Works (14)
Updated: July 5th, 2023 16:02
2019 chapter
Conducting cost-benefit analyses using scanner and label data
In Using Scanner Data for Food Policy Research (pp. 203–229).
TL;DR:
This chapter describes the different ways that store scanner data and household scanner data, with or without linking to label data, can be used as the basis for cost-benefit analyses and provides an overview of a labeling cost model and a reformulation cost model that were constructed using 2012 Nielsen Scantrack store scanners.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger
(OpenAlex)
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: June 10, 2021
2019 chapter
Estimating food demand systems using scanner data
In Using Scanner Data for Food Policy Research (pp. 141–175).
UN Sustainable Development Goal Categories
2. Zero Hunger
(OpenAlex)
9. Industry, Innovation and Infrastructure
(Web of Science)
Source: Web Of Science
Added: June 10, 2021
2019 chapter
Insights from past food research using scanner data
In Using Scanner Data for Food Policy Research (pp. 59–140).
TL;DR:
This chapter provides an overview of novel and interesting ways scanner data have been used to address food policy issues in the literature.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger
(OpenAlex)
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: June 10, 2021
2019 chapter
Label and nutrition data at the barcode level
In Using Scanner Data for Food Policy Research (pp. 31–39).
TL;DR:
This work provides a list of suggested considerations when researchers seek to acquire label data for conducting food policy research.
(via Semantic Scholar)
Source: Web Of Science
Added: June 10, 2021
2019 chapter
Measuring the food environment using scanner data
In Using Scanner Data for Food Policy Research (pp. 177–202).
TL;DR:
Using scanner data purchases and the estimated parameters from regression models with dietary recall data, household-level and retail chain-level approximate healthy eating indexes (aHEI) are calculated and used in reduced-form models of the nutritional quality of household food purchases while accounting for the endogeneity associated with household and store location choice.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger
(OpenAlex)
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: June 10, 2021
2019 chapter
Methodological approaches for using scanner data
In Using Scanner Data for Food Policy Research (pp. 41–57).
TL;DR:
Methodological issues that arise when working with scanner data vary depending on whether researchers are using data reported at the store level or household level or are using product-level data aggregated to geographic areas, marketing channels, or demographic categories.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(Web of Science)
Source: Web Of Science
Added: June 10, 2021
2019 chapter
Sources of scanner data across the globe
In Using Scanner Data for Food Policy Research (pp. 13–30).
TL;DR:
This work provides a list of suggested considerations when researchers seek to purchase or otherwise acquire scanner data for use in conducting food policy research.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: June 10, 2021
2019 chapter
What is scanner data and why is it useful for food policy research?
In Using Scanner Data for Food Policy Research (pp. 1–12).
UN Sustainable Development Goal Categories
2. Zero Hunger
(OpenAlex)
Source: Web Of Science
Added: June 10, 2021
2007 article
Did the pathogen reduction and hazard analysis and critical control points regulation cause slaughter plants to exit?
REVIEW OF AGRICULTURAL ECONOMICS, Vol. 29, pp. 596–611.
TL;DR:
This multiperiod analysis tested whether the 1996 Pathogen Reduction and Hazard Analysis and Critical Control Points food safety regulation affected the probability of slaughter plant exit and suggested very small and small meat slaughter plants were more likely to exit during implementation than during preimplementation but less likely after implementation.
(via Semantic Scholar)
open access via academic.oup.com (publisher PDF)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
2003 journal article
The fable of the bees revisited: Causes and consequences of the US honey program
JOURNAL OF LAW & ECONOMICS, 46(2), 479–516.
UN Sustainable Development Goal Categories
15. Life on Land
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
2002 journal article
Exit of meat slaughter plants during implementation of the PR/HACCP regulations
Journal of Agricultural and Resource Economics, 27(1), 187–203.
Source: NC State University Libraries
Added: August 6, 2018
1999 journal article
A test for market power using marginal input and output prices with application to the US beef processing industry
AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS, 81(3), 638–643.
UN Sustainable Development Goal Categories
2. Zero Hunger
(OpenAlex)
9. Industry, Innovation and Infrastructure
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
1999 journal article
Measuring the degree of oligopsony power in the beef packing industry in the absence of marketing input quantity data
Journal of Agricultural and Resource Economics, 24(2), 299–312.
Source: NC State University Libraries
Added: August 6, 2018
1995 journal article
Why support the price of honey?
Choices (Ames, Iowa), (2), 19.
Source: NC State University Libraries
Added: August 6, 2018
Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.
Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.