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S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E
SO C I A L M OV EM EN T S
Department of Sociology, University of Maryland, 2112 Parren Mitchell Art-Sociology
Building, 3834 Campus Drive, College Park, MD 20742, USA.
*Corresponding author. Email: drfisher@umd.edu

Fisher, Dow, Ray, Sci. Adv. 2017;3 :eaao1390 20 September 2017
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License 4.0 (CC BY-NC).
Intersectionality takes it to the streets: Mobilizing
across diverse interests for the Women’s March
Dana R. Fisher,* Dawn M. Dow, Rashawn Ray

Can a diverse crowd of individuals whose interests focus on distinct issues related to racial identity, class, gender,
and sexuality mobilize around a shared issue? If so, how does this process work in practice? To date, limited research
has explored intersectionality as a mobilization tool for social movements. This paper unpacks how intersectionality
influences the constituencies represented in one of the largest protests ever observed in the United States: the
Women’s March on Washington in January 2017. Analyzing a data set collected from a random sample of parti-
cipants, we explore how social identities influenced participation in the Women’s March. Our analysis demon-
strates how individuals’ motivations to participate represented an intersectional set of issues and how
coalitions of issues emerge. We conclude by discussing how these coalitions enable us to understand and predict
the future of the anti-Trump resistance.
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INTRODUCTION
Following the presidential election of Donald Trump, which was won
while the candidate explicitly insulted large swaths of the American
public, there has been substantial and continuous protest against the
direction that the new Administration is taking the United States.
One of the more visible responses to the new regime is the numerous
street demonstrations that have been organized to express opposition to
the Administration and its policies. Since President Trump’s inaugura-
tion, demonstrations have taken place to express concern about a range
of progressive issues. The first and largest protest to date was the
Women’s March, a coordinated effort around the United States that
mobilized more than 2 million people on the day after the Inaugu-
ration. The biggest demonstration on this historic day of action took
place in Washington, DC.

A key theme of the scholarship on contentious politics is
understanding mobilization processes and how individuals become
involved in various forms of collective action (1–4). A subset of this re-
search has explored the strategies and mechanisms through which so-
cial movements broaden their societal reach and mobilize more
participants (5–9). Large-scale street demonstrations and marches have
been documented as a site where social movement expansion is partic-
ularly visible to the general public (10, 11). Although social movement
scholarship has examined movement-to-movement transmission,
focusing on tactical overlap (12), social movement spillover (13), and
the sequencing of social movements (14), research has yet to explore
how overlapping motivations influence participants who join a protest
that is concentrated on one specific issue [but see the study of Goss and
Heaney (15)].

In addition, few studies have examined how intersectionality
contributes to social movements (15–19). Scholars of intersectionality
examine how intersections of race, class, gender, sexual orientation, le-
gal status, and other categories of identity are linked to structures of in-
equality and produce different life experiences and forms of oppression
(20–23). Some scholars suggest that these intersections divide people
into silos with distinct and competing interests that deter the coalition
building necessary for robust social movements (24, 25). Moreover, re-
search has found that, when these interests are incorporated into move-
ments, some interests become marginalized in favor of broader
movement goals (17). Intersectionality has been criticized as producing
“identity politics” that focuses on narrow group interests at the expense
of broader political claims.

More recently, however, a handful of studies of collective action have
focused on how intersectional interests can be used to build coalitions
within and across social movements, thereby increasing the number
and diversity of activists (26–30). In her influential work, Crenshaw
(22) suggests that intersectionality can promote coalitions instead of
divisions. A small number of studies have specifically explored inter-
sectional mobilization processes and how shared grievances play a role
(19). In her work that explores how organizations cross movement
boundaries, Van Dyke (11) comes to similar conclusions without ex-
plicitly discussing intersectionality. To date, intersectionality has been
understood as a theory (21, 22), an analytical framework (20, 23, 31),
and/or a method (32) that focuses on understanding how experiences
of inequality are complicated by intersections of race, gender, social
class, and other social categories. Here, we extend the application of
the notion of intersectionality to analyze how it influences the motiva-
tions of individuals within social movements, thereby mobilizing
them to engage in collective action in the form of a large-scale street
demonstration.

