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Abstract

This study examines whether self-monitoring—a ubiquitous social psychological construct that captures the extent to which individuals regulate their self-presentation to match the expectation of others—varies across demographic and social contexts. Building on Erving Goffman’s classic insights on stigma management, the authors expect that the propensity for self-monitoring will be greater among sexual minorities, especially in areas where the stigma surrounding minority sexual orientations is strong. The authors’ survey of U.S. adults shows that sexual minorities report significantly higher levels of self-monitoring than heterosexuals and that this difference disappears in large cities. These findings speak to sociological research on self-presentation, with implications for the literatures on identity formation, stigma management, and labor markets.

Keywords

self-monitoring sexual orientation stigma self-presentation urban settings

At least since Erving Goffman’s work in the 1960s, sociologists have investigated the wide-ranging consequences of self-presentation and impression management for the construction of individuals’ social lives. Self-presentation and impression management, for example, have become important topics in research on labor markets (Bolino 1999; Gardner and Avolio 1998; Rivera 2012; Roberts 2005), social psychology (Asch 1956; Leary and Kowalski 1990), stigma and social inequalities (Link and Phelan 2001), personal identity (Howard 2000), and boundary drawing (Lamont and Molnár 2002). Across diverse social arenas, how and why individuals manage and alter their self-presentation to match the demands of their social surroundings have been central to the study of social selves.

As sociologists have amassed extensive evidence about the consequences of impression management, social psychologists have investigated the origins of the disposition toward this behavior. In social psychology, self-monitoring refers to a disposition that captures the extent to which an individual adjusts his or her self-presentation to match the expectations of others (Burt, Kilduff, and Tasselli 2013; Day et al. 2002; Gangestad and Snyder 2000; Snyder 1974). Social psychologists find that this trait varies across individuals: high self-monitors are more attentive to social cues, group norms, and the behavior of others and are more likely to adjust their public appearance accordingly across different social situations. By contrast, low self-monitors respond less readily to situational norms and present themselves more consistently across social settings (Day et al. 2002; Mehra, Kilduff, and Brass 2001).

In this study, we draw on an interactionist perspective to consider the social correlates of a self-monitoring disposition. Although self-monitoring is an inherently social orientation, we know little about how the propensity to self-monitor varies across different social groups and contexts. Early research on the etiology of self-monitoring found little evidence of demographic differences and instead largely focused on identifying self-monitoring’s biological origins (Gangestad 1986; Snyder 1987). For instance, research using twin studies has indicated that self-monitoring has a heritable component (Gangestad 1986). Moreover, biological differences in how high and low self-monitors react to social stress (Bono and Vey 2007; Hofmann 2006) suggest the potential for a “hard-wired biological” basis for self-monitoring differences (Leone 2011:10). Contemporary work often portrays self-monitoring as a purely individual personality trait.

Yet even if the disposition to monitor and adjust one’s self-presentation has a heritable component, past research on identity has shown that social context and early interactions shape individuals’ notions of their social selves (Owens, Robinson, and Smith-Lovin 2010). With regard to self-monitoring, in particular, Snyder (1987) suggested that “although one may be ‘born’ with a genetically given predisposition to become high or low in self-monitoring, one must actually be ‘made’ high or low in self-monitoring through relevant experiences of socialization” (p. 134). Indeed, levels of self-monitoring have been shown to differ across countries (Gudykunst et al. 1989; Gudykunst, Yang, and Nishida 1987), indicating that “environmental forces play a role in the development of self-monitoring propensities” (Leone 2011:10). However, little is known about how self-monitoring varies systematically across demographic factors or how social context structures such variation. This is surprising given that self-monitoring is one of the most ubiquitous personality measures in social research (Day et al. 2002).

In this paper, we build on Goffman’s (1963) classic insights on stigma management to bring a more sociological perspective to bear on self-monitoring. We predict that self-monitoring will be systematically higher among certain demographic groups than others and that this variation is likely to be contingent on contextual factors. In particular, we hypothesize that the propensity for self-monitoring tends to be greater among sexual minorities than their otherwise similar heterosexual peers and that this difference is most pronounced in geographic areas where the stigma surrounding minority sexual orientations is particularly strong. A survey of a diverse sample of U.S. adults, which combines data on demographic factors, geographic location, and a standard scale of self-monitoring (Day et al. 2002; Snyder and Gangestad 1986), allows us to test this hypothesis.

