Vocal Pitch as a Measure of Emotional Intensity

Although understudied within legislatures, the nonverbal elements of speech have clear political ramifications. A growing body of work demonstrates that vocal pitch affects evaluations of candidates (Anderson et al. Reference Anderson, Klofstad, Mayew and Venkatachalam2014; Anderson and Klofstad Reference Anderson and Klofstad2012; Klofstad Reference Klofstad2016; Klofstad, Anderson, and Nowicki Reference Klofstad, Anderson and Nowicki2015; Klofstad, Anderson, and Peters Reference Klofstad, Anderson and Peters2012). This research shows both that differences in baseline vocal pitch can influence candidates’ political prospects and also that these effects are deeply gendered. Experimental studies, for example, suggest that citizens make inferences about competence and trustworthiness based on vocal characteristics (Anderson et al. Reference Anderson, Klofstad, Mayew and Venkatachalam2014), and prefer female leaders with lower pitched voices (Klofstad, Anderson, and Peters Reference Klofstad, Anderson and Peters2012).

Despite the interest in political speech broadly, and newer work on the nonverbal elements of speech in particular, political science research has overlooked a central feature of speech: subtle variations within an individual’s vocal pitch. These small deviations convey information about a speaker’s emotional state. When individuals become emotionally activated, a typical physiological response is a tightening of the vocal cords. This tightening, in turn leads to a higher-than-average vocal pitch when speaking. Indeed, studies have “routinely shown that [pitch]-related measures…are influenced by affect-related arousal” (Owren and Bachorowski Reference Owren, Bachorowski, Coan and Allen2007, 240), and “higher levels of arousal have been linked to higher-pitched vocal samples” (Mauss and Robinson Reference Mauss and Robinson2009, 222).^{Footnote 1}

Although novel within political science research, the link between higher vocal pitch and emotional arousal is well established in the psychology literature. Indeed, on pages S4–S16 of our Supplemental Information, we provide an extended discussion of this link between vocal pitch and emotional intensity, including validation exercises.^{Footnote 2} These include studies that generate emotional intensity via acting prompts and those that induce emotional states via the Velten procedure. Here, we note that Bachorowski and Owren (Reference Bachorowski and Owren1995) induced specific emotional states within respondents by asking them to complete a 210-trial word identification task on a computer. After each block of 10 words, the respondents received either positive (“Good Job”) or negative (“Try Harder”) feedback. Subjects were then asked to answer a battery of questions about their emotional state and read a block of text aloud. The authors showed that vocal pitch was higher when individuals reported higher levels of emotional intensity, leading them to conclude that pitch can be used to assess respondents’ levels of emotional arousal.

These shifts in vocal pitch are subtle and difficult for the speaker to control. This physiological response, like many automatic responses, is thought to occur largely below conscious awareness. In this way, minor changes in vocal pitch serve as an “inherently honest indicator” of a speaker’s “internal state” (Ekman et al. Reference Ekman, O’Sullivan, Friesen and Scherer1991, 133–4). This latter argument is supported by Zuckerman and Driver (Reference Zuckerman, Driver, Siegman and Feldstein1985), who argue that nonverbal behaviors reveal (or “leak”) information that speakers are trying to hide. “Tone of voice,” in particular, has been identified as an especially telling indicator. Indeed, “several studies have shown that…the tone of a person’s voice leaks information that is not revealed by the verbal content or facial expressions associated with the message” (Zuckerman and Driver Reference Zuckerman, Driver, Siegman and Feldstein1985, 129). For these reasons, verbal and nonverbal behavior can be thought of in terms of a “leakage hierarchy” with “verbal content” (i.e., the words spoken) located in the “controllable end of the continuum, whereas the body and tone of voice may be classified as less controllable and more leaky channels” (Zuckerman and Driver Reference Zuckerman, Driver, Siegman and Feldstein1985, 130). In fact, when individuals attempt to control their vocal pitch, they often sound “more tense and less pleasant or compelling than someone speaking sincerely,” which is in turn associated with “increased vocal pitch” (Elkins et al. Reference Elkins, Zafeiriou, Pantic, Burgoon, Calvo, D’Mello, Gratch and Kappas2014, 505).^{Footnote 3} This difficulty in controlling vocal pitch makes it uniquely well suited for studying the emotional states of strategic actors.

