Research

The ideological relaxation and fiscal autonomy of the 1980s initiated over three decades of bold economic reform in China, driving sustained growth. However, an analysis of over 50,000 bond issuance records reveals a sharp increase in local debt, which has progressively constrained policy choices at the local level. Our cross-sectional and dynamic examination demonstrates that China's once-nimble developmentalism has largely transitioned into debt-driven crisis management across most provinces. In 27 provinces, high debt servicing costs for bond obligations relative to local revenue have left authorities heavily dependent on central government transfers and new bond issuance to sustain basic governance. This reliance has significantly eroded the fiscal autonomy enjoyed in previous decades. Furthermore, we provide evidence that these growing fiscal constraints have hindered the capacity of high-debt provinces to pursue policy innovation, even in critical areas such as carbon neutrality. Our findings highlight the profound shift in China's local governance from local innovation to high dependence on the center, with significant implications for future economic and policy trajectories.

Authoritarian regimes regularly disseminate messaging designed to strengthen support for nondemocratic political systems. Increasingly, pro-state narratives are disseminated by social media influencers rather than traditional media. To gain understanding of the extent of this phenomenon and the persuasive capabilities of these "foreign influencers," we collect comprehensive data on pro-China influencers on TikTok. We show that pro-China influencers have more engagement than state media and estimate that many influencers generate large personal financial returns. We then create a realistic, fully functional version of the TikTok app and conduct a randomized experiment in which over 8500 Americans view a random sample of actual TikTok content over 150,000 times. We show that pro-China foreign influencers are strikingly effective at increasing favorability toward China, while traditional state media causes backlash. The findings highlight the importance of influencers in shaping global public opinion.

Effective climate action depends not only on technological effects and policy innovations but also on the leadership driving their implementation. Despite extensive research on solutions to mitigate climate change and reduce carbon emissions, progress often stalls at the implementation stage, prompting a crucial question: Which leaders are best equipped to deliver meaningful climate outcomes? Here, I show that political leaders with local embeddedness demonstrate advantages in advancing climate action. Analyzing two decades of city-level data in China, I leverage the central government’s leadership assignment system to reveal that cities led by embedded bureaucrats exhibit larger progress in low-carbon transitions. My findings indicate that such leaders are 1.5 times more likely to prioritize climate issues in public discourse, implement twice as strong emissions-reduction policies, and lead to significant reductions in air pollution and CO₂ emissions. I distinguish between two forms of embeddedness: emotional and professional. Emotional embeddedness, driven by personal connections to the locality, emerges as the primary driver of enhanced climate action, shifting leaders' preferences towards sustainable policies. In contrast, professional embeddedness, characterized by local administrative experience, plays a more modest role. This paper calls attention to the potential of appointing embedded leaders to accelerate progress toward carbon neutrality and sustainable development.

Corpus curation is routine in text-as-data research, yet it is often under-reported and rarely treated as a source of inferential uncertainty. I surveyed 44 AJPS and APSR articles (2019--2024) and found that 48\% do not describe curation steps. This paper examines three common decision domains---document selection, text-quality enhancement, and metadata validation---and evaluates their downstream consequences through classification illustrations and replications of published political science papers. Across applications, curation choices shift baseline descriptives and, in some cases, substantive estimates. However, I find that these effects are context-dependent, often non-additive, and can introduce risks such as sample alteration and context loss. In response, I propose a transparency-first curation audit centered on explicit logging and diagnostics across plausible alternatives. I also introduce an open-source software package, corpuscurator, to implement these workflows and support reproducible reporting. Treating curation decisions as explicit, testable design choices strengthens the credibility and replicability of text-based inference.

While institutions are often assumed to promote development, excessive regulation may hurt economic growth. We examine the effect of local regulatory activity on economic growth using a dataset of city-level legislation and government work reports across 273 Chinese cities from 2008 to 2023. Using a shift-share instrument strategy based on the aggregation of exogenous topic salience, we identify the causal impact of local lawmaking on growth. We find that increased local lawmaking reduces GDP growth: a 0.1 rise in legislative activity leads to a 1.1 to 1.4 percentage point decline in annual GDP growth, about a 20% reduction relative to our sample mean of 6.3%. While cities benefit from adapting national priorities, formalizing these into local laws decreases growth, especially in the industrial sector, where it leads to firm exit. These findings highlight a central institutional challenge in China's regional decentralization framework: the same mechanism that signals responsive local government can generate bureaucratic overreach that constrains economic performance.

How can prediction markets affect politics? While traditional markets have long influenced politics, we argue that some of prediction markets' unique features allow them to affect political speech through distinct forms of manipulation, fulfillment, and legitimation. We then test for the possibility that one or more of these mechanisms are operating by analyzing the relationship between "mention markets," which allow traders to wager on specific words spoken by political elites, and public comments by Donald Trump and the Chair of the Federal Reserve. We find that words introduced into mention markets become significantly more likely to appear in Trump's remarks during the days following market creation, with larger effects for markets when there is higher trading volume. By contrast, we detect no comparable effects in Federal Reserve press conferences. These results suggest that prediction markets are playing a role in shaping public political speech when financial stakes are high and speech is less constrained by accountability. The findings also encourage further research directly examining the mechanisms linking prediction markets to political speech, as well as other political outcomes.

