Bilateral trade imbalances: Hidden causes and hidden effects
Throughout his tenure in office, President Trump has kept the spotlight on the bilateral trade deficits of the US with some of its key trade partners – most notably China. Recently, the White House hailed the Phase One Trade Agreement with China as a stepping stone in “rebalancing the United States-China trade relationship” (White House 2020). Figure 1 displays the average US trade balances with 39 trade partners and the rest of the world (‘RoW’) from 2010 to 2014. Each trade balance is expressed as a percentage of US GDP. In the figure, the US-China trade deficit stands out for its size: during the 2010-2014 period, it amounted to 1.3% of US GDP.
Note: “Net exports (% GDP)” refers to the total net exports of the United States vis-à-vis the horizontal-axis country, expressed as a percentage of U.S. GDP. All data is based on WIOD (2016 release), average for the years 2010-14.
Aside from China, the US has had large bilateral deficits with its NAFTA trade partners, as well as with the trade giants Germany and Japan. However, there are also many countries with which the US has negligible trade imbalances. Figure 1 reveals several US bilateral trade surpluses with countries such as France, Brazil, and the Netherlands. This pattern is not unique to the US; most countries exhibit large variation in their bilateral balances across trade partners, with both bilateral deficits and surpluses present. But what explains this variation?
By way of an illustration, it is possible to deconstruct the US bilateral trade balances in Figure 1 into two components. The figures can be split into the (geometric) average value of bilateral trade flows between the US and the corresponding trading partner, as a percentage of US GDP, and the difference in bilateral flows relative to the (geometric) average value. The former is shown in Panel A of Figure 2. The latter is shown in Panel B of Figure 2 and shall be referred to as the “proportional imbalance”.
Note: The “geom. avg. of bilateral trade flows (% GDP)” refers to the geometric average of horizontal-axis country imports from the U.S. and U.S. imports from the horizontal-axis country. The “proportional bilateral imbalance” refers to the difference between horizontal-axis country imports from the U.S. and U.S. imports from the horizontal-axis country, expressed relative to their geometric average. All data is based on WIOD (2016 release), average for the years 2010-14.
Figure 2 shows that the US-China trade deficit is especially large. This is both because the value of US-China trade flows is large relative to US GDP and because the two countries have a large proportional imbalance. By contrast, the US proportional trade deficit with Slovakia is comparable to its proportional deficit with China. However, the small average value of US-Slovakia trade flows implies that the US-Slovakia deficit is barely visible in Figure 1. The variation in US bilateral trade balances in Figure 1 therefore reflects the interplay of the variation in average bilateral trade values and the variation in proportional imbalances.
It is well understood that the variation in average bilateral trade values across country pairs is shaped by “gravity” forces (Tinbergen 1962) Countries tend to trade more with larger economies, and with economies that are geographically closer to them. In Panel A (Figure 2), the US trades in large volumes with China, Germany, Japan and the rest of the world because these countries/regions make up a large share of the world economy. It trades substantially with Mexico and Canada because these economies are its direct neighbours. Such gravity patterns in the data can be rationalised using standard trade models (Anderson 1979, Anderson and van Wincoop 2003, Costinot and Rodríguez-Clare 2014). Appropriately estimated gravity trade models have been shown to account for most of the variation in average bilateral trade values across country pairs (Head and Mayer 2014).
In a recent paper (Cuñat and Zymek 2019), we demonstrate that gravity trade models can also be used to analyse the variation in proportional imbalances across trade partners. In doing so, we use recent advances in the quantitative modelling of international trade patterns to formalise insights first outlined by Davis and Weinstein (2002). In line with their discussion, we find that there are three drivers of proportional imbalances across country pairs.
The first of these drivers are countries’ aggregate (‘multilateral’) net export positions. For example, the US had a large aggregate trade deficit in the 2010-2014 period whereas China had a large aggregate trade surplus. Structural gravity models imply that the proportional imbalance between an ‘overall surplus’ country and an ‘overall deficit’ country should be larger than that between two ‘overall surplus’ countries or between two ‘overall deficit’ countries. The second driver is differences in expenditure and production patterns across countries. For example, if US consumers like buying German goods, German consumers like buying Dutch goods, and Dutch consumers like buying American goods, the resulting ‘triangular trade’ may give rise to (proportional) bilateral imbalances, even if the countries’ trade is balanced in the aggregate. The third possible driver are asymmetries in trade barriers – such as transport costs, tariffs and non-tariff barriers to trade ¬– that impede trade flows in one direction more than in the other.
