# How exporters neutralized an increase in tariffs††thanks: I thank Elena
Cifuentes, Raúl Mínguez, Rafael Pico, Juán Manuel Sánchez, Cristina Vega,
Albert Vinaixa and participants at ETSG 2022 Groningen and the 47th Simposio
de Análisis Económico for their valuable comments and suggestions. I
acknowledge the Department of Customs and Excise of the Spanish Tax Agency
(AEAT) and the Spanish Chamber of Commerce for providing customs data. This
research was conducted as part of the Project PID2021-122133NB-I00 financed by
MCIN/AEI/10.13039/501100011033/FEDER, EU. I also gratefully acknowledge the
financial support from the Basque Government Department of Education
(IT1429-22). Part of the research presented in this paper was done while I
visited the Department of Economics and the Real Colegio Complutense at
Harvard University. I gratefully acknowledge their hospitality. I also thank
the Basque Government’s Ikermugikortasuna 2022 program for financing the
research stay.

Asier Minondo
Deusto Business School, University of Deusto, Camino de Mundaiz 50, 20012
Donostia - San Sebastián (Spain). Email<EMAIL_ADDRESS>
( This version:
)

###### Abstract

I use the unanticipated and large additional tariffs the US imposed on
European Union products due to the Airbus-Boeing conflict to analyze how
exporters reacted to a change in trade policy. Using firm-level data for Spain
and applying a difference-in-differences methodology, I show that the export
revenue in the US of the firms affected by the tariff hike did not
significantly decrease relative to the one of other Spanish exporters to the
US. I show that Spanish exporters were able to neutralize the increase in
tariffs by substituting Spanish products with products originated in countries
unaffected by tariffs and shifting to varieties not affected by tariffs. My
results show that tariff avoidance is another margin exporters can use to
counteract the effects of a tariff hike.

JEL: F10, F14

Keywords: tariffs, tariff avoidance, exporting firms, Spain, US, Airbus-Boeing
conflict.

##  1 Introduction

With the arrival of Donald Trump to the US presidency, tariffs became a major
policy tool of the US administration. As recorded by the Peterson Institute’s
Trump’s Trade War Timeline, the Trump administration opened six major trade
wars, which affected all its main trade
partners.111https://www.piie.com/blogs/trade-investment-policy-watch/trump-
trade-war-china-date-guide. The US also withdrew from the Trans-Pacific
Partnership and forced a renegotiation of the North American Free Trade
Agreement seeking to restrict its free-trade provisions. The regression from
trade integration was not a trend exclusive to the US (Colantone et al.,,
2021). For example, in January 2020, the United Kingdom disengaged from the
European Union (EU), culminating the biggest reversal in deep economic
integration in recent history (Dhingra and Sampson,, 2022).

The shift toward more aggressive and unilateral trade policies has raised
interest in understanding how exporters adjust to a more hostile environment.
However, there is scant evidence on how economies and firms in practice
respond to tariffs (Cavallo et al.,, 2021). This paper analyzes how Spanish
exporters adjusted to the tariffs imposed by the Trump Administration because
of the Airbus-Boeing conflict. In October 2019, the US introduced an
additional 25% tariff on 64 Spanish products, which represented almost 5% of
the total Spanish merchandise exports to the US in 2018.222The US was the
top-6 destination of Spanish merchandise exports, accounting for 4.4% of its
total exports in 2018. Furthermore, the importance of the US market for the
Spanish exporters affected by the tariff was high: 32%. A key feature of this
trade conflict is that the US imposed tariffs on many products that had no
relationship with the aircraft industry. For example, in the case of Spain,
the products most affected by the additional tariff were olive oil, wine,
cheese, and olives. Hence, exporters of those products could not anticipate
the imposition of a tariff. This creates a suitable environment to identify
how exporters adjust to a change in trade policy.

Using firm-level monthly exports data to the US and a difference-in-
differences specification, I find that Spanish exporters affected by the
increase in tariffs experienced an insignificant decrease in export revenue in
the US relative to other Spanish exporters to the US. Spanish exporters
affected by the additional tariff did not have either a larger probability of
exiting the US market than the rest of the exporters to the US.

How were Spanish exporters to the US able to counteract the increase in
tariffs? They used two major strategies to _avoid_ tariffs. The first one was
to substitute a product originated in Spain by a product originated in a
country not targeted by tariffs. For example, Spanish olive oil exporters to
the US increased their imports of olive oil from Tunisia and Portugal,
countries not affected by the US tariff, bottled it in Spain, and shipped it
to the US with a label declaring that the origin of the product was Tunisia or
Portugal. Spanish Customs recorded these shipments as Spanish exports to the
US. However, as was the goal of Spanish exporters, US Customs considered that
the bottling of the olive oil in Spain had not introduced a substantial change
in the product, and thus recorded the shipments from Spain as imports from
Tunisia or Portugal, with no tariffs imposed. Due to this recording
discrepancy, the value of olive oil imports from Spain recorded by US customs
accounted for less than 50% of the mirror export value recorded by Spanish
customs. I provide evidence showing that this gap was not the result of tariff
evasion, but the outcome of a tariff-avoidance strategy followed by Spanish
exporters.

The second strategy, used by wine and cheese producers, was to shift exports
from tariff-targeted varieties to similar non-targeted varieties. For example,
wines with an alcohol strength by volume of 14% or less were subject to an
additional tariff, whereas wines with a higher alcohol strength were not. The
alcoholic strength of red wines, the most important variety exported by Spain
to the US, is in the 13-15 range. Therefore, it was feasible for exporters of
red wine with an alcoholic strength equal or lower than 14% to shift their
exports to higher alcoholic strength varieties to avoid the tariff. Regarding
cheese, some varieties of cheese made from sheep’s milk, the most important
cheese variety exported by Spain to the US, were targeted by a tariff, whereas
other similar ones were not. This enabled Spanish exporters to shift from the
targeted to the non-targeted variety and avoid the negative impact of the
tariff. I show that there was an actual change in the exported varieties and
not a product reclassification to evade tariffs. I document that targeted
varieties that were “far” in product characteristics from the non-targeted
ones (e.g., white wine and cheese made from cow’s milk) experienced a much
larger decrease in exports than the “close” ones.

The strategies implemented by Spanish firms during the Airbus-Boeing conflict
reveal that exporters have another margin to adjust to changes in trade
policy: trade avoidance. Like the trade evasion margin highlighted by Demir
and Javorcik, (2020), trade avoidance enables exporters, at least the ones
specialized in primary and the manufacturing of primary products, to respond
swiftly to changes in trade policy. This paper shows that this margin can be
powerful enough to neutralize the effect of an increase in tariffs.

This paper contributes to four strands of literature. Firstly, this work
relates to the margins exporters have to adjust to changes in trade policy.
Models of international trade with export fixed costs and firm heterogeneity
in productivity show that some exporters will reduce their sales (the
intensive margin) and others will exit export markets (the extensive margin)
in response to a raise in tariffs (Melitz,, 2003). Firms exporting multiple
products (the product margin) will concentrate their sales in their most
productive, or core, product in response to a raise in trade costs (Eckel and
Neary,, 2010; Bernard et al.,, 2011). Models with strategic pricing (the mark-
up margin) predict that firms will reduce their mark-ups in response to a
raise in tariffs (Atkeson and Burstein,, 2008). Ludema and Yu, (2016) showed
that an increase in tariffs lead exporters to reduce the quality of products
(the quality margin). Finally, Demir and Javorcik, (2020) showed that
exporters in Turkey increased tariff evasion after taxes were introduced on
some types of foreign transactions (the tariff-evasion margin). I show that
tariff avoidance is another margin exporters can use to mute the negative
effects of a tariff increase.