Emotional Intensity in Legislators’ Speech

Finding a reliable indicator of emotional activation or intensity is especially important in the domain of legislative speech. Scholars have long recognized that much of a legislator’s behavior is best understood as strategic actions used to meet her reelection or institutional goals. That is, the verbal content of speech is often seen as cheap talk (Austen-Smith Reference Austen-Smith1990). Indeed, “to the extent [that a] behavior furthers the actor’s short-term self-interests,” Kraut (Reference Kraut1978) argues that we should “discount an actor’s behavior as a reflection of his or her true nature.” For this reason, with legislative speech, it is difficult to disentangle legislators’ internal states from their strategic behavior.

Although lawmakers have ulterior motives when speaking in the legislature, some aspects of their speech may be outside their conscious control. Because “one should believe most in those aspects of a person’s performance that the person is least able to deliberately and consciously control” (Kraut Reference Kraut1978, 381), subconscious aspects of speech should serve as more meaningful signals of legislators’ emotional states. With respect to legislative speech, features like “verbal content, speech rate and fluency, most body movements, and the large easy-to-see facial expressions are all more susceptible to deliberate control” (Ekman et al. Reference Ekman, O’Sullivan, Friesen and Scherer1991, 134). Speech topic, choice of words, and the length or extent of remarks each have a high degree of “controllability,” and should thus be driven largely by legislators’ strategic concerns. Vocal pitch, on the other hand, typically lies beyond the control of the individual (Ekman et al. Reference Ekman, O’Sullivan, Friesen and Scherer1991, 134). Because changes in pitch are less “controllable,” they are a more honest indicator of a lawmaker’s emotional intensity on a given issue.

This argument about the difficulty of controlling nonverbal aspects of speech has not been entirely lost on political scientists. Citing Goffman (Reference Goffman1959), Fenno (Reference Fenno1977) recognizes the importance of nonverbal expressions for evaluating the sincerity of legislators’ behaviors. He says:

When MCs speak, they do so with a combination of controllable and manipulable elements, and relatively uncontrolled and sincere elements. The less easily controlled an element, the more likely that it measures the emotional disposition the speaker has toward the issues she is advancing. There is significant evidence that changes in pitch can arise from affective arousal, and that these variations in vocal pitch are associated with other noticeable emotional displays. Because such changes in vocal pitch are difficult to purposefully manipulate, discussing a topic with higher-than-average vocal pitch signals one’s emotional intensity about that issue. We turn now to outlining a test case for our claims about emotional intensity: women’s and men’s legislative speech on women.

Measuring Emotional Intensity via Vocal Pitch

From our raw audio data, we compute changes in speakers’ vocal pitch as our measure of emotional intensity. We first calculate speakers’ baseline levels of vocal pitch, and then measure variations in vocal pitch across speeches. Generally speaking, “voice pitch is the perceived ‘highness’ or ‘lowness’ of a voice and is influenced by the fundamental frequency” (Klofstad Reference Klofstad2016, 2). Following Titze (Reference Titze2000), the fundamental frequency (F ₀) can be defined using the following equation:

(1)$${F_0} = {1 \over {2L}}\sqrt {{\sigma \over \rho }} ,$$

where L is the vocal fold length, σ is the longitudinal stress on the vocal folds, and ρ is the vocal fold tissue density. Individual variations in vocal fold length (L) and density (ρ) are largely determined by genetics (e.g., Debruyne et al. Reference Debruyne, Decoster, Van Gijsel and Vercammen2002; Przybyla, Horii, and Crawford Reference Przybyla, Horii and Crawford1992).^{Footnote 7} Conversely, variations in longitudinal stress (σ) are specific to the speaker and speech. Puts, Gaulin, and Verdolini (Reference Puts, Gaulin and Verdolini2006, 285) demonstrate that “[e]motional activation raises F ₀ by increasing tension on the vocal fold mucosa [σ, in equation (1)], mainly via contraction of the cricothyroid muscles and consequent lengthening of the vocal folds.”^{Footnote 8}

To measure pitch, we extract the mean fundamental frequency (F ₀)—that is, the average vocal pitch—from each floor speech using Praat.^{Footnote 9} This commonly used speech analysis software estimates the fundamental frequency by dividing the autocorrelation of a windowed signal by the autocorrelation of the window itself.^{Footnote 10} Given that the pitch window can influence the vocal pitch estimate, on pages S22–S30 in the Supplemental Information, we reestimate all of our models using different Praat settings. The results remain essentially unchanged, regardless of the pitch window used.

