Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2604.09187

The Geoeconomics of Venture Capital An Economic Complexity Approach to Emerging Technological Sovereignty

Benjamin Leroy·Davi Marim·El Ghali Benjelloun·Arthur Rozan Debeaurain·Jean-Michel Dalle
View original source ↗Read source document ↗Export metadata ↓

Abstract

We explore a quantitative approach to emerging technological sovereignty and geoeconomic power by assessing the relative positioning of countries with economic complexity methods applied to the structure of national venture-capital (VC) portfolios and their associated Revealed Venture Advantage (RVA) metrics. Using Crunchbase firm- and deal-level data, we map venture-backed startups to 18 emerging technology domains via a probabilistic multi-label large-language-model classifier, and construct an RVA-based country-technology specialization matrix for the 17 countries with the highest aggregate VC funding. From this matrix, we derive two eigenvector-based measures: a Geoeconomic Complexity Index (GCI) that ranks countries by the composition of their venture specializations, and an Emerging Technology Geoeconomic Complexity Index (ETGCI) that ranks domains by the extent to which specialization is concentrated among high-GCI countries. Empirically, Cloud Computing, Cybersecurity Tools, and Medtech exhibit the highest ETGCI values, reflecting concentration of specialization in a small set of leading countries. The United States and Israel consistently occupy a marked "high-diversity/low-ubiquity" position and lead the GCI ranking, followed by China, France, Japan, and Germany; both country and domain rankings are stable from 2021-2024. Finally, relatedness-based simulations identify, when it exists, for each country the Simplest Single Sovereignty Enhancing Technology (SSSET), i.e., the most feasible single new technological direction associated with the largest expected improvement in relative geoeconomic positioning.

Explore related subjects

econ.GNphysics.soc-phq-fin.ECq-fin.GN

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Benjamin Leroy, Davi Marim, El Ghali Benjelloun, Arthur Rozan Debeaurain, Jean-Michel Dalle. 2026-04-10. The Geoeconomics of Venture Capital An Economic Complexity Approach to Emerging Technological Sovereignty. https://arxiv.org/abs/2604.09187

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Critical Mathematical Economics and Progressive Computer Science

The aim of this article is to present elements and discuss the potential of a research program at the intersection between mathematics and heterodox economics, which we call Criticial Mathematical Economics (CME). We propose to focus on the mathematical and model-theoretic foundations of controversies in economic policy, and aim at providing an entrance to the literature as an invitation to mathematicians that are potentially interested in such a project. From our point of view, mathematics has been partly misused in mainstream economics to justify `unregulated markets'. We identify two key parts of CME, which leads to a natural structure of this article: The first part focusses on an analysis and critique of mathematical models used in mainstream economics, like e.g. the Dynamic Stochastic General Equilibrium (DSGE) in Macroeconomics and the so-called ``Sonnenschein-Mantel-Debreu''-Theorems. The aim of the second part is to improve and extend heterodox models using ingredients from modern mathematics and computer science, a method with strong relation to Complexity Economics. We exemplify this idea by describing how methods from Non-Linear Dynamics have been used in Post-Keynesian Macroeconomics', and also discuss (Pseudo-) Goodwin cycles and possible Micro- and Mesofoundations. Finally, we outline in which areas a collaboration between mathematicians and heterodox economists could be most promising, and discuss both existing projects in such a direction as well as areas where new models for policy advice are most needed. In an outlook, we discuss the role of (ecological) data, AI and the need for what we call Progressive Computer Science.

econ.GN↗
arXiv2026

Don't Fake It If You Can't Make It: Driver Misconduct in Last-Mile Delivery

In the last two decades, last-mile delivery (LMD) firms have seen immense growth fueled by the success of e-commerce, leading to faster and cheaper deliveries. Operating on thin margins, LMD firms strive for successful first-time deliveries to avoid the financial and reputational costs of reattempts. Delivery Agents (DAs) are integral to LMD efficiency, influencing customer experience, delivery success, and productivity. However, most LMD performance enhancement research focuses on process, technology, and incentives, which presume workers will conform to procedures and monitoring tools will function flawlessly. Nevertheless, in practice, DAs deviate from expected behaviors, i.e., indulge in misconduct, negatively affecting delivery efficiency, often resulting in returned parcels. One of the major misconducts is fake remarked deliveries, wherein DAs intentionally do not deliver the parcels and provide a fake reason for it. For instance, even without reaching a delivery address, a DA remarks 'customer unavailable' and records a delivery failure. In this study, we collaborated with a leading Indian LMD firm and, using instrumental variable regression, find that such misconduct leads to a spillover productivity loss. This effect reduces the next day's successful deliveries by 1.60% and first-time-right deliveries by 1.86%. We discuss misconduct's correlation with factors such as task complexity and offer novel insights into how opportunistic circumstances can influence worker behavior.

econ.GN↗
arXiv2026

Decomposing Wage Stagnation: Employment Reallocation, Wage Structure,and Demographics

Japan's average log real hourly wages rose until the mid-1990s, declined through the mid-2010s, and partially recovered thereafter. This paper decomposes these changes over 1980-2024 into four components: demographic change across worker types, changes in relative employment shares across job types, changes in relative log wages across job types, and unweighted mean wage growth. The framework combines a shift-share decomposition across worker types with an extension of the Olley-Pakes decomposition across job types within worker types, separating employment reallocation from changes in relative wage structure. The four components vary across periods. Before 1996, unweighted mean wage growth and changes in relative wage structure contribute positively, while demographic change and employment reallocation contribute negatively. During 1996-2014, all four components are negative. After 2014, the recovery mainly reflects unweighted mean wage growth. Employment reallocation and changes in relative wage structure contribute differently across dimensions of job type.

econ.GN↗