Concealable Stigma, Self-monitoring, and Sexual Orientation

Only limited research has examined potential linkages between self-monitoring and demographic variables. Although there is some evidence that men tend to exhibit higher levels of self-monitoring than women (e.g., Frazier and Fatis 1980), there is little systematic research on differences by race and mixed results with respect to age (Day et al. 2002). With regard to sexual orientation, although researchers have examined whether self-monitoring is associated with outcomes such as life satisfaction among lesbians and gay men (Anderson and Randlet 1993), they have not systematically compared the level of self-monitoring between sexual minorities and their otherwise similar heterosexual counterparts.

Goffman’s (1963) Stigma is a useful starting point for understanding sexual orientation and self-monitoring. As Goffman noted, individuals who are subject to a concealable social stigma—one that is not immediately apparent in most social interactions—tend to control information about the stigmatized aspect of their identities differently in different social settings, ranging from the workplace and the doctor’s office to various circles of friends and places of recreation. A key challenge for these individuals is to avoid inadvertent revelation of their stigmatized status in potentially hostile settings, while selectively disclosing their status in other social situations. Goffman argued that doing so requires dividing the social world into different segments and appropriately managing the presentation (or concealment) of stigma-related information as one traverses these segments. Thus, people with a concealable stigma must be vigilant of expectations in different social settings and need to make frequent decisions about whether and how to disclose potentially stigmatizing information. As Goffman put it, a person with a concealable stigma has “to be alive to aspects of the social situation which others treat as uncalculated and unattended” and must be a constant “scanner of possibilities” of how others might react to information about various aspects of his or her identity (p. 88). Echoing Goffman’s early insights, recent scholars have also suggested that individuals who have a status they wish to conceal or only selectively reveal may develop greater sensitivity to social cues about how such a status is likely to be received (see Flynn, Chatman, and Spataro 2001; Pachankis 2007).

Consistent with these general observations, research on the socialization of lesbian and gay youth suggests that early threats of social stigmatization from peers and family may encourage the development of important social strategies (McDavitt et al. 2008; Radkowsky and Siegel 1997). Because many gay and lesbian adolescents are aware of their status as sexual minorities from an early age (American Psychological Association 1992), they may develop concealment strategies to better control to whom, when, and how they reveal their sexual identities. This concealment often involves nearly “constant monitoring of that which should be unconscious or automatic for others” (Radkowsky and Siegel 1997:195). Indeed, “at a time when heterosexual adolescents are learning how to socialize, young gay people are learning to conceal large areas of their lives from family and friends” (Hetrick and Martin 1984:11), which in turn requires close observation of social cues and control of one’s self-presentation for situational appropriateness. These strategies may carry on into adulthood. For instance, research suggests that compared with heterosexual men, gay men may rely more on social strategies such as monitoring the content of their speech in interactions with others (Pachankis and Goldfried 2006).

To the extent that the threat of stigmatization contributes to the development of self-monitoring among sexual minorities, it is likely to be a particularly important social skill in contexts in which the stigma of a minority sexual orientation is especially severe, such as in geographic areas with largely negative public attitudes toward sexual minorities. In such areas, it may be especially important for sexual minorities to maintain and cultivate a self-monitoring orientation. It may also be the case that sexual minorities with a greater propensity for self-monitoring are more likely to remain in, or move to, such areas because they are better prepared to handle the challenges of these contexts than their low self-monitoring peers. Similarly, low self-monitors who are less skilled at, or less willing to engage in, stigma management may choose to exit areas of high stigmatization as an alternative means to lower the risk of exposure. As Goffman (1963) argued, “By residing in a region with a mobile population, [the stigmatized person] can limit the amount of continuous experience others have of him” (p. 100). An example of such relocation includes “small-town homosexuals going to New York, Los Angeles, or Paris” (p. 100). For these reasons, the tendency for self-monitoring among sexual minorities may be more pronounced in geographic areas with less accepting attitudes (e.g., rural parts of the South) than in areas more tolerant of minority sexual orientations (e.g., urban centers in the Northeast; see Wilson 1995 and Tilcsik 2011).

