JOURNAL OF GLOBAL TRADE, ETHICS AND LAW
Volume 2 Issue 2, 2024
THE ROLE OF GEOGRAPHIC AND SECTORAL
DIVERSIFICATION, AND THE HERFINDAHL-
HIRSCHMAN INDEX THEORY: INSIGHTS FROM
ITALIAN PROVINCES AND REGIONS
Luigi Capoani, 1 Piergiorgio Martini 2
1 University of Trieste, Trieste, Italy. luigi.capoani@units.it.
2 European Youth Think Tank, Strasbourg, France. piergio.martini@gmail.com.
Abstract. This study examines the role of the Herfindahl-Hirschman Index (HHI)
and a new diversification index that integrates geographical and sectoral factors to
analyze economic activities in Italian provinces. The research highlights how local
territorial heterogeneity influences urban and regional economics, emphasizing
diversification as a strategy for growth. The findings underscore diversification’s
importance in enhancing economic stability and resilience, while comparing it with
specialization. Although it can boost competitive advantage through efficiency and
innovation, specialization also increases vulnerability to sector-specific risks. The
study calculates the HHI by using three categories: geographical diversification of
export destinations, sectoral exports, and geographical import diversification. These
insights provide a groundbreaking understanding of the economic dynamics within
Italian provinces and regions, showcasing the advantages and trade-offs between
diversification and specialization in regional economic development.
Keywords: Regional Economies, HHI index, Imports, Exports, Geographic Diversification.
JEL: R11, L25, F14, O18
© 2024 Durham & Thunmann, London
Journal of Global Trade, Ethics and Law
ISSN 2977-0025 (Online). Published under Creative Common (CC) BY 4.0 license.
https://doi.org/10.70150/wq5d0b20
Luigi Capoani & Piergiorgio Martini, 2024
74
1. Introduction
On the topic of research diversification, scholars frequently debate whether better
results in terms of future research advancement are accomplished through diversification
or specialization, as explored by Abramo et al. (2019). The analyzed dualism is a
longstanding economic issue that has intrigued scholars for centuries (Wang and Nie
2019). This debate lies at the core of strategic decision-making, with increasing interest
in the choice between concentrating resources in a specific sector to achieve the
economies of scale necessary for firm survival and growth or diversifying economic
activities across multiple fields to mitigate corporate risk and enhance enterprise success.
(Wang and Nie 2019). This tension reflects the ongoing struggle between the pursuit of
excellence in a particular activity and the requirement of mitigating risks associated with
overreliance on a single sector, a debate that societies have been dealing with since the
most ancient times—finding solutions that have led to the most diverse outcomes in terms
of both economic and social structures—, with early forms of division of labor being
attested, for instance, in ancient Greece, with the population being employed,
alternatively, in agriculture, crafts, and trade. In more recent times, this debate has taken
on a renewed importance, with industrial and post-industrial economies being marked by
growing global interconnection and rapid technological advancements.
While Kaulich (2012) notes that diversification of product and exports can indeed make
a country less prone to negative economic shocks, it is unquestionable that specialization
is a key driver of efficiency and innovation, particularly in densely populated regions
where competitive advantages may be maximized.
Additionally, a region’s dominance in particular industries, especially if characterized
by a concentration of enterprises in related fields, fosters innovation, which involves the
exchange of knowledge between actors and institutions and, as such, it can be thought of
as a collective learning process that occurs in a network of interconnected participants
(Fritsch and Slavtchev 2010). Specializing can create competitiveness because it propels
continuous product amelioration while also enabling the invention of new products and
tapping into unfamiliar market niches. Moreover, it enhances responsiveness to the ever-
changing customer needs in the business community. Consequently, firms are set to
compete effectively and expand their knowledge across markets. Concentrating all their
resources on the development of a specific sector allows firms to successfully employ an
economically targeted and specialized strategy, thus preventing resource dispersion over
different industries, which in turn promotes advanced technology that is resourceful,
refined and sophisticated. Such firms can also maintain a better hold of competitive edge
over their rivals by sticking to the most beneficial areas therein. On the other hand, when
entering related or unrelated sectors, firms should continuously and steadily grow in their
main activities to keep pace with markets’ demand (Wang and Nie 2019). Considering
all these factors, it is also important to recognize the role of trade theories in explaining
patterns of specialization. For this reason, it is crucial to examine the relationship between
agglomeration and industry concentration, alongside the role of comparative advantage
described by political economist David Ricardo in 1817 (Ricci 1999). As posed by
agglomeration effects, firms operating in close proximity benefit from shared resources,
Luigi Capoani & Piergiorgio Martini, 2024
75
knowledge exchange, and increased productivity; at the same time, Ricardo’s model on
comparative advantage specifically elucidates how nations gain from trade by
specializing in industries on which they hold a competitive edge, thereby enhancing
returns, fostering product differentiation, and stimulating monopolistic competition,
while also affecting trade costs (Ricci 1999). Such synergistic effects between
agglomeration and comparative advantage illuminate the dynamics of specialization and
its impact on global trade patterns. In accordance with Ricardo’s ([1817] 2004) and
Heckscher and Ohlin’s (1933) early trade theories, the concept of comparative advantage
was determined by disparities in productivity or the relative availability of production
factors. Nevertheless, in the Ricardian model, the size of a country does influence the
benefits from trade, with smaller countries often gaining more in relation to their size
because their relative prices in autarchy tend to be further from the new world relative
price established by trade. In the Heckscher-Ohlin framework, the size of a country does
not directly impact comparative advantage: this is because countries can have different
sizes but similar relative factor endowments, which determine the pattern of
specialization (Ricci 1999). Indeed, goods are produced at reduced transportation costs,
encouraging a focal point (Dornbusch, Fischer and Samuelson 1977). Simultaneously,
the presence of demand externalities encourages greater producer collaboration, which
could result in industrial concentration.