Although there have been numerous claims that the election of
Donald Trump will galvanize the progressive movement, this so-called
merging of movements has yet to be documented. The Women’s March
provides an ideal opportunity to explore how intersectionality may be
used as a mechanism to increase activism. Although the Women’s
March was initiated by a white grandmother in Hawaii who posted a
call to action on Facebook on the day after the 2016 election, it soon
transitioned into a broader, intersectional coalition of seasoned activists.
The four national co-chairs of the Women’s March were a racially di-
verse group of women who were already engaged in a range of political
activism and social mobilization. Together, these activists aimed to cre-
ate an inclusive event that responded to Donald Trump’s rhetoric,
which encouraged women’s marginalization and social inequality. By
the day of the event, the Women’s March’s website listed more than
400 organizational partners, sending a clear signal that the Women’s
March intended to appeal to participants across social categories of race,
class, gender, sexual orientation, and legal status. It is worth noting that
the Women’s March’s organizers made the decision to exclude anti-
abortion groups from their list of partners.
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The day after average crowds came out for Donald Trump’s inau-
guration, hundreds of thousands of people descended on Washington,
DC to participate in the Women’s March. The Women’s March in
Washington, DC was part of a broader day of action that took place
in other cities across the United States and around the globe. Indi-
viduals with a range of demographic backgrounds turned out at these
“sister marches.” As participants flooded the same streets that had
hosted the inaugural parade only 24 hours beforehand, chants op-
posing the new Administration reverberated through the air. The
protesters themselves held signs and wore T-shirts that suggested
intersectional issues as motivations for attending. Much speculation
has focused on who attended the Women’s March and what issues
motivated them to raise their voices in protest. Moreover, although
the progressive movement spans issues of race, class, gender, and sex-
ual orientation, more research is needed to understand how indi-
viduals motivated by certain issues come together to participate
in intersectional forms of social protest. Accordingly, this paper
presents analysis of a unique data set collected from a random sam-
ple of participants in the 2017 Women’s March in Washington, DC to
examine the issues that motivated individuals to protest the new U.S.
President and his policies.
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RESULTS

Table 1 shows the distribution of issues that motivated participants
to attend. Not surprisingly, Women’s Rights (53%) was the top mo-
tivating reason. Four other issues—Equality (41.5%), Reproductive
Rights (23.4%), Environment (22.5%), and Social Welfare (21.7%)—
were reported by more than 20% of respondents. In addition, more
than 15% of respondents reported that Racial Justice, Lesbian, Gay,
Bisexual, Transgender, and Queer (LGBTQ) issues, Politics/Voting,
and Immigration were issues that motivated them to attend. Although
this march was called the “Women’s March,” these findings demon-
strate that participants were not just motivated by issues related to
Fisher, Dow, Ray, Sci. Adv. 2017;3 :eaao1390 20 September 2017

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women but were actually motivated by a diverse set of issues con-
nected to intersectional concerns (21).

Table 2 shows a series of regression models for the association
among various sociodemographic variables and the issues that moti-
vated individuals to participate in the Women’s March. Women were
significantly more likely to mention Reproductive Rights than others
(B = 1.313, P < 0.01). Men were significantly more likely than women
to mention Trump as a motivation to participate. Blacks were signifi-
cantly more likely to mention Racial Justice than whites and all other
racial groups (B = 0.388, P < 0.01). Hispanics were significantly more
likely to mention Immigration than whites and other racial groups (B =
0.550, P < 0.01). These findings are consistent with the research that
connects intersectionality and identity politics (24). It also suggests that
the Women’s March’s unity principles and its organizational coalition
were successful in mobilizing a crowd with diverse interests.

In addition, nonorganization members were significantly less likely
to mention Politics as a motivating issue than organization members
(B = −1.880, P < 0.001). In addition to these relationships among
gender, race, ethnicity, and organizational affiliation and the stated
motivations of the Women’s March participants, other findings re-
lated to age are noteworthy. Age is significantly and negatively asso-
ciated with Women’s Rights, Reproductive Rights, and Racial Justice.
In other words, older protesters relative to younger protesters were less
likely to mention these issues as their motivations for attending the
Women’s March.

Table 3 shows a regression graph for the associations among the
different issues that motivated participants to attend the Women’s
March. These models control for the number of protests attended in
the past 5 years, gender, race, age, and organizational membership. In
addition, a cross-tabulation of percentages of overlapping motivations
for attending the Women’s March is included as Table 4. Through
these models, we are able to examine the extent to which individuals
reported intersectional motivations for participating in the Women’s
March. Participants who mentioned Women’s Rights were signifi-
cantly more likely to mention Racial Justice, Immigration, and Social
Welfare. However, they were significantly less likely to mention Repro-
ductive Rights and Equality. Similarly, participants who mentioned
Reproductive Rights were significantly more likely to mention Immi-
gration and Social Welfare. These participants were also significantly
more likely to mention Labor. In addition to the negative association
with Women’s Rights, these Reproductive Rights respondents were
significantly less likely to mention Equality and Trump.

