Opioid Mortality in the US: Quantifying the Impact of Key Determinants Using a Spatial Panel Data Approach

Sucharita Gopal, Manfred M. Fischer*

*Corresponding author for this work

Publication: Working/Discussion PaperWU Working Paper

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Abstract

This paper employs a spatial Durbin panel data model, an extension of the cross-sectional spatial Durbin model to a panel data framework, to estimate the impact of a set of demographic and economic factors on state-level opioid-related mortalities in the US. The empirical model uses a pool of US states over six years from 2014 to 2019 and a nearest-neighbor matrix that represents the topological structure between the states. Calculation of direct (own state) and indirect (cross-state spillovers) effects estimates -- based on Bayesian estimation and inference -- reflects a proper interpretation of the marginal effects for our nonlinear model that involves lags of the dependent variable vector. The study provides evidence for the existence of spatial effects working through the dependent variable vector and points to the importance of larger indirect effects of Asian and Hispanic/Latino minorities on the one side and the population age groups 35-44 years and 65 years and older on the other. This finding echoes the first law of geography that "everything is related to everything else, but near things are more related than distant things" (Tobler 1970). Space -- largely neglected in previous research -- matters for gaining a valid and better understanding of why and how neighboring states contribute to opioid-related mortality in the states.
Original languageEnglish
Number of pages19
DOIs
Publication statusPublished - 1 Dec 2022

Publication series

SeriesWorking Papers in Regional Science
Volume2022/02

WU Working Paper Series

  • Working Papers in Regional Science

Keywords

  • Spatial Durbin panel data model
  • Bayesian econometrics
  • Markov Chain Monte Carlo
  • direct (own state) effects
  • indirect (cross-state spatial spillover) effects
  • inferential statistics

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