ECONOMIC CONTAGION UNDER UNCERTAINTY: CGE WITH A MONTE CARLO EXPERIMENT

Hiroshi SAKAMOTO

Research Associate Professor Asian Growth Research Institute (AGI) 11-4 Otemachi, Kokurakita, Kitakyushu, 803-0814 JAPAN Tel: +81 93 583 6202; Fax: +81 93 583 4602

sakamoto@agi.or.jp

Abstract

Economic contagion is increasingly felt as economic interdependence deepens in today’s economy. This study quantitatively investigates how economic shocks of a certain country influence a different country. Usually, a positive shock has a positive influence, and a negative shock has a negative influence. For instance, the monetary crisis of Europe affected the Asian economy as well as the economy of Europe itself. The Chinese economy, which recently accomplished the most remarkable economic growth in the Asian region, has also declined in rates of growth, and has become a risk factor for the global economy. The downturn of the economy in regions with economic power may have a negative influence on the economy of other countries. Under such circumstances, this study quantitatively analyzes the economic shock influence of a certain country to other countries, at the same time there is a possibility of influence to the opposite direction supposing the economic shock occurs under uncertainty. The model employed in the study uses the general algebraic modeling system (GAMS), it uses the global trade analysis project (GTAP) database, which is compiled as a computable general equilibrium (CGE) model using multiple countries’ data. Moreover, this database is constantly updated to a recent year to feature more realistic knowledge. Furthermore, this study uses the Monte Carlo experiment to model uncertainty. This is realizable by adding the random number of a normal distribution to the exogenous variables of the model.

Keywords: Economic Contagion, Multi-country Computable General Equilibrium Model, Monte Carlo Experiment

JEL classification: C15, C68, D58, O53, R13

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HOW THE REPLACEMENT OF BASEL II BY BASEL III HAS AN EFFECT ON ECONOMIC GROWTH

Nikiforos CHATZIGAKIS

n.chatzigakis@gmail.com

Abstract

After the recent crisis, the Basel Committee decided to create a new regulatory framework, Basel III. This is because the recession demonstrated the inability of Basel II accord to prevent the economic crisis. Basel III on the other hand, has come to rectify all these weaknesses, however its focus is on liquidity risk and on regulatory capital requirements. For this reason, Basel III makes changes on capital definition and has increased the capital charges for derivatives and securities. Also, Basel III has introduced the liquidity and coverage ratio that the former is separated into Liquidity coverage ratio (LCR) and Net stable funding ratio (NSFR), which their main objective is to increase liquidity during economic stress periods. Even though Basel III has not been fully implemented and it’s under construction, its main provisions of capital requirements, liquidity coverage ratio (LCR), Net stable funding ratio (NSFR) and the leverage ratio have been criticized as increasing the cost of bank lending to borrowers. Finally, it is argued that Basel III could have a dampening effect on economic growth.

Keywords: Basel ΙΙ, Basel III, regulation, Liquidity coverage ratio (LCR), Net stable funding ratio (NSFR)

JEL classification:
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A MODEL OF A SYSTEM OF MONITORING AND ALERT SYSTEM OF THE RISK OF UNEMPLOYMENT – ROMANIAN CASE

Cristina LINCARU

National Scientific Research Institute for Labour and Social Protection – INCSMPS, Bucharest,, Romania
cristina.lincaru@yahoo.de

Speranta PÎRCIOG

Scientific Manager, National Scientific Research Institute for Labour and Social Protection – INCSMPS, Bucharest,, Romania
pirciog@incsmps.ro

Draga Atanasiu

National Scientific Research Institute for Labour and Social Protection – INCSMPS, Bucharest,, Romania
incsmps1@incsms.ro

Abstract

Public Employment Services (PES) have to “react efficiently and effectively to unceasingly changing public and political demand” and also to cope successfully to the growing “competitive environment’s “demand. (Public Employment Services’ Contribution to EU 2020: PES 2020 Strategy Output Paper, 2013). One direction that allow PES to “enhancing labour market transparency and providing evidence to support policy design” is to fully exploit the informational potential provided by the registered unemployment indicator in a systemic way.

In Romania the registered unemployment administrative unit is AJOFM – County Agency for Employment and Training of the Labour Force (CAE) – the PES provider at NUTS 3 level, while the lowest administrative unit is represented by localities at LAU 2 / NUTS 5 level. The ANOFM – The National Employment Agency for Labour Force in short NEA implements the policies and strategies of Labour Ministry in the field of employment and training for the persons seeking a job. The following 4 dimensions of the unemployment risks, expressed through aggregate indices for: the level, seasonality (with 2 aggregate indices), of cohesion tendency and of the density of unemployment served to make a sketch for an System of Monitoring and Alert system of the Risk of Unemployment. The Unemployment risk for each county by 10 sub-indices (5 urban and 5 rural) by the 4 dimensions of the unemployment risk at county level is represented in radar graphs. The 10 scores for each county, are compared to scores obtained at national average and with the theoretical thresholds (maximum and minimum of the intermediary categorical scale) and finally all the counties are grouped by 4 cluster types in regard with the unemployment risk: Alarm, Alert, Balance, Low risk of unemployment and figured in Maps.

Keywords: unemployment risk, registered unemployment, local level, composite indicators, monitoring and alarm system

JEL classification:
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