BIS Working Papers
No 1249
The role of geopolitics in
international trade
by Han Qiu, Dora Xia and James Yetman
Monetary and Economic Department
March 2025
JEL classification: F13, F14, F15, F51, F60.
Keywords: International trade, geoeconomics,
geopolitics, fragmentation.
BIS Working Papers are written by members of the Monetary and Economic
Department of the Bank for International Settlements, and from time to time by other
economists, and are published by the Bank. The papers are on subjects of topical
interest and are technical in character. The views expressed in them are those of their
authors and not necessarily the views of the BIS.
This publication is available on the BIS website ().
© Bank for International Settlements 2025. All rights reserved. Brief excerpts may be
reproduced or translated provided the source is stated.
ISSN 1020-0959 (print)
ISSN 1682-7678 (online)
The role of geopolitics in international trade 1
The role of geopolitics in international trade1
March 2025
Abstract
Geopolitical considerations have seen economies impose trade restrictions on trading
partners with whom they have large geopolitical differences. Here we use granular
bilateral trade data across finely disaggregated sectors for 47 economies to examine
the effect of geopolitics on trade, and whether this is due to a change in trade
quantities or trade prices. We first corroborate existing results in terms of the value
of trade – that economies that are less geopolitically aligned tend to trade less with
each other. Quantitatively, we find that year-on-year trade values between more
geopolitically distant economies grew around 12 percentage points more slowly than
between closer ones, on average, over 2017–2023. We then take advantage of our
detailed data and show that the decline in trade values mostly reflects a fall in the
quantity of goods traded. By contrast, the prices received by exporters (measured in
US Dollars) are largely unaffected, indicating that higher costs associated with
geopolitical factors, due to measures such as tariffs, were mostly passed on to
importers.
Keywords: international trade, geoeconomics, geopolitics, fragmentation
JEL classification: F13, F14, F15, F51, F60.
1 We thank our colleagues at the Bank for International Settlements (BIS), especially Ryan Banerjee,
Guilio Cornelli, Fiorella de Fiore, Leonardo Gambacorta, Vatsala Shreeti, Goetz von Peter and Phil
Wooldridge, for helpful comments. We are grateful to Luca Iavarone and Jimmy Shek for excellent
research assistance. The views expressed are ours and do not necessarily reflect those of the BIS.
Han Qiu
@
Dora Xia
@
James Yetman
@
2 The role of geopolitics in international trade
1. Introduction
Following a relatively benign period of increasing trade integration, concerns about
“deglobalisation” have grown along with geopolitical tensions recently. The US
administration from 2017 to 2021 saw increasing restrictions, often concentrated on
trade with China (Brown, 2021). The Russian Invasion of Ukraine (in February 2022)
saw further trade restrictions (Borin et al, 2023). Against this backdrop, world exports
as a share of GDP peaked around the time of the GFC and has been trending down
since then (Graph 1).
World trade export value1
As a percentage of world GDP Graph 1
a Russian invasion of Ukraine (24 February 2022).
1 Calculated as global exports divided by global GDP at current prices.
Source: Macrobond.
While the decline in world trade in recent years coincides with heightened
geopolitical risks, a correlation alone is far from conclusive evidence of a link between
them. For one, geopolitics may alter the configuration of trade without affecting its
overall value, for example if countries move to favour trading with geopolitically
aligned trading partners over others more strongly. In addition, many other factors
also influence global trade, including the strength of supply in exporting countries
and demand in importing countries. Thus, if some major importers grow more slowly,
this is likely to translate into reduced global trade flows that has nothing to do with
geopolitics.
In this paper, we investigate the relationship between geopolitics and trade.
Building on Amiti et al (2024a,b), we use granular bilateral trade data, across around
5,000 finely disaggregated sectors. This allows us to focus on not just trade values, as
is standard in much of the extant literature, but also on the underlying quantities and
the prices at which trade takes place.
As explained later, this empirical approach uses source economy fixed effects to
attribute any empirical effect in a given sector that is common across exports from
one source economy to all destination economies as due to supply factors in the
source economy. Conversely, destination economy fixed effects attribute any
empirical effect that is common between exports from all source economies to one
destination economy to demand factors in the destination economy.
The role of geopolitics in international trade 3
To these elements, we add the geopolitical distance between each pair of
economies, as in Qiu et al (2024). This is specified as a continuous variable, and the
estimated coefficients on this at each point in time are our primary focus. We expect
these to vary with geopolitical tensions if countries impose different trade policies
(such as tariffs, restrictions or licensing requirements) towards geopolitical allies than
adversaries. For example, less geopolitically aligned economies may be more likely to
put in place trade barriers or make existing trade barriers higher when tensions are
higher.
Using our model, and granular bilateral trade data across finely disaggregated
sectors, we are able to corroborate a key finding from the existing literature on the
effects of geopolitics on trade, and go further. Previous work has mostly found that
geopolitics plays an important role in reducing the value of trade between
geopolitically distant countries relative to that between geopolitically close ones. Our
estimates indicate that the value of trade between geopolitical adversaries grew
around 12 percentage points more slowly over 2017-2023 than between geopolitical
allies, and this effect strengthened in the aftermath of the Russian invasion of Ukraine.
