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Supanika Leurcharusmee

Researcher at Chiang Mai University

Publications -  17
Citations -  97

Supanika Leurcharusmee is an academic researcher from Chiang Mai University. The author has contributed to research in topics: Medicine & Wage. The author has an hindex of 2, co-authored 13 publications receiving 47 citations.

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Journal ArticleDOI

Trust and power as determinants of tax compliance across 44 nations

Larissa Batrancea, +63 more
TL;DR: In this article, the authors found that trust in authorities and power of authorities, as defined in the slippery slope framework, increase tax compliance intentions and mitigate intended tax evasion across societies that differ in economic, sociodemographic, political, and cultural backgrounds.
Journal ArticleDOI

Do Bitcoin and Traditional Financial Assets Act as an Inflation Hedge during Stable and Turbulent Markets? Evidence from High Cryptocurrency Adoption Countries

TL;DR: In this paper , the authors employ the Markov Switching Vector Autoregressive (MSVA) to examine whether Bitcoin, gold, oil, and stock have the ability to hedge against inflation in high cryptocurrency adoption countries in the periods from January 2010 to March 2021.
Journal Article

Child-Gender Preference Generalized Maximum Entropy Approach

TL;DR: In this article, the authors measured child-gender preference in Thailand by examining the probability of having an additional child given genders of the previous children using the generalized maximum entropy (GME) approach.
Book ChapterDOI

Thailand’s Household Income Inequality Revisited: Evidence from Decomposition Approaches

TL;DR: This study decomposes income inequality across household in Thailand in three dimensions: sources of income, industrial subgroups, and household characteristics, and found that the key contributors of income inequality are heterogeneous across industrial sub groups.
Book ChapterDOI

The Classifier Chain Generalized Maximum Entropy Model for Multi-label Choice Problems

TL;DR: The Classifier Chain (CC) method was applied to transform the Generalized Maximum Entropy choice model from a single-label model to a multi- label model, indicating that the incorporation of the information on dependence patterns among alternatives can improve prediction performance.