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This research suggests that no single country conforms entirely to classical liberalism. Fragile states – many of which have long communist, socialist and patrimonial histories – exhibit a cocktail of economic personalities. They may best be referred to as “liberal-hybrids”. Research shows that while such states are highly exposed to global transmission channels for liberal market policies, many of these liberal hybrids fared better through the global financial crisis because of their adaptive mechanisms. There is, therefore, a great need to deepen understanding of the drivers of fragility and resilience in fragile states, and redefine proscriptive ideological approaches that drive economic and development policies in different directions. This paper focuses on four key pillars of liberal order policies: financial liberalisation, trade liberalisation, foreign direction investment and exchange rate management. These aspects are fundamental to growth, but “test” fragile institutions and societies too severely in many cases – aggravating fragility and creating inequitable growth patterns. Policy responses to mitigate risks and maximise benefits from adoption of these liberal order policies in fragile contexts have been stronger in theory (as the Post-Washington consensus era draws to a close) than in practice; fragile states are still subject to blueprint prescriptions and competitive political pressures.
Drawing on country examples, this paper proposes future avenues for international research and action: (i) grouping fragile states according to a new set of vulnerability criteria on which to base support; (ii) developing a set of leading or proxy indicators to close the action-research time gap for fragile states; (iii) modelling fragile state responses to global risks towards early warning; (iv) integrating economic and development policies at national level; (v) staggering liberalisation policies to keep pace with institutional capacities; and (vi) prioritising internal economic cohesion. To create the analytical base, three fragile state case studies could be produced exploring liberalisation adoption from ideology and prescription to uptake pattern over time. Results could be synthesised by a newly established Global & Fragile Systems Contact Group, empowered to create the new metrics required to turn the New Deal into the “real deal” for fragile and conflict-affected states.
- Le pourcentage le plus important d’élèves qui espèrent obtenir un diplôme universitaire s’observe en Corée (80 %), et le plus faible, en Lettonie (25 %).
- De nombreux élèves très performants n’envisagent pas d’aller à l’université, soit autant de talents potentiels perdus pour l’économie et la société, tandis que de nombreux élèves peu performants pensent qu’ils y parviendront, même si leurs résultats scolaires actuels semblent présager le contraire.
- Un élève sur quatre environ envisage de terminer sa scolarité à la fin du deuxième cycle de l’enseignement secondaire et nécessite donc les compétences qui lui permettront de faire une transition en douceur de la scolarité au monde du travail et à l’âge adulte. < /LI>
- The percentage of students who expect to complete university is highest in Korea (80%) and lowest in Latvia (25%).
- Many high-performing students do not expect to go to university, representing potentially lost talent to an economy and society while many low-performing students think they will make it to university, even if their current performance suggests they are not likely to succeed.
- Around one in four students expects to end his or her formal schooling at the upper secondary level and thus needs the skills to make a smooth transition into work and adulthood.
The production of heat is responsible for a large share of final energy demand. In 2009, heat accounted for 47% of total energy used worldwide. Expanding the use of modern biomass, geothermal energy, solar energy and ambient energy to produce heat could contribute substantially to meeting energy security objectives and mitigating climate change.
Governments can encourage private investment in LCR infrastructure by improving the risk-return profile of projects. The paper provides a ranking of the most significant risks in financing LCR projects showing that policy (or sovereign) risks rank amongst the highest. The potential to finance LCR infrastructure in low income nations is challenging due to basic banking services, lack of non-bank financial services, weak risk management capacity and limited availability of long term funding. Drawing on OECD?s work on the water sector, the paper reviews financing mechanisms that help to increase access to commercial banks, bond finance, project finance and equity finance in developing countries. Green bonds are an example of a financing mechanism with strong potential for LCR infrastructure in developed countries, but supportive government policies are required. The paper concludes by considering governance arrangements that can enable and secure private engagement in LCR infrastructure investment, including public private partnerships (PPPs). Where governments have opted to use PPPs, government PPP units may be suitable administrative units for managing delivery of LCR performance as an integral part of the infrastructure project.
The aim of this paper is to provide an interpretation of the measure of capacity utilisation provided by the European Union harmonised survey on the Italian manufacturing sector. In doing so, we evaluate its ability to correctly track cyclical turning points and its contribution in explaining consumer price index (CPI) inflation. The survey based measure results are a good co-incident indicator of business cycle, however it is generally outperformed by time series models in explaining inflation. We conclude that the standard “output gap” interpretation of the survey results is broadly confirmed by the data, however we cannot rule out at this stage that survey respondents may also consider the alternative “variable capacity utilisation” concept in answering the survey question.
Keywords: Capacity utilisation, co-integration, unobserved component models, VAR.
JEL Classification: E32, C22, E37