scholarly journals Does Car-Sharing Reduce Car-Use? An Impact Evaluation of Car-Sharing in Flanders, Belgium

2020 ◽  
Vol 12 (19) ◽  
pp. 8155
Author(s):  
Donald A. Chapman ◽  
Johan Eyckmans ◽  
Karel Van Acker
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Experimental Methods ◽  
Car Ownership ◽  
Car Sharing ◽  
Car Use ◽  
Binomial Regression ◽  
Quasi Experimental ◽  
Private Car ◽  
The Impact

Private car-use is a major contributor of greenhouse gases. Car-sharing is often hypothesised as a potential solution to reduce car-ownership, which can lead to car-sharing users reducing their car-use. However, there is a risk that car-sharing may also increase car-use amongst some users. Existing studies on the impacts of car-sharing on car-use are often based on estimates of the users’ own judgement of the effects; few studies make use of quasi-experimental methods. In this paper, the impact of car-sharing on car-ownership and car-use in Flanders, Belgium is estimated using survey data from both sharers and non-sharers. The impact on car-use is estimated using zero-inflated negative binomial regression, applied to matched samples of car-sharing users and non-users. The results show that the car-sharing may reduce car-use, but only if a significant number of users reduce their car-ownership. Policy intervention may therefore be required to ensure car-sharing leads to a reduction in car-use by, for example, discouraging car-ownership. Further research using quasi-experimental methods is required to illuminate whether the promise of car-sharing is reflected in reality.

scholarly journals Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA

2019 ◽  
Vol 11 (17) ◽  
pp. 1958 ◽  
Author(s):  
Hanlin Zhou ◽  
Lin Liu ◽  
Minxuan Lan ◽  
Bo Yang ◽  
Zengli Wang
Keyword(s):  
Negative Binomial ◽  
Infrared Imaging ◽  
Negative Impact ◽  
Positive Impact ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Binomial Regression ◽  
Street Robbery ◽  
The Impact

Previous research has recognized the importance of edges to crime. Various scholars have explored how one specific type of edges such as physical edges or social edges affect crime, but rarely investigated the importance of the composite edge effect. To address this gap, this study introduces nightlight data from the Visible Infrared Imaging Radiometer Suite sensor on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS) to measure composite edges. This study defines edges as nightlight gradients—the maximum change of nightlight from a pixel to its neighbors. Using nightlight gradients and other control variables at the tract level, this study applies negative binomial regression models to investigate the effects of edges on the street robbery rate and the burglary rate in Cincinnati. The Akaike Information Criterion (AIC) of models show that nightlight gradients improve the fitness of models of street robbery and burglary. Also, nightlight gradients make a positive impact on the street robbery rate whilst a negative impact on the burglary rate, both of which are statistically significant under the alpha level of 0.05. The different impacts on these two types of crimes may be explained by the nature of crimes and the in-situ characteristics, including nightlight.

scholarly journals Customer reactions to a webshop’s service quality

Empirica ◽  
2019 ◽  
Vol 47 (4) ◽  
pp. 699-731
Author(s):  
Franz Hackl ◽  
Rudolf Winter-Ebmer
Keyword(s):  
Service Quality ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Product Categories ◽  
Service Characteristics ◽  
Binomial Regression ◽  
Quality Aspects ◽  
Quality Dimensions ◽  
The Impact

Abstract E-commerce has become an integral part of the world’s economy. In this study we investigate the impact of service quality in e-tailing on site visits and consumer demand. Such an analysis is important given the almost Bertrand-like competitive structure. Our analysis is based on a large representative data set obtained from a price comparison site covering essentially the complete Austrian e-tailing market. Customer evaluations for a broad range of 15 different service characteristics are condensed using factor analysis. Negative binomial regression analysis is used to measure the impact of service quality dimensions on referral requests to online shops for different product categories. Our results show that the most important service quality aspects are those related to the ordering process and the firm’s website performance.

Mandated Representation: Exploring the Consequences of Gender Quota Design on Parliamentary Activity

2020 ◽  
Author(s):  
Eva-Maria Euchner ◽  
Elena Frech
Keyword(s):  
Negative Binomial ◽  
Gender Diversity ◽  
Negative Binomial Regression ◽  
Gender Quotas ◽  
Binomial Regression ◽  
Interdisciplinary Framework ◽  
Research Gap ◽  
And Gender ◽  
Gender Quota ◽  
The Impact

