guide", Striebing, C. , Müller, J. and Schraudner, M. (Ed.) Diversity and Discrimination in Research Organizations , Emerald Publishing Limited, Leeds, pp. 421-441. https://doi.org/10.1108/978-1-80117-956-020221012

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Copyright © 2023 Clemens Striebing, Jörg Müller, Martina Schraudner, Irina Valerie Gewinner, Patricia Guerrero Morales, Katharina Hochfeld, Shekinah Hoffman, Julie A. Kmec, Huu Minh Nguyen, Jannick Schneider, Jennifer Sheridan, Linda Steuer-Dankert, Lindsey Trimble O’Connor and Agnès Vandevelde-Rougale

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Transfer to Practice

It is a particular concern of ours to provide practitioners in academic organizations with the insights that they can draw from the contributions presented in this edited collection for their work and their specific organizational contextual conditions. With this essay, we therefore want to offer a comprehensive orientation on the question of what measures can be taken in practice to create discrimination-free working conditions for a diverse workforce, whereby we especially address academic leadership and research managers. Our prototypical program is described in the following steps:

Based on research on effective gender equality policies in research organizations, we derive four conditions that policy-makers should consider to provide sufficient framework conditions for reducing social and systemic discrimination in academia (see “Recommendations for Policy-Makers” section).

We outline a compact program of measures at the organizational level, which is essentially based on the studies of the US National Academies of Sciences, Engineering, and Medicine (2018) about the sexual harassment of women in science and experience of this article’s authors, which we have gained in our own projects (see “Recommendations for the Design of a Discrimination Resuction Program” section).

We discuss the role that research management can – or should – play in creating a diversity-inclusive team climate as well as preventing and managing cases of discrimination (see “Recommendations for Academic Leaders and Research Managers” section).

Finally, we discuss how the contributions in this edited collection add to the current state of research on the effective prevention and fair treatment of discrimination in the scientific workplace (see “Our Lessons Learned” section).

Recommendations for Policy-Makers

For more than two decades now, the European Commission has been funding research projects that address the question of how to increase the participation of women researchers in research teams and decision-making positions in the European Research Area. Without claiming to be exhaustive, examples include the Helsinki Group on Women in Science reports first published in 2002 ( EC, 2008 ), the PRAGES project ( Cacace, 2009 ), and the STAGES project ( Kalpazidou Schmidt and Cacace, 2017 ).

A subsequent assessment of the Organisation for Economic Co-operation and Development’s ( OECD, 2018 ) Science, Technology and Innovation Outlook appears to be rather skeptical concerning the impact of gender equality interventions in science, technology, engineering, and mathematics (STEM). The authors recognize the strong prevalence of gender equality measures among OECD countries, mainly aiming to increase the number of students in the STEM fields and the provision of support to individual women scientists. However, they criticize the fragmentation of current policy actions “[…] characterised by multiple institutions acting independently, and limited co-ordination between education, science and innovation actors” ( OECD, 2018 : 178). They attest an insufficient sustainability of the various initiatives and the need for more systemic evaluations and indicators as well as mutual learning formats. Especially regarding the importance of long-term monitoring and evaluation of gender equality challenges and measures, the OECD report confirms the policy recommendations of the mentioned EC reports. Moreover, the nub of equality measures addresses the quantitative equalization of women and men, yet the quality of work and working climate are a rare issue.

The following framework conditions for success in promoting gender equality in research – and, by analogy, promoting underrepresented or disadvantaged groups of people – can be derived from the reports mentioned above.

Gender monitoring: Highly institutionalized gender monitoring that comprises a high number of research institutions and indicators keeps gender equality on the broader political and organizational agenda and enables problem-framing and impact evaluation of gender equality measures.

Leadership: A clear commitment of political and organizational leaders gives legitimacy to those actors like working groups, equality officers or intrapreneurs who work every day to improve gender equality in their organizations.

Networks: Networks enable mutual learning for research organizations and enable coordinating extensive actions at multiple levels between versatile actors from local to global.

A fourth condition for success – which is not explicitly mentioned in the reports above but should not be underestimated – is the binding nature of anti-discrimination measures. Research shows that a lack of consequences often restricts the effectiveness of gender equality measures ( Matthies and Zimmermann, 2010 ; van den Brink and Benschop, 2012 ). Firm accountability provides measures such as quotas, voluntary agreements and gender equality plans with the necessary binding force and therefore will be considered in the following discussion, along with the other policy approaches.

Recommendations for Designing a Discrimination Reduction Program

Structured according to a simplified policy cycle that distinguishes the phases of policy formulation, implementation and evaluation and has an iterative sequence, Fig. 22 lists a number of measures to reduce, prevent and manage experiences of discrimination in the research workplace (see also Marquis et al., 2008 : 4–6).

Building Blocks of a Coherent and Comprehensive Program to Ensure a Discrimination-Free and Diversity-Friendly Workplace.

Evaluating the Status Quo and the Achievement of Objectives

The basis for developing an effective anti-discrimination program is a sound knowledge base on the distribution of employees according to different socio-demographic characteristics (e.g. age, gender, care responsibilities, ethnicity, etc.). For the purpose of evidence-based development of a discrimination reduction program, ideally data is collected that relates the respective socio-demographic characteristics to organizational status characteristics (e.g., hierarchical position, function, income) or employee perceptions and experiences (e.g., survey of work climate, experiences of social misconduct, compatibility of professional and private obligations). 1

The finer the units of analysis, the more meaningful the evaluation of the status quo and the achievement of objectives. For example, to identify potential outcomes of systemic discrimination, data should be differentiable by scientific or non-scientific activity or hierarchical level. The work climate may considerably vary between individual teams and across disciplines, depending on conflict constellations that are very situation-specific. 2

For an evaluation to be successful and – above all – practically relevant, it is important to plan for budget and working time. Evaluations not only involve sending out an online survey and presenting the results in PowerPoint; rather, they require a person or group of persons with sufficient expertise to develop an evaluation concept (key questions are: What do we want to know and why?), implement it using suitable survey instruments (questionnaires, interviews, focus groups, document analyses, etc.) in compliance with data protection regulations, and generate meaningful data that meet social science quality standards (e.g., validity and reliability, transferability, representativeness). In the meantime, there are a number of tools that enable an easily applicable organizational survey tailored to research organizations, e.g., on gender equality. 3 However, without social science expertise, even these tools cannot be used optimally, nor can the data generated be interpreted well.

Statistical methods such as questionnaire surveys often reach their limits when researching minority groups such as employees with health impairments or LGBTQI+ employees. Since social minorities are obviously often small groups in terms of numbers and therefore difficult to reach, collecting data on them often violates data protection regulations. Person-related inferences are easily made possible when – for example – two out of 80 respondents assign themselves to a third gender category. In these cases, qualitative methods such as interviews or focus groups, must be used to gather information about any experiences of discrimination. Another strength of qualitative methods is that they enable understanding correlations in data (e.g., why one social group evaluates the work climate worse than another), whereas the strength of quantitative methods lies in detecting and confirming such correlations.

Another necessity for an evaluation that holds practical relevance is a process for its utilization. Within this framework, questions arise concerning how often an evaluation should be carried out, what happens to the results of the evaluation, what happens in the case of conspicuous or critical values at the organizational or team level, who determines the threshold values for the critical values, and who manages this process. The clearer and more binding that the process is for utilizing the evaluation results, the stronger the practical impact of the evaluation.

The data collected and the evaluations carried out on it should be handled transparently to counteract the creation of organizational myths within the workforce about positive and negative discrimination among them, potentially compromising the effectiveness of anti-discrimination policies. 4 The results of the status quo and progress evaluation can be reported in the annual or equality report of a research organization. Continuous progress monitoring requires that the data collected meet social science standards from the outset (see the discussion of evaluation teams above), since data are no longer comparable between two or more time periods if the questionnaire design is changed in significant ways.

The knowledge base generated by the evaluation can be used to develop targeted policies. Noteworthy, the evaluation of the policy program to be established should already be considered during its development ( Palmén et al., 2019 ). Key questions are which indicators can be used to determine whether a program has been successful or whether adjustments are necessary. Furthermore, how are the data needed to answer this question generated, and who collects and evaluates them? Adequate human resources must be planned for ongoing evaluation.

Policy Formulation: Defining Clear Behavioral Expectations and Consequences

When designing a social intervention such as an anti-discrimination program, it is important to formulate a set of goals that are as specific as possible for the state to aim for. Specific goals enable the effective planning and use of the human and financial resources available to implement the program, means-ends relationships can be assessed for appropriateness, and goal achievement can be evaluated. Insofar as an organizational cultural change is aimed for, it should be clearly presented accordingly which behavior is expected from the employees in concrete terms, which complaint channels are open in the event of violations and which consequences may occur ( Daley et al., 2018 ).

A code of conduct can be formulated as a key document that provides a framework of orientation for employees and the anti-discrimination program. The code of conduct should be short and compact. It should not be formulated only by the leadership team but in a participatory process involving employees. This promotes the acceptance and implementation of such a code of conduct. In practice, such codes of conduct regularly address the key issues of workplace integrity and the prevention of workplace incivility. Such broad framing signals that protection against discrimination requires the active cooperation of all employees and that not only extreme cases of discrimination that can be proven in court are to be prevented, but rather that the general aim is to create a positive inclusive working environment in which even minor forms of discrimination cannot flourish in the first place.

Broad framing as workplace integrity or incivility also emphasizes the integrated nature of an anti-discrimination policy. In practice, in most academic institutions, equality officers, disability officers, anti-racism officers, work councils and other bodies are separate institutions that often have to establish mutual intersections. For example, if a sexist work environment prevails at a university or other academic institution, organizational change should not only be the responsibility of the equal opportunity officers, but must be driven by the management level and lived by all employees. Moreover, it is very likely that other types of discrimination are also taking place. A smart anti-discrimination policy takes into account and bridges the functional differentiation of institutional discrimination prevention and management.

In the sense of an integrated approach with clear behavioral expectations, it is also important to explicitly include personnel management competencies in job profiles and subsequently also evaluate academic leaders based on these competencies. At present, the suitability of researchers for leadership positions is often assessed solely based on their academic performance and very few leaders are trained to recognize or effectively address inequitable behaviors. Management and personnel leadership skills are expected in very few job requirements, although “team science” ( Wang and Barabási, 2021 ) is on the rise.

When designing policies, it is also important to encourage bottom-up approaches, i.e., initiatives coming from employee representatives, team members, and not decided by an institution’s management. Such initiatives are more likely to promote equity in a grounded and reflexive approach that might challenge dominant views on personnel management in academia and research organizations. Bottom-up approaches could inter alia help thinking research policies and practices outside a neoliberal managerial grid (see Vandevelde-Rougale and Guerrero Morales in this collection) and thus contribute to fostering a more caring environment, with more time and resources allocated to thinking and creating, and less to complying with evaluation indicators based on international rankings that tend to reinforce power imbalance and competition both between individuals and between organizations instead of acknowledging the contribution of research to society ( Hodgins and McNamara, 2021 ).

Policy Implementation: Embedding Objectives Through Context-Specific Measures

An anti-discrimination program should generally be implemented through context-specific interventions ( Palmén et al., 2019 ). This means that the program should be tailored as appropriately as possible for the specific situation and challenges in an organization. Individual interventions should be adapted to the requirements and needs of different target groups, such as research managers, early career researchers, administrative staff, and others. Measures should also take into account organizational characteristics: for example, in a research organization with low staff turnover, targets for the representation of certain social groups will only be realized in the long term.

In terms of content, a wide range of measures is available, which should be coordinated with evaluating the status quo and formulating goals. Typical measures include welcome actions for new staff, training for employees to enable them to implement the goals of the anti-discrimination program in their daily work; for example, to recognize and overcome implicit prejudices against certain social groups, work productively in diverse teams, or behave appropriately as a bystander to discriminatory behavior in the workplace. Training such as anti-discrimination or anti-gender bias training as part of institutional onboarding after hiring and repeated refresher courses can also help to ensure that managers have the appropriate skills for inclusive leadership and conflict management.

As already mentioned above, the commitment of the academic leaders in a research organization is a central condition for the success of an anti-discrimination program. This commitment should be visible in the organization; for example, through speeches or circulars (provided that these discourses are linked to means and practical actions). 5

Fig. 22 lists a range of other possible measures through which the goals of an anti-discrimination policy can be implemented: regular career-related and documented development discussions between leaders and their employees promote joint career development and partly counteract biased preference or disadvantage in interactions between leaders and their employees (vertical discrimination), especially early career researchers and their supervisors, as well as among employees (horizontal discrimination). Low-threshold, confidential, and well-advertised reporting channels – which can not only be consulted in cases of tangible discrimination – may enable leaders to intervene at an early stage. In cases where the personal supervisor is excluded as a reporting channel due to a conflict, research organizations should offer “neutral” reporting channels that are not embedded in local hierarchies and dependencies. Depending on the context, measures aimed at improving the reconciliation of scientific work and private life are potentially suitable for reducing gender-related discrimination, e.g., crediting parental leave and care responsibilities when assessing the scientific performance of an early- or mid-career researcher, waiving meetings at off-peak times, or offering childcare.

Recommendations for Academic Leaders and Research Managers

Research managers are considered to be those individuals who provide support services to researchers and academics and themselves have an academic education and – in some cases – experience in research and teaching ( WR, 2018 : 85). 6 They work in staffs or decentralized units, monitor compliance with quality standards, supervise committees, and are involved with personnel processes in a variety of ways.

While the integration and productive use of diversity in research teams in everyday work is the task of traditional academic leaders – e.g., chair holders, research group leaders or the dean – research managers are regularly entrusted with diversity monitoring and developing and implementing strategic action programs (as exemplified above) from an organizational perspective. A comparable division of labor also exists for preventing and handling discrimination, which are regularly to be resolved initially by “line management,” i.e., the immediate leader in accordance with the academic hierarchical order, but which can be handed over under certain criteria or alternatively to specially established committees, staff units or service providers. Examples include academic ombudspersons, equal opportunity officers, compliance officers, representatives of the severely disabled, staff councils, psycho-social counseling centers, lawyers or other external reporting offices. Nonetheless, as studies in this volume show, these organs do not always interfere flawlessly, which require further optimization of their work and anti-discrimination actions.

Integrating Diverse Teams

Regarding gender-diverse teams, Nielsen et al. (2018) discuss how to create a diversity-inclusive team climate in research and innovation development. First, the quality of collaboration and problem-solving ability of diverse teams (and homogeneous teams as well) is considerably influenced by their diversity belief and openness to diversity. Diversity belief refers to the conviction of individual team members that their difference is a strength in the work process ( van Dick et al., 2008 ). Openness to diversity refers to the awareness of – for example – visible, informational or value differences in a team and the willingness of a team member to engage with dissimilar individuals and learn from them ( Hobman et al., 2004 ). Accordingly, it is recommended that academic leaders interact with their teams to determine whether they view themselves as homogeneous or heterogeneous in terms of the professional and socio-demographic characteristics of their members and whether they view each as positive or negative. A low openness to diversity or a low diversity belief would have to be explored in an exchange with the team or a bilateral exchange with the team members.

Second, teams that work productively are those whose interactions (i.e., conversations and collaboration) between team members are determined by the expertise and experience of individual team members rather than social relationships ( Joshi and Knight 2015 ). For leaders, this implies clearly identifying and communicating to the team the competencies and responsibilities of each member of their team. Larger work tasks in research projects should be differentiated according to the competencies that they require to be mastered and how the team members can optimally complement each other in their competencies.

Third, the same applies to the integration of diverse teams that applies to team processes in general, namely teams need team players. Team members should have a certain level of identification with their team, a shared sense of purpose and they must trust the team’s ability to accomplish tasks, the team’s processes should be transparently coordinated, and team members should treat each other with mutual respect and openness ( Nielsen et al., 2018 ). The team structure should thereby regulate itself based on the competencies and expertise of the team members, as noted above. Too much team cohesion in turn can lead to isolation and silo thinking in an organization and may even be more conducive to exclusion and discrimination processes ( Feldblum and Lipnic, 2021 ).

Preventing and Managing Discrimination

The expectations placed on leaders and research managers to prevent and deal with discrimination in the workplace are sometimes high and sometimes seem contradictory. An idealistic and a realistic perspective can be distinguished.

In the idealistic perspective, organizations strive for rationally acting leadership and management personnel. These personnel are sensitized through training and show zero tolerance toward discriminatory behavior and structures in the workplace. They regularly and perceptibly commit to zero tolerance in the organization, set an example through their own behavior, and deal with discrimination claims promptly and fairly (prototypical Daley et al., 2018 ).

On the other hand, a realistic perspective takes better account of the complexity of social conflicts in the workplace. It is often not possible to say clearly who are the perpetrators and who are the victims in a conflict case. Typical of this are claims of systemic discrimination based on institutions – i.e., implicit and explicit rules and practices – in an organization or in cases of scandalization. In his studies on academic mobbing, Westhues (2021) recommends a sober and critical approach to complaints of workplace misconduct within the line authority. The respective academic leaders in charge would have a broader perspective to deal with claims sensitively and fairly, whereas individuals and committees specifically appointed to investigate would sometimes tend toward zealotry. Westhues emphasizes that social conflict in the workplace is often borne out of social relationships. The individuals involved in each case seek empathy and allies, which can lead to the aforementioned scandalization, i.e., criticism by a group against an individual (also conceivable in relation to accusations of inaction regarding dismantling discriminatory institutions), without there being any concrete misconduct against the group.

In turn, the realistic perspective reaches its limits where problem-solving by academic leaders does not take place; for example, because they are involved in the conflict themselves, they are not willing to adjust supposedly discriminatory structures and rules, or an adjustment of the structures simply exceeds their work capacities.

In summary, it can be deduced from the comparison of the two approaches that universities and research institutions need sensitized leadership and management personnel who are aware of their role model function and trained to deal with employee complaints objectively, discreetly and rationally. At the same time, due to their embeddedness in the work processes of their own organization, academic leadership personnel are also only capable of objectively and conclusively resolving cases of social misconduct and discrimination complaints to a certain extent. This requires contact points that deal with preventing and managing discrimination on a structural basis (and not exclusively based on a specific case).

Our Lessons Learned

Lesson 1: identifying and knowing the majority group in a research organization is key to understanding discrimination processes.

Our first lesson learned is anything but a novel insight; rather, it is the core of social identity theory. The theoretical assumption that there are so-called in- and out-groups in (research) organizations, whose boundaries are constitutive of experiences of discrimination partly formed through experiences of discrimination, is supported in particular by the contributions of Sheridan et al., Striebing, Pantelmann and Wälty, Nguyen et al. and Gewinner. The contributions discuss and/or provide evidence of the negative consequences of deviating from a norm type that can typically be described as male, healthy, and belonging to the ethnic majority in a country. In their paper, Pantelmann and Wälty comprehensively explain the historically formative role of this in-group, leading to what the authors describe as an androcentric academia. A typical example of the androcentric character of work processes in academia is the traditionally very high proportion of men in scientific leadership positions and the low proportion of men in administrative assistant functions [ e.g., Kolboske (2021) shows this for the German Max Planck Society].

The respective in-groups – which vary in their composition depending on the local context – have defined the implicit and explicit rules and practices in research organizations over time and continue to play a major role in determining their interpretation. Examples of such indirectly exclusionary rules include processes that appear to create rationality and transparency, such as evaluation rules or review committees. These kind of rational processes are problematic when they only aim to create decision legitimacy through processes seen as legitimate rather than a truly legitimate, just, “good” outcome, free of cognitive bias (van den Brink and Benschop, 2012, on the concept of legal legitimacy: Mayntz, 2010 ). The Covid-19 pandemic and the associated problem of double jeopardy – especially for the parents of young children – is an example of how processes that appear objective can lead to systemic discrimination when research organizations evaluate process outcomes as “neutral.” The constraints associated with the pandemic have led to an average decline in publication output among female researchers, which will disadvantage their long-term career development if research organizations maintain their unilateral focus on process justice rather than outcome justice ( Squazzoni et al., 2021 ; Nature Editorial, 2021 ).

Examples of informal practices shaped and reproduced by an in-group that can have an indirectly exclusionary effect may seem trivial in some cases, but they can be highly meaningful in individual research organizations. One can think of regulars’ tables, meetings in the evening hours, 24/7 lab hours, hiking groups, and other forms of interaction that promote exchanges based on expectations of presence and personal sympathies rather than professional skills and expertise ( Nielsen et al., 2018 ).

In their study of Vietnamese social scientists, Nguyen et al. illustrate that individuals who assume a higher level of effort in informal household and care work are disproportionately less able to meet academic performance expectations than individuals who assume fewer household duties. In Vietnamese society, it is also usually women who are influenced in their career advancement by more informal work.

In his study on work climate in the Max Planck Society, Striebing also shows for Germany that women with responsibility for minor children rate their work climate lower than men with children or women without children. In Striebing’s studies on work climate and bullying, women generally rate their work climate lower than men and experience bullying more often. 7 Moreover, according to Sheridan et al., it is the employees who deviate from the norm due to their sexual orientation, skin color or health impairments who seem to most frequently experience hostile and intimidating behavior in the academic workplace (see lesson 5).

Using the example of women researchers from the former Soviet Union working in Germany, Gewinner provides a comprehensive picture of the extent to which institutions shaped by the respective national majority society and the in-groups in academic organizations pose special challenges to individuals who deviate from the in-groups; for example, due to their gender, living circumstances, or nationality.

Since academia – shaped by its respective local in-groups – cannot necessarily provide equal opportunities for a diverse workforce, good academic leaders and research managers strive in a self-reflective manner to dismantle those structures and processes that can lead to implicit and indirect disadvantage for certain groups of employees. This means that strengthening disadvantaged groups through mentoring and networking programs as well as training can only be one part, but it is equally important to be attentive to structures and processes that can lead to disadvantage, and to dismantle them.

Lesson 2: Managers Are Not Neutral Regulators and Conflict Resolvers

Creating an inclusive work culture, designing and implementing anti-discrimination prevention programs, reducing discrimination, and intervening in cases of conflict in the workforce are especially the tasks of academic leaders and research managers. A number of the studies in the edited collection imply that this group of people is not itself a neutral entity and is itself part or non-part of organizational in- and out-groups, as well as one of the most important levers for successful diversity management.

The study by Kmec et al. supports the relevance of belief systems in the interpretation of illegal harassment behaviors. The authors found that individuals who hold more gender egalitarian beliefs (that women and men are equal) are more likely to recognize factually illegal acts of sexual harassment than individuals with traditional gender beliefs. Their study also points to the special importance of merit beliefs: people who believe that they live in a just society tend to regard sexual harassment as neither illegal nor inappropriate in cases that are (in everyday perception) ambiguous.

Striebing’s work climate and bullying studies show that a gender gap in the perception of the work climate and the experience of bullying narrows from the PhD level to group or institute leadership. The author interprets this observation as a filtering mechanism of the science system. His results suggest that the “successful” women and men who hold scientific leadership positions perceive and evaluate their work environment differently than early career researchers and – as a conjecture – may have limited empathy for problems of their employees due to this different perception.

Vandevelde-Rougale and Guerrero Morales’ case studies demonstrate the high complexity of bullying constellations. They argue that management ideology and practices force individuals who perceive themselves to be affected by bullying or discrimination into a formalized discourse. They highlight that what a person complains about and how they do so is not only essential for perceiving conflict dynamics but also for how managers and research management perceive and evaluate the person, and that it can influence the likelihood of success of a complaint:

[…] even in organizations where policies to guarantee dignity and respect have been adopted, showing one’s hurt to managers or human resources department is not sufficient so that steps would be taken to ensure a saner working atmosphere; it can even be detrimental to the person showing his/her vulnerability. (Vandevelde-Rougale and Guerrero Morales in this collection)

The two authors also highlight that it can be problematic to apply seemingly rational approaches (e.g., measures to reduce discrimination and strengthen reconcilability) to issues that primarily have an emotional impact on those involved. For example, a person’s perceived work-life balance is not only influenced by organizational factors such as the range of flexible working time models and workload, and not only by cognitive-psychological factors such as a person’s ability to cope with stress or the pace at which a person works, but also by situational aspects such as individual career prospects or the management style, or societal aspects such as traditional views on parenting or care. If the individual work–life balance is nevertheless not right in an organization with comprehensive reconciliation offerings, it is therefore not necessarily the individual who is “defective,” but rather the broader social context must also be taken into account.

The contributions of Kmec et al. and Vandevelde-Rougale and Guerrero Morales imply the strong importance of patience and reflexivity – or “attentive listening” – in academic leadership. Thus, on the one hand, leaders and research managers are required to reconcile the different interests and personalities of individual team members and – in cases of conflict – weigh the perspectives of all stakeholders, including both co-workers and organizational goals. In doing so, it is important that academic leaders and research managers not only obtain a comprehensive picture – i.e., take all perspectives into account – but they also need a detailed picture, and they should perceive employees in their entirety as the people they are, with their multiple overlaps of status, character or social background. In doing so, evaluating leaders and research managers must also be aware of the relativity of their own perspective: Why might I find one person in a conflict more sympathetic than another or be better able to understand their perspective?

The article by Kmec et al. also shows the importance of drawing clear boundaries for misconduct in the workplace and sensitizing management personnel to this. Only in this way can clear decisions be made – even in “gray areas” – concerning what is judged to be appropriate or inappropriate, and managers must be supported in setting an example of the conduct desired in the workplace. In this context, with reference to their case study at the University of Wisconsin–Madison, Sheridan et al. state that most academic leaders and supervisors had no knowledge of how to deal with misconduct in general. They recommend that universities should essentially develop a process and disciplinary measures for this.

Lesson 3: The System Can Tend to Individualize and Normalize Discrimination

Just because a problem is not visible, this does not mean it is not there: in their case study of a German university, Pantelmann and Wälty form a diagnosis that could certainly be extended to other types of organizations:

The university approach to the problem [of sexual violence] paints a picture of sexual harassment as an individual (women’s) problem for which individual solutions must be found. Acts of harassment and violence are normalized, minimized, and dismissed by patriarchal gender norms and power relations […] as well as by complex and uneven systems of loyalty and hierarchy […]. (Pantelmann and Wälty in this collection)

By the university approach, the authors mean the interplay of patriarchal institutions (see lesson 1), the self-image of a non-discriminatory, neutral and enlightened academy, combined with market-oriented organizational and management structures (e.g., performance evaluation, dependency and competition situations reinforced by fixed-term employment relationships, competition for external funding).

