A Big Data Approach to Cargo Type Prediction and Its Implications for Oil Trade Estimation (With Xiwen Bai, Qi Wang, and Zhongjun Ma, 2022) - Transportation Research Part E: Logistics and Transportation Review, 165(9), 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, also known as LOGMS 2021).

  • My presentation of the paper at LOGMS 2021 can be accessed here.

Working Papers

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.

  • 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''.

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).


Supply Chain Disruption and Rising Inflation (With Xiwen Bai and Francesco Zanetti)

Abstract: this paper assesses the causal effects of global supply chain disruption on the macroeconomic outcomes following the Covid-19 pandemic. Applying a density-based clustering algorithm, exploiting satellite data of containerships, we construct a novel measure of port congestion that is applicable to ports worldwide and free from measurement errors and endogeneity issues. We use such a measure as information for estimating the structural vector autoregressions (SVARs) that quantify the causal effects of global supply chain disruption; in particular, we disentangle a supply chain disruption shock from a demand shock resulting from the post-pandemic recovery in economic activities. Our results show that (1) for the U.S. economy, a global supply chain disruption shock leads to an immediate drop of its real GDP, a sharp and persistent increase in inflation, a large deterioration of trade balance, and a significant jump in unemployment; (2) estimations using other measures of global supply chain disruption, such as the global supply chain pressure index (GSCPI) by the Federal Reserve Bank of New York, would lead to significant biases in terms of historical decompositions of the U.S. inflation during 2017M2-2022M7; and (3) the effects of monetary policy exhibit state-dependency as a monetary policy shock would tame inflation at smaller costs of activities when the U.S. economy is more supply-constrained.

  • Presentation: University of International Business and Economics (2022).

The Scarring Effect of Natural Disasters on Labor Market: Evidence from the Philippines (With Eugenio Cerutti and Wenzhang Zhang)

Policy Work

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?