Going further than most previous work, we show that the effect is primarily real:
quantities grew around 12 percentage points slower between geopolitical adversaries
compared to allies, whereas the equivalent difference in free on board (FOB) prices
was close to
Our approach differs from that used elsewhere in terms of methodology, data
granularity and/or scope. Most existing work on the effects of geopolitics on trade
builds on the “gravity model” of bilateral trade flows between countries. This
modelling approach has a long history, going back to Isard (1954). It seeks to explain
the aggregate level of trade between pairs of countries as a function of the
geographical (ie physical) distance between them (as a proxy of trade costs) and their
GDP levels (as proxies for supply and demand factors).3 To this, these papers add the
geopolitical distance between the pair of trading countries. A common measure to
gauge this distance focuses on how countries vote at the United Unions (Bailey et al,
2017): the more similar are their votes, the more closely aligned are the two countries
and the smaller is the geopolitical distance between them. An alternative measure,
due to Signorino and Ritter (1999), relates to the similarity of trading partners’ treaty
portfolios. In some cases researchers have divided countries into blocs on the basis
of these measures, and trade levels within blocs are then compared with those
between blocs.
These papers have mostly found that geopolitical distance has adversely affected
trade flows. For example, Nana and Ouedraogo (2023) found that while both
geopolitical and geographical distances were important drivers of bilateral trade
flows, the former exacerbated the negative impact of the latter. Jakubik and Ruta
(2023) considered the effect of geopolitical distance on its own, as well as interacted
with a measure of macroeconomic uncertainty proposed by Ahir et al (2022). In all
their specifications they found that geopolitical distance alone was not statistically
significant, whereas the interaction term was. Gopinath et al (2024) compared trade
between one country bloc based around the US and Europe and another based
around China and Russia, with other countries considered to be non-aligned. They
2 These are the prices received by exporters and exclude trade costs and any tariffs.
3 Sometimes exporter-and importer-time fixed effects (and country-pair fixed effects) are used instead
to absorb these factors as well as any others that are common to all trading partners (or relate to
particular bilateral trade relationships).
4 The role of geopolitics in international trade
found that trade between countries in different blocs fell by around 12% compared
with trade within the same bloc following the Russian invasion of Ukraine in contrast
with the five years prior. Meanwhile Campos et al (2023) estimated that if the world
fragments into three different trade blocs (western, eastern and neutral) based on
how countries voted on a key UN resolution following the Russian invasion of Ukraine,
trade between opposing blocs would fall by 22%–57% compared to its level in
A few papers moved beyond aggregate bilateral trade data and used gravity
models to analyse bilateral trade at the level of a limited number of sectors. Hakobyan
et al (2023) took a two-stage approach on bilateral trade values for 10 sectors. They
first ran a regression on data across these sectors to identify fixed effects for each
importing and exporting economy, as well as each country pair, in each sector. They
then used the coefficients on the country-pair fixed effects in a second stage
regression that included gravity variables and a measure of geopolitical alignment.
They found that closer geopolitical alignment was generally associated with lower
trade barriers, but with most of the effect concentrated in a few sectors – notably
“transport equipment”, “food and beverages” and “other manufacturing”. Relatedly,
Blanga-Gubbay and Rubínová (2023) used a one-stage version of this approach, but
with an additional regressor representing either the geopolitical distance between
trading partners or if they’re members of the same geopolitical Dividing trade
data into 22 product groups, they found that the value of inter-bloc trade between
countries has been four per cent lower than intra-bloc trade since the start of the war
in Ukraine.
Another approach used event-study methodologies. These tend to limit the
scope of the study by examining specific events, but provide sharper answers to the
effects of geopolitics on trade. Corsetti et al (2024) focused on the effects on Türkiye’s
trade flows following the Russian invasion of Ukraine. They used difference-in-
difference methods – comparing Turkish exports to Russia with those to Europe – and
found that Turkish exports to Russia rose sharply. 6 Another key event that has
received considerable attention is the effect of tariffs introduced during the US
administration from 2017 to 2021. Amiti et al (2019) found that these led to a
substantial decline in US import quantities but little change in FOB In contrast,
they reported that both prices and quantities of US exports declined as a result of the
retaliatory tariffs imposed by other countries in Cavallo et al (2021) found
similar results for trade prices, but that the effects on retail prices were more
heterogenous, with some of the higher cost being offset by shrinking margins.
Assessing the same event, Fajgelbaum et al (2020) reported only quantity declines,
with little change in FOB prices on both exports and imports. Meanwhile Flaaen et al
4 See, also, Bosone et al (2024) for an analysis of the effect of the Russian invasion of Ukraine on
European exports based on a gravity model. Note, however, that not all gravity model-based studies
report significant effects of geopolitics on trade. Cevik (2023) found contradictory and statistically
insignificant effects, which were dependent on the level of economic development, with positive
effects for advanced countries and negative ones for developing countries.
5 They considered two hypothetical eastern and western blocs, made up of countries geopolitically
close to the United States and China respectively.
6 They also went beyond trade flows to investigate the effects of geopolitics on financing and invoicing
currency choices.
7 Jiao et al (2022) explained the limited price response by Chinese exporters as resulting from their
already compressed profit margins.
8 Amiti et al (2020) extended this analysis, which was based on tariffs introduced in 2018, to the
following year and reported qualitatively similar results for the effect of tariffs on US exports.
The role of geopolitics in international trade 5
(2020) focused on one narrow sector affected by the US tariffs – washing machines –
and found that consumer prices, which reflect not just FOB prices but also transport
costs, tariffs and retailer margins, were significantly higher following their imposition.
We add to the literature on the impact of geopolitics on trade in three ways. First,
our approach allows us to control for the strength of supply and demand at the level
of individual sectors in different economies (instead of simply using GDP or aggregate
economy fixed effects as in the gravity model).