Secondly, the paper contributes to the empirical literature that analyzes how
exporters react to changes in trade policy. Fitzgerald and Haller, (2018)
showed that exporters are more responsive to changes in tariffs than changes
in the real exchange rate. Albornoz et al., (2021) concluded that fewer
Argentinian firms exported to the US after an unanticipated tariff was imposed
on their products. They also showed that surviving exporters began to sell new
products in the US to counteract the reduction in revenue from the tariff-
targeted products. Jiao et al., (2022) found that exports of Chinese firms to
the US dropped significantly after the US imposed tariffs on their products.
This drop was only partially compensated with higher sales to the EU. This
paper contributes to this literature showing that exporters can mute the
negative effects of tariffs exporting products originated in other countries
and shifting to varieties not targeted by tariffs.

Thirdly, this paper is related to the tariff _evasion_ literature. In a
seminal article, Bhagwati, (1964) compared the value of exports reported by
the exporter country and the value of mirror imports reported by the importer
country to evaluate the magnitude of tariff evasion in Turkey. This
methodology was recovered and refined by Fisman and Wei, (2004), who showed
that misreporting in Chinese imports from Hong-Kong was positively correlated
with tariff rates. Misreporting happened mainly because importers
misclassified high-tariff products as similar low-tariff products. Javorcik
and Narciso, (2008) found that tariff evasion is more prevalent in
differentiated than homogeneous products, because it is more difficult to
identify a price misreporting in the former than in the latter. This study
contributes to this literature showing that the positive correlation between
the trade gap between exports and mirror imports and tariffs may also be
explained by a tariff-avoidance strategy rather than a tariff-evasion one.

Fourthly, this paper contributes to the tariff avoidance literature. One of
the best known examples of tariff avoidance is the one related to the so-
called chicken tax. In the early 1960s, Germany imposed a tax on frozen
chicken imported from the US. In retaliation, the US imposed a 25% global
tariff on light trucks, with the goal of making the import of German
Volkswagen pick-ups, which were very popular in the US at that time, less
attractive. The tariff remained in force even when disputes between Germany
and the US over chicken disappeared. Paradoxically, the chicken tax ended up
hurting US car manufacturers, which used very awkward procedures to avoid the
tariff when importing the vans produced in their production facilities abroad
into the US.333See, for example, Wall Street Journal “To Outfox the Chicken
Tax, Ford Strips Its Own Vans” published on September 23, 2009. Available at
https://www.wsj.com/articles/SB125357990638429655#pr. There is also literature
showing that firms shift production across export platforms to avoid the
effect of tariffs (Flaaen et al.,, 2020). This paper contributes to this
literature showing that exporters can implement tariff-avoidance strategies
very rapidly in response to a change in trade policy.

The paper is organized as follows. Section 2 presents the timeline of the
Airbus-Boeing conflict, highlighting the lengthy process that culminated in
the imposition of tariffs by the US. It also identifies the Spanish products
that were more affected by tariffs. Section 3 uses econometric analyses to
estimate the impact of tariffs on exports at the product and firm level.
Section 4 describes the tariff-avoidance strategies followed by Spanish
exporters, and the final section concludes.

##  2 The Airbus-Boeing conflict

On October 6, 2004, the US requested consultations in the World Trade
Organization (WTO) with France, Germany, Spain, United Kingdom, and the EU
related to potential subsidies received by Airbus.444The code granted by the
WTO to this case was DS316. All the documents related to this dispute can be
found at https://www.wto.org/english/tratop_e/dispu_e/cases_e/ds316_e.htm. The
main complaint was that Airbus had received preferential loans to design and
develop new aircraft models. Allegedly, this had enabled Airbus to market new
products faster and at a lower cost than in a situation in which loans had
been granted on market terms. After a lengthy process, in June 2011, the WTO
Dispute Settlement Body ruled that Airbus had received subsidies that were
inconsistent with the WTO rules and requested the EU to remove them. In
December 2011, the EU asserted that it had removed the illegal subsidies.
However, the US argued that the illegal subsidies remained in force and
requested the formation of a compliance panel. On May 28, 2018, the Dispute
Settlement Body ruled that the EU had not removed the illegal subsidies and
allowed the US to take retaliatory measures. In October 2019, the WTO
Arbitrator concluded that the US could apply countermeasures on $7.5 billion
of imports from the EU, the largest retaliatory measure granted in the WTO
history at that time.555In a parallel case that began the very same day the US
requested consultations in the WTO, the EU requested consultations with the US
because it suspected that Boeing had received illegal subsidies from some US
states. After an exceptionally long process, in March 2019, the WTO ruled that
the US had not withdrawn the illegal subsidies, that those subsidies were
damaging EU interests, and allowed the EU to take retaliatory measures against
the US. These measures, which amounted to $4 billion of imports from the US,
were implemented in November 2020.

On April 12, 2019, the US government presented a preliminary list of 326 EU
products that could be targeted by an additional tariff. Figure 1 presents the
timeline of the conflict since that date. As expected, aircrafts were included
in the list. However, most of the products included in the list were not
related to the aircraft industry (e.g., salmon fillets, yogurt, handbags, or
printed books). In some products, all EU countries were potential targets of
the additional tariff. However, in other products, only some EU countries were
affected. The US specifically targeted the EU countries where Airbus
production facilities were located and which have granted subsidies to the
aircraft company: France, Germany, Spain, and the United Kingdom. On the 5th
of July 2019, the list of products potentially targeted by tariffs was
enlarged with 89 new goods.

Figure 1: Timeline of the Airbus-Boeing conflict since April 2019

[startyear=2019,stopyear=2022,startdate=FALSE,stopdate=FALSE,arrow=FALSE]textstyle=,datestyle=
[markdepth=-60pt]12/4/20191st list of potential (326)
[markdepth=10pt]5/7/20192nd list of potential (89)
[markdepth=-20pt]18/10/20191st tariffs (159) [markdepth=50pt]26/6/20203rd list
of potential (32) [markdepth=-50pt]1/9/20202nd tariffs (8)

[markdepth=-20pt]6/1/20213rd tariffs (13)

[markdepth=20pt]11/3/2021Tariffs suspended for 4 months

[markdepth=-60pt]15/6/2021Tariffs suspended for 5 years Note: The number in
parentheses corresponds to the number of products affected by the measure.

On October 9, 2019, the US announced the products that were affected by an
additional tariff. Specifically, 159 out of 405 potential products were
affected by the additional tariff (39%). For all products, except for the
aircraft industry, the additional tariff was set at 25%.666The additional
tariff for aircraft-related products was set at 10%. However, the tariff was
increased to 15% in February 2020. These tariffs came into force on October
18, 2019. In June 2020, 30 more products were added to the potential list of
targeted products. In September 2020 and January 2021, additional tariffs were
applied on 8 and 13 more products, respectively.777In March 2020, the tariff
was lifted from one product (prune juice) and applied to another product
(butchers’ or kitchen chopping or mincing knives). 138 out of the 180 products
targeted by tariffs were primary products or manufactures of primary products,
such as cheese, fruits and nuts, olive oil, olives, wine, and liquors. The
remaining 42 products were manufactures, such as aircrafts, printed pictures,
or sweaters. Finally, in March 2021, the US and the EU agreed to suspend the
tariffs for 4 months. The temporary suspension was widened to a 5-year period
in June 2021.888The US government reached a similar agreement with the UK,
which had already left the EU’s single market and customs union at the
beginning of January 2021.

Regarding Spain, 277 products were potentially subject to an additional
tariff. Among them, 64 products were targeted by an additional tariff in
October 2019.999No Spanish product was added to the products targeted by
tariffs after October 2019. The Spanish exports to the US that were targeted
by tariffs amounted to 5% of all Spanish exports to the US in 2018. Figure 2
shows that the exports subjected to tariffs were concentrated in four
products: olive oil, which accounted for 37% of all Spanish exports targeted
by tariffs in 2018, wine (25%), olives (17%), and cheese (13%). The remaining
tariff-targeted products only accounted for 8% of tariff-targeted
exports.101010None of the aircraft products exported by Spain to the US were
targeted by tariffs.

Figure 2: Distribution of Spanish exports to the US by tariff-targeted
products (based on Spanish exports to the US in 2018

Source: author’s own elaboration based on US Census trade data.

