PDPRIETI Policy Discussion Paper Series 23-P-008

Policy Agenda and Trajectory of the Xi Jinping Administration
Textual Evidence from 2012 to 2022
LIM, Jaehwan

Aoyama Gakuin University

ITO, Asei
RIETI

ZHANG, Hongyong
RIETI

The Research Institute of Economy, Trade and Industry

https://www.rieti.go.jp/en/

RIETI Policy Discussion Paper Series 23-P-008
July 2023

Policy Agenda and Trajectory of the Xi Jinping Administration
Textual Evidence from 2012 to 2022 １
Jaehwan LIM (Aoyama Gakuin University)
Asei ITO (The University of Tokyo/RIETI)
Hongyong ZHANG (RIETI)
Abstract
How many agendas has Xi Jinping put forth and promoted since taking office in 2012, and what are thetypes of agendas? What is the relationship between the agendas? How much of political attention has eachagenda received, and how has the allocation of attention changed over time? Moreover, how do we know
this? Despite the scholarly interest in policy development during the Xi era, few studies havesystematically mapped the overall structure – the number, substance, and underlying relationship – ofpolicy agendas pursued by the Xi administration. To address this research gap, we utilize a dataset ofpresidential statements, speeches, and reports from 2012 to 2022 and employed automated text analysis
to identify major topics and terms associated with each topic. Our analysis reveals the identification of
about 25 distinct policy agendas across diverse policy domains, with remarkable temporal variations
between agendas in terms of the amount of leadership attention. We find a significant shift in both thesubstance and relative weight of policy agendas between the first and second term of Xi’s tenure,indicating his adaptation and responses to changing domestic and foreign policy environments.
Keywords: policy agenda, political attention, structural topic model, Xi Jinping, China
Abbreviations— CCP: the Chinese Communist Party; LDA: Latent Dirichlet Allocation;
STM: Structural Topic Model

JEL classification: P20, O53

The RIETI Policy Discussion Paper Series was created as part of RIETI research and aims tocontribute to policy discussions in a timely fashion. The views expressed in these papers are solely
those of the author(s), and neither represent those of the organization(s) to which the author(s)belong(s) nor the Research Institute of Economy, Trade and Industry.

１

This study is conducted as a part of a Chinese economy project, “Empirical Research on the Changing Chinese
Economy: Upgrading, Expansion, Structural Reform,” in “Studies on Transformations of International Systems
and their impact on Japan’s mid & long-term competitiveness” project undertaken at the Research Institute of
Economy, Trade, and Industry (RIETI). We thank Shujiro Urata, Yasuhiko Yoshida, Haruhiko Ando, andparticipants in The Japan Association for Asian Studies (JAAS), Society for Computational Social Science ofJapan, and RIETI DP research seminars for their valuable comments and suggestions. LIM:
t13025@aoyamagakuin.jp (corresponding author); ITO: asei@iss.u-tokyo.ac.jp; ZHANG: zhang-hongyong@rieti.go.jp.

11. Introduction
There is a general agreement that Xi Jinping’s rise to power as the supreme leader of China has led
to significant changes in the policy-making process of the country. Of particular note is Xi’s deep
involvement in the policy process in which the party leader now engages in nearly every stage ofpolicy development, from agenda setting to implementation and evaluation (Heilmann and Stefan
2016; Lampton 2015). Certain scholars argue that Xi has expanded his influence beyond the
traditional areas of focus of the General Secretary, such as economic, and social policies, whichwere previously the purview of the premier and the State Council. This has earned him thenickname “The Chairman of Everything” (Shirk 2018; Economy 2018).
Despite the widely held perception of the transformative leadership of the new leader, observers
increasingly disagree over whether or not Xi has made significant progress in transforming thepolicy-making process and policy outcomes. For instance, scholars have expressed skepticism about
the state of policy implementation and suggested that the reform efforts by Xi may have exerted alimited impact on pre-established norms and practices on the ground (Pei 2022; Jaros and Tan 2019;
Ang 2022). Additionally, in light of the lack of tangible accomplishments, doubts are being raised
regarding Xi’s ability and willingness to remain committed even to his signature policy initiatives(Zhu 2022; McGregor 2019). In summary, as signs of policy deadlock and agenda shifts emerge
under Xi’s continued rule, reassessing the relationship between Xi’s characteristic leadership andpolicy outcomes is important.
This study aims to contribute to the discussion by analyzing policy outcomes during the Xi era, witha special attention to the identification of the evolving policy agendas and priorities of Xi. The
underlying assumption is that Xi has adapted to changes in domestic and external conditions, whichleads to shifts in policy substance, priority, and enforcement strategy. To achieve this goal, the study
uses probabilistic topic modeling on a vast collection of presidential statements, speeches, andreports from 2012 to 2022. The ultimate objective is to construct a comprehensive and reliable setof policy agenda and to analyze the structural and temporal dimensions of agenda dynamics
throughout Xi’s entire reign.
Our analysis on the dynamics of agenda in the Xi era in particular and authoritarian regimes ingeneral presents two contributions. Despite the increased interest in policy development during Xi's
2