To control for inter- and intra-speaker variations, we scale vocal pitch to standard deviations above or below the speaker’s baseline. Dietrich, Enos, and Sen (Reference Dietrich, O’Brien and Yao2019) argue that this should be done for two reasons. First, in this project, we are not interested in whether a lawmaker generally speaks at a higher vocal pitch. Rather, we are concerned with whether a legislator’s vocal pitch changes from its baseline level when speaking about women. Standardizing vocal pitch not only helps capture whether a speaker is higher or lower than her average but also gives the relative magnitude of the change. Second, although studies find few gender differences in the vocal characteristics used to convey emotions—such as laryngeal tension, lip rounding, pitch level and range, loudness, clarity, and rate (Bezooyen Reference Bezooyen1984; Brody Reference Brody2009; Davitz Reference Davitz1964)—because women’s vocal cords tend to be smaller and shorter, they do typically speak at a higher baseline vocal pitch than men. By standardizing vocal pitch using each speaker’s baseline (or mean) vocal pitch, we account for this inherent sex difference.

Identifying Speech Topic

Individual-level variation in vocal pitch provides a measure of emotional intensity. We expect that levels of emotional activation vary by speech topic. We use the closed-captioning text data from HouseLive to determine whether a MC addressed women in a given speech. We opt for closed-captioned transcripts because they more accurately report what is said on the House floor than the Congressional Record. Legislators can change the Congressional Record after the fact, and often read in text that was not spoken on the House floor (e.g., asking that a letter from a constituent be added to the Congressional Record instead of reading it aloud). Because it is directly transcribed, closed-captioning information does not suffer from this limitation.^{Footnote 11}

To establish whether the speaker addressed women, we create a binary variable indicating whether the speech used any of the Pearson and Dancey (Reference Pearson and Dancey2011b) dictionary terms related to women. These include “woman,” “women,” “woman’s,” “women’s,” “girl,” “girl’s,” “girls,” “girls’,” “female,” “female’s,” “females,” “females’,” “servicewoman,” “servicewoman’s,” “servicewomen,” and “servicewomen’s.” If a speech contains any of these terms, it is coded as a 1, otherwise 0. Given that this is a coarse measure of whether a speech addresses women, we also estimate all models using two alternative operationalizations of the dependent variable. The first considers the proportion of words in the speech drawn from these dictionary terms. The second identifies speech about women using a Structural Topic Model (STM) (Roberts et al., Reference Roberts, Stewart, Tingley and Airoldi2013, Reference Roberts, Stewart, Tingley, Lucas, Leder-Luis, Kushner Gadarian, Albertson and Rand2014; Roberts, Stewart, and Tingley Reference Roberts, Stewart and Tingley2014). Irrespective of the way we measure the degree to which a speech addressed women, the results are identical to those outlined below. The models using alternative approaches are reported on pages S30–S36 in the Supplemental Information.

Modeling Strategy

Our key predictor of interest is speaker sex, which we obtain from GovTrack.^{Footnote 12} We view each speech as a unique opportunity to address women. We thus use the legislative speech (rather than the legislator) as our unit of analysis. Aggregating to the legislator level would obfuscate the important within-individual variation that takes place from speech to speech. For example, a single emotionally activated speech could artificially inflate a MC’s mean vocal pitch—giving the impression that she is an emotive legislator when in reality she generally delivers subdued speeches (with one extreme exception). At the same time, because speeches given by the same MC will likely share commonalities, we estimate multilevel linear and logistic regressions with random intercepts for each legislator. This modeling approach also helps to account for other unobserved differences between legislators that could influence the parameter estimates.

We also control for other factors that may bias the results if omitted. Members who are institutionally disadvantaged are forced to take to the floor more often in an attempt to influence legislation (Maltzman and Sigelman Reference Maltzman and Sigelman1996), because they have fewer other tools at their disposal. We therefore include measures of speaker race and seniority from GovTrack, and use data from Stewart and Woon (Reference Stewart and Woon2016)^{Footnote 13} to determine whether the speaker was a committee chair. We also incorporate data on partisanship and ideology from Voteview,^{Footnote 14} both of which affect floor speeches (Harris Reference Harris2005; Morris Reference Morris2001). For similar reasons, we include dummy variables for whether the speech was less than one minute and whether it was delivered during an election year, both of which have been shown to influence speaking behavior (Maltzman and Sigelman Reference Maltzman and Sigelman1996). We also incorporate a control for the speech duration because spikes in vocal pitch will have a greater effect on mean vocal pitch when a speech is shorter. Finally, on pages S51–S86 of the Supplemental Information, we further replicate these results with a number of different model specifications.