Second, given our use of disaggregated data, we can distinguish between the
effects of geopolitics on prices versus quantities generally, as opposed to looking at
the response to specific events as in some of the literature discussed above. In
principle, a decline in trade values could be real – ie driven by a fall in the quantity of
goods traded; or nominal – driven by a decline in the prices at which goods are
traded. The relative importance of each of these implies differences in economic
welfare, and hence has implications for policy.
Third, we show that results are not driven by China or the United States: even
when we drop all trade with either of these countries from the sample, we obtain
qualitatively similar results.
The paper proceeds as follows. In the following section, we outline our data
sources. Next, we outline our empirical model of the effects of geopolitics on trade
values, quantities and prices. We then provide some empirical results, before
considering some robustness checks and concluding.
2. Data
To examine the effect of geopolitics on trade, we make use of three sources of
data. Our first is a measure of the geopolitical distance between trading partners
based on UN voting records proposed by Bailey et al (2017). This has often been used
in the literature as a proxy for geopolitical alignment. Based on observed votes, they
estimate a time-varying annual measure of each country’s political preferences,
referred to as its “ideal point”. They then calculate the “geopolitical distance” between
each pair of countries as the distance between their ideal points, as illustrated in
Graph 2 based on data for 2022. The larger the distance, the less aligned the two
countries are. 9 Based on this approach, country pairs within the European Union are
generally close to each other, whereas the United States and China are far
9 In the case of Hong Kong SAR, for which we have trade data but not separate geopolitical data, we
treat their geopolitical positioning as identical to the rest of China.
10 The geopolitical distance data are available up until 2022, at annual frequency. Given concerns about
the possibility of reverse causality, we lag this variable by four quarters in our estimation. We assume
that the same value holds for each quarter throughout the year.
6 The role of geopolitics in international trade
Geopolitical distance between countries1
Ideal point distance Graph 2
1 This heatmap is constructed using 2022 data. The colour of cells indicates the distance between country pairs, with lighter colours indicating
a smaller geopolitical distance.
Source: Bailey et al (2017).
While the above measure has been very commonly used in studies similar to this
one, it has come in for criticism. Some countries are geopolitically distant from almost
all others (eg the United States) and/or even those with which they have strong
alliances (eg fellow NATO or EU members).
Our second source of data is therefore an alternative measure of geopolitical
alignment, as used in Hakobyan et al (2023), which we consider as a robustness check.
This measures geopolitical alignment based on the similarity of trading partners’
military treaty portfolios. The measure is due to Signorino and Ritter (1999), based on
data from Chiba et al (2015).11 This measure ranges from -1 to +1, with a higher
number indicating a more similar treaty portfolio between the two trading partners,
which is analogous to a smaller geopolitical distance between them. Thus, the
expected sign on the coefficient on this measure is the opposite of the one from
Bailey et al (2017).
Our third source is for trade data. For this, we use the six-digit Harmonized
System (HS) of bilateral trade from the UN Comtrade data set. This includes bilateral
export quantities and values (measured in US dollars) for each of around 5,000 sectors
at monthly frequency. We aggregate these data from monthly to quarterly frequency
to reduce their volatility. Prices are computed as values (measured in US dollars)
divided by quantities.
11 The data are available from our variable of interest is
s_un_atop.
The role of geopolitics in international trade 7
We focus on an unbalanced panel consisting of the 47 largest trading economies
(based on 2023 merchandise trade – imports plus exports – as assessed by the World
Bank). We start with the top 50 traders, and then drop three based on their data being
available for less than 50% of our 2016-2023 sample Data availability is
displayed in Graph 3.
Trade data availability1 Graph 3
1 This graph shows the availability for each economy in our sample over the 2016-2023 period. For each economy, if data is missing in one
or more months in a given quarter, it will be indicated as being “unavailable” for the quarter and dropped from our estimation.
Source: United Nations Comtrade; authors’ calculations.
Our dependent variables are the four-quarter change in the natural log of the
level of the values, quantities and prices of bilateral trade flows. Using the log
transformation across all three variables implies that the changes in each are
comparable, aiding our interpretation of the results. In addition, it ensures that the
change in value will equal the change in quantity plus the change in price. In all cases,
the use of the four-quarter change should help to address any effects due to
seasonality. Table 1 displays data summary statistics.
Table 1. Summary statistics
12 In order of the size of 2023 trade, our economy sample is made up of China, the United States,
Germany, the Netherlands, Japan, France, Italy, the United Kingdom, South Korea, Hong Kong SAR,
Mexico, Canada, Belgium, India, Singapore, Spain, Switzerland, Poland, Russia, Vietnam, Australia,
Türkiye, Brazil, Malaysia, Thailand, Czechia, Indonesia, Sweden, Ireland, Hungary, Norway, Denmark,
South Africa, Romania, Slovakia, Iraq, the Philippines, Portugal, Chile, Finland, Israel, Iran, Slovenia,
Greece, Argentina, Kazakhstan and Qatar. The three dropped economies are Austria, Saudi Arabia
and the United Arab Emirates. The starting point for our estimation period is due to the lack of data
for China, a key exporter, before 2016.
8 The role of geopolitics in international trade
Variable Obs. Mean Std. Min. Max.
Geopolitical distance 50,033,422
𝛥𝛥4log(𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉) 50,033,422
𝛥𝛥4log(𝑄𝑄𝑉𝑉𝑉𝑉𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄) 50,033,422
𝛥𝛥4log(𝑃𝑃𝑃𝑃𝑄𝑄𝑃𝑃𝑉𝑉) 50,033,422
3. Model
To quantify the impact of geopolitics on trade, we model the growth rate of bilateral
trade, for each of nearly 5,000 narrowly defined sectors. We incorporate supply and
demand factors, from exporters and importers respectively, in the spirit of Amiti et al
(2024a,b).13 We then add the geopolitical distance between trading partners, with the
coefficient on this assumed to be equal across all sectors and economy pairs.