Conversely, neo-protectionism and mercantilism can, indeed, encourage more
diversification than economic and industrial specialization and free trade, even though
these two aspects are not directly oriented towards economic diversification as a primary
objective. Neo-protectionism entails state intervention in international trade relations to
preserve economic influence and address social issues (Panchenko and Reznikova 2017),
whereas the mercantilist doctrine advocates for a nationalist economic stance that
strategically enhances exports while minimizing imports in order to maximize the nation's
resource base and unilateral trade benefits. This economic strategy seeks to prevent
account deficits by building up monetary reserves through favorable trade balances,
primarily by exporting finished goods. Historically, mercantilist policies pursued a
“favorable” trade balance, primarily to attract precious metals such as gold and silver,
thereby enriching the national treasury. Furthermore, these policies were designed to
bolster domestic employment by protecting home industries from foreign competition, as
Smith extensively discussed in his analysis of economic behavior that influences national
prosperity (Smith [1776] 1904). Together, neo-protectionism and mercantilism usually
promote policies that shield domestic industries from foreign competitors or unfair trade
practices, supporting those companies’ growth through tax incentives, subsidies, or other
supportive measures (Panchenko and Reznikova 2017). This approach can boost
economic diversification by investing in new sectors or by promoting existing industries
(McCusker and Morgan 2001), and by mitigating dependency on vulnerable sectors or
exposure to foreign competition. Historical mercantilism favored resource accumulation
and market dominance through monopoly policies and aggressive trade practices, with
diversification being a strategy to fully leverage the resources gathered and to
subsequently secure a competitive advantage across different sectors. On this note,
several modern economic theories focusing on monopolistic competition (Helpman and
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Krugman 1985; Melitz 2003) delve on the notion of intra-industry trade, i.e. the exchange
of similar goods between countries within the same industry. Another key perspective is
offered by Adam Smith’s theory of absolute advantage as discussed in The Wealth of
Nations (1776), where specialization is seen as essential for economic efficiency, with
individuals having a tendency to focus naturally on their strongest skills thereby
maximizing their earnings and ultimately contributing to economic growth and societal
well-being.
Thus, individuals become specialized because they can locate a gap in their skill set
which allows them to barter with one another in relation to specific aspects. They,
therefore, improve on their tasks through the division of labor, by increasing innovation
and enhancing the overall economic performance of the entire society. Yet, studies have
consistently shown a strong correlation between income levels and export basket
diversification, though the evidence is not always conclusive (Bahar 2016). As previously
stated, it is worth noting that as countries increase their economic diversity and wealth,
they become increasingly interconnected in the global economy. This enhanced
integration enables them to deliberately refocus on certain items while leveraging their
comparative advantages through international trade. Within the framework of general
equilibrium, global integration has the potential to accelerate knowledge dispersion and
increase production through diversification rather than concentration (Bahar et al. 2014).
Delving further into this issue, Keynes (1933), based on his direct experience with severe
economic shocks (most notably the Great Depression in 1929), rejects productive
specialization—fearing that it would lead to trade imbalances and the emergence of new
economic shocks—in favor of national diversity, which, he argued, would enhance
countries’ resilience.
This paper is structured in two main parts: the first section (parr. 2, 3, and 4) begins
with a brief introduction to the concept of resilience, followed by a comprehensive review
of the existing literature on diversification as an economic development strategy.
Additionally, through reference works, it explores export diversification and the
application of the Hirschman-Herfindahl Index (HHI). The second part (par. 5) of the
paper specifically focuses on Italy, presenting a case study that examines the application
and impacts of diversification in the Italian regions and provinces. This bifocal approach
not only enriches the theoretical understanding but also aims to offer an applied and
concrete perspective through the analysis of a well-defined national context. This paper
applies the Herfindahl-Hirschman Index (HHI) as a foundational measure of economic
concentration. Building on this, three distinct diversification indices are developed to
assess geographic diversification of export destination, sectoral diversification of export,
and geographic diversification of import origin. To achieve this, dynamic data was
gathered within Italian regions and provinces (Barbieri et al. 2024). However, it is
important to acknowledge the limitation of this approach, particularly regarding the
relatively small geographic areas represented by regions and provinces, which might lead
to less precise estimates due to sample size constraints (Ghosh and Rao 1994).
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2. Complexity and the Vital Role of Diversification in Today’s
Landscape
Recently, the world had to face some urgent problems: markets have been overturned
since the pandemic and its aftermath, while Russia’s invasion of Ukraine also had a great
impact on them. In contexts of this kind, resilience is an aspect that proved itself to be
beneficial: but what exactly is resilience? The term refers to a component of a region’s
economic potential that can shield its economy from negative external shocks or minimize
the magnitude and scope of the consequences of said shocks (Bogdański 2021).
Reflecting on Harry Markowitz’s seminal “Portfolio Selection” theory (1952), which
posits that diversification reduces risk, its principles extend beyond financial markets to
regional economics. In this context, sectoral diversification plays a crucial role in
reducing disparities in regional gross value-added growth rates. Conroy (1974; 1975)
adapted this theory to demonstrate how diversification within regions could reduce
employment instability during times of economic stress. By diversifying their industrial
base, regions can reduce the impact of sector-specific downturns, leading to more stable
employment levels. Building on these foundations, this study develops a comprehensive
industrial diversification estimate. Its focus on absolute growth contributions, rather than
sector-specific shares, helps better understand how diversification can serve as a buffer
against the type of economic disruptions recently experienced. This method helps to
measure the real impact each sector has on economic growth, thus determining which
sectors are most resilient and those that are more prone to economic shocks. Analyzing
this point further, Walker et al. (2004) define economic resilience as a system’s ability to
absorb disturbances and reorganize during change while maintaining essentially the same
purpose and identity. It denotes a system’s capacity to sustain a positive functioning
condition, rather than deteriorating into a less functional state when confronted with
external threats or challenges. Resilience is acclaimed as pertinent to every current global
crisis, whether political, biological, or educational in character (Jackson 2020). At its
core resilience relies on its adaptive capacity, which refers to the ability of economic
systems to adjust and evolve in response to external pressures while maintaining stability
(Simmie and Martin 2010). This perspective emphasizes that regions and industries with
greater adaptability—whether through diversified supply chains, institutional flexibility,
or knowledge diffusion—are better equipped to withstand economic shocks. However,
resilience is not the only aspect that has been reconsidered. In recent years, there has also
been a renewed interest in diversification, especially among academics and broker
research (Banz 2001), with scholars often debating whether diversification should focus
on specific regions or sectors. In the midst of these debates, both academic and practical
perspectives have increasingly aligned with economic complexity theories, a shift that is
largely due to the growing recognition that traditional economic models—with their
emphasis on specialization and efficiency—may no longer be sufficient to navigate the
complexities marking contemporary global economy. More particularly, economic
complexity theories offer a deeper understanding of the role of varied and interconnected
economic activities in contributing to an economy’s growth, stability, and resilience in
the face of economic disruptions.
