The papers here are available upon request. All my publications (including non-peer reviewed papers) are listed on my CV.
Under Review & Revision
Political Science:
From Gun to Gavel: Protest, Repression, and the Limits of Autocratic Liberalization.
Under Review at Comparative Political Studies.
Abstract: How do authoritarian regimes manage dissent during political liberalization? While prior research documents a shift toward more ‘open’ forms of authoritarianism, the micro-level mechanisms underlying this transformation remain underexplored. I examine two mechanisms autocrats employ to liberalize without democratizing, a phenomenon I call autocratic liberalization: substituting high-cost violence with lower-cost legal prosecution and selectively deploying legal repression against key mobilization threats. I study them during Myanmar’s early-2010s liberalization, a critical case of transition from closed autocracy to hybrid rule. Drawing on data on legal reforms, protest events, and state responses, I show that the regime quickly adopted both strategies. Legal reforms are associated with a fourfold increase in protest, yet repression shifted away from informal violence toward disproportionate prosecution of pro-democracy actors. These findings qualify claims that liberalization inherently destabilizes autocrats; instead, autocratic liberalization enhances resilience by transforming political challengers into legal defendants, revealing when and how autocrats shift from gun to gavel.
Authoritarian Impediments to Civil Society in Hybrid Regimes: Findings from the Myanmar Civil Society Survey. Under Review at Journal of East Asian Studies.
Abstract: Prior to the 2021 coup, Myanmar saw unprecedented civil society expansion. Despite liberalization, civil society actors still faced regular obstacles. Whereas egregious repression was documented case-by-case, systematic knowledge about day-to-day impediments, which I call authoritarian impediments, has been missing. The Myanmar Civil Society Survey compiled first-hand data from civil society organizations in June 2019. Specifically, I present findings across five domains: regulatory restrictions, funding constraints, corruption, rights to information and participation, and safe space. The data reveal the prevalence of authoritarian impediments. Critically, many appeared to stem from limited bureaucratic capacity and institutional inertia rather than deliberate repression. Nonetheless, CSO experiences varied: roughly one-third reported improvements under the NLD government, while two-thirds faced unchanged or worsening conditions. The 2021 coup has made further research impossible, rendering the survey crucial historical documentation and a comparative resource for understanding authoritarian impediments in hybrid regimes.
Management:
From Burden to Breakthrough: Off-the-Shelf AI/ML Enables New Research with Unstructured Data. Dissertation Chapter. Under Review at (blinded as per journal policy).
Abstract: Strategy and management researchers increasingly rely on unstructured data such as news articles, website texts, and regulatory filings to study innovation, adaptation, and strategic behavior. Yet transforming such data into structured formats has remained labor-intensive, costly, and dependent on advanced programming skills, limiting broader adoption. This paper conceptualizes and evaluates the use of off-the-shelf artificial intelligence and machine learning (AI/ML) models, including large language models (LLMs) and topic modeling techniques, as scalable and cost-effective solutions for classifying and coding unstructured text. To assess their practicality, I apply off-the-shelf models to two domains that frequently require extensive unstructured data processing: bibliometric analysis of articles published in top management journals (2004-2024) and protest event analysis (PEA). In bibliometric analysis, the LLM reliably classified full-text academic articles. The analysis shows that up to 75 percent of recent articles rely on unstructured data, yet most still depend on manual coding, highlighting the potential value of accessible automation. In PEA, the LLM matched or outperformed trained human coders in both classification and nuanced coding tasks, in some cases achieving more than twice the accuracy with far less effort. Topic modeling approaches performed considerably worse. Rather than advocating full replacement of human or customized AI/ML pipelines, the paper clarifies where off-the-shelf AI/ML performs well, where trade-offs emerge, and how they can augment existing methods. A companion website provides tutorials, code, and best practices to help researchers apply these tools in their own studies.
Shooting for the Right Stars? Three Critical Challenges in Pursuit of Commercial Markets in the ’New’ Space Economy. R&R at Research Policy.