The authors note – similar to Vandevelde-Rougale and Guerrero Morales (see lesson 2) – that there seems to be a contradiction between the rational world of science and experiences of discrimination, harassment, and bullying that primarily take place on an emotional level. The latter are seen as remote from science and more societal in nature. On the part of research managers, this led to a failure to accept their (co-)responsibility for the campus as part of society and a good working atmosphere to the necessary extent, as well as combatting social misconduct and systemic discrimination, even if it remained below a threshold punishable by criminal or labor law.

From these considerations, it can be concluded that in most research organizations an institutional commitment to responsibility for a good research culture and combating discriminatory behavior and structures (as well as other forms of social misconduct) is an essential milestone. Often reviled as “paper tigers,” in this sense codes of conduct are important markers of the way forward and institutional self-assurances that can then have an indirect impact on an organization’s discrimination policies. However, due to the tendency to normalize, relativize, and downplay discrimination as described by Wälty and Pantelmann, one or the other skeptical leader must be convinced that the formulation of a formal institutional commitment against discrimination is desirable (but not sufficient per se ). In this regard, Sheridan et al. emphasize the added value of employee surveys, not least to counter skeptics of the need for anti-discrimination measures with data.

Lesson 4: How Identity Characteristics Shape Conflicts and Conflict Perceptions Is Difficult to Predict and Strongly Depends on Situational Circumstances (in Individual Cases)

In particular, the contribution of Vandevelde-Rougale and Guerrero Morales conveys how the multiple socio-demographic characteristics of individuals involved in conflict can shape conflicts and conflict dynamics. Identity categories such as gender, class, nation or race can be intertwined with different power positions. These identity-related power positions may be the starting point of conflicts, and they can be mobilized by participants in conflicts to place themselves in a stronger position (e.g., as part of the search for allies or to normatively underpin their own position), and they also shape the way in which third parties (such as leaders and research managers) perceive and interpret a conflict.

Accordingly, Sheridan et al. highlight that in practice they have found that individuals who receive and process complaints against social misconduct must be well trained in implicit/explicit bias and discrimination. Accordingly, there is a possibility that the view of persons making a report against social misconduct is biased. Thus, the reported person’s behaviors would sometimes be interpreted depending on their gender, sexual orientation, race, or other socio-demographic factors.

Striebing’s paper builds on this consideration and explores whether a person’s gender is related to whether that person perceives one or a series of negative experiences as bullying or sexual discrimination. In practice, it is possible for individuals who complain to a leader or other entity about misconduct or discrimination to be (implicitly) confronted with accusations of being too sensitive ( Hinze, 2004 ). A reference to the identity of the reporting individuals then functions as an easy legitimation for leaders and research managers to justify doing nothing or decide and act along their sympathies and (maybe biased) intuition.

Striebing concludes that the relationship between experience(s) of negative acts in the workplace and their assessment as bullying or sexual discrimination is indeed influenced by the gender of the person concerned. However, the pattern of this correlation – i.e. which specific negative acts are more often seen as “transgressive” by women or men – is so complex and weak in its entirety that a practical effect is questionable.

As a result of these considerations, leaders and research managers should be sensitized to perceive and deal with the identitarian dimension of workplace conflicts and reflect their own positioning appropriately. At the same time, leaders and research managers should be sensitized to be attentive and critical whenever a person’s credibility is placed in the context of his/her socio-demographic characteristics.

Lesson 5: Measures Aimed at Very General Groups of People Waste Financial and Personnel Resources

Often academic support programs target very open groups of people, such as “the women,” “the students with an immigrant background,” or “the working-class children.” However, this does not sufficiently take into account the fact that people have a variety of identities and balance them with each other.

The studies by Gewinner, Nguyen et al., Striebing and Sheridan et al. show that – for example – women are not fundamentally less able than men to compete academically and in the working environment, experience a qualitatively poorer working environment or misconduct more frequently. Moreover, women might perceive programs addressing women as discriminatory by themselves, since they subtly and unconsciously label them as less productive, thus manifesting the gender or national differences. Even women in a conservative male breadwinner partnership who take on the main responsibility of raising children in their partnership are not necessarily at a disadvantage if – for example – they are supported by their (in-)parents, as Nguyen et al. show. Therefore, it is necessary to pay attention to gender aspects in organizing the most suitable form of support programs such as training courses for female researchers. Striebing also shows for the German Max Planck Society that self-perceptions of bullying experiences are more frequent – for example – among male social scientists than among women in the STEM disciplines. In Sheridan et al., among the group of women, women of color and those with disabilities most frequently report experiences of hostile and intimidating behavior in the workplace, and in the group of men, gay men and those with disabilities.

Research management should apply an intersectional perspective 8 when analyzing the need for organizational support measures and conceptualizing these measures. Vulnerable target groups and their needs should be defined and analyzed as precisely as possible. For example, if a measure is to be developed to increase the proportion of women, it should be asked in as much detail as possible which women can benefit from the measure and under which circumstances, as well as which ones cannot. If a measure is to be developed to prevent, e.g., sexism or racism, it should be asked which groups of people are to be protected from which groups of people in particular.

Lesson 6: It Is a Long Way from Raising Awareness through Trainings to Factual Effects on the Incidence of Discrimination Experiences

Sheridan et al. show in their study that short-term effects of anti-discrimination measures such as training or information campaigns cannot be expected. Based on the authors’ data, it can be surmised that such measures can immediately and quite persistently increase sensitivity to discriminatory and inappropriate behavior in the workplace and knowledge about how to deal with it, but that there are pitfalls for a long-term effect on reported cases of social misconduct in the workplace (see also Chang et al., 2019 ). The authors conclude: “We have found supplemental education and resources are necessary to empower individuals to interrupt HIB [hostile and intimidating behavior] in their work environments” (Sheridan et al. in this collection).

It also seems conceivable that local efforts to promote diversity in academia may also be undermined by developments at the regional or national level. For example, Sheridan et al. emphasize a more adversarial political and social climate under Donald Trump’s presidency in the United States. They speculate that this overall climate change might provide a possible explanation for why counterintuitively LGBT individuals were the only ones among the groups of individuals studied to even report an increase in experiences of misconduct in the academic workplace during the study period.

Steuer-Dankert and Leicht-Scholten also highlight the challenges of a multi-level perspective in diversity management. In doing so, they adopt a holistic perspective by analyzing the framework conditions of the German science system and reflecting on the different influencing factors. They link this perspective to a systems theory approach, which highlights the complexity of key positions and emphasizes the need to develop measures that address the specific framework conditions of the respective organization. Using the example of a complex research organization with several management levels – i.e., the institute and network level or the chair and university management level as well as institute-specific cultures – Steuer-Dankert and Leicht-Scholten identify the general challenge in the fact that the diversity climate experienced by the research teams is ultimately a function of the diversity management of the different levels. The authors therefore point to the importance of a common diversity strategy that is co-formulated and supported by all levels of an organizational network and fits the needs of the respective organizational levels. Steuer-Dankert and Leicht-Scholten emphasize the potential of academic leaders as multipliers for establishing an open diversity belief and climate. In their case study of a large German research association, Steuer-Dankert and Leicht-Scholten found that the leadership style attributed to management and the leadership style that they aspired to themselves were closely linked. The authors see these effects of homosocial reproduction as an explanation for this ideational similarity between managers (managers hire and promote people if they feel connected to them due to perceived similarities) and the role model effect of top managers whose style is adopted in practice by team members. Linked to the examined perception of diversity, Steuer-Dankert and Leicht-Scholten also see a direct effect of leadership behavior in the diversity management context on the next generation of scientists. In order to counteract these effects in the long term, they recommend a stronger link between diversity management and the change management approach, which at the same time underpins the long-term nature of corresponding measures.

We Can only go Ahead

Within the framework of the texts published in this collection, not only the extent of discrimination in research organizations was measured and described, but often implicit or direct criticism of established structures was also voiced. The main object of criticism was the effects of “neoliberalization” of universities ( Block, Gray, and Holborow, 2012 ; Hodgins and McNamara 2021 ) and “bureaucratization” and “corporatization” of research administration ( Sørensen and Traweek, 2021 ), and in particular the role of academic leaders, research managers as well as representatives and officers for the concerns of the employees. The critique collected here highlights that restructuring the research system does not necessarily lead to a rationalization of personnel processes and career paths. Moreover, academic leaders and research managers are also by no means neutrally administering, measuring, evaluating, and deciding entities, but rather these are embedded in and emerged from the very research system to whose rationalization they are supposed to contribute.

Finally, it should be emphasized once again that we do not believe that the “old research system” – in which research organizations hardly conducted any performance evaluations, academic leaders had more discretion, and third-party funding was not awarded in open competition – could have integrated or managed diversity better. We welcome the increasing reduction of power imbalances in the scientific workforce and see major potential in the professionalization of diversity management and the handling of experiences of discrimination in research institutions, especially in the newly-created professional field of research managers ( WR, 2018 ).

The fact that we increasingly talk about and problematize diversity and discrimination in research organizations can also be seen as a positive sign. The idea of the “integration paradox” ( Mafaalani, 2018 ) highlights that equal treatment of social groups is only demanded when a group and society (or an organization) have become aware that the respective group is to be treated equally. In this sense, it remains to be hoped for the future that conflicts and disputes – as an indicator of an increased awareness for discrimination processes – around the diverse socio-demographic character of the scientific workforce will continue to increase in the future.

Funding Note

The present contribution is not related to externally funded research.

Potential guidelines concerning the assessment of diversity initiatives: J. Marquis, N. Lim, L. Scott, C. Harrell, and J. Kavanagh (2008) , [online] Rand.org. https://www.rand.org/content/dam/rand/pubs/occasional_papers/2007/RAND_OP206.pdf accessed 10 February 2022. Guidance on measuring socio-demographic characteristics: J. H. P. Hoffmeyer-Zlotnik and U. Warner, Measuring Ethnicity in Cross-National Comparative Survey Research; GESIS-Schriftenreihe Band 4 (Bonn: GESIS – Leibniz Institute for the Social Sciences, 2010); J. H. P. Hoffmeyer-Zlotnik and U. Warner, Measuring Occupation and Labour Status in Crossnational Comparative Surveys ; GESIS-Schriftenreihe Band 7 (Bonn: GESIS – Leibniz Institute for the Social Sciences, 2011). Guidance on measuring diversity and inclusion: K. April and E. Blass, Measuring Diversity Practice and Developing Inclusion (2010). https://www.researchgate.net/profile/Kurt-April/publication/228668437_Measuring_Diversity_Practice_and_Developing_Inclusion/links/0a85e534e003f59ba3000000/Measuring-Diversity-Practice-and-Developing-Inclusion.pdf , accessed 10 February 2022.; S. Thompson, “Defining and measuring ‘inclusion’ within an organization”, K4D Helpdesk Report (Brighton, UK: Institute of Development Studies, 2017).

At the same time, the units of analysis should not be chosen too finely. Data protection requirements are crucial here. The data collected and reported regularly must not allow drawing any personal conclusions, i.e., the identification of a respondent based on the data shared by him or her (which can quickly become the case, especially for research organizations with a three-digit or lower number of employees). Furthermore, when surveying the work climate, opinions and experiences of employees, valid results can only be expected if “shaming” is excluded. The results should not be used to compare individual teams or groups to identify high- or low-performers.

See for example the GEAM Tool: “The Gender Equality Audit and Monitoring (GEAM) tool is an integrated environment for carrying out survey-based gender equality audits in academic organizations or organizational units”, https://act-on-gender.eu/nes/gender-equality-audit-and-monitoring-geam-tool accessed 15 March 2022. For another example, see the Immunity to Change Tool, which helps people identify and subsequently alter “competing commitments” that conflict with change (e.g. a change in the gender composition of research spaces), https://www.gse.harvard.edu/hgse100/story/changing-better , accessed 16 March 2022.

Organizational interventions such as diversity measures or data collection in the context of such measures are naturally questioned by organizational members. Organizational members interpret such measures based on how they perceive their organization. These assessments can tend to be positive or negative, which is why proactive communication management in relation to diversity policies is important. For a detailed discussion of the causes and effects of diversity resistance, see Thomas (2020) .

Of course, visibility per se is insufficient and adverse effects can be observed where there is a discrepancy between managerial discourse (including against discrimination and/or workplace bullying) and organizational practice (see inter alia: Clasches, 2019; Bereni, 2020 ; Vandevelde-Rougale, 2016 ).

With the emergence of professional research management, the status of faculty changes from autonomous members of their respective scientific profession to employees of the respective university or research institution, as Gerber (2014) states for the United States. In the European research area, the emergence of the professional group of research managers has been accelerated by the Bologna reform (to harmonize the system of higher education teaching across Europe) and the increased importance of third-party funding for research financing, as a result of which universities have been increasingly entrusted with management tasks ( WR, 2018 : 85).

The influence of nationality presents a more complex picture, for which an obvious explanation is that nationality groups are attributed different statuses and possibly also different stereotypes.

For us, this means considering the complexity of identities and that, e.g., two positive linear effects do not necessarily add up to each other. It also means taking into account “power domains” and “power vectors” ( Bilge, 2013 ).

Bereni, 2020 Bereni , L. , “La diversité, ruse ou dévoiement de l’égalité?” , L’Observatoire , 56 ( 2020 ): 30 – 32 .

Bilge, 2013 Bilge , S. , “Intersectionality undone. Saving intersectionality from feminist intersectionality studies” , Du Bois Review , 10 no. 2 ( 2013 ): 405 – 424 .

Block, Gray, and Holborow, 2012 Block , D. , J. Gray , and M. Holborow , Neoliberalism and Applied Linguistics ( New York : Routledge , 2012) .

Cacace, 2009 Cacace , M. , “Guidelines for Gender Equality Programmes in Science: Prages - Practising Gender Equality in Science” ( Rome : Ministry for Economy and Finance , 2009 ).

Chang, Milkman, Gromet, Rebele, Massey, Duckworth, and Grant, 2019 Chang , E.H. , K.L. Milkman , D.M. Gromet , R.W. Rebele , C. Massey , A.L. Duckworth , and A.M. Grant , “The mixed effects of online diversity training” , Proceedings of the National Academy of Sciences of the United States of America , 116 no. 16 ( 2019 ): 7778 – 7783 .

Clasches, 2019 Clasches , “Retourner l’arme du droit. Contre le harcèlement sexuel dans l’enseignement supérieur et la recherche” , Travail, genre et sociétés , 42 ( 2019 ): 191 – 194 .

Daley, Travis, and Shaffer, 2018 Daley , L.P. , D.J. Travis , and E.S. Shaffer , “Sexual harassment in the workplace: how companies can prepare, prevent, respond, and transform their culture” ( Catalyst , 2018 ).

European Commission, 2008 European Commission , “Benchmarking policy measures for gender equality in science” (Updated 2008 ). Available at: https://ec.europa.eu/research/swafs/pdf/pub_gender_equality/benchmarking-policy-measures_en.pdf accessed 23 October 2017.

Feldblum, and Lipnic, 2021 Feldblum , C.R. and V.A. Lipnic , “Select task force on the study of harassment in the workplace | U.S. equal employment opportunity commission” ( 2021 ). Available at: https://www.eeoc.gov/select-task-force-study-harassment-workplace accessed 14 December 2021.

Gerber, 2014 Gerber , L.G. , The Rise and Decline of Faculty Governance: Professionalization and the Modern American University ( Baltimore, MD : Johns Hopkins University Press , 2014) .

Hinze, 2004 Hinze , S.W. , “‘Am I being over-sensitive?’ Women’s experience of sexual harassment during medical training” , Health , 8 no. 1 ( 2004 ): 101 – 127 .

Hobman, Bordia, and Gallois, 2004 Hobman , E.V. , P. Bordia , and C. Gallois , “Perceived dissimilarity and work group involvement” , Group & Organization Management , 29 no. 5 ( 2004 ): 560 – 587 .

Hodgins, and McNamara, 2021 Hodgins , M. and P.M. McNamara , “The Neoliberal University in Ireland: institutional bullying by another name?” , Societies , 11 no. 2 ( 2021 ): 52 .

Joshi, and Knight, 2015 Joshi , A. and A.P. Knight , “Who defers to whom and why? dual pathways linking demographic differences and dyadic deference to team effectiveness” , Academy of Management Journal , 58 no. 1 ( 2015 ): 59 – 84 .

Kalpazidou Schmidt, and Cacace, 2017 Kalpazidou Schmidt , E. and M. Cacace , “Addressing gender inequality in science: the multifaceted challenge of assessing impact” , Research Evaluation , 26 ( 2017 ): 1 – 13 .

Kolboske, 2021 Kolboske , B. , “Hierarchies. Lotta support, little science? scientists and secretaries in the Max Planck Society” , in Fundamental Questions: Gender Dimensions in Max Planck Research Projects , 1st ed., Eds U. Weber ( Baden-Baden : Nomos , 2021 ): 105 – 134 .

Mafaalani, 2018 Mafaalani , A. el, Das Integrationsparadox: Warum gelungene Integration zu mehr Konflikten führt [The integration paradox: Why successful integration leads to more conflict.] , 2nd ed. ( Cologne : Kiepenheuer & Witsch , 2018 ).

Marquis, Lim, Scott, Harrell, and Kavanagh, 2008 Marquis , J.P. , N. Lim , L.M. Scott , C.H. Harrell , and J. Kavanagh , Managing diversity in corporate America – an exploratory analysis ( RAND Corporation , 2008 ). Available at: https://www.rand.org/content/dam/rand/pubs/occasional_papers/2007/RAND_OP206.pdf

Matthies, and Zimmermann, 2010 Matthies , H. and K. Zimmermann , “Gender equality in the research system [Gleichstellung in Der Wissenschaft]” , in Handbook Science Policy [Handbuch Wissenschaftspolitik] , Eds D. Simon , A. Knie , and S. Hornbostel ( Wiesbaden : VS Verlag , 2010 ): 193 – 209 .

Mayntz, 2010 Mayntz , R. , “Legitimacy and compliance in transnational governance” , MPIfG Working Paper, No. 10/5 ( Cologne : Max Planck Institute for the Study of Societies , 2010 ).

National Academies of Sciences, Engineering, and Medicine, 2018 National Academies of Sciences, Engineering, and Medicine , Sexual Harassment of Women: Climate, Culture, and Consequences in Academic Sciences, Engineering, and Medicine ( Washington, DC : The National Academies Press , 2018) .

Nature Editorial, 2021 Nature Editorial , “COVID is amplifying the inadequacy of research-evaluation processes” , Nature , 591 no. 7 ( 2021 ). doi: 10.1038/d41586-021-00527-9

Nielsen, Bloch, and Schiebinger, 2018 Nielsen , M.W. , C.W. Bloch , and L. Schiebinger , “Making gender diversity work for scientific discovery and innovation” , Nature Human Behaviour , 2 ( 2018 ): 726 – 734 .

OECD, 2018 OECD , “Gender in a changing context for STI” , in OECD Science, Technology and Innovation Outlook 2018 Adapting to Technological and Societal Disruption , Ed. OECD, OECD Science, Technology and Innovation Outlook ( Paris : OECD Publishing , 2018 ): 163 – 284 .

Palmén, Schmidt, Striebing, Reidl, Bührer, and Groó, 2019 Palmén , R. , E.K. Schmidt , C. Striebing , S. Reidl , S. Bührer , and D. Groó , “Measuring gender in R&I – theories, methods, and experience” , Interdisciplinary Science Reviews , 44 no. 2 ( 2019 ): 154 – 165 .

Sørensen, and Traweek, 2021 Sørensen , K.H. and S. Traweek , Questing Excellence in Academia ( London : Routledge , 2021) .

Squazzoni, Bravo, Grimaldo, García-Costa, Farjam, and Mehmani, 2021 Squazzoni , F. , G. Bravo , F. Grimaldo , D. García-Costa , M. Farjam , and B. Mehmani , “Gender gap in journal submissions and peer review during the first wave of the COVID-19 pandemic. A study on 2329 Elsevier journals” , PLOS ONE 16 , no. 10 ( 2021 ).

Thomas, 2020 Thomas , K.M. , Diversity Resistance in Organizations ( New York : Routledge , 2020) .

van den Brink, and Benschop, 2012 van den Brink , M. and Y. Benschop , “Slaying the seven-headed dragon: the quest for gender change in academia” , Gender, Work and Organization , 19 no. 1 ( 2012 ): 71 – 92 .

van Dick, van Knippenberg, Hägele, Guillaume, and Brodbeck, 2008 van Dick , R. , D. van Knippenberg , S. Hägele , Y.R.F. Guillaume , and F.C. Brodbeck , “Group diversity and group identification: the moderating role of diversity beliefs” , Human Relations , 61 no. 10 ( 2008 ): 1463 – 1492 .

Vandevelde-Rougale, 2016 Vandevelde-Rougale , A. , “Discours managérial, lissage de la parole et vacillement du rapport au langage: l’empêchement de l’expression subjective des émotions” , Langage et Société , 158 ( 2016 ): 35 – 50 .

Wang, and Barabási, 2021 Wang , D. and A.-L. Barabási , Eds, The Science of Science ( Cambridge : Cambridge University Press , 2021) .

Westhues, 2021 Westhues , K. , “Three stories and five questions arising from research on academic mobbing1” , Beiträge zur Hochschulforschung , 1–2 ( 2021 ): 118 – 127 .

WR (German Research Council), 2018 WR (German Research Council) , Empfehlungen zur Hochschulgovernance (Recommendations on university governance). Print 7328-18 ( 2018 ). Available at: https://www.wissenschaftsrat.de/download/archiv/7328-18.pdf?__blob=publicationFile&v=15 accessed on 13 August 2020.

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More people globally see racial, ethnic discrimination as a serious problem in the U.S. than in their own society

Concerns about racial and ethnic discrimination are widespread in most of the 17 advanced economies surveyed by Pew Research Center this spring. Majorities of adults in 14 of these places say discrimination on the basis of race or ethnicity is a somewhat or very serious problem in their own society – including around three-quarters or more in Italy, France, Sweden, Germany and the United States. Only in Japan, Singapore and Taiwan do fewer than half say such discrimination is a serious problem.

This Pew Research Center analysis focuses on comparing attitudes about whether racial and ethnic discrimination is a problem within a given survey public and whether it is a problem in the United States. For non-U.S. data, this post draws on nationally representative surveys of 16,254 adults from March 12 to May 26, 2021, in 16 advanced economies. All surveys were conducted over the phone with adults in Canada, Belgium, France, Germany, Greece, Italy, the Netherlands, Spain, Sweden, the United Kingdom, Australia, Japan, New Zealand, Singapore, South Korea and Taiwan.

In the U.S., we surveyed 2,596 adults from Feb. 1 to 7, 2021. Everyone who took part in the U.S. survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories.

This study was conducted in places where nationally representative telephone surveys are feasible. Due to the coronavirus outbreak, face-to-face interviewing is not currently possible in many parts of the world.

Here are the questions used for this analysis, along with responses. Visit our methodology database for more information about the survey methods outside the U.S. For respondents in the U.S., read more about the ATP’s methodology .

But even as sizable majorities in these places see racial and ethnic discrimination as a serious problem, even bigger majorities see it as an issue in the U.S. A median of 89% across the 16 non-U.S. publics surveyed describe racial and ethnic discrimination in the U.S. as a somewhat or very serious problem. That includes at least nine-in-ten who take this position in New Zealand, South Korea, Canada, Japan, the Netherlands, Spain and Sweden.

Across most of the places surveyed, younger adults tend to be more likely than older people to see discrimination as a problem, whether in their own society or in the U.S. For example, among Spaniards, 69% of those under age 30 think racial and ethnic discrimination in their own society is a serious problem, compared with 44% of those ages 65 and older. Younger Spaniards are also more likely than older Spaniards to see discrimination in the U.S. as a serious problem – though age-related differences in opinion about American discrimination are less pronounced, both in Spain and elsewhere.

Women in most of the advanced economies surveyed tend to see discrimination at higher rates than men. In the U.S., for example, 80% of women say discrimination against people based on their race or ethnicity is a somewhat or very serious problem, compared with 68% of men. Gender differences of around 10 percentage points are also evident in Canada, Germany, Greece, the Netherlands, Spain, Sweden, New Zealand and South Korea, both when it comes to discrimination locally and in the U.S. (though differences for the U.S. are again less pronounced).

In many places surveyed, those on the ideological left are more likely than those on the right to see racial and ethnic discrimination as a serious problem, both in their own society and in the U.S. The ideological gap on this question is widest in the U.S. itself: 92% of those on the left (liberals, in common U.S. parlance) say racial and ethnic discrimination is a serious problem, compared with 47% of those on the right (conservatives), a difference of 45 points. The next-largest ideological gap is in Australia, where 80% of those on the left and 50% of those on the right hold the view that discrimination is a serious problem in Australia. In general, people on the ideological left are also more likely than those on the right to say discrimination in the U.S. is a serious problem.

Attitudes sometimes also differ by educational level, especially when it comes to discrimination in the U.S. In Taiwan, for example, 95% of those with at least a postsecondary degree describe discrimination as a serious problem in the U.S., compared with 77% of those with less than a postsecondary degree. On the other hand, when it comes to perceptions of domestic discrimination, education only plays a role in Singapore, Japan and South Korea, with more educated people more likely to cite discrimination as a serious problem.

Note: Here are the questions used for this analysis, along with responses. Visit our methodology database for more information about the survey methods outside the U.S. For respondents in the U.S., read more about the ATP’s methodology .

• Discrimination & Prejudice
• International Political Values
• Racial Bias & Discrimination

Laura Silver is an associate director focusing on global attitudes at Pew Research Center .

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Discrimination: What it is and how to cope

For many people, discrimination is an everyday reality. Discrimination is the unfair or prejudicial treatment of people and groups based on characteristics such as race, gender, age, or sexual orientation.