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The Wealth of Nations, mentioned previously, underscores the importance of a myriad
of interrelated economic activities in creating stronger communities that can withstand
shocks resulting from any kind of economic disruptions. Populations with high
concentrations of different kinds of productive activities redistribute resources efficiently
and adapt to production changes easily: during these times, there are few reasons why an
economy should solely rely on internal transformation. This is how this wider generality
should result in the spread of wisdom and competence, thereby enhancing innovation in
economies, as well as their adaptability to new challenges. When one sector falters, it is
this interconnectedness that allows for other sectors to intervene. Again, new industries
and innovations can only be created by mixing different kinds of abilities: this gives
support to the growth and stability achieved in the long run. Essentially, an economy that
has diversified adequately is likely less vulnerable to disturbances of global origin
compared to non-diversified ones based on old economic thoughts that emphasize
efficiency through division of labor alone without incorporating critical aspects of today’s
turbulent market cycles. This perspective has revived interest in strategic diversification,
where the synergy between related and unrelated diversification activities is seen as
crucial for fostering economic resilience and adaptive capacity in response to global
economic shifts (Banz 2001). The role of diversification has been reconsidered in relation
to the major events that have happened in recent times. Locations, sectors, and enterprises
have all been affected differently, thus leading investors to wonder where to seek refuge.
In this case, the basic answer is diversification (Kellands 2023), that, in turn, can be linked
to the concept of resilience, as it improves the transfer and dissemination of external
shocks in many directions (sectors and branches) of the economy, resulting in a speedier
economic recovery and adaptation to changing conditions (Nazarczuk et al. 2020).
In a rapidly globalized economy filled with political and financial vulnerabilities, a
region’s ability to promote financial development while enduring external shocks is
critical. Areas with a high concentration of linked sectors are more vulnerable to outside
shocks due to the risk-spreading effect. Similarly, firms with highly concentrated
activities are also at risk (Joya and Rougier 2019; Prasanna et al. 2011). Moreover,
diversification can generate new areas of expertise through the combination of diverse
skills (Saviotti et al. 2020).
Resilience also refers to an economic ecosystem’s capacity to adjust and evolve. At the
core of this evolution lies the idea of geographic and sectoral diversification. This notion
embodies adaptability, akin to viewing economic events as “evolutionary processes”
(adaptive economic changes), where markets act as selection mechanisms, much like the
environment does in the biological realm. This perspective finds its roots in the works of
Penrose (1959), Winter (1935), and Nelson and Winter (1982), who emphasized the
importance of trial and error, survival of the fittest, and the replication of effective
techniques in business evolution theory. This stands in contrast to the neoclassical
concept of firms as consciously optimizing production functions, marginal costs, demand
functions, and revenues. Furthermore, diversification is most likely to produce economies
of scale in research and development (R&D) and it implies that a company with a diverse
product offering has numerous options to capitalize on the findings of a research program
(Jovanovic and Gilbert 1993). This is because the paths in which research will create
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outcomes are highly unpredictable. As a result, the broader the scope of activity, the more
likely it is that a technological discovery or development will fit into the firm’s existing
product structure (Jovanovic and Gilbert 1993). On a different note, evolutionary
perspectives (which have recently gained quite a lot of attention; Nelson 2020) embody
a valuable tool for interpreting the mechanisms of economic adaptation and survival in
the everchanging contemporary economic landscape. This evolutionary approach, deeply
rooted in the principles articulated by Hayek (1988), emphasizes how firms and
economies, much like organisms in nature, evolve through learning and adaptation. More
particularly, Universal Darwinism, an extension of these ideas, suggests that Darwinian
principles of variation, selection, and retention, far from being limited to biological
entities, are equally applicable to economic systems at both macro and micro levels
(Hodgson 2002).
Evolutionary frameworks help explain not only economic balances and imbalances but
also highlight the intrinsic capacity of firms to adjust to new environmental pressures by
diversifying their activities and strategies in order to spread risks and increase resilience,
thus mimicking biological diversity which enhances survival prospects in the face of
external shocks. Diversification strategies are informed, then, by the understanding that
complex economic ecosystems—much like their biological counterparts—are better
equipped to handle changes and disruptions if they comprise several interconnected
elements rather than a single, unitary entity.
Hayek ([1976] 1982; 1988) and Campbell’s (1974; 1987) framework for cultural
evolution extends Darwin’s theory to the inheritance of civilization, encompassing
institutions, customs, practices, tools, and methods of performing activities. It also
involves the selective accumulation of skills, technology, behavioral patterns, beliefs,
organizational structure, and economic systems. Cultural evolution can be seen as a
method of acquiring knowledge through iterative experimentation and learning from the
tools, rules, belief systems, and cultural achievements of previous generations (Krstić
2012). However, challenges such as monopolies and oligopolies can modify the
competitive environment, while moral hazard and tax evasion can skew the selection
process, allowing inefficient enterprises to remain in operation (Rogers 1972).
Diversification is closely linked to complexity, which may be affected, as explored by
Ben Saad et al. (2023), by spatial heterogeneity and discontinuity. If the profound
economic transformations faced my most global economy have created opportunities for
both developing and developed countries alike, only few actually manage to adapt to these
new conditions, resulting in significant differences in growth rates among countries that
have been largely discussed in theories of economic growth. Economic progress
necessarily involves the adaptation of production processes, with enhanced productive
capacities, greater product diversity, and increased export competitiveness culminating in
the trade of more complex products whose added value generates advantage to the
exporting country (Ben Saad et al. 2023). Product composition reflects, in turn, the
productive knowledge of countries (Hausman and Hidalgo 2013), which may as well
exhibit levels of productive complexity comparable to those of regional spaces beyond
national borders, thus giving credit to the hypothesis of interdependencies existing at
transnational level (Ben Saad et al. 2023).
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Complex systems theory in economics has become well-known research tool,
employed in economic geography, international development, and innovation (Hidalgo
2021). Furthermore, complex systems in economics provide a framework for
understanding social issues like growth, development, technological change, income
inequality, spatial disparities, and resilience as visible results of hidden systemic
interactions. Geographical heterogeneity (spatial discontinuities) and temporal
heterogeneity (historical discontinuities) in the processes of complexification of regional
productive systems can either spur regional economic convergence, reinforced by
strengthened regional cooperation, or instigate a regional dynamic of competition and
economic predatory behavior (Ben Saad et al. 2023). These methods draw their scientific
foundation from multiple studies conducted in different states and cities on a wide variety
of economic activities.
