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The regional and sectoral mobility of high-tech workers: insights from Finland

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Abstract

In this paper we employ data on 156,000 workers working within the Finnish high-tech industries in order to identify the extent to which labour mobility between sectors and regions is influenced by the characteristics of the locality in which the worker works. With these data we are able to estimate different types of binary, multinomial and ordered logit models to capture different types of inter- or intra-sector or region employment mobility. As we will see the different categories of employment mobility are influenced by different factors such that we cannot simply talk about ‘labour mobility’, but rather need to be specific regarding each particular form of employment mobility. Our results show that urbanisation and industrial diversity are not just associated with greater intra-regional mobility, as is emphasised by the agglomeration literature, but also greater inter-regional mobility.

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Notes

  1. In Finland high-tech 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-tech 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-tech sector was about 6 % (in 1989 3.6 %) of the total labour force and almost 18 % of the total export (in 1991, 6 %).

  2. We exclude the workers who left the high-tech sector in 2005 and employees who entered to the high-tech sector in 2006. So we are analysing the labour mobility within the high-tech sector, both across industries and regions in 2005–2006. The data, on level of detail we have available in terms of sectoral-spatial movements, do not include individual level information. Therefore, we have adopted this particular estimation strategy. Instead of using sample of the total data, as it is the case in most of studies which use the individual data, our data covers all the high-tech workers in Finland in year pair 2005–2006.

  3. The general labour mobility values are based on the data which cover 71 regions and 1,441,298 employees in a year pair 2005–2006. All the values presented in Table 1 are based on region-specific values, and for instance, they do not show the variance across regions. As a comparison to these figures approximately 3–3.5 % of the total population migrated between the sub-regions in Finland (average per year over the period of 1996–2006) (Nivalainen 2010).

  4. Because we do not have information about the regional wages of the high-tech sector in all 71 regions, the number of regions used in estimations is 64.

  5. More information about the definition of high-tech sectors. See Eurostat Pocketbooks (2011): science, technology and innovation in Europe.

  6. Using the median wage overcomes the problem that some dominant regions may skew the average wage significantly, although we find that in the case of Finnish high-tech sectors the average value is very close to the median value. We use nominal values because of the lack of real values.

  7. 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. 2014). The maximum value of the Shannon index is ln(m). In this case all high-tech branches are present in a particular region and employment is evenly distributed within these branches.

  8. Eriksson et al. (2008) have argued that in smaller regions (where the number of firms is pretty low in general) firms probably know and meet each other more frequently. This can promote intra-regional labour mobility as firms view workers from other local companies more attractive because of their local knowledge of norms and routines.

  9. Unfortunately we do not have data available to measure the concentration of workplaces and use that as a proxy for urbanisation.

  10. This variable is not measured as a difference to the regional average but our tests demonstrate that it does not matter whether we use difference values or levels values.

  11. We have checked how the possible correlations between the independent variables affect results. Various estimation results show that results and conclusions remain the same as shown in this paper. For instance, results do not change significantly if we leave, e.g. “the number of high-tech establishments” variable away from the estimations. We also ran the reduced form estimations, where we left variables out one by one (based on p-values) and all variables which are statistically significant stay significant and their signs do not change.

  12. If the estimate of dispersion after fitting (measured by the deviance or Pearson’s chi-square divided by the degrees of freedom) is \({>}1\), then we have reason to believe that data might be overdispersed. Without adjusting for the overdispersion, the standard errors are likely to be underestimated, causing the Wald tests to be too very sensitive. The only thing which will be different to the normal formation of point estimates (when we control the overdispersion) is that we shall make all conclusions little bit more cautiously since we scale the standard errors upwards.

  13. The average marginal effects of the variables on probability of changing the region as well as the marginal effect at the mean values of the variables are presented in the “Appendix” (Table 9).

  14. The average marginal effects of the variables on probability of changing the sector and the marginal effect at the mean values of the variables are presented in the “Appendix” (Table 10).

  15. Because of technological specificities, we assume that switching sector is more difficult than changing regional location, thereby justifying the ranking of our categories 3 and 4. Moreover, this is also borne out by the numbers of actual movements, with inter-regional movements between firms within the same high-tech sector outnumbering employment movements between firms in different sectors but within the same region.

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Acknowledgments

The authors are grateful for valuable suggestions from two anonymous referees and financial support from the Academy of Finland and Yrjö Jahnsson foundation. Previous versions of this paper were presented at 53rd Congress of the European Regional Science Association, Palermo, Italy, 2013, and at 42nd annual conference of the Regional Science Association International - British and Irish Section, Sidney Sussex College, University of Cambridge, August, 2013.

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Authors and Affiliations

  1. Department of Economics, Oulu Business School, University of Oulu, P.O. Box 4600, 90014, Oulu, Finland

    J. Simonen & R. Svento

  2. Department of Economic Geography, Faculty of Spatial Sciences, The University of Groningen, Landleven 1, 9747 AD, Groningen, The Netherlands

    P. McCann

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  1. J. Simonen
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  3. P. McCann
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Correspondence to J. Simonen.

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Simonen, J., Svento, R. & McCann, P. The regional and sectoral mobility of high-tech workers: insights from Finland. Ann Reg Sci 56, 341–368 (2016). https://doi.org/10.1007/s00168-016-0742-0

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