Intersectionality takes it to the streets: Mobilizing across diverse interests for the Women's March.

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 participants, we explore how social identities influenced participation in the Women's March. Our analysis demonstrates 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.

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 inauguration, 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 Inauguration. 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 (–). A subset of this research has explored the strategies and mechanisms through which social movements broaden their societal reach and mobilize more participants (–). Large-scale street demonstrations and marches have been documented as a site where social movement expansion is particularly visible to the general public (, ). Although social movement scholarship has examined movement-to-movement transmission, focusing on tactical overlap (), social movement spillover (), and the sequencing of social movements (), 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 ()].

In addition, few studies have examined how intersectionality contributes to social movements (–). Scholars of intersectionality examine how intersections of race, class, gender, sexual orientation, legal status, and other categories of identity are linked to structures of inequality and produce different life experiences and forms of oppression (–). 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 (, ). Moreover, research has found that, when these interests are incorporated into movements, some interests become marginalized in favor of broader movement goals (). 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 (–). In her influential work, Crenshaw () suggests that intersectionality can promote coalitions instead of divisions. A small number of studies have specifically explored intersectional mobilization processes and how shared grievances play a role (). In her work that explores how organizations cross movement boundaries, Van Dyke () comes to similar conclusions without explicitly discussing intersectionality. To date, intersectionality has been understood as a theory (, ), an analytical framework (, , ), and/or a method () 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 motivations 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 diverse group of women who were already engaged in a range of political activism and social mobilization. Together, these activists aimed to create 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 antiabortion groups from their list of partners.

The day after average crowds came out for Donald Trump’s inauguration, 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. Individuals 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 opposing 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 sexual orientation, more research is needed to understand how individuals 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 sample 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.

RESULTS

Table 1 shows the distribution of issues that motivated participants to attend. Not surprisingly, Women’s Rights (53%) was the top motivating 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 demonstrate that participants were not just motivated by issues related to women but were actually motivated by a diverse set of issues connected to intersectional concerns ().

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%

Table 2 shows a series of regression models for the association among various sociodemographic variables and the issues that motivated 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 significantly 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 (). It also suggests that the Women’s March’s unity principles and its organizational coalition were successful in mobilizing a crowd with diverse interests.

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.

	Women’s Rights	Reproductive Rights	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**
	(1.77)	(−1.27)	(0.32)	(1.54)	(0.83)	(2.94)
Asian	0.751	−1.139	−0.446	0.370	−0.069	0.241
	(1.38)	(−1.49)	(−0.68)	(0.63)	(−0.38)	(1.28)
Multiracial	0.099	−0.586	−0.515	−0.037	−0.021	−0.188
	(0.26)	(−1.14)	(−1.01)	(−0.07)	(−0.15)	(−1.32)
Age	−0.020**	−0.022**	0.003	−0.006	−0.008**	−0.003
	(−2.92)	(−2.58)	(0.41)	(−0.62)	(−3.00)	(−1.41)
Nonorganization member	−0.437	−0.211	−0.099	−0.304	−0.164	−0.288*
	(−0.97)	(−0.41)	(−0.20)	(−0.57)	(−1.49)	(−2.53)
Passive organization member	−0.462	0.196	0.804	0.266	0.000	0.000
	(−0.90)	(0.34)	(1.44)	(0.44)	(0.00)	(0.00)
Constant	1.604	−1.185	−1.368	−1.273	0.449	0.408
	(2.73)	(−1.62)	(−2.00)	(−1.73)	(2.30)	(2.03)
	Social Welfare	Labor	Peace	Equality	Politics	Trump
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)
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.

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 related to age are noteworthy. Age is significantly and negatively associated 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 significantly more likely to mention Racial Justice, Immigration, and Social Welfare. However, they were significantly less likely to mention Reproductive Rights and Equality. Similarly, participants who mentioned Reproductive Rights were significantly more likely to mention Immigration 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.

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).

