Abstract: We study the causal effects and policy implications of global supply chain disruptions. We construct a new index that measures the state of the global supply chain from the mandatory automatic identification system data of container ships and propose a novel spatial clustering algorithm that determines real-time congestion from the positions, speeds, and headings of container ships in major ports around the globe. We develop a model with search frictions between producers and retailers that links spare productive capacity with congestion in the goods market and the responses of output and prices to supply chain shocks. The co-movements of output, prices, and spare capacity yield unique identification restrictions that allow us to study the causal effects on macroeconomic outcomes. We document how supply chain shocks drove U.S. inflation during 2021 but that, from 2022 onward, traditional demand and supply shocks also played an important role in explaining inflation. Finally, we show how monetary policy is more effective in taming inflation amid supply chain disruptions than in regular circumstances.

• My presentations: University of International Business and Economics (2022), 4th UIBE CCTER Workshop on Transportation Research (2022), University of Oxford (2023), 2nd SaM Asia-Pacific Spring Online Workshop (2023), KU Leuven Summer Event (2023), 26th Theories and Methods in Macroeconomics Conference (2023), Commodity & Energy Markets Association Annual Conference, China Meeting of the Econometric Society (2023), 11th Shanghai Macroeconomics Workshop (2023), Asian Meeting of the Econometric Society, Singapore, EEA-ESEM Barcelona (2023), Inflation: Drivers and Dynamics Conference (2023), NBU-EABCN Workshop on Monetary Policy in Emerging Markets (2023), Chinese Academy of Social Sciences (2024), Central University of Finance and Economics (2024), RES Annual Conference (2024), Inaugural Conference of the ESCB Research Network on Monetary Transmission (2024), Shanghai Jiaotong University Macro Workshop (2024), NBER East Asian Seminar on Economics (2024), SED Annual Conference (2024), AMLEDS webinar, Peking University, ASSA Annual Meeting (scheduled).

• Our presentation slides can be accessed here. 

• We created a website to present the latest progress of our research and to share findings and related data. Interested audiences can also download the aggregate indices of port congestion (ACR and ACT indices) from the website. 

• My presentation at the Inflation: Drivers and Dynamics Conference 2023 (hosted by ECB and the Cleveland Fed) can be accessed here. 

• Jesús' presentation at the NBER Monetary Economics Program Meeting Fall 2023 can be accessed here. 

• Jerome Powell, the Chairman of the Federal Reserve, cited our work several times in his Review and Outlook speech at the "Reassessing the Effectiveness and Transmission of Monetary Policy" economic symposium, sponsored by the Federal Reserve Bank of Kansas City in Jackson Hole, Wyoming. 

• A more accessible piece, titled "Supply Chain Disruptions and Pandemic-Era Inflation," is featured in the NBER Digest. Media coverage by the Wall Street Journal, under the title "How Supply-Chain Snarls Made Everyone Wrong on Inflation," can be accessed here. Further coverage can be found on VoxEU, Marginal Revolution, and Omnia Podcast. 

Abstract: We examine the impact of dark shipping - oil tankers disabling AIS (Automatic Identification System) transceivers to evade detection - in response to Western sanctions on crude oil exports from Iran, Syria, Venezuela, and Russia, focusing on the U.S., EU, and China. Using a novel multi-attribute ship clustering model and leveraging AIS data even during signal gaps, we assess the likelihood that vessels transport oil from sanctioned countries or engage in unauthorized ship-to-ship transfers. Our analysis reveals significant differences between tankers likely involved in suspicious activities ("dark") and those that are not ("white"). From 2017 to 2023, we estimate that an average of 7.8 million metric tons of crude oil per month was exported from sanctioned countries via dark ships, equating to about 43% of total world seaborne crude oil exports in official data. China imports the largest share, accounting for 15% of total sanctioned oil exports. Using local projections, we find that intensified sanctions drive higher exports of discounted, dark-shipped oil, mitigating the fall in recorded world seaborne oil exports but causing divergent aggregate implications. The U.S., a net oil exporter, suffers from lower oil prices but benefits from cheaper imports from China, experiencing deflationary pressure and supply-driven growth. Conversely, the EU, a net oil importer, faces higher energy costs but gains from increased demand from China, leading to inflation and demand-driven growth. Meanwhile, China's access to discounted oil significantly boosts its industrial production, playing a key role in driving aggregate dynamics for the U.S. and EU.

• Draft available upon request.

• My presentations: Shanghai University of Finance and Economics.

Abstract: This paper introduces a multi-attribute ship clustering model designed to detect dark shipping activities, particularly involving oil tankers that deactivate their AIS (Automatic Identification System) transceivers to avoid detection, often in relation to Western sanctions targeting oil-exporting countries such as Iran, Venezuela, and Russia. The model combines vessel-specific attributes, kinematic behaviors, and trip-level AIS data to differentiate between tankers likely involved in suspicious practices ("dark") and those less likely ("white"). The model develops two key measures during periods when a tanker's AIS signal disappears - one assessing the vessel's geographical proximity to sanctioned ports and another examining suspicious ship-to-ship transfers - offering a comprehensive framework for detecting the dark shipping of sanctioned oil. Our findings reveal significant differences between dark and white ships in terms of vessel age, flag state, and travel patterns, as well as key geographical hotspots for dark shipping, such as the Black and Baltic Seas, where activity surged following the imposition of sanctions on Russian oil in 2022. Validated against external sources, the model demonstrates more than a 28% improvement in prediction accuracy compared to simpler methods. Beyond contributing to theoretical insights, our model has the potential to support real-time monitoring of illegal oil flows and inform the development of more effective sanctions enforcement.

