HGX: the anatomy of high growth exporters

Abstract

Previous work has found that a small number of export superstars contribute disproportionally to the economy’s overall exports. Differently from export superstars, this study is the first to define high growth exporters (HGXs) (that are not export superstars) as a new firm category. We provide their economic importance and depict their micro-level anatomy. By tracking HGXs in Croatia for over a quarter of a century, 44 out of 100 export superstars in 2019 were previously HGXs. HGXs represent only 0.5% of all firms and 18% of high growth firms (HGFs) in the economy, but are responsible for about 25% of new exports and 5% of new jobs. During their growth episode, HGXs hire more employees from technology intensive industries with previous experience in exporting. They often hire on a single year work contract, and more frequently send new employees to work abroad. HGX also increase their presence in more advanced markets, increase the number of new export products and decrease their reliance on the largest product or largest export market. We argue HGXs represent an under-researched category of firms.

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https://doi.org/10.1007/s11187-024-00884-5
RESEARCH ARTICLE
HGX: theanatomy ofhigh growth exporters
StjepanSrhoj · AlexCoad · JanetteWalde
Accepted: 18 January 2024
© The Author(s) 2024
Abstract Previous work has found that a small num-
ber of export superstars contribute disproportionally to
the economy’s overall exports. Differently from export
superstars, this study is the first to define high growth
exporters (HGXs) (that are not export superstars) as a
new firm category. We provide their economic impor-
tance and depict their micro-level anatomy. By track-
ing HGXs in Croatia for over a quarter of a century,
44 out of 100 export superstars in 2019 were previ-
ously HGXs. HGXs represent only 0.5% of all firms
and 18% of high growth firms (HGFs) in the economy,
but are responsible for about 25% of new exports and
5% of new jobs. During their growth episode, HGXs
hire more employees from technology intensive indus-
tries with previous experience in exporting. They
often hire on a single year work contract, and more
frequently send new employees to work abroad. HGX
also increase their presence in more advanced mar-
kets, increase the number of new export products and
decrease their reliance on the largest product or largest
export market. We argue HGXs represent an under-
researched category of firms.
Keywords Exporting· Firms· High growth firms·
High growth exporters
JEL Classification F2· D22· L1
1 Introduction
Considerable policy interest surrounds exporters
because they are found to be larger firms, more pro-
ductive, more skill- and capital-intensive, and to pay
higher wages than non-exporting firms (Bernard
etal., 1995, 2007). In addition, firms that grow via
exporting tend not to cannibalize the market share
of their domestic rivals, nor engage in domestic
business-stealing, but bring in revenues from abroad
(Wagner, 2019). Unsurprisingly, there is consid-
erable policy interest in the manufacturing sector
(e.g., European Industrial Renaissance, European
Commission, 2014), on the grounds of manufactur-
ing’s alleged superiority in terms of exporting, pro-
ductivity growth, and innovation (Coad & Vezzani,
2019). Policy-makers have introduced a large range
Supplementary Information The online version
contains supplementary material available at https:// doi.
org/ 10. 1007/ s11187- 024- 00884-5.
S.Srhoj
Department ofEconomics, Faculty ofEconomics,
Business andTourism, University ofSplit, Split, Croatia
e-mail: ssrhoj@efst.hr
A.Coad(*)
Waseda Business School, Waseda University, Tokyo,
Japan
e-mail: alex.coad@waseda.jp
J.Walde
Department ofStatistics, Faculty ofEconomics
andStatistics, University ofInnsbruck, Innsbruck, Austria
e-mail: janette.walde@uibk.ac.at
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of short-term export boosting policies, initiatives to
encourage firms to start exporting and deepen their
exporting activity (for a review: Srhoj et al., 2023),
including public grants, tax credits, subsidized export
loans, export credit guarantees, public institutions
offering partner search, matchmaking, intelligence,
analysis and organizing participation on trade fairs, or
providing vouchers for outgoing economic missions,
fairs, and external counseling. A special focus of
policy-makers is high growth firms (HGFs) because
of their contributions to job creation, productivity
growth and innovation (Benedetti Fasil etal., 2021).
Export growth is a target for HGF policy (OECD,
2013, p. 27, Table2.7); however, not much research
has been done on the intersection of exporters and
HGFs. This gap was also recently highlighted by
international business scholars who called for more
research on scale-ups and scaling in an international
business context (Reuber et al., 2021; Tippmann
etal., 2023).
Export superstars, defined either as single-largest,
top five, top 100 or even top decile exporters by export
value have received academic attention (Ciliberto
& Jäkel, 2021; Eaton etal., 2008; Freund & Pierola,
2015, 2020). Freund and Pierola (2015) show that top
five export superstars contribute to 30% of all non-oil
exports in their sample of 32 countries. A narrow focus
on export superstars could lead to excluding important
and upcomingplayers in theeconomy. Export super-
stars are already perceived as relevant stakeholders,
and they seem unlikely to have large export growth
rates in future, although their small growth rates may
already lead to large absolute values in exports or
job creation. Due to creative destruction or structural
change, export superstars might be displaced in the
future. The question is which firms (besides export
superstars) can bring about new products and jobs
necessary for economic growth. Candidates with the
potential to become export superstars include small
and medium enterprises (SMEs). However, SMEs
comprise over 95% of all firms, which makes it dif-
ficult to manage and tailor targeted policies for high
growth (Coad et al., 2022; Shane, 2009). Another
potential category could be all current exporters except
the export superstars; however, these are still a broad
category of firms (Wagner, 2007, 2019) of which many
will presumably lack high-growth potential. HGFs are
another category of firms that could displace export
superstars in the future. HGFs are 3–5% of all firms,
but are a heterogeneous category, making it difficult to
predict which firms will be HGFs next year, and HGFs
often lack growth persistence (Coad & Srhoj, 2020;
Esteve-Pérez etal., 2022). This paper investigates a cat-
egory of firms that is more homogeneous and could be
effectively addressed by policy measures: high growth
exporters (HGXs) not yet being export superstars.
We contribute to the literature in several ways.
Despite research on the characteristics of exporters
(e.g., Atkin etal., 2017; Bernard etal., 2007; Wagner,
2007, 2012), HGFs (e.g., Benedetti Fasil etal., 2021;
Coad & Srhoj, 2020; Esteve-Pérez etal., 2022), Born
Globals (e.g., Knight & Cavusgil, 1996, 2004, 2005;
Rennie, 1993), and export superstars (Ciliberto &
Jäkel, 2021; Freund & Pierola, 2015, 2020), there is
scarce literature on firms having high export growth.
We define the category of HGX, which is a sub-
category of high growth firms (HGFs) not yet being
export superstars, and track these firms over time to
investigate whether they become export superstars. In
detail, we elaborate differences of HGXs compared to
HGFs not categorized as HGXs, to export superstars
and to remaining exporters that are not belonging to
the category of HGF. HGXs appear worthy of more
academic and policy attention.
Our results show five cross-cutting themes. First,
HGXs are dynamic and global: growing fast, hir-
ing fast, introducing new products, expanding into
new markets, and placing employees abroad. Sec-
ond, there is some evidence that HGXs are relatively
knowledge intensive, being more active in medium-
tech or high-tech sectors such as manufacturing,
information and communication technology (ICT),
R&D-intensive sectors, and knowledge-intensive
services (KIS). Third, HGXs hire from other former
HGXs and offer more short-term contracts on aver-
age. Flexible labor arrangements may benefit HGXs
in terms of facilitating the reallocation of talent
between HGXs, as well as facilitating the placement
of employees in relatively precarious roles. Fourth,
HGXs are not growing by concentrating on products
or export markets. HGXs are not growing vulnerable,
instead their growth involves reducing their reliance
on individual products and export markets, and diver-
sifying their sales portfolios. Fifth, HGXs are not the
stereotypical cost-cutting entrants selling cheap gim-
micks, but their growth occurs alongside increases in
unit prices. HGXs differ from other exporters in that
they have been successful in growing their exporting
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activity in the EU Single Market, which is one of the
largest and most sophisticated export markets in the
world. HGXs therefore constitute a genuine competi-
tive threat to incumbents.
The paper unfolds as follows. Section2 provides
a literature review. Section3 presents the three cen-
sus datasets used in the analysis. Section4 presents
the mutually-exclusive categories that form the basis
of our analysis: export superstars, HGXs, HGfs (i.e.,
non-HGX HGFs), non-HGF exporters, and non-HGF
domestic traders. Section5 presents the anatomy of
HGXs, investigating the HGXs at the firm-level, firm-
employee level, firm-product level, and firm-market
level. Section6 discusses the results, academic and
policy implications in the short and long-run. Sec-
tion7 concludes.
2 Literature review
Three literature streams provide the background for
our analysis of HGXs: the literature on HGFs, the
literature on exporter dynamics (in particular, the
export superstars), and the literature on early interna-
tionalizing firms (EIFs, also known as Born Globals).
2.1 High growth firms and internationalization
Export growth is a target for high growth firm
(HGF) policy (OECD, 2013), although little
research has focused on the intersection of export-
ers and HGFs. The HGFs literature has mainly
considered the internationalization dimension by
including additional explanatory variables in a
standard HGF regression framework. For example,
Coad and Srhoj (2020) show that having higher
export growth or higher exports is associated with
becoming an HGF in Croatia and Slovenia. Ter-
uel et al. (2022) review several existing articles
and suggest that being internationally active in
general and exporting in particular are important
for HGFs. More closely related to our work, Cruz
etal. (2022) find that importing, exporting, foreign
ownership, or benefiting from offshore regimes is
associated with a higher probability of achieving
high-growth status compared to other similar firms
in terms of size, age, sector, and region. Cruz etal.
(2022) highlight the particularly important role of
imports in fueling HGFs. Teruel etal. (2022) use
ORBIS and EIBIS survey data for 27 EU Member
States and the UK to show a positive association
between being an HGF, conducting international
activities (particularly FDI), and adopting new
digital technologies. While the HGF literature has
not focused centrally on the internationalization
dimension, the literature on exporter dynamics has
made considerable progress while nevertheless not
connecting with the literature on HGFs.
2.2 Exporter dynamics
The literature on exporter dynamics is well-estab-
lished in the field of economics and international
trade. Bernard et al. (1995) highlighted the superi-
ority of exporters compared to domestic traders, in
terms of size, profitability, capital intensity, produc-
tivity, and wages (see also Wagner, 2007). These
empirical findings gave rise to new trade theories,
such as models that emphasize firm heterogeneity in
productivity in markets with trade costs that allows
the most productive firms to become exporters (e.g.,
Melitz, 2003). New datasets enabled delving deeper
into exporter dynamics. Therefore, this new empiri-
cal literature stream used new datasets to analyze the
changes between and within exporters, leading to a
rich set of stylized facts and empirical regularities.