Abstract: The rise of NewSpace is widely described as a pivotal shift from government-led space exploration to a commercially-driven sector fueled by private investment. Yet, Earth observation (EO) satellite operators remain heavily dependent on government funding while struggling to develop viable commercial markets. Through a hypothesis-generating case study of the EO segment, we identify three interconnected challenges constraining commercial development. First, government (co-)dependence incentivizes upstream technological advances aligned with government rather than commercial priorities, keeping data prices too high to enable thorough downstream exploration. Second, limited commercial demand encourages EO ventures to pre-maturely integrate, creating barriers for entry in the downstream. Third, high prices and vertical integration prevent innovation ecosystem formation necessary for discovering new commercial applications, perpetuating limited demand. Using multi-source data triangulation combining quantitative analysis of satellite data and firm-level financial data with qualitative analysis of procurement documents and firm publications, we show that despite upstream advances, commercial revenue remains limited while government dependence increased during commercialization efforts. Our analysis contributes to debates about procurement as innovation policy, nascent markets, and innovation ecosystems while providing managerial and policy recommendation for realizing NewSpace’s commercial potential. Overall, our effort is to encourage further research and dialogue to foster sustainable, inclusive, and accountable futures for space commerce and governance.
Working Papers
The Liability of Loyalty: How Political Hedging Shapes Firm Survival After Democratic Breakdown. Job Market Paper.
Abstract: How do firms’ political strategies perform when democracy collapses? This paper investigates the effectiveness of corporate political hedging—cultivating ties across rival political camps—under conditions of democratic breakdown. While prior research finds that hedging helps firms navigate regime transitions and electoral turnover in democratizing and consolidated democracies, we know little about its value when democratic progress reverses. I exploit Myanmar’s 2021 military coup as a natural experiment to examine how hedging shaped firm survival following the country’s abrupt return to dictatorship. Linking the universe of firms and their owners to all politicians from the preceding democratic period (2010–2021), I construct a novel dataset from nearly one million leaked registry documents using machine learning and extensive human validation. Difference-in-differences analyses reveal that hedged firms were 20–30 percentage points more likely to survive two years after the coup than both unconnected and pro-democracy-only firms—and even outperformed firms exclusively tied to the military. Follow-up experimental evidence with Burmese activists suggests a mechanism: focused boycott behavior disproportionately targeted firms with exclusive military ties, while hedgers avoided activist punishment and investor flight. The study extends theories of nonmarket strategy and political risk by showing that under democratic breakdown, political hedging primarily protects firms not through balanced access to power, but by reducing exposure to non-governmental retaliation.
Digital Natives: Conceptualizing and Measuring Innovation on the Internet, Introducing the Internet Dynamism Index (IDX). Dissertation Chapter.
Abstract: Standard innovation measures are operationalized using observable artifacts like patent applications or R&D spending, which are by-products of ‘good’ institutions or resources allocated to innovation-related activity. Unlike conventional innovators, however, internet innovators engage much less with local resources and institutions; and in some countries, these are practically absent altogether. To address this ‘observation problem,’ I propose the Internet Dynamism Index (IDX), a novel comparative measure of internet innovation. I leverage domain name zone file data to construct the general population of all websites, randomly sample a large subset of domains each month, scrape their current and historic content, and apply various classification algorithms to identify key dimensions of Schumpeterian innovation, geographic origin, and more. After aggregation and standardization, the IDX generates ordinal rankings at the country and city levels that diverge significantly from those produced by conventional measures, also suggesting internet innovation provides unique opportunities for growth in regions lacking traditional comparative advantage in innovation. I validate the IDX through regressions showing distinct patterns of association with predictors of innovation and its correlation with economic performance. Using an instrumental variable approach in the context of Russia’s invasion of Ukraine, I show that internet innovation performance is more resilient to crises than conventional innovation. These findings highlight the economic distinctiveness of internet innovation and its potential to foster sustainable growth, particularly in regions that are unable to maintain or entirely lack conventional innovation. I offer strategic insights for managers and policymakers to optimize location decisions and innovation policies.
Detecting AI-Generated Text Through Syntactic Artifacts: A Character Encoding Approach.
Abstract: Generative AI threatens empirical research integrity by enabling machine-generated responses to surveys, experiments, and academic assessments. Existing detection tools rely on semantic analysis via opaque machine learning models, which may become less accurate as AI sophistication increases. I introduce a syntax-based alternative exploiting a structural artifact: character encoding inconsistencies when users copy AI-generated text into documents. Validated on 347 written responses with definitive ground truth, my method achieves 96.3% accuracy with zero false positives, outperforming leading commercial detectors by 5.8-14.4 percentage points (both p < 0.0001). To assess external validity, I further evaluate the method in an online survey experiment where participants complete open-ended writing tasks under varying instructions regarding AI use. The same encoding signature reliably distinguishes responses produced under AI-permissive conditions from those produced without AI encouragement, while maintaining near-zero false positives. Unlike computationally intensive machine-learning approaches, this transparent algorithm requires no training data and exploits architectural features unlikely to disappear as models improve. Open-source implementations enable immediate deployment in research and educational settings.