reign, only a few studies have systematically analyzed the overall structure of the policy agenda of
the Xi administration. To address this research gap, we provide a comprehensive overview of thepolicy agenda of Xi, including both revealed and latent policies, by identifying their number,substance, and underlying relationships. Our findings not only improve the understanding of Xi's
evolving policy priorities, which is particularly important given his extended tenure, but also shedlight on the policy outcomes of the resurgence of strongman politics in China.
After applying the topic modeling method, we have identified approximately twenty-five uniquepolicy agendas that span various policy areas. Among the most frequently occurring topics are
China’s relations with developing countries and global economic governance. Structurally, ouranalysis finds that each policy agenda is nested within one of three broad clusters that are only
loosely connected, namely, foreign policy, domestic policy, and crisis response. We also
demonstrate that China’s relations with developing countries, which is the most dominant agenda at
the corpus level, is less significant in relational terms, as programmatic agendas, such as Socialism,
and Civilization, become salient due to their strong connections with a wide range of topics. Lastly,
the findings indicate a significant shift in the relative amount of attention given to each agenda
between Xi’s first and second terms, which is particularly pronounced in agendas related to
implementation strategy.
The remainder of this paper is structured as follows. Section 2 introduces the relevant literature andhow this study enhances the existing research. Section 3 outlines the data sources and methodology
used in this study. Section 4 displays the model output and presents the main findings supported byevidence from text analysis. Section 5 discusses various approaches to validating the findings.
Finally, Section 6 summarizes the findings and draws conclusions.
2. Relevant Literature: Authoritarian Agenda Dynamics and China Under Xi
Theories of policy agenda have traditionally emphasized democratic systems and focused onprocesses that ensure that policy priorities remain responsive to stakeholder demands. However,
applying these theories to authoritarian regimes poses two challenges. First, in contrast to
democracies, authoritarian policy processes are insulated from stakeholder activities external to theestablishment (Han 2022). Second, under authoritarian regimes, representation within bounds is amore common form of agenda entrepreneurship instead of direct competition embedded in a broad
network of policy venue (Truex 2016). Despite the growing interest in authoritarian responses to3societal demands, the majority of scholars agree that autocrats possess significant freedom in
deciding on the extent and the form of external preferences to incorporate into government policy(Chen et al. 2016; Lorentzen 2014). Furthermore, autocrats are generally skeptical of greater public
involvement in the policy process, as asserted by Wintrobe (2000).
Regarding internal processes, a significant discourse exists regarding the interplay between policy
priorities, specifically the choice between the provision of public or private goods and regime
survival (Mesquita, Smith, & Siverson, 2003). However, the intense interest of autocrats inmitigating threats to the survival of their regime does not necessarily indicate that such threats alone
dictate all government priorities determined by officials dispersed throughout the broader politicalsystem. Despite the strong emphasis on top-down control, bureaucratic deviation from central
priorities can be relatively prevalent in authoritarian regimes. As a response, autocrats may
occasionally employ drastic measures to suppress non-compliant activities, which may also result inchanges in policy priorities.
How then does China under the leadership of Xi fit into the existing literature? The prevalent viewis that the Chinese Communist Party (CCP) under Xi’s rule has shifted toward more repression andless responsiveness in managing external demands, and has been more skeptical of incorporating
societal preferences into the policy-making process (Dickson, 2021). Many observers agree that Xi
has significantly strengthened his personal control over the entire party-state bureaucracy using
various methods, including anti-corruption campaigns. As a result, a popular characterization of thepast decade is the emergence of the strongman rule in which a single leader, often aided by a small
group of loyal supporters, wields disproportionate power over the party-state system (Shirk, 2018).
The concentration of power in Xi’s hands has significant implications for the policy process andagenda outcomes. Notably, it has led to remarkable erosion of collective decision-making norms,
which enables Xi to make decisions more quickly and with less input from other leaders or interest
groups (Shirk, 2023). Policymakers in the broader political system, who face intense demands forloyalty, are reluctant to pursue independent agendas and instead focus on a smaller set of priorities
within the boundaries of political acceptability (Stern and Hassid, 2012). Consequently, Chan et al.
(2021) argue that Xi’s tenure is associated with more frequent changes in policy agendas, greater
unevenness in the distribution of attention across priorities, and more alignment in agenda focus
across policy venues than did Hu Jintao’s era (Chan et al. 2021).
4

