Perceiving the Black female body: Race and gender in police constructions of body weight
1 and Abstract
Representations of Black women in United States popular culture and public discourse frequently depict them stereotypically as fat and in need of policing for moral failures. As well, research has shown that Black women are perceived and constructed as non-prototypical for their gender. Taken together, observers within a White dominant social frame could be said to have difficulty correctly seeing Black women’s bodies and gender presentations. In this study we examined how Black women are seen in the context of New York City Police Department (NYPD) stops and searches (known as Stop & Frisk). We examined how officers categorized Black women’s body weight; investigated whether stops took place in public or private space; and assessed the extent to which body weight brought additional sanctions (i.e., being frisked). We used publicly available datasets from the NYPD’s Stop & Frisk program, in which stops numbering in the hundreds of thousands were recorded in yearly databases from 2003 to 2012. For each stop, officers record a number of attributes about the potential suspect and context, including race, gender, physique, date, and precinct. We conducted logistic regressions to model the odds of being categorized as heavy by race and gender, controlling for age, calculated BMI, location in a Black precinct, and season of the year. Results showed that across 10 years of data, Black women were more likely than White women to be labeled heavy. Black women were also much more likely than all other subgroups to be stopped inside rather than outside. Body size showed little association with stop locations or frisks. We interpret these findings as a reflection of Black women’s positioning with regard to racial and gender representations and the disciplinary projects of the state.
Cultural representations of Black women often exist at opposite ends of a corporeal spectrum. On the one hand there is the Mammy stereotype. Named after a character in the novel Gone With The Wind, this stereotype depicts Black women as overweight, asexual and unthreatening servants, hypermaternal if not ultrafeminine. In the novel, Mammy’s first appearance reveals her to be “a huge old woman with the small, shrewd eyes of an elephant. She was shining black, pure African, devoted to her last drop of blood to the O’Haras” (Mitchell, 1936, 2011, p. 43).
“Sapphire” stereotypes, on the other hand, construct Black women as overbearing, masculine and emasculating (Means Coleman, 1998; Newsome, 2003). Indeed, Black women are often portrayed by male actors in films, deriding Black women as violations of dominant beauty ideals and femininity (Masullo Chen, Williams, Hendrickson, & Chen, 2012). The stereotype of the Black woman as masculine recurs in fiction. For example, Motherless Brooklyn’s hero Lionel Essrog recalls his youth growing up as an orphan in a boys’ home. So sexually segregated was his life that high school presents the protagonist with his first opportunity to interact with girls. He notes, “We mixed with girls for the first time, about as well as chunks of road salt in ice cream, though ice cream might be a generous comparison for the brutal, strapping black girls of Sarah J., gangs of whom laid after-school ambushes for any white boy daring enough to have flirted, even made eye contact, with one inside the building. They comprised the vast majority there, and the handful of white or Latin girls survived by a method of near-total invisibility” (Lethem, 1999, p. 54). This account of Black girls inspiring fear among racially diverse boys and girls alike is credible to the reader because the physicality described —brutal and strapping—is consonant with pervasive notions of Black women as aggressive, masculine, and angry.
Though polar opposites, both representations preclude any characterization of Black women as feminine, delicate, or frail; these are bodily and character traits often accorded to White women. Abel (2010) argues that Jim Crow signage in the segregated U.S. South at times was rhetorically redundant in announcing admittance to “White Ladies Only”: “race is already written into the class-inflected ladies, a noun reserved so exclusively for whites that signs for ladies and women could serve without further modification to signal racial difference” (Abel, 2010, p. 81, emphasis in original). Dogged in shaping expectations and social censures around Black women’s physical and behavioral comportment, Mammy continues to define an ideal posture for Black women in relation to White elites; the public face expected from Black women is one of obedience, attentiveness to the physical and emotional needs of the White body and thus consummately nurturing of that body and of acceptance of placement in the racial hierarchy (Hill Collins, 2000). And yet, Mammy’s body concomitantly seems to reject the possibility of an oppressed status, given an exaggerated overweight physiology that suggests an overabundance of maternal resources and an infinite supply of strength and emotional resources (Shaw, 2005). In this view, however, the mammy, and indeed the Black female body itself is perceived to be out of place in Black domestic space.
Black women whose bodies and character might resist these well-scripted and subordinating characterizations are perceived as anomalous and therefore present no challenge to the explanatory power of the dominant representations. The intersectionality invisibility framework (Purdie-Vaughns & Eibach, 2008) posits that individuals with intersecting identities are construed as non-prototypical group members. For example, the influence of heterocentrism and ethnocentrism renders prototypical women as straight and White. Individuals at the intersection of subordinate identities are invisible because they are not fully recognized as group members, and because their characteristics and experiences are distorted or discounted in order to fit them into more prototypical models (Purdie-Vaughns & Eibach, 2008). Empirical research has demonstrated the conflation in the White American imaginary of whiteness with female, and blackness with male. In one study, participants (82% White) were presented with images of Black and White men and women, and asked to identify the gender of the depicted person. Respondents often miscategorized Black women as men, making more errors when categorizing them compared to any other group (Goff, Thomas, & Jackson, 2008). Respondents were more accurate when categorizing White, compared to Black women; and Black men, compared to Black women. In sum, blackness appeared to call up maleness, and femaleness was most readily associated with whiteness. The authors further claimed that Black women were not ascribed the kinds of valued characteristics that are associated with masculinity, such as intelligence. Instead, “rather than being seen as similar to men, Black women were miscategorized as being men—which may constitute an altogether different form of social comparison”(Goff et al., 2008, p. 402, emphasis in original).
There is evidence that the tendency to misperceive Black women also carries over into medical setting assessments of body size. Physician’s subjective classifications of obesity among respondents to the National Health and Nutrition Examination Study were discordant from anthropometric data. For example, anthropometric data indicated 17% of the sample was obese, but physicians so labeled 23% of respondents. As well, of individuals with a BMI of 30 or more, only 81% were designated by physicians as obese, while false positives registered at 12% (Ferraro & Holland, 2002). In gender-stratified models, physicians were less likely to categorize Black women as obese, contrary to hypotheses that the high prevalence of obesity among Black women would induce higher likelihoods of appending an obese label (Ferraro & Holland, 2002). In other words, if Black women are generally seen as overweight, physicians may be less likely to perceive them as such, because a higher body weight is seen as normative.
In this paper we investigate how Black women are perceived by actors who, like physicians, are in positions of disciplinary authority—but are in a different social location, engaged in a quite different disciplinary project. Specifically, we examine how law enforcement officers categorize Black women’s bodies. While physicians are tasked with disciplining the overweight and the obese body, and are thus charged with prescribing dietary and other behavioral regimens intended to change it, they are arguably a source of medical care and other support. Police officers, on the other hand surveil, control, and punish constituents—and may be particularly compromised in “seeing” Black women “correctly”. The juxtaposition of law enforcement and the perception of Black women’s bodies is also instructive because fatness itself provokes intense social policing by the general public, who insist that thinness is within reach for those with willpower (Bailey, 2010).
