IDEAS home Printed from https://ideas.repec.org/a/spr/lsprsc/v16y2023i1d10.1007_s12076-023-00350-y.html
   My bibliography  Save this article

Opioid mortality in the US: quantifying the direct and indirect impact of sociodemographic and socioeconomic factors

Author

Listed:
  • Sucharita Gopal

    (Boston University)

  • Manfred M. Fischer

    (Vienna University of Economics and Business)

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 quantify the impact of a set of sociodemographic and socioeconomic factors that influence opioid-related mortality in the US. The empirical model uses a pool of 49 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 spillover) effects estimates is based on Bayesian estimation and inference reflecting a proper interpretation of the marginal effects for the model that involves spatial lags of the dependent and independent variables. The study provides evidence that opioid mortality depends not only on the characteristics of the state itself (direct effects), but also on those of nearby states (indirect effects). Direct effects are important, but externalities (spatial spillovers) are more important. The sociodemographic structure (age and race) of a state is important whereas economic distress of a state is less so, as indicated by the total impact estimates. The methodology and the research findings provide a useful template for future empirical work using other geographic locations or shifting interest to other epidemics.

Suggested Citation

  • Sucharita Gopal & Manfred M. Fischer, 2023. "Opioid mortality in the US: quantifying the direct and indirect impact of sociodemographic and socioeconomic factors," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-17, December.
    • Handle: RePEc:spr:lsprsc:v:16:y:2023:i:1:d:10.1007_s12076-023-00350-y
    DOI: 10.1007/s12076-023-00350-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12076-023-00350-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12076-023-00350-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. James Lesage & Manfred Fischer, 2008. "Spatial Growth Regressions: Model Specification, Estimation and Interpretation," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(3), pages 275-304.
    2. Manfred M. Fischer & Jinfeng Wang, 2011. "Spatial Data Analysis," SpringerBriefs in Regional Science, Springer, number 978-3-642-21720-3, March.
    3. Cheng Hsiao, 2007. "Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 1-22, May.
    4. Baltagi, Badi H. & Heun Song, Seuck & Cheol Jung, Byoung & Koh, Won, 2007. "Testing for serial correlation, spatial autocorrelation and random effects using panel data," Journal of Econometrics, Elsevier, vol. 140(1), pages 5-51, September.
    5. Manfred M. Fischer & James P. LeSage, 2020. "Network dependence in multi-indexed data on international trade flows," Journal of Spatial Econometrics, Springer, vol. 1(1), pages 1-26, December.
    6. James Paul LeSage, 2020. "Fast MCMC estimation of multiple W-matrix spatial regression models and Metropolis–Hastings Monte Carlo log-marginal likelihoods," Journal of Geographical Systems, Springer, vol. 22(1), pages 47-75, January.
    7. Barbara Blake-Gonzalez & Richard J. Cebula & James V. Koch, 2021. "Drug-overdose death rates: the economic misery explanation and its alternatives," Applied Economics, Taylor & Francis Journals, vol. 53(6), pages 730-741, February.
    8. King, N.B. & Fraser, V. & Boikos, C. & Richardson, R. & Harper, S., 2014. "Determinants of increased opioid-related mortality in the united states and canada, 1990-2013: A systematic review," American Journal of Public Health, American Public Health Association, vol. 104(8), pages 32-42.
    9. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    10. Cheng Hsiao, 2007. "Rejoinder on: Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 56-57, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Naeem Tabassum & Satwinder Singh, 2020. "Corporate Governance and Organisational Performance," Springer Books, Springer, number 978-3-030-48527-6, December.
    2. Lukas Dargel, 2021. "Revisiting estimation methods for spatial econometric interaction models," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-41, December.
    3. Giuseppe Arbia, 2011. "A Lustrum of SEA: Recent Research Trends Following the Creation of the Spatial Econometrics Association (2007--2011)," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(4), pages 377-395, July.
