Thomas Li

We study the international transmission of inflation uncertainty and its economic consequences across a panel of thirty-four advanced and emerging market economies from 1980 to 2023. Building on the forecastabilty-based measure of inflation uncertainty of Londono, Ma, and Wilson (2024), we document that inflation uncertainty has remained exceptionally elevated since the onset of the COVID-19 pandemic, reaching a record high of roughly four standard deviations above its historical mean in March 2023. Estimating the domestic and cross-border effects of inflation uncertainty, we find that a rise in foreign inflation uncertainty exerts an effect on the domestic economy that is independent of domestic inflation uncertainty and of the level of inflation at home and abroad: it raises future domestic inflation uncertainty, lowers domestic monetary policy rates, and reduces domestic investment. These spillovers are economically meaningful but smaller than the corresponding domestic effects—a three-standard-deviation increase in foreign (domestic) inflation uncertainty is followed by a decline in investment of 2.55 (4.90) percent after six quarters. The effect on U.S. investment is highly heterogeneous across source countries: inflation uncertainty originating in economies with policy rates below those in the United States—that is, with negative monetary policy rate differentials—has a more pronounced impact, suggesting that a low policy rate differential amplifies the transmission of foreign inflation uncertainty to domestic activity. Our findings imply that the global rise in inflation uncertainty during the COVID-19 period represented an additional headwind to real activity.

We examine how luxury brands can navigate the exclusivity paradox—the tension between maintaining prestige through scarcity and embracing digital accessibility. Our study develops a rigorous, data-driven framework, integrating choice-based conjoint analysis with equilibrium modeling, for determining how online strategies affect both consumer preferences and competitive dynamics in the luxury market. Using data collected from a sample of high net-worth U.S. consumers, we assess responses to combinations of offline and digital strategies across three leading brands in the fashion industry (Louis Vuitton, Chanel, and Hermès). Our results challenge the traditional belief that e-commerce and digital openness dilute luxury’s allure. We find that thoughtfully designed digital strategies, such as immersive technologies and brand-led online communities, can strengthen brand prestige when aligned with a brand’s offline positioning. Simulations with competitive responses reveal distinct optimal strategies across the three brands. Louis Vuitton and Chanel gain from more open online spaces, whereas Hermès benefits from maintaining exclusivity through invite-only digital experiences. These findings collectively demonstrate that digital transformation is not antithetical to luxury. Rather, digital channels offer new avenues for expressing exclusivity, controlling narrative, and deepening consumer engagement. We offer practical guidance for managers to optimize their online strategy in a competitive luxury market.

Human mobility datasets have seen increasing adoption in the past decade, enabling diverse applications that leverage the high precision of measured trajectories relative to other human mobility datasets. However, there are concerns about whether the high sparsity in some commercial datasets can introduce errors due to lack of robustness in processing algorithms, which could compromise the validity of downstream results. The scarcity of “ground-truth” data makes it particularly challenging to evaluate and calibrate these algorithms. To overcome these limitations and allow for an intermediate form of validation of common processing algorithms, we propose a synthetic trajectory simulator and sandbox environment meant to replicate the features of commercial datasets that could cause errors in such algorithms, and which can be used to compare algorithm outputs with “ground-truth” synthetic trajectories and mobility diaries. Our code is open-source and is publicly available alongside tutorial notebooks and sample datasets generated with it.

2025: International Conference on Computational Social Science. “A Synthetic Dataset and Framework to Test Pre-processing Algorithms of Human Mobility Data.” (Poster)

2024: International Conference on Computational Social Science. “Trajectory Data Mining in Highly Sparse Location Datasets.” (Parallel Talk)