• Racism, Bias, and Discrimination
• Race and Ethnicity
• Socioeconomic Status

What is discrimination?

Discrimination is the unfair or prejudicial treatment of people and groups based on characteristics such as race, gender, age, or sexual orientation. That’s the simple answer. But explaining why it happens is more complicated.

The human brain naturally puts things in categories to make sense of the world. Very young children quickly learn the difference between boys and girls, for instance. But the values we place on different categories are learned—from our parents, our peers, and the observations we make about how the world works. Often, discrimination stems from fear and misunderstanding.

Stress and health

Discrimination is a public health issue. Research has found that the experience of discrimination—when perceived as such—can lead to a cascade of stress-related emotional, physical, and behavioral changes . Stress evokes negative emotional responses, such as distress, sadness, and anger, and can often lead to an increase in behaviors that harm health, such as alcohol, tobacco, and other substance use, and a decrease in healthy activities, such as sleep and physical activity.

Discrimination can be damaging even if you haven’t been the target of overt acts of bias. Regardless of your personal experiences, it can be stressful just being a member of a group that is often discriminated against, such as racial minorities or individuals who identify as lesbian, gay, bisexual, or transgender.

The anticipation of discrimination creates its own chronic stress. People might even avoid situations where they expect they could be treated poorly, possibly missing out on educational and job opportunities.

Discrimination, big and small

Laws are in place to protect people from discrimination in housing and employment.

• The Fair Housing Act prohibits discrimination in the sale, rental, and financing of dwellings on the basis of race, color, national origin, religion, sex, familial status, and disability.
• The Civil Rights Act, the Age Discrimination in Employment Act, and the Americans with Disabilities Act prohibit discrimination in employment on the basis of race, color, sex, ethnic origin, age, and disabilities.

Unfortunately, discrimination still occurs.

Yet experts say that smaller, less obvious examples of day-to-day discrimination—receiving poorer service at stores or restaurants, being treated with less courtesy and respect, or being treated as less intelligent or less trustworthy—may be more common than major discrimination. Such day-to-day discrimination frequently comes in the form of “microaggressions” such as snubs, slights, and misguided comments that suggest a person doesn’t belong or invalidates his or her experiences.

Though microaggressions are often subtle, they can be just as harmful to health and well-being as more overt episodes of major bias. People on the receiving end of day-to-day discrimination often feel they’re in a state of constant vigilance, on the lookout for being a target of discrimination. That heightened watchfulness is a recipe for chronic stress.

Dealing with discrimination

Finding healthy ways to deal with discrimination is important, for your physical health and your mental well-being.

Focus on your strengths. Focusing on your core values, beliefs, and perceived strengths can motivate people to succeed, and may even buffer the negative effects of bias. Overcoming hardship can also make people more resilient and better able to face future challenges.

Seek support systems. One problem with discrimination is that people can internalize others’ negative beliefs, even when they’re false. You may start to believe you’re not good enough. But family and friends can remind you of your worth and help you reframe those faulty beliefs.

Family and friends can also help counteract the toll that microaggressions and other examples of daily discrimination can take. In a world that regularly invalidates your experiences and feelings, members of your support network can reassure you that you’re not imagining those experiences of discrimination. Still, it’s sometimes painful to talk about discrimination. It can be helpful to ask friends and family how they handle such events.

Your family and friends can also be helpful if you feel you’ve been the victim of discrimination in areas such as housing, employment, or education. Often, people don’t report such experiences to agencies or supervisors. One reason for that lack of reporting is that people often doubt themselves: Was I actually discriminated against, or am I being oversensitive? Will I be judged negatively if I push the issue? Your support network can provide a reality check and a sounding board to help you decide if your claims are valid and worth pursuing.

Get involved. Support doesn’t have to come from people in your family or circle of friends. You can get involved with like-minded groups and organizations, whether locally or online. It can help to know there are other people who have had similar experiences to yours. And connecting with those people might help you figure out how to address situations and respond to experiences of discrimination in ways you haven’t thought of.

Help yourself think clearly. Being the target of discrimination can stir up a lot of strong emotions including anger, sadness, and embarrassment. Such experiences often trigger a physiological response, too; they can increase your blood pressure, heart rate, and body temperature.

Try to check in with your body before reacting. Slow your breathing or use other relaxation exercises to calm your body’s stress response. Then you’ll be able to think more clearly about how you want to respond.

Don’t dwell. When you’ve experienced discrimination, it can be really hard to just shake it off. People often get stuck on episodes of discrimination, in part because they’re not sure how to handle those experiences. You might want to speak out or complain, but you’re not sure how to go about it, or are afraid of the backlash. So instead, you end up ruminating, or thinking over and over about what you should have done.

In a calmer moment, it might be helpful to talk over the ways you can cope with similar experiences in the future. Try to come up with a plan for how you might respond or what you could do differently next time. Once you’ve determined how to respond, try to leave the incident behind you as you go on with your day.

Seek professional help. Discrimination is difficult to deal with, and is often associated with symptoms of depression. Psychologists are experts in helping people manage symptoms of stress and depression, and can help you find healthy ways to cope. You can find a psychologist in your area by using APA’s Psychologist Locator Service.

Discrimination resources

If you have questions about policies or concerns about discrimination in your workplace, the human resource department is often a good place to start. To learn more about discrimination in housing and employment, or to file a complaint, visit:

• Equal Opportunity Employment Commission
• U.S. Department of Housing and Urban Development

Recommended Reading

Related reading.

• Psychology topics: Racism, bias, and discrimination
• Stress in America
• Talking to your kids about discrimination

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Gender inequalities in the workplace: the effects of organizational structures, processes, practices, and decision makers’ sexism

Cailin s stamarski, leanne s son hing.

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Edited by: Frédérique Autin, University of Lausanne, Switzerland

Reviewed by: Peggy Stockdale, Indiana University – Purdue University Indianapolis, USA; Tina C. Elacqua, LeTourneau University, USA

*Correspondence: Leanne S. Son Hing, Department of Psychology, University of Guelph, Guelph, ON N1G 2W1, Canada, [email protected]

† These authors have contributed equally to this work.

This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology

Received 2015 Jan 27; Accepted 2015 Sep 2; Collection date 2015.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Gender inequality in organizations is a complex phenomenon that can be seen in organizational structures, processes, and practices. For women, some of the most harmful gender inequalities are enacted within human resources (HRs) practices. This is because HR practices (i.e., policies, decision-making, and their enactment) affect the hiring, training, pay, and promotion of women. We propose a model of gender discrimination in HR that emphasizes the reciprocal nature of gender inequalities within organizations. We suggest that gender discrimination in HR-related decision-making and in the enactment of HR practices stems from gender inequalities in broader organizational structures, processes, and practices. This includes leadership, structure, strategy, culture, organizational climate, as well as HR policies. In addition, organizational decision makers’ levels of sexism can affect their likelihood of making gender biased HR-related decisions and/or behaving in a sexist manner while enacting HR practices. Importantly, institutional discrimination in organizational structures, processes, and practices play a pre-eminent role because not only do they affect HR practices, they also provide a socializing context for organizational decision makers’ levels of hostile and benevolent sexism. Although we portray gender inequality as a self-reinforcing system that can perpetuate discrimination, important levers for reducing discrimination are identified.

Keywords: hostile sexism, benevolent sexism, institutional discrimination, human resources practices, gender harassment, personal discrimination

Introduction

The workplace has sometimes been referred to as an inhospitable place for women due to the multiple forms of gender inequalities present (e.g., Abrams, 1991 ). Some examples of how workplace discrimination negatively affects women’s earnings and opportunities are the gender wage gap (e.g., Peterson and Morgan, 1995 ), the dearth of women in leadership ( Eagly and Carli, 2007 ), and the longer time required for women (vs. men) to advance in their careers ( Blau and DeVaro, 2007 ). In other words, workplace discrimination contributes to women’s lower socio-economic status. Importantly, such discrimination against women largely can be attributed to human resources (HR) policies and HR-related decision-making. Furthermore, when employees interact with organizational decision makers during HR practices, or when they are told the outcomes of HR-related decisions, they may experience personal discrimination in the form of sexist comments. Both the objective disadvantages of lower pay, status, and opportunities at work, and the subjective experiences of being stigmatized, affect women’s psychological and physical stress, mental and physical health ( Goldenhar et al., 1998 ; Adler et al., 2000 ; Schmader et al., 2008 ; Borrel et al., 2010 ),job satisfaction and organizational commitment ( Hicks-Clarke and Iles, 2000 ), and ultimately, their performance ( Cohen-Charash and Spector, 2001 ).

Within this paper, we delineate the nature of discrimination within HR policies, decisions, and their enactment, as well as explore the causes of such discrimination in the workplace. Our model is shown in Figure 1 . In the Section “Discrimination in HR Related Practices: HR Policy, Decisions, and their Enactment,” we explain the distinction between HR policy, HR-related decision-making, and HR enactment and their relations to each other. Gender inequalities in HR policy are a form of institutional discrimination. We review evidence of institutional discrimination against women within HR policies set out to determine employee selection, performance evaluations, and promotions. In contrast, discrimination in HR-related decisions and their enactment can result from organizational decision makers’ biased responses: it is a form of personal discrimination. Finally, we provide evidence of personal discrimination against women by organizational decision makers in HR-related decision-making and in the enactment of HR policies.

A model of the root causes of gender discrimination in HR policies, decision-making, and enactment .

In the Section “The Effect of Organizational Structures, Processes, and Practices on HR Practices,” we focus on the link between institutional discrimination in organizational structures, processes, and practices that can lead to personal discrimination in HR practices (see Figure 1 ). Inspired by the work of Gelfand et al. (2007) , we propose that organizational structures, processes, and practices (i.e., leadership, structure, strategy, culture, climate, and HR policy) are interrelated and may contribute to discrimination. Accordingly, gender inequalities in each element can affect the others, creating a self-reinforcing system that can perpetuate institutional discrimination throughout the organization and that can lead to discrimination in HR policies, decision-making, and enactment. We also propose that these relations between gender inequalities in the organizational structures, processes, and practices and discrimination in HR practices can be bidirectional (see Figure 1 ). Thus, we also review how HR practices can contribute to gender inequalities in organizational structures, processes, and practices.

In the Section “The Effect of Hostile and Benevolent Sexism on How Organizational Decision Makers’ Conduct HR Practices,” we delineate the link between organizational decision makers’ levels of sexism and their likelihood of making gender-biased HR-related decisions and/or behaving in a sexist manner when enacting HR policies (e.g., engaging in gender harassment). We focus on two forms of sexist attitudes: hostile and benevolent sexism ( Glick and Fiske, 1996 ). Hostile sexism involves antipathy toward, and negative stereotypes about, agentic women. In contrast, benevolent sexism involves positive but paternalistic views of women as highly communal. Whereas previous research on workplace discrimination has focused on forms of sexism that are hostile in nature, we extend this work by explaining how benevolent sexism, which is more subtle, can also contribute in meaningful yet distinct ways to gender discrimination in HR practices.

In the Section “The Effect of Organizational Structures, Processes, and Practices on Organizational Decision Makers’ Levels of Hostile and Benevolent Sexism,” we describe how institutional discrimination in organizational structures, processes, and practices play a critical role in our model because not only do they affect HR-related decisions and the enactment of HR policies, they also provide a socializing context for organizational decision makers’ levels of hostile and benevolent sexism. In other words, where more institutional discrimination is present, we can expect higher levels of sexism—a third link in our model—which leads to gender bias in HR practices.

In the Section “How to Reduce Gender Discrimination in Organizations,” we discuss how organizations can reduce gender discrimination. We suggest that, to reduce discrimination, organizations should focus on: HR practices, other closely related organizational structures, processes, and practices, and the reduction of organizational decision makers’ level of sexism. Organizations should take such a multifaceted approach because, consistent with our model, gender discrimination is a result of a complex interplay between these factors. Therefore, a focus on only one factor may not be as effective if all the other elements in the model continue to promote gender inequality.

The model we propose for understanding gender inequalities at work is, of course, limited and not intended to be exhaustive. First, we only focus on women’s experience of discrimination. Although men also face discrimination, the focus of this paper is on women because they are more often targets ( Branscombe, 1998 ; Schmitt et al., 2002 ; McLaughlin et al., 2012 ) and discrimination is more psychologically damaging for women than for men ( Barling et al., 1996 ; Schmitt et al., 2002 ). Furthermore, we draw on research from Western, individualistic countries conducted between the mid-1980s to the mid-2010s that might not generalize to other countries or time frames. In addition, this model derives from research that has been conducted primarily in sectors dominated by men. This is because gender discrimination ( Mansfield et al., 1991 ; Welle and Heilman, 2005 ) and harassment ( Mansfield et al., 1991 ; Berdhal, 2007 ) against women occur more in environments dominated by men. Now that we have outlined the sections of the paper and our model, we now turn to delineating how gender discrimination in the workplace can be largely attributed to HR practices.

Discrimination in HR Related Practices: HR Policy, Decisions, and their Enactment

In this section, we explore the nature of gender discrimination in HR practices, which involves HR policies, HR-related decision-making, and their enactment by organizational decision makers. HR is a system of organizational practices aimed at managing employees and ensuring that they are accomplishing organizational goals ( Wright et al., 1994 ). HR functions include: selection, performance evaluation, leadership succession, and training. Depending on the size and history of the organization, HR systems can range from those that are well structured and supported by an entire department, led by HR specialists, to haphazard sets of policies and procedures enacted by managers and supervisors without formal training. HR practices are critically important because they determine the access employees have to valued reward and outcomes within an organization, and can also influence their treatment within an organization ( Levitin et al., 1971 ).

Human resource practices can be broken down into formal HR policy, HR-related decision-making, and the enactment of HR policies and decisions. HR policy codifies practices for personnel functions, performance evaluations, employee relations, and resource planning ( Wright et al., 1994 ). HR-related decision-making occurs when organizational decision makers (i.e., managers, supervisors, or HR personnel) employ HR policy to determine how it will be applied to a particular situation and individual. The enactment of HR involves the personal interactions between organizational decision makers and job candidates or employees when HR policies are applied. Whereas HR policy can reflect institutional discrimination, HR-related decision-making and enactment can reflect personal discrimination by organizational decision makers.

Institutional Discrimination in HR Policy

Human resource policies that are inherently biased against a group of people, regardless of their job-related knowledge, skills, abilities, and performance can be termed institutional discrimination. Institutional discrimination against women can occur in each type of HR policy from the recruitment and selection of an individual into an organization, through his/her role assignments, training, pay, performance evaluations, promotion, and termination. For instance, if women are under-represented in a particular educational program or a particular job type and those credentials or previous job experience are required to be considered for selection, women are being systematically, albeit perhaps not intentionally, discriminated against. In another example, there is gender discrimination if a test is used in the selection battery for which greater gender differences emerge, than those that emerge for job performance ratings ( Hough et al., 2001 ). Thus, institutional discrimination can be present within various aspects of HR selection policy, and can negatively affect women’s work outcomes.

Institutional discrimination against women also occurs in performance evaluations that are used to determine organizational rewards (e.g., compensation), opportunities (e.g., promotion, role assignments), and punishments (e.g., termination). Gender discrimination can be formalized into HR policy if criteria used by organizational decision makers to evaluate job performance systematically favor men over women. For instance, “face time” is a key performance metric that rewards employees who are at the office more than those who are not. Given that women are still the primary caregivers ( Acker, 1990 ; Fuegen et al., 2004 ), women use flexible work arrangements more often than men and, consequently, face career penalties because they score lower on face time ( Glass, 2004 ). Thus, biased criteria in performance evaluation policies can contribute to gender discrimination.

Human resource policies surrounding promotions and opportunities for advancement are another area of concern. In organizations with more formal job ladders that are used to dictate and constrain workers’ promotion opportunities, women are less likely to advance ( Perry et al., 1994 ). This occurs because job ladders tend to be divided by gender, and as such, gender job segregation that is seen at entry-level positions will be strengthened as employees move up their specific ladder with no opportunity to cross into other lines of advancement. Thus, women will lack particular job experiences that are not available within their specific job ladders, making them unqualified for advancement ( De Pater et al., 2010 ).

In sum, institutional discrimination can be present within HR policies set out to determine employee selection, performance evaluations, and promotions. These policies can have significant effects on women’s careers. However, HR policy can only be used to guide HR-related decision-making. In reality, it is organizational decision-makers, that is, managers, supervisors, HR personnel who, guided by policy, must evaluate job candidates or employees and decide how policy will be applied to individuals.

Personal Discrimination in HR-Related Decision-Making

The practice of HR-related decision-making involves social cognition in which others’ competence, potential, and deservingness are assessed by organizational decision makers. Thus, like all forms of social cognition, HR-related decision-making is open to personal biases. HR-related decisions are critically important because they determine women’s pay and opportunities at work (e.g., promotions, training opportunities). Personal discrimination against women by organizational decision makers can occur in each stage of HR-related decision-making regarding recruitment and selection, role assignments, training opportunities, pay, performance evaluation, promotion, and termination.

Studies with varying methodologies show that women face personal discrimination when going through the selection process (e.g., Goldberg, 1968 ; Rosen and Jerdee, 1974 ). Meta-analyses reveal that, when being considered for male-typed (i.e., male dominated, believed-to-be-for-men) jobs, female candidates are evaluated more negatively and recommended for employment less often by study participants, compared with matched male candidates (e.g., Hunter et al., 1982 ; Tosi and Einbender, 1985 ; Olian et al., 1988 ; Davison and Burke, 2000 ). For example, in audit studies, which involve sending ostensibly real applications for job openings while varying the gender of the applicant, female applicants are less likely to be interviewed or called back, compared with male applicants (e.g., McIntyre et al., 1980 ; Firth, 1982 ). In a recent study, male and female biology, chemistry, and physics professors rated an undergraduate science student for a laboratory manager position ( Moss-Racusin et al., 2012 ). The male applicant was rated as significantly more competent and hireable, offered a higher starting salary (about $4000), and offered more career mentoring than the female applicant was. In summary, women face a distinct disadvantage when being considered for male-typed jobs.

There is ample evidence that women experience biased performance evaluations on male-typed tasks. A meta-analysis of experimental studies reveals that women in leadership positions receive lower performance evaluations than matched men; this is amplified when women act in a stereotypically masculine, that is, agentic fashion ( Eagly et al., 1992 ). Further, in masculine domains, women are held to a higher standard of performance than men are. For example, in a study of military cadets, men and women gave their peers lower ratings if they were women, despite having objectively equal qualifications to men ( Boldry et al., 2001 ). Finally, women are evaluated more poorly in situations that involve complex problem solving; in these situations, people are skeptical regarding women’s expertise and discredit expert women’s opinions but give expert men the benefit of the doubt ( Thomas-Hunt and Phillips, 2004 ).

Sometimes particular types of women are more likely to be discriminated against in selection and performance evaluation decisions. Specifically, agentic women, that is, those who behave in an assertive, task-oriented fashion, are rated as less likeable and less hireable than comparable agentic male applicants ( Heilman and Okimoto, 2007 ; Rudman and Phelan, 2008 ; Rudman et al., 2012 ). In addition, there is evidence of discrimination against pregnant women when they apply for jobs ( Hebl et al., 2007 ; Morgan et al., 2013 ). Further, women who are mothers are recommended for promotion less than women who are not mothers or men with or without children ( Heilman and Okimoto, 2008 ). Why might people discriminate specifically against agentic women and pregnant women or mothers, who are seemingly very different? The stereotype content model, accounts for how agentic women, who are perceived to be high in competence and low in warmth, will be discriminated against because of feelings of competition; whereas, pregnant women and mothers, who are seen as low in competence, but high in warmth, will be discriminated against because of a perceived lack of deservingness ( Fiske et al., 1999 , 2002 ; Cuddy et al., 2004 ). Taken together, research has uncovered that different forms of bias toward specific subtypes of women have the same overall effect—bias in selection and performance evaluation decisions.

Women are also likely to receive fewer opportunities at work, compared with men, resulting in their under-representation at higher levels of management and leadership within organizations ( Martell et al., 1996 ; Eagly and Carli, 2007 ). Managers give women fewer challenging roles and fewer training opportunities, compared with men ( King et al., 2012 ; Glick, 2013 ). For instance, female managers ( Lyness and Thompson, 1997 ) and midlevel workers ( De Pater et al., 2010 ) have less access to high-level responsibilities and challenges that are precursors to promotion. Further, men are more likely to be given key leadership assignments in male-dominated fields and in female-dominated fields (e.g., Maume, 1999 ; De Pater et al., 2010 ). This is detrimental given that challenging roles, especially developmental ones, help employees gain important skills needed to excel in their careers ( Spreitzer et al., 1997 ).

Furthermore, managers rate women as having less promotion potential than men ( Roth et al., 2012 ). Given the same level of qualifications, managers are less likely to grant promotions to women, compared with men ( Lazear and Rosen, 1990 ). Thus, men have a faster ascent in organizational hierarchies than women ( Cox and Harquail, 1991 ; Stroh et al., 1992 ; Blau and DeVaro, 2007 ). Even minimal amounts of gender discrimination in promotion decisions for a particular job or level can have large, cumulative effects given the pyramid structure of most hierarchical organizations ( Martell et al., 1996 ; Baxter and Wright, 2000 ). Therefore, discrimination by organizational decision makers results in the under-promotion of women.

Finally, women are underpaid, compared with men. In a comprehensive US study using data from 1983 to 2000, after controlling for human capital factors that could affect wages (e.g., education level, work experience), the researchers found that women were paid 22% less than men ( U.S. Government Accountability Office, 2003 ). Further, within any given occupation, men typically have higher wages than women; this “within-occupation” wage gap is especially prominent in more highly paid occupations ( U.S. Census Bureau, 2007 ). In a study of over 2000 managers, women were compensated less than men were, even after controlling for a number of human capital factors ( Ostroff and Atwater, 2003 ). Experimental work suggests that personal biases by organizational decision makers contribute to the gender wage gap. When participants are asked to determine starting salaries for matched candidates that differ by gender, they pay men more (e.g., Steinpreis et al., 1999 ; Moss-Racusin et al., 2012 ). Such biases are consequential because starting salaries determine life-time earnings ( Gerhart and Rynes, 1991 ). In experimental studies, when participants evaluate a man vs. a woman who is matched on job performance, they choose to compensate men more ( Marini, 1989 ; Durden and Gaynor, 1998 ; Lips, 2003 ). Therefore, discrimination in HR-related decision-making by organizational decision makers can contribute to women being paid less than men are.

Taken together, we have shown that there is discrimination against women in decision-making related to HR. These biases from organizational decision makers can occur in each stage of HR-related decision-making and these biased HR decisions have been shown to negatively affect women’s pay and opportunities at work. In the next section, we review how biased HR practices are enacted, which can involve gender harassment.

Personal Discrimination in HR Enactment

By HR enactment, we refer to those situations where current or prospective employees go through HR processes or when they receive news of their outcomes from organizational decision makers regarding HR-related issues. Personal gender discrimination can occur when employees are given sexist messages, by organizational decision makers, related to HR enactment. More specifically, this type of personal gender discrimination is termed gender harassment, and consists of a range of verbal and non-verbal behaviors that convey sexist, insulting, or hostile attitudes about women ( Fitzgerald et al., 1995a , b ). Gender harassment is the most common form of sex-based discrimination ( Fitzgerald et al., 1988 ; Schneider et al., 1997 ). For example, across the military in the United States, 52% of the 9,725 women surveyed reported that they had experienced gender harassment in the last year ( Leskinen et al., 2011 , Study 1). In a random sample of attorneys from a large federal judicial circuit, 32% of the 1,425 women attorneys surveyed had experienced gender harassment in the last 5 years ( Leskinen et al., 2011 , Study 2). When examining women’s experiences of gender harassment, 60% of instances were perpetrated by their supervisor/manager or a person in a leadership role (cf. Crocker and Kalemba, 1999 ; McDonald et al., 2008 ). Thus, personal discrimination in the form of gender harassment is a common behavior; however, is it one that organizational decision makers engage in when enacting HR processes and outcomes?

Although it might seem implausible that organizational decision makers would convey sexist sentiments to women when giving them the news of HR-related decisions, there have been high-profile examples from discrimination lawsuits where this has happened. For example, in a class action lawsuit against Walmart, female workers claimed they were receiving fewer promotions than men despite superior qualifications and records of service. In that case, the district manager was accused of confiding to some of the women who were overlooked for promotions that they were passed over because he was not in favor of women being in upper management positions ( Wal-Mart Stores, Inc. v. Dukes, 2004/2011 ). In addition, audit studies, wherein matched men and women apply to real jobs, have revealed that alongside discrimination ( McIntyre et al., 1980 ; Firth, 1982 ; Moss-Racusin et al., 2012 ), women experience verbal gender harassment when applying for sex atypical jobs, such as sexist comments as well as skeptical or discouraging responses from hiring staff ( Neumark, 1996 ). Finally, gender harassment toward women when HR policies are enacted can also take the form of offensive comments and denying women promotions due to pregnancy or the chance of pregnancy. For example, in Moore v. Alabama , an employee was 8 months pregnant and the woman’s supervisor allegedly looked at her belly and said “I was going to make you head of the office, but look at you now” ( Moore v. Alabama State University, 1996 , p. 431; Williams, 2003 ). Thus, organizational decision makers will at times convey sexist sentiments to women when giving them the news of HR-related decisions.

Interestingly, whereas discrimination in HR policy and in HR-related decision-making is extremely difficult to detect ( Crosby et al., 1986 ; Major, 1994 ), gender harassment in HR enactment provides direct cues to recipients that discrimination is occurring. In other words, although women’s lives are negatively affected in concrete ways by discrimination in HR policy and decisions (e.g., not receiving a job, being underpaid), they may not perceive their negative outcomes as due to gender discrimination. Indeed, there is a multitude of evidence that women and other stigmatized group members are loath to make attributions to discrimination ( Crosby, 1984 ; Vorauer and Kumhyr, 2001 ; Stangor et al., 2003 ) and instead are likely to make internal attributions for negative evaluations unless they are certain the evaluator is biased against their group ( Ruggiero and Taylor, 1995 ; Major et al., 2003 ). However, when organizational decision makers engage in gender harassment during HR enactment women should be more likely to interpret HR policy and HR-related decisions as discriminatory.