Vocal Pitch and Interest Group Ratings

If increases in vocal pitch reflect legislators’ emotional intensity about a topic, then vocal pitch should be associated with other types of legislative activity related to the issue area. To this end, we examine whether female MCs’ emotional intensity when speaking about women is associated with differences in voting behavior on women’s issues. To do so, we use legislative scorecards from 24 prominent women’s interest groups, as reported by Project Vote Smart. These scorecards are used by interest groups to inform their members about which lawmakers are more or less likely to cast votes that are consistent with the organization’s mission.^{Footnote 18} We collapse these scores into the average vote score for each legislator across all 24 groups for all available years of data, and use this information to examine voting patterns among female lawmakers.

Although on average Congresswomen speak with higher-than-baseline pitch when referencing women, Table 3 demonstrates that there is substantial variation in female MCs’ emotional intensity when addressing this topic. Importantly, we find that the Congresswomen who speak with the greatest intensity about women receive significantly higher scores from women’s interest groups than those who speak with the lowest intensity. In fact, there is a statistically significant difference between the average vote scores for the 25 most (Table 3a) and least (Table 3b) activated women (t = 2.59, df = 48, p ≤ 0.02). This suggests that the female legislators who are especially emotionally intense when referencing women also have voting records that reflect their interest in representing women.

TABLE 3. Emotionally Intense Congresswomen are Rated More Highly by Women’s Interest Groups

Note: Measurements of vocal pitch are in Hertz (Hz). In the first column, we restricted our data to speeches which used at least one of the terms outlined by Pearson and Dancey (Reference Pearson and Dancey2011b). In the second column, we restricted our data to speeches which did not use any of these terms. The “Pitch Difference” column (abbreviated “Pitch Diff.”) is the difference between these two columns. The 25 most (see Panel A) and least activated (see Panel B) female MCs had the highest and lowest “Pitch Difference,” respectively. The average vote score from 24 prominent women’s interest groups (as reported by Project Vote Smart) is found in the column labeled “Vote Smart.” A full list of the groups we used can be found in Table S4 in the SI. Higher values imply the MC cast more votes that are consistent with the mission of these groups. Column averages for Democratic and Republican women can be found in the “Groups” section.

Of course, interest groups associated with women tend to skew Democratic, so our results are likely to be stronger for female Democratic MCs. As expected, when we conducted separate tests for each party, we found a statistically significant difference between the average vote scores for the most and least emotionally intense Democratic women (t = 2.83, df = 39, p ≤ 0.01). These results did not hold for Republican women (t = 0.14, df = 7, p > 0.05), although we note that there are far fewer observations in this category. Moreover, although falling outside of the conventional bounds of statistical significance, the most intense Republican women are still rated higher than the least intense women. Our strong findings for Democratic women—and suggestive results for Republican MCs—together offer additional support for our assertion that changes in vocal pitch can be used to capture legislators’ emotional intensity around a given issue.

To further validate this claim, on pages S81–S84 in the SI, we interact our vocal pitch measure with the MC’s average women’s interest group rating. Here too we find that female lawmakers who tend to vote in the preferred direction of women’s interest groups (as defined by Project Vote Smart) also speak with greater emotional intensity when talking about women as compared with their speech on other topics. We also replicate this analysis on pages S80–S81 in the SI with a second measure of legislative activity on behalf of women: the number of women’s issue bills introduced as defined by Volden, Wiseman, and Wittmer (Reference Volden, Wiseman and Wittmer2018). We find that Congresswomen who introduce more women’s bills also tend to speak with higher vocal pitch when referencing women than when speaking about other issues. These results are not only consistent with our broader argument but also demonstrate that vocal pitch may yield additional insights when used in conjunction with more traditional measures of substantive representation.^{Footnote 19}

Vocal Pitch and Partisan Issue Ownership

Our work on female lawmakers suggests that we will observe heightened vocal pitch among MCs who have a broader commitment to the policy under discussion. As a further exploration of this claim, we turn now to an analysis of the speaking behavior of partisans in the US House on issues that are traditionally “owned” by the Democratic and Republican Parties. If emotional intensity is associated with legislators’ issue commitments, then we should observe Democrats in Congress speaking with heightened pitch on traditionally Democratic issues as compared with both Republican issues and also those that are owned by neither party. Likewise, we expect Republicans to express greater emotional intensity when speaking about topics that are especially important to their party. To assess this claim, we examine variations in vocal pitch above or below the speaker’s baseline on issues that are more likely to be associated with Democrats and Republicans.