Most firms sell only in the domestic market, and
exporters usually export to a single foreign market,
while only a minority of exporters export to many
markets (Eaton et al., 2004). Eaton et al. (2008)
report many interesting findings from their analysis of
Colombian customs data: (i) about one-third to one-
half of all exporters are new exporters in a given year;
(ii) these new exporters contribute little to aggregate
export growth or decline (either because they are
tiny or because they do not export for more than a
year); (iii) among firms surviving in export markets,
export growth is especially large in the initial years,
particularly in the first 3 years, after which export
growth diminishes; (iv) a switch from non-exporting
to exporting usually comes from gradually adding
markets (i.e., starting with a single market, and then
gradually increasing in number of export markets);
(v) firms usually start exporting small quantities to
a neighboring country; (vi) exports increase gradu-
ally by increasing presence in current export mar-
kets, while a sizable fraction of exporters expand
to other export markets. Eaton etal. (2008) showed
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that exports growth is highest among new firms and
firms in low-exports quintiles, because such firms are
tiny, and subsequently their contribution to aggregate
growth is trivial. Firms in the high-sales quintiles
had low growth, but their contribution to aggregate
growth was considerable. Eaton et al. (2008) pro-
vide two potential interpretations of the difference in
growth rates. A first explanation refers to increasing
resistance in foreign markets as firms’ exports grow,
for example, initially firms sell to easy-to-access
buyers, and then further growth becomes difficult as
demand elasticities fall with sales, or firms encounter
capacity constraints in production facilities. A second
explanation is that new exporters are tried-out by for-
eignbuyers on a limited scale (cf. Rauch & Watson,
2003), and once this try-out is up, exports stop or
increase. Alternatively, new exporters experiment on
the market with small quantities to resolve uncertain-
ties regarding product-market fit, and then either stop
exports or expand.
Mayer and Ottaviano (2008) use a sample of
mostly large firms from seven countries (Germany,
France, UK, Italy, Hungary, Belgium, and Nor-
way) to establish several stylized facts on exporter
dynamics.1 Notably, (i) aggregate exports are
driven by a small number of top exporters (i.e.,
export superstars), (ii) only a few firms have high
export intensity (defined as exports as a fraction of
total sales), (iii) these high export-intensity firms
export to many markets, (iv) exporters are more
likely to be foreign-owned, (v) foreign-owned firms
generally perform better than domestic exporters,
while exporters perform better than domestic trad-
ers (c.f. Bernard et al. 1995; Wagner, 2007), and
(vi) historical ties such as former colonial links
and a common language foster exports, making
it easier for less efficient firms to export. A sub-
stream of literature on export superstars emerged
from the literature on exporter dynamics. Export
superstars can have different definitions—includ-
ing single-largest, top five, top 100, or even top
decile exporters by export value (Freund & Pierola,
2015, 2020; Mayer & Ottaviano, 2008). Freund and
Pierola (2015) show that top five export superstars
contribute to 30% of all non-oil exports in their
sample of 32 countries. Export superstars are typi-
cally born large and there is interest in their export
growth and diversification. Freund and Pierola
(2020) use firm-level data on manufacturing sec-
tor trade from 32 developing countries and report
that, over a 5-year period, the five largest export-
ers account for around one-third of exports, 47% of
export growth, and a third of the growth that comes
from export diversification. Within countries and
within industries, export growth is positively asso-
ciated with the share of exports in the five largest
firms. Most of the top five exporters were already
large (5–8 years earlier) and these export super-
stars rarely emerge from the bottom half of the firm
size distribution. Available evidence suggests that
export superstars are producers, not traders, and
are mainly foreign-owned.
Lawless (2009) analyzed a sample of 751 firms
in Ireland over a 5-year period (2000–2004) and
showed that productivity differences can explain
firms’ number of export markets. She further
highlighted the dynamic nature of firms’ involve-
ment in export markets. Entry into or exit from
markets was an important component of overall
market-specific export flows, particularly for less
popular markets. Lawless (2009) confirmed pre-
vious empirical results (e.g., Eaton et al., 2008),
showing that the smallest exporters have the high-
est export growth alongside a tiny contribution to
overall exports, while the largest exporters had
small growth rates, but contribute the most to the
overall exports growth. To put this into numbers,
the largest exporters grew 4%, but this was 73% of
aggregate exports growth, while smallest export-
ers grew on average 21%, but this was only 2% of
aggregate exports growth. This finding of the rela-
tive unimportance of new export entries was also
confirmed by Buono etal. (2008) on a sample of
French exporters (1995–1999)who also highlight
that trade relations are much more stable in more
popular export destinations (where firms from
France export more often).
Findings that small exporters have high growth
rates, while large exporters have low growth rates
was also confirmed in Argentinian customs data
(2002–2007) by Albornoz etal. (2012), who observe
an empirical pattern among a share of exporters—the
growth at intensive and extensive margins—and name
1 We highlight just those most relevant for our study. Inter-
ested readers can read all twelve facts in Mayer and Ottaviano
(2008).
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this pattern sequential exporting. This idea of sequen-
tial exporting has important implications for trade
policies or export boosting policies (cf. Srhoj etal.,
2023). Lowering tariffs or providing export boosting
policies will induce some firms to start exporting, and
once they learn about their ability to export to a par-
ticular market, if successful they will further expand
to other export markets. Therefore, initial sunk costs
of entering an export market can be worthwhile given
the possibility of “sequential exporting.” Albornoz
etal. (2012) also find that “sequential exporting” is
less evident in the following cases: (i) during export
restart following a break; (ii) among firms exporting
simultaneously to multiple markets; and (iii) export-
ers of homogeneous goods.
Eaton etal. (2021) develop a model of firm-level
export dynamics focusing on firm’s customer base
and knowledge of a market, together with analysis
of Colombian data on manufacturers shipping to
the United States of America, to show the following
three insights. Firstly, most exporters ship a small
quantity to a very small number of foreign cli-
ents. Secondly, these foreign relationships usually
are not long-lived. Thirdly, in each cohort of new
exporters, there is a small number of firms that sur-
vive and grow several times faster than aggregate
exports. The suggested mechanism is not by selling
ahigher quantity to the same foreign client, but by
finding new customers. In other words, firms have
to undertake costly search to find potential buyers,
and these buyers can reject their product or enter
in finite-lived business relationship. When buyers
form a business relationship, they send exporters a
signal of appeal to encourage them to continue their
search for additional buyers (learning effects), in
addition, successful business relationships reduce
exporters’ search costs by improving their visibility
(network effects) (Eaton etal., 2021).
What do firms do to expand the customer base
after entry into an export market? This ques-
tion was investigated by Fitzgerald et al. (2023)
on Irish customs data, drawing on two prominent
theories of customer base accumulation: (1) firms
can reach more customers by engaging in non-
price activities such as marketing and advertis-
ing, or (2) the future customer base is increasing
in today’s sales, so firms can expand in a market
by first charging markups below the statically opti-
mal level, and then gradually increase markups
as the customer base rises towards a possible
“steady state.” Fitzgerald etal. (2023) find export
quantities increase (relative to old markets) in the
years following export entry, but prices do not
increase.They find that investment in the customer
base through marketing and advertising (captured
by the ratio of advertising and marketing expendi-
tures to sales) explains the dynamics of quantities.
Successful episodes of export market entry seem to
be associated with important post-entry dynamics
of quantities, but not of markups.
2.3 Born globals
Another stream of literature in international business
and entrepreneurship focuses on Early International-
izing Firms (EIFs), also known as Born Globals.2
First, who are Born Globals? The term Born
Global was introduced by management consultants
(i.e., McKinsey & Company; Rennie, 1993). This
subgroup of firms is defined as “entrepreneurial start-
ups that, from or near their founding, seek to derive
a substantial proportion of their revenue from the
sale of products in international markets” (Knight
& Cavusgil, 2004; p. 124). This definition is usually
operationalized based on two dimensions: age and
export intensity. The usual Born Global definition
(i.e., Meuric & Favre-Bonté, 2023; Kuivalainen etal.,
2007; Knight & Cavusgil, 2004; Madsen etal., 2000)
refers to firms that have started exporting within the
first 3years after foundation and have an export inten-
sity (share of exports in total sales) of at least 25%.
Note that there is nothing in this definition regarding
whether Born Globals are actually growing. Some
famous examples satisfying this definition of Born
Globals include Microsoft, Apple, Google, Amazon,
Facebook, and eBay. About one-fifth of new enter-
prises in Europe across sectors are found to be Born
Globals (cf. Cavusgil & Knight, 2015; Eurofound,
2012).
Second, what triggers Born Globals (i.e., early
internationalization)? Countries with smaller domes-
tic markets have more born global firms, hence the
2 There may be fine distinctions between EIFs and Born
Globals, for example, if EIFs are regional but not truly global
(Sheppard and McNaughton, 2012). Such fine distinctions are
beyond the scope of our present study.
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size of the firms’ home market matters (Cavusgil
& Knight, 2015). Other important triggers of Born
Globals include technological development, changes
in production networks, global market conditions
(e.g., new niche markets), and organizational capa-
bilities. Fernhaber et al. (2007) find Born Globals
are more common in rapidly growing industries, high
knowledge intensity industries, and globally intercon-
nected industries.
Third, who leads Born Globals to capture the
emerging opportunities? Founders of Born Globals
have the vision, commitment, entrepreneurial orienta-
tion, and innovativeness to offer high-quality products
or services to global markets. Their founders focus on
international markets from the beginning (Acedo &
Jones, 2007) and also have prior international experi-
ence (Criaco etal., 2022). Zahra etal. (2005) high-
light the ability of managers to conceive, recognize,
and exploit opportunities in foreign markets, which
fosters higher proactivity, tolerance for ambiguity, and
decreased perceptions of risk in internationalization
(Acedo & Jones, 2007; Cavusgil & Knight, 2015).
Founders’ motivation for early internationalization is
improved performance compared to domestic market-
oriented firms, also known as the international pre-
mium. The international premium occurs because of
learning from and networking with international part-
ners, lower costs of information, lowering uncertainty
about foreign markets, and efficient logistics.
