All common real effective exchange rate indexes assume trade is only in final goods, despite the growing presence of global supply chains. Extending effective exchange rate indexes to include such intermediate goods can imply radically different effective exchange rate weights, depending on the relative substitutability of goods in final demand and in production. Unfortunately, the effect of these shifts in weights are difficult to identify empirically because the two currencies most affected—the dollar and the renminbi—have moved closely together. As the renminbi becomes more flexible, however, it will be important to determine which assumptions are the most realistic.
We present and discuss a set of indicators to help assess countries’ trade policies. The indicators relate to three policy areas – trade in goods, trade in services, and FDI. Given concerns about the direction of global trade policy, we also consider a set of more granular measures that reflect the evolution of countries’ policies since the 2008 financial crisis. We propose a simple approach to present the multidimensional aspects of trade policy that, by shedding light on relative openness across areas, can facilitate policy discussions. In the cross-section of countries, we find a diversity in the type of measures adopted, both between and (since the 2008 financial crisis) within policy areas, lending support to the approach based on multiple indicators. The indicators’ time series suggest that advanced and, especially, emerging economies are moving toward more open regimes over time, although recently progress has, with some exceptions, slowed across the board. Lastly, our findings also call for stronger efforts to objectively quantify the different aspects of countries’ trade regimes. More data, both across countries and in terms of policy areas that significantly affect trade, are needed for better-informed policy discussions.
This paper studies the potential long-term effects of three illustrative scenarios using a multi-sector computable general equilibrium (CGE) trade model calibrated to 165 countries. The first scenario estimates effects from potential U.S. auto tariffs. The second analyzes a ‘transactional deal’ between the U.S. and China to close their bilateral deficit. The third, in the absence of such a deal, considers a potential escalation in bilateral tariffs between the two countries. Some common features emerge across all three scenarios: the overall effects on GDP tend to be relatively small albeit negative in most cases, including for the U.S. However, sectoral disruptions and positive and negative spillovers to highly exposed ‘by-stander’ economies can be large. There is also heterogeneity at the subnational level in the U.S. -- richer states tend to benefit from certain scenarios. We discuss how estimated impacts depend on the extent to which the U.S. is able to re-shore production in protected sectors. These results can usefully complement estimates obtained through macroeconomic models that are better suited to capture dynamic effects, such as those stemming from trade policy uncertainty. More generally, our results both underscore the value of adhering to the existing levels of liberalization, and highlight the risks associated with a fragmentation or even a complete breakdown of the trading system.
Maritime data from the Automatic Identification System (AIS) have emerged as a potential source for real time information on trade activity. However, no globally applicable end-to-end solution has been published to transform raw AIS messages into economically meaningful, policy-relevant indicators of international trade. Our paper proposes and tests a set of algorithms to fill this gap. We build indicators of world seaborne trade using raw data from the radio signals that the global vessel fleet emits for navigational safety purposes. We leverage different machine-learning techniques to identify port boundaries, construct port-to-port voyages, and estimate trade volumes at the world, bilateral and within-country levels. Our methodology achieves a good fit with official trade statistics for many countries and for the world in aggregate. We also show the usefulness of our approach for sectoral analyses of crude oil trade, and for event studies such as Hurricane Maria and the effect of measures taken to contain the spread of the novel coronavirus. Going forward, ongoing refinements of our algorithms, additional data on vessel characteristics, and country-specific knowledge should help improve the performance of our general approach for several country cases.
Rising prices and reports of empty shelves in major economies have drawn attention to the functioning of supply chains that normally operate smoothly in the background. Among the issues, the long delays that port congestion may have caused in delivering goods to consumers and firms have been gathering increasing attention. We shed light on these issues leveraging a unique data set on maritime transport. Two main features emerge. First, at the world level, we find that shipping times jumped upwards as soon as the COVID crisis hit, and after a marked acceleration from end-2020, delays surpassed 1.5 days on average by December 2021 – or roughly a 25 percent increase in global travel times. The estimated additional days in transit for the average shipment in December 2021 can be compared to an ad-valorem tariff of 0.9 to 3.1 percent. The midpoint of this range is approximately equal, in absolute value, to the global applied tariff reduction achieved over the 14-year period from 2003 to 2017. Second, not all congestion appears related to increased demand. Many ports, especially since mid-2021, exhibit longer wait times despite handling less cargo than pre-pandemic. Infrastructure upgrading is therefore likely a necessary, but not sufficient condition for building resilience during a crisis where other factors (such as labor shortages) may also become binding.
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