Competitive Effects of Front-of-Package Nutrition Labeling Adoption on Nutritional Quality: Evidence from Facts Up Front–Style Labels

2020 ◽  
Vol 84 (6) ◽  
pp. 3-21 ◽  
Author(s):  
Joon Ho Lim ◽  
Rishika Rishika ◽  
Ramkumar Janakiraman ◽  
P.K. Kannan
Keyword(s):  
Nutritional Quality ◽  
Large Scale ◽  
Product Category ◽  
Underlying Mechanism ◽  
The United States ◽  
Product Line ◽  
Nutrition Labeling ◽  
Competitive Response ◽  
Data Set ◽  
Packaged Food

“Facts Up Front” nutrition labels are a front-of-package (FOP) nutrition labeling system that presents key nutrient information on the front of packaged food and beverage products in an easy-to-read format. The authors conduct a large-scale empirical study to examine the effect of adoption of FOP labeling on products’ nutritional quality. The authors assemble a unique data set on packaged food products in the United States across 44 categories over 16 years. By using a difference-in-differences estimator, the authors find that FOP adoption in a product category leads to an improvement in the nutritional quality of other products in that category. This competitive response is stronger for premium brands and brands with narrower product line breadth as well as for categories involving unhealthy products and those that are more competitive in nature. The authors offer evidence regarding the role of nutrition information salience as the underlying mechanism; they also perform supplementary analyses to rule out potential self-selection issues and conduct a battery of robustness checks and falsification tests. The authors discuss the implications of the findings for public policy makers, consumers, manufacturers, and food retailers.

scholarly journals JUSTFAIR: Judicial System Transparency through Federal Archive Inferred Records

2020 ◽  
Author(s):  
Maria-Veronica Ciocanel ◽  
Chad M. Topaz ◽  
Rebecca Santorella ◽  
Shilad Sen ◽  
Christian Michael Smith ◽  
...  
Keyword(s):  
Large Scale ◽  
Federal District ◽  
The United States ◽  
Judicial System ◽  
Public Access ◽  
Data Set ◽  
Scale Free ◽  
The Public ◽  
Sentencing Decisions ◽  
Constitutional Right

In the Unites States, the public has a constitutional right to access criminal trial proceedings. In practice, it can be difficult or impossible for the public to exercise this right. We present JUSTFAIR: Judicial System Transparency through Federal Archive Inferred Records, a database of criminal sentencing decisions made in federal district courts. We have compiled this data set from public sources including the United States Sentencing Commission, the Federal Judicial Center, the Public Access to Court Electronic Records system, and Wikipedia. With nearly 600,000 records from the years 2001 - 2018, JUSTFAIR is the first large scale, free, public database that links information about defendants and their demographic characteristics with information about their federal crimes, their sentences, and, crucially, the identity of the sentencing judge.

Red Bull (Abridged)

2017 ◽  
pp. 1-8
Author(s):  
Paul W. Farris ◽  
Ervin R. Shames ◽  
Richard R. Johnson ◽  
Jordan Mitchell
Keyword(s):  
United States ◽  
Word Of Mouth ◽  
Product Category ◽  
New Product ◽  
The United States ◽  
Product Line ◽  
Brand Building ◽  
History Of ◽  
Brand Product ◽  
Red Bull

This case (an abridged version of UVA-M-0663) describes the history of the Red Bull brand and how the company stimulated and harnessed word of mouth to build a new product category (functional energy drinks) and brand franchise. The case concludes by asking the reader to consider where Red Bull will take its brand, product line, and marketing next, in light of many competitive challenges in the United States. The case was written to foster discussion of nontraditional brand-building strategies and the growing globalization of brands and products targeted toward younger consumers.

