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Abstract
This paper examines the degree of business cycle synchronicity among Greek regions and investigates the determinants of the business cycle co-movements of output associated with specific functional and spatial aspects of the integration process among the Greek regions. We analyse nearly 30 years (1980–2008) of data at the NUTSIII level (prefectures). We conclude that the business cycles of prefectures are more synchronized with the NUTSII regional cycle than with the national business cycle revealing a regional (NUTSII) border effect. Moreover, the intensification of the integration process seems to diachronically affect the structural characteristics of the Greek regions and the geography of cyclical synchronization. Our study reveals a two-stage integration process where in the first stage we detect the existence of urbanization economies, while in the second one the existence of localization economies. Furthermore, our study reveals that the metropolitan regions have a low level of business cycle synchronicity with the other regions, stressing Greece's pattern of economic and structural dualism.
Notes
1. It is calculated by the equation , with
as the minimum value in the range of values αj; i denotes the studied region, J indicates the total number of branches in which region i specializes and
is the share of each branch in the studied area. The merits of the Theil index are described broadly in the literature as it is neither scale nor mean dependent, it is not excessively affected by extreme values, it is independent of the number of regions and it is decomposable in between-group and within-group coefficients.
2. Bhagwati and Dehejia (1994) call this “kaleidoscope comparative advantage”, as firms shift location quickly; Krugman (1996) uses the phrase “slicing the value chain”; Leamer (1996) prefers “delocalisation” whereas Antweiler and Trefler (1997) introduce “intra-mediate trade”.
3. In the econometric model a dummy variable has been included, which takes the value 1 when the distance among two regions is smaller than 300 km (or 200 or 100 km as it has also been estimated) and 0 otherwise. The statistical significance of the variable is assessed in any case as non-important. Similar results have been found by the study of Panteladis and Tsiapa (2012).