Data and Methods

We recruited 1,000 participants who completed a standard self-monitoring scale and a questionnaire focusing on sexual orientation, other demographic factors, and geographic location. Our sample size was determined in advance, and we report all data exclusions below.¹

Participants

A critical requirement of our survey was that it generate sufficient numbers of self-identified sexual minorities, such as gay men, lesbians, and bisexual men and women. We recruited participants (n = 993) from Amazon’s Mechanical Turk (MT), an online platform that serves as a source of crowdsource-recruited samples.² MT tends to produce more diverse pools of research subjects than is possible with population-based online survey panels (Buhrmester, Kwang, and Gosling 2011). In addition, the quality of data from MT has been shown to be comparable (or slightly superior) to that of data provided by population-based online panels (Weinberg, Freese, and McElhattan 2014).

Table 1 presents results for how our sample differed from the U.S. population. Although our sample included a higher percentage of gay men and lesbians than that reported by nationally representative estimates (Black et al. 2000), our sample underrepresented black and Hispanic populations and included a higher percentage of men. Individuals in our sample were also less likely to be employed full-time (as well as more likely to be unemployed), more likely to have received a bachelor’s degree, and substantially more likely to have completed high school. In addition, our sample skewed left politically, overrepresenting Democrats and underrepresenting Republicans. We control for these differences in our analyses (see Weinberg et al. 2014).

Table 1.

Representativeness of Mechanical Turk Survey Sample.

Variable	Mechanical Turk Sample	U.S. Adult Population
Median age (years)^a	36.8	37.7
Female	47.8%	50.8%
White^b	75.0%	77.4%
Black	10.3%	13.2%
Hispanic	4.5%	17.4%
Employed full-time^c	53.9%	59.0%
Unemployed	9.7%	6.2%
Republican^d	17.3%	25.0%
Democrat	55.1%	31.0%
Less than high school^e	0.8%	12.0%
High school	11.1%	19.0%
Some college	27.1%	19.4%
Associate’s degree	10.4%	9.4%
Bachelor’s degree	38.6%	18.9%
Graduate degree	11.9%	10.4%

U.S. estimate from American Community Survey 1-year estimate (U.S. Census Bureau 2014a).

U.S. gender, race, and ethnicity estimates from U.S. Census Bureau (2014c).

U.S. employment estimates from U.S. Bureau of Labor Statistics (2015).

U.S. estimates from Gallup (Jones 2014).

U.S. estimates calculated from the Current Population Survey (U.S. Census Bureau 2014b).

Self-monitoring

To measure self-monitoring, we relied on an adaptation of the 18-item self-monitoring scale from Snyder and Gangestad (1986). This revised scale has been shown to be more reliable and factorially pure than the original 25-item scale used by Snyder (1974). Sample items include “In different situations and with different people, I often act like very different persons” and “I have trouble changing my behavior to suit different people and different situations” (reverse coded). Although a majority of studies using this scale use dichotomous scoring systems (i.e., true or false on each item), we use a continuous seven-point, Likert-type scale because a meta-analysis of self-monitoring research suggests that continuous scoring provides higher scale reliability than dichotomous scoring (Day et al. 2002). We calculated the overall self-monitoring score as the average of the seven-point scores on each of the 18 items. The scale reliability coefficient was .840. Although this is a self-reported scale (and no observed measure of self-monitoring exists), studies have demonstrated consistent associations of this scale with a variety of behavioral outcomes and consequences in the field (Day and Schleicher 2006; Day et al. 2002).

Sexual Orientation

To assess sexual minority status, we used a sexual orientation item from the National Longitudinal Study of Adolescent Health questionnaire. This item asks respondents to identify their sexual orientation as one of the following: 100 percent heterosexual, mostly heterosexual but somewhat attracted to the same sex, bisexual, mostly homosexual but somewhat attracted to the opposite sex, 100 percent homosexual, not attracted to either sex, and don’t know. We excluded from the sample those respondents who indicated that they did not know their sexual orientation (n = 3) or who were not attracted to either sex (n = 11), resulting in a final sample size of 979. For the purposes of the analyses, we consider a minority sexual orientation to be indicated by those responses other than 100 percent heterosexual. We therefore consider the response “100 percent heterosexual” as indicating a heterosexual orientation (n = 830) and responses indicating a degree of attraction to the same sex (mostly heterosexual but somewhat attracted to the same sex, bisexual, mostly homosexual but somewhat attracted to the opposite sex, and 100 percent homosexual) as indicating a minority sexual orientation (n = 149). Table 2 provides the distribution of sexual orientations across all response categories.