Except for aircraft-related products, the US administration did not explain
the criteria it had followed to select the products that could be affected by
tariffs. Therefore, except for the aircraft manufacturers, the inclusion on
the list was unanticipated by EU exporters. To confirm this, following
Albornoz et al., (2021), I estimate the following equation:

$y_{k}=\beta_{0}+\beta_{1}Tariff_{k}+\beta_{2}M_{k}+\beta_{3}\Delta
M_{k}+\epsilon_{k}$ (1)

where $y_{k}$ is an indicator variable that turns one if product $k$ was
included in the list of products that could potentially be targeted by
tariffs, $\beta_{0}$ is a constant, $Tariff_{k}$ is the duty imposed by the US
on EU product $k$ before the Airbus-Boeing conflict, $M_{k}$ is the value of
US imports of product $k$ from the EU, and $\Delta M_{k}$ is the growth in
imports from the EU between 2015-2016 and 2017-2018. Products are defined at
the HTS 8-digit level, because tariffs, except for aircrafts, were imposed on
that level.111111In a few products, the potential tariff only affected some EU
countries. I exclude these products from the sample. Column 1 of Table 1 shows
that products that had an initial higher tariff were more likely to be
included in the potential list. However, this effect becomes statistically
insignificant once I control for other confounding variables. The level and
growth of imports do not have a significant effect on the probability to be
included in the list of products targeted by tariffs.

As explained above, in virtually all targeted products the tariff duty
increased by 25 percentage points. The lack of variation in the additional
tariff across products and industries indicates that industry-specific
pressure groups had little influence in the determination of tariffs
(Fajgelbaum et al.,, 2020).

Table 1: Selection of products that could potentially be targeted by tariffs

| (1) | (2) | (3) | (4) | (5)
---|---|---|---|---|---
Tariff rate |  0.110b | 0.066 | 0.066 | 0.065 |  $-$0.020
| (0.044) | (0.049) | (0.049) | (0.049) | (0.049)
Imports EU 2018 | 1.112 | 5.471 | 5.471 | 6.755 | 5.094
| (3.231) | (5.504) | (5.504) | (6.063) | (5.612)
Imports growth EU |  |  | 0.000 | 0.000 |  $-$0.002
|  |  | (0.002) | (0.002) | (0.002)
Imports non-EU 2018 |  |  |  |  $-$0.776 | 0.910
|  |  |  | (1.553) | (1.688)
Imports growth non-EU |  |  |  |  $-$0.001 |  $-$0.002
|  |  |  | (0.003) | (0.003)
Conditional on growth sample | No | Yes | Yes | Yes | Yes
HS-4 digits fixed effects | No | No | No | No | Yes
Observations | 8,926 | 8,247 | 8,247 | 8,247 | 8,247
Adjusted R2 | 0.001 | 0.000 | 0.000 | 0.000 | 0.371

Note: Standard errors in parentheses. b: Statistically significant at 5%.
Imports EU and Imports non-EU refer to the year 2018. Import growth is
computed as the log change in imports between 2015-2016 and 2017-2018. Columns
(1) and (2) differ in that column (2) conditions on the sample of columns (3)
to (5), that is, product observations for which growth rate can be computed.

Once the first list of potential tariffs was announced, firms could determine
whether they could be affected by an increase in tariffs and take
precautionary measures. However, as will be shown in the next section, there
was no significant change in the behavior of exporters that were finally
targeted by tariffs during the period that elapses between the publication of
the list of potentially-affected products and the announcement of products
that were finally targeted by tariffs. This result suggests that potentially-
targeted exporters did not take precautionary measures, such as anticipating
sales and building stocks in the US, to counteract the potential raise in
tariffs (Alessandria et al.,, 2019; Khan and Khederlarian,, 2021).

##  3 The impact of tariffs on Spanish exports to the US

This section analyzes the impact of tariffs on Spanish exports to the US.
Firstly, I explore the impact of tariffs at the product level using import
data from the US Census. Next, I analyze the impact of tariffs on Spanish
exporters revenue and survival in the US market using data from Spanish
customs. As will be seen, the impact of tariffs was different when analyzed at
the product or exporter level.

###  3.1 Product-level analyses

I use data on US monthly imports from Spain from the US Census to compare the
evolution of products that were targeted by a tariff with that of those that
were not.121212To obtain a cleaner comparison, I excluded the products that
had been included in the list of products that potentially could be targeted
by a tariff but, finally, no tariff was imposed. I calculate a 12-month
rolling sum of imports to avoid the noise introduced by monthly data. For
example, the value corresponding to January 2019 is the sum of the value of
imports in that month and the previous 11 months. The value of imports in
September 2019, one month before the introduction of tariffs, is set at 100.

Figure 3 shows that tariffs led to a large decline in imports. During the
period in which tariffs were in force, October 2019-March 2021, imports of
products targeted by tariffs (blue solid line) decreased by 62%. Imports of
products not targeted by tariffs (red dashed line) only declined by 3%.
Imports of products targeted by tariffs began to increase once tariffs were
lifted in March 2021.131313Tariffs were suspended on the 4th and 11th of March
for UK and EU products, respectively. However, the import level one year after
tariffs had been lifted, March 2022, was still 32% lower than the one in the
month before tariffs were imposed.

Figure 3: US imports from Spain: tariff vs. non-tariff products, 2019-2022
(rolling 12-month sum; September 2019=100)

Source: author’s own elaboration based on the US Census database.

Figure 4 presents the evolution of imports in the four main Spanish products,
in terms of value, targeted by tariffs: olive oil, cheese, wine, and
olives.141414For each product category, I only compute the imports of the
varieties that were hit by a tariff. The largest decrease happened in olive
oil, where imports dropped by 84% during the tariff period. Decreases were
also large for wine and cheese: 65% and 67% respectively. The lowest decrease
happened in olives: 10%.

Figure 4: Impact of tariffs on the four main products targeted by tariffs,
2019-2022 (rolling 12-month sum; September 2019=100)

Source: author’s own elaboration based on the US Census database.

To confirm the visual appreciation from Figure 3, I estimate the following
difference-in-differences specification:

$y_{ktm}=exp[\beta(Tariff_{k}*Post_{tm})+\gamma_{km}+\gamma_{t}+\epsilon_{ktm}]$
(2)

where $y_{ktm}$ is the import value of US imports of product $k$ at year $t$
and month $m$ from Spain. $Tariff_{k}$ turns one if the US imposed a tariff on
product $k$ imported from Spain. $Post_{tm}$ turns one if the year-month
combination is after October 2019.151515Since tariffs were imposed in the
middle of October 2019, I exclude this month’s observations from the sample.
The specification includes product $\times$ month ($\gamma_{km}$) and year
($\gamma_{t}$) fixed effects. $\epsilon_{ktm}$ is the disturbance term. To
exploit the granularity of data, products are defined at the 10-digit HTS
classification. The $\beta$ coefficient captures whether the difference in the
dependent variable between non-tariff-targeted and tariff-targeted products
(first difference) changed after tariffs were imposed (second difference). I
cluster standard errors at the product level.

Table 2: Impact of tariffs on US imports from Spain. Product-level analysis | PPML | OLS
---|---|---
| (1) | (2) | (3) | (4) | (5)
| Value | Value | Value | Quantity | Price
Tariff x Post | -0.781a | -0.762a | -0.529a | -0.509a | -0.020
| (0.266) | (0.257) | (0.124) | (0.128) | (0.046)
Observations | 339220 | 140340 | 140340 | 140340 | 140340
Pseudo-R2 | 0.744 | 0.778 | . | . | .
Adj.-R2 | . | . | 0.604 | 0.707 | 0.774
Note: Regressions estimated with monthly export data from January 2017 to
February 2021. The dependent variables are import value in columns 1 and 2,
and log import value, log import quantity, and log import price in columns 3,
4 and 5, respectively. All regressions include product$\times$month and year
fixed effects. Tariff$\times$Post turns 1 if a product was subject to a tariff
and the month-year combination is within the tariff-imposition period.
Standard errors clustered at the product level are in parentheses. a
represents statistical significance at the 1% level.