Using a 30-topic structural topic model (STM) to categorize the content of legislative speeches (see pages S30–S32 in the Supplemental Information for more information), we select a number of topic areas that correspond to widely acknowledged partisan-owned issues. For Democratic-owned issues, we identify nine topic categories that generally correspond to broader issues related to social welfare, land management/infrastructure, and civil rights. For Republican-owned issues, we identify nine topic categories that generally correspond to defense, immigration, and budget/tax policy.^{Footnote 20} Although it is beyond the scope of this article to resolve debates about which issues are owned by the respective parties (e.g., Petrocik Reference Petrocik1996; Petrocik, Benoit, and Hansen Reference Petrocik, Benoit and Hansen2003), these topics are widely recognized by the general public as Democratic and Republican issues (Goggin and Theodoridis Reference Goggin and Theodoridis2017).

As a validation of our identification of Democratic- and Republican-owned issues, we should find that legislators’ speeches contain more language that falls under their party’s owned issues. Indeed, we find that Republicans talk more about Republican-owned issues as compared with Democrats (t = 30.51, df = 74,135, p < 0.001), and Democrats talk more about Democratic-owned issues than Republicans (t = 18.43, df = 74,047, p < 0.001). This corresponds to Democrats using 9.02% more words—and Republicans using 17.21% more words—related to their respective party-owned issues. Although these differences may seem relatively small, the topics we extract from our STM capture less than 8% of the words in a given speech on average. This is because STMs—like other forms of Latent Dirichlet Allocation (LDA) topic modeling—view speeches as mixtures of several “topics,” many of which are not issues but rather reflect different types of speaking styles (e.g., being more collegial or deferential). Words from a single issue are thus rare as compared to the entire corpus of words used in a speech.

Table 4 displays our results for changes in vocal pitch for Democratic- and Republican-owned issues. The positive and statistically significant interaction term in Models 4.1 and 4.2 indicates that Democrats speak with higher vocal pitch when speeches reference more Democratic-owned issues. Conversely, the statistically significant interaction term in Models 4.3 and 4.4 indicates that Republicans speak with a higher vocal pitch when speeches reference more Republican-owned issues. Moreover, the negative main effects for Democratic- and Republican-owned issues suggests that Democrats and Republicans speak at a significantly lower vocal pitch when referencing issues owned by the opposing party. On pages S20–S22 in the Supplemental Information, we further show that MCs who are closest to their party’s median voting behavior (as measured by DW-NOMINATE scores) are more emotionally intense on their party’s issues as compared with legislators who are further from the party median.^{Footnote 21}

TABLE 4. Partisans Talk about Party Issues with Greater Intensity

Note: The dependent variable is the speaker’s vocal pitch in standard deviations above or below the speaker’s baseline. In Panel A, we consider whether Democrats tend to raise their vocal pitch when speaking about Democratic-owned issues. In Panel B, we consider whether Republicans tend to raise their vocal pitch when speaking about Republican-owned issues. The issues themselves are derived from the Structural Topic Model (STM) outlined on pages S30–S32 in the Supplemental Information. All models are multilevel linear regressions and include randomly varying intercepts for each member of Congress. Levels of significance are reported as follows: *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors are reported in parentheses.

Petrocik, Benoit, and Hansen (Reference Petrocik, Benoit and Hansen2003) argue that partisans have more credibility when talking about their party’s issues in part “because typical Democrats (or Republicans) believe in the concerns of their party” (602). This argument is consistent with our results, as Democrats and Republicans speak with more emotional intensity when discussing their party’s core issues as compared with those owned by the opposition. Moreover, the slight changes we isolate are also consistent with vocal pitch being an indicator of a lawmaker’s emotional disposition toward an issue. Policy topics that are central to a party’s identity should elicit a stronger emotional reaction from party members, which is exactly what we observe in the rising vocal pitch of Democrats and Republicans in Congress when discussing issues owned by their parties.^{Footnote 22}

Heightened emotional intensity thus appears to correspond to policy commitments. Female MCs who speak at higher standard deviations above their baselines when referencing women also demonstrate their commitment to women through their voting behavior (as measured by interest group ratings). And, Democrats and Republicans speak on issues owned by their party with greater intensity than on other issues. Together, these supplementary analyses provide further evidence consistent with our claim that a legislator’s vocal pitch signals her emotional intensity about the issue she is discussing.

Amount and Intensity of Men’s Speech on Women

To examine men’s response to female MCs’ speech on women, we first extend our main analysis to consider men’s willingness to address women in their floor speeches (see Table 5). In Models 5.1 and 5.2, the dependent variable is whether a given Congressman’s floor speech contained one of Pearson and Dancey’s (Reference Pearson and Dancey2011b) women’s dictionary terms. Our primary independent variables are (1) the number of speeches delivered by women on a given legislative day that reference women (Female Speeches), and (2) the average vocal pitch of those speeches (Female Pitch). We are thus interested in the interaction between Female Speeches and Female Pitch. A positive and statistically significant interaction term would be consistent with male MCs becoming more likely to mention women when female MCs deliver a large number of speeches about women at an increased vocal pitch.

