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Beyond bipartisan support: analyzing the TikTok ban votes in the U.S. house of representatives

July 2024
Global Public Policy and Governance 4(2)

Authors:

Prince of Songkla University

The U.S. House of Representatives made a significant move by passing legislation on the TikTok ban ‘twice’ in less than 40 days. The passed legislation requires that ByteDance—its China-based parent company—divest from TikTok within 270 days. Despite overwhelming bipartisan support, this research aims to provide evidence that there is significant variation in legislator voting behavior attributable to partisanship, ideology, and demographic factors. This study employs logistic regression to analyze the voting decisions of 417 legislators in the first vote and 418 in the second. The results showed that partisanship had a strong effect on the first vote, with Republicans in red states more likely to support the ban. However, the second vote saw a decline in party influence, with white legislators from both parties emerging as key proponents of the ban. Across both votes, the strongest predictor was ideology, with ideological extremists on either the conservative or liberal side more likely to vote against the ban. Younger but longer-tenured legislators as well as non-lawyers also showed a higher likelihood of opposing the ban. This study contributes to the discourse on congressional voting in the context of evolving geopolitical and technological landscapes between the U.S. and China.

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Beyond Bipartisan Support:

Analyzing the TikTok Ban Votes in the U.S. House of Representatives

Dr. Wisanupong Potipiroon

Associate Professor, Prince of Songkla University

potipiroon@gmail.com, wisanupong.p@psu.ac.th

Wisanupong Potipiroon is Associate Professor at the Faculty of Management Sciences, Prince of

Songkla University in Thailand and has served as Director of the PhD Program in Management

since 2015. He conducts research on leadership, work motivation, and public-sector corruption.

His work can be found in several leading journals in public management and organizational

behavior. He currently serves on the editorial boards of Review of Public Personnel

Administration, Public Personnel Management, Asia Pacific Journal of Public Administration, and

International Review of Public Administration.

Citation:

Potipiroon, W. (2024). Beyond bipartisan support: analyzing the TikTok ban votes in the US house

of representatives. Global Public Policy and Governance, 4(2), 197-223.

*Requests for reprints should be addressed to Wisanupong Potipiroon; at e-mail:

potipiroon@gmail.com

Beyond Bipartisan Support:

Analyzing the TikTok Ban Votes in the U.S. House of Representatives

Abstract

The U.S. House of Representatives made a significant move by passing legislation on the

TikTok ban ‘twice’ in less than 40 days. The passed legislation requires that ByteDance—its

China-based parent company—divest from TikTok within 270 days. Despite overwhelming

bipartisan support, this research aims to provide evidence that there is significant variation in

legislator voting behavior attributable to partisanship, ideology, and demographic factors. This

study employs logistic regression to analyze the voting decisions of 417 legislators in the first vote

and 418 in the second. The results showed that partisanship had a strong effect on the first vote,

with Republicans in red states more likely to support the ban. However, the second vote saw a

decline in party influence, with white legislators from both parties emerging as key proponents of

the ban. Across both votes, the strongest predictor was ideology, with ideological extremists on

either the conservative or liberal side more likely to vote against the ban. Younger but longer-

tenured legislators as well as non-lawyers also showed a higher likelihood of opposing the ban.

This study contributes to the discourse on congressional voting in the context of evolving

geopolitical and technological landscapes between the U.S. and China.

Keywords: TikTok ban; congressional voting; legislative behavior; partisanship; ByteDance;

Communist Party of China (CPC).

1. INTRODUCTION

The U.S. House of Representatives made a significant move by passing legislation on the

TikTok ban ‘twice’ in less than 40 days—first on March 13 and again on April 20, 2024. Both

instances saw overwhelming bipartisan support, with decisive votes of 362-65 and 360-58,

respectively. This legislation—later passed overwhelmingly by the Senate on April 23 and signed

into law on the very next day by President Joe Biden—could lead to a nationwide ban of TikTok

unless its China-based owner—ByteDance—divests itself from the company within 270 days.

While the swift action by Congress and the executive branch reflects legitimate concerns about the

app’s data governance as well as its impact on the U.S. national security (Clausius, 2022) and

American youth (Amnesty International, 2024; Cao, 2023; Weimann & Masri, 2023), it also raises

questions about its potential impact on 170 million users, 5 million small businesses, and 7,000

employees in the U.S. (Deloitte, 2024; Francis, 2024) as well as First Amendment rights (Joukov,

2022). Furthermore, the ban has a broader implication for the geopolitical tensions between the

U.S. and China (Bouvier et al., 2024; Minghao, 2020), which have escalated since the start of the

trade war under the Trump administration in 2018 (Liu & Woo, 2018; Steinbock, 2018).

The cross-party agreement on banning TikTok reflects a longstanding trend in U.S. foreign

policy (Tama, 2024), which indicates that Congress tends to be more unified on international

security matters (Bryan & Tama, 2022; McCormick & Wittkopf, 1990; Prins & Marshall, 2001)

and issues concerning China (Smeltz, 2022). Nonetheless, this study aims to demonstrate that,

despite the overwhelming bipartisan support, there exists significant variation in legislative

behavior.

Specifically, this research aims to address two key research questions: First, did

This study focuses exclusively on the two votes by the U.S. House of Representatives, omitting the Senate

vote, due to the bill’s inclusion in a larger foreign aid package passed by Congress. However, analyzing the

second vote by the House is deemed valuable as it provides insights into any developments or changes in

voting behaviors across the two bills.

Republicans and Democrats exhibit distinct voting patterns on the ban, despite the lopsided nature

of the vote? Secondly, what factors influenced legislators from both sides of the aisle to deviate

from their party’s position on this issue? This inquiry not only contributes to the scholarly

discourse on congressional voting (Ansolabehere et al., 2001; Bryan & Tama, 2022; Jessee &

Theriault, 2014; McCormick & Wittkopf, 1990; Snyder Jr & Groseclose, 2000) especially on

China trade policy (Galantucci, 2015; Kuk et al., 2018; Seo, 2010; Smeltz, 2022; Xie, 2006), but

also has important implications for the evaluation of American democracy on the global stage in

the context of evolving technological and geopolitical landscapes (Steinbock, 2018).

The above research questions are predicated on two related yet competing arguments:

partisanship and ideological preferences (Ansolabehere et al., 2001; Jenkins, 2006; McCormick &

Wittkopf, 1990). To date, there is still much debate over partisanship or ideology is more important

in explaining legislative voting (Ansolabehere et al., 2001; Jenkins, 2012; Snyder Jr & Groseclose,

2000), especially in American foreign policy (Friedrichs & Tama, 2022) as well as specific policy

domains (Bendix & Jeong, 2022; Bryan & Tama, 2022; Myrick, 2021; Xie, 2006). Part of the

ambiguity is due to the changing nature of these factors across different Congresses and policy

issues (Snyder Jr & Groseclose, 2000). The first argument suggests that political parties matter the

most for explaining congressional voting behavior (Cox & McCubbins, 1993, 2004). Partisanship

reflects not only how parties vote on key issues but also broader party polarization within the

electorate (Ansolabehere et al., 2001). Nonetheless, Krehbiel (1993) challenged the ‘strong

congressional party’ hypothesis, arguing that, for party influence to be significant, it must be

observed independently of legislators’ own personal preferences. The underlying assumption is

that each legislator has an ideological position, often referred to as an ideal point, that dictates his

or her voting behavior (Jessee & Theriault, 2014). As will be discussed below, this study contends

that ideological extremists on either the conservative or liberal side are more likely to oppose the

ban. While partisanship and ideology tend to be highly correlated (Barber & Pope, 2019; Thomsen,

2014), empirical evidence indicates that legislators often hold ideological views that do not fully

align with their partisan affiliation (see McCarty, 2016 for a discussion).

