IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v305y2022ics0277953622004099.html
   My bibliography  Save this article

What factors drive state firearm law adoption? An application of exponential-family random graph models

Author

Listed:
  • Clark, Duncan A.
  • Macinko, James
  • Porfiri, Maurizio

Abstract

Guns are a ubiquitous feature of contemporary US culture, driven, at least partly, by firearms' constitutional enshrinement. However, the majority of laws intended to restrict or expand firearm access and use are formulated and passed in the states, leading to 50 different firearm-related legal environments. To date, little is known about why some states pass more restrictive or permissive firearm laws than others. In this article, we identify patterns of firearm law adoption across states, by framing the problem as a bipartite network (states connected to laws and laws connected to states) that is the result of a complex, and interconnected system of unobserved forces. We employ Exponential-family Random Graph Models (ERGMs), a class of statistical network models that allow for the dispensing of the assumptions of statistical independence, to identify factors that increase or decrease the likelihood of states adopting permissive or restrictive firearms laws over the period 1979 to 2020. Results show that more progressive state governments are associated with a higher chance of enacting restrictive firearm laws, and a lower chance of enacting permissive ones. Conservative state governments are associated with the analogous reversed association. States are more likely to adopt laws if bordering states have also adopted that law. For both restrictive and permissive laws the presence of a law in a neighboring state increased the conditional likelihood of a state having that law, that is laws diffuse across state borders. High levels of homicides are associated with a state having adopted more permissive, but not more restrictive, firearm laws. In summary, these results point to a complex interplay of state internal and external factors that seem to drive different patterns of firearm law adoption Based on these results, future work using related classes of models that take into account the time evolution of the network structure may provide a means to predict the likelihood of future law adoption.