Dependentvariables	Women’sRights	ReproductiveRights	Environment	LGBTQ	RacialJustice	Immigration	SocialWelfare	Labor	Peace	Equality	Politics	Trump
Independentvariables
Women’s Rights		−			+		+			−		−
Reproductive Rights	−					+	+			−		−
Environment							+			+
LGBTQ issues					+	+
Racial Justice	+			+		+		+		+	+	+
Immigration	+	+		+	+					+
Social Welfare	+	+	+					+
Labor		+			+		+		+	+
Peace			+		+	+		+				+
Equality	−	−	+		+	+		+			−	−
Politics					+	+	+
Trump		−			+					−

Table 4

The Women’s March survey cross-tabulations of motivations for attending (n = 463).

	Women’sRights	ReproductiveRights	Environment	LGBTQ	RacialJustice	Immigration	SocialWelfare	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%
ReproductiveRights	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%

The negative association between Women’s Rights and Reproductive 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 example, 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 selected Women’s Rights and Racial Justice but did not mention Reproductive Rights, whereas less than 20% of respondents mentioned Reproductive 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 motivation 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 motivated by the Environment. Instead, they were significantly more likely to mention Racial Justice and Immigration.

Individuals who mentioned Racial Justice indicated the most number of additional issues as motivating them to participate in the Women’s March. Specifically, they were significantly more likely to mention Women’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 Equality, they were significantly more likely to mention Racial Justice and Peace. Together, these findings strongly support the view that intersectionality can promote alliances across identity-based issues (, , ). In contrast to the studies that focus on coalitions among organizations (, , ), this paper identifies coalitions among the motivations of individual participants, particularly those associated with identity-based issues.

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 Justice, Hispanic participants mobilized for Immigration, and women mobilized 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 [(, , ); see also the study of Van Dyke ()]. Although we find that individuals were often motivated by issues related to their own social 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 protest on the basis of issues that fall outside their narrow interests or specific social identities. In contrast to the extant research that focuses on how smaller-scale movements use intersectionality to mobilize and expand their constituencies (, ), we find that members of these coalitions 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 individuals and organizational constituencies that were motivated by intersectional 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, 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 [(, ); but see the study of Goss et al. ()], 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 intersectional 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 important 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 reputation as a predominantly white social movement (, ), 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 constituencies 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 (, ). Similarly, those motivated by Peace were also motivated by most of the other dominant issues. This finding indicates that the 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. For issues where overlapping interests may 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.

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 approximation of random selection throughout the Women’s March. This methodology has been developed over a series of empirical investigations of activism and protest in the United States and abroad (, –). 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” (–). 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 encourage the highest level of participation possible and facilitate data collection 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 individuals over the age of 18 were eligible to participate in the study. Researchers 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 () and is substantially lower than those studies that rely on mailed-back questionnaires, which can suffer from delayed refusal bias (, ).

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 responses to this question were coded into 14 categories including Women’s Rights, Reproductive 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” (α = 0.45). Although the 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 created a variable that combines Immigration and Religion (α = 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 Reproductive 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, Congress, or the potential Russian hack were coded as Politics. The newly inaugurated President Trump received his own category as many respondents 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 traditionally 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 regression models, women are compared to the other two groups. Race was coded as White/Caucasian (77.4%), Hispanic/Latino (4.2%), Black/African American (6.5%), Asian (3.8%), or Multiracial (7.9%). Age was an open-ended variable. In contrast to claims by Milkman () that “millennials 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%) (); or active member (6.4%).

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%

Like recent studies of large-scale protest events (), we asked respondents 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 variable 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 select as many responses that applied. This variable was coded into four categories: attended alone (5%), attended with family (61.2%), attended with friends (69.8%), and attended with colleagues (10.2%). This pattern is consistent with the existing research that finds social networks to play an important role in mobilization (, , ).

Respondents were also asked about their levels of educational attainment, place of employment, and political ideology. More than 86% of the sample reported having a bachelor’s degree. Although it is not unusual for protesters to represent an educated group, note that middle-class participants are overrepresented in our sample relative to the general 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% reported voting in the 2016 presidential election with 90% of all participants 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.

Statistical analysis

This paper examines the issues that motivated protesters to participate in the Women’s March in Washington, DC. Accordingly, we performed two sets of analyses. First, using ordinary least squares and logistic regression analysis, we examined the association between sociodemographic 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 systematic patterns to the missing data.