Search results

All countries need to track the implementation of their educational policies and legislation. The justifications and pressures for mapping such developments are very clear at the international level, as can be seen in the 2006 United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) call for data collection and monitoring at State level. They are also apparent at the European level, as outlined in the European Council priorities and targets outlined in the education and training ET 2020 strategy. This chapter will focus on the current situation in European Union member countries. It will attempt to identify the issues that must be addressed by systems of data collection at international and national levels in order to monitor learners’ rights to inclusive education.

Introduction: The Great Financial Crisis of 2008 highlighted the importance of financial cycle fluctuations. While the regulatory response was to mandate higher bank capital requirements during the financial cycle upswing, academic research focussed on identifying the best performing early warning indicators to forecast financial cycle fluctuations that have proven to be often unrelated to business cycle changes. To safeguard the global financial system against the financial cycle fluctuations, Basel Committee of Banking Supervisors, based on first strands of empirical evidence, proposed the credit-to-GDP gap as the headline indicator tied to the countercyclical capital buffer. However, later research on this indicator identified certain concerns, among them subpar performance for economies with short available data series.

Aim of the Study: To this end this study aims to analyse various financial cycle indicators from a unique perspective of their potential viability under limited historical data availability.

Methods: For this purpose, a meta-study of existing research is carried out as well as an empirical study to compare performance of certain indicators for the sample of six countries in the Central, Eastern and South-Eastern European region, where long data series are not available.

Main Findings: It was found that certain approaches, among them calculation of raw credit growth rate and application of Hamilton filter, can supplement or possibly even outperform the Basel credit-to-GDP gap indicator under limited data availability.

Conclusion: Author concludes that for limited time series Basel credit-to-GDP gap can be potentially outperformed by other indicators and further research in this currently under-studied field is warranted.

Originality of the Paper: By using various financial cycle indicators that already proven their early warning prediction powers from previous research, this study focusses on their potential viability under limited historical data availability. Respective findings might be appreciated for supplementing policy-makers’ toolkits as complementary indicators in cases where there is no available long time series for financial cycle estimation, for example, such as countries that entered market economies relatively late.

There is widespread awareness that evidenced-based policy-making is critical for the long-term development of inclusive education systems. Policy-makers, data collection experts and researchers are aware of the need for data collection at national level that not only meets the requirements of international policy guidelines, but also works within a shared approach so as to promote a synergy of efforts at national and international levels.

Monitoring inclusive education at the system level is increasingly seen as a priority for country and EU level decision-makers. However, what form this monitoring should take and what issues it should focus upon are less clear.

This chapter looks across a number of recent European Agency studies in order to highlight and consider key issues and questions in relation to monitoring the implementation of inclusive education in terms of a system’s efficiency, effectiveness and ability to be equitable for all learners.

By drawing upon the findings of European Agency work considering a range of policy priority areas, it is possible to highlight a number of common factors that apply to monitoring the dimensions of efficiency, effectiveness and equity in different educational contexts or systems.

International construction joint ventures (ICJVs) are an effective strategy for construction companies worldwide for delivering large and complex projects. Despite numerous ICJVs studies, there is a lack of comprehensive empirical examination of what drives ICJVs implementation. This study aims to investigate the key drivers for implementing ICJVs through an international survey.

Grounded on a comprehensive literature review and structured questionnaire survey, 123 ICJV experts' responses from 24 different countries/jurisdictions were analyzed using inferential and descriptive statistics. Mann–Whitney U test was used to determine any divergence of ranking of the drivers by the experts. Factor analysis (FA) was used to identify the clusters underlying the key drivers. Rank agreement analysis was later used to investigate the consensus between experts from developing and developed countries/jurisdictions on their ranking of the clusters.

Out of 34 factors, 26 factors greatly drive the implementation of ICJVs. Mann–Whitney U test results prove the absence of significant disparity among the experts in the ranking of the drivers. Six clusters were obtained through factor analysis (FA), namely, market-penetration and innovation-driven drivers, legal and market-driven drivers, fiscal incentives and market expansion drivers, personal branding drivers, sustainable advantage/power drivers and industrial and organizational promotion drivers. Rank agreement analysis exhibited varied levels of concurrence between professionals from developed and developing countries/jurisdictions.

The appreciation of the factors motivating ICJVs is beneficial to the successful implementation of ICJV strategies. A clear understanding of the drivers can help practitioners and policymakers to customize their ICJVs to reap the expected benefits.

The study has generated valuable insights into the factors that are greatly driving the implementation of ICJVs worldwide. While the findings of this study provide a profound contribution to theory and practice, it contributes to sustainable growth in different perspectives.

The purpose of this paper is to look at the journey and experience of the University of Hong Kong (HKU) Research Data Management (RDM) practice to respond to the needs of researchers in an academic library.

The research data services (RDS) practice is based on the FAIR data principle. And the authors designed the RDM Stewardship framework to implement the RDS step by step.

The HKU Libraries developed and implemented a set of RDS under a research data stewardship framework in response to the recent evolving research needs for RDM amongst the academic communities. The services cover policy and procedure settings for research data planning, research data infrastructure establishment, data curation services and provision of online resources and instructional guidelines.