The negative association between Women’s Rights and Reproduc-
tive Rights seems counterintuitive and deserves more attention. Upon
further examination, we found that 22% of the respondents who
selected Women’s Rights as a motivation for attending also selected
Reproductive Rights (see Table 4). In comparison, 50% of respondents
who selected Reproductive Rights as a motivation for attending also
selected Women’s Rights. Demographically, respondents who selected
either Women’s Rights or Reproductive Rights are similar. For exam-
ple, although a higher percentage of women are represented among
those who selected Reproductive Rights (94%) relative to Women’s
Rights (85%), this difference is nonsignificant because men in this
group were significantly less likely to mention Reproductive Rights.
Individuals who selected both Women’s Rights and Reproductive
Rights were more likely to be a member of an organization, as either
a passive or an active member.

Racial Justice is the driver of the negative association between
Women’s Rights and Reproductive Rights. Nearly 30% of respondents
Table 1. Reasons for attending the Women’s March (respondents
selected all that applied) (n = 516).
Women’s Rights
 52.9%
Equality
 41.5%
Reproductive Rights
 23.4%
Environment
 22.5%
Social Welfare
 21.7%
Racial Justice
 18.6%
LGBTQ
 17.4%
Politics/Voting
 16.9%
Immigration
 15.3%
Labor
 9.1%
Police Brutality/Black Lives Matter
 6.8%
Peace
 5.6%
Religion
 5.2%
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Table 2. Regression analysis of reasons for attending (n = 463). z statistics are in parentheses except for Racial Justice and Immigration, which includes t
statistics in parentheses because those models are ordinary least squares regression rather than logistic regression.
Fish
er, Dow, Ray, Sci. Adv. 2017;3 :eaao
Women’s Rights
1390 20 September
Reproductive Rights
2017
Environment
 LGBTQ
 Racial Justice
 Immigration
First ever protest
 −0.034
 −0.167
 −0.410
 −0.378
 −0.111
 −0.108
(−0.15)
 (−0.61)
 (−1.53)
 (−1.25)
 (−1.12)
 (−1.06)
First in 5 years
 −0.276
 0.254
 −0.539
 −0.236
 0.000
 0.000
(−1.06)
 (0.81)
 (−1.70)
 (−0.69)
 (0.00)
 (0.00)
Women
 −0.127
 1.313**
 0.339
 0.309
 −0.014
 0.012
(−0.46)
 (2.93)
 (0.97)
 (0.80)
 (−0.14)
 (0.12)
Black
 −0.386
 −0.854
 −0.790
 −0.281
 0.388**
 −0.092
(−0.99)
 (−1.53)
 (−1.41)
 (−0.50)
 (2.73)
 (−0.63)
Hispanic
 0.970
 −0.839
 0.177
 0.823
 0.151
 0.550**
D
o
(1.77)
 (−1.27)
 (0.32)
 (1.54)
 (0.83)
 (2.94)
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Asian
 0.751
 −1.139
 −0.446
 0.370
 −0.069
 0.241
a

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d
(1.38)
 (−1.49)
 (−0.68)
 (0.63)
 (−0.38)
 (1.28)
 
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Multiracial
 0.099
 −0.586
 −0.515
 −0.037
 −0.021
 −0.188
h
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(0.26)
 (−1.14)
 (−1.01)
 (−0.07)
 (−0.15)
 (−1.32)
://a
Age
 −0.020**
 −0.022**
 0.003
 −0.006
 −0.008**
 −0.003
d
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n
(−2.92)
 (−2.58)
 (0.41)
 (−0.62)
 (−3.00)
 (−1.41)
c
e
s.
Nonorganization member
 −0.437
 −0.211
 −0.099
 −0.304
 −0.164
 −0.288*
 s
cie
n
(−0.97)
 (−0.41)
 (−0.20)
 (−0.57)
 (−1.49)
 (−2.53)
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m
Passive organization member
 −0.462
 0.196
 0.804
 0.266
 0.000
 0.000
a

g
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(−0.90)
 (0.34)
 (1.44)
 (0.44)
 (0.00)
 (0.00)
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Constant
 1.604
 −1.185
 −1.368
 −1.273
 0.449
 0.408
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(2.73)
 (−1.62)
 (−2.00)
 (−1.73)
 (2.30)
 (2.03)
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Social Welfare
 Labor
 Peace
 Equality
 Politics
 Trump
1