Improving teleseismic event locations using a three-dimensional Earth model

1996 ◽  
Vol 86 (3) ◽  
pp. 788-796 ◽  
Author(s):  
Gideon P. Smith ◽  
Göran Ekström
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
The United States ◽  
Earth Model ◽  
Published Data ◽  
S Wave ◽  
Origin Time ◽  
Data Set ◽  
Station Corrections ◽  
Earth Models

Abstract A comparison is made between seismic event locations derived from standard spherically symmetric Earth models (JB, PREM, IASP91) and a recent Earth model (S&P12/WM13) that incorporates large-scale lateral heterogeneity of P- and S-wave velocities in the mantle. Events with known hypocentral coordinates are located in the different Earth models using standard methods. Two sets of events are considered: a data set of 26 explosions, including primarily nuclear weapons test explosions and peaceful nuclear explosions in the United States and former USSR; and a published data set of 82 well-located earthquakes with a more even global distribution. IASP91 and PREM are shown to offer similar errors in event location and origin time estimates with respect to the JB model. The three-dimensional (3D) model S&P12/WM13 offers improvement in event locations over all three one-dimensional (1D) models with, or without, station corrections. For the explosion events, the average mislocation distance is reduced by approximately 40%; for the earthquakes, the improvements are smaller. Corrections for crustal thickness beneath source and receiver are found to be of similar magnitude to the mantle corrections, but use of station corrections together with the three-dimensional mantle model provide the best locations.

scholarly journals EXISTING STRATEGIES FOR ELECTRONIC DATA COLLECTION BY ELDER ABUSE MULTI-DISCIPLINARY TEAMS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S423-S423
Author(s):  
Tony Rosen ◽  
David Burnes ◽  
Darin Kirchin ◽  
Alyssa Elman ◽  
Risa Breckman ◽  
...  
Keyword(s):  
Data Collection ◽  
Social Services ◽  
Large Scale ◽  
Elder Abuse ◽  
The United States ◽  
Third Party ◽  
Snowball Sampling ◽  
Electronic Data ◽  
Data Set ◽  
Electronic Data Collection

Abstract Elder abuse cases often require integrated responses from social services, medicine, civil legal, and criminal justice. Multi-disciplinary teams (MDTs), which meet periodically to discuss and coordinate interventions for complex cases, have developed in many communities. Little is known about how these MDTs collect case-level data. Our objective was to describe existing strategies of case-level electronic data collection conducted by MDTs across the United States as a preliminary step in developing a comprehensive database strategy. To identify MDTs currently collecting data electronically, we used a snowball sampling approach discussing with national leaders. We also sent an e-mail to the National Center for Elder Abuse listserv inviting participation. We identified and reviewed 11 databases from MDTs. Strategies for and comprehensiveness of data collection varied widely. Databases used ranged from a simple spreadsheet to a customized Microsoft Access database to large databases designed and managed by a third-party vendor. Total data fields collected ranged from 12-338. Types of data included intake/baseline case/client information, case tracking/follow-up, and case closure/outcomes. Information tracked by many MDTs, such as type of mistreatment, was not captured in a single standard fashion. Documentation about data entry processes varied from absent to detailed. We concluded that MDTs currently use widely varied strategies to track data electronically and are not capturing data in a standardized fashion. Many MDTs collect only minimal data. Based on this, we have developed recommendations for a minimum data set and optimal data structure. If widely adopted, this would potentially improve ability to conduct large-scale comparative research.

scholarly journals Reduction of COVID-19 Incidence and Nonpharmacologic Interventions: Analysis Using a US County–Level Policy Data Set (Preprint)

2020 ◽  
Author(s):  
Senan Ebrahim ◽  
Henry Ashworth ◽  
Cray Noah ◽  
Adesh Kadambi ◽  
Asmae Toumi ◽  
...  
Keyword(s):  
Large Scale ◽  
Time Series Data ◽  
Main Tool ◽  
The United States ◽  
Policy Formulation ◽  
Contact Tracing ◽  
Series Data ◽  
County Level ◽  
Data Set ◽  
The Relationship