Economic diversification has traditionally been employed to promote positive
economic expansion and development. In the context of climate change adaptation, its
importance grows as a strategy to move away from susceptible products, markets, and
jobs and towards revenue sources that are low-emission and climate resilient (United
Nations Framework Convention on Climate Change [UNFCCC] n.d.). This process, by
which locations engage in new economic activities, is thought to be a combination of
related and unrelated diversification, with the first occurring especially in low-complexity
contexts. The study by Pinheiro et al. (2022) indeed concludes that the less complex an
economy is, the less diversification options it has to shift to unrelated activities, thereby
being more tied to relatedness. Related diversification is the most common between the
two. Countries (Hidalgo et al. 2007; Pinheiro et al. 2022), regions, and cities (Boschma
and Capone 2015; Pinheiro et al. 2022) have been shown to be more inclined to engage
in activities that are related to those currently existing in them. However, while the
principle of relatedness has been successful in establishing economic path dependency in
countries and regions, there is still much to learn about scenarios in which economies
engage in unrelated activities (Boschma 2016; Pinheiro et al. 2022). It is important to
consider that unrelated diversification is difficult to achieve because becoming
competitive in new activities necessitates the acquisition of new capabilities, ranging
from human capital to institutions, which can be demanding to amass all at once
(Hausmann and Hidalgo 2011). Thus, unconnected diversification is not only uncommon,
but also a risky development strategy. Even though related diversification is more
frequent than unrelated diversification, the latter is gaining significant attention as a
crucial strategy for economic renewal and long-term development. Studies like those by
Saviotti and Frenken (2008) have highlighted how unrelated diversification can prevent
economic lock-in by diversifying investment into new and innovative unrelated sectors,
thus fostering new avenues for growth and adaptation. This aspect of diversification
strategy underscores a key shift in economic thinking: the need to continuously evolve
and innovate, avoiding over-dependence on traditional industries that might no longer
guarantee sustainable growth. Using Penrose’s (1959) resource-based perspective,
unrelated diversification can be described by an organization’s “three pillars”: firstly,
dynamic capacities; secondly, absorptive capacity; and thirdly, weak ties. The three
pillars’ job is to uncover new resource applications or uses in market failure scenarios
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known as ‘incomplete’ markets. This model allows for greater diversification than
Penrose’s (1959) and other resource-based models (Teece et al. 1980). Furthermore,
unconnected diversification can be advantageous, as demonstrated by Desmond W. Ng
(2007), who, in his study, elaborates on how such diversification strategies, albeit
complex, can yield significant competitive advantages by leveraging the discussed pillars
in diverse market environments. As a matter of fact, Frenken et al. introduced the concept
of “affiliated variety” (Frenken et al. 2007), which promotes job creation and indigenous
growth by fostering knowledge exchanges within a sector. This concept can be used to
test the belief that industrial clusters can benefit from a wider range of products and
services, as greater diversity increases inter-industry knowledge spillover. However, a
consensus on the importance of diversity for economic development has not been reached
yet. Most research supports the thesis of variety leading to employment growth, but some
suggest it is more pronounced in knowledge-intensive industries. They argued that
knowledge from one industry is more valuable and can be utilized more effectively by
another related industry.
Nevertheless, Boschma and Capone (2015), criticized unrelated diversification,
arguing that it ignores key components that influence industry relatedness, such as
institutions, infrastructure, and the combination of production factors. Moreover, they
argued that institutions inform on the direction of diversification, thus determining
whether countries gain a competitive advantage from new sectors either close or far from
their present industrial framework. Pinheiro et al. (2022) discovered that nations with
intermediate levels of economic growth and greater levels of human capital enter more
unrelated products. Their research found that countries with greater diversification levels
in unrelated businesses, enjoy a small but significant increase in economic growth
compared to their counterparts with comparable levels of income, human capital, capital
stock per worker and economic complexity. Indeed, the pursuit of diversification into
unrelated products should result in higher export product-sophistication and increased
value-added (Hausmann et al. 2006). This elevation in complexity not only boosts
productivity, but also enhances the overall export value, thereby reinforcing the
interconnectedness between diversification, complexity, and economic growth.
Delving deeper into this matter, Songwe (2019) suggests that disparities in export
success and product sophistication propose the possibility of further diversification,
vertical integration, and globally competitive regional value chains. The likelihood of
countries surviving in global value chains is intricately linked to commodity and trade
specialization. Countries that supply advanced commodities are more likely to maintain
their position in the network, while those with lower manufacturing requirements face a
lower risk of failure in trade relationships (Lall et al. 2006). Research on international
production fragmentation emphasizes the importance of coordination across value chain
trading partners (Arndt and Kierzkowski 2001). The complexity of knowledge is
determined by regional economic actors’ capabilities and organizations. A broader and
diverse knowledge base leads to more complex and integrated understanding sets,
forming long-term regional advantages. For example, European regions diversify their
creative efforts by growing into complex technical industries, which are more appealing
but require more management (Becker and Murphy 1992).
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Another important topic analyzed in this paper is Italy’s case study. Sbardella and
Pietronero’s (2021) research analyses regional differences in Italy and discovers that the
complexity of the production system is intricately related to regional performance in this
country. These findings confirm the existence of the North-South economic divide, as
seen by the different levels of complexity of each region’s production system, which has
a direct impact on the depth and breadth of the skill level underlying local economies.
Sbardella and Pugliese (2018) study the impact of economic fitness (that is, a measure
of productivity found using advanced network techniques) on economic development.
They use the Economic Fitness and Complexity (EFC) algorithm to predict the suitability
of a country c (Fc) and the quality (complexity) of a product p (Qp), by non-linearly
coupling fitness and complexity. Furthermore, Sbardella et al. (2017) used the Fitness and
GDP per capita to examine the complicated link between a country’s growth and
industrialization and economic inequality. They discovered that the movement of pay
disparity with industrialization follows a longitudinally consistent trend.
3. The Role of Export and Geographic Diversification in
Economic Development
Export diversification has once again become a key focus thanks to its role as a vital
driver of economic growth, particularly in regard with boosting productivity, harnessing
economies of scale, reducing income volatility, and, more generally, generating positive
spillover effects across various industries.
