Customs brokers as intermediaries in international trade

Abstract

Recent studies suggest that intermediaries like merchants facilitate international trade by reducing fixed trade costs for producers that trade through them instead of exporting or importing directly. This study argues that customs brokers–a type of intermediary rarely studied in economics before–play a similar role by reducing fixed costs of clearing goods through customs for firms that use them instead of self-declaring. Using panel data of Norwegian trade transactions, the paper shows that the majority of manufacturing producers participating in international trade use such brokers, and that the brokers typically handle large trade values on behalf of several different produces. In an econometric analysis, the author finds that the share of a producer’s market specific trade that is self-declared rather than handled by brokers increases with the traded value. This is in line with predictions from theoretical models on trade intermediaries and holds after controlling for observed as well as unobserved factors at the producer, country and product level. Results are similar for exporting and importing, indicating that brokers facilitate both modes of trade.

Full text

Vol.:(0123456789)
Review of World Economics
https://doi.org/10.1007/s10290-020-00396-w
1 3
ORIGINAL PAPER
Customs brokers asintermediaries ininternational trade
HegeMedin1
© The Author(s) 2020
Abstract
Recent studies suggest that intermediaries like merchants facilitate international
trade by reducing fixed trade costs for producers that trade through them instead
of exporting or importing directly. This study argues that customs brokers–a type
of intermediary rarely studied in economics before–play a similar role by reducing
fixed costs of clearing goods through customs for firms that use them instead of self-
declaring. Using panel data of Norwegian trade transactions, the paper shows that
the majority of manufacturing producers participating in international trade use such
brokers, and that the brokers typically handle large trade values on behalf of sev-
eral different produces. In an econometric analysis, the author finds that the share of
a producer’s market specific trade that is self-declared rather than handled by bro-
kers increases with the traded value. This is in line with predictions from theoretical
models on trade intermediaries and holds after controlling for observed as well as
unobserved factors at the producer, country and product level. Results are similar for
exporting and importing, indicating that brokers facilitate both modes of trade.
Keywords International trade· Trade costs· Intermediaries· Customs brokers·
Customs clearing
JEL Classication F12· F14
1 Introduction
A recent strand in economic research concerns how intermediaries like merchants
facilitate international trade by making it possible for firms unable to handle all
trade-related issues by themselves to participate in international markets. This,
in turn, can boost aggregated trade flows. I argue that another type of intermedi-
ary–customs brokers–can play a similar role. In Norway, all exported or imported
* Hege Medin
hege.medin@nupi.no
1 Norwegian Institute ofInternational Affairs (NUPI), St. Olavs Plass, P. O. Box7024,
0130OSLO, Norway

H.Medin
1 3
goods must pass through customs and be declared, and a producer can choose
between handling the customs declaration by itself (henceforth: self-declare) or
engaging the services of a broker to do this. I use an exhaustive panel of Norwe-
gian manufacturing producers’ trade transactions containing information on usage
of customs brokers. The data reveal that outsourcing of customs brokerage is very
common. To my knowledge, customs brokers have rarely been studied in econom-
ics before, and this is the first article to document the use of such brokers in a whole
population or even representative sample of firms.
Building on the seminal Melitz (2003) model of international trade, Ahn et al.
(2011) proposed a model in which exporting firms avoid paying high fixed costs of
reaching foreign customers by using a merchant (referred to as a trading company)
to sell their goods in foreign markets. This comes at the expense of lower operating
profits, however, as the exporter has to pay a de facto fee to the merchant (which
is proportional to the traded value). Other, similar models have been proposed by
scholars like Felbermayr and Jung (2011), Crozet etal. (2013), Bai etal. (2017) and
Akerman (2018), focusing on, respectively, wholesaler subsidiaries; quality differ-
ences; learning by exporting and economies of scope. The survey article of Blum
etal. (2018, p. 20) refers to these models jointly as the canonical model of export
intermediation. The model has the feature that a third type of firm emerges in equi-
librium in addition to the exporters and non-exporters in Melitz (2003). Like in that
model, a producer’s operating profits from exporting to a given market is propor-
tional to the value exported, and a sorting pattern arises where only the most pro-
ductive firms export, because they are the only ones that earn enough to cover the
market specific fixed exporting costs. The new feature is that firms of intermediate
productivity levels export indirectly through merchants, as this involves lower fixed
exporting costs (but also higher variable ones).1 Consequently, the model predicts
that larger and more productive producers exporting sizeable values to more profit-
able markets with lower trade costs are less likely to use intermediaries.
There are some empirical studies that, for selected countries, find patterns in
accordance with the intermediation model. In contrast to the present study, all of
these define the intermediary as a merchant like a wholesaler or retailer, and such
merchants are found to account for significant shares of trade (see footnote 5). Two
different approaches are used in the literature. In what will here be referred to as
the intermediary approach, the intermediaries as such are compared to producers
that trade without using them (henceforth: direct trading producers). Ahn et al.
(2011), Crozet etal. (2013) and Akerman (2018) all used customs declaration data
from, respectively, China, France and Sweden, and showed that the share of exports
accounted for by merchants was larger in destination markets with lower profitabil-
ity (e.g. lower GDP) and higher trade costs (e.g. higher tariffs).2 However, such data
1 In some of the models, the indirect exporters do not pay a fee to the merchants, but there is a dou-
ble marginalisation problem instead, which reduces their revenue from exporting indirectly instead of
directly: the merchants have market power and impose a mark-up to the producer price when selling to
foreign consumers.
2 Using a somewhat different regression design, Bernard etal. (2015) reached a similar conclusion for
Italy.

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Customs brokers asintermediaries ininternational trade
typically do not contain information on the intermediary-users; that is, the producers
that export or import through the merchants. The scholars were therefore not able
study how producer-features affected intermediary-use.
In what will be referred to as the intermediary-user approach, in contrast, the
direct trading producers are compared to producers who channel their trade through
intermediaries. Studies applying this approach found that a producer’s inclination
to trade directly rather than via a merchant was positively associated with meas-
ures of firm level profitability, such as the number of employees (Abel-Koch 2013;
Ahn etal. 2011; Maurseth and Medin 2019), labour-productivity (Ahn etal. 2011;
McCann 2013; Grazzi and Tomasi 2016) and foreign ownership (Abel-Koch 2013;
McCann 2013; Maurseth and Medin 2019). All these studies used firm level survey
data from one or several developing countries. The data were based on interviews
with producers that traded, either directly or via merchants, but not the merchant
used. The scholars were therefore not able to study the intermediaries as such. Nor
were they able to study how intermediary-use related to partner country characteris-
tics, as such information was not available in the data.3 The data also had other limi-
tations. Being based on interviews of samples of firms rather than register data, they
yielded less accurate information; and they were mostly cross-sectional and hence
provided limited possibilities for controlling for unobserved heterogeneity.
Fixed trade costs may not only accrue when searching for foreign contacts, but
also when complying with trade and customs procedures. Grainger (2008) points
out that many costs like that are fixed, as they involve, for instance, ‘purchase of
specialist IT systems and employment of dedicated staff’ (p. 26). This is also the
case in Norway. The Directorate of Norwegian Customs informs that declarants
must purchase special software, familiarise themselves with regulations and com-
plete an electronic form for each declaration (shipment). Relevant certificates, like
health certificates for food, must also be obtained. Further, declarants must hold
customs credit, and must calculate and pay taxes and duties.4 Such customs related
transaction costs are not negligible. Verwaal and Donkers (2003) found that they, on
average, constituted 2% of the traded value among Dutch firms. They varied largely
among firms, however, and there were indications of economies of scale. Relatedly,
WTO (2016) found that border procedures were the third most important trade bar-
rier for EU firms operating in the US (after product standards).
In the model of intermediation, the merchants face lower fixed trade costs, either
by assumption or due to economies of scope. In addition, they may be better able to
3 In addition to applying the intermediary approach on Chinese customs declaration data, Ahn et al.
(2011) did a separate analysis applying the intermediary-user approach on Chinese survey data. They
were not able to combine these data, however. Bai etal. (2017), in contrast, combined Chinese cus-
toms declaration data for producers of rubber and plastic with other survey data for large enterprises
and inferred intermediary-use from this. They studied learning effects, and like in the intermediary-user
approach, they compared intermediary-users to direct exporters. However, they did not study how coun-
try- and product characteristics influenced intermediary-use, as this information was not available for the
intermediary-users. Nor did they compare the intermediary-users to the intermediaries as such.
4 Information based on in depth-interviews with two representatives from the Directorate of Norwegian
Customs, conducted by the author in January 2016.

