A stylized fact in modern economies is the more developed a country is, the greater the weight of the service sector. In this sense, the study of economic complexity through the standar measure of complexity index presents an increasingly relevant omission to understand the economic process and its growth. This paper proposes a new methodology to retrieve information on economic complexity in services. For this purpose, the US input-output matrix is used. This work is novel because, thanks to the structure of the data as a network, it is possible to infer the missing information of complexity of services at a level of disaggregation that is strikingly higher than in other works. Using the k-NN method is possible to learn 146 services sectors complexity index. The index recuperated by this method are consistent with previous works and this index is highly correlated with the GDP of States and US economy.

We extend the opinion formation model to study the global influence of economic organizations, particularly through currency preferences in international trade. Using data from the United Nations Comtrade database, we construct the world trade network for the years 2010–2020. The model simulates the competition between currencies, focusing on two core groups: five Anglo-Saxon countries that predominantly trade in US dollars (USD) and the 11 BRICS+ nations that prefer a hypothetical currency, BRI, pegged to their collective economies. Countries' currency preferences are determined using a Monte Carlo process, influenced by their direct trade transactions. Our results indicate that, starting in 2014, the majority of countries would have preferred to trade in BRI rather than USD. The Monte Carlo simulations converge into three distinct groups: one favoring USD, another favoring BRI, and a third group that swings between the two currencies based on initial conditions. We further analyze a scenario with three currencies—USD, BRI, and EUR—where the EUR currency is pegged by the core group of nine EU countries. We show that the countries preferring EUR are mainly the swing countries obtained in the frame of the two currencies model. Additionally, we investigate the competition between USD, the Chinese yuan (CNY), and OPE, a currency pegged to major OPEC+ economies, to assess their economic influence. We also describe the reduced Google matrix of the trade relationships between the Anglo-Saxon countries and the BRICS+, offering insight into the shifting balance of power in international trade currencies.

This paper maps Ecuadorian firm-to-firm domestic and international trade linkages from 2016 to 2023. This novel longitudinal dataset is constructed from granular transaction data compiled following the introduction of electronic billing in Ecuador in 2014. Based on this data, we contribute along several dimensions to the literature on firm productivity, networks and trade. First, we explore whether trade patterns and the structure of the production network obtained in the small, developing, and open economy context of Ecuador match the stylized facts documented in the literature. The firm-to-firm trade network induces upstream and downstream network spillovers, presenting a major challenge for estimating firm-level productivity that is not accounted for in standard approaches. Our second contribution is to use state-of-the-art techniques to estimate firm-level productivity, where we account for dynamics, sectoral clustering, and firm-level observables. To do so, we employ a control-function approach that exploits properties of the bilateral network along with finite mixture methods that allow us to control for unobserved firm heterogeneity. Third, we compare our estimates to standard approaches to quantify the magnitude of the network effect. Finally, with these productivity estimates in hand, we study whether existing tax policies target any specific segment of firms along the productivity distribution, and to what extent the policymakers' observed targeting behavior aligns with competing theories. The recovered productivity distribution can also inform optimal targeting of firms for the design of industrial policy instruments, for example, through leveraging tax rebates or direct subsidies.

This paper presents a data-driven review of research on global production networks (GPNs) using data science methodologies. A dataset of 1,431 journal articles published since 1984 was collected from OpenAlex (formerly Microsoft Academic Graph). Through bibliometric analysis, natural language processing, and network analysis, this study examines the evolution of research trends, key contributions, and patterns of scholarly collaboration in the field. The findings highlight the increasing integration of network science in economic research, revealing methodological shifts and interdisciplinary engagement. Additionally, the paper identifies underexplored network tools that offer new opportunities for advancing GPN research.

This study visualizes topological structural changes in international finance by analyzing interactions between trade and portfolio investment networks. Using Master Stability Function (MSF) analysis with intra- and inter-layer matrices, we quantify each country’s impact on financial markets. Results indicate that market stability depends on network interdependence, with key countries acting as hubs. This research lays the groundwork for a new field: the "architecture of international finance."