Fourth, what do we know about Born Globals’
strategy-making? Harms and Schiele (2012) observe
that experienced entrepreneurs are less focused on
planning activities (i.e., causation-oriented entre-
preneurs), but more on an adaptive and iterative
process by looking at available resources and then
experimenting with alternative possibilities based on
emerging opportunities (i.e., effectuation-oriented
entrepreneurs). Cavusgil and Knight (2015) argue that
Born Globals tend to follow advances in technology,
science, and design and then market the cutting-edge,
unique, and innovative offer. It is assumed that Born
Globals have the capacity to develop innovation and
deliver it in innovative ways (Leonidou & Samiee,
2012). Knight and Cavusgil (2005) suggest different
sub-types of Born Globals including: poor perform-
ers “stuck-in-the-middle,” entrepreneurs emphasiz-
ing cost leadership, high-tech focusers, and entrepre-
neurial strategy and technology leaders. Born Globals
tend to avoid cost leadership and tend to emphasize
differentiation or focus as their strategies (Knight &
Cavusgil, 2005; 2015). On the one hand, firms with
a differentiation strategy usually target niches for
which they develop and market distinct offerings. On
the other hand, firms with a focus strategy are con-
centrated on developing superb capabilities in a par-
ticular product category or are focused on specific
groups of buyers who have high expertise. Previous
work (Cooper & Kleinschmidt, 1985) emphasized
the importance of being oriented to the world-market
as opposed to being oriented towards a “convenient”
export market that is geographically and psychologi-
cally close. Kuivalainen etal. (2007) divided Born
Globals into Apparently Born Globals and True Born
Globals, where the first have lower export intensity
and export to nearby markets, while the second have
high export intensity and export to far-away markets.
Kuivalainen etal. (2007) do not find risk-taking or
proactiveness (two dimensions of entrepreneurial
orientation) to be necessarily higher among True
Born Globals compared to Apparently Born Globals;
however, True Born Globals were higher on another
entrepreneurial orientation dimension (i.e., competi-
tive aggressiveness).
Fifth, what is specific about Born Globals’ learn-
ing? Born Globals are thought to allocate their
resources efficiently under the principle of asset par-
simony (Cavusgil & Knight, 2015). They focus on
early and rapid internationalization to learn about for-
eign markets, which is enhanced with social capital
and network relationships (Cavusgil & Knight, 2009;
Chetty & Campbell-Hunt, 2003). Early internation-
alization benefits from innovative culture (Cavusgil
& Knight, 2015), internal and market-focused learn-
ing capability, as well as knowledge gathered from
the firm’s network relationships (Weerawardena
et al., 2007). Research has identified different types
of organizational learning and innovation among
Born Globals (Freixanet et al., 2020), and recently,
dynamic explanation of the capability development
given by Meuric and Favre-Bonté (2023) on a sample
of 15 firms in two French regions (Auvergne Rhône-
Alpes and Brittany). Related to our paper, Meuric
and Favre-Bonté (2023) use a standard HGF defini-
tion (OECD-Eurostat, 2007) combined with the Born
Globals definition and show that the interactions
between three main microfoundations (rapid proto-
typing, experiential learning, and international strate-
gic agility) generate a feedback loop that allows the
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HGX: theanatomy ofhigh growth exporters
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HGF Born Globals to continuously gain new knowl-
edge and improve their own products. This feedback
loop, composed of the three microfoundations, gen-
erates HGF Born Globals’ commercial intensity. The
core of growth is suggested to come from the interac-
tion between two microfoundations (i.e., navigating
international networks and improving international
maturity) which facilitate continuous support for mar-
ket entries and enhance rapid international expansion
(Meuric & Favre-Bonté, 2023).
2.4 HGX as an important new category
While considerable research on exporting firms
has been done, we argue the HGX segment remains
under-researched.
First, the HGF literature does not analyze exports
in depth (Section2.1). For example, no research has
been done on HGFs using customs data (i.e., firm-
product-market level), nor is there sufficientresearch
on HGFs and their exporting growth.
Second, in the exporter dynamics literature there
are several sophisticated and broad analyses of cus-
toms data (e.g., Eaton et al., 2008, 2021); however,
the main findings relate to export superstars as a
small number of important exporters (Section 2.2)
and highest export growth among tiny exporters
(Section2.2). This literature uses quintiles, which is
problematic, as there are many tiny firms with excep-
tional relative export growth of negligible absolute
export-size importance. Consider the example of a
firm that sells USD 10,000 domestically and USD
1000 abroad, if this firm in the next year sells again
USD 10,000 domestically, and USD 4000 abroad,
this would make it an impressive 400% export growth
of no aggregate importance.
Third, the Born Globals definition focuses on
firms that have at least 25% of exports in the first
3years (Section2.3). However, this definition faces
several drawbacks. On the one hand, while it is
well-suited for technology-intensive firms, there are
many successful exporters which do not fit the defi-
nition. Cavusgil and Knight (2015) highlight some
of these: Parker Pen, General Motors, Philips, Sony,
and Honda, to which we can add: McDonalds, Star-
bucks, Hewlett-Packard, L’Occitane, Nokia, Ferrero
Group, orRed Bull. On the other hand, about one-
fifth of new enterprises in Europe across sectors
are considered to be Born Globals (cf. Cavusgil &
Knight, 2015; Eurofound, 2012). The Born Globals
definition captures many firms with a mediocre per-
formance in terms of economic contribution and job
creation (Ferguson etal., 2021). Imagine a firm that
makes USD 10,000 a year and exports USD 3000 a
year in the third year from its foundation. This firm
would be satisfying both criteria for Born Globals,
the time dimension (starts exporting up to 3 years
from foundation) and export intensity criteria (30%
of sales are from exports), but this would be a bad
example for a Born Global, and would be exactly
the type of firm that Eaton etal. (2008) or Lawless
(2009) describe as negligible. The international
business literature (i.e., Cooper & Kleinschmidt,
1985; Freixanet et al., 2020; Knight & Cavusgil,
2004; Kuivalainen et al., 2007; Meuric & Favre-
Bonté, 2023) avoids this issue by cherry-picking
better examples satisfying Born Globals criteria in
tiny sample sizes, but an elite firm-type definition
should not pick-up tiny and/or non-growing firms.
Elements of critique of the Born Global definition
and operationalization can be found in multiple
studies (i.e., Kuivalainen etal., 2007; Madsen etal.,
2000; Moen & Servais, 2002).
Fourth, Cavusgil and Knight (2015; p.3) argue
that the “… issue of why some firms international-
ize early, others late in their evolution, and still others
choose to remain local, is a fundamental question for
international business scholarship.” Ferguson et al.
(2021) observe no benefits of early internationaliza-
tion compared to later internationalization, in their
comprehensive analysis of Swedish manufacturing
firms, 1998–2014. With regards to the firm interna-
tionalizations that come with somewhat later firm
age, a notable example is Bell etal. (2001) who con-
duct 50 semi-structured interviews to suggest there
are important exporters that do not necessarily sat-
isfy the EIF definition. However, Bell etal.’s (2001)
study is exploratory in nature, and fails to provide a
definition for a group they call born-again globals
(Sheppard & McNaughton, 2012). Thus, we suggest,
there is a need for a different definition capturing firm
growth in exports independent of firm age.
3 Data
Our analysis focuses on Croatia. World Bank classi-
fies Croatia as a high-income country since 2017, and
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prior to 2017 as an upper middle-income country.
Historically, Croatia fought its war for independence
and became an internationally-recognized independ-
ent country in 1992. Prior to independence, Croatia
was part of the Socialist Federal Republic of Yugo-
slavia, an economy which was organized as a mixture
of a planned socialist economy and a market social-
ist economy (Horvat, 1971, 1986). After independ-
ence, Croatia transitioned to an open market economy
and became part of the World Trade Organization in
2000 and of the Central European Free Trade Agree-
ment (CEFTA) from 2002 to 2013. Since July 2013,
it became an EU Member State, therefore part of
the EU Single Market and no longer part of CEFTA
(Josic and Basic, 2021).3 Policymakers in Croa-
tia have shown considerable interest in supporting
exporters, with studies documenting mixed success
(e.g., Srhoj & Walde, 2020).
In 2019, Croatia’s exports of goods and services as
a percentage of GDP were 57%, considerably above
the world average of 28.2%, or OECD member states’
average of 28%. This indicator was already high in
Croatia since its first measurement (1995, 27.2%),
while the world and OECD member states averages
were at 20.9% and 18.7%, respectively. Merchandise
exports to high-income economies as percentage of
total merchandise exports in Croatia was 79.9% in
1995 and 74.1% in 2019, while these figures were
76.9% and 68.2% for the world and 79.6% and 75.6%
for the OECD member states averages. Medium and
high-tech exports as a percentage of manufactured
exports in Croatia was 37% in 1995 and 48% in 2019,
which is more than in Australia (19%), Russia (27%),
India (37%), or Brazil (39%), but less than in Italy
(54%), Spain (55%), Belgium (56%), or the USA
(64%).4
Although several indicators of the Croatian econ-
omy suggest findings can be generalized to other
countries, we are cautious about generalizing our
findings due to Croatia’s historical peculiarity. To
address the issue of generalizability, most of our
analysis is calculated for the period after entering the
EU. Our initial study of HGXs should trigger further
research to replicate or extend the generalizability of
the economic importance of HGXs.
The following subsections outline the micro-level
datasets.
3.1 Firm financial data
Firm demographic and financial data come from cen-
sus data stemming from the Financial Agency (FINA)
of Croatia. This dataset encompasses all publicly-
listed and private limited companies incorporated
in the Republic of Croatia. It includes full balance
sheet and profit and loss statements of firms including
information on firm employees, firm sales, imports,
and exports,5 along with demographic information
such as firm employment, age, NACE 4-digit indus-
try, and micro location of the firms’ headquarters (i.e.,
county and municipality). This full panel census data
includes 2,297,130 observations and 234,176 unique
firms with 410 variables over the period 1993–2019.
3.2 Firm product data
Firm-level international trade data stems from the
Customs Administration, Ministry of Finance of the
Republic of Croatia, and was assembled by the Cro-
atian Bureau of Statistics (DZS) and accessed in the
DZS safe room. The Customs Administration data
encompasses all imports and exports of goods disag-
gregated at the firm–market level encompassing the
period 2008–2016. The variables include firm ID,
the 8-digit Combined Nomenclature (CN8) product
codes, the country market to which a firm exports
or imports (i.e., destination market), together with
amount of exports in tons and value of product
exports of a firm in a particular market. To illustrate
with a fictional example, we could identify a firm
“Car Equipment Ltd.” which exports to the USA,
500 tons of batteries, with a value of 5,000,000
3 Online Appendix (Figure OA1) gives the geographical posi-
tion of Croatia in Europe.