When Stakeholder Engagement Isn’t Enough: When and How Home-Country Reputation Undermines Foreign Infrastructure Projects (with Jake Grandy).
Abstract: We examine when home-country reputation conditions the effectiveness of firm-level stakeholder engagement in foreign infrastructure projects. Integrating nonmarket strategy, CSR, and liability-of-foreignness research, we theorize that unfavorable country reputations dampen the marginal returns to engagement, and can even trigger backlash if outreach is perceived as instrumental rather than responsive. We study Myanmar’s post-2010 opening using a multi-method design: (a) an observational dataset linking foreign projects to georeferenced protest events and coded engagement practices, and (b) survey experiments that randomize project country-of-origin and the intensity of “standard” versus “fully engaged“ practices. We expect engagement to raise approval on average, but less so for firms from poorly regarded countries; even at full engagement, approval gaps may persist. These dynamics illuminate a societal spillover in which project-level responses aggregate into anti- country sentiment, feeding geopolitical tensions. Findings inform firms’ political-risk assessments (e.g., when engagement yields diminishing or negative returns), host-country regulation of foreign investors, and home-country efforts to repair reputational liabilities.
Surviving Dictatorship: Organizational Responses to Democratic Backsliding.
Abstract: This study examines how sudden democratic regime breakdown—an increasingly common outcome of democratic backsliding—affects the organizational environment and how organizations respond to these changes. While existing theory suggests that extreme environmental uncertainty leads organizations to either exit or fall into paralysis, I argue that organizations in dictatorships often deploy a wider range of strategic responses, including operating in exile or underground. Drawing on Oliver’s (1991) seminal work on responses to institutional pressures, I theorize how these response choices are shaped by organizational resources, stakeholder preferences, and perceptions of the regime’s coercive capacity. I explore the plausibility of my theory using a mixed-methods approach that triangulates pre-coup survey data, topic modeling of organizational texts, and in-depth interviews to track sixty-six civil society organizations in Myanmar before and after the 2021 military coup. By examining how organizations navigate contradictory institutional pressures in contexts of autocratic restoration, this research offers implications for organizational resilience that extend beyond Myanmar.
Obligated by Memory: Collective Memory and Intergenerational Protest Mobilization.
Abstract: Why are youths so often at the forefront of street protests, even if the state represses them the most? I investigate this question in the context of transitional Myanmar. I argue that many factors commonly associated with youth activism elsewhere were largely absent when Myanmar began liberalizing in 2011, providing opportunities for new theoretical insights. To demonstrate that youths were indeed among the first to mobilize after political opportunity structures changed, and that they were the most active social group in the years after, I examine protest event data from Myanmar. Based on in-depth interviews that I conducted with a diverse set of youth activists in 2017 and 2018, as well as survey experiments conducted via face-to-face interviews with a representative sample of Yangon residents in 2019, I argue that collective memory of the 8888 Uprising, a past youth-led struggle, has been instrumental in mobilizing youths after political opportunity changed in 2011. I find that collective memory has instilled a sense of moral obligation in those self-identifying as youth to ‘finish’ what previous youth generations have started. Activists appeal to this individual-level trait in order to mobilize youths. For autocrats, collective memory is difficult to repress as it can be shared in the safety of one’s home; it may even become ‘stickier’ when the state tries to repress public sharing. While derived from Myanmar, my theory has broader applicability to contexts where the state suppresses the open commemoration of focal events, which can fuel the intergenerational transmission of collective memory and help mobilize protests in the future.
Does Economic Salience Reduce Support for Radical Movements?.
Abstract: This paper examines how economic salience shapes support for radical political movements, focusing on the case of Hong Kong. While many observers attribute the city’s recent social unrest to economic grievances—especially among younger generations—such accounts remain largely untested. Drawing on a survey experiment embedded in a representative household survey conducted in early 2020, I randomly exposed respondents to varying degrees of economic salience before asking about their support for Hong Kong independence in a hypothetical, central government–approved referendum. Framing the question in this way isolates attitudes toward independence itself, apart from concerns about legality or conflict. Contrary to the prevailing economic grievance narrative, I find that greater economic salience increases support for independence rather than dampening it. These findings challenge conventional explanations linking economic hardship to moderation and suggest that material concerns may, under certain conditions, heighten support for radical political change.