Industries oriented towards export can undergo dynamic learning processes by
interacting with foreign markets and facing global competition (Newfarmer et al. 2009).
Export diversification is commonly considered a factor of economic growth, as it leads
to the improvement of productivity, the use of economies of scale and the reduction of
fluctuation. Indeed, expanding horizontally into new export sectors may yield beneficial
spillover effects for the rest of the economy. Similarly, vertical diversification from
primary to manufactured exports has been associated with economic growth, as primary
export industries typically lack significant spillover effects (Herzer and Nowak-
Lehnmann 2006). In theory, this should guarantee a greater per capita income growth. In
this regard, technological developments and infrastructure expenditures over the past 20
years have made it possible for numerous developing states, even those with the least
resources, to diversify their exports. States with low incomes and heavy reliance on
commodities have long attempted to improve their export diversifications as means of
regaining their lost growth momentum and simultaneously reducing income insecurity.
Economic development refers to a systemic change in a country’s production from “poor-
country products” to “rich-country products”. This transition typically calls for the
creation of flexible export demand in international markets, which allows for countries to
reel in the benefits of those markets without preoccupation for their adverse trade terms.
Sectoral diversification enhances industry growth via innovation, while geographic
diversification stabilizes economies by spreading activities across markets (Jiraporn et al.
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83
2008). It is critical to investigate the valuation implications of both industrial and
geographic diversification concurrently (Jiraporn et al. 2008), considering that a company
might diversify across different product markets, geographic borders, or both (Berger et
al. 1995). Generally, industrial diversification can be considered as a value-reducing
approach. In this context, agency theory and inefficient resource allocation are the
primary explanations for the diversification discount. On the other hand, empirical study
on spatial diversity has yielded inconclusive outcomes. To further explore this topic, a
significant amount of literature in the business and trade domain focuses on examining
the relationship between the geographic and product diversification-performance (Boehe
and Jiménez 2016; Borda et al. 2017; Geringer et al. 2000; Hennart 2007; Hitt et al. 2006;
Li et al. 2012; Qian et al. 2013). The conclusion was that, although exports and
multinational foreign investments share certain similarities, their distinct characteristics
limit the applicability of findings from one context to the other (Solano et al. 2019).
Furthermore, it is important to understand the relationship between sectoral and
geographic diversification. In line with Gerard et al. (2002), who observed that
geographic diversification prevails over industry diversification under short sale
restrictions, it was then highlighted the significance of country diversification compared
to industry diversification at the national level (Ehling and Ramos 2005). Recent studies
(Solano et al. 2019; Qian et al. 2013; Qian et al. 2010) examined firms’ diversification
strategies within a single geographic region versus those spanning multiple geographic
regions. It was soon discovered that intra-regional diversification allows firms to
penetrate similar markets with lower marginal costs (Johanson and Vahlne 1977; 2009),
whereas inter-regional diversification may pose challenges in terms of organization and
management (Fernández Olmos and Díez-Vial 2015; Thomas 2006).
Siegel et al. (1995) pursued two main objectives. First, they sought to resolve the
inconsistencies and gaps in the definitions and measures of economic diversity and
diversification. In this context, diversity refers to the variety within an economic system,
while diversification describes the process of achieving this variety, typically through
changes in sectors and markets. Second, they proposed a framework that clearly
highlights the relationship between a region's evolving economic structure and its
performance. This was carried out through the exploration of the terms of ‘diversity’ and
‘diversification’, used in similar contexts but different in meaning. To explain this point
further, economic diversification can be defined as a systematic process of change that
transfers resources from primary (natural) to secondary (manufacturing) and tertiary
(service) sectors; leading to increased diversity. This transformative movement is
grounded on the assumption that there is an evident unevenness in the structure of the
economy, which supports the development of some industries while obstructing that
of others. In this respect, certain connections between economic variety and stability have
been researched by means of interregional comparisons. Scholars developed a range of
scalar indicators of regional economic diversity to investigate these theories: instead of
utilizing regional economic integration (REI, i.e. a regional trade agreement that aims to
lower or eliminate tariffs and non-tariff obstacles to the free flow of products, services,
and factors of production between countries), researchers have examined the link between
diversity and growth by analyzing employment or income growth (Wagner and Deller
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1993; 1998). Most economic diversity definitions and metrics, however, lack a solid
conceptual basis: for this reason, industrial organization theory uses concentration
indicators to measure market dominance, and diversity is frequently linked to a varied
industry. Although the connection between stability and diversity are yet to be properly
formalized, researchers have long been testing the assumption that a diversified economic
activity in each region contributes to stabilizing the economic performance of said
region.
1
As described by Kluge (2018), “Neither portfolio theory nor input-output analysis has
provided fully convincing diversification measures due to methodological problems and
issues of data availability.” Therefore, the author contends that these methods have not
been widely adopted by experts and that in more recent works (such as Brown 2012;
Chandra 2003; Essletzbichler 2007; Ezcurra 2010) researchers seem to be rediscovering
the Herfindahl Index, which can be used to measure diversity in a single area economy.
In doing so, it is possible to show that a smaller index value entails less employment
concentration and, thus, more diversity. On the contrary, a higher index value implies
greater concentration, meaning that employment is unevenly distributed among different
sectors. Finally, the aforementioned indicator was also used by Ezcurra (2010) to measure
the level of industrial diversity in different regions of the European Union. At the end of
his research, he concluded that the higher the index value, the less diverse a region’s
sectors are. In this respect, sectoral diversification can be expected to increase a region’s
overall financial stability, thereby working as an insurance.
2
In this framework, a heated debate commonly referred to as MAR vs. Jacobs arose.
This was the clash between so-called MAR theories, that is, the theories of Marshall,
Arrow, and Romer, and Jacobs’s theory. The former argued that the concentration of an
industry in an area promotes knowledge spillovers between firms and facilities innovation
in that specific industry within that region. This specialization promotes the transmission
and sharing of knowledge, ideas, and information of, whether tacit or codified, as well as
goods and processes, through imitation, business interactions, and skilled worker
1
Wagner and Deller collaborated on several works, notably A Measure of Economic Variety: An Input-
Output Approach (1993), which explores economic diversity as a means of achieving stability. While
previous studies have proposed links between diversity, stability, and economic activity, empirical evidence
remains limited. This study argues that disparities in findings arise from differing conceptualizations and
measurements of economic diversity. By integrating multiple metrics that characterize the (I - A) matrix in
regional input-output models relative to a base economy, a more comprehensive measure of diversity can
be achieved.