Abstract Although the scholarship on legislative behaviour widely agrees that electoral rules determine parliamentary activities, surprisingly little is known on the impact of gender quotas. We contribute to this research gap by developing an innovative interdisciplinary framework and by exploring it based on a unique dataset on varying gender quota designs throughout EU countries and parties running for the 7th term of the European Parliament (2009–2014). Based on the scholarship on gender diversity in management teams and the research on gendered processes in political parties, we argue that especially mandated gender quotas stimulate processes of social categorisation, intergroup biasing and competition due to a normative mis-fit between conceptions of gender equality and gender quotas, which in turn influences coordination and communication and hence, parliamentary activity more generally. Combining negative-binomial regression models and expert interviews, we indeed find that mandated gender quotas promote ‘individual’ parliamentary activities (e.g. speeches) and tend to impede ‘collaborative’ parliamentary activities (e.g. reports).

scholarly journals The impact of political instability upon the increase of terrorism in the Middle East

2021 ◽  
pp. 43-62
Author(s):  
Andrey Vadimovich Novikov
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Political Instability ◽  
Negative Binomial Regression ◽  
Severe Form ◽  
Political Regime ◽  
Level Of Education ◽  
Binomial Regression ◽  
Different Types ◽  
The Impact

The key goal of the article is to examine whether the domestic political instability associated with the “Arab Spring” caused the subsequent surge of global terrorism, which reached its peak in 2014. The author reviews six different types of domestic political instability: antigovernment demonstrations, national strikes, government crises, government repression, disturbances, and revolutions. Using the regression models, the author clarifies the impact of such factors as the level of education, Internet access, economic development, democratization indexes, and the degree of religious and ethnic fragmentariness. Analysis is conducted on the results of the models separately for different types of political regimes, forms of domestic political instability, and global regions. The results of construction and analysis a number of negative binomial regression models testify to the support of “escalation effect”, which implies that heightened intensity of domestic political instability leads to the surge of terrorist attacks. More severe forms of domestic political instability, namely repression and disturbances, generate a higher level of terrorism; however, revolution, as the most severe form of domestic political instability does not produce such effect. The formulated conclusions are also substantiated by the fact that certain forms of political instability have a different impact upon terrorism and its peculiarities, depending on the geographical region and the type of political regime.

The relationship between citations, downloads and alternative metrics in rheumatology publications: a bibliometric study

Rheumatology ◽  
2019 ◽  
Vol 59 (2) ◽  
pp. 277-280 ◽  
Author(s):  
Winnie M Y Chen ◽  
Marwan Bukhari ◽  
Francesca Cockshull ◽  
James Galloway
Keyword(s):  
Negative Binomial ◽  
Positive Association ◽  
Negative Binomial Regression ◽  
Significant Positive Association ◽  
H Index ◽  
Binomial Regression ◽  
Number Of Citations ◽  
The Impact ◽  
The Relationship ◽  
Alternative Metrics

Abstract Objective Scientific journals and authors are frequently judged on ‘impact’. Commonly used traditional metrics are the Impact Factor and H-index. However, both take several years to formulate and have many limitations. Recently, Altmetric—a metric that measures impact in a non-traditional way—has gained popularity. This project aims to describe the relationships between subject matter, citations, downloads and Altmetric within rheumatology. Methods Data from publications in Rheumatology were used. Articles published from 2010 to 2015 were reviewed. Data were analysed using Stata 14.2 (StataCorp, College Station, TX, USA). Correlation between citations, downloads and Altmetric were quantified using linear regression, comparing across disease topics. Relationship between downloads and months since publications were described using negative binomial regression, clustering on individual articles. Results A total of 1460 Basic Science and Clinical Science articles were identified, with the number of citations, downloads and Altmetric scores. There were no correlations between disease topic and downloads (R2 = 0.016, P = 0.03), citations (R2 = 0.011, P = 0.29) or Altmetric (R2 = 0.025, P = 0.02). A statistically significant positive association was seen between the number of citations and downloads (R2 = 0.29, P < 0.001). No correlations were seen between Altmetric and downloads (R2 = 0.028, P < 0.001) or citations (R2 = 0.004, P = 0.445). Conclusion Disease area did not correlate with any of the metrics compared. Correlations were apparent with clear links between downloads and citations. Altmetric identified different articles as high impact compared with citation or download metrics. In conclusion: tweeting about your research does not appear to influence citations.

The Effects of Intensive Postrelease Correctional Supervision on Recidivism: A Natural Experiment

2021 ◽  
pp. 088740342199843
Author(s):  
Grant Duwe ◽  
Susan McNeeley
Keyword(s):  
Negative Binomial ◽  
Cox Regression ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Benefit Analysis ◽  
Intensive Supervision ◽  
Binomial Regression ◽  
The Impact

In July 2018, the Minnesota Department of Corrections revised the criteria it uses to place soon-to-be-released prisoners on intensive supervision by shifting from mostly offense-based conditions to those based exclusively on risk. In doing so, this policy change provided a unique opportunity to evaluate not only the impact of intensive supervision on recidivism but also whether risk-based policies lead to better outcomes. Using Cox regression and negative binomial regression on a sample of 1,818 persons released in 2018, we found that intensive supervised release (ISR) significantly reduced the hazard for general, felony, and violent reoffending. We also found, however, that ISR significantly increased the risk of a technical violation revocation. The findings from our cost–benefit analysis showed that, despite the relatively high costs it incurred, ISR was a cost-effective intervention because it reduced reoffending for those with a higher risk of committing serious, violent crimes.