• Draft available upon request.

Abstract: this paper uses a unique dataset, encompassing the entire product tanker fleet from 2017-2020, to study congestion in oil transportation markets. From this we establish three novel facts: (1) congestion significantly reduces tankers' contracting probability; (2) tankers switch frequently between markets; and, (3) oil trade shocks propagate in the economy through a transportation network. We develop a novel search and matching model by incorporating these facts with the interactions between oil exporters, tankers, and a final good producer. We show that shipping market congestion attenuates over one third of the volatility in oil trade and final good production, while increasing price fluctuations by more than tenfold. We also show that the endogenous adjustment of the oil transportation network and the resulting congestion effect are almost irrelevant for aggregate fluctuations; it is the exogenous oil trade shocks that generate large fluctuations. 

• My presentations: Commodity & Energy Markets Association Annual Conference (2022), Jinan University (2022), Xi'an Jiaotong-Liverpool University (2021), Cardiff Business School (2021), International Association of Maritime Economists Conference (2021), Hong Kong Polytechnic University (2021), Australasia Meeting of the Econometric Society (2021), China Meeting of the Econometric Society (2021), 8th Annual MMF PhD Conference (2021), Tsinghua University (2021), Shandong University (2021), Oxford Macro Working Group Seminar (2021). 

• The paper has been awarded the 2021 Peter Sinclair Prize at the 8th Annual MMF PhD Conference . 

• The paper has been awarded the 2022 PhD Prize Award at the CEMA 2022 Conference.

• A short presentation of this paper at the Econ Job Market Vlog 2021 (organized by the Applied Young Economist Webinar (AYEW) series) can be accessed here. 

• An earlier version of the paper circulated under the title "The 'Missing' Link: How Does Oil Transportation Trigger Macroeconomic Implications''.

Abstract: lagging labor reallocations outside agriculture amid sustained low agricultural productivity have been a key feature in the Philippines over the past 15 years. An analysis of the labor adjustments in and out of agriculture shows that a variety of factors have influenced this process. We find that the widening of wage differentials with non-agricultural sectors, improvements in labor market efficiency, and better transport infrastructure are largely associated with growing outflows of labor from agriculture, whilst the lack of post-primary education and the presence of agricultural clusters hinder such outflows. In contrast to the traditional view that agricultural employment outflows are largely driven by productivity differences and wage differentials, our results emphasize the roles of education as well as transport infrastructure in facilitating labor reallocations from agriculture to non-agriculture.

• My presentations: Philippine Institute for Development Studies Research Workshop (2021), IMF APD Seminar (2020).

• The paper was previously circulated as an IMF Working Paper (No. 2021/220). 

Abstract: To estimate global crude oil trade flows, current research either considers only crude oil tankers, or simply applies external information to distinguish between crude and refined product oil cargoes transported by coated product tankers; these limitations often reduce an estimation’s accuracy or compromise replicability. Our methodology directly addresses these issues by applying the random forest (RF) ensemble learning technique to Automatic Identification System (AIS) data in order to predict the cargo types of coated product tankers. By leveraging domain knowledge, we construct a set of unique input variables for the RF model, and use its predictions to quantify the global crude oil trade in a more accurate manner. Our estimation shows that coated product tankers were responsible for approximately 8% of global seaborne crude oil trade from 2017–2020. Further, unanticipated variations in the crude oil volume carried by these tankers are consistent with several major historical oil trade disruptions. Our study further extends current applications of AIS data in the domains of operations management and maritime transportation, and facilitates the exploration of the more minute characteristics of oil transportation. The resulting shipping dataset and associated decomposition strategy also enable relevant stakeholders to quickly identify emerging trade flow risks and adapt more effectively.

• My presentations: 10th International Conference on Logistics and Maritime Systems (2021).

Abstract: Container port congestion threatens the effectiveness and sustainability of the global supply chain because it stagnates cargo flows and triggers ripple effects across connected, multimodal freight transport networks. This study aims to develop a novel and tangible method to measure port congestion by investigating ship behaviors between different zones in port waters. Different port zones have varying ship densities because ships moor in the anchorage area randomly but dock at berths in an orderly and close fashion. This observation leads us to apply the density-based clustering method for port zone identification and differentiation. In order to ensure the method is globally applicable and accurate, we develop a new clustering algorithm, an iterative, multi-attribute DBSCAN (IMA-DBSCAN), which incorporates an iterative process, together with both spatial information and domain knowledge. The necessary input data for the algorithm is extracted from the Automatic Identification System (AIS), a satellite-based tracking system with real-time ship positioning and sailing data. An illustrative case suggests that our algorithm can rapidly and precisely identify anchorage areas and individual berths (even in a port with complicated geographic features), while other methods cannot. The algorithm is applied to measure congestion at 20 major container ports in the world, and the results prove its efficiency and practical applicability.