4 Source: World Bank Data. Link: https:// datab ank. world
bank. org/ home. aspx [Accessed: 24th July 2022]. For indicator
medium and high-tech exports (% manufactured exports), it is
not possible to obtain world or OECD member state average.
5 There were inconsistencies in the variable exports. For
example, a share of hotels reported their sales as exports
because hotels are mostly used by foreign citizens (i.e., tour-
ists). We examined such firms in a case-by-case manner, since
this is not exporting in the definition of this study, we set the
export values of firms in NACE 1-digit sectors Accommoda-
tion and food service activities and Administrative and support
service activities to zero.
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euros. CN8 product codes change over time and har-
monizing product codes over time is needed in order
to measure the number of exported products (or
newly introduced products, and the value of new
products)in a valid way (Baumgartner etal., 2023).
We use the Harmonizer package in R (Baumgartner
etal., 2023) to harmonize CN8 product codes over
time. Finally, we enrich this data with informa-
tion on bilateral distance between Croatia and the
destination market, whether a destination market
is a European Union (EU) Member State, CEFTA
market, or other market (CEPII GeoDist database,
Mayer & Zignago, 2011).6
3.3 Firm employee data
The employer–employee dataset stems from the
Croatian Pension Insurance Institute (HZMO).
Pensions are mandatory in the Republic of Croa-
tia, and HZMO tracks all pension registrations that
are either started or ceased; i.e., we have a census
firm–employee dataset. Importantly, the employee
is anonymized, but in a structured way so that we
can track employees changing jobs over time. While
employees are anonymized, the data includes vari-
ables such as start of work at the job, type of work
contract, the date of work contract termination, rea-
son for termination, employee occupation, qualifi-
cations and education, gender, and age. In addition
to these variables, the firm ID of the firms in which
each employee works is also included. This full
panel census data includes 6,329,064 observations,
312,769 unique employers7 and 2,009,420 unique
employees over the period 2014–2020.
We used these three rich data sets for our analy-
ses, albeit with the limitation that the time peri-
ods do not fully overlap. To show the number of
HGXs and their transitions to export superstars,
we have the full period from 1993 till 2019. Most
importantly, for describing the economic contribu-
tion and firm-level features of HGXs, we used the
period 2013–2016 (for which product-level data was
also available). Replicating the results for the period
2016–2019 yields further insights into the economic
contribution and firm-level characteristics of HGXs.
Furthermore, to examine HGXs’ hiring practices, we
obtained the firm-employee level data for the period
2016–2019, as this data did not exist in a structured
form before 2014.
HGXs
HGfs
i.e. non-HGX HGFs
Non-HGF exporters
i.e. exporters (excluding HGFs and export
superstars)
Non-HGF domestic traders
i.e. domestic traders (excluding HGFs)
HGFs
Non HGFs
Export superstars
in t
Non export
superstars in t
Fig. 1 Assignment of firms into mutually exclusive firm categories according to their salesandexport volume or growth
6 Several countries did not have bilateral distances, including
Serbia, Kosovo, and Montenegro. We therefore constructed
them ourselves based on the GeoDist website instructions.
7 This dataset includes firms, crafts, public institutions, and
NGOs.
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4 Definition offirm categories
This study specifies several firm categories and
defines the category of high growth exporters. Fig-
ure1 illustrates the five mutually exclusive firm cate-
gories. Export superstars are defined as top 100 firms
by export value in period t (category (i)).8 All other
firms, i.e., the ones not being an export superstar, we
classified into high growth firms (HGFs) or non-
HGFs.9 Our high growth firms (HGFs) definition
aligns closely with the OECD-Eurostat (2007) defini-
tion of the revenue-based HGFs, except that we use a
more inclusive lower-bound size threshold of 5
employees instead of 10 employees, because of con-
cerns in the literature that the threshold of 10 employ-
ees could be overly restrictive (Daunfeldt et al.,
2015). HGFs are defined as firms having 5 or more
employees and an average yearly sales growth rate of
at least 20% over three consecutive years, i.e.,
S
t+3
−St
St
≥
0.728, where St denotes sales at time t.10
All HGF firms are divided into (ii) high growth
exporters (HGXs) and (iii) the other high growth
firms which are not HGXs (abbreviated as HGfs).
Within the category of HGFs, HGXs are firms whose
sales growth is driven by the growth of exports, i.e.,
firms for which a share of at least 50% of the mini-
mum yearly sales growth by HGF definition is gener-
ated by exports. Thus, the additional criteria
where
Xt
denotes export at time t, holds for HGXs.
We chose 50% as the percentage because we wanted
a substantial part of the growth to be due to growth
in exports. The chosen approach does not exclude the
possibility of becoming an HGX if an HGF does not
export in the first period of interest (t).11 All other
HGFs that fail to satisfy the HGX definition are HGfs.
Among the firms not being export superstars and
failing to satisfy the HGF definition, we further clas-
sify these firms into (iv) non-HGF exporters and (v)
non-HGF domestic traders which do not export. The
main analysis does not focus on domestic traders
because the literature (Bernard et al., 2007) already
answered many questions related to the difference
between exporters and domestic traders (non-export-
ing firms). With this approach, we obtain five mutu-
ally exclusive categories for investigation: export
superstars, HGXs, HGfs, non-HGF exporters, and
non-HGF domestic traders. Figure 1 presents the
various (mutually-exclusive) categories of firms and
their connections.
Finally, firm entries and exits are important for job
creation and innovation (Haltiwanger et al., 2009),
which is why we considered firm entry and exit for
calculating the number of jobs and the exports crea-
tion of different types of firms in the economy. Given
the 3-year period that features prominently in the
OECD-Eurostat (2007) definition of HGFs, firm entry
is defined as any firm that does not exist in period t
but exists in period t + 3, while firm exit is defined as
any firm that exists in period t but not in period t + 3.
Figure2 shows the number of firms per firm typefor
each of Croatia’s 21 NUTS 3 regions per 10,000 resi-
dents. Regardless of the mutually-exclusive category
of firm (HGXs, HGfs, non-HGF exporters, and export
superstars), the highest shares are documented in the
capital city probably due to agglomeration or spillover
effects (Puga, 2010; Roca & Puga, 2017). The north-
west regions bordering Slovenia, which also has a his-
tory of exporting and higher shares of exporters (Bačić
(1)
Δ
Xt+3=Xt+3−Xt≥
0.728
2
∙St
,
8 We use census dataset on all exporters to analyze export
superstars in the Republic of Croatia with several definitions,
the top 1, top 5, and the top 100 exporters by the absolute
export value. Table13 shows exports in total economy exports
(%) of single, top 5, and top 100 exporters. Top 100 export-
ers account for 40–50% of total exports in the economy over
the period 2013–2019. Table14 gives descriptive statistics of
Superstars, showing they have 730 employees, 167 million
euro sales, and 82 million euro exports at the mean, with high
mean market shares at 38%.
9 We would like to point out that at first the export superstars
are categorized. Therefore, all firms in the category of HGX
are not export superstars and thus HGXs may have the poten-
tial to become export superstars. From 2013 to 2016, there are
3 export superstars being also a high growth firm and having
a growth in exports accounting for at least 50% of the sales
growth; in the full sample with all years, out of 2534 HGXs
21 firms are export superstars fulfilling also the HGX criteria.
However, this sequential classification step allows that each
firm is categorized in a unique firm category.
10 Revenue-based HGFs are associated with higher aggregate
productivity, which was not found for employee-based HGFs
(Bisztray etal., 2023).
11 There are two consequences of not defining HGXs as sim-
ply HGFs that export in period t. Firstly, in the analyzed sam-
ple, this would lead to a 55% increase in the number of HGX,
and secondly, 25% of actual HGXs would be dropped as they
have no exports in period t.
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& Aralica, 2016) have higher shares of HGXs. These
Croatian regions have strong international ties to Slo-
venia, Austria, Germany, and Italy (Croatian Chamber
of Commerce, 2018). Dalmatia (coast, south-west)
and Slavonia (continent; north-east) have very low
shares of HGXs, but also have low shares of non-HGX
exporters and export superstars. South Dalmatian
regions (i.e., Dubrovnik-Neretva and Split-Dalmatia)
have very high shares of HGfs-these regions are tour-
ism-intensive with HGfs stemming from sectors such
as construction, accommodation, restaurants, and bev-
erage service activities.
5 Results
Starting from 1995 until the last pre-pandemic year
(2019) for each of the eight 3-year periods (Fig.3),
we used the definitions and classified firms into the
corresponding categories (i.e., Fig.1).
In each 3-year period, there are 100 export super-
stars, thus, 800 export superstar observations with 290
unique export superstars over the period, while there
are 2515 unique HGXs. Compared to export super-
stars, HGXs are more volatile. From the 100 export
superstars in 2019, as many as 44 were previously
HGXs. Depending on the decades, 5% of export super-
stars in 2019 were HGXs in 1990s, 21% were HGXs
in 2000s, and 26% were HGXs in the 2010s (details
in Table15).12 Additional analysis shows that the larg-
est export superstar was previously not an HGX in the
period 1995–2019, but was large already at the start of
Fig. 2 Number of firms per NUTS 3 population dependent on firm types. Note: The number of firms per firm type is divided by
NUTS 3 population in 10,000
12 Note that individual firms can be HGXs in more than one
decade.
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the analyzed period and did not come from the low-
end of the firm size distribution. Among the top five
largest export superstars, two export superstars were
previously HGXs. With 44% of export superstars in
2019 having been HGXs, we conclude that previous
HGXs have a chance to become export superstars
over a long enough period. This chance may even be
higher when targeted by policy but this needs further
research. In other words, current HGXs are potential
future export superstars which give additional moti-
vation for further investigating their characteristics in
comparison to HGfs, export superstars, and non-HGF
exporters.
5.1 Sector distribution
We examine the industrial differences among the firm
categories in the period 2013–2016 (Table 16). To
some extent, differences in the within and between
distributions of sectors dependent on the firm cat-
egories are expected for tradeable and non-tradeable
sectors. Export superstars (63%) and HGXs (31%)
are predominantly in the manufacturing sector. HGXs
are more frequently in the transportation and storage
sector (9%) than HGfs (4%), but similar to non-HGF
exporters (7%) and export superstars (6%). Wholesale
and retail trade; repair of motor vehicles and motor-
cycles is a sector where there is considerable share of
HGXs (13%), but less than HGfs (20%) and non-HGF
exporters (28%), with a similar share to export super-
stars (13%).