H.Medin
1 3
overcome such costs by pooling trade from many producers. Similarly, customs bro-
kers are likely to face lower fixed customs clearance costs than produces since they
specialise in handling border procedures. Furthermore, in the article it is shown that
brokers handle trade from several different producers, and that each broker-handle
considerably larger values than each producer. Consequently, not only merchants,
but also customs brokers may facilitate trade by offering reduced fixed trade costs
for producers that use their services. Using brokers is costly, however, their services
must be purchased, and thus, the choice between hiring brokers and self-declare is
likely to be a trade-off between fixed and variable trade costs, just as in the model
of intermediation. If this holds, the predictions from that model should also be rel-
evant for broker-use, and we should expect that producers trading small values hire
brokers, and those trading large values self-declare. This prediction is studied here.
In addition to providing evidence of a new type of intermediary and focusing on
a country not previously studied in this regard, this article also offers several contri-
butions to the empirical literature on trade intermediaries. In contrast to other stud-
ies, I have in the same data information on the intermediaries themselves (customs
brokers), as well as on the two types of producers; those that trade directly (self-
declarants) and those that rely on intermediaries (broker-users). I am therefore able
to merge the intermediary and intermediary-user approaches, thereby providing a
more consistent and comprehensive investigation of intermediary-use.
I first use highly disaggregated data–at the declaration level, to show that there
is a positive correlation between the probability of a producer self-declaring a sin-
gle declaration and its trade value. This is consistent with producers avoiding fixed
customs clearing costs by hiring brokers when they trade small values. The corre-
lation may be spurious, however, and unlike other studies, I therefore control for
characteristics of producers, countries and products at the same time–observed as
well as unobserved–by aggregating the data and using the panel dimension. The cor-
relation between tendency to self-declare and trade value is still positive. Apart from
that, only producer level features affect intermediary-use. This contrasts studies of
merchant-intermediaries, especially those using the intermediary approach, which
generally found that country- and product characteristics also affected the tendency
to trade directly. This underlines the importance of controlling for all these features
at the same time.
Most other studies of intermediary-use, whether theoretical or empirical, have
focused solely on how intermediaries facilitate exports. But, importing may also
involve fixed costs that can be reduced by using intermediaries. This is likely to be
the case as regards customs clearance because procedures are similar for clearing
goods into as well as out of Norway. To compare the trade-facilitating role of bro-
kers in exports and imports, I conduct all analyses for both modes of trade. In line
with what a few other studies have found for merchant-intermediaries, I find that
brokers-intermediaries are more commonly used in exporting than importing, and
that effects are similar for the two modes of trade.5
5 For example, Bernard etal. (2010) found that in the USA, 8% of the export value and 15% of import
value was accounted for by wholesalers. Blum et al. (2018) found that the corresponding figures for
Chile were 6% and 41%, and Utar (2017) found them to be 32% and 57% for Denmark. Whereas these
scholars used the intermediary approach, Maurseth and Medin (2019) applied the intermediary-user

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Customs brokers asintermediaries ininternational trade
In the following, Sect.2 discusses some concepts, Sect.3 presents the data and
provides some descriptive statistics, Sect.4 presents empirical evidence of customs-
brokers acting as trade facilitators and Sect.5 offers some conclusions.
2 Concepts
This section discusses the relevance of the intermediation model for guiding the
empirical analysis of the factors that influence a producer’s inclination to self-
declare in a given market (country-product combination). First, an objection to
the model could be that it assumes that fixed trade costs are country-and product
specific, whereas the decision to self-declare is done at the shipment level. Conse-
quently, the fixed costs of clearing customs–at least part of them–are likely to accrue
at the level of the shipment. In the context of this article, however, the crucial feature
of the intermediation model is that a productivity sorting pattern arises in equilib-
rium due to a trade-off between fixed and variable trade costs. Several authors have
built Melitz (2003) style models with trade-offs like that at the level of the ship-
ment rather than market. More shipments yield higher total fixed shipment costs, but
also lower variable costs, for instance due to lower inventory costs (Kropf and Sauré
2014). A sorting pattern does indeed arise, where only more productive producers
earn enough profits from a single shipment to cover the fixed trade cost. As a conse-
quence, higher shipment frequency implies that a higher (present value of) revenue
from all shipments is required for exporting to take place. Combined with the ideas
from the intermediation model, this should here imply that, ceteris paribus, produc-
ers with a larger number of shipments are less likely to self-declare.6
Another factor supporting the intermediation model is that parts of the fixed cus-
toms clearing costs are likely to accrue at country- and product level. If a declara-
tion contains products (to/from countries) that are subject to stricter trade regula-
tions, it becomes more complicated, and fixed costs of acquiring knowledge of how
to handle it increase. Food products, for instance, are not only likely to be subject
to tariffs and quotas, but often also require more documentation (in exports as well
as imports). Extra documentation may alsobe required when trading with certain
countries, for example those that have preferential agreements with Norway. In addi-
tion, a declaration may contain various products (from various sources in the case
of imports), in which case the complexity of customs clearance is likely to be larger.
6 Note, however, that larger firms have more shipments in equilibrium, and that such firms are more
likely to self-declare in the intermediation model. As pointed out by an anonymous referee, it is also
likely that larger firms find it easier to manipulate the number of shipments in order to minimise fixed
costs. They may for instance schedule shipments so that they are able to fill whole containers in one ship-
ment. This can put them in an even better position to self-declare, and adds to the argument that large
firms are more likely to self-declare from the intermediation model.
approach and found that in representative samples of manufacturing firms from 117 developing coun-
tries, 10.4% of the exporters and 22.8% of the importers traded solely via a merchant.
Footnote 5 (continued)

H.Medin
1 3
Effects like these are accounted for in the regression analysis by controlling for the
number of declarations, traded products and partner countries a producer has.
It is not obvious that such measures influence negatively on the inclination to
self-declare however. Any economies of scope in self-declaring pull in the oppo-
site direction. Procedures are often similar, and the fixed costs of self-declaring in
a given market may therefore decline with the producer’s declaration-, product- or
country scope. Indeed, Verwaal and Donkers (2003) found that customs related
transaction costs decreased with transaction frequency (as well as transaction size).7
Second, it could be argued that the predictions from the intermediation model are
not applicable to imports. The model regards exports, and no corresponding canoni-
cal model of import intermediation exists (Blum etal. 2018). Antràs etal. (2017)
point out that Melitz-style models of exporting are not necessarily applicable for
importing, as those models generally assume constant marginal production costs,
which results in export decisions across markets being independent. In importing,
in contrast, such decisions are likely to be interdependent because importing from
an increased number of markets is likely to reduce producers’ marginal production
costs (because a greater variety of inputs becomes available). Even so, also in the
Antràs etal. (2017) model a sorting pattern arises in equilibrium where only more
productive producers import from markets with higher fixed importing cost. Com-
bined with the ideas from the intermediation model, this should indicate a positive
relationship between inclination to self-declare and trade value, also in imports.
Moreover, Antràs etal. (2017) show that a reduction in market specific-fixed import-
ing costs, under what they argue are empirically plausible assumptions, leads to an
increase in the profitability of importing from all other markets.8 Again, such effects
are accounted for in the regression analysis by including as controls the producer’s
country- and product scopes.
Third, customs clearance may not be the main reason for hiring brokers, and if
so, the motive of reduced fixed trade costs can be questioned. In Sect.3 we shall
see that much of the broker-handled trade in Norway is cleared by companies in
the logistics sector, in particular companies whose core activity is organisation of
transport, namely freight forwarders. Other studies have found that such companies
commonly handle customs clearance. Based on a survey, Andersen and Eidham-
mer (2009) showed that about 10–15% of the revenue of freight forwarders handling
Norwegian exports came from services other than transport, distribution and stor-
age, and that customs handling was the largest component (also see Frémont 2009).
Furthermore, studies of nonrepresentative samples of firms in the business literature
reported that bundling of logistics services is very common. For example, Lieb and
7 In Akerman (2018), merchant-intermediaries possess a technology involving economies of scope, so
that average export costs per product declines with the number of products exported. It is assumed that
this technology is not available for producers, however. Similarly, various studies of multi-product pro-
duces have held that these face economies of scope in the number of products exported (e.g. Bernard
etal. 2011).
8 As mentioned above, similar results may arise in exporting if there are economies of scope. Moreover,
they may arise due to learning and network effects, which are discussed by e.g. Albornoz etal. (2012)
and Chaney (2014).