In addition, Xi’s consolidation of power also has implications for the substance of policy agendas.
Scholars have pointed out that Xi has pursued an elitist or particularistic type of policy that satisfies
the relatively narrow set of interests of his allies and trusted institutions (Li, 2016; Lam, 2015). If
the case is true, it suggests a significant departure from the Hu era, during which significant
attention was given to redistributive agendas such as fiscal rebalancing between urban and rural
regions and the establishment of a basic social safety net (Kroeber, 2016). Moreover, the perceived
need to centralize political authority in response to economic slowdown has led to a renewed focus
on the role of ideology as a unifying mechanism (Cheek, 2021). Additionally, previous studies
suggest that national policy priorities have reflected more of the personal political agenda of theleader with further consolidation of his personal power, particularly after the 19th Party Congress(Schatz, 2009; Fewsmith, 2021).
Despite the broad and potentially significant theoretical implications of Xi’s concentration of power
and authority on policy outcomes, only a few systematic, evidence-driven approaches are used to
identify and explain the entire spectrum of constituent elements and their relationships in the policy
agenda in the Xi era. Instead, many studies focus on specific issue areas (e.g., economics, security,
or social issues) or individual policy initiatives (e.g., the Belt and Road Initiative [BRI], anticorruption campaign, or common prosperity). Although a few analyses employ a comprehensive
focus, the majority rely on a deep yet selective reading of Xi’s speeches and writings, which results
in an impressionistic portrayal of policy priorities. For example, Pei (2022) argues that Xi’s agenda
consists of three main components, namely, establishing personal political dominance, revitalizing
the Leninist party-state, and expanding China's global power and influence. However, this type ofaccount lacks falsifiability and does not enable empirical testing through alternative observations.
Furthermore, the existing research fails to address the level of leadership attention given to eachpolicy agenda and its evolution over time ２. A grasp of these factors is critical for understanding
variations in policy outcomes, particularly in the Chinese context (Lieberthal and Oksenberg 1988;

２

Notable exceptions include Chan et al. (2021) and Qin and Huang (2023). In addition to using different
collections of policy documents, their research differs from ours in two crucial aspects. Firstly, those studies
primarily aim to examine whether China's policy process is characterized by punctuations and, if so, to what
extent. Secondly, while they focus on the dynamics of elite bargains, their main object of observation is thepolicy process within the central bureaucracy, rather than the policy preferences and priorities of central
leaders.

5

Wang 2008). Therefore, a more comprehensive and evidence-based approach is required to identify
and explain the entire spectrum of constituent elements and their relationships in the policy agendaof the Xi era.
In summary, this brief literature review highlights three main points. First, the authoritarian agenda
setting frequently reflects the process and outcome of elite bargains but does not suggest that nonelitist societal preferences are irrelevant to the agenda outcome. Second, reports emerge that theconcentration of power by Xi has introduced new dynamics to Chinese policy-making processes.
Third, scholars have not reached a consensus on the specific agenda outcomes of the Xi era.
This study aims to fill the gap in the literature by answering three questions: (1) How many policy
agendas has Xi Jinping proposed and promoted since taking office in 2012, and what are these
agendas? (2) How much attention has each agenda received, and how has the allocation of attention
evolved over time? (3) What is the relationship among policy agendas, and how has it changed?
Notably, the objective of the study is to discover a comprehensive set of policy agendas proposed
and pursued by Xi using political texts to explore their temporal evolution and structure. Although
theoretical implications are inevitable and will be discussed, the immediate goal is not to test aparticular hypothesis derived from the theories of agenda setting and authoritarian policy-making.
3. Data and Method
3.1. Data
We collected textual data from the Xi Jinping Xilie Zhongyao Jianghua Shujuku (习近平系列重要讲话数据库; hereafter, XIS), which is a database of important speeches made by Xi Jinping ３. XIS
is a web-based source that mainly collects speeches and reports related to Xi Jinping from Chinesestate media, including the People’s Daily (domestic and international editions), Xinhua News
Agency, local newspapers, and several state, or CCP publications, such as Qiushi. The database
contains speeches, activity reports, field visits, press conferences, meetings, telegrams (e.g.,
ceremonial), and other types of articles. The articles date back to November 15, 2012, which is the
day Xi Jinping took office as the General Secretary of the CCP.
The pre-cleaning dataset included a total of 12,532 articles spanning from November 15, 2012, to

３

The URL of the database: http://jhsjk.people.cn/6October 31, 2022, which comprises a total of 18,456,212 Chinese characters, with an average of
1,478 characters per article. To ensure the quality of the dataset, we eliminated duplicate articles,
particularly those published by People’s Daily and Xinhua News Agency, and articles that were
either very long (>30,000 characters) or too short (<200 characters). After cleaning, the final dataset
for analysis consisted of 9,016 articles, which contain a total of 14,117,728 characters, with anaverage of 1,565 characters per article. Figure 0 provides a detailed summary of the dataset.

Using these data poses advantages and limitations at the same time. One significant advantage isthat the dataset contains a high concentration of policy statements made by Xi, allowing us toconcentrate on his policy agenda and priorities, in contrast to collections of newspaper articles.
However, the dataset's limitations arise from its direct compilation from articles published by stateowned media, particularly the People’s Daily and the Xinhua News Agency ４. As a result, apossibility exists that the articles in the dataset were selected for propagandistic purposes, such as

４Previous research has demonstrated that media bias is more evident in news sources that are subject tostrong state or party control. For example, using Chinese domestic newspaper data from 2000 to 2010,
Piotroski et al. (2017) illustrates that news sources under the control of the central government or party
organizations are more likely to display a favorable sentiment. Similarly, Qin et al. (2018) examines 117
newspapers across China to evaluate the degree of bias in each media outlet. The results indicate that themedia at the central level tend to report more on highest political leaders, while topics emphasized by the antiCCP media tended to be less frequent.