This study also aims to contribute to this body of work by showing that demographics such

as gender, age, tenure, and race are important determinants of legislative decisions. Hambrick

(2007) indicates that demographic characteristics can serve as important proxies for the cognitive

base and values of the upper echelon. Although this theoretical perspective is generally not

explicitly employed by congressional politics scholars, the importance of legislator demographics

in roll-call voting is evident in various contentious policy areas, including abortion (Daynes &

Tatalovich, 1984; Jenkins, 2012; Rolfes-Haase & Swers, 2022; Swers, 1998), gun control (Goel

& Nelson, 2024) and environmental policy (MacPepple, 2023).

To summarize, this research aims to shed light on the factors that can explain variation in

the votes on the TikTok ban in the U.S. House of Representatives. Across the two roll-call votes,

this study finds compelling evidence, both within and across parties, that congressional voting

choice on the ban was strongly associated with the relative ideological positions of the incumbent

legislators, even after accounting for the influence of party affiliation (partisanship) and

demographic factors. To date, the underlying factors driving the TikTok ban remain shrouded in

some mystery, and research on this timely topic is needed to shed light on this crucial geopolitical

and technological landscapes between the U.S. and China.

The following sections begin by setting the stage for the relationship between the U.S. and

China and provide background information on the TikTok ban. They then discuss the two

aforementioned theoretical perspectives and examine the roles of demographics in legislative

voting in greater detail. These lay the groundwork for the formulation of the study hypotheses.

2. THEORY AND HYPOTHESES

2.1 Tensions between the U.S. and China

The relationship between the U.S. and China has seen its share of ups and downs, but

tensions have notably heightened in recent years. This has led some to liken the current state of

affairs to a new Cold War (Harris & Marinova, 2022; Layne, 2020; Rachman, 2020). Scholars in

the U.S. largely depict China as reminiscent of the Soviet Union—a formidable, hostile,

communist, and expansionist power that poses a threat to the global order (Harris & Marinova,

2022, p.337). This characterization is supported by China’s increasing technological and military

prowess. The military gap between the U.S. and China has narrowed considerably, while

technological competition and cybersecurity tensions have intensified. The U.S. perceives China

as aiming to diminish its influence in the Western Pacific, evidenced by China’s assertive military

presence in the South China Sea and its actions towards Taiwan and Hong Kong (Rachman, 2020).

Conversely, China sees the U.S. as resistant to an increasingly multipolar world and suspects it of

subverting its government (Rachman, 2020).

Trade relations between the U.S. and China have also been a source of contention, with the

U.S. adopting an aggressive stance. Liu and Woo (2018) offer three primary reasons behind the

U.S.-initiated trade war with China: (a) China’s perceived trade surplus impacting U.S. job

creation, (b) China’s alleged unfair technology acquisition practices, and (c) China’s perceived

threat to U.S. national security and global influence. In the aftermath of the Tiananmen Square

incident in 1989, Congress was deeply divided over whether to revoke or impose conditions on

China’s most favored nation (MFN) trade status, now known as normal trade relations (NTR) (Seo,

2010; Xie, 2006). What began as a concern over gross violation of human rights evolved into an

increasing focus on the trade deficit. Throughout the 1990 decade, the U.S. president and Congress

clashed over China’s trade status (Seo, 2010; Xie, 2006). In a significant turn of events in 2000,

Congress voted to grant China ‘permanent’ NTR (PNTR), paving the way for China’s accession

to the World Trade Organization (WTO). This decision led to China’s soaring export growth in

the course of the 2000s (Steinbock, 2018), prompting members of Congress from economically

vulnerable districts impacted by import competition to adopt a more confrontational stance

towards China (Kuk et al., 2018).

Tensions between the U.S. and China escalated further, largely driven by former President

Donald Trump’s ‘America First’ policy, which has undermined “the post-World War II liberal

international order that the U.S, and its allies had constructed in the postwar era” (Steinbock, 2018,

p.521). President Trump’s decision to impose a 25% tariff on Chinese imports in July 2018, in

response to a staggering 46% trade deficit in 2016–2017, marked a significant escalation in the

trade dispute, prompting a reciprocal response from China (Liu & Woo, 2018). President Trump

also intensified efforts to counter China’s global technological influence to purportedly safeguard

American innovation from the ‘Chinese IP [Intellectual Property] theft’ (Clausius, 2022;

Steinbock, 2018; Xuetong, 2020). In 2019, the Trump administration issued Executive Order

13873 to protect the information and communications technology (ICT) supply chain in the U.S.

from potential threats posed by entities controlled or influenced by foreign adversaries, which in

effect blocked companies like Huawei from expanding its operations in the U.S. (Rinehart, 2024).

President Trump’s attribution of blame to China for the COVID-19 outbreak also adds salt to the

wound, further straining the already tense relations between the two countries.

Given the evolving geopolitical and technological landscapes, U.S. leaders appear to adopt

a more assertive stance (Harris & Marinova, 2022) in an effort to reclaim postwar supremacy in

the face of China’s burgeoning influence on the global stage (Chang, 2023; Steinbock, 2018).

However, China also receives its share of criticism from foreign countries regarding its

cybersecurity protectionism (Clausius, 2022). This is exemplified by the Chinese government’s

decision to block American-owned sites such as Facebook, Twitter (now X), and Google services

in July 2009 through its Great Firewall (Zucchi, 2021). As discussed further below, the TikTok

ban marks another important development in the escalating tension between the U.S. and China.

2.2 The TikTok Ban

TikTok has rapidly risen to prominence as the world’s fastest-growing social media

company under ByteDance—a tech giant based in Beijing. In 2017, ByteDance acquired the

popular short-video app Musical.ly and integrated it into its new platform TikTok (Rinehart, 2024).

Launched in 2018, TikTok quickly soared in popularity. What initially started as a platform for

dance videos has now evolved into a diverse hub for various content genres, including news,

movies, travel, food, social and political commentary, and commerce. Increasingly, TikTok has

become the go-to app for online search, positioning it as a major competitor to traditional search

engines such as Google and Facebook (Huang, 2022).

The app’s user base surged during the COVID-19 pandemic, further solidifying its status

as a mainstream platform (Feldkamp, 2021). At present, TikTok boasts over 1 billion users

worldwide (Woodward, 2024), with the U.S. alone accounting for 170 million users, 5 million

small businesses, and 7,000 employees (Deloitte, 2024). Approximately half of TikTok’s user base

in the U.S. is under the age of 29, with 25% of active users aged 10-19 (Woodward, 2024). This

demographic group has raised significant concerns among U.S. lawmakers about children’s mental

health (Amnesty International, 2024) and data privacy (Cao, 2023). According to an independent

study conducted by Amnesty International (2024), almost half of the videos shown to children on

TikTok have potential mental health implications.

Beyond concerns about the app’s impact on youth, TikTok draws serious scrutiny over the

U.S. national security and data privacy issues (Clausius, 2022; Rinehart, 2024).

Under article 7

of the 2017 China National Intelligence Law, private companies based in China are mandated to

“assist and cooperate with the state intelligence work in accordance with the law” (Clausius, 2022;

Kwande, 2023). Simply put, any Chinese tech companies are obliged to share user data with the

Communist Party of China (CPC), when required. Arguably, TikTok collects personal data such

as usernames, dates of birth, phone numbers, and email addresses as well as location, IP address,

browsing, and search history to tailor content for users, raising fears that the CPC could exploit

the app to access Americans’ data or influence their views through propaganda and misinformation

(Kwande, 2023).

While the data collection is no more extensive than that of other American-

owned social media apps such as Facebook or Google, the key difference, as often argued by U.S.

lawmakers, is that TikTok is an apparatus operated by a foreign adversary (Bouvier et al., 2024;

Rinehart, 2024).

On August 8, 2020, the Trump Administration made an unprecedented move by issuing

Executive Order 13942 to ban TikTok from app stores and force its sale to a U.S. company

See Full Committee Hearing: “TikTok: How Congress Can Safeguard American Data Privacy and Protect

Children from Online Harms”, https://energycommerce.house.gov/events/full-committee-hearing-tik-tok-

howcongress

In a congressional hearing on March 23, 2023, TikTok CEO Shou Zi Chew said “TikTok has never shared,

or received a request to share, US user data with the Chinese government. Nor would TikTok honor such a

request if one were ever made.”

