Joohun Han Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Researcher

University of Arkansas at Fayetteville

unknown

3 h-index 9 pubs 31 cited

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Biography and Research Information

OverviewAI-generated summary

Joohun Han's research investigates factors influencing decision-making and market participation across various domains. His work has explored the determinants of earthquake insurance uptake through supervised machine learning and analyzed the relationships between different crop acreages using Bayesian time series analysis. Han has also examined the economic effects of sin taxes on legal and illicit markets, particularly within the US cannabis market, and studied the supply-side impacts of cannabis legalization. Additionally, his research has delved into the role of social networks in the adoption of new agricultural technologies, as evidenced by an analysis of Twitter data on turfgrass varieties. He has also investigated income disparity between agricultural and non-agricultural households, considering factors like aging and underemployment, and applied a Bayesian triple-hurdle model to study maize market participation in Zambia. Han's scholarship metrics include an h-index of 3, with 9 total publications and 31 total citations.

Metrics

  • h-index: 3
  • Publications: 9
  • Citations: 31

Selected Publications

  • Stock-to-Use Ratio and Price of Rice: Deciphering the Relationships (2025) DOI
  • A Bayesian triple-hurdle model of maize market participation in Zambia (2025) DOI
  • Vitamin A fortification: key factors and considerations for effective implementation (2025) DOI
  • Does sin tax on the legal market facilitate the illicit market? An ex‐ante assessment on the US cannabis market (2024) DOI
  • The Role of the Social Network in Adopting New Turfgrass Varieties: An Analysis of Twitter Data (2024) DOI
  • Uncovering the factors that affect earthquake insurance uptake using supervised machine learning (2023) DOI
  • The relation between wheat, soybean, and hemp acreage: a Bayesian time series analysis (2023) DOI

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