We are pleased to announce the funding results of IEMS Research Grants 2025. Out of a total of 16 applications, 6 grants valued at about HKD 480,000 were awarded. 

Awardees

Effects of internet connectivity on the economic lives of people in the global south

Abhiroop Mukherjee, Professor of Finance, HKUST

Sumit Agarwal, Low Tuck Kwong Distinguished Professor of Finance, National University of Singapore

How exactly did the internet–the “greatest invention of our time” (The Economist, 2012) change the lives of people in developing countries? In India, for example, less than 0.5% of the population had internet access in 2000; but by March 2023, there were 881 million internet subscribers (TRAI, 2023). These 881 million people today use the internet to socialize, search, shop, study, share ideas, seal deals, save money–—raising important questions about its economic impacts. Who benefits from digital expansion, and who is left behind? Given that 96% of Indian users access the internet via mobile devices, this study focuses on the rollout of 3G, which marked the first widespread mobile internet access. Using two separate proprietary datasets from India’s largest banks, including one that contains over 75 million bank transactions from 429,000 individuals over eight years, we aim to measure how mobile internet affects income, consumption, payment preferences, insurance uptake, and inequality. To ensure causality, we exploit a natural experiment: delays in 3G rollout caused by a telecom corruption scandal. We use the staggered acquisitions of affected firms as an instrument for internet expansion. This allows us to isolate the impact of internet access from other economic factors.

Choices for work and family policy in low fertility BRI Emerging Markets in Southeast Asia

Stuart Gietel-Basten, Professor of Social Science and Public Policy, HKUST

Lu Gram, Senior Research Fellow in Global Health, Institute for Global Health, University College London

Yeqing Zhang, PhD student, University College London

Putu Geniki Lavinia Natih, Lecturer, Faculty of Economics, Univeristas Indonesia

Emerging markets benefit significantly from their demographic circumstances, particularly during the"demographic dividend" phase, characterized by declining fertility rates and high old-age mortality. This phase creates a substantial working-age population that, when coupled with employment opportunities, human capital investment, and gender equity, can stimulate economic growth. However, this demographic window is temporary, as many emerging markets, including China with a Total Fertility Rate (TFR) of 1.0, face low fertility and rapid aging, leading to potential population decline. The Belt and Road Initiative (BRI) relies on these markets, yet many are transitioning to similarly low fertility rates, diminishing their demographic advantages. Southeast Asia, a critical region for the BRI, illustrates these trends, with countries experiencing some of the lowest fertility rates globally. In response, governments have implemented various pronatalist policies, although their effectiveness is debated. Our recent research in China employed a discrete choice experiment to assess married women's preferences for family formation policies, revealing that gender equity in the workplace is vital for meeting reproductive aspirations. We propose to replicate this study in other Southeast Asian emerging markets to compare low-fertility settings, focusing on urban areas. By utilizing a validated questionnaire and engaging local participants, we aim to understand better the dynamics of fertility intentions and the impact of policy interventions in diverse demographic contexts. This research seeks to inform policies that adequately address the underlying causes of low fertility while promoting sustainable economic and social development.

Counterproductive Transparency: Why Disclosure of AI Generated Content Backfires

David Hagmann, Assistant Professor of Management, HKUST

George Loewenstein, Herbert A. Simon University Professor of Economics and Psychology, Department of Social and Decision Sciences, Carnegie Mellon University

Zaidan Chen (Amanda), PhD Student, Department of Management, HKUST

Ching Pang (Christie), PhD Student, Individualized Interdisciplinary Program–Immersive Technologies, HKUST

Policymakers are looking for ways to regulate AI content, driven by concerns that AI-generated media may mislead audiences and shape perceptions on important social issues. The EU has been taking the lead, requiring disclosure of such content, which, the argument goes, can help people avoid misinformation because they will know that the content was not generated by a human. Drawing on research by the PI and a Co-PI, as well as a proof-of-concept study conducted by all applicants, we propose that this intuitive policy can backfire and increase engagement with misinformation. Specifically, we hypothesize that (1) labeling AI-generated content does not meaningfully reduce how persuasive it is, which we document in a recently conducted and unpublished study, (2) people are more curious about AI-generated content than real content, such that adding labels increases willingness to view the content, and (3) people are propelling to share knowingly AI-generated content. Thus, mandating AI labeling could backfire and increase exposure on the supply and demand side, without reducing the impact of the AI-generated information. Our proposed research can inform policymakers in Hong Kong, the GBA, and beyond in how to tackle the risks around misinformation in the AI era. While disclosure requirements and AI content labeling might seem like effective ways of addressing the problem, the preliminary and hypothesized findings of this project may help policymakers avoid regulation that could cause more harm than good.