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Customs brokers asintermediaries ininternational trade
Bentz (2002) found that of the 500 largest US manufacturers in 2002, 65% reported
using so called third-party logistics services (3PL), defined as ‘the use of an out-
side company to perform all or parts of another company’s material management or
product distribution’ (Leahy etal. 1995, p. 5). Langley etal. (2004), in turn, reported
results from a survey among firms in North America, Western Europe, Asia–Pacific,
and Latin America. Firms were (non-randomly) selected from a few manufacturing
sectors and from wholesale/retail/distribution. The percentage of firms using 3PL
ranged from 67 to 84 in the different regions. These studies also indicated wide-
spread use of customs brokers. 67% of the 3PL users studied in Lieb and Bentz
(2002) outsourced customs brokerage, whereas the corresponding number ranged
from 34 to 88% in Langley etal. (2004), depending on the region studied.
The data used in this article do not contain information about purchase of other
services than customs clearance, but these findings may indicate that the broker-
users do in fact also purchase other logistics services from the brokers. The inter-
mediation model should still be relevant, though. The crucial feature in this context
is that the intermediary offers trade-related services likely to reduce fixed costs for
producers that use them, and, as pointed out by WTO (2016), logistics costs are
indeed likely to contain large fixed components. They hold that advanced logistics
services require quick adaption to new technologies, ICT in particular, and that aver-
age logistics costs typically constitute a greater share of overall revenue for small
than for large firms. Such firms often lack international shipment experiences and
have to rely on other firms for handling logistics and transport. Though these argu-
ments support the relevance of the intermediation model, they may imply that when
the concept fixed customs clearing costs is used throughout the article it should be
understood more broadly to also include other types of fixed shipment costs asso-
ciated with logistics. Like pure customs clearing costs, such costs are likely to be
higher for certain countries (more distant ones, for instance) or products (for exam-
ple perishable food products). In the regression analysis, such differences are cap-
tured by controlling for observed as well as unobserved heterogeneity of countries
and products (in addition to producers).
Fourth, the intermediation model has previously only been studied in the con-
text of merchant-intermediaries, and there are several important differences between
these and broker-intermediaries. For instance, merchants take hold of the products
they trade, whereas brokers are agents who don’t. A merchant’s main purpose is to
connect buyers and suppliers, while a broker-handle border procedures and possibly
also logistics and transports. Again, however, the crucial feature is that customs bro-
kers are likely tooffer reduced fixed trade costs for producers that use them. Moreo-
ver, below we will see that the services offered by merchants and brokers are likely
to be complementary; producers seldom purchase customs clearance services from
merchants, and the merchants themselves often buy such services from customs
brokers.9
9 Though the Norwegian data also contain information about wholesalers and retailers, it is not the pur-
pose of this article to compare such merchant-intermediaries to the trading producers. Several studies
like that have already been carried out for other countries, and such a study would suffer the same prob-
lems as those do regarding lack of information about the intermediary-users; the data do not contain
information about producers selling or buying their goods to/from the merchants in Norway, and none of

H.Medin
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3 Data anddescriptives
The dataset used for the empirical analyses is a panel that, with a few exceptions,
contains all trade transactions of firms exporting and importing goods to and from
Norway between years 2003 and 2013.10 These data are confidential and provided
by Statistics Norway (SSB). They are based on information from the customs decla-
ration forms that firms are legally obliged to complete and submit. One declaration
regards one shipment and is declared by the same actor, but may contain various
transactions of different products (from different countries of origin in the case of
imports). It contains information on the firm that trades (the owner of the product),
the partner countries, the traded products and the declaration date. Importantly, it
also contains information on the identity of the declarant, and thus on whether the
trading firm handles the declaration itself or hires an intermediary—a broker—to do
this. The data can be merged with firm-level balance-sheet data, also provided by
the SSB, which i.a. contain information about the firms’ sectoral affiliation. Since
the intermediation model regards intermediary-use among producing firms, all anal-
yses will be limited to trading produces in the manufacturing sector.
Table1 shows all trade declarations by such firms, categorised according to trade
mode (export/import) and declaration mode (self-declared/broker-handled). In line
with the findings from the business literature reviewed in Sect.2, it is evident that
Table 1 Self-declared and broker-handled trade
Norwegian trade by manufacturing producers during years 2003–2013, all observations pooled together.
Values are given in 1 000 (constant year 2014) NOK
a Producers listed under self-declared trade may also use brokers for some declarations in some years,
whereas producers listed under broker-handled trade always use brokers during the whole sample period.
Many producers only trade during some of the sample years, and thus the (# of producer-year observa-
tions) < (# of producers) (the number of sample years)
(1) (2) (3)a(4) (5)
# of declarations Meandecl.
value
Median
decl. value
# of
producer-year
obs*
# of producers
Exporters
Self-declared trade 2,065,799 374 97 5139 744
Broker-handled trade 4,062,147 367 62 33,034 7946
Importers
Self-declared trade 340,335 354 99 7930 1146
Broker-handled trade 6,448,878 218 40 68,833 15,723
Footnote 9 (continued)
the trading producers in the sample can be classified as merchant-users because all of them are registered
as the exporter/importer in the customs declaration.
10 Raw oil and natural gas are excluded; further, a transaction must have a minimum value of 1000 NOK
(122 = USD in year 2018) to be included in the data.

1 3
Customs brokers asintermediaries ininternational trade
brokers play an important role in trade. Comparisonof # of declarations (column 1)
and mean decl. value (column 2) between the two declaration modes, shows that two
thirds of the export declarations as well as the value are broker-handled. In import-
ing, even more trade is handled by brokers, 95% of the declarations and 92% of the
value. This is similar to previous studies of merchant-intermediaries, which have
found that these are more commonly used in importing than exporting (see footnote
5).
Comparison of the declaration values indicates that self-declared declarations are
larger than broker-handled ones, and comparison of the mean and median values
demonstrates that the distribution of declaration values is highly skewed, with many
small declarations and a few large ones. This holds regardless of trade- and declara-
tion mode.
The importance of brokers becomes even more prominent when we focus on pro-
ducers and not single declarations. There are 17 468 trading producers (exporters
plus importers) in the sample, and while a declaration is either handled by a broker
or self-declared, producers may switch between the two declaration modes in dif-
ferent declarations. In fact, almost no producers only self-declare in the course of
all sample years. Some switch between self-declaring and hiring brokers even for
the same country, product and year despite evidently possessing the equipment and
knowledge necessary for self-declaring. A reason for this can be that producers do
not always find it worthwhile to pay the fixed declaration-specific costs even though
the product- and country level parts are already paid for (if the transaction is rela-
tively unimportant, for instance). Another reason can be that the producer primar-
ily demands other services than customs clearance – organisation of transport for
instance, and that the broker bundles services with customs clearance being included
in the sale, as discussed in Sect.2.
The widespread use of customs brokers makes it reasonable to categorise the
trading producers into two groups: self-declarants, which self-declare at least once
during the sample period, and broker-users, which always use brokers. The last two
columns of Table1, demonstrate that the vast majority of producers belong to the
latter group. Of all producers that trade in a given year, only 13% of the exporters
and 10% of the importers are self-declarants (see # of producer-year obs, column
4). Though not shown in the table, the data also reveal that even among the self-
declarants, broker-use is more common than self-declaring. In exporting, the median
self-declarant only handles one fifth of its trade value in a given year itself. The
mean self-declarant handles 42%, though. For importers, self-declaring is even less
common; the median self-declarant lets brokers handle all its imports during a given
year (but may self-declare in other years), whereas the mean share is about 0.15.
In Sect.2, we saw that previous surveys of nonrepresentative samples of firms
indicated that customs clearance is often provided by freight forwarders or other
logistics firms. The Norwegian data confirm this. There are in total 884 brokers serv-
ing the manufacturing producers, and for some 60% of them it is possible to obtain