(Rinehart, 2024).

ByteDance challenged this order in Federal Court, where TikTok received a

preliminary injunction, allowing it to remain available on app stores operated by Apple and

Google.

President Biden later revoked Trump’s executive orders, replacing them with a broader

review of social media apps connected to foreign adversaries (Clausius, 2022). In Congress, six

bills were proposed, some specifically targeting TikTok, but none gained traction (Rinehart, 2024).

Nonetheless, on December 23, 2022, Congress passed the ‘No TikTok on Government Devices

Act,’ mandating the removal of TikTok from official government devices. Ten states also

implemented similar bans on government mobile devices, with Montana enacting the most

stringent measure, banning the app for all Montanans (Rinehart, 2024). However, a U.S. District

Judge issued a preliminary injunction on the Montana ban, citing constitutional violations (the

First Amendment).

Following years of various attempts by the U.S. lawmakers to ban TikTok, on March 13,

2024, the House successfully passed the bill, known as ‘the Protecting Americans from Foreign

Adversary Controlled Applications Act,’ with an overwhelming bipartisan support of 352-65.

The

legislation mandates TikTok to sever ties with its parent company within 180 days or face a

nationwide ban in the U.S. The bill, which would also affect other companies under the influence

of a US foreign adversary, would force Apple and Google to remove TikTok from their app stores

and require internet service providers to make TikTok inaccessible in the U.S. However, due to

the Senate’s deliberate pace, the House proposed another bill to the chamber on April 20, 2024.

Trump first indicated his consideration to ban TikTok in July, 2020 and implied that the ban “would be a

way of punishing China for the coronavirus” (Hamilton, 2020). It is also worth noting that Trump issued

another order (Executive Order 13942) to ban WeChat, an app owned by a China-based company.

MARLAND v. TRUMP, 2:20-cv-04597, (E.D. Pa. Nov 12, 2020) ECF No. 38,

https://www.courtlistener.com/docket/18454269/38/marland-v-trump/

See https://www.lawfaremedia.org/article/montana-judge-blocks-tiktok-ban

The bill was sponsored by Republican Representative Mike Gallagher from Wisconsin.

This bill, known as the 21st Century Peace through Strength Act (H. R. 8038), was placed within

the broader foreign-aid package as part of the National Security Act, 2024.

This second bill, which

was also passed by the House with a bipartisan vote of 360-58, gives ByteDance 270 days to divest

from the app. The Senate swiftly approved this latter legislation with a vote of 79 to 18 on April

23, 2024, and President Joe Biden signed it into law the following day (Lima-Strong, 2024).

The overwhelming support across both chambers is not entirely unexpected, as Congress

typically rallies behind the president on foreign policy in the presence of external threats (Myrick,

2021). Indeed, when it comes to China, there is a broad consensus in Congress that the U.S. must

maintain its global leadership role (Rachman, 2020). Tama’s (2024) analysis of U.S. congressional

votes from 1991 to 2020 provides further evidence for cross-party cooperation on foreign policy,

reinforcing the adage that ‘politics stops at the water’s edge.’ On national security matters,

lawmakers are also generally less swayed by constituent opinions, allowing them greater freedom

to align with the executive branch (Edwards, 1989). This dynamic may explain why Congress

resolutely passed the bill, despite declining public support for banning TikTok among American

adults and teenagers (Mcclain, 2023) and strong opposition from TikTok users (Deloitte, 2024).

2.3 Partisanship

Despite the bipartisan support, it is important to note that polarization has been a defining

feature of the U.S. congressional politics (McCarty et al., 2016; Mellow & Trubowitz, 2005; Neal,

2020; Porter et al., 2005) especially in the post-cold war era (McCormick & Wittkopf, 1990).

Evidence suggests that party influence was significant in both the House and the Senate in almost

all congresses over the period of 1871 to 1998 (Snyder Jr & Groseclose, 2000) with a notable

increase in party influences on final passage votes since the early 1990s (Jessee & Theriault, 2014).

The bill was sponsored by Republican Representative Michael McCaul from Texas.

In terms of U.S.-China relations, Xie (2006) find that House partisanship plays an important role

in determining whether China’s MFN status was renewed, with Republicans much more

supportive than Democrats.

Scholars indicate that parties play a prominent role in determining congressional voting

choice. Politicians tend to support their party positions because parties make it easier to achieve

their policy goals (Aldrich & Rohde, 1997). Evidence shows that lawmakers are often hesitant to

take policy positions that might antagonize party activists, campaign contributors, and core

supporters, as they rely on the support of these key constituencies for re-election (Cox &

McCubbins, 1993; Mellow & Trubowitz, 2005; Poole & Rosenthal, 2000). These strategic

considerations may thus motivate lawmakers to avoid voting for policy positions that might be

considered too soft or weak by their partisans (Mellow & Trubowitz, 2005). This understanding is

especially relevant as many voters tend to support political candidates from their preferred party

regardless of the candidates’ specific positions on policy issues (Shor & Rogowski, 2018).

Furthermore, one must consider that politicians may align with their party due to their allegiance

and commitment (Jenkins, 2012; Snyder Jr & Groseclose, 2000), as well as the desire to show

discipline and preserve party unity (Ansolabehere et al., 2001). Under this scenario, legislators

may vote along party lines even if they personally disagree with a policy.

According to Ansolabehere et al. (2001), one of the key principle of party politics is that

party influence should be strongest on issues that define parties. To illustrate, Snyder Jr and

Groseclose (2000) find strong party influence on roll-call voting in Congress from 1871 to 1998,

notably in budget resolutions, tax policy, social security, social welfare policy, and the national

debt limit. In contrast, party influence was less common on moral and religious issues and civil

rights, and entirely absent on topics like gun control (Snyder Jr & Groseclose, 2000). Likewise,

analyzing roll-call behavior in the 103rd, 104th, and 105th Congresses, Ansolabehere et al. (2001)

find that party effects were highest on key ‘party’ issues such as budgeting and taxation and lowest

on affirmative actions and gun control. Specifically, Democrat legislators tend to support more

progressive policy platforms, such as environmental protection (Fowler & Kettler, 2021;

McAlexander & Urpelainen, 2020) and humanitarian efforts (Hildebrandt et al., 2013). Democrats

also tend to advocate for higher taxes on the wealthy and corporations to fund social programs and

infrastructure development (Donovan & Bowler, 2022). Republican legislators, on the other hand,

generally advocate for conservative positions including pro-life policy (Annas, 2010), strong

national defense and immigration policy (Hammer & Kafura, 2022) and free trade (Xie, 2006).

Nonetheless, partisanship reflects not only the differences in how the two parties vote on

key issues but also the broader party polarization at the electorate level (Ansolabehere et al., 2001).

Accordingly, this study proposes that, apart from party affiliation (partisanship), political leaning

at the state level—to the extent that it reflects the views of the constituency—may also shape

legislator voting behavior on the TikTok ban. State political leaning refers to the political

orientation within a particular state (red [Republican] states and blue [Democrat] states). Mellow

and Trubowitz (2005) indicate that, in ‘red versus blue state’ politics, Republicans and Democrats

have become more geographically divided, with fewer independents, while there is greater unity

within each party around their presidential candidates since the 1970s. Likewise, Abramowitz and

Saunders (2008) show that red and blue states have become more sharply divided along party lines,

a division that is attributed more to religious beliefs than to class differences. Specifically, red state

voters tend to prioritize gun ownership rights and support wars more than those in blue states

(Abramowitz & Saunders, 2008). This polarized sentiment is also reflected in public opinion

especially on the TikTok ban, as polls show that Republicans are more likely than Democrats to

support the ban, highlighting a significant divide between party bases (Mcclain, 2023). This is

consistent with the 2020 Chicago Council Survey, which finds that while both Republicans and

Democrats have negative views of China, Republicans (67%) are significantly more likely than

Democrats (47%) to consider China a critical threat to the US (Smeltz & Kafura, 2020). This study

thus hypothesizes the following.