Theories of gendered embodiment, racialized gender identity, and bio- and necropower provide a profitable point of departure for analyzing how Black women are perceived and how power intersects with the Black female body. Social theory on the embodiment of gender suggests that first, the physical material of the body expresses a biological sex to which we assign the cultural meaning and cultural weight of gender (Rubin, 1975; Stoller, 1968); that, second, gender depends on our embodied performance through a set of “gender acts” (e.g., how we move in space, whether we keep our legs closed or apart) that produce the illusion of gender and eventually come to constitute interior and exterior gendered beings (Butler, 1997, 1999); and that, third, gender is inscribed externally, on the surface of the body (Bartky, 1990; Bordo, 1993). All cultures require members to write gender on the surface of the body through dress, hair and make up, and people also inscribe gender internally, through exercise that changes the body’s biological processes (Grosz, 1994). Taken together, if we create the illusion of gender through a series of gendered acts, Goff et. al’s (2008) research suggests several possibilities regarding the embodied performance of blackness. One is that the body’s performance of blackness trumps gender performance and Black women and men perform blackness in ways that aren’t distinct enough for White viewers to discern a difference. A second is that White viewers can only perceive blackness, and this overrides an ability to perceive gender—viewers essentially become too frightened to finish reading the text on which race and gender are written, and they therefore skip part of the story. A third possibility is that gender is differentiated, but within a social frame where White women are the reference point, Black women simply cannot be read as such and thus encourage the perceiver to understand the body as male by default.
This raises the possibility of a generalized White perception problem—that may well become the problem of disciplinary authorities within a White dominated state— regarding the Black female body. These perceptual deficits are akin to those around the generic Black body. We have evidence, for example, that when Whites see Black persons, they are more likely to perceive guns and criminality (Eberhardt, Goff, Purdie, & Davies, 2004; Payne, 2001). Could the same faulty perception make Whites and the agents of a White dominated state only able to see Black female bodies as fat?
van Amsterdam (2013) argues that fatness is socially constructed as a failure to make appropriate lifestyle choices and to exert self-surveillance and self-control, thereby making those who are not slender deserving of their fate. As Bailey (2010) argues, “imagined as lazy and hedonistic, fat people signify both the physical incompetence and the moral turpitude that weaken America. They jeopardize the nation’s vigor, security, and dominance” (p.443). For Guthman (2011), disquiet about the moral failures of fat people invokes Foucault’s notion of biopower—state concern with the health and vigor of the population. Biopower seeks to ensure national productivity and thus evinces a state charged with producing properly embodied forms of life. Biopower disciplines the body politic by regulating health status through diffuse power relations (Lupton, 1995). It subtends neoliberal views of obesity, casting good citizens as those that make minimal use of state health and welfare services, and censuring fat people for failing to police their own bodies and maintain ideally efficacious bodies that create economic and social burdens on the nation (Guthman, 2011). On this account, moral panic around body size is likely to be particularly acute for Black women, who are already stereotyped as excessive, slothful and dependent on the state.
Yet Mbembe (2003), argues that Foucault’s concept of biopower and states’ efforts to produce a specific kind of docile obedient body and correct forms of life must be supplemented with the concept of necropower —power formations that produce properly embodied forms of death. Necropower arose and was perfected in states of exception, such as sub-Saharan African colonies and plantations in the Americas, and is brought into sharp relief in the efforts of those tasked with the production and management colored bodies. These actors include police, military personnel, teachers and school administrators, welfare personnel, medical professionals and agents of population control. In contrast to a biopower framework, which sees state agents as marshaling collective productivity from the populace, necropower sees them as acting to produce an entirely opposite lived experience for Black populations. That is, the preferred forms of embodied Black life within U.S. neighborhoods are not bodies that are docile, healthy and able to take their place in the formal economic system, but that are harassed, stressed and resource deprived. Thus it may be productive to foreground necropower over disciplinary power in theorizing police interaction with and the force they exert on the Black female body. Here we might expect not disciplinary efforts to produce a thin, self-policing and productive docile body that can take its proper place within the formal economic system, but management and containment designed to produce underdeveloped and undernurtured bodies with no real place in the economic system, but bodies nonetheless with no need to be disciplined, to be made thin.
Media representations of Black women are replete with imagery of fat bodies, and we would not expect police officers to be immune. A comparison of top sitcom television shows airing during “Black prime time” and “General prime time” found that African American programs featured a greater proportion of overweight characters (17%) than General counterparts (4%). With regard to Black women specifically, depictions of fatness are not only common, but are central to whom characters are constructed to be. These constructions are pointedly rendered more masculine via the use of Black male actors in drag, as in movies such as Big Momma and the Madea series. In these films, masculinity, fatness, and social pathology stew inextricably in Black female bodies. Depictions of “male mammies…usurped a familiar image of a grandmother or matriarch and turned it into an absurdity portrayed by men…these characters violate the dominant beauty ideal not only by being overweight and ugly but also by not being female at all” (Masullo Chen et al., 2012, p. 125).
Study Objectives
We take advantage of a unique dataset—stops and searches by the New York Police Department (NYPD)—to examine perceptions of and the forms of power that operate on Black female bodies. Police stops provide a window into Black women’s positioning in relation to the state and in relation to the broader society. The empirical studies we reviewed above suggest that Black women’s gender and bodily attributes are misperceived in varied contexts, based on a White dominant social frame. It is therefore likely that misperceptions would also extend to encounters with police officers. We underscore here that these misperceptions are driven by perceptual conflations in the White dominated state, which trains both White and non-White police officers in its preferred modes of perception. The result is a failure to accurately “see” Black women by this group of disciplinary/necropolitical actors as a whole—not only those officers who are White. Misperceptions from the diverse group of individuals comprising the NYPD would be consonant from the racially mixed sample in Goff et al.’s (2008) work. We sought to examine whether police officers perceive Black women’s bodies differently from other racial and gender groups, and whether officers tend to interact with the Black female body in distinct ways based on those perceptions.
Our research aims were as follows. In Aim 1, we assessed whether Black women are more or less likely than Black and White men and women to be categorized by police officers as having a “heavy” body. We hypothesized that Black women would have differential probabilities of being classified as heavy. Recognizing that the probabilities could lie in either direction—extant stereotypes and social hierarchies could create perceptual schemas in which seeing a Black female body equals seeing a fat body, or in which fat bodies are seen as normative for Black female bodies, and are therefore not read as such—we anticipated the former direction. That is, given the pervasiveness of stereotypes of fat Black women, it is likely that they would have higher probabilities of being classified as heavy by police officers.
Second, we attempted to tease out the particularities of police stops and how they relate to public discourses about Black women. In Aim 2 we asked whether Black women are more or less likely to be stopped in public or private space and the extent to which this depends on a heavy body. Racialized constructions of Black women may activate actions to control and contain them in the spheres that are most suggestive of deviance and threat; this is the province of the stereotypical overweight lazy Black woman who would drain resources and threatens the state. Black Welfare Queens who are constructed as extracting undeserved state monies and reproducing irresponsibly are thought to do so in their homes. In this regard, we hypothesized that police officers would be more likely to target domestic (private) space rather than public space. In Aim 3 we examined the implications of Black women’s body categorizations, by asking whether Black women who are rated as heavy face additional sanctions in police stops in the form of being frisked. On one hand, we might hypothesize that if the American imaginary prefers Black women as mammies—subservient and overweight subjects who “know their place”—embodying this symbol of domesticity and racial subservience would lessen the risk of additional police action. On the other hand, it is possible that the large Black female body no longer connotes an unthreatening posture of domesticity, but rather, after years of anti-welfare rhetoric, domesticity and threat may have come to coexist in the large Black woman’s body. If stereotypically constructed blackness now summons ideas about moral turpitude, heavy Black women may activate strong associations with criminality. This would mark them for greater control and punitive measures, and therefore greater probabilities of being frisked.