2
The method’s implicit premise is that the chosen base economy is diversified and serves as a reasonable
reference economy. The paper explores numerous techniques to quantifying regional economic diversity,
proposes a new way of thinking and analysing economic diversity, and empirically evaluates its diversity
index for the United States and its 50 states. Measuring the Effects of Economic Diversity on Growth and
Stability (1998) investigates the role of economic diversity in ensuring regional stability and prosperity.
Conventional definitions of diversity are frequently restricted and fail to take into consideration inter-
industrial linkages. An alternate technique to conceptualize and compute variety for the 50 states is
described and computed, demonstrating that variety within the theoretical construct of input-output is
associated to higher degrees of stability and growth.
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circulation between firms, without monetary transactions (Beaudry and Schiffauerova
2009; Martin and Ottaviano 1999). On the other hand, Jacobs claimed that the most
important sources of knowledge spillovers are outside the industry in which the firm
operates. This is caused by the fact that, since cities have the greatest diversity of various
information sources, they are also source of creativity. Jacobs’ thesis highlights that a
geographic region’s diverse industries produce knowledge externalities, which lead to
inventive activity and economic success. The proximity to a different industrial fabric
promotes possibilities to replicate, share, and recombine ideas and techniques from many
industries, a concept also explored by Beaudry and Schiffauerova (2009). In the end,
according to De Groot et al. (2016), the numerous empirical investigations on MAR and
Jacobs that have been conducted since the initial study by Glaeser et al. (1992) have
reached dissimilar conclusions, with an equal number of studies supporting or rejecting
MAR and Jacobs’ theories.
4. The HHI and Its Application to Export, Import, and Sectoral
Export
As noted by Ben Saad et al. (2023), export diversification may be evaluated through a
variety of concentration indices, such as the Gini coefficient and the Shannon entropy.
However, the most commonly used of such methods is certainly the Hirschman-
Herfindahl Index (HHI), which plays a crucial role in assessing market competitiveness,
particularly in the context of mergers and acquisitions (M&A) transactions (Roberts
2014). Initially proposed by economist Albert O. Hirschman in his book National Power
and the Structure of Foreign Trade (1945) illustrating market concentration, the HHI
was later refined by Orris C. Herfindahl in his 1950 doctoral dissertation at Columbia
University, (Concentration in the U.S. Steel Industry), where he analyzed market
concentration. By comparing a company’s size to that of a specific industry, the HHI is
primarily used to evaluate the former’s market competitiveness. However, it should be
noted that, through the same index, it is also possible to assess the extent of industrial
diversification (Wu et al. 2022).
Some scholars have questioned the index’s ability to effectively explain the role of
market share inequalities in the banking system (Alegria and Schaeck 2008; Hannan
1997). Unlike traditional indexes, the HHI offers a more comprehensive assessment of
concentration levels by considering all firms and assigning greater weight to larger
enterprises. This is because it provides a more complete picture of the industry’s true
status, considering all firms. It also allows for a greater weighting of major enterprises’
influence on the sector against smaller firms. However, in the lack of prior knowledge of
all firms’ real market shares, the difficulty of its calculation makes it an ineffective, if not
misleading, estimation tool (Carluccio et al.).
Within this context, Albert Hirschman’s work of 1945 regarding the dynamics national
power in international trades introduced the reciprocal of the HHI, which assesses both
geographic and sectoral diversification, providing an easily interpretable indicator of
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economic activities. Hirschman’s approach diverged from previous concentration
measures by assigning higher weighting to larger enterprises, reflective of their
substantial market share. Market conditions are determined by variables such as quantity,
size, entry, and service differentiation. On the other hand, firm behavior, product quality,
production, profit, and distribution efficiency all influence competitiveness.
Concentration ratios and HHI indices serve as industry benchmarks, highlighting the
dominance of major firms. Understanding both opportunities and risks is paramount in
competitiveness analysis, with mergers often intensifying concentration while
diminishing competition.
To assess the extent of diversity and systemic risk in the construction business, it is
important to measure market concentration, if market share is concentrated it means that
one firm possesses most of it. High market concentration results in larger shares for
certain organizations than others in a similar line of business. However, when there is low
concentration, each company selling similar products faces an equally competitive
environment within the entire industry. In this case, the HHI can be used as an indicator
of market concentration since the index can be calculated as a positive number (the HHI
typically ranges from 0 to 10,000, or from 0 to 1 in decimal notation). The HHI quantifies
market concentration by squaring the market share of each firm within an industry and
summing these squared values. HHI is used to examine the possibility of two rival firms
to combine. However, to identify how the market impacts merger performance, a specific
method is adopted. First, competition authorities obtain the information by developing a
description of the researched market and analyzing its competitive circumstances. HHI
values below 1000 often indicate a competitive market, while those exceeding 2000
suggest critical competition dynamics (Bardellone 2022). Nevertheless, establishing a
precise correspondence between the numerical HHI value and the qualitative indication
of market concentration remains elusive. Antitrust authorities, such as the U.S.
Department of Justice, utilize the HHI to evaluate potential merges and their impact on
market competition. Then, market shares and concentration measurements are used to
assess a company’s market power or the intensity of its competitive constraints. Finally,
Cournot’s quantitative competition technique is used to create a model based on the idea
of market concentration and HHI where enterprises manufacture identical items at fixed
marginal prices. This concept connects average margins to HHI, which quantify demand
price elasticity. When one company controls a substantial portion of a market, other
smaller companies earn a sizable HHI payment, making market power impractical.
Stigler’s study shows that homogenous product marketplaces correspond with HHI, while
Cournot’s approach shows that price increases relate to index disparities. When the
market is extremely concentrated, agreements that are inversely related to the stability of
coordinated behavior can detect and discourage deceitful enterprises. Wealth is
anticipated to fall with concentration.
Furthermore, the Herfindahl-Hirschman Index is used to quantify the concentration of
the construction industry. The identification of regions is done to balance the need to
concentrate facilities and organize them appropriately with the evolving service
requirements in the production system. They allow various parties to use facilities
“continuously” in the case of operator aggregation and concentration based on multi-
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customer and multi-product operating models. The number of elements in a series,
namely the concentration of which is being measured, is thus an important consideration.