HGXs are actually more frequently in the infor-
mation and communication technology (ICT) sector
(15%) than HGfs (3%) and non-HGF exporters (10%),
while there are no export superstars in ICT in 2013.
Similarly, HGXs (9%) are more active in profes-
sional, scientific, and technical activities than export
superstars (1%), similar to HGfs (10%), and less than
non-HGF exporters (17%). There are many HGfs in
the construction sector (23%), but far fewer HGXs
(8%), non-HGF exporters (4%), and export superstars
(1%). A similar distribution occurs for the period
2016–2019 (Table OA1) with a noticeable increase of
HGXs in ICT, as well as emergence of an ICT export
superstar in 2016.
5.2 New exports
Table1 provides insights into the net export creation
of active firms in 2013, and their additional exports
3years later (2016). HGXs are only about 0.5% of all
the firms in the economy, they account for 24.9% of
export growth (1456 out of 5841 million euro), while
considering only the active firms (excluding firm
entry) HGXs account for 28.8% of export growth.
HGXs are 18.6% of the number of HGFs (528 out of
2846) and account for almost all of the export growth
of HGFs (1456 out of 1562 million euro). HGXs have
about 2.5 times the exports as HGfs in 2013, despite
being only about 23% of HGfs by number. By the end
of the growth period (i.e., 2016), HGXs have almost
10 times more exports than HGfs. In absolute exports
value, in 2013, Superstars are almost 20 times larger
than HGXs (6013 vs 309 million euro). The differ-
ence between the Superstars and HGXs falls by the
end of the growth period (i.e., 2016) to 3.3 times
(5789 vs 1765 million euro). Table OA2 repeats the
analysis in Table1 for the period 2016–2019, finding
qualitatively the same results.
In the period 2013–2016, the 528 HGXs contribute
more to the overall export growth than the 9113 export
1995 (1)1998 (2)2001 (3)2004 (4)2007 (5) 2010 (6)2013 (7)2016 (8)2019
1990s 2000s 2010s
Fig. 3 Time periods for classifying the firms into corresponding categories. The number of the time period is provided in brackets
next to the start date
13 From 100 export superstars, nine export superstars are not
active in 2016, with deeper case-by-case analysis showing
these nine export superstars are merged or liquidated.
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superstars (24.9% vs 16.5%, respectively), firm entry
contributes 13.6% of new exports, and the rest of the
contribution to new exports essentially comes from
slow-growth non-export superstar firms. The contri-
bution to exports from entries (793 million euro) is
larger than the loss due to exit (−689 million euro).
Export superstars decline in exports (−3.7%) while
the “other active firms” category14 grew (27.8%).
HGXs as a group grew their exports by 471%. In
2013, Croatia entered the EU, so the increase of
exports during 2013–2016 could have benefitted from
the EU accession. We also conduct the analysis for
2016–2019 and show HGXs have a similar absolute
growth in exports as in 2013–2016 (1439 million euro
in 2016–2019 vs 1456 million euro in 2013–2016).
5.3 New employment
We investigate the job creation of different firm types
over the period 2013–2016. Table2 shows that the
creation of new jobs from firm entries is smaller
than the destruction of jobs from firm exits (85,470
vs−87,232). There are 32,771 new firms from 2013
to 2016, which are important for jobs in the economy,
as they account for about 10% of all jobs (85,470 out
of 864,662), and 38.2% of all new jobs (85,470 out
of 223,741). HGXs make up a much smaller category
of firms, only 528 firms, which make a dispropor-
tionate contribution to job creation. In 2016, the 528
HGXs account for about 38% of the number of the
91 export superstars jobs (23,277 vs 59,916, respec-
tively), although when it comes to job creation (i.e.,
changes rather than levels), HGXs are growing fast
(73.3% growth) while export superstars are actually
destroying jobs overall (i.e.,−11.9% growth). Table2
also shows HGXs employ about 28% of the num-
ber of HGf employees in 2013 (13,430 vs 48,533),
while this percentage grows to 33% in 2016 (23,277
vs 70,392). In relative terms, HGXs have a higher
growth rate than HGfs (71% vs 45%).
Since there are 4 times fewer HGXs than HGfs, the
HGXs make a disproportionately large contribution to
job creation. In particular, HGXs grow on average by
18.6 employees (9847/528), while HGfs grow by 9.4
Table 1 Exports 2013 and 2016
Active firms are those existing both in 2013 and in 2016. Firm entry counts firms existing in 2016, but not in 2013. Firm exit counts
firms existing in 2013, but not in 2016. We firstly split firms into export superstars and non-export superstars. Export superstars
are 100 largest exporters in absolute value in 2013. From 100 export superstars, nine export superstars are not active in 2016, with
deeper case-by-case analysis showing these nine export superstars are merged or liquidated. Among those non-export superstars,
active firms are split into HGFs and other firms. HGFs cannot be export superstars in 2013. HGFs are split into HGXs and other
HGFs (HGfs). All growing firms are a subgroup of firms that had positive change in exports from 2013 to 2016. Export values are
given in million euro. Column % of new exports calculates share of new exports from all new exports between year 2013 and 2016
Number of
firms
Exports2013 Exports 2016 Difference % change Only grow-
ing firms
Exports
growth
% of new
exports
Entry and
active firms
114,651 11,323 14,767 3444 30.4 13,173 5841 100
Firm entry 32,771 0 793 793 - 3380 793 13.6
Firm exit 23,519 689 0 − 689 - 0 0 0
Active firms 81,880 11,323 13,974 2651 23.4 9793 5048 86.4
HGFs 2846 432 1953 1521 352.1 978 1562 26.7
HGXs 528 309 1765 1456 471.2 528 1456 24.9
HGfs 2318 123 188 65 52.8 450 106 1.8
Export super-
stars
91 6013 5789 − 224 − 3.7 52 963 16.5
All other firms 78,934 4878 6232 1354 27.8 8763 2524 43.2
All firms 2013 105,399 12,012 -
All firms 2016 114,642 - 14,767
14 All other firms active in t and t + 3 that are not HGFs or
export superstars.
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employees on average (21,859/2318). Relatedly, look-
ing only at the firms growing in employees from 2013
to 2016, HGXs are 0.5% of all firms in the economy
but contribute 4.7% of all new employees in the econ-
omy, thus about 10 times more than expected consid-
ering their number (share of firms in the economy).
HGfs are 2.2% of all firms in the economy, but con-
tribute 11.2% of all new jobs, which is about 5 times
more than expected by their share in the economy.
Analysis for 2016–2019 (Table OA3) shows sim-
ilar results to Table 2. During 2013–2016, HGXs
create 9847 jobs, while for 2016–2019 HGXs create
11,351 jobs. However, for the period 2016–2019, we
also have data on firm-employee spells which allows
more detailed analysis. Firm types are merged by
firm IDs with firm-employee employment spells in
the same period. Differences between HGXs and
the other categories are not huge (Table17 and 18),
although a few observations can be made. In par-
ticular, new hires at HGXs are more likely to have
previous work experience in the mid high-tech sec-
tor, and in particular HGXs employ from non-HGF
exporters, HGXs, and export superstars. HGXs are
more likely to hire employees on 1-year contracts
(i.e., short-term) and have considerably more new
work contracts for working abroad (20.8%).
5.4 Firm-level characteristics
We summarize firm-level characteristics for HGXs
compared to other firm categories using a linear
probability model based on variables from 2013
(period t). In this sample, as many as 70% of HGX
did not satisfy the Born Globals definition (Table
OA4), while the mean export intensity of HGX was
Table 2 Job creation 2013–2016
Mean per firm is provided in brackets. Active firms are those existing both in 2013 and in 2016. Firm entry counts firms existing in
2016, but not in 2013. Firm exit counts firms existing in 2013, but not in 2016. We firstly split firms into export superstars and non-
export superstars. Export superstars are the 100 largest exporters in absolute value in 2013. From 100 export superstars, nine export
superstars are not active in 2016, with deeper case-by-case analysis showing these nine export superstars are merged or liquidated.
Among those non-export superstars, active firms are split into HGFs and other firms. HGFs cannot be export superstars in 2013.
HGFs are split into HGXs and other HGFs (HGfs). All growing firms are a subgroup of firms that had positive change in employ-
ment from 2013 to 2016. Column % of new jobs calculates share of new jobs from all new jobs between year 2013 and 2016
Number of
firms
Jobs 2013 Jobs 2016 Difference % change Only grow-
ing firms
Jobs created % of
new jobs
Entry and
active firms
114,651 779,192 864,662 85,470 11.0 44,969 223,741 100
Firm entry 32,771 0 85,470 85,470 - 21,824 85,470 38.2
Firm exit 23,519 87,232 0 − 87,232 - 0 0 0
Active firms 81,880 779,192 772,279 − 6913 − 0.9 23,145 138,271 61.8
From active firms:
HGFs 2846 61,963 93,669 31,706 51.2 2034 35,469 15.9
HGXs 528 13,430 23,277 9847 73.3 420 10,521 4.7
HGfs 2318 48,533 70,392 21,859 45.0 1614 24,948 11.2
Export
super-
stars
91 68,046 59,916 − 8130 − 11.9 39 3226 1.4
All other
firms
78,934 649,102 618,650 − 30,452 − 4.7 21,070 99,572 44.5
Raw sample:
All firms
2013
105,399 866,424 -
All firms
2016
114,642 - 857,718
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0.40 in 2013, and 0.63 in 2016 (Table OA5). The
median HGX sales in year 2013 were € 564,000,15
thus, the median HGX had to grow 72.8%–this
implies growth to € 974,592 in year 2016; therefore,
a growth of € 410,592, from which at least half (€
205,296) had to be exports. Furthermore, to shed
light on whether HGXs are not simply re-export-
ing products (Bernard etal., 2019; Damijan etal.,
2013), we provide statistics on export-to-imports
share to show that more than half of HGX do not
import, and 25% of HGX export three times more
than they import (Table OA6).
Monetary variables are log-transformed to address
skewness. The dependent variable is a dummy indicat-
ing whether the firm is an HGX or not (= 0). We com-
puted the linear probability model for three subsamples:
HGXs vs HGfs, HGXs vs non-HGF exporters, and
HGX vs non-HGF exporter with 5 or more employees.
