Search results

This study intends to explore the impact of World Trade Organization (WTO) which came into existence from January 1, 1995, on the export share of developing counties in the world exports of all goods together in US$, that is, in global merchandise trade. This study endogenously determines the structural break in changing export share of developing countries and how are they related to the major changes in the multilateral trading systems of international trade, in particular, the introduction of the WTO by following a multiple breakpoint analysis due to Bai–­Perron. In this context, it would be worthwhile to note that the shift toward more export-oriented strategies by a large number of developing countries has accelerated the growth of LDC exports. This study also compares the changing share of merchandise exports and trade in commercial ­services for developing countries and the LDCs in the Post-WTO regime. The ­authors follow a univariate time-series exploratory analysis to understand the trend in world export shares of all goods and commercial services for different regions of the developing world and demonstrate the potential of these regions in the expansion of trade. The study, while evaluating the impact of WTO in changing export share in terms of structural change analysis, enables us to understand the role tariff cut in the developed countries on the imports from developing countries. This study also observes increasing inequality in terms of export share among different regions of the developing world.

Financial sustainability in emerging market economies crucially depends on stable foreign capital inflows as these countries lack adequate domestic capital and sophisticated technology. This study attempts to examine the impact of major political risk factors in the emerging market economies along with basic economic fundamentals such as institutional variables like per capita electric consumption, trade openness, and real rate of interest. We have followed a static panel data approach in studying the impact of these crucial variables in Foreign Direct Investment (FDI) inflows in 15 major emerging economies for the period 2000–2014. Risk perceptions, i.e., political risk data, have been collected from the International Country Risk Guide (ICRG) provided by the Political Risk Services (PRS) Group. In our research purpose, we have considered dependent variable as FDI inflows for 15 emerging countries during the period 2000–2014, which are drawn from the United Nations Conference on Trade and Development (UNCTAD, 2014, 2015) FDI database. Our results demonstrate that there are six subcomponents of risk perception (political risk) which are statistically significant in explaining variation in FDI inflows of the major emerging countries. The results show that government stability, socioeconomic conditions, religious tension, and bureaucracy quality have a positive impact on FDI inflows of emerging countries, whereas internal conflict and law and order have a negative impact on FDI inflows of these countries. Stable government is more attractive to foreign investors. Again, an improvement in the socioeconomic conditions is positively related with FDI inflows in emerging countries. Decreasing bureaucracy leads to a reduction in corruption, and assists expanding FDI flows in the emerging country.

The present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface roughness (Ra) and cutting tool flank wear (VB) while hard turning of AISI 4340 steel (35 HRC) under dry environment.

In this study, Taguchi L16 design of experiments methodology was chosen. The experiments were performed under dry machining conditions using TiSiN-TiAlN nanolaminate PVD-coated cutting tool on which Taguchi and responses surface methodology (RSM) for single objective optimization and MCDM methods like the multi-objective optimization by ratio analysis (MOORA) were applied to attain optimal set of machining parameters. The predictive models for each response and multiresponse were developed using RSM-based regression analysis. S/N ratios, analysis of variance (ANOVA), Pareto diagram, Tukey's HSD test were carried out on experimental data for profound analysis.

Optimal set of machining parameters were obtained as cutting speed: at 180 m/min., feed rate: 0.05 mm/rev., and depth of cut: 0.15 mm; cutting speed: 145 m/min., feed rate: 0.20 mm/rev. and depth of cut: 0.1 mm for Ra and VB, respectively. ANOVA showed feed rate (96.97%) and cutting speed (58.9%) are dominant factors for Ra and VB, respectively. A remarkable improvement observed in Ra (64.05%) and VB (69.94%) after conducting confirmation tests. The results obtained through the MOORA method showed the optimal set of machining parameters (cutting speed = 180 m/min, feed rate = 0.15 mm/rev and depth of cut = 0.25 mm) for minimizing the Ra and VB.

This work contributes to realistic application for manufacturing industries those dealing with AISI 4340 steel of 35 HRC. The research contribution of present work including the predictive models will provide some useful guidelines in the field of manufacturing, in particular, manufacturing of gear shafts for power transmission, turbine shafts, fasteners, etc.