First ever protest
 −0.452
 0.0254
 −0.441
 −0.239
 0.162
 0.363
−1.57)
 (0.06)
 (−0.88)
 (−1.05)
 (0.48)
 (1.31)
First in 5 years
 0.029
 0.037
 −0.297
 0.088
 0.630
 0.637*
(0.09)
 (0.08)
 (−0.50)
 (0.34)
 (1.77)
 (2.15)
Women
 0.246
 0.196
 −0.081
 0.453
 −0.749*
 −0.864**
(0.71)
 (0.39)
 (−0.14)
 (1.61)
 (−2.25)
 (−2.97)
Black
 −0.030
 −0.358
 −0.533
 −0.257
 0.131
 0.640
(−0.07)
 (−0.47)
 (−0.51)
 (−0.66)
 (0.25)
 (1.58)
Hispanic
 −0.201
 0.628
 0.661
 −0.964
 0.430
 0.689
(−0.34)
 (0.93)
 (0.81)
 (−1.78)
 (0.70)
 (1.33)
Asian
 −1.542
 0.000
 0.000
 0.224
 0.00
 −0.188
(−1.48)
 (0.00)
 (0.00)
 (0.46)
 (0.00)
 (−0.29)
continued on next page
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selected Women’s Rights and Racial Justice but did not mention Repro-
ductive Rights, whereaslessthan 20% of respondents mentioned Repro-
ductive Rights and Racial Justice but did not mention Women’s Rights.
This difference is driving the negative association between Women’s
Rights and Reproductive Rights. These findings suggest that identity
is playing a complex role in these overlapping motivations.

In contrast, participants who mentioned Environment as a motiva-
tion did not mention the identity-based themes that are associated with
intersectionality. Rather, they were significantly more likely to mention
Social Welfare, Peace, and Equality. Participants who mentioned
LGBTQ issues did not overlap at all with participants who were moti-
vated by the Environment. Instead, they were significantly more likely
to mention Racial Justice and Immigration.
Fisher, Dow, Ray, Sci. Adv. 2017;3 :eaao1390 20 September 2017
Individuals who mentioned Racial Justice indicated the most num-
berof additionalissuesasmotivating themto participateintheWomen’s
March.Specifically,theyweresignificantlymorelikelytomentionWomen’s
Rights, LGBTQ issues, Immigration, Labor, Peace, Equality, Politics,
and Trump. People who reported attending because of Immigration
issues were significantly more likely to mention Peace, Equality, and
Politics in addition to Reproductive Rights, LGBTQ issues, and Racial
Justice. Although participants who mentioned Trump were significantly
less likely to mention Women’s Rights, Reproductive Rights, and Equal-
ity, they were significantly more likely to mention Racial Justice and
Peace. Together, these findings strongly support the view that intersec-
tionality can promote alliances across identity-based issues (26, 28, 29).
In contrast to the studies that focus on coalitions among organizations
Women’s RightsSocial Welfare
 Reproductive RightsLabor
 EnvironmentPeace
 LGBTQEquality
 Racial JusticePolitics
 ImmigrationTrump
Multiracial
 −0.636
 −0.446
 0.107
 −0.113
 0.038
 −0.051
(−1.15)
 (−0.59)
 (0.14)
 (−0.30)
 (0.08)
 (−0.11)
Age
 0.004
 −0.006
 −0.014
 −0.007
 0.003
 0.012
(0.42)
 (−0.53)
 (−0.93)
 (−0.97)
 (0.32)
 (1.54)
Nonorganization member
 0.313
 −0.520
 −0.611
 1.141*
 −1.880***
 −0.092
(0.54)
 (−0.78)
 (−0.75)
 (2.18)
 (-3.90)
 (−0.17)
Passive organization member
 0.881
 −0.469
 0.229
 1.277*
 −0.804
 0.725
(1.40)
 (−0.59)
 (0.26)
 (2.22)
 (−1.50)
 (1.23)
Constant
 −1.861
 −1.730
 −1.535
 −1.399
 0.154
 −1.343
(−2.52)
 (−1.85)
 (−1.37)
 (−2.18)
 (0.23)
 (−2.00)
*P < 0.05. **P < 0.01. ***P < 0.001.
Table 3. The Women’s March survey regression models by motivation for attending (n = 463). These models control for number of protests, gender, race,
age, and organizational membership. +, significant and positive association (P < 0.05); −, significant and negative association (P < 0.05).
Dependent
variables
Women’s
Rights
Reproductive
Rights
Environment
 LGBTQ