TABLE 5. The Quantity and Intensity of Women’s Speech Affects the Quantity of Men’s Speeches about Women

Note: Dependent variable equals 1 if the speech included any of the Pearson and Dancey (Reference Pearson and Dancey2011b) women’s dictionary terms, 0 otherwise. These models report the results from a multilevel logistic regression. All models also include a randomly varying intercept for each member of Congress. Levels of significance are reported as follows: *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors are reported in parentheses.

We restrict our analysis to male MCs who delivered speeches on the same legislative day as female speeches about women for which we have vocal pitch data. This restriction allows us to focus on those male legislators who are most likely to have been influenced by Congresswomen’s behavior.^{Footnote 24} We also include two additional controls for the types of issues being debated on a given legislative day. First, we want to isolate the impact of female MCs’ speech from the general effects of women’s heightened issue activity. To do so, we draw on Volden, Wiseman, and Wittmer’s (Reference Volden, Wiseman and Wittmer2018) “women’s issues” measure to include a variable counting the number of bills debated in their six “women’s issues” categories.^{Footnote 25} This provides a general proxy for whether issues highly salient to women in Congress appeared on the day’s agenda. Second, we are concerned that both male and female MCs may speak with greater emotional intensity when important issues are being debated. In these instances, we would expect the vocal pitch of male and female legislators to be heightened, leading to the impression that women’s speech is influencing male behavior when in reality both groups are responding to the importance of the issue itself. To account for this possibility, we use CQ Weekly’s “Bills to Watch”^{Footnote 26} to create a count variable of the number of major bills debated on each legislative day.^{Footnote 27}

Our results are in line with what we would expect if women’s speeches influence male legislators’ discussion of women. Although the interaction term in Model 5.1 is not statistically significant at the 0.05-level, calculating predicted probabilities suggests that vocal pitch can have a substantively meaningful effect on whether a male MC mentions women in his speech, particularly when large numbers of female MCs give emotionally intense speeches. Introducing controls (see Model 5.2) strengthens this interaction effect and also indicates that this relationship holds even after accounting for individual speakers’ characteristics and the types of bills on the agenda.

To assess the substantive significance of our finding, Figure 1 plots the predicted values from Model 5.2. The x-axis shows the range of our Female Speeches variable (from 0 to 43). The y-axis plots the likelihood that a male MC’s speech includes at least one Pearson and Dancey (Reference Pearson and Dancey2011b) term. We show average Female Pitch set to 2 standard deviations above and below its baseline in the solid and dashed lines, respectively.

FIGURE 1. The Quantity and Intensity of Women’s Speech Affects the Quantity of Men’s Speeches about Women

Note: Predicted male speaking behavior from Model 2 in Table 5 holding all other variables constant. Solid and dashed lines indicate Female Pitch was set to two standard deviations above (1.41) and below (−1.28) the mean, respectively. On the x-axis, Female Speeches is allowed to vary from its minimum (0) to maximum (43). The y-axis is the probability that the male speech included any of the Pearson and Dancey (Reference Pearson and Dancey2011b) women’s dictionary terms. The gray ribbons represent 90% confidence intervals. The 95% confidence intervals overlap until the x-axis reaches approximately 25 speeches.

From this figure, it is clear that as more female MCs take to the floor to give emotionally intense speeches about women, their male colleagues become more likely to mention women in their own speeches. For example, when Female Pitch is two standard deviations above its mean, going from the minimum number of female speeches referencing women (0) to the maximum (43) results in an increase in the likelihood that male MCs mention women from 0.05 to 0.56. Yet, it is important to note that such an effect only occurs when the number of female speeches becomes quite large. Going from one standard deviation below (6) to one standard deviation above (11) the mean number of female speeches, yields only a 3% gain in the likelihood of a male MC referencing women (from 0.07 to 0.10). This underscores the hurdles female lawmakers face when trying to advance women’s issues in the US House of Representatives.^{Footnote 28}

We next turn to investigating whether women’s speeches might also influence the emotional intensity of male speeches referencing women (Table 6). Here, the main dependent variable is the vocal pitch of male MCs who spoke on the same day as women, scaled to standard deviations above and below their baseline. We are primarily interested in the interaction between (1) the number of female speeches using any of the Pearson and Dancey (Reference Pearson and Dancey2011b)’s terms (Female Speeches), (2) the average vocal pitch of those speeches (Female Pitch), and (3) whether a male MC mentioned women (“Women” Mentioned). If women’s speeches increase not only the quantity, but also the emotional intensity, of male references to women, then we would expect this interaction term to be positive and statistically significant.