Methods
Data Sources
The NYPD instituted Stop and Frisk in the mid-1990s as part of “broken windows” policing (Harris, 2013), an aggressive approach that targeted individuals perceived as potentially engaging in criminal behavior as well as “quality of life” offenses such as panhandling. This policing strategy fell heavily on Black and Latino residents, particularly young men. The U.S. Supreme Court held in Terry v. Ohio (1968) that police could stop (temporarily detain and investigate) and frisk (cursory pat down) individuals with less evidence than probable cause (Harris, 2013). Since then, police departments across the country have deployed the practice. What makes NYC unique is the extensive, publicly available data on these stops (Harris, 2013). In the fallout from the shooting death of Amadou Diallo, and a class action lawsuit against the NYPD in 1999, the NYPD was required to maintain databases with information on police stops (Center for Constitutional Rights, none; Harris, 2013).
NYPD officers who make stops record information on a form called a UF-250. Data from the forms have been made publicly available for download on the NYPD website in yearly SPSS portable file format datasets organized from 2003 through 2012 (New York City Police Department, 2014). In each dataset, each row comprises a stop, such that individuals who are stopped multiple times in the year will appear multiple times in the dataset. Each stop specifies a number of attributes. Central to our inquiry were: the suspect’s sex, race, age, height and weight; whether or not he or she was frisked; the precinct where it occurred; the date of the stop, and whether the stop took place inside or outside. Although the majority of stops take place on city streets, officers also conduct patrols within public housing stairwells, hallways, and lobbies, and some housing projects are intensely policed in this way (Center for Constitutional Rights, 2012; Rivera, Baker, & Roberts, 2010). At least some of the stops categorized as inside were conducted inside transit stations, which also constitute a kind of public space, but data coding did not allow us to separate out these instances. Across all years, hundreds of thousands of stops were recorded, increasing each year to a high point of 685,724 times in 2011 before declining slightly in 2012. Note that the dataset does not contain information about individuals who were not stopped, precluding assessments of whether body size contributes to the probability of being stopped. Our focus was to tease out how police officers have categorized those they do stop, the context of those stops as a function of race and gender, and the penalties that are differentially meted out to Black women across body classifications. Because the dataset records information about potential suspects, but not about officers, we were unable to examine the extent to which the associations we observed varied by the race and gender of the police officers making the stops.
We analyzed stops for which the suspect’s race (dataset variable = “race”) was classified as “Black” or “White” (excluding “Black Hispanic” and “White Hispanic”) and gender (dataset variable = “sex”) was classified as “female” or “male” (excluding the small number of “unknown”). In addition to height and weight, officers recorded the suspect’s perceived body type (dataset variable= “physique”), which was initially recorded as thin, muscular, medium, average, and heavy. Although we do not have direct information about how police officers use the term heavy, it is reasonable to infer that it is meant to connote overweight. It stands apart from other classifications and deploys a euphemism that is more socially acceptable.
Because the rating criteria for each are not defined in the data, these labels would seem to invite a great deal of inter- and even intra-officer variability. But as these labels are based on officer self-report, rather than anthropometric measures, it is impossible to verify accuracy. However, these are not data for which we might anticipate police officers would be motivated to purposefully give inaccurate reports, as could be true for other aspects of the stop (e.g., whether physical force was deployed).
Analytic Plan
Our first research aim was to estimate the probability of a heavy vs. not heavy body type categorization. We dichotomized the outcome by collapsing the categories thin and medium together and recoding these as “not heavy,” while retained heavy as coded. Compared to the tens or hundreds of thousands of stops using these classifications, each year, a much smaller number of stops were characterized as “muscular” (M=1630, range= 451 – 2,423); these were omitted from the analysis. We then used logistic regression in Stata/MP 13.1, with statistical significance assessed as p<0.05, to model in each year of the data the probability of a heavy label. We used a binary rather than multinomial logistic regression because our interests were specifically in whether Black women would be seen as heavy, rather than the combined ordered comparisons rendered by a multinomial model. As well, the logistic model provides greater clarity in interpreting the results.
The primary predictor in our models was an indicator for race and gender, which spanned White women (reference), Black women, White men, and Black men. Models were adjusted to control for variables that may explain a heavy classification: BMI, age, neighborhood racial composition, and time of year. First, individuals may be classified as heavy simply because they weigh more. Using reported height and weight, we calculated BMI using a standard formula of weight (lb.) / [height (in)]2 × 703 (Centers for Disease Control and Prevention, 2014). Implausible values (<12 or >75) that may reflect data entry or other errors were excluded. It is worth noting that BMI does not necessarily indicate body fatness, and as a “universal” measure, it does not account well for interethnic differences (Solomons & Kumanyika, 2000); Guthman (2011) interrogates the social and cultural history of this measure. We use it as a control because it is the best available measure. As with physique, BMI is reported but not measured anthropometrically; and we do not know whether or in which instances officers obtained this data from ID (e.g., driver’s licenses) or from visual estimation. Therefore, if officers are biased towards using certain labels to describe the physiques of Black women, they may also be biased in the quantitative ratings of height and weight. However, if this bias were present, it would drive our results towards null findings. For example, if a woman who is 5’5 and 125 lbs. is rated as 5’4 and 140 lbs., she will receive a BMI calculation that is higher than is accurate. However, predicting the probability of her receiving a heavy label should be attributable to that higher BMI, whether or not it is accurate; we would not expect her to be categorized as heavier than White women referents after controlling for BMI. Although the BMI measure is imperfect, we assume that police officers are at least reasonably accurate in judging height and weight, given the centrality of this task in identifying and describing suspects. If so, BMI can be used as a relatively objective assessment of the individual’s body type.
Second, models controlled for age, given positive correlations between weight and age (Ferraro & Holland, 2002) and higher prevalence of obesity with increasing age, particularly among White women (Flegal, Carroll, Kit, & Ogden, 2012). Third, is possible that individuals stopped in Black neighborhoods may be more or less likely to be viewed as heavy via a contextual effect. That is, given that Black New Yorkers have higher obesity rates than White counterparts (e.g., 41% of White women were overweight or obese in 2011, compared to 71% of Black women (New York City Department of Health and Mental Hygiene, 2011), officers may differentially categorize individuals based on reference to nearby inhabitants in areas with many Black residents. To control for this possibility, stop location was characterized as a Black or non-Black precinct by overlaying a GIS shapefile of NYPD precincts over a map of percent Black using Census 2000 data. Precincts that partially or completely encompassed census tracts that were greater than or equal to 65% Black were coded as Black precincts. Finally, officers may misperceive someone as heavy as a function of season in the year: people may appear heavy when wearing bulky coats and other warm garments. Thus, the model controlled for cold weather, where the months April through September were scored 0 and October through March were scored 1. Models for one year (2006) did not control for season because the data in the original dataset did not allow recoding in this format.
We then explored the meaning underlying police perceptions of Black women’s bodies. As noted earlier, dominant narratives see Black women as the guardians of deviant domesticity. We tested this possibility by examining whether they were more likely to come under police scrutiny in public (outside) vs. private (inside) space. On the overall sample, to examine associations between race, gender and stop location, we used two sample tests for proportions to compare the percentage of stops that were conducted inside and outside across race and gender subgroups. We then restricted analyses to Black women and used logistic regression to estimate whether a heavy body was associated with being stopped in public or private space, controlling for age, stop location, and season.
Finally, we sought to assess the consequences of police perceptions of Black women. Specifically, we examined whether among Black women, body classification was associated with a differential probability of being frisked once stopped. To do so we conducted logistic regression analyses with odds of being frisked as the outcome, and age, body classification (heavy vs. non-heavy), a Black precinct, and season as explanatory variables.
