“Billionaires” and Capitalist Development in World-Historical Perspective

The extraordinary increase in the number and wealth of billionaires in the past two decades has yielded a groundswell of analyses of the shifting patterns of economic growth around the globe and the reconfiguration of transnational elites. But data limitations have hampered attempts to situate these trends in world-historical context. This paper introduces a new data set to begin overcoming such limitations. This original data set provides relevant information on what we call “world-magnates”: the wealthiest individuals in the capitalist world-system from the fifteenth to the twentieth century, who can be understood as the structural and historical analogs to today’s billionaires. The data not only identify the “who,” “when,” and “where” of these magnates but also capture information on the diverse innovations in technology, organizations of production, labor arrangements, and relationships to state power that were associated with the presence of these individuals at the pinnacle of wealth over time. In this contribution, we introduce the first version of this new data set, describe the data-collection procedures and methodological approach, present some aggregate trends derived from the data, and conclude by outlining how this data set can be used to help overcome some persistent challenges to the study of capitalist development in a world-historical perspective.

Authors

Institutions

Publication Details

Journal
Sociology of Development
Published
2025-01-01
DOI
https://doi.org/10.1525/sod.2025.2611172
Primary Topic
World Systems and Global Transformations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

“Billionaires” and Capitalist Development in World-Historical Perspective

Corey R. Payne, Roberto Patricio Korzeniewicz
Sociology of Development
World Systems and Global Transformations
article

“Billionaires” and Capitalist Development in World-Historical Perspective

Corey R. Payne, Roberto Patricio Korzeniewicz
article en

Abstract

The extraordinary increase in the number and wealth of billionaires in the past two decades has yielded a groundswell of analyses of the shifting patterns of economic growth around the globe and the reconfiguration of transnational elites. But data limitations have hampered attempts to situate these trends in world-historical context. This paper introduces a new data set to begin overcoming such limitations. This original data set provides relevant information on what we call “world-magnates”: the wealthiest individuals in the capitalist world-system from the fifteenth to the twentieth century, who can be understood as the structural and historical analogs to today’s billionaires. The data not only identify the “who,” “when,” and “where” of these magnates but also capture information on the diverse innovations in technology, organizations of production, labor arrangements, and relationships to state power that were associated with the presence of these individuals at the pinnacle of wealth over time. In this contribution, we introduce the first version of this new data set, describe the data-collection procedures and methodological approach, present some aggregate trends derived from the data, and conclude by outlining how this data set can be used to help overcome some persistent challenges to the study of capitalist development in a world-historical perspective.

Sociology of DevelopmentVol. 11(3)
University of Richmond (US), University of Maryland, College Park (US)
Openalex Percentile: Top 93%
World Systems and Global Transformations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.