Does mayoral partisanship matter for local politics and finance? Theory predicts that mayors, as chief executives with concentrated power, should shape fiscal outcomes through partisan policy choices. Yet empirical tests consistently find null or minimal effects of mayoral ideology on city fiscal conditions, leading scholars to question whether partisanship matters in municipal governance. This paper resolves the puzzle by demonstrating that partisan identity profoundly shapes outcomes not through immediate policy changes, but through the market expectations it generates. Even when policies remain unchanged, partisan signals convey valuable information that diverse economic actors use to update their beliefs about future governance. Using a regression discontinuity design around close U.S. mayoral elections, I find that municipal bond prices increase by 0.4-1.5% when Democrats narrowly defeat Republicans. These market responses occur within one month of the election outcome, before newly elected mayors take office, and direct tests reveal no detectable changes in fiscal policy variables (revenues, expenditures, debt levels). This timing rules out policy-based explanations and instead points to an expectations-based mechanism: bond investors update beliefs about future governance based on partisan signals alone. To understand what expectations drive this response, I examine how job postings, housing prices, and rental markets respond to the same partisan signal. My results are aligned with a pro-development interpretation. All responses occur in the same month, before any policy implementation, confirming that diverse economic actors-firms, landlords, home buyers, and bond investors-interpret partisan identity as conveying credible information about future local conditions. Applying a regression discontinuity design to more than 16,000 municipal credit reports further confirms this expectations channel: Democratic victories generate significant increases in analyst net sentiment scores and in the frequency of formal credit rating upgrades. The findings demonstrate that partisan signals may be even more powerful than actual policy changes in shaping economic outcomes, as expectations coordinate private-sector behavior long before governance shifts materialize.

Not all local elections nationalize equally. While scholarship documents the broad nationalization of American politics, we know less about what causes variation in nationalization across local races. I argue that features of the electoral environment, incumbency, partisan competition, and mayoral partisan identity might shape whether municipal contests attract national attention or prompt strategic nationalization by candidates. I assemble a new corpus of 440,000+ newspaper articles covering mayoral races over 40 years across 700 U.S. cities and construct a text-based Nationalization Index that captures mentions of non-local political figures in race coverage. Using stacked difference-in-differences, panel fixed effects models, and regression-discontinuity designs, I show that incumbent reruns lead to an increase of about 11% in nationalization, while partisan-race and partisan-identity effects are generally small and not significant. Mechanism tests using novel data on mayoral campaign donations and mayoral candidate expenditures from ten states indicate incumbents attract more out-of-state donations but do not systematically increase out-of-state consulting, consistent with an increase in national attention rather than strategic candidate adaptation. The paper contributes a new scalable measure of local nationalization and evidence on when and how national politics penetrate municipal elections.

Conventional accounts of authoritarian politics argue that elites prioritize political survival, not ideology. In this paper, we challenge that view by demonstrating how ideology shapes elite competition in China. We argue that autocratic leaders use ideology to signal policy preferences and rely on personal networks to identify officials aligned with their ideological vision. We build a new dataset of over 50,000 speeches and 40,000 policy documents from local officials in China and develop a novel method to measure ideological alignment with Xi Jinping. We find that elite conflict revolves around socialism and economic issues. Local officials with personal ties to Xi who publicly align with his socialist ideology are more likely to advance in their careers. They are also more likely to implement socialist policies, with negative consequences for economic growth. These findings suggest that, contrary to dominant theories, ideology plays a central role in structuring elite politics under authoritarianism.

Hamilton’s Paradox highlights the moral hazard faced by local governments due to the implicit expectation of central government bailouts. Where the central fiscal authorities can credibly deny bailouts to the localities, local debt can be constrained. This paper sets forth a framework where moral hazard intensifies at both the central and local levels when financial repression eliminates pressure and policing from external creditors. In such cases, central authorities, even if they could discipline local authorities, may repeatedly raise debt limits for local governments out of two considerations. First, the central government prefers to avoid the deleterious policy and political effects of sudden stops in credit provision to local governments. Second, the central government aims to transfer fiscal obligations from its balance sheet to local balance sheets in order to maintain its international reputation as a reliable steward of the economy. Empirically, we demonstrate the benefits of financial repression to the central government by showing that rising government debt levels do not impact bond spreads, unlike in most developing countries. We then show that when local debts mature, Chinese local governments, backed by central approval, issue additional debt rather than impose austerity, regardless of outstanding debt levels. Third, by matching a comprehensive geospatial dataset of major floods with China’s provincial boundaries, we show that the central government does not provide substantial fiscal transfers to provinces affected by major floods but permits localities to borrow further for disaster relief and reconstruction. This pattern reveals that strict capital control protects China from external financial scrutiny, enabling local authorities to intensify debt accumulation at the behest of the central government.

Building on an independent database of 180,000 UN recommendations and a novel computational method, we present the most comprehensive study of HR debates to date. We develop a unique empirical model that measures topical density of discourse. This innovative instrument measures the discursive activity of UN HR bodies through a machine-learning textual analysis of their outputs, offering a dynamic map of evolving trends in human rights, both over time (diachronically) and across different mechanisms (synchronically) within the UN HR ecosystem. Leveraging this comprehensive dataset and sophisticated computational methodologies, we identify which protected groups are central to different mechanisms’ attention and highlight the major human rights issues that have witnessed significant changes in attention. Our research presents significant findings on the density of UN HR discourse and its implications for two major debates in the field of human rights law – HR proliferation and the structural critique of UN HR bodies.

Type inference methods based on deep learning are becoming increasingly popular as they aim to compensate for the drawbacks of static and dynamic analysis approaches, such as high uncertainty. However, their practical application is still debatable due to several intrinsic issues such as code from different software domains will involve data types that are unknown to the type inference system. In order to overcome these problems and gain high-confidence predictions, we thus present TIPICAL, a method that combines deep similarity learning with novelty detection. We show that our method can better predict data types in high confidence by successfully filtering out unknown and inaccurate predicted data types and achieving higher F1 scores to the state-of-the-art type inference method Type4Py. Additionally, we investigate how different software domains and data type frequencies may affect the results of our method.