scholarly journals Loneliness, physical activity and mental health during Covid-19: a longitudinal analysis of depression and anxiety between 2015 and 2020

2020 ◽  
Author(s):  
Byron Creese ◽  
Zunera Khan ◽  
William Henley ◽  
Siobhan O’Dwyer ◽  
Anne Corbett ◽  
...  
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Risk Factors ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Annual Data ◽  
Activity Levels ◽  
Binomial Regression ◽  
Mental Health Questionnaire ◽  
The Impact

BackgroundLoneliness and physical activity are important targets for research into the impact of COVID-19 because they have established links with mental health, could be exacerbated by social distancing policies and are potentially modifiable.MethodWe analysed mental health data collected during COVID-19 from adults aged 50 and over alongside comparable annual data collected between 2015 and 2019 from the same sample. Trajectories of depression (PHQ-9) and anxiety (GAD-7) were analysed with respect to loneliness, physical activity levels and a number of socioeconomic and demographic characteristics using zero-inflated negative binomial regression.Results3,281 people completed the COVID-19 mental health questionnaire, all had at least one data point prior to 2020. In 2020, the adjusted PHQ-9 score for loneliness was 3.2. (95% CI: 3.0-3.4), an increase of one point on previous years and 2 points higher than people not rated lonely, whose score did not change in 2020 (1.2, 95% CI: 1.1-1.3). PHQ-9 was 2.6 (95% CI: 2.4-2.8) in people with decreased physical activity, an increase of 0.5 on previous years. In contrast, PHQ-9 in 2020 for people whose physical activity had not decreased was 1.7 (95% CI: 1.6-1.8), similar to previous years. A similar relationship was observed for GAD-7 though the differences were smaller and the absolute burden of symptoms lower.ConclusionsAfter accounting for pre-COVID-19 trends, we show that experiencing loneliness and decreased physical activity are risk factors for worsening mental health during the pandemic. Our findings highlight the need to examine policies which target these potentially modifiable risk factors.

Managing Threats on the High Seas: The Role of Naval Bases on Reducing Maritime Piracy

2021 ◽  
pp. 0095327X2110494
Author(s):  
Orlandrew E. Danzell ◽  
Jacob A. Mauslein ◽  
John D. Avelar
Keyword(s):  
Global Economy ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Security Issue ◽  
Maritime Piracy ◽  
Time Series Dataset ◽  
Binomial Regression ◽  
The Impact

Weak coastal states often lack an adequate, sustained naval presence to monitor and police their territorial waters. Unpatrolled waters, both territorial and otherwise, may provide pirates with substantial financial opportunities that go far beyond any single country. Maritime piracy costs the global economy on average USD 24 billion per year. This research explores the impact of naval bases on acts of piracy to determine if naval presence can decrease the likelihood of piracy. To examine this important economic and national security issue, our research employs a zero-inflated negative binomial regression model. We also rely upon a newly constructed time-series dataset for the years 1992–2018. Our study shows that the presence of naval bases is essential in helping maritime forces combat piracy. Policymakers searching for options to combat piracy should find the results of this study especially useful in creating prescriptive approaches that aid in solving offshore problems.

A Network of Neighborhoods

2017 ◽  
Vol 54 (6) ◽  
pp. 824-846 ◽  
Author(s):  
Rémi Boivin ◽  
Maurizio D’Elia
Keyword(s):  
Social Disorganization ◽  
Negative Binomial ◽  
Large City ◽  
Negative Binomial Regression ◽  
Risk Level ◽  
Multistage Process ◽  
Census Tracts ◽  
Binomial Regression ◽  
Suitable Area ◽  
The Impact

Objectives: Criminal target choice has been described as a multistage process: An offender first selects a suitable area from a set of alternatives and then chooses a specific target. This article studies area selection and attempts to distinguish between crime generators/visit detractors (elements that could affect anyone) and crime attractors/offense detractors (elements that affect offenders specifically). Methods: Trips that resulted in violent or property crimes between 506 census tracts in a large city ( n = 11,411) are analyzed. Multilevel negative binomial regression is used to assess the impact of measures relating to pairs of tracts and characteristics of destination tracts. Results: Various factors are significantly related to the number of crime-associated trips per pair of tracts: differences in reward (residential and visiting population size, presence of schools or bars), differences in effort (distance between tracts, major roads linking both tracts), and differences in risk (level of social disorganization). Conclusions: This article supports an “opportunistic perspective” on crime: Crime-associated trips are more likely when advantages are high and risks and efforts are low.

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