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 Meeting (2022), Jinan University (2022), Xi'an Jiaotong - Liverpool University (2021), Cardiff Business School (2021), International Association of Maritime Economists Conference (2021, also known as IAME 2021), The 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''.
A Big Data Approach to Cargo Type Prediction and Its Implications for Oil Trade Estimation (With Xiwen Bai, Qi Wang, & Zhongjun Ma) - 2nd Round R&R, Transportation Research Part E: Logistics and Transportation Review
Abstract: in estimating global crude oil trade flows, existing studies either only consider crude oil tankers or apply external information to distinguish between crude and product oil trips taken by product tankers; these limitations often render the estimation less accurate or lacking replicability. Our methodology directly addresses such issues by applying in a pioneering way the random forest (RF) ensemble learning technique to the Automatic Identification System (AIS) data so as to predict the cargo types of product tankers during their loaded trips. In the process, by leveraging domain knowledge, we propose and construct ten unique input features for the RF model; given the model predictions, we quantify global crude oil trade in a more accurate and tractable manner. As an external validation, we apply the resulting shipping dataset and perform a quantitative exercise that studies global crude oil trade fluctuations, which enables relevant stakeholders to quickly identify emerging trade flow risks and better engage in decision-making. Our study further extends existing applications of the AIS data in the domains of operations management and maritime transportation, and facilitates the exploration of the most microscopic characteristics of oil transportation.
Presentations: 10th International Conference on Logistics and Maritime Systems (2021, also known as LOGMS 2021).
My presentation of the paper at LOGMS 2021 can be accessed here.
The Agricultural Exodus in the Philippines: Are Wage Differentials Driving the Process? (With Eugenio Cerutti) - Under Review
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 (PIDS) Research Workshop (2021), IMF APD Seminar (2020).
The paper was previously circulated as an IMF Working Paper (No. 2021/220).
The Scarring Effect of Natural Disasters on Labor Market: Evidence from the Philippines (With Eugenio Cerutti and Wenzhang Zhang)
Separate Less but Switch More: How Skill-Biased Technical Change Affects the Worker’s Job Separation and Occupation Switching Behaviors
Presentations: Oxford Macro Working Group Seminar (2020), Oxford Inter-Departmental Macro-Finance Doctoral Research Workshop (2019).
On the Cyclicality of Human Capital Investment: Heterogeneity in Time Preference and Borrowing Constraint
Appendix I. Impact Of COVID-19 On The Philippines' Labor Market
Chapter 1. Economic Update
Chapter 3. The Future We Want: Is It Affordable?