BACKGROUND Worldwide, nonpharmacologic interventions (NPIs) have been the main tool used to mitigate the COVID-19 pandemic. This includes social distancing measures (closing businesses, closing schools, and quarantining symptomatic persons) and contact tracing (tracking and following exposed individuals). While preliminary research across the globe has shown these policies to be effective, there is currently a lack of information on the effectiveness of NPIs in the United States. OBJECTIVE The purpose of this study was to create a granular NPI data set at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases. METHODS Using a standardized crowdsourcing methodology, we collected time-series data on 7 key NPIs for 1320 US counties. RESULTS This open-source data set is the largest and most comprehensive collection of county NPI policy data and meets the need for higher-resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states (<i>P</i>&lt;.001). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases (<i>P</i>=.004). We found weak correlations between shelter-in-place enforcement and measures of Democratic local voter proportion (R=0.21) and elected leadership (R=0.22). CONCLUSIONS This study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this data set will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.

scholarly journals Understanding vehicular routing behavior with location-based service data

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Yanyan Xu ◽  
Riccardo Di Clemente ◽  
Marta C. González
Keyword(s):  
Travel Time ◽  
Large Scale ◽  
Census Data ◽  
The United States ◽  
Location Based Services ◽  
Smartphone Applications ◽  
United States Census ◽  
Data Set ◽  
High Resolution Data ◽  
The Impact

AbstractProperly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation.

“Big” Treaties, Small Effects

2018 ◽  
Vol 70 (2) ◽  
pp. 165-193 ◽  
Author(s):  
Joanne Gowa ◽  
Raymond Hicks
Keyword(s):  
Economic History ◽  
Original Data ◽  
The United States ◽  
Significant Rise ◽  
Product Line ◽  
Most Favored Nation ◽  
Data Set ◽  
Bilateral Agreements ◽  
Trading Partners ◽  
Line Level

It seems obvious that agreements to cut tariffs will raise trade between their signatories. But recent studies show that some agreements widely considered to be landmarks in economic history had either a remarkably small impact on trade or none at all. Among those agreements are the Cobden-Chevalier Treaties and the long series of tariff accords concluded under the auspices of the GATT/WTO. Both sets of agreements cut import duties on many goods that applied to all trading partners entitled to most-favored-nation treatment, but neither increased aggregate trade between their members. This article examines the agreements concluded by the United States under the 1934 Reciprocal Trade Agreements Act (RTAA). The authors use an original data set that records changes in tariffs and US imports at the product-line level for each of the twenty-seven bilateral agreements. No comparable data exist either for the nineteenth-century trade network or for the postwar trade regime. The results show that the RTAA treaties failed to raise aggregate US imports from its treaty partners. They also show that these agreements did lead to a large and significant rise in US imports of specific products from specific countries. Because the same bargaining protocol that produced the RTAA agreements also governed the European treaty network and the GATT/WTO, the argument advanced in this article can also help to explain why neither treaty exerted a significant impact on aggregate trade between their signatories.

scholarly journals PREDICTION OF SHARE OF VISUAL INVENTORY USING STORE DISPLAY, CHANNEL, AND PRODUCT VARIABLES

2021 ◽  
Vol 29 (No.1) ◽  
pp. 163-183
Author(s):  
Avil Terrance Saldanha ◽  
Rajendra Desai ◽  
Rekha Aranha
Keyword(s):  
Soft Drink ◽  
National Income ◽  
Product Category ◽  
The United States ◽  
Soft Drinks ◽  
Data Set ◽  
Food And Beverage ◽  
Research Gap ◽  
The Impact ◽  
Group Product

The purpose of this study is to predict the share of visual inventory (SOVI), which is defined as the number of stock-keeping units (SKUs) of a company’s products, calculated as a percentage of the total SKUs on the display of all products. Research studies in the past have focused mainly on the impact of inventory, which includes back end and visual inventory, on sales but less attention has been given to the impact of SOVI on sales. To address this research gap, this study attempted to create an analytics model to predict SOVI at the category of soft drinks level using four predictor variables namely point of purchase display, channel/sub-channel, package group, product category, and derived variable gross national income (GNI). The results were encouraging confirming the effectiveness of such a model. The researchers utilized a data set collected over a period of 18 months (February 2016 to July 2017) by a soft drink firm headquartered in the United States. Based on the findings, it is suggested that this prediction model can be utilized by other researchers and practitioners to predict SOVI of other soft drinks, fast-moving consumer goods (FMCG), and food and beverage companies.