I estimate Equation (2) using a Poisson pseudo-maximum likelihood estimator
(Santos-Silva and Tenreyro,, 2010). This model keeps the zero-import values in
the estimation sample and addresses ordinary least squares (OLS) estimates’
heteroscedasticity bias. Column 1 shows that the interaction coefficient is
negative and statistically significant, indicating that imports of products
targeted by tariffs decreased relative to non-targeted products after tariffs
were introduced. Specifically, US imports from Spain of tariff-targeted
products decreased by 54% relative to the non-targeted ones (1-exp(-.781)).

Columns 4 and 5 decompose the impact of tariffs on the value of imports into
its quantity and price components. Since this decomposition can only be
performed with an OLS model, I re-estimate Equation (2) with it.161616As the
OLS is a linear estimator, and errors have an expected value of zero, the sum
of the quantity and price estimates are equal to the value estimate. To
compare point estimates, in column 2, I re-estimate the PPML model using the
same sample used in the OLS estimations. The OLS point estimate (column 3) is
similar to that reported in column 1, confirming that tariffs had a large
negative effect on the value of imports. According to the difference-in-
differences OLS coefficient, the value of imports of a product targeted by a
tariff decreased relative to a non-targeted product by 41% after the tariff
was imposed (1-exp(-0.529)). This decrease is slightly lower than that
estimated with the PPML model. Interestingly, almost all the reduction in the
value of imports is explained by a reduction in quantity (column 4). The
change in prices is statistically not different from zero (column 5). This
latter result suggests that there was a complete pass-through of tariffs to
import prices. This result is surprising given the large size of the US
market. However, it is in line with previous studies that found complete pass-
through of tariffs to import prices during the US-China trade war (Amiti et
al.,, 2019; Fajgelbaum et al.,, 2020).

The period in which tariffs were in force coincided with the Covid-19
pandemic. The products targeted by a tariff could coincide with those that
were negatively affected in demand due to social distancing measures. If that
were the case, the interaction coefficient would be capturing the negative
shock of the pandemic on the demand for tariff-targeted products rather than
the negative effect of tariffs. To rule out this potential bias, I estimate a
triple-difference specification, which compares the evolution of US imports
from Spain with those from other countries that were not affected by tariffs.
The triple-difference specification is defined as follows:

$y_{kjtm}=exp[\beta(Tariff_{kj}*Post_{tm})+\gamma_{kj}+\gamma_{tj}+\gamma_{mj}+\gamma_{kt}+\gamma_{km}+\epsilon_{ktm}]$
(3)

where $y_{kjtm}$ is the value of US imports of product $k$ from country $j$ at
year $t$ and month $m$. Note that the new equation includes five different
fixed-effects combinations: product$\times$origin, year$\times$origin,
month$\times$origin, product$\times$year, and product$\times$month. Now, the
$\beta$ coefficient captures whether the difference between US imports of
tariff-targeted products from Spain and other countries (first difference)
changed relative to the difference between US imports of non-targeted products
from Spain and other countries (second difference) after tariffs were imposed
(third difference). To keep the sample computationally feasible, I select the
top 30 origins of US imports in 2018, the year before tariffs were imposed,
and estimate the model with OLS.171717I exclude other EU members from the list
of origin-of-imports countries because they could also be targeted by tariffs.
China was also excluded from the sample because many of its products had been
targeted by tariffs during the same period. As shown in Table A1 in the
Appendix, the triple-difference interaction term remains negative and
significant, and has a point value similar to that reported in column 3 of
Table 2. This result confirms that imports of tariff-targeted products did not
decrease relative to the non-targeted ones because of Covid-19.

###  3.2 Exporter-level analyses

In this subsection, I analyze how tariffs affected Spanish exporters’ revenue
and probability of survival in the US. I estimate the following specification:

$x_{ftm}=exp[\beta(ShareTariff_{f}*Post_{tm})+\alpha_{1}Medium_{ft}+\alpha_{2}Large_{ft}+\gamma_{f}+\gamma_{tm}+\epsilon_{ftm}]$
(4)

where $x_{ftm}$ is the export revenue of firm $f$ in year $t$ and month $m$ in
the US. $ShareTariff_{f}$ is the share of tariff-targeted products in firm
$f$’s total exports to the US before tariffs were introduced. It captures the
extent to which an exporter was affected by the tariffs imposed due to the
Airbus-Boeing conflict. The specification also introduces two dummy variables
controlling for the export-size of the firm in year $t$. Export size is
defined as total exports of firm $f$ at year $t$. $Medium_{ft}$ turns 1 if the
export size of the firm is above the median and below or equal to the 75
percentile in year $t$. $Large_{ft}$ turns 1 if the export size of the firm is
above the percentile 75. $\gamma_{f}$ is a firm fixed effect, $\gamma_{tm}$ is
a year$\times$month fixed effect, and $\epsilon_{ft}$ is the disturbance term.
The key variable is the difference-in-differences interaction
$ShareTariff_{f}*Post_{t}$, which captures whether the dependent variable
changed for Spanish firms exporting tariff-targeted products to the US
relative to the one of other Spanish exporters to the US after tariffs were
introduced.

Data on Spanish exporters were collected from the Customs and Excise
Department of the Spanish Tax Agency and included the universe of Spanish
exporters of goods. The data set contains a firm identifier, export
destination, the product classification, the value of exports, and exported
quantities. The product classification used by Spanish customs, the Combined
Nomenclature, is equivalent to the US HTS classification at the 6-digit level
only. I define that a 6-digit product was targeted by a tariff if any of its
HTS 8-digit varieties was targeted by a tariff.181818When calculating the
firm-level exports, I excluded all products that were threatened by a tariff,
but a tariff was not imposed. Products related to the aircraft industry were
excluded as well. It is reasonable that these manufacturers anticipated a
raise in tariffs on their products in the US after the WTO ruling in May 2018
and, hence, had enough time to prepare for this change in trade policy.

Figure 5: Pre-trends. Median exports to the US by a tariff-targeted firm vs a
matched firm, 2017-2021 (thousand euros; rolling 12-month sum)

Source: author’s own elaboration based on Customs data.

The key identifying assumption of the difference-in-differences strategy is
that other exporters provide an appropriate counterfactual of the trend that
exporters hit by tariffs would have followed had the US not introduced the
tariffs. To ensure parallel pre-trends, I use the Mahalanobis distance
algorithm to match each tariff-targeted exporter with a non-targeted exporter
using export values to the US in 2016, 2017, and 2018. Figure 5 presents the
evolution of exports to the US of the median firm targeted by tariffs (solid
blue line) and the median matched firm (dashed red line). By construction, the
trend followed by the two types of firms is very similar in the three years
before the introduction of tariffs. After tariffs were introduced, the trends
are also similar: exports decrease for both tariff-targeted and non-targeted
exporters.

Table 3 presents descriptive statistics of Spanish exporters of products that
were targeted by tariffs and the matched exporters. They are calculated using
data from 2016 to 2018, before tariffs were introduced. The number of
exporters that were affected by tariffs was 1,216. Wine was the most important
product for 78% of the exporters that were hit by a tariff in the US; olive
oil was the most important product for 8% of the exporters, cheese for 3%, and
olives for 3%. These exporters were matched with 964 firms that were not hit
by tariffs. Due to the criteria used for matching, the median value of exports
to the US for both groups is similar. This is also true for the number of
products that both groups sell in the US market. However, the overall export
revenue of the tariff-targeted exporters is smaller than that of matched
exporters. The matched firms also export to more destinations and more
products than the tariff-targeted firms. Interestingly, the importance of the
US market in total exports for the firms that were hit by tariffs was larger
than for the matches.