Now that we have specified the nature of institutional gender discrimination in HR policy and personal discrimination in HR-related decision-making and in HR enactment, we turn to the issue of understanding the causes of such discrimination: gender discrimination in organizational structures, processes, and practices, and personal biases of organizational decision makers.

The Effect of Organizational Structures, Processes, and Practices on HR Practices

The first contextual factor within which gender inequalities can be institutionalized is leadership. Leadership is a process wherein an individual (e.g., CEOs, managers) influences others in an effort to reach organizational goals ( Chemers, 1997 ; House and Aditya, 1997 ). Leaders determine and communicate what the organization’s priorities are to all members of the organization. Leaders are important as they affect the other organizational structures, processes, and practices. Specifically, leaders set culture, set policy, set strategy, and are role models for socialization. We suggest that one important way institutional gender inequality in leadership exists is when women are under-represented, compared with men—particularly when women are well-represented at lower levels within an organization.

An underrepresentation of women in leadership can be perpetuated easily because the gender of organizational leaders affects the degree to which there is gender discrimination, gender supportive policies, and a gender diversity supportive climate within an organization ( Ostroff et al., 2012 ). Organizational members are likely to perceive that the climate for women is positive when women hold key positions in the organization ( Konrad et al., 2010 ). Specifically, the presence of women in key positions acts as a vivid symbol indicating that the organization supports gender diversity. Consistent with this, industries that have fewer female high status managers have a greater gender wage gap ( Cohen and Huffman, 2007 ). Further, women who work with a male supervisor perceive less organizational support, compared with those who work with a female supervisor ( Konrad et al., 2010 ). In addition, women who work in departments that are headed by a man report experiencing more gender discrimination, compared with their counterparts in departments headed by women ( Konrad et al., 2010 ). Some of these effects may be mediated by a similar-to-me bias ( Tsui and O’Reilly, 1989 ), where leaders set up systems that reward and promote individuals like themselves, which can lead to discrimination toward women when leaders are predominantly male ( Davison and Burke, 2000 ; Roth et al., 2012 ). Thus, gender inequalities in leadership affect women’s experiences in the workplace and their likelihood of facing discrimination.

The second contextual factor to consider is organizational structure. The formal structure of an organization is how an organization arranges itself and it consists of employee hierarchies, departments, etc. ( Grant, 2010 ). An example of institutional discrimination in the formal structure of an organization are job ladders, which are typically segregated by gender ( Perry et al., 1994 ). Such gender-segregated job ladders typically exist within different departments of the organization. Women belonging to gender-segregated networks within organizations ( Brass, 1985 ) have less access to information about jobs, less status, and less upward mobility within the organization ( Ragins and Sundstrom, 1989 ; McDonald et al., 2009 ). This is likely because in gender-segregated networks, women have less visibility and lack access to individuals with power ( Ragins and Sundstrom, 1989 ). In gender-segregated networks, it is also difficult for women to find female mentors because there is a lack of women in high-ranking positions ( Noe, 1988 ; Linehan and Scullion, 2008 ). Consequently, the organizational structure can be marked by gender inequalities that reduce women’s chances of reaching top-level positions in an organization.

Gender inequalities can be inherent in the structure of an organization when there are gender segregated departments, job ladders, and networks, which are intimately tied to gender discrimination in HR practices. For instance, if HR policies are designed such that pay is determined based on comparisons between individuals only within a department (e.g., department-wide reporting structure, job descriptions, performance evaluations), then this can lead to a devaluation of departments dominated by women. The overrepresentation of women in certain jobs leads to the lower status of those jobs; consequently, the pay brackets for these jobs decrease over time as the number of women in these jobs increase (e.g., Huffman and Velasco, 1997 ; Reilly and Wirjanto, 1999 ). Similarly, networks led by women are also devalued for pay. For example, in a study of over 2,000 managers, after controlling for performance, the type of job, and the functional area (e.g., marketing, sales, accounting), those who worked with female mangers had lower wages than those who worked with male managers ( Ostroff and Atwater, 2003 ). Thus, gender inequalities in an organization’s structure in terms of gender segregation have reciprocal effects with gender discrimination in HR policy and decision-making.

Another contextual factor in our model is organizational strategy and how institutional discrimination within strategy is related to discrimination in HR practices. Strategy is a plan, method, or process by which an organization attempts to achieve its objectives, such as being profitable, maintaining and expanding its consumer base, marketing strategy, etc. ( Grant, 2010 ). Strategy can influence the level of inequality within an organization ( Morrison and Von Glinow, 1990 ; Hunter et al., 2001 ). For example, Hooters, a restaurant chain, has a marketing strategy to sexually attract heterosexual males, which has led to discrimination in HR policy, decisions, and enactment because only young, good-looking women are considered qualified ( Schneyer, 1998 ). When faced with appearance-based discrimination lawsuits regarding their hiring policies, Hooters has responded by claiming that such appearance requirements are bona fide job qualifications given their marketing strategy (for reviews, see Schneyer, 1998 ; Adamitis, 2000 ). Hooters is not alone, as many other establishments attempt to attract male cliental by requiring their female servers to meet a dress code involving a high level of grooming (make-up, hair), a high heels requirement, and a revealing uniform ( McGinley, 2007 ). Thus, sexist HR policies and practices in which differential standards are applied to male and female employees can stem from a specific organizational strategy ( Westall, 2015 ).

We now consider institutional gender bias within organizational culture and how it relates to discrimination in HR policies. Organizational culture refers to collectively held beliefs, assumptions, and values held by organizational members ( Trice and Beyer, 1993 ; Schein, 2010 ). Cultures arise from the values of the founders of the organization and assumptions about the right way of doing things, which are learned from dealing with challenges over time ( Ostroff et al., 2012 ). The founders and leaders of an organization are the most influential in forming, maintaining, and changing culture over time (e.g., Trice and Beyer, 1993 ; Jung et al., 2008 ; Hartnell and Walumbwa, 2011 ). Organizational culture can contribute to gender inequalities because culture constrains people’s ideas of what is possible: their strategies of action ( Swidler, 1986 ). In other words, when people encounter a problem in their workplace, the organizational culture—who we are, how we act, what is right—will provide only a certain realm of behavioral responses. For instance, in organizational cultures marked by greater gender inequality, women may have lower hopes and expectations for promotion, and when they are discriminated against, may be less likely to imagine that they can appeal their outcomes ( Kanter, 1977 ; Cassirer and Reskin, 2000 ). Furthermore, in organizational cultures marked by gender inequality, organizational decision makers should hold stronger descriptive and proscriptive gender stereotypes: they should more strongly believe that women have less ability to lead, less career commitment, and less emotional stability, compared with men ( Eagly et al., 1992 ; Heilman, 2001 ). We expand upon this point later.

Other aspects of organizational culture that are less obviously related to gender can also lead to discrimination in HR practices. For instance, an organizational culture that emphasizes concerns with meritocracy, can lead organizational members to oppose HR efforts to increase gender equality. This is because when people believe that outcomes ought to go only to those who are most deserving, it is easy for them to fall into the trap of believing that outcomes currently do go to those who are most deserving ( Son Hing et al., 2011 ). Therefore, people will believe that men deserve their elevated status and women deserve their subordinated status at work ( Castilla and Benard, 2010 ). Furthermore, the more people care about merit-based outcomes, the more they oppose affirmative action and diversity initiatives for women ( Bobocel et al., 1998 ; Son Hing et al., 2011 ), particularly when they do not recognize that discrimination occurs against women in the absence of such policies ( Son Hing et al., 2002 ). Thus, a particular organizational culture can influence the level of discrimination against women in HR and prevent the adoption of HR policies that would mitigate gender discrimination.

Finally, gender inequalities can be seen in organizational climates. An organizational climate consists of organizational members’ shared perceptions of the formal and informal organizational practices, procedures, and routines ( Schneider et al., 2011 ) that arise from direct experiences of the organization’s culture ( Ostroff et al., 2012 ). Organizational climates tend to be conceptualized and studied as “climates for” an organizational strategy ( Schneider, 1975 ; Ostroff et al., 2012 ). Gender inequalities are most clearly reflected in two forms of climate: climates for diversity and climates for sexual harassment.

A positive climate for diversity exists when organizational members perceive that diverse groups are included, empowered, and treated fairly. When employees perceive a less supportive diversity climate, they perceive greater workplace discrimination ( Cox, 1994 ; Ragins and Cornwall, 2001 ; Triana and García, 2009 ), and experience lower organizational commitment and job satisfaction ( Hicks-Clarke and Iles, 2000 ), and higher turnover intentions ( Triana et al., 2010 ). Thus, in organizations with a less supportive diversity climate, women are more likely to leave the organization, which contributes to the underrepresentation of women in already male-dominated arenas ( Miner-Rubino and Cortina, 2004 ).

A climate for sexual harassment involves perceptions that the organization is permissive of sexual harassment. In organizational climates that are permissive of harassment, victims are reluctant to come forward because they believe that their complaints will not be taken seriously ( Hulin et al., 1996 ) and will result in negative personal consequences (e.g., Offermann and Malamut, 2002 ). Furthermore, men with a proclivity for harassment are more likely to act out these behaviors when permissive factors are present ( Pryor et al., 1993 ). Therefore, a permissive climate for sexual harassment can result in more harassing behaviors, which can lead women to disengage from their work and ultimately leave the organization ( Kath et al., 2009 ).

Organizational climates for diversity and for sexual harassment are inextricably linked to HR practices. For instance, a factor that leads to perceptions of diversity climates is whether the HR department has diversity training (seminars, workshops) and how much time and money is devoted to diversity efforts ( Triana and García, 2009 ). Similarly, a climate for sexual harassment depends on organizational members’ perceptions of how strict the workplace’s sexual harassment policy is, and how likely offenders are to be punished ( Fitzgerald et al., 1995b ; Hulin et al., 1996 ). Thus, HR policies, decision-making, and their enactment strongly affect gender inequalities in organizational climates and gender inequalities throughout an organization.

In summary, gender inequalities can exist within organizational structures, processes, and practices. However, organizational leadership, structure, strategy, culture, and climate do not inherently need to be sexist. It could be possible for these organizational structures, processes, and practices to promote gender equality. We return to this issue in the conclusion section.

The Effect of Hostile and Benevolent Sexism on How Organizational Decision Makers’ Conduct HR Practices

In this section, we explore how personal biases can affect personal discrimination in HR-related decisions and their enactment. Others have focused on how negative or hostile attitudes toward women predict discrimination in the workplace. However, we extend this analysis by drawing on ambivalent sexism theory, which involves hostile sexism (i.e., antagonistic attitudes toward women) and benevolent sexism (i.e., paternalistic attitudes toward women; see also Glick, 2013 ), both of which lead to discrimination against women.

Stereotyping processes are one possible explanation of how discrimination against women in male-typed jobs occurs and how women are relegated to the “pink ghetto” ( Heilman, 1983 ; Eagly and Karau, 2002 ; Rudman et al., 2012 ). Gender stereotypes, that is, expectations of what women and men are like, and what they should be like, are one of the most powerful schemas activated when people encounter others ( Fiske et al., 1991 ; Stangor et al., 1992 ). According to status characteristics theory, people’s group memberships convey important information about their status and their competence on specific tasks ( Berger et al., 1974 ; Berger et al., 1998 ; Correll and Ridgeway, 2003 ). Organizational decision makers will, for many jobs, have different expectations for men’s and women’s competence and job performance. Expectations of stereotyped-group members’ success can affect gender discrimination that occurs in HR-related decisions and enactment ( Roberson et al., 2007 ). For example, men are preferred over women for masculine jobs and women are preferred over men for feminine jobs ( Davison and Burke, 2000 ). Thus, the more that a workplace role is inconsistent with the attributes ascribed to women, the more a particular woman might be seen as lacking “fit” with that role, resulting in decreased performance expectations ( Heilman, 1983 ; Eagly and Karau, 2002 ).

Furthermore, because women are associated with lower status, and men with higher status, women experience backlash for pursuing high status roles (e.g., leadership) in the workplace ( Rudman et al., 2012 ). In other words, agentic women who act competitively and confidently in a leadership role, are rated as more socially deficient, less likeable and less hireable, compared with men who act the same way ( Rudman, 1998 ; Rudman et al., 2012 ). Interestingly though, if women pursue roles in the workplace that are congruent with traditional gender expectations, they will elicit positive reactions ( Eagly and Karau, 2002 ).

Thus, cultural, widely known, gender stereotypes can affect HR-related decisions. However, such an account does not take into consideration individual differences among organizational decision makers (e.g., managers, supervisors, or HR personnel) who may vary in the extent to which they endorse sexist attitudes or stereotypes. Individual differences in various forms of sexism (e.g., modern sexism, neosexism) have been demonstrated to lead to personal discrimination in the workplace ( Hagen and Kahn, 1975 ; Beaton et al., 1996 ; Hitlan et al., 2009 ). Ambivalent sexism theory builds on earlier theories of sexism by including attitudes toward women that, while sexist, are often experienced as positive in valence by perceivers and targets ( Glick and Fiske, 1996 ). Therefore, we draw on ambivalent sexism theory, which conceptualizes sexism as a multidimensional construct that encompasses both hostile and benevolent attitudes toward women ( Glick and Fiske, 1996 , 2001 ).

Hostile sexism involves antipathy and negative stereotypes about women, such as beliefs that women are incompetent, overly emotional, and sexually manipulative. Hostile sexism also involves beliefs that men should be more powerful than women and fears that women will try to take power from men ( Glick and Fiske, 1996 ; Cikara et al., 2008 ). In contrast, benevolent sexism involves overall positive views of women, as long as they occupy traditionally feminine roles. Individuals with benevolently sexist beliefs characterize women as weak and needing protection, support, and adoration. Importantly, hostile and benevolent sexism tend to go hand-in-hand (with a typical correlation of 0.40; Glick et al., 2000 ). This is because ambivalent sexists, people who are high in benevolent and hostile sexism, believe that women should occupy restricted domestic roles and that women are weaker than men are ( Glick and Fiske, 1996 ). Ambivalent sexists reconcile their potentially contradictory attitudes about women by acting hostile toward women whom they believe are trying to steal men’s power (e.g., feminists, professionals who show competence) and by acting benevolently toward traditional women (e.g., homemakers) who reinforce conventional gender relations and who serve men ( Glick et al., 1997 ). An individual difference approach allows us to build on the earlier models ( Heilman, 1983 ; Eagly and Karau, 2002 ; Rudman et al., 2012 ), by specifying who is more likely to discriminate against women and why.

Organizational decision makers who are higher (vs. lower) in hostile sexism should discriminate more against women in HR-related decisions ( Glick et al., 1997 ; Masser and Abrams, 2004 ). For instance, people high in hostile sexism have been found to evaluate candidates, who are believed to be women, more negatively and give lower employment recommendations for a management position, compared with matched candidates believed to be men ( Salvaggio et al., 2009 ) 1 . In another study, among participants who evaluated a female candidate for a managerial position, those higher in hostile sexism were less likely to recommend her for hire, compared with those lower in hostile sexism ( Masser and Abrams, 2004 ). Interestingly, among those evaluating a matched man for the same position, those higher (vs. lower) in hostile sexism were more likely to recommend him for hire ( Masser and Abrams, 2004 ). According to ambivalent sexism theorists ( Glick et al., 1997 ), because people high in hostile sexism see women as a threat to men’s status, they act as gatekeepers denying women access to more prestigious or masculine jobs.

Furthermore, when enacting HR policies and decisions, organizational decision makers who are higher (vs. lower) in hostile sexism should discriminate more against women in the form of gender harassment. Gender harassment can involve hostile terms of address, negative comments regarding women in management, sexist jokes, and sexist behavior ( Fitzgerald et al., 1995a , b ). It has been found that people higher (vs. lower) in hostile sexism have more lenient attitudes toward the sexual harassment of women, which involves gender harassment, in the workplace ( Begany and Milburn, 2002 ; Russell and Trigg, 2004 ). Furthermore, men who more strongly believe that women are men’s adversaries tell more sexist jokes to a woman ( Mitchell et al., 2004 ). Women also report experiencing more incivility (i.e., low level, rude behavior) in the workplace than men ( Björkqvist et al., 1994 ; Cortina et al., 2001 , 2002 ), which could be due to hostile attitudes toward women. In summary, the evidence is consistent with the idea that organizational decision makers’ hostile sexism should predict their gender harassing behavior during HR enactment; however, more research is needed for such a conclusion.

In addition, organizational decision makers who are higher (vs. lower) in benevolent sexism should discriminate more against women when making HR-related decisions. It has been found that people higher (vs. lower) in benevolent sexism are more likely to automatically associate men with high-authority and women with low-authority roles and to implicitly stereotype men as agentic and women as communal ( Rudman and Kilianski, 2000 ). Thus, organizational decision makers who are higher (vs. lower) in benevolent sexism should more strongly believe that women are unfit for organizational roles that are demanding, challenging, and requiring agentic behavior. Indeed, in studies of male MBA students those higher (vs. lower) in benevolent sexism assigned a fictional woman less challenging tasks than a matched man ( King et al., 2012 ). The researchers reasoned that this occurred because men are attempting to “protect” women from the struggles of challenging work. Although there has been little research conducted that has looked at benevolent sexism and gender discrimination in HR-related decisions, the findings are consistent with our model.

Finally, organizational decision makers who are higher (vs. lower) in benevolent sexism should engage in a complex form of gender discrimination when enacting HR policy and decisions that involves mixed messages: women are more likely to receive messages of positive verbal feedback (e.g., “stellar work,” “excellent work”) but lower numeric ratings on performance appraisals, compared with men ( Biernat et al., 2012 ). It is proposed that this pattern of giving women positive messages about their performance while rating them poorly reflects benevolent sexists’ desire to protect women from harsh criticism. However, given that performance appraisals are used for promotion decisions and that constructive feedback is needed for learning, managers’ unwillingness to give women negative verbal criticisms can lead to skill plateau and career stagnation.

Furthermore, exposure to benevolent sexism can harm women’s motivation, goals and performance. Adolescent girls whose mothers are high in benevolent (but not hostile) sexism display lower academic goals and academic performance ( Montañés et al., 2012 ). Of greater relevance to the workplace, when role-playing a job candidate, women who interacted with a hiring manager scripted to make benevolently sexist statements became preoccupied with thoughts about their incompetence, and consequently performed worse in the interview, compared with those in a control condition ( Dardenne et al., 2007 ). These findings suggest that benevolent sexism during the enactment of HR practices can harm women’s work-related motivation and goals, as well as their performance, which can result in a self-fulfilling prophecy ( Word et al., 1974 ). In other words, the low expectations benevolent sexists have of women can be confirmed by women as they are undermined by paternalistic messages.

Ambivalent sexism can operate to harm women’s access to jobs, opportunities for development, ratings of performance, and lead to stigmatization. However, hostile and benevolent sexism operate in different ways. Hostile sexism has direct negative consequences for women’s access to high status, male-typed jobs ( Masser and Abrams, 2004 ; Salvaggio et al., 2009 ), and it is related to higher rates of sexual harassment ( Fitzgerald et al., 1995b ; Mitchell et al., 2004 ; Russell and Trigg, 2004 ), which negatively affect women’s health, well-being, and workplace withdrawal behaviors ( Willness et al., 2007 ). In contrast, benevolent sexism has indirect negative consequences for women’s careers, for instance, in preventing access to challenging tasks ( King et al., 2012 ) and critical developmental feedback ( Vescio et al., 2005 ). Interestingly, exposure to benevolent sexism results in worsened motivation and cognitive performance, compared with exposure to hostile sexism ( Dardenne et al., 2007 ; Montañés et al., 2012 ). This is because women more easily recognize hostile sexism as a form of discrimination and inequality, compared with benevolent sexism, which can be more subtle in nature ( Dardenne et al., 2007 ). Thus, women can externalize hostile sexism and mobilize against it, but the subtle nature of benevolent sexism prevents these processes ( Kay et al., 2005 ; Becker and Wright, 2011 ). Therefore, hostile and benevolent sexism lead to different but harmful forms of HR discrimination. Future research should more closely examine their potentially different consequences.

Thus far, we have articulated how gender inequalities in organizational structures, processes, and practices can affect discrimination in HR policy and in HR-related decision-making and enactment. Furthermore, we have argued that organizational decision makers’ levels of hostile and benevolent sexism are critical factors leading to personal discrimination in HR-related decision-making and enactment, albeit in different forms. We now turn to an integration of these two phenomena.

The Effect of Organizational Structures, Processes, and Practices on Organizational Decision Makers’ Levels of Hostile and Benevolent Sexism

Organizational decision makers’ beliefs about men and women should be affected by the work environments in which they are embedded. Thus, when there are more gender inequalities within organizational structures, processes, and practices, organizational decision makers should have higher levels of hostile sexism and benevolent sexism. Two inter-related processes can account for this proposition: the establishment of who becomes and remains an organizational member, and the socialization of organizational members.

First, as organizations develop over time, forces work to attract, select, and retain an increasingly homogenous set of employees in terms of their hostile and benevolent sexism ( Schneider, 1983 , 1987 ). In support of this perspective, an individual’s values tend to be congruent with the values in his or her work environment (e.g., Holland, 1996 ; Kristof-Brown et al., 2005 ). People are attracted to and choose to work for organizations that have characteristics similar to their own, and organizations select individuals who are likely to fit with the organization. Thus, more sexist individuals are more likely to be attracted to organizations with greater gender inequality in leadership, structure, strategy, culture, climate, and HR policy; and they will be seen as a better fit during recruitment and selection. Finally, individuals who do not fit with the organization tend to leave voluntarily through the process of attrition. Thus, less (vs. more) sexist individuals would be more likely to leave a workplace with marked gender inequalities in organizational structures, processes, and practices. The opposite should be true for organizations with high gender equality. Through attraction, selection, and attrition processes it is likely that organizational members will become more sexist in a highly gender unequal organization and less sexist in a highly gender equal organization.

Second, socialization processes can change organizational members’ personal attributes, goals, and values to match those of the organization ( Ostroff and Rothausen, 1997 ). Organizational members’ receive both formal and informal messages about gender inequality—or equality—within an organization through their orientation and training, reading of organizational policy, perceptions of who rises in the ranks, how women (vs. men) are treated within the organization, as well as their perception of climates for diversity and sexual harassment. Socialization of organizational members over time has been shown to result in organizational members’ values and personalities changing to better match the values of the organization ( Kohn and Schooler, 1982 ; Cable and Parsons, 2001 ).

These socialization processes can operate to change organizational members’ levels of sexism. It is likely that within more sexist workplaces, people’s levels of hostile and benevolent sexism increase because their normative beliefs shift due to exposure to institutional discrimination against women, others’ sexist attitudes and behavior, and gender bias in culture and climate ( Schwartz and DeKeseredy, 2000 ; Ford et al., 2008 ; Banyard et al., 2009 ). These processes can also lead organizational decision makers to adopt less sexist attitudes in a workplace context marked by greater gender equality. Thus, organizational members’ levels of hostile and benevolent sexism can be shaped by the degree of gender inequalities in organizational structures, processes, and practices and by the sexism levels of their work colleagues.

In addition, organizational decision makers can be socialized to act in discriminatory ways without personally becoming more sexist. If organizational decision makers witness others acting in a discriminatory manner with positive consequences, or acting in an egalitarian way with negative consequences, they can learn to become more discriminatory in their HR practices through observational learning ( Bandura, 1977 , 1986 ). So, organizational decision makers could engage in personal discrimination without being sexist if they perceive that the fair treatment of women in HR would encounter resistance given the broader organizational structures, processes, and practices promoting gender inequality. Yet over time, given cognitive dissonance ( Festinger, 1962 ), it is likely that discriminatory behavior could induce attitude change among organizational decision makers to become more sexist.

Thus far we have argued that gender inequalities in organizational structures, processes, and practices, organizational decision makers’ sexist attitudes, and gender discrimination in HR practices can have reciprocal, reinforcing relationships. Thus, it may appear that we have created a model that is closed and determinate in nature; however, this would be a misinterpretation. In the following section, we outline how organizations marked by gender inequalities can reduce discrimination against women.

How to Reduce Gender Discrimination in Organizations

The model we present for understanding gender discrimination in HR practices is complex. We believe that such complexity is necessary to accurately reflect the realities of organizational life. The model demonstrates that many sources of gender inequality are inter-related and have reciprocal effects. By implication, there are no simple or direct solutions to reduce gender discrimination in organizations. Rather, this complex problem requires multiple solutions. In fact, as discussed by Gelfand et al. (2007) , if an organization attempts to correct discrimination in only one aspect of organizational structure, process, or practice, and not others, such change attempts will be ineffective due to mixed messages. Therefore, we outline below how organizations can reduce gender discrimination by focusing on (a) HR policies (i.e., diversity initiatives and family friendly policies) and closely related organizational structures, processes, and practices; (b) HR-related decision-making and enactment; as well as, (c) the organizational decision makers who engage in such actions.

Reducing Gender Discrimination in HR Policy and Associated Organizational Structures, Processes, and Practices

Organizations can take steps to mitigate discrimination in HR policies. As a first example, let us consider how an organization can develop, within its HR systems, diversity initiatives aimed at changing the composition of the workforce that includes policies to recruit, retain, and develop employees from underrepresented groups ( Jayne and Dipboye, 2004 ). Diversity initiatives can operate like affirmative action programs in that organizations track and monitor (a) the number of qualified candidates from different groups (e.g., women vs. men) in a pool, and (b) the number of candidates from each group hired or promoted. When the proportion of candidates from a group successfully selected varies significantly from their proportion in the qualified pool then action, such as targeted recruitment efforts, needs to be taken.

Importantly, such efforts to increase diversity can be strengthened by other HR policies that reward managers, who select more diverse personnel, with bonuses ( Jayne and Dipboye, 2004 ). Organizations that incorporate diversity-based criteria into their performance and promotion policies and offer meaningful incentives to managers to identify and develop successful female candidates for promotion are more likely to succeed in retaining and promoting diverse talent ( Murphy and Cleveland, 1995 ; Cleveland et al., 2000 ). However, focusing on short-term narrowly defined criteria, such as increasing the number of women hired, without also focusing on candidates’ merit and providing an adequate climate or support for women are unlikely to bring about any long-term change in diversity, and can have detrimental consequences for its intended beneficiaries ( Heilman et al., 1992 , 1997 ). Rather, to be successful, HR policies for diversity need to be supported by the other organizational structures, processes, and practices, such as strategy, leadership, and climate.