Results
Descriptive analyses
Table 1 shows the annual number of stops in the present analyses and citywide, and the proportions of stops by race, gender, body type, and precinct type. Black men comprise the overwhelming majority of stops, and stops were slightly more prevalent in non-Black precincts. “Medium” was the most common body classification, and heavy labels comprised less than 10% of stops.
Table 1
Percentages of stops by race & gender, body classification, and precinct, 2003–2012
| 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Total number of NYPD stops | 97,296 | 1,60,851 | 3,13,523 | 3,98,191 | 5,06,491 | 4,72,096 | 5,40,302 | 5,81,168 | 6,01,285 | 6,85,724 |
| Number of stops: total sample | 83,770 | 1,59,495 | 2,07,899 | 3,06,594 | 2,81,067 | 3,18,824 | 3,50,483 | 3,60,842 | 3,95,921 | 3,20,962 |
| Number of stops: Black women and men | 72,590 | 1,43,307 | 1,83,219 | 2,62,071 | 2,39,977 | 2,72,020 | 3,07,163 | 3,11,760 | 3,46,527 | 2,81,124 |
| White women | 1.7 | 1.6 | 1.8 | 1.7 | 1.8 | 1.9 | 1.5 | 1.5 | 1.6 | 1.5 |
| White men | 16.8 | 14.1 | 15.4 | 15 | 16 | 15.4 | 13.3 | 13.3 | 13.4 | 13.5 |
| Black women | 5.2 | 6.1 | 5.9 | 5.9 | 6 | 5.9 | 5.5 | 5.7 | 5.4 | 6 |
| Black men | 76.3 | 78.1 | 77 | 77.4 | 76.2 | 76.8 | 79.8 | 79.4 | 79.4 | 79 |
| Thin | 35.3 | 32.4 | 32.8 | 31.6 | 30.5 | 30.7 | 30 | 30.8 | 32.2 | 32.9 |
| Medium | 56.1 | 54.4 | 57.6 | 59.4 | 60.6 | 60.5 | 61.3 | 60.3 | 59.3 | 58.5 |
| Muscular | 0.4 | 0.1 | 0.2 | 0.4 | 0.4 | 0.4 | 0.3 | 0.3 | 0.4 | 0.4 |
| Heavy | 8.2 | 9.2 | 9.4 | 8.5 | 8.5 | 8.4 | 8.3 | 8.6 | 8.2 | 8.2 |
| Black precinct | 45.7 | 37.2 | 40.4 | 42.4 | 43.8 | 45.8 | 42 | 42.7 | 41.3 | 42.3 |
| Non-Black precinct | 54.3 | 62.8 | 59.6 | 57.6 | 56.2 | 54.2 | 58 | 57.3 | 58.7 | 57.7 |
Note: "Total number of NYPD stops" are as reported by the NYCLU. "Number of stops: total sample" refers to observations in the present analysis which includes White and Black persons, and excludes Latinos, Asians, and those of other racial and ethnic backgrounds. "Number of stops: Black women and men" also refer to observations in the present analyses. Finally, percentages for race & gender, body classification and precinct are for the subsamples analyzed in this study; each category sums to 100%.
Computed BMI values showed that heavy, compared to non-heavy stops had higher values, across all years. BMI values for heavy individuals ranged from 29.88 (SE=.035) in 2005 to 31.38 (SE=.441) in 2003, with an average of 30.84 across the 10 years of data. In the total sample, BMI values were in the normal weight range for women (White women=22.84, Black women=24.31), and just crossed the cutoff for overweight for men (White men=25.24, Black men=25.06).
Primary analyses: Aim 1—Probability of a heavy body classification
We conducted logistic regression models estimating the odds of being classified as heavy across race and gender categories and controlling for potential confounds. First, we used crude models with only race and gender categories, but no controls. With White women as the reference, both Black men and Black women showed statistically significant greater odds of being classified as heavy. For Black men, the odds ratio of 1.31, p=.009 (95% CI = 1.07 – 1.60) indicates 31% greater odds of a heavy label; for Black women, the odds ratio of 2.38, p<.0001 (95% CI = 1.92 – 2.96) indicates that they were more than twice as likely as White women to be labeled heavy. We then used the full model, controlling for BMI, age, precinct type and season. Inclusion of these covariates attenuated the odds of a heavy classification for Black women, but did not eliminate it entirely, showing that Black women’s greater likelihood being labeled heavy is attributable in part to anthropometric characteristics, but does not fully explain officers’ categorizing. Table 2 reports coefficients from the fully adjusted models for each year of data, and we report ROC curves suggesting that the models were a good fit to the data.
Table 2
Regression models estimating the probability of a "heavy" classification by race, gender and covariates, 2003–2012
| 2003 | 2004 | 2005 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Odds Ratio | Std. Err. | 95% Conf. | Inter val | Odds Ratio | Std. Err. | 95% Conf. | Interv al | Odds Ratio | Std. Err. | 95% Conf. | Interv al | |||
| White women | White women | White women | ||||||||||||
| White men | 0.53 | 0.071 | 0.404 | 0.686 | White men | 0.57 | 0.054 | 0.472 | 0.685 | White men | 0.66 | 0.054 | 0.558 | 0.771 |
| Black women | 1.61 | 0.228 | 1.222 | 2.129 | Black women | 1.50 | 0.148 | 1.241 | 1.824 | Black women | 1.67 | 0.144 | 1.408 | 1.974 |
| Black men | 0.69 | 0.091 | 0.534 | 0.895 | Black men | 0.76 | 0.070 | 0.633 | 0.908 | Black men | 0.89 | 0.072 | 0.760 | 1.042 |
| Age | 1.00 | 0.001 | 1.001 | 1.006 | Age | 1.00 | 0.001 | 0.998 | 1.001 | Age | 1.00 | 0.001 | 0.997 | 1.000 |
| BMI | 1.46 | 0.006 | 1.449 | 1.472 | BMI | 1.47 | 0.004 | 1.465 | 1.481 | BMI | 1.45 | 0.003 | 1.438 | 1.452 |
| Black precinct | 0.99 | 0.033 | 0.931 | 1.059 | Black precinct | 0.95 | 0.022 | 0.905 | 0.992 | Black precinct | 0.91 | 0.018 | 0.879 | 0.950 |
| Cold weather | 1.07 | 0.033 | 1.012 | 1.140 | Cold weather | 1.05 | 0.021 | 1.005 | 1.087 | Cold weather | 1.03 | 0.018 | 1.000 | 1.071 |
| Area under ROC curve=.894 | Area under ROC curve=.868 | Area under ROC curve=.856 | ||||||||||||
| 2006 | 2007 | 2008 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Odds Ratio | Std. Err. | 95% Conf. | Inter val | Odds Ratio | Std. Err. | 95% Conf. | Interv al | Odds Ratio | Std. Err. | 95% Conf. | Interv al | |||
| White women | White women | White women | ||||||||||||
| White men | 0.62 | 0.045 | 0.540 | 0.716 | White men | 0.60 | 0.044 | 0.525 | 0.697 | White men | 0.58 | 0.038 | 0.513 | 0.662 |
| Black women | 1.63 | 0.122 | 1.408 | 1.887 | Black women | 1.62 | 0.122 | 1.398 | 1.879 | Black women | 1.39 | 0.095 | 1.216 | 1.590 |
| Black men | 0.84 | 0.059 | 0.731 | 1.003 | Black men | 0.85 | 0.060 | 0.739 | 0.973 | Black men | 0.77 | 0.049 | 0.684 | 1.590 |
| Age | 1.00 | 0.001 | 1.001 | 1.003 | Age | 1.00 | 0.001 | 1.003 | 1.005 | Age | 1.00 | 0.001 | 1.004 | 1.006 |
| BMI | 1.47 | 0.003 | 1.465 | 1.477 | BMI | 1.48 | 0.003 | 1.471 | 1.484 | BMI | 1.46 | 0.003 | 1.453 | 1.464 |
| Black precinct | 0.97 | 0.016 | 0.938 | 1.002 | Black precinct | 0.95 | 0.017 | 0.920 | 0.985 | Black precinct | 0.96 | 0.016 | 0.927 | 0.989 |
| Cold weather | n/a | Cold weather | 1.02 | 0.016 | 0.987 | 1.051 | Cold weather | 1.02 | 0.016 | 0.987 | 1.049 | |||
| Area under ROC curve=.877 | Area under ROC curve=.877 | Area under ROC curve=.877 | ||||||||||||
| 2009 | 2010 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Odds Ratio | Std. Err. | 95% Conf. | Inter val | Odds Ratio | Std. Err. | 95% Conf. | Interv al | ||