7

disseminating preferential messages for relevant organizations, instead of conveying true
information about the policy priorities of the leader. Thus, Grimmer et al. (2021) stress that whenusing documents as a reflection of government inner workings, researchers should seek tounderstand the incentives that underlie document collection and the process through which the data
are made available.
Nevertheless, given Xi’s firm grip on the party-state system, it is unlikely that any organizationalbiases systematically influence document selection in ways that could conceal or distort the policy
preferences of the leader. Notably, the policy priorities of a politician can frequently be inferredfrom their statements or speeches in meetings and congresses, which is particularly true in thehighly controlled media environment of China. Furthermore, the current study focuses on policy
priorities in the public domain, which must be effectively communicated to approximately 90
million CCP members and other stakeholders. Thus, assuming that media types, such as the
propaganda apparatus of the Party, would not bias political priorities is reasonable, because it could
be extremely misleading and costly given the magnitude of the potential audience.
3.2. Method
Text has long been a crucial resource for social scientists, but advances in technology in recent
years have rendered possible the analysis of vast amounts of text using quantitative methods. With
the proliferation of the Internet, social scientists now have access to more data than ever before, andthe research community has responded by creating user-friendly software packages. As a result,scholars are now able to draw on a wider range of data sources and use more diverse researchmethods (Wilkerson and Casas 2017).
To analyze our corpus of the XIS, this study utilizes topic modeling methods. This approach wasselected, because topic models are well-suited to our analytic goals: identifying policy agendas andexploring their structural and temporal properties. Originally developed as an unsupervised machinelearning algorithm for document classification and information retrieval (Blei et al. 2003), topic
modeling not only saves significant labeling efforts but also enables researchers to summarize theunderlying structure of large collections of documents. Notably, although topic modeling was
initially intended for document classification, it has become a widely used tool in the social sciences
for measuring latent concepts and generating new conceptualizations (Ying et al. 2021). Moreover,practitioners suggest that topic models are particularly effective for analyzing newspaper articles or8well-crafted statements from political or social elites (Grimmer et al. 2021), which makes themsuitable for our objective.
In our topic modeling applications, we leverage a recently introduced framework, the structural
topic model (STM). The STM is a mixed-membership model built on the popular latent Dirichlet
allocation (LDA) model, which is particularly useful when documents likely contain multiple
complex and overlapping topics. ５ In contrast to LDA, however, the STM provides a flexible
approach for incorporating document metadata, such as author characteristics, writing location, andtime of writing, into the analysis using document-level covariates (Lucas et al. 2015, Roberts et al.
2019). The inclusion of this information within the model can improve the quality of learned topics
and facilitate hypothesis testing. Additionally, as Roberts et al. (2014) demonstrate in theirapplication to open-ended survey responses, the STM is sensitive to the time period in which thetext was created, which renders it advantageous for tracking changes in topic over time (Roberts etal. 2014, 17).
Prior to examining the applications of STM, an essential aspect to note is that unsupervised
methods, such as topic modeling, require significant input from the researcher despite their name.
The researcher must specify, among other things, the number of topics in advance and interpret their
meanings. Furthermore, the preprocessing decisions made can exert a profound impact on the
outputs of the topic model (Denny and Spirling 2018, Wilkerson and Casas 2017, Eshima et al.
2020). For instance, manipulating only one feature of a topic model (e.g., removing or retaining
stop words) can produce vastly different results. Fundamentally, the agnostic perspective that notrue, correct model exists for language analysis highlights the need for significant effort for
devotion toward the validation of the model and outputs (Lucas et al. 2015, Quinn et al. 2010). Thisissue will be revisited in Section 5.
4. Main Findings
In this section, we conduct a detailed analysis of the dataset to present three sets of findings on thesubstance, structure, and temporal evolution of the policy agenda during the last decade under theXi era.
５

Mixed membership models can provide more accurate and nuanced analysis of complex topics because
they allow for a document to be associated with multiple topics, which facilitates generating the documenttopic matrix and topic-word matrix simultaneously.

94.1. Number and Substance of Policy Agendas
As previously noted, topic modeling requires input from the researcher, including the specificationof the number of topics, which is one of the most important and challenging tasks from themethodological standpoint. The optimal number of topics (parameter K) is dependent on various
factors. If K is very small, then the corpus will be divided into a few general semantic contexts,
whereas if K is very large, then the collection will be divided into numerous topics, a few of which
may overlap, such that topics may be difficult to interpret. No definitive answer exists to theappropriate number of topics for a given corpus, and human judgment is used to select the final
model (Grimmer and Stewart 2013). Political scientists who have used topic models typically selectthe number of topics on the basis of whether or not the discovered topics are substantively
meaningful (Grimmer 2010; Moser and Reeves 2015; Catalinac 2016). However, the existing
software packages for topic modeling offer multiple options for measuring topic quality andassisting with model selection. Given that the quantities of interest to our study include the numberof topics, a brief discussion of model selection is necessary.

