Working Papers

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.

High-Frequency Satellite Data and A Weekly Maritime Supply Index (With Xiwen Bai, Jesús Fernández-Villaverde, & Francesco Zanetti)

Abstract: We develop a Maritime Supply Index (MSI) using high-frequency Automatic Identification System data of container ships to track weekly changes in the global provision of shipping services. This index accounts for factors such as total shipping capacity, vessel speed adjustments, non-blank sailings, vessel rerouting, port congestion, and congestion at maritime chokepoints. Spanning from 2017 to 2024, our MSI provides a real-time indicator of global maritime health and reflects the effects of key external events on containerized trade, including the China-U.S. trade war, the COVID-19 pandemic, the Russia-Ukraine war, and the recent Red Sea crisis. Our analysis also reveals the key factors driving changes in global maritime supply, with vessel speed adjustments often playing a dominant role, alongside other factors such as port congestion during 2021 and vessel rerouting during the recent Red Sea disruptions. Lastly, using a time series causality analysis, we find that the spike in shipping prices in early 2021 was largely demand-driven, while the decline in late 2022 resulted from both diminishing demand and recovering supply.

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. 

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.


A Big Data Approach to Cargo Type Prediction and Its Implications for Oil Trade Estimation (With Xiwen Bai, Qi Wang, & Zhongjun Ma, 2022) - Transportation Research Part E: Logistics and Transportation Review, 165, 102831.

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.

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.

Policy Work

Box II.2. Developing Country Central Banks Need a Broad Range of Tools to Manage the Spillover Effects of Global Monetary Cycles

(World Economic Situation and Prospects 2024)

Appendix I. Impact Of COVID-19 On The Philippines' Labor Market

(IMF 2021 Article IV Consultation Staff Report)

Chapter 1. Economic Update

(Jordan Economic Monitor – Spring 2020 : Weathering the Storm)

Chapter 3. The Future We Want: Is It Affordable?