Mathematically, for sector ℎ from economy 𝑄𝑄 to economy 𝑗𝑗 in period 𝑄𝑄 , our
estimated equation is:
𝛥𝛥4log(Tradeℎ𝑖𝑖𝑖𝑖𝑖𝑖) = 𝛼𝛼ℎ𝑖𝑖𝑖𝑖 + 𝛽𝛽ℎ𝑖𝑖𝑖𝑖 + 𝜃𝜃𝑖𝑖𝐷𝐷𝑖𝑖𝑖𝑖 ,𝑖𝑖−4 + 𝜀𝜀ℎ𝑖𝑖𝑖𝑖𝑖𝑖 ,
where 𝛥𝛥4log(Tradeℎ𝑖𝑖𝑖𝑖𝑖𝑖) is our dependent variable of interest. This is the four-quarter
change in the natural log of one of the following trade variables for a sector and an
economy pair: the value of trade measured in US dollars (𝑉𝑉ℎ𝑖𝑖𝑖𝑖𝑖𝑖); the quantity of goods
traded (𝑄𝑄ℎ𝑖𝑖𝑖𝑖𝑖𝑖); or the price of traded goods measured in US dollars (𝑃𝑃ℎ𝑖𝑖𝑖𝑖𝑖𝑖).14 𝛼𝛼ℎ𝑖𝑖𝑖𝑖 is an
importer economy-sector-time fixed effect to capture demand conditions in the
importing economy; 𝛽𝛽ℎ𝑖𝑖𝑖𝑖 is an exporter economy-sector-time fixed effect to capture
supply conditions in the exporting economy; and 𝐷𝐷𝑖𝑖𝑖𝑖,𝑖𝑖−4 is a measure of geopolitical
distance between the importing and exporting economies, lagged by four quarters.
We use lagged distance to mitigate against the possibility of reverse causality: that
countries could choose to geopolitically align with their major trading
Intuitively, we assume that a change in trade that is common across all exporting
economies to a given importing economy 𝑗𝑗, captured by 𝛼𝛼ℎ𝑖𝑖𝑖𝑖 , is related to demand
factors in the importing economy. Likewise, we assume that a change that is common
across all importing economies from a given exporting economy 𝑄𝑄, captured by 𝛽𝛽ℎ𝑖𝑖𝑖𝑖 , is
related to supply factors in the exporting economy. Meanwhile changes in the
dependent variable that correlate with the geopolitical distance between economies
𝑄𝑄 and 𝑗𝑗 after controlling for supply and demand factors that are common across traded
sectors are considered to be due to geopolitical factors; these will be captured by 𝜃𝜃𝑖𝑖 .
Before showing the empirical results, we briefly discuss what we might expect to
find for the effect of geopolitics on trade, based on previous studies. We know that
trade values have tended to decline between geopolitical adversaries compared to
13 BIS (2024) uses the same approach to assess the effect of China’s exports on imported inflation across
a panel of 12 countries.
14 One concern with this specification is that the commencement or cessation of bilateral trade in any
sector is disregarded in the estimation. As a robustness check, we will consider estimation using less
granular four-digit HS trade data, which reduces the number of such missing observations, at the
expense of grouping more diverse goods into a single trade sector.
15 We consider a further robustness check for possible reverse causality later.
The role of geopolitics in international trade 9
allies. Thus, we can expect 𝜃𝜃𝑖𝑖 to be negative for trade values, especially during periods
when geopolitical tensions were heightened. If this adjustment plays out over time,
then 𝜃𝜃𝑖𝑖 will remain in negative territory in subsequent periods as well.
While the existing literature has less to say about the effect on the trade
quantities and prices that underlie trade values, if the effect on values is mainly
working through one of these variables, then we might expect to see comparable
results for estimated 𝜃𝜃𝑖𝑖 for this variable as for values, and limited effects for the other.
There are other possibilities as well. For example, quantities could have fallen by more
than values, with the gap made up by increasing prices.
That said, there are good reasons to think that the effect on quantities is most
likely to be negative. Quantitative restrictions (such as quotas or export bans on
geopolitical adversaries) would directly lead to this outcome, while tariffs could have
a similar effect, working indirectly through prices. Additionally, if restrictions led to
trade being re-routed through third-party countries that were geopolitically closer,
this would reduce direct trade quantities between
On prices received by exporters, however, we had weaker priors. On the one
hand, for tariffs imposed on trade between geopolitical adversaries, exporters could
in principle cut their prices to offset part of the tariff and protect their market share,
although recent US evidence (discussed above) suggests that any such effect can be
limited, partly because of constrained profit margins. On the other hand, if some
exporters choose to exit the market altogether, prices for the remaining exporters
could rise, especially if adversaries’ and allies’ exports are poor substitutes for each
other. Likewise for quantitative restrictions: prices from adversaries could rise due to
their relative scarcity.
Complicating matters, any expectation of future trade restrictions could see
adversaries move trade forward in time to avoid the constraints, temporarily
increasing trade quantities before they decline. The impact on near-term prices in this
case, however, could go in either direction, depending on the elasticity of supply
versus demand: if importers were the driving force behind the temporary surge, their
increased demand would be likely to see prices rise, whereas if exporters where, their
increased supply could see prices fall.