Racial
Justice
Immigration

Social
Welfare
Labor
 Peace
 Equality
 Politics
 Trump
Independent
variables
Women’s Rights
 −
 +
 +
 −
 −
Reproductive Rights
 −
 +
 +
 −
 −
Environment
 +
 +
LGBTQ issues
 +
 +
Racial Justice
 +
 +
 +
 +
 +
 +
 +
Immigration
 +
 +
 +
 +
 +
Social Welfare
 +
 +
 +
 +
Labor
 +
 +
 +
 +
 +
Peace
 +
 +
 +
 +
 +
Equality
 −
 −
 +
 +
 +
 +
 −
 −
Politics
 +
 +
 +
Trump
 −
 +
 −
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(5, 8, 9), this paper identifies coalitions among the motivations of
individual participants, particularly those associated with identity-
based issues.
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DISCUSSION
As one might expect and consistent with the intersectional character of
the 2017 Women’s March in Washington, DC, individuals were more
likely to be motivated by issues connected to the social identities that
were most salient for them: Black participants mobilized for Racial Jus-
tice, Hispanic participants mobilized for Immigration, and women mo-
bilized for Reproductive Rights. Our analysis supports previous studies
that find that individuals concerned with a range of social issues can
establish and build coalitions informed by intersectional motivations
[(22, 28, 29); see also the study of Van Dyke (11)]. Although we find
that individuals were often motivated by issues related to their own so-
cial identities, we also find that individuals reported being motivated by
reasons that extended beyond their social identities.

Our findings demonstrate that individuals can be mobilized to pro-
test on the basis of issues that fall outside their narrow interests or spe-
cific social identities. In contrast to the extant research that focuses on
how smaller-scale movements use intersectionality to mobilize and ex-
pand their constituencies (19, 29), we find that members of these coali-
tions participated together in one large-scale protest event while still
coalescing around a suite of intersectional interests that sometimes
overlapped. In many ways, we believe that the large turnout at the
Women’s March, which organizers and others see as an indicator of
success, is the direct result of the effective mobilization of various indi-
viduals and organizational constituencies that were motivated by in-
tersectional issues. One has only to review the expansive list of the
organizational partners for the Women’s March to see how it aimed
to mobilize people whose interests lie at the intersections of race,
Fisher, Dow, Ray, Sci. Adv. 2017;3 :eaao1390 20 September 2017
class, gender, sexual orientation, legal status, and other categories of
identity, along with less identity-based sympathizers.

In contrast to social movement–oriented research that tends to take
as a given that people who turn out for a particular march are explicitly
motivated by that specific issue and focus their inquiry on movement-
to-movement transmission [(13, 14); but see the study of Goss et al.
(15)], we find that there is much to learn from looking at the varied
issues that motivated participants to join the Women’s March. Future
research should examine the degree to which other marches and
movements can also mobilize participants who are motivated by an in-
tersectional set of issues. Moreover, research should be devoted to
understanding how and why people come to see their interests as
linking to certain issues but not others.

On a more practical level, the results from this research shed im-
portant light on potential cleavages and enduring coalitions within the
progressive movement. For example, although the environmental
movement has worked for many years to break free from its reputa-
tion as a predominantly white social movement (33, 34), our findings
from the Women’s March on Washington, DC show that people who
are mobilizing around the environment continue to be less concerned
about issues that mobilize people of color. In contrast, individuals who
identify issues that more directly relate to people of color, such as Racial
Justice or Immigration, also tend to be more likely motivated by issues
related to the LGBTQ community and vice versa. This overlap suggests
that, rather than existing as silos with distinct aims, these political con-
stituencies have come to view their fates as connected to one another.