Table 2.

Sexual Orientation among the Mechanical Turk Sample.

Orientation	n
100% heterosexual	830
Mostly heterosexual but somewhat attracted to same sex	87
Bisexual	32
Mostly homosexual but somewhat attracted to opposite sex	8
100% homosexual	22
Total	979

Geographic Location

Urban areas tend to have a greater diversity of lifestyles and are thought to promote tolerance toward difference (Simmel 1903 [1997]), including sexual differences (Adler and Brenner 1992; Chauncey 1994; Ghaziani 2014; Kennedy and Davis 1993; Wilson 1995). Indeed, city size has been shown to be very strongly related to the level of tolerance toward minority sexual orientations (e.g., Stephan and McMullin 1982), even though the culture of a city can also shape minorities’ experiences (Brown-Saracino 2015). Accord-ingly, we created a dummy variable for individuals who live in relatively large cities—those with populations of more than 200,000. Our results were substantively identical when we set the bar to 500,000, but we report results with a threshold of 200,000 because doing so created more balanced subsamples.³

To capture regional differences in attitudes, our main models include a regional-level dummy for respondents living in the South or Midwest. Public opinion surveys and field experiments measuring discrimination (e.g., Lax and Phillips 2009; Tilcsik 2011) consistently indicate greater acceptance of sexual minorities in the Northeast and the West than in the Midwest and the South. In addition, we conducted robustness checks with a more fine-grained regional variation measure as well, using Lax and Phillips’s (2009) survey-based state-level gay rights opinion scores (see Tilcsik 2011). Our substantive conclusions remained the same when using this alternative measure.

Other Demographic Factors

We account for several additional factors that may be associated with our outcome. Because self-monitoring and the need for peer approval might change over the life course (Reifman, Klein, and Murphy 1989; Sears 1986), we asked respondents to indicate their age. Previous studies suggest that women report somewhat lower self-monitoring than men (Day et al. 2002), so we control for sex. As self-monitoring has been linked with status attainment outcomes (e.g., Kilduff and Day 1994), we asked respondents to indicate their employment status (full-time employed, part-time employed, or unemployed) and level of completed education. We also asked respondents to report their political party identification.

Table 3 presents self-monitoring scores and demographic characteristics of our sample by sexual orientation. In terms of self-monitoring, the simple bivariate relationship indicates that nonheterosexuals report significantly higher level of self-monitoring than heterosexuals. Nevertheless, nonheterosexuals differ in several key respects from heterosexuals. Compared with heterosexual participants, individuals who report a minority sexual orientation tend to be younger, less likely to be employed full-time, more likely to be female, and more likely to be Democrats. There are no significant statistical differences in race, ethnicity, educational attainment, or regional location between the two groups.

Table 3.

Self-monitoring and Demographic Characteristics by Sexual Orientation.

	Total Sample		Heterosexual		Minority Sexual Orientation		Difference (Heterosexual–Minority Orientation)
Variable	Mean	SD	Mean	SD	Mean	SD	Difference in Means	SE
Self-monitoring	3.64	.88	3.59	.87	3.94	.91	−.35***	.08
Age (years)	36.78	12.58	37.38	12.70	33.45	11.33	3.93***	1.11
Female	.48	.50	.46	.50	.59	.49	−.13**	.04
Black	.10	.30	.11	.31	.09	0.28	0.02	0.03
Hispanic	.04	.21	.05	.21	.03	0.16	0.02	0.02
Employment status
Employed, full-time	.54	.50	.55	.50	.46	0.50	0.10*	0.04
Employed, part-time	.18	.39	.18	.38	.22	0.42	−0.04	0.03
Unemployed	.10	.30	.09	.29	.11	0.32	−0.02	0.03
Party identification
Republican	.17	.38	.19	.39	.07	0.26	0.12***	0.03
Democrat	.55	.50	.53	.50	.68	0.47	−0.15***	0.04
Educational attainment
High school diploma or less	.12	.32	.12	.33	.11	0.32	0.01	0.03
Some college	.27	.44	.26	.44	.32	0.47	−0.05	0.04
Associate’s degree	.10	.31	.11	.31	.07	0.26	0.04	0.03
Bachelor’s degree or higher	.50	.50	.51	.50	.49	0.50	0.02	0.04
Geographic location
South or Midwest	.53	.50	.53	.50	.53	0.50	<0.00	0.04
City with population > 200,000	.30	.46	.30	.46	.29	0.45	0.01	0.04
n	979		830		149

p < .05. **p < .01. ***p < .001.

