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Metropolitan Regions and Export Renewal

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Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

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Abstract

Metropolitan regions are advantageous location for new export products due to factors such as external economies, diversified industry environment and a large share of skilled labour. This is the main assumption of this paper. What happens to these products when the technology becomes common knowledge? Using empirical data on exports, we find that products with a high specialisation in the metropolitan region have a tendency to be successful in the non-metropolitan regions subsequent years. Also, this export product diffusion does not seem to be related to a location in the immediate proximity to the metropolitan region. Instead, the recipient regions are mainly characterised as being centrally located in its labour market region, having a high share of highly educated individuals. Features related product standardisation such as a large manufacturing sector and low labour costs cannot be distinguished as prominent features.

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Notes

  1. 1.

    This hypothesis has been criticised on theoretical grounds by, for example, Taylor (1986) and on empirical grounds by Howells (1983) and Kleinklencht and Poot (1992).

  2. 2.

    The role of metropolitan regions for the development of new products was stressed already by Hoover and Vernon (1959).

  3. 3.

    However, results for the London conurbation reported by Gordon and McCann (2005) indicate that the importance of specifically local informal information spillovers is much more limited than has been suggested.

  4. 4.

    The product life cycle theory can be seen as an extension of spatial theories of industrial dynamics developed by Marshall (1890), Kuznets (1929, 1930), Burns (1934), Schumpeter (1939), Clark (1940), and others.

  5. 5.

    Duranton and Puga (2001) present a model where new products are developed in diversified cities, and relocated to specialised cities, when firms have found their ideal production process.

  6. 6.

    For further discussion on this discussion on old and new products in a setting of north–south trade please see Krugman (1979).

  7. 7.

    Ciccone and Hall (1996) emphasize the importance of density for productivity.

  8. 8.

    CN = Combined Nomenclature based on the Harmonized Commodity Description and Codifying System. Exports of services are not included and the agriculture, fishery and forestry are excluded when the sample is extracted.

  9. 9.

    Specialisation is calculated with a location quotient: \( \sigma = \left[ {\left( {EV_{Sthlm}^k} \right)/\left( {{\Sigma_{SthlmEV }}} \right)} \right]/\left[ {EV_{Sweden}^k/{\Sigma_{SwedenEV }}} \right] \), where \( EV \) is the export value, \( k \) is the 8-digit product. \( \sigma > 1 \): The product’s share of Stockholm’s export is larger than the product’s share of Sweden’s total export, i.e. the product is over represented. \( \sigma < 1 \): The product’s share of Stockholm’s exports is smaller than the product’s share of Sweden’s total exports, i.e. the product is under represented.

  10. 10.

    741 of the export products with a low export specialization in Stockholm in 1997 also had a negative growth in all other regions in Sweden between 1997 and 2003.

  11. 11.

    No distinction is made with respect to novelty. That is, this group also comprises products that were not exported in 1997 but exported in 2003 in regions 2–81.

  12. 12.

    For a discussion on distance friction parameters (Hugosson and Johansson 2001).

  13. 13.

    The number of observations is 257. That is, the island Gotland has been removed from the spatial estimation since it creates bias in the distance matrix. An OLS estimation has been performed without Gotland but the results did not diverge from prior results. An estimation of a spatial error model has also been executed and the results are highly robust. The spatial error model considers the error process and not the model itself. The spatial lag model affects the dependent variable by values of the variables in the nearby locations (Anselin 1990).

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Author information

Authors and Affiliations

  1. Jönköping International Business School, 1026, Jönköping, 551 11, Sweden

    Lina Bjerke

  2. CESIS, Royal Institute of Technology, Stockholm, Sweden

    Lina Bjerke & Charlie Karlsson

Authors
  1. Lina Bjerke
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  2. Charlie Karlsson
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Corresponding author

Correspondence to Lina Bjerke .

Editor information

Editors and Affiliations

  1. Economics, Finance & Statistics, Jönköping Int. Business School, Jönköping University, Gjuterigatan 5, Jönköping, 551 11, Sweden

    Johan Klaesson

  2. Economics, Finance and Statistics, Jönköping Int. Business School, University Jonköping, Gjuterigatan 5, Jönköping, 551 11, Sweden

    Börje Johansson

  3. Economics, Finance and Statistics, Jönköping Int. Business School, Jönköping University, Gjuterigatan 5, Jönköping, 551 11, Sweden

    Charlie Karlsson

Appendices

Appendix 1

Table 10.7 Absolute value growth of products in H++ in functional regions 2–81, share of highly educated labor, distance to Stockholm, share of employees within the manufacturing (In ascending order w.r.t export value growth in H++)
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Appendix 2

Table 10.8 Moran’s I test for spatial dependence for all models in Table 10.4 for year 2003 model
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Fig. 10.1
figure 00101

Export value in H++ in all Swedish urban areas 1997, export unit value = 31.24

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Fig. 10.2
figure 00102

Export value in H++ in all Swedish urban areas 2003, export unit value = 19.31

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Fig. 10.3
figure 00103

Absolute growth of export value in H++ in all Swedish urban areas 1997–2003

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Bjerke, L., Karlsson, C. (2013). Metropolitan Regions and Export Renewal. In: Klaesson, J., Johansson, B., Karlsson, C. (eds) Metropolitan Regions. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32141-2_10

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  • DOI: https://doi.org/10.1007/978-3-642-32141-2_10

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