Table 3: Tariff-targeted exporters vs. matched exporters to the US before
tariffs

|  Tariff-targeted exporters |  Matched exporters
---|---|---
Number of exporters to the US | 1216 | 964
Export revenue (thousand euros) | 233 | 1458
Export revenue in the US (thousand euros) | 54 | 57
Number of export destinations | 6 | 13
Number of exported products | 3 | 6
Number of exported products to the US | 2 | 2
Exports to the US as a share of total exports (%) | 33 | 8

Note: average yearly values for the period 2016-2018. A tariff-targeted
exporter is a firm that exports at least one product that was targeted by
tariffs. Matched exporters are firms that exported no products hit by tariffs.

Table 4 presents the estimations of Equation (4) when export revenue is the
dependent variable. I estimate a Poisson pseudo-maximum likelihood model.
Column 1 shows that the interaction coefficient, although negative, is
statistically insignificant. In line with the visual impression from Figure 5,
this result indicates that the export revenue of firms that exported products
targeted by tariffs did not significantly decrease relative to the one of the
control firms after tariffs were introduced.

Table 4: Impact of tariffs on Spanish exporters to the US | PPML | OLS
---|---|---
| (1) | (2) | (3) | (4)
| Value | Value | Value | Exit
Share tariff x Tariff period | -0.124 | -0.105 | -0.119 | 0.015
| (0.111) | (0.100) | (0.115) | (0.016)
Tariff period 3 lags |  | 0.040 |  |
|  | (0.097) |  |
Tariff period 2 lags |  | -0.025 |  |
|  | (0.065) |  |
Share tariff x Potential period |  |  | 0.045 |
|  |  | (0.075) |
Observations | 114106 | 97168 | 114106 | 8587
Pseudo-R2 | 0.858 | 0.862 | 0.858 | .
Adj.-R2 | . | . | . | 0.105
Note: The dependent variable is monthly exports to the US in columns 1, 2, and
3. In column 4 the dependent variable turns if the firm ceases to export to
the US. All regressions include firm and year$\times$month fixed effects, and
year-specific dummies to control for firm size. Standard errors clustered at
the firm level are in parentheses.

As explained above, the majority of exporters that were hit by the increase in
tariffs were wine exporters. Hence, the insignificant effect of tariffs on
export revenue reported in column 1 may be reflecting the particular situation
in that industry. To determine whether tariffs had a similar effect in other
industries, I classify exporters according to their most important product,
and estimate Equation (4) for each group. Table A2, in the Appendix, reports
the results for the top-4 Spanish product categories targeted by a tariff:
olive oil, wine, olives, and cheese. In all products, the export revenue in
the US of the exporters targeted by tariffs did not decrease significantly
relative to their matched exporters after tariffs were imposed.

To further test the validity of the parallel pre-trends’ assumption, I divide
the period before the imposition of tariffs into three sub-periods. The first
sub-period, Tariff period 1 lag, covers the October 2018-September 2019
period; Tariff period 2 lags covers the October 2017-September 2018 period,
and the Tariff period 3 lags covers the October 2016-September 2017 period.
Column 2 presents the results of estimating Equation (4) with Tariff period 3
lags and Tariff period 2 lags as additional variables. Tariff period 1 lag is
the reference category. The Tariff period 3 lags and Tariff period 2 lags
coefficients are statistically insignificant, confirming that exporters that
were hit by tariffs had a similar trend to the one of exporters that were not
hit by tariffs before they were applied.

In column 3, I analyze whether the export revenue changed between the date in
which the potential list of targeted products was announced (April 2019) and
the final list of targeted products was published (October 2019). The Share
tariff x Potential period interaction is statistically insignificant,
indicating that the export revenue of potentially-targeted exporters did not
change relative to the one of other exporters to the US during the period in
which the potential products that could be targeted by a tariff was known.

Next, I analyze whether the imposition of tariffs increased the risk of
ceasing to export to the US for firms that were hit by tariffs. I estimate the
following equation:

$Exit_{fz}=\beta(ShareTariff_{f}*Post_{z})+\alpha_{1}Medium_{fz}+\alpha_{2}Large_{fz}+\gamma_{f}+\gamma_{z}+\epsilon_{fz}$
(5)

where $Exit_{fz}$ turns 1 if the firm exited the US market during period $z$.
To estimate Equation (5), I divide the period of analysis into four sub-
periods. Since tariffs were in force for 16 full months (November
2019-February 2021), I define three 16-months sub-periods for the pre-tariff
period: 1) October 2015-January 2017; 2) February 2017-May 2018, and 3) June
2018-September 2019. For each sub-period, I compute whether the firm exported
to the US and estimate a linear probability model. Column 4 of Table 4 shows
that the risk of ceasing to export to the US did not increase significantly
for exporters that were hit by tariffs relative to other exporters after
tariffs were introduced.

I also analyzed whether exporters hit by tariffs in the US changed their
export performance relative to other Spanish exporters to the US in third
foreign markets. I do not find any significant change between the two groups
of exporters.191919To conserve space, estimates are available upon request.

In sum, the firm-level analysis indicates that Spanish exporters targeted by
tariffs had similar economic outcomes in the US as not-targeted exporters. The
next section analyzes how Spanish exporters targeted by tariffs were able to
neutralize the negative impact of the tariff hike.

##  4 Spanish exporters’ tariff-avoidance strategies

Spanish exporters followed two major strategies to counteract the introduction
of tariffs: 1) the substitution of Spanish products by products originated in
countries that had not been affected by a tariff; and 2) the substitution of
tariff-targeted varieties by non-targeted ones. This section describes these
tariff-avoidance strategies.

###  4.1 Substitution of Spanish products by products originated in non-
targeted countries

The first tariff-avoidance strategy followed by Spanish exporters to the US
was to substitute a Spanish product targeted by a tariff with the same product
originated in a non-targeted country. This strategy was used by olive oil
exporters.

Figure 6 shows the imports of olive oil by the Spanish olive oil exporters to
the US. These firms accounted for 86% of all Spanish olive oil imports in
2018, the year before tariffs were introduced. Tunisia was the most important
origin of imports for these firms in 2018, accounting for 41% of total
imports, followed by Portugal with 27%. These two countries were not targeted
by a tariff on olive oil. There was a decrease in the value of imports from
all countries until tariffs were imposed by the US. During the tariff period,
Spanish exporters of olive oil to the US increased imports from Portugal and,
specially, from Tunisia. There is also a very sharp decrease in the value of
imports from Tunisia once tariffs were lifted.

Figure 6: Imports of olive oil by Spanish exporters of olive oil to the US,
2019-2022 (Million euros. Rolling 12-month sum)

Source: author’s own elaboration based on US Customs data.

As reported by the media and confirmed by the author in personal
communications with industry representatives, a large share of the olive oil
in bulk imported from Tunisia and Portugal during the tariff period was
bottled in Spain and exported to the US in substitution of the tariff-targeted
olive oil originated in Spain.202020In the media, see, among others,
https://www.elconfidencial.com/economia/2020-10-10/espana-aceite-trump-compra-
otros-paises_2782899/ and
https://www.elconfidencial.com/espana/andalucia/2021-02-17/aceite-envasado-
espana-vendio-eeuu-tunez-portugal_2954147/. I interviewed Rafael Pico,
Director of Asoliva, Spanish Association of Industry and Exporters of Olive
Oil. The label of these olive oil containers exported by Spanish firms to the
US stated that the origin of the product was Tunisia or Portugal. This was key
for Spanish exporters’ tariff-avoidance strategy. US customs considered that
the bottling that had taken place in Spain did not constitute a major product
transformation. Hence, US Customs recorded the shipments from Spain as imports
of olive oil from Tunisia or Portugal. Since these countries had not been
targeted by a tariff, Spanish exporters could avoid the additional import
duties.

Figure 7 shows the evolution of US imports of olive oil between 2019 and the
first quarter of 2022 from its top-4 suppliers in 2018: Italy, Spain, Tunisia,
and Turkey, which accounted for 89% of total olive oil imports in that year.
It also plots imports from Portugal, the top-9 supplier, which accounted for
1% of total imports in 2018. There is an increase in olive imports from
Portugal and, particularly, from Tunisia after tariffs were imposed. Imports
from these countries decreased once tariffs were lifted. There is no
significant change in the value of imports from Italy and Turkey, countries
that were not targeted by tariffs and occupied the top and the top-4 position,
respectively as suppliers of olive oil to the US in 2018. These trends are in
line with the narrative that Spanish exporters substituted Spanish olive oil
with olive oil originated in Tunisia and Portugal in their US exports during
the period in which tariffs were in force.