We take a dynamic many-country, many-sector trade model to the sector-level trade, production and expenditure data for 40 countries (and the rest of the world) from the 2016 release of the World Input-Output Database. Crucially, the model assumes that sectoral bilateral trade flows obey a standard structural gravity equation. As a result, the model encapsulates the three drivers of proportional bilateral imbalances described above. Since the full extent of trade barriers cannot be measured directly, the calibrated model captures them as ‘residual’ wedges. These wedges explain all ‘importer-exporter-sector’ variation in trade flows that cannot be attributed to differences in importer-sector or exporter-sector characteristics.
Through a simple variance decomposition and fully-fledged counterfactuals, we explore how much of the variation in proportional bilateral imbalances across country pairs is due to each of the three drivers. Our main findings are as follows:
Our model relies on sizeable trade-wedge asymmetries to account for more than half of the variation in proportional bilateral imbalances. These may reflect asymmetries in trade barriers or determinants of sector-level trade patterns that are simply not captured well by standard models.
Most of the remaining variation is due to ‘triangular trade’, arising from differences in production and spending patterns.
Aggregate net exports play only a minor role. This finding is striking because aggregate trade surpluses and deficits, arising from differences in macroeconomic conditions, are frequently cited by economists as an explanation of bilateral imbalances in the public discourse.
The reliance of our model on inferred trade-wedge asymmetries suggests that there is a significant portion of the variation in proportional imbalances which is difficult to explain using standard trade models and data sources. This resurrects, in a new guise, the ‘mystery’ of bilateral imbalances originally found by Davis and Weinstein (2002). Our computations highlight that this issue merits further study. A counterfactual move to symmetric trade wedges would not only significantly reduce the variation in bilateral imbalances, but also materially affect countries’ real income and consumption levels.
Note: “Net exports (% GDP)” refers to the total net exports of the United States vis-à-vis the horizontal-axis country, expressed as a percentage of U.S. GDP. “Data” refers to actual bilateral net exports. “Counterfactual” refers to the new steady-state net exports if the tariffs imposed by the U.S. and China on each other between January 2018 and June 2019 are introduced into a dynamic many-sector model calibrated to match the “data” net exports.
Finally, our model can also be used to assess the long-run impact of the new tariffs introduced during US-China trade war (and left in place by the Phase One agreement). The dark blue bars in Figure 3 illustrate what new steady-state US bilateral imbalances are obtained if we introduce into our calibrated model the tariffs imposed by the US and China on each other between January 2018 and June 2019. As can be seen from the figure, the US deficit with China is reduced by nearly one half. This suggests that, at least in terms of the Trump administration’s stated objective of reducing the US-China trade deficit, the trade war may ultimately prove a ‘success’.
However, our counterfactual shows this to be a Pyrrhic victory for two reasons.
First, most of this reduction is due to a decline in the average value of trade flows between the US and China, not the proportional imbalance. The flip side of this trade destruction is a reduction in steady-state US real GDP by 0.21%, and in Chinese real GDP by 0.25%.
Second, the tariffs have little effect on the US aggregate trade deficit. As a result, the fall in the US-China trade deficit is offset almost one-for-one by deteriorations in the US trade balance with other trade partners – most prominently, the rest of the world.
Anderson, J E (1979), “A Theoretical Foundation for the Gravity Equation”, American Economic Review 69(1):106-116.
Anderson, J E and E van Wincoop (2003), “Gravity with Gravitas: A Solution to the Border Puzzle”, American Economic Review 93(1): 170-192.
Costinot, A and A Rodríguez-Clare (2014), “Trade Theory with Numbers: Quantifying the Consequences of Globalization”, in G Gopinath, E Helpman and K S Rogoff (eds), Handbook of International Economics, pp. 197-261.
Cuñat, A and R Zymek (2019), “Bilateral Trade Imbalances”, Edinburgh School of Economics Discussion Paper 292, August.
Davis, D R and D E Weinstein (2002), “The Mystery of the Excess Trade (Balances),” American Economic Review, Papers and Proceedings 92(2): 170-174.
Head, K and T Mayer (2014), “Gravity Equations: Workhorse, Toolkit, and Cookbook”, in G Gopinath, E Helpman and K S Rogoff (eds), Handbook of International Economics, pp. 131-195.
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White House (2020), “President Donald J. Trump is Signing a Landmark Phase One Agreement with China”, White House Fact Sheet, 15 January 2020.