TABLE 6. The Quantity and Intensity of Women’s Speech Affects Men’s Vocal Pitch

Note: The dependent variable is the speaker’s vocal pitch in standard deviations above or below the speaker’s baseline. Levels of significance are reported as follows: *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors are reported in parentheses.

In Model 6.1, the interaction term is positive and statistically significant at the 0.05-level. To help interpret this result, we present predicted values in Figure 2. When average female vocal pitch is set to two standard deviations above the mean (1.41), increasing the number of female speeches mentioning women from one standard deviation below the mean (6) to one standard deviation above the mean (11) raises men’s predicted vocal pitch when talking about women from 0.17 standard deviations to 0.26 standard deviations above their baseline. Increasing the number of female speeches mentioning women from the minimum (0) to the maximum (43) raises men’s predicted vocal pitch when talking about women from 0.12 standard deviations to 1.79 standard deviations above their baseline. This suggests that when female MCs deliver a large number of speeches on women with higher vocal pitch, male MCs are not only more likely to mention women but also do so with increased emotional intensity.^{Footnote 29} These results hold even after the addition of a number of controls for legislator characteristics and the legislative agenda (see Table S14 on page S37 in the Supplemental Information). Together, this provides strong suggestive evidence of the link between female MCs’ emotionally intense speeches about women and male MCs’ speaking behavior.

FIGURE 2. The Quantity and Intensity of Women’s Speech Affects Men’s Vocal Pitch

Note: Predicted vocal pitch derived from Model 2 in Table 6 holding all other variables constant. Solid lines indicate the speech included at least one of the Pearson and Dancey (Reference Pearson and Dancey2011b) women’s dictionary terms. Dashed lines indicate all other speeches. For a given legislative day, Female Speeches is the total number of female speeches that used any of the Pearson and Dancey (Reference Pearson and Dancey2011b) women’s dictionary terms and Female Pitch is the average vocal pitch of those speeches. Female Speeches is allowed to vary from the minimum (0) to maximum (43), whereas Female Pitch is set to two standard deviations above (1.41) and below (−1.28) the mean in the right and left panel, respectively. The gray ribbons represent 95% confidence intervals.

Voting Behavior

Our results thus far are consistent with male MCs talking more about women, and with greater emotional intensity, when female lawmakers deliver a large number of emotionally intense speeches referencing women. On the one hand, these findings may represent (some) male legislators’ desire to speak to women’s concerns. In both judicial settings (Boyd, Epstein, and Martin Reference Boyd, Epstein and Martin2010)—and in deliberation more generally (Karpowitz and Mendelberg Reference Karpowitz and Mendelberg2014, 288)—when women talk, men listen. This can, in turn, alter group behavior and decisions. On the other hand, men’s behavior could also reflect a backlash against female MCs’ speeches. Past research has shown that as women become more prevalent in legislatures, male politicians act to minimize their influence to maintain dominance (Heath, Schwindt-Bayer, and Taylor-Robinson Reference Heath, Schwindt-Bayer and Taylor-Robinson2005; Kanthak and Krause Reference Kanthak and Krause2012; Krook Reference Krook2015), including becoming more aggressive and controlling of deliberation (Kathlene Reference Kathlene1994). In this way, when female MCs take to the floor to deliver emotionally intense speeches, it could signal to male legislators that their dominance is under threat and thus result in an adverse reaction. We provide a preliminary examination of men’s voting behavior to investigate whether changes in male MCs’ speaking patterns can be better characterized as supportive of, or a backlash against, women’s speech. To do so, we construct a measure of whether those Congressmen referencing women became more or less likely to vote with female MCs’ who spoke about women on the House floor.