For instance, diversity initiatives should be linked to strategies to create a business case for diversity ( Jayne and Dipboye, 2004 ). An organization with a strategy to market to more diverse populations can justify that a more diverse workforce can better serve potential clientele ( Jayne and Dipboye, 2004 ). Alternatively, an organization that is attempting to innovate and grow might justify a corporate strategy to increase diversity on the grounds that diverse groups have multiple perspectives on a problem with the potential to generate more novel, creative solutions ( van Knippenberg et al., 2004 ). Furthermore, organizational leaders must convey strong support for the HR policies for them to be successful ( Rynes and Rosen, 1995 ). Given the same HR policy within an organization, leaders’ personal attitudes toward the policy affects the discrimination levels found within their unit ( Pryor, 1995 ; Pryor et al., 1995 ). Finally, diversity programs are more likely to succeed in multicultural organizations with strong climates for diversity ( Elsass and Graves, 1997 ; Jayne and Dipboye, 2004 ). An organization’s climate for diversity consists of employees’ shared perceptions that the organization’s structures, processes, and practices are committed to maintaining diversity and eliminating discrimination ( Nishii and Raver, 2003 ; Gelfand et al., 2007 ). In organizations where employees perceive a strong climate for diversity, diversity programs result in greater employee attraction and retention among women and minorities, at all levels of the organization ( Cox and Blake, 1991 ; Martins and Parsons, 2007 ).

As a second example of how HR policies can mitigate gender inequalities, we discuss HR policies to lessen employees’ experience of work-family conflict. Work-family conflict is a type of role conflict that workers experience when the demands (e.g., emotional, cognitive, time) of their work role interfere with the demands of their family role or vice versa ( Greenhaus and Beutell, 1985 ). Work-family conflict has the negative consequences of increasing employee stress, illness-related absence, and desire to turnover ( Grandey and Cropanzano, 1999 ). Importantly, women are more adversely affected by work-family conflict than men ( Martins et al., 2002 ). Work-family conflict can be exacerbated by HR policies that evaluate employees based on face time (i.e., number of hours present at the office), as a proxy for organizational commitment ( Perlow, 1995 ; Elsbach et al., 2010 ).

Formal family friendly HR policies can be adopted to relieve work-family conflict directly, which differentially assists women in the workplace. For instance, to reduce work-family conflict, organizations can implement HR policies such as flexible work arrangements, which involve flexible schedules, telecommuting, compressed work weeks, job-shares, and part-time work ( Galinsky et al., 2008 ). In conjunction with other family friendly policies, such as the provision of childcare, elderly care, and paid maternity leave, organizations can work to reduce stress and improve the retention of working mothers ( Burke, 2002 ).

Unfortunately, it has been found that the enactment of flexible work policies can still lead to discrimination. Organizational decision makers’ sexism can lead them to grant more flexible work arrangements to white men than to women and other minorities because white men are seen as more valuable ( Kelly and Kalev, 2006 ). To circumvent this, organizations need to formalize HR policies relating to flexible work arrangements ( Kelly and Kalev, 2006 ). For instance, formal, written policies should articulate who can adopt flexible work arrangements (e.g., employees in specific divisions or with specific job roles) and what such arrangements look like (e.g., core work from 10 am to 3 pm with flexible work hours from 7 to 10 am or from 3 to 6 pm). When the details of such policies are formally laid out, organizational decision makers have less latitude and therefore less opportunity for discrimination in granting access to these arrangements.

To be successful, family friendly HR policies should be tied to other organizational structures, processes, and practices such as organizational strategy, leadership, culture, and climate. A business case for flexible work arrangements can be made because they attract and retain top-talent, which includes women ( Baltes et al., 1999 ). Furthermore, organizational leaders must convey strong support for family friendly programs ( Jayne and Dipboye, 2004 ). Leaders can help bolster the acceptance of family friendly policies through successive interactions, communications, visibility, and role modeling with employees. For instance, a leader who sends emails at 2 o’clock in the morning is setting a different expectation of constant availability than a leader who never sends emails after 7:00 pm. Family friendly HR policies must also be supported by simultaneously changing the underlying organizational culture that promotes face time. Although it is difficult to change the culture of an organization, the leaders’ of the organization play an influential role in instilling such change because the behaviors of leaders are antecedents and triggers of organizational culture ( Kozlowski and Doherty, 1989 ; Ostroff et al., 2012 ). In summary, HR policies must be supported by other organizational structures, processes, and practices in order for these policies to be effective.

Adopting HR diversity initiative policies and family friendly policies can reduce gender discrimination and reshape the other organizational structures, processes, and practices and increase gender equality in them. Specifically, such policies, if successful, should increase the number of women in all departments and at all levels of an organization. Further, having more women in leadership positions signals to organizational members that the organization takes diversity seriously, affecting the diversity climate of the organization, and ultimately its culture ( Konrad et al., 2010 ). Thus, particular HR policies can reduce gender inequalities in all of the other organizational structures, processes, and practices.

Reducing Gender Discrimination in HR-Related Decision-Making and Enactment

A wealth of research demonstrates that an effective means of reducing personal bias by organizational decision makers in HR practices is to develop HR policies that standardize and objectify performance data (e.g., Konrad and Linnehan, 1995 ; Reskin and McBrier, 2000 ). To reduce discrimination in personnel decisions (i.e., employee hiring and promotion decisions) a job analysis should be performed to determine the appropriate knowledge skills and abilities needed for specific positions ( Fine and Cronshaw, 1999 ). This ensures that expectations about characteristics of the ideal employee for that position are based on accurate knowledge of the job and not gender stereotypes about the job ( Welle and Heilman, 2005 ). To reduce discrimination in performance evaluations, HR policies should necessitate the use of reliable measures based on explicit objective performance expectations and apply these practices consistently across all worker evaluations ( Bernardin et al., 1998 ; Ittner et al., 2003 ). Employees’ performance should be evaluated using behaviorally anchored rating scales ( Smith and Kendall, 1963 ) that allow supervisors to rate subordinates on examples of actual work behaviors. These evaluations should be done regularly, given that delays require retrieving memories of work performance and this process can be biased by gender stereotypes ( Sanchez and De La Torre, 1996 ). Finally, if greater gender differences are found on selection tests than on performance evaluations, then the use of such biased selection tests needs to be revisited ( Chung-Yan and Cronshaw, 2002 ). In summary, developing HR policies that standardize and objectify the process of employee/candidate evaluations can reduce personal bias in HR practices.

Importantly, the level of personal discrimination enacted by organizational decision makers can be reduced by formalizing HR policies, and by controlling the situations under which HR-related decisions are made. We have articulated how HR-related decisions involve social cognition and are therefore susceptible to biases introduced by the use of gender stereotypes. This can occur unwittingly by those who perceive themselves to be unprejudiced but who are affected by stereotypes or negative automatic associations nonetheless ( Chugh, 2004 ; Son Hing et al., 2008 ). For instance, when HR policies do not rely on objective criteria, and the context for evaluation is ambiguous, organizational decision makers will draw on gender (and other) stereotypes to fill in the blanks when evaluating candidates ( Heilman, 1995 , 2001 ). Importantly, the context can be constructed in such a way as to reduce these biases. For instance, organizational decision makers will make less biased judgments of others if they have more time available to evaluate others, are less cognitively busy ( Martell, 1991 ), have higher quality of information available about candidates, and are accountable for justifying their ratings and decisions ( Kulik and Bainbridge, 2005 ; Roberson et al., 2007 ). Thus, if they have the time, motivation, and opportunity to make well-informed, more accurate judgments, then discrimination in performance ratings can be reduced.

Reducing Organizational Decision Makers’ Sexism

Another means to reduce gender discrimination in HR-related decision-making and enactment is to focus directly on reducing the hostile and benevolent sexist beliefs of organizational decision makers. Interventions aimed at reducing these beliefs typically involve diversity training, such as a seminar, course, or workshop. Such training involves one or more sessions that involve interactive discussions, lectures, and practical assignments. During the training men and women are taught about sexism and how gender roles in society are socially constructed. Investigations have shown these workshop-based interventions are effective at reducing levels of hostile sexism but have inconsistent effects on benevolent sexism ( Case, 2007 ; de Lemus et al., 2014 ). The subtle, and in some ways positive nature of benevolent sexism makes it difficult to confront and reduce using such interventions. However, levels of benevolent sexism are reduced when individuals are explicitly informed about the harmful implications of benevolent sexism ( Becker and Swim, 2012 ). Unfortunately, these interventions have not been tested in organizational settings. So their efficacy in the field is unknown.

Gender inequality in organizations is a complex phenomenon that can be seen in HR practices (i.e., policies, decision-making, and their enactment) that affects the hiring, training, pay, and promotion of women. We propose that gender discrimination in HR-related decision-making and the enactment of HR practices stems from gender inequalities in broader organizational structures, processes, and practices, including HR policy but also leadership, structure, strategy, culture, and organizational climate. Moreover, reciprocal effects should occur, such that discriminatory HR practices can perpetuate gender inequalities in organizational leadership, structure, strategy, culture, and climate. Organizational decision makers also play an important role in gender discrimination. We propose that personal discrimination in HR-related decisions and enactment arises from organizational decision makers’ levels of hostile and benevolent sexism. While hostile sexism can lead to discrimination against women because of a desire to keep them from positions of power, benevolent sexism can lead to discrimination against women because of a desire to protect them. Finally, we propose that gender inequalities in organizational structures, processes, and practices affect organizational decision makers’ sexism through attraction, selection, socialization, and attrition processes. Thus, a focus on organizational structure, processes, and practices is critical.

The model we have developed extends previous work by Gelfand et al. (2007) in a number of substantive ways. Gelfand et al. (2007) proposed that aspects of the organization, that is, structure, organizational culture, leadership, strategy, HR systems, and organizational climates, are all interrelated and may contribute to or attenuate discrimination (e.g., racism, sexism, ableism, homophobia). First, we differ from their work by emphasizing that workplace discrimination is most directly attributable to HR practices. Consequently, we emphasize how inequalities in other organizational structures, processes, and practices affect institutional discrimination in HR policy. Second, our model differs from that of Gelfand et al. (2007) in that we focus on the role of organizational decision makers in the enactment of HR policy. The attitudes of these decision makers toward specific groups of employees are critical. However, the nature of prejudice differs depending on the target group ( Son Hing and Zanna, 2010 ). Therefore, we focus on one form of bias—sexism—in the workplace. Doing so, allows us to draw on more nuanced theories of prejudice, namely ambivalent sexism theory ( Glick and Fiske, 1996 ). Thus, third, our model differs from the work of Gelfand et al. (2007) by considering how dual beliefs about women (i.e., hostile and benevolent beliefs) can contribute to different forms of gender discrimination in HR practices. Fourth, we differ from Gelfand et al. (2007) by reviewing how organizational decision makers’ level of sexism within an organization is affected by organizational structures, processes, and practices via selection-attraction-attrition processes and through socialization processes.

However, the model we have developed is not meant to be exhaustive. There are multiple issues that we have not addressed but should be considered: what external factors feed into our model? What other links within the model might arise? What are the limits to its generalizability? What consequences derive from our model? How can change occur given a model that is largely recursive in nature? We focus on these issues throughout our conclusion.

In this paper, we have illustrated what we consider to be the dominant links in our model; however, additional links are possible. First, we do not lay out the factors that feed into our model, such as government regulations, the economy, their competitors, and societal culture. In future work, one could analyze the broader context that organizations operate in, which influences its structures, processes, and practices, as well as its members. For instance, in societies marked by greater gender inequalities, the levels of hostile and benevolent sexism of organizational decision makers will be higher ( Glick et al., 2000 ). Second, there is no link demonstrating how organizational decision makers who are more sexist have the capacity, even if they sit lower in the organizational hierarchy, to influence the amount of gender inequality in organizational structures, processes, and practices. It is possible for low-level managers or HR personnel who express more sexist sentiments to—through their own behavior—affect others’ perceptions of the tolerance for discrimination in the workplace ( Ford et al., 2001 ) and others’ perceptions of the competence and hireability of female job candidates ( Good and Rudman, 2010 ). Thus, organizational decision makers’ levels of hostile and benevolent sexism can affect organizational climates, and potentially other organizational structures, processes, and practices. Third, it is possible that organizational structures, processes, and practices could moderate the link between organizational decision makers’ sexist attitudes and their discriminatory behavior in HR practices. The ability of people to act in line with their attitudes depends on the strength of the constraints in the social situation and the broader context ( Lewin, 1935 , 1951 ). Thus, if organizational structures, processes, and practices clearly communicate the importance of gender equality then the discriminatory behavior of sexist organizational decision makers should be constrained. Accordingly, organizations should take steps to mitigate institutional discrimination by focusing on organizational structures, processes, and practices rather than focusing solely on reducing sexism in individual employees.

Our model does not consider how women’s occupational status is affected by their preferences for gender-role-consistent careers and their childcare and family responsibilities, which perhaps should not be underestimated (e.g., Manne, 2001 ; Hakim, 2006 ; Ceci et al., 2009 ). In other words, lifestyle preferences could contribute to gender differences in the workplace. However, it is important to consider how women’s agency in choosing occupations and managing work-life demands is constrained. Gender imbalances (e.g., in pay) in the workplace (e.g., Moss-Racusin et al., 2012 ; Sheltzer and Smith, 2014 ) and gender imbalances in the home (e.g., in domestic labor, childcare; Bianchi, 2000 ; Bianchi et al., 2000 ) shape the decisions that couples (when they consist of a woman and a man) make about how to manage dual careers. For instance, research has uncovered that women with professional degrees leave the labor force at roughly three times the rate of men ( Baker, 2002 ). Women’s decisions to interrupt their careers were difficult and were based on factors, such as workplace inflexibility, and their husbands’ lack of domestic responsibilities, rather than a preference to stay at home with their children ( Stone and Lovejoy, 2004 ). Thus, both factors inside and outside the workplace constrain and shape women’s career decisions.

Our model is derived largely from research that has been conducted in male-dominated organizations; however, we speculate that it should hold for female-dominated organizations. There is evidence that tokenism does not work against men in terms of their promotion potential in female-dominated environments. Rather, there is some evidence for a glass-escalator effect for men in female-dominated fields, such as nursing, and social work ( Williams, 1992 ). In addition, regardless of the gender composition of the workplace, men are advantaged, compared with women in terms of earnings and wage growth ( Budig, 2002 ). Finally, even in female-dominated professions, segregation along gender lines occurs in organizational structure ( Snyder and Green, 2008 ). Thus, the literature suggests that our model should hold for female-dominated environments.

Some might question if our model assumes that organizational decision makers enacting HR practices are men. It does not. There is evidence that decision makers who are women also discriminate against women (e.g., the Queen Bee phenomenon; Ellemers et al., 2004 ). Further, although men are higher in hostile sexism, compared with women ( Glick et al., 1997 , 2000 ), they are not necessarily higher in benevolent sexism ( Glick et al., 2000 ). More importantly, the effects of hostile and benevolent sexism are not moderated by participant gender ( Masser and Abrams, 2004 ; Salvaggio et al., 2009 ; Good and Rudman, 2010 ). Thus, those who are higher in hostile or benevolent sexism respond in a more discriminatory manner, regardless of whether they are men or women. Thus, organizational decision makers, regardless of their sex, should discriminate more against women in HR practices when they are higher in hostile or benevolent sexism.

In future work, the consequences of our model for women discriminated against in HR practices should be considered. The negative ramifications of sexism and discrimination on women are well known: physical and psychological stress, worse physical health (e.g., high blood pressure, ulcers, anxiety, depression; Goldenhar et al., 1998 ); lower job satisfaction, organizational commitment, and attachment to work ( Murrell et al., 1995 ; Hicks-Clarke and Iles, 2000 ); lower feelings of power and prestige ( Gutek et al., 1996 ); and performance decrements through stereotype threat ( Spencer et al., 1999 ). However, how might these processes differ depending on the proximal cause of the discrimination?

Our model lays out two potential paths by which women might be discriminated against in HR practices: institutional discrimination stemming from organizational structures, processes, and practices and personal discrimination stemming from organizational decision makers’ levels of sexism. In order for the potential stressor of stigmatization to lead to psychological and physical stress it must be seen as harmful and self-relevant ( Son Hing, 2012 ). Thus, if institutional discrimination in organizational structures, processes, and practices are completely hidden then discrimination might not cause stress reactions associated with stigmatization because it may be too difficult for women to detect ( Crosby et al., 1986 ; Major, 1994 ), and label as discrimination ( Crosby, 1984 ; Stangor et al., 2003 ). In contrast, women should be adversely affected by stigmatization in instances where gender discrimination in organizational structures, processes, and practices is more evident. For instance, greater perceptions of discrimination are associated with lower self-esteem in longitudinal studies ( Schmitt et al., 2014 ).

It might appear that we have created a model, which is a closed system, with no opportunities to change an organization’s trajectory: more unequal organizations will become more hierarchical, and more equal organizations will become more egalitarian. We do not believe this to be true. One potential impetus for organizations to become more egalitarian may be some great shock such as sex-based discrimination lawsuits that the organization either faces directly or sees its competitors suffer. Large corporations have been forced to settle claims of gender harassment and gender discrimination with payouts upward of $21 million ( Gilbert v. DaimlerChrysler Corp., 2004 ; LexisNexis, 2010 ; Velez, et al. v. Novartis Pharmaceuticals Crop, et al., 2010 ). Discrimination lawsuits are time consuming and costly ( James and Wooten, 2006 ), resulting in lower shares, lower public perceptions, higher absenteeism, and higher turnover ( Wright et al., 1995 ). Expensive lawsuits experienced either directly or indirectly should act as a big driver in the need for change.

Furthermore, individual women can work to avoid stigmatization. Women in the workplace are not simply passive targets of stereotyping processes. People belonging to stigmatized groups can engage in a variety of anti-stigmatization techniques, but their response options are constrained by the cultural repertoires available to them ( Lamont and Mizrachi, 2012 ). In other words, an organization’s culture will provide its members with a collective imaginary for how to behave. For instance, it might be unimaginable for a woman to file a complaint of sexual harassment if she knows that complaints are never taken seriously. Individuals do negotiate stigmatization processes; however, this is more likely when stigmatization is perceived as illegitimate and when they have the resources to do so ( Major and Schmader, 2001 ). Thus, at an individual level, people engage in strategies to fight being discriminated against but these strategies are likely more constrained for those who are most stigmatized.

Finally, possibly the most efficacious way for organizational members (men and women) to challenge group-based inequality and to improve the status of women as a whole is to engage in collective action (e.g., participate in unions, sign petitions, organize social movements, recruit others to join a movement; Klandermans, 1997 ; Wright and Lubensky, 2009 ). People are most likely to engage in collective action when they perceive group differences as underserved or illegitimate ( Wright, 2001 ). Such a sense of relative deprivation involves feelings of injustice and anger that prompt a desire for wide scale change ( van Zomeren et al., 2008 ). Interestingly, people are more likely to experience relative deprivation when inequalities have begun to be lessened, and thus their legitimacy questioned ( Crosby, 1984 ; Kawakami and Dion, 1993 ; Stangor et al., 2003 ). If organizational leaders respond to such demands for change by altering previously gender oppressive organizational structures, processes, and practices, this can, in people’s minds, open the door for additional changes. Therefore, changes to mitigate gender inequalities within any organizational structure, policy, or practice could start a cascade of transformations leading to a more equal organization for men and women.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This research was supported by funding from the Canadian Institute for Advanced Research (CIFAR) awarded to Leanne S. Son Hing.

In this study, candidates were identified with initials and participants were asked to indicate the presumed gender of the candidate after evaluating them.