| White women | White women | ||||||||
| White men | 0.65 | 0.045 | 0.565 | 0.741 | White men | 0.72 | 0.049 | 0.630 | 0.824 |
| Black women | 1.40 | 0.101 | 1.220 | 1.615 | Black women | 1.69 | 0.120 | 1.470 | 1.941 |
| Black men | 0.80 | 0.053 | 0.697 | 0.907 | Black men | 0.93 | 0.062 | 0.815 | 1.058 |
| Age | 1.01 | 0.001 | 1.006 | 1.009 | Age | 1.01 | 0.001 | 1.004 | 1.007 |
| BMI | 1.45 | 0.003 | 1.440 | 1.451 | BMI | 1.47 | 0.003 | 1.464 | 1.475 |
| Black precinct | 1.04 | 0.017 | 1.009 | 1.075 | Black precinct | 0.96 | 0.015 | 0.935 | 0.994 |
| Cold weather | 1.01 | 0.015 | 0.980 | 1.037 | Cold weather | 0.99 | 0.014 | 0.958 | 1.013 |
| Area under ROC curve=.879 | Area under ROC curve=.885 | ||||||||
| 2011 | 2012 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Odds Ratio | Std. Err. | 95% Conf. | Inter val | Odds Ratio | Std. Err. | 95% Conf. | Interv al | ||
| White women | White women | ||||||||
| White men | 0.73 | 0.047 | 0.643 | 0.829 | White men | 0.58 | 0.040 | 0.504 | 0.661 |
| Black women | 1.72 | 0.116 | 1.507 | 1.962 | Black women | 1.51 | 0.108 | 1.313 | 1.737 |
| Black men | 0.91 | 0.057 | 0.803 | 1.028 | Black men | 0.77 | 0.051 | 0.673 | 0.873 |
| Age | 1.01 | 0.001 | 1.004 | 1.006 | Age | 1.01 | 0.001 | 1.005 | 1.007 |
| BMI | 1.47 | 0.003 | 1.465 | 1.475 | BMI | 1.46 | 0.003 | 1.454 | 1.465 |
| Black precinct | 0.94 | 0.014 | 0.916 | 0.972 | Black precinct | 0.93 | 0.016 | 0.902 | 0.964 |
| Cold weather | 1.00 | 0.014 | 0.974 | 1.029 | Cold weather | 0.99 | 0.015 | 0.956 | 1.017 |
| Area under ROC curve=.886 | Area under ROC curve=.886 | ||||||||
We first discuss the effect of covariates, followed by the impact of race and gender categories. Age was weakly related, with each additional year associated with <1% greater odds of a heavy classification across almost all years. For example, in the first year of data, 2003, the odds ratio for age was 1.003. Cold weather showed little relationship to body categorization, with 7 out of the 9 years with seasonal data failing to reach statistical significance. BMI was consistently positively related to body classification. Each unit increase in BMI was associated with greater odds of being labeled heavy. For example, in 2003, the odds ratio of 1.46 indicates that all else being equal, each unit increase in BMI was associated with 46% greater odds (which was the average across all years) of being classified as heavy. In contrast, individuals who were stopped in a Black precinct were less likely to be labeled heavy. Of the eight years for which associations were statistically significant, seven years showed a lower probability (on average 5.3% lower odds), and one year a higher probability.
After controlling for these effects, we find that compared to the reference group of White women, Black women continue to have greater odds of being classified as heavy, and this was true for every year of data. For example, in 2003 Black women had an odds ratio of 1.61, p=.001 (95% CI= 1.22 – 2.13) indicating 61% greater odds of being labeled heavy than White women, after controlling for BMI and other variables. Across all years, odds ranged from 39% to 72% greater probability. Notably, Black women were the only group for which a higher probability of a heavy classification exists. Across all years, after controls, White men and Black men are always less likely than White women to be labeled heavy (for Black men, differences were not statistically significant in three years). Figure 1 graphs the estimates and 95% confidence intervals across all study years for three key variables: Black women, Black precinct, and BMI.
Primary Analyses: Aim 2—Public vs. private stops
Table 3 reports the proportions of inside vs. outside stops for each race and gender group. As shown in panel a, for White women, White men, and Black men, the overwhelming majority (at least 70%) of stops took place outside. This is to be expected if the Stop and Frisk program seeks to deploy street officers to evaluate and respond to potential crimes. However, Black women have much lower rates of outside stops (percentages in the 50s) and exceed all other groups in the proportion of indoor stops. Panel b reports the results of testing the differences in proportion. Black women’s proportion of inside stops were compared to White women and to Black men; all differences were substantial and statistically significant.
Table 3
| panel a | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Percentage of stops conducted inside vs. outside by race and gender and tests of proportion | ||||||||||||
| Year | White women | White men | Black women | Black men | ||||||||
| N | % Inside | % Outside | N | % Inside | % Outside | N | % Inside | % Outside | N | % Inside | % Outside | |
| 2003 | 1635 | 26.06 | 73.94 | 15681 | 11.17 | 88.83 | 4897 | 42.70 | 57.30 | 71222 | 22.34 | 77.66 |
| 2004 | 2954 | 24.96 | 75.04 | 25469 | 12.39 | 87.61 | 11113 | 43.24 | 56.76 | 141319 | 23.75 | 76.25 |
| 2005 | 4133 | 29.01 | 70.99 | 35904 | 13.60 | 86.4 | 13660 | 43.70 | 56.30 | 179627 | 25.36 | 74.64 |
| 2006 | 5342 | 27.46 | 72.54 | 47882 | 14.64 | 85.36 | 18701 | 46.77 | 53.23 | 246947 | 28.31 | 71.69 |
| 2007 | 5294 | 26.69 | 73.31 | 47283 | 14.55 | 85.45 | 17599 | 45.15 | 54.85 | 224626 | 29.33 | 70.67 |
| 2008 | 6153 | 25.52 | 74.48 | 51137 | 13.45 | 86.55 | 19444 | 42.18 | 57.82 | 254676 | 26.91 | 73.09 |
| 2009 | 5361 | 27.01 | 72.99 | 48024 | 13.94 | 86.06 | 19888 | 42.13 | 57.87 | 289162 | 26.85 | 73.15 |
| 2010 | 5624 | 25.71 | 74.29 | 48912 | 15.38 | 84.62 | 21164 | 38.27 | 61.73 | 292421 | 23.96 | 76.04 |
| 2011 | 6617 | 24.33 | 75.67 | 54877 | 15.35 | 84.65 | 23204 | 33.39 | 66.61 | 325665 | 22.01 | 77.99 |
| 2012 | 5110 | 27.57 | 72.43 | 44992 | 15.36 | 84.64 | 19869 | 38.61 | 61.39 | 262932 | 22.90 | 77.1 |
| panel b | ||||||
|---|---|---|---|---|---|---|
| Percentage of stops conducted inside vs. outside for Black women vs. gender and race counterparts | ||||||
| Year | Black women vs. White women | Black women vs. Black men | ||||
| % Difference | S.E. | 95% CI | % Difference | S.E. | 95% CI | |
| 2003 | 17 | 0.013 | .145–.195 | 21 | 0.007 | .195–.224 |
| 2004 | 18 | 0.009 | .162–.198 | 19 | 0.005 | .180–.199 |
| 2005 | 15 | 0.008 | .134–.166 | 19 | 0.004 | .181–.199 |
| 2006 | 20 | 0.007 | .186–.214 | 19 | 0.004 | .183–.197 |
| 2007 | 18 | 0.007 | .166–.194 | 16 | 0.004 | .152–.168 |
| 2008 | 16 | 0.007 | .147–.173 | 15 | 0.004 | .142–.157 |
| 2009 | 15 | 0.007 | .136–.164 | 15 | 0.004 | .143–.157 |
| 2010 | 12 | 0.007 | .107–.133 | 14 | 0.003 | .133–.147 |
| 2011 | 9 | 0.006 | .078–.102 | 11 | 0.003 | .219–.221 |
| 2012 | 11 | 0.008 | .096–.124 | 16 | 0.004 | .153–.167 |
Note: % Difference is of the proportion stopped inside. All tests of proportion were significant at the p<.0001 level.