Hypothesis 1: Republican legislators are more likely to vote in favor of the TikTok ban in

comparison to Democrats.

Hypothesis 2: Legislators in red states are more likely to vote in favor of the TikTok ban in

comparison to those in blue states.

2.4 Ideological Preferences

Krehbiel (1993) challenges the ‘strong congressional party’ argument, emphasizing that

legislators' personal convictions also a crucial role. According to Ansolabehere et al. (2001), party

effects were present in only about 40% of roll calls. Furthermore, party exerts less influence in

policy areas of conscience (Ansolabehere et al., 2001) and high public visibility (Jessee &

Theriault, 2014; Milyo et al., 2000) especially during final (vs. procedural) passage voting (Carson

et al., 2014). According to Wagner and Gruszczynski (2018), even in periods with strong partisan

polarization, there is substantial within-party heterogeneity in the ideological orientations of

individual members of Congress. The underlying assumption is that each legislator has an

ideological position, often referred to as an ideal point, that dictates their voting behavior, subject

to some random errors (Jessee & Theriault, 2014). In this respect, Kalt and Zupan (1984) suggest

that legislators base their voting decisions on ideology for two main reasons: first, to advance their

beliefs for either the satisfaction of helping others or because they truly believe it is the right thing

to do, and second, as a convenient signaling mechanism when other political information is too

costly.

This study employs roll call voting records (DW-NOMINATE) for characterizing the

ideology of incumbent legislators (Poole & Rosenthal, 1997). Despite the need for establishing a

valid measure of legislators’ ideological beliefs independent of roll call voting (Jenkins, 2006),

McCarty (2016) indicates that NOMINATE provides a reasonable measure of ideology that

explains both within-party and within-state variation in legislators’ behaviors. In the area of

foreign policy, Bendix and Jeong (2022) find that legislator’s ideological preferences strongly

influence both their cosponsorship and voting decisions, with liberals favoring a limit on defense

spending and conservatives favoring a limit on foreign aid. Nonetheless, this current study does

not aim to test the hypothesis that conservatives are more likely to vote for the TikTok ban than

are liberals, or vice versa. Rather, this study aims to show that ideological extremists on either

political aisle (left or right) are more likely to oppose the ban.

Empirical evidence indicates that ideological extremists seek to signal their independence

from party leadership and agenda in order to appeal directly to a specific ideological group (also

known as ‘ideological grandstanding’) (Kirkland & Slapin, 2017). They tend to “care more about

their personal ideological brand and they are less beholden to the party message” (Kirkland &

Slapin, 2017, p.27). Ideological extremists achieve this by mobilizing voters who share their views

through media platforms or rallies to connect with this ideological base. To illustrate, research has

shown that legislators with strong ideological views are more likely to have a twitter account

(Peterson, 2012) and use more intense, partisan rhetoric (Morris, 2001). Furthermore, Wagner and

Gruszczynski (2018) find that ideological extremists in the House tend to receive more political

news coverage than ideological moderates, with extreme Republicans more likely to earn media

attention than extreme Democrats.

In contrast, ideological moderates, i.e., conservative Democrats and liberal Republicans,

are more likely to succumb to pressure from their party leaders especially when they hold the

majority because they rely on their party’s support to pass legislation and secure resources for their

constituents (Kirkland & Slapin, 2017; Snyder Jr & Groseclose, 2000). Bonica and Cox (2018)

also show that since the 1994 midterm election, ideological moderates have increasingly voted

with their party. This trend is attributed to the lack of incentives for moderates to align their votes

with their constituencies, as there are no penalties for doing so (Bonica & Cox, 2018). Wiseman

and Wright (2008) further show that the median legislator—who represents the midpoint of the

ideological spectrum in the House (Krehbiel, 1993)—tends to align more closely with the policy

positions of the majority party than with those of the minority party. Even without explicit

influence from the majority party organization, if legislative decisions were based solely on the

preferences of the median legislator, policy outcomes would still tend to favor the majority party

(Wiseman & Wright, 2008). This suggests that ideological moderates, on both sides of the aisle,

are more likely to support the majority party’s policy agenda and be open to compromise. This

study thus hypothesizes the following.

Hypothesis 3: Legislators with strong ideological views on either side of the political aisle

are more likely to vote in favor of the TikTok ban.

2.5 Legislator Demographics

Upper echelons theory (UET) argues that the individual characteristics of the upper

echelons who are responsible for strategic decision making can “greatly influence their

interpretations of the situations they face and, in turn, affect their choices” (Hambrick, 2007,

p.334). Specifically, observable demographics such as age, gender, education, race, and

professional backgrounds can influence how individuals perceive reality, shaping their values,

cognitive base, personality traits, and other psychological factors (Anessi-Pessina & Sicilia, 2020).

This study focuses on various factors that hold relevance to the decision on the banning of TikTok

including gender, age, tenure, race, and lawyer status.

First, feminist scholars indicate that gender is a significant factor in shaping attitudes and

perceptions of legislators, leading to differences in their policy preferences and voting decisions

both at the state level (Hogan, 2008) and within Congress (Frankovic, 1977; Frederick, 2010;

Norton, 1999). To illustrate, evidence indicates that congresswomen are more supportive of

abortion (Daynes & Tatalovich, 1984; Jenkins, 2012; Rolfes-Haase & Swers, 2022; Swers, 1998)

and firearm control (Goel & Nelson, 2024; Price et al., 2002). Studies have shown that women

legislators tend to hold different national security policy preferences than men, with greater

representation by women in the legislature resulting in a decrease in conflict in foreign policy (see

Koch & Fulton, 2011 for further discussion). Accordingly, this study proposes that gender may

influence individuals’ perceptions of national security and play a role in how legislators,

particularly women, decide to vote on the TikTok ban. This study thus hypothesizes the following.

Hypothesis 4: Female legislators are more likely to vote against the ban.

Given that a significant portion of TikTok users are from younger generations (Woodward,

2024), age could be another important factor that affects legislators’ positions on the ban. This

could be due to differences in digital literacy (Jin et al., 2020) as well as attitudes toward social

media (Keating et al., 2016). While any lawmaker should prioritize national security (Clausius,

2022), younger legislators might be more receptive to solutions that address national security

concerns without a complete ban, compared to their older counterparts. Thus, they may vote

differently than their party members. Research has shown that millennial congressman tend to be

supportive of progressive policy such as environmental protection (MacPepple, 2023). This study

therefore hypothesizes the following.

Hypothesis 5: Older legislators are more likely to vote in favor of the ban.

Legislators with longer tenure typically enjoy greater job security and are expected to have

relatively more freedom to vote based on their ideological preferences (Zupan, 1990). Indeed,

research suggests that tenure acts as a kind of ‘brand name’ for legislators (Lott Jr, 1986). Long-

tenured legislators have cultivated relationships with the media, taken varied public stances, and

accrued individual influence over time (Wagner & Gruszczynski, 2018). Analyzing the House roll

call votes over China trade policy from 1990 through 2000, Seo (2010) find that senior members

of Congress who are more electorally secure tend to switch their policy positions more often.

Consequently, the relationship between tenure and voting behavior may not be straightforward.

Nonetheless, given their relative security in office, it is expected that longer-tenured legislators are

less likely to be influenced by their party positions and thus are more likely to vote against the ban.

This study therefore hypothesizes the following.

Hypothesis 6: Longer-tenured legislators are more likely to vote against the ban.

This study also considers the possibility that racial backgrounds could influence legislators’

views on the TikTok ban. The debates surrounding the TikTok ban have brought to light issues of

racism toward China (Fung, 2023; Minghao, 2020) and discrimination based on the identity of

TikTok’s parent company (Clausius, 2022), which echo the historical ‘yellow peril’ stereotype

within American patriotism (Kim, 2007). According to Danielson (2021, p.71), “U.S. domestic

racism directly and profoundly influences the conduct of U.S. foreign affairs.” To illustrate, the

questioning of TikTok CEO Shou Zi Chew by members of Congress raises significant concerns

about racial insinuations (Fung, 2023; Soo, 2024). These signs of xenophobia are not new: The

former President’s rhetoric and characterization of COVID-19 as the ‘Chinese Virus’ has

contributed to this narrative, framing China as the pandemic’s origin in a racially charged manner.

