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.
Presentations: 10th International Conference on Logistics and Maritime Systems (2021).
A Data-Driven Iterative Multi-Attribute Clustering Algorithm and Its Application in Port Congestion Estimation (With Xiwen Bai, Zhongjun Ma, Yao Hou, & Dong Yang, 2023) - IEEE Transactions on Intelligent Transportation Systems, 1-12.
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.
Abstract: We estimate the causal effects of the pandemic-induced global supply chain disruption on the U.S. economy. To achieve this, we first construct a unique spatial clustering algorithm to transform the Automatic Identification System (AIS) data of containerships into an original granular index of global supply chain disruption. Next, we develop a novel analytic theory that features search frictions on the international product market and endogenous separation of exporter-importer matches on transportation cost. We then integrate our index of global supply chain disruption and theory-based identification restrictions of a supply chain disruption shock with Structural Vector Autoregressions (SVARs) in the causality assessment. Our results reveal that, (1) a supply chain disruption shock leads to an immediate drop of real GDP, a sharp and persistent increase in prices, a large deterioration of trade balance, and a significant jump in unemployment; (2) estimations using other measures of global supply chain disruption would lead to significant biases in both the stagflationary nature of such a shock, and more importantly, the main force behind the accelerating inflation in the U.S. since mid-2020; and (3) the effects of monetary policy exhibit state-dependency as a contractionary monetary policy shock would tame inflation at smaller costs of activities and employment when the global supply chain disruption takes hold.
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), Workshop on Methods and Applications for Dynamic Stochastic General Equilibrium (DSGE) Models (scheduled).
My presentation at the Inflation: Drivers and Dynamics Conference 2023 (hosted by ECB and the Cleveland Fed) can be accessed here.
Market Concentration and Freight Rates in the Product Tanker Market: Theory and Evidence (With Xiwen Bai & Qi Wang) - Under Review
Abstract: This study explores the impact of market concentration on freight rates in the product tanker market, which has experienced increasing market concentration over the past decades. A game theoretical model and empirical analysis using both OLS and IV approaches are utilized to investigate the relationship between market concentration and freight rates. The results indicate that higher market concentration is associated with higher freight rates, suggesting increased market power of shipowners and a non-perfectly competitive market. These findings have important implications for shipping participants and regulators, emphasizing the role of market structure in shaping freight rates.
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.
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.
An earlier version of the paper circulated under the title "The 'Missing' Link: How Does Oil Transportation Trigger Macroeconomic Implications''.
The Agricultural Exodus in the Philippines: Are Wage Differentials Driving the Process? (With Eugenio Cerutti)
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.
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).
Appendix I. Impact Of COVID-19 On The Philippines' Labor Market
Chapter 1. Economic Update
Chapter 3. The Future We Want: Is It Affordable?