Abstract
Why are some people more likely than others to recognize hostile or unfair interactions in local environments such as their workplaces? We argue that awareness of chilly climates is not simply a tally of instances of discrimination but an interpretive process framed by cultural schemas of inequality, deeply held cultural accounts of broad ascriptive group differences. We contend that schemas of inequality frame the way individuals interpret their day-to-day work environments, sharpening or distorting their ability to recognize unfair circumstances therein. To investigate the relationship between these cultural schemas and recognition of chilliness, we analyze survey data from a theoretically useful case of academic science and engineering (STEM) faculty. When accounting for patterns of under-representation in STEM generally, roughly half of respondents rely on meritocratic schemas, while half use schemas emphasizing structural barriers. Yet even net of demographics and personal experiences of marginalization at work, those using meritocratic schemas are less likely than those using structural schemas to recognize chilly departmental climates and chilly professional cultures. Our focus pivots analytical attention beyond individuals’ experiences of disadvantage toward the cultural schemas that shape whether co-workers, both dominant and non-dominant, recognize chilly interactions in their work environments that disadvantage women and minorities.
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Notes
These deep schemas are “pervasive… [and] relatively unconscious, in the sense that they are taken-for-granted mental assumptions or modes of procedure that actors normally apply without being aware that they are applying them” (Sewell, 1992, p. 22).
Supporting the assertion that some schemas are “deeper” than others, Alexander (2010) examines the “deep code” at the heart of political discourse in the U.S. and finds that the schema of democracy shapes and constrains more surface-level political meanings and discourse.
Cognitive biases about gender and race are also learned from the culture (Ridgeway, 2011). We conceptualize cognitive heuristics such as confirmation bias as one part of the broader interpretive process of using schemas of inequality to frame whether respondents recognize instances of unfair treatment in a range of workplace interactions. Confirmation bias has often been studied in terms of a specific belief influencing the use of new information on the same topic in ways that confirms that belief (e.g., Koehler, 1993; Snyder and Swann, 2003; Westen et al, 2003). In contrast, the broader cultural processes of schemas of inequality we theorize here are generalizable and transposable and can frame the understanding of multiple types of interactions within different interactional environments.
This paper is part of a broader research project, for which we also conducted interviews with 85 STEM faculty in our sample. In this paper, quotations are used solely to help articulate our conceptual framework.
Because Asian/Asian-Americans are not under-represented in our sample or in STEM more broadly (NSF, 2014), we do not include this group within the URM category.
Our population is 73.8 per cent white, 20.4 per cent Asian, and 5.8 per cent under-represented minority, while the national STEM faculty is 71.5 per cent white, 20.7 per cent Asian, and 7.7 per cent under-represented minority (NSF 2014). Additionally, our population is 84 per cent men and 16 per cent women and the national STEM faculty workforce is 72.5 per cent men and 27.5 per cent women (NSF, 2014). Women are somewhat less well represented in our population than in a national sample of all STEM faculty; however, the national data include both universities and 4-year colleges. Women tend to be even more under-represented in tenure-track positions at Research 1 Universities (Ginther and Kahn, 2012).
We used the chained equations technique in Stata, generating twenty multiply imputed datasets. The results of the analysis of each dataset are pooled to produce the resulting coefficient estimates. Following standard procedure, we imputed missing values for all variables in the analysis, including the chilliness measures; all variables in the model were included in the imputation procedure regardless of whether they had any missing values. Because we rely on academic personnel data for the demographic measures, there are no missing values for gender, URM status, rank, department, or the teaching faculty indicator. Missingness on the survey-based attitudinal measures is as follows: twelve percent for the chilly climate and chilly culture measures, sixteen percent for the schemas of inequality measures, and seven percent for the personal experiences of disadvantage measure. Indicating that the multiple imputation procedure does not skew our findings, the results presented here are consistent with models that use listwise deletion. They are also consistent with models that were imputed to the full population size (N = 506).
As the sample has significantly different response rates by gender and rank compared to the population, we use these to construct our weights. Specifically, we created an inverse probability weight that accounted for the differential likelihood of being in the sample by gender and by rank. Because our models already control for the measures used to construct the weights and because the inclusion of weights can artificially inflate standard errors, we excluded the weights from our models. As noted above, the size and significance of the model coefficients are consistent regardless of whether weights are used.
This section of the questionnaire was introduced as follows: “The following questions ask about the climate in your department…. Please indicate your level of agreement with the following statements regarding the climate in your department.”.
Because LGB status is a form of difference that cannot as easily be “read off” the body as gender or race/ethnicity, colleagues my not know who is LGB in their department. As such, we asked the question about treatment of LGB individuals in this more general way.
This section of the questionnaire was introduced as follows: “Please indicate your level of agreement with the following statements regarding the climate in your discipline.”.
We ran two supplemental analyses to assess the empirical distinctiveness of the chilliness and schemas of inequality measures. First, we factor analyzed the three chilly departmental climate measures alongside the two structural explanations of inequality measures (one for gender and the other for race/ethnicity). The five measures loaded onto two factors, one containing the chilly climate measures and the other containing the structural schemas measures. Second, we conducted a discriminant validity test with structural equation models (SEM). Specifically, we compared the χ2 and degrees of freedom between a model where all five measures predicted a single latent measure and a second model where the three chilly climate measures predicted a latent measure and the two structural schemas measures were included in the model as manifest measures. The difference in χ2 and degrees of freedom was large and highly significant (∆χ2 = 19.44; ∆df = 1; p < .001). We repeated these tests with the chilly professional culture measures and found similar evidence of their empirical distinctiveness from the structural schemas of inequality measures: the factor analysis produced two factors and the discriminant validity test was highly significant (∆χ2 = 18.61; ∆df = 1; p < .001).
