Search arXivSearch

arXiv · 2401.12118

Measures of the Capital Network of the U.S. Economy

Abstract

About two million U.S. corporations and partnerships are linked to each other and human investors by about 15 million owner-subsidiary links. Comparable social networks such as corporate board memberships and socially-built systems such as the network of Internet links are "small worlds," meaning a network with a small diameter and link densities with a power-law distribution, but these properties had not yet been measured for the business entity network. This article shows that both inbound links and outbound links display a power-law distribution with a coefficient of concentration estimable to within a generally narrow confidence interval, overall, for subnetworks including only business entities, only for the great connected component of the network, and in subnetworks with edges associated with certain industries, for all years 2009-2021. In contrast to other networks with power-law distributed link densities, the network is mostly a tree, and has a diameter an order of magnitude larger than a small-world network with the same link distribution. The regularity of the power-law distribution indicates that its coefficient can be used as a new, well-defined macroeconomic metric for the concentration of capital flows in an economy. Economists might use it as a new measure of market concentration which is more comprehensive than measures based only on the few biggest firms. Comparing capital link concentrations across countries would facilitate modeling the relationship between business network characteristics and other macroeconomic indicators.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ben Klemens. 2024-01-22. Measures of the Capital Network of the U.S. Economy. https://arxiv.org/abs/2401.12118

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

KEEP EXPLORING

Related papers

Access to Live AI Advice and Behavior Under Risk: An Incentivized Experiment

Generative AI has become an everyday advisor, and the systems people consult are live and interactive, not pre-scripted. We ask whether access to such a system changes behavior under risk. In an incentivized experiment (N = 158), participants made lottery choices with an optional decision aid presented as a conventional pre-written tool, a live one-shot AI, or a live interactive AI they could query, with information format held equivalent across conditions. Risk preferences are elicited via DOSE. We find no evidence that access to a live AI advisor changes risk aversion.

econ.GN

Bricks or Cash? Externalities of Housing Upgrading in High-density Cities

We estimate housing externalities in a high-density city, exploiting the staggered rollout of Singapore's nationwide Main Upgrading Programme for public housing. Controlling for nonrandom neighborhood exposure, we find that upgrading raises treated buildings' prices by 11.5% upon completion and neighboring buildings' resale prices by about 2% within 500 meters, decaying to zero beyond. A model with distance-decaying externalities shows that in dense settings spillovers justify the distortions of in-kind provision; this advantage diminishes and reverses at lower densities. Administrative data on over 2 million residents show that upgrading disproportionately retains older incumbents, suggesting age-specific amenities as an underexplored externality channel.

econ.GN

The Joneses Visit an Economics Lab

Existing literature offers persuasive evidence that individuals care about how their consumption compares to that of peers, and proposes a large variety of explanatory models. The present paper proposes a common framework for many of those models, and compares their ability to predict behavior in a laboratory experiment. We find evidence of Keeping up with the Joneses motivations but also find that conspicuous consumption is enhanced by Veblen motivations arising from peers' ability to observe one's own choice. Among the seven quasi-linear preference models we compare, our data are best explained by a model that contrasts envy and pride (upward vs downward comparisons) using a value function borrowed from Prospect Theory.

econ.GN