Results

To estimate the relationship between minority sexual orientation and self-monitoring net of controls, we used ordinary least squares models that included covariates for gender, age, race, ethnicity, political orientation, educational attainment, employment status, region, and whether the respondent lived in a large city. All models were estimated using robust standard errors.

Table 4 presents the results for regression models of self-monitoring on sexual orientation. Model 1 estimates the relationship between sexual orientation and self-monitoring including demographic controls, and model 2 adds location variables. These models indicate that gay men and lesbians score about a third of a standard deviation higher on the self-monitoring scale than their otherwise similar heterosexual peers.

Table 4.

Regression Results for Self-monitoring Score on Sexual Orientation, Geographic Context, and Covariates.

Variable	Model 1	Model 2	Model 3	Model 4
Minority sexual orientation	.33***	.32***	.43***	.19
Minority sexual orientation	(.08)	(.08)	(.10)	(.11)
Female	−.21***	−.21***	−.21**	−.21***
Female	(.06)	(.06)	(.06)	(.06)
Age	−.01***	−.01***	−.01***	−.01***
Age	(<0.00)	(<0.00)	(<0.00)	(<0.00)
Black	−.02	−.02	−.03	−.02
Black	(.08)	(.09)	(.09)	(.09)
Hispanic	−.20	−.20	−.19	−.20
Hispanic	(.14)	(.14)	(.14)	(.14)
Democrat	.04	.05	.04	.05
Democrat	(.05)	(.05)	(.05)	(.05)
Full-time employed	.20**	.20**	.20**	.19**
Full-time employed	(.06)	(.06)	(.06)	(.06)
Part-time employed	.11	.11	.11	.11
Part-time employed	(.08)	(.08)	(.08)	(.08)
Some college	.16	.16	.16	.16
Some college	(.10)	(.10)	(.10)	(.10)
Associate’s degree	.02	.02	.02	.01
Associate’s degree	(.12)	(.12)	(.12)	(.12)
Bachelor’s degree or higher	.16	.17	.17	.16
Bachelor’s degree or higher	(.09)	(.09)	(.09)	(.09)
South or Midwest		.03	.02	−.01
South or Midwest		(.05)	(.05)	(.06)
City with population > 200,000		(<0.00)	.06	(<0.00)
City with population > 200,000		(.06)	(.06)	(.06)
Minority Sexual Orientation × City with Population > 200,000			−.36*
			(.17)
Minority Sexual Orientation × South or Midwest				.26
Minority Sexual Orientation × South or Midwest				(.15)
Constant	3.87***	3.85***	3.83***	3.88***
Constant	(.13)	(.13)	(.13)	(.13)
n	979	979	979	979
R ²	.09	.09	.09	.09

p < .05. **p < .01. ***p < .001.

In line with previous research, we also find that women tend to engage in lower levels of self-monitoring compared with men (Day et al. 2002; Frazier and Fatis 1980). The level of self-monitoring also tends to decrease with age, and individuals employed full-time report higher levels of self-monitoring than respondents in other employment categories.

We then test whether the relationship between sexual orientation and self-monitoring varies with the broader social context. Models 3 and 4 add interaction terms between minority sexual orientation and geographic location. Model 3 shows that the generally higher level of self-monitoring observed among sexual minorities is significantly mitigated among those living in a large city. Thus the relationship between sexual orientation and self-monitoring is most pronounced outside relatively large cities. In model 4, the coefficient on the interaction of sexual orientation and our region dummy is positive, which would suggest that sexual minorities’ tendency for self-monitoring is stronger in the South and the Midwest than in other regions, but the relatively wide confidence interval around this coefficient implies that our model estimates this relationship imprecisely. Likewise, when using Lax and Phillips’s (2009) more fine grained gay rights opinion score as a state-level indicator of tolerance in supplementary analyses, the coefficient on the interaction term indicating regional variation was estimated with a wide confidence interval.

