Abstract
Much of the literature on agglomeration emphasises labour mobility between firms as a potential source of externalities. However, while there is a large literature on interregional migration, the empirical literature on the employment-mobility of workers within the local arena is surprisingly thin. Furthermore, there is almost no empirical evidence on the relationship between local and non-local employment movements, especially across industries. In this paper we analyse how agglomeration of the high technology industry as well as regional amenities affects labour mobility. In order to do this we employ panel data on the regional and industrial labour mobility of the Finnish high technology firms and regional economies on a period of 1991–2007. Analysing this dataset allows us to identify the roles which the structure of the high technology sector, regional economic and amenity variables have played both in the inter-regional and inter-sectoral labour mobility of high technology workers over the industry life-cycle. Our findings confirm that the structure of the high technology sector as well as regional economic and amenity variables have an influence on the migration decisions of the high technology workers, and their roles vary in within-region and across-region mobility. In addition, the effects of the variables seem to vary at different stages of the industry life cycle.
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Notes
- 1.
In Finland high technology firms and their success in international markets has been an engine of economic growth over the past two decades. The strong growth of information and communication technology cluster in the 1990s (led by Nokia Corporation) made Finland internationally known as a small technology intensive economy where economic growth is mainly based on technology know-how. The strong high technology sector had an extremely important role especially in early 1990s when the Finnish economy was recovering from deep recession. For instance in 2008, the share of high technology sector was about 6% (in 1989, 3.6%) of the total labour force and almost 18% of the total export (in 1991, 6%) (Simonen et al. 2015).
- 2.
Due to the availability of the data, the number of regions in our study is 70 for the period of 1991–2007.
- 3.
The difference between commonly used Herfindahl-Hirschmann index (HHI) and Shannon index is that the HHI assigns higher weights to the largest branches than does the Shannon Index. Therefore the value of HHI is largely driven by the share of the dominant branch, whereas the value of the Shannon Index depends more strongly on shares of several industries. Therefore, it reflects more accurately the variety of the high technology sector in terms of how many industries, including even small ones, are present in a region (Aiginger and Davies 2004; Simonen et al. 2015).
- 4.
We decided to use Random effect models based on the Hausman test. Only in a case of model 1, column 2 in Table 8.4 have we used a Fixed effect model.
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Simonen, J., Svento, R., Karhinen, S., McCann, P. (2018). Inter-regional and Inter-sectoral Labour Mobility and the Industry Life Cycle: A Panel Data Analysis of Finnish High Technology Sector. In: Biagi, B., Faggian, A., Rajbhandari, I., Venhorst, V. (eds) New Frontiers in Interregional Migration Research. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-75886-2_8
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