Figure 7: US imports of olive oil by countries, 2019-2022 (Million USD.
Rolling 12-month sum)

Source: author’s own elaboration based on US Customs data.

The tariff-avoidance strategy of Spanish olive oil exporters also led to a
large discrepancy between the value of exports reported by Spanish customs and
the mirror value of imports reported by US customs. The dashed red line in
Figure 8 captures the rolling 12-month sum value of exports of olive oil from
Spain to the US reported by Spanish customs. The solid blue line is the mirror
imports reported by US Customs. I set the value of these flows at 100 in
September 2019, the month before tariffs were introduced.212121To make the
figures comparable, the Spanish tariff-targeted olive oil exports and the
mirror US imports are calculated at the HS 6-digit level. The Spanish and US
lines are close and move parallel in the months before the introduction of
tariffs. However, since tariffs were introduced the two lines move in opposite
directions: US imports decrease whereas Spanish exports rise. The gap between
the lines is remarkable. By the end of the tariff period, US customs had
recorded a 53% decline in the value of olive oil imports from Spain relative
to the pre-tariff period, whereas Spanish customs had recorded a 14% increase
in the value of olive oil exports to the US.

Figure 8: Olive oil. US imports from Spain vs. Spanish exports to the US,
2019-2022 (rolling 12-month sum; September 2019=100)

Source: author’s own elaboration based on Customs and US Census data.

This difference is explained by the fact that Spanish Customs recorded as
exports to the US all containers shipped from Spain to the US, although a
large share of them had not been filled with olive oil produced in Spain. This
recording criteria explains why Spanish exports of olive oil to the US did not
decline, and even increased, despite the fact that an additional 25% tariff
had been imposed. It also provides the first explanation on why a large
decline is observed in US imports of Spanish tariff-targeted products (Figure
3) and, at the same time, we find no significant effect on the export revenue
of Spanish exporters of tariff-targeted products to the US relative to other
Spanish exporters (Table 4). Note that the gap between Spanish exports and the
mirror US imports began to narrow once tariffs were lifted in March 2021.
However, a large gap remained even one year after tariffs had been removed.
This suggests that some Spanish exporters continued to export olive oil
originated in other countries after the elimination of tariffs.

As mentioned in the introductory section of the paper, many studies have
relied on the gap between the value of exports and the mirror value of imports
to identify tariff-evasion. The present case highlights that the gap can also
be the outcome of exporters’ tariff-avoidance strategies and the differences
in the criteria to record trade flows between the exporting and importing
customs offices.

As additional evidence, I ran a simple OLS regression:

$x_{i}=\alpha+\beta\Delta m_{i}+\epsilon_{i}$ (6)

where $x_{i}$ is the value of olive oil exports to the US by firm $i$ during
the tariff period, $\alpha$ is a constant, $\Delta m_{i}$ is the change in
imports of olive oil from Tunisia and Portugal between the tariff and pre-
tariff period, and $\epsilon_{i}$ is the disturbance term.

The $\beta$ coefficient is expected to be positive: only Spanish olive oil
exporters that increased imports from Tunisia and Portugal could substitute
the Spanish olive oil with the olive oil of non-targeted countries and keep
their export revenue in the US. Table A3 in the Appendix reports a positive
and statistically significant $\beta$ coefficient: a 1000 euro increase in
olive oil imports from Tunisia and Portugal was correlated with a 627 euros
increase in olive exports to the US market. This result is in line with the
argument that Spanish olive oil exporters substituted the Spanish olive oil
with olive oil from non-targeted countries to avoid the negative impact of
tariffs on their exports to the US.

The substitution of olive oil produced in Spain with olive oil originated in
non-tariff countries was a feasible strategy for Spanish exporters whose
competitive strategy in the US was based on prices. The main goal of these
exporters was to maintain their relationship with large US retailers. These
retailers would not have accepted a rise in prices, forcing the Spanish
exporters to find alternatives to offer a price-competitive product.222222For
example, the export manager of a major Spanish olive oil firm asserted that
large US distributors would not have allowed even a 2% increase in prices
(https://issuu.com/olimerca/docs/vermultimedia/s/10169409). In another related
context, Iacovone et al., (2015) showed that Walmart demanded a lower price
than traditional retailers to its suppliers in exchange of a larger market in
Mexico. In contrast, results by Cavallo et al., (2021) suggested that in the
US-China trade war retailers absorbed a large share of the increase in costs
due to tariffs. They explained this fact based on the differentiated nature of
the products that the US imported from China. However, product substitution
was not an option for exporters that had built their competitive strategy on
the relationship between a high-quality olive oil and its Spanish origin.

Spanish firms could also use two additional strategies to avoid tariffs in the
US. Firstly, olive oil was not targeted by tariffs if it was exported in bulk.
This benefited Spanish exporters that were already bottling olive oil in the
US. Bottling in the US might become an option for the rest of exporters.
However, US imports of olive oil in bulk from Spain decreased during the
tariff period, indicating that Spanish exporters did not follow this strategy.
The main reason was that the cost of bottling olive oil in the US was too high
for many
exporters.232323https://issuu.com/olimerca/docs/vermultimedia/s/10169409.
Secondly, Spanish exporters that already had production facilities in
countries that had not been targeted by tariffs (e.g., Portugal and Tunisia)
increased their exports to the US directly from those
countries.242424https://www.elconfidencial.com/empresas/2019-10-04/aceite-
oliva-arancel-aceituna-exportacion_2268007/.

Figure 9 shows that the gap between the value of Spanish exports to the US and
the mirror value of US imports from Spain also widened during the tariff
period for wine, olives, and cheese. However, the increase in those products
was negligible compared to that in the olive oil industry. One explanation for
these differences lies in product regulations. For example, most of the wine
imported by the US from Spain are of varieties protected with designation of
origin. Hence, these exporters cannot substitute the origin of their product.
Another explanation, as analyzed in the next subsection, is that, in the case
of wine and cheese, Spanish exporters had an alternative strategy to avoid
tariffs.

Figure 9: Analysis by product: US imports from Spain vs. Spanish exports to
the, 2019-2021 (rolling 12-month sum; September 2019=100)

Olive oil | Wine
---|---
|
Olives | Cheese
|

Source: author’s own elaboration based on US Census and Agencia Tributaria
trade data.

###  4.2 Substitution of tariff-targeted varieties with non-targeted ones

The second strategy followed by Spanish exporters to avoid the negative
effects of tariffs was to substitute the tariff-targeted varieties with
similar non-targeted ones. Figure 10 compares the evolution of US imports from
Spain of tariff-targeted varieties and non-targeted varieties in the top-4
products targeted by tariffs. For each HTS 8-digit variety targeted by a
tariff, I identify other varieties belonging to the same HTS 6-digit product
that were not targeted by a tariff. There is a decrease in US imports of the
tariff-targeted varieties when tariffs enter into force in the four products
analyzed. For wine and cheese, there is a parallel increase in imports of non-
targeted varieties. In the case of wine, the increase in imports of non-
targeted varieties covered 57% of the decrease in imports of targeted
varieties (75 out of 132 million USD); in the case of cheese, 80% of the
decrease was covered by non-targeted varieties (48 out of 60 million USD).

Figure 10: Substitution of tariff-targeted varieties with non-targeted ones in
US imports from Spain, 2019-2021 (rolling 12-month sum; million USD)

Olive oil | Wine
---|---
|
Olives | Cheese
|

Source: author’s own elaboration based on US Census trade data.