H.Medin
1 3
information about the sectoral affiliation from SSB’s balance-sheet data.11 Table2
demonstrates that almost half of them are freight forwarders, and alone such com-
panies clear three quarters of the trade value (exports plus imports) for whichinfor-
mation on the sectoral affiliation of the broker exists. Other logistics companies
and transporters are also of some importance, clearing slightly less than 10% of the
value each. Most of these brokers operate in both exporting and importing.
Some companies from other sectors also provide customs clearance, but
togetherthese handle less than 10% of the value. Perhaps somewhat surprisingly, the
most important sector among these is manufacturing. As many as 14% of the cus-
toms brokers in Table2 are manufacturing producers that clear customs on behalf of
other producers. They only account for 4% of the tradevalue, though.12
Some 6% of the brokers in Table2 are wholesalers or retailers–the types of inter-
mediary previously studied in the literature, but these clear a negligible share of the
trade; less than 0.3%. Such merchant-intermediaries are not the focus in this article,
but it is worth mentioning that they themselves are common users of brokers. In
exports, they are even more pronounced buyers of customs clearance services than
the produces, self-declaring less than 20% of their declarations. In imports the % of
self-declared declarations is about the same for merchants and produces: 5%. Fur-
thermore, freight forwarders are the most frequent type of broker used, accounting
for 62% of the merchants’ broker-handled trade were information about the broker’s
sectoral affiliation exists. Other companies in logistics and transport account for
an additional 33%, whereas manufacturing producers account for less than 1% and
other wholesalers/retailers for less than 4%. These figures indicate that merchants do
Table 2 Sectoral affiliation of customs brokers serving Norwegian manufacturing producers
Broker-handled trade (exports + imports) where information about the broker’s sector affiliation is avail-
able, 2003–2013. Values are given in one billion (constant, year 2014) NOK. In the 2007 version of
SSB’s Standard Industrial Classification (SIC), which is based on NACE, the Other logistics category
consists of all sub-categories in SIC 52 and 53, except for the sub-category 52.291, which is reserved
for the Freight forwarder. The Transport category consists of 49–51, Manufacturing of 10–33 and
Wholesale/retail of 45–47, except 45.2 and 45.403. In the 2002 version, the following applies: Freight
forwarder = 63.401, Other logistics = all other sub-categories in 63 and 64, Transport = 60–62, Manufac-
turing = 15–37, Wholesale/retail = 50–52, except 50.2 and 50.403 and 52.7. More information about SIC
can be found at https ://www.ssb.no/en/klass /klass ifika sjone r/6 (accessed 11.06.2020)
(1) (2) (3) (4) (5) (6) (7)
Freight
forwarder
Other
logistics
Transport Manufac-
turing
Wholesale/
retail
Other Total
Value 1292 163 145 72 4 64 1739
# brokers 224 62 111 74 30 20 521
11 These brokers also cover about 60% of the broker-handled trade (in terms of value as well as number
of declarations).
12 Also other scholars have found that certain producers perform trade-related services for other pro-
ducers. Bernard etal. (2018) demonstrate that many Belgian manufacturers export products they do not
produce, so-called Carry-Along Trade (CAT). However, the extent of CAT seems to be much larger than
the extent of producers clearing customs for other produces; among the Belgian firms a whole 30% of the
export value was CAT.

1 3
Customs brokers asintermediaries ininternational trade
not to handle customs clearing to any significant extent, neither for themselves nor
for others.
Table1 showed that declarations handled by broker-intermediaries were smaller
than those handled by producers. An interesting question is whether this translates
into differences in aggregated trade handled by the intermediaries (brokers) and
the two types of produces; those that rely on the intermediaries (broker-users) and
those that don’t (self-declarants). Studies of merchant-intermediaries have only been
able to assess this question by either comparing the intermediary (the merchant) to
the direct trading producer, as in the intermediary approach; or the direct trading
producer to the merchant-user, as in the intermediary-user approach. The data used
here, in contrast, allow for comparison of all three actors at the same time. Table3
shows their mean and median annual traded value. In addition, it shows their coun-
try- and product scopes; the latter both for disaggregated product categories, classi-
fied according to the 6-digit Harmonised System [henceforth: HS6], and for aggre-
gated ones, classified according to the 21 main chapters in the customs tariffs, where
each category represents quite distinct products, for example, Manufactured food,
Textiles and Chemicals.
Table 3 Characteristics of trading producers and customs brokers
Trade by Norwegian manufacturing producers, 2003–2013. Figures are means and medians for all pro-
ducer-year observations. Self-declarants may also use brokers, whereas broker-users never self-declare
during the period. Trade value is given in million constant (year 2014) NOK. # of different products are
reported for two different levels of aggregation. Hs6 refers to 6-digit product categories in the Harmo-
nised System; aggr. refers to the 21 main chapters in the customs tariffs (2002 versions in both cases). #
of firms used/served refers to the number of brokers used for the producers, and the number of producers
served for the brokers
(1) (2) (3) (4)
Producers Subgroups of producers that Brokers
Self-declare Use brokers
Mean Median Mean Median Mean Median Mean Median
Exporters
Trade value 56.71 0.503 228.8 14.42 29.94 0.352 336.7 54.87
# of partner countries 6.596 2.000 14.85 8.000 5.311 2.000 20.55 13.00
# of different products (HS6) 9.114 3.000 17.02 8.000 7.885 3.000 70.79 24.00
# of different products (aggr.) 2.506 2.000 3.709 3.000 2.319 2.000 7.817 7.000
# of firms used/served 4.887 2.000 7.975 4.000 4.406 2.000 44.05 12.00
Importers
Trade value 19.05 0.243 99.61 5.684 9.764 0.193 319.3 62.04
# of partner countries 4.894 3.000 9.766 7.000 4.333 2.000 17.67 13.00
# of different products (HS6) 15.39 5.000 41.57 19.00 12.38 4.000 153.1 52.00
# of different products (aggr.) 3.500 2.000 5.979 5.000 3.215 2.000 9.877 11.00
# of firms used/served 7.186 3.000 15.90 11.00 6.182 3.000 131.1 24.00

H.Medin
1 3
The table clearly demonstrates that self-declarants trade much more than broker-
users, the median traded value being 30–40 times greater for the former. They trade
with more countries and in more products, and also in a greater variety of products.
Brokers in turn, score higher on all these measures than the self-declarants. The
table also reveals that brokers pool trade from several different produces. As can be
seen from the last row in each subpanel, the median broker serves, respectively, 12
and 24 different produces in exporting and importing during one year, and the mean
broker serves considerably more. The producers, in turn, tend to stick to only a few
brokers. Admittedly, the self-declarants use a higher number of brokers than thebro-
ker-users, but this is not surprising as they trade in far more countries and products.
From Table3 it is also clear that the mean values are generally much greater
than the median values, indicating highly skewed distributions of producers and
brokers. The majority of both actors handle small values in a few countries and
products, and the major part of trade is accounted for by a few large actors. In fact,
the top 10% of all produces (in terms of trade value) account for 76% of all trade
(exports + imports). The same holds for brokers, where the top 10% account for 71%
of all broker-handled trade. Above, we saw similar patters at the declaration level.
For trading producers, these findings are in accordance with previous studies by e.g.
Bernard et al. (2012), which show that most firms participating in trade generally
account for low trade values, whereas a small group of firms account for the major
part of trade. Similar findings have also been reported for merchant-intermediaries
by e.g. Blum etal. (2018), but to my knowledge, this is the first article that docu-
ments a pattern like this for customs brokers.13
4 Econometric analyses
4.1 Trade bybrokers, self‑declarants andbroker‑users
In accordance with the intermediation model, the descriptive statistics presented
in Table3 suggested a sorting pattern where broker-users trade smaller values than
self-declarants. Self-declarants, in turn, trade smaller values than brokers, and each
broker serves several producers. This is in accordance with brokers being better able
at overcoming fixed customs clearing costs by pooling trade from many producers.
The descriptives also suggested that broker-users are more specialised than the self-
declarants as regards countries and products, and that self-declarants are more spe-
cialised than brokers. However, this may not hold if we control for differences in the
traded value. Similarly to how Bernard etal. (2010) compared merchants to direct
trading producers, I pool together brokers and producers during all sample years and
13 Table3 also shows large differences in trade values between producers that export and import. The
median value of the former is almost twice as high as for the latter. Brokers, in contrast, handle similar
values of imports and exports.