Reflecting varying patterns of diversity in STEM fields nationally, the proportion of white men by department ranges from 59 per cent in one of the biology specialties to 93 per cent in one engineering specialty (departmental-level mean: 77 per cent white men). To ensure that the assessments of departmental climate are not simply reflections of this demographic variation, we re-ran the models in Tables 4 and 5 replacing the departmental controls with a single measure of the percent white men in respondents’ department. This measure was not a statistically significant predictor of chilly departmental climates or chilly professional cultures.
While men and women are equally likely to give structural and meritocratic explanations for gender inequality, women are marginally more likely than men to give structural explanations of racial/ethnic inequality. Similarly, white and Asian-American and racial/ethnic minority faculty do not give different explanations for race inequality, but URM faculty are marginally more likely to give structural explanations of gender inequality (see Online Appendix Table 2).
The personal experience of marginalization measure mediates (or helps explain part of the relationship between) gender and recognition of chilly climates: a Clogg et al (1995) test with listwise-deleted models shows that the gender coefficient is significant (p < .001) when personal experiences of marginalization are included in the model. Thus, part of the reason that women are more likely to recognize chilly climates in their department is that they are more likely to experience marginalization. Although racial/ethnic minority status is not significant in these models, the unstandardized coefficient drops dramatically (from .196 to .095) controlling for experiences of marginalization, suggesting a similar mediation process (Clogg et al test is significant at the p < .001 level). However, this mediation does not fully explain the relationship between disadvantaged group status and perceptions of chilly climates: women and are still more likely than their male colleagues to recognize chilly climates.
Personal experience of marginalization appears to mediate—but does not completely eliminate—the relationship between gender and perceptions of professional culture in Model B2 (Clogg et al, 1995 test for mediation was significant at the p < .001 level).
We conducted two model checks to help rule out the possibility that these relationships between schemas of inequality and recognizing chilliness are simply an artifact of our modeling strategy: first, we re-ran the models in Tables 4 and 5 removing one control measure at a time. We found that the strength and significance of the schemas of inequality measures remain in each iteration of the models. Second, because we control for variation in fourteen interactional environments (departments in this case), we wanted to make sure that this is not adversely affecting the stability of the models—and thus possibly artificially elevating the strength of the schemas of inequality measures. We replicated models A3 and A4 (Table 4), and B3 and B4 (Table 5), controlling for three broad disciplinary categories (engineering, physical sciences, and biological sciences and the interdisciplinary department) and we found the strength and significance of the schemas of inequality measures remained the same. These two supplemental analyses suggest that the focal results are robust to the particular configuration of covariates in the models.
We use models with interaction terms between gender*schemas of inequality measures to produce the predicted values for women and men and models with interaction terms between URM status*schemas of inequality measures to produce the predicted values for URM and non-URM respondents. These interaction terms help account for any possible differences in the relationships between schemas of inequality and recognition of chilliness by gender or URM status. Suggesting that these relationships are widespread in the data, we find that none of these interaction terms are significant.
Specifically, this measure asks respondents whether “overall, over the last three years, have you ever been aware of any instances in which others in your department may possibly have been treated differently” on the basis of race, gender, sexual orientation or physical disability. One response option for this question is, “No observed instances occurred.” We transformed this response into a dichotomous indicator, where 1 indicates respondents who report no instances of differential treatment (else = 0). The wording of this measure is important: it requires that respondents affirm that they have not observed instances of differential treatment (rather than requiring the authors to deduce this among those who simply failed to mark any observed instances of differential treatment of a list of demographic categories).
Category frequencies for schemas of inequality relating to gender: all meritocratic = 27 per cent, ambivalent = 45 per cent; all structural = 28 per cent. Category frequencies for schemas of inequality relating to race/ethnicity: all meritocratic = 15 per cent, ambivalent = 56 per cent, all structural = 29 per cent.
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Acknowledgments
We are grateful to Jeanne Ferrante for her assistance and advice throughout the project and Erica Bender for her expert research assistance on the survey. We thank Marbella Allen, Maria Charles, Sergio Chavez, Elaine Howard Ecklund, Elizabeth Long, Leslie McCall, Robin Paige, William Rothwell, Andrew Perrin, and Heidi Sherick for valuable advice on earlier versions of this manuscript. We are grateful to the anonymous reviewers and the editor for their detailed and thoughtful feedback. This research was supported by a grant from the National Science Foundation (Grant 1107074; PI: Mary Blair-Loy; Co-PIs: Jeanne Ferrante and Erin Cech). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Cech, E.A., Blair-Loy, M. & Rogers, L.E. Recognizing chilliness: How schemas of inequality shape views of culture and climate in work environments. Am J Cult Sociol 6, 125–160 (2018). https://doi.org/10.1057/s41290-016-0019-1
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DOI: https://doi.org/10.1057/s41290-016-0019-1