In many ways, these findings are consistent with the work of Ghaziani
and Baldassarri who have found that activists involved in LGBTQ
marches take advantage of what they call “cultural anchors” to address
internal diversity while also responding to unfolding historical events
(35, 36). Similarly, those motivated by Peace were also motivated by
most of the other dominant issues. This finding indicates that the
Table 4. The Women’s March survey cross-tabulations of motivations for attending (n = 463).
Women’s
Rights
Reproductive
Rights
Environment
 LGBTQ

Racial
Justice
Immigration

Social
Welfare
Labor
 Peace
 Equality
 Politics
 Trump
n = 273
 n = 121
 n = 116
 n = 90
 n = 110
 n = 87
 n = 112
 n = 47
 n = 29
 n = 214
 n = 83
 n = 132
Women’s Rights
 49.59%
 63.79%
 71.11%
 80.00%
 75.86%
 64.29%
 68.09%
 82.76%
 48.60%
 55.42%
 51.52%
Reproductive
Rights
21.98%
 37.07%
 45.56%
 38.18%
 42.53%
 48.21%
 59.57%
 75.86%
 25.70%
 43.37%
 23.48%
Environment
 27.11%
 35.54%
 45.56%
 40.00%
 44.83%
 49.11%
 55.32%
 79.31%
 30.84%
 39.76%
 25.76%
LGBTQ issues
 23.44%
 33.88%
 35.34%
 37.27%
 44.83%
 32.14%
 46.81%
 62.07%
 20.09%
 33.73%
 21.21%
Racial Justice
 32.23%
 34.71%
 37.93%
 45.56%
 54.02%
 37.50%
 51.06%
 68.97%
 26.17%
 40.96%
 29.55%
Immigration
 24.18%
 30.58%
 33.62%
 43.33%
 42.73%
 32.14%
 40.43%
 65.52%
 23.83%
 33.73%
 18.94%
Social Welfare
 26.37%
 44.63%
 47.41%
 40.00%
 38.18%
 41.38%
 61.70%
 75.86%
 26.64%
 45.78%
 24.24%
Labor
 11.72%
 23.14%
 22.41%
 24.44%
 21.82%
 21.84%
 25.89%
 68.97%
 15.89%
 28.92%
 16.67%
Peace
 8.79%
 18.18%
 19.83%
 20.00%
 18.18%
 21.84%
 19.64%
 42.55%
 11.21%
 22.89%
 15.15%
Equality
 38.10%
 45.45%
 56.90%
 47.78%
 50.91%
 58.62%
 50.89%
 72.34%
 82.76%
 43.37%
 31.82%
Politics
 16.85%
 29.75%
 28.45%
 31.11%
 30.91%
 32.18%
 33.93%
 51.06%
 65.52%
 16.82%
 22.73%
Trump
 24.91%
 25.62%
 29.31%
 31.11%
 35.45%
 28.74%
 28.57%
 46.81%
 68.97%
 19.63%
 36.14%
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threat of war and conflict is seen as a cross-cutting issue for many
protest participants.

There is much to be learned from the 2017 Women’s March and its
successful mobilization of hundreds of thousands of participants. In
particular, our findings may be of use to organizations that are seeking
to mobilize sympathizers who are motivated by an intersectional set of
issues.Forissueswhere overlapping interestsmay already exist,activists,
organizers, and policymakers can apply our findings to develop more
effective strategies for sustainable cross-movement coalitions. In cases
where overlap does not exist, our findings help to explain the work that
has yet to be carried out to broaden the base.
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MATERIALS AND METHODS
This research draws on unique survey data collected from a random
sample of protest participants in the Women’s March in Washington,
DC on 21 January 2017. Participants were selected using a field ap-
proximation of random selection throughout the Women’s March.
This methodology has been developed over a series of empirical investi-
gationsofactivismandprotest in the UnitedStatesandabroad(7, 37–39).
An eight-member research team spread out across the area designated
for the staging of the Women’s March: from 3rd Street southwest to
14th Street southwest on the National Mall. Pairs of researchers entered
the crowd at the entrances designated by the organizers (on 4th Street,
7th Street, and 12th Street). Snaking through the crowd as people
gathered, researchers “counted off” protesters, selecting every fifth
person to participate. This method avoids the potential of selection
bias by preventing researchers from selecting only “approachable peers”
(40–42). The Women’s March participants were sampled throughout
the morning and early afternoon of the 21st as they listened to speeches
and performances during the rally.