1 3
Customs brokers asintermediaries ininternational trade
regress their trade value as well as country-and product scopes on an indicator vari-
able for brokers. In contrast to Bernard etal. (2010) however, I can also study the
intermediary-users; this is done by including an additional indicator for self-declar-
ants among the independent variables (broker-users being the base category). Also
included among the independent variables are the trade value (in the scope regres-
sions only), and dummies for years and main aggregated product traded. Equality
between the values of the coefficients for brokers and self-declarants is tested using
a Wald test.
Table4 shows the results, and the patterns of trade values are still evident. The
estimated coefficients for brokers (column 1) as well as for self-declarants (column
2) are significantly positive, and those for the former are larger and significantly
different than those for the latter (column 3). This holds for exporting and import-
ing alike.14 The regressions also show that when conditioning upon trade value, the
higher degree of specialisation among self-declarants relative to brokers suggested
by the descriptives no longer applies, except for in exported products. In import-
ing, the converse is true; self-declarants have wider scopes in both countries and
products than brokers (and the brokers do not have significantly larger scopes than
the broker-users).15 This may suggest that brokers build competence on importing
certain products from certain countries, and this may put them in a better position
to offer reduced fixed trade costs for producers that hire their services. The behav-
iour of the producers in turn, is likely to be more dependent on the products they
produce and the inputs they use, and they may therefore be more concerned with
selling and buying in the currently best suited markets rather than specialising their
market-specific customs clearing competence. This dependency may also be a likely
explanation for the producers’ relative narrower product scope in exporting than
importing if the variety of goods a producer makes is smaller than the inputs it uses.
The results further show that producers that self-declare still have wider country-
and product scopes than those that use brokers, with one exception; self-declarants
export a smaller number of products, also relative to broker-users. This is similar to
what McCann (2013) found comparing merchant-users to direct trading produces. It
may indicate that for producers, product-specific fixed costs of clearing customs in
exporting more than cancel out any economies of scope.16
16 To my knowledge, there are no studies documenting differences in country scope between direct trad-
ing produces and merchants-user.
14 Similarly, studies of merchant-users showed that these traded smaller values than direct trading pro-
duces (Abel-Koch 2013; McCann 2013; Grazzi and Tomasi 2016; Ahn etal. 2011). Furthermore, some
studies comparing direct trading producers to merchants found that the latter traded larger values than the
former (Ahn etal. 2011; Bernard et al. 2015). The literature is ambiguous on this point, however. Ber-
nard etal. (2010), Akerman (2018) and Blum etal. (2018) all found the converse, and Utar (2017) found
different results for importing and exporting; merchants traded smaller values than direct trading produc-
ers in exporting, but larger ones in importing.
15 Similar results for product scope in exporting was found for merchant-intermediaries by Bernard etal.
(2010), Ahn etal. (2011) and Akerman (2018). They showed that (conditional upon firm size) wholesal-
ers had a wider product scope than direct trading producers. Furthermore, similar to what was found
here for country scope, Bernard et al. (2010) showed that wholesalers had a narrower country scope
than direct trading producers in importing, but not in exporting. Ahn etal. (2011) and Akerman (2018)
reached similar conclusions for exporters, though.

H.Medin
1 3
Table 4 Regressions of trade values and scope
The table shows results from OLS regressions of the dependent variable on dummy variables for broker and self-declarant, broker-users being the base category. Trade
by Norwegian manufacturing producers, 2003–2013. Self-declarants may also use brokers, whereas broker-users never self-declare during the period. Trade is given in
million constant (year 2014) NOK. # of different products are reported for two different levels of aggregation. HS6 refers to 6-digit product categories in the Harmonised
System; aggr. refers to the 21 main chapters in the customs tariffs (2002 versions in both cases). All dependent variables are given in logs. Dummies for years and main
(aggr.) product traded are included among the explanatory variables in all regressions. In addition, log of trade is included except when it is the dependent variable. Stand-
ard errors are clustered at the firm-level.*** indicate significance levels at 1%
(1) (2) (3)
Brokers Self-declarants p-val test
Dependent variable Coeff Std. error Coeff Std. erro5 |Coef (1)| =|Coef(2)|
Exporters
Trade 4.055*** 0.108 2.913*** 0.127 0.000
# of partner countries 0.091*** 0.032 0.129*** 0.029 0.331
# of products (HS6) 0.454*** 0.050 − 0.102*** 0.034 0.000
# of products (aggr.) 0.464*** 0.031 − 0.010 0.021 0.000
Importers
Trade 4.785*** 0.099 2.545*** 0.103 0.000
# of partner countries –0.009 0.030 0.090*** 0.017 0.002
# of products (HS6) 0.029 0.048 0.127*** 0.021 0.057
# of products (aggr.) 0.036 0.027 0.073*** 0.014 0.195

1 3
Customs brokers asintermediaries ininternational trade
4.2 The choice ofself‑declaring vs. using brokers
4.2.1 Declaration level
Above, I have argued that brokers are better able at overcoming fixed costs of clear-
ing customs than producers, and possibly also face lower costs like that. I now turn
to studying whether a producer’s choice of self-declaring rather than using brokers
is related to its trade value and hence ability to pay the fixed cost, as the intermedia-
tion model predicts when applied to brokers. As mentioned, at least parts of such
costs are likely to accrue at the shipment level, as a separate declaration form must
be completed for each shipment. Consequently, there should be an incentive for pro-
ducers to self-declare when the shipment is large in value and to hire brokers when
it is small. Indeed, this is what the descriptive statistics in Table1 suggested. To
more formally test this, I regress an indicator variable for self-declaring on the value
declared using a probit model, where year dummies are included as controls. The
average partial effects (APEs) are presented in Table5 and confirm that the prob-
ability of self-declaring increases with shipment value, in exports as well as import.
A 10% increase in the value is associated with an increase in the probability of self-
declaring by 0.25 per cent points for exporters and 0.13 per cent points for import-
ers. Evaluated relative to the share of declarations that are self-declared, which are
33.7% for exporters and 4.9% for importers (see Table1), this implies that the prob-
ability of self-declaring increases by, respectively, 0.75% for exporters and 2.67%
for importers when the shipment value increases by 10%.
4.2.2 Producer‑country‑product‑year level
There is a chance that the correlations found in the previous section are spurious.
Due to computational constraints, it was not possible to include other controls than
year dummies in the regressions, but features of producers, countries and products
are likely to have an influence. To better assess the effect of trade value on choice
of declaration mode, I aggregate the data and calculate the share of trade from pro-
ducer i to/from country j of product v in year t that is traded without using interme-
diaries, i.e. the share that is self-declared (ShSDijvt). This variable is then used as
dependent variable in estimation of the following equation:
α and β are coefficients to be estimated, ηt is a year dummy and εijvt is noise dis-
tributed as Normal[0, σε2]. xijvt is a vector of explanatory variables of which the
main variable of interest is Tradeijvt; producer i’s trade in country j in product v (at
the HS6 level) in year t. In the intermediation model, Tradeijvt is proportional to the
producer’s operating profits in market jv and should therefore control for that. How-
ever, this may not hold in reality, and xijvt therefore also contains a number of other
attributes of the producer, country and product that are likely to affect market spe-
cific profitability or for some other reason may affect the inclination to self-declare.
These include the producer’s size in terms of number of employees (NoEmpit) and
(1)
ShSDijvt =
𝛼
+𝛃𝐱𝐢𝐣𝐯𝐭 +cijv +
𝜂
t+
𝜀
ijvt

H.Medin
1 3
labour productivity (Prodit); an indicator variable for foreign ownership (ForOwnit);
market-size in terms of partner country’s GDP (GDPjt); partner country’s remote-
ness (Remjt); and trade costs, reflected in an indicator variable for free trade agree-
ment with country j (FTAjt) in addition to country specific tariffs on product v (Tar-
iffjvt). Furthermore, to account for the fact that different parts of the fixed customs
clearing costs may accrue atdifferent levels – shipment, country, product, and for
possible interdependencies between market specific trade decisions as discussed in
Sect. 2, xijvt also includes the producer’s total number of declarations (NoDeclit),
partner countries (NoCoit) and traded products (NoPrit). Not only observed, but also
unobserved characteristics are likely to affect intermediary-use. For example, there
may be some underlying productivity differences at the producer-country-product
level, not captured by the control variables mentioned. The panel dimension of the
data allows for controlling for such differences when they are time invariant – they
are captured by cijv. How cijv is controlled for is described in Sect. 4.2.2.1, but it
involves including as additional controls a number of auxiliary variables, which are
listed in the notes to Table6a and b in Sect.4.2.2.2.
Equivalently to most studies using the intermediary approach, this design implies
studying how the share of trade accounted for by intermediaries to/from a given
market is associated with features of that market. But, in contrast to those studies,
the share is here calculated for each producer rather than at the more aggregated
level, which permits controlling for producer attributes as in the intermediary-user
approach. Consequently, the strengths from both approaches are combined.
Data for producer features are taken from SSB’s balance-sheet data, and data for
market characteristics (country and product) are taken from various publicly avail-
able sources. Table7 in the Appendix provides more details on the regression vari-
ables, including summary statistics and source information. Observations where any
element in xijvt is missing are excluded from the analysis. For the export data, this
leads a loss of 26% of all observations, covering about 19% of all exports; whereas
for the import data, 13% of all observations, covering 12% of all imports are lost.
4.2.2.1 Econometric issues There are a number of issues that has to be dealt with
when estimating Eq.(1). Firstly, the dependent variable is a share including a con-
siderable amount of 0s (see Table7 in the Appendix). Ahn et al. (2011), Crozet
etal. (2013) and Akerman (2018) dropped observations like that and applied linear
Table 5 Regression of probability of self-declaring on the value declared (shipment value)
Trade by Norwegian manufacturing producers, 2003–2013. Values in log. Year dummies are included
and standard errors are clustered at the level of the producer
*, **, *** indicatesignificance levels at, respectively, 10%, 5% and 1%
Exporters Importers
APE Std. error APE Std. error
Shipment value 0.025** 0.011 0.013*** 0.002
# obs 6,127,946 6,575,156
# clusters (produces) 8690 14,438
Log pseudolikelihood − 3,891,667 − 1,238,170