• Abrams K. (1991). Social construction, roving biologism, and reasonable women: a response to Professor Epstein. DePaul Law Rev. 41 1021–1040. [ Google Scholar ]
• Acker J. (1990). Hierarchies, jobs, bodies: a theory of gendered organizations. Gend. Soc. 4 139–158. 10.1177/089124390004002002 [ DOI ] [ Google Scholar ]
• Adamitis E. M. (2000). Appearance matters: a proposal to prohibit appearance discrimination in employment. Wash. Law Rev. 75 195–223. [ Google Scholar ]
• Adler N. E., Epel E. S., Castellazzo G., Ickovics J. R. (2000). Relationship of subjective and objective social status with psychological and physiological functioning: preliminary data in healthy White women. Health Psychol. 19 586–592. 10.1037/0278-6133.19.6.586 [ DOI ] [ PubMed ] [ Google Scholar ]
• Baker J. G. (2002). The influx of women into legal professions: an economic analysis. Mon. Labor Rev. 125 12–24. [ Google Scholar ]
• Baltes B. B., Briggs T. E., Huff J. W., Wright J. A., Neuman G. A. (1999). Flexible and compressed workweek schedules: a meta-analysis of their effects on work-related criteria. J. Appl. Psychol. 84 496–513. 10.1037/0021-9010.84.4.496 [ DOI ] [ Google Scholar ]
• Bandura A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84 191–215. 10.1037/0033-295X.84.2.191 [ DOI ] [ PubMed ] [ Google Scholar ]
• Bandura A. (1986). The explanatory and predictive scope of self-efficacy theory. J. Soc. Clin. Psychol. 4 359–373. 10.1521/jscp.1986.4.3.359 [ DOI ] [ Google Scholar ]
• Banyard V. L., Moynihan M. M., Crossman M. T. (2009). Reducing sexual violence on campus: the role of student leaders as empowered bystanders. J. Coll. Stud. Dev. 50 446–457. 10.1353/csd.0.0083 [ DOI ] [ Google Scholar ]
• Barling J., Dekker I., Loughlin C. A., Kelloway E. K., Fullagar C., Johnson D. (1996). Prediction and replication of the organizational and personal consequences of workplace sexual harassment. J. Manag. Psychol. 11 4–25. 10.1108/02683949610124771 [ DOI ] [ Google Scholar ]
• Baxter J., Wright E. O. (2000). The glass ceiling hypothesis: a comparative study of the United States, Sweden, and Australia. Gend. Soc. 14 275–294. 10.1177/089124300014002004 [ DOI ] [ Google Scholar ]
• Beaton A. M., Tougas F., Joly S. (1996). Neosexism among male managers: is it a matter of numbers? J. Appl. Soc. Psychol. 26 2189–2203. 10.1111/j.1559-1816.1996.tb01795.x [ DOI ] [ Google Scholar ]
• Becker J. C., Swim J. K. (2012). Reducing endorsement of benevolent and modern sexist beliefs: differential effects of addressing harm versus pervasiveness of benevolent sexism. Soc. Psychol. 43 127–137. 10.1027/1864-9335/a000091 [ DOI ] [ Google Scholar ]
• Becker J. C., Wright S. C. (2011). Yet another dark side of chivalry: benevolent sexism undermines and hostile sexism motivates collective action for social change. J. Pers. Soc. Psychol. 101 62–77. 10.1037/a0022615 [ DOI ] [ PubMed ] [ Google Scholar ]
• Begany J. J., Milburn M. A. (2002). Psychological predictors of sexual harassment: authoritarianism, hostile sexism, and rape myths. Psychol. Men Masc. 3 119–126. 10.1037/1524-9220.3.2.119 [ DOI ] [ Google Scholar ]
• Berdhal J. L. (2007). The sexual harassment of uppity women. J. Appl. Psychol. 92 425–437. 10.1037/0021-9010.92.2.425 [ DOI ] [ PubMed ] [ Google Scholar ]
• Berger J., Conner T. L., Fisek M. H. (eds). (1974). Expectation States Theory: A Theoretical Research Program. Cambridge, MA: Winthrop Publishers. [ Google Scholar ]
• Berger J., Fisek M. H., Norman R. Z., Wagner D. G. (1998). “Formation of reward expectations in status situations,” in Status, Power, and Legitimacy eds Berger J., Zelditch M., Jr. (New Brunswick, NJ: Transaction Publishers; ) 121–153. [ Google Scholar ]
• Bernardin H. J., Hagan C. M., Kane J. S., Villanova P. (1998). “Effective performance management: a focus on precision, customers, and situational constraints,” in Performance Appraisal: State of the Art in Practice ed. Smither J. W. (San Francisco, CA: Jossey-Bass; ) 3–48. [ Google Scholar ]
• Bianchi S. M. (2000). Maternal employment and time with children: dramatic change or surprising continuity? Demography 47 401–414. 10.1353/dem.2000.0001 [ DOI ] [ PubMed ] [ Google Scholar ]
• Bianchi S. M., Milkie M. A., Sayer L. C., Robinson J. P. (2000). Is anyone doing the housework? Trends in the gender division of household labor. Soc. Forces 79 191–228. 10.1093/sf/79.1.191 [ DOI ] [ Google Scholar ]
• Biernat M., Tocci M. J., Williams J. C. (2012). The language of performance evaluations: gender-based shifts in content and consistency of judgment. Soc. Psychol. Personal. Sci. 3 186–192. 10.1177/1948550611415693 [ DOI ] [ Google Scholar ]
• Björkqvist K., Österman K., Hjelt-Bäck M. (1994). Aggression among university employees. Aggress. Behav. 20 173–184. [ DOI ] [ Google Scholar ]
• Blau F. D., DeVaro J. (2007). New evidence on gender differences in promotion rates: an empirical analysis of a sample of new hires. Ind. Relat. 46 511–550. 10.1111/j.1468-232X.2007.00479.x [ DOI ] [ Google Scholar ]
• Bobocel R. D., Son Hing L. S., Davey L. M., Stanley D. J., Zanna M. P. (1998). Justice-based opposition to social policies: is it genuine? J. Pers. Soc. Psychol. 75 653–669. 10.1037/0022-3514.75.3.653 [ DOI ] [ Google Scholar ]
• Boldry J., Wood W., Kashy D. A. (2001). Gender stereotypes and the evaluation of men and women in military training. J. Soc. Issues 57 689–705. 10.1111/0022-4537.00236 [ DOI ] [ Google Scholar ]
• Borrel C., Artazcoz L., Gil-González D., Pérez G., Rohlfs I., Pérez K. (2010). Perceived sexism as a health determinant in Spain. J. Womens Health 19 741–750. 10.1089/jwh.2009.1594 [ DOI ] [ PubMed ] [ Google Scholar ]
• Branscombe N. R. (1998). Thinking about one’s gender group’s privileges or disadvantages: consequences for well-being in women and men. Br. J. Soc. Psychol. 37 167–184. 10.1111/j.2044-8309.1998.tb01163.x [ DOI ] [ PubMed ] [ Google Scholar ]
• Brass D. J. (1985). Men’s and women’s networks: a study of interaction patterns and influence in an organization. Acad. Manag. J. 28 327–343. 10.2307/256204 [ DOI ] [ Google Scholar ]
• Budig M. J. (2002). Male advantage and the gender composition of jobs: who rides the glass escalator? Soc. Probl. 49 258–277. 10.1525/sp.2002.49.2.258 [ DOI ] [ Google Scholar ]
• Burke R. J. (2002). Organizational values, job experiences and satisfactions among managerial and professional women and men: advantage men? Women Manag. Rev. 17 228–236. 10.1108/09649420210433184 [ DOI ] [ Google Scholar ]
• Cable D. M., Parsons C. K. (2001). Socialization tactics and person-organization fit. Pers. Psychol. 54 1–23. 10.1111/j.1744-6570.2001.tb00083.x [ DOI ] [ PubMed ] [ Google Scholar ]
• Case K. A. (2007). Raising male privilege awareness and reducing sexism: an evaluation of diversity courses. Psychol. Women Q. 31 426–435. 10.1111/j.1471-6402.2007.00391.x [ DOI ] [ Google Scholar ]
• Cassirer N., Reskin B. (2000). High Hopes: organizational position, employment experiences, and women and men’s promotion aspirations. Work Occup. 27 438–463. 10.1177/0730888400027004002 [ DOI ] [ Google Scholar ]
• Castilla E. J., Benard S. (2010). The paradox of meritocracy in organizations. Admin. Sci. Q. 55 543–576. 10.2189/asqu.2010.55.4.543 [ DOI ] [ Google Scholar ]
• Ceci S. J., Williams W. M., Barnett S. M. (2009). Women’s underrepresentation in science: sociocultural and biological considerations. Psychol. Bull. 135 218–261. 10.1037/a0014412 [ DOI ] [ PubMed ] [ Google Scholar ]
• Chemers M. M. (1997). An Integrative Theory of Leadership. Mahwah, NJ: Lawrence Erlbaum Associates. [ Google Scholar ]
• Chugh D. (2004). Societal and managerial implications of implicit social cognition: why milliseconds matter. Soc. Justice Res. 17 203–222. 10.1023/B:SORE.0000027410.26010.40 [ DOI ] [ Google Scholar ]
• Chung-Yan G. A., Cronshaw S. F. (2002). A critical re-examination and analysis of cognitive ability tests using the Thorndike model of fairness. J. Occup. Organ. Psychol. 75 489–509. 10.1348/096317902321119709 [ DOI ] [ Google Scholar ]
• Cikara M., Lee T. L., Fiske S. T., Glick P. (2008). “Ambivalent sexism at home and at work: how attitudes toward women in relationships foster exclusion in the public sphere,” in Social and Psychological Bases of Ideology and System Justification eds Jost J. T., Kay A. C., Thorisdottir H. (New York: Oxford University Press; ) 444–462. [ Google Scholar ]
• Cleveland J. N., Stockdale M., Murphy K. R., Gutek B. A. (2000). Women and Men in Organizations: Sex and Gender Issues at Work. Mahwah, NJ: Lawrence Erlbaum Associates. [ Google Scholar ]
• Cohen P. N., Huffman M. L. (2007). Working for the woman? Female managers and the gender wage gap. Am. Sociol. Rev. 72 681–704. 10.1177/000312240707200502 [ DOI ] [ Google Scholar ]
• Cohen-Charash Y., Spector P. E. (2001). The role of justice in organizations: a meta-analysis. Organ. Behav. Hum. Decis. Process. 86 278–321. 10.1006/obhd.2001.2958 [ DOI ] [ Google Scholar ]
• Correll S. J., Ridgeway C. L. (2003). “Expectation states theory,” in Handbook of Social Psychology ed. Delamater J. (New York, NY: Kluwer Academic Press; ) 29–51. [ Google Scholar ]
• Cortina L. M., Lonsway K. A., Magley V. J., Freeman L. V., Collinsworth L. L., Hunter M., et al. (2002). What’s gender got to do with it? Incivility in the federal courts. Law Soc. Inq. 27 235–270. 10.1111/j.1747-4469.2002.tb00804.x [ DOI ] [ Google Scholar ]
• Cortina L. M., Magley V. J., Williams J. H., Langhout R. D. (2001). Incivility in the workplace: incidence and impact. J. Occup. Health Psychol. 6 64–80. 10.1037/1076-8998.6.1.64 [ DOI ] [ PubMed ] [ Google Scholar ]
• Cox T. (1994). Cultural Diversity in Organizations: Theory, Research, and Practice. San Francisco, CA: Berrett-Koehler Publishers. [ Google Scholar ]
• Cox T. H., Blake S. (1991). Managing cultural diversity: implications for organizational competitiveness. Executive 5 45–56. 10.5465/AME.1991.4274465 [ DOI ] [ Google Scholar ]
• Cox T. H., Harquail C. V. (1991). Career paths and career success in the early career stages of male and female MBAs. J. Vocat. Behav. 39 54–75. 10.1016/0001-8791(91)90004-6 [ DOI ] [ Google Scholar ]
• Crocker D., Kalemba V. (1999). The incidence and impact of women’s experiences of sexual harassment in Canadian workplaces. Can. Rev. Sociol. 36 541–558. 10.1111/j.1755-618X.1999.tb00963.x [ DOI ] [ Google Scholar ]
• Crosby F. (1984). The denial of personal discrimination. Am. Behav. Sci. 27 371–386. 10.1177/000276484027003008 [ DOI ] [ Google Scholar ]
• Crosby F., Clayton S., Alksnis O., Hemker K. (1986). Cognitive biases in the perception of discrimination: the importance of format. Sex Roles 14 637–646. 10.1007/BF00287694 [ DOI ] [ Google Scholar ]
• Cuddy A. J. C., Fiske S. T., Glick P. (2004). When professionals become mothers, warmth doesn’t cut the ice. J. Soc. Issues 60 701–718. 10.1111/j.0022-4537.2004.00381.x [ DOI ] [ Google Scholar ]
• Dardenne B., Dumont M., Bollier T. (2007). Insidious dangers of benevolent sexism: consequences for women’s performance. J. Pers. Soc. Psychol. 93 764–779. 10.1037/0022-3514.93.5.764 [ DOI ] [ PubMed ] [ Google Scholar ]
• Davison H. K., Burke M. J. (2000). Sex discrimination in simulated employment contexts: a meta-analytic investigation. J. Vocat. Behav. 56 225–248. 10.1006/jvbe.1999.1711 [ DOI ] [ Google Scholar ]
• de Lemus S., Navarro L., Velázquez M. J., Ryan E., Megías J. L. (2014). From sex to gender: a university intervention to reduce sexism in Argentina, Spain, and El Salvador. J. Soc. Issues 70 741–762. 10.1111/josi.12089 [ DOI ] [ Google Scholar ]
• De Pater I. E., Van Vianen A. E. M., Bechtoldt M. N. (2010). Gender differences in job challenge: a matter of task allocation. Gend. Work Organ. 17 433–453. 10.1111/j.1468-0432.2009.00477.x [ DOI ] [ Google Scholar ]
• Durden G. C., Gaynor P. E. (1998). More on the cost of being other than white and male. Am. J. Econ. Sociol. 57 95–103. 10.1111/j.1536-7150.1998.tb03259.x [ DOI ] [ Google Scholar ]
• Eagly A. H., Carli L. L. (2007). Through the Labyrinth: The Truth about How Women become Leaders. Boston, MA: Harvard Business School Publishing. [ Google Scholar ]
• Eagly A. H., Karau S. J. (2002). Role congruity theory of prejudice toward female leaders. Psychol. Rev. 109 573–598. 10.1037/0033-295X.109.3.573 [ DOI ] [ PubMed ] [ Google Scholar ]
• Eagly A. H., Makhijani M. G., Klonsky B. G. (1992). Gender and the evaluation of leaders: a meta-analysis. Psychol. Bull. 111 3–22. 10.1037/0033-2909.111.1.3 [ DOI ] [ PubMed ] [ Google Scholar ]
• Ellemers N., Heuvel H., Gilder D., Maass A., Bonvini A. (2004). The underrepresentation of women in science: differential commitment or the queen bee syndrome? Br. J. Soc. Psychol. 43 315–338. 10.1348/0144666042037999 [ DOI ] [ PubMed ] [ Google Scholar ]
• Elsass P. M., Graves L. M. (1997). Demographic diversity in decision-making groups: the experiences of women and people of color. Acad. Manag. Rev. 22 946–973. 10.5465/AMR.1997.9711022111 [ DOI ] [ Google Scholar ]
• Elsbach K. D., Cable D. M., Sherman J. W. (2010). How passive ‘face time’ affects perceptions of employees: evidence of spontaneous trait inference. Hum. Relat. 63 735–760. 10.1177/0018726709353139 [ DOI ] [ Google Scholar ]
• Festinger L. (1962). A Theory of Cognitive Dissonance. Stanford, CA: Stanford University Press. [ Google Scholar ]
• Fine S. A., Cronshaw S. F. (1999). Functional Job Analysis: A Foundation for Human Resources Management. Mahwah, NJ: Lawrence Erlbaum Associates, Inc. [ Google Scholar ]
• Firth M. (1982). Sex discrimination in job opportunities for women. Sex Roles 8 891–901. 10.1007/BF00287858 [ DOI ] [ Google Scholar ]
• Fiske A. P., Haslam N., Fiske S. T. (1991). Confusing one person with another: what errors reveal about the elementary forms of social relations. J. Pers. Soc. Psychol. 60 656–674. 10.1037/0022-3514.60.5.656 [ DOI ] [ PubMed ] [ Google Scholar ]
• Fiske S. T., Cuddy A. J. C., Glick P., Xu J. (2002). A model of (often mixed) stereotype content: competence and warmth respectively follow from perceived status and competition. J. Pers. Soc. Psychol. 82 878–902. 10.1037//0022-3514.82.6.878 [ DOI ] [ PubMed ] [ Google Scholar ]
• Fiske S. T., Xu J., Cuddy A. J. C., Glick P. (1999). (Dis)respecting versus (dis)liking: status and interdependence predict ambivalent stereotypes of competence and warmth. J. Soc. Issues 55 473–489. 10.1111/0022-4537.00128 [ DOI ] [ Google Scholar ]
• Fitzgerald L. F., Gelfand M. J., Drasgow F. (1995a). Measuring sexual harassment: theoretical and psychometric advances. Basic Appl. Soc. Psychol. 17 425–445. 10.1207/s15324834basp1704_2 [ DOI ] [ Google Scholar ]
• Fitzgerald L. F., Hulin C. L., Drasgow F. (1995b). “The antecedents and consequences of sexual harassment in organizations: an integrated model,” in Job Stress in a Changing Workforce: Investigating Gender, Diversity, and Family Issues eds Keita G., Hurrell J., Jr. (Washington, DC: American Psychological Association; ) 55–73. [ Google Scholar ]
• Fitzgerald L. F., Shullman S. L., Bailey N., Richards M., Swecker J., Gold Y., et al. (1988). The incidence and dimensions of sexual harassment in academia and the workplace. J. Vocat. Behav. 32 152–175. 10.1016/0001-8791(88)90012-7 [ DOI ] [ Google Scholar ]
• Ford T. E., Boxer C. F., Armstrong J., Edel J. R. (2008). More than “just a joke”: the prejudice-releasing function of sexist humor. Pers. Soc. Psychol. Bull. 34 159–170. 10.1177/0146167207310022 [ DOI ] [ PubMed ] [ Google Scholar ]
• Ford T. E., Wentzel E. R., Lorion J. (2001). Effects of exposure to sexist humor on perceptions of normative tolerance of sexism. Eur. J. Soc. Psychol. 31 677–691. 10.1002/ejsp.56 [ DOI ] [ Google Scholar ]
• Fuegen K., Biernat M., Haines E., Deaux K. (2004). Mothers and fathers in the workplace: how gender and parental status influence judgements of job-related competence. J. Soc. Issues 60 737–754. 10.1111/j.0022-4537.2004.00383.x [ DOI ] [ Google Scholar ]
• Galinsky E., Bond J., Sakai K. (2008). 2008 National Study of Employers. Available at: http://familiesandwork.org/site/research/reports/2008nse.pdf [ Google Scholar ]
• Gelfand M. J., Nishii L. H., Raver J. L., Schneider B. (2007). Discrimination in Organizations: An Organizational-Level Systems Perspective (CAHRS Working Paper #07-08). Ithaca, NY: Cornell University. Retrieved from Cornell University, School of Industrial and Labor Relations, Center for Advanced Human Resource Studies. [ Google Scholar ]
• Gerhart B., Rynes S. (1991). Determinants and consequences of salary negotiations by male and female MBA graduates. J. Appl. Psychol. 76 256–262. 10.1037/0021-9010.76.2.256 [ DOI ] [ Google Scholar ]
• Gilbert v. DaimlerChrysler Corp. (2004). 470 Mich. 749, 685 N.W.2d 391 2004 Lansing, MI: Supreme Court of Michigan. [ Google Scholar ]
• Glass J. (2004). Blessing or curse? Work-family policies and mother’s wage growth over time. Work Occup. 31 367–394. 10.1177/0730888404266364 [ DOI ] [ Google Scholar ]
• Glick P. (2013). “BS at work: how benevolent sexism undermines women and justifies backlash,” in Paper Presented at the Harvard Business School symposium Gender & Work: Challenging Conventional Wisdom Boston, MA. [ Google Scholar ]
• Glick P., Diebold J., Bailey-Werner B., Zhu L. (1997). The two faces of Adam: ambivalent sexism and polarized attitudes toward women. Pers. Soc. Psychol. Bull. 23 1323–1334. 10.1177/01461672972312009 [ DOI ] [ Google Scholar ]
• Glick P., Fiske S. T. (1996). The ambivalent sexism inventory: differentiating hostile and benevolent sexism. J. Pers. Soc. Psychol. 70 491–512. 10.1037/0022-3514.70.3.491 [ DOI ] [ Google Scholar ]
• Glick P., Fiske S. T. (2001). An ambivalent alliance: hostile and benevolent sexism as complementary justifications for gender inequality. Am. Psychol. 56 109–118. 10.1037/0003-066X.56.2.109 [ DOI ] [ PubMed ] [ Google Scholar ]
• Glick P., Fiske S. T., Mladinic A., Saiz J. L., Abrams D., Masser B., et al. (2000). Beyond prejudice as simple antipathy: hostile and benevolent sexism across culture. J. Pers. Soc. Psychol. 79 763–775. 10.1037/0022-3514.79.5.763 [ DOI ] [ PubMed ] [ Google Scholar ]
• Goldberg P. (1968). Are women prejudiced against women? Transaction 5 316–322. [ Google Scholar ]
• Goldenhar L. M., Swanson N. G., Hurrell J. J., Jr., Ruder A., Deddens J. (1998). Stressors and adverse outcomes for female construction workers. J. Occup. Health Psychol. 3 19–32. 10.1037/1076-8998.3.1.19 [ DOI ] [ PubMed ] [ Google Scholar ]
• Good J. J., Rudman L. A. (2010). When female applicants meet sexist interviewers: the costs of being a target of benevolent sexism. Sex Roles 62 481–493. 10.1007/s11199-009-9685-6 [ DOI ] [ Google Scholar ]
• Grandey A. A., Cropanzano R. (1999). The conservation of resources model applied to work-family conflict and strain. J. Vocat. Behav. 54 350–370. 10.1006/jvbe.1998.1666 [ DOI ] [ Google Scholar ]
• Grant R. M. (2010). Contemporary Strategy Analysis, Seventh Edition. New York, NY: Wiley. [ Google Scholar ]
• Greenhaus J. H., Beutell N. J. (1985). Sources of conflict between work and family roles. Acad. Manag. Rev. 10 76–88. 10.5465/AMR.1985.4277352 [ DOI ] [ Google Scholar ]
• Gutek B. A., Cohen A. G., Tsui A. (1996). Reactions to perceived sex discrimination. Hum. Relat. 49 791–813. 10.1177/001872679604900604 [ DOI ] [ Google Scholar ]
• Hagen R. L., Kahn A. (1975). Discrimination against competent women. J. Appl. Soc. Psychol. 5 362–376. 10.1111/j.1559-1816.1975.tb00688.x [ DOI ] [ Google Scholar ]
• Hakim C. (2006). Women, careers, and work-life preferences. Br. J. Guid. Counc. 34 279–294. 10.1080/03069880600769118 [ DOI ] [ Google Scholar ]
• Hartnell C. A., Walumbwa F. O. (2011). “Transformational leadership and organizational culture,” in The Handbook of Organizational Culture and Climate eds Ashkanasy N. M., Wilderom C. P. M., Peterson M. F. (Thousand Oaks, CA: Sage Publications; ) 225–248. [ Google Scholar ]
• Hebl M. R., King E. B., Glick P., Singletary S. L., Kazama S. (2007). Hostile and benevolent reactions toward pregnant women: complementary interpersonal punishments and rewards that maintain traditional roles. J. Appl. Psychol. 92 1499–1511. 10.1037/0021-9010.92.6.1499 [ DOI ] [ PubMed ] [ Google Scholar ]
• Heilman M. E. (1983). “Sex bias in work settings: the lack of fit model,” in Research in Organizational Behavior Vol. 5 eds Staw B., Cummings L. (Greenwich, CT: JAI press; ) 269–298. [ Google Scholar ]
• Heilman M. E. (1995). Sex stereotypes and their effects in the workplace: what we know and what we don’t know. J. Soc. Behav. Pers. 10 3–26. [ Google Scholar ]
• Heilman M. E. (2001). Description and prescription: how gender stereotypes prevent women’s ascent up the organizational ladder. J. Soc. Issues 57 657–674. 10.1111/0022-4537.00234 [ DOI ] [ Google Scholar ]
• Heilman M. E., Block C. J., Lucas J. A. (1992). Presumed incompetent? Stigmatization and affirmative action efforts. J. Appl. Psychol. 77 536–544. 10.1037/0021-9010.77.4.536 [ DOI ] [ Google Scholar ]
• Heilman M. E., Block C. J., Stathatos P. (1997). The affirmative action stigma of incompetence: effects of performance information ambiguity. Acad. Manag. J. 40 603–625. 10.2307/257055 [ DOI ] [ Google Scholar ]
• Heilman M., Okimoto T. G. (2007). Why are women penalized for success at male tasks? The implied communality deficit. J. Appl. Psychol. 92 81–92. 10.1037/0021-9010.92.1.81 [ DOI ] [ PubMed ] [ Google Scholar ]
• Heilman M., Okimoto T. G. (2008). Motherhood: a potential source of bias in employment decisions. J. Appl. Psychol. 93 189–198. 10.1037/0021-9010.93.1.189 [ DOI ] [ PubMed ] [ Google Scholar ]
• Hicks-Clarke D., Iles P. (2000). Climate for diversity and its effects on career and organisational attitudes and perceptions. Person. Rev. 29 324–345. 10.1108/00483480010324689 [ DOI ] [ Google Scholar ]
• Hitlan R. T., Pryor J. B., Hesson-McInnis M. S., Olson M. (2009). Antecedents of gender harassment: an analysis of person and situation factors. Sex Roles 61 794–807. 10.1007/s11199-009-9689-2 [ DOI ] [ Google Scholar ]
• Holland J. L. (1996). Exploring careers with a typology: what we have learned and some new directions. Am. Psychol. 51 397–406. 10.1037/0003-066X.51.4.397 [ DOI ] [ Google Scholar ]
• Hough L. M., Oswald F. L., Ployhart R. E. (2001). Determinants, detection, and amelioration of adverse impact in personnel selection procedures: issues, evidence, and lessons learned. Int. J. Sel. Assess. 9 152–194. 10.1111/1468-2389.00171 [ DOI ] [ Google Scholar ]
• House R. J., Aditya R. N. (1997). The social scientific study of leadership: quo vadis? J. Manag. 23 409–473. 10.1177/014920639702300306 [ DOI ] [ Google Scholar ]
• Huffman M. L., Velasco S. C. (1997). When more is less: sex composition, organizations, and earnings in U.S. firms . Work Occup. 24 214–244. 10.1177/0730888497024002005 [ DOI ] [ Google Scholar ]
• Hulin C. L., Fitzgerald L. F., Drasgow F. (1996). “Organizational influences on sexual harassment,” in Sexual Harassment in the Workplace: Perspectives, Frontiers, and Response Strategies. Women and Work: A Research and Policy Series Vol. 5 ed. Stockdale M. S. (Thousand Oaks, CA: Sage Publications; ) 127–150. [ Google Scholar ]
• Hunter J. E., Schmidt F. L., Jackson G. B. (1982). Meta-Analysis: Cumulating Research Findings Across Studies. Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Hunter L. W., Bernhardt A., Hughes K. L., Skuratowicz E. (2001). It’s not just the ATMs: technology, firm strategies, jobs, and earnings in retail banking. Ind. Labor Relat. Rev. 54 402–424. 10.1177/001979390105400222 [ DOI ] [ Google Scholar ]
• Ittner C. D., Larcker D. F., Meyer M. W. (2003). Subjectivity and the weighting of performance measures: evidence from a balanced scorecard. Account. Rev. 78 725–758. 10.2308/accr.2003.78.3.725 [ DOI ] [ Google Scholar ]
• James E. H., Wooten L. P. (2006). Diversity crises: how firms manage discrimination lawsuits. Acad. Manag. J. 49 1103–1118. 10.5465/AMJ.2006.23478091 [ DOI ] [ Google Scholar ]
• Jayne M. E., Dipboye R. L. (2004). Leveraging diversity to improve business performance: research findings and recommendations for organizations. Hum. Resour. Manag. 43 409–424. 10.1002/hrm.20033 [ DOI ] [ Google Scholar ]
• Jung D., Wu A., Chow C. W. (2008). Towards understanding the direct and indirect effects of CEOs’ transformational leadership on firm innovation. Leadersh. Q. 19 582–594. 10.1016/j.leaqua.2008.07.007 [ DOI ] [ Google Scholar ]
• Kanter R. M. (1977). Men and Women of the Corporation. New York, NY: Basic Books. [ Google Scholar ]
• Kath L. M., Swody C. A., Magley V. J., Bunk J. A., Gallus J. A. (2009). Cross-level, three-way interactions among work-group climate, gender, and frequency of harassment on morale and withdrawal outcomes of sexual harassment. J. Occup. Organ. Psychol. 82 159–182. 10.1348/096317908X299764 [ DOI ] [ Google Scholar ]
• Kawakami K., Dion K. L. (1993). The impact of salient self-identities on relative deprivation and action intentions. Eur. J. Soc. Psychol. 23 525–540. 10.1002/ejsp.2420230509 [ DOI ] [ Google Scholar ]
• Kay A. C., Jost J. T., Young S. (2005). Victim derogation and victim enhancement as alternate routes to system justification. Psychol. Sci. 16 240–246. 10.1111/j.0956-7976.2005.00810.x [ DOI ] [ PubMed ] [ Google Scholar ]
• Kelly E. L., Kalev A. (2006). Managing flexible work arrangements in US organizations: formalized discretion or a ‘right to ask’. Soc. Econ. Rev. 4 379–416. 10.1093/ser/mwl001 [ DOI ] [ Google Scholar ]
• King E. B., Botsford W., Hebl M. R., Kazama S., Dawson J. F., Perkins A. (2012). Benevolent sexism at work: gender differences in the distribution of challenging developmental experiences. J. Manag. 38 1835–1866. 10.1177/0149206310365902 [ DOI ] [ Google Scholar ]
• Klandermans B. (1997). The Social Psychology of Protest. Oxford: Basic Blackwell. [ Google Scholar ]
• Kohn M. L., Schooler C. (1982). Job conditions and personality: a longitudinal assessment of their reciprocal effects. Am. J. Sociol. 87 1257–1286. 10.1086/227593 [ DOI ] [ Google Scholar ]
