A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

Global Networks of Trade and Bits

Considerable efforts have been made in recent years to produce detailed topologies of the Internet. Although Internet topology data have been brought to the attention of a wide and somewhat diverse audience of scholars, so far they have been overlooked by economists. In this paper, we suggest that such data could be effectively treated as a proxy to characterize the size of the "digital economy" at country level and outsourcing: thus, we analyse the topological structure of the network of trade in digital services (trade in bits) and compare it with that of the more traditional flow of manufactured goods across countries. To perform meaningful comparisons across networks with different characteristics, we define a stochastic benchmark for the number of connections among each country-pair, based on hypergeometric distribution. Original data are thus filtered by means of different thresholds, so that we only focus on the strongest links, i.e., statistically significant links. We find that trade in bits displays a sparser and less hierarchical network structure, which is more similar to trade in high-skill manufactured goods than total trade. Lastly, distance plays a more prominent role in shaping the network of international trade in physical goods than trade in digital services.Comment: 25 pages, 6 figure

Surviving the global financial crisis : foreign ownership and establishment performance

This paper examines how different establishments performed during the recent global financial crisis, focusing on the role of foreign ownership. The paper investigates how foreign ownership affected establishments'responses to negative economic shocks, using a cross-country panel dataset with detailed information on operation, location and industry for more than 12 million establishments from 2005-2008. The evidence shows that multinational subsidiaries on average fared better than local counterfactuals with similar economic characteristics. Among multinational subsidiaries, establishments with stronger production and financial linkages with parent companies showed greater resilience. Finally, in contrast to the crisis period, the impact of foreign ownership and linkages on an establishment's performance was insignificant in non-crisis years.Economic Theory&Research,Investment and Investment Climate,Bankruptcy and Resolution of Financial Distress,Emerging Markets,Economic Conditions and Volatility

Which Sectors of a Modern Economy are most Central?

We analyze input-output matrices for a wide set of countries as weighted directed networks. These graphs contain only 47 nodes, but they are almost fully connected and many have nodes with strong self-loops. We apply two measures: random walk centrality and one based on count-betweenness. Our findings are intuitive. For example, in Luxembourg the most central sector is “Finance and Insurance” and the analog in Germany is “Wholesale and Retail Trade” or “Motor Vehicles”, according to the measure. Rankings of sectoral centrality vary by country. Some sectors are often highly central, while others never are. Hierarchical clustering reveals geographical proximity and similar development status.

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

Introducing clustering model for auto parts manufacturing companies

The index of development of every country is measured by the amount of its industrialization. The comparative statistics show that the level of industrial GDP in developed countries is much higher than in Iran. The influence of automotive industry on the redevelopment of satellite sectors such as steel, rubber, Polymer, petrol and petrochemicals, electrical and electronics, ceramics and etc. is very high. In view of the fact that the cluster system defines a collection of united small and medium enterprises which are distributed in a particular region, these companies do face with global and local threats, and opportunities in manufacturing of their products. In particular, the auto industry as one of the most important sector in the region qualifies for the application of clustering method, in order to unify efforts of the SME companies. A suitable cluster model if carefully applied, can result in increasing output and quality of parts, decreasing overhead costs, specialization of skills, increase of employment, equal opportunities and etc. This paper represents a thorough study of all SME’s for auto parts manufacturers in North West province of Azerbaijan. On the basis of these finding and the studies carried out, a suitable Cluster model within UNIDO requirements has been devised and developed, to suit the needs of all SME’s auto parts manufacturers in the region. The paper discusses and analysis the cluster model in details

Conservation priorities for Prunus africana defined with the aid of spatial analysis of genetic data and climatic variables

Conservation priorities for Prunus africana, a tree species found across Afromontane regions, which is of great commercial interest internationally and of local value for rural communities, were defined with the aid of spatial analyses applied to a set of georeferenced molecular marker data (chloroplast and nuclear microsatellites) from 32 populations in 9 African countries. Two approaches for the selection of priority populations for conservation were used differing in the way they optimize representation of intra-specific diversity of P. africana across a minimum number of populations. The first method (Si) was aimed at maximizing genetic diversity of the conservation units and their distinctiveness with regard to climatic conditions, the second method (S2) at optimizing representativeness of the genetic diversity found throughout the species' range. Populations in East African countries (especially Kenya and Tanzania) were found to be of great conservation value, as suggested by previous findings. These populations are complemented by those in Madagascar and Cameroon. The combination of the two methods for prioritization led to the identification of a set of 6 priority populations. The potential distribution of P. africana was then modeled based on a dataset of 1,500 georeferenced observations. This enabled an assessment of whether the priority populations identified are exposed to threats from agricultural expansion and climate change, and whether they are located within the boundaries of protected areas. The range of the species has been affected by past climate change and the modeled distribution of P. africana indicates that the species is likely to be negatively affected in future, with an expected decrease in distribution by 2050. Based on these insights, further research at the regional and national scale is recommended, in order to strengthen P. africana conservation efforts

International Sourcing, Product Complexity and Intellectual Property Rights

In this paper, we propose the technological complexity of a product and the level of Intellectual Property Rights (IPRs) protection to be the co-determinants of the mode through which multinational firms purchase their goods. We study the choice between intra-firm trade and outsourcing given heterogeneity at the product- (complexity), firm- (productivity) and country- (IPRs) level. Our findings suggest that the above three dimensions of heterogeneity are crucial for complex goods, where firms face a trade-off between higher marginal costs in the case of trade with an affiliate and higher imitation risks in the case of sourcing from an independent supplier. We test these predictions by combining data from a French firm-level survey on the mode choice for each transaction with a newly developed complexity measure at the product-level. Our fractional logit estimations confirm the proposition that although firms are generally reluctant to source highly complex goods from outside the firm’s boundaries, they do so when a strong IPR regime in the host country guarantees the protection of their technology.Sourcing Decision, Product Complexity, Intellectual Property Rights, Fractional Logit Estimation