1 3
Customs brokers asintermediaries ininternational trade
estimation methods. However, dropping such observations may lead to selection bias,
and linear estimation may lead to specification bias–both potentially important issues
in the present case due to the large prevalence of 0s. Therefore, I apply a fractional
probit estimation model.
Secondly, the cijv’s cannot be appropriately controlled for by using conventional
panel data methods like fixed or random effects. Including fixed effects in the frac-
tional probit model yields biased coefficients estimates when there are relatively
few time periods like here, due to the incidental parameters problem (Neyman and
Scott 1948). Furthermore, the cijv’s are likely to be correlated with elements in xijvt.
For example, unobserved productivity differences (captured by cijv) are likely to be
correlated with the producer’s size (included in xijvt) as more productive produc-
ers are also likely to be larger. Consequently, random effects will also yield biased
estimates.
Thirdly, the panel is unbalanced; each producer-country-product combination
may appear for a different number of years, and selection into the sample is also
likely to be correlated with cijv. For example, if a producer has employees well expe-
rienced in international commerce, it is likely to trade in more countries and prod-
ucts for longer time periods.
To deal with these issues, I apply a correlated random effects fractional probit
model for unbalanced panels (henceforth: CRE fprobit), suggested by Wooldridge
(2019). Let sijvt be equal to 1 if producer-country-product combination ijv appears in
year t (and 0 otherwise) and Tijv be the number of sample years the combination
appears:
Tijv
=
∑T
t=1
s
ijvt
(where T is the total number of sample years in the whole
panel). Furthermore, let givjr be an indicator variable for sample selection:
gijvr = 1[Tijv = r] where r goes from 1 to T. In other words, there are T different givjr,
each reflecting a specific number of time periods it is possible for a combinationto
appear in the sample. The method implies specifying a distribution of the firm-coun-
try-product specific effect–cijv–conditional on all the explanatory variables–xijvt–and
appearance in the sample–sijvt. Specifically, it is assumed that this distribution is
Normal with conditional expectation depending on time-averages of each element of
xijvt, denoted x̄ijv, and the indicator variables for sample selection:
E
c
ijv 
s
ijvt
,s
ijvt
x
ijvt
=
T
r=1
𝜓
r
g
ijvr
+g
ijvr
x
ijv
ξ
r
, for t = 1,…,T. Furthermore, the
conditional variance of cijv also depends on sample selection:
Var 
cijv

sijvt,sijvt xijvt

=exp

𝜏+

T−1
r=1gijvr𝜔r

. The sum in the variance goes to T-1
because the group of observations appearing in all years (T) is the base group. τ is
the variance of this group, and the ωr’s are the deviations from τ for each r. In other
words, together τ and the ωr’s denote all the variances (one variance for each num-
ber of years acombination may appear in the sample). Assuming that all the ele-
ments of xijvt are strictly exogenous (conditional on cijv), and that the sijvt’s are inde-
pendent of the εijt,’s17 Eq.(1) can now be estimated by replacing cijv with:
(2)
cijv
=𝛏(𝐱
𝐢𝐣𝐯
,…,𝐱
𝐢𝐣𝐯
)+
(
g
ijv3
,…,g
ijvT )
17 This implies assuming that any shock to the self-declared share of trade in one year does not affect
being in the sample in other years, which seems fairly plausible. Note that the same assumption is made
in linear fixed effects models when the panel is unbalanced.

H.Medin
1 3
Table 6 Regression results. Share of self-declared trade, (a) exporters, (b) importers
(1) (2) (3) (4) (5) (6)
Model CRE fprobit CRE fprobit Red FE OLS CRE fprobit Exp CRE fprobit CRE fprobit
APE Std. error APE Std. error APE Std. error APE Std. error APE Std. error APE Std. error
(a)
Tradeijvt 0.002*** 0.000 0.006*** 0.002 0.003*** 0.001 0.002*** 0.000
NoDeclit 0.022*** 0.003 0.046 0.032 0.019 0.013 0.014** 0.006 0.015*** 0.002
NoCoit − 0.002 0.004 − 0.005 0.029 − 0.003 0.010 − 0.008 0.009 0.007** 0.003
NoPrit − 0.002 0.002 − 0.006 0.020 − 0.002 0.008 0.002 0.006 − 0.014*** 0.002
ForOwnit 0.023*** 0.003 0.066 0.043 0.021 0.019 0.021* 0.012 0.001 0.005
Prodit − 0.003 0.002 − 0.004 0.015 − 0.001 0.008 − 0.002 0.005 0.000 0.002
NoEmpit 0.020*** 0.003 0.051 0.032 0.022* 0.013 0.007 0.007 0.009** 0.004
Tariffjvt 0.047 0.036 0.096 0.104 0.066 0.064 − 0.019 0.063 0.018 0.035
GDPjt 0.021 0.015 0.048 0.044 0.024 0.027 0.023 0.015 0.042*** 0.015
FTAjt − 0.005 0.004 − 0.014 0.014 − 0.006 0.007 − 0.006 0.004 0.021*** 0.006
Remjt 0.012 0.012 − 0.014 0.018
SD_Saijvt-1 0.022*** 0.003
Br_Saijvt-1 − 0.007** 0.003
SD_Oijvt-1 0.037*** 0.009
Br_Oijvt-1 − 0.011 0.008
Aggr. level Producer-
country-
product
Producer-
country-
product
Producer-
country-
product
Producer-
country-
product
Producer Country-
product
# of obs 433 151 177 905 433 151 253 152 32 303 247 806
Log p.l. − 172 076 − 108 119 − 22 239 − 5 446 − 118 225
(b)
Tradeijvt 0.002*** 0.000 0.004*** 0.001 0.002*** 0.000 0.001*** 0.000
NoDeit 0.005 0.004 0.016 0.014 0.005 0.004 − 0.002 0.005 0.003*** 0.001

1 3
Customs brokers asintermediaries ininternational trade
Table 6 (continued)
(1) (2) (3) (4) (5) (6)
Model CRE fprobit CRE fprobit Red FE OLS CRE fprobit Exp CRE fprobit CRE fprobit
APE Std. error APE Std. error APE Std. error APE Std. error APE Std. error APE Std. error
NoCoit 0.003 0.004 0.011 0.013 0.000 0.002 0.001 0.004 0.001 0.001
NoPrit − 0.009 0.006 − 0.028* 0.017 − 0.006 0.005 − 0.005 0.004 − 0.003** 0.001
ForOwnit 0.000 0.002 − 0.003 0.006 − 0.002 0.002 − 0.001 0.002 0.000 0.002
Prodit 0.002 0.003 0.007 0.008 0.002 0.002 0.002 0.002 0.000 0.001
NoEmpit 0.014 0.009 0.045 0.028 0.012 0.010 0.009 0.006 − 0.001 0.001
Tariffjvt − 0.010 0.011 − 0.028 0.030 − 0.042 0.051 − 0.014 0.010 − 0.016** 0.007
GDPjt − 0.007 0.008 − 0.025 0.023 − 0.006 0.014 − 0.006 0.008 0.021*** 0.007
FTAjt 0.008 0.006 0.025 0.017 0.019 0.020 0.008** 0.004 0.002 0.004
Remjt 0.006 0.012 0.010 0.009
SD_Saijvt-1 0.009*** 0.002
Br_Saijvt-1 − 0.005*** 0.001
SD_Oijvt-1 0.007* 0.004
Br_Oijvt-1 0.000 0.002
Aggr. level Producer-
country-
product
Producer-
country-
product
Producer-
country-
product
Producer-
country-
product
Producer Country-
product
# of obs 976 148 336 327 976 148 543 485 62 952 329 689
Log p.l. − 112 630 − 90 152 − 16 878 − 4 739 − 66 224
APE = average partial effect. Log p.l. = log pseudo-likelihood. In all regressions, year dummies are included. Standard errors are clustered at the producer level except in the country- product level
regression, where they are clustered at the country-level
*, **, *** indicate significance levels at, respectively, 10%, 5% and 1%. CRE fprobit = cor related random effects heteroskedastic fractional probit, estimated according to Wooldridge (2019), where a
number of auxiliary variables are included to account for unobserved heterogeneity. These include: distance between Norway and the partner country; a dummy equal to 1 if the partner country is either
Sweden, Denmark or Finland; dummies for 7 different geographical regions; dummies for 3 different categories describing degree of product homogeneity according to Rauch (
1999); dummies for 21
different product groups corresponding to the main chapters in the customs tariffs (version 2002); year dummies; the time-constant means of all the time-varying independent variables (including the
year dummies); dummies for the number of periods each producer-country-product combinationappears in the sample (also included as explanatory variables in the variance). See the text for further
description of the method.
Exp
= four additional independent variables indicating experience are included.
Red = reduced sample, excluding firms that never self-declare. Fe OLS = fixed effects OLS