Based on a Pew survey conducted during the Trump’s era, American sentiments towards China

declined, with only 38% of Americans holding a favorable view of China, down from 44% in 2017

(Wike & Devlin, 2018). This consideration leads to the following final hypothesis.

Hypothesis 7: White legislators are more likely to vote in favor of the ban.

Lastly, this study considers whether being a lawyer-legislator is associated with the ban of

TikTok. Matter and Stutzer (2015) indicate that lawyers-legislators bring valuable legal expertise

and rhetorical skills, offering various benefits to Congress. Given the intricate legal nature of the

TikTok bill (Clausius, 2022; Kwande, 2023), lawyer-legislators may weigh the broader legal

ramifications of such legislation, potentially showing reluctance to endorse policies that could pose

legal risks to civil liberties and the First Amendment. This leads to the final hypothesis.

Hypothesis 8: Lawyer-legislators are more likely to vote against the ban.

3. DATA AND METHODS

The above hypotheses were tested using publicly available data collected from multiple

sources. Roll-call data were obtained directly from the website of the Clerk’s Office of the U.S.

House of Representatives. Demographic information was sourced from the official Congress.gov

website and also GovTrack.us as well as the Clerk’s office’s website. The author conducted the

entire data collection process, which resulted in two datasets that include factors at both the

legislator and state levels (see Table 1).

3.1 Measures

3.11 Dependent Variable

The dependent variable, votes on the TikTok ban, was dichotomous, coded as 1 if the

legislator voted in favor of the proposed ban and 0 if the legislator voted against it. The first vote

was based on legislators’ recorded votes during Roll Call 86 on Bill Number H.R. 7521 (Protecting

Americans from Foreign Adversary Controlled Applications Act) on March 13, 2024, at 10:33

AM in the 118th Congress, 2nd Session. The second vote was based on recorded votes during Roll

Call 145 on Bill Number H.R. 8038 (21st Century Peace through Strength Act) on April 20, 2024,

at 01:12 PM.

3.12 Independent Variables

Partisanship as assessed through party affiliation, with legislators from the Democrat Party

coded as 1 and those from the Republican Party coded as 0 (McCormick & Wittkopf, 1990; Snyder

Jr & Groseclose, 2000). State political leaning was determined by whether the legislator was from

a Democratic (blue) state (1) or a Republican (red) state (0), based on the outcomes of the 2020

presidential election. The latter data were collected from Ballotpedia.

Political ideology was measured using Poole and Rosenthal’s DW-NOMINATE scores,

which address how the U.S. legislators position themselves on policy issues and how much they

deviate from their party lines (Poole & Rosenthal, 2001). The scores are structured to represent an

ideological spectrum ranging from −1 (most liberal) to 1 (most conservative) with 0 serving as the

midpoint. Because the primary aim of this research is to assess ideological extremity on either end

of the political spectrum, the absolute value of the scores was used, irrespective of direction, with

1 indicating extreme ideology and 0 indicating moderate ideology (Peterson, 2012; Xie, 2006).

This study examines political leanings at the state level rather than the district level, as party affiliation

demonstrates a strong positive correlation of 0.83 with the 2020 presidential election outcomes in each

district.

Without this transformation, the NOMINATE scores would be almost perfectly correlated with party

affiliation (r > .91 in both samples). See also Xie (2006, p.753) for a discussion of this issue.

Despite criticisms that NOMINATE scores may capture factors beyond ideology including

partisanship and constituency interests, McCarty (2016) forcefully argue that the scores largely

capture ‘ideology-like substance’ that is relatively consistent across policy issues and over time.

This is supported by evidence of substantial within-party and within-state heterogeneity among

legislators with different NOMINATE scores (McCarty, 2016).

Other legislator-level variables include gender, age, tenure, race and lawyer status. Gender

was coded as dichotomous (1 = female and 0 = male). Age and tenure were recorded as continuous

variables (in years). Race was coded as dichotomous (1 = White; 0 = otherwise). Lawyer status

was also dichotomous, coded as 1 if the legislator holds a Juris Doctorate (J.D.) and 0 if otherwise.

This information was obtained directly from the official records of the Clerk’s office. Lastly, this

study considers TikTok’s popularity as a policy-specific factor. Legislators from states with higher

TikTok popularity may be less inclined to antagonize their support base. This variable was coded

ordinally (1 = low, 2 = moderate, 3 = high) and was measured using the data from Start.io.

This

data was based on an anonymized sample of 11 million Americans with Android phones, to

estimate the relative market share of TikTok.

--- Insert Table 1 right about here ----

3.2 Data Analytics

Logistic regression was performed in STATA Version 13.0 (StataCorp, 2012) with the

‘logit’ command. To address the potential non-independence of observations, the ‘VCE cluster’

command was used to calculate cluster-robust standard errors. Clustered standard errors account

for possible correlations in the error term between the votes of individual members within the same

states (Peterson, 2012). Odd ratios (OR) were used to interpret the coefficients, indicating the

See “TikTok popularity by U.S. state” at https://www.start.io/blog/tiktok-popularity-by-u-s-state/

(accessed on April 20, 2024)

probability of a legislator voting in favor of the TikTok ban. A series of logistic regressions were

performed to examine both the interparty (across-party) and intraparty (within-party)

heterogeneity on the TikTok ban votes. Pseudo R2 is calculated automatically in STATA. In terms

of data management, continuous variables including age, tenure, and ideological extremity were

found to be relatively normally distributed, with no extreme outliers. As a result, it was determined

that data transformation for these variables was unnecessary. Descriptive statistics of the sample,

bivariate correlations and logistic regression results are provided in the following sections.

4. RESULTS

4.1 Descriptive Statistics

Table 2 presents information on the two votes on the TikTok ban by party affiliation. The

first vote involved a total of 417 voting members (excluding those who did not vote, abstained, or

were not present). Among these, 352 voted ‘yea’ and 65 voted ‘nay’ on the bill. Of the 65 who

voted ‘nay,’ 50 were Republicans and 15 were Democrats. In the second vote, there were a total

of 418 voting members (excluding those who did not vote or were not present). Out of these, 360

voted ‘yea’ and 58 voted ‘nay’ on the bill. Of the 65 who voted ‘nay,’ 25 were Republicans and

33 were Democrats.

It is important to note that the composition of the voting members across

these two votes differed significantly, with 15 new voting members in the second vote, comprising

8 Democrats and 7 Republicans, whereas 14 legislators did not participate in this voting.

Notably,

while the majority of Democrats and Republicans remained unchanged in their positions on the

ban, 25 legislators (21 Democrats and 4 Republicans) became more accepting of the new bill. In

The study by Galantucci (2015) also examined a lopsided vote (342-77) on the passage of the Currency

Reform for Fair Trade Act H.R. 2378 in the 111th Congress.

Among those who did not participate in the second vote was North Carolina Representative Jeff Jackson

who lost more than 200,000 of his TikTok followers after he voted ‘yea’ in the first vote.

contrast, 15 Republicans became opposed of it. Other descriptive statistics are provided in Table

--- Insert Tables 2 and 3 right about here ----

4.2 Bivariate Correlations

In the first vote, all the independent variables, except for tenure and lawyer status, were

found to be significantly correlated with the voting. Females (r = -.097, p < .05), Democrats (r = -

.239, p < .01), and blue states (r = -.157, p < .01) show negative correlations, whereas age (r =

.092, p < .10), white (r = .161, p < .01) and TikTok popularity (r = .092, p < .10) showed positive

correlations. Interestingly, ideological extremity was not correlated with the voting. In the second

vote, only three variables were found to be significantly correlated with the voting including age

(r = .092, p < .10), white (r = .169, p < .01) and ideological extremity (r = -.265 p < .01).