• Konrad A. M., Cannings K., Goldberg C. B. (2010). Asymmetrical demography effects on psychological climate for gender diversity: differential effects of leader gender and work unit gender composition among Swedish doctors. Hum. Relat. 63 1661–1685. 10.1177/0018726710369397 [ DOI ] [ Google Scholar ]
• Konrad A. M., Linnehan F. (1995). Formalized HRM structures: coordinating equal employment opportunity or concealing organizational practices? Acad. Manag. J. 38 787–820. 10.2307/256746 [ DOI ] [ Google Scholar ]
• Kozlowski S. W., Doherty M. L. (1989). Integration of climate and leadership: examination of a neglected issue. J. Appl. Psychol. 74 546–553. 10.1037/0021-9010.74.4.546 [ DOI ] [ Google Scholar ]
• Kristof-Brown A. L., Zimmerman R. D., Johnson E. C. (2005). Consequences of individuals’ fit at work: a meta-analysis of person-job, person-organization, person-group, and person-supervisor fit. Pers. Psychol. 58 281–342. 10.1111/j.1744-6570.2005.00672.x [ DOI ] [ Google Scholar ]
• Kulik C. T., Bainbridge H. T. J. (2005). “Psychological perspectives on workplace diversity,” in Handbook of Workplace Diversity eds Konrad A. M., Prasad P., Pringle J. K. (Thousand Oaks, CA: Sage Publications; ) 25–52. [ Google Scholar ]
• Lamont M., Mizrachi N. (2012). Ordinary people doing extraordinary things: responses to stigmatization in comparative perspective. Ethn. Racial Stud. 35 365–381. 10.1080/01419870.2011.589528 [ DOI ] [ Google Scholar ]
• Lazear E. P., Rosen S. (1990). Male-female wage differentials in job ladders. J. Labor Econ. 8 106–123. 10.1016/j.sapharm.2011.06.003 [ DOI ] [ Google Scholar ]
• Leskinen E. A., Cortina L. M., Kabat D. B. (2011). Gender harassment: broadening our understanding of sex-based harassment at work. Law Hum. Behav. 35 25–39. 10.1007/s10979-010-9241-5 [ DOI ] [ PubMed ] [ Google Scholar ]
• Levitin T., Quinn R. P., Staines G. L. (1971). Sex discrimination against the American working woman. Am. Behav. Sci. 15 237–254. 10.1177/000276427101500207 [ DOI ] [ Google Scholar ]
• Lewin K. (1935). A Dynamic Theory of Personality. New York, NY: McGraw-Hill. [ Google Scholar ]
• Lewin K. (1951). Field Theory in Social Science: Selected Theoretical Papers. Oxford: Harpers. [ Google Scholar ]
• LexisNexis. (2010). Mealy’s Daily News Update: Pharmaceutical Firm Settles Gender Bias Class Claims for $ 175 Million. Available at: http://www.lexis.com [accessed July 15, 2010] [ Google Scholar ]
• Linehan M., Scullion H. (2008). The development of female global managers: the role of mentoring and networking. J. Bus. Ethics 83 29–40. 10.1007/s10551-007-9657-0 [ DOI ] [ Google Scholar ]
• Lips H. M. (2003). The gender pay gap: concrete indicator of women’s progress toward equality. Anal. Soc. Issues Public Policy 3 87–109. 10.1111/j.1530-2415.2003.00016.x [ DOI ] [ Google Scholar ]
• Lyness K. S., Thompson D. E. (1997). Above the glass ceiling? A comparison of matched samples of female and male executives. J. Appl. Psychol. 82 359–375. 10.1037/0021-9010.82.3.359 [ DOI ] [ PubMed ] [ Google Scholar ]
• Major B. (1994). From social inequality to personal entitlement: the role of social comparisons, legitimacy appraisals, and group membership. Adv. Exp. Soc. Psychol. 26 293–355. 10.1016/S0065-2601(08)60156-2 [ DOI ] [ Google Scholar ]
• Major B., Quinton W. J., Schmader T. (2003). Attributions to discrimination and self-esteem: impact of group identification and situational ambiguity. J. Exp. Soc. Psychol. 39 220–231. 10.1016/S0022-1031(02)00547-4 [ DOI ] [ Google Scholar ]
• Major B., Schmader T. (2001). “Legitimacy and the construal of social disadvantage,” in The Psychology of Legitimacy: Emerging Perspectives on Ideology, Justice, and Intergroup Relations eds Jost J. T., Major B. (Cambridge: Cambridge University Press; ) 176–200. [ Google Scholar ]
• Manne A. (2001). Women’s preferences, fertility and family policy: the case for diversity. People Place 9 6–25. [ Google Scholar ]
• Mansfield P., Koch P., Henderson J., Vicary J., Cohn M., Young E. (1991). The job climate for women in traditionally male blue-collar occupations. Sex Roles 25 63–79. 10.1007/BF00289317 [ DOI ] [ Google Scholar ]
• Marini M. M. (1989). Sex differences in earnings in the United States. Annu. Rev. Sociol. 15 343–380. 10.1146/annurev.so.15.080189.002015 [ DOI ] [ Google Scholar ]
• Martell R. F. (1991). Sex bias at work: the effects of attentional and memory demands on performance ratings of men and women. J. Appl. Soc. Psychol. 21 1939–1960. 10.1111/j.1559-1816.1991.tb00515.x [ DOI ] [ Google Scholar ]
• Martell R. F., Lane D. M., Emrich C. (1996). Male-female differences: a computer simulation. Am. Psychol. 51 157–158. 10.1037/0003-066X.51.2.157 [ DOI ] [ Google Scholar ]
• Martins L. L., Eddleston K. A., Veiga J. F. (2002). Moderators of the relationship between work-family conflict and career satisfaction. Acad. Manag. J. 45 399–409. 10.2307/3069354 [ DOI ] [ Google Scholar ]
• Martins L. L., Parsons C. K. (2007). Effects of gender diversity management on perceptions of organizational attractiveness: the role of individual differences in attitudes and beliefs. J. Appl. Psychol. 92 865–875. 10.1037/0021-9010.92.3.865 [ DOI ] [ PubMed ] [ Google Scholar ]
• Masser B. M., Abrams D. (2004). Reinforcing the glass ceiling: the consequences of hostile sexism for female managerial candidates. Sex Roles 51 609–615. 10.1007/s11199-004-5470-8 [ DOI ] [ Google Scholar ]
• Maume D. J. (1999). Glass ceilings and glass escalators: occupational segregation and race and sex differences in managerial promotions. Work Occup. 26 483–509. 10.1177/0730888499026004005 [ DOI ] [ Google Scholar ]
• McDonald P., Backstrom S., Dear K. (2008). Reporting sexual harassment: claims and remedies. Asia Pac. J. Hum. Resour. 46 173–195. 10.1177/1038411108091757 [ DOI ] [ Google Scholar ]
• McDonald S., Lin N., Ao D. (2009). Networks of Opportunity: gender, race, and job leads. Soc. Probl. 56 385–402. 10.1525/sp.2009.56.3.385 [ DOI ] [ Google Scholar ]
• McGinley A. (2007). Babes and beefcake: exclusive hiring arrangements and sexy dress codes. Duke J. Gend. Law Policy 14 257–283. [ Google Scholar ]
• McIntyre S., Moberg D. J., Posner B. Z. (1980). Preferential treatment in preselection decisions according to sex and race. Acad. Manag. J. 23 738–749. 10.2307/255560 [ DOI ] [ Google Scholar ]
• McLaughlin H., Uggen C., Blackstone A. (2012). Sexual harassment, workplace authority, and the paradox of power. Am. Sociol. Rev. 77 625–647. 10.1177/0003122412451728 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Miner-Rubino K., Cortina L. M. (2004). Working in a context of hostility toward women: implications for employees’ well-being. J. Occup. Health Psychol. 9 107–122. 10.1037/1076-8998.9.2.107 [ DOI ] [ PubMed ] [ Google Scholar ]
• Mitchell D., Hirschman R., Angelone D. J., Lilly R. S. (2004). A laboratory analogue for the study of peer sexual harassment. Psychol. Women Q. 28 194–203. 10.1111/j.1471-6402.2004.00136.x [ DOI ] [ Google Scholar ]
• Montañés P., de Lemus S., Bohner G., Megías J. L., Moya M., Garcia-Retamero R. (2012). Intergenerational transmission of benevolent sexism from mothers to daughters and its relation to daughters’ academic performance and goals. Sex Roles 66 468–478. 10.1007/s11199-011-0116-0 [ DOI ] [ Google Scholar ]
• Moore v. Alabama State University. (1996). 980 F. Supp. 426 (M.D. Ala. 1996) M.D. Alabama: United States District Court. [ Google Scholar ]
• Morgan W. B., Walker S. S., Hebl M. R., King E. B. (2013). A field experiment: reducing interpersonal discrimination toward pregnant job applicants. J. Appl. Psychol. 98 799–809. 10.1037/a0034040 [ DOI ] [ PubMed ] [ Google Scholar ]
• Morrison A. M., Von Glinow M. A. (1990). Women and minorities in management. Am. Psychol. 45 200–208. 10.1037/0003-066X.45.2.200 [ DOI ] [ Google Scholar ]
• Moss-Racusin C. A., Dovidio J. F., Brescoll V. L., Graham M. J., Handelsman J. (2012). Science faculty’s subtle gender biases favor male students. PNAS 109 16474–16479. 10.1073/pnas.1211286109 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Murphy K. R., Cleveland J. (1995). Understanding Performance Appraisal: Social, Organizational, and Goal-Based Perspectives. Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Murrell A. J., Olson J. E., Frieze I. H. (1995). Sexual harassment and gender discrimination: a longitudinal study of women managers. J. Soc. Issues 51 139–149. 10.1111/j.1540-4560.1995.tb01313.x [ DOI ] [ Google Scholar ]
• Neumark D. (1996). Sex discrimination in restaurant hiring: an audit study. Q. J. Econ. 111 915–942. 10.2307/2946676 [ DOI ] [ Google Scholar ]
• Nishii L. H., Raver J. L. (2003). “Collective climates for diversity: evidence from a field study,” in Paper Presented at the Annual Conference of the Society for Industrial and Organizational Psychology Orlando, FL. [ Google Scholar ]
• Noe R. A. (1988). Women and mentoring: a review and research agenda. Acad. Manag. Rev. 13 65–78. 10.5465/AMR.1988.4306784 [ DOI ] [ Google Scholar ]
• Offermann L. R., Malamut A. B. (2002). When leaders harass: the impact of target perceptions of organizational leadership and climate on harassment reporting and outcomes. J. Appl. Psychol. 87 885–893. 10.1037/0021-9010.87.5.885 [ DOI ] [ PubMed ] [ Google Scholar ]
• Olian J. D., Schwab D. P., Haberfeld Y. (1988). The impact of applicant gender compared to qualifications on hiring recommendations: a meta-analysis of experimental studies. Organ. Behav. Hum. Decis. Process. 41 180–195. 10.1016/0749-5978(88)90025-8 [ DOI ] [ Google Scholar ]
• Ostroff C., Atwater L. E. (2003). Does whom you work with matter? Effects of referent group gender and age composition on managers’ compensation. J. Appl. Psychol. 88 725–740. 10.1037/0021-9010.88.4.725 [ DOI ] [ PubMed ] [ Google Scholar ]
• Ostroff C., Kinicki A. J., Muhammad R. S. (2012). “Organizational culture and climate,” in Handbook of Psychology, Industrial and Organizational Psychology 2nd Edn Vol. 12 eds Schmitt N. W., Highhouse S. (New York, NY: Wiley and Sons; ) 643–676. [ Google Scholar ]
• Ostroff C., Rothausen T. J. (1997). The moderating effect of tenure in person—environment fit: a field study in educational organizations. J. Occup. Organ. Psychol. 70 173–188. 10.1111/j.2044-8325.1997.tb00641.x [ DOI ] [ Google Scholar ]
• Perlow L. A. (1995). Putting the work back into work/family. Group Organ. Manag. 20 227–239. 10.1177/1059601195202009 [ DOI ] [ Google Scholar ]
• Perry E. L., Davis-Blake A., Kulik C. T. (1994). Explaining gender-based selection decisions: a synthesis of contextual and cognitive approaches. Acad. Manag. Rev. 19 786–820. 10.5465/AMR.1994.9412190219 [ DOI ] [ Google Scholar ]
• Peterson T., Morgan L. A. (1995). Separate and unequal: occupation-establishment sex segregation and gender wage gap. Am. J. Sociol. 101 329–365. 10.1086/230727 [ DOI ] [ Google Scholar ]
• Pryor J. B. (1995). The psychosocial impact of sexual harassment on women in the US Military. Basic Appl. Soc. Psychol. 17 581–603. 10.1207/s15324834basp1704_9 [ DOI ] [ Google Scholar ]
• Pryor J. B., Giedd J. L., Williams K. B. (1995). A social psychological model for predicting sexual harassment. J. Soc. Issues 51 69–84. 10.1111/j.1540-4560.1995.tb01309.x [ DOI ] [ Google Scholar ]
• Pryor J. B., LaVite C. M., Stoller L. M. (1993). A social and psychological analysis of sexual harassment: the person/situation interaction. J. Vocat. Behav. 42 68–83. 10.1006/jvbe.1993.1005 [ DOI ] [ Google Scholar ]
• Ragins B. R., Cornwall J. M. (2001). Pink triangles: antecedents and consequences of perceived workplace discrimination against gay and lesbian employees. J. Appl. Psychol. 86 1244–1261. 10.1037/0021-9010.86.6.1244 [ DOI ] [ PubMed ] [ Google Scholar ]
• Ragins B. R., Sundstrom E. (1989). Gender and power in organizations: a longitudinal perspective. Psychol. Bull. 105 51–88. 10.1037/0033-2909.105.1.51 [ DOI ] [ Google Scholar ]
• Reilly K. T., Wirjanto T. S. (1999). Does more mean less? The male/female wage gap and the proportion of females at the establishment level. Can. J. Econ. 32 906–929. 10.2307/136410 [ DOI ] [ Google Scholar ]
• Reskin B. F., McBrier D. B. (2000). Why not ascription? Organizations’ employment of male and female managers. Am. Sociol. Rev. 65 210–233. [ Google Scholar ]
• Roberson L., Galvin B. M., Charles A. C. (2007). When group identities matter: bias in performance appraisal. Acad. Manag. Ann. 1 617–650. 10.1080/078559818 [ DOI ] [ Google Scholar ]
• Rosen B., Jerdee T. H. (1974). Effects of applicant’s sex and difficulty of job on evaluations of candidates for management positions. J. Appl. Psychol. 59 511–512. 10.1037/h0037323 [ DOI ] [ Google Scholar ]
• Roth P. L., Purvis K. L., Bobko P. (2012). A meta-analysis of gender group differences for measures of job performance in field studies. J. Manag. 38 719–739. 10.1177/0149206310374774 [ DOI ] [ Google Scholar ]
• Rudman L. A. (1998). Self-promotion as a risk factor for women: the costs and benefits of counterstereotypical impression management. J. Pers. Soc. Psychol. 74 629–645. 10.1037/0022-3514.74.3.629 [ DOI ] [ PubMed ] [ Google Scholar ]
• Rudman L. A., Kilianski S. E. (2000). Implicit and explicit attitudes toward female authority. Pers. Soc. Psychol. Bull. 26 1315–1328. 10.1177/0146167200263001 [ DOI ] [ Google Scholar ]
• Rudman L. A., Phelan J. E. (2008). Backlash effects for disconfirming gender stereotypes in organizations. Res. Organ. Behav. 28 61–79. 10.1016/j.riob.2008.04.003 [ DOI ] [ Google Scholar ]
• Rudman L. A., Moss-Racusin C. A., Phelan J. E., Nauts S. (2012). Status incongruity and backlash effects: defending the gender hierarchy motivates prejudice against female leaders. J. Exp. Soc. Psychol. 48 165–179. 10.1016/j.jesp.2011.10.008 [ DOI ] [ Google Scholar ]
• Ruggiero K. M., Taylor D. M. (1995). Coping with discrimination: how disadvantaged group members perceive the discrimination that confronts them. J. Pers. Soc. Psychol. 68 826–838. 10.1037/0022-3514.68.5.826 [ DOI ] [ PubMed ] [ Google Scholar ]
• Russell B. L., Trigg K. Y. (2004). Tolerance of sexual harassment: an examination of gender differences, ambivalent sexism, social dominance, and gender roles. Sex Roles 50 565–573. 10.1023/B:SERS.0000023075.32252.fd [ DOI ] [ Google Scholar ]
• Rynes S., Rosen B. (1995). A field survey of factors affecting the adoption and perceived success of diversity training. Pers. Psychol. 48 247–270. 10.1111/j.1744-6570.1995.tb01756.x [ DOI ] [ Google Scholar ]
• Salvaggio A. N., Streich M., Hopper J. E. (2009). Ambivalent sexism and applicant evaluations: effects on ambiguous applicants. Sex Roles 61 621–633. 10.1007/s11199-009-9640-6 [ DOI ] [ Google Scholar ]
• Sanchez J. I., De La Torre P. (1996). A second look at the relationship between rating and behavioral accuracy in performance appraisal. J. Appl. Psychol. 81 3–10. 10.1037/0021-9010.81.1.3 [ DOI ] [ Google Scholar ]
• Schein E. H. (2010). Organizational Culture and Leadership , Vol. 2 New York, NY: Jossey-Bass [ Google Scholar ]
• Schmader T., Johns M., Forbes C. (2008). An integrated process model of stereotype threat effects on performance. Psychol. Rev. 115 336–356. 10.1037/0033-295X.115.2.336 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schmitt M. T., Branscombe N. R., Kobrynowicz D., Owen S. (2002). Perceiving discrimination against one’s gender group has different implications for well-being in women and men. Pers. Soc. Psychol. Bull. 28 197–210. 10.1177/0146167202282006 [ DOI ] [ Google Scholar ]
• Schmitt M. T., Branscombe N. R., Postmes T., Garcia A. (2014). The consequences of perceived discrimination for psychological well-being: a meta-analytic review. Psychol. Bull. 140 921–948. 10.1037/a0035754 [ DOI ] [ PubMed ] [ Google Scholar ]
• Schneider B. (1975). Organizational climates: an essay. Pers. Psychol. 28 447–479. 10.1111/j.1744-6570.1975.tb01386.x [ DOI ] [ Google Scholar ]
• Schneider B. (1983). “The attraction–selection–attrition framework,” in Organizational Effectiveness: A Comparison of Multiple Models eds Cameron K. S., Whetten D. A. (New York, NY: Academic Press; ). [ Google Scholar ]
• Schneider B. (1987). The people make the place. Pers. Psychol. 40 437–453. 10.1111/j.1744-6570.1987.tb00609.x [ DOI ] [ Google Scholar ]
• Schneider B., Ehrhart M. G., Macey W. H. (2011). “Organizational climate research: achievements and the road ahead,” in The Handbook of Organizational Culture and Climate 2nd Edn eds Ashkanasy N. M., Wilderom C. P. M., Peterson M. F. (Thousand Oaks, CA: Sage; ) 29–49. [ Google Scholar ]
• Schneider K. T., Swan S., Fitzgerald L. F. (1997). Job-related and psychological effects of sexual harassment in the workplace: empirical evidence from two organizations. J. Appl. Psychol. 82 401–415. 10.1037/0021-9010.82.3.401 [ DOI ] [ PubMed ] [ Google Scholar ]
• Schneyer K. L. (1998). Hooting: public and popular discourse about sex discrimination. Univ. Mich. J. Law Reform 31 551–636. [ Google Scholar ]
• Schwartz M. D., DeKeseredy W. S. (2000). Aggregation bias and woman abuse: variations by male peer support, region, language, and school type. J. Interpersh. Violence 15 555–565. 10.1177/088626000015006001 [ DOI ] [ Google Scholar ]
• Sheltzer J. M., Smith J. C. (2014). Elite male faculty in the life sciences employ fewer women. PNAS 111 10107–10112. 10.1073/pnas.1403334111 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Smith P. C., Kendall L. M. (1963). Retranslation of expectations: an approach to the construction of unambiguous anchors for rating scales. J. Appl. Psychol. 47 149–155. 10.1037/h0047060 [ DOI ] [ Google Scholar ]
• Snyder K. A., Green A. I. (2008). Revisiting the glass escalator: the case of gender segregation in a female dominated occupation. Soc. Probl. 55 271–299. 10.1525/sp.2008.55.2.271 [ DOI ] [ Google Scholar ]
• Son Hing L. S. (2012). Responses to stigmatization: the moderating roles of primary and secondary appraisals. Du Bois Rev. 9 149–168. 10.10170/S1742058X11000592 [ DOI ] [ Google Scholar ]
• Son Hing L. S., Bobocel D. R., Zanna M. P. (2002). Meritocracy and opposition to affirmative action: making concessions in the face of discrimination. J. Pers. Soc. Psychol. 83 493–509. 10.1037/0022-3514.83.3.493 [ DOI ] [ PubMed ] [ Google Scholar ]
• Son Hing L. S., Bobocel D. R., Zanna M. P., Garcia D. M., Gee S. S., Orazietti K. (2011). The merit of meritocracy. J. Pers. Soc. Psychol. 101 433–450. 10.1037/a0024618 [ DOI ] [ PubMed ] [ Google Scholar ]
• Son Hing L. S., Chung-Yan G. A., Hamilton L. K., Zanna M. P. (2008). A two-dimensional model that employs explicit and implicit attitudes to characterize prejudice. J. Pers. Soc. Psychol. 94 971–987. 10.1037/0022-3514.94.6.971 [ DOI ] [ PubMed ] [ Google Scholar ]
• Son Hing L. S., Zanna M. P. (2010). “Individual differences,” in The Sage Handbook of Prejudice, Stereotyping and Discrimination eds Dovidio J. F., Hewstone M., Glick P., Esses V. (London: SAGE Publications Ltd.) 163–179. [ Google Scholar ]
• Spencer S. J., Steele C. M., Quinn D. M. (1999). Stereotype threat and women’s math performance. J. Exp. Soc. Psychol. 35 4–28. 10.1006/jesp.1998.1373 [ DOI ] [ Google Scholar ]
• Spreitzer G. M., McCall M. W., Mahoney J. D. (1997). Early identification of international executive potential. J. Appl. Psychol. 82 6–29. 10.1037/0021-9010.82.1.6 [ DOI ] [ Google Scholar ]
• Stangor C., Lynch L., Duan C., Glass B. (1992). Categorization of individuals on the basis of multiple social features. J. Pers. Soc. Psychol. 62 207–218. 10.1037/0022-3514.62.2.207 [ DOI ] [ Google Scholar ]
• Stangor C., Swim J. K., Sechrist G. B., DeCoster J., Van Allen K. L., Ottenbreit A. (2003). Ask, answer, and announce: three stages in perceiving and responding to discrimination. Eur. Rev. Soc. Psychol. 14 277–311. 10.1080/10463280340000090 [ DOI ] [ Google Scholar ]
• Steinpreis R. E., Anders K. A., Ritzke D. (1999). The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: a national empirical study. Sex Roles 41 509–528. 10.1023/A:1018839203698 [ DOI ] [ Google Scholar ]
• Stone P., Lovejoy M. (2004). Fast-track women and the “choice” to stay home. Ann. Am. Acad. Polit. Soc. Sci. 596 62–83. 10.1177/0002716204268552 [ DOI ] [ Google Scholar ]
• Stroh L. K., Brett J. M., Reilly A. H. (1992). All the right stuff: a comparison of female and male managers’ career progression. J. Appl. Psychol. 77 251–260. 10.1037/0021-9010.77.3.251 [ DOI ] [ Google Scholar ]
• Swidler A. (1986). Culture in action: symbols and strategies. Am. Sociol. Rev. 51 273–286. 10.2307/2095521 [ DOI ] [ Google Scholar ]
• Thomas-Hunt M. C., Phillips K. W. (2004). When what you know is not enough: expertise and gender dynamics in task groups. Pers. Soc. Psychol. Bull. 30 1585–1598. 10.1177/0146167204271186 [ DOI ] [ PubMed ] [ Google Scholar ]
• Tosi H. L., Einbender S. W. (1985). The effects of the type and amount of information in sex discrimination research: a meta-analysis. Acad. Manag. J. 28 712–723. 10.2307/256127 [ DOI ] [ Google Scholar ]
• Triana M. D. C., García M. F. (2009). Valuing diversity: a group-value approach to understanding the importance of organizational efforts to support diversity. J. Organ. Behav. 30 941–962. 10.1002/job.598 [ DOI ] [ Google Scholar ]
• Triana M. D. C., García M. F., Colella A. (2010). Managing diversity: how organizational efforts to support diversity moderate the effects of perceived racial discrimination on affective commitment. Pers. Psychol. 63 817–843. 10.1111/j.1744-6570.2010.01189.x [ DOI ] [ Google Scholar ]
• Trice H. M., Beyer J. M. (1993). The Cultures of Work Organizations. Englewood Cliffs, NJ: Prentice-Hall. [ Google Scholar ]
• Tsui A. S., O’Reilly C. A. (1989). Beyond simple demographic effects: the importance of relational demography in superior-subordinate dyads. Acad. Manag. J. 32 402–423. 10.2307/256368 [ DOI ] [ Google Scholar ]
• U.S. Census Bureau. (2007). Statistical Abstract of the United States: 2007. Washington, DC. Available at: http://www.census.gov/prod/www/statistical_abstract.html [ Google Scholar ]
• U.S. Government Accountability Office. (2003). Women’s Earnings: Work Patterns Partially Explain Difference Between Men’s and Women’s Earnings (GAO-04-35). Available at: http://www.gao.gov [ Google Scholar ]
• van Knippenberg D., De Dreu C. K., Homan A. C. (2004). Work group diversity and group performance: an integrative model and research agenda. J. Appl. Psychol. 89 1008–1022. 10.1037/0021-9010.89.6.1008 [ DOI ] [ PubMed ] [ Google Scholar ]
• van Zomeren M., Postmes T., Spears R. (2008). Toward an integrative social identity model of collective action: a qualitative research synthesis of three socio-psychological perspectives. Psychol. Bull. 134 505–535. 10.1037/0033-2909.134.4.504 [ DOI ] [ PubMed ] [ Google Scholar ]
• Velez et al. v. Novartis Pharmaceuticals Crop et al. (2010). No. 04 Civ. 9194 (S.D. N.Y., May 19, 2010). [ Google Scholar ]
• Vescio T. K., Gervais S. J., Snyder M., Hoover A. (2005). Power and the creation of patronizing environments: the stereotype-based behaviors of the powerful and their effects on female performance in masculine domains. J. Pers. Soc. Psychol. 88 658–672. 10.1037/0022-3514.88.4.658 [ DOI ] [ PubMed ] [ Google Scholar ]
• Vorauer J. D., Kumhyr S. M. (2001). Is this about you or me? Self-versus other-directed judgments and feelings in response to intergroup interaction. Pers. Soc. Psychol. Bull. 27 706–719. 10.1177/0146167201276006 [ DOI ] [ Google Scholar ]
• Wal-Mart Stores Inc. v. Dukes. (2004/2011). 222 F.R.D. 137 (N.D. Cal. 2001) aff’d 509 F.3d. 1168 (9C 2007) aff’d 603 F.3d 571 (9C 2010) rev’d 564 U.S. ___ (2011), Docket No. 10–277. [ Google Scholar ]
• Welle B., Heilman M. E. (2005). “Formal and informal discrimination against women at work: the role of gender stereotypes,” in Research in Social Issues in Management eds Steiner D., Gilliland S. W., Skarlicki D. (Westport, CT: Information Age Publishers; ) 23–40. [ Google Scholar ]
• Westall R. (2015). Restaurant Dress Codes Open to Sexual Discrimination Complaints. CBC. Available at: http://www.cbc.ca/news/canada/restaurant-dress-codes-open-to-sexual-discrimination-complaints-1.3012522 [accessed March 31, 2015] [ Google Scholar ]
• Williams C. L. (1992). The glass escalator: hidden advantages for men in the “female” professions. Soc. Probl. 39 253–267. 10.2307/3096961 [ DOI ] [ Google Scholar ]
• Williams J. C. (2003). Beyond the glass ceiling: the maternal wall as a barrier to gender equality. T. Jefferson L. Rev. 26 1–14. [ Google Scholar ]
• Willness C. R., Steel P., Lee K. (2007). A meta-analysis of the antecedents and consequences of workplace sexual harassment. Pers. Psychol. 60 127–162. 10.1111/j.1744-6570.2007.00067.x [ DOI ] [ Google Scholar ]
• Word C. O., Zanna M. P., Cooper J. (1974). The nonverbal mediation of self-fulfilling prophecies in interracial interaction. J. Exp. Soc. Psychol. 10 109–120. 10.1016/0022-1031(74)90059-6 [ DOI ] [ Google Scholar ]
• Wright P., Ferris S. P., Hiller J. S., Kroll M. (1995). Competitiveness through management of diversity: effects on stock price valuation. Acad. Manag. J. 38 272–287. 10.2307/256736 [ DOI ] [ Google Scholar ]
• Wright P. M., McMahan G. C., McWilliams A. (1994). Human resources and sustained competitive advantage: a resource-based perspective. Int. J. Hum. Resour. Manag. 5 301–326. 10.1080/09585199400000020 [ DOI ] [ Google Scholar ]
• Wright S. C. (2001). “Strategic collective action: social psychology and social change,” in Blackwell Handbook of Social Psychology: Intergroup Processes eds Brown R., Gaertner S. (Oxford: Blackwell Publishing; ) 409–430. 10.1002/9780470693421.ch20 [ DOI ] [ Google Scholar ]
• Wright S. C., Lubensky M. E. (2009). “The struggle for social equality: collective action versus prejudice reduction,” in Intergroup Misunderstandings: Impact of Divergent Social Realities eds Demoulin S., Leyens J. P., Dovidio J. F. (New York, NY: Psychology Press; ) 291–310. [ Google Scholar ]
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Discrimination

