SPATIAL ANALISIS OF EFFECT OF GOVERNMENT EXPENDITURES ON ECONOMIC GROWTH

Ziba KARJOO
MA student of economics, Department of Economics, Azad Islamic University, khorasgan Branch
Department of Economics, Islamic Azad University, khorasgan Branch, Isfahan, Iran
ziba.karjoo@gmail.com
(corresponding author)

Majid SAMETI
Associated professor of economics, Department of Economics, University of Isfahan
Department of Economics, University of Isfahan, Isfahan, Iran
mj.sameti@ase.ui.ac.ir

Abstract
Among many factors which affect the economic growth of a country, governments are considered to be the most influential stimulants. Because of the importance of studying the government expenditures on economic growth, many techniques have been suggested so far.
In this article we have applied a new technique, namely Spatial Econometrics Method. This method examines “neighborhood” and “location” factors, which are influential in weakening or reinforcing the effects. In this article, by using Ram’s growth model (1986) and applying geographic aspect to global regression models, attempts are made to discover the effect of U.S states government expenditures on economic growth of states. Finally, it became clear that the growth of each state is influenced by the growth of neighbor states. Also state government expenditures have no effect on economic growth. In addition, the growth of labor force is introduced as one of the influential elements on the states’ economic growth.

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GREAT EXPECTATIONS FOR TOURISM AND REGIONAL DEVELOPMENT IN ROMANIA: WHY ARE NOT THEY MET?

Tudorel ANDREI
The Bucharest Academy of Economic Studies, 6, Romana Square, district 1, Bucharest, postal code: 010374, postal office 22, Romania, Phone: +4.021.319.19.00, Fax: +4.021.319.18.99
andreitudorel@ase.ro

Constantin MITRUT
The Bucharest Academy of Economic Studies, 6, Romana Square, district 1, Bucharest, postal code: 010374, postal office 22, Romania, Phone: +4.021.319.19.00; Fax: +4.021.319.18.99
cmitrut@ase.ro

Daniela-Luminita CONSTANTIN
The Bucharest Academy of Economic Studies, 6, Romana Square, district 1, Bucharest, postal code: 010374, postal office 22, Romania, Phone: +4.021.319.19.00; Fax: +4.021.319.18.99
danielaconstantin_2005@yahoo.com

Bogdan OANCEA
“Nicolae Titulescu” University,Calea Văcăreşti, Nr. 185, Sector 4, postal code 040051, Bucharest, Romania, Phone: +4.021.330.90.32, Fax: +4.021.330.86.06
bogdanoancea@univnt.ro
(corresponding author)

Abstract
Despite the high potential of the Romanian tourism competitiveness and reducing interregional disparities, the results obtained in the last fifteen-twenty years are far below expectations. This paper aims to identify national and regional characteristics of tourism in Romania during the period 1990 to 2010 and to evaluate the most important factors that influenced foreign tourists’ arrivals in Romania and the departures of Romanian tourists abroad. As infrastructure is one of the main obstacles to tourism development we have used data from development regions in order to explore the changes in the concentration of accommodation capacities. We have developed econometric models estimated on panel data to assess the implications of road infrastructure development and accommodation capacity utilization on economic results of tourism. The results indicate the important relationship between the territorial distribution of road infrastructure and the concentration of accommodation capacity.

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PROVINCIAL CLUSTERING IN THE SOUTH OF THAILAND: CONCEPTUAL AND EMPIRICAL

Kiatkajon CHAIRAT
Department of Economics, Faculty of Economics, Kasetsart University, Bangkok, Thailand.
Corresponding author: Kiatkajon Chairat Tel: +6681 870 0219
kiatkajon2111@gmail.com

Sumalee SANTIPOLVUT
Department of Economics, Faculty of Economics, Kasetsart University, Bangkok, Thailand.

Supachart SUKHAROMANA
Department of Economics, Faculty of Economics, Kasetsart University, Bangkok, Thailand.

Abstract
This paper aims to determine the cluster of 14 provinces in the Southern part of Thailand. We formulated 24 indicators for provincial clustering based on three major concepts: spatial, functional, and micro-foundational. Factor analysis shows that 10 of these indicators significantly determine provincial clustering. Cluster analysis obviously categorises 14 provinces into five cases of three to seven provincial clusters. In each case, the formation of groups is determined using the proximity criteria. Discrimination analysis helps to classify the most appropriate form, and in each case shows that clusters three and four are appropriate for provincial clustering.

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