--- Insert Table 4 right about here ----

4.3 Logistic Regression Analyses

Table 5 provides logistic regression results for the first vote. Model 1 examines the factors

influencing voting patterns across both parties. The result indicates that age (OR = 1.77, p < .01),

while tenure (OR = 0.942, p < .05) showed a negative relationship with supporting the ban.

Unexpectedly, being a lawyer-legislator (OR = 2.20, p < .10) showed a positive relationship.

Gender, race and TikTok popularity showed no significant relationships with the ban. Democrats

(OR = 1.33, p < .001) and blue state affiliation (OR = 0.549, p < .05) were negatively associated

with supporting the ban, indicating that Democrat legislators from blue states were less likely to

support the ban. Ideological extremity (OR = 0.003, p < .001) emerged as the strongest predictor,

indicating that more extreme ideological positions were associated with opposition to the ban. This

model explained 19% of the variance in voting behavior. Thus, Hypotheses 1, 2, 3, 5 and 6 were

supported, whereas Hypotheses 4, 7 and 8 were not supported.

Models 2 and 3 in Table 5 compare the voting patterns among Democrats and Republicans,

respectively. Ideological extremity emerged as the strongest negative predictor for both Democrats

(OR = 0.005, p < .001) and Republicans (OR = 0.001, p < .001), indicating that more extreme

ideological positions within either party were associated with opposition to the ban. As shown in

Models 2 and 3, the results also showed that age (OR = 1.07, p < .01), tenure (OR = .942, p < .05),

being a lawyer-legislator (OR = 2.20, p < .05) and blue state affiliation (OR = 0.373, p < .05) were

only significant for Democrats but not for Republicans. Overall, these two models explained 14%

and 11% of the variance in voting behavior, respectively.

--- Insert Table 5 right about here ----

Table 6 provides results for the second vote. Similar to the first vote, the result, which can

be seen in Model 4, indicates that age (OR = 1.071, p < .001) and being a lawyer-legislator (OR =

1.891, p < .10) were positively related with supporting the ban, while tenure (OR = 0.937, p < .05)

show a negative relationship. Gender and TikTok popularity showed no significant associations

with the ban. Blue state affiliation (OR = 0.549, p < .05) was negatively related with supporting

the ban. However, white (OR = 2.572, p < .05) became highly significant, suggesting that white

legislators more likely to support the ban. Furthermore, ideological extremity (OR = .000, p <

.001) emerged as the strongest predictor, whereas Democrats (OR = .420, p < .10) was marginally

significant. This suggests that partisanship waned in influence in the second vote. These results

were fairly consistent with the within-party analyses in Models 5 and 6. These three models

Further analyses revealed that gender influence alone—without other variables—was actually a

significant predictor in the first vote. Furthermore, TikTok’s popularity also emerged as a significant

predictor of the voting behavior.

explained 22%, 19% and 36% of the variance in voting behavior, respectively. Thus, Hypotheses

1, 2, 3, 5, 6 and 7 were supported, whereas Hypotheses 4 and 8 were not supported.

--- Insert Table 6 right about here ----

5. DISCUSSION

The TikTok ban casts a shadow over the growing geopolitical tensions between the U.S.

and China, which are already likened by some to a new Cold War (Minghao, 2020). Based on the

two roll-call votes, it is evident that the majority of legislators from both Republican and Democrat

Parties supported the ban on TikTok, showing a united front to the rest of the world. This

bipartisan support indicates a continued hawkish stance towards China from both parties in

Congress (see also Smeltz, 2022; Tama, 2024). Nonetheless, even with the lopsided nature of

the votes, this study observes significant variation in legislative behavior both within and across

parties. This current research not only responds to Friedrichs and Tama’s (2022, p.778) calls for

research on “the relative importance of interparty polarization and intraparty divisions [in

American foreign policy]” but also demonstrates that variation in legislator voting behavior can

be attributed to legislators’ demographic factors. This study also expands the work of Bendix and

Jeong (2022) on how ideological preferences and partisan calculations influence legislative

decisions related to defense and foreign aid spending by focusing on how partisanship (party

affiliation) and ideological extremity shape public opinion on the TikTok ban. To date, while many

studies have examined voting patterns across different congresses on various policy issues, the

current emphasis on a contemporary event offers further insights into how legislators make their

voting decisions on foreign policy issues (Friedrichs & Tama, 2022).

First, this study lends further credence to the political divide that has shaped legislative

behavior within Congress (Ansolabehere et al., 2001; Bryan & Tama, 2022; Jessee & Theriault,

2014; McCormick & Wittkopf, 1990; Snyder Jr & Groseclose, 2000). Despite the bipartisan vote,

there was a significant difference in support between Republicans and Democrats and between

those in red and blue states. Whether this reflects key party principles or pressure from constituents

(Ansolabehere et al., 2001), partisanship remains a key factor that determines legislative voting

especially on foreign policy (Bryan & Tama, 2022; McCormick & Wittkopf, 1990; Seo, 2010;

Xie, 2006). Although lopsided votes typically indicate minimal party influence due to

preconceived knowledge of voting outcomes (Snyder Jr & Groseclose, 2000, p.193), partisanship

still exerted a significant effect on the TikTok ban votes. Nonetheless, the fact that the second vote

saw less division along party lines highlights the evolving nature of legislative behavior, which

points to the influence of other underlying factors at play including party leadership and

presidential signaling. In the period between the two votes, party leaders likely engaged in

extensive negotiations and whipping efforts. The extension of the divestiture period for TikTok

from 180 days to 270 days also represents a compromise that could have appeased some opponents.

Furthermore, the Biden administration’s strong support for the ban may have swayed Democratic

legislators who were initially hesitant. These underlying elements align with the logic of party

cartel theory (Cox & McCubbins, 1993, 2004).

A particularly intriguing finding is that ideological extremity emerged as the strongest

predictor of legislative behavior. Both ideologically extreme conservatives and liberals voted

against their party positions on the TikTok ban. The current findings provide support to Homan

and Lantis’s (2022) free agency model, which suggests that politicians at the ideological extremes

tend to find common grounds in their distrust of the political establishment and concerns over

executive overreach. This convergence may lead them to break ranks with the party leadership.

Conservative extremists may oppose the ban due to libertarian principles advocating for minimal

government intervention in the market, while liberal extremists might view the ban as an overreach

that infringes on individual freedom.

Representatives Alexandria Ocasio-Cortez and Ro Khanna

from the Democratic Party, and Representative Matt Gaetz from the Republican Party are notable

examples of those who have voiced strong opinions on these issues.

These findings provide further credence to the notion that legislators do behave differently

from their party when it comes to highly contentious issues (Ansolabehere et al., 2001; Xie, 2006)

with high public visibility (Jessee & Theriault, 2014; Milyo et al., 2000). This raises questions

about the prevailing theory of party discipline (Cox & McCubbins, 1993, 2004). The influential

role of ideological extremists in shaping legislative behavior also has important implications for

Congressional voting, particularly in the current political climate. With Congress frequently split

nearly evenly between the two parties, these lawmakers can sway crucial votes, potentially

determining legislative outcomes. Nonetheless, it is important to not overly romanticize the role

of ideological extremists. After all, congressional incumbents may strategically choose their

ideological positions to maximize their media visibility (Peterson, 2012; Wagner & Gruszczynski,

2018) and the chances of winning re-elections (Adams et al., 2011; Kalt & Zupan, 1984; Kirkland

& Slapin, 2017; McCarty & Poole, 1998; Shor & Rogowski, 2018).

This current study also enriches our understanding of voting behavior by highlighting the

influence of legislators’ demographics. First, this study provides the first evidence that younger

legislators are less inclined to support a ban on social media apps. This is despite the predominant

presence of older incumbents in Congress (Roberts & Wolak, 2023). This finding has important

implications for the representation of younger constituents in Congress. It is also intriguing to find

that longer-tenured legislators were more likely to oppose the ban. Despite common assumptions

In reality, the ideological divide among these legislators may not be as clear-cut, as many of them have

expressed concerns about both free speech and business impacts.

that the effects of age and tenure should align, the results suggest that longer-tenured legislators

may feel more emboldened to vote based on their preferences rather than simply following party

lines (Zupan, 1990). Longer-tenured legislators may have also accumulated an understanding of

various policy issues and their potential repercussions, leading them to take more nuanced and

independent positions (Seo, 2010), even if these positions diverge from the mainstream or their

party’s stance.