    4. Hany Eldemerdash & Hugh Metcalf & Sara Maioli, 2014. "Twin deficits: new evidence from a developing (oil vs. non-oil) countries’ perspective," Empirical Economics, Springer, vol. 47(3), pages 825-851, November.
    5. Parent, Olivier & LeSage, James P., 2011. "A space-time filter for panel data models containing random effects," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 475-490, January.
    6. Ding Luo & Oded Cats & Hans Lint, 2020. "Can passenger flow distribution be estimated solely based on network properties in public transport systems?," Transportation, Springer, vol. 47(6), pages 2757-2776, December.
    7. Liv Osland & Inge Thorsen, 2013. "Spatial Impacts, Local Labour Market Characteristics and Housing Prices," Urban Studies, Urban Studies Journal Limited, vol. 50(10), pages 2063-2083, August.
    8. Li, Larry & McMurray, Adela & Sy, Malick & Xue, Jinjun, 2018. "Corporate ownership, efficiency and performance under state capitalism: Evidence from China," Journal of Policy Modeling, Elsevier, vol. 40(4), pages 747-766.
    9. Lynn, Peter & Bosch, Oriol, 2021. "Methodological lessons from the pilot longitudinal survey on debt advice," ISER Working Paper Series 2021-03, Institute for Social and Economic Research.
    10. Yasser Razak Hussain & Pranab Mukhopadhyay, 2023. "How Much do Education, Experience, and Social Networks Impact Earnings in India? A Panel Data Analysis Disaggregated by Class, Gender, Caste and Religion," SAGE Open, , vol. 13(4), pages 21582440231, December.
    11. Trabelsi, Emna & Hichri, Walid, 2021. "Central Bank Transparency with (semi-)public Information: Laboratory Experiments," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    12. Noor Zainab.Tunggal & Shariff Umar Shariff Abd. Kadir & Venus-Khim Sen Liew, 2018. "Panel Analysis of Monetary Model of ASEAN-5 Exchange Rates," International Business Research, Canadian Center of Science and Education, vol. 11(11), pages 1-7, November.
    13. Wanglin Ma & Kathryn Bicknell & Alan Renwick, 2019. "Feed use intensification and technical efficiency of dairy farms in New Zealand," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(1), pages 20-38, January.
    14. Martey, Edward & Etwire, Prince Maxwell & Abdoulaye, Tahirou, 2020. "Welfare impacts of climate-smart agriculture in Ghana: Does row planting and drought-tolerant maize varieties matter?," Land Use Policy, Elsevier, vol. 95(C).
    15. Wenhao Song & Chunhui Ye & Yuheng Liu & Weisong Cheng, 2021. "Do China’s Urban–Environmental Quality and Economic Growth Conform to the Environmental Kuznets Curve?," IJERPH, MDPI, vol. 18(24), pages 1-15, December.
    16. Jahn, Vera, 2017. "The Importance of Mittelstand Firms for Regional Apprenticeship Activity," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168260, Verein für Socialpolitik / German Economic Association.
    17. Elkhan Richard Sadik-Zada & Wilhelm Loewenstein, 2020. "Drivers of CO 2 -Emissions in Fossil Fuel Abundant Settings: (Pooled) Mean Group and Nonparametric Panel Analyses," Energies, MDPI, vol. 13(15), pages 1-24, August.
    18. Vecci, Joseph & Zelinsky, Tomas, 2017. "A Spatial Analysis of Foreign Aid and Civil Society," Working Papers in Economics 688, University of Gothenburg, Department of Economics.
    19. Vishal Gupta & Sandra C. Mortal & Tina Yang, 2018. "Entrepreneurial orientation and firm value: Does managerial discretion play a role?," Review of Managerial Science, Springer, vol. 12(1), pages 1-26, January.
    20. Merello, Paloma & Barberá, Antonio & la Poza, Elena De, 2022. "Is the sustainability profile of FinTech companies a key driver of their value?," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

    More about this item

    Keywords

    Spatial Durbin panel data model; Bayesian econometrics; Markov Chain Monte Carlo; Direct (own state) effects; Indirect (cross-state spatial spillover) effects; Inferential statistics;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • I19 - Health, Education, and Welfare - - Health - - - Other
    • O51 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - U.S.; Canada

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lsprsc:v:16:y:2023:i:1:d:10.1007_s12076-023-00350-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.