H.Medin
1 3
and applying a heteroskedastic fractional probit model with the indicator variables
(gijv3, …, gijvT-1) as explanatory variables in the variance. This accounts for the fact
that the variance varies according to the unbalancedness of the sample.
The method yields estimates of the β’s scaled by a positive factor, which again
can be used directly to calculate the APEs of the elements of xijvt. However, the
(scaled) coefficients are only identified when there is some time variation in xijvt,
otherwise xijvt and x̄ijv will be perfectly collinear. As a consequence, all observations
that appear only once during the sample years must be dropped, which is the reason
why neither givj1 nor gijv2 are included in Eq.(2) (the latter being the base group).18
Furthermore, while time constant explanatory variables (like distance to country j)
can be included in (1), it is not possible to separate their partial effect on ShSDijvt
from their partial correlation with cijvt. Estimated effects of variables like that are
therefore not reported below, nor are those of the elements in x̄ijv.
The CRE fprobit model resembles a fixed effects model in that it allows for cor-
relation between cijv and the explanatory variables. The estimated β’s therefore, to
a large degree, reflect the effect of changes within each producer-country-product
combination overtime (in contrast to differences between combinations). In fact,
Wooldridge (2019) points out that estimating Eq.(1) with cijv given by Eq.(2) using
OLS is equivalent to a linear fixed effects model (i.e. a within model). But, unlike
the CRE fprobit model, the linear model makes no assumptions about the distribu-
tion of cijv. As a robustness check, I also estimate a model like that.
Also see Wooldridge (2005) and Papke and Wooldridge (2008) for, respectively,
dichotomous and fractional dependent variable balanced-panel CRE models, and
Bluhm (2013) for additional explanations of the Wooldridge (2019) method.19
4.2.2.2 Results The results are reported in Table6a and b. To be able to compare the
estimates between exporters and importers and also among different models, APEs
rather coefficients are displayed. The results from the main estimation (column1)
clearly demonstrate a positive association between the self-declared share of a pro-
ducer’s trade to a given market and the traded value. Hence, the tendency of larger
trade values being self-declared and smaller ones being handled by brokers found in
Sect.4.2.1 also holds when controlling for observed and unobserved factors at the
producer, country and product level. This indicates that the model of intermediation
is not only relevant in the context of merchant-intermediaries, but also in the con-
text of broker-intermediaries. Furthermore, the results are similar for exporters and
importers, suggesting that customs brokers facilitate both modes of trade.
For exporters, the APE for NoDeclit is positive and significant. A plausible inter-
pretation is that any negative influence from larger fixed costs due to more ship-
ments are cancelled out by a positive influence of economies of scope in the num-
ber of declarations. For importers, the APE for NoDeclit is not significant, however.
18 This leads to an additional drop in the number of observations of about 30% for exporters and import-
ers alike, covering about 3% of all exports and 5% of all imports.
19 Note that year dummies are included in x̄ijv. Due to the unbalancedness of the panel, the means of the
year dummies may differ for different ijv combinations. Unlike in the balanced CRE fractional probit
model, these means are therefore included in x̄ijv.

1 3
Customs brokers asintermediaries ininternational trade
Country and product scope also do not seem to influence much, neither in exporting
nor in importing. Regarding other controls, for exporters the APE for NoEmplit is
positive and significant, which is in line with predictions from the intermediation
model. Furthermore, the APE for ForOwit is significantly positive, and a reason for
this might be that foreign owners, by bringing capital and knowledge, increase the
producer’s ability to pay the fixed costs required for being able to self-declare. No
other controls are found to be significantly associated with the share of self-declared
trade, neither for exporters nor importers. Also note that the results for variables
other than Tradeijvt are not robust to alternativemodel specifications.
The estimated APEs for Tradeijvt are moderate in size, indicating that a 10%
increase in a producer’s market specific traded value increases its predicted % of self-
declared trade to that market by 0.023 percentage points for exporters and 0.016 percent-
age points for importers. The average % of a producer’s self-declared trade in a given
market is 20.3% for exporters and 3.2% for importers (see Table7 in the Appendix),
and evaluated relative to this, the APEs indicate that the probability of self-declaring
increases by 0.16% for exporters and 0.17% for importers when the market specific
traded value increases by 10%. For exporters, the estimated effect of trade valueis mod-
erate relative to that of producer-size, which is about 10 times larger. The moderately
sized effects are not surprising given that many producers never self-declare during the
course of all sample years they trade, resulting in a large share of the ShSDijvt observa-
tions being equal to 0 (see the last two rows of Table7 in the Appendix). To check how
the results are influenced by this large group of producers, I run the CRE fprobit estima-
tion on a reduced sample where they are dropped (CRE fprobit Red). As can be seen in
the second columns of Table6a and b, the APEs for trade value have the same sign as
those in the full sample, but the sizes are larger. The APE for exporters have tripled and
that for importers have more than doubled.
To check how sensitive the results are to the assumptions about the correlation struc-
ture between the explanatory variables and cijv, I also estimate Eq.(1) using a linear fixed
effects model (FE OLS)). The results, displayed in the third columns of Table6a and b,
are similar to those from the main model; the estimated effects from Tradeijvt are still
significantly positive, and their economic significance is also similar. This may indicate
that any bias from misspecification of the correlation structure is not severe in the main
model.
In the main model, it was assumed that a producer’s customs clearing experience
didn’t affect its current declaration modes. However, experience from self-declaring
is likely to reduce the current cost of self-declaring, because producers that have self-
declared in previous years have already familiarised themselves with the relevant pro-
cedures and regulations. If this holds, the share of self-declared trade of experienced
self-declarants is likely to increase or maintain from one year to another. Similarly, expe-
rience from using brokers may affect current broker-use if procedures related to the pur-
chase of brokerage services have to be learnt. However, if the fixed costs of self-declar-
ing are larger than those for broker-use, and experience leads to a larger decline in the
former than in the latter, experience will matter more for self-declaring.
To check for effects like these, I run the main estimation again, adding to the inde-
pendent variables dummies for, respectively, having self-declared and used brokers for
at least one trade transaction with market jv in year t-1 (SD_Saijvt-1 and Br_Saijvt-1). Both

H.Medin
1 3
dummy variables may be positive at the same time, as the producer may have used bro-
kers for some transactions and self-declared others. Experience in other markets may
also affect declaration mode, as procedures can be similar; and thus, I include two addi-
tional dummy variables for experience like that (SD_Oijvt-1 and Br_Ojvt-1). Significantly
positive APEs for the SD variables indicate that experience matters for self-declaring,
whereas significantly negative APEs for the Br variables indicate that it matters for
broker-use.
The results, displayed in the fourth columns of Table6a and b, clearly indicate
that experience in the same market matters for both modes of declaration.20 How-
ever, the absolute values of the estimated effects are significantly larger for self-
declaring than for broker-use (at the 1% level at least). With regards to experience
in other markets, only the estimated effect from SD_Oijvt-1 are significant. Conse-
quently, experience seems to matter more for self-declaring than broker-use, as
expected. The estimated effects from trade value are still positive and significant,
and hence, the result from the main analysis holds.
A few other studies analysed how experience mattered for intermediary-use in the
context of merchants. McCann (2013) found that persistence in exports was larger
for producers trading directly rather than via merchants, and in an analysis supple-
mentary to that of China, Ahn et al. (2011) found that Ghanaian firms were more
likely to export directly if they had previously exported via a merchant. Further-
more, Bai etal. (2017) found that sunk (as well as fixed) costs were larger when
exporting directly than through a merchant, but that the direct exporters improved
their performance more (so-called ‘learning by exporting’).
The lack of influence from variables at the market level in the analyses con-
trasts findings in studies of merchant-intermediaries, which generally found that
market features were important. However, those analyses were done at more
aggregated levels than here. To check whether analysing my data at higher
aggregation levels would influence the results, I perform two additional analy-
ses. Firstly at the producer level, as in the intermediary-user approach; and sec-
ondly at the market level, as in the intermediary approach. These regressions are
estimated with CRE fprobit as in the main model, using as dependent variables,
respectively, the self-declared share of producer i’s total exports or imports; and
the self-declared share of total exports and imports to/from market jv. In both
regressions, independent variables at finer aggregation levels than the dependent
variable are dropped.21 To some extent, the results from these estimations diverge
from those from the main model. In the regressions at the producer level (columns 5),
the APE for declaration scope is significantly positive for both exporters and import-
ers, whereas in the main model it is only significant for exporters. Furthermore, in
contrast to the main model, the APE for product scope is significantly negative for
20 Note that in these regressions, only producer-country-product observations with trade in at least two
consecutive periods are included, as information on lagged declaration mode are only available for obser-
vations like that. Thus, the sample size is smaller than in the main analysis.
21 This implies that the regressions do not contain any variable reflecting the traded value. This is equiva-
lent to the regressions carried out in Ahn etal. (2011), Crozet etal. (2013) and Akerman (2018) studying
the share of exports accounted for by merchants using the intermediary approach. Studies applying the
intermediary-user approach sometimes include a variable for total sales (domestic and foreign), but, like
here, the number of employees is often used as a measure of firm size instead.