Estimating the relationship between women’s speech and men’s voting behavior is inherently difficult, as male legislators may be more likely to vote with female MCs who gave emotionally intense speeches about women simply because of shared ideology or partisanship.^{Footnote 30} To overcome this challenge, our dependent variable in the models presented in Table 7 scales the proportion of male MC votes cast with female speakers based on their previous shared voting behavior. Our measure includes three components: (1) the proportion of votes male MCs cast in the same direction as the female speakers on a given legislative day, (2) the proportion of votes those male MCs typically cast with those same female speakers on all previous legislative days, and (3) the degree to which those proportions vary. Combining these three pieces of information yields a standardized measure where positive values indicate that male speakers were more likely to vote in line with female speakers than their past voting history would predict. Negative values indicate that male speakers were less likely to vote in line with female speakers, and would thus provide evidence of a backlash effect among those men who spoke with emotional intensity about women. A detailed working example can be found on pages S36–S41 in the Supplemental Information. We also provide alternative model specifications that yield similar results on pages S41–S42.^{Footnote 31} Likewise, a dyadic estimation of this relationship (and accompanying placebo tests) can be found in Section S8.2 of the Supplemental Information which starts on page S55.

TABLE 7. The Quantity and Intensity of Women’s Speech Affects Men’s Voting Patterns

Note: Outcome is the proportion of time male MCs voted with women, as described on page Voting Behavior. Levels of significance are reported as follows: *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors are reported in parentheses.

The results of our model predicting male voting behavior can be found in Table 7. Because our primary variable of interest is the interaction term between Female Speeches and Female Pitch, we plot predicted percentage of co-voting in Figure 3. Model 7.2 shows a positive and statistically significant relationship between this interaction and the percentage of votes male MCs cast with female speakers. This provides evidence consistent with male legislators becoming more likely to vote with their female counterparts when women give a large number of emotionally intense speeches about women. When female MCs’ average vocal pitch is set to two standard deviations above the mean (1.41) and women’s speeches on women range from the minimum (0) to maximum (43), the rate at which male MCs vote with women on a given day increases from 0.23 standard deviations below to 0.53 standard deviations above what we would expect given past voting behavior.^{Footnote 32}

FIGURE 3. The Quantity and Intensity of Women’s Speech Affects Men’s Voting Patterns

Note: Predicted male voting behavior from Model 2 in Table 7 holding all other variables constant. Solid and dashed lines indicate Female Pitch was set to two standard deviations above (1.41) and below (−1.28) the mean, respectively. On the x-axis, Female Speeches is allowed to vary from its minimum (0) to maximum (43). The y-axis has the percentage of time the male MC voted with women, as described on page Voting Behavior. The gray ribbons represent 95% confidence intervals.

Together, these results hint at the importance of women’s collective speaking efforts. In the 111th–113th Congresses, there were between 77 and 81 women in the US House. The maximum number of female speeches we observe in our data (43) thus represents 56% of all women in the legislature taking to the floor to reference women. When we observe this high degree of women’s participation, our models suggest that we are also likely to see men talk more (and with greater intensity) about women. And, our preliminary evidence indicates that they may be more likely to vote in line with those female speakers. This suggests that increasing women’s descriptive representation in legislatures, particularly by electing female candidates who are champions of women, could help female representatives further advance the interests of women among their male colleagues.

At the same time, two caveats are in order. First, given the observational nature of our data, we do not claim that these findings reflect a causal relationship. Indeed, our aim is not to make claims about the effect of female MCs’ emotionally intense speeches about women on men’s voting behavior, but instead to simply determine whether men’s heightened vocal pitch represents a backlash effect. Our examination of voting behavior yields no evidence of male backlash, which suggests that when Congresswomen speak with greater intensity about women, they do not encounter any immediate detrimental consequences. More generally, although we find an association between women’s speeches and male MCs’ behavior—and present dyadic models and placebo tests that lend further support to this relationship (see Section S8.2 of the Supplemental Information)—legislators’ speeches on women simply cannot be randomly assigned. We thus cannot definitively rule out alternative explanations for this relationship.

Second, it is important to note that at lower levels of female floor participation, we see no positive (and sometimes negative) estimates of the effect of women’s emotionally intense speech. This finding in some ways echoes the broader literature on gendered speaking behavior. Research focusing on ordinary citizens finds that women speak in a way that is characterized as more feminine than men, which includes speaking with more emotional content (Hogg Reference Hogg1985). Importantly, in mixed-gender settings, women often speak less than men (Karpowitz and Mendelberg Reference Karpowitz and Mendelberg2014) and are perceived as less influential than their male counterparts (Carli, LaFleur, and Loeber Reference Carli, LaFleur and Loeber1995). Although there are important differences in elite women’s speaking behavior as compared to ordinary citizens, our results suggest that when speaking alone, or in small numbers, Congresswomen may also find it difficult to achieve the standing and influence necessary to affect their male colleagues’ behavior. Building on our preliminary findings, future work should thus explore the broader consequences of women’s collective legislative efforts.