Does the Prevalence of Obesity Affect the Demand for Soft Drinks? Evidence from Cross-Country Panel Data

The impact of soft drinks on obesity has been widely investigated during the last decades. Conversely, the role of obesity as a factor influencing the demand for soft drinks remains largely unexplored. However, understanding potential changes in the demand for soft drinks, as a result of changes in the spread of obesity, may be useful to better design a comprehensive strategy to curb soft drink consumption. In this paper, we aim to answer the following research question: Does the prevalence of obesity affect the demand for soft drinks? For this purpose, we collected data in a sample of 97 countries worldwide for the period 2005–2019. To deal with problems of reverse causality, an instrumental variable approach and a two-stage least squares method were used to estimate the impact of the age-standardized obesity rate on the market demand for soft drinks. After controlling for several demographic and socio-economic confounding factors, we found that a one percent increase in the prevalence of obesity increases the consumption of soft drinks and carbonated soft drinks by about 2.37 and 1.11 L per person/year, respectively. Our findings corroborate the idea that the development of an obesogenic food environment is a self-sustaining process, in which obesity and unhealthy lifestyles reinforce each other, and further support the need for an integrated approach to curb soft drink consumption by combining sugar taxes with bans, regulations, and nutrition education programs.

Hierarchical clustering for multiple nominal data streams with evolving behaviour

Over the decade, a number of attempts have been made towards data stream clustering, but most of the works fall under clustering by example approach. There are a number of applications where clustering by variable approach is required which involves clustering of multiple data streams as opposed to clustering data examples in a data stream. Furthermore, a few works have been presented for clustering multiple data streams and these are applicable to numeric data streams only. Hence, this research gap has motivated current research work. In the present work, a hierarchical clustering technique has been proposed to cluster multiple data streams where data are nominal. To address the concept changes in the data streams splitting and merging of the clusters in the hierarchical structure are performed. The decision to split or merge is based on the entropy measure, representing the cluster’s degree of disparity. The performance of the proposed technique has been analysed and compared to Agglomerative Nesting clustering technique on synthetic as well as a real-world dataset in terms of Dunn Index, Modified Hubert $$\varGamma $$ Γ statistic, Cophenetic Correlation Coefficient, and Purity. The proposed technique outperforms Agglomerative Nesting clustering technique for concept evolving data streams. Furthermore, the effect of concept evolution on clustering structure and average entropy has been visualised for detailed analysis and understanding.

Probabilistic assessment of complex system consisting three subsystems, multi-failure threats and copula repair approach

PurposeThe purpose of this paper is to deliberate the system reliability of a system in combination of three subsystems in a series configuration in which all three subsystems function under a k-out-of-n: G operational scheme. Based on computed results, it has been demonstrated that copula repair is better than general repair for system better performance. The supplementary variable approach with implications of copula distribution has been employed for assessing the system performance.Design/methodology/approachProbabilistic assessment of complex system consisting three subsystems, multi-failure threats and copula repair approach is used in this study. Abbas Jubrin Bin, V.V. Singh, D.K. Rawal, in this research paper, have analyzed a system consisting of three subsystems in a series configuration in which all three subsystems function under a k-out-of-n: G operational scheme. The supplementary variable approach with implications of copula distribution has been employed for assessing the system performance. Based on computed results, it has been demonstrated that copula repair is better than general repair for system better performance.FindingsIn this analysis, four different cases of availability are analysed for Gumbel–Hougaard family copula and also four cases for general repair with similar failure rates are studied. The authors found that when failure rates increase, the system availability decreases, and when the system follows copula repair distribution, the system availability is better than general repair.Research limitations/implicationsThis research may be implemented in various industrial systems where the subsystems are configured under k-out-of-n: G working policy. It is also advisable that copula repair is highly recommended for best performances from the system. On the basis of mean time to system failure (MTSF) computations, the failure rate which affects system failure more needs to be controlled by monitoring, servicing and replacing stratagem.Practical implicationsThis research work has great implications in various industrial systems like power plant systems, nuclear power plant, electricity distributions system, etc. where the k-out-of-n-type of system operation scheme is validated for system operations with the multi-repair.Originality/valueThis work is a new work by authors. In the previously available technical analysis of the system, the researchers have analyzed the repairable system either supplementary variable approach, supplementary variable and system which have two subsystems in a series configuration. This research work analyzed a system with three subsystems with a multi-repair approach and supplementary variables.

Corporate social responsibility and systematic risk: international evidence

PurposeThis paper aims to close gaps in the current literature according to whether there are differences regarding the relationship between corporate social performance (CSP) and systematic risk when diverse regions of the world are considered, and what the respective drivers for this relationship are. Furthermore, it tests the robustness to alternative measures for CSP and systematic risk.Design/methodology/approachThis study focuses on the impact of corporate social responsibility on systematic firm risk in an international sample. The authors measure CSP emerging from a company's social responsibility efforts by utilizing a CSP rating framework that covers a variety of dimensions. The instrumental variable approach is applied to mitigate endogeneity and identify causal relationships.FindingsThe impact of overall CSP on systematic risk is most distinct for North American firms and, in descending order, weaker in Europe, Asia–Pacific and Japan. Risk mitigation applies across all four regions. However, the magnitude of impact differs. While the most critical drivers in North America and Japan include product responsibility, Europe is affected most by the employees category and Asia–Pacific by environmental innovation.Practical implicationsThe findings help firms to control their cost of equity and investors may identify low-risk stocks by considering certain aspects of CSP.Originality/valueThis study distinguishes itself from previous literature addressing the connection between systematic risk and CSP by focusing on regional differences in an international sample, using the very transparent CSP measures of Asset4, identifying underlying impact drivers, and testing for robustness to alternative measures of systematic risk.

On the Macroeconomic Consequences of Over-Optimism

Analyzing International Monetary Fund (IMF) data, we find that overly optimistic growth expectations for a country induce economic contractions a few years later. To isolate the causal effect, we take an instrumental variable approach—exploiting randomness in the country allocation of IMF mission chiefs. We first document that IMF mission chiefs differ in their individual degrees of forecast optimism, yielding quasi-experimental variation in the degree of forecast optimism at the country level. The mechanism appears to run through excessive accumulation of debt (public and private). Our findings illustrate the potency of unjustified optimism and underline the importance of basing economic forecasts upon realistic medium-term prospects. (JEL C53, E23, E27, E32, F33, H63)

Impact of foreign domestic workers on the fertility decision of households: evidence from Hong Kong

A potential solution to low fertility is the employment of foreign domestic workers (FDWs), who substitute child-rearing and housework duties, thus reducing child-rearing costs. Recent studies argue that the flow of low-skilled foreign workers into the childcare sector influences fertility choice. However, these studies mainly use the availability of FDWs in the local area as the causal inference and focus on Western countries, making it difficult to identify individual direct effects or generalize the findings to other countries. To bridge this research gap and examine the impacts, this study uses household data from the Hong Kong census. Employing ordinary least squares, the inverse probability weighted regression adjustment, and the instrumental variable approach, we find that households that employ live-in FDWs give birth to more children. Moreover, the heterogeneous analysis reveals that women's greater proportional contribution to household income has a positive impact on households' fertility response after employing the FDWs.