This is particularly true when concentration denotes “control by a few”, as in the case of
market phenomena.
Hirschman stated that “One of the well-known conditions of perfect competition is that
no individual seller should command a significant share of the total market supply; this
condition entails the presence of both relative equality of distribution and of large
numbers”. Hirschman (1945) asserts that in a situation of perfect competition, the
industrial concentration index should decrease as the number of firms and their equality
in the distribution of market share increases. As a result, in an economy where one sector
completely dominates, the concentration index attains a value of unity and decreases with
increased diversification. In the article written by Toby Roberts (2014), the limitations of
the HHI Index are analyzed. The focus is on the application to mergers within network
industries.
(1)
where n is the number of firms, is the output (or sales, profits, etc.) of the j firm, and
Q is the industry’s total output. He demonstrated that his index could be expressed
equivalently as
(2)
where v represents the coefficient of variation, equal to σ/ μ, or the standard deviation
of the series divided by its arithmetic mean. By breaking down the index into two
components, (one dependent on the number of firms, n, and the other dependent on the
relative inequality of market shares, v), Hirschman fulfilled his self-imposed criteria. The
index grows smaller as n increases and larger as v increases.
After 5 years, Orris Herfindahl came up with a similar approach by suggesting an index
to illustrate the disparities in the Lorenz curve’s depiction of industry concentration:
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(3)
This index shares similarities with Hirschman’s Index, differing only in scale and sign
of the square root. Like Hirschman, Herfindahl argued that the correlation between the
number of enterprises and the coefficient of variation might be used to establish his index.
However, he outperformed Hirschman in terms of his understanding of the index’s legal
applications. Herfindahl’s Index offers several benefits, but the most important one is that
it gives large businesses more weight. Other advantages include the fact that it requires
little publicly available data for calculation making the calculating process
straightforward. The latter, although it is very easy to calculate, fails to account for market
complexity and does not produce an accurate evaluation of monopolistic and competitive
market conditions. Its disadvantages, thus, result from the inability to identify the market
in question.
Let us consider a scenario where the HHI is used to assess an industry with ten active
firms, each with a roughly 10% market share. Using the fundamental HHI calculation, it
appears that the market is highly competitive. However, one company may control up to
80%–90% of the market for a single market sector, such as the sale of a specific item,
granting it a near-total monopoly over the production and sale of that commodity. Another
disadvantage is the influence of regional factors, which arises when businesses within a
particular industry operate only in specific regions of the nation while holding a roughly
equal market share. As a result, each company has a monopoly on the market where it
operates. For these very reasons, for HHI to be used correctly, additional factors must be
considered, and markets must be precisely defined (Woerheide and Persson 1993).
(4)
Where:
DI = Diversification Index
HI = Herfindahl Index
The percentage of the portfolio’s market value that is (in decimal notation) allocated to
security I, where N is the total number of securities in the portfolio and the proportion
of portfolio market value invested in security i.
The main purpose of this index is to change the index value so that 1.0 represents
maximum diversification and 0 represents a portfolio with only one security, that is, no
diversification. The index values of nine sample portfolios are shown in Table 1A, to
help the reader better comprehend the various indexes. The first portfolio, called Portfolio
A, only includes one security, and lacks diversification as a result. On the other hand,
Portfolio I, the eighth one, has 100 securities that are equally allocated. In our first index,
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the portfolio with a single asset has an index value of 0, whereas the one with 100 stocks
has an index value of 0.99.
Table 1: Values of different diversification indexes for various portfolios and composition of the
portfolios used in Table 1A (Woerheide and Persson 1993).
The HHI for Coeweb data must be adjusted to reflect evolving export destinations
across provinces, with the goal of creating a diversification index for both export
destinations and sectors akin to the HHI. By capturing shifts in export patterns, this index
provides insight into the degree of diversification across regions.
A criticism of the HHI argues about a structural measure applied uniformly across
industries, it misses the industry-specific nuances relating structure to market power and
thus wholly fails to provide regulators with useful information. For example, HHI
provides no information about barriers to entry, economies of scale or scope, rapidly
changing technology, or firm-specific characteristics, all of which may bear on the degree
of competition in the industry. Given that the HHI quantifies concentration, interpreting
it inversely offers great insight: higher HHI values means less diversified export
geography with provinces’ rankings remaining unchanged. These territories characterized
by ports, complicated economies, and strong industrial bases are clearly the most
diversified.
Regions vary not just in terms of productive diversification but also in their economic
structure. Different perspectives, such as internal regional processes and dimensions
requiring further research, pose challenges for conventional financial analysis techniques.
Additionally, we can evaluate how each region deviates from the average across other
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product categories, such as industrial groups or Pavitt categorization. This more in-depth
study allows us to track the diverse effects of the pandemic on manufacturing sectors. A
check was introduced to control for the abbreviation of regions. Since several regions
have merged over time, we assigned two residual values, 98 and 99, to a group of different
provinces. The HHI serves a useful role in measuring changes in industrial concentration.
In many industries it provides a reasonable initial indication, prior to an efficiencies
calculation, of harm to consumers from potential consolidation.
5. Diversification Types: Geographic (Origin and Destination) and
Sectoral. A Focus on The Italian Provinces
Geographic diversification is a valuable and effective strategy to pursue during
economic downturns. Studies confirm the importance of understanding the overall impact
of product or geographic diversification on corporate performance by investigating their
interplay, a relationship that, as suggested by research in this area (Hautz et al. 2014;
Kuppuswamy and Villalonga 2016; Rajan et al. 2000) may be influenced by the business
environment itself.
Graph 1: Overview of the percentages in terms of export diversification for every macro-area of the
globe.
Italy is divided into twenty first-level administrative divisions called “regions,” which
may be informally grouped into three main areas, namely the North (comprising Liguria,
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Lombardy, Piedmont, Aosta Valley, Veneto, Friuli-Venezia Giulia, Emilia-Romagna,
and Trentino-Alto-Adige), the Center (with Tuscany, Lazio, Marche, and Umbria), and,
finally, the South along with the Islands (Abruzzo, Basilicata, Calabria, Campania,
Molise, Apulia, Sicily, and Sardinia). The economic situation is marked by a significant
gap between the northern and southern regions, with disparities in growth, GDP,
unemployment, and capital formation (Daniele and Malanima 2011; Ciaschini et al. 2022)
further worsen by the 2008 financial crisis (Franco 2010).