This study provides an example of an approach to respond to the needs of the academic libraries about how to start the RDS including the data policy, data repository, data librarianship and data curation.

The onset of climate change is expected to result in variations in weather patterns which can exacerbate water scarcity issues. This can potentially impact the economic productivity of nations as economic activities are highly dependent on water especially for agricultural countries. In response to this, the concepts of virtual water and water footprint have been introduced as metrics for measuring the water intensity of products, services and nations. Researchers have thus looked into virtual water trade flows as a potential strategy for alleviating water scarcity. The paper aims to discuss these issues.

Environmentally extended input-output models (IOMs) are often used to analyze interactions between economic and ecological systems. This work thus develops a multi-regional input-output model for optimizing virtual water trade between different geographic regions in consideration of local environmental resource constraints, product demands and economic productivity.

A case study on agriculture crop production and trade in different regions of the Philippines is utilized to demonstrate the capabilities of the model. The results show that the optimal strategy does not necessarily limit a water-scarce region to produce less water-intensive crops.

The model uses an input-output framework whose fixed coefficients reflect a fixed technological state. As such, the model is best used for short-term projections, or projections for mature technological state (i.e. where no major gains in efficiency or yield can be foreseen).

The proposed modeling framework can be used in any geographic region (provided relevant statistical data are available for calibration) to provide decision support for optimal use of limited water resources.

The model proposed in this work has general applicability to the optimal planning of agro-industrial systems under water footprint constraints. This modeling approach will be particularly valuable in the future, as climate change causes changes in precipitation patterns and water availability.

The purpose of this paper is to (a) examine the current state of the digital economy in Southeast Asia (SEA), the challenges and opportunities derived from digital transformation and the digital economy, and the impact of the digital economy on SEA, especially human and physical capital development, and (b) propose policy recommendations for SEA countries to better manage digital transformation.

This is a conceptual paper. The theoretical framework has been built from the three-sector governance approach to identify the issues of the digital economy and propose solutions to address the issues. Specifically, it examines the role and activities of the public sector, the private sector and the third sector to address the challenges posed by the digital economy, especially human and physical capital development.

This paper revealed challenges and opportunities derived from the experience of Southeast Asian countries and proposed several policies recommendations, including a common data policy and payment platform for the Asian region, a good training and development policy to equip the workforce with digital skills, and digital mindset as well as build cybersecurity capability and capacity at the regional level.

This paper is significant as it examines the development of the digital economy from an interdisciplinary perspective – including economics, digitalisation, governance, management, public policy, technology and human resource development. It also provides better insights into how SEA's digital economic development can be further improved to contribute to a sustainable regional economy.

This study aimed to know the effect of cross-sectional risk, which comprises business-specific risk and stock market volatility, as a variable for estimating macroeconomic risk in Indonesia. This study observes public companies in Indonesia and Indonesian macroeconomic data from 2004 to 2020. In this study, the author uses term spread as the dependent variable that reflects macroeconomic risk. The cross-sectional risk comprises financial friction (FF), cash flow (CF), debt–service ratio, and stock market volatility as independent variables. By using the Autoregressive Distributed Lag (ARDL) Model method, this study shows that business-specific and stock market risk can estimate macroeconomic risk, so that it becomes an early signal of economic shock, such as recession or high inflation, in the future. The model in this study also examines the cross-sectional risk relationship with other macroeconomic indicators, such as the Consumer Confidence Index (CCI), money supply (M0), and Indonesia’s trade balance (TB).

In the past two decades, there has been a remarkable decline in inflation in both developed and developing countries, in sharp contrast to the period immediately preceding it. Interestingly, the behaviour of inflation in India broadly exhibits such a pattern. For much of the 1970s and 1980s, India experienced recurrent bouts of high inflation together with sub‐par economic performance. Since the 1990s the inflation record has been far better. The purpose of this paper is to answer an important question about what ultimately brought on this improved economic outcome.

A time‐varying parameter model for inflation is proposed which nests all the plausible explanations. The time variation in parameters is modelled as driftless random walks, and is estimated using the median unbiased estimator. The median unbiased estimate helps in addressing the pile‐up problem, which arise if variances of the state specification are small. In such cases the maximum likelihood estimates are biased towards zero. Kalman Filter algorithm is used to obtain the time path of the parameters of the reduced form equation.

The estimated time paths of the reaction function coefficients suggest gradual changes in the rule coefficients. It has been found that while better monetary policy and structural change have played a non‐trivial role, good luck and exchange rate regime have played a major role in the moderation of inflation in the 1990s. This interpretation suggests that to prevent a resurgence of 1970s‐style inflation, the central bank should reinforce as much as possible its commitment to low inflation by institutional, operational, and rhetorical means. Otherwise, sooner or later, luck will dry out and high inflation could return.

A time‐varying parameter model for inflation in India is proposed which nests the various plausible explanations for moderate inflation in the recent decade. Most empirical and theoretical studies on inflation dynamics have concentrated on developed economies. This paper pays attention to the international dimension of the issue. The reduced form model is estimated using time‐varying parameter estimation technique.