To further explore the relationship between self-monitoring and sexual orientation, we tested for any potential moderating effects of our demographic controls (Weinberg et al. 2014). For instance, age may be a moderator in that older nonheterosexual respondents may have more experience with concealment than younger respondents and might therefore be more likely to be high self-monitors. At the same time, other factors may counteract this potential positive moderation effect. For instance, older nonheterosexual individuals may experience less of a need for social approval than their younger peers and therefore, on average, be less likely to self-monitor (see Sears 1986). To test for this and other potential moderation possibilities, we reestimated model 2, iteratively including an interaction effect between sexual orientation and each demographic control. With only one control did we find evidence of a statistically significant moderation effect: black respondents who are sexual minorities are more likely to be high self-monitors than white respondents who are sexual minorities (b = 0.500, SE = .230, p = .040). Because black participants are underrepresented in our sample, this moderating effect would, if anything, make our results regarding the relationship between sexual orientation and self-monitoring more conservative.

To better interpret these results, Figure 1 graphically presents the adjusted means of self-monitoring by sexual orientation, urban location, and regional context. Among individuals living the South or the Midwest and not living in large cities, nonheterosexual respondents average a self-monitoring score of 4.090, while heterosexual respondents average a self-monitoring score of 3.530. This difference corresponds to about a three-fifths of a standard deviation increase in self-monitoring for nonheterosexuals, which is generally considered to be a medium to large effect size (Cohen 2013).⁴ Similarly, for individuals who live in the Northeast or the West and who do not reside in large cities, sexual minorities average a self-monitoring score of 3.910, while heterosexuals report a self-monitoring score of 3.590, a small to medium effect size of about a third of a standard deviation. In large cities, in contrast, the estimated level of self-monitoring does not substantially differ by sexual orientation, and this fact applies to the South and the Midwest as well as the Northeast and the West. Thus, regardless of region, the degree of urbanity is an important contextual variable that moderates the relationship between sexual orientation and self-monitoring.

Figure 1.

Self-monitoring by sexual orientation, region, and city.

Discussion and Conclusion

In this article, we examine how an important psychological trait, self-monitoring, relates to a fundamental sociodemographic characteristic, sexual orientation. Our analysis reveals that sexual minorities in the United States report higher levels of attentiveness and responsiveness to social cues than their otherwise similar heterosexual peers and that this difference is most pronounced among individuals who do not live in large cities. In fact, in certain areas, particularly nonurban areas in the South, this difference can be quite large, amounting to an increase in self-monitoring of more than half a standard deviation among nonheterosexuals. These results suggest that self-monitoring is not simply an individual-level trait but also, importantly, a social one, varying systematically across demographic categories and social contexts, particularly geographies.

Our findings not only contribute to the literature on self-monitoring but also extend two classic insights in sociology. First, the association between minority sexual orientation and self-monitoring dovetails with Goffman’s (1963) notion that managing a concealable social stigma requires ongoing vigilance of social cues. Given the diverse and well-documented relationship between self-monitoring and numerous individual outcomes, our finding of a nontrivial increase in self-monitoring among nonheterosexuals may have meaningful implications for understanding important outcomes in social networks, labor markets, and other domains. For instance, high self-monitors have been found to be more likely to span structural holes between disconnected social worlds (Oh and Kilduff 2008; Sasovova et al. 2010) and tend to form looser and more diverse friendship circles than low self-monitors (Snyder, Gangestad, and Simpson 1983). In the realm of work, self-monitoring is positively associated with the attainment of leadership roles in groups (Dobbins et al. 1990) and favorable evaluations from supervisors (Mehra et al. 2001). Indeed, numerous studies find that even relatively modest differences in self-monitoring can be associated with fairly large differences in outcomes. For example, Kilduff and Day (1994) found that a one standard deviation increase in self-monitoring was associated with a 55 percent boost in the likelihood of promotions and a 109 percent increase in the chances of mobility across employers. Given these large effects, the differences in self-monitoring that we identify between nonheterosexuals and heterosexuals in nonurban areas might meaningfully influence labor market outcomes and constitute a potential “distinctiveness” for nonheterosexual job seekers (Anteby and Anderson 2014:16). Additionally, our result may also help explain the observed segregation of gay and lesbian workers into occupations that require a high level of social perceptiveness (Tilcsik, Anteby, and Knight 2015).