Geopolitics and Innovation Decoupling: Impact of US Entity List on Chinese Firms

Jiatao Li, Lee Quo Wei Professor of Business and Chair Professor of Management, School of Business and Management

In recent years, escalating geopolitical tensions between China and the United States—marked by the rise of techno-nationalism and de-globalization—have dramatically reshaped the global institutional landscape, posing unprecedented challenges for Chinese firms. According to this context, this project aims to examine how Chinese firms strategically adjust their innovation activities in response to escalating geopolitical tensions between the United States and China, particularly those shaped by techno-nationalism and de-globalization. We plan to focus on the phenomenon of innovation decoupling from U.S. technologies by analyzing three key dimensions: knowledge sourcing, technology combinations, and invention content. Drawing on a longitudinal dataset of Chinese firms listed on the Growth Enterprise Market (GEM) and the Science and Technology Innovation Board(STIB), we plan to employ a staggered difference-in-differences model to identify the causal effects of industry peer entity listing events on firms’ innovation strategies.

To further explore the heterogeneity in strategic responses, we will apply unsupervised machine learning methods, such as K-means clustering, to identify patterns across firms, and focus on firm-level outcomes such as total factor productivity (TFP), Tobin’s Q, and return on invested capital (ROIC).

This study aims to contribute to the literature on innovation, geopolitical risk, and firm performance by providing a comprehensive analysis of how firms in emerging markets adapt their innovation strategies under institutional and geopolitical pressures. It will also introduce methodological improvements in capturing technological recombination, enhancing the measurement of invention novelty and conventionality.

Trade War 'Winners' Revisited: Environmental Consequences of Manufacturing Relocation

Deyu Rao, Assistant Professor of Economics, HKUST

Yuta Antai Suzuki, Assistant Professor, College of Economics and Management, Shanghai Jiao Tong University

Sifan Xue, Assistant Professor, National School of Development, Peking University

The US-China trade war has reshaped global production networks, with some countries experiencing economic gains from redirected manufacturing activity. While these relocations may boost employment and growth, their environmental consequences remain poorly understood. This study investigates whether nations benefiting from trade diversion face unintended environmental costs due to expanded manufacturing scales, even as they reap economic rewards. We propose an integrated framework combining international trade theory and environmental economics to analyze how multinational firm relocations—particularly to Southeast Asian countries like Vietnam and Malaysia—affect emissions through three key channels: (1) sectoral composition shifts, (2) production scale effects, and (3) technology transfer through firm-level abatement investments. Our methodology develops a quantitative model incorporating sector-specific energy intensities and productivity-dependent abatement technologies, building on recent advances in trade and environmental economics literature.

This research makes two primary contributions: First, it provides systematic evidence on whether trade war-induced production shifts exacerbate pollution in host countries. Second, it examines the conditions under which multinational firms' technology advantages might mitigate environmental damage. The analysis will distinguish between local pollutants (e.g., SO₂) and global emissions (e.g., CO₂), as their policy implications differ substantially.

By quantifying these complex interactions, the study aims to inform debates on sustainable trade policy. Potential findings could help policymakers design targeted interventions—such as green FDI incentives or carbon-adjusted tariffs—to align economic diversification with environmental objectives. The project underscores the need to evaluate trade conflicts through both economic and ecological lenses.

Large language model-powered smartphone systems for real-time behavioral analysis  and personalized interventions for mild dementia in Hong Kong

Xiaomin Ouyang, Assistant Professor of Computer Science and Engineering, HKUST

Polly Wai Chi Li, Associate Professor, School of Nursing, The University of Hong Kong

Allen Ting Chun Lee, The Clinical Associate Professor, Department of Psychiatry, The Chinese University of Hong Kong

Guoliang Xing, Professor, Information Engineering, The Chinese University of Hong Kong

Hong Kong’s rapidly aging population is grappling with a dementia crisis, where over 150,000 individuals are affected, yet diagnosis delays average 18–24 months due to overburdened clinics and costly, episodic assessments. This proposal presents a collaborative initiative to develop an AI and smartphone-based digital intervention system that leverages large language models (LLMs) to analyze multimodal sensor data (e.g., motion, location, app usage)collected by smartphones for real-time detection of dementia behavior biomarkers and generation of personalized, just-in-time health interventions. Targeting community-dwelling elderly with mild dementia who can use smartphones independently, our solution leverages ubiquitous smartphone sensors and LLMs to create a low-cost, scalable tool for proactive care. The system employs LLMs to interpret subtle, longitudinal behavioral changes—such as physical inactivity, sleep fragmentation, and social withdrawal—based on sensor data, and generates personalized intervention suggestions accordingly, delivering them in the right time based on run-time contextual data. Unlike conventional methods, our platform operates passively via ubiquitous smartphones, enabling continuous monitoring of behavior and lifestyle changes, and generating personalized intervention suggestions in daily living environments. Such digital health systems have the potential to slow the progression of dementia and reduce the burden on nurses and healthcare systems.