Recent years have seen several calls to take time “seriously” in organization and management studies. This also applies to the field of interorganizational relationships (IORs) and networks (IONs), where attention needs to be devoted to network change, co-evolution of network and actor attributes, and relational events (i.e., sequences of discrete actions between actors, such as economic transactions). While relational (i.e., network) effects such as repetition and reciprocation shape these events, the speeds at which they unfold remain largely underexplored. Addressing this research gap, we argue that relational effect speeds hold substantive meaning. Studying them can provide insights into IOR functioning and dynamics. To better understand the variance in relational effect speeds, we introduce a computational algorithm rooted in relational event modeling methodology that accounts for effect censoring. Applying this algorithm to compute the speeds of relational effects guiding emission allowance exchanges in the EU Emission Trading System (ETS), we find that their variance is non-random and systematically differs within and between pairs of transacting firms. Exploring the possible sources of observed variability using survival analysis, we find that country, industry, ownership similarity, and the number of prior transactions among organizations play a determining role in the speed of transaction repetition, transaction reciprocation, and transactions with partners of partners. With this, we advance the currently limited understanding of relational effects speeds, specifically their antecedents, paving the way for future empirical research while further enriching social network theory with a dimension.

The global transition towards sustainable energy has significantly impacted the European trade landscape for battery and automotive products. This study analyzes official European trade data (Comext) from 2020 to 2024, focusing on the evolution of supply relationships in response to the ecological transition.

Regarding automotive, the findings indicate a significant drop around early 2020, likely due to the impact of the COVID-19 pandemic. This is followed by a strong recovery and fluctuating but generally stable volumes between 2021 and 2023. In late 2024, a sharp increase indicates a surge in trade activity. Meanwhile, the battery market shows an overall steady growth, with cyclic behavior including a strong dip in early 2020 and 2023, followed by a recovery phase in 2024.

These trends suggest a dynamic restructuring of supply chains, likely driven by the accelerated adoption of electric vehicles and related technological advancements. The observed fluctuations may indicate market stabilization or shifts in trade policies and regional production strategies. Using network analysis to assess the centrality and the strategic significance of nations within the trade network, this study provides deeper insights into the evolving structure of European trade. It highlights the ongoing transformation of trade patterns in response to the global ecological transition and underscores the need for continuous monitoring of these supply relationships.

The literature on product space and economic complexity suggests that countries are more likely to begin exporting new products closely related to those already in their export basket. Complex products, in turn, foster economic development by paving the way for new capabilities and a broader range of exports. However, for developing countries, the products closest to their existing export baskets tend to be less complex. Recent research indicates that certain countries have managed to transform their export baskets in ways that reverse this negative correlation between nearness and complexity. In this study, I investigate how these countries overturned the negative correlation, enabling complex products—initially located far from their export baskets—to move closer. To do this, I decompose the positive shift in the correlation between nearness and complexity into three components: complex products moving closer to the export basket, nearby products becoming more complex, and products both moving closer and becoming more complex. My findings show that in past cases of successful export transformation, the dominant factor was the movement of complex products closer to the export basket. I examine how countries such as Ireland and South Korea brought complex products closer to their export baskets. By tracing the network links among products, I identify the connections that enabled these countries to export increasingly complex products.

A key focus of international trade literature is understanding the formation of trade agreements. One major challenge lies in capturing the fact that country pairs form treaties based on bilateral characteristics and in response to the broader web of agreements. This interdependence between agreements violates the assumption of independent observations. Empirical studies have adopted proxies and modelling techniques to mitigate multicollinearity and address endogenous processes, identifying key determinants across economic, institutional, geographical, and political dimensions. Foundational papers have explored political economy theories, such as the domino effect—where signing agreements may lead to new ones—and path dependence, emphasising how past agreements shape future decisions. While these studies provide valuable insights, they fall short of capturing the complexity of today's interconnected trade landscape. They focus primarily on predicting dyadic agreement formation. Recent efforts have employed stochastic actor-oriented models to study trade agreements, demonstrating the importance of network effects. However, these models fall short of incorporating all dimensions previously identified as determinants in tie-based models.

