Job Market Paper
Firewall for Innovation
Abstract: Do protectionist policies foster domestic growth and innovation in the digital economy, and if so, how? This paper investigates the impact of the Great Firewall (GFW) in China – the world's largest system of internet regulation – on the development of domestic mobile apps. By blocking foreign apps at times determined mostly by political considerations, the GFW prompted a 30% user base expansion for Chinese substitute apps (identified through their baseline text descriptions). Monthly data on these apps’ underlying technologies, extracted from their compiled source code, reveal that Chinese substitute apps accelerated their innovation efforts, with in-house development increasing by 14% two years after the blockage. This technological progress spilled over broadly post-blockage, as both domestic and foreign apps adopted more Chinese technologies. I further show that increased access to data was one important driver. Chinese apps requested more types of sensitive data and were more likely to share user data access with outside firms after their foreign substitutes were blocked. These increased types of user data generate innovation; quasi-random variation in the introduction of new data access raises in-house technology development. Finally, using data-sharing networks between app developers, I show that in-house development also increased at the firms that user data was shared with. In summary, protectionist policies brought about through China's GFW boosted its app industry, potentially contributing to China's leadership role in this fast-growing industry.
Working Papers
Power and the Direction of Research: Evidence from China's Academia
(with Daron Acemoglu and David Yang) | [link to the latest version]
Abstract: Can China stimulate and sustain innovation with its juxtaposition of top-down emphasis on innovation and the presence of powerful leaders within academic institutions? In this paper, we investigate whether powerful actors curtail academic autonomy and freedom, and impact the direction and quality of innovation. We collect comprehensive data on the scientific publications of researchers in the leading 109 Chinese universities and the leadership changes in these universities. We use NLP methods to measure the similarity between faculty members' and their leaders' research portfolios. We find that immediately after --- and not before --- the leaders take office, faculty members begin to shift their research direction towards that of their leaders. Such shifts cannot be explained by the signaling of star researchers' activities, but can be attributed to leaders' political power over faculty members' career trajectories. Leaders appointed by the Communist Party exert greater influence on faculty members' research directions, and leaders' influence is stronger among disciplines and institutions that have historically or recently experienced academic persecution. We also document significant costs of leaders' influence on research quality. Below-median productivity leaders lead to even greater increases in similarity, and switches from above-median to below-median leaders is associated with sizable declines in citations. Such decline is driven by citations to papers that are most similar to new leaders.
Research In Progress
From Choice to Compulsion: Does A/B Testing Drive Behavioral Manipulation?
[slides available upon request]
Abstract: This study examines the impact of A/B testing -- a widely adopted method by internet companies to leverage user data and inform data-driven decisions -- on the escalation of temptation levels in digital products. I develop a model in which individuals with intertemporally inconsistent preferences make daily decisions regarding the optimal duration for blocking apps, aimed at temptation mitigation. In collaboration with Freedom, one of the largest and most comprehensive commitment applications for blocking distracting apps and websites, I estimate the temptation levels of over 2,000 apps on a monthly basis from 2021 to 2023 using detailed session-level data. Preliminary findings indicate that approximately 20% of app usage can be attributed to temptation and that temptation levels have intensified over time. This trend is strongly correlated with the increasing adoption of A/B testing practices within these applications.
Increasing Revenue Collection with Computer Vision: Experiments in Pakistan
(with Sher Afghan Asad, Adnan Khan, Ben Olken, and Mahvish Shaukat)
Abstract: Economic growth in developing countries is often limited by the state’s inability to raise tax revenue. In many countries, tax administration systems rely on infrequently updated and out-of-date property tax valuations, and tax officials often employ significant discretion when assessing properties. These factors can lead to errors that could increase tax leakages or lower citizen trust in the state. This study addresses this challenge in two steps: first, by developing a computer vision algorithm that can use property images to predict property assessments and second, by testing how well the algorithm performs in identifying properties for reassessment.
Data Sovereignty and Sustainability
(with Yulu Tang)
Abstract: Developing countries face a trilemma in building their digital economies: (1) the increasing demand for data centers in the digital era, (2) the significant costs of constructing them in warmer regions, particularly in the Global South, and (3) their environmental impact due to a substantial carbon footprint. To address these challenges, we have compiled extensive datasets tracking global internet firms' data center location decisions, user bases, and operating costs across 167 countries over the past two decades. Leveraging this data, we will develop a model to quantify the influence of critical factors -- such as market demand, operational expenses, policy changes related to data security -- on firms' data center site selection. This model enables us to conduct policy counterfactuals, identifying the most effective strategies for reducing costs and minimizing environmental impact. It also provides valuable insights for shaping policy interventions that promote the development of efficient and sustainable data centers, particularly in developing countries.