The following linear probability model was esti-
mated dependent on the reference group for HGX:
(2)
HGX
=
𝛽
0+
𝛽
1⋅
ForeignOwnership
+
𝛽
2⋅
Exporter
+
𝛽
3⋅
Importer
+
𝛽
4⋅
Log intangible assets+
𝛽5⋅Log R&D+𝛽6⋅Log average wage +𝛽7⋅Log labor productivity +𝛽8⋅Log age
𝛽9⋅Surplus +𝛽10 ⋅
EBIT
Total assets +𝛽11 ⋅
Retained earnings
Total assets +𝛽12 ⋅
Book value of equity
Total liabilities +
𝛽
13
⋅
Firm size categories
+𝛽
14
⋅
NACE
1−
digit sectors
+𝜀,
Table 3 Predicting HGX status (2013–2016)
*p < 0.10, **p < 0.05, ***p < 0.01; R2 denotes the coefficient of determination; the first linear probability model (LPM) includes
HGXs and HGfs, the second LPM includes HGXs and non-HGF exporters, while the third LPM includes HGXs and non-HGF
exporters with 5 or more employees. All models include firm size and NACE 1-digit industry categorical variables, but are not
reported for brevity reasons. Heteroscedastic robust standard errors are used and provided in brackets
Dependent variable
Firm characteristics HGX vs HGf HGX vs non-HGF exporter HGX vs non-HGF
exporter with 5 or more
employees
(1) (2) (3)
Foreign ownership 0.211*** (0.029) 0.049*** (0.008) 0.080*** (0.011)
Exporter 0.303*** (0.021) − 0.935*** (0.006) − 0.860*** (0.022)
Importer − 0.015 (0.016) 0.006 (0.004) 0.007 (0.008)
Log intangible assets 0.001 (0.002) 0.0002 (0.0005) − 0.0002 (0.001)
Log R&D 0.004 (0.005) 0.001 (0.001) 0.001 (0.002)
EBIT/total assets 0.089*** (0.033) 0.015 (0.011) 0.031 (0.023)
Quick ratio 0.008 (0.008) 0.001 (0.002) 0.008* (0.004)
Log average wage − 0.0001 (0.007) 0.010*** (0.002) − 0.007 (0.006)
Log labor productivity − 0.008** (0.003) − 0.009*** (0.002) − 0.017*** (0.004)
Log age − 0.002 (0.007) − 0.017*** (0.003) − 0.053*** (0.005)
Surplus dummy 0.020 (0.021) 0.001 (0.007) − 0.004 (0.012)
Retained earnings/total assets 0.002 (0.017) 0.006 (0.005) 0.005 (0.011)
Book value of equity/total liabilities 0.004 (0.004) − 0.002** (0.001) − 0.004** (0.002)
Observations 2843 9674 5287
R20.267 0.301 0.308
Residual Std. error 0.335 (df = 2809) 0.190 (df = 9640) 0.250 (df = 5254)
15 The 90th percentile HGX sales were € 3,575,000 in 2013.
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where the regression parameters/vectors are
𝛽0,
⋯
,𝛽14
and
𝜀
is the remainder noise. Variance
inflation factors indicate no strong multicollinearity
(Table OA10; GVIF < 4),16 heteroscedastic robust
standard errors are used.
Results in Table3 show foreign ownership is asso-
ciated with higher probability of being an HGX com-
pared to HGfs and also compared to non-HGF export-
ers. Being export active in period t is associated with
a higher probability of being an HGX compared to
HGf. Since non-HGF exporters per definition have to
be exporters in period t, but about 25% of HGXs are
not exporters in period t, in the second model, there
is actually a negative association having a non-HGF
exporter or an HGX status due to the firm category
definitions.
HGXs are associated with higher
EBIT
Total assets
and
lower labor productivity compared to HGfs (Table3).
Compared to non-HGF exporters, HGXs are asso-
ciated with higher average wage, younger age, but
lower
Book value of equity
Total liabilities
and labor productivity. Regard-
ing the latter, theliterature on exporters (e.g., Melitz,
2003; Wagner, 2019) uses productivity as a key
measure explaining why firms start exporting. As our
results show, this does not hold for predicting HGX.
HGXs are less productive than for example non-HGF
exporters. Our further investigation shows that by the
period t + 3, HGXs’ labor productivity triples on aver-
age which is probably due to learning-by-exporting
(Atkin etal., 2017); however, more research is needed
on this topic. Log R&D and log intangible assets
are not statistically significantly associated with
HGX status once controlling for sectors, firm size,
and other firm characteristics. Relatedly, having a
positive surplus is not different between HGX, HGfs
and non-HGF exporters, nor is the quick ratio17 or
Retained earnings
Total assets
. Further investigations show that HGXs
are less likely to exit compared to other HGfs or non-
HGF exporters (Table OA11 – OA12).
We also run the model on the three subsamples
(Table 3) by adding variables with higher bivariate
correlations (r = 0.23 up to 0.67) stepwise into the
regression. Either no collinearity issues (Table OA13-
OA14) or not-at-all-severe collinearity issues (Table
OA15) are present. We check robustness using three
alternative HGX definitions, lower (40%) and higher
(60%) minimum growth coming from exports as well
as net export HGX definition (detailed definitions in
the OA). We obtained similar results across alterna-
tive HGX definitions (Tables OA16 – OA19). The
same model for the three subsamples as in Table3 are
run for period 2016–2019 which show quite similar
results to Table3 (Table OA20).
5.5 Product mix
Products are subject to customs regulation and are
tracked in detail. For comparisons among firm cat-
egories, we focus on export-active firms in 2013, but
allow changes in any direction in 2016. Table4 shows
several interesting results. In the category of HGXs,
the number of export products grows rapidly, faster
than for export-active HGfs. Although a smaller cat-
egory of firms, HGXs start from a similar number
Table 4 Export products
Brackets provide mean per firm
Firms Export
products
2013
Export
products
2016
Diff Growth (%) Only
growing
firms
Product growth % of all
additional
HGfs 325 2628[8.1] 4098 [12.6] 1470 [4.5] 155.9 120 2266 [18.9] 7.9
HGXs 180 2864[15.9] 5865 [32.6] 3001 [16.7] 204.8 135 3181 [23.6] 11.1
Non-HGF exporters 5792 56,146 [9.7] 59,517
[10.3]
3371 [0.6] 6.0 1536 22,402 [14.6] 78.5
Export superstars 94 5154 [54.8] 4697 [50.0] − 457 [− 4.9] − 8.9 30 701 [23.4] 2.5
17 Quick ratio is defined as cash/current liabilities (nonfinan-
cial).
16 Tables OA7-OA9 and Figures OA2-OA4 in the Online-
Appendix show correlation matrices of the independent vari-
ables for the three subsamples (HGX vs HGfs, HGX vs non-
HGF exporters, and HGX vs non-HGF exporter with 5 or more
employees).
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of export products as HGfs (2628 vs 2864), but their
growth is about twice that of HGfs (3001 vs 1470).
The rapid growth of HGXs export products takes
place while export superstars actually decrease the
number of export products (from 5154 to 4697).
The non-HGF exporter category increases their
number of export products per firm, but not as much
as HGXs (0.6 vs 16.7 per firm). A total of 75%
of HGXs increase the number of export products,
which is a considerably higher percentage than in
the other firm categories (export superstars = 32%;
HGfs = 37%; non-HGF exporters = 27%). Clearly,
HGXs are not just expanding the same products in
new and/or existing markets, but are expanding the
number of export products.
On average HGXs grow their number of export
products, but to investigate the change in the dis-
tribution of the number of products, we examine
the deciles of number of products across firm cat-
egories (Table 5). HGXs increase in number of
products across all deciles, for example, at the 30%
decile, HGXs grow from 3 to 7, at the median they
grow from 6 to 11, while at 70th percentile they
grow from 11 to 25 export products. As a robust-
ness check, we also analyze the increase in export
products when products are defined at the first
6-digit code of the CN8 code. Robustness results
show similar patterns (although of course smaller
in absolute values) for HGXs growth of export
products (Table19).18 Thus, HGXs grow, and this
growth is not just due to sales growth of the same
products, but due to more export products, with
different CN8 codes. The growth in number of
export products is substantially different from other
firm categories, for example, the first three deciles
of HGfs do not export anymore, and they only
increase in number of export products at the 80th
and 90th percentiles. Similarly, non-HGF exporters
Table 5 Export products:
beyond averages Obs 10% 20% 30% 40% 50% 60% 70% 80% 90%
Exported products 2013
HGfs 325 1 1 1 2 3 4 7 12 19
HGXs 180 1 2 3 4 6 9 11 22.2 45
Non-HGF exporters 5791 1 1 2 2 3 4 7 11 24
Export superstars 94 7.6 15.8 21 29 39 48.8 62 84.4 132
Exported products 2016
HGfs 325 0 0 0 1 2 4 7 16.2 36
HGXs 180 3 5 7 9 11 16 25.3 45 82
Non-HGF exporters 5791 0 0 0 1 2 3 6 11 25
Export superstars 94 6 9 15 20 27 34.8 51.4 76 114
Table 6 Share of largest
export product: beyond
averages
Obs 10% 20% 30% 40% 50% 60% 70% 80% 90%
Share of largest export product in total exports 2013
HGfs 325 0.33 0.46 0.57 0.69 0.78 0.89 1 1 1
HGXs 180 0.33 0.44 0.57 0.68 0.76 0.87 0.95 0.99 1
Non-HGF exporters 5791 0.34 0.46 0.56 0.66 0.79 0.91 1 1 1
Export superstars 94 0.32 0.38 0.45 0.56 0.63 0.79 0.85 0.95 0.99
Share of largest export product in total exports 2016
HGfs 325 0 0 0 0.18 0.38 0.48 0.68 0.86 1
HGXs 180 0.3 0.41 0.49 0.54 0.62 0.71 0.86 0.95 0.99
Non-HGF exporters 5791 0 0 0 0.25 0.43 0.56 0.73 0.92 1
Export superstars 94 0.3 0.38 0.47 0.55 0.64 0.75 0.85 0.96 0.99
18 In addition, we also analyze first 4-digit code of the CN8
code (Table OA21). Main results do qualitatively not change.
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and export superstars decrease in number of export
products across all or the majority of deciles.
While HGXs grow in the number of export prod-
ucts, they might in addition explore new foreign mar-
kets. To examine this, we calculate the share of firms’
top export product in total export value (Table 6).
More than 50% of HGXs have 75% or more exports
from a single product; also, more than 70% of HGXs
have 50% or more exports from a single product. Over
the 3 years (i.e., 2013–2016), at the median, HGXs
decrease in the share of top export product in total
exports. At the 70th percentile, HGXs largest export
product represents 95% of total exports, 3years later,
this share is 86%; at the median, HGXs had 76% in
2013, and 62% in 2016; and at the 30th percentile,
HGXs decrease from 57 to 49%. At all deciles, the
share of HGXs’ top export product decreases over
time; however, at the 60th, 70th, 80th, and 90th
deciles HGXs’ top product in 2016 is more than 70%
of total exports. In contrast, export superstars have
almost the same share of top export product in total
exports over all deciles. HGfs and non-HGF exporters
decrease in top export product share; however, this
finding is driven by the first three percentiles where
firms stop exporting by 2016.