Additionally, this study highlights the role of racial backgrounds on foreign policy voting

patterns (see also Danielson, 2021). The higher likelihood of white legislators supporting the ban

in the second vote was striking both within and across parties, with white Republicans showing a

much more pronounced effect compared to white Democrats. While it is beyond the scope of this

research to delve into the origins of individual attitudes towards China—whether rooted in genuine

concerns about China’s technological expansion (Chang, 2023; Harris & Marinova, 2022) or in

underlying racial biases towards the Chinese (Fung, 2023; Minghao, 2020)—it is crucial to note

the marked significance of the racial effect compared to other demographic factors.

An unexpected finding was the tendency of lawyer-legislators to support the ban. Despite

expectations that their legal background might make them less likely to support measures with

potential legal risks to civil liberties and the First Amendment, it appears that their legal expertise

Following the suggestion of a reviewer, this study conducted additional analyses (using both Chi-square

tests and logistic regression) to examine the voting patterns of Asian-American legislators. The results

indicate that Asian-American legislators did not have a different voting pattern compared to non-Asian-

American legislators, as most of them voted in favor of the ban (13 out of 16 in the 1st vote and 15 out of

17 in the 2nd vote). Notably, two Democrats of Indian descent—Pramila Jayapal (D-WA-7) and Ro Khanna

(D-CA-17)— opposed the ban in both votes. In contrast, Grace Meng (D-NY-6), of Chinese descent,

changed her stance, initially opposing the ban but later voting in favor. Further analysis also indicates that

Black legislators were more likely to vote against the ban, compared to non-Black legislators. In particular,

17 out of 56 in the 1st vote and 15 out of 55 in the 2nd vote were opposed to the ban. This finding indicates

the broader concerns around the economic impact of the ban on Black communities (Dodgeson, 2024).

may shape heightened perceptions of security risks associated with TikTok, leading to greater

support for the ban. With more than one-third of all legislators in Congress being lawyers (Matter

& Stutzer, 2015), future research on congressional voting should consider this important factor.

Despite these noteworthy findings, several limitations warrant consideration. First, like

previous research in this area, the measurement of ideology may conflate members’ foreign policy

positions and their domestic policy positions (Jeong, 2018). However, it is important to note that

obtaining direct, independent measures of legislators’ ideology is inherently difficult (Jenkins,

2006; McCarty et al., 2016). Secondly, it is important to note that this study did not consider the

economic characteristics of a legislator’s district, which reflect the interests of constituents who

may be affected by the ban (Milner & Tingley, 2010). Furthermore, this study did not examine the

influence of interest groups or lobbying efforts (Edwards, 1989; Fordham, 2008; Fordham &

McKeown, 2003; Galantucci, 2015; Milner & Tingley, 2010) including those from hawkish

organizations such as the Foundation for Defense of Democracies and the anti-China group State

Armor Action (Oprysko, 2024). It is plausible that legislators with greater economic ties to China

might be more likely to vote against the TikTok ban.

6. CONCLUSION

This research reveals that partisanship, ideology, and demographic factors significantly

influenced legislative behavior, with ideology emerging as the most potent force. This study

enhances our understanding of congressional voting patterns, particularly on a specific and timely

issue. This understanding can lead to improved predictions that inform the public about legislators’

stances and actions on critical policies.

The bipartisan support for the TikTok ban, while demonstrating a unified front on national

security, raises questions about the delicate balance between cybersecurity, citizen liberties and

regulatory actions. Given the U.S.’s role as a beacon of democratic values in a globalized economy,

there arises a crucial inquiry into how such values can be preserved in congressional policymaking.

Moreover, the potential repercussions of barring foreign developers may set a precedent,

potentially triggering a cascade of similar actions by other nations.

The fate of TikTok in the U.S. remains uncertain, with China staunchly opposing any

forced sale. The approaching November 2024 election and Trump’s changing stance further

complicate the enforcement prospects of the TikTok ban. Regardless of the immediate outcome,

protracted legal challenges concerning First Amendment rights can be anticipated, along with

ongoing debates over the substantiation of national security threats. These issues are likely to

persist in the courts and public discourse for the foreseeable future.

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Tables

Table 1 Summary of Study Variables and Coding Schemes

Variables

Coding Schemes

Sources

Dependent variable

Votes on TikTok Ban

Dichotomous: 1 = yea; 0 = nay

The Clerk’s Office of the U.S.

House of Representatives website

Legislator-level variables

Gender

Dichotomous: 1 = female; 0 =

male

Congress.gov website and also

GovTrack.us

Age

Continuous: in years

Congress.gov website and also

GovTrack.us

Tenure

Continuous: in years

Congress.gov website and also

GovTrack.us

White

Dichotomous: 1 = White; 0 =

otherwise

Congress.gov website and also

GovTrack.us

Lawyer-Legislator

Dichotomous: 1 = with a J.D.; 0 =

without a J.D.

https://clerk.house.gov/document

s/Lawyers.pdf

Partisanship

Dichotomous: 1 = Democrats; 0 =

Republicans

Congress.gov website and also

GovTrack.us

Ideology

Continuous (0 = most moderate

and 1 = most extreme)

https://www.voteview.com

State-level variables

State Political

Leaning

Dichotomous: 1 = Blue States; 0 =

Red States

Ballotpedia (based on the 2020

Presidential Election)

TikTok Popularity

Ordinal:

1 = low (TikTok is losing

Instagram)

2 = moderate (TikTok is fighting

Instagram)

3 = high (TikTok is leading

Instagram)

https://www.start.io/blog/tiktok-

popularity-by-u-s-state/

Table 2 Crosstabulation of Votes on the TikTok Ban and Party Affiliation

TikTok Ban

Republicans

Democrats

Total

First Vote (n = 417)

Yea

197

155

352

Nay

Total

212

205

417

Second Vote (n = 418)

Yea

186

174

360

Nay

Total

211

207

418

Table 3 Sample Characteristics

Variables

First Vote

March 13, 2024

Second Vote

April 20, 2024

Frequency

Gender

Male

299

71.7

294

70.3

Female

118

28.3

124

29.7

Age (by generations)

Silent: Born 1928-1945 (aged 79 or above)

4.1

4.5

Baby Boomers: Born 1946-1964 (aged 60-78)

179

42.9

181

43.3

Gen X: Born 1965-1980 (aged 44-59)

158

37.9

156

37.3

Gen Y: Born 1981-1996 (aged 28-43)

14.9

14.7

Gen Z: Born 1997-2012 (aged 27)

0.2

Tenure (in years)

1-10

285

68.3

281

67.2

11-20

20.1

20.6

21-30

7.4

8.1

31-40

3.1

41-43

1.0

Lawyer Legislator

Yes

131

31.4

132

31.6

286

68.6

286

68.4

Race

White

347

83.2

347

83.0

Otherwise

17.8

17.0

TikTok Popularity (state level)

Low

17.0

17.2

Moderate

186

44.6

186

44.5

High

160

38.4

160

38.3

State Political Leaning

Republican (Red State)

179

42.9

181

43.3

Democrat (Blue State)

238

57.1

237

56.7

Partisanship

Republican

212

50.8

211

50.5

Democrat

205

49.2

207

49.5

Ideological Extremity

Low (.10 - .42)

188

45.08

188

45.0

High (.43 - .86)

229

54.92

230

55.0

Total

417

100

418

100

Note. Ideological extremity was categorized as low and high based on the median value.

