Conference Agenda

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."