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Investigating Cohort Effects on Deer Hunter Participation in Michigan and Wisconsin

Investigating Cohort Effects on Deer Hunter Participation in Michigan and Wisconsin. Richelle Winkler & Chris Henderson Dept of Social Sciences/Environmental & Energy Policy Grad Program Michigan Tech University.

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Investigating Cohort Effects on Deer Hunter Participation in Michigan and Wisconsin

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  1. Investigating Cohort Effects on Deer Hunter Participation in Michigan and Wisconsin Richelle Winkler & Chris Henderson Dept of Social Sciences/Environmental & Energy Policy Grad Program Michigan Tech University

  2. Sources: US Fish and Wildlife Service Hunting License Reports, US Census Bureau

  3. Common Explanations • Time demands • Land conversion/accessibility • Alternative activities • Urbanization • Fewer animals • Changing social support/counter-movement, etc. • Policy Change • Population Aging Cohort Theory

  4. Cohort Theory

  5. Research Questions • To what extent are cohort effects driving hunter participation? • How does this vary across space, by sex, by species hunted? • What are the implications for future hunting numbers and the North American Model? • How can we explain cohort effects?

  6. Age-Period-Cohort Models Theoretically Distinct …But Empirically Blurred?

  7. Data & Analysis • State license records by single year age, sex, residence • Not by race/ethnicity • General population estimates and projections (US Census Bureau) • APC Analysis using Intrinsic Estimator (IE) approach in Stata (Yang et al. 2008)

  8. In Summary/Future Questions • Growing body of research suggesting cohort effects are key. We need to design research and planning strategies that consider this approach. • Michigan and Wisconsin very similar-- effects of policy, env? • Models predict exacerbated decline to come • lasting social change away from hunting? • Serious implications for North American model • Could be spatial variation, sex, race… • Important to understand WHY cohort effects? Anglers too? Small game? Etc.

  9. Potential for Spatial Variation

  10. Rates highest in rural areas, especially northwest.

  11. Middle-aged decline greatest in east. Most Drop-out (2004-2009): Forest (-19%) Vilas (-18%) Juneau (-16%) Florence (-14%) Oneida (-14%)

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