1 3
Customs brokers asintermediaries ininternational trade
both modes of trade, and that for country scope is positive for exporters. For export-
ers, the APE for firm size is also significantly positive as in the main model, but that
for foreign ownership is not. The regressions at the market level (columns 6), in turn,
yield significant APEs for several country- and product specific characteristics, includ-
ing positive APEs for GDPjt for exporters as well as importers. Also trade costs mat-
ters – FTAjt influences positively on the share of self-declared exports, and Tariffivt
influences negatively on the share of self-declared imports. These results demonstrate
that market specific effects are only significant when each produces’ market specific
profitability from trade are not controlled for.
5 Concluding remarks
This study has focused on customs brokers, a type of intermediary in interna-
tional trade rarely examined in economics. Recent research has suggested that
merchant-intermediaries like wholesalers facilitate trade by offering the oppor-
tunity of reducing fixed trade costs for firms that use them. As a result, firms
with small trade values will use intermediaries, whereas firms that trade a lot or
increase their trade above a certain threshold will manage without them. I have
found indications of a similar role for customs brokers.
This study draws on an exhaustive panel of the trade transactions of Norwe-
gian manufacturing producers, with information on the use of brokers. The data reveal
that the vast majority of producers participating in international trade engage brokers to
clear their goods through customs rather than self-declare. Brokers handle larger trade
values than producers do, and producers that self-declare trade more than those that rely
solely on brokers.
The tendency of producers self-declaring large trade values was further confirmed in
econometric analyses where observed and unobserved characteristics of produces, coun-
tries and products combined where controlled for. These showed that the share of trade
from agiven producer in a given market that was self-declared was positively associated
with the producer’s market specific trade value – a result that was very robust to alter-
native model specifications. Apart from trade value, however, few other variables were
found to significantly affect broker-use consistently across different model specifications.
The only exception was perhaps producer size for exporters.
The lack of influence from country- and product characteristics contrasts empirical
studies of merchant-intermediaries studying the share of trade accounted for by whole-
salers, which generally found that this was significantly correlated with variables like
GDP and tariffs. However, these studies did not control for producer-specific character-
istics. Nor did they control for the trade value or other observed and unobserved charac-
teristics at the producer, country and product level combined. The results presented here
may indicate that controlling for such variables is important.
The results are consistent with customs brokers offering an opportunity for producers
to avoid paying fixed customs clearing costs by purchasing customs clearing services
for a variable fee. They are probably also consistent with brokers offering reductions of

H.Medin
1 3
other types of fixed trading cost related to organisation of transport because thebrokers
operate in logistics sectors where bundling of services is very common. Without the pos-
sibility of hiring brokers, some of the broker-users might not have been able to handle
customs clearing or other logistical challenges in certain markets, or even participate in
international trade at all. Hence, customs brokers are intermediaries that seem to facili-
tate trade in the manner described in Ahn etal. (2011) and similar models. The results
are similar for exporters and importers, suggesting that the trade facilitating role of bro-
ker-intermediaries is not only important in exporting, but also in importing.
Whereas policymakers concerned with international market participation often
emphasise ensuring sound business conditions for producing firms, the results presented
here indicate that having well-functioning intermediary sectors is also highly relevant.
Furthermore, it can be important to continue efforts aimed at streamlining and simpli-
fying customs procedures, also in a highly developed country like Norway, in order to
reduce fixed costs of exporting and importing.
Not only customs brokers and merchants, but also other types of intermediaries, like
transporters and marketing agents, may be crucial for producers’ ability to sell and buy in
international markets. Future research should study the facilitating role of these. Another
important theme is how developments in new digital methods, such as blockchain tech-
nology, can facilitate trade, perhaps changing the need for customs brokers and other
intermediaries in the future.
Acknowledgements I am grateful to the following people for providing useful information: Morten Aas-
gaard, Reidar Knutsen and Guillaume Lanquepin from the Norwegian Customs Directorate (on customs
brokers, procedures and customs declaration data in Norway); and Øyvind Hagen from Statistics Norway
(on customs declaration data). Copyediting by Susan Høivik is highly appreciated. I also wish to thank
Jens Andvig, Frank Asche, Neil Balchin, Fenella Carpena, Fulvio Castellacci, Jon Fiva, Per Botolf Maur-
seth, Arne Melchior, Hans Martin Straume, Tommy Sveen and anonymous referees for useful comments.
Funding Research was funded by the Research Council of Norway, project 233836 ‘Traders in the Food
Value Chain: Firm Size and International Food Distribution’. The funder had no role in study design; in
the collection, analysis and interpretation of data; in the writing of the report; and in the decision to sub-
mit the article for publication.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is
not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission
directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen
ses/by/4.0/.
Appendix
See Table7.

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Customs brokers asintermediaries ininternational trade
Table 7 Summary statistics and description of variables
Variables Exporters Importers Description
Mean Median Mean Median
Independent variables Tradeijvt 4.026 0.092 1.234 0.042 Producer i’s trade value of product v to/from country j, in million real (year 2014) NOK
NoDeclit 1 951 545.0 977.6 355.0 Number of declarations producer i conducts
NoCoit 35.73 28.00 19.31 17.00 Number of countries producer i trades with
NoPrit 66.34 33.00 98.07 68.00 Number of products producer i trades
ForOwnit 0.317 0.000 0.276 0.000 Equals 1 if producer i’s ultimate owner is foreign
Prodit 0.946 0.800 0.856 0.722 Value-added per employee for producer i in one thousand real (year 2014) NOK
NoEmpit 352.3 87.00 275.2 63.00 Number of employees for producer i
Tariffjvt 1.016 1.000 1.021 1.000 Weighted average preferential ad valorem tariffs for products in Norway (for importers) or the
destination countries (for exporters). Specific tariffs are converted into ad valorem equivalents
(AVEs) using the UNCTAD method. Source: UNCTAD’s TRAINS database, https ://wits.world
bank.org/wits/
GDPjt 11 094 3 083 14 851 3 849 GDP in one thousand million real (year 2014) NOK. Source: World development indicators
(WDI), https ://datac atalo g.world bank.org/datas et/world -devel opmen t-indic ators (accessed
09.09.2019)
FTAjt-1 0.798 1.000 0.847 1.000 Equals 1 if there was a free trade agreement between Norway and the partner country in the previ-
ous year. Source: The Norwegian Ministry of Trade, Industry and Fisheries, https ://www.regje
ringe n.no/no/tema/narin gsliv /hande l/nfd---innsi ktsar tikle r/friha ndels avtal er/partn er-land/id438
843/

H.Medin
1 3
Table 7 (continued)
Variables Exporters Importers Description
Mean Median Mean Median
Remjt 2 498 2 223 2 049 1 928
Rem
j�=log

n

j=1
xj
dj�j

−
1
; xjt = GDPjt/GDPwt, where GDPw is world GDP; dj’i is distance from
country j’ to country j; n is the number of countries. Indicates remoteness (multilateral resist-
ance) of partner country. Source of distance data: CEPII database dist_cepii (Mayer and Zignago
2011). Distance is great circle distance in kilometres between largest cities (the dist variable).
Internal distance dj’j’ equals the square root of the country’s area multiplied by about 0.4 (Head
and Mayer 2000). The formula, has the advantage of not putting too much weight on very small
and distant countries and is taken from Head’s “Gravity for beginners”, available at https ://
vi.uncta d.org/tda/backg round /Intro ducti on%20to%20Gra vity%20Mod els/gravi ty.pdf (accessed
09.09.2019)
ShSDijvt 0.203 0.000 0.032 0.000 Share of producer i’s trade value with country j and product v that is self-declared (dependent
variable)
Share of 1′s 0.169 0.026 Share of all observations of ShSDijvt that takes the value 1
Share of 0′s 0.781 0.963 Share of all observations of ShSDijvt that takes the value 0
Products classified according to the 6 digit Harmonised System nomenclature. Norwegian customs declaration data and producer level balance sheet data are from Statis-
tics Norway

1 3
Customs brokers asintermediaries ininternational trade
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