Are global Phanerozoic marine diversity curves truly global? A study of the relationship between regional rock records and global Phanerozoic marine diversity

Paleobiology ◽  
2008 ◽  
Vol 34 (1) ◽  
pp. 80-103 ◽  
Author(s):  
Alistair J. McGowan ◽  
Andrew B. Smith
Keyword(s):  
Large Scale ◽  
Western Europe ◽  
The United States ◽  
Future Research ◽  
Small Scale ◽  
Data Set ◽  
Diversity Patterns ◽  
Small Areas ◽  
Geological Maps ◽  
Marine Diversity

The consensus view that the amount of rock available for sampling does not significantly and systematically bias Phanerozoic marine diversity patterns has broken down. How changes in rock availability and sampling intensity affect our estimates of past biodiversity has been investigated with a variety of new approaches. A number of proxies for the amount of rock available for sampling have been used, but most of these proxies do not rely directly on evidence from large-scale geological maps (maps that cover small areas) and accompanying memoirs. Most previous map-based studies focused on single regions or relied on small-scale synoptic maps. We collected data from published geological maps and memoirs from western Europe, Australia, and Chile, which we combined with COSUNA data from the United States to generate the first multiregional data set for investigating whether the global Phanerozoic marine diversity record is a true global record, or is instead biased toward North America and Western Europe as has long been suspected. Both short and long-term trends in variation in the amount of outcrop display limited correlation among the regions studied. A series of diversification models obtained better matches to observed fossil diversity from the European and U.S. records than for the Chilean and Australian records, further supporting suspicions that the global Phanerozoic diversity curve is disproportionately influenced by European and U.S. fossil data. These results indicate that future research into Phanerozoic marine diversity patterns should not continue to apply global eustatic curves as a proxy for rock at outcrop, but should use regional data on rock occurrence.

scholarly journals Using Twitter to track immigration sentiment during early stages of the COVID-19 pandemic

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Francisco Rowe ◽  
Michael Mahony ◽  
Eduardo Graells-Garrido ◽  
Marzia Rango ◽  
Niklas Sievers
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Large Scale ◽  
Public Attitudes ◽  
Temporal Frequency ◽  
The United States ◽  
Data Set ◽  
Economic Implications ◽  
Early Stages ◽  
Negative Messages

Abstract Large-scale coordinated efforts have been dedicated to understanding the global health and economic implications of the COVID-19 pandemic. Yet, the rapid spread of discrimination and xenophobia against specific populations has largely been neglected. Understanding public attitudes toward migration is essential to counter discrimination against immigrants and promote social cohesion. Traditional data sources to monitor public opinion are often limited, notably due to slow collection and release activities. New forms of data, particularly from social media, can help overcome these limitations. While some bias exists, social media data are produced at an unprecedented temporal frequency, geographical granularity, are collected globally and accessible in real-time. Drawing on a data set of 30.39 million tweets and natural language processing, this article aims to measure shifts in public sentiment opinion about migration during early stages of the COVID-19 pandemic in Germany, Italy, Spain, the United Kingdom, and the United States. Results show an increase of migration-related Tweets along with COVID-19 cases during national lockdowns in all five countries. Yet, we found no evidence of a significant increase in anti-immigration sentiment, as rises in the volume of negative messages are offset by comparable increases in positive messages. Additionally, we presented evidence of growing social polarization concerning migration, showing high concentrations of strongly positive and strongly negative sentiments.

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