Optimal policy design for the sugar tax
Clicks: 78
ID: 282109
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
23.1
/100
78 views
20 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #251 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Healthy nutrition promotions and regulations have long been regarded as a
tool for increasing social welfare. One of the avenues taken in the past decade
is sugar consumption regulation by introducing a sugar tax. Such a tax
increases the price of extensive sugar containment in products such as soft
drinks. In this article we consider a typical problem of optimal regulatory
policy design, where the task is to determine the sugar tax rate maximizing the
social welfare. We model the problem as a sequential game represented by the
three-level mathematical program. On the upper level, the government decides
upon the tax rate. On the middle level, producers decide on the product
pricing. On the lower level, consumers decide upon their preferences towards
the products. While the general problem is computationally intractable, the
problem with a few product types is polynomially solvable, even for an
arbitrary number of heterogeneous consumers. This paper presents a simple,
intuitive and easily implementable framework for computing optimal sugar tax in
a market with a few products. This resembles the reality as the soft drinks,
for instance, are typically categorized in either regular or no-sugar drinks,
e.g. Coca-Cola and Coca-Cola Zero. We illustrate the algorithm using an example
based on the real data and draw conclusions for a specific local market.
| Reference Key |
nedelko2018optimal
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Kelly Geyskens; Alexander Grigoriev; Niels Holtrop; Anastasia Nedelko |
| Journal | arXiv |
| Year | 2018 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.