We then restricted analyses to Black women and used logistic regression to evaluate the impact of body classification on the probability of being stopped in public vs. private space. We found that being labeled heavy was not consistently associated with where stops took place (models not shown). Controlling for covariates, cold weather was consistently positively related to the odds of being stopped inside, and Black precincts had consistently lower odds of inside stops. After accounting for these effects, in four of ten years there was a significant association between a heavy classification and probability of being stopped inside. In 2004 and 2006, heavy Black women were less likely to be stopped inside than non-heavy counterparts (ORs= .866 and .907, respectively); in 2011 and 2012, a heavy classification meant a greater probability of being stopped inside (ORs=1.10 and 1.17).
Primary analyses: Aim 3—Probability of frisks for Black women
Finally, we estimated the probability of Black women being frisked based on body type. In the interest of space, we present estimates from the full model for the first and last year only, and odds ratios for the remaining years alone, in Table 4. In only one year did a statistically significant association emerge: in 2005, a heavy label resulted in 15% greater odds of being frisked. Thus, a heavy body did not appear to carry greater penalties for Black women in police encounters. As a point of comparison, the same general picture emerged for White women: those with a heavy label were no more likely to be frisked than those without. Two years showed an association (2009 and 2012), with a heavy classification resulting in greater odds of being frisked. In 2009, the OR=1.37, 95% CI= 1.08 – 1.74, and in 2012, the OR= 1.38, 95% CI= 1.09 – 1.76.
Table 4
Regression models estimating the probability of heavy Black women being frisked Full models, 2003 & 2012
| 2003 | 2012 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Odds Ratio | Std. Err. | 95% Conf. Interval | Odds Ratio | Std. Err. | 95% Conf. Interval | ||||
| Age | 0.989 | 0.003 | 0.984 | 0.996 | Age | 0.989 | 0.001 | 0.986 | 0.991 |
| Heavy classification | 1.02 | 0.099 | 0.847 | 1.24 | Heavy classification | 0.937 | 0.045 | 0.853 | 1.03 |
| Black precinct | 1.28 | 0.095 | 1.11 | 1.48 | Black precinct | 0.997 | 0.035 | 0.93 | 1.07 |
| Cold weather | 1.11 | 0.078 | 0.963 | 1.27 | Cold weather | 1.01 | 0.033 | 0.948 | 1.08 |
| Odds ratios (variable=heavy classification), 2004–2011 | ||||
|---|---|---|---|---|
| Year | Odds Ratio | Std. Err. | 95% Conf. Interval | |
| 2004 | 1.12 | 0.073 | 0.988 | 1.28 |
| 2005 | 1.15 | 0.068 | 1.02 | 1.29 |
| 2006 | 1.01 | 0.056 | 0.905 | 1.13 |
| 2007 | 1.04 | 0.053 | 0.945 | 1.15 |
| 2008 | 0.965 | 0.46 | 0.879 | 1.06 |
| 2009 | 1.1 | 0.051 | 1.003 | 1.2 |
| 2010 | 1.04 | 0.046 | 0.958 | 1.14 |
| 2011 | 1.06 | 0.043 | 0.982 | 1.15 |
Discussion
We exploited publicly available New York Police Department data from 2003 to 2012 to examine perceptions of Black women’s bodies in the context of Stop and Frisk encounters. We asked whether Black women were more or less likely to be classified as heavy; whether they were more or less likely to be stopped in public or private space; and whether Black women who were labeled heavy faced additional sanctions in terms of being frisked in addition to being stopped. Across 10 years of data, we found that after controlling for several covariates, Black women were more likely than White women to be labeled heavy. In fact, they were the only group for whom this was true; Black men and White men did not differ from White women in the probability of being labeled heavy. In contrast to physicians, who were less likely to label Black women as obese (Ferraro & Holland, 2002), our data suggest that police officers disproportionately label them as heavy. Black women were also much more likely than all other subgroups to be stopped inside rather than outside. Among Black women, body type was inconsistently associated with the probability of being stopped inside, with two years each showing positive and negative associations. Finally, Black women labeled as heavy were no more likely than non-heavy counterparts to face police frisking. Some limitations should be noted, most of which stem from the constraints of the data.
Study limitations
Because we conducted secondary analyses of a publicly available dataset, we did not have some information that would further enrich our study. First, the dataset contains only information about individuals who were stopped by police. Ideally, in addition to assessing whether Black women are differently labeled than others, we would also examine whether being labeled heavy incurred greater odds of being stopped. To do so, we would compare individuals who were stopped to those who were not; the NYPD datasets do not allow such a comparison. Second, as noted earlier, because the dataset records information about potential suspects, but not about officers, we were unable to examine the extent to which the associations we observed varied by the race and gender of the police officers making the stops. In 2009, the uniformed police force at the NYPD was 18% female (New York City Police Department, 2009), making the majority of stops the province of male officers. This primarily male police force was 47% White and 53% Black, Latino, or Asian in 2010 (El-Ghobashy, 2011), so it is unlikely that the patterns we observed were driven by White officers only. Here it is important to reiterate that the patterns we observed reflect the lens of a White dominated state, not that of individual Whites. And, because the representations of Black women on which we focus deeply pervade American consciousness, it is logical to expect that officers—who are overwhelmingly male—from different racial and ethnic backgrounds could respond in similar ways. Indeed, even Black women police officers face overt racism and sexism on the job, ranging from racial slurs and epithets and exclusion from information and social networks, to failure to receive backup from other officers in potentially life-threatening situations. Moreover, exclusion and denigration arose from Black male counterparts as well (Pogrebin, Dodge, & Chatman, 2000). If Black women on the other side of the blue wall are marginalized, it stands to reason that civilians could be misperceived by NYPD officers regardless of officer race and ethnicity. We would argue that the marked consistency of racially gendered patterning in body ratings findings across all the years of data we studied is suggestive of the monolithic ways Black women are perceived.