10Figures 1 and 2 depict the results of the application of the existing built-in metrics to our cleaned
text corpus and multiple model outputs, respectively, to assess the quality of topic structures. A
good topic structure should consist of understandable, meaningful, and distinct semantic clusters,with each topic being mutually exclusive. Figure 1 illustrates that two metrics were used to
calculate intra-topic density and inter-topic distance, which, when taken together, indicate that the11

optimal number of topics falls in the range of 20 to 30. Another common strategy for evaluating
topic quality is by combining semantic coherence and the exclusivity of words to topics (Roberts etal. 2019), as shown in Figure 2. The results demonstrate that models tend to perform well with 20 to
25 topics.
After applying various assessment metrics, we selected 25 topics that produce an acceptabletradeoff across the metrics and a relatively interpretable result. Table 1 presents the STM output inwhich each topic is labeled on the basis of the highest probability and the frequent and exclusive(FREX) words in each topic. ６ In a number of instances, we further examined exemplar documents
to better understand a topic, that is, the documents that contain the highest proportion of words
pertaining to the topic.
Our model generated a broad array of topics across diverse policy domains, except for a limited
number of topics related to Xi’s official activities as the head of the party and state, such as Foreignand Local Visits (topics 5 and 16), Press Conference (topic 13), and Congresses and Meetings (topic
17). This finding is notable, because it exceeds the original classification of policy areas of the CCP(Shao 2019). ７ To provide an overview of topical prevalence in the entire corpus, Figure 3 displays
the expected proportion of each topic in the corpus. The figure shows that China and Developing
Countries (topic 9) is the most prevalent theme in the corpus, which indicates that foreign economic
policy issues, along with those pertaining to Global Economy and Governance (topic 3), were themajor aspects of Xi’s policy agenda.
Table 1 highlights an agenda that merits attention: Poverty Alleviation (topic 12). It is the only
agenda in the model output that has a redistributive appeal. A close examination of the mostfrequently occurring words and FREX words suggests that the inclusion of this agenda may reflect
the traditional emphasis of the CCP on the so-called agricultural issues instead of indicating apreference for Xi’s personal policy priorities. However, this agenda received significantly more
attention

６

Frequent and exclusive (FREX) words combine the frequency of a term in the corpus with its exclusivity to
the topic, as measured by the ratio of the frequency of the term within a topic to its frequency in the corpus asa whole (Roberts et al. 2019).
７
The official categories are economics, politics, culture, society, ecology, party-building, military, anddiplomacy.

12

Table 1. Topics and Top Words in the Structural Topic Model#

Topic Labels

1

Culture and Civilization

2

Ethnic Unity

3

China and Asia-Pacific4China and Youth

5

Foreign Visits

6

Rule of Law

7

Covid-198

Speech and Writing

9

China and Developing Countries

10

Socialism with Chinese Characteristics

11

Innovation and Economy

12

Poverty Alleviation

13

Press Conference

14
15

Anti-Corruption and Cadre
management
China and Major Powers

16

Local Visits

17

Congresses and Meetings

Top words
Highest Prob: 文化, 中国, 文明, 历史, 教育, 发展, 世界

FREX: 社会科学, 哲学, 中华文明, 文物, 文化遗产, 思政课, 学术
Highest Prob: 工作, 发展, 民族, 群众, 坚持, 社会, 人民FREX: 西藏, 新疆, 民族团结, 妇女, 宗教, 各族群众, 工人阶级Highest Prob: 中国, 经济, 领导人, 亚太, 增长, 会议, 亚太经合组织FREX: 澳大利亚, 亚太, 亚太经合组织, 中澳, 马来西亚, 亚太地区, 新西兰
Highest Prob: 青年, 习近平, 中国, 广大, 时代, 希望, 实现FREX: 青春, 广大青年, 青年, 回信, 共青团, 老师, 人生
Highest Prob: 中国, 习近平, 主席, 访问, 友谊, 两国, 人民

FREX: 比利时, 荷兰, 中法, 墨西哥, 驻华大使, 法国, 到访Highest Prob: 改革, 制度, 法治, 国家, 全面, 推进, 工作
FREX: 法治, 司法, 立法, 宪法, 法律, 依法治国, 政法

Highest Prob: 疫情, 防控, 工作, 安全, 肺炎, 新冠, 加强FREX: 防控, 疫情, 肺炎, 新冠, 公共卫生, 抗疫, 物资

Highest Prob: 讲话, 习近平, 十八, 问题, 学习, 不能, 没有FREX: 论述, 中央文献出版社, 年版, 文献选编, 摘编, 讲话Highest Prob: 合作, 发展, 关系, 两国, 习近平, 中方, 中国

FREX: 中非, 建交, 非洲, 传统友谊, 中方, 战略伙伴, 两国关系

Highest Prob: 社会主义, 发展, 全面, 中国, 党, 坚持, 特色

FREX: 特色, 社会主义, 现代化, 十九, 改革开放, 四个, 小康社会

Highest Prob: 发展, 创新, 经济, 科技, 改革, 我国, 推动FREX: 供给, 科技, 驱动, 结构性, 侧, 结构, 实体Highest Prob: 生态, 发展, 脱贫, 建设, 农村, 工作, 保护
FREX: 脱贫, 扶贫, 生态环境, 乡村, 贫困人口, 攻坚, 生态Highest Prob: 习近平, 主席, 国家, 记者, 摄, 出席, 举行

