Search results

This study aims to analyze the relationship between global value chain (GVC) participation and poverty levels. Additionally, it investigates the impact of education levels, specifically analyzing literacy rates and tertiary education rates, on the correlation between GVC participation and poverty in Organization for Economic Co-operation and Development (OECD) and Association of Southeast Asian Nations (ASEAN) countries.

Fixed effect and random effect models will be employed to quantify the relationships between the dependent and independent variables. The Hausman test is applied to determine the appropriate estimator between fixed and random effects. Also, in the model, time-fixed effect or two-way fixed effect has been used to control for unobserved heterogeneity both across entities and over time in panel data analysis.

The findings demonstrate that engagement in GVCs presents a promising avenue for stimulating development, advancing income per capita growth and facilitating job creation. Notably, the results illuminate that the poverty-alleviating impacts of GVC participation are most conspicuous in nations boasting elevated levels of educational attainment among their populace.

This research aims to promote a better understanding of the connection between GVC participation and the level of poverty, with GVC participation decomposed into forward participation and backward participation.

Acquiring a foreign language may be a lifelong endeavor, and this requires one to approach it from a lifelong learning perspective. However, learners may not always be ready for such an approach. It is important to know where learners stand in their orientations toward learning and consider this when planning educational activities. Therefore, it is necessary to determine language learners’ readiness for lifelong learning (LLL) in order to support their language development. This paper reports the findings of a study conducted to identify the LLL propensities of some Turkish and Emirati university students learning English as a foreign language in their local contexts. The study included 61 Emirati and 47 Turkish students, with a mean age of 19. Data were collected using a research tool with three sections: Demographics, the Lifelong Learning Tendency Scale (LLLTS – developed by Coskun & Demirel (2012)), and a survey with six open-ended questions. Student’s t-test, the Kruskal-Wallis test and the Mann-Whitney U-test were used to compare the quantitative data in terms of the participants’ nationalities, gender and length of study. The results showed that both Turkish and Emirati students had a moderate level of propensity for LLL. However, the Turkish students’ overall LLLTS scores as well as certain sub-skills were found to be higher than those of the Emirati students. Gender was not found to make a significant difference in the students’ LLL orientations, while motivation was found to be lower at a statistically significant level for those learning English for more than a year. Suggestions are offered for the development of language learners’ LLL skills.

This paper aims to test the hypothesis that the effect of production slowdown on labour demand can be muted by labour hoarding.

This study adopts a production function approach, using data from Malta, a small state in the EU.

The results confirm the hypothesis and indicate that firms are normally prepared to employ and dismiss more workers in the long run than in the short run.

This finding has important implications for developed countries, including that labour hoarding can be of certain relevance in times of economic slowdown as shocks are absorbed by internal flexibility.

The results of this study add on to the existing literature in two ways. First, this study compares two industries –manufacturing and financial services– for which the former sector received support to hoard labour after the financial turmoil of 2008. Consequently, the dominance of labour hoarding in manufacturing relative to financial services is uncovered and the effect of hoarding practices on labour demand is estimated. Second, Malta is an interesting case because it is one of the smallest economies in the world and faces a high degree of vulnerability because of constraints associated with small size and insularity. As a result, firms adopt policy-induced measures to minimise adjustment costs.

The purpose of this paper is to examine the influence of export logistics components: shipment arrangements, timely delivery, customs quality, trade infrastructure, and tracking and tracing on export competitiveness of firms in the East African Community (EAC).

The study adopted the Structural Gravity Model and the Poisson pseudo-maximum likelihood (PPML). PPML a nonlinear estimation method was applied in STATA on a balanced panel data for the period of 2007–2018. Data were obtained from World Bank International Trade Centre (ITC), World Bank Logistics Performance Index (LPI) and World Bank development indicators.

Results show that timely delivery and tracking and tracing of exports are positive and significant predictors of export competitiveness in EAC countries. Conversely, shipment arrangements, customs quality and trade infrastructure have no influence on export competitiveness.

The results of this study show that export logistics components of shipment arrangements, customs quality and trade infrastructure do not matter at the present in improving export competitiveness in the EAC. There is a need to examine the intricate nature of the EAC economy to further this study's findings.

The EAC partner states should embrace deep integration by removing the behind the border trade barriers in addition to other trade restrictions, to create a common economic space among member states. This will further shrink the delivery time and the tracking and tracing of exports hence improving the competitiveness of EAC exports within the region and outside. Also, common and harmonized trade policies and regulations should be implemented through mutual recognition agreements where countries agree to recognize one another's conformity assessments.

This study explains the complex dynamic interactions of export logistics factors in the EAC using quantitative data and that this interaction has an effect on the export competitiveness in import-dominated countries with less harmonization in their trade policies.

This study aims to consider how migrants may act as channel of diffusion of knowledge which contributes to the dynamics of trade and comparative advantages of EU and MENA countries for the period 1990–2015.

Adopting an IV approach and a gravity framework to instrument for migration, the authors document how variations in stocks of migrants coming from (in) countries that are already competitive exporters of a given product impact on the probability that the destination (home) country starts to export competitively new products or succeed in exporting more intensively.

Controlling for potential confounding factors which can be correlated to knowledge flows and productivity shifts, the authors find trade-promoting effects via migration flows (mostly immigration) between the two areas, testing our hypotheses by different technology classes of products and different specifications.

The contribution of this work to the literature is threefold. First, by providing evidence on international knowledge diffusion induced by migration flows between MENA and EU regions, like no other work before, the authors document the effects of migration on trade and comparative advantages. Second, unlike standard literature on migration-trade link, the authors focus more on long-term structural changes in comparative advantages than on trade volumes. Third, we exploit how the effect of migration on margins of trade varies according to different types of goods, classified by technological level.

Special and differential treatment (SDT) in the World Trade Organisation (WTO) has failed to integrate developing countries into the international trading system, as contemplated by the WTO Agreement, itself. This paper aims to interrogate the current application of SDT by WTO members as the possible undermining factor for SDT not delivering on its objective.

The research uses a qualitative legal methodology. This study conducts desk analysis of primary legal materials and existing literature to assess current reflections of SDT and draw lessons for reforms in the WTO.

From interrogating current SDT practice in the WTO and a comparative analysis with a similar differential treatment under the Montreal Protocol, this paper finds that indeed, the problem lies in the current approach to SDT application in the WTO. This study finds that the existing absence of eligibility criteria for determining access to SDT by countries is the core reason for the abuse and sub-optimal outcome from its application.

While making a case for a rules-based approach to differentiation in the WTO, this paper proposes a unique methodology for differentiating between developing countries for SDT, including the use of a composite indicator to ensure that indicators that are used sufficiently reflect their heterogeneous needs. Drawing inspiration from Gonzalez et al. (2011a), this study introduces an adaptation for selecting a threshold for graduation. Specifically, the proposal on the value of the standard deviation of countries from the weighted mean of the composite indicator as the threshold for graduating countries from SDT is novel.

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.