Third, it is possible that persons appear multiple times in the dataset in a given year, which would produce correlated outcomes for which we cannot make a correction. We do not have the means to assess the extent to which there is repetition in the data. UF-250 forms do ask officers to record the name of the person stopped, but assuming it is recorded, this personally identifying information was removed for the publicly available datasets. However, we do not believe it is likely to be a serious problem for our data because the unit of analysis is stops, not people. In other words, even where the same person repeats in the dataset, the real unit of analysis is person-officer interaction. If an individual is stopped multiple times in a year, it is unlikely that his or her physique will be categorized exactly the same way each time by each officer. Thus, these observations are less correlated than may be assumed, because the outcome is less a function of the individual’s attributes than of the officers’ perception of that person. Similarly, correlated scores could exist if individuals were repeatedly stopped by the same officer, but we believe this kind of repetition is unlikely to be frequent in the data. And, given the size of the dataset, not adjusting for any such correlation should have minimal effects on our results.
A fourth limitation is that we categorized stops conducted inside to operationalize Black domestic space, but these stops included an unknown number of encounters in transit stations, a quasi-public space. We were unable to stratify by, or omit stops that took place in these locations because most stops did not include codes for transit/non-transit. Again, however, the key point is the consistency with which Black women were stopped in sites that were either within buildings or other spaces that were not city streets, and at rates that exceeded all other groups. Another spatial limitation is that our measure of Black precincts was somewhat crude. Because the city is highly racially segregated, Black precincts were easily demarcated by joining precinct polygons to census data. However, the ideal would have been more granular analyses in which we mapped each stop using x/y coordinates and examined how our outcomes varied by census data. This was beyond the scope of this project.
Finally, we defined overweight within the context of the limited set of labels police used to classify the physiques of individuals they stopped. We interpreted the label “heavy” to mean “overweight” or “fat”. This is supported by the fact that measures of BMI calculated from the height and weight reported by the police officer were positively associated with the probability of a heavy label. Additionally, the classification did not appear to indicate a larger overall stature; men are generally larger than women, but in these datasets Both Black and White men were less likely than White women to be classified as heavy. Still, without access to officers’ coding protocols, we cannot state unequivocally that that is what the police meant to convey. As well, the meanings behind, and applications of the different labels may vary across racial, ethnic and gender groups. For example, “heavy” may mean “fat” for White women, but given Black women’s intersectional invisibility, the heavy label could also be interpreted as an indicator of masculinity. For Black women, who are not only perceived as being fat but as being men, heaviness may indicate either or both simultaneously. Qualitative research probing the ways in which officers construct and use these labels could be fruitful in substantiating our interpretations, but even here, officers’ renderings of Black women may rely strongly on representations and perceptual schemas that operate out of awareness.
Study implications
The limitations discussed above mean that the interpretations of the data that follow remain somewhat speculative, and the findings raise additional questions. We noted earlier that one way to interpret Goff et al.’s (2008) data is that White viewers are too frightened to fully read gender scripts associated with blackness, causing them to abort a complete reading and arriving preemptively at an inaccurate gender classification for Black women. In contrast, police officers who are in the midst of a Stop and Frisk encounter may prematurely stop reading the gender text of alleged suspects not because they are frightened, but because they believe they know the whole story. That is, if officers are more likely to read Black female bodies as heavy, it may be because dominant representations of fat Black women preclude the need for a close reading of their bodies. If the prototypical Black woman is fat, then officers may read that prototypicality on the bodies of the citizens they stop. Although we may not be able to infer the processes underlying body classifications, it is evident is that NYPD officers view Black women differently than others.
We found that Black women stood apart in the proportion of stops experienced in private space. Black women are more often than not understood to connote problematic bodies and behavior, and this deviance is frequently situated within Black domestic contexts. Distinct stereotypical scripts of Black women may shape the locations in which Black women are stopped. For example, scholars have distinguished between such representations as Mammy, Jezebel, Welfare Mother and Matriarch (Stephens & Phillips, 2003). Black women are constructed as Mammy when serving the needs of White bodies and opportunity structures, and are perceived as such outside of Black domestic space; but they are perceived as Welfare Mothers/Queens in Black domestic space, suggesting the need for sanction in that space specifically, in a way that other embodied subjects do not provoke. Welfare Mothers/Queens collect government checks, breed uncontrollably and pass on pathologies to progeny (Stephens & Phillips, 2003), but scholarship across the social sciences and humanities has revealed the ways in which Black women in general are perceived to atypically and pathologically embody gender and social mores. For example, Carby (1992) reads the urban and northern migration of Black women as generating moral panics around sexually degeneracy and social danger that broadly viewed Black female urban behavior as pathological. In the early 1900s, reformers such as Frances Kellor viewed Black women as lacking the desire for hard work, and only too eager to therefore sell their bodies; a situation that merited policing and discipline (Carby, 1992). In the late nineteenth century, White women who participated in the sex trade became the focus of social reformers’ concern and protection, while Black women were seen as active and willing participants owing to their moral bankruptcy and were a threat to White men and the city as a whole. One New York City police officer argued that they were “filthy and beastly beyond belief” (Sacks, 2005, p. 802). Thus, White women were viewed as at risk from sexual predation, but Black women embodied moral degeneracy and lack of sexual decorum” (Sacks, 2005). Although our data do not allow analyses of NYPD officer motivations in Stop and Frisk, the end result of their surveillance is a racially stratified and gendered system of policing domestic space.
Our analysis showed that Black women labeled heavy were no more likely than non-heavy counterparts to be frisked by police. Thus, although fatness stimulates significant policing of the citizenry in general and for women in particular, for Black women embodying this discredited status bore no additional sanctions above and beyond being Black. We anticipated that fatness would invite sanctions aimed at bringing the Black female body in line with the normative thin body, but we did not find evidence of these efforts. We do not mean to imply that police officers purposefully stop potential suspects with the conscious and explicit aim of inducing conformity with ideal body types. Rather, a healthy body and appropriate diet and lifestyle choices have “become an alternative to prayer and righteous living in providing a means of making sense of life and death” (Lupton, 1995, p. 4). Therefore, to the extent that fatness is devalued and provokes social policing, Black women who are classified as heavy might bear additional scrutiny in police encounters.
There are several possible explanations for the lack of an observed relationship between heaviness and probability of frisking (which also did not exist for White women). First, given that women of any race are less likely than male counterparts to be stopped, perhaps being stopped is sanction enough; these women were clearly perceived as norm violators in some way. Second, perhaps heavy Black female bodies did not invite sanctions because Black women are perceived as less than prototypically female—as argued in a intersectional invisibility framework (Purdie-Vaughns & Eibach, 2008)—and are therefore less subject to expected disciplinary critiques around weight. Third, as we noted above, heavy Black women outside of Black domestic space are perceived as Mammy; this is an acceptable gender presentation to the White dominated state, and thus it may not invite additional sanctions. Finally, our hypotheses about frisking are conditional on police accurately perceiving the gender of Black female bodies. That is, in order for heavy Black women to face additional sanctions, officers must first perceive them as women, and therefore subject to discipline regarding weight. But we know that Black women’s gender scripts are misread (Goff et al., 2008). Experimental research could investigate the point at which gender accurately registers in Black women’s encounters with police, and what consequences that brings.
