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Dynamic Structure of Competition Networks in Affordable Care Act Insurance Market
Author(s) -
David A. Gianetto,
Mohsen Mosleh,
Babak Heydari
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2800659
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Stimulating competition is one of the main topics in most health care reform debates, and it has been a central issue in the Affordable Care Act (ACA), USA, since 2009. The goal of this paper is to use complex network methods to study dynamic and structure of competition under ACA and its evolution over time since its beginning until 2017. Using publicly available data, we construct a bipartite network of counties and insurance providers, create associated weighted single-mode networks, and analyze the evolution of network parameters that are related to competition and potential collusion in complex networks. These parameters have been previously tied to the dynamics of collaboration and competition in earlier theoretical works. We argue that three parameters (network modularity, and the mean and skewness of eigenvector centrality) are appropriate indicators of the overall competition in the insurance market. Based on these parameters, we show that the level of systemic competition among insurers as a function of time is an inverse U-shape trend, and that competition has returned back to what it was at the very beginning of ACA, indicating an undesirable resilience in the national health care system.

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