In this paper, I build on insights from trade literature by employing a Dynamic Actor Network Model (DyNAM) to study trade agreement formation. This allows me to test how political economy theories hold under models that fully account for relational dependencies and endogenous processes. I analyse over 600 treaties notified to the World Trade Organisation spanning 1948-2023, complementing this with country- and dyad-level data from well-established trade databases. Finally, I assess the model's predictive power by evaluating whether its calibration aligns with ongoing trade agreement negotiations.

Current models of status competitions in online, networks-supported markets (aka influencer economies) are meritocratic: they emphasize the superior skills of influencers as preceding network initiation. As a consequence of displaying better skills, influencers receive requests for connections and form networks of followers. Alternative to these models, we highlight the role of network initiation as an informal mechanism of competition control and in attaining influencer positions. Using a dataset with over 51,000 ties from an influencer economy, we explore how participants control competition for status by initiating link requests. Participants initiate ties and seek competitions following a change in performance. They have a higher likelihood of accepting ties after improved performance. Status competitors are more likely to accept ties from senders who perform worse and rank lower in their networks. The outcomes of this initiation dynamic are multiple networks in which members maintain ties with those performing worse than them. Members performing less well repeatedly seek new ties and are more likely to receive link requests from others. We argue that status competitions lead to multiple networks (instead of a unique one), within which influencers would dominate, performance-wise, a group of followers. Theoretically, we draw attention to initiation processes as informal mechanisms of competition control. Empirically, we highlight the dynamics of status competitions in social media-supported economies.

It is acknowledged that great powers inevitably aspire to dominate different global topics. Topics like Artificial Intelligence (AI) have consolidated new perspectives on national and foreign policy strategies. The race for AI supremacy began several years ago. However, as AI advancements continue to transform industries, organizations, and strategies, there is a tendency to overlook the geopolitical implications of this ecosystem and its supply chains.

Some aspects of the AI ecosystem, such as data, algorithms, hardware, raw materials, and energy, are already defining several issues on the national security and diplomatic agendas of countries. These AI components define the implications of its development, usage, and diffusion.

A supply chain is a network that transforms inputs into outputs. This network conceptualization implies a relational approach where nodes and links interact, and several dimensions could be included. There is a growing interest in supply chains because price hikes, shortages of several goods, and tariff imposition affect economic growth and how businesses conduct operations. These phenomena, however, are a consequence of geopolitics. On the contrary, the supply chain of AI (the network that transforms minerals, data, microchips, energy, and work for developing and deploying AI) is a cause of global competition. However, there are no studies about the global implications of the AI supply chain to date.

So, in the age of AI, questions arise: What are the strategic supply chains? What does it mean for lagging countries and their organizations? To address these questions, we aim to analyze whether the race for AI supremacy is shaping two aspects: first, an AI gap among countries and their organizations, and second, an AI ecosystem, especially, hardware, energy, knowledge, and data centers. By empirically demonstrating the existence of the AI gap and its increase over time, this paper sheds new light on the implications of the AI ecosystem for different countries and their organizations. As a result, we provide evidence for the AI capabilities gap. It implies that only a few countries possess systemic AI capabilities, while many others will need to leverage them. Thus, AI methods are at the center of the AI ecosystem, serving as a tool for the advancement of organizations but also as a tool for power.

In this paper, we analyze four networks from different domains: the materials layer, the semiconductors layer, the energy layer, and the AI knowledge layer. However, the results obtained here also hold for a wide spectrum of layers that we can integrate into an AI ecosystem or another technological ecosystem that works with a supply chain pipeline. We first describe their network structure, namely, ordered graphs with the same vertices and similar degree definitions. Thus, each layer $L$ has the same number of nodes, $N$, as all countries are represented in each layer.