4. Estimation results
We estimate the model using weighted least squares, with 4-quarter lagged trade
values serving as Importer demand factors and exporter supply factors,
16 Eg Qiu et al (2023) documented the lengthening of supply chains on the back of re-routing of trade
through third-party countries.
17 Gravity models have often been estimated using a Poisson pseudo-maximum likelihood (PPML)
estimator, which provides consistent estimates when the dependent variable can take non-negative
integer values and there are many zeros. This estimator would not be appropriate in our context since
our dependent variable takes on both positive and negative values. Later we will consider as a
robustness check using HS4 instead of HS6 data, which substantially reduces the number of missing
observations.
10 The role of geopolitics in international trade
𝛼𝛼ℎ𝑖𝑖𝑖𝑖 and 𝛽𝛽ℎ𝑖𝑖𝑖𝑖 respectively, are identified with fixed effects, and we use standard error
estimates that are robust to
So what do we find? We first analyse the data by looking at the estimates of 𝜃𝜃𝑖𝑖 ,
our measure of how sensitive trade in each sector is to geopolitical distance. This
estimate applies to a particular point in time, across all sectors and economies in our
sample. To summarise these estimates, we focus on the point estimates and 95%
confidence bands, as displayed in Graph 4.
The impact of geopolitical distance on trade
In per cent Graph 4
A. Value B. Quantity C. Price
Sources: Bailey et al (2017); United Nations Comtrade; authors’ calculations.
Our estimates indicate that the effect of geopolitics on the value of trade was
negative throughout our sample, and that this effect has generally become more
pronounced over time (Graph ). Quantitatively, a one-unit increase in geopolitical
distance corresponded to an average decrease in bilateral trade of percentage
points across the sample. The effect was also highly statistically significant, as
indicated by the confidence bands. Given that our dependent variable is the four-
quarter change in the log of the value of trade, this implies that a greater geopolitical
distance correlated with falling trade quantities throughout the sample, after
controlling for demand and supply factors. The rate of growth decline approximately
doubled at the start of 2018 before partially recovering, but approximately doubled
again by 2023 following the Russian invasion of Ukraine. While the impact had been
attenuating towards the end of the sample period, the coefficient remained negative,
indicating that trade between geopolitically distant countries continued to contract
in relative terms. This result for the value of trade is broadly consistent with the extant
literature that geopolitical distance has an adverse impact on trade flows.
The effect of geopolitical distance on the value of trade is made up of two
components: the effect on the quantity of goods traded (Graph ) and its price ().
Here we can see the change in values was primarily real: if we were to plot the lines
for value and quantity on the same axes, they would sit almost atop each other for
18 We use the “reghdfe” package for STATA. Regarding standard errors, we also examined clustering by
sector and by importing and exporting economy, both separately and jointly. All reported results are
qualitatively similar.
The role of geopolitics in international trade 11
much of the sample. By contrast, changes in prices were much smaller and
insignificantly different from zero in most periods.
How sensitive are our results to our choice of the US dollar as a unit of measure?
Whereas our quantity data are independent of currency units, our value (and thus
price) data are measured in terms of US dollars. Hence our results indicate that there
is little impact of geopolitics on trade prices measured in US dollars. While around
half of all global trade is invoiced in dollars,19 there is wide variation by
However, our estimation approach is already somewhat robust to alternative
invoicing currencies. Recall that we include fixed effects at the sectoral level for every
exporting and importing economy (𝛽𝛽ℎ𝑖𝑖𝑖𝑖 and 𝛼𝛼ℎ𝑖𝑖𝑖𝑖 respectively). To the extent that all
exports from a given country in a given sector are invoiced in the exporter’s currency,
or imports to a given country in a given sector are invoiced in the importer’s currency,
any influence of this on trade will be absorbed by the fixed effects. Thus, for example,
explicitly accounting for the fact that most trade between EU countries is
denominated in euros would not change our results for the effects of geopolitics.
We next assess the economic magnitude of the effect of geopolitics on bilateral
trade flows. To do this, we replace our geopolitical distance variable with three
dummies: one for the 25% of economy pairs with the smallest geopolitical distances
at each point in time (“geopolitical allies”), another for the 25% with the greatest
distance (“geopolitical adversaries”), and a third for the The difference
in the coefficients between the top 25% and the bottom 25% provides a visual
illustration of how large a role geopolitics played in trade across economies. These
estimates are given in Graph 5, for each of value, quantity and price.
19 See Maronoti (2022).
20 More than two-thirds of trade is invoiced in US dollars in Asia, Latin America and the Middle East,
whereas three-quarters of euro area trade is invoiced in euros (see Annex 4B, ADB (2024)).
21 Comparing trade between blocks of countries was also examined in Gopinath et al (2024) and
Campos et al (2023), among others.
12 The role of geopolitics in international trade
The impact of geopolitical distance on trade between adversaries less allies1
In per cent Graph 5
A. Value B. Quantity C. Price
1 Allies are economy pairs whose ideal point distance is below the 25th percentile in a given period in our sample, while adversaries are those
above the 75th percentile.
Sources: Bailey et al (2017); United Nations Comtrade; authors’ calculations.
Not surprisingly, the results qualitatively track the time-evolution of the median
estimates of 𝜃𝜃𝑖𝑖 presented in the previous graph. First, the coefficients in Graph
are negative in all periods, indicating that trade values between adversaries fell
continuously compared with those between allies over the sample. Second, the
greatest rate of decline followed the Russian invasion of Ukraine in 2022. And third,
the decline in values was largely real: declines in trade quantities roughly match those
in trade values, while changes in the prices at which trade took place between allies
and adversaries were close to zero throughout the sample.