Table 20 shows additional information for
2013–2016, including the number of export prod-
ucts, the number of new products and the number of
dropped products. Results show that 60% of HGXs
have at least 2 products growing from 2013 to 2016.
Up to 30% of HGfs have at least 1 product growing in
exports, and up to 40% of non-HGF exporters have
at least 1 product growing in exports. After export
superstars, HGXs have the second highest number of
dropped products, for example, 70% of HGXs drop at
least 1 product. As many as 80% of HGXs introduce
3 or more new products.
As the final firm-product analysis, we focus on the
mean export product unit price (Table7). In particular,
we divide the export value for each export product and
the number of units in which products are exported. We
then calculate the mean unit price. In 2013, HGXs show
a wide range of mean unit costs, from as little as € 12 at
the 10th percentile, € 141 at the 30th percentile, € 631
at the median, € 10,736 at the 70th percentile up to as
much as € 152,653 at the 90th percentile. By the end
of the growth period (i.e., 2016), the mean unit prices
increase to € 30 (10th percentile), € 202 (30th percen-
tile), € 1719 (median), € 20,357 (70th percentile), and €
350,253 (90th percentile). On the other hand, HGfs and
export superstars decrease in the mean unit prices across
all deciles, while non-HGF exporters increase across all
deciles. Comparing HGXs and non-HGF exporters, it
seems that HGXs increase their unit prices substantially
more. However, it should be clearly stated that the share
of this increase in unit prices stems from introduction of
new products by the HGXs (i.e., Table4 and 5) which
could have very different units, and therefore results in
Table 7 are not necessarily strong evidence on quality
upgrading, but could be a combination of both qual-
ity upgrading and introduction of new export products
(Table5, 19, OA21).
5.6 Export markets
This subsection analyzes whether HGXs growth is
driven by more export products to the same markets
or by diversifying export markets. Table8 shows the
Table 7 Unit price: beyond averages
Obs 10% 20% 30% 40% 50% 60% 70% 80% 90%
Unit price 2013
HGfs 325 11 45 188 417 1197 1941 4357 10,795 25,129
HGXs 180 12 49 141 338 631 3324 10,736 38,655 152,653
Non-HGF exporters 5791 6 25 80 221 486 1174 2893 7145 27,185
Export superstars 94 5 17 47 190 1572 3700 43,329 177,209 2,806,756
Unit price 2016
HGfs 325 5 39 112 353 997 2136 4315 10,097 23,190
HGXs 180 30 88 202 441 1719 7681 20,357 68,680 350,253
Non-HGF exporters 5791 7 31 99 266 567 1389 3762 11,059 44,538
Export superstars 94 4 18 47 116 857 2917 7682 119,183 1,476,783
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HGX: theanatomy ofhigh growth exporters
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number of firm-market observations across firm cat-
egories. HGXs are the only category of firms that have
an increasing number of export markets. Their growth
is impressive, the number of export markets in 2016 is
almost twice the number of export markets they had
in 2013. Between the 2013 and 2016, HGXs increase
by 3.7 export markets on average. For HGfs, non-HGF
exporters, and export superstars, the number of export
markets decreases over the period 2013–2016.
Table9 shows HGXs increase the number of export
markets in all deciles from 2013 to 2016. By the end of
the growth period (i.e., 2016), 50% of HGXs have more
than 5 export markets. On the other hand, 30% of HGfs
and non-HGF exporters stop exporting, and it seems
that a majority decrease the number of export product-
markets. At the upper deciles (70th, 80th, and 90th per-
centiles); however, some HGfs grow by increasing the
export markets. Hence, HGfs display heterogeneity:
some HGfs stop exporting, while other HGfs intensify
their exporting by exporting more products and being
active in more export markets.
Table10 investigates whetherHGXs grow by con-
centrating on their largest export market (Table10).
More than 80% of HGXs have 50% of their exports
from a single export market in 2013, while about
60% of HGXs have 75% of their exports from a sin-
gle export market. The share of exports in the larg-
est export market decreases among HGXs over
the growth period. In other words, HGXs growth
leads to less reliance on any individual export mar-
ket, because HGXs growth spreads out into a more
diversified portfolio of export markets. Apart from
the first decile, export superstars are decreasing their
share of largest market in total exports; however, the
changes are much smaller than among HGXs. HGfs
decrease their reliance on the single largest export
market; however, at the end of the growth period, the
upper deciles (70th, 80th, and 90th percentile) focus
their exports on a single export market (share of larg-
est export market = 1). Finally, non-HGF exporters
decrease their reliance on their top export market, but
at a much smaller rate compared to HGXs.
Table 8 Export markets
Brackets provide mean per firm. Export market is defined as a unique firm-market observation. Thus, if two firms both export mul-
tiple product to the USA and the UK in 2013, this is two export markets per firm and would be four markets in the column Export
market 2013
Firms Export mar-
ket 2013
Export mar-
ket 2016
Diff Growth (%) Only
growing
firms
Growth % of new
markets
HGfs 325 788 [2.4] 752 [2.3] − 36 [− 0.1] − 4.6 77 212 [2.8] 4.9
HGXs 180 739 [4.1] 1401 [7.8] 662 [3.7] 89.6 137 686 [5.0] 15.8
Non-HGF exporters 5791 17,999 [3.1] 15,008 [2.6] − 2991 [− 0.5] − 16.6 1189 3267 [2.7] 75.2
Export superstars 94 1785 [19.0] 1708 [18.2] − 77 [− 0.8] − 4.3 34 178 [5.2] 4.1
Table 9 Export markets:
beyond averages Obs 10% 20% 30% 40% 50% 60% 70% 80% 90%
Export markets 2013
HGfs 325 1 1 1 1 1 2 2 3 5
HGXs 180 1 1 2 2 3 3 4.3 6.2 10
Non-HGF exporters 5791 1 1 1 1 2 2 3 4 7
Export superstars 94 4 7.6 11 13.2 16 20.8 22.1 29.4 35
Export markets 2016
HGfs 325 0 0 0 1 1 2 3 4 6
HGXs 180 2 3 4 5 6 7 9 11 15
Non-HGF exporters 5791 0 0 0 1 1 2 2 3 7
Export superstars 94 2.3 5 8 10.2 14 18 25.1 32 38
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Results in Table11 show in 2013, 78% of HGXs
export to the EU Single Market, while at the end of
the growth period, as many as 90% export to the EU
Single Market. HGXs also increase their presence in
CEFTA markets (from 61 to 68%) and other export
markets (from 39 to 51%). Interestingly, the only cat-
egory of firms growing in the EU market is HGXs,
HGXs can benefit from new business opportunities
by offering competitive products.
In 2013, 57% of HGfs export to EU Single Market,
65% export to CEFTA, and 23% export to other markets.
By the end of the growth period, HGfs decrease their
presence in the EU Single Market (to 37%) but increase
their presence in CEFTA (83%) and other markets
(29%). Finally, Superstars remain present in the EU Sin-
gle Market (97%) and other markets (78%) but decrease
their presence in CEFTA markets (from 90 to 82%).
HGXs increase mean distance to the active export
markets across all deciles except for the largest decile
(Table 12). In 2013, 50% of HGXs have mean dis-
tance of 573km; while in 2016, 50% of HGXs had
a mean distance over 716km. In 2013, about 70%
of HGXs export to markets on average more than
850km distant from Croatia, in 2016, 70% of HGXs
Table 10 Share of largest
export market: 2013 and
2016
Obs 10% 20% 30% 40% 50% 60% 70% 80% 90%
Share of largest market in total exports 2013
HGfs 325 0.5 0.64 0.77 0.92 1 1 1 1 1
HGXs 180 0.43 0.51 0.61 0.74 0.81 0.93 0.99 1 1
Non-HGF exporters 5791 0.49 0.61 0.75 0.89 0.99 1 1 1 1
Export superstars 94 0.23 0.29 0.33 0.4 0.53 0.6 0.69 0.88 0.98
Share of largest market in total exports 2016
HGfs 325 0.42 0.52 0.64 0.71 0.85 0.97 1 1 1
HGXs 180 0.34 0.41 0.47 0.57 0.65 0.74 0.84 0.92 1
Non-HGF exporters 5791 0.42 0.54 0.65 0.77 0.89 0.98 1 1 1
Export superstars 94 0.25 0.28 0.33 0.42 0.48 0.58 0.69 0.8 0.98
Table 11 Activity at export markets
2013 2016
Obs EU CEFTA Other markets Obs EU CEFTA Other markets
HGfs 325 0.57 0.65 0.23 207 0.37 0.83 0.29
HGXs 180 0.78 0.61 0.39 180 0.90 0.68 0.51
Non-HGF exporters 5791 0.60 0.65 0.26 3704 0.41 0.78 0.30
Export superstars 94 0.98 0.90 0.79 92 0.97 0.82 0.78
Table 12 Mean distance of
firms to their active export
markets in 2013 and 2016
Obs 10% 20% 30% 40% 50% 60% 70% 80% 90%
Mean firm distance in km to export markets 2013
HGfs 325 226 226 331 394 485 610 853 1149 2040
HGXs 180 226 367 444 518 573 703 865 1532 2988
Non-HGF exporters 5791 226 226 342 394 499 588 774 1086 2373
Export superstars 94 502 657 791 1071 1456 1731 2100 2792 3491
Mean firm distance in km to export markets 2016
HGfs 325 226 226 310 414 458 546 692 989 2449
HGXs 180 354 447 559 648 716 875 1197 1798 2765
Non-HGF exporters 5791 226 230 351 407 460 551 692 1073 2578
Export superstars 94 507 621 830 1174 1670 2044 2658 3008 3744
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HGX: theanatomy ofhigh growth exporters
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have their mean distance to export market higher
than 1150 km. Non-HGF exporters increase their
mean distance in the majority of deciles, although
some deciles (70th and 80th percentile) experience a
decrease in mean distance. Export superstars increase
their distance to export markets across all deciles,
while exporters increase their distance in the mid and
upper deciles.
The main finding is that HGXs are further away
from their export markets than HGfs and non-HGF
exporters, but not as much as export superstars.