The survey was designed to be short and noninvasive, so as to en-
courage the highest level of participation possible and facilitate data col-
lection in the field: It took less than 10 min for participants to fill out the
one-page, two-sided survey. Data were collected in accordance with the
University of Maryland Institutional Review Board Protocol(UMD IRB
#332104-1). On the basis of the requirements of this protocol, only in-
dividuals over the age of 18 were eligible to participate in the study. Re-
searchers completed 528 surveys with participants. Forty-three people
refused to participate in the study, representing a refusal rate of 7.5%. It
is worth noting that our refusal rate is lower than other studies that have
used this methodology (7) and is substantially lower than those studies
that rely on mailed-back questionnaires, which can suffer from delayed
refusal bias (40, 41).

Dependent variables
To understand the potentially intersectional motivations of the protest
participants, the survey instrument included an open-ended question
that asked respondents: “What issues motivated you to participate
today?” Respondents could write in as many issues as they wanted.
On average, respondents wrote in 2.74 issues. The responsesto this ques-
tion were coded into 14 categories including Women’s Rights, Repro-
ductive Rights, Environment, LGBTQ issues, Racial Justice, Police
Brutality, Immigration, Religion, Social Welfare, Labor, Peace, Equality,
Politics, and Trump. We reduced these 14 categories to 12. First, using
factor analysis, we consolidated Racial Justice and Police Brutality into
“Racial Justice” (a = 0.45). Althoughthe scale reliability coefficient is not
as high as expected, we believe that these two categories are theoretically
related and should be viewed as one larger motivating issue. Second, we
Fisher, Dow, Ray, Sci. Adv. 2017;3 :eaao1390 20 September 2017
created a variable that combines Immigration and Religion (a = 0.53),
which we call, “Immigration.” Many respondents specifically mentioned
the threat of a Muslim ban, which combines Immigration and Religion.
These 12 motivating issues serve as the dependent variables for our
analysis.

Although many respondents explicitly mentioned these issues, there
were other terms that we interpreted as referring to these issues. Any
mention of “women” or references to specific women in people’s lives,
such as a mother, sister, or partner, was coded as Women’s Rights. Any
mention of abortion or a woman’s right to choose was coded as Repro-
ductive Rights. Mentions of climate change or pollution were coded as
Environment. Mentions of same-sex or gay marriage were coded as
LGBTQ issues. Mentions of Racism, Police Brutality, or Black Lives
Matter were coded as Racial Justice. Mentions of the threat of a Muslim
ban or threats to religious freedom were coded as Immigration. Mentions
of health care reform, the Affordable Care Act, Medicaid, Medicare,
education, or housing were coded as Social Welfare. Mentions of equal
pay were coded as Labor. Many respondents also mentioned Equality
in addition to Peace. Thus, Equality received its own category. In these
cases, Equality was a broader category for respondents that spoke to
their desire to have everyone treated the same without noting a specific
gender, race, or sexual orientation. Mentions of government, Con-
gress, or the potential Russian hack were coded as Politics. The newly
inaugurated President Trump received his own category as many re-
spondents wrote specific and poignant comments about his election
and their fears about his presidency. The Women’s March participants
were viewed as having intersectional motivations when they reported
being motivated by multiple issues related to social identities tradi-
tionally associated with intersectionality such as race, gender, class,
and sexual orientation.

Independent variables
Table 5 shows a series of sociodemographic variables that were used as
controls. Gender was an open-ended question. Participants responded
as woman (85.3%), man (14.1%), or transgender (0.6%). In the regres-
sion models, women are compared to the other two groups. Race was
coded as White/Caucasian (77.4%), Hispanic/Latino (4.2%), Black/Af-
rican American (6.5%), Asian (3.8%), or Multiracial (7.9%). Age was an
open-ended variable. In contrast to claims by Milkman (16) that “mil-
lennials comprise the bulk of those involved in the new movements that
emerged on the left” since 2008, the median age of the sample was 43.3
years old. We also collected data about participants’ connections to the
400-plus organizational partners of the Women’s March. Organization
membership was coded into three categories: not a member (81.8%);
passive member, or what some scholars would call a “tertiary” or
“mail-in member” (11.8%) (43); or active member (6.4%).

Like recent studies of large-scale protest events (44), we asked re-
spondents about their protest experience. Responses were coded into
three categories: first protest attended (34.7%), first protest in 5 years
(24.8%), or more than one protest in the past 5 years (40.5%). This var-
iable was created on the basis of the responses from two questions: one
question that asks what was the first protest/demonstration ever
attended and another question that asks how many protests the
respondent had participated within the past 5 years. The Women’s
March was identified as the first protest experience for respondents
whether they listed it as their first protest or they checked the option
that said, “this is the first protest that I have ever attended.”