These estimates indicate that geopolitics played a material role in bilateral trade
growth during our sample. We find that the year-on-year growth in both trade
quantities and trade values between adversaries was around 12 percentage points
slower than between allies on average, with a maximum gap of around 25 percentage
points at the beginning of 2023. By contrast, the average gap in price growth between
the two groups was small, at percentage points, and always smaller than four
percentage points (in absolute terms).
5. Robustness checks
We now examine how robust our results are to alternative specifications. First we
consider sectors at the four-digit HS level (instead of six); second we consider an
alternative measure of geopolitical alignment; third we examine how robust our
results are to excluding trade with China and the United States; fourth we consider
EU countries as a single trading entity (instead of including each country separately);
fifth we check that reverse causality – ie authorities’ adjusting their geopolitical
positioning in response to trade flows – is not driving the results; and sixth we add
pair-wise country fixed effects to control for any additional fixed factors that influence
The role of geopolitics in international trade 13
the To summarise, we find that our results are robust across all the checks
that we consider.
Four-digit HS trade data
One challenge to our econometrics is the number of missing observations within the
sample, due to zero trade in many sectors between pairs of countries. Our dependent
variable is the four-quarter log change in trade, which is only defined if data is
available for both period 𝑄𝑄 and period 𝑄𝑄 − 4.
Of all the theoretically available HS6 observations (5,830 sectors x 28 quarterly
observations x 1081 pairs of economies), the set of observations on the dependent
variable that we have constitutes %. These are observations on the change in
trade at the intensive margin. The rest of the hypothetical data set is made up of
changes in trade at the extensive margin (ie moving from zero to positive trade, or
vice versa; %), or being absent in both period 𝑄𝑄 and period 𝑄𝑄 − 4 (%).
In principle, these missing observations could be informative for identifying the
impact of geopolitics on trade, so their absence from the estimation could bias the
results. For example, suppose that trade commenced in some sectors between
geopolitical allies, but ceased in other sectors between geopolitical allies. This would
be consistent with geopolitics influencing trade, but would have no effect on our
estimates. Even for sectors with zero trade in both period 𝑄𝑄 and period 𝑄𝑄 − 4, the
absence of change could be interpreted as implying a ceiling on the impact of
geopolitics on trade between allies.
One way to assess the sensitivity of our results to this is to use less dis-
aggregated measures of trade, for which there are fewer missing observations. In
contrast to the % of theoretically available HS6 observations, by aggregating to
the HS4 level, the share available jumps to %.
Aggregation from HS6 to HS4 in this way is not without cost, however. It implies
the grouping of more diverse goods into a single trade sector category; changes in
the composition of goods within the category are likely to render price and quantity
measures less informative, hence our preference for HS6 data for our primary results.
Results for four-digit HS level trade are given in Graph 6. The key point to note
is that they are qualitatively very similar to those in our base results reported earlier,
indicating that focusing on the effects of geopolitics on the intensive margin of trade
at the HS6 level does not appear to be materially distorting our estimation results.
22 Besides the robustness checks discussed in the text, we also considered the inclusion of the year-on-
year log change in the pair-wise exchange rate. Whereas most of our specifications involve estimating
period-by-period, this entailed estimating the full sample at once (similarly, see also the robustness
check adding pairwise fixed effects below). The coefficient on this variable was insignificant in all
three regressions, and the estimated effect of geopolitical distance on trade was almost unchanged
(results available upon request).
14 The role of geopolitics in international trade
The impact of geopolitical distance on trade: HS4 level
In per cent Graph 6
A. Value B. Quantity C. Price
Sources: United Nations Comtrade; authors’ calculations.
Alternative measure of geopolitical alignment
Our second robustness check is to consider the results with an alternative measure of
geopolitical alignment, focused on the degree of similarity of trading partners’
military treaty portfolios, as introduced by Signorino and Ritter (1999).
Given that this data for geopolitical alignment is only available up until 2018, and
that treaty portfolios move only slowly through time, we use the value for 2015 –
shortly before the start of our trade data – for all periods. Graph 7 displays a
comparison of this measure against that in the baseline model, for all economy-pairs
and time periods in our sample.
The role of geopolitics in international trade 15
Scatter plot of measures of geopolitical distance / alignment Graph 7
Sources: Bailey et al (2017); Chiba et al (2015); Signorino and Ritter (1999); authors’ calculations.
As previously noted, the measures are inversely related: a high degree of
similarity of treaty portfolios corresponds to a relatively small distance based on UN
votes, and vice versa. Note, however, that there is considerable divergence between
the two measures: the two series have a Pearson correlation coefficient of only .
Nevertheless, given that military alliances are likely to be an important factor
underlying geopolitical actions, we believe that this measure provides a reasonable
check of the “robustness” of the results from our baseline In addition, the
negative relationship between the two variables is highly statistically significant (the
estimated slope coefficient of a fitted linear relationship between the two datasets –
shown in red in Graph 7 – has a t-statistic of 47).
The results based on this alternative geopolitical measure are given in Graph 8
(with the vertical axes inverted to ease comparison with the previous graphs). While
there are some important differences in the estimated 𝜃𝜃𝑖𝑖’s (besides their expected
opposite sign), the results are qualitatively similar. First, the effect of geopolitics on
both value and trade is statistically highly significant, with country pairs that are
geopolitically strongly aligned trading more in terms of both values and quantities
than those that were weakly aligned. Second, the estimated effect is largest across
our sample during 2023. And third, the effect of geopolitics on prices is close to zero
and statistically insignificant for much of the sample.