6 Discussion
Previous literature on the economic contribution of
exporters drew attention to large export superstars
(Freund & Pierola, 2015, 2020). This study provides
evidence for focusing also on the newly-defined
category of high growth exporters (HGXs). While
export superstars have achieved a large size, HGXs
demonstrate growth. Awareness of HGXs may result
in growth policies that (1) contrast the stability of
export superstars’ workforce with HGX’s need for
new employees, (2) ponder export superstars’ require-
ments in terms of the education level of their employ-
ees compared to the skill requirements of HGXs, and
(3) consider the different relationships that export
superstars and HGXs have with foreign markets. An
exclusive focus on export superstars seems inap-
propriate, and an analysis considering both, export
superstars and HGXs, enriches knowledge with
respect to effective policy. While export superstars
are important because of their current large size, in
Croatia, they are declining in terms of total exports
(Table1), number of exported products (Table4), and
overall job creation (Table 2). Forward-looking and
proactive policy should focus on HGXs, which may
be the export superstars of tomorrow.
We began with a sectoral analysis of exporting
activity. HGXs are present in various sectors, but are
more prevalent in manufacturing, wholesale and retail
trade, and ICT. Tradeable sectors such as manufactur-
ing are associated with R&D investment and produc-
tivity growth (Coad & Vezzani, 2019), highlighting
the necessary policy interest in HGXs. Interestingly,
there are no export superstars in the ICT sector in
2013, perhaps because the ICT sector is relatively
young, and insufficient time has elapsed to allow
promising ICT firms to grow into the export superstar
category. Since there are no ICT firms in the export
superstar category, we could look for leading ICT
firms in the HGXs category, which (given that HGXs
could become export superstars in the following peri-
ods) serve as promising candidates for tomorrow’s
export superstars. Indeed, a first export superstar in
ICT appears in 2016 (Table OA1) and finally an addi-
tional (second) export superstar in ICT in 2019.
6.1 HGXs and employment
We investigate the sources and characteristics of new
HGXs employees. Do HGXs employees differ from
HGfs, non-HGF exporters, and export superstars with
respect to the new hires? During their growth epi-
sode, HGXs hire more employees from technology-
intensive industries and employees with previous
experience in exporting. HGXs are also observed to
be more likely to hire on the basis of a single year
work contract. In addition, HGXs are more likely to
send new employees to work abroad. This is in line
with the literature claiming the necessity of export-
ers to facilitate the connections to export markets
(Srhoj etal., 2023). HGXs are particularly sensitive
to information flows across countries and prefer to
have employees abroad as a way of organizing their
international operations. Therefore, HGXs can benefit
from flexible labor markets and contracts that allow
workers to move between firms, contributing with
their knowledge to enable growth in export markets
of firms and thus HGXs.
6.2 HGXs and their product offerings
HGXs substantially increase export value over the
3-year period. While HGXs grow in exports, their
growth is not driven only by the top export product,
because the share of the top export product in total
exports substantially decreases (at the median from
76 to 62%). Instead, a robust finding is that HGXs
increase in number of export products. Further-
more, growth in export products is not driven exclu-
sively by new export products with incremental
changes, but competitive products with higher unit
prices that are sold in sophisticated export markets
(such as the EU Single Market). In fact, a striking
result is that the category that appears to benefit the
most from new business opportunities (i.e., from
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S.Srhoj et al.
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the EU Single Market) by introducing new competi-
tive products are HGXs.
HGXs’ growth involves diversification in involv-
ing export products with high unit prices. In addition,
our findings suggest that HGXs are not the stereotypi-
cal cost-cutting entrants, but are able to competitively
sell high-cost products in developed export markets
such as the EU. Therefore, HGXs are not just expand-
ing the same products in new markets, but expanding
the number of markets and also the number of prod-
ucts at the same time.
The ability of HGXs to introduce sophisticated
new export goods is reminiscent of Braguinsky etal.
(2021) who observe that it is often the same firms
that push forward their technological frontier with
new products that also simultaneously push forward
their sales growth in known technologies and familiar
products. Thinking about our results regarding num-
ber of export products, we could make the distinction
between incremental innovation and GPT (General
Purpose Technology) innovation. The latter is prob-
ably associated with a new technology that leads to a
swelling of product offerings in many different direc-
tions at the same time, whereas the former is prob-
ably more limited in terms of growth directions. In
this case, the export growth of HGXs resembles the
phenomenon of growing like yeast, whereby yeast
makes bread grow evenly in many directions at the
same time.
HGXs defy the trend observed for other groups
of firms (e.g., HGfs, export superstars) that tend to
prefer growth in closer export markets. HGXs are
increasingly active in highly-developed EU markets
rather than potentially less-developed export mar-
kets on poorer continents further afield. In contrast to
HGXs, HGfs do not achieve growth in the large, rich,
and developed EU market but in other markets. This
suggests that the growth of HGfs may be less inter-
esting than the growth of HGXs, in the eyes of poli-
cymakers. A similar pattern is observed for non-HGF
exporters. Export superstars, for their part, are more
globally competitive (presumably due to their previ-
ously-accumulated competitive advantage), although
they are less dynamic than they used to be.
6.3 Policy interest in HGX
The impressive export performance of HGXs justifies
policy interest in this category of firms. Note that the
rapid growth of exports by HGXs is not merely a tau-
tology, because (i) the HGX category refers to export
growth, not export size, and (ii) if an export superstar
also satisfies the HGX definition, it is categorized as
an export superstar in our analysis.
Our results show the importance of HGXs and pro-
vide new knowledge on their micro-level characteris-
tics. A first step in designing policy to support HGXs,
even without having yet causal knowledge, is aware-
ness and recognition of this category of firms, their
relative frequencies, their growth patterns in terms of
new products and markets, and their overall economic
importance. By providing a first look into the HGX
category, we intend to spur on policy discussions.
A second step in designing policy to support HGXs
relates to empirical evidence on the determinants and
causes of HGXs performance. An important question
is what policy-makers can do in the short-term and
long-term to increase the probability of non-HGX
firms becoming HGXs.
In the short run (1–2 years), a key question will
be the ability to predict HGXs, and the ability of
policy makers to nudge HGfs and non-HGF export-
ers (and perhaps even non-HGF domestic traders)
to become HGXs, as well as supporting HGXs on
the way to becoming export superstars. A monitor-
ing system for policymakers could be established
to better tailor a large set of existing short-term
export boosting policies and initiatives to encour-
age exporting activity (for a review, see Srhoj etal.,
2023). These policy initiatives to stimulate export-
ing activity include public grants, tax credits, subsi-
dized export loans, export credit guarantees, public
institutions offering partner search, matchmaking,
intelligence, analysis and organizing participation
on trade fairs, or providing vouchers for outgo-
ing economic missions, fairs, and external coun-
seling. There is also evidence for positive effects of
demand-driven instruments, such as foreign market
access programs (Atkin etal., 2017) or public pro-
curement for innovation (Stojčić etal., 2020).
Medium-term (3–5 years) policy instruments
could include R&D grants and changes to incentives
via tax reform (Dimos etal., 2022). Large and smaller
EU or US funded grants for innovation or technology
upgrading have been shown to have a positive effect
for those firms that apply (for a review: Dvouletý
et al. 2021; for empirical studies: Santoleri et al.,
2022; Howell, 2017).
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HGX: theanatomy ofhigh growth exporters
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In the long-term (5 + years), a standard innovation
toolkit (Bloom etal., 2019) could assist in stimulat-
ing more HGXs. Firstly, countries could increase the
supply of skilled labor, for example, by an immigra-
tion policy that targets highly skilled individuals and
by improving the education system so that it devel-
ops the next generation of researchers and innovators.
Secondly, since HGXs hire more on 1-year contracts,
changes to labor markets in order to enable more flex-
ible work contracts might be beneficial, although of
course, more research is needed on this topic. Finally,
developing the venture capital market could benefit
the emergence and scaling-up of HGXs.
In short, policy recommendations for support-
ing HGX refer to addressing the liabilities of rapid
growth. This contrasts with policy recommendations
for Born Globals which often focus on addressing
the liabilities of newness, e.g., addressing the low
survival rates of newborns, and addressing their lack
of reputation and/or experience, and/or lack of capa-
bilities through training and knowledge development
(Knight & Cavusgil, 2005, p. 32; Acedo & Jones,
2007, p. 248). Policy support for Born Globals may
not be effective for achieving the intended policy tar-
gets, because many Born Globals have a disappoint-
ing performance in terms of growth of employment
and sales (Ferguson etal., 2021).
7 Conclusion
Policymakers interested in job creation and eco-
nomic development have shown a keen interest in
high growth firms (HGFs; Grover etal., 2019; Bene-
detti Fasil et al., 2021) as well as export boosting
(e.g., involving export superstars, Freund & Pierola,
2015, 2020; Srhoj etal., 2023). This paper presents
evidence on a novel category of firms operating
roughly at the intersection of these two groups, i.e.,
high growth exporters (HGXs), defined as a subgroup
of revenue-based high growth firms with a substan-
tial share of their growth (over 50%) coming from
exports. We present a detailed analysis of HGXs, pro-
viding newinsights at the level of region, sector, firm,
firm-employee, firm-product, and firm-market. These
dynamic and export-active firms have an impressive
performance in a variety of areas, including entry into
high unit-price product markets, entry into sophisti-
cated export markets (such as the EU Single Market),
and avoiding over-reliance on single products by
engaging in growth through broad-based diversifica-
tion. A limitation of our analysis is its focus on a sin-
gle country, which is why we call for further research
on the topic of HGX.
Acknowledgements The authors thank Bruno Ćorić, Dario
Diodato, Clemens Domnick, Melko Dragojević, Nicola Gras-
sano, Emanuele Pugliese, Francesco Rentocchini, and Alex
Tuebke, as well as seminar participants at the European Com-
mission’s Joint Research Centre and two anonymous review-
ers, for many helpful comments and suggestions. The authors
thank the Croatian Bureau of Statistics for data access and
support. This work was supported in part by the Research
Platform Empirical and Experimental Economics of the Uni-
versity of Innsbruck and by the Croatian Science Foundation
under the project IP-CORONA-2020-12-1064. Alex Coad
gratefully acknowledges financial support from the Basic Sci-
ence Research Program through the National Research Foun-
dation of Korea (NRF) funded by the Ministry of Education
(2021R1A6A1A14045741), and from the Japan Society for the
Promotion of Science, Grant-in-Aid for Scientific Research (A:
B1K401072101; and B: 21H00719).
Open Access This article is licensed under a Creative Com-
mons 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 Crea-
tive Commons 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
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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://creativecommons.org/licenses/by/4.0/.
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