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Proximity effects in obesity rates in the US: A Spatial Markov Chains approach

Massimiliano Agovino, Alessandro Crociata () and Pier Luigi Sacco

Social Science & Medicine, 2019, vol. 220, issue C, 301-311

Abstract: In this paper, we investigate, by means of a Spatial Markov Chains approach, the existence of proximity effects at State level for US data on obesity rates in the period 1990–2011. We find that proximity effects do play an important role in the spatial diffusion of obesity (the obesity ‘epidemics’), and that the actual health geography of nearby States in terms of high vs. low obesity rates makes an important difference as to the future evolution of the State's own obesity rate over time. This means, in particular, that clusters of States characterized by uniformly high levels of obesity rates, as it happens for instance in the US Southern macro-region, may suffer from a perverse ‘geographical lock-in’ effect that calls for coordinated action across States to implement effective countervailing policies.

Keywords: Obesity rates; Obesity epidemics; Proximity effects; Spatial Markov chain; Ergodic distribution (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.socscimed.2018.11.013

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