23 When proposing this measure, Signorino and Ritter (1999) suggested using it “in combination with
data on alliances, trade, UN votes, diplomatic missions, and other types of state interaction.”
16 The role of geopolitics in international trade
The impact of geopolitical alignment on trade1
In per cent Graph 8
A. Value B. Quantity C. Price
1 Given the negative relationship between geopolitical alignment and geopolitical distance, the vertical axis is flipped to ease comparison
with earlier graphs.
Sources: Chiba et al (2015); Signorino and Ritter (1999); United Nations Comtrade; authors’ calculations.
We also repeat the assessment of the top and bottom quartiles of the sample
based on geopolitical alignment (equivalent to those presented in Graph 5). This
presents the results in terms of economically meaningful magnitudes, and thus aids
comparison with our previous specification. The results are reported in Graph 9.
The impact of geopolitical alignment on trade between adversaries less allies1
In per cent Graph 9
A. Value B. Quantity C. Price
1 Allies are country pairs whose geopolitical alignment is above the 75th percentile in a given period in our sample, while adversaries are those
below the 25th percentile. The vertical axis is flipped to ease comparison with earlier graphs.
Sources: Chiba et al (2015); Signorino and Ritter (1999); United Nations Comtrade; authors’ calculations.
The results are qualitatively similar to those based on our previous measure of
geopolitical distance. The effect of geopolitics on trade value and quantity has tended
The role of geopolitics in international trade 17
to get larger over time, hitting a maximum in terms of the effect between adversaries
versus allies of around 25% in 2023. The average effect, however, is a little larger than
before (16% vs 12%). For prices, the difference remains small and statistically
insignificant in most periods except for a small negative effect in 2018, indicating
relatively lower prices between allies, reversing to a small positive effect in 2019.
Taken together, our results are robust to this alternative measure of geopolitical
alignment.
Excluding the effect of China and the United States
Our third robustness check is to determine how dependent our results are on China
(including Hong Kong SAR) and the United States. As discussed in the introduction,
an important component of the effect of geopolitics on trade in recent years has been
measures introduced by the United States, often targeting China, and retaliation to
those measures. Indeed, much of the cited literature specifically looks at the effect of
geopolitics on US trade. However, many trade measures have been taken by other
economies as well.
To investigate how dependent results are on China and the United States, we
consider two variations to our baseline model. Graph 10 reports the results excluding
all bilateral trade in either direction between the United States and China (including
Hong Kong SAR) but retains trade between all other economies and either of them.
Meanwhile Graph 11 excludes all trade between any economy and either the United
States or China (again, treating Hong Kong SAR as part of China).
The impact of geopolitical distance on trade, excluding bilateral CN-US trade
In per cent Graph 10
A. Value B. Quantity C. Price
Sources: Bailey et al (2017); United Nations Comtrade; authors’ calculations.
18 The role of geopolitics in international trade
The impact of geopolitical distance on trade, excluding all trade with CN or US
In per cent Graph 11
A. Value B. Quantity C. Price
Sources: Bailey et al (2017); United Nations Comtrade; authors’ calculations.
As can be seen in the graphs, excluding CN-US trade has only a limited effect on
the estimated effects. The estimated effect of geopolitics on trade volumes and
quantities is reduced by around percentage points. When we exclude all trade
with either economy, the effect on the estimates actually goes the opposite direction:
the average impact on values and quantities increases by close to one percentage
point relative to baseline. In both cases, the effect on prices remains small and
statistically insignificant in most periods. Thus, our results are not driven by China
and/or the United States.
The European Union (EU) as a single entity
Our fourth robustness check is to treat all EU countries as a single trading entity,
instead of separately. The free flow of goods across EU country boundaries
encourages longer and more complex international supply chains. This could bias
estimates of the consequences of geopolitics on trade, since intra-EU trade is unlikely
to be heavily affected by geopolitics. Treating all EU countries as a single trading
partner allows us to examine the importance of this for our results. Indeed, in other
contexts, EU countries have sometimes been considered to be analogous to US states
and have been compared against
Treating EU countries as a single entity entails dropping all intra-EU trade and
then combining remaining EU trade to each other country into a single data series
for each sector. We do this for each of values and quantities and then reconstruct our
price variable as combined values divided by combined quantities. We then repeat
our baseline estimation on this smaller dataset and present the results in Graph 12.
24 See eg Head and Mayer (2021), who compare the movement of goods, services, people and capital
between EU and US states using a gravity framework.
The role of geopolitics in international trade 19
The impact of geopolitical distance on trade, with all EU countries as a single entity
In per cent Graph 12
A. Value B. Quantity C. Price
Sources: Bailey et al (2017); United Nations Comtrade; authors’ calculations.
The results are qualitatively very similar to the baseline case: with greater
geopolitical distance between trading partners, trade values and quantities grew
significantly more slowly while trade prices were much less affected.
However, the differences for trade values and quantities between these results
and the baseline model are quantitatively meaningful: the average gap in the
estimated 𝜃𝜃𝑖𝑖’s is smaller by around for values and for quantities units. This is
because intra-EU trade grew relatively quickly in the aftermath of the Russian invasion
of Ukraine, 25 and EU countries are relatively close to each other geopolitically.
Removing this trade from the panel means that geopolitically more distant
economies then seem to perform relatively better, hence yielding smaller estimates
on the impact of geopolitical distance on trade.
Reverse causality
Our fifth robustness check is to confirm that results are not driven by reverse causality.