Table 4 Bivariate Correlations

Variables

1. Votes on the TikTok Ban

-.073

.092 t

-.004

.079

.169**

.021

-.057

-.059

-.265**

2. Female

-.097*

-.024

-.051

-.058

-.194**

-.081

.113*

.289**

-.108*

3. Age (years)

.092 t

-.026

.609**

-.032

-.019

.011

.034

.040

.105*

4. Tenure (years)

-.050

-.051

.601**

.086 t

-.056

-.044

.060

.155**

-.063

5. Lawyer Legislator

.049

-.070

-.017

.101*

.019

-.056

.033

.161**

-.097*

6. White

.161**

-.188**

-.025

-.053

.041

.080

-.087 t

-.393**

.042

7. TikTok Popularity (state Level)

.092 t

-.098*

.009

-.036

-.058

.071

-.574**

-.278**

.238**

8. Blue State

-.157**

.122*

.052

.062

.030

-.076

-.571**

.412**

-.283**

9. Democrats

-.239**

.287**

.060

.178**

.161**

-.380**

-.274**

.412**

-.394**

10. Ideological Extremity

-.072

-.098*

.114*

-.050

-.102*

.034

.225**

-.275**

-.405**

Note. t p < .10; * p < .05; ** p < .01. The numbers below the diagonal are from the first vote (n =417) and the numbers above the diagonal

are from the second vote (n = 418).

Table 5 Logistic Regression (1st Vote on March 13, 2024 [118th Congress])

Predictors

Model 1

(Both Parties)

Model 2

(Democrats)

Model 3

(Republicans)

Constant

2.89***

(.956)

18.03

.501

(1.202)

1.65

4.20*

(1.72)

67.17

Legislator-level

Female

-.025

(.305)

1.06

-.100

(.333)

.904

1.132

(.981)

1.14

Age (years)

.063**

(.016)

1.77

.069**

(.022)

1.07

.044 t

(.025)

1.04

Tenure (years)

-.054*

(.021)

1.20

-.058*

(.025)

.942

-.068

(.042)

.933

Lawyer Legislator

.573 t

(.299)

.899

.790*

(.394)

2.20

.064

(.736)

.937

White

.184

(.239)

.461

.274

(.286)

1.31

State-level

TikTok Popularity

-.105

(.259)

.901

-.143

(.342)

.866

.112

(.404)

1.11

Blue State

-.773*

(.385)

.549

-.984 t

(.519)

.373

-331

(.581)

.717

Partisanship

Democrats

-2.011***

(.321)

.133

Ideology

Ideological Extremity

-5.663***

(1.216)

.003

-5.191***

(1.493)

.005

-6.663***

(2.255)

.001

Number of Legislators

417

205

212

Number of States

Pseudo R2

.18

.14

.11

Note. t p < .10; * p < .05; ** p < .01; *** p < .001. na = white predicts success perfectly and was

removed from the analysis.

Table 6 Logistic Regression (2nd Vote on April 20, 2024 [118th Congress])

Predictors

Model 4

(Both Parties)

Model 5

(Democrats)

Model 6

(Republicans)

Constant

3.351***

(1.215)

28.532

.840

(1.483)

2.317

3.567*

(1.640)

35.442

Legislator-level

Female

-.281

(.421)

.754

-.419

(.470)

.657

-.246

(.825)

.781

Age (years)

.068***

(.019)

1.071

.072**

(.026)

1.07

.078**

(.028)

1.081

Tenure (years)

-.064*

(.027)

.937

-.064*

(.028)

.937

-.146*

(.065)

.863

Lawyer Legislator

.637 t

(.386)

1.891

1.300*

(.604)

3.67

-.186

(.618)

.829

White

.945*

(.470)

2.572

.899*

(.419)

2.45

4.447**

(1.426)

85.454

State-level

TikTok Popularity

-.193

(.193)

.824

-.207

(.313)

.813

.062

(.435)

1.064

Blue State

-.803*

(.378)

.447

-.765

(.669)

.465

-337

(.600)

.713

Partisanship

Democrats

-.867 t

(.525)

.420

Ideology

Ideological Extremity

-8.754***

(1.574)

.000

-5.619*

(2.231)

.003

-15.269***

(2.961)

.000

Number of Legislators

418

207

211

Number of States

Pseudo R2

.22

.19

.36

Note. t p < .10; * p < .05; ** p < .01; *** p < .001. The large odds ratio (OR) for White in Model

6 was influenced by the two cells with values of less than 5 (specifically, 4 non-white Republicans

voted for the ban, while only 1 non-white Republican voted against it).

Trotta, A. M. (2018). Advances in E-governance: Theory and application of technological initiatives. Routledge

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Aug 2023

Jordan Tama

Bipartisanship and US Foreign Policy shows that, even as polarization in American politics reaches new heights, Democrats and Republicans in Washington continue to cooperate on many international issues. A close look at congressional voting patterns and major foreign policy debates of recent years—including over military intervention, the use of economic sanctions, international trade, and foreign policy spending—reveals that bipartisanship remains surprisingly common when elected officials turn their attention overseas. Yet bipartisanship today rarely involves unity in Washington. Instead, bipartisan coalitions often coexist with intra-party divisions or disagreement between Congress and the president, making it difficult for the United States to speak with one voice on the global stage. In short, the politics of contemporary US foreign policy are more nuanced than either headlines highlighting extreme polarization or truisms suggesting that politics stops at the water’s edge would suggest. Drawing on new data and interviews of more than 100 US foreign policy practitioners, the book highlights key factors that influence political alignments among elected officials and provides takeaways for efforts to foster more bipartisanship on important foreign policy challenges.

SAFEGUARDING CHILDREN'S PRIVACY: A STUDY OF REGULATION AND PRACTICE IN THE UNITED KINGDOM AND THE UNITED STATES

Article

May 2023

Juncheng Cao

In recent years, there has been a growing emphasis on protecting privacy in the global internet economy and innovation. Consequently, governments have implemented strict regulations on the issue. However, information society service providers (ISS providers) may approach this problem differently due to their unique service domains. Compliance with regulations such as the ICO code and COPPA may present challenges for operators due to technical difficulties and unclear guidelines. Unfortunately, these issues ultimately harm users, especially children. To address this problem, this note examines the key elements and regulations of the ICO code and analyzes the privacy policies for children issued by five major technology companies. The aim is to clarify existing protective measures and identify areas for improvement. Additionally, this note highlights the challenges ISS providers face when trying to identify children. The author's objective is to provide a clear understanding of the current system for protecting children's privacy, with the goal of improving the situation

Comrades or Foes: Did the Russians Break the Law or New Ground for the First Amendment?

Article

Apr 2019

Legislative Leviathan: Party Government in the House

Book

Jun 2012

The second edition of Legislative Leviathan provides an incisive new look at the inner workings of the House of Representatives in the post-World War II era. Re-evaluating the role of parties and committees, Gary W. Cox and Mathew D. McCubbins view parties in the House - especially majority parties - as a species of 'legislative cartel'. These cartels seize the power, theoretically resident in the House, to make rules governing the structure and process of legislation. Most of the cartel's efforts are focused on securing control of the legislative agenda for its members. The first edition of this book had significant influence on the study of American politics and is essential reading for students of Congress, the presidency, and the political party system.

American Primacy and US–China Relations: The Cold War Analogy Reversed

Article

Sep 2022

Does today’s US–China relationship resemble the bygone rivalry between the United States and the Soviet Union? In this article, we suggest that there are some instructive similarities between US–China and US–Soviet relations, but the Cold War analogy works best when the contemporary United States is cast in the role of the historical Soviet Union. Specifically, the United States (1991–present) has in common with the Soviet Union (1945–91) the fact that it occupies a position of near dominance on the Eurasian continent during a prolonged era of relative peace. It was Soviet hegemony in Eastern Europe that gave the bipolar international system of the Cold War era its defining characteristic—that is, a geographic distribution of power assets in Eurasia that the United States and its allies mobilized to resist and overturn. Now, it is US primacy in Eurasia that serves to define the basic contours of the present (if ailing) unipolar international system: a geopolitical configuration that denies any power other than the United States the opportunity to carve out an effective sphere of influence. In short, the legacy forward deployment of US forces shapes the context within which Sino-American relations are unfolding just as the legacy occupation of Eastern Europe by the Soviet Union structured superpower relations during the Cold War.

At the Margins: Presidential Leadership of Congress

Book