Do special issues offer something special? An overview of research trends in Telecommunications Policy special issues

This study explores the value of special issues by analyzing the characteristics of special issues of Telecommunications Policy (TP). Specifically, this study examines the status of special issue publications, the characteristics of their articles compared with those of general issues, and the factors affecting article citations. For the analysis, all information on TP articles published from 1976 to 2018 was collected from the Scopus database. A comparative analysis as well as a negative binomial regression were conducted on papers published after 1994, when the first special issue was published. The results show that special issues are distinguished from general issues by various characteristics, such as multinational author collaboration, number of references, keywords, and funding. While general issue papers are more frequently cited in academia, special issue papers show better performance in terms of online captures and social media sharing. Moreover, the smaller number of variables affects the frequency of citations in the case of special issues, revealing that content and format are more influential than author factors.     

Monetary policy and food inflation in South Africa: A quantile regression analysis

Although optimal monetary policy stabilizes food inflation theoretically, empirical studies remain limited not only in the context of volumes and the estimation approaches, but are focused on selected advanced and emerging countries to the neglect of Africa where poverty and dominance of food in the consumption basket are more pronounced. We provide empirical evidence in the context of South Africa using quantile regressions. Rising food prices are destabilized even further by restrictive monetary policy; a finding that has ramifications for inflation targeting, especially given that a quarter of the country’s population is food poor.     

Reconciling Alternative Theories for the Safety of Owner–Operators

Motor carrier safety is an important concern of shippers, carriers, policy makers, consignees, insurance providers, and the motoring public. One aspect of carrier safety that has garnered substantial attention is whether carriers making greater use of owner–operators are more or less safe vis‐à‐vis carriers making greater use of employee drivers. Currently, conflicting theoretical predictions exist regarding the direction of this relationship. In this article, we offer a reconciliation of the alternative theoretical predictions by developing a coherent theory that merges sociological rational choice theory and theory regarding motor carrier safety. We subject our theory to empirical testing by fitting a series of seemingly unrelated regression models to a vector of safety measures tracked as part of the Federal Motor Carrier Safety Administration's Compliance, Safety, and Accountability program. Our results are consistent with our proposed theory of owner—operator safety and provide meaningful theoretical and managerial implications and directions for future research.     

Labor market analysis through transformations and robust multivariate models

The work presents a robust approach to labor share analysis. The estimate of labor share presents various complexities related to the nature of the data sets to be analyzed. Typically, labor share is evaluated by using discriminant analysis and linear or generalized linear models, that do not take into account the presence of possible outliers. Moreover, the variables to be considered are often characterized by a high dimensional structure. The proposed approach has the objective of improving the estimation of the model using robust multivariate regression techniques and data transformation.     

Victory margins and the paradox of voting

This paper addresses a simple question: why do people vote? Though simple, this question remains unanswered despite the considerable attention it has received. In this paper, I show that purely rational–instrumental factors explain a large fraction of turnout variations, provided that the effect of the margin of victory on implemented policy is considered. I extend Myerson's models of elections based on Poisson games, and show that, when platforms are responsive to vote shares, the predictions of the model become consistent with several stylized facts, including the secular fall in turnout rates in the US.     

Estimating the Size of a Criminal Population from Police Records Using the Truncated Poisson Regression Model

The truncated Poisson regression model is used to arrive at point and interval estimates of the size of two offender populations, i.e. drunk drivers and persons who illegally possess firearms. The dependent capture–recapture variables are constructed from Dutch police records and are counts of individual arrests for both violations. The population size estimates are derived assuming that each count is a realization of a Poisson distribution, and that the Poisson parameters are related to covariates through the truncated Poisson regression model. These assumptions are discussed in detail, and the tenability of the second assumption is assessed by evaluating the marginal residuals and performing tests on overdispersion. For the firearms example, the second assumption seems to hold well, but for the drunk drivers example there is some overdispersion. It is concluded that the method is useful, provided it is used with care.     

Modelling soccer matches using bivariate discrete distributions with general dependence structure

In this paper copulas are used to generate bivariate discrete distributions. These distributions are fitted to soccer data from the English Premier League. An interesting aspect of these data is that the primary variable of interest, the discrete pair shots-for and shots-against, exhibits negative dependence; thus, in particular, we apply bivariate Poisson-related distributions that allow such dependence. The paper focuses on Archimedian copulas, for which the dependence structure is fully determined by a one-dimensional projection that is invariant under marginal transformations. Diagnostic plots for copula fit based on this projection are adapted to deal with discrete variables. Covariates relating to within-match contributions such as numbers of passes and tackles are introduced to explain variability in shot outcomes. The results of this analysis would appear to support the notion that playing the 'beautiful game' is an effective strategy—more passes and crosses contribute to more effective play and more shots on the goal.     

Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method

Product life cycle (PLC) prediction plays a crucial role in strategic planning and policy definition for high-technology products. Forecast methodologies which can predict PLCs accurately can help to achieve successful strategic decision-making, forecasting, and foresight activities in high-technology firms, research institutes, governments, and universities. Over the past few decades, even though analytic framework strategies have been proposed for production, marketing, R&D (research and development), and finance, aiming at each stage of PLCs, forecast methodologies with which to predict PLCs are few. The purpose of this research is to develop a novel forecast methodology to allow for predictions of product life time (PLT) and the annual shipment of products during the entire PLC of multiple generation products. A novel two-stage fuzzy piecewise regression analysis method is proposed in this paper. In the first stage, the product life-time of the specific generation to be analyzed will be predicted by the fuzzy piecewise regression line that is derived based upon the product life-time of earlier generations. In the second stage of the forecast methodology, the annual shipment of products of the specified generation will be predicted by deriving annual fuzzy regression lines for each generation, based upon the historical data on the earlier generations' products. An empirical study predicting the life-time and the annual shipment of the 16 Mb (Mega bit) DRAM (Dynamic Random Access Memory) PLC is illustrated to validate the analytical process. The results demonstrate that two-stage fuzzy piecewise regression analysis can predict multiple generation PLT and PLC precisely, thereby serving as a foundation for future strategic planning, policy definitions and foresights.     

Valuing Food Store Access: Policy Implications for the Food Stamp Program

Food stamp recipients may lack access to larger stores, reducing the availability of nutritious food. Reliance on smaller stores may have undesirable impacts. Policy alternatives include limiting food stamp redemption to larger stores and subsidizing store development. I estimate that limiting redemption to supermarkets and grocery stores, or supermarkets alone, results in losses ranging from $499.2 million to $1.05 billion, or $1.17 billion to $2.44 billion (respectively) annual loss in food stamp recipient welfare nationwide. The impact of improving access is also significant, ranging from $333.6 million to $931.2 million.