Contrary to wine and cheese, there is no substitution in imports between
targeted and non-targeted varieties in olive oil and olives. In the former, as
explained above, firms could have avoided the tariff exporting olive oil in
bulk. However, firms did not use this strategy, because it was costly and they
had the alternative of exporting olive oil originated in non-targeted
countries. In the latter, it was the green-olive variety that was targeted by
the additional tariff. The other main variety, black olive, had already been
targeted by countervailing and anti-dumping measures since August
2018.252525The combined tariff equivalent of these measures was similar to the
25% additional tariff applied on the green variety. See
https://www.govinfo.gov/content/pkg/FR-2018-08-01/pdf/2018-16449.pdf and
https://www.govinfo.gov/content/pkg/FR-2018-08-01/pdf/2018-16450.pdf. Hence,
shifting from targeted to non-targeted varieties was not an option for olive
exporters. It is interesting to observe that the relative decrease in imports
after tariffs were introduced was smaller in olives than in the rest of
products. I address this fact at the end of this section.

The substitution of the exported varieties in wine and cheese was feasible
because some targeted and non-targeted varieties were similar. Regarding wine,
the variety targeted by the tariff was wine in containers holding 2 liters or
less with an alcoholic strength by volume not over 14%. However, the variety
with an alcohol strength over 14% in the same type of containers was not
targeted by tariffs. Figure 11 shows that there was substitution in imports
between these varieties once tariffs were introduced.

Figure 11: Wine. US imports by alcoholic strength, 2019-2022 (rolling 12-month
sum; million USD)

Source: author’s own elaboration based on US Census trade data.

Spanish red wine, which accounted for 75% of total Spanish exports of wine in
containers holding 2 liters or less before tariffs were introduced, has an
alcohol strength in the 13%-15% range.262626https://catatu.es/blog/graduacion-
vino/. Hence, it was feasible for Spanish red wine exporters to substitute
red-wine varieties with an alcohol strength equal or below 14% with those of
an alcohol strength above 14%. This may explain why exports of red wine in
containers holding 2 liters or less only declined by 7.6% during the tariff
period. However, Spanish white wine, which accounted for 18% of total Spanish
exports of wine in containers holding 2 liters or less before tariffs were
introduced, has an average alcohol strength between 10% and 11%. In this case,
the shift to varieties with an alcohol strength above 14% was not feasible.
For this type of wine, exports declined by 23.4% relative to the pre-tariff
period.

The US also imposed an additional tariff on wines originating from France and
Germany. However, contrary to Spain, the tariff was imposed on wines with an
alcoholic strength equal to 14% or less and wines with an alcoholic strength
over 14%. No substitution between wine varieties happened in US imports from
these countries once tariffs were imposed.

It would have been ideal to confirm the shift between wine varieties using
exporter-level data. However, this was not possible since the alcohol strength
threshold used by the EU trade classification is set at 15% rather than 14%,
concealing the shift between varieties that happens at the latter threshold.
Finally, it is interesting to observe that US imports of wines with an
alcoholic strength equal to or below 14% began to increase after tariffs were
lifted. Imports of varieties with an alcohol strength above 14% only began to
decline several months after tariffs were lifted.

Figure 12 plots the evolution of US imports of cheese from Spain before,
during, and after the tariff period. Three categories account for most of
imports: cheese made from sheep’s milk, fresh cheese, and other cheeses
containing cow’s milk. Tariffs were imposed on the three varieties. However,
the cheese made from sheep’s milk category had a close variety which was not
targeted by a tariff.272727Specifically, HTS codes 04069056 and 04069057 were
targeted by an additional 25% tariff, whereas code 04069059 was not. In the
rest of the categories, all alternative varieties were targeted by a tariff.
The figure shows a large decrease in imports of the cheese made from sheep’s
milk targeted by tariffs and a big increase in the cheese made from sheep’s
milk not targeted by tariffs. In fact, the non-targeted variety more than
compensated for the decrease in imports in the targeted varieties. These
trends suggest that Spanish firms could switch rapidly from the targeted
varieties to the non-targeted ones in their exports to the US. In contrast, in
the categories without an alternative non-targeted variety, the decrease in
imports is less intense than in the targeted sheep category.

Figure 12: Cheese. US imports from Spain by top categories, 2019-2022 (rolling
12-month sum; million USD)

Source: author’s own elaboration based on US Census trade data.

Note that the cheese made from sheep’s milk varieties reversed their trends
once tariffs were lifted. This further suggests that Spanish firms had shifted
the targeted with the non-targeted varieties in their exports to avoid the
negative effect of tariffs.

As was the case for wine, it would have been ideal to explore the shift across
varieties using data at the exporter level. However, the product
classification used by the US at the 8-digit level, the one at which tariffs
were imposed, is different to the one used by the EU at the 8-digit level
disaggregation. This difference in classification precluded me from
identifying the varieties targeted by tariffs and the non-targeted ones in the
EU classification at the 8-digit level.

Figure 4 and 10 showed that the negative impact of tariffs on US imports of
Spanish olives was much smaller than in other tariff-targeted products. To
understand this differential impact, I estimate the following regression:

$m_{jktm}=exp[\beta(Tariff_{jk}*Post_{tm})+\gamma_{jkm}+\gamma_{t}+\epsilon_{ktm}]$
(7)

where $m_{jktm}$ are US imports of olive variety $k$ from country $j$ at year
$t$ and month $m$. The difference-in-differences interaction term,
$Tariff_{jk}*Post_{tm}$ turns 1 if the US imposed a tariff on country $j$ and
the period analyzed is when tariffs were in force. The sample covers the
period January 1997-February 2021 and includes all countries the US imported
olives from.282828In addition to Spain, olives imported from France, Germany,
and the UK were targeted by tariffs. However, the share of these countries in
US total imports of tariff-targeted olives in 2018 was only 0.5%. The sample
only includes the olive varieties that were targeted by tariffs.

Column 1 of Table 5 shows that olives imports from countries targeted by
tariffs decreased in the period in which the tariff was in force. However, the
coefficient is statistically insignificant. This result was unexpected, since
one would anticipate a significant decrease in imports from countries targeted
by an additional 25% tariff. To understand this result, it is important to
consider the share of Spain in the US total olives imports targeted by
tariffs. In 2018, this share was 62%. The next countries in the ranking were
Greece and Italy, with 26% and 7% of total US imports, respectively. Spain’s
large share suggests that Spanish olives were competitive in the US, difficult
to substitute, and commanded a low import demand elasticity. This low demand
elasticity could explain why a 25% increase in tariffs did not lead to a
significant decrease in imports.

Table 5: Impact of tariffs on US imports of olives | PPML | OLS
---|---|---
| (1) | (2) | (3) | (4) | (5)
| Value | Value | Value | Quantity | Price
Tariff x Post | -0.273 | -0.255 | -0.553a | -0.551a | -0.002
| (0.196) | (0.196) | (0.132) | (0.105) | (0.062)
Observations | 4240 | 2265 | 2265 | 2265 | 2265
Pseudo-R2 | 0.920 | 0.955 | . | . | .
Adj.-R2 | . | . | 0.825 | 0.801 | 0.618
Note: Regressions estimated with monthly export data from January 2017 to
February 2021. The dependent variables in columns 1, 2, 3, and 4 are import
revenue, log import revenue, log import quantity, and log import price,
respectively. All regressions include product$\times$destination, year, and
month fixed effects. Tariff$\times$Post turns 1 if a product was subject to a
tariff in a specific import origin and the month-year combination is within
the tariff-imposition period. Standard errors clustered at the product level
are in parentheses. a represents statistical significance at the 1%.

In columns 3 to 5, I decompose the change in the value of US imports into the
quantity and price margins. Since this decomposition can only be performed
with an OLS estimator, in column 2 I repeat the PPML estimation in column 1
with the sample used for the OLS decomposition. The point estimate barely
changes relative to the estimate reported in column 1. However, when an OLS
model is used to estimate Equation (7), the interaction coefficient becomes
negative and significant (column 3). The table also shows that the reduction
in revenue was explained by a reduction in quantities. Prices, which do not
include tariff duties, did not change, indicating a complete pass-through of
tariffs to prices. This result is in line with the low import demand
elasticity of Spanish olives in the US (Fajgelbaum and Khandelwal,, 2022).