Differences exist not only in the economic field, but also in other domains, such as in
the healthcare system. However, this is also due to regional economic inequality
(Lucchini et al. 2009). Nonetheless, the dualism between Northern and Southern Italian
territories has been an object of concern for decades, as the disparities between them are
significant and seemingly intractable, despite extensive efforts to find a solution. Before
Italian unification, inequalities between regions were far less pronounced and did not
involve the North-South divide, with roughly equal average incomes. This began to
change by the end of the nineteenth century, when the Northern areas, such as the famous
industrial triangle (Milan, Turin, and Genoa), embarked on the so-called
“industrialization process”, with industrial progress that can be thus considered as the
primary cause of today’s inequities between North and South (Daniele and Malanima
2011).
In our endeavor to compare diverse specialized provinces, we have devised three
diversification indices for the year 2019 utilizing Coeweb (Barbieri et al. 2024).
Diversification indices for each specific province (or region) can be calculated in terms
of sector, export destination, and import origin through the reciprocal of the Herfindahl-
Hirschman Index (HHI) , which provides a more intuitive measure of diversification, with
higher values indicating greater diversification, and lower values suggesting higher
concentration:
(5)
In the formula above, S is either the share of a province’s exports or imports to a certain
country in the world, or the share of a province’s exports in a sector compared to other
sectors. The HHI index has a maximum value of 10,000 (calculated as 100 × 100).
For instance, if the province of Rome exclusively exports in a single sector, we will
have:
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(5a)
However, the index obtained is a concentration index. In order to get the diversification
index it is necessary to take the reciprocal:
(6)
Thus, in the aforementioned example, the diversification index for the province of
Rome is:
(6a)
It is possible to observe that by taking the reciprocal, the diversification index must
range from 0.0001 to an arbitrarily large number. The formulas above referred to the
regional diversification index for export by sector. However, the same formula can be
applied for import by origin:
(5b)
and for export by destination:
(5c)
In both cases, the diversification index is calculated in the same way as in the case of
export by sector.
Since economics is not a static science, but rather one that constantly evolves due to
changing trade agreements and other socio-political factors, it is crucial to examine
connections from another perspective, namely as a system of relations among numerous
countries, referred to as “linkages”. Hirschman (1945) describes linkages as “unique
constellations of experiences and consequences of direct and indirect effects’’: an
investment in a particular market indeed leads to changes that affect the economy in a
succession of ripple effect events in different sectors. Interdisciplinarity, alongside a
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multidisciplinary perspective, is crucial in economic approaches: the first attempt to
combine the two was carried out precisely by Hirschman in his book National Power and
the Structure of Foreign Trade (1945).
6. Statistical Analysis
Shifting the focus on Italian provinces can help study diversifications and market
structures. Compatible development can be accomplished at both sectoral and territorial
levels through coordinated efforts within a logistical system. This coordination can
enhance the region’s competitiveness, attract new investments, and spark major
endogenous and self-propulsion growth processes (Gereffi et al. 2005). On a similar note,
dialogue should be encouraged between entities, stakeholders, and the public to identify
specific problems and articulate possible solutions.
Graph. 2: The diversification of five Italian provinces (Treviso, Ferrara, Milano, Vicenza, Venezia)
compared using the HHI index.
Invariance encompasses study of transportation and logistics, which create and attract
traffic flows, as well as the benefits and drawbacks of the latter. The input cost differential
between alternative localization solutions can be used to deduce the localization
indifference or “Muth condition” for productive activities (Muth 1969). Companies can
plan production and distribution processes over long distances using global networks of
industrial relations, transportation, and logistics infrastructure, as well as international-
scale facilities and information systems. The process of analyzing and assessing
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alternative routes and logistical solutions that substitute all-road traffic with
multimodal/intermodal transportation is referred to as “transversality.”
Specialization and variety in urban production structure are crucial for regional and
urban economies, contributing to technological externalities into localization and
urbanization effects (Isard [1965] 1972). The concentration of printing activity within a
specific region, for instance, improves information spillovers and encourages innovation.
This form of externality has been identified with Marshall (1890), Arrow (1962), and
Romer (1986) (1, according to Glaeser et al. 1992). As noted by Jacobs (1969), the highest
sources of knowledge spillovers are often located outside the innovating firm’s industry
(Beaudry and Schiffauerova 2009). The empirical literature, particularly influenced by
Glaeser’s work, has focused on measuring specialization and diversity in urban life using
positive indicators whose variability is determined by the robustness and accuracy of
quantitative analyses. On this note, Salvati and Zitti (2009) argues for assessing factors
such as the geographic environment of economic activities as well as the size of the
enterprises located in the corresponding area. In Italy, regional authorities serve as levels
of territorial subdivision, while enjoying—as public entities acknowledged by the
Constitution (Art. 114)—legal standing and broad autonomy. Among the primary
structural constraints in Italy, the 34th Italy Report by independent research institute
Eurispes—a reputable source of regional analysis—claims that social and economic
fragmentations, despite being among the most visible issues, are also the least addressed.
Further inconsistencies include 41% of Italy’s population residing in economically
depressed areas. Notably, regional disparities in GDP per-capita persist, with Italy’s
population distribution reflecting substantial economic imbalances since 1973.
The Coeweb NACE codes are classification codes used to categorize economic
activities in the EU, widely applied in trade, businesses, and statistics to standardize data
across countries. Coeweb refers to the Eurostat trade database, which includes data on
imports and exports and helps analyze trade flows. These codes allow policymakers to
assess which industries contribute the most to exports and imports, track changes in
industrial activity, and guide decisions for diversification or investment.
The data available on Coeweb focuses on customs-based information, leading to
greater import and export values in bordering Italian regions. Coeweb tracks domestic
trade flows at the province level, with province 98 reflecting residual values related to
electricity and natural gas, and province 99 indicating changes over time in some
provinces. This information provides insight into provincial trade activities across Italy.
Additionally, the TEC platform presents a comprehensive view of Italian trade,
encompassing all manufacturing enterprises in importing and exporting goods. A zero
value in the platform indicates manufacturing activities with no trade involvement. In
conclusion, despite the pandemic’s challenges, Italian export sectors have remained
resilient. However, it is essential to approach the data with an open mind and
acknowledge the limitations of customs-based analysis to gain a deeper understanding of
the nuances within trade activities across Italy.