Second, the fact that the relationship between sexual orientation and self-monitoring is contingent on whether the focal individual resides in an urban setting is consistent with Simmel’s (1903 [1997]) observation that large cities tend to foster tolerance (or at least indifference) toward different lifestyles. To the extent that stigmatization of minority sexual orientations tends to be less severe in large cities, the observed interaction between sexual orientation and geography might reflect a treatment effect or a selection process, or both. Outside urban settings, it may be especially important for sexual minorities to develop or maintain a tendency for self-monitoring (i.e., a treatment effect). Past studies of rural queer youth point to such a possibility (Gray 2009). At the same time, it is also possible that sexual minorities who are high self-monitors are more likely to choose to reside outside urban settings because they can more effectively manage the potential challenges associated with living in such areas than their low self-monitoring peers (i.e., a selection effect). Although we cannot determine the relative contributions of these two processes, our study highlights the importance of geographic context in structuring individuals’ dispositions toward self-presentation.

Finally, our findings speak to a much broader literature on self-presentation, interaction, and identity formation. The importance of self-presentation, and in particular the interpersonal dynamics involved in impression management, continually reappears as a key theme in research on labor markets, cognition, stigma, and identity. If these literatures have shown the ways in which the social selves we present are managed, then research on self-monitoring suggests that some selves might be better at such management than others. Our work, in turn, pushes this body of work forward by moving beyond the psychological, individual, and microinteractional levels to link self-monitoring to broader sociodemographic and contextual antecedents.

Limitations and Future Research

This study was focused on identifying the social and demographic correlates of self-monitoring behavior. A promising avenue for further research would be to adjudicate whether treatment or selection processes better account for the patterns we identify here. For example, a laboratory experiment that prompts individuals to conceal some stigma and then determines whether this concealment alters self-monitoring would provide additional evidence that it is the process of concealment that contributes to the development and enactment of self-monitoring. Similarly, by manipulating whether respondents believe themselves to be interacting with individuals likely to stigmatize their concealed attribute, researchers could simulate more or less stigmatizing environments, thereby testing for the effects of a stigmatizing context. Such studies could lend support to a partly socially acquired view of self-monitoring.

Furthermore, previous research posits self-monitoring as a general, rather than domain-specific, trait (Gangestad and Snyder 2000). Future work should investigate whether this is also the case for groups with a concealable stigmatized status. At issue is whether those with concealable stigmas might self-monitor in one domain and possibly therefore erroneously consider themselves to be self-monitors across other domains. For instance, could self-monitoring be limited to those domains in which a concealable stigma is salient (such as nonheterosexuals’ discussions with colleagues about their private lives)? Or does self-monitoring occur across diverse kinds of interactions, even those where concealment is less relevant (such as technical discussions about work)? To address this possibility, and to ensure the reliability of the self-monitoring scale for these subgroups, future research could use observational data, rather than self-reports, to identify whether self-monitoring among those with concealable stigmatized statuses varies across different kinds of interactions. Such an approach would not only help overcome the limitation, associated with many studies that use survey methods, of assessing self-monitoring through a self-reported measure but might also provide qualitative insight into the nature of self-monitoring among those with concealable stigmas.

More broadly, this study opens the door to an exploration of self-monitoring as a socially shaped disposition, one that varies by demography and geography. Although much previous work has investigated the effects of self-monitoring on social interactions and relationships, our results suggest that a self-monitoring disposition, and its distribution across geographic space, may itself be, in part, a product of social processes.

Footnotes

Notes

Author Biographies

Carly Knight is a PhD candidate in sociology at Harvard University. Her research concerns how cultural meanings influence economic behavior. Her dissertation investigates anticorporate discourse during the early twentieth century and the role such discourse played in shaping business regulation.

András Tilcsik is an assistant professor of strategic management and a Lee-Chin Institute Fellow at the Rotman School of Management, University of Toronto. His research focuses on the mutual influences between organizations and the individuals who constitute them. He holds a PhD from Harvard University.

Michel Anteby is an associate professor of organizational behavior at Boston University’s Questrom School of Business. His research looks at how people relate to their work, their occupations, and the organizations they belong to. He received a joint PhD in management from New York University and sociology from EHESS (France).

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The Geography of Stigma Management

Abstract

Keywords

Concealable Stigma, Self-monitoring, and Sexual Orientation

Data and Methods

Participants

Self-monitoring

Sexual Orientation

Geographic Location

Other Demographic Factors

Results

Discussion and Conclusion

Limitations and Future Research

Footnotes

Notes

Author Biographies

References