FREX: 抵达, 摄, 记者, 机场, 兰红光, 古巴, 杜尚泽
Highest Prob: 干部, 政治, 党, 问题, 工作, 领导, 建设

FREX: 党内, 党风廉政, 纪律, 八项, 党章, 规矩, 从严治党

Highest Prob: 合作, 发展, 组织, 上海, 金砖, 共同, 双方
FREX: 中美, 中拉, 成员国, 中美关系, 金砖, 上海, 俄Highest Prob: 习近平, 总书记, 考察, 来到, 情况, 群众, 了解
FREX: 正定, 乡亲们, 村民, 浙江, 福州, 察看, 村里

Highest Prob: 代表, 习近平, 会议, 委员, 工作, 大会, 李克强
FREX: 全国人大常委会, 表决, 主席团, 赵乐际, 汪洋, 全国工商联, 最高人民检察院
18Party History

19

Military Modernization

20

Olympics

21

Internet and Propaganda

22

Chinese Nationalism and Taiwan

Highest Prob: 党, 同志, 人民, 革命, 学习, 历史, 精神
FREX: 初心, 长征, 共产党人, 党史, 红军, 革命, 毛泽东Highest Prob: 军队, 建设, 习近平, 部队, 强军, 主席, 中央军委FREX: 全军, 部队, 强军, 我军, 打仗, 军队, 官兵

Highest Prob: 习近平, 北京, 中国, 香港, 澳门, 国家, 主席
FREX: 冬奥会, 澳门, 冬, 香港, 残奥会, 筹办, 冰雪Highest Prob: 工作, 文化, 人民, 互联网, 中国, 文艺, 宣传

FREX: 文艺, 互联网, 舆论, 新闻, 网络空间, 文艺工作者, 网络安全Highest Prob: 中国, 人民, 伟大, 中华民族, 发展, 实现, 历史

FREX: 两岸, 两岸关系, 抗日战争, 台湾同胞, 同胞, 抗战, 近代

13

23

Global Economy and Governance24

Disaster Relief

25

Belt and Road Initiative

Highest Prob: 发展, 世界, 中国, 国际, 全球, 经济, 合作

FREX: 全球, 发展中国家, 联合国, 全球化, 气候变化, 复苏, 主义Highest Prob: 习近平, 同志, 表示, 人民, 主席, 国家, 仪式
FREX: 遇难者, 哀悼, 防汛, 人员伤亡, 逝世, 受灾, 颁授Highest Prob: 合作, 中国, 发展, 一路, 一带, 亚洲, 建设

FREX: 亚洲, 东盟, 一带, 一路, 丝绸之路, 互联互通, 博览会

during the second term of Xi’s tenure, as evidenced below (Figure 6), which suggests the presenceof a different motivation behind its presentation.
To gain a better understanding of the content of each policy agenda, Figure 4 displays the results ofthe calculation of the probability of each term being generated from a particular topic. For instance,
Military Modernization (topic 19) generates not only general terms, such as “军队 military” or “军
事 military affairs,” but also specific terms, such as “中央军委 Central Military Commission” or“强军 building a strong and capable military,” which indicates the central agenda-setter and major
policy objectives. Notably, we observe a significant variation in the probability of the term “习近平Xi Jinping” appearing as the most common word in each topic, which is of particular interest to our
research. In general, foreign policy agendas are more likely to include this term than domestic
policy agendas do, although variations can also be observed among foreign policy topics, such as
those between China and Developing Countries (topic 9) and China and Major Powers (topic 15).

14

4.2. Correlations and Structures of Policy Agendas
In addition to the inclusion of covariates, another distinctive feature of the STM is the explicit

estimation of correlation between topics. ８ The model output allows the analyst to produce a
network graph of topics in which each topic is a node (point), and two nodes are connected when
they are highly likely to co-occur in a document, which offers insight into the organizational
structure at the corpus level and enables researchers to identify large themes that transcend topics.

８

Correlations are estimated by replacing the Dirichlet distribution in the standard LDA framework with alogistic normal distribution as in the correlated topic model (Blei and Lafferty 2007). When no covariates are
specified, the STM reduces to an instance of the correlated topic model.

15

Figure 5 illustrates the structure of correlations among the discovered agendas. The size of eachnode represents betweenness centrality, a metric frequently used to capture the importance of nodes
that act as intermediaries or bridges in a network, while the edge (line) width is proportional to thestrength of correlation between the two connected topics. In general, the more strongly connected
any two topics are (as measured by the thickness of the connected line), the more similar are theirword use patterns are in Xi’s political text.
Our model has identified three broad policy clusters that are loosely connected to one another,namely, foreign policy, domestic policy, and crisis response. The foreign policy and domestic policy
clusters are linked by connecting topics, such as Foreign Visits (topic 5) in the former and Culture
and Civilization (topic 1) in the latter. A few topics in each cluster present obvious connections,such as the relationship between global economic governance topics (e.g., Global Economy andGovernance and BRI) or between topics related to Xi’s ideological contribution (e.g.,