##  5 Conclusion

In contrast to previous decades characterized by a process of trade
liberalization at the global and regional level, there has been a shift
towards more unilateral and aggressive trade policies since the middle 2010s.
This paper has analyzed how exporters behave in this hostile trade
environment.

I use the retaliatory tariffs that the US imposed on some EU products between
October 2019 and March 2021 due to the Airbus-Boeing trade dispute as a case
study. I show that US imports of Spanish products targeted by tariffs declined
by 62% relative to other US imports from Spain. However, the export revenue of
Spanish exporters of tariff-targeted products did not significantly decrease
relative to the one of other Spanish exporters to the US during the period in
which tariffs were in force. I show that Spanish exporters used two trade-
avoidance strategies to mute the negative effects of tariffs. Firstly, instead
of exporting Spanish products targeted by tariffs, they exported the same
products but originated in countries not targeted by tariffs. Secondly, they
shifted from varieties targeted by tariffs to varieties that had not been
targeted by tariffs within the same product category.

The trade avoidance strategy followed by Spanish exporters to the US enabled
them to neutralize the negative effects of tariffs. This shows that exporters,
at least those specialized in primary and manufactures of primary products,
have an additional margin to counteract a tariff hike.

## References

  * Albornoz et al., (2021) Albornoz, F., Brambilla, I., and Ornellas, E. (2021).  Firm export responses to tariff hikes.  Centre for Economic Performance Working Paper 1683.
  * Alessandria et al., (2019) Alessandria, G. A., Khan, S. Y., and Khederlarian, A. (2019).  Taking stock of trade policy uncertainty: evidence from China’s Pre-WTO accession.  NBER Working Paper 25965.
  * Amiti et al., (2019) Amiti, M., Redding, S. J., and Weinstein, D. E. (2019).  The impact of the 2018 tariffs on prices and welfare.  Journal of Economic Perspectives, 33(4):187–210.
  * Atkeson and Burstein, (2008) Atkeson, A. and Burstein, A. (2008).  Pricing-to-market, trade costs, and international relative prices.  American Economic Review, 98(5):1998–2031.
  * Bernard et al., (2011) Bernard, A. B., Redding, S. J., and Schott, P. K. (2011).  Multiproduct firms and trade liberalization.  The Quarterly Journal of Economics, 126(3):1271–1318.
  * Bhagwati, (1964) Bhagwati, J. (1964).  On the underinvoicing of imports.  Bulletin of the Oxford University Institute of Economics and Statistics, 29:61–77.
  * Cavallo et al., (2021) Cavallo, A., Gopinath, G., Neiman, B., and Tang, J. (2021).  Tariff pass-through at the border and at the store: Evidence from US trade policy.  American Economic Review: Insights, 3(1):19–34.
  * Colantone et al., (2021) Colantone, I., Ottaviano, G. I., and Stanig, P. (2021).  The backlash of globalization.  Centre for Economic Performance Working Paper 1800.
  * Demir and Javorcik, (2020) Demir, B. and Javorcik, B. (2020).  Trade policy changes, tax evasion and Benford’s law.  Journal of Development Economics, 144:102456.
  * Dhingra and Sampson, (2022) Dhingra, S. and Sampson, T. (2022).  Expecting Brexit.  Annual Review of Economics, 14(1):495–519.
  * Eckel and Neary, (2010) Eckel, C. and Neary, J. P. (2010).  Multi-product firms and flexible manufacturing in the global economy.  The Review of Economic Studies, 77(1):188–217.
  * Fajgelbaum et al., (2020) Fajgelbaum, P. D., Goldberg, P. K., Kennedy, P. J., and Khandelwal, A. K. (2020).  The return to protectionism.  The Quarterly Journal of Economics, 135(1):1–55.
  * Fajgelbaum and Khandelwal, (2022) Fajgelbaum, P. D. and Khandelwal, A. K. (2022).  The economic impacts of the US-China trade war.  Annual Review of Economics, 14(1):205–28.
  * Fisman and Wei, (2004) Fisman, R. and Wei, S. J. (2004).  Tax rates and tax evasion: evidence from “missing imports” in China.  Journal of Political Economy, 112(2):471–496.
  * Fitzgerald and Haller, (2018) Fitzgerald, D. and Haller, S. (2018).  Exporters and shocks.  Journal of International Economics, 113(July):154–171.
  * Flaaen et al., (2020) Flaaen, A., Hortaçsu, A., and Tintelnot, F. (2020).  The production relocation and price effects of US trade policy: the case of washing machines.  American Economic Review, 110(7):2103–27.
  * Iacovone et al., (2015) Iacovone, L., Javorcik, B., Keller, W., and Tybout, J. (2015).  Supplier responses to Walmart’s invasion in Mexico.  Journal of International Economics, 95(1):1–15.
  * Javorcik and Narciso, (2008) Javorcik, B. S. and Narciso, G. (2008).  Differentiated products and evasion of import tariffs.  Journal of International Economics, 76(2):208–222.
  * Jiao et al., (2022) Jiao, Y., Liu, Z., Tian, Z., and Wang, X. (2022).  The impacts of the US trade war on Chinese exporters.  Review of Economics and Statistics.  Conditionally accepted.
  * Khan and Khederlarian, (2021) Khan, S. Y. and Khederlarian, A. (2021).  How does trade respond to anticipated tariff changes? Evidence from NAFTA.  Journal of International Economics, 133:103538.
  * Ludema and Yu, (2016) Ludema, R. D. and Yu, Z. (2016).  Tariff pass-through, firm heterogeneity and product quality.  Journal of International Economics, 103:234–249.
  * Melitz, (2003) Melitz, M. J. (2003).  The impact of trade on intra-industry reallocations and aggregate industry productivity.  Econometrica, 71(6):1695–1725.
  * Santos-Silva and Tenreyro, (2010) Santos-Silva, J. and Tenreyro, S. (2010).  Further simulation evidence on the performance of the Poisson pseudo-maximum likelihood estimator.  Economics Letters, 112(2):220 – 222.

Table A1: Triple-difference. Impact of tariffs on US imports from Spain. Product-level analysis | (1) | (2) | (3)
---|---|---|---
| Value | Quantity | Price
Tariff x Post | -0.578a | -0.558a | -0.020
| (0.147) | (0.151) | (0.046)
Observations | 3065942 | 3065942 | 3065942
Adj.-R2 | 0.669 | 0.760 | 0.823
Note: Regressions estimated with monthly export data from January 2017 to February 2021. The dependent variables in column 1, 2, and 3 are log import revenue, log import quantity, and log import price, respectively. All regressions include product$\times$destination, year$\times$destination, month$\times$destination, product$\times$year, and product$\times$month fixed effects. Tariff$\times$Post turns 1 if a product was subject to a tariff and the month-year combination is within the tariff-imposition period. Standard errors clustered at the product level are in parentheses. a represents statistical significance at the 1% level.  Table A2: Top-4 product categories. Impact of tariffs on US exports | (1) | (2) | (3) | (4)
---|---|---|---|---
| Olive oil | Wine | Olives | Cheese
Share tariff x Tariff period | -0.096 | -0.118 | 0.112 | -0.049
| (0.195) | (0.096) | (0.255) | (0.403)
Observations | 11048 | 86353 | 3721 | 4313
Pseudo-R2 | 0.860 | 0.771 | 0.868 | 0.853
Note: The dependent variable is monthly exports to the US. All regressions include firm, year$\times$month fixed effects, and year-specific dummies to control for firm size. Standard errors clustered at the firm level are in parentheses.  Table A3: Correlation between the increase in imports of olive oil from Tunisia and Portugal and exports to the US | (1)
---|---
$\Delta$ Imports from Portugal and Tunisia | 0.627b
| (0.251)
Constant | 2360.698a
| (829.509)
Observations | 198
$R^{2}$ | 0.031
Note: The dependent variable is the value of olive oil exports to the US
during the tariff period. Standard errors are in parentheses. a and b denote
statistical significance at the 1 and 5% levels, respectively.