One concern with our baseline specification is that reverse causality could influence
the results. In that case, a negative 𝜃𝜃𝑖𝑖 would not necessarily reflect more or higher
trade restrictions imposed by countries on geopolitical adversaries. Instead, it could
result from authorities taking account of the consequences for trade when choosing
their geopolitical positioning. For example, they could choose to become more
geopolitically distant from countries with whom trade flows are falling for other
reasons. Or, fearing the loss of big suppliers or customers in key sectors, they could
seek to align geopolitically with their most important trading partners. We argue that
this is unlikely to be the case and add a robustness exercise in support of this.
There are a number of reasons to believe that reverse causality is not a major
concern in this context. For one, existing evidence suggests that such reverse causality
plays a role only at longer horizons than we have assumed in our timing of our
25 As a share of total trade in our dataset, the share of intra-EU trade increased from 32% to around
35% following the Russian invasion of Ukraine.
20 The role of geopolitics in international trade
geopolitical distance For example, Kleinman et al (2024) finds that while
countries tend to realign towards trade partners that they depend on, this plays out
slowly. In the case of South-East Asian countries and China, they identify a significant
effect as China became an increasingly important trading partner, but spread out over
three decades (1980-2010). In addition, average changes in geopolitical distances are
generally small over our sample (the mean geopolitical distance in 2016 was
versus in 2022).
That said, the second moment of changes in geopolitical distances is high
enough to leave some room for doubt. The change in geopolitical distance between
pairs of countries from 2016 to 2022 has a standard deviation of . A scatter plot
comparing the geopolitical distance in 2016 versus that in 2022 illustrates the same
point: some country pairs do see notable changes in their distance measure (Graph
13). In principle these changes could reflect countries adjusting their voting records
– and hence ideal points – in ways that would bias our estimates.
Ideal point distance Graph 13
Source: Bailey et al (2017); authors’ calculations.
As a check, we therefore repeat our estimation assuming that geopolitical
distances were fixed at their level in 2000 – long before the start of our sample. This
is even before China became a member of the World Trade Organisation, when the
lure of trade with China could have become an important driver of geopolitical
positioning.
The results are presented in Graph 14 and are again quantitatively similar to our
baseline scenario. This indicates that even after going to extreme lengths to ensure
that we have eliminated the possibility of reverse causality, our findings remain robust.
26 In our baseline specification, we use geopolitical distance lagged by four quarters in our regression,
with a dependent variable of trade growth over four quarters, consistent with our view that any
reverse causality is unlikely to show up within a year.
The role of geopolitics in international trade 21
The impact of geopolitical distance on trade, with distances as in 2000
In per cent Graph 14
A. Value B. Quantity C. Price
Sources: Bailey et al (2017); United Nations Comtrade; authors’ calculations.
Pair-wise country fixed effects
Our sixth and final robustness check is to assess the effects of adding fixed effects for
each pair of economies in our sample. Their addition is intended to control for any
factors that might influence trade behaviour and do not vary over time. For example,
gravity models of the level of trade include either the physical distance between each
pair of countries as a regressor (or else pair-wise fixed effects, which controls for the
presence of all such time-invariant factors).
In our context, the rationale for including pair-wise fixed effects is different from
gravity models. Given that our dependent variable is the log change in the value,
quantity or price of trade, the effects of any fixed factors (such as distance) on the
level of trade would already drop out. Nonetheless, it seems possible that the growth
of trade could also be influenced by fixed factors. For example, maybe trade between
countries that are closer to each other is more reactive and thus responds more
quickly than trade between countries that are further apart. We hence consider
including pair-wise fixed effects as a robustness check.
Adding pairwise fixed effects requires a change in our estimation approach. In
most of our specifications there are no temporal connections, so we estimate the
model separately at each point in time. However, since pair-wise fixed effects apply
to all periods, these require estimation across the full sample simultaneously.
The results are given in Graph 15. Note that these are both qualitatively and
quantitatively very similar to our base results presented previously. Thus our results
are robust to the inclusion of any fixed factors related to pairs of countries.
22 The role of geopolitics in international trade
The impact of geopolitical distance on trade, including pair-wise country fixed effects
In per cent Graph 15
A. Value B. Quantity C. Price
Sources: Bailey et al (2017); United Nations Comtrade; authors’ calculations.
6. Conclusions
Our analysis shows that global trade has become increasingly fragmented due to
geopolitical considerations. Using granular bilateral trade data between 47 countries
across approximately 5,000 finely disaggregated sectors, we explore the impact of
geopolitical distance on trade dynamics, carefully controlling for confounding factors
such as country-specific supply and demand at the sectoral level. We corroborate
previous findings that countries that are more geopolitically distant tend to trade less
with each other, all else equal. Leveraging granular data, we go further and find that
the impact of geopolitical distance on the value of trade is primarily driven by its
impact on quantities, with prices being relatively unaffected.
On prices, it is important to bear in mind that the small effects we identify are for
FOB prices – those received by exporters, excluding shipping costs, tariffs and retailer
margins. This implies that, over our sample, exporters have not systematically
responded to geopolitical tensions by reducing the prices they charge for their
exports. That suggests that any costs associated with geopolitical measures, such as
tariffs, have been largely borne by the importing firms, retailers and/or consumers,
rather than exporters, consistent with evidence discussed earlier based on US tariffs
introduced in 2018-19.
The role of geopolitics in international trade 23
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BIS Working Papers No 1249
The role of geopolitics in international trade
Abstract
1. Introduction
2. Data
3. Model
4. Estimation results
5. Robustness checks
Four-digit HS trade data
Alternative measure of geopolitical alignment
Excluding the effect of China and the United States
The European Union (EU) as a single entity
Reverse causality
Pair-wise country fixed effects
6. Conclusions
References
Previous volumes in this series
