This project develops a structured empirical pathway toward constructing a Rupee Internationalisation Index (RII) by addressing a central methodological constraint: the absence of a defensible framework for aggregating heterogeneous indicators of currency usage. Rather than imposing a composite index ex ante, the research adopts a decomposed, multi-channel divergence framework, using China’s renminbi trajectory as an empirical benchmark to identify the structural conditions under which a domestic currency attains international relevance. The project constructs a harmonised country–year panel integrating data from the World Bank, IMF, and BIS, and evaluates divergence across four channels—external scale, external balance structure, financial depth, and exchange rate regime—using a standardised metric centred on a post-2013 structural break. Preliminary findings indicate that currency internationalisation is not driven by broad macroeconomic convergence, but by asymmetric dominance in specific dimensions, particularly global trade centrality and external balance configuration. Divergence in China’s global export share highlights the primacy of scale and network position over conventional openness measures. Within this framework, the RII is an endogenously reconceptualised construct, where variable selection and weighting are informed by empirically validated divergence signals. The project establishes a scalable analytical foundation for rigorously evaluating India’s position within the evolving global monetary system.

This study analyzes local and national housing market dynamics using new data published by Zillow. In previous research, much of the work does not provide complete direct evidence on the activity from the buyer’s side before a sale occurs. To address this gap, this paper uses monthly Zillow data on the metropolitan level from 2018 to 2025, with variables including market heat, days to close, days to pending, list price, sale-to-list ratio, and new listings. With the market heat index variable specifically, this variable indicates how favorable the market is tilted in favor of sellers, with a higher value corresponding towards being favorable to sellers and a lower value being more favorable to buyers. After cleaning the Zillow data, we're able to craft a new database in Google Colab and use several methods including descriptive statistics, correlation analysis, and STL decomposition of logged series to identify long-run trends, seasonality, and residual variation. The results show that the faster a home took to go pending on the market, the less it would often fall below the asking price of the seller. This means that hotter markets tend to move faster and produce stronger outcomes for sellers, allowing them to sell their home closer to the asking price. Overall, the study shows that Zillow’s data, specifically its heat index variable, adds useful evidence on buyer competition and bargaining pressure, providing an extended housing market analysis.

This paper challenges the "Attenuation Principle" (Brainard, 1967) in the context of structural model uncertainty. While conventional wisdom suggests policymakers should act cautiously when facing uncertainty, we demonstrate that a passive ("myopic'') approach significantly reduces the signal-to-noise ratio, prolonging model ambiguity. By solving the dual control problem using a high-fidelity Finite Difference Method on a 125^3 grid, we identify a distinct "probing wedge''---an optimal deviation where the policymaker induces strategic volatility to accelerate belief convergence. We show that this "anti-attenuation'' strategy generates a welfare gain reaching up to a 0.73 percentage point reduction in inflation volatility during periods of peak uncertainty, proving that the long-run value of model identification outweighs the short-run costs of stabilization.

As AI development concentrates in the United States and China, technology dependencies from development financing risk constraining developing countries’ digital governance trajectories. China’s Digital Silk Road (DSR) has contributed to this by diffusing surveillance technologies (facial recognition, smart city platforms, and predictive policing infrastructure) across Belt and Road Initiative (BRI) partners. This technological adoption creates plausible pathways for governance convergence, but domestic factors complicate this as states may adopt Chinese surveillance infrastructure while maintaining individual privacy frameworks, or converge toward sovereignty-centered governance without significant Chinese hardware presence. For Southeast Asian states adopting Chinese surveillance technologies while formalizing AI governance frameworks, this raises the question: does DSR surveillance technology diffusion translate into governance-level alignment in Southeast Asian AI strategies, and under what conditions? AI strategies are the unit of analysis because their normative content, whether centering individual rights or collective security, reflects future visions for digital state-society relations. Using a most-similar systems design across three Southeast Asian BRI members selected for variation in regime trajectory, relationship with China, and pre-existing privacy laws, this study compares AI strategy documents against a Chinese policy corpus to assess whether governance convergence follows surveillance technology diffusion.