Conclusions
By taking advantage of a law enforcement dataset in order to investigate constructions of Black women’s bodies, we gained important insight into the imposition of racially-driven embodiment projects, broadly defined. We find that unique intersections of race and gender representations of Black women inform how they are seen by state actors. Discussions about proactive policing such as the street-level encounters that constitute Stop and Frisk tell a story primarily about poor Black men (Desmond & Valdez, 2012); and in this regard, new research has shown that Stop and Frisk has been shown to be deleterious to the mental health of young men in New York City (Geller, Fagan, Tyler, & Link, 2014). But our data suggest that Stop and Frisk may have hidden consequences for Black women. That Black women were much more likely to be stopped in private space suggests that police profiling is raced and gendered. Our data are instructive in thinking through how the state interacts not only with male bodies in civic space but also with female bodies in private space. Research is needed to shed light on the unique liabilities inhabiting a Black female body invokes in police encounters, and on the consequent health and social outcomes.
References
- Abel E. Signs of the Times: The Visual Politics of Jim Crow. Berkeley: University of California Press; 2010. [Google Scholar]
- Bailey C. Supersizing America: Fatness and post-9/11 cultural anxieties. The Journal of Popular Culture. 2010;43(3):441–462. [Google Scholar]
- Bartky SL. Femininity and Domination: Studies in the Phenomenology of Oppression. New York, NY: Routledge; 1990. [Google Scholar]
- Bordo S. Unbearable Weight: Feminism, Western Culture and the Body. Berkeley, CA: University of California Press; 1993. [Google Scholar]
- Butler J. Performative Acts and Gender Constitution: An Essay in Phenomenology and Feminist Theory. In: Conboy K, Medina N, Stanbury S, editors. Writing on the Body: Female Embodiment and Feminist Theory. New York, NY: Columbia University Press; 1997. [Google Scholar]
- Butler J. Gender Trouble: Feminism and the Subversion of Identity. New York, NY: Routledge; 1999. [Google Scholar]
- Carby HV. Policing the Black woman’s body in an urban context. Critical Inquiry. 1992;18(4):738–755. [Google Scholar]
- Center for Constitutional Rights. Daniels, et al., v. the City of New York. (none). Retrieved February 20, 2015, from http://ccrjustice.org/ourcases/past-cases/daniels,-et-al.-v.-city-new-york.
- Center for Constitutional Rights. Stop and Frisk The Human Impact. New York: Author; 2012. [Google Scholar]
- Centers for Disease Control and Prevention. About BMI for Adults. 2014 Retrieved December 4, 2014, from http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html.
- Desmond M, Valdez N. Unpolicing the Urban Poor: Consequences of Third-Party Policing for Inner-City Women. American Sociological Review. 2012;78(1):117–141. [Google Scholar]
- Eberhardt JL, Goff PA, Purdie VJ, Davies PG. Seeing Black: Race, crime, and visual processing. Journal of Personality and Social Psychology. 2004;87(6):876–893. [PubMed] [Google Scholar]
- El-Ghobashy T. Minorities gain in NYPD ranks. Wall Street Journal. 2011 Jan 7; 2011. [Google Scholar]
- Ferraro KF, Holland KB. Physician evaluation of obesity in health surveys: “Who are you calling fat?” Social Science & Medicine. 2002;55:1401–1413. [PMC free article] [PubMed] [Google Scholar]
- Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA. 2012;307(5):491–497. [PubMed] [Google Scholar]
- Geller A, Fagan J, Tyler T, Link BG. Aggressive policing and the mental health of young urban men. American Journal of Public Health. 2014;104(12):2321–2327. [PMC free article] [PubMed] [Google Scholar]
- Goff PA, Thomas MA, Jackson MC. “Ain’t I a woman?”: Towards an intersectional approach to person perception and group-based harms. Sex Roles. 2008;59:392–403. [Google Scholar]
- Grosz EA. Toward a Corporeal Feminism. Bloomington, IN: Indiana University Press; 1994. [Google Scholar]
- Guthman J. Obesity, Food Justice, and the Limits of Capitalism. Berkeley, CA: University of California Press; 2011. [Google Scholar]
- Harris DA. Across the Hudson: Taking the Stop and Frisk debate beyond New York City. Legislation and Public Policy. 2013;16:853–882. [Google Scholar]
- Hill Collins P. Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment. 2nd ed. New York: Routledge; 2000. [Google Scholar]
- Lethem J. Motherless Brooklyn. New York: Vintage Books; 1999. [Google Scholar]
- Lupton D. The Imperative of Health: Public Health and the Regulated Body. London, UK: Sage Publications; 1995. [Google Scholar]
- Masullo Chen G, Williams S, Hendrickson N, Chen L. Male mammies: A social-comparison perspective on how exaggeratedly overweight media portrayals of Madea, Rasputia, and Big Momma affect how Black women feel about themselves. Mass Communication. 2012;15:115–135. [Google Scholar]
- Mbembe A. Necropolitics. Public Culture. 2003;15(1):11–40. [Google Scholar]
- Means Coleman R. African American Viewers and the Black Situation Comedy: Situating racial humor. New York: Garland; 1998. [Google Scholar]
- Mitchell M. Gone With the Wind. New York: Scribner; 1936, 2011. [Google Scholar]
- New York City Department of Health and Mental Hygiene. Community Health Survey. 2011 Retrieved December 4, 2014, from https://a816-healthpsi.nyc.gov/SASStoredProcess/guest?_PROGRAM=%2FEpiQuery%2FCHS%2Fchsindex&year=2011.
- New York City Police Department. NYPD Celebrates Women in Policing [Press release] 2009 [Google Scholar]
- New York City Police Department. NYPD Stop Question and Frisk Report Database: The Stop, Question and Frisk Data. 2014 from http://www.nyc.gov/html/nypd/html/analysis_and_planning/stop_question_and_friskreport.shtml.
- Newsome YD. Border patrol: The U.S. customs service and the racial profiling of African American women. Journal of African American Studies. 2003;7(3):31–57. [Google Scholar]
- Payne BK. Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology. 2001;81:1–12. [PubMed] [Google Scholar]
- Pogrebin M, Dodge M, Chatman H. Reflections of African-American Women on their Careers in Urban Policing. Their Experiences of Racial and Sexual Discrimination. Internation Journal of the Sociology of Law. 2000;28(4):311–326. [Google Scholar]
- Purdie-Vaughns V, Eibach RP. Intersectional invisibility: The distinctive advantages and disadvantages of multiple subordinate-group identities. Sex Roles. 2008;59:377–391. [Google Scholar]
- Rivera R, Baker A, Roberts J. A few blocks, 4 years, 52,000 police stops. The New York Times. 2010 Jul 11; 2010. [Google Scholar]
- Rubin G. The traffic in women: Notes on the ‘Political Economy of Sex’ In: Reiter R, editor. Toward an Anthropology of Women. New York: Monthly Review Press; 1975. [Google Scholar]
- Sacks MS. “To Show Who was in Charge”: Police Repression of New York City’s Black Population at the Turn of the Twentieth Century. Journal of Urban History. 2005;31:799–819. [Google Scholar]
- Shaw A. The other side of the looking glass: The marginalization of fatness and blackness in the construction of gender identity. Social Semiotics. 2005;15(2):143–152. [Google Scholar]
- Solomons NW, Kumanyika S. Implications of racial distinctions for body composition and its diagnostic assessment. American Journal of Clinical Nutrition. 2000;71:1387–1389. [PubMed] [Google Scholar]
- Stephens DP, Phillips LD. Freaks, gold diggers, divas, and dykes: The sociohistorical development of adolescent African American women’s sexual scripts. Sexuality & Culture. 2003;7:3–49. [Google Scholar]
- Stoller RJ. Sex and Gender: On the Development of Masculinity and Feminity. New York: Science House; 1968. [Google Scholar]
- van Amsterdam N. Big fat inequalities, thin privilege: An intersectional perspective on ‘body size’ European Journal of Women’s Studies. 2013;20(2):155–169. [Google Scholar]