Suggested Citation

  • Clark, Duncan A. & Macinko, James & Porfiri, Maurizio, 2022. "What factors drive state firearm law adoption? An application of exponential-family random graph models," Social Science & Medicine, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:socmed:v:305:y:2022:i:c:s0277953622004099
    DOI: 10.1016/j.socscimed.2022.115103
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953622004099
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.socscimed.2022.115103?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Nicole Abaid & James Macinko & Diana Silver & Maurizio Porfiri, 2015. "The Effect of Geography and Citizen Behavior on Motor Vehicle Deaths in the United States," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    2. Desmarais, Bruce A. & Harden, Jeffrey J. & Boehmke, Frederick J., 2015. "Persistent Policy Pathways: Inferring Diffusion Networks in the American States," American Political Science Review, Cambridge University Press, vol. 109(2), pages 392-406, May.
    3. Craig Volden, 2006. "States as Policy Laboratories: Emulating Success in the Children's Health Insurance Program," American Journal of Political Science, John Wiley & Sons, vol. 50(2), pages 294-312, April.
    4. Spitzer, S.A. & Staudenmayer, K.L. & Tennakoon, L. & Spain, D.A. & Weiser, T.G., 2017. "Costs and financial burden of initial hospitalizations for firearm injuries in the United States, 2006-2014," American Journal of Public Health, American Public Health Association, vol. 107(5), pages 770-774.
    5. Walker, Jack L., 1969. "The Diffusion of Innovations among the American States," American Political Science Review, Cambridge University Press, vol. 63(3), pages 880-899, November.
    6. repec:aph:ajpbhl:10.2105/ajph.2017.303684_9 is not listed on IDEAS
    7. Macinko, J. & Silver, D., 2015. "Diffusion of impaired driving laws among US states," American Journal of Public Health, American Public Health Association, vol. 105(9), pages 1893-1900.
    8. Shipan, Charles R. & Volden, Craig, 2014. "When the smoke clears: expertise, learning and policy diffusion," Journal of Public Policy, Cambridge University Press, vol. 34(3), pages 357-387, December.
    9. Gray, Virginia, 1973. "Innovation in the States: A Diffusion Study," American Political Science Review, Cambridge University Press, vol. 67(4), pages 1174-1185, December.
    10. Saavedra, Luz Amparo, 2000. "A Model of Welfare Competition with Evidence from AFDC," Journal of Urban Economics, Elsevier, vol. 47(2), pages 248-279, March.
    11. Yu, Jinhai & Jennings, Edward T. & Butler, J. S., 2020. "Lobbying, learning and policy reinvention: an examination of the American States’ drunk driving laws," Journal of Public Policy, Cambridge University Press, vol. 40(2), pages 259-279, June.
    12. Ronald L. Wasserstein & Nicole A. Lazar, 2016. "The ASA's Statement on p -Values: Context, Process, and Purpose," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 129-133, May.
    13. Walker, Jack L., 1969. "The Diffusion of Innovations among the American States," American Political Science Review, Cambridge University Press, vol. 63(3), pages 880-899, November.
    14. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2008. "Goodness of Fit of Social Network Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 248-258, March.
    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. Côme Billard & Anna Creti & Antoine Mandel, 2020. "How Environmental Policies Spread? A Network Approach to Diffusion in the U.S," Working Papers 2020.12, FAERE - French Association of Environmental and Resource Economists.
    2. Valente, Thomas W. & Pitts, Stephanie & Wipfli, Heather & Vega Yon, George G., 2019. "Network influences on policy implementation: Evidence from a global health treaty," Social Science & Medicine, Elsevier, vol. 222(C), pages 188-197.
    3. Felix Strebel & Thomas Widmer, 2012. "Visibility and facticity in policy diffusion: going beyond the prevailing binarity," Policy Sciences, Springer;Society of Policy Sciences, vol. 45(4), pages 385-398, December.
    4. Thomas Holyoke & Jeffrey Henig & Heath Brown & Natalie Lacireno-Paquet, 2009. "Policy dynamics and the evolution of state charter school laws," Policy Sciences, Springer;Society of Policy Sciences, vol. 42(1), pages 33-55, February.
    5. Nadiya Kostyuk, 2024. "Allies and diffusion of state military cybercapacity," Journal of Peace Research, Peace Research Institute Oslo, vol. 61(1), pages 44-58, January.
    6. Fabrizio Gilardi, 2010. "Who Learns from What in Policy Diffusion Processes?," American Journal of Political Science, John Wiley & Sons, vol. 54(3), pages 650-666, July.
    7. Lanahan, Lauren & Feldman, Maryann P., 2015. "Multilevel innovation policy mix: A closer look at state policies that augment the federal SBIR program," Research Policy, Elsevier, vol. 44(7), pages 1387-1402.
    8. Derek Glasgow & Shuang Zhao & Saatvika Rai, 2021. "Rethinking Climate Change Leadership: An Analysis of the Ambitiousness of State GHG Targets," Review of Policy Research, Policy Studies Organization, vol. 38(4), pages 398-426, July.
    9. Suk Joon Hwang & Frances Berry, 2019. "Deterring Drunk Driving: Why Some States Go Further Than Others in Policy Innovation," IJERPH, MDPI, vol. 16(10), pages 1-18, May.
    10. Yunxiang Zhang & Shichen Wang, 2021. "How does policy innovation diffuse among Chinese local governments? A qualitative comparative analysis of River Chief Innovation," Public Administration & Development, Blackwell Publishing, vol. 41(1), pages 34-47, February.
    11. Nico Heiden & Felix Strebel, 2012. "What about non-diffusion? The effect of competitiveness in policy-comparative diffusion research," Policy Sciences, Springer;Society of Policy Sciences, vol. 45(4), pages 345-358, December.
    12. Charles R. Shipan & Craig Volden, 2008. "The Mechanisms of Policy Diffusion," American Journal of Political Science, John Wiley & Sons, vol. 52(4), pages 840-857, October.
    13. Brian Y. An & Adam Butz & Min-Kyeong Cha & Joshua L. Mitchell, 2023. "Following neighbors or regional leaders? Unpacking the effect of geographic proximity in local climate policy diffusion," Policy Sciences, Springer;Society of Policy Sciences, vol. 56(4), pages 825-868, December.
    14. Amy Y. Li, 2017. "Covet Thy Neighbor or “Reverse Policy Diffusion”? State Adoption of Performance Funding 2.0," Research in Higher Education, Springer;Association for Institutional Research, vol. 58(7), pages 746-771, November.
    15. Cécile Hoareau, 2011. "GLOBALIZATION AND DUAL MODES OF HIGHER EDUCATION POLICYMAKING IN FRANCE: Je t’aime moi non plus," University of California at Berkeley, Center for Studies in Higher Education qt9r38v416, Center for Studies in Higher Education, UC Berkeley.
    16. Xiaohan Li & Yang Lv & Md Nazirul Islam Sarker & Xun Zeng, 2022. "Assessment of Critical Diffusion Factors of Public–Private Partnership and Social Policy: Evidence from Mainland Prefecture-Level Cities in China," Land, MDPI, vol. 11(3), pages 1-15, February.
    17. Jäkel Tim, 2019. "Performance Gaps, Peer Effects, and Comparative Behaviour: Empirical Evidence from Swedish Local Government," Statistics, Politics and Policy, De Gruyter, vol. 10(1), pages 27-53, June.
    18. Christoph Engel & Alon Klement & Karen Weinshall Margel, 2017. "Diffusion of Legal Innovations: The Case of Israeli Class Actions," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2017_11, Max Planck Institute for Research on Collective Goods, revised Jan 2018.
    19. Weixing Liu & Hongtao Yi, 2020. "What Affects the Diffusion of New Energy Vehicles Financial Subsidy Policy? Evidence from Chinese Cities," IJERPH, MDPI, vol. 17(3), pages 1-15, January.
    20. Million, Anthony J., 2017. "Bureaucratic organization and innovation: a mixed-methods study of US state department of transportation website adoption," Thesis Commons z6p3j, Center for Open Science.

    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:eee:socmed:v:305:y:2022:i:c:s0277953622004099. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    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.