16Socialism with Chinese Characteristics, and Speeches and Writings). However, certain connections(or the lack thereof) are subtle and require further investigation. For instance, MilitaryModernization is firmly situated in the domestic policy cluster and only linked to the topics of AntiCorruption and Rule of Law, while Chinese Nationalism and Taiwan lack a connection to any
foreign policy agendas.
Furthermore, it's worth noting that China and Developing Countries, which is the most prevalent
topic at the corpus level, loses much of its significance in relational terms, which suggests that the17agenda might be located at a lower level in the latent agenda hierarchy. In contrast, programmatic
agendas, such as Socialism, Culture and Civilization, and Global Governance, become structurally
salient due to their strong connection with a wide range of topics. This finding not only aligns with
our previous discussion on authoritarian agenda politics, in which the intense need of an autocrat forpolitical survival does not solely dictate all policy priorities but also prompts further inquiry into theconditions under which this particular type of policy agendas emerges and its influence on theoverall distribution of agendas.
4.3. Temporal Evolution of Policy Agenda in the Xi Era
As previously noted, STM distinguishes itself from other topic modeling methods, such as LDA, byallowing document-level covariates to be incorporated into the model as a means of poolinginformation. A covariate may influence either the prevalence or the content of a topic. Building onthese advances, we introduced a time covariate into the model to explore the temporal dimension of
the agenda outcomes during the Xi era and examined whether and how the weight of policy agendas
in the XIS has shifted.
Figure 6 displays the estimated difference in topic prevalence for each topic between Xi’s first(2012-2017) and second (2017-2022) terms. Among the top agendas that demonstrated a significant
18increase in prevalence during Xi’s second term are COVID-19 (topic 7), China and Developing
Countries (topic 9), Socialism with Chinese Characteristics (topic 10), Poverty Alleviation (topic
12), and Global Economy and Governance (topic 23). In contrast, topics, such as Press Conference(topic 13), China and Major Powers (topic 15), Foreign Visits (topic 5), and Anti-Corruption (topic
14) have witnessed a significant decrease in estimated prevalence. Although these policy agendas
suggest a possible discontinuity or agenda shift after Xi articulated and confirmed his intention tobreak the pre-existing party norm regarding leadership retirement, other issues also demonstratesignificant continuity in policy development throughout Xi’s tenure, including MilitaryModernization (topic 19), BRI (topic 25), Ethnic Unity (topic 2), Chinese Nationalism and Taiwan(topic 22), and Local Visits (topic 16), which have consistently received attention during this period.
One intriguing discovery is the change in the agenda within a group of categories that may fallunder the rubric of enforcement strategy. In contrast to another group of agendas that focus onpolicy content, these agendas outline how the leader seeks to unify and cohere the bureaucracy toensure effective policy implementation, which is a significant concern for the General Secretary.
Figure 7 illustrates the trajectories of the four relevant agendas. It clearly demonstrates that
although Xi emphasized an institutional approach to policy implementation during his first term, his
second term was marked by a more ideological approach that seemingly placed greater emphasis on
the sense of loyalty of individuals.
Additionally, the analysis provides insight into the effect of the COVID-19 outbreak on the
distribution of Xi’s policy attention. It reveals that, in general, the arrival of COVID-19 has diverted
significant attention away from many but not all of the existing agendas. Specifically, we observethat the policy agendas that have experienced a similar surge in attention to COVID-19 include
Global Economy and Governance and Innovation and Economy. This finding is largely consistent
with the broad observation that the earlier response of the CCP to the pandemic emphasized thesuperiority of the Chinese system and the need to reform global institutions to align with the policy
priorities of China (Huang 2021; Yang and Chen 2021).
However, the finding suggests that the adoption and strict adherence of the CCP to the Zero Covid19 policy, which resulted in increased discontent among the population, has led to the increased
reliance on upholding Xi’s authority and symbolically justifying it through appeals to Chinese Culture
and Civilization in which the youth is the main target of this effort (Wing-Chung 2022). This stance19

is evident in the recent increase of attention to agendas such as Socialism with Chinese Characteristics,
Culture and Civilization, and China and Youth. In summary, although COVID-19 has diverted
attention from many policy agendas, it has also reinforced the emphasis of the CCP on the uniquesystem and culture in China, which presents implications for the future of policy priorities.
5. Validation: Tracking the Belt and Road Initiative and Anti-Corruption Campaign
We have used the model fit from STM to measure the prevalence of various categories of policy
agenda and sought to understand the relationship with one another and their evolution over time. To
establish how many and which topics capture these categories, we already engaged in extensive
validation. For example, in Section 4.1, we pursued several possible approaches for identifying the
optimal number of topic categories by utilizing a set of evaluation metrics for topic quality. Anotheravenue for exploring the quality of topics involved the examination of the highest probability andexclusivity words assigned to the topic along with the documents that the model indicates are mostclosely associated with each topic.
Notably, the validation conducted by the study differs from the commonly employed validation
techniques in the machine learning literature, which tends to focus on the degree to which a model
can predict unseen words in a document. For the objective of the study, it is not important that themodel can predict unseen words – we have all the words! Instead, our validation is designed toensure that the agenda categories learned from the XIS are not idiosyncratic or do not reflect awildly different process than that examined by other relevant work.
With these caveats in mind, this section presents another approach for validating the reliability andusefulness of the discovered topics. Following the practices of Grimmer et al. (2013) and Catalinac(2016), we demonstrate that our measures align with well-known facts about policy development
during the Xi’s era. This validation approach is designed to assess the notion that Quinn et al.
(2010) refer to as “predictive validity,” which represents an expected correspondence between a20

measure and exogenous events uninvolved in the measurement process. ９

９However, as the authors caution, the correspondence need not be a pure forecast of events from measures,
but can be concurrent or postdictive, and causality can run from events to measure (Quinn et al. 2010, 27).