Social and Community Context

About This Literature Summary

This summary of the literature on Discrimination as a social determinant of health is a narrowly defined examination that is not intended to be exhaustive and may not address all dimensions of the issue. Please note: The terminology used in each summary is consistent with the respective references. For additional information on cross-cutting topics, please see the Access to Foods That Support Healthy Dietary Patterns , Housing Instability , Incarceration , and Quality of Housing literature summaries. 

Related Objectives (5)

Here's a snapshot of the objectives related to topics covered in this literature summary. Browse all objectives .

• Increase the proportion of people whose water supply meets Safe Drinking Water Act regulations — EH‑03
• Reduce bullying of lesbian, gay, or bisexual high school students — LGBT‑05
• Reduce bullying of transgender students — LGBT‑D01
• Increase employment in working-age people — SDOH‑02
• Reduce the proportion of children with a parent or guardian who has served time in jail or prison — SDOH‑05

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Literature Summary

Discrimination is a socially structured action that is unfair or unjustified and harms individuals and groups. 1 , 2 , 3 , 4 Discrimination can be attributed to social interactions that occur to protect more powerful and privileged groups at the detriment of other groups. 3 , 4 Stressful experiences related to discrimination can negatively impact health. 5 Discrimination, especially racial discrimination, has also been known to cause symptoms of trauma. 6 , 7 , 8  

This summary discusses types of structural discrimination (e.g., residential segregation, disparities in access to quality education, and disparities in incarceration) and individual discrimination (e.g., discrimination based on race, gender, sexuality, gender identity, disability, and age). 

Levels of Discrimination

The impact of discrimination occurs at both structural and individual levels. Structural discrimination refers to macro-level conditions (e.g., residential segregation) that limit “opportunities, resources, and well-being” of less privileged groups. 9 Individual discrimination refers to negative interactions between individuals in their institutional roles (e.g., health care provider and patient) or as public or private individuals (e.g., salesperson and customer) based on individual characteristics (e.g., race, gender, etc.). 10 Individual and structural discrimination can cause either intentional or unintentional harm, whether or not it is perceived by the individual. 3 , 11 Discrimination can be understood as a social stressor that has a physiological effect on individuals (e.g., irregular heartbeat, anxiety, heartburn) that can be compounded over time and can lead to long-term negative health outcomes. 5 Discrimination can occur as everyday discrimination or as major discriminatory events. 12 , 13 Everyday discrimination taps into more ongoing and routine experiences of unfair treatment. 12 , 14 Major discriminatory events capture important or more significant experiences of unfair treatment. 12 , 13  

Structural Discrimination

Major discriminatory events are often the result of structural discrimination that can negatively affect individuals and communities. Residential segregation, disparities in access to quality education, and disparities in incarceration rates are some specific forms of structural discrimination. 15 , 16 , 17 , 18 , 19

Residential segregation is a form of structural discrimination in the housing market. Redlining is a form of discrimination where individuals living in neighborhoods mainly populated by certain racial/ethnic groups are denied loans. 20 Basing credit lending decisions on property location has created a legacy of urban areas experiencing chronic health inequities. 21 In one study there was an association found between redlining and poor mental health, a nondermatological cancer diagnosis, and a lack of health insurance. 21 Another study found that small-for-gestational-age birth, prenatal mortality, and preterm birth have a higher prevalence in redlined neighborhoods than in other areas. 22 Residential segregation is a major cause of differences in health status between African American and White people because it can determine the social and economic resources for not only individuals and families, but also for communities. 19  

Residential segregation also affects disparities in access to quality education. 16 , 23 Most school districts generate their income locally through property taxes, so residential segregation by income translates into very different possibilities for funding across school districts. 16 , 24 Children who enroll in poor-quality schools with limited health resources, increased safety concerns, and low teacher support are more likely to have poorer physical and mental health. 15 Another example of structural discrimination is variance in the implementation of criminal justice policy. Some of these variances include the rates at which some racial/ethnic groups are arrested, convicted, and incarcerated for criminal offenses. 25 , 26 , 27 Research shows that some of the racial disparities seen in the incarceration rate may be heavily influenced by state and federal policies such as “3 strikes,” mandatory minimum sentences, and life without parole. 27 These state and federal policies impact incarceration rates for some racial/ethnic groups and in turn may have negative impacts on families, housing, employment, political participation, and health. 16 , 17 , 18 , 27 , 28  

Individual Discrimination

Along with the examples of structural discrimination provided above, individual discrimination may have high physical and emotional health costs. 5 , 29 , 30 , 31 Some examples of individual discrimination include being treated with less courtesy or respect than other people, receiving poorer service than other people at restaurants or stores, or being threatened or harassed. 12 , 14 Research suggests that repeated experiences of discrimination may cause the body to be more physically sensitive in stressful or potentially stressful social situations. 5 , 30 Routine discrimination can be a chronic stressor and increase vulnerability to physical illness. 31 As with other forms of sustained stress, discrimination “may lead to wear and tear on the body.” 5

Discrimination is a fairly common experience; 31 percent of U.S. adults report at least 1 major discriminatory occurrence in their lifetime, and 63 percent report experiencing discrimination every day. 3 While only 8 percent of all U.S. adolescents report experience with racial/ethnic discrimination, there is significant variation between White (2 percent), non-Hispanic Black (17.1 percent), and Hispanic (11.0 percent) youths. 32 Experiencing discrimination may be related to health behaviors that have clear associations with particular disease outcomes, such as smoking 33 , 34 or alcohol abuse. 35 It may also be related to not participating in health-promoting behaviors, such as cancer screening, diabetes management, and condom use. 5 , 36 , 37 , 38 Various forms of discrimination impact different population groups, including certain racial/ethnic groups, 29 , 39 , 40 women, 11 , 41 , 42 lesbian, gay, bisexual, transgender, and queer (LGBTQ) individuals, 43 , 44 , 45 , 46 , 47 people with disabilities, 48 , 49 , 50 and older adults. 3 , 51

Discrimination Based on Race

Discrimination on the basis of race (commonly referred to as racism) has been linked to disparities in health outcomes for some racial/ethnic groups. 39 Racism has been linked to low birth weight, high blood pressure, and poor health status. 29 , 40 For example, infant mortality rates among the non-Hispanic Black population is 11.11 infant deaths out of 1,000 live births while the overall rate in the United States is 5.96 infant deaths per 1,000 live births. 52 Further, the 2019 National Healthcare Disparities Report indicated that White patients receive better quality of care than 40.6 percent of Black patients, 40.5 percent of American Indian/Alaska Native patients, 34.5 percent of Hispanic patients, and 28.6 percent of Asian and Pacific Islander patients. 53 This differential quality of care may be based on racial discrimination. 39 , 53

Discrimination Based on Gender    

Experiences of discrimination based on gender (commonly referred to as sexism) have been shown to have negative health impacts for women. 11 , 41 , 42 Gender refers to “the cultural roles, behaviors, activities, and attributes expected of people based on their sex” while sex is “an individual’s biological status as male, female, or something else assigned at birth.” 54 One study found that after adjusting for other influences, levels of unhappiness, loneliness, and depression are about 30 percent higher for women who reported experiencing recent discrimination compared with those who did not. 11 Additionally, in a national sample of U.S. women ages 18 to 55 years, perceived discrimination was associated with lower likelihood of self-reported excellent/very good health. 41 Another study with a sample of U.S. women found that reports of discrimination due to physical appearance or gender were strongly related to reduced self-reported receipt of Pap smears, mammography, and clinical breast exams. 42 These findings suggest that perceived discrimination may be related to reduced utilization of health care services and worse self-reported health for women. 41 , 42

Discrimination Based on Sexuality and Gender Identity

People who identify as LGBTQ also endure frequent exposure to discrimination due to sexual orientation and gender identity (commonly referred to as sexualism). 43 , 44 , 45 , 46 Sexual orientation refers to “a person’s sexual and emotional attraction to another person (i.e., lesbian, gay, bisexual, etc.)” while gender identity refers to an “individual’s sense of their self as man, woman, transgender, or something else.” 54 Research has found that LGBTQ people reported more lifetime and day-to-day experiences with discrimination when compared with heterosexual individuals. 43 Evidence suggests that adolescents who identify as LGBTQ are more likely than heterosexual adolescents to exhibit symptoms of emotional distress, including depressive symptoms, suicidal ideation, and self-harm. 44 Elevated risk of emotional distress among LGBTQ adolescents may be related to the stress of having a stigmatized identity. 44 , 45 Specifically, LGBTQ adolescents may be in settings where they experience social rejection and isolation, decreased social support, and verbal or physical abuse. 44 , 46 , 47 Additionally, research has found that transgender individuals’ increased risk of discrimination and violence contribute to high rates of suicide attempts among this population — some of the highest of any marginalized group. 55

Discrimination Toward People With Disabilities

People with disabilities are especially vulnerable to experiences of discrimination (commonly referred to as ableism). 48 , 49 In 2014, about 85.3 million people in the United States (27.2 percent of the population) had a disability (minor to more severe). 50 “A history of discrimination and institutionalization” for people with disabilities has caused health inequalities in this population. 49 Adults with disabilities are more likely to report their health to be fair or poor than people without disabilities. 53 , 56 Specifically, 50.8 percent and 31.5 percent of adults with complex activity limitation (e.g., work limitation, self-care limitation) and basic actions difficulty (e.g., movement difficult, cognitive difficulty, seeing or hearing difficulty), respectively, reported their health to be fair or poor, compared with 3.4 percent of adults with no disability. 53 , 55 Adults with disabilities are 2.5 times more likely to report skipping or delaying health care because of cost. 57 People with disabilities consistently report higher rates of obesity, lack of physical activity, and smoking. 55 These disparities in health could also be the result of insufficient or no health insurance coverage, patient choice, or inaccessible transportation. 48

Discrimination Based on Age

The health vulnerabilities of older adults may amplify the health effects of discrimination (commonly referred to as ageism). 58 One study found that experiences of discrimination are frequent among the elderly population, with 63 percent and 31 percent of older adults reporting everyday discrimination and major discriminatory events, respectively. 3 Discrimination based on age was the most common type of discrimination. 3 After controlling for general stress, everyday discrimination still had effects on emotional health, such as depressive symptoms and self-reported health in older adults. 3 Although older adults perceive lower levels of discrimination as they get older, they are more likely to associate experiences of discrimination with their age. 3

Intersectionality Within Discrimination

Although categories such as race or gender alone may influence how individuals experience discrimination, it is equally important to understand how being a part of several affected groups simultaneously (e.g., by race, gender, and place of birth) can impact experiences of discrimination. For example, Black women are differentially situated economically, socially, and politically — and may experience discrimination differently — than other women or Black men; this may affect health outcomes. 10 , 40 , 59 , 60 Specifically, racial discrimination as a psychosocial stressor may increase the risk of preterm and low-birth-weight deliveries for Black women. 40 , 61 , 62

Gaps in Research

Given the health impacts of discrimination on various populations, there is an ongoing need for innovative research methods, improved instrumentation, and new approaches for identifying all types of discrimination and its impact on health and health care. 39 Cumulative and structural consequences of racism are hard to determine through traditional research methods. Innovative advancements in examining this area of discrimination are needed for future examinations. 16 Additional research is needed to increase the evidence base on the effects of discrimination on health outcomes or disparities. This additional evidence will help facilitate public health efforts to address discrimination as a social determinant of health.

Abramson, C. M., Hashemi, M., & Sánchez-Jankowski, M. (2015). Perceived discrimination in US healthcare: Charting the effects of key social characteristics within and across racial groups. Preventive Medicine Reports, 2 , 615–621.

Dovidio, J. F., Penner, L. A., Albrecht, T. L., Norton, W. E., Gaertner, S. L., & Shelton, J. N. (2008). Disparities and distrust: The implications of psychological processes for understanding racial disparities in health and health care. Social Science & Medicine, 67 (3), 478–486.

Luo, Y., Xu, J., Granberg, E., & Wentworth, W. M. (2012). A longitudinal study of social status, perceived discrimination, and physical and emotional health among older adults. Research on Aging, 34 (3), 275–301.

Feagan, J. R. (2000). Racist America: Roots, current realities, and future reparations . Routledge & CRC Press.

Pascoe, E. A., & Smart Richman, L. (2009). Perceived discrimination and health: A meta-analytic review. Psychological Bulletin, 135 (4), 531.

Mendez, L., Mozley, M. M., & Kerig, P. K. (2022). Beyond trauma exposure: Discrimination and posttraumatic stress, internalizing, and externalizing problems among detained youth. Journal of Interpersonal Violence, 37 (3-4), 1825–1851.

Polanco-Roman, L., Danies, A., & Anglin, D. M. (2016). Racial discrimination as race-based trauma, coping strategies, and dissociative symptoms among emerging adults. Psychological Trauma: Theory, Research, Practice, and Policy, 8 (5), 609.

Vines, A. I., Ward, J. B., Cordoba, E., & Black, K. Z. (2017). Perceived racial/ethnic discrimination and mental health: A review and future directions for social epidemiology. Current Epidemiology Reports, 4 (2), 156–165.

Lukachko, A., Hatzenbuehler, M. L., & Keyes, K. M. (2014). Structural racism and myocardial infarction in the United States. Social Science & Medicine, 103 , 42–50.

Krieger, N. (2000). Discrimination and health. Social Epidemiology, 1 , 36–75.

Pavalko, E. K., Mossakowski, K. N., & Hamilton, V. J. (2003). Does perceived discrimination affect health? Longitudinal relationships between work discrimination and women’s physical and emotional health. Journal of Health and Social Behavior , 18–33.

Williams, D. R., Yu, Y., Jackson, J. S., & Anderson, N. B. (1997). Racial differences in physical and mental health: Socio-economic status, stress and discrimination. Journal of Health Psychology, 2 (3), 335–351.

Kessler, R. C., Mickelson, K. D., & Williams, D. R. (1999). The prevalence, distribution, and mental health correlates of perceived discrimination in the United States. Journal of Health and Social Behavior , 208–230.

Essed, P. (1991). Understanding everyday racism: An interdisciplinary theory (Vol. 2) . Sage Publications.

Huang K. Y., Cheng S., & Theise R. (2013). School contexts as social determinants of child health: Current practices and implications for future public health practice. Public Health Reports. 128 (Suppl 3), 21–28.

Pager, D., & Shepherd, H. (2008). The sociology of discrimination: Racial discrimination in employment, housing, credit, and consumer markets. Annu. Rev. Sociol, 34 , 181–209.

Pattillo, M., Western, B., & Weiman, D. (Eds.). (2004). Imprisoning America: The social effects of mass incarceration . Russell Sage Foundation.

Manza, J., & Uggen, C. (2008). Locked out: Felon disenfranchisement and American democracy . Oxford University Press.

Williams, D. R., & Collins, C. (2001). Racial residential segregation: A fundamental cause of racial disparities in health. Public Health Reports, 116 (5), 404–416. https://pubmed.ncbi.nlm.nih.gov/12042604/

Lynch, E. E., Malcoe, L. H., Laurent, S. E., Richardson, J., Mitchell, B. C., & Meier, H. C. (2021). The legacy of structural racism: Associations between historic redlining, current mortgage lending, and health. SSM-Population Health, 14 , 100793.

Nardone, A., Chiang, J., & Corburn, J. (2020). Historic redlining and urban health today in US cities. Environmental Justice, 13 (4), 109–119.

Nardone, A. L., Casey, J. A., Rudolph, K. E., Karasek, D., Mujahid, M., & Morello-Frosch, R. (2020). Associations between historical redlining and birth outcomes from 2006 through 2015 in California. PloS One, 15 (8), e0237241.

Massey, D. S., & Fischer, M. J. (2006). The effect of childhood segregation on minority academic performance at selective colleges. Ethnic and Racial Studies, 29 (1), 1–26. 

Orfield, G., & Lee, C. (2005). Why segregation matters: Poverty and educational inequality . The Civil Rights Project at Harvard University.

Freudenberg, N. (2002). Adverse effects of US jail and prison policies on the health and well-being of women of color. American Journal of Public Health, 92 (12), 1895–1899.

The Pew Charitable Trusts (2010). Collateral Costs: Incarceration’s effect on economic mobility . 

Travis, J., Western, B., & Redburn, F. S. (2014). The growth of incarceration in the United States: Exploring causes and consequences . The National Academies Press.

Pager, D. (2008). Marked: Race, crime, and finding work in an era of mass incarceration . University of Chicago Press.

Cuffee, Y. L., Hargraves, J. L., Rosal, M., Briesacher, B. A., Schoenthaler, A., Person, S., ... & Allison, J. (2013). Reported racial discrimination, trust in physicians, and medication adherence among inner-city African Americans with hypertension. American Journal of Public Health, 103 (11), e55–e62.

Guyll, M., Matthews, K. A., & Bromberger, J. T. (2001). Discrimination and unfair treatment: Relationship to cardiovascular reactivity among African American and European American women. Health Psychology, 20 (5), 315.

Gee, G. C., Spencer, M. S., Chen, J., & Takeuchi, D. (2007). A nationwide study of discrimination and chronic health conditions among Asian Americans. American Journal of Public Health, 97 (7), 1275–1282.

Sykes, B. L., Piquero, A. R., & Gioviano, J. P. (2017). Adolescent racial discrimination and parental perceptions of safety in American neighborhoods and schools. Sociological Forum, 32 , 952–974.

Corral, I., & Landrine, H. (2012). Racial discrimination and health-promoting vs damaging behaviors among African-American adults. Journal of Health Psychology, 17 (8), 1176–1182.

Bennett, G. G., Wolin, K. Y., Robinson, E. L., Fowler, S., & Edwards, C. L. (2005). Perceived racial/ethnic harassment and tobacco use among African American young adults. American Journal of Public Health, 95 (2), 238–240.

Martin, J. K., Tuch, S. A., & Roman, P. M. (2003). Problem drinking patterns among African Americans: The impacts of reports of discrimination, perceptions of prejudice, and “risky” coping strategies. Journal of Health and Social Behavior , 408–425.

Crawley, L. M., Ahn, D. K., & Winkleby, M. A. (2008). Perceived medical discrimination and cancer screening behaviors of racial and ethnic minority adults. Cancer Epidemiology and Prevention Biomarkers, 17 (8), 1937–1944.

Ryan, A. M., Gee, G. C., & Griffith, D. (2008). The effects of perceived discrimination on diabetes management. Journal of Health Care for the Poor and Underserved, 19 (1), 149–163.

Yoshikawa, H., Alan-David Wilson, P., Chae, D. H., & Cheng, J. F. (2004). Do family and friendship networks protect against the influence of discrimination on mental health and HIV risk among Asian and Pacific Islander gay men? AIDS Education and Prevention, 16 (1: Special issue), 84–100.

Shavers, V. L., Fagan, P., Jones, D., Klein, W. M., Boyington, J., Moten, C., & Rorie, E. (2012). The state of research on racial/ethnic discrimination in the receipt of health care. American Journal of Public Health, 102 (5), 953–966.

Mustillo, S., Krieger, N., Gunderson, E. P., Sidney, S., McCreath, H., & Kiefe, C. I. (2004). Self-reported experiences of racial discrimination and Black–White differences in preterm and low-birthweight deliveries: The CARDIA Study. American Journal of Public Health, 94 (12), 2125–2131.

Fazeli Dehkordy, S., Hall, K. S., Dalton, V. K., & Carlos, R. C. (2016). The link between everyday discrimination, healthcare utilization, and health status among a national sample of women. Journal of Women’s Health, 25 (10), 1044–1051.

Jacobs, E. A., Rathouz, P. J., Karavolos, K., Everson-Rose, S. A., Janssen, I., Kravitz, H. M., ... & Powell, L. H. (2014). Perceived discrimination is associated with reduced breast and cervical cancer screening: The Study of Women's Health Across the Nation (SWAN). Journal of Women’s Health, 23 (2), 138–145.

Mays, V. M., & Cochran, S. D. (2001). Mental health correlates of perceived discrimination among lesbian, gay, and bisexual adults in the United States. American Journal of Public Health, 91 (11), 1869–1876. 

Almeida, J., Johnson, R. M., Corliss, H. L., Molnar, B. E., & Azrael, D. (2009). Emotional distress among LGBT youth: The influence of perceived discrimination based on sexual orientation. Journal of Youth and Adolescence, 38 (7), 1001–1014.

Rosario, M., Schrimshaw, E. W., Hunter, J., & Gwadz, M. (2002). Gay-related stress and emotional distress among gay, lesbian and bisexual youths: A longitudinal examination. Journal of Consulting and Clinical Psychology, 70 (4), 967.

Wyss, S. E. (2004). “This was my hell”: The violence experienced by gender non‐conforming youth in US high schools. International Journal of Qualitative Studies in Education, 17 (5), 709–730.

Lombardi, E. L., Wilchins, R. A., Priesing, D., & Malouf, D. (2002). Gender violence: Transgender experiences with violence and discrimination. Journal of Homosexuality, 42 (1), 89–101.

Kirschner, K. L., Breslin, M. L., & Iezzoni, L. I. (2007). Structural impairments that limit access to health care for patients with disabilities. JAMA, 297 (10), 1121–1125.

Krahn, G. L., Walker, D. K., & Correa-De-Araujo, R. (2015). Persons with disabilities as an unrecognized health disparity population. American Journal of Public Health, 105 (S2), S198–S206.

Brault, M. W. (2012). Americans with disabilities: 2010. Household economic studies. Current Population Reports . 70–131.

Lewis, T. T., Barnes, L. L., Bienias, J. L., Lackland, D. T., Evans, D. A., & Mendes de Leon, C. F. (2009). Perceived discrimination and blood pressure in older African American and white adults. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, 64 (9), 1002–1008.

Alhusen, J. L., Bower, K. M., Epstein, E., & Sharps, P. (2016). Racial discrimination and adverse birth outcomes: An integrative review. Journal of Midwifery & Women’s Health, 61 (6), 707–720.

2019 National Healthcare Quality and Disparities Report. (2020). Agency for Healthcare Research and Quality . https://www.ahrq.gov/sites/default/files/wysiwyg/research/findings/nhqrdr/2019qdr.pdf

Centers for Disease Control and Prevention. (2019). Terminology . https://www.cdc.gov/healthyyouth/terminology/sexual-and-gender-identity-terms.htm#:~:text=Gender%3A%20The%20cultural%20roles%2C%20behaviors,dress%2C%20voice%2C%20or%20movement

Dickey L. M., & Budge S. L. (2020). Suicide and the transgender experience: A public health crisis. American Psychologist, 75 (3), 380.

Altman, B. M., & Bernstein, A. (2008). Disability and health in the United States, 2001–2005 . National Center for Health Statistics.

Centers for Disease Control and Prevention. (2010). QuickStats: Delayed or forgone medical care because of cost concerns among adults aged 18–64 years, by disability and health insurance coverage status. National Health Interview Survey, United States, 2009. Morbidity and Mortality Weekly Report, 59 (44), 1456.

Rich‐Edwards, J., Krieger, N., Majzoub, J., Zierler, S., Lieberman, E., & Gillman, M. (2001). Maternal experiences of racism and violence as predictors of preterm birth: rationale and study design. Paediatric and Perinatal Epidemiology, 15 , 124–135.

Crenshaw, K. (1989). Demarginalizing the intersection of race and sex: A Black feminist critique of antidiscrimination doctrine, feminist theory, and antiracist politics. The University of Chicago Legal Forum, 1989 (1).

Collins, J. W., Jr., David, R. J., Symons, R., Handler, A., Wall, S. N., & Dwyer, L. (2000). Low-income African-American mothers’ perception of exposure to racial discrimination and infant birth weight. Epidemiology, 11 (3), 337–339.

Dole, N., Savitz, D. A., Hertz-Picciotto, I., Siega-Riz, A. M., McMahon, M. J., & Buekens, P. (2003). Maternal stress and preterm birth. American Journal of Epidemiology, 157 (1), 14–24.

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