Respondents were also asked who they attended with and could se-
lect as many responses that applied. This variable was coded into four
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categories: attended alone (5%), attended with family (61.2%), attended
withfriends(69.8%), andattendedwithcolleagues (10.2%).Thispattern
is consistent with the existing research that finds social networks to play
an important role in mobilization (1, 2, 6).

Respondents were also asked about their levels of educational attain-
ment, place of employment, and political ideology. More than 86% of
the sample reported having a bachelor’s degree. Although it is not un-
usual for protesters to represent an educated group, note that middle-
class participants are overrepresented in our sample relative to the gen-
eral population, and thus, our analysis of intersectionality speaks best to
the motivations of that group. It is also worth noting that the racial
distribution of our sample was relatively consistent with the national
averages for college-educated Americans. Most of the sample report
working in either the public or private sector (only 7% were students)
and about 90% report being left-leaning politically. Roughly 93% re-
ported voting in the 2016 presidential election with 90% of all partici-
pants reporting that they voted for Hillary Clinton. Given the similarities
of these variables across the sample, we do not control for these variables
in the models. Note that non-whites were no less likely to fall into these
categories than their white counterparts.
Fisher, Dow, Ray, Sci. Adv. 2017;3 :eaao1390 20 September 2017
Statistical analysis
This paper examines the issues that motivated protesters to participate
in the Women’sMarchin Washington, DC.Accordingly, we performed
two sets of analyses. First, using ordinary least squares and logistic re-
gression analysis, we examined the association between sociodemo-
graphic factors and the issues that respondents reported motivated
their participation. Second, we examined the relationship among each
of the motivating issues to determine whether patterns emerge among
individuals with certain motivations for participating. Ordinary least
squares regression analysis was used for Racial Justice and Immigration
as these two variables are transposed from a factor analysis that joins
two existing issues, which we previously discussed. Because of missing
data on some of the sociodemographic variables (roughly 13% of the
sample), our sample size decreased from 528 to 463. There were no sys-
tematic patterns to the missing data.
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Table 5. Sample demographics (n = 528).
Gender
Women
 85.3%
Men
 14.1%
Transgender
 0.6%
Race
White
 77.4%
Hispanic
 4.2%
Black
 6.5%
Asian
 3.8%
Multiracial
 7.9%
Age
 43.3
Organization member
Not a member
 81.8%
Passive member
 11.8%
Active member
 6.4%
Attended protests
First ever protest
 34.7%
First in 5 years
 24.8%
More than one in 5 years
 40.5%
Attended with
Alone
 5.0%
Family
 61.2%
Friends
 69.8%
Colleagues
 10.2%
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Acknowledgments: We would like to thank the three anonymous reviewers who provided
comments on this paper, as well as B. Powell and R. Vanneman, and the Workshop on Cultural
and Political Sociology at the University of North Carolina for comments on previous versions of this
paper. We also would like to thank the following students for their help with data collection:
T. Barnes, D. Chen, A. Dewey, S. Genter, H.-Y. Ho, and D. Koonce, as well as G. Fuentes for her coding
assistance. Funding: This project received no financial support, grants, or funding. Author
contributions: D.R.F. directed the data collection. D.R.F. and D.M.D. collected the data.R.R. managed
the data entry and cleaned and analyzed the data. All authors prepared the survey instrument
and contributed to writing and revisions of the manuscript. Competing interests: The authors
declare that they have no competing interests. Data and materials availability: All data needed to
evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.
Additionaldatarelated to thispapermaybe requested from the authors. Data analyzed in thispaper,
along with a codebook and the survey instrument, are available on the Principal Investigator’s
website (www.drfisher.umd.edu/protest.html).

Submitted 18 June 2017
Accepted 24 August 2017
Published 20 September 2017
10.1126/sciadv.aao1390

Citation: D. R. Fisher, D. M. Dow, R. Ray, Intersectionality takes it to the streets: Mobilizing
across diverse interests for the Women’s March. Sci. Adv. 3, eaao1390 (2017).
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March
Intersectionality takes it to the streets: Mobilizing across diverse interests for the Women's

Dana R. Fisher, Dawn M. Dow and Rashawn Ray

DOI: 10.1126/sciadv.aao1390
 (9), eaao1390.3Sci Adv 

ARTICLE TOOLS http://advances.sciencemag.org/content/3/9/eaao1390

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