21

Figure 8 illustrates the relationship between the topical prevalence of the BRI (topic 25) andChina’s distribution of overseas development finance, which we sourced from the Chinese Overseas
Development Finance Database of Boston University. １０ The graph demonstrates a close correlation
between the topical coverage of BRI and China’s investment in BRI-related projects, particularly
during Xi’s first term. However, since 2019, the actual implementation of BRI has not aligned as
closely with Xi’s discursive patterns, which could be due to the outbreak of COVID-19.
Nevertheless, the study observed a significant increase in the proportion of the Global Economy andGovernance agenda since the pandemic (see the Appendix), which indicates that resources may
have been redirected to other areas or that BRI has been integrated into a broader agenda (Ray2023). Consequently, further investigation is required to determine whether or not changes can beobserved in the language used to discuss BRI by examining the effect of the time covariate on
topical content.
Additionally, Figure 9 illustrates the correlation between the topical prevalence of Anti-corruption(topic 14) in the XIS and the actual implementation of a nation-wide anti-corruption campaign. １１
The figure reveals that Xi’s focus on anti-corruption aligns well with the progress of the anticorruption campaign targeted at centrally-managed cadres. The number of central leaders underinvestigation peaked during the early years of Xi’s leadership and has since steadily decreased.
However, there exists a noticeable time delay between the leader’s agenda setting and policy
implementation at the provincial level, as evidenced by the number of provincially-managed cadres
accused of corruption. Nevertheless, this time inconsistency does not indicate that the leader’sagenda failed to attract the attention of provincial leaders; rather, they presumably required moretime to assess the genuineness and sustainability of Xi’s commitment to combatting corruption(Ding et al. 2020). Notably, despite the observed decrease in the topical proportion of Anticorruption during the second term, Xi continued to exhibit rhetorical commitment to the agenda,
which in turn explains the ongoing implementation of the campaign both at the central andprovincial levels.

１０

https://www.bu.edu/gdp/chinas-overseas-development-finance/

１１

The data is sourced from Yang, Milanovic, and Lin 2023, which is derived from official information
published by the Central Commission for Discipline Inspection (ccdi.gov.cn). Several studies have explored
the anti-corruption campaign using datasets compiled from various sources, such Gang and Yano (2017),
Lorentzen and Lu (2018), and Aidt et al. (2020), and Wang and Dickson (2020). However, the data utilized inthis study represents the most recent, and the only variant that encompasses the period of XI’s second term.

22

6. Conclusion
This study leveraged probabilistic topic modeling on a vast collection of presidential speeches andconducted qualitative and quantitative interpretations of the discovered topics. This process enabled
the presentation of a comprehensive, structural, and dynamic overview of agenda outcomes during
the entire period of Xi’s leadership.
The analysis revealed approximately 25 distinct policy agendas, which exhibit significant temporalvariations in terms of leadership attention. We estimated the difference in topical prevalence
between the first and second terms of Xi’s tenure, which indicates the shifting policy priorities anddifferent enforcement strategies of the leader. Structurally, our model output implies that foreignand domestic agendas separately cluster. Agendas that pose implications for foreign affairs, such as
the military and Taiwan, are discussed in the domestic context, which suggests that Xi appeals tointernational and domestic audiences with different themes and rhetoric. Additionally, we validated
the reliability and usefulness of the model outputs and topic labeling by examining the relationshipbetween the discursive patterns and the actual policy execution of the BRI and anti-corruption
campaign, which is widely known as Xi’s flagship policy initiatives.
The empirical findings of this study shed new light on previously overlooked aspects of the policy
agenda of the Xi Jinping administration. While prior studies have produced valuable insights into
Xi’s key policy agendas, most of them have focused on the initial years of his tenure or specificpolicy domains, offering only a partial and fragmented account of agenda dynamics. Instead, thisstudy aims to present a comprehensive and reliable list of policy agendas that covers the entire
duration of Xi’s leadership, while also seeking to investigate the evolving priorities associated witheach agenda. For instance, the estimation results reveal that in addition to the three major agendas
identified by Pei (2020) – establishing personal political dominance, revitalizing the party-statesystem, and expanding China’s global power and influence – poverty alleviation and domestic
innovation have also gained significant rhetorical attention, a tendency that has further intensified
during Xi’s second term.
From a theoretical perspective, the findings highlight the importance of examining the dynamics ofauthoritarian agendas. The analysis suggests that a dictator’s personal accumulation of power andauthority can potentially influence policy outcomes by shaping their interactions with regime elites.
23

This dynamic may lead to a shift from agenda items driven by particularistic goals to thosecharacterized by programmatic or ideological connotations, particularly when the dictator further
centralizes power. It is worth noting that assuming a fixed preference for a dictator is bothchallenging